Digital Technologies and Applications: Proceedings of ICDTA 21, Fez, Morocco (Lecture Notes in Networks and Systems, 211) 3030738817, 9783030738815

Citation preview

Lecture Notes in Networks and Systems 211

Saad Motahhir Badre Bossoufi   Editors

Digital Technologies and Applications Proceedings of ICDTA 21, Fez, Morocco

Lecture Notes in Networks and Systems Volume 211

Series Editor Janusz Kacprzyk, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Advisory Editors Fernando Gomide, Department of Computer Engineering and Automation—DCA, School of Electrical and Computer Engineering—FEEC, University of Campinas— UNICAMP, São Paulo, Brazil Okyay Kaynak, Department of Electrical and Electronic Engineering, Bogazici University, Istanbul, Turkey Derong Liu, Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, USA; Institute of Automation, Chinese Academy of Sciences, Beijing, China Witold Pedrycz, Department of Electrical and Computer Engineering, University of Alberta, Alberta, Canada; Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Marios M. Polycarpou, Department of Electrical and Computer Engineering, KIOS Research Center for Intelligent Systems and Networks, University of Cyprus, Nicosia, Cyprus Imre J. Rudas, Óbuda University, Budapest, Hungary Jun Wang, Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong

The series “Lecture Notes in Networks and Systems” publishes the latest developments in Networks and Systems—quickly, informally and with high quality. Original research reported in proceedings and post-proceedings represents the core of LNNS. Volumes published in LNNS embrace all aspects and subfields of, as well as new challenges in, Networks and Systems. The series contains proceedings and edited volumes in systems and networks, spanning the areas of Cyber-Physical Systems, Autonomous Systems, Sensor Networks, Control Systems, Energy Systems, Automotive Systems, Biological Systems, Vehicular Networking and Connected Vehicles, Aerospace Systems, Automation, Manufacturing, Smart Grids, Nonlinear Systems, Power Systems, Robotics, Social Systems, Economic Systems and other. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution and exposure which enable both a wide and rapid dissemination of research output. The series covers the theory, applications, and perspectives on the state of the art and future developments relevant to systems and networks, decision making, control, complex processes and related areas, as embedded in the fields of interdisciplinary and applied sciences, engineering, computer science, physics, economics, social, and life sciences, as well as the paradigms and methodologies behind them. Indexed by SCOPUS, INSPEC, WTI Frankfurt eG, zbMATH, SCImago. All books published in the series are submitted for consideration in Web of Science.

More information about this series at http://www.springer.com/series/15179

Saad Motahhir Badre Bossoufi •

Editors

Digital Technologies and Applications Proceedings of ICDTA 21, Fez, Morocco

123

Editors Saad Motahhir ENSA Sidi Mohamed Ben Abdellah University Fez, Morocco

Badre Bossoufi Faculty of Sciences Sidi Mohamed Ben Abdellah University Fez, Morocco

ISSN 2367-3370 ISSN 2367-3389 (electronic) Lecture Notes in Networks and Systems ISBN 978-3-030-73881-5 ISBN 978-3-030-73882-2 (eBook) https://doi.org/10.1007/978-3-030-73882-2 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

We are honored to dedicate the proceedings of ICDTA’21 to all the participants and committees of ICDTA’21.

Foreword

It is with deep satisfaction that I write this Foreword to the proceedings of the ICDTA’21 organized by École Nationale des Sciences Appliquées and Faculté des sciences which belong to SMBA University, Fez, Morocco, on January 29 and 30, 2021. This conference was bringing together researchers, academics, and professionals from all over the world, experts in digital technologies and their applications. This conference particularly encouraged the interaction of research students and developing academics with the more established academic community in an informal setting to present and to discuss new and current work. The papers contributed the most recent scientific knowledge known in the field of digital technologies and their applications. Their contributions helped to make the conference as outstanding as it has been. The organizing and technical program committees put much effort into ensuring the success of the day-to-day operation of the meeting. We hope that this book proceedings will further stimulate research in digital technologies such as artificial intelligence, Internet of things, embedded systems, network technology, information processing and their applications, in several areas including hybrid vehicles, renewable energy, robotic, COVID-19, etc. We feel honored and privileged to serve the best recent developments to you through this exciting book proceedings. We thank all authors and participants for their contributions. Saad Motahhir Badre Bossoufi

vii

Preface

This conference proceedings volume contains the written versions of most of the contributions presented during the ICDTA’21. The conference provided a setting for discussing recent developments in a wide variety of topics including artificial intelligence, Internet of things, embedded systems, network technology, information processing and their applications, in several areas such as hybrid vehicles, renewable energy, robotic, and COVID-19. The conference has been a good opportunity for participants from various destinations to present and discuss topics in their respective research areas. ICDTA’21 tends to collect the latest research results and applications on digital technologies and their applications. It includes a selection of 166 papers from 328 papers submitted to the conference from universities and industries all over the world. All of accepted papers were subjected to strict peer reviewing by 2–4 expert referees. The papers have been selected for this volume because of quality and the relevance to the conference. ICDTA’21 would like to express our sincere appreciation to all authors for their contributions to this book. We would like to extend our thanks to all the referees for their constructive comments on all papers; especially, we would like to thank organizing committee for their hardworking. Finally, we would like to thank the Springer publications for producing this volume. Saad Motahhir Badre Bossoufi

ix

Acknowledgments

We request the pleasure of thanking you for taking part in the first edition of the International Conference on Digital Technologies and Applications (ICDTA’21). We are very grateful for your support, so thank you everyone for bringing your expertise and experience around the conference and engaging in such fruitful, constructive, and open exchanges throughout the two days of the ICDTA’21. We would like to extend our deepest thanks and gratitude to all the speakers for accepting to join us from different countries. Thank you for being such wonderful persons and speakers. Again, thanks for sharing your insight, knowledge, and experience. Of course, this event could not be that successful without the effort of the organizing and technical program committees. Therefore, Pr. Badre and I would like to express our sincere appreciation to all of you who generously helped us. We would like to especially thank all the participants for the confidence and trust you have placed in our conference. We hope we lived up to your highest expectations. Finally, our acknowledgement would be incomplete without thanking the biggest source of support. Our deepest gratitude goes to Pr. REDOUANE MRABET, President of Sidi Mohamed Ben Abdellah University, Prof. ABDERRAHIM LAHRACH, Director of the National School of Applied Sciences, Prof. MOHAMMED BELMLIH, Dean of the Faculty of Sciences, Professor ABDELMAJID SAKA, Deputy Director of the National School of Applied Sciences, and Professor MOHAMMED ELHASSOUNI, Vice Dean of Faculty of Sciences. Thank you all for your support and for being all-time open to hosting such events. Saad Motahhir Badre Bossoufi

xi

Contents

Artificial Intelligence, Machine Learning and Data Analysis New Reconfiguration of the Radial Distribution Network by Using the Chaotic Mapping and the Success Rate to Improve the PSO Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Meriem M’dioud, Ismail ELkafazi, and Rachid Bannari Convolutional Neuronal Networks for Tumor Regions Detection in Histopathology Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mohammed Lamine Benomar, Nesma Settouti, Rudan Xiao, Damien Ambrosetti, and Xavier Descombes Early Detection of Wheat Diseases in Morocco by Convolutional Neutral Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kenza Aitelkadi, Noureddine Chtaina, Saber Bakouri, and Mohamed Belebrik

3

13

25

Genetic Algorithm Design Optimization for Non-standard Spur Gears . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Samya Belarhzal and El Mostapha Boudi

37

A Robust Method for Face Classification Based on Binary Genetic Algorithm Combined with NSVC Classifier . . . . . . . . . . . . . . . . . . . . . . M. Ngadi, A. Amine, B. Nassih, Y. Azdoud, and A. El-Attar

47

A Proposed Solution to Road Traffic Accidents Based on Fuzzy Logic Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Halima Drissi Touzani, Sanaa Faquir, Saloua Senhaji, and Ali Yahyaouy

57

Multi-objective Archimedes Optimization Algorithm for Optimal Allocation of Renewable Energy Sources in Distribution Networks . . . . Ahmad Eid and Hassan El-Kishky

65

xiii

xiv

Contents

The Recurrent Neural Network for Program Synthesis . . . . . . . . . . . . . Achraf Berrajaa and El Hassane Ettifouri MaskNet: CNN for Real-Time Face Mask Detection Based on Deep Learning Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ismail Nasri, Mohammed Karrouchi, Hajar Snoussi, Abdelhafid Messaoudi, and Kamal Kassmi Machine Learning System for Fraud Detection. A Methodological Approach for a Development Platform . . . . . . . . . . . . . . . . . . . . . . . . . . Salma El Hajjami, Jamal Malki, Mohammed Berrada, Harti Mostafa, and Alain Bouju

77

87

99

U-Net: Deep Learning for Extracting Building Boundary Collected by Drone of Agadir’s Harbor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 Tarik Chafiq, Hayat Hachimi, Mohammed Raji, and Soufiane Zerraf Analysis and Classification of Plant Diseases Based on Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 Assia Ennouni, Noura Ouled Sihamman, My Abdelouahed Sabri, and Abdellah Aarab A Novel Approach to Data Augmentation for Document Image Classification Using Deep Convolutional Generative Adversarial Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 Aissam Jadli, Mustapha Hain, and Abderrahman Jaize Knowledge Driven Paradigm for Anomaly Detection from Tweets Using Gated Recurrent Units . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 S. Manaswini, Gerard Deepak, and A. Santhanavijayan Stacked Deep Learning LSTM Model for Daily Solar Power Time Series Forecasting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155 Soufiane Gaizen, Ouafia Fadi, and Ahmed Abbou A Comparison Study of Machine Learning Methods for Energy Consumption Forecasting in Industry . . . . . . . . . . . . . . . . . . . . . . . . . . 165 Mouad Bahij, Moussa Labbadi, Mohamed Cherkaoui, Chakib Chatri, and Soufian Lakrit The Review of Objectives, Methods, Tools, and Algorithms for Educational Data Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177 Mohamed Timmi, Adil Jeghal, Said EL Garouani, and Ali Yahyaouy Brain-Computer Interface: A Novel EEG Classification for Baseline Eye States Using LGBM Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189 Said Abenna, Mohammed Nahid, and Abderrahim Bajit A Light Arabic POS Tagger Using a Hybrid Approach . . . . . . . . . . . . . 199 Khalid Tnaji, Karim Bouzoubaa, and Si Lhoussain Aouragh

Contents

xv

RSEPUA: A Recommender System for Early Predicting University Admission . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209 Inssaf El Guabassi, Zakaria Bousalem, Rim Marah, and Aimad Qazdar Forecasting Students’ Academic Performance Using Different Regression Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221 Inssaf El Guabassi, Zakaria Bousalem, Rim Marah, and Aimad Qazdar Computational Analysis of Human Navigation Trajectories in a Spatial Memory Locomotor Task . . . . . . . . . . . . . . . . . . . . . . . . . . 233 Ihababdelbasset Annaki, Mohammed Rahmoune, Mohammed Bourhaleb, Jamal Berrich, Mohamed Zaoui, Alexandre Castilla Ferro, and Alain Berthoz An Electrocardiogram Data Compression-Decompression Technique Based on the Integration Filtering of Peaks’ Amplitude Signal . . . . . . . 245 Skander Bensegueni Internet of Things, Embedded Systems, Blockchain and Security Narrowband IoT Evolution Towards 5G Massive MTC: Complete Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259 Adil Abou El Hassan, Abdelmalek El Mehdi, and Mohammed Saber A Fuzzy Ontology Driven Integrated IoT Approach for Home Automation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271 Levin Varghese, Gerard Deepak, and A. Santhanavijayan Towards an IoT/Big-Data Platform for Data Measurements, Collection and Processing in Micro-grid Systems . . . . . . . . . . . . . . . . . . 279 Abdellatif Elmouatamid, Youssef Alidrissi, Radouane Ouladsine, Mohamed Bakhouya, Najib Elkamoun, Mohammed Khaidar, and Khalid Zine-Dine Smart Hospitals and Cyber Security Attacks . . . . . . . . . . . . . . . . . . . . . 291 Yassine Chahid, Mohammed Benabdellah, and Nabil Kannouf Design of an Automatic Control and IoT Monitoring System for Dual Axis Solar Tracker . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 301 Fatima Cheddadi, Hafsa Cheddadi, Youssef Cheddadi, Fatima Errahimi, and Ikram Saber Analysis Jamming Attack Against the Protocol S-MAC in IoT Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 311 Imane Kerrakchou, Sara Chadli, Mohamed Emharraf, and Mohammed Saber Simulation and Analysis of Jamming Attack in IoT Networks . . . . . . . . 323 Imane Kerrakchou, Sara Chadli, Amina Kharbach, and Mohammed Saber

xvi

Contents

A Brief Survey on Internet of Things (IoT) . . . . . . . . . . . . . . . . . . . . . . 335 Fatima Zahra Fagroud, Lahbib Ajallouda, El Habib Ben Lahmar, Hicham Toumi, Ahmed Zellou, and Sanaa El Filali Design and Implementation of Smart Irrigation System Based on the IoT Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 345 Asmae Hafian, Mohammed Benbrahim, and Mohammed Nabil Kabbaj Lightweight Key Exchange Algorithms for MQTT Protocol in Its Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 355 Anwar D. Alhejaili and Omar H. Alhazmi IoT Design and Realization of a Supervision Device for Photovoltaic Panels Using an Approach Based on Radiofrequency Technology . . . . . 365 Zaidan Didi and Ikram El Azami ESP8266 Wireless Indoor Positioning System using Fingerprinting and Trilateration Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 377 Safae El Abkari, Jamal El Mhamdi, Abdelilah Jilbab, and El Hassan El Abkari High Performance Predictive Control for Permanent Magnet Synchronous Machine Drive: FPGA-Based Implementation . . . . . . . . . . 387 Badre Bossoufi and Ahmed Lagrioui High-Level Synthesis Implementation of Monocular SLAM on Low-Cost Parallel Platforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 399 Ayoub Mamri, Mohamed Abouzahir, Mustapha Ramzi, and Mohamed Sbihi Improving Multispectral Image Processing for Real-Time Agricultural Indices Tracking Using an Embedded System . . . . . . . . . . 411 Amine Saddik, Rachid Latif, and Abdelhafid El Ouardi Design and Implementation of an Open Source and Low-Cost Nanosatellite Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 421 Ahmed Hanafi, Aziz Derouich, Mohammed Karim, and Assiya Lemmassi Design and Realization of Fire Safety System for Controlling and Monitoring a Siren Using Arduino Uno . . . . . . . . . . . . . . . . . . . . . 433 Abdennabi Morchid, Mustapha El Alaoui, Rachid El Alami, Hassan Qjidaa, Karim El Khadiri, and Youness Mehdaoui Water-Purifying Distiller with a Cooling System Controlled by a Photovoltaic Panel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 447 Kamilia Mounich, Aicha Wahabi, and Mohammed Chafi Security at the Application Layer and the Physical Layer in 5G . . . . . . 457 Hafida Amgoune and Tomader Mazri

Contents

xvii

Blockchain Based Security and Privacy in VANETs . . . . . . . . . . . . . . . 469 Sana Iqbal, Narmeen Zakaria Bawany, and Ayesha Zulfiqar Dynamic Data Protection for Mobile Agents: XACML/ABAC Policies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 483 Hassan Razouki Securing the Storage of Passwords Based on the MD5 HASH Transformation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 495 Hamza Touil, Nabil El Akkad, and Khalid Satori Is Blockchain Technology Applicable in Small and Medium-Sized Enterprises? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 505 Zakariya Chabani, Salima Hamouche, and Raed Said Anomaly Detection from Log Files Using Multidimensional Analysis Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 515 Yassine Azizi, Mostafa Azizi, and Mohamed Elboukhari Web-Based Techniques and E-Learning A Review on Content Based Recommender Systems in Tourism . . . . . . 527 Asmae Bentaleb, Younès El Bouzekri El Idrissi, and Ayoub Ait Lahcen Towards Semantic Web Services Density Clustering Technique . . . . . . . 543 Naoufal El Allali, Mourad Fariss, Hakima Asaidi, and Mohamed Bellouki KSTAR: A Knowledge Based Approach for Socially Relevant Term Aggregation for Web Page Recommendation . . . . . . . . . . . . . . . . . . . . . 555 Deepak Surya, Gerard Deepak, and A. Santhanavijayan MKSTD: Multiple Knowledge Sourcing Approach for Topic Directories Construction and Linking Web Pages Using Fusion Semantics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 565 Gerard Deepak and A. Santhanavijayan A Fuzzy Near Neighbors Approach for Arabic Text Categorization Based on Web Mining Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 575 Mounir Gouiouez Recommendation System Based on Semantic Web Approach . . . . . . . . 585 Sara Belattar, Otman Abdoun, and Haimoudi El Khatir Sentiment Analysis of Moroccan Tweets Using Text Mining . . . . . . . . . 597 Moncef Garouani, Hanae Chrita, and Jamal Kharroubi Contribution of Digital Collaboration and E-Learning to the Implementation of Smart Mobility in Morocco . . . . . . . . . . . . . . 609 Mohammed Mouhcine Maaroufi, Laila Stour, and Ali Agoumi

xviii

Contents

MOOCs Semantic Interoperability: Towards Unified and Pedagogically Enriched Model for Building a Linked Data Repository . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 621 Hanane Sebbaq and Nour-eddine El Faddouli IBN TOFAIL UNIVERSITY: From Classical University to Smart University . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 633 Khawla Mazwa and Tomader Mazri Collaborative Metacognitive Technique in Smart Tutoring System . . . . 643 Hibbi Fatima-Zohra, Abdoun Otman, and Haimoudi Elkhatir Enhancing Education System with a Q&A Chatbot: A Case Based on Open edX Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 655 Rachid Karra and Abdelali Lasfar A Multi-agent and Content-Based Course Recommender System for University E-learning Platforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . 663 Meryem Amane, Karima Aissaoui, and Mohammed Berrada Image and Information Processing Sentiment Classification for Moroccan Arabic Using the Lexicon-Based Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 675 Chorouk Ferroud and Abdeljalil Elouardighi Forecasting Tourism Demand in Marrakech with SQD-PCA-SVR . . . . 685 Houria Laaroussi, Fatima Guerouate, and Mohamed Sbihi Toward Improving Arabic Text Preprocessing in Sentiment Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 695 Mohcine Maghfour and Abdeljalil Elouardighi IIMDR: Intelligence Integration Model for Document Retrieval . . . . . . 707 S. Aditya, P. Muhil Aditya, Gerard Deepak, and A. Santhanavijayan Effective Application of Information Technology Tools for Real-Time Project Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 719 Mahmoud Ershadi, Marcus Jefferies, Peter Davis, and Mohammad Mojtahedi HCODF: Hybrid Cognitive Ontology Driven Framework for Socially Relevant News Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 731 V. Adithya, Gerard Deepak, and A. Santhanavijayan Understanding Customer Adoption of E-banking . . . . . . . . . . . . . . . . . . 741 Jaride Chama, Taqi Ahmed, and El Hachimi Mohamed Amine Amalgamation of Novel Objective Function and Multi-sink Solution for a Reliable RPL in High Traffic Monitoring Applications . . . . . . . . . 749 Abdelhadi Eloudrhiri Hassani, Aicha Sahel, and Abdelmajid Badri

Contents

xix

Wireless Sensor Network Fault Detection and Isolation for Smart Irrigation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 759 Nassima Jihani, Mohammed Nabil Kabbaj, and Mohammed Benbrahim Multi-resolution Texture Analysis for Osteoporosis Classification . . . . . 769 Meriem Mebarkia, Abdallah Meraoumia, Lotfi Houam, Seddik Khemaissia, and Rachid Jennane High Performance Decoding by Combination of the Hartmann Rudolph Decoder and Soft Decision Decoding by Hash Techniques . . . . 781 Hamza Faham, My Seddiq El Kasmi Alaoui, Saïd Nouh, and Mohamed Azzouazi Automated Assessment of Question Quality on Online Community Forums . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 791 Harish Rithish, Gerard Deepak, and A. Santhanavijayan QFRDBF: Query Facet Recommendation Using Knowledge Centric DBSCAN and Firefly Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 801 Deepak Surya, Gerard Deepak, and A. Santhanavijayan SentiFusion (SF): Sentiment Analysis of Twitter Using Fusion Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 813 Sampurn Anand, Chaya Vijaykumar, Gerard Deepak, and A Santhana Vijayan Using Internet of Things to Increase Efficient Collaboration in PLM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 825 Narjiss Tilioua, Fatima Bennouna, and Zakaria Chalh ECC Image Encryption Using Matlab Simulink Blockset . . . . . . . . . . . 835 Sara Chillali and Lahcen Oughdir Development of Large Chaotic S-boxes for Image Encryption . . . . . . . . 847 Younes Qobbi, Abdeltif Jarjar, Mohamed Essaid, and Abdelhamid Benazzi Evaluation of Feature Extraction Methods Combined with Support Vector Machines for Powerline Component Recognition in Aerial Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 859 Jamila Garfaf, Lamyae Fahmani, and Hicham Medromi A Novel Brain Tumor Detection Approach Based on Fuzzy C-means and Marker Watershed Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 871 Hanae Moussaoui, Mohamed Benslimane, and Nabil El Akkad Image Retrieval Based on MPEG-7 Feature Selection Using Meta-heuristic Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 881 Naoufal Machhour and M’barek Nasri

xx

Contents

A Powerful and Efficient Method of Image Segmentation Based on Random Forest Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 893 Zahra Faska, Lahbib Khrissi, Khalid Haddouch, and Nabil EL Akkad Digital Technologies in Battle Against COVID-19 Towards Overlapped Objects Recognition Using Capsule Network . . . . 907 Merieme Mansouri, Samia Benabdellah Chaouni, and Said Jai Andaloussi The Spread of the Corona Virus Disease (Covid-19) and the Launch of 5G Technology in China: What Relationship . . . . . . . . . . . . . . . . . . . 919 Abdelhakim Moutaouakil, Younes Jabrane, Abdelati Reha, and Abdelaziz Koumina Analysis of Covid-19 Impact on Gender (Male and Female) in South Asian Country Pakistan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 925 Saba Malik, Mubbra Habib, Mehmood Ahmed Husnain Hashmi, Muhammad Tariq Saeed, Anwaar ul Huda, Syeda Anam Zahra, Muhammad Nisar, Muhammad Sajid, Shahbaz Ahmad Shahzad, Saddam Hussain, and Muhammad Sakandar Majid Deep Residual Convolutional Neural Network Based Detection of Covid-19 from Chest-X-Ray Images . . . . . . . . . . . . . . . . . . . . . . . . . . 939 Valaparla Rohini, M. Sobhana, Ch. Smitha Chowdary, Mukesh Chinta, and Deepa Venna A New Optimized Approach to Resolve a Combinatorial Problem: CoronaVirus Optimization Algorithm and Self-organizing Maps . . . . . . 947 Omayma EL Majdoubi, Farah Abdoun, and Otman Abdoun Study of a Dual-Function Intelligent Electronic Pin to Help Compliance with Security Measures Related to Covid-19 . . . . . . . . . . . . 959 Laince Pierre Moulebe, Abdelwahed Touati, Eric Obar Akpoviroro, and Nabila Rabbah DenTcov: Deep Transfer Learning-Based Automatic Detection of Coronavirus Disease (COVID-19) Using Chest X-ray Images . . . . . . 967 Youssra El Idrissi El-Bouzaidi and Otman Abdoun Network Technology Explainable Deep Learning Model for COVID-19 Screening in Chest CT Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 981 Mostafa El Habib Daho, Amin Khouani, Mohammed El Amine Lazouni, and Sidi Ahmed Mahmoudi

Contents

xxi

A Novel Tool for Automating the Trace File Analysis in Vehicular Ad Hoc Networks with Multi-agents Method . . . . . . . . . . . . . . . . . . . . . 991 Sanaa Achchab, Souad El Houssaini, Amal Tmiri, Souad Ajjaj, and Mohammed-Alamine El Houssaini A Fuzzy On/Off Switching Strategy for Green Cellular Networks . . . . . 1003 Soufiane Dahmani, Mohammed Gabli, Abdelhafid Serghini, and El Bekkaye Mermri Multi-band Planar-Inverted-F-Antenna Design for WIFI WIMAX and WLAN Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1013 Asma Khabba, Layla Wakrim, Saida Ibnyaich, and Moha M’Rabet Hassani A New Approach for Evaluating the Performance of AODV Routing Protocol in VANETs Using the Plackett-Burman Method . . . . . . . . . . . 1021 Souad Ajjaj, Souad El Houssaini, Mustapha Hain, and Mohammed-Alamine El Houssaini Design and Simulation of a New Dual Band Fractal Antenna for TM Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1033 Abdelhakim Moutaouakil, Younes Jabrane, Abdelati Reha, Abdelaziz Koumina, and Nadia El Makoussi Design and Simulation of Three Bands Planar Inverted F Antenna Array for 5G Communication Systems . . . . . . . . . . . . . . . . . . . . . . . . . 1039 Sara Arsalane, Nabil Arsalane, and Mounir Rifi RFID System for Hospital Monitoring and Medication Tracking Using Digital Signature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1051 Safae El Abkari, Soufiane Kaissari, Jamal El Mhamdi, Abdelilah Jilbab, and El Hassan El Abkari Building a Domain Ontology for the Construction Industry: Towards Knowledge Sharing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1061 Hayat El Asri, Fatine Jebbor, and Laila Benhlima Optimization of the Ad Hoc Network by Using Hybridization of Genetic Algorithm with a Two-Optimization Algorithm . . . . . . . . . . 1073 Hala Khankhour, Otman Abdoun, and Jâafar Abouchabaka Design of a Microstrip Antenna Two-Slot for Fifth Generation Applications Operating at 27.5 GHz . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1081 Salah-Eddine Didi, Imane Halkhams, Mohammed Fattah, Younes Balboul, Said Mazer, and Moulhime El Bekkali Performance Improvement of Corner-Truncated Sierpinski Carpet Fractal Antenna Using U-tree Slots for Vehicular Communications and Other Bands . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1091 Fatima Ez-Zaki, Abdelilah Ghammaz, and Hassan Belahrach

xxii

Contents

A Novel System Based V2V Communications to Prevent Road Accidents in Morocco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1101 Zakaria Sabir and Aouatif Amine Detection of Underground Cavities Using Electromagnetic GPR Method in Bhalil City (Morocco) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1111 Oussama Jabrane, Driss El Azzab, Mahjoub Himi, Mohammed Charroud, and Mohammed El Gettafi An Adapted Routing Protocol for Mobile Edge Computing . . . . . . . . . . 1121 Ali Ouacha Advanced Technologies in Energy and Electrical Engineering Improved Geographic Routing Protocol for Wireless Sensor Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1135 Kenza Redjimi, Mohammed Redjimi, and Mehdi Boulaiche Supervision and Monitoring of Photovoltaic Systems Using Siemens PLC and HMI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1147 Ahmed Bouraiou, Ammar Neçaibia, Saad Motahhir, Mohammed Salah Bouakkaz, Issam Attoui, Nadir Boutasseta, Rachid Dabou, and Abderrezzaq Ziane Maximum Power Point Tracking Using SEPIC Converter and Double Diode Solar Cell Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1159 Khalid Chennoufi and Mohammed Ferfra Global Maximum Power Point Tracking Using Genetic Algorithm Combined with PSO Tuned PID Controller . . . . . . . . . . . . . . . . . . . . . . 1171 Mohammed Salah Bouakkaz, Ahcene Boukadoum, Omar Boudebbouz, Abdel Djabar Bouchaala, Nadir Boutasseta, Issam Attoui, Ahmed Bouraiou, and Saad Motahhir Experimental Validation of a Neuro-Fuzzy Model of Open Circuit Voltage and Maximum Power Voltage Case of Amorphous Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1181 Brahim Bouachrine, Mustapha Kourchi, Driss Yousfi, Kaoutar Dahmane, Mhand Oubella, and Mohamed Ajaamoum Simulation of Natural Ventilation on Building with Solar Chimney Under Climatic Conditions of Errachidia Morocco Zone . . . . . . . . . . . . 1191 Herouane Aboubakr, Thami Ait Taleb, and Mourad Taha Janan Investigating the Effect of the Air Gap of a Solar Air Heater Intended for an Indirect Drying System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1205 Mourad Salhi, Dounia Chaatouf, Benyounes Raillani, Samir Amraqui, and Ahmed Mezrhab

Contents

xxiii

Experimental Investigation of Efficiency and Dynamic Losses for a Constructed Solar Boost Converter . . . . . . . . . . . . . . . . . . . . . . . . 1213 Weam El Merrassi, Abdelouahed Abounada, and Mohamed Ramzi Improvement of the Non-linear Control Strategy of a Wind Turbine by a High-Gain Observer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1223 Azeddine Loulijat, Najib Ababssi, and Mohamed Makhad Optimization of DFIG Wind Turbine Power Quality Through Adaptive Fuzzy Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1235 Hamid Chojaa, Aziz Derouich, Mohammed Taoussi, Othmane Zamzoum, and Mourad Yessef Use of a Doubly-Fed Induction Generator for the Conversion of Wind Energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1245 Wahabi Aicha, Elmoudden Abdelhadi, and Senhaji Rhazi Kaoutar Improvement of the Wind Power System Based on the PMSG . . . . . . . 1255 Btissam Majout, Badre Bossoufi, Mohammed Karim, and Chakib El Bekkali Control of Wind Water Pumping Using Input-Output Feedback Linearization Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1267 Atarsia Loubna, Toufouti Riad, and Meziane Salima Improved Hybrid Control Strategy of the Doubly-Fed Induction Generator Under a Real Wind Profile . . . . . . . . . . . . . . . . . . . . . . . . . . 1279 Mourad Yessef, Badre Bossoufi, Mohammed Taoussi, Ahmed Lagrioui, and Hamid Chojaa Flow-Oriented Control Design of Wind Power Generation System Based on Permanent Magnet Synchronous Generator . . . . . . . . . . . . . . 1291 Nada Zine Laabidine, Chakib El Bakkali, Karim Mohammed, and Badre Bossoufi Households Energy Consumption Forecasting with Echo State Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1305 Wadie Bendali, Mohammed Boussetta, Ikram Saber, and Youssef Mourad Management of Multi-agents in a Smart Grid Network with the Python Using the Contract Net Protocol . . . . . . . . . . . . . . . . . 1317 Saber Ikram, El Bachtiri Rachid, Bendali Wadie, Boussetta Mohammed, El hammoumi Karima, and Cheddadi Fatima Smart Grid Operation Using Hybrid AI Systems . . . . . . . . . . . . . . . . . . 1327 Chakib Alaoui and Hajar Saikouk

xxiv

Contents

Economic Comparison Between Two Hybrid Systems (Wind-Hydrogen) and (Wind-Hydroelectric) for Electricity Production in Socotra, Yemen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1341 Saif Serag, Outhman Elbakkali, and Adil Echchelh A New Hybrid System Used to Connect PV to a Microgrid . . . . . . . . . . 1353 Alexandru Dusa, Petru Livinti, Daniel Ciprian Balanuta, and Gelu Gurguiatu Coordinated Control and Optimization Dispatch of a Hybrid Microgrid in Grid Connected Mode . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1365 Marouane Lagouir, Abdelmajid Badri, and Yassine Sayouti Smart Energy Management System: SCIM Diagnosis and Failure Classification and Prediction Using Energy Consumption Data . . . . . . . 1377 Oussama Laayati, Mostafa Bouzi, and Ahmed Chebak Intelligent Technique Proposed for Nonlinear Inductor Modelling for DC/DC Converters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1387 Rafika El idrissi, Ahmed Abbou, Abderrahim Taouni, and Mohcine Mokhlis High Order Sliding Mode Power Control of Doubly Fed Induction Generator Based on Wind Turbine System . . . . . . . . . . . . . . . . . . . . . . 1399 Yahya Dbaghi, Sadik Farhat, Mohamed Mediouni, Hassan Essakhi, and Aicha Elmoudden Robust Voltage Control for Four-Phase Interleaved DC-DC Boost Converter for Electric Vehicle Application . . . . . . . . . . . . . . . . . . . . . . . 1409 Mohamed Barara, M. R. Barakat, Nabil Elhaj, and Ghania Belkacem MPPT and Pitch Angle Based on Neural Network Control of Wind Turbine Equipped with DFIG for All Operating Wind Speed Regions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1421 Moussa Reddak, Ayoub Nouaiti, Anass Gourma, and Abdelmajid Berdai Cairo Metro Power Analysis and Harmonic Mitigation . . . . . . . . . . . . . 1433 Ahmed Abd Elsadek, O. A. Monem, Zaki Matar, Mokhtar Hussien, and Yasser Elsayed Dual Fuzzy Direct Power Control for Doubly-Fed Induction Generator: Wind Power System Application . . . . . . . . . . . . . . . . . . . . . 1445 Hala Alami Aroussi, El Mostafa Ziani, Manale Bouderbala, and Badre Bossoufi DFIG-WECS Non Linear Power Controls Application . . . . . . . . . . . . . 1455 Manale Bouderbala, Badre Bossoufi, Hala Alami Aroussi, Mohammed Taoussi, and Ahmed Lagriou

Contents

xxv

Behaviour of Concrete and Steel for the Construction of Storage Tanks: Comparative Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1467 Salma El Aamery, El Hassan Achouyab, and Hassan OuajjiOuajji Sensorless Control of DC-DC Converter Using Integral State Feedback Controller and Luenberger Observer . . . . . . . . . . . . . . . . . . . 1477 Djamel Taibi, Toufik Amieur, Mohcene Bechouat, Moussa Sedraoui, and Sami Kahla Optimization and Improvement of the Efficiency of a Drying System Based on the Dimensional Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . 1489 Dounia Chaatouf, Benyounes Raillani, Mourad Salhi, Samir Amraqui, and Ahmed Mezrhab Monitoring and Control System of a Hybrid Micro-CSP/Biomass Boiler System for Traditional Baths . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1497 Said Lamghari, Hassan Hamdi, Mehdi Khaldoun, Mickael Benhaim, Fatima Ait Nouh, and Abdelkader Outzourhit Low Capacity Diffusion Absorption Refrigeration: Experimental Investigations and Thermal Characterization . . . . . . . . . . 1505 Ikram Saâfi, Ahmed Taieb, and Ahmed Bellagi The Synchronized Electrical Charge Extraction Regulator for Harvesting Energy Using Piezoelectric Materials . . . . . . . . . . . . . . . 1517 Youssef El Hmamsy, Chouaib Ennawaoui, Ikrame Najihi, and Abdelowahed Hajjaji Calculation of the Magnetic Field in the Vicinity of the Overhead Transmission Lines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1529 S. Houicher and R. Djekidel Implementation of the Vehicle Speed and Location Monitoring System to Minimize the Risk of Accident . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1539 Mohammed Karrouchi, Ismail Nasri, Hajar Snoussi, Abdelhafid Messaoudi, and Kamal Kassmi Comparison Study of the Resonant Inductive Power Transfer for Recharging Electric Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1549 Naima Oumidou, Ali Elkhatiri, Sara Khalil, Moussa Labbadi, and Mohamed Cherkaoui Magnetic Chargers in Electrical Models: Operating Principle and Efficiency Analysis of an Inductively Coupled Power Transfer System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1561 Ali Elkhatiri, Naima Oumidou, Moussa Labbadi, Mouna Lhayani, and Mohamed Cherkaoui

xxvi

Contents

Improve the Energy Harvesting Alternatives Using the Bond Graph Approach for Powering Critical Autonomous Devices . . . . . . . . . . . . . . 1573 Souad Touairi and Mustapha Mabrouki Online Energy Management Strategy of FC/UC Hybrid Electric Vehicle Based on Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1585 Nada Rifai, Jalal Sabor, and Chakib Alaoui The Potential Outcomes of Artificial Intelligence Applied to the Powered Two-Wheel Vehicle: Analytical Review . . . . . . . . . . . . . 1595 F. Jalti, B. Hajji, and A. Mbarki The Design of an EEG Bio-Amplifier for the Primary Motor Cortex Zone in 180 nm CMOS Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1607 Younes Laababid, Karim El Khadiri, Hassan Qjidaa, Ahmed Lakhssassi, and Ahmed Tahiri Design of a Non-inverting Buck-Boost Converter Controlled by Voltage-Mode PWM in TSMC 180 nm CMOS Technology . . . . . . . 1619 Anas Boutaghlaline, Karim El Khadiri, Hassan Qjidaa, and Ahmed Tahiri Low-Power High-Speed On-Chip 5 to 1 Serializer in 180 nm Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1631 Aicha Menssouri, Karim El Khadiri, Hassan Qjidaa, Ahmed Lakhssassi, and Ahmed Tahiri Computing and Analyzing Through Silicon Via (TSV) Noise Coupling in 3D-IC Structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1643 Khaoula Ait Belaid, Hassan Belahrach, Hassan Ayad, and Fatima Ez-zaki Design of a CMOS Bandgap Reference Voltage Using the OP AMP in 180 nm Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1655 Ahmed Rahali, Karim El Khadiri, Zakia Lakhliai, Hassan Qjidaa, and Ahmed Tahiri Mechatronic, Robotic and Control System Analytical Study of the CMOS Active Inductor . . . . . . . . . . . . . . . . . . . 1665 Imane Halkhams, Wafae El Hamdani, Said Mazer, Moulhime El Bekkali, and Mohammed Fattah SM Versus PI Tunning for PWM Rectifier for SEIG in Wind Energy Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1675 Ouafia Fadi, Soufiane Gaizen, and Ahmed Abbou Improved DTC of the PID Controller by Using Genetic Algorithm of a Doubly Fed Induction Motor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1687 Said Mahfoud, Aziz Derouich, Najib El Ouanjli, Mohammed Taoussi, and Mohammed El Mahfoud

Contents

xxvii

State Feedback Control of DC-DC Converter Using LQR Integral Controller and Kalman Filter Observer . . . . . . . . . . . . . . . . . . . . . . . . . 1699 Djamel Taibi, Toufik Amieur, Mohcene Bechouat, Sami Kahla, and Moussa Sedraoui Backstepping and Indirect Vector Control for Rotor Side Converter of Doubly Fed-Induction Generator with Maximum Power Point Tracking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1711 Elmostafa Chetouani, Youssef Errami, Abdellatif Obbadi, and Smail Sahnoun Integral Backstepping Power Control of DFIG Based Nonlinear Modeling Using Voltage Oriented Control . . . . . . . . . . . . . . . . . . . . . . . 1725 Mourad Loucif, Abdelkader Mechernene, and Badre Bossoufi Smart Monitoring PID of Solenoid Response Based on Fiber Squeezer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1735 Said Amrane, Abdallah Zahidi, Nawfel Azami, Mostafa Abouricha, Naoual Nasser, and Mohamed Errai Fuzzy Logic Based MPPT Control of an Isolated Hybrid PV-Wind-Battery Energy System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1745 Hadjer Abadlia and Riad Toufouti Implementation of a Digital Control for PV Power Systems . . . . . . . . . 1757 Badreddine Lahfaoui Towards a Digital Modeling of the Optimal Mechanical Properties of a Green Eco Composite Based on Renewable Resources . . . . . . . . . . 1765 Aziz Moumen, Abdelghani Lakhdar, Mustapha Jammoukh, and Khalifa Mansouri Experimental Evaluation of MEMS Accelerometers Integrated into Smartphones: A Case Study of Bearing Condition Monitoring . . . . 1777 Abdelbaset Ait Ben Ahmed, Abdelhamid Touache, Abdelhadi El Hakimi, and Abderrahim Chamat MPPT Using Adaptive Genetic-Fuzzy Logic Control for Wind Power System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1787 Chakib Alaoui, Hajar Saikouk, and Anass Bakouri Development of an Automated Measurement Platform for an Electrical Traction System Test Bench . . . . . . . . . . . . . . . . . . . . 1799 Amine El Houre, Driss Yousfi, Zakaria Bourzouk, and Mohammed Chaker Simulation of a Guidance Law on a ROS-Based Nonholonomic Mobile Robot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1809 Nabil Rezoug and Mokhtar Zerikat

xxviii

Contents

Speed Sensorless Direct Torque Control of Doubly Fed Induction Motor Using Model Reference Adaptive System . . . . . . . . . . . . . . . . . . 1821 Mohammed El Mahfoud, Badre Bossoufi, Najib El Ouanjli, Said Mahfoud, Mourad Yessef, and Mohammed Taoussi Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1831

Artificial Intelligence, Machine Learning and Data Analysis

New Reconfiguration of the Radial Distribution Network by Using the Chaotic Mapping and the Success Rate to Improve the PSO Algorithm Meriem M’dioud, Ismail ELkafazi, and Rachid Bannari

Abstract The inertia weight is one of the inspection parameters that improve the reliability of the Particle Swarm Optimization (PSO). To define this inertia, several techniques have been suggested to improve the quality of the PSO algorithm. In this paper, the aim objective is to determine the new reconfiguration of the radial system by using two strategies: the chaotic descending inertia weight, and the combination strategy of the chaotic inertia weight and the success rate technique aiming to improve the result. To define the reliability and the solidity of these suggested techniques, IEEE 33 bus is used, and the results are compared with other recent studies where they have also used the particle swarm optimization by using other strategies to define the inertia weight parameter. These strategies of inertia weight used in this paper are selected for their features of speed convergence and precision of solution that is near to the optimal solution. Keywords Radial system distribution · Particle swarm optimization · Load flow analysis · Power losses · Chaotic inertia weight · Success rate

1 Introduction In recent years, several companies have been oriented towards the research about the new strategies, to improve and optimize energy exploitation by finding suitable solutions, so these new strategies help to minimize the losses. This paper focuses on the distribution network’s reconfiguration. Many researchers have proposed different methods based on mathematical programming for solving problems of losses and cost minimization.

M. M’dioud (B) · R. Bannari Laboratory Engineering Sciences Ensa, Ibn Tofail University Kenitra, Kenitra, Morocco I. ELkafazi Laboratory SMARTILAB, Moroccan School of Engineering Sciences, EMSI, Rabat, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_1

3

4

M. M’dioud et al.

In this vision, the authors of [1], have used an evolutionary technique using the particle swarm optimization, to find the optimal solution of restoration of radial power distribution. The authors of [2], have solved the same problem using the minimum spanning tree, and in the end, they have compared their solution with the solution of the authors [1]. For the authors of [3], they have considered that the reactive power is the weights of lines to reduce losses. On the other hand, the authors of [4] have chosen the genetic algorithm that provides good quality solutions in reduced execution time. In this article, i will use an improved and adaptative PSO algorithm, thanks to its high speed to converge towards an optimal solution, it easy to implement and it focuses on simple mathematical equations. For testing the solidity and the reliability of this suggested method, this paper uses the IEEE 33 bus, and compare the results with other recent studies. In this vision, the authors of [5], have used in their study, the Particle Swarm Optimization based on the decreasing inertia weight w to update position and velocity of the particle then they have used the Backward Forward Sweep to do load flow analysis. For the authors [6], they have used also the linear decreasing inertia weight by applying the sigmoid transformation to limit the velocities. On another side, the authors of [7] have applied the linear decreasing weight by canceling the wend in the second term. The authors of [8] have tried to solve this problem of reconfiguration by using a modified shark smell optimization, that based on the same idea of particle swarm optimization, and he concludes that this method solves the problem in a significant time and help to improve the profile voltage at each node. Considering the authors of [9], where they have made a comparative study between the different strategies used to calculate the inertia weight parameter, and to validate the performance of their study they based on five famous mathematic equations, at the end of their study, they have concluded that the chaotic inertia weight is the best strategy for better accuracy, however, the random inertia weight strategy is best for better efficiency. On the other hand, the authors of [10] have based on the chaotic inertia weight by combined the swarm success rate parameter with the chaotic mapping to set the new inertia weight coefficient, aiming to verify the quality and the efficiency of this proposed method, they tested their strategy by searching the solution of these function (Griewank, Rastrigin, Rosenbrock, Schaffer f6, Sphere), and they have concluded that the swarm success rate is a useful tool to improve the reliability of any swarm focused on optimization algorithms. Hence, in this paper, the aim objective is to adjust the inertia weight parameter by using the two tools described in the previous paragraph (the chaotic inertia weight and the swarm success rate combined with the chaotic mapping). To study this problem, we have divided the article into four sections. Section two presents the objective function and define the constraints. Describe the algorithm of the chosen method and present the case study. Section three discusses and analyses the results with the other recent works. And in the five and the final section, i conclude the research and i determine the future works.

New Reconfiguration of the Radial Distribution Network …

5

2 Problematic Reformulation As described in the previous section, the peak demand period is when all resources are operating at maximum, and among the drawback of this period is that give rise to unnecessary expenses for the electric companies resulting from losses. As the load increases, these losses are more significant. Aiming to reduce the losses, this paper is focused on reconfiguration of the radial system by using two kinds of inertia weight to implement the PSO algorithm.

2.1 Objective Function In this paper, to calculate losses, it seems important to use the following expression: Pj =

 k∈S

Rk ∗ Ik 2

(1)

With S is the set of the network’s lines and Ik is the current at line k, Rk is the current at line k. This problem of the loss’s optimization solved under the following constraints [11]: Kirchhoff’s law: I∗X=0

(2)

Where; I: row vector of current and X: incidence matrix of graph (Xij = 0 if there are no arcs between i and j; Xij = 1 else); Tolerance limit: |(Vin − Vi )/Vin | ≤ εimax

(3)

Vin nominal voltage at node i, Vi is the voltage at node i, and εimax is tolerance limit at node i [4] (±5% for HTA and +6%/−10%BT). Admissible current constraint: Ik ≤ Ik,maxadm

(4)

Ik : current of the line k and Ik,maxadm : current maximum admissible of the line k. Radial topology constraint: For the reasons to have a simple, inexpensive operation and keeping the security and protection of distribution power grid, the radial configuration is requested. It means that each loop should have an open line. To check this topology, the following constraints should be considered: Total number of main loops:

6

M. M’dioud et al.

Nmain loops = (Nbr − Nbus ) + 1

(5)

Where Nbr is the total branches of the network, Nbus is the total number of buses and Nmain loops are the total number of loops in the network. The Total number of sectionalizing switches Nbr = Nbus − 1

(6)

The total number of open switches must be equal to the number of main loops in the network. To solve this problem, it seems useful to break up this problem into two important elements, the first element concerning the reconfiguration of the radial distribution system using the PSO algorithm and the second element concerning the NewtonRaphson methods to apply the power flow module to check the set of the constraints. This load flow method is chosen due to its feature of a fast convergence rate [12].

2.2 PSO Algorithm PSO is a metaheuristic of optimization, invented by the authors [13]. This optimization tool is focused on the collaboration of individuals among themselves. In this study, thanks to the characteristic of the simple travel rules in the solution space, the particles can gradually find the global minimum. This algorithm works following these steps: Step 1: in the beginning, initialize the number of particles and the number of tie lines by respecting the condition of the system is in radial nature. Step 2: Initialize iteration number (maxiter), inertia coefficient (wmin and wmax), and acceleration coefficients (C1 and C2), the initial velocity of each particle is randomly generated. Step 3: Identify the search space for each D dimension (all possible configuration). Step 4: apply newton-Raphson method [12] to load flow analysis. Step 5: define the best value among all pbest values. Step 6: find the global best and identify the new tie switches. Step 7: update the velocity and new position for each D dimension of the ith particle using the following equation: Select a random number z in the interval [0, 1] and use a chaotic mapping by using the logistic mapping to set inertia weight coefficient [9]: z(iter + 1) = 4 ∗ z(iter) ∗ (1 − z(iter))

(7)

or use the success rate [10]:  Successti =

  1 f  Pbestti  < 0 f Pbestti ≥

  i f  Pbestt−1  i f Pbestt−1

(8)

New Reconfiguration of the Radial Distribution Network …

Succrate =

n i=1

Successti /n

z(iter + 1) = 4 ∗ Succrate ∗ (1 − Succrate )

7

(9) (10)

According to the choice of the inertia weight calculation strategy, adjust and calculate the inertia coefficient by using [13]: W = (wmin − wmax) ∗ (maxiter − iter)/maxiter+ wmax*z(iter+1)

(11)

After adjusting the inertia weight by selecting one of the previous equations that define this parameter: Update the velocity by using this equation [13]: Vi(iter + 1) = W ∗ Vi (iter) + C1 ∗ r1 ∗ (Pi (iter) − Xi(iter)) + C2 ∗ r2 ∗ (G(t) − Xi (iter))

(12) Update the position by using this equation [13]: Xi (iter + 1) = xi (iter) + vi (iter + 1)

(13)

Define the new fitness function value for the new position [13]:  = Pbestt+1 t

  Pbestti f xit+1  > Pbestit xit+1 f xit+1 ≤ Pbestit

(14)

Define the global best by using [13]  Gbest = min Pbestit }

(15)

Step 8: until iter = maxiter, go to step 4. Else print the optimal results. Step 9: display result.

2.3 Test Case In IEEE graph has 33 buses. There are 37 branches, 32 close switches, and 5 open switches. The red line indicates the tie-line switches. In the initial case, we assume that {33-34-35-36-37} is the set of the opened lines. The IEEE 33 features (electrical and topological) are given in the reference [14], the following figure shows the architecture of the network at the initial case (Fig. 1): The respective loops, hence, dimensions, the search space for respective dimension and the parameters of PSO are given in [8].

8

M. M’dioud et al.

Fig. 1 Distribution system IEEE “33 node before reconfiguration”

Fig. 2 IEEE 33 bus after reconfiguration using chaotic inertia weight

3 Test and Results After the implementation of the IEEE 33 bus data in MATLAB, we obtained the new radial distribution that presented in the next schema (Figs. 2 and 3): In this case, and after reconfiguration by using the chaotic inertia weight, it’s clear that the new tie lines are 7-9-14-32-37. For the second suggested method using the combination of the success rate and the chaotic inertia weight, the tie lines are 7-11-14-28-36. To define the updated loss value of the new distribution network and improve the voltage profile at each node, the Newton Raphson method [12] (applicated by using Matpower version 4.1). The next table summarizes the result of

New Reconfiguration of the Radial Distribution Network …

9

Fig. 3 Network distribution after reconfiguration by using PSO with combination of the success rate and the chaotic inertia weight

Table 1 Comparative study Base case

[5]

[7]

[8]

PSO using the chaotic inertia weight

PSO using the combination of success rate and chaotic inertia weight

Tie switch

33-34-35-36-37

7-9-14-28-31

9-7-14-37-32

7-9-14-32-37

7-9-14-32-37

7-11-14-28-36

Real power losses (kW)

208.459

148.3075

136.3

138.9276

138.9275

143.9227

Vmin(p.u)

0.91075

0.9395

0.940

0.94234

0.9423

0.93682

Node

18

32

22

32

32

18

this paper and gives the result of other studies to make difference and conclude the feature and the benefits of this study. As presented in the above table, concerning the tie switch, for the case of reconfiguration by using the chaotic inertia weight, the new tie lines are 7-9-14-32-37, while the new tie lines in the case of using the combination of success rate and the chaotic inertia are 7-11-14-28-36. When we talk about the role of these strategies to reduce losses, we notice that the losses in the case of PSO using the chaotic inertia weight equal to 138.9276 kW, and in the case of the using a combination of success rate and the chaotic inertia weight the losses become 143.9227 kW. These values are lesser than the losses in the base case (208.459 kW) and the case [5] where the value of losses is 148.3075 kW. But they are greater than the value found by [7] which equals 136.3 kW. For the voltage profile side, the Table 1 proves that in the case of PSO using the chaotic inertia weight, the minimum voltage profile equal to 0.9423 p.u at node 32, and in the case of PSO using the combination of success rate and the chaotic inertia weight the losses equal to 0.93682 p.u at node 18.these values are much improved

10

M. M’dioud et al.

Fig. 4 Voltage profile before and after the reconfiguration using chaotic inertia weight

than the base case 0.91075 p.u at bus 18, and also improved than [5] where the minimum voltage profile is 0.9395 p.u at bus 32. Also, this value found by using the chaotic inertia weight is perfect than [7], where the minimum voltage in this study equal to 0.940 p.u at node 22. To conclude, it seems clear that the PSO using the chaotic inertia weight gives the best result than the PSO using the combination of the success rate and the chaotic inertia weight. This first proposed strategy gives the same result as [8]. However, this first suggested method reduces the losses with voltage profile improved than [7]. The following figure shows the difference between the voltage profile in the case of the chaotic inertia weight method and in the base case (Fig. 4): The Fig. 5 shows the difference between the voltage profile in the case of the combination method and in the base case. In addition, it is important to note that this first proposed algorithm takes 46.68s and the second one takes about 55.178566 s for that they run.

New Reconfiguration of the Radial Distribution Network …

11

Fig. 5 Voltage profile before and after the reconfiguration using the combination of success rate and the chaotic inertia weight

4 Conclusion This paper is concerned with a study of two kinds of strategies to determine the inertia weight, the chaotic inertia weight, and the combination strategy. The main objective of this research is to define a new reconfiguration of the radial distribution, aiming to reduce losses and improve voltage profile. To check the reliability and the performance of these strategies, we use IEEE 33 bus, to test these strategies. The results found are compared with another recent research focused on the PSO algorithm. According to the results, it’s clear that using the chaotic inertia weight help to reduce losses better than the combination strategy of the success rate and the chaotic inertia weight. In the framework of the profile voltage improvement, these two strategies found the same result. But the combination strategy takes more time than the chaotic inertia weight strategy to execute. Finally, the chaotic inertia weight is the best strategy for better reconfiguration and improved losses. In the next study, I will use this perfect strategy to find the best allocation and sizing of a distributed generation.

References 1. Oliveira LW, Oliveira EJ, Silva IC, Gomes FV, Borges TT, Marcato AL, Oliveira ÂR (2015) Optimal restoration of power distribution system through particle swarm optimization. In: IEEE Eindhoven PowerTech, p 13

12

M. M’dioud et al.

2. Mohamad H (2019) Power system restoration in distribution network using kruskal’s algorithm. Indonesian J Electr Eng Comput Sci 16(1):8 3. Sudhakar TD (2017) Loss minimization in distribution network by using Prim’s algorithm. Int J Appl Eng Res 9(24):15 4. Tomoiag˘a B, Chindri¸s M, Sumper A, Villafafila-Robles R, Sudria-Andreu A (2013) Distribution system reconfiguration using genetic algorithm based on connected graphs. Electric Power Syst Res J 104(216–225):10 5. Sudhakara Reddy AV, Damodar Reddy M (2016) Optimization of network reconfiguration by using particle swarm optimization. In: 1st IEEE international conference on power electronics, intelligent control and energy systems (ICPEICES) 6. Tandon A, Saxena D (2014) Optimal reconfiguration of electrical distribution network using selective particle swarm optimization algorithm 7. Atteya II, Ashour H, Fahmi N, Strickland D (2017) Radial distribution network reconfiguration for power losses reduction using a modified particle swarm optimisation 8. Juma SA (2018) Optimal radial distribution network reconfiguration using modified shark smell optimization. Pan African University Institute for Basic Sciences Technology and Innovation 9. Bansal JC, Singh PK, Saraswat M, Verma A, Jadon SS, Abraham A (2011) Inertia weight strategies in particle swarm optimization 10. Arasomwan MA, Adewumi AO (2013) On adaptive chaotic inertia weights in particle swarm optimization 11. Enacheanu FB (2008) outils d’aide à la conduite pour les opérateurs des réseaux. https://tel. archives-ouvertes.fr/tel-00245652 12. Sharma A, Saini M, Ahmed M (2017) Power flow analysis using NR method. In: International conference on innovative research in science, technology and management 13. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: IEEE international conference on neural networks, proceedings, vol 4 14. Baran ME, Wu FF (1989) Network reconfiguration in distribution systems for loss reduction and load balancing. EEE Trans Power Deliv 4(2):7

Convolutional Neuronal Networks for Tumor Regions Detection in Histopathology Images Mohammed Lamine Benomar, Nesma Settouti, Rudan Xiao, Damien Ambrosetti, and Xavier Descombes

Abstract Recent advances in deep learning and image sensors present the opportunity to unify these two complementary fields of research for a better resolution of the image classification problem. Deep learning provides image processing with the representational power necessary to improve the performance of image classification methods. The deep networks are considered as the most powerful tool in visual recognition, notably on the automated analysis of histological slides using Whole Slide Image (WSI) scanning techniques. In this paper, we proposed a comparative study analysis for efficient localization of tumor regions in kidney histopathology WSIs by employing state-of-the-art convolutional deep learning networks such as ResNet and VGGNet which can be fine-tuned to the target problem and exploit the powerful transfer learning techniques that save low computational costs. Experimental results conducted on an independent set of 17.300 images derived from 12 WSIs show that ResNet50 and VGG19 combined with VGG16 achieve almost 97% and 96% of accuracy respectively, for classifying tumor and normal tissues to identify relevant regions of interest (ROIs). Keywords Convolutional neuronal network · Whole Slide Image (WSI) · Renal Cell Carcinoma (RCC) · Histopathology image M. L. Benomar (B) University of Ain Temouchent Belhadj Bouchaib, Ain Temouchent, Algeria e-mail: [email protected] M. L. Benomar · N. Settouti Biomedical Engineering Laboratory, University of Tlemcen, Tlemcen, Algeria e-mail: [email protected] R. Xiao · X. Descombes Inria, Sophia Antipolis, Biot, France e-mail: [email protected] X. Descombes e-mail: [email protected] D. Ambrosetti Laboratoire Central d’Anatomo Pathologie, CHU Nice/Hôpital Pasteur, Nice, France e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_2

13

14

M. L. Benomar et al.

1 Introduction Histopathological WSI analysis is the microscopic examination of biological tissues in order to study the manifestations and evolution of a disease, highlighting the interactions between different cells and other components of the tissue. This study helps to determine what type of cancer it is and what treatments to use. Pathologists must observe in detail wide-field images, images of hundreds of millions of pixels, in order to extract characteristic areas representative of the patient’s condition. In recent years, numerous contests for the automatic processing of such images have been launched [1]. The medical field, and more specifically computational pathology has recently received increasing attention, partially due to the competition emerging, where deep learning approaches have been awarded first place in various challenges: MoNuSAC2020 [2], BACH18 [3], ANHIR2019 [4], CAMELYON17 [5], CAMELYON16 [6] and TUPAC16 [7]. In general, competition winners have used one or more architectures that have performed particularly well in other popular image recognition competitions. These deep models allow end-to-end learning to extract and classify. The extracted features are then automatically determined during the learning process to optimize the classification. Despite the very interesting results obtained in the execution of certain tasks [8– 11] using deep learning approaches, the underlying theoretical aspects are not yet well understood and we still have not a very clear understanding of the architectures or networks that offer better performance than others. It is therefore difficult to say what structure, how many layers or even the number of neurons that must be available on each layer in order to obtain good results for a very specific task. In addition, the lack of knowledge about the choice of critical values, namely the learning rate and the reliability of the optimization, etc…. Therefore, the design of the architecture has always been done in an ADHOC way. In order to reduce the field of investigation and direct users to the best network, we propose a comparative study to find the accurate Convolutional Neuronal Network RCC (renal cell carcinoma) region tumors detection in histopathology images. In this work, we propose a comparative study of several deep learning architectures among these approaches, we are particularly interested in two deep networks: ResNet [12] the winner of the ILSVRC 2015 challenge and VGGNet [13], the finalist of the ILSVRC 2014 competition. Both networks are widely used and known for their power, with architectures that are far from a traditional CNN. The remainder of the paper is organized as follows. In Sect. 2, we provide an overview of the related deep learning works for tumor regions detection in histopathology images, Sect. 3 describes the proposed approach with different deep learning architectures. Section 4 presents the image dataset and discuss the experimental results achieved by the models. Finally, conclusions and perspectives for future work are reported in Sect. 5.

Convolutional Neuronal Networks for Tumor Regions Detection …

15

2 Related Works Research on deep learning through convolutional and other neural networks has paved the way for a representation machine learning approach based on an unsupervised extraction of descriptors (low, medium and high level), i.e. independent of any human intervention that could affect these performances. In recent years, several papers have been published showing the potential of deep learning in digital histopathology. According to the review of state-of-the-art deep learning approaches that have been used in the context of histopathological image analysis presented by Srinidhi et al. [1], among the supervised learning techniques, there are three major canonical deep learning models based on the nature of tasks that are solved in digital histopathology: classification, regression and segmentation-based models. Classification: related to pixel-wise/sliding-window classification-based approaches in a local-level tasks (based on a region) with methods based on multi-scale CNN [14] and transfer learning approaches [15], or global-level tasks (image-level prediction like WSI disease grading) [16] with the popular attention-based models [17]. Regression: consists in detecting or localizing objects by directly regressing the likelihood of a pixel, as the position of cells [18] or nuclei [19] or cancer severity score prediction with constrained CNN models [20]. Most of deep regression models proposed in the literature are mainly based on either Convolutional Neuronal Networks [21] or Fully Convolutional Networks architectures [22]. Segmentation: related mainly to fully convolutional network (FCN) based approach [23] and its variants, which are generally formulated as a semantic segmentation task [24] or instance aware semantic segmentation task [25] in computer vision. Most deep learning methods applied in digital pathology have been successfully evaluated in renal clear cell carcinoma [26], breast cancer [11], cervical cancer [16], prostate cancer [27], where several multiple publicly databases are available. However, there is a need for a relevant one which is more significant and representative of real clinical task. In this paper, we proposed a histopathological Kidney database, it includes 17.300 images issue from 12 WSIs Hematoxylin and eosin (H&E) stained slides of Kidney tumor collected at the University Hospital Center of Nice (France). Whilst large part of the literature deals with tumor regions segmentation [28, 29] or cell and nuclei morphology analysis [26, 30]. Meanwhile, [31] investigate the vascular network on the same dataset study in this paper, in scanning the network irregularity indicating the severity of the cancer i.e. malignant tumor and inversely, the regularity of the network for low grade tumor. In our case, we are particularly interested in the highest level of magnification, and in the segmentation of tumor regions, although not requiring the intervention of an expert, this task could gain in precision to become sufficiently reliable and thus avoid having use of manual segmentation. We propose to study two deep learning architectures, VGGNet and ResNeT which their weight configuration is publicly

16

M. L. Benomar et al.

available and has been used in many other applications and challenges as a basic functionality extractor. However, these two architectures include several parameters, which can be a bit difficult to manage and interesting to study. Indeed, VGGNet 16 & 19 consists of 16 and 19 convolutional layers respectively and are very attractive due to their very uniform architecture. Like the canonical approach of LeNet and AlexNet, with only 3 × 3 convolutions, but lots of filters. It is currently the most popular choice in the community for extracting functionality from images. The ResNet residual neural network is characterized by an architecture with “jump connections” and presents a very heavy batch normalization. These jump connections are also called gated 15 units or gated recurrent units and have a strong similarity to recent successful elements applied in RNNs.

3 Proposed Approach In this paper, we evaluate different deep learning networks to deal with histopathology images task that may assist pathologists in locating tumor regions and allowing further complex image analysis exams to be performed only on relevant regions within the entire slide image (WSI) as can be seen from Fig. 1, thereby achieving more computational efficiency increase pathologists throughput. To develop this system for identifying tumor tissues on histopathological images, our approach focused on data augmentation and the implementation of ResNet and a combination of VGG19 & VGG16. To perform data augmentation, the whole slide image was divided into patches by cropping (500 × 500) regions from the high resolution image to increase the amounts of training images [32, 33], where the goal is

Fig. 1 Tumor Regions Detection Pipeline. 1 Tissue whole slide images (WSI); 2 Extracted patches from whole slide images; 3 Feeding the Deep Network with the Normal and Cancerous slides patches extracted by sliding window; 4 Patches classification as Tumor or Not-tumor; 5 Binary mask generated where each pixel of the map corresponds to a (500 × 500 pixels) patch in the input WSI; 6 Small patches removed using morphological operations. The resulting image is then displayed by sliding window to obtain the final tumor map for the entire whole slide image

Convolutional Neuronal Networks for Tumor Regions Detection …

17

to classify the WSI patches as belonging to tumor regions (malignant) or normal tissue (benign class). Additionally, we applied affine (low-level) transformations to modify images without biasing the classification by using more morphological operators. Besides the data augmentation, computational burden and memory load are also impacted; consequently, each patch (sub-image) was processed independently. The major strength of ResNet architecture lies in its ability to train successfully very deep neural networks with over 150 layers, and addresses the problem of vanishing gradients (i.e. the repeated multiplication can make the gradient extremely small when back-propagating to earlier layers) by providing shortcuts connection at each layer of the network allowing the gradient to pass through the layers more easily [12]. As discussed, we investigate the way of applying popular models such as the VGG network [13] due to its simplicity and success in the ImageNet challenge. VGG network uses small kernels (only 3 × 3) in every convolution layer in order to extract fine features present in images. Additionally, a significant improvement can be achieved by pushing the depth to 16–19 weight layers. Therefore, to correctly classify tumor and other tissue regions from histopathology whole slide images we combine both VGG16 and VGG19 to improve the performance. Indeed, combining different feature extraction methods increases the classification accuracy [34].

4 Experiments and Results 4.1 Data Collection The histopathology dataset used is composed of 17.300 images distributed to 2 classes (ROI tumor and not-ROI) with (500 × 500) pixels originate from a set of 12 digitized hematoxylin and eosin (H&E) stained slides of Kidney tumor collected at the University Hospital Center of Nice (France). The WSIs were acquired using Leica SCN400 scanner on 400 × magnification with a resolution of 0.25 µm per pixel and stored in ‘.scn’ format (RGB encoding with 8 bits per channel), the images are around 100 000 × 100 000 pixels in size. The histopathology whole slide image (WSI) contains different types of deviations from a healthy structure which do not necessarily form a continuous spectrum of changes and their detection can be difficult for pathologists. Therefore, pathologists examined all slides to identify and delineate manually tumor regions for the evaluation of models’ performance. Consequently, each slide has a labeled ground truth image where the tumor tissue regions are marked in black. Additionally, Fig. 2 shows various patches samples from our whole slide images dataset. Finally, the dataset is split into training and test set. We randomly partitioned 14.300 of these images (about 82.5% of the dataset) for model training, and the remaining 3.000 images (about 17.5% of the dataset) for model testing. Furthermore, the 14.300 images used for training were split into a training set of 12.000 images and a validation set of 2.300 images.

18

M. L. Benomar et al.

Fig. 2 Histopathologic patches examples resize to 224 × 224 pixels at 400× magnification

4.2 Experimental Setup We implemented ResNet-50 architecture pre-trained on ImageNet dataset weights with resized square patches from 500 × 500 to 224 × 224 for inputs since the shape of the input image is defined as 224 × 224 pixels and reducing the image size nearly by half does not significantly impact convolutional models accuracy for histopathology images analysis [32]. Data augmentation techniques are widely used in medical image analysis for model generalization. To this end, we performed rotation (90°) and random flipping across the horizontal and vertical axes on the training images dataset. Furthermore, before inputting a patch into the model, the color channels were normalized by rescaling all the values to [0 1] interval. This makes the training approach more convenient by reducing memory and processing needs, and prevents falling into exploding gradients problem without large impact on prediction results. We train the ResNet-50 on the histopathology WSI dataset, however, during the process we freeze ResNet50 layer until “res5a branch2a” of the convolutional part in order to train only the last few layers, where the number 5 indicates the block and the letter “a” the sub-block. The second architecture based on VGG19 and VGG16 fusion, to fine-tuning networks we use the same image patch size 224 × 224 pixels and rescaling all the pixel values to [0 1] interval in order to decrease the colored intensive rate. Additionally, to limit over-fitting we apply the same data augmentation transformations (rotation and flip) to modify original images and avoid biasing the classification process. Unlike the ResNet50 setup, VGG19 and VGG16 were initialized with random weights and all layers are not freezing in order to retrain it on our histopathology dataset with low learning rate. For training the RestNet and VGGs models, a stochastic gradient optimizer was used to fine-tune the networks and cross-entropy as a loss function. Regarding the model’s parameters, batch size and epochs numbers were set to 32 and 20 respectively, with an initial learning rate of 1e−4 and decay according to the epochs. Note that the learning rate is one of the most important hyper-parameters that needs to be tuned when building the model, since it controls the adjustment speed of our network weights with respect to the local minima. Moreover, to avoid over-fitting during the experiments, the early stopping method is required, hence the average number of epochs was between 8 and 12 as the training stopped when the validation accuracy

Convolutional Neuronal Networks for Tumor Regions Detection …

19

did not improve for a certain number of epochs (3 epochs). The tests for patch classification were implemented using Keras library and trained in 25 and 29 min for ResNet50 and VGG19 & VGG16 respectively, on Colab (Google Colaboratory) GPU environment.

4.3 Results The two deep networks described in the previous section were evaluated on the histopathology whole slide images dataset and the experiments were carried out on the training, validation and test subset, where we apply the exact same preprocessing by rescaling images before evaluating the network in order to reach maximum performance. The number of trainable parameters for each model used are 40.666.881 for ResNet50 and 34.740.097 for VGG19 and VGG16. Figures 3 and 4 present the training and validation set loss of ResNet50 and VGGs models respectively, the variance is low ensuring that our models are not over-fitted. Additionally, the accuracy results show that both ResNet50 and VGG19 & VGG16 models perform well and correctly classify cancerous regions. However, ResNet50 achieved slightly better performance than VGG19 & VGG16 models, as shown in Figs. 3 and 4 we plotted the Receiver Operating Characteristic (ROC) Curve of our models, where it achieved an area under the curve (AUC) equal to 0.972 and 0.967 respectively. These results are in good agreement with the ResNet50 model which use the transfer learning techniques that can improve the classification performance with a limited training dataset. Moreover, Deep Residual Network uses bottleneck residual

Fig. 3 ResNet50 training performances

Fig. 4 VGG training performances

20

M. L. Benomar et al.

Fig. 5 ROIs identification results (from left to right): input image, ground truth image, ResNet50 and VGG19&VGG16 result respectively

block design to mitigate the vanishing gradient problem by allowing alternate shortcut path for gradient to flow through. The fusion from VGG19 and VGG16 convolutional layers also achieved important improvements by incrementally adapting the pre-trained features and correlate the sizes of the receptive fields of the convolutional layers with the basic image elements (nucleus, vessels or a particular tissue pattern) to increase the recognition accuracy on tumor vs not-tumor regions classification in histopathology images dataset. Besides the training and validation graphs, the visual results are also important as an evaluation tool. As shown in Fig. 5, the two networks provide important precision in locating the tumor regions for a given whole slide image and assure that no critical region is overlooked during diagnosis. Subsequently, for future work some tumor characteristics must be indicated in order to retain particular regions, since the model provides tumor regions not delimited by the physician in the ground truth.

5 Conclusion In this study, we compare ResNet and VGG convolutional deep learning models that can identify RCC (renal cell carcinoma) regions from normal tissue in histopathology whole slide images. The CNN’s successfully classify the cancerous regions where the ResNet50 achieves the best performance accuracy by adding shortcuts connections to address the accuracy degradation caused by gradient vanishing effect. Moreover, transfer learning and data augmentation can be considered to overcome the lack of data in medical field. As a future work, a post-processing step is necessary to obtain tumor probability maps for the whole slide image. Also, we need to visualize the overall information captured by the networks and the critical learned features in differentiation of cancer regions to further improve the results by adding more data. This opens the possibility to perform classification of the extracted regions into RCC subtypes. Acknowledgements The authors would like to thank the Directorate-General of Scientific Research and Technological Development (Direction Géenérale de la Recherche Scientifique et

Convolutional Neuronal Networks for Tumor Regions Detection …

21

du Développement Technologique, DGRSDT, URL: www.dgrsdt.dz, Algeria) for the financial assistance towards this research.

References 1. Srinidhi CL, Ciga O, Martel AL (2019) Deep neural network models for computational histopathology: a survey. arXiv preprint arXiv:1912.12378 2. Verma R, Kumar N, Patil A, Kurian NC, Rane S, Sethi A (2020) Multi-organ nuclei segmentation and classification challenge 2020 3. Aresta G, Araujo T, Kwok S, Chennamsetty SS, Safwan M, Varghese A, Marami B, Prastawa M, Chan M, Donovan MJ, Fernandez G, Zeineh J, Kohl M, Walz C, Ludwig F, Braunewell S, Baust M, Vu QD, To MNN, Kim E, Kwak JT, Galal S, Sanchez-Freire V, Brancati N, Frucci M, Riccio D, Wang Y, Sun L, Ma K, Fang J, Koné I, Boulmane L, Campilho A, Eloy C, Polonia A, Aguiar P (2018) BACH: grand challenge on breast cancer histology images. CoRR abs/1808.04277 4. Borovec J, Kybic J, Arganda-Carreras I, Sorokin DV, Bueno G, Khvostikov AV, Bakas S, Chang EI, Heldmann S, Kartasalo K, Latonen L, Lotz J, Noga M, Pati S, Punithakumar K, Ruusuvuori P, Skalski A, Tahmasebi N, Valkonen M, Venet L, Wang Y, Weiss N, Wodzinski M, Xiang Y, Xu Y, Yan Y, Yushkevic P, Zhao S, Munoz-Barrutia A (2020) ANHIR: automatic non-rigid histological image registration challenge. IEEE Trans Med Imaging 1 (2020) 5. Bandi P, Geessink O, Manson Q, Dijk MV, Balkenhol M, Hermsen M, Bejnordi BE, Lee B, Paeng K, Zhong A, Li Q, Zanjani FG, Zinger S, Fukuta K, Komura D, Ovtcharov V, Cheng S, Zeng S, Thagaard J, Dahl AB, Lin H, Chen H, Jacobsson L, Hedlund M, Cetin M, Halici E, Jackson H, Chen R, Both F, Franke J, Kusters-Vandevelde H, Vreuls W, Bult P, van Ginneken B, van der Laak J, Litjens G (2019) From detection of individual metastases to classification of lymph node status at the patient level: the camelyon17 challenge. IEEE Trans Med Imaging 38(2):550–560 6. Ehteshami Bejnordi B, Veta M, Johannes van Diest P, van Ginneken B, Karssemeijer N, Litjens G, van der Laak JAWM, the CAMELYON16 Consortium (2017) Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA 318(22), 2199–2210 7. Veta M, Heng YJ, Stathonikos N, Bejnordi BE, Beca F, Wollmann T, Rohr K, Shah MA, Wang D, Rousson M, Hedlund M, Tellez D, Ciompi F, Zerhouni E, Lanyi D, Viana MP, Kovalev V, Liauchuk V, Phoulady HA, Qaiser T, Graham S, Rajpoot NM, Sjoblom E, Molin J, Paeng K, Hwang S, Park S, Jia Z, Chang EI, Xu Y, Beck AH, van Diest PJ, Pluim JPW (2018) Predicting breast tumor proliferation from whole-slide images: the TUPAC16 challenge. CoRR abs/1807.08284 8. Qaiser T, Rajpoot NM (2019) Learning where to see: a novel attention model for automated immunohistochemical scoring. IEEE Trans Med Imaging 38:2620–2631 9. Serag A, Ion-Margineanu A, Qureshi H, McMillan R, Saint Martin MJ, Diamond J, O’Reilly P, Hamilton P (2019) Translational ai and deep learning in diagnostic pathology. Front Med 6:185 10. Dimitriou N, Arandjelovi O, Caie PD (2019) Deep learning for whole slide image analysis: an overview. Front Med 6:264 11. Singh OV, Choudhary P, Thongam K (2020) A study on deep learning for breast cancer detection in histopathological images. In: Nain N, Vipparthi SK, Raman B (eds) Computer vision and image processing. Springer, Singapore, pp 36–48 12. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition, CVPR 2016, Las Vegas, NV, USA, 27–30 June 2016. IEEE Computer Society, pp 770–778

22

M. L. Benomar et al.

13. Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: Bengio Y, LeCun Y (eds) 3rd International conference on learning representations, ICLR 2015, San Diego, CA, USA, 7–9 May 2015, Conference Track Proceedings 14. Albarqouni S, Baur C, Achilles F, Belagiannis V, Demirci S, Navab N (2016) Aggnet: deep learning from crowds for mitosis detection in breast cancer histology images. IEEE Trans Med Imaging 1313–1321 15. Valkonen M, Isola J, Ylinen O, Muhonen V, Saxlin A, Tolonen T, Nykter M, Ruusuvuori P (2020) Cytokeratin-supervised deep learning for automatic recognition of epithelial cells in breast cancers stained for ER, PR, and Ki-67. IEEE Trans Med Imaging 39(2):534–542 16. Halicek M, Shahedi M, Little JV, Chen AY, Myers LL, Sumer BD, Fei B (2019) Head and neck cancer detection in digitized whole-slide histology using convolutional neural networks. Sci Rep 9(1):14043 17. Zhang Z, Chen P, McGough M, Xing F, Wang C, Bui M, Xie Y, Sapkota M, Cui L, Dhillon J, Ahmad N, Khalil FK, Dickinson SI, Shi X, Liu F, Su H, Cai J, Yang L (2019) Pathologist-level interpretable whole-slide cancer diagnosis with deep learning. Nat Mach Intell 1(5):236–245 18. Xie Y, Xing F, Shi X, Kong X, Su H, Yang L (2018) Efficient and robust cell detection: a structured regression approach. Med Image Anal 44:245–254 19. Xie Y, Kong X, Xing F, Liu F, Su H, Yang L (2015) Deep voting: a robust approach toward nucleus localization in microscopy images. In: Navab N, Hornegger J, Wells WM, Frangi AF (eds) Medical image computing and computer-assisted intervention - MICCAI 2015. Springer, Cham, pp 374–382 20. Sirinukunwattana K, Raza SEA, Tsang Y, Snead DRJ, Cree IA, Rajpoot NM (2016) Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE Trans Med Imaging 35(5):1196–1206 21. Naylor P, Lae M, Reyal F, Walter T (2019) Segmentation of nuclei in histopathology images by deep regression of the distance map. IEEE Trans Med Imaging 38(2):448–459 22. Xing F, Cornish TC, Bennett T, Ghosh D, Yang L (2019) Pixel-to-pixel learning with weak supervision for single-stage nucleus recognition in Ki67 images. IEEE Trans Biomed Eng 66(11):3088–3097 23. Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR), pp 3431–3440 24. Chan L, Hosseini MS, Rowsell C, Plataniotis KN, Damaskinos S (2019) Histosegnet: semantic segmentation of histological tissue type in whole slide images. In: The IEEE international conference on computer vision (ICCV), October 2019 25. Qu H, Riedlinger G, Wu P, Huang Q, Yi J, De S, Metaxas D (2019) Joint segmentation and fine-grained classification of nuclei in histopathology images. In: 2019 IEEE 16th international symposium on biomedical imaging (ISBI 2019), pp 900–904 26. Tian K, Rubadue CA, Lin DI, Veta M, Pyle ME, Irshad H, Heng YJ (2019) Automated clear cell renal carcinoma grade classification with prognostic significance. PLoS ONE 14(10):1–16 27. Nagpal K, Foote D, Liu Y, Chen C, Wulczyn E, Tan F, Olson N, Smith J, Mohtashamian A, Wren JH, Corrado G, MacDonald R, Peng L, Amin M, Evans A, Sangoi A, Mermel C, Hipp J, Stumpe M (2019) Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer. Nature Partner J (NPJ) Digit Med 2:48 28. Cheng J, Mo X, Wang X, Parwani A, Feng Q, Huang K (2017) Identification of topological features in renal tumor microenvironment associated with patient survival. Bioinformatics 34(6):1024–1030 29. de Bel T, Hermsen M, Litjens G, van der Laak J (2018) Structure instance segmentation in renal tissue: a case study on tubular immune cell detection. In: Stoyanov D, Taylor Z, Ciompi F, Xu Y, Martel A, Maier-Hein L, Rajpoot N, van der Laak J, Veta M, McKenna S, Snead D, Trucco E, Garvin MK, Chen XJ, Bogunovic H (eds) Computational pathology and ophthalmic medical image analysis. Springer, Cham, pp 112–119 30. Tabibu S, Vinod PK, Jawahar CV (2019) Pan-renal cell carcinoma classification and survival prediction from histopathology images using deep learning. Sci Rep 9(1):10509

Convolutional Neuronal Networks for Tumor Regions Detection …

23

31. Zubiolo A, Debreuve E, Ambrosetti D, Pognonec P, Descombes X (2016) Is the vascular network discriminant enough to classify renal cell carcinoma? In: 14th international workshop on content-based multimedia indexing, CBMI 2016, Bucharest, Romania, 15–17 June 2016. IEEE, pp 1–6 32. de Matos J, de Souza Britto A, de Oliveira LES, Koerich AL (2019) Texture CNN for histopathological image classification. In: 2019 IEEE 32nd international symposium on computer-based medical systems (CBMS), pp 580–583 33. Spanhol FA, Oliveira LS, Petitjean C, Heutte L (2016) Breast cancer histopathological image classification using convolutional neural networks. In: 2016 international joint conference on neural networks (IJCNN), pp 2560–2567 34. Thuy MBH, Hoang VT (2020) Fusing of deep learning, transfer learning and GAN for breast cancer histopathological image classification. In: Le Thi HA, Le HM, Pham Dinh T, Nguyen NT (eds) Advanced computational methods for knowledge engineering. Springer, Cham, pp 255–266

Early Detection of Wheat Diseases in Morocco by Convolutional Neutral Network Kenza Aitelkadi , Noureddine Chtaina, Saber Bakouri, and Mohamed Belebrik

Abstract Convolutional Neural Network is a deep learning method that covers several fields, including the agronomy sector. This new method provides fast and very encouraging results for diagnosis disease. As part of this work, we built a model of early diagnosis of the wheat plant in Moroccan region based on the Convolutional Neutral Network. Our goal is to allow rapid recognition of contaminated plants at early stages of appearance in order to provide the necessary treatment and limit propagation damage. Our study begins with a set image collection of wheat plants in the field and in the laboratory. We collect two type of images. Images of healthy plants and others that display three diseases, namely Brown rust, Septoria and Powdery Mildew. A first phase of data processing consisted in segmenting and augmenting these images. The next step is the model architecture choice, the network training and finally the evaluation phase. The validation of our model by test images, allowed us to reach 97% as an accuracy level in the wheat diseases diagnosis. Keywords Early diagnosis · Wheat disease · Convolutional neural network

1 Introduction Plant diseases cause considerable crops damage, which greatly reduces production. Several types of diseases are destructive [1] especially for the cereals that are the subject of our study. In Morocco, wheat is the most consumed cereal, its consumption is estimated at 173 kg/year/person with a production in 2016 of 2.7 Mt [2]. However, this production is affected by many biotic constraints; including cryptogamic diseases. Septoria and brown rust caused by Zymoseptoria tritici and K. Aitelkadi (B) · S. Bakouri · M. Belebrik School of Geomatic Sciences and Surveying, Hassan II Agronomic and Veterinary Institute, Rabat, Morocco e-mail: [email protected] N. Chtaina Plant Production Department, Hassan II Agronomic and Veterinary Institute, Rabat, Morocco e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_3

25

26

K. Aitelkadi et al.

Puccinia triticina, respectively, are our main wheat diseases [3]. The symptoms of these two leaf diseases are specific, they cause significant losses [4]. In order to avoid agricultural yield damage, protecting wheat from disease is essential to guarantee the quality and quantity of crops [5]. An effective protection strategy must begin with early detection of the disease in order to choose the appropriate treatment at the right time to prevent its spread [6]. Usually, in the absence of an agricultural warning system, the diagnosis of diseases is done in two stages. The first consists of periodic inspections to detect anomalies in plant growth. The second consists in taking samples of diseased plants which will be analyzed in the laboratory by phytopathologists [1]. For farmer, continuous monitoring is difficult and time consuming. These limitations make important the automation of an early diagnosis diseases for crop protection [5]. In this context, the image processing by machine learning approaches provides quick and easily usable solutions. For disease diagnosis, the classifying images by machine learning are divided in two classical methods and deep forms. Classification methods based on classical learning use a limited number of images [7]. They allow iteration more quickly and therefore open the possibility of trying several techniques in a shorter time [8].These algorithms are easier to interpret and understand [9]. However, their accuracy are lower than those of deep learning methods, in particular the CNN method. The introduction of deep learning techniques in agriculture, especially in phytopathology field [10], has only started to take place in the last six years, to a rather limited extent. The author in reference [11] presented in their research the importance and potentialities of deep learning in agriculture and plant disease. The research [10] presented a new method for identifying rice diseases based on CNN techniques, using a data set of 500 natural images of healthy and diseased rice leaves. Reference [12] used deep learning architectures for the diagnosis of various plant diseases. Experimental results of [13] demonstrate that VGG-FCN-VD16 and VGG-FCN-S system outperforms conventional CNN architectures for wheat disease recognition. In this work, we try to experiment the performance of new CNN architectures in Moroccan wheat disease context using our specific image and by comparing results of small and large database.

2 Material and Methods 2.1 Data Data acquisition is the first preparing step. Data can be acquired from the web, using a database, or manually by taking pictures with RGB cameras. They are prepared and inoculated in plant protection laboratory of the Agronomic and Veterinary Hassan II Institute. We have taken pictures using smartphone camera with 27 Mega Pixels. Three diseases are chosen: brown rust, Septoria leaf spot and powdery mildew. We obtained 100 images for each disease. Figure 1 shows an example of the images.

Early Detection of Wheat Diseases in Morocco …

27

Fig. 1 Sample images of affected wheat leaves: a Healthy, b Septoria, c Powdery mildew, d Brown rust

For training and testing our deep learning algorithm, it is important to separate the images. The goal is to keep some of the data outside of training, which will be used to test the reliability of the model. The most popular separation is 80% for training data and 20% for test data. This is to make sure that the model can work in unexpected cases.

2.2 Software Tools We chose Python as the programming language. It’s an easy language to learn. It has simple syntax and has many libraries [14]. In the context of deep learning, we used the “Keras” library on python. This library uses tensors to do the various deep learning calculations which are mathematical objects generalizing scalars, vectors and matrices to higher dimensions. Also, we chose the Kaggle free workspace since it makes easy the integration and management of large data. It allows deep learning models to converge successfully. The storage space of our workspace is 16 GB of disk space and 16 GB of the Central Processing Unit RAM and 16 GB of the Graphics Processing Unit RAM.

2.3 Methodology In this work, we expose the adopted methodology whose main objective is the construction of early detection model for wheat diseases diagnosis. Our approach begins by the image processing. Next, we choose the most suitable model architecture for the recognition of wheat diseases. We will also study the effect of hyper parameters variations and their influences on the model. To finish, we present and discuss the obtained results. Image Processing Image Labeling

28

K. Aitelkadi et al.

Fig. 2 Segmentation process

Image labeling consists of associating the images with labels, for example associating the image of wheat disease with its label which is the name of this disease. These labels are a knowledge base for learning the model. The purpose of the model will be to associate the appropriate label with each test image. So you have to make sure you create correct labels. Image Transformation Neural networks perform better when the input data has common information, the same background and the same image size. Our images are characterized by the variation of the backgrounds. Following this variation we decided to proceed with segmentation to homogenize the background, this procedure is explained in Fig. 2. The segmentation was done using OpenCV graphics library. We started by converting the RGB image to an HSV format. Subsequently, we applied an edge detection algorithm. Finally we created a mask to fill the detected part. Figure 3 shows an image before and after segmentation. Wheat Leaf Segmentation According to the Verticality In order to duplicate the number of images and allow our algorithm to recognize the different parts of a wheat leaf, we proceeded to a second segmentation. It is a separation step of the three leaf parts according to the verticality. Figure 4 shows the obtained images. We got three new images. The first representing the top of the leaf, the second the middle and the third the bottom of the leaf. Resize and Data Augmentation We have resized the wheat images to 400 * 300 format because the high resolution of the images requires powerful equipment for training. Large image sizes bring to memory problems and can stop training. Image resizing was done using the OpenCV graphics library and a resizing algorithm [15]. In order to generalize the wheat diseases recognition on other images with different orientations and positions of the leaves, we adopted an augmentation step. The purpose of the augmentation is Fig. 3 Segmentation result: a before segmentation, b after segmentation

Early Detection of Wheat Diseases in Morocco …

29

Fig. 4 Segmentation result according to the leaf verticality: a top, b middle, c bottom

to increase the data amount. This augmentation is done by several transformations: rotations, translations, vertical and horizontal symmetries and horizontal and vertical stretching. The transformed images will be associated with training data. Convolutional Neural Network Architecture One of the deep learning approaches is the Convolutional Neural Network. CNN models use multiple layers. The input layers, the hidden layers and the output layer. The depth of the model depends on the network layers number, especially the hidden layers. The adopted model depends on several parameters, namely the type of data, its volume, its quality and the type of processing or result expected by the network. The richness and the performance level of the architectures evolves rapidly and changes according to the need. In this work, we tried to find the most used architectures, in a similar context, to solve our problem of diagnosing wheat diseases. We preferred to experiment architectures which are often used in the field of human, animal and vegetable diseases diagnosis. We choose to experiment three architectures which are: “Tairu” architecture [16], “Alexnet” architecture and “LeNet” architecture [12]. The Tairu architecture is a CNN architecture that uses five convolutional layers. It is implemented for training converges faster. This is useful because the architecture is deep and the number of parameters logically increases which will require a long training period. This architecture showed very good performance for “PlantVillage” database during our experimentation on tomato diseases diagnosis.”Alexnet” is popular architecture. Its model includes five convolutional layers and 3 fully connected layers. In a single convolutional layer, there are usually several filters of the same size. The activation function used is ReLU. For the first and second convolutional layers, the activation function is followed by a local normalization step before pooling. LeNet architecture is a simple model of 3 layers with a convolution activation function ReLU, followed by max-pooling layers. The main advantage of this architecture is that it is efficient even if you don’t have a large amount of data. It’s characterized by a reduced number of layers and therefore it is relatively shallow learning. In addition of architecture choice, we should determine the hyper-parameters before the learning process. A right determination avoids the over-learning problem

30

K. Aitelkadi et al.

and provides better prediction accuracy. Indeed, when a model has learned too much the peculiarities of training data, it has a very high success rate (up to 100%). Instead of setting generalized learning on the data test, the model projects closely training results which provides an unstable model. There are no generic or statistical means to determine the best hyper-parameters. It’s based on experimentation with empirical values. The hyper-parameters that we study in this document are: • The size of image filter • The Number of Epochs • The size of the images

3 Results 3.1 Effect of Hyper Parameters Variation Hyper-parameters are parameters whose value is defined before the start of the learning process. They cannot be learned directly from data and must be predefined. Unfortunately, there are still no statistical means to determine the best. So we experimented with different values. We train our model and we choose at the end the parameters that provide the best precision. Filter Size A filter is a matrix of values. It is generally small and often square. It is used to show some characteristics of a given image (color, outline, brightness, sharpness, etc.). By scanning the image in successive steps, according to the values and the size of the filter matrix, we obtain a new image more or less modified. The purpose of this process is to reveal some characteristics of the image. It is not best to use very small filters as this can cause over-learning. We tested LeNet model which contains three convolutional layers. Through tests we carried out on the size, we evaluated the precision obtained. If a number of pixels is required to recognize the object, large filters should be used. If the objects present a small part of the image, the small filters are better [17]. Since the symptoms of the diseases are in a small part of the images (for example small spots for septoria), it seems logical to use small filters to extract the characteristics. The obtained results confirm this hypothesis (Fig. 5). By reducing the size of the filter, we obtained a better precision, we reached a precision of 97% with a filter size (3 × 3), 92% for (12 × 12) and 89% for (40 × 40). We found similar results when we tested the small sizes of filters in the neural network for the tomato diseases diagnosis. The Number of Epochs The epoch number is the number of times the algorithm identifies the complete data set. Thus, each time the algorithm has all the samples in the dataset, an epoch is over [18]. We have tried to vary the number of epochs, between 1 and 100. Figure 6

Early Detection of Wheat Diseases in Morocco …

31

Fig. 5 Two filters of different sizes on an image of a diseased leaf (case of septoria)

achieved by the “matplotlib” visualization library, shows the evolution of two details as a function of the epoch number. The first accuracy is that of the training data and the second is the precision of the tested data. In all the graphs presented in this document, the x-axis refers to the number of epochs and the y-axis corresponds to the reached prediction rate. By growing the number of epochs, the training takes more time and the precision increases but stagnates from epoch 40. We also notice that the blue curve (training data) exceeds the red curve (test curve) in the thirtieth epoch which means that our model did over learning and therefore good performance on training data and poor generalization on other data. We therefore worked with 25 epochs because this number offers good precision and at the same time prevents us an over-learning. The Image Size The adopted image size depends on the network size and the capacity of the GPU (Graphics Processing Unit) used to be able to train it. It should add a lot of reasonable size in GPU memory. We tested three different sizes of images to compare the details obtained with the optimal training time: 300 * 400, 256 * 256 and 50 * 50. We obtained 97.61% for 300 * 400 size with an interval time of 768 ms per epoch. Using the reduced size of 256 * 256, we obtained an accuracy of 97.32% with an interval of 424 ms per epoch. Finally, the small size of 50 * 50 gives us an accuracy of 94.04% with 33 ms per epoch. So it offers a very short duration, but the accuracy is relatively low compared to other sizes. Fig. 6 The accuracy depending on the epoch’s number

32

K. Aitelkadi et al.

3.2 Choice of Model Tairu Architecture Experimentation with this architecture provided us a 90.47% prediction accuracy. This is a slightly low diagnostic accuracy. However, despite the precision obtained, we had an over-learning. AlexNet Architecture The “Alexnet” architecture achieved an accuracy of 94.04%. We preferred to test for 100 epochs to see the accuracy evolution of the precision. Precision increased until epoch 60 where it stabilizes. LeNet Architecture By using the LeNet architecture, we obtained an accuracy of 97.61%. The precision increases according to the time. The synthesis of our experimentation with the three architectures mentioned above, allowed us to adopt the appropriate model according to the precision of predictions obtained (Fig. 7). We obtained the best predictions from the LeNet model. Indeed, its architecture has a limited depth which makes it more efficient when we have less data. In the case of our study, we obtained a prediction accuracy of 97.61% since we have few images. We could only use 400 images obtained after the segmentation and augmentation operations. The results also show the effectiveness of CNN architectures regardless of the data masses. The good precision of certain architectures requires thousands of images in order to adapt to their depths (in the case of Tairu and AlexNet architectures). Others provide more relevant results with average mass of images (case of LeNet architecture). Although it is true, it is not always easy to get millions of images and to process them on ordinary machines or servers.

Fig. 7 The obtained accuracy according to the architectures

Early Detection of Wheat Diseases in Morocco …

33

4 Discussion It should be noted that phytopathologists do not need machine learning methods to accurately interpret the condition of plants and provide assumptions about the diseases. Rather, think about using them to confirm their hypotheses or to make an early diagnosis. This could be done by a mobile application using the deep learning algorithm. For example, if the disease begins to spread in an area and has not spread over the entire field, machine learning algorithms can effectively help phytopathologists to accelerate decision-making. This tool offers new possibilities that were not available before. First, early diagnosis and detection of the disease by the farmer himself will allow the farmer to know the type of disease and make an early diagnosis. He can then immediately apply treatments or contact an expert and ensure rapid intervention. Secondly help the expert to confirm his hypothesis. Through this document, we have tried to simplify the various experiments made either on hyper parameters or on network architectures, without noting the mathematical formulations since our main concern was to try to obtain good details on the detection of diseases. This was accomplished with a prediction accuracy of 97.62% of the three wheat diseases experienced. Figure 8 shows the results of an iteration of our prediction model on a septoria leaf. The possibilities offered [healty leaf, brown rust, powdery mildew, septoria]. We obtained a perfect score for the appearance of septoria (score of 1 or otherwise 100%). The probability that the leaf will be affected by the other two diseases and the probability that it will be healthy is very low and tends to zero. Following these results, we conclude that the convolutional neural networks hold great promise for the detection of diseases and allow very interesting applications such as the intelligent use of pesticides. In this sense, if deep learning algorithms use both drone images and terrestrial images of a crop field, the farmer will be able to guide the phytosanitary treatment of affected areas in an intelligent and optimal way. However, like any new technology and experiment, some difficulties arise, especially the complexity of the calculations generated by different models. This complexity makes the training phase time consuming. In order to improve the work carried out and with a view to developing other researches, we recommend setting up an international database of plant disease images which will serve as training data Fig. 8 The scores obtained by our prediction model respectively from left to right: healthy leaves, brown rust, Oidum and Septoria leaf spot

34

K. Aitelkadi et al.

for the deep learning model. The addition of other parameters could improve the accuracy such as the location, the plant age, etc.

5 Conclusion In this work, we have integrated deep learning for the early detection of certain wheat diseases that spread in Morocco. The process requires RGB images of sick and sick leaves. We have concluded from the results of our experiment that the use of convolutional neural networks is very promising. We managed to predict the various diseases of wheat at 97.62%. Particular attention is considered in the availability of training data, the adequate choice of network structures and adopted hyper-parameters.

References 1. Blancard D (2012) Tomato diseases: identification, biology and control. Manson Publishing, London, 688 p. eBook ISBN 9780429158445 2. USDA Foreign Agricultural Service. Morocco: grain and feed update (2016). https://gain.fas. usda.gov/Recent%20GAIN%20Publications/Grain%20and%20Feed%20Annual_Rabat_Mor occo_3-30-2016.pdf 3. Lyamani A (1989) Fréquence et sévérité des maladies d’orge, blé dur et blé tendre dans les principales régions céréalières du Maroc. Rapport d’activité 1988–89. INRA/Centre régional Settat, Maroc, pp 75–76 4. Eyal Z, Sharen AL, Prescott JM, Van Ginkel M (1987) The Septoria diseases of wheat: concepts and methods of disease management. CIMMYT, Mexico, 52 p 5. Brahimi M, Boukhalfa K, Moussaoui A (2017) Deep learning for tomato diseases: classification and symptoms visualization. Appl Artif Intell 31(4):1–17 6. Al Hiary H, Bani SA, Reyalat M, Braik M, ALRahamneh Z (2011) Fast and accurate detection and classification of plant diseases. Int J Comput Appl 17:31–38 7. Zubiolo A (2017) Extraction de caractéristiques et apprentissage statistique pour l’imagerie biomédicale cellulaire et tissulaire. PhD thesis, p 134 8. Tzu-Liang T, ReddyAletia H, Zhong H, Yongji K (2015) E-quality control: a support vector machines approach. J Comput Des Eng 2015(2):91–101 9. Padmavath J (2012) Logistic regression in feature selection in data mining. University SRM, India, pp 1–3 10. Yang X, Guo T (2017) Machine learning in plant disease research. Eur J BioMed Res 3:6–9. https://doi.org/10.18088/ejbmr.3.1.2016m 11. Kamilaris A, Francesc X, Prenafeta B (2018) Deep learning in agriculture: a survey. Comput Electron Agric 147:70–90 12. Ferentinos KP (2018) Deep learning models for plant disease detection and diagnosis. Comput Electron Agric 145:311–318 13. Lu J, Hu J, Zhao G, Mei F, Zhang C (2017) An in-field automatic wheat disease diagnosis system. Comput Electron Agric 142:369–379 14. Karkare P (2019) First Steps — Deep Learning using Python and Keras. https://medium.com/ x8-the-ai-community/first-steps-deep-learning-using-python-and-keras-3ae6bcb37d0e

Early Detection of Wheat Diseases in Morocco …

35

15. Wang SY, Wang O, Zhang R, Owens A, Efros AA (2020) CNN-generated images are surprisingly easy to spot... for now. In: Proceedings of the IEEE conference on computer vision and pattern recognition, vol 7 16. Tairu O (2018) plant disease detection using convolutional neural network. https://towardsda tascience.com/plant-ai-plant-disease-detection-using-convolutional-neural-network-9b58a9 6f2289 17. Oliveira DCD (2016) How do we choose the filters for the convolutional layer of a Convolution Neural Network (CNN)? https://www.researchgate.net/post/How_do_we_choose_the_fil ters_for_the_convolutional_layer_of_a_Convolution_Neural_Network_CNN 18. Sharma A (2018) What is an epoch in deep learning? https://www.quora.com/What-is-anepoch-in-deep-learning

Genetic Algorithm Design Optimization for Non-standard Spur Gears Samya Belarhzal and El Mostapha Boudi

Abstract Spur gear design is one of the understood subjects that can not be treated by limited classical methods. For this reason, an optimization process by the genetic algorithm was conducted in order to minimize the structure volume. Mechanical problems such as gear designs present many variables to analyze and several constraints to respect. In order to gain in terms of weight cost and transmission, Genetic Algorithms are used to improve the design process of the gearbox and find the optimum gear parameters. In this paper, the influence of the profile shift factor on one spur gear volume and clearance volume is studied including the bottom clearance volume equation in the fitness function. The optimal results of the model with corrected pinion and gear are compared with the standard spur gear model without a profile shift factor in bottom clearance equation volume or structure equation volume. Keywords Genetic algorithms · Optimization · Volume · Profile shift factor · Bottom clearance · Spur gear

1 Introduction Genetic algorithms are frequently used for optimization purposes to solve theoretical understanding problems. They have been often used in different fields of science, engineering, or biomechanics [1]. Genetic algorithms adopt the approach of organic evolution, inspired by populations of individuals with a high quality of genetic materials. Collecting and modelling different organic evolutions lead to form the basis of GA [2]. The greatest application of this technique is in the optimization sector, as discussed by De Jong [3]. Thomas Bäck, Ulrich Hammel, and Hans-Paul Schwefel talked about the history of evolutionary computation, their objectives, and different structures, including genetic algorithms [4]. S. Belarhzal (B) · E. M. Boudi Mechanical Department, Mohammadia School of Engineers, Mohammed V University, Rabat, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_4

37

38

S. Belarhzal and E. M. Boudi

In the mechanical field, the gearbox is known for its primary role in power transmission for machines. They are often used in rough environments leading to severe damages, system failures, and high maintenance costs [5]. For this reason, mechanical designers are always recommended to improve designs to give better results and reduce costs. Cevdet Gologlu used GA to minimize the volume of a parallel axis gearbox with two-stage helical gear trains. He compared the design results with static and dynamic penalty function to the solutions of a deterministic design already developed [6]. Said Golabi and Javad Jafari Fesharaki analyzed the volume/weight minimization of one, two, and three-stage gear train. Depending on different values of input power, the hardness of gears and, the gear ratio, the results were compared to previous works solutions [7]. Particle swarms (PS) are used too on many occasions in gearbox volume optimizations. Ketan Tamboli and Sunny Patel took the volume geometry for a pair of helical gear reducer as an objective function. The results were enough to improve performance requirements [8]. Even if sometimes the particle swarm optimization (PSO) solutions outperform the GA results on the gearbox minimization volume/weight in some points, Genetic algorithms still the most used method in gears optimization design [9]. One of the most interesting parameters to study in the gears optimization field is the profile shift factor and his influence on different aspects of design, stress, and transmission capacity. Diez-Ibarbia studied the impact of profile shift factor on the efficiency of spur gears. He concluded that increasing the profile shift factor reduces the transmission efficiency [10]. Coming to Genetic Algorithm optimization Paridhi Rai and Aman Agrawal included the profile shift as a design variable, with the module, the face width, and the number of teeth in their volume optimization of the helical gear, using Real Coded Genetic Algorithm (RCGA). They found that optimum volume with profile shift factor is lower than a volume without it [11]. Daniel Miler and Antonio Loncar treated the impact of profile shift factor on spur gear pair optimization using the Genetic Algorithm. The pair gear volume was considered as an objective function. Its a function of addendum diameter instead of the pitch diameter to highlight the impact of shift coefficient in the objective function. The obtained results were confirmed by the specialized commercial software results. The inclusion of the profile shift as a variable leads to a lower volume and weight [12]. The same authors have made a multi-objective GA optimization on spur gears based on volume and efficiency as objective functions. The aim was to decrease the power loss and design volume. The optimum results were achieved by reducing the module, the face width, and raising the profile shift and teeth number [13]. A lot of work has been achieved on gearbox design. In this work, the authors studied the impact of the shift coefficient on one pair of spur gear volume by the process of genetic algorithms. Two models of the geometry structure are compared. The first model with a simple volume equation is calculated as a function of pitch diameter, the face width b and, the transmission ratio i. The second model included the profile shift factor in the bottom clearance equation to make clear his influence on the optimization results.

Genetic Algorithm Design Optimization …

39

The pinion and gear shift coefficients x 1 and x 2 are taken as function variables in this model besides the module m, the face width b, and the pinion teeth number Z 1 in the first and second model. The novelty of this paper is the inclusion of the bottom clearance volume equation in the structure volume equation for more accurate results and to spotlight more the influence of the shift coefficient on optimization results.

2 Problem Definition The aim of this paper is the volume minimization of one simple single-stage spur gear system transmission, then analyze the effect of the bottom clearance volume equation, and profile shift factor of the pinion and the gear on the optimization result.

2.1 Gear Basic Volume Equation for the First Model The basic equation of the gear volume can be written as below:   V = f (m, b, Z 1 ) = π b (m Z )2 − d B2 /4

(1)

2.2 Gear Volume Equation with Bottom Clearance Equation Volume Using the Profile Shift Factor X for Second Model When the reference line of the basic rack is at a distance (r + y) from the spur gear center, and conjugate with the gear involute part of the profile teeth. Then the spur gear became a corrected gear (Non-standard), with a profile shift y [14]. The clearance is the difference between the addendum of one gear and the addendum of the mating gear [15]. In the solid-structure of spur gear volume computation, the tooth part is the most complicated part to calculate. That’s because the tooth part begins from the root circle to the pitch circle. The bottom clearance is created when the bottom of the first gear tooth and the tip of the second operation gear tooth don’t match (Fig. 1). Sa is the tooth tip thickness of the mating spur gear (Eq. (3)). To simplify the calculations Sa is considered as thickness or broad of the clearance cube. Z is the number of gear teeth, and b is the gear face width. The clearance volume V c is described by the Eq. (2), with r is the pitch radius, r a is the addendum radius, r b is the base radius, α is the pressure angle, and S is the thickness of the teeth at the pitch circle.

40

S. Belarhzal and E. M. Boudi

Fig. 1 The bottom clearance between two spur gears teeth in contact

Vc = Sa ∗ h ∗ b ∗ Z Sa = S ∗

(2)

ra − 2ra ∗ (invαa − invα) r

(3)

The relation between the profile shift of gear and the thickness of its teeth is presented in the Eq. (4): S=m∗  ra = m ∗

π 2

+ 2xtanα

 (4)

 Z Z Z + x + 1 ; r = m ∗ ; r = m ∗ ∗ cosα 2 2 2

invαa = tanαa − αa ; αa = ar ccos

rb ra

(5) (6)

h is the height of the bottom clearance. It presents the radial distance between the top of the first spur gear and the bottom of the second gear in contact. For corrected spur gears the height of the bottom clearance is written as: h = (0, 25 − 2x) ∗ m

(7)

Using the bottom clearance volume while taking the profile shift factor in consideration, transform the equation of gear volume into equation right bellow:   V = f (m, b, Z 1 ) = π b (m Z )2 − d B2 /4 − Vc

(8)

3 Method This optimization research combine between genetic algorithms method and ISO 6336:2006 Standards for spur gear strength calculations. The author’s first objective

Genetic Algorithm Design Optimization …

41

was to study the effect of the profile shift factor on structure volume. For this reason, besides modules, pinion number of teeth, and teeth face width used in the first model, pinion profile shift factor and wheel profile shift factor were included as functions variable for the second model, using genetic algorithms. The optimization results for the first and the second model were compared. Corrected gears (with positive profile shift factor) use an extended part of the involute to form the tooth flank compared to standard gears; (without profile shift factor), which leads to the creation of the clearance between tooth profiles in contact while meshing. In this paper, the authors analyze the influence of positive values of shift coefficient on the clearance between teeth in contact. The bottom clearance equation was considered as a fitness function besides the structure volume equation for a multi-objective optimization using genetic algorithms. The aim is to consider the variation of the entire volume structure as well as the bottom clearance volume according to profile shift modifications. The same input power, input speed, and shaft diameter are used for the three models. The module, the face width, the number of pinion teeth, and the profile shift factor for the gear are limited, to have possible results in practical situations. Data about boundary conditions are presented in Table 1. The profile shift factor for the pinion is related to the tip tooth thickness by the equation: x1max = 0.4m

(9)

3.1 Genetic Algorithm The objective of this paper is the minimization of the spur gear volume, which is considered as an objective function for the genetic algorithm. To come up with the results, the genetic algorithm toolbox of software MATLAB is used [16]. The Table 2 shows the chosen input power, torque, and rotational speed. The gear number of teeth Z 2 is calculated using the transmission ratio i. The material considered for this structure is the standard steel with ρ = 7830 kg/m3 as density. The pinion and gear shaft diameters are fixed on 20 mm. In this paper, the population size adopted is 1000 individuals with a random vector for the initial population variables instead of the uniform vectors [17]. For more precision, the optimization was repeated five times for each volume model.

3.2 The Constraint Functions and Strength Calculation Most spur gear failures came from bending stress on the root of the teeth and surface durability limited by the contact stress between teeth in contact while meshing [18]. For this reason, the contact stress and the bending stress are considered as constraints

42

S. Belarhzal and E. M. Boudi

Table 1 Optimization boundary conditions

Table 2. GA optimization input data

Input variables

Range

Module m [mm]

[2, 10]

Pinion number of teeth Z 1

[18, 30]

Face width b

[14, 65]

Pinion profile shift factor x 1

[0.2, 0.7]

Gear profile shift factor x 2

[0.2, 0.4]

Parameter

Value

Pinion shaft diameter [mm]

20

Gear shaft diameter [mm]

20

Input power [w]

5000

input speed [tr/min]

1500

Poisson’s ratio

0.3

Elasticity factor [GPa]

210

Torque [N.mm]

35000

Transmission ratio i

2.5

Maximum bending stress σ F max [MPa]

750

Maximum contact stress σ Hmax [MPa]

450

to satisfy the gear strength conditions (10), and (11):  g1 (x) = Z H Z E Z ε Z β g2 (x) =

Wt i + 1 K A K v K Hβ K H α − σ Hlim bd i

Ft Z F Z S Z ε Z β − σ F Lim b.m

(10) (11)

The contact ratio factor Z ε is calculated for each generation since it is directly influenced by the profile shift factor value [19]. The helix factor Z β is equal to 1 for the spur gears [20]. The zone factor Z H , Elasticity factor Z E , the stress factor Z S , and the form factor Z F are calculated using the ISO 6336:2006 Standards. k is the face width coefficient, with 6 < k < 12 and b = k.m, which lead to tooth face with constraints (12), and (13): g3 (x) = 6 − g4 (x) =

b m

b − 12 m

(12) (13)

Genetic Algorithm Design Optimization …

43

Changing the profile shift value could increase or decrease the contact ratio CR value [21], therefore with a greater number of CR corrections the gearing action is disturbed by tooth gear tip interference [22]. To avoid this undesirable phenomenon the tooth interference constraints are used (14), and (15): g5 (x) =

17 − Z 1 − x1 17

(14)

g6 (x) =

17 − Z 2 − x2 17

(15)

Z2 = 2, 5 Z1

(16)

i is the transmission ratio. i=

4 Results and Discussion The optimization without profile shift factor converged faster. The program running was for more than 30 times and didn’t present any deviation. First of all, the generation number fixed on 500 generations, then reduced to 100 generations to decrease the computation time. The computation time could be influenced too by the initial population. Generally, using pseudo-random numbers for initial population gives accurate results in less time [23]. The results of the genetic algorithm optimization for both equation volume of one-stage spur gear are presented in Table 3. It showed that the smallest volume was obtained by including the bottom clearance volume and the profile shift factor in the spur gear equation volume. It was reduced by 48%, the volume minimization is clear. Increasing the profile shift factor by the amount of a positive value leads to a higher clearance volume between the gear tooth flank as shown in Fig. 2. It can be justified by the difference in shape between standard and corrected spur gear teeth. During the meshing process, a prolonged part of the involute is taken as a flank. Which explains the larger space between gear and pinion in contact. As shown in Fig. 3, the Pareto front illustrates the best optimization solutions between global structure volume and clearance volume. As the volume increases, so does the clearance volume.

44

S. Belarhzal and E. M. Boudi

Table 3. Output AG optimization variables Function volume variables

Standard gears

Corrected gears

Number of teeth of the pinion Z1

27

25

The module m

4,7

4,2

The face width b

46

33

The face width coefficient k

9,8

7,8

The profile shift factor of the pinion x1

0

0,34

The profile shift factor of the gear x2

0

0,14

The structure volume V [mm3]

5114600

2640900

The clearance volume %V c

0,15

0,18

Fig. 2 Impact of profile shift factor on the percent of clearance volume compared to the global structure volume

Fig. 3 Pareto front of GA multi-optimization for global structure volume and clearance volume for corrected spur gear pair

Genetic Algorithm Design Optimization …

45

5 Conclusion Gears are the most important part in the transmission power field. They are widely working in the industry. For this reason, many researchers worked on how to improve their different aspects. Gears design is a complicated problem to treat. It contains several variables and deals with different constraints. Genetic Algorithms present a metaheuristic approach to treat complicated gearbox design that cannot be solved with classical methods. In this work, the effect of the profile shift factor for pinion and gear on the genetic algorithm volume and bottom clearance volume optimization of one-stage of the spur gear are studied. The results are significant since the low volume was obtained using the profile shift factor on the spur gear volume equation. With a difference of 48% less than the first model using only the module m, the face width b, and the pinion profile shift factor Z 1 as function variables. Including the clearance volume equation helps to have more accurate optimization volume results and avoid interference problems that appear with corrected gears since V c increases while increasing the profile shift factor. It is to be noted that this study has a limitation concerning the constraint functions involved in the genetic algorithm. Spur gear’s dynamic properties were not considered but will be developed in future studies.

References 1. Bensghaier A, Romdhane L, Benouezdou F (2012) Multi-objective optimization to predict muscle tensions in a pinch function using genetic algorithm. CR Mec 340:139–155 2. Back T (1966) Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms. Oxford University Press, Oxford (1966) 3. De Jong KA (1992) Are genetic algorithms function optimizers? Elsevier, The Netherlands, pp 3–13 4. Back T, Hammel U, Schwefel HP (1997) Evolutionary computation: comments on the history and current state. IEEE Trans Evol Comput 1:3–17 5. Graja O, Zghal B, Dziedziech K, Chaari F, Jablonski A, Barszcz T, Haddar M (2019) Simulating the dynamic behavior of planetary gearbox based on improved Hanning function. CR Mec 374:49–61 6. Gologlu C, Zeyveli M (2009) A genetic approach to automate preliminary design of gear drives. Comput Ind Eng 57:1043–1051 7. Golabi S, Fesharaki JJ, Yazdipoor M (2014) Gear train optimization based on minimum volume/weight design. Mech Mach Theory 73:197–217 8. Tamboli K, Patel S, George PM, Sanghvi R (2014) Optimal design of a heavy duty helical gear pair using particle swarm optimization technique. Procedia Technol 14:513–519 9. Savsani V, Rao RV, Vakharia DP (2010) Optimal weight design of a gear train using particle swarm optimization and simulated annealing algorithms. Mech Mach Theory 45:531–541 10. Diez-Ibarbia A, Fernandez del Rincon A, Iglesias M, de-Juan A, Garcia P, Viadero F (2015) Efficiency analysis of spur gears with a shifting profile. Meccanica 51:707–723 11. Rai P, Agrawal A, Saini ML, Jodder C, Barman AG (2018) Volume optimization of helical gear with profile shift using real coded genetic algorithm. Procedia Comput Sci 133:718–724

46

S. Belarhzal and E. M. Boudi

12. Miler D, Lonˇcar A, Žeželj D, Domitran Z (2017) Influence of profile shift on the spur gear pair optimization. Mech Mach Theory 117:189–197 13. Miler D, Žeželj D, Lonˇcar A, Vuˇckovi´c K (2018) Multi-objective spur gear pair optimization focused on volume and efficiency. Mech Mach Theory 125:185–195 14. Michael GM (2013) Effect of change of contact ratio on contact fatigue Michael Gmariam these. Addis Ababa University (2013) 15. Colbourne JR (1987) The Geometry of Involute Gears. Springer, New York 16. Mei W, Na J, Yang F, Shen G, Chen J (2016) The optimal design method and standardized mathematical model of tooth profile modification of spur gear. Math Probl Eng 1–7. https:// doi.org/10.1155/2016/6347987 17. Maaranen H, Miettinen K, Mäkelä MM (2004) Quasi-random initial population for genetic algorithms. Comput Math Appl 47:1885–1895 18. Samya B, Bachir A, Boudi EM, Amarir I (2019) The effect of addendum factor on contact ratio factor and contact stress for spur gears. In: 2019 7th international renewable and sustainable energy conference (IRSEC), Agadir, Morocco, pp 1–6. https://doi.org/10.1109/IRSEC48032. 2019.9078205 19. Samya B, Boudi EM (2020) Profile shift factor’s effect on contact stress and natural frequencies of spur gears using the finite element method. Int Rev Model Simul (IREMOS) 13:1–12 20. Samya B, Boudi EM, Bachir A, Amadane Y (2020) Analysis of profile shift factor’s effect on bending stress of spur gears using the finite element method. In: 2020 IEEE 6th international conference on optimization and applications (ICOA), Beni Mellal, Morocco, pp 1–6. https:// doi.org/10.1109/ICOA49421.2020.9094486 21. Gebremariam M, Thakur A, Leake E, Tilahun D (2018) Effect of change of contact ratio on contact fatigue stress of involute spur gears. Int J Current Eng Technol 8:719–731 22. Maiti R, Roy AK (1966) Minimum tooth difference in internal-external involute gear pair. Mech Mach Theory 31:475–485 23. Maaranen H, Miettinen K, Penttinen A (2007) On initial populations of a genetic algorithm for continuous optimization problems. J Glob Optim 37:405. https://doi.org/10.1007/s10898006-9056-6

A Robust Method for Face Classification Based on Binary Genetic Algorithm Combined with NSVC Classifier M. Ngadi, A. Amine, B. Nassih, Y. Azdoud, and A. El-Attar

Abstract Image features selection consists on reducing the number of features of an image, used for training or testing a classifier, by eliminating irrelevant, noisy and redundant data without decreasing significantly the prediction accuracy of the classifier. In this paper we consider the problem of feature selection. We give a quick technique dependent on a binary Genetic Algorithm (GA) combined with Neighboring Support Vector Classifier NSVC. More concretely, we utilize the k-Nearest Neighbors KNN classifier as a fitness function for each feature selection decision during the evaluation step of the genetic algorithm. GA-NSVC is implemented and tested on BOSS and MIT-CBCL Face dataset. The results show its robustness and high performances. Keywords NSVC · KNN · Feature selection · Genetic algorithm

1 Introduction The issue of choosing a subset of factors can be viewed as an inquiry in a space of speculations (called a bunch of potential arrangements). The nature of a chose subset is evaluated by an exhibition model. On account of a directed characterization issue, this basis is regularly the exactness of a classifier (classification rate) worked from the arrangement of chose factors. Choosing a subset of qualities disposes of insignificant and excess data relying upon the standards utilized. GA is probably the most recent strategy in the field of the element determination [1, 2]. It comes to locate a particular capacity that permits a decent separation among chromosomes and characterize the hereditary administrators [3, 4]. Our objective is to evaluate the ability of the GA-NSVC for the improvement of the classification rate of images. M. Ngadi (B) · A. Amine · B. Nassih · Y. Azdoud · A. El-Attar Systems Engineering Laboratory, ENSA, Ibn Tofail University of Kenitra, Kenitra, Morocco e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_5

47

48

M. Ngadi et al.

We have chosen to use the NSVC of the classification function [5]. Next, to demonstrate the effectiveness of our proposed algorithm GA-NSVC, we have tested on two real Face datasets. Our trials on the distinctive datasets show that our strategy prevailing with regards to lessening the quantity of highlights while keeping up great precision surpassing 99%. The remainder of this article is coordinated as follows: The essential ideas of GA are given in area Sect. 2. Section 3 presents the NSVC classifier. In Sect. 4 we conduct a comprehensive experimental study and we conclude the article and give perspectives to our work in section.

2 Genetic Algorithm (GA) Genetic algorithm is an iterative algorithm that search for the optimum of a constant size population [3, 6]. The constant population size leads to a phenomenon of competition between chromosomes. The making of another populace from the past is finished by: Selection The choice is a cycle by which a chromosome is replicated in the new populace dependent on the estimations of the capacity to advance for this chromosome. Crossover The straightforward intersection comprises on, right off the bat picking several chromosomes with a likelihood p and afterward in a subsequent advance, cutting the agent directs in an indistinguishable arbitrary situation in the two guardians. This produces two “head fragments” and two “tail portions”. At last, we drive two guardians’ tail portions to get two youngsters who acquire a few attributes of their folks (Fig. 1a). Mutation A transformation is characterized as the in-rendition of somewhat in a chromosome (Fig. 1b). This is comparable to haphazardly change the estimation of a boundary. Transformations go about as commotion and forestall the development to freeze. They permit guaranteeing worldwide (just as nearby) search, contingent upon the weight and the quantity of pieces moved. Moreover, they guarantee numerically that the general ideal will be accomplished. Fitness Function The fundamental objective of highlight choice is to utilize less highlights to get the equivalent or better presentation [1, 7]. The inquiry’s procedure will likely discover a component subset boosting this capacity. The hybrid and change capacities are the primary administrators that haphazardly sway the wellness esteem.

A Robust Method for Face Classification …

49

Fig. 1 Genetic crossover and mutation process

Fitness = K N N _{accuracy}

3 Neighboring Support Vector Classifier SVM is first presented by Vapnik and partners for the issues of arrangement and relapse can be viewed as another preparation method dependent on conventional straight, polynomial and Spiral Premise Capacity (RBF). In any case, the suspicion that the preparation information are indistinguishably produced from obscure likelihood circulations may restrict the use of SVM to the issues of regular daily existence. To loosen up the supposition of indistinguishable circulation, the NSVC [8, 9] utilizes a bunch of vicinal centers capacities fabricated dependent on directed grouping in the element space actuated by the portion. Signifying a non-direct change of the info space X to a high-dimensional space utilizing a piece work as:  : Rn → F xi → (xi ),

i = 1, . . . , l

Where (xi ) is the changed point xi . All preparation information focuses are circulated in c regions/groups in the component space, where k (z) is the focal point of mass of the k-th region dwelling

50

M. Ngadi et al.

in F. This is a comparable portrayal to bunching dependent on the trademark space of k-means: k =

l 

αki z i ,

k = 1, 2, . . . , c

(1)

i=1

Where c is the quantity of groups, αki are the boundaries to be characterized by the bunching strategy (SKDA) and zi = yi (xi ) signifies the information focuses named in the component space [10, 11]. That is, the issue of bunching by SKDA [12, 13] is formulated as minimizing. F = J − TH

(2)

Where J is the distortion function, T are the Lagrange multiplier and H is Shannon entropy [14, 15]. To decide the αki parameter, the free energy work F is limited regarding the likelihood of affiliation, which is identified with the Gibbs dispersion. p(zi )p(k |zi ) αki = l     j=1 p zj p k |zj

(3)

Let characterize the mono- and bi-vicinal kernels as: (x) =

l 

yi αki K(x, xi ) + b , k = 1, 2, . . . . . . , K

(4)

  yi yj αki αmj K xi , yj + b , k, m = 1, 2, . . . . . . , K

(5)

i=1

Mkm (x) =

l  l  i=1 j=1

The decision boundary is: f(x) =

c  k=1

βk yk Lk (x) + b

(6)

A Robust Method for Face Classification …

51

Fig. 2 Typical face images of BOSS dataset

Fig. 3 Face pictures used for classification

4 Results and Discussion 4.1 Datasets Description BOSS Dataset contains 2000 images with 949 faces and 1051 non-faces. Figure 2 shows some face pictures in the preparation and test sets. MIT-CBCL Face Dataset: is a preparation set comprising on 6,977 trimmed pictures (2,429 countenances and 4,548 nonfaces). Figure 3 shows some face pictures in the preparation and test sets.

4.2 NSVC Classifier The classification accuracy without dimension reduction on the two datasets is given in Table 1:

52 Table 1 The order precision results With NSVC

Table 2 Boundaries utilized in GA

M. Ngadi et al. Dataset

Kernel

Parameter kernel

Accuracy (%)

BOSS

Poly

q=2

99.95

MIT-CBCL

Poly

q=2

99.79

GA parameter

Value

Population size

100

Genome length

20

Population type

bitstrings

Fitness function

KNN

Number of generations

100

Crossover

Arithmetic crossover

Crossover probability

0.8

Mutation

Uniform mutation

Mutation probability

0.1

The NSVC can improve the quality of classification, confirming that NSVC is performing well.

4.3 Genetic Algorithm Based Feature Selection In view of the GA setup (Table 2), the accompanying outcomes were acquired. The deliberately picked wellness work empowered the GA to limit grouping mistake from KNN. Figures 4 and 5 shows the development of the fitness estimation of the best chromosome in every age. This curve shows that the error in the classification for the best individual that represents the subset of features to select decreases with changes in population at every generation.

4.4 GA and NSVC Classifier Parameters We used the GA approach to select a subset of features for the NSVC classifier (Fig. 6). The characterization precision on the two subsets is given in Table 3: Our experiments on different databases show that our method has succeeded in reducing the number of features while maintaining a good precision which exceeds 99%.

A Robust Method for Face Classification …

53

Fig. 4 GA-KNN simulation diagram on BOSS dataset

Fig. 5 GA-KNN simulation diagram on MIT-CBCL dataset

Finally, we can confirm the effectiveness of our method to select the most relevant features and give a better classification.

54

M. Ngadi et al.

Fig. 6 The phases of our methodology

Table 3 Precision results With GA-NSVC

Subset

Kernel

Parameter kernel

Accuracy (%)

BOSS

Linear

–

99.80

MIT-CBCL

Poly

q=2

99.37

5 Conclusion In this paper another methodology dependent on GA for feature selection is introduced. The calculation of the fitness function is done using the KNN classifier for each primitive. This method was tested on two real datasets using NSVC classifier. The results show the robustness of our approach GA-NSVC, with a precision exceeding 99%. Our objective sooner rather than later is to proceed with the investigation of the AG to test it on a few datasets from other examination regions, and attempt to locate the best trade off among precision and execution time.

References 1. Babatunde O et al (2014) A genetic algorithm-based feature selection. Int J Electron Commun Comput Eng 5(4):2278–4209 2. Oluleye B, Leisa A, Leng J, Dean D (2014) Zernike moments and genetic algorithm: tutorial and application. Br J Math Comput Sci 4(15):2217–2236

A Robust Method for Face Classification …

55

3. Amine A, El Akadi A, Rziza M (2009) Aboutajdine D (2009) GA-SVM and mutual information based frequency feature selection for face recognition. INFOCOMP J Comput Sci 8(1):20–29 4. Gunavathi C, Premalatha K (2014) Performance analysis of genetic algorithm with KNN and SVM for feature selection in tumor classification. Int J Comput Electr Autom Control Inf Eng 8(8):1490–1497 5. Yang X, Cao A, Song Q, Schaefer G, Su Y (2014) Vicinal support vector classifier using supervised kernel-based clustering. Artif Intell Med 60(3):189–196 6. Huang C-L, Wang C-J (2006) A GA-based feature selection and parameters optimization for support vector machines. Expert Syst Appl 31:231–240 7. Mathworks T (2013) Statistics toolbox user’s guide. The MathWorks, Inc, Natick 8. Ngadi M, Amine A, Hachimi H, El-Attar A (2016) A new optimal approach for breast cancer diagnosis classification. Int J Imaging Robot 16(4):25–36 9. Cao A, Song Q, Yang X (2004) Mammographic mass detection by vicinal support vector machine. In: IEEE international joint conference on neural networks, Budapest, Hungary, vol 3. IEEE, pp 1953–1958 10. Ngadi M, Amine A, Nassih B, Hachimi H, El-Attar A (2016) The performance of LBP and NSVC combination applied to face classification. Appl Comput Intell Soft Comput 2016:10. Article ID 8272796 11. Ngadi M, Amine A, Nassih B, Hachimi H, El-Attar A (2017) Uniformed two local binary pattern combined with neighboring support vector classifier for classification. Int J Artif Intell 15(2):102–115 12. Ngadi M, Amine A, Hachimi H, El-Attar A (2017) Vicinal support vector classifier: a novel approach for robust classification based on SKDA. Pattern Recognit Image Anal 27(3):403–411 13. Ngadi M, Amine A, Nassih B, Hachimi H (2019) A highly efficient system for mammographic image classification using NSVC algorithm. Procedia Comput Sci 148:135–144 14. Nassih B, Ngadi M, Amine A, El-Attar A (2018) New proposed fusion between DCT for feature extraction and NSVC for face classification. Cybern Inf Technol 18(2):89–97 15. Ngadi M, Amine A, Nassih B, Hachimi H, El-Attar A (2018) Intelligent classification of heart disease using neighboring support vector classifier. Int J Tomogr Simul 31(4):54–64

A Proposed Solution to Road Traffic Accidents Based on Fuzzy Logic Control Halima Drissi Touzani, Sanaa Faquir, Saloua Senhaji, and Ali Yahyaouy

Abstract Road Traffic accidents are one of the main problems causing death and fatal injuries in all countries. Several general suggestions were presented to the ministry of transport to try to overcome this problem. This paper discusses a specific approach based on fuzzy logic control to train semi-autonomous cars to prevent accidents from occurring. These types of cars equipped with many devices along with decision making algorithms based on learning will make cars more intelligent to take the right decision when no reaction of the driver is noticed. Keywords Road accidents · Fuzzy logic control · Data analytics · Semi-autonomous cars

1 Introduction National Highway Traffic Safety Administration (NHTSA), states that more than 90% of the crashes are caused by human errors, and 40% of deadly ones imply drunk, drugged, tired or distracted drivers [1, 2]. Therefore the technology of Autonomous vehicles has the possibility to impact vehicle safety, traffic jam and travel behavior [2], so to avoid potential collision and vehicle accidents lot of studies has been applied on cars system to became automatic or semi-automatic, Triveni et al. equipped the car with an ultrasonic sensor which will continuously track for any obstacles from the front side, In this way they can ensure that a safe distance is always maintained between the two cars and thus accident can be avoided [3]. Keeping up with evolution, these last years’ vehicles are being more equipped with AEB (Automatic Emergency Braking) systems as stated in Erik et al. article, they have also described one of the latest AEB systems called Collision Warning with H. D. Touzani (B) · S. Faquir · A. Yahyaouy LISAC Laboratory, Faculty of Science Dhar Mehraz, Sidi Mohamed Ben Abdallah University, Fez, Morocco S. Faquir · S. Senhaji LSED Laboratory, Faculty of Sciences of Engineering, Private University of Fez, Fez, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_6

57

58

H. D. Touzani et al.

Full Auto Brake and Pedestrian Detection (CWAB-PD) which helps drivers avoid both rear-end and pedestrian accidents by providing a warning and, if necessary, automatic braking using full braking power [4]. To find solutions to traffic accidents, data should be collected and analyzed to discover insights from it and present solutions to the government. In the previous paper, a research study on road accidents in the country of Morocco was conducted using a real database provided by the Ministry of transport and equipment’s and composed of 1953 Instances of data on road accidents taken in 2014. The data has been maintained using data transformation techniques such as filters and then analyzed by three different methods, MS Excel, K-means classifier. The two methods showed almost similar results presenting reasonable causes on different types of accidents which can help to provide valuable suggestions to the Ministry to reduce road accidents in the country like for example [5]: • Install roundabouts that help reduce collisions and control speed. • Build overpasses and underpasses in the places knowing more traffic. Since the above mentioned suggestions are general and mostly related to infrastructure, a second solution is proposed which relies on using the data gathered to train Semi-autonomous cars to be able to take decisions. These types of cars are equipped with many devices along with decision making algorithms based on learning such as deep learning or linguistic variables such as fuzzy logic will make cars more intelligent to take the right decision when no reaction of the driver is noticed. Fuzzy logic system have the potential to improve the performance of a system by applying reasoning and are able to describe complex systems in linguistic terms rather than numerical values [6]. This approach is very suitable in several areas within many researches as it represents data by rules in place of using specific equations [7]. Fuzzy logic has been excelled in various domain such as medicine, Jason et al. describe how this method works by representing its application in a simplified model of fluid resuscitation of intensive care unit patients [8]. Blood pressure has been used for regulation of depth of anesthesia by fuzzy logic control and in some cases exceeded a human operator [9]. In energy domain, Faquir et al. adopted Fuzzy logic to control the energy between production and consumption and also confirm the availability of power on demand for an isolated house in the city of Essaouira in Morocco [10]. In electric vehicle Rachid et al. apply fuzzy control to ensure a longer life for a hybrid electrical vehicle by controlling the flow of energy inside [11]. We propose in this paper an approach based on Fuzzy logic control to help semi autonomous cars to decide and take action when it is needed.

A Proposed Solution to Road Traffic Accidents …

59

Table 1 Results provided by K-means algorithm Day

Night

Rate

Type of collision

15%

Initial shock

Movement

Rate

Type of collision

Initial shock

Movement

By the side Front

By inserting

17%

By the back

Back

Same sense

12%

By the side Front Left

Against the direction

10%

Other

Front

Against the direction

12%

By the side Front

By inserting

8%

By the side Front right

Same sense

9%

By the side Front left

By inserting

6%

Front

Back left

Crossing the road

7%

Front

Left changing 5% wire

By the back

Front

Same sense

Front

2 Summary of Data Analytics Phase We based our work on the previous result which analyzed 1953 instances using statistics graphs and k-means classifier to find similarities between data and extract different types of accidents that frequently happen and important shock points [5]. The most important similarity criteria determined by K-means algorithm are described in Table 1. Based on the previous statistical analysis it was found that the majority of accidents occur during the day, so we decided to partition the results by day and night, we noticed also that the k-means algorithm could identify the most important attributes that represents the 10 different classes and one of the attributes was day/night. Other attributes such as the type of collision, the initial shock point and the movement of the vehicles in the road were also considered as criteria to separate different classes between them, so the majority of accidents that have occurred in the day have a type of collision by the side of the car, front and by entering the road. On the other hand, the accidents that happened at night took place between cars of the same sense [5].

3 Fuzzy Logic Control Fuzzy logic is a method of reasoning convenient to natural language and human thinking than the traditional logical systems. Basically, it provides an effective means of capturing the approximate, inexact nature of the real world [12]. Historically, the term of fuzzy logic was used for the first time by the professor Lotfi Zadeh in 1956, when he observed that the logic of conventional computer was not able to manipulate data which represent subjective or unclear human ideas [13]. Fuzzy logic architecture is composed from four main parts as represented in Fig. 1.

60

H. D. Touzani et al.

Fig. 1 Architecture of fuzzy logic

• Rules: Fuzzy Inference Rules are the most important operation of the fuzzy inference system, including a number of inference rules connecting the input fuzzy variables to the output fuzzy variables [7], and the experts offered all the rules and the IF-THEN conditions to control the decision making system [13]. • Fuzzification: where the fuzzifier performs the act of fuzzification, it transform a numeric value to a fuzzy set [14], and for further processing the crisp inputs are measured by sensors and passed into the control system. For example Human body temperature, pressure, etc. [13]. • Inference Engine: in this step we could determine the degree of match between fuzzy input and the rules. Based on the % match, it helps to determine which rules need implement according to the given input field. And then the applied rules are combined to develop the control actions [13]. • Defuzzification: which is an important operation in the process of fuzzy set, because it transforms a fuzzy set to numeric value [14].

4 Semi-autonomous Car A semi-autonomous car is a vehicle that dispenses with the action of the driver in many areas. It is equipped with several sensors and software that automatically cooperate using artificial intelligence. It will use various techniques to sense its surrounding, such as radar, Lidar, and camera. Lidar (Light Sensing and Telemetry), It is a technology which is based on remote sensing that calculates distance by illuminating a target with a light beam and analyzes the reflected light. It is mounted on the roof of the vehicle on a cylindrical enclosure which rotates 360° [15]. Lidar consists of a transmitter, a mirror and a receiver, It can also use laser, ultraviolet and infrared light to create images of objects. RADAR (Radio Detection and Ranging) is becoming an important automotive technology. This equipment helps to estimate the mutual speed of the vehicle and the object, based on electromagnetic waves. Radar technologies generally used are: frequency-modulated continuous wave radar or FMCW, pulse Doppler radar [16].

A Proposed Solution to Road Traffic Accidents …

61

VIDEO CAMERAS: These are attached to the top of the front window, near the rearview mirror and reproduce real-time 3D images of the road in front of you. These are adopted to detect traffic lights and signs, unforeseen everything like pedestrians, or animals…. [17]. A semi-autonomous car will equipped with such devices along with decision making algorithms based on learning such as deep learning or linguistic variables such as fuzzy logic will make cars more intelligent to take the right decisions when no reaction of the driver is noticed.

5 Proposed Solutions Based on the results we found by both Excel graphs and K-means classifier algorithm, we provided the following suggestions to the ministry of transportation to help reduce accidents: • Widen the most frequented roads, and install warning signs that alert drivers who exceed the speed limit, especially where there are schools, Public services, hospitals… • More control over non-controlling intersection and giving more priority to turning. • Install roundabouts that help reduce collisions and control speed. • Build overpasses and underpasses in the places knowing more traffic. Aging causes a loss of meaning. For example, an elderly person sees a sign only 65 m away, while a 20-year-old sees it at 100 m. So, it is important for the ministry to work on the visibility and legibility of signage [5]. Concerning the behavior of the driver it remains relative to the education received by each individual as well as to the values acquired by each one of them. Since all of these suggestions are general and most of them requires building a new infrastructure, a second solution is proposed which relies on using the data gathered to train Semi-autonomous cars that can take decisions like breaking, reducing speed, and stopping when it is needed in case the driver didn’t take any related action and this to limit accidents and help ensure greater road safety. This system will be implemented by the fuzzy logic control.

6 The Proposed Approach Based on results presented earlier in the data analytics phase; a scenario was generated to help a semi autonomous car take right decisions when there is a bad or no reaction from the driver which will prevent accidents from happening. The Flowchart presented in Fig. 2. represents the parameters which are Type of collision, initial shock point, Movement, braking system in S (second), and depending on the case an action will be generated, if the driver takes action its will be no

62

H. D. Touzani et al.

Fig. 2 Flowchart presenting our approach

reaction from the semi-autonomous car otherwise the car will take action either braking immediately, braking and attention left or right or reducing speed. This scenario will be Implemented using fuzzy logic control since the decisions can be based on linguistic values.

7 Conclusion Thousands of people die in traffic crashes yearly; this involves not only loss of human life but also property damage, this makes road safety one of the most studied subjects nowadays. In this paper, a data analytics was done by three different tools to analyze a big database of road traffic accidents collected by the ministry of transportation in Morocco. The purpose of the analysis was to identify the hidden parts in the data and locate the most important initial points and the types of intersection in the road that mostly causes accidents. As a better solution, a semi-autonomous car equipped

A Proposed Solution to Road Traffic Accidents …

63

with different types of sensors and cameras connected to intelligent models can help the car take the right decisions when there is no driver reaction. This paper presented an algorithm based on fuzzy logic to consider different scenarios and try to help the car take the right decision and prevent the accident from occurring when the driver doesn’t react. Our future work will be the implementation of the algorithm and try it on different scenarios.

References 1. NHTSA | National Highway Traffic Safety Administration. https://www.nhtsa.gov/. Accessed 27 Jun 2020 2. Fagnant DJ, Kockelman K (2015) Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations. Transp Res Part A: Policy Pract 77:167–181 3. Shinde T (2013) Car anti-collision and intercommunication system using communication protocol. Int J Sci Res (IJSR) 2(6):187–191 4. Coelingh E, Eidehall A, Bengtsson M (2010) Collision warning with full auto brake and pedestrian detection - a practical example of automatic emergency braking. In: IEEE conference on intelligent transportation systems, proceedings, ITSC, pp 155–160 5. Drissi Touzani H, Faquir S, Yahyaouy A (2020) Data mining techniques to analyze traffic accidents data: case application in Morocco. In: The fourth international conference on intelligent computing in data sciences ICDS 2020 conference, pp 1–3 6. Zadeh LA (1975) The concept of a linguistic variable and its application to approximate reasoning-III. Inf Sci 9(1):43–80 7. Driss M, Saint-Gerand T, BenSaïd A, Benabdeli K, Hamadouche MA (2013) A fuzzy logic model for identifying spatial degrees of exposure to the risk of road accidents (Case study of the Wilaya of Mascara, Northwest of Algeria). In: 2013 international conference on advanced logistics and transport, ICALT 2013, pp 69–74 8. Bates JHT, Young MP (2003) Applying fuzzy logic to medical decision making in the intensive care unit. Am J Respir Crit Care Med 167(7):948–952 9. Zbinden AM, Feigenwinter P, Petersen-Felix S, Hacisalihzade S (1995) Arterial pressure control with isoflurane using fuzzy logic. Br J Anaesth 74(1):66–72 10. Faquir S, Yahyaouy A, Tairi H, Sabor J (2016) Energy management in a hybrid PV/wind/battery system using a type-1 fuzzy logic computer algorithm. Int J Intell Eng Inform 4(3–4):229–244 11. El Amrani R, Faquir S, Yahyaouy A, Tairi H (2018) Modelling and implementation of an energy management simulator based on agents using optimised fuzzy rules: Application to an electric vehicle. Int J Innovative Comput Appl 9(4):203–215 12. Lee CC (1990) Fuzzy logic in control systems: fuzzy logic controller—part I. IEEE Trans Syst Man Cybern 20(2):404–418 13. Guru99. Fuzzy Logic Tutorial: What is, Application & Example. https://www.guru99.com/ what-is-fuzzy-logic.html. Accessed 08 Oct 2020 14. Roychowdhury S, Pedrycz W (2001) A survey of defuzzification strategies. Int J Intell Syst 16(6):679–695 15. Ondruš J, Kolla E, Vertaˇl P, Šari´c Ž (2020) How do autonomous cars work? Transp Res Procedia 44(2019):226–233 16. Sarkan B, Stopka O, Gnap J, Caban J (2017) Investigation of exhaust emissions of vehicles with the spark ignition engine within emission control. Procedia Eng 187:775–782 17. Yun HS, Kim TH, Park TH (2019) Speed-bump detection for autonomous vehicles by lidar and camera. J Electr Eng Technol 14(5):2155–2162

Multi-objective Archimedes Optimization Algorithm for Optimal Allocation of Renewable Energy Sources in Distribution Networks Ahmad Eid and Hassan El-Kishky

Abstract This paper presents the optimal allocation of the distributed generations (DGs) into the distribution networks (DNs) using the Archimedes Optimization Algorithm (AOA). The AOA optimizes three DGs operating at unity power factor representing the PV renewable energy sources (RES). The standard 33-bus DN is used as a test system to verify the AOA’s effectiveness in both single-objective and multiobjective optimizations. In addition to the overall power loss elimination, the total voltage deviation (TVD) of the DN is reduced by the optimum allocation of three DGs. In multi-objective optimization, the Pareto Optimal Front (POF) method is adopted to determine the non-dominated solutions. A fuzzy linear function decides the Best Compromise Solution (BCS) between the points set by the POF. The AOA’s obtained results in both single and multi-objective optimizations are compared to the Particle Swarm Optimization (PSO) and Atom Search Optimization (ASO) algorithms. The three algorithms are effective in solving the optimization problem or both single and multiple dimensions. Moreover, the AOA outperforms the ASO and PSO algorithms in different case studies. Keywords Archimedes optimization algorithm · Multi-objective optimization · Distributed generations

A. Eid (B) Department of Electrical Engineering, Aswan University, Aswan 81542, Egypt e-mail: [email protected] Department of Electrical Engineering, College of Engineering, Qassim University, Unaizah 56452, Saudi Arabia H. El-Kishky Electrical Engineering Department, The University of Texas at Tyler, Tyler, TX 75799, USA e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_7

65

66

A. Eid and H. El-Kishky

1 Introduction In recent years, environmental issues, fuel cost volatility, energy liberalization, and technological developments have contributed to the rise in distributed generation (DG) investment. This development has generated fantastic benefits but produced a range of difficulties in planning and operating distribution networks (DNs). The primary objective of inserting a DG into any DN is to provide extra real and/or reactive power feeding the load that reduces the total power supplied by the mains. More advantages to utilities of DG allocations include power loss and TVD reductions, enhancing the stability of DNs, increasing the system reliability [1–3]. At the same time, DG technologies provide a secure operation of the electricity markets during the deregulation of the power industry. Integration of DG units into DNs plays a vital role in providing support for reactive power, spinning reserve, and frequency control. However, poorly designed and incorrectly controlled DGs will contribute to reversing power flows, increasing power losses, and resulting in feeders’ overload. Recently, many researchers applied different metaheuristic optimization algorithms to allocate DG units into DNs. In [4, 5], the authors used the particle swarm optimization (PSO) algorithm or enhanced versions to reduce the distribution networks’ power losses. Grey wolf optimizer (GWO) algorithm is adopted in [6, 7] to minimize the power losses and reactive losses and enhance the voltage profiles of different DNs. A bacterial foraging optimization algorithm (BFOA) has been applied in [8, 9] to optimally determine the size and site of multiple DG units with DNs. The study also included allocating STATCOM devices with different load models for power loss minimization, voltage profile improvement, and increasing the reliability of the systems. A hybrid of ant and artificial bee colonies is applied in [10] for optimal placement and sizing of different DG sources to minimize the power losses and energy costs and enhance voltage stability. An enhanced artificial ecosystem-based optimization (EAEO) algorithm is developed and applied in [11] for optimal allocations of multiple DG units to reduce the power losses and TVD and enhance the system stability. The analysis included the application of the DG under fixed, unity, and optimal power factors. The authors of [12] applied a combination of the genetic multi-objective solver (GMOS) with linear programming to optimize the DG units attached to DNs for reducing energy price arbitrage, energy losses and enhance the power quality of the systems. In [13] adaptive PSO and modified gravitational search algorithm (GSA) are adopted to optimize the size and locations of different DG units with single and multi-objective problems. The main objectives were to reduce the total power losses and TVD and to enhance voltage stability. A hybrid salp swarm algorithm (SSA) with the loss sensitivity is used in [14] for renewable energy integration considering the annual load growth. The authors in [15] have proposed a newly published algorithm known as Archimedes Optimization Algorithm (AOA). It is one of the most recent natureinspired algorithms formulated from the physics Archimedes law of gravity. It imitates the concept of buoyant force imposed on an entity, partly or entirely submerged in a fluid, which is proportional to the weight of the fluid being displaced.

Multi-objective Archimedes Optimization Algorithm …

67

The application of AOA for DG allocation in distribution systems has not been considered yet, which encourages us to implement it to deal with these optimization problems. In this paper, the AOA is implemented to determine the optimal sizes and locations of three DG units operating with UPF to reduce the power losses and TVD of the 33bus DN. The obtained results of AOA are compared favorably with other algorithms such as PSO and ASO. The results show the superiority of the AOA algorithm in the single- and multi-objective optimization case studies.

2 Problem Formulation The DG units’ integration to the DNs enhances their performance in terms of loss reduction and improvement of the voltage profile. The AOA algorithm is implemented to optimally determine the sizes and locations of three DG units to minimize the total power losses and the total voltage deviation. The DG type includes the PV operating at unity power factor (PF). The Forward/Backward sweep method is used for solving the DN before and after the DG integrations. For single-objective optimization, the branch power loss is calculated as: br 2 = Ibr Rbr Ploss

(1)

where Ibr represents the branch current and Rbr represents the branch current. The total power loss of the system, Ploss, is: Ploss = 3

 N br 1

br Ploss

(2)

where N br is the number of branches and three accounts for the three-phase system, the power loss objective function is expressed as: floss = min(Ploss)

(3)

The TVD is the sum of the voltage differences between the bus voltages and unity, as: TVD =

N b i=1

|Vi − 1.0|

(4)

where N b represents the number of buses, and Vi is the bus voltage, the TVD objective function is as: f T V D = min(T V D)

(5)

For multi-objective (MO) optimization, both functions are optimized together as:

68

A. Eid and H. El-Kishky

f M O = min[ floss , f T V D ]

(6)

In this case, the non-dominated points are determined using the Pareto Optimal Front (POF) method. The fuzzy-based function selects the BCS in the POF collection of points. For each objective function f i with a solution range of k points, the linear membership function µik is calculated as:

µik

=

⎧ ⎪ ⎨1 ⎪ ⎩

f imax − f i f imax − f imin

0

f i ≤ f imin f imin ≤ f i ≤ f imax fi ≥

(7)

f imax

The normalized function µk for each solution k, is as follows:  Nob

µik µ =  Nnd i=1  Nob k

k=1

i=1

µik

(8)

where f imin is the minimum value while f imax is the maximum value of the i th objective function; Nob is the objective function numbers and Nnd is the number of the non-dominated solutions. The minimum value of this set is the BCS of the POF points.

3 Optimization Algorithms The newly published AOA [15] is implemented here to optimize the three DG units in order to minimize power loss and TVD. The performance of the AOA is compared to the ASO [16] and PSO [13] algorithms. The details of each algorithm are found in the respective literature.

4 Results and Discussions In this section, the obtained results of the adopted optimization algorithms are demonstrated and compared. The study includes the sizing and siting of two different RES of PVs and WTs in the 33-bus RDS to decrease the total power losses and total voltage deviation. Three RESs are optimized, working at unity power factor (UPF). The following case studies are investigated.

Multi-objective Archimedes Optimization Algorithm …

23 24 25

1

2

3

4

5

6

69

26 27 28 29 30 31 32 33

7

8

9 10 11 12 13 14 15 16 17 18

19 20 21 22

Fig. 1 The 33-bus RDS layout

4.1 Test System The paper discusses the DG allocations using different optimization algorithms to the standard 33-bus radial distribution system. This system has 32 branches and 33 nodes, as shown in Fig. 1, with a total load of 3715 kW and 2300 kVar with a voltage level of 12.66 kV. The complete system data is presented in [17].

4.2 Simulation Strategies In this paper, the optimal placement and sizing of three PVs and three WTs are proposed using the AOA algorithm to reduce the losses and TVD. The study includes both single-objective optimization (SOO) and multi-objective optimization (MOO) based on a fuzzy function to determine the BCS among the POF points. In all case studies, the obtained results of the AOA are compared to ASO and PSO algorithms.

4.3 Simulation Results of Single-Objective Functions In this section, the single-objective functions of power loss and TVD are optimized separately using the AOA, ASO, and PSO algorithms. The obtained results are compared in terms of total power loss, reactive loss, and TVD. The comparison also includes statistical analysis showing the average, median, standard deviation, and standard error for each algorithm. Power Loss Reduction: The three optimization algorithms PSO, ASO, and AOA, are simulated and compared, as listed in Table 1 with the base case for minimum loss objective function. Investigating the table shows that the AOA algorithm obtains the lowest power loss of 72.7837 kW while it is a little bigger of 72.9759 kW by using the

70

A. Eid and H. El-Kishky

Table 1 Optimal sizing of three UPF DGs with power loss reduction as the objective function Parameters

No DGs

PSO

ASO

AOA

Power loss (kW)

210.9822

72.7951

72.8291

72.7837

Reactive loss (kVar)

143.0313

50.6530

50.7958

50.6530

TVD

1.8044

0.6164

0.6185

0.6164

Minimum voltage (pu)

0.9038

0.9686

0.9683

0.9687

DG size & location

–

801.80/13

742.03/14

801.79/13

1091.31/24

1081.62/24

1091.31/24

1053.60/30

1114.29/30

1053.59/30

ASO algorithm and 72.7951 kW for the PSO case. The power loss for the base case is 210.9822 kW. Allocating three DGs working at UPF of optimal sizes, as shown in Table 1 at optimal buses 13, 24, and 30, reduces the power loss by 65.5% and 64.6% for reactive power loss. Moreover, TVD is reduced by 65.8%, which means improving the system’s voltage profile. As a result, the minimum voltage of the base case of 0.9038 pu has been elevated to 0.9687 pu after DGs allocations. The complete voltage profile is shown in Fig. 2. The node voltages are improved at all buses after the DG allocations. Although the three algorithms’ power loss values are relatively equal, the AOA outperforms the other algorithms regarding the convergence speed of the objective function against the iteration number, as shown in Fig. 3. The PSO and ASO algorithms come in the second and third stages, respectively, compared to the AOA algorithm. Statistical analysis is performed to compare the performance of the three algorithms for solving the DG allocation problem. Every algorithm solves the system 30 consecutive times, allocating the three DGs to minimize the power loss. The average, median, standard deviation, and standard error are calculated for each algorithm and listed in Table 2. It is clear from the table that AOA is the best algorithm in all listed parameters. These results show the superiority of the AOA algorithm compared to others. Reduction of TVD: In the same manner as the previous case of power loss reduction, the three algorithms are simulated to reduce the TVD as a single-objective function. The TVD values are 0.0715, 0.0693, and 0.0636 V, respectively, for the PSO, ASO, and AOA, as listed in Table 3. The AOA outperforms the other two algorithms of ASO and PSO. The allocation of three DGs also contributes to decrease the power losses and enhance the voltage profile, as shown in Fig. 4. The bus voltages are all improved (except the slack bus) and approximately equal to unity, where the minimum bus voltage is 0.9943 pu. The speed convergence of the three algorithms is shown in Fig. 5. It is clear that the AOA is much faster and more accurate than the other PSO and ASO algorithms. A statistical analysis based on 30 consecutive runs of the three algorithms to minimize the TVD as presented in Table 4. The statistical parameters include average, mean, median, standard deviation, and standard error.

Multi-objective Archimedes Optimization Algorithm …

Voltage, pu

without DGs

71

with DGs

1.02 1 0.98 0.96 0.94 0.92 0.9 0.88 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 Bus number

Fig. 2 Voltage profile before and after DG allocations with power loss reduction

Fig. 3 Convergence speed of different algorithms for minimum power loss

Table 2 Statistical analysis for power loss reduction

Parameters

PSO

ASO

AOA

Average

72.8058

73.3148

72.7846

Median

72.8005

73.1597

72.7837

Standard deviation

20.372E-3

0.48053

1.781E-3

Standard error

3.719E-3

0.08773

0.325E-3

4.4 Simulation Results of Multi-objective (MO) Functions This section presents the multi-objective (MO) optimization of both the power losses and the T V D of the distribution system. The PSO, ASO, and AOA algorithms are applied to the 33-bus system to optimize the two objective functions. The decisionmaker decides the best operation point depending on the system situations and

72

A. Eid and H. El-Kishky

Table 3 Optimal sizing of three UPF DGs with TVD reduction as the objective function Parameters

No DGs

PSO

ASO

AOA

TVD

1.8044

0.0715

0.0693

0.0636

Power loss (kW)

210.98

162.18

114.88

113.18

Reactive loss (kVar)

143.03

114.91

79.89

78.95

Minimum voltage (pu)

0.9038

0.9637

0.9944

0.9943

DG size/location

–

1350.6/12

1170.0/13

1168.9/13

1481.7/24

1762.3/24

1558.7/24

1759.6/30

1782.1/30

1847.9/30

without DGs

1.02

with DGs

Voltage, pu

1 0.98 0.96 0.94 0.92 0.9 0.88 0

2

4

6

8 10 12 14 16 18 20 22 24 26 28 30 32 34 Bus number

Fig. 4 Voltage profile before and after DG allocations with TVD reduction

Table 4 Statistical analysis for TVD reduction

Parameters

PSO

ASO

AOA

Average

0.076021

0.078834

0.068913

Median

0.072150

0.078150

0.070401

Standard deviation

9.211E-3

7.906E-3

2.654E-3

Standard error

1.682E-3

1.444E-3

0.484E-3

attributed conditions. The power loss (Ploss) and T V D objectives are optimized by optimally sizing and sitting different DG units. The MO-AOA, MO-ASO, and MO-PSO algorithms are used to optimize a single DG operating at UPF. The objective is to minimize both the power losses and the T V D using POF techniques accompanied by the fuzzy distribution function. The POF set of points and the BCS points for all the algorithms are shown in Fig. 6. Although the BCS points for the three algorithms are close, the MO-AOA has the minimum values for Ploss and T V D. With two DGs, the optimal solutions with their BCS points are shown in Fig. 6. For three DGs optimization, the POF points with the

Multi-objective Archimedes Optimization Algorithm …

73

Fig. 5 Convergence speed of different algorithms for minimum TVD

Fig. 6 Optimal solutions for optimizing a single-DG (left) and two-DG (right)

BCS points of the different algorithms are shown in Fig. 7. In this case, the MO-ASO and MO-PSO solutions are close together regarding the BCS points. The MO-AOA has a lower BCS for both objectives, and hence, it beats the other algorithms. If one investigates Figs. 6, 7 carefully, it is clear that the expansion ranges of the AOA when optimizing the TVD and power losses are larger than with the PSO and ASO algorithms. Subsequently, the AOA finds more non-dominated solutions than the other algorithms. Moreover, the effectiveness of the AOA appears more clearly with increasing the number of DG units. The BCS is much lower for AOA than for other algorithms with three DG units, as shown in Fig. 7. Hence, the AOA is more suitable for large DNs operations.

74

A. Eid and H. El-Kishky

Fig. 7 Optimal solutions of different algorithms for optimizing three DGs

5 Conclusions The optimal sizing and siting of multiple distributed generations are determined using the new metaheuristic Archimedes optimization algorithm (AOA) to reduce total power losses and total voltage deviation. The AOA is implemented in a singleobjective as well as multi-objective optimizations. It is found that the allocation of the DG units to the distribution networks decreases the power losses and enhances the voltage profile of the system. The AOA’s obtained results surpass those obtained by the ASO or PSO algorithms regarding the optimized functions or convergence speed.

References 1. Home-Ortiz JM, Pourakbari-Kasmaei M, Lehtonen M, Sanches Mantovani JR (2019) Optimal location-allocation of storage devices and renewable-based DG in distribution systems. Electr Power Syst Res 172:11–21 2. Nduka OS, Pal BC (2018) Quantitative evaluation of actual loss reduction benefits of a renewable heavy DG distribution network. IEEE Trans Sustain Energy 9(3):1384–1396 3. Muthukumar K, Jayalalitha S (2016) Optimal placement and sizing of distributed generators and shunt capacitors for power loss minimization in radial distribution networks using hybrid heuristic search optimization technique. Int J Electr Power Energy Syst 78:299–319 4. Prakash DB, Lakshminarayana C (2016) Multiple DG placements in distribution system for power loss reduction using PSO algorithm. Procedia Technol 25:785–792 5. Eid A, Abdel-Akher M (2019) Power loss reduction using adaptive PSO in unbalanced distribution networks. In: 2019 21st International Middle East Power Systems Conference, MEPCON 2019 - Proceedings, pp 675–680 6. Sanjay R, Jayabarathi T, Raghunathan T, Ramesh V, Mithulananthan N (2017) Optimal allocation of distributed generation using hybrid grey Wolf optimizer. IEEE Access 5:14807–14818 7. Sultana U, Khairuddin AB, Mokhtar AS, Zareen N, Sultana B (2016) Grey wolf optimizer based placement and sizing of multiple distributed generation in the distribution system. Energy 111:525–536

Multi-objective Archimedes Optimization Algorithm …

75

8. Devabalaji KR, Ravi K (2016) Optimal size and siting of multiple DG and DSTATCOM in radial distribution system using bacterial foraging optimization algorithm. Ain Shams Eng J 7(3):959–971 9. Mohammadi M, Rozbahani AM, Montazeri M (2016) Multi criteria simultaneous planning of passive filters and distributed generation simultaneously in distribution system considering nonlinear loads with adaptive bacterial foraging optimization approach. Int J Electr Power Energy Syst 79:253–262 10. Kefayat M, Lashkar Ara A, Nabavi Niaki SA (2015) A hybrid of ant colony optimization and artificial bee colony algorithm for probabilistic optimal placement and sizing of distributed energy resources. Energy Convers Manag 92:149–161 11. Eid A, Kamel S, Korashy A, Khurshaid T (2020) an enhanced artificial ecosystem-based optimization for optimal allocation of multiple distributed generations. IEEE Access 8:178493– 178513 12. Jannesar MR, Sedighi A, Savaghebi M, Anvari-Moghadam A, Guerrero JM (2020) Optimal multi-objective integration of photovoltaic, wind turbine, and battery energy storage in distribution networks. J Energy Manag Technol 4(4):76–83 13. Eid A (2020) Allocation of distributed generations in radial distribution systems using adaptive PSO and modified GSA multi-objective optimizations. Alexandria Eng J 59(6):4771–4786 14. Abdel-Mawgoud H, Kamel S, Yu J, Jurado F (2019) Hybrid Salp Swarm Algorithm for integrating renewable distributed energy resources in distribution systems considering annual load growth. J King Saud Univ Comput Inf Sci (2019, in press) 15. Hashim FA, Hussain K, Houssein EH, Mabrouk MS, Al-Atabany W (2020) Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems. Appl Intell 51(3):1531–1551 16. Zhao W, Wang L, Zhang Z (2019) Atom search optimization and its application to solve a hydrogeologic parameter estimation problem. Knowl-Based Syst 163:283–304 17. Aman MM, Jasmon GB, Bakar AHA, Mokhlis H (2014) A new approach for optimum simultaneous multi-DG distributed generation units placement and sizing based on maximization of system loadability using HPSO (hybrid particle swarm optimization) algorithm. Energy 66:202–215

The Recurrent Neural Network for Program Synthesis Achraf Berrajaa and El Hassane Ettifouri

Abstract The program synthesis is a new and ambitious field consists of generating from short and natural descriptions a source code. The program synthesis is rapidly becoming a popular research problem, interesting potential has been offered by using artificial intelligence to support new tools in almost all areas of program analysis and software engineering. The goal of our long-term research project is to enable and facilitate any user to create an application from a description written in natural language by specifying the need for a complete system. This involves carrying out a study of the user’s needs, the design and implementation of an intelligent system allowing the automatic realization of an Informatic project (architecture, initialization scripts, configuration, etc.) expressed in natural language. We propose a recurrent neural network with LSTM cells and we publish a dataset of more than 145 765 questions and their best answer, especially in Java. The performance of the proposed model is very interesting such that the success rate is 94%. Keywords Artificial intelligence · The program synthesis · RNN · LSTM and new dataset

1 Introduction The dream of using a natural language to generate source code in a programming language has existed almost as long as the programming task itself. Natural language as a support for computer programming would be universally accessible and allow the automation of an application [1]. However, the ambiguity and diversity of the text, the

A. Berrajaa (B) INSA Euro-Mediterranean UEMF, Fez, Morocco e-mail: [email protected] E. H. Ettifouri Novelis Lab, Paris, France © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_8

77

78

A. Berrajaa and E. H. Ettifouri

compositional nature of the source code, and the architecture of the layers in the software make it too difficult to generate the source code from natural language descriptions. Artificial intelligence offers interesting potential to support new tools in almost all areas of program analysis and software engineering. In recent years, artificial intelligence has grown exponentially in the field of natural language processing (NLP) and software engineering. For example, researchers have developed for several languages several tools for analyzing and extracting useful information in digital documents in order to generate automatic coding [2]. However, the differences between the linguistic structures of different languages and the compositional nature of the code do not always make it possible to extend the use of the programs developed for a given language. Program synthesis occupies an important place in the field of artificial intelligence, and consists of automatically building a program from a specification [2]. It is a field of multidisciplinary research, which makes collaborate linguists, computer scientists, logicians, lexicographers or translators. Computing has long regarded programs as data objects. With the rise of machine learning, there is renewed interest in treating programs as data to feed into learning algorithms. However, programs have special features, including several layers of structure, such as the contextless syntax structure of a program, name and type constraints that are not without context, and program semantics. As a result, programs do not start in a form that is immediately compatible with most standard learning techniques [3]. Overall, the program synthesis consists in designing a solution allowing the automatic generation of the source code of an application based on descriptions and specifications written in natural languages (French/English) taking into account the context and the field of activity of this application, which forces the proposal of solutions of artificial intelligence [4]. This paper presents a recurrent neural network (RNN) with LSTM cells with a learning process based on a new data set from the program synthesis. The new dataset (Corpus) is based on over 145 765 Java Q&A from different topics from the “Stack OverFlow” website. In the rest of the paper, we start with the Sect. 2, reviewing recent work on program synthesis, also a description of some algorithms which are considered to be the most used and presenting models of machine learning and deep learning. In Sect. 3, we will present the problem statement, then in Sect. 4 we present the construction of the corpus and we describe our network of recurrent neurons with LSTM cells. In the Sect. 5, we discuss our results and we will conclude with some thoughts on future work.

2 Related Work Linking a natural language description with source code has many topical applications, such as program synthesis, comment or text prediction, code autocompletion, prediction of properties of a program, bugs detection and anomalies, the automation of the migration and the analysis of the quality of the code. The goal of program

The Recurrent Neural Network for Program Synthesis

79

synthesis is to create and generate complete programs based on a [1] specification. Generally, a specification is a set of requirements to be met by the program. The models proposed in the program overview assumed that a specification is the desired user intention to be achieved, which in many cases has also proven to be more complicated than writing the program itself. Synthesis of programs is too difficult a field of research. Its challenge can be summed up in two main components: the size of the program’s search space and the diversity of the user’s expression. In other words, the main challenge is to search a large space for possible programs to find a program that matches the specification. This has several lines of research in inductive synthesis. This field has made many progress in different communities, including programming languages and artificial intelligence (machine learning and deep learning). The first automation approaches were proposed in [5]. The basic idea behind this kind of models was to use theorem provers to build a proof of the user specification, then use the proof to provide the corresponding logic program. Currently, researchers have considered incomplete or partial specifications, such as [6] input/output pairs or a natural language description. When the specification is a natural language description, this is the task of semantic analysis. Semantic analysis methods have been implemented by the NLP community, they are based on data that attempts to map natural languages to structured logical forms that can be executed by simple computers. These logical forms are general purpose meaning representations [7], instructions or orders for personal assistants or robots [8] or formalisms for querying knowledge bases [9]. These proposals have a great advantage that they are comparable to data, compared to programming languages used by developers, the languages specific to these areas targeted by this research have a relatively simple syntax and scheme. In 2016, Ling et al. [4] proposed a datadriven code generation approach for high-level programming languages like Java and Python. However, unlike most semantic analysis jobs, it does not take into account that the source code must be welldefined programs in the target syntax. The deep learning community in turn sparked interest in the program synthesis. Deep neural networks (Deep learning) have two major advantages for modeling and translating natural language. With the invention of [10] word embedding, informal instruction sequences have become a possible entry for neural models. The recurrent neural networks (RNN) have made it possible to make great progress in terms of modeling the structure of natural language [3]. The “Seq2Seq: sequence-to-sequence neural network” sequence-to-sequence models [11] with new methods such as the attention technique [12] allowed the results of translation to go beyond automatic expression-based in production systems such as Google Translate [13]. Until recently, neural modeling of natural language dominated the idea that neural networks do not require any information about the syntactic structure of sentences. The outstanding results of Deep Learning in learning representation and in analyzing structure suggested using a simple sequence of words as input for a neural model and allowing it to learn a representation. Of all other important characteristics reestablishing an error gradient (based on the gradient backpropagation). However, recent results of using syntactic structures for machine translation [14] and reading comprehension [15] exceed the results of Seq2Seq models. To analyze the syntactic

80

A. Berrajaa and E. H. Ettifouri

structures, recursive neural networks [16, 17] and a grammar of recurrent neural networks [18] were used. Recursive neural networks have also shown good results for the analysis of [19] and dependency [20] semantics. Reed and Freitas [21] and Neelakantan et al. [22] introduce differentiable neuronal architectures for program induction. This is an interesting emerging area of research [23], but one that does not yet adapt to the kinds of problems that the programming language and software engineering community is considering [24]. The generation of source code from descriptions and sentences written in natural language (Text-to-Code) aims to help developers, end-users, to easily generate programs. There is growing interest in this area, with applications such as converting natural language to: Excel software macros [25], Java expressions [26], Shell commands [27], if-then simple programs [8], regular expressions [28], SQL queries [29], variable names, methods and their types [30, 31] and entity names [32]. This work presents a network of recurrent neurons (RNN) with LSTM cells with a learning process based on a new dataset (Corpus) that contains more than 145 765 questions and their best response specifically in Java in order to automatically generate source code for java applications with an n-tier (multilayer) architecture.

3 Problem Statement The creation of an application or software, meeting a user need, is a project that requires several steps as: framing meetings of the need, functional and technical design, implementation of the technical architecture, development of the application, code reviews, tests and validations, correction of quality anomalies and various technical and functional bugs. The project requires the intervention of several profiles: project manager, technical experts, architect, developers and test and validation teams. Our objective is to design a solution allowing the automatic generation of the source code of an application from sentences (specifications and descriptions) written in natural languages (program synthesis) in order to minimize this cost. This objective involves the resolution of several major technological and scientific challenges: • Application and implementation of approaches allowing the machine to understand the user’s need, expressed in natural language (text) in a specific field, by exploiting the results of the state of the art and by participating through scientific contributions to the resolution of the limitations encountered. • Automatic generation of a computer project meeting user needs (Architecture, configuration, initialization scripts, …). • Generation of algorithms that take into account the constraints and requirements expressed in the user’s need in order to integrate them into the generated project. We will focus our work on the generation of applications and programs with language java with an n-tier (multilayer) architecture, for that we propose a network of recurrent neurons (RNN) with LSTM cells with a learning process based on a

The Recurrent Neural Network for Program Synthesis

81

new set of data more than 145 765 questions and their best answer, especially in Java. This dataSet represents the learning data of our recurrent neural network with LSTM.

4 The Proposed Contribution 4.1 Stack OverFlow The dataset we propose in this article is based on the dataset provided by the “Stack Overflow” site. We rely on it because it is both the largest and the newest platform for exchange between computer programming experts. Stack Overflow offers questions and answers on a wide range of topics related to computer programming (java, c, c++, hp, javascript, python, …). It is part of the Stack Exchange site network. The proposed dataset is based on the questions and their best answers available in stackoverflow.

4.2 Data Preparation The main goal behind creating a new data set is to enable the scientific community to evaluate new techniques using the same test conditions. Also, this dataset represents an interesting and recent sample of questions and answers from computer experts which will allow it to be a good learning sample, and it will be a strong point for a learning model because it will be learned from experts in the field. These benefits are usually not yet offered by the program synthesis researchers. The dataset we use in this article is based on all the data provided by the Stack OverFlow Questions and Answers website via the Kaggle web platform in a prerelease version. We used this dataset because it is both an interesting and recent sample of the questions and answers from the Stack OverFlow site. Initially, we did the extraction of dataset containing a 1.2 m sample of questions and answers since 2008 until October 2016 [33] and to have a larger and updated sample with the new scripts, we have added a second dataset with 143 690 questions and answers since October 2016 until September 2020. Then we processed the data of the two datasets checking the validity of the fields (empty fields and quotation marks) and we filtered the lines taking only the questions and their best response from the java language, furthermore we combined the two samples to have a single structure. The result is a dataset of 145 765 questions and their best answer specifically in Java. For the latter to be usable for machine learning.

82

A. Berrajaa and E. H. Ettifouri

4.3 LSTM Neural Networks Long Short-Term Memory (LSTM) is an alternative solution was proposed in [34]: The network architecture is modified such that the vanishing gradient problem is explicitly avoided, whereas the training algorithm is left unchanged. But what is the strong point of LSTM? why are we using it in our implementation? A common LSTM unit is composed of a cell, a forget gate, an input gate and an output gate. The cell remembers values over arbitrary time intervals and the three gates regulate the flow of information into and out of the cell. LSTM networks are well-suited to classifying, processing and making predictions based on time series data, since there can be lags of unknown duration between important events in a time series. And also as we explained LSTMs were developed to deal with the exploding and vanishing gradient problems that can be encountered when training traditional RNNs. Figure 1 shows the internal architecture of an LSTM. – Forget gate: it’s the power to forget information. In a long sequence concretely to the classic neural network where you have to memorize all the information on the long term. In the LSTM the power to forget non-useful information is represented by the function ft = α (Wf ∗ [ht − 1, xt] + bf), where α is a sigmoid function, Wf is the weights and [ht − 1, xt] is the concatenation of the two vectors ht − 1 and xt. – Input gate: is the responsible to add relevant information, the function that will offer new information is Ct = Ct − 1 ∗ ft + it ∗ At where it = α (Wi ∗ [ht − 1, xt] + bi) and At = tanh (Wc ∗ [ht − 1, xt] + bc). – Output gate: Finally, this operation allows to define the current state of the cell. So far we have used the status of the president cell, we have forgotten information and we have added new information to the memory. We still have to define the state of the current cell. This is summarized by the following function: ht = α (W o ∗ [ht − 1, xt] + bo) ∗ tanh(Ct).

Fig. 1 LSTM recurrent neural networks

The Recurrent Neural Network for Program Synthesis

83

4.4 Network Architecture and Choice of Parameters Although there are several differences and several architectures in neural network language models that have been successfully applied so far, all present and share some basic principles like: The input sequence (the input words), in our implementation it is encoded by an encoding from 1 to K, where K is the unique number of letters in our dataSet. This involves listing single letters and the code using the One Hot Encoding [35]. Note that the sequenes are generated in 64 batch with 10% diversity (in the objective to avoid overfitting). The model makes a prediction of a sequence where the first element of each sequence represents the next letter of the input. Concerning the network topology, an architecture with five hidden layers has been applied each with 128 LSTM cells, or each uses the “relu” activation function. While the output layer, a “softmax” activation function is used to produce properly normalized probability values in order to choose a single output letter. As a learning criterion, Adam optimizers is used with a precision of 0,001. It is generally advisable to normalize the input data of a neural network [36], which means that a linear transformation is applied so that the data has zero mean and unit variance. Only one normalization was to use is to keep all the letters in lower case. Otherwise we kept the symbols because they represent part of the source code. The following algorithm summarizes the main steps. Algorithm 1 : The proposed RNN architecture with LSTM - Initialization of the weights by random drawing according to a uniform law on [0;1]. - Normalization: keep the text in lowercase. - Codage: list all elements in text, represent each one by a number and coded it using the One Hot Encoding. - Creating an RNN network: Creating seven layers, the six hidden layers has been applied each with 128 LSTM cells. While the output layer, a "softmax" activation function is used to predict the continuation of the text. while counter ≤ iterMax do 1. Batch generation with 10% of noise. 2. Have the model trained. 3. Adam optimizers is used with a precision of 0.001 in order to assign to each entry a responsibility in the overall error. 4. Update each weight end while

84

A. Berrajaa and E. H. Ettifouri

5 Experimental Results In this section, we present the types and percentage of data in our dataSet, also the learning rate achieved by our neural network. The Content of the dataSet In order to identify the categories of questions asked by StakOverFlow users for the Java language, we ran through all the questions in the dataset and symbolized each question, then counted the occurrences of each to rank the words by categories. Each class contains words with convergent occurrences. Surprisingly, we find subjects of different categories. For example, there are general topics with words like “error” or this class deals with the problems of errors. Also there are topics specific to the exception problem or references to specific techniques (for example “java mail”) or topics that can rarely occur (like “keyevent” or “jaxrs”). We recognize categories of questions that cannot be explained with code because they do not always coincide with identifiers. These are emerging code properties, such as library or version problems. By performing the tokenization of the title and whenever possible, we analyzed the java code snippets, we identify the fact that 83% of the text, 16.95% contains both text and java code and that the questions 0.05% only contain java code. This reaffirms that our dataSet contains a diversity to present a specification. The Learning and Test Rate The learning rate makes it possible to get an idea of the quality of the model. In the following figure we present a graph that represents the learning rate (94%) and the test rate (87%).

Fig. 2 The learning rate and the loss rate

The Recurrent Neural Network for Program Synthesis

85

6 Conclusion In order to design and propose a model that meets the need for program synthesis. Specifically, the generation of frontend/backend web applications based on Java technology and which respect an architecture with n levels (multilayer). We proposed a project architecture in order to meet the different user needs expressed in natural language on different software engineering domains. We started by introducing a recurrent neural network with LSTM cells and we publish a set of learning data of more than 145 765 questions and their best answer, especially in Java. The performance of the proposed model is very interesting such that the success rate is 94%. The perspectives are set by proposing new methods, new neural network architectures and implementing them in GPUs to improve the learning face.

References 1. Gulwani S, Polozov O, Singh R (2017) Program synthesis. Found Trends Program Lang 4(1– 2):1–119 2. Berrajaa A, Ettifouri EH, Dahhane W, Bouchentouf T, Rahmoun M (2019) Nl2code: a corpus and semantic parser for natural language to code. In: International conference on smart information and communication technologies. Springer, Cham, pp 592–599 3. Jozefowicz R, Vinyals O, Schuster M, Shazeer N, Wu Y (2016) Exploring the limits of language modeling. arXiv preprint arXiv:1602.02410 4. Ling W, Grefenstette E, Hermann KM, Koˇciský T, Senior A, Wang F, Blunsom P (2016) Latent predictor networks for code generation. arXiv preprint arXiv:1603.06744 5. Manna Z, Waldinger RJ (1971) Toward automatic program synthesis. Commun ACM 14(3):151–165 6. Lau T (2001) Programming by demonstration: a machine learning approach. Doctoral dissertation 7. Banarescu L, Bonial C, Cai S, Georgescu M, Griffitt K, Hermjakob U, Knight K, Koehn P, Palmer M, Schneider N (2013) Abstract meaning representation for sembanking. In: Proceedings of the 7th linguistic annotation workshop and interoperability with discourse, pp 178–186 8. Quirk C, Mooney R, Galley M (2015) Language to code: learning semantic parsers for if-thisthen-that recipes. In: Proceedings of the 53rd annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing, vol 1, pp 878–888 9. Berant J, Chou A, Frostig R, Liang P (2013) Semantic parsing on freebase from questionanswer pairs. In: Proceedings of the 2013 conference on empirical methods in natural language processing, pp 1533–1544 10. Bengio Y, Ducharme R, Vincent P, Jauvin C (2003) A neural probabilistic language model. J Mach Learn Res 3:1137–1155 11. Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. In: Advances in neural information processing systems, pp 3104–3112 12. Bahdanau D, Cho K, Bengio Y (2014) Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 13. Wu Y, Schuster M, Chen Z, Le QV, Norouzi M, Macherey W, Krikun M, Cao Y, Gao Q, Macherey K, Klingner J (2016) Google’s neural machine translation system: bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144

86

A. Berrajaa and E. H. Ettifouri

14. Chen H, Huang S, Chiang D, Chen J (2017) Improved neural machine translation with a syntax-aware encoder and decoder. arXiv preprint arXiv:1707.05436 15. Xie P, Xing E (2017) A constituent-centric neural architecture for reading comprehension. In: Proceedings of the 55th annual meeting of the association for computational linguistics (Volume 1: Long Papers), vol 1, pp 1405–1414 16. Goller C, Kuchler A (1996) Learning task-dependent distributed representations by backpropagation through structure. In: Proceedings of international conference on neural networks (ICNN 1996), vol 1, pp 347–352 17. Socher R, Lin CC, Manning C, Ng AY (2011) Parsing natural scenes and natural language with recursive neural networks. In: Proceedings of the 28th international conference on machine learning (ICML 2011), pp 129–136 18. Dyer C, Kuncoro A, Ballesteros M, Smith NA (2016) Recurrent neural network grammars. arXiv preprint arXiv:1602.07776 19. Tai KS, Socher R, Manning CD (2015) Improved semantic representations from treestructured long short-term memory networks. arXiv preprint arXiv:1503.00075 20. Zhu C, Qiu X, Chen X, Huang X (2015) A re-ranking model for dependency parser with recursive convolutional neural network. arXiv preprint arXiv:1505.05667 21. Scott R, de Freitas N (2016) Neural programmer-interpreters. In: Proceedings of the international conference on learning representations (ICLR) 22. Neelakantan A, Le QV, Sutskever I (2015) Neural programmer: inducing latent programs with gradient descent. arXiv preprint arXiv:1511.04834 23. Kant N (2018) Recent advances in neural program synthesis. arXiv preprint arXiv:1802.02353 24. Gaunt AL, Brockschmidt M, Singh R, Kushman N, Kohli P, Taylor J, Tarlow D (2016) Terpret: a probabilistic programming language for program induction. arXiv preprint arXiv:1608.04428 25. Gulwani S, Marron M (2014) Nlyze: interactive programming by natural language for spreadsheet data analysis and manipulation. In: Proceedings of the 2014 ACM SIGMOD international conference on management of data. ACM, pp 803–814 26. Gvero T, Kuncak V (2015) Synthesizing java expressions from free-form queries. Acm Sigplan Notices 50(10):416–432 27. Lin XV, Wang C, Zettlemoyer L, Ernst MD (2018) Nl2bash: a corpus and semantic parser for natural language interface to the linux operating system. arXiv preprint arXiv:1802.08979 28. Kushman N, Barzilay R (2013) Using semantic unification to generate regular expressions from natural language. In: Proceedings of the 2013 conference of the North American chapter of the association for computational linguistics: human language technologies, pp 826–836 29. Zhong V, Xiong C, Socher R (2017) Seq2sql: generating structured queries from natural language using reinforcement learning. arXiv preprint arXiv:1709.00103 30. Alon U, Zilberstein M, Levy O, Yahav E (2019) code2vec: learning distributed representations of code. Proc ACM Program Lang 3(POPL):40 31. Gao S, Chen C, Xing Z, Ma Y, Song W, Lin SW (2019) A neural model for method name generation from functional description. In: 2019 IEEE 26th international conference on software analysis, evolution and reengineering (SANER). IEEE, pp 414–421 32. Parvez MR, Chakraborty S, Ray B, Chang KW (2018) Building language models for text with named entities. arXiv preprint arXiv:1805.04836 33. StackOverflow (2008–2016) Stacksample: 10 percent of stack overflow questions and answers. https://www.kaggle.com/stackoverflow/stacksample 34. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780 35. Buckman J, Roy A, Raffel C, Goodfellow I (2018) Thermometer encoding: one hot way to resist adversarial examples 36. Bishop CM (1995) Neural networks for pattern recognition. Oxford University Press, Oxford

MaskNet: CNN for Real-Time Face Mask Detection Based on Deep Learning Techniques Ismail Nasri, Mohammed Karrouchi, Hajar Snoussi, Abdelhafid Messaoudi, and Kamal Kassmi

Abstract Coronavirus disease 2019 (COVID-19) is currently spreading in several countries around the world. The wearing of face masks during the COVID-19 pandemic is one of major protection method that has received varying recommendations from different public health agencies and governments. In this paper we have proposed a face mask detector based on deep learning techniques to classify each face as with mask or without mask in real-time. This study will focus on the Prajna Bhandary dataset. In this work a comparison between transfer learning and training from scratch has been provided, this comparison based on accuracy, model size and computer time. We have also analyzed the role of number of images dataset and learning rate in the classification accuracy and training time. In additions, the extracted model can achieve an accuracy of more than 98% and can be implemented in embedded system and used for many applications. Keywords COVID-19 · Face mask detection · Convolutional neural networks · Transfer learning · Training from scratch

1 Introduction The World Health Organization and several countries have recommended that masks can limit the spread of certain respiratory viral diseases, including COVID-19 [1]. This recommendation is meant to reduce the spread of the disease by asymptomatic and pre-symptomatic individuals and is a complementary measure to established preventive measures such as social distancing. In this paper we have proposed a solution for mask detection that can be very effectively used at airports to detect travelers without masks and at other public places like Malls, schools, etc. Figure 1

I. Nasri (B) · M. Karrouchi · H. Snoussi · A. Messaoudi · K. Kassmi Electrical Engineering and Maintenance Laboratory, High School of Technology, Mohammed First University, BP. 473, Oujda, Morocco e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_9

87

88

I. Nasri et al.

Fig. 1 General architecture of face mask detection process

presents the general architecture of face mask detection, in three phases: face detection, feature extraction, and classification. From the existing method of face recognition, we have used the Viola-Jones algorithm [2, 3] to detect and crop faces from images. We get the image with the face and run it through the Viola-Jones algorithm. If the face is found, the classifier will give the region of interest of the face, and pass it to the proposed CNN. CNN with Feature Detection Layers are used to extract the deep features and those features are passed to Classification Layers. Softmax layer in CNN classifies the images as With Mask or Without Mask in real time with the probabilities of each classes. The remainder of this paper is organized as follows. Section 2 introduces a brief description of the related work. The Sect. 3 describes the proposed solution and approach adopted to prepare the developed CNN models. The obtained results are discussed in Sect. 4. Finally, in Sect. 5, conclusions, as well as suggestions for future research, are presented.

2 Related Work In this section, a review of the previous methods and approaches for classification images based on deep learning and computer vision techniques will be provided. Ququ Chen and Lei Sang [4] proposed Face mask recognition to prevent fraud and to limit the problem of identifying the face and mask in the field of financial security precaution. For that, OpenCV [5] and Dlib [6] are used to recognize the face and extract it, and they can predict whether the image of which they test is a human face or a mask. Loey et al. [7] propose a hybrid model using deep and classical machine learning for face mask detection using Resnet50. In this study, we detect the region of interest for helping proposed algorithms to learn more precise features for the classification. For that, we use the cascade object detector uses the Viola-Jones algorithm for detecting and extracting face from images, the images extracted will act as the datasets for training and testing the Convolutional Neural Networks CNNs proposed. The Viola-Jones object detection algorithm [2, 3] is the first object detection framework used in real-time, which was proposed by Paul Viola and Michael

MaskNet: CNN for Real-Time Face Mask Detection …

89

Fig. 2 With mask and without mask image samples from Prajna Bhandary dataset

Jones in 2001. It can be used to detect various object classes, but it was mainly used for the problem of face detection.

3 Proposed Method This section provides an overview of the proposed method: Dataset description and approach adopted to prepare the developed CNN models that will be able to classify whether the person is wearing a mask or not.

3.1 Dataset Description This study will focus on the Prajna Bhandary dataset [8]. It contains the full component of the dataset for training and validation the proposed CNN model. For dataset creation, Prajna takes normal images of faces and adds face masks to them using custom computer vision Python script (Fig. 2).

3.2 Proposed Approach In the rest of this section, an overview of the proposed approach (see Fig. 3) to prepare a convolutional neural network model will be provided. The proposed approach consists of five main steps:

90

I. Nasri et al.

Fig. 3 Approach proposed to prepare a deep learning model

• Step 1: Load and Explore images: Loading images from Prajna Bhandary dataset for processing. • Step 2: Detecting and Cropping faces from images: In the second step, we use the cascade object detector that uses the Viola-Jones algorithm to detect and crop faces from images (see Fig. 8). These images will be used to train and test the proposed models. In the present study, datasets utilized are explained in two categories as training and test sets, 70% for training and 30% for validation and testing. • Step 3: Creating and Configuring Network Layers: In this step, we define the convolutional neural networks CNN [9] architecture MaskNet. • Step 4: Model training and testing: The cropped face from dataset will provided as the input to the training algorithm, detailed in (algorithm 1). • Step 5: Extract the model: Finally, the CNN model can be saved as a file and used to classify images with the predicted label and probability. Figure 4 illustrates the flowchart of the proposed algorithm for mask detection.

3.3 Transfer Learning For transfer learning, four pre-trained network such as convolutional neural networks CNN namely AlexNet [10], VGG16 [11], GoogLeNet [12] and ResNet18 [13] were used to classify images as with mask or without mask. Table 1 shows an overview of the pretrained CNN used in this work. AlexNet is a CNN that contains eight layers; 3 fully connected layers and 5 convolutional layers. The AlexNet can classify images into 1000 object classes, such as a laptop, pen and a variety of objects. This network was trained on ImageNet database, with inputs at size 227 × 227 × 3 images. VGG16 consists of 16 layers; 13 convolutional layers and 3 fully connected layers; the convolutional layers are followed by the rectified linear unit (ReLU) layer for faster and more effective training, this layer performs a threshold operation to each element of the input by mapping any negative values to zero and maintaining positive values. The image input size of VGG16 is 224 × 224 × 3. GoogLeNet is a CNN that that contains 22 layers, each convolutional layer followed by pooling layer for simplifies the output by reducing the number of parameters that the network needs to learn about.

MaskNet: CNN for Real-Time Face Mask Detection …

91

Fig. 4 Flowchart of the proposed algorithm

Table 1 Overview of the pretrained CNN used in this work Network

Year

Parameters

AlexNet

2012

60M

Nbr layers 8

Size input 227 × 227 × 3

VGG16

2014

138M

16

224 × 224 × 3

GoogLeNet

2014

7M

22

224 × 224 × 3

ResNet 18

2015

11M

18

224 × 224 × 3

Figure 5 shows the training progress plot for transfer learning using AlexNet, and Fig. 6 shows the classification result with the predicted label and the predicted probability.

92

I. Nasri et al.

Fig. 5 The training progress plot for transfer learning

Fig. 6 Classification images with the predicted label and the predicted probability %

3.4 Training from Scratch For training from scratch, we creating and configuring network layers by defining the convolutional neural network architecture. In the proposed CNN model MaskNet, we use 3 convolutional layers and one fully connected layer. Softmax classifier is used to classify images as With Mask or Without Mask. The network was trained by the following algorithm. Figure 7 shows the architecture of the proposed deep CNN MaskNet.

MaskNet: CNN for Real-Time Face Mask Detection …

93

Fig. 7 Proposed deep CNN MaskNet

Algorithm 1 : Training from Scratch Input: Processed images dataset. Output: Learned CNN model MaskNet. 1. Load and Explore Image data from My PC. 2. Split data into training and test sets. 3. Create and Configure Network Layers by defining the convolutional neural network architecture. 4. Specify Training Options. 5. Train the Network Using Training Data (imdsTrain). 6. Review Network Architecture (see Fig. 7). 7. Compute accuracy.

4 Experimental Results 4.1 Dataset Preparation The processor for training and test processing platform was a 3.6 GHz Intel (R) Core (TM) Core i5-8350U with 8 GB memory and 256 GB SSD hard disk, and the development platform for the algorithm was Matlab R2018b. About the dataset processing, the faces was detected and cropped from images (see Fig. 8) using the Viola-Jones algorithm as it appears in the Table 2 below.

4.2 Result and Analysis Once the face mask detector is trained from scratch based on our proposed CNN model, we can then proceed on to loading our CNN mask detector, performing face detection first, and then classify each face as with or without mask. For testing the accuracy of the proposed model we use a dataset including images and videos of subjects of various ethnicities, scenarios, and conditions.

94

I. Nasri et al.

Fig. 8 Extracted image samples by the Viola-Jones algorithm from dataset

Table 2 Overview of images processed for each class

Dataset With mask Without mask Total

Nbr images

Nbr images extracted by Viola-Jones

690

648

686

677

1376

1325

In this part, a comparison of performances of the proposed CNN model MaskNet and pretrained AlexNet is presented. The two models were trained and evaluated by the same number of images dataset (1325 images). The following Table 3 shows the network performance of these models. Training the model from scratch and achieving reasonable results requires a lot of effort and computer time. The reason for that is we should test the performance of the network. If it is not adequate, we should try modifying the CNN architecture and adjusting some of the training options and retraining. The training time of the CNN Scratch model is 15 min and 14 s. Experimental results show that size of the proposed CNN model is small (2.12 Mbit) comparing to AlexNet (621Mbit) size, but results of proposed CNN model are more accurate than pretrained AlexNet results. The accuracy rate of the developed model is almost 99% for proposed CNN model and 96% for AlexNet. In this part, a comparison between the pretrained networks is provided. The model performance was evaluated by modifying the number of images datasets for training the CNN model (Fig. 9). Table 3 Comparison between the proposed models (CNN Transfer & CNN Scratch) Model

Accuracy

Computer time

Size

Nbr images

AlexNet

96%

12 min 4 s

621Mbit

1325

MaskNet

99%

15 min 14 s

2.12Mbit

1325

MaskNet: CNN for Real-Time Face Mask Detection …

95

100

Accuracy %

80 60 40 20 0

VGG16

ResNet18

GoogLeNet

AlexNet

MaskNet

100

91

97

80

75

95

500

93

98

91

89

98

1325

96

98

97

96

99

Fig. 9 Accuracy of classification with different image number of dataset

The accuracy of classification obtained using VGG16 net, ResNet18, GoogLeNet, and AlexNet were 96%, 98%, 97%, 96% respectively for 1325 images. Achieving a higher accuracy requires a large dataset. The Accuracy rate of all CNN models is very important for a number of images of 1325. Table 4 shows the model size of the pretrained network and the proposed CNN MaskNet. The model size of VGG16 was 528 Mbit with 138M of parameters, and GoogLeNet was 94.2 Mbit with 7M parameters. The obtained results show that the size of the model is directly proportional to the number of parameters. Figure 10 provides the computer time of the pretrained networks and the proposed CNN with different number of dataset. The results shows that the computer time Table 4 Comparison between the CNN models CNN model

VGG16 net

ResNet-18

GoogLeNet

AlexNet

MaskNet

Model size (Mbit)

528

121

94.2

621

2.12

45

Fig. 10 Training Time of CNNs with different number of dataset

100

Computer Time (min)

40

500

35

1325

30 25 20 15 10 5 0 AlexNet

MaskNet

VGG16

GoogLeNet ResNet18

96

I. Nasri et al. MaskNet

Accuracy %

100%

99%

98%

94%

85%

80% 60% 40% 20% 0%

AlexNet

120% Accuracy %

120%

100%

96%

95%

93%

80% 60%

60%

40% 20%

0.0001 0.001

0.01

0.1

0%

0.0001 0.001

0.01

0.1

Fig. 11 Analysis of classification accuracy for varying learning rate value

depends on a lot of factors including the number of image dataset, networks architecture, and the number of parameters, etc. The CNN models used in this work achieve a higher accuracy when the learning value is 0.0001. This parameter controls how quickly the model adapted to the problem. A small learning rate value can lead to good results in terms of accuracy but with long computer time, whereas when the learning rate increased to 0.1, we have too fast learning with an unstable training process and less accuracy rate. Figure 11 below shows the classification accuracy of MaskNet and AlexNet for varying Learning Rate value.

5 Conclusion In this work, we have proposed a method for face mask detection using deep learning techniques. The face is detected using the Viola-Jones algorithm. The proposed convolutional neural network with Feature Detection Layers is used to extract the deep features, and those features are passed to Classification Layers. A Softmax layer in the CNN provides the classification output as With Mask or Without Mask with the probabilities for each class. The proposed model has been trained and evaluated using the Prajna Bhandary dataset by two commonly used approaches for deep learning: transfer learning and training from scratch. For that, we have used four pre-trained networks, and one proposed CNN, namely MaskNet. The performances of the models such as accuracy and computer time have been evaluated by modifying the number of images dataset and the learning Rate value. Further work will focus on the implementation of the proposed CNN MaskNet in an embedded system for mask detection in the era of the COVID-19 pandemic.

MaskNet: CNN for Real-Time Face Mask Detection …

97

References 1. World Health Organization. Coronavirus disease (COVID-19) advice for the public. https:// www.who.int/emergencies/diseases/novel-coronavirus-2019/advice-for-public. Accessed 20 Oct 2020 2. Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features. In: The 2001 IEEE computer society conference on computer vision and pattern recognition, CVPR 2001, vol 1. IEEE 3. Viola P, Jones M (2001) Robust real-time object detection. Int J Comput Vis 4(34–47):4 4. Chen Q, Sang L (2018) Face-mask recognition for fraud prevention using Gaussian mixture model. J Vis Commun Image Represent 55:795–801 5. Bradski G, Kaehler A (2008) Learning OpenCV: computer vision with the OpenCV library. O’Reilly Media, Inc. 6. Dlib C++ toolkit, 08 January 2018. https://dlib.net/ 7. Loey M, Manogaran G, Taha MHN, Khalifa NEM (2020) A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic. Measurement 167:108288 8. Prajna Bhandary dataset. Mask classifier. https://github.com/prajnasb/observations/tree/mas ter/experiements/data 9. O’Shea K, Nash R (2015) An introduction to convolutional neural networks. arXiv pre-print arXiv:1511.08458 10. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems 11. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 12. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1–9 13. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition

Machine Learning System for Fraud Detection. A Methodological Approach for a Development Platform Salma El Hajjami, Jamal Malki, Mohammed Berrada, Harti Mostafa, and Alain Bouju

Abstract The democratization and massification use of credit cards lead inexorably to a high number of fraudulent transactions. Generally, the fraud detection is part of the anomaly detection problem. In this field, current approaches and techniques are constantly looking for optimized solutions to detect anomalies. Faced with a massive and growing data volume, these methods are put to the test, and thus lead to a large number of undetected anomalies. Real time fraud detection requires the design and implementation of scalable techniques capable of ingesting and analyzing massive amounts of data continuously. Recent advances in storage, data analytics processing, and open-source solutions open up new perspectives in the anomaly detection field and in particular fraud. In this article, we are interested in the design of a fraud detection system (FDS) based on open-sources Big Data technologies. Thus, a general methodology is proposed based on the formalization, the implementation and the technical design of a platform for fraud detection. The formalization part consists of four layers: distributed storage, data processing, model building, and finally the model evaluation. The implementation part uses Spark distributed data processing system. In particular, we are based on its framework dedicated to machine learning, called MLlib. The technical design part of the platform is based on the latest Big Data technologies such as Hadoop, Yarn, Livy etc. Keywords Machine learning · Anomaly detection · Fraud detection · Big data · Spark and Hadoop platforms

Projet UE-FEDER PLAIBDE https://plaibde.ayaline.com S. El Hajjami (B) · M. Berrada IASSE Laboratoire, ENSA, USMBA, Fès, Marocco e-mail: [email protected] J. Malki · A. Bouju L3i Laboratoire, La Rochelle Université, La Rochelle, France H. Mostafa LIMS Laboratoire, FSDM, USMBA, Fès, Marocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_10

99

100

S. El Hajjami et al.

1 Introduction In recent years, the volume of credit card transactions has increased considerably due to the popularization of their uses, the rapid development of associated services, such as e-commerce, e-finance and all mobile payments. The large-scale adoption of credit card coupled with the development of different use cases where transactions take place without rigid verification and supervision inevitably result in large losses [1]. In this area, it becomes necessary, even crucial, to immediately process the collected data to detect any potential fraud. However, traditional detection tools are unable to handle the captured data volume [2]. Therefore, they cannot detect anomalies and threats. It has therefore become essential to overcome the bottlenecks of existing techniques by using a new generation of artificial intelligence approaches, such as those based on machine learning. These approaches must work closely with techniques for data processing, known as Big Data. They must also provide near real-time responses. Ultimately, we believe that Machine Learning (ML) models can provide a reliable solution to the anomaly detection problem and therefore its application to the fraud detection [3]. Depending on the needs and scope of ML, there are several scenarios, how these models can be constructed and applied. Recent work in the field of ML highlights two kinds of important platforms: development platform and deployment platform. Development platforms dramatically reduce the time and cost of building ML models. They guarantee the organization of all levels of maturity of these models. They can be out of the box or tailor-made, based on open-sources or commercial software. Some development platforms can act as model deployment platforms as well, and in a few cases, model life cycle management platforms, but their core is running model building pipelines. The deployment platform brings the models to the production environment. Suppose an ML model has been created and evaluated on a given development platform, the next step is to use that model in a production application. The latter can be a simple web application, embedded in an IoT device, or a service or micro-service forming part of a complex architecture. This is the role of the deployment platform. The two platforms are linked by what is known as the machine learning pipeline. Indeed, the life cycle of a model should not stop at the development and evaluation stage. It is put into production and then returned to the development stage as much as possible to be refined and improved. In this work, we present a methodology for building ML models in a development platform approach based on open-source software. This platform dedicated to a fraud detection system (FDS) brings two important contributions: – A fraud detection methodology which contains most of the design ideas common in the latest FDS, which greatly facilitates the integration of detection algorithms into the workflow. – A four-layer framework that includes a distributed storage layer, data processing layer, model building layer, and finally model evaluation layer.

Machine Learning System for Fraud Detection …

101

– A development platform whose central core is based on Hadoop and Spark. With these technologies, we are able to build a scalable and reliable system. The remainder of the paper is organized as follows. In Sect. 2, data and system challenges for fraud detection are described. Section 3 describes our methodology for building ML models for fraud detection. The results of the experimental evaluation are illustrated in Sect. 4. Section 5 gives all the details about the development platform. Finally, conclusion and future work terminate our article.

2 Fraud Detection: Data and System Challenges The design of an effective, real-time, and scalable based FDS is subjected to several data and system challenges enumerated as follows [4–7]: – Data imbalance: generally, fraudulent transactions represent less than 0.05\% of total transactions. This ratio results in the very imbalanced dataset. A good FDS should be able to handle asymmetric distributions of data. – Data overlap: some fraudulent patterns behave like normal ones. A good FDS should be able to detect fraudulent transactions that mimic legitimate transactions. – Concept Drift: FDS targeting fraudulent behavior suffer from the fact that in the real world, the profile of normal and fraudulent behavior changes over time. So, a model that performs well over a period of time can quickly become obsolete and produces inaccurate predictions. Therefore, a good FDS should be dynamic and able to adapt to changes in fraudulent patterns. – Evaluation metric: evaluation metric used for fraud detection techniques should be chosen with care. Indeed, some measures such as accuracy are not suitable for asymmetric distribution. – Misclassification cost problem: cost of misclassifying each transaction varies. Fraud detection is a very cost sensitive area. Generally, undetected fraudulent transactions are much more serious and costly than the detection of normal behavior as fraud. Therefore, a good FDS should prioritize transactions with a higher misclassification cost. – Lack of Real-Time FDS: most of the existing FDS reported in the literature work on archival data to drive future security policies. The fraud must be detected and blocked-in real time to avoid future fraud.

3 Machine Learning Methodology and Application to Fraud Detection The fraud detection is a Big Data problem. Our ML methodology and application to fraud detection takes into account all the data properties intrinsic to a Big Data

102

S. El Hajjami et al.

system. It includes four main layers: data collection and storage, data processing, model building and evaluation before pre-production.

3.1 Data Collection and Storage This layer consists of collecting data transactions made by customers. In their raw state, they contain information classified as confidential. The vast majority of payment systems provide interfaces to access transaction data. Legal data protection rules must be observed. The quality of ML models is measured, among other things, by the quality of the data from which they are built. Therefore, knowing how to use good data collection practices is essential to develop high performing models. The data must be, as much as possible, error-free and contain information relevant to the task at hand [2].

3.2 Data Processing Data processing is an important and time-consuming task due to its importance to overall performance. The purpose of data processing is to create what is called prepared data. Data processing includes various operations: 1. 2.

3. 4.

Data cleaning: deletion or correction of records containing corrupted or invalid values, or for which a large number of columns are missing. Data transformation: converting a numeric characteristic to a categorical characteristic and converting categorical characteristics to a numeric representation. Some models only work with numeric or categorical characteristics, while others can handle characteristics of different types. Feature Selection: selecting a subset of the input features for training the model while ignoring irrelevant or redundant ones. Data Sampling: sampling the dataset before training the predictive model in order to have more balanced data.

3.3 Model Building Model development begins by partitioning the datasets into one dataset used to train a model, another dataset used to test the trained model. This splitting of the data ensures that the model does not remember a particular subset of data. There are two steps in a learning model building:

Machine Learning System for Fraud Detection … Table 1 Kaggle credit card fraud dataset details

103

Transactions

Majority class

Minority class

Columns

284 807

284 315

492

31

– Training phase: mainly consists of the building or adjusting a model. The fundamental goal is to learn a pattern of trends that lend themselves well to generalization to new data instead of simply memorizing the data he was able to see during his training. – Test phase: once the model is trained, it is important to check whether it behaves correctly on new examples that are not used for training the model. To do this, the model is used to predict the response on the test dataset. Then, the predicted target is compared to the actual response to measure the performance of the model.

3.4 Model Evaluation It’s important to test, measure and monitor the performance of a predictive model. We must then define measures to be used for performance evaluation. The assessment metrics used depends on several factors. Such as, the task of modeling (classification, regression or segmentation), the context of the problem we are trying to solve as well as the data distribution. These metrics are also used to compare the model’s performance and select those that give the best performance.

4 Credit Card Fraud Detection Use Case: Experimentation and Evaluation 4.1 Data Collection For data collection, we use the Kaggle Credit Card Fraud Detection dataset1 to illustrate these different steps. It contains the transactions carried out by credit card during two days of September 2013 by holders of European cards. The Table 1 provides statistics for the dataset and shows that the minority class (fraud) accounts for 0.172% of all transactions. Therefore, this dataset is very imbalanced [8]. It contains 31 digital features. Because some of the input features contain financial information, the PCA transformation of 28 digital input features (named V 1 , …, V 28 ) was performed due to privacy concerns. Three of the given features have not been transformed. Time feature displays the time between the first transaction and each other transaction in the dataset. The Amount feature is the value of the amount 1 Anonymized

credit card transactions labeled as fraudulent or genuin From Kaggle https://www. kaggle.com/mlg-ulb/creditcardfraud.

104

S. El Hajjami et al.

spent in a single credit card transaction. The Class feature represents labels and takes two values: value 1 for fraudulent transaction and value 0 otherwise.

4.2 Data Processing During the processing phase, we proceed to remove missing data. Then, it was analyzed and all features except Amount and Time were scaled using the PCA transformation technique. Therefore, the Time and Amount columns are scaled and normalized to ensure consistency. For selection features, we are only interested in data features that are able to separate the two classes (fraudulent, normal). Visualization techniques can be helpful in this process. Consider the example shown in Fig. 1 showing the class distribution for some features of our dataset. We can see for V 12 a significant divergence in the distribution of the two classes. It is therefore a feature with high predictive power. We therefore keep it when building models. Similarly, we can see for feature V 13 that the distribution of normal transactions (majority class) corresponds to the distribution of fraudulent transactions (minority class). This feature cannot effectively contribute to the separation between the two classes, we eliminate it from the dataset. We carried out this process for all the 28 features. As a result, 11 relevant features were selected for our experiments: V 3 , V 4 , V 9 , V 10 , V 11 , V 12 , V 14 , V 16 , V 17 , V 18 and V 19 . The main difficulties are the skewness and data overlap in fraud detection cases. Our goal is to correctly classify the fraudulent transactions. So, we use One Side Behavioral Noise Reduction (OSBNR) [9, 10] as a sampling approach. This approach manages behavioral noise in order to improve the classification of fraudulent transactions.

Fig. 1 Class distribution histogram on some features

Machine Learning System for Fraud Detection …

105

4.3 Model Building Since there is no rule of thumb for dividing a data set into training and test sets, we choose the 70/30 rule for training/test sets. Several studies reported Random Forest and Multilayer Perceptron to get the best performance [1, 3, 11, 12], this is one of the reasons we are adopting Random Forest and Multilayer Perceptron in our experiments: – Random forest (RF) is an algorithm that consist of many decision trees. This algorithm works best when there are more trees in the forest. Each decision tree in the forest gives results. These results are merged in order to obtain a more precise and stable prediction [13]. – MultiLayer perceptron (MLP) is an artificial neural network with direct action which is made up of at least 3 layers of nodes: entry layer, hidden layer and exit layer. Each node uses an activation function. The activation function calculates the weighted sum of its inputs and adds a bias. This allows us to decide which neuron should be removed and not taken into account in the external connections.

4.4 Model Evaluation Various metrics can be used to measure the predictive accuracy of a model. In our case, the major challenge is to tackle the imbalance problem, since legitimate transactions are much more numerous than fraudulent transactions (less than 1% of total transactions). This problem often leads to extremely high accuracy where a model can reach up to 99% of the prediction accuracy, ignoring the 1% of minority class cases. In other words, accuracy does not reflect reality in this data imbalance case. The ML model performance is evaluated using AUC (Area Under The Curve) of the ROC Curve (Receiver Operating Characteristics) [14]. The ROC curve is generated by plotting the true positives rate TPR, against the false positives rate FPR, on all decision thresholds. The true positive rate TPR is the proportion of real positives (fraud) that are correctly identified as the positive class, and the false positive rate FPR measures the proportion of real positives (fraud) that are wrongly classified. AUC (1) is a concise measure of the performance of the ROC curve with a single value between 0 and 1, where a perfect model has a score close to 1. In addition, AUC has been shown to be effective for class imbalance [14, 15]. AUC = (1 + TPR − FPR)/2

(1)

106

S. El Hajjami et al.

Fig. 2 AUC results for MLP

1 AUC Metric Relevant Features

Case (80%,20%) Case (70%,30%)

0.98

Case (60%,40%) 0.96

0.94

AUC Metric

0.92

0.9

0.88

0.86

0.84

0.82

0.8

MLP

MLP_CNN

MLP_ENN

MLP_AKNN

MLP_RENN

MLP_TL

MLP_OSS

MLP_NCR MLP_OSBNR

ML Models used

Fig. 3 AUC results for RF

1

0.98

AUC Metric Relevant Features

Case (80%,20%) Case (70%,30%) Case (60%,40%)

0.96

0.94

AUC Metric

0.92

0.9

0.88

0.86

0.84

0.82

0.8

RF

RF_CNN

RF_ENN

RF_AKNN

RF_RENN

RF_TL

RF_OSS

RF_NCR

RF_OSBNR

ML Models used

4.5 Results Analysis Figures 2 and 3 show some results analysis done for both MLP anf RF models using the AUC metric. They also show the model’s performance of a new approach called OSBNR. In~\cite{Salma2020b,el2020machine}, MLP and RF algorithms are combined with different resampling methods to study the performance of the resulting ML models.

5 ML Model Development Platform 5.1 General Architecture Horizontal scaling ensures the platform profitability when data and processing requirements vary between models. Adding more machines to a cluster would not be of much value on its own. What we need is a system that can take advantage of horizontal scalability and that runs on multiple machines seamlessly, regardless of the number of machines in the cluster. The choice of a distributed system for the ML model development platform is then necessary because it operates transparently on a cluster of machines and automatically manages the necessary resources. Figure 4 shows our cluster formed by a master node and three slave nodes. The cluster is

Machine Learning System for Fraud Detection …

107

Fig. 4 General architecture of the ML model development platform

managed by a private cloud-type platform within the L3i laboratory as part of the FEDER-PLAIBDE project2 . It is based on: – – – –

Operating system: Ubuntu-16.04.3-server-amd64 Hard disk: type SSD 500 Go Processors: 16 Go de RAM (4 sockets, 4 cores) Network: 2x10 Go SFP +

5.2 Distributed Data System In this design, we made the choice of data storage in raw format. Thus, the system does not depend on any data model. It is therefore natural that the choice of Apache Hadoop Distributed File System (HDFS) is necessary to manage our distributed data system. HDFS is a well-known example of a distributed file system in the big data ecosystem. In HDFS, we use a «master/slave» architecture consisting of a single NameNode which manages the distributed file system and several DataNodes, which generally reside on each node of the cluster and manage the physical disks attached to this node as well as data that is stored physically.

5.3 Distributed Treatment System In our ML model development platform, we use Apache Spark. It is a versatile distributed processing engine capable of handling large volumes of data. The versatility of Apache Spark is that it is suitable for a wide variety of large-scale use cases. In this work, we are particularly interested in its machine learning library called «Spark ML». 2 https://plaibde.ayaline.com/

108

S. El Hajjami et al.

Spark uses a «maître/esclave» architecture with a central coordinator called «Driver» and a set of executable workflows called «Executors» located on different nodes of the cluster. Each application written in Apache Spark gives rise to a pilot program or «Driver». It segments a Spark application into tasks which are then partitioned between the worker nodes in the distributed cluster. The pilot program also creates a SparkContext that tells the application how to connect to the cluster and its underlying services. The slave nodes are where the computational processing physically occurs. Typically, they are located on the same nodes where the underlying data is available. These nodes generate processes called «Spark Executor». They are responsible for performing computational tasks and storing locally cached data. Executors communicate with the pilot program to receive scheduled functions which are then executed.

5.4 Spark ML Algorithms Spark ML is an essential software brick of our ML model development platform. It offers all the services dedicated to the implementation of these models. In Apache Spark, can distinguish two APIs: – Spark ML: based on the DataFrame API. It is a distributed, column-oriented data structure suitable for learning algorithms. – Spark MLlib: based on RDD API (Resilient Distributed Datasets). This is a distributed, object-oriented data representing data. As part of our ML model development platform, we use Spark ML. At the heart of it are several layers of software for different services.

5.5 Integrated Development Environment In our ML model development platform, we use a development environment based on Jupyter Lab. It is a web application for recording the entire process of development, testing, evaluating and documenting ML models. The Jupyter product was originally developed as part of the IPython project. It allows interactive development in several languages. The name Jupyter itself is derived from the combination of Julia, Python, and R. Our development environment provides three cores: – PySpark: for applications written in Python2. – PySpark3: for applications written in Python3. – Spark: for applications written in Scala.

Machine Learning System for Fraud Detection …

109

6 Conclusion and Future Work This work presents a machine learning methodology and a development platform. We presented the fraud detection problem and approached its resolution using an ML approach. Also, we showed that this approach requires the design and implementation of an adequate platform. The presented solution is based on open-source solutions from the world of Big-Data. Our future work covers other aspects of ML modeling and deployment platform. Acknowledgment This work is carried out thanks to the support of the European Union through the PLAIBDE project of the FEDER-FSE operational program for the Nouvelle-Aquitaine region, France.

References 1. Hajjami SE, Malki J, Berrada M, Bouziane F (2020) Machine learning for anomaly detection. Performance study considering anomaly distribution in an imbalanced dataset. In: Cloudtech 2020. IEEE 2. Roh Y, Heo G, Whang SE (2018) A survey on data collection for machine learning: a big data - AI integration perspective. arXiv:abs/1811.03402 3. Dal Pozzolo A, Caelen O, Le Borgne YA, Waterschoot S (2014) Bontempi G (2014) Learned lessons in credit card fraud detection from a practitioner perspective. Expert Syst Appl 41(10):4915–4928 4. Maes S, Tuyls K, Vanschoenwinkel B, Manderick B (2002) Credit card fraud detection using Bayesian and neural networks. In: Proceedings of the 1st international Naiso congress on neuro fuzzy technologies, pp 261–270 5. Sahin ¸ YG, Duman E (2011) Detecting credit card fraud by decision trees and support vector machines 6. Adewumi AO, Akinyelu AA (2017) A survey of machine-learning and nature-inspired based credit card fraud detection techniques. Int J Syst Assur Eng Manage 8(2):937–953 7. Puh M, Brki´c L (2019) Detecting credit card fraud using selected machine learning algorithms. In: 42nd international convention on information and communication technology, electronics and microelectronics. IEEE, pp 1250–1255 8. Dal Pozzolo A, Caelen O, Johnson RA, Bontempi G (2015) Calibrating probability with undersampling for unbalanced classification. In: IEEE symposium series on computational intelligence. IEEE, pp 159–166 9. Hajjami SE, Malki J, Bouju A, Berrada M (2020) Machine learning facing behavioral noise problem in an imbalanced data using one side behavioral noise reduction: application to a fraud detection. J Comput Inf Eng 10. Hajjami SE, Malki J, Bouju A, Berrada M (2020) A machine learning based approach to reduce behavioral noise problem in an imbalanced data: application to a fraud detection. In: International conference on intelligent data science technologies and applications (IDSTA). IEEE, pp 11–20 11. Bhattacharyya S, Jha S, Tharakunnel K, Westland JC (2011) Dataminingforcredit card fraud: a comparative study. Decis Support Syst 50(3):602–613 12. Dal Pozzolo A, Boracchi G, Caelen O, Alippi C, Bontempi G (2015) Credit card fraud detection and concept-drift adaptation with delayed supervised information. In: International joint conference on Neural networks. IEEE, pp 1–8

110

S. El Hajjami et al.

13. Breiman L (2001) Random forests. Mach Learn 45(1):5–32 14. Bekkar M, Djemaa HK, Alitouche TA (2013) Evaluation measures for models assessment over imbalanced data sets. J Inf Eng Appl 3(10) (2013) 15. Jeni LA, Cohn JF, De La Torre F (2013) Facing imbalanced data–recommendations for the use of performance metrics. In: 2013 Humaine association conference on affective computing and intelligent interaction. IEEE, pp 245–251

U-Net: Deep Learning for Extracting Building Boundary Collected by Drone of Agadir’s Harbor Tarik Chafiq, Hayat Hachimi, Mohammed Raji, and Soufiane Zerraf

Abstract Identification of a specific object in an image might be a trivial task for humans but are often quite challenging for machines. Recently the field has witnessed groundbreaking research with cutting-edge results. However, for real-world application problems of this research remain a challenge. The approach used is based on a training model from a Dataset, and this model will be used in all processing to detect homes from sample images. All the images were extracted from the unmanned aerial vehicle (UAV) recordings. This paper presents a method to detect segmentation of the Building footprint using U-Net architecture; in order to build footprints without needing manual digitizing with higher accuracy. Keywords Image classification · U-NET · Deep Learning · UAV

1 Introduction The automation of work carried out by the human being in order to reduce the execution time, quality of production, and precision has always made dreams to researchers in the computer science field while simplifying workers’ routine activities and helping [1, 2] companies provide next-generation services. In recent years, Artificial intelligence has progressed rapidly, approaching or even exceeding human precision in certain cases, object detection represents one of the most significant tasks [3] in the field of computer vision and building footprint detection and outlining satellite or drone imagery is a very useful tool in many types of applications [4], from population mapping to the monitoring of illegal development, from urban expansion control to planning a prompt and more reliable rescue response within the case of catastrophic events. Detecting building footprints in optical, multi-spectral satellite or drone data is not easy to unravel in a general way due to the acute heterogeneity

T. Chafiq (B) · H. Hachimi · M. Raji · S. Zerraf Faculty of Sciences, Ben M’sick-University Hassan II, Casablanca, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_11

111

112

T. Chafiq et al.

of material [5], shape, spatial and spectral patterns that will result in diverse environmental conditions and construction activities embedded in various parts of the world. Deep learning, also known as artificial intelligence approaches to self-learning, provides new doors for data processing and vision of machines [6]. The possibilities of object detection and pattern recognition are currently being revolutionized by convolutional neural networks (CNN) that are used in remote sensing [7]. CNN enables efficient analysis of image textures, i.e. the contextual signal of multiple neighboring pixels, unlike common pixel-based methods. The self-learning capabilities of CNN allow these textures to be analyzed effectively [7]. Even in complex urban scenes, accurate information on urban objects automatically detected by drone images is a fundamental approach to obtaining a reliable classification result [6]. At this stage, the technologies of remote sensing have become a promising tool and are continuing to grow at an unparalleled pace [6, 8, 9]. Due to modern platforms and sensors, such as very high-resolution satellite or unmanned aerial vehicles (UAVs), the availability of optical Earth observation data revealing both spatial details and high temporal urban patterns is increasing [10]. Using deep learning methods, this optical feature space can be expanded further with details on the 3D urban structure [11, 12]; Effective techniques are required to make full use of this source of knowledge for urban mapping.

2 Related Works Some of the previous work contributions use the images segmentation for the automatic detection of buildings based on High Resolution Satellite (VHRS) images are many [13–18]; in this section, two examples are cited: the works of Menarched [17], whom was present the potential of SVMs to automatically extract buildings in suburban area using Very High Resolution Satellite (VHRS) images. Then, SVM classifier was used to extract buildings. The proposed method has been applied on a suburban area in Tetouan city (Morocco) and 83.76% of existing buildings have been extracted by only using color features [17]. Regarding the work of Vakalopoulou [19], they work for the approach exploits a decomposed interconnected graphical model formulation where registration similarity constraints are relaxed in the presence of change detection wish gave the accuracy of 74% [19].

U-Net: Deep Learning for Extracting Building Boundary Collected …

113

3 Materiel and Method 3.1 Study Area The study of the harbor area of Agadir (Fig. 1), which is the fishermen’s village south of Safi, Morocco. It is 30 km south of Safi, and 90 km north of Essaouira. The active maritime population in AGADIR is estimated at 1000 sailors including fishing related activities. The study area is covered by 1218 images with a surface area of 15 ha and a maximum altitude of 10 m. Determination of the coordinates of the boundary markers the first was carried out in relation to the Merchich (national Projected coordinate system), which adopts the Lambert conformal conical projection.

4 Methodology In this stage, ArcGIS Pro will be used to classify the UAV using statistical classification methods. As defined above, deep learning is a form of machine learning that depends on many layers of non-linear processing listed in a model for feature identification and pattern recognition [20]. For object recognition and image processing, Deep Learning models can be combined with ArcGIS [21]. Using the Classification Training Sample Manager tools to generate training samples of entities or objects of interest in ArcGIS Pro, and then using the “Label Object for Deep Learning” tool to

Fig. 1 Map of the Agadir harbor area

114

T. Chafiq et al.

Fig. 2 Schematic diagram, showing overall methodology of the work

label them. Finally converted to a format for the in-depth learning structure. These training examples are used to train the model using the “Train Deep Learning Model” rid analysis tool or the ArcGIS API for Python. The trained model associated with the model definition (and possibly the Python raster function script file) is packaged and shared as a dlpk (deep learning package) element, which is then used to generate the raster analysis inference tools. The inference tool extracts specific features or classifies pixels in the image. The dlpk element can be used multiple times as input to the raster analysis tool, making it easy to evaluate multiple images at different locations and over different time periods after model formation (Fig. 2).

4.1 Data Preparation The orthophoto of the Tan-Tan harbor (Fig. 3) will be used as a training dataset, then the orthophoto’s harbor of Agadir as a test of the accuracy of the model, and produce the vector data. In fact, the training dataset has been exported using a tool available

U-Net: Deep Learning for Extracting Building Boundary Collected …

115

Fig. 3 BF on the Orthophoto of Tan-Tan’s harbor

Fig. 4 Thematic raster 8bit unsigned of Tan’s harbor

in ArcGIS Pro which is called Export Training Data For Deep Learning in order to generate a thematic raster 8bit unsigned (Fig. 4). The preparation of data can be a very long process involving the selection and massaging of training in the format needed for any deep learning model [22, 23]. Typical data processing command lines include the splitting of the data into training and test (validation) sets, various data techniques are implemented, data structures are created to load the data into the model, batch sizing is specified, etc. All these long-term activities can be automated by ArcGIS, and the training dataset exported by ArcGIS is read directly by the planning process.

4.2 Model Architecture Convolutional Neural Networks (hereafter referred to as CNN) can be seen as MLPs neural networks particularly adapted to 2D signal processing. These networks were inspired by the work of Hubel and Wiesel on the visual cortex in mammals [24].

116

T. Chafiq et al.

Fig. 5 The architecture of a convolutional neural network [27]

The first CNNs date back to the 1980s with the work on the Necognitron by K. Fukushima [25], but it is in the 1990s that these networks became popularized with the work of Y LeCun & al about character identification [26]. A CNN is a network of neurons whose layers are linked not by a matrix operation but by a two-dimensional convolution. NDCs allow the direct processing of large amounts of data such as images for several reasons: – The images are spatially correlated (the values of adjacent pixels are generally very close) and the convolution layers make it possible to create links between these spatially correlated data. – Convolution layers are usually followed by sub-sampling (pooling) layers that greatly reduce the size of the data. A CNN architecture is formed by a stack of independent processing layers (Fig. 5):

4.3 Modeling Approached Instead of developing a model from scratch, an existing convolutional neural network model is used for the segmentation of the U-net image, originally designed for the segmentation of biomedical image [28, 29]. Once formed, the network was capable of producing a binary at the classification pixel level (whether under construction or not) with high accuracy. U-net is a particular form of FCN [30] that has gained a great deal of interest in the segmentation of biomedical images using a reduced data collection but is known to be very effective for the pixel-by-pixel classification of satellite and aerial images. U-net has been created by Olaf Ronneberger [28]. For the segmentation of biomedical images (Fig. 6). The architecture contains two paths. The first path is the path of contraction or encoder [31] used to catch the context in the image. The encoder is a conventional stack full of clustering layers. The second path is the symmetrical expansion path or decoder [32] which is used to allow precise localization using transposed convolutions. It is therefore a fully

U-Net: Deep Learning for Extracting Building Boundary Collected …

117

Fig. 6 U-NET architecture [28]

convolutional network (FCN) end-to-end, i.e. it contains only convolutional layers and no dense layers, allowing it to accept images of any size.

4.4 Optimal Learning Rate Tuning hyper-parameters means optimisation of profound learning. We will find an ’optimal learning rate’ for our training data template in this phase. The learning rate is an important parameter in the stage of training the model. An extremely high rate of learning would contribute to the convergence of our paradigm into an unstable approach. we have found optimum research speeds at which a robust model can be trained fast enough.

4.5 Evaluating Model Accuracy The complete training data was divided into training set and validation was defined at the preparatory data level, and the validation set is by default 0,22 or 20% and the rest of the training data is 80%. Here, it lets us know how well our models generalize data that they have never observed, and how they avoid overfitting of the model. As we can see in this case, our model (Fig. 7) can also categorize pixels in construction or not with an accuracy of 96%.

118

T. Chafiq et al.

Evaluang Model Accuracy 100% 80% 60% 40% 20% 0% 1 2 3 4 5 6 7 8 9 101112131415161718192021222324252627282930 train_loss

valid_loss

accuracy

Fig. 7 Evaluating model accuracy

Fig. 8 Thematic raster 8bit unsigned of Agadir’s harbor

5 Results 5.1 Preview Results In this step, the Geoprocessing tool which called Classify Pixels Using Deep Learning is available in ArcGIS Pro was used to process the images and generate a classified raster (Fig. 8). The results of this tool will take the form of a classified raster that contains both background and building footprints.

5.2 Extracting Building Footprints To extract building footprint, the Pixel Classification tool using deep learning to segment the imagery through the model and post-processed the resulting raster in ArcGIS Pro. For post-processing the classified imagery, the Model Builder (MB) has been used, which is a visual programming language for building workflows [33].

U-Net: Deep Learning for Extracting Building Boundary Collected …

119

Fig. 9 Building footprints extracted using automated process

The results of the building footprint model generated on Agadir’s Harbor (Fig. 9); this is few subsets of results overplayed on the drone recording. furthermore, it works well when any space divides buildings from each other.

6 Conclusion The model described in this paper offers many advantages among which the ability to elucidate better identification and automatic detection of building-footprint based on the Training-Model. Moreover, it shows that the benefit of using improved ground truth labels for building segmentation without human intervention is a testament of the progress made in AI. Furthermore, such goals have been completed by making the system capable of recognizing buildings in different situations, achieving a precision rate of 96% after a training period on images acquired and processed for this purpose. This would also enable future research to focus on adding a third dimension to generate an indexable object 3D model that could give more value and precision to the building- footprint.

References 1. Naya F, et al (2006) Workers’ routine activity recognition using body movements and location information. In: 2006 10th IEEE international symposium on wearable computers. IEEE 2. Chafiq T, Ouadoud M, Elboukhari K (2020) Covid-19 forecasting in Morocco using FBprophet Facebook’s Framework in Python. Int J 9(5) 3. Ding P et al (2018) A light and faster regional convolutional neural network for object detection in optical remote sensing images. ISPRS J Photogram Remote Sens 141:208–218 4. Martin F-M et al (2018) Using single-and multi-date UAV and satellite imagery to accurately monitor invasive knotweed species. Remote Sens 10(10):1662 5. Darraj B, Gourja B, Faiq A, Zerraf S, Tace EM, Belaaouad S (2019) The use of concept maps to evaluate learning strategies for nursing students. Int J Innov Technol Explor Eng 9(1) 6. Kattenborn T, Eichel J, Fassnacht FE (2019) Convolutional Neural Networks enable efficient, accurate and fine-grained segmentation of plant species and communities from high-resolution UAV imagery. Sci Rep 9(1):1–9

120

T. Chafiq et al.

7. Huang B, et al (2018) Large-scale semantic classification: outcome of the first year of Inria aerial image labeling benchmark. In: IGARSS 2018–2018 IEEE international geoscience and remote sensing symposium. IEEE 8. Corbane C et al (2015) Remote sensing for mapping natural habitats and their conservation status–New opportunities and challenges. Int J Appl Earth Obs Geoinf 37:7–16 9. White JC et al (2016) Remote sensing technologies for enhancing forest inventories: A review. Can J Remote Sens 42(5):619–641 10. Hirschmugl M et al (2017) Methods for mapping forest disturbance and degradation from optical earth observation data: a review. Current Forestry Reports 3(1):32–45 11. Kattenborn T et al (2019) UAV data as alternative to field sampling to map woody invasive species based on combined Sentinel-1 and Sentinel-2 data. Remote Sens Environ 227:61–73 12. Fassnacht FE, et al (2017) Estimating stand density, biomass and tree species from very high resolution stereo-imagery–towards an all-in-one sensor for forestry applications? Forestry: Int J Forest Res 90(5):613–631 13. Porway J, Wang Q, Zhu SC (2010) A hierarchical and contextual model for aerial image parsing. Int J Comput Vision 88(2):254–283 14. Lin Y et al (2014) Rotation-invariant object detection in remote sensing images based on radial-gradient angle. IEEE Geosci Remote Sens Lett 12(4):746–750 15. Zhang F et al (2016) Weakly supervised learning based on coupled convolutional neural networks for aircraft detection. IEEE Trans Geosci Remote Sens 54(9):5553–5563 16. Moranduzzo T, Melgani F (2014) Detecting cars in UAV images with a catalog-based approach. IEEE Trans Geosci Remote Sens 52(10):6356–6367 17. Benarchid O, Raissouni N (2013) Support vector machines for object based building extraction in suburban area using very high resolution satellite images, a case study: Tetuan, Morocco. IAES Int J Artif Intell 2(1) 18. Liu Z et al (2016) Ship rotated bounding box space for ship extraction from high-resolution optical satellite images with complex backgrounds. IEEE Geosci Remote Sens Lett 13(8):1074– 1078 19. Vakalopoulou M, et al (2015) Simultaneous registration and change detection in multitemporal, very high resolution remote sensing data. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops 20. Liu W et al (2017) A survey of deep neural network architectures and their applications. Neurocomputing 234:11–26 21. Zhang X et al (2020) How well do deep learning-based methods for land cover classification and object detection perform on high resolution remote sensing imagery? Remote Sens 12(3):417 22. Chafiq T, et al (2018) Application of data integrity algorithm for geotechnical data quality management12(8) 23. Ouadoud M, Chafiq T, Chkouri MY (2018) Designing an IMS-LD model for disciplinary information space of learning management system. In: Proceedings of the 3rd international conference on smart city applications (2018) 24. Hubel DH, Wiesel TN (1962) Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. J Physiol 160(1):106 25. Fukushima M (1980) Dirichlet forms and Markov processes, vol. 23. Elsevier 26. LeCun Y, Denker JS, Solla SA (1990) Optimal brain damage. In: Advances in eural information processing systems 27. Krut P (2019) MNIST Handwritten digits classification using a Convolutional Neural Network (CNN). https://towardsdatascience.com/mnist-handwritten-digits-classific ation-using-a-convolutional-neural-network-cnn-af5fafbc35e9. Accessed 12 Apr 2020 28. Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International conference on Medical image computing and computer-assisted intervention. Springer 29. Ouadoud M, et al (2019) Generate a meta-model content for collaboration space of learning management system compatible with IMS-LD

U-Net: Deep Learning for Extracting Building Boundary Collected …

121

30. Zhou X et al (2017) Deep learning of the sectional appearances of 3D CT images for anatomical structure segmentation based on an FCN voting method. Med Phys 44(10):5221–5233 31. Guo X, et al (2017) Deep clustering with convolutional autoencoders. In: International conference on neural information processing. Springer 32. Forney GD Jr (1974) Convolutional codes III. Sequential decoding. Inf Control 25(3):267–297 33. Allen DW (2011) Getting to know ArcGIS modelBuilder. Esri Press

Analysis and Classification of Plant Diseases Based on Deep Learning Assia Ennouni, Noura Ouled Sihamman, My Abdelouahed Sabri, and Abdellah Aarab

Abstract Plant diseases identification is generally done by visual evaluation. The diagnosis quality depends strongly on professional knowledge. However, agricultural expertise is not easily learned. To overcome this issue, automatic analysis and classification of plant diseases through image processing and artificial intelligence is an encouraging solution and can reduce the lack of agricultural knowledge. This alliance between image processing and machine learning and/or deep learning approaches has produced very good results in medical imaging and has enabled the development of robust systems to assist in the diagnosis of several diseases. Deep learning (DL) and Machine learning approaches became the most promising tools for image analysis and classification. The objective of our work is to conduct a comparative study between different deep learning architectures for the analysis and classification of plant diseases. Four DL-based architectures have been implemented such as MobileNet, AlexNet, Inception V3, and VGG16. Simulation results have shown that the MobileNet architecture, even simple, has allowed better classification accuracy. Keywords Plant diseases detection · Plant diseases classification · Image classification · Deep learning · Machine learning · Convolutional neuron network

1 Introduction Plant diseases are considered to be alterations of plants that affect and may damage their vital functions. Plant diseases are caused mainly by viruses, fungi or bacteria. Plant diseases have a great impact on agricultural productivity. Plant diseases have increased considerably in recent years, according to the FAO (Food and Agriculture A. Ennouni (B) · N. O. Sihamman · M. A. Sabri Department of Computer Science, LISAC Laboratory, Faculty of Sciences Dhar-Mahraz, University Sidi Mohamed Ben Abdellah, Fez, Morocco A. Aarab Department of Physics, LISAC Laboratory, Faculty of Sciences Dhar-Mahraz, University Sidi Mohamed Ben Abdellah, Fez, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_12

123

124

A. Ennouni et al.

Organization of the United Nations). This is mainly due to climate change, globalization, and the low resilience of production systems, … Early detection of plant diseases based on visible symptoms on leaves is one of the main challenges to protect cultures. This mainly depends on the farmer’s knowledge and expertise. However, not all actors in the agricultural domain are well experienced and especially small farmers. To overcome this problem and reduce the lack of agricultural knowledge problem, automatic detection, and recognition of plant diseases combining image processing and artificial intelligence is a promising solution [1]. Machine Learning (ML) and Deep Learning (DL) techniques have become more promising approaches in the medical field because of their ability to learn from reliable and discriminating visual features [2–4]. For their success, ML and DL have been used in many fields including agriculture [5, 6]. Artificial Intelligence (AI) and the Internet of Things (IOT) have been used to develop the concept of Intelligent Agriculture [7, 8]. The objective of our research is to propose a comparative study between four different deep learning architectures for the analysis and classification of plant diseases. Namely MobileNet, AlexNet, Inception V3, and VGG16. These architectures have been implemented and tested. Simulation results have shown that the MobileNet architecture, even simple, has allowed better classification accuracy. The rest of this article is as follows: in the following section, we will present the different plant diseases and then present the different DL-based image classification architectures and more precisely the classification of plant diseases. We will present in detail the different DL architectures used and describe our dataset. Implementation results will be presented afterward. And we end this work with a conclusion.

2 Plant Diseases Plants are living beings. They are one of the three main groups in which living things are usually classified (the other two are animals and fungi). Unlike other living beings, most plants are fixed to the ground by roots, which make them highly dependent on the conditions of their environment [9]. Plant diseases are damages that disrupt or modify their vital duties. All plant species are exposed to diseases. Some plants are immune to certain kinds of diseases while others are particularly prone to them [10]. Plant diseases can be classified as either they are infectious or not or to the causal agent. Therefore, leaf diseases are either fungal, bacterial, or viral [9, 10].

2.1 Bacterial Plant Diseases Bacterial plant infection symptoms are very similar to the symptoms of fungal plant diseases. Infections are recognized by the presence of leaf spots, wilting, burns, cankers, scabies, and rot. They can appear on fruits, leaves, stems, and/or flowers.

Analysis and Classification of Plant Diseases Based on Deep Learning

125

Fig. 1 Bacterial leaf blight on wheat and Bacterial strand test on cut stems

Fig. 2 Sclerotinia Infected by Soybeans

When they affect plants, pathogenic bacteria can be responsible for serious and potentially disastrous diseases [11]. The following figure presents two bacterial plant diseases (Fig. 1).

2.2 Fungal Plant Diseases Fungi cover rust, mildew, mildew, fungus, and others are the cause of diseases that are not risky but are harmful. There are 144000 known species. Some fungi are used in medicine. A distinction is made between those that live in the soil or water and that considered parasitic for plants and animals [12]. An example of the fungal disease Sclerotinia is presented in Fig. 2.

2.3 Viral Plant Diseases Organisms can be infected with viruses or viroids. Viruses differ from viroids by their protein layer. Both are mainly transmitted by insects or nematodes. The symptoms of this infection vary and depend on the type of virus infecting. The viral disease can affect all areas of the plant (see Fig. 3). Plant viruses are mainly isometric (polyhedral) or stem-shaped [13].

126

A. Ennouni et al.

Fig. 3 Necrotic spot virus on chili leaves

3 Image Classification Based on Deep Learning Computer vision has made prodigious steps in recent years thanks to machine learning and deep learning [3, 3]. CNN (Convolutional Neural Network), denoted also as ConvNet, is a classification algorithm that can be used as both machine learning (one layer) and deep learning (many layers). CNN does not require much pre-processing as other classification algorithms. A standard CNN architecture is generally composed of a succession of convolution and pooling layers and at the end there one or more fully connected layers. An average pooling layer replaces the fully connected in some architectures. To optimize the performance of the CNN deep learning architecture, some regulatory units like dropout and batch normalization are incorporated. Designed Deep Learning architectures differ essentially by the arrangement of the CNN components which play a fundamental role in their performance. A large number of parameters have to be defined for a given deep learning architecture such as the layers number, the neurons number in each layer, the size of the filters mask, the weights of the neurons, the activation function to be used, the best learning rate obtained, … [25, 25]. Several CNN architectures have been proposed in the literature. The following table lists the most successful architectures, specifying for each the number of parameters, the authors, the year, and the depth (Table 1). Table 1 The most successful CNN architectures Architectures

References

Year

Nb of parameters

Depth

LeNet-5

[14]

1998

60,000

5

AlexNet

[15]

2012

60 million

8

VGG

[16]

2014

138 million

19

GoogleNet

[17]

2015

4 million

22

Inception V3

[18]

2015

23 million

159

Inception V4

[19]

2016

35 million

70

ResNet

[20]

2016

25 million

152

MobileNetV1

[21]

2017

4.2 million

28

MobileNetV2

[22]

2018

3.47 million

53

Analysis and Classification of Plant Diseases Based on Deep Learning

127

Fig. 4 VGG16 macro architecture

4 Plant Diseases Classification In this work, we will conduct a comparative study between 4 Deep Learning architectures for plant diseases classification. The chosen architectures are AlexNet, InceptionV3, VGG16, and MobileNet. The database used contains images of diseased crop leaves classified into 38 classes and healthy ones [23].

4.1 VGG K. Simonyan and A. Zisserman proposed a convolutional neural network called VGG in their paper published in 2015 entitled “Very Deep Convolutional Networks for Large-Scale Image Recognition” [16]. VGG is characterized by its simplicity because it uses 3 × 3 convolutional stacked layers on top of each other at an increasing depth. The reduction in volume size is managed by max-pooling which is used as a downsampling strategy. Two fully connected layers (each layer contains 4,096 nodes) are then followed by the “softmax” activation function. Two versions were proposed; VGG16 and VGG19. Where 16 and 19 refers to the number of layers in the network. In 2014, the 16 and 19 layer-networks were considered as very deep architectures. Figure 4 shows the VGG16 macro-architecture.

4.2 Inception V3 Authors Szegedy et al. introduced in 2015 the micro-architecture “Inception” in their article entitled “Going deeper with convolutions” [17]. The original implementation of this architecture was called GoogLeNet. Later releases were then called Inception

128

A. Ennouni et al.

Fig. 5 Inception modules with dimension reduction [17]

VX where X refers to the number of the version Google published. Figure 5 presents the GoogLeNet modules. The purpose of the start-up module is to calculate 1 × 1, 3 × 3, and 5 × 5 convolutions in the same network module. The output is then concatenated and feeds into the next layer of the network. Compared to VGG and ResNet the Inception V3 weights (96 MB) being the smallest.

4.3 MobileNet MobileNet architecture is a lightweight deep neural network that provides a robust model for embedded and mobile applications [22]. It is a simplified architecture that uses separable convolutions. Figure 6 shows the MobileNet architecture where we can see the depth-wise separable convolutions.

Fig. 6 MobileNet architecture

Analysis and Classification of Plant Diseases Based on Deep Learning

129

Fig. 7 AlexNet architecture

4.4 AlexNet In 2012, AlexNet considerably outperformed all the prior competitors and won the challenge by reducing the top-5 error from 26% to 15.3%. The second-place top-5 error rate, which was not a CNN variation, was around 26.2% (Fig. 7). The network had a very similar architecture as LeNet by Yann LeCun et al. but was deeper, with more filters per layer, and with stacked convolutional layers. It consisted 11 × 11, 5 × 5, 3 × 3, convolutions, max pooling, dropout, data augmentation, ReLU activations, SGD with momentum. It attached ReLU activations after every convolutional and fully-connected layer. AlexNet was trained for 6 days simultaneously on two Nvidia Geforce GTX 580 GPUs which is the reason for why their network is split into two pipelines. AlexNet was designed by the SuperVision group, consisting of Alex Krizhevsky, Geoffrey Hinton, and Ilya Sutskever [23]. Plant Village Dataset Description In our study, we will use the Plant Village (PV) Dataset for the evaluation of the deep learning architectures. PV dataset is one of the most used dataset for the evaluation of plant disease classification algorithms. It contains images of infected and healthy crop leaves on a uniform background. In our simulations, we will use an offline augmentation of the dataset which is still available in GitHub [23]. The PV dataset contains 87K RGB images classified into 38 subsets. The dataset is provided with training and validation subsets. Performance Measures In the pattern recognition field, information retrieval and automatic classification, accuracy (or positive predictive value) are the proportion of relevant items among all proposed items; recall (or sensitivity) is the proportion of relevant items proposed among all relevant items. These two notions thus correspond to a design and a relevance measure. Precision can thus be seen as a measure of accuracy or classification quality, while recall is a completeness measure or classification quantity. In the multiclass context (where the number of classes is more than 1), the recall and precision average over all classes is calculated by the macro-mean. It calculates first the recall and precision over each class and then calculates the average of the precision and recall over the classes:

130

A. Ennouni et al.

n pr ecision =

i=1

pr ecision i n

(1)

n

i=1 r ecall i

r ecall =

n

n r ecall =

i=1

accuracy i n

(2) (3)

Where n is the number of classes and i the current class. precisioni =

TPi TPi TPi + TNi recalli = accuracyi = TPi + FPi TPi + FNi TPi + TNi + FPi + FNi

TP is The True Positives number, FP is the False Positives number, FP is the False Positives number, and FN is the False Negatives number.

5 Experimental Results Simulations have been tested on a Windows 10 computer with Intel Core i7 3.6 GHz CPU and 16 GB of RAM. In our study, we chose 4 different depth sizes CNN architectures: VGG16, InceptionV3, AlexNet and MobileNet. The number of epoch for the 4 DL architectures is 25. Table 2 presents the evolution of training and validation accuracies, Training and validation losses, precision, recall, and the accuracy of each of the 4 DL architectures. As it is a multi-classification problem with 38 classes in total, we can conclude that the 4 architectures performed well and allowed an acceptable classification rate. The classification errors are detected particularly at the level of the “tomato late blight" class. This is mainly due to a confusion between several classes, which are mainly tomato related diseases, namely: “early tomato blight, septoria leaf spot tomato blight, and tomato target spot”. Even visually, as shown in Fig. 8, the different diseases represent visual similarities on tomato leaves. We have also noticed that there are visual similarities between other diseases which did not allow an excellent classification. To remedy this problem, it is possible to look for other information that would allow the discrimination of these different diseases and to consider using ensemble learning by concatenating features extracted from DL architectures and handcrafted features. The classification accuracies of Alexnet and MobileNet are better than Inception V3. However, since the precision and recall values obtained by MobilNet are higher than Alexnet, we can conclude that the model build using MobileNet architecture is the most suitable to better classify plant disease.

Analysis and Classification of Plant Diseases Based on Deep Learning

131

Fig. 8 Examples of classification confusion of leaf samples Table 2 The evolution of Training and validation accuracies, Training and validation losses, precision, recall, and the accuracy of each of the 3 DL architectures A

AlexNet

R

0.86 0.96 0.95

VGG16

P

0.89 0.96 0.92

IncepƟon V3

Training and ValidaƟon Loss

0.89 0.95 0.91

MobileNet

Training and validaƟon Accuracy

0.94 0.97 0.95

132

A. Ennouni et al.

6 Conclusion Farmers usually identify plant diseases by visual assessment. The quality of the diagnosis depends strongly on the knowledge of the profession. However, human expertise is not easily acquired by all actors in the agricultural world. Image processing and analysis in combination with artificial intelligence represent an effective solution to assist in the recognition and classification of plant diseases. Deep learning and especially convolutional neural networks (CNN) was recently used in the medical field and sees itself as a very promising solution. In this work, several DL-based architectures such as AlexNet, VGG16, Inception V3, and MobileNet have been tested and compared using the Plants Village database. The MobileNet architecture, even though very simple in comparison with the others, showed very encouraging results.

References 1. Prakash RM, Saraswathy PG, Ramalakshmi KH, Mangaleswari TK (2017) Detection of leaf diseases and classification using digital image processing, In: International conference on innovations in information, embedded and communication systems (ICIIECS), pp 1–4, Coimbatore 2. Sabri MA, Filali Y, Ennouni A, Yahyaouy A, Aarab A (2019) An overview of skin lesion segmentation features engineering, and classification. In: Intelligent decision support systems: applications in signal processing, De Gruyter, Berlin, Boston, pp 31–52 3. Filali Y, EL Khoukhi H, Sabri MA, Aarab A (2020) Efficient fusion of handcrafted and pretrained CNNs features to classify melanoma skin cancer. Multimed Tools Appl (2020) 4. Sabri MA, Filali Y, Khoukhi H, Aarab A (2020) Skin cancer diagnosis using an improved ensemble machine learning model. In: 2020 international conference on intelligent systems and computer vision (ISCV), Fez, Morocco, pp 1–5 5. Sue HL, Hervé G, Pierre B, Alexis J (2020) New perspectives on plant disease characterization based on deep learning. Comput Electron Agri 170:105220. ISSN 0168–1699 6. Shiv RD, Anand SJ (2012) Detection and classification of apple fruit diseases using complete local binary patterns. In: IEEE computer and communication technology (ICCCT), 2012 third international conference on computer and communication technology, Allahabad, pp 346–351 7. Badreddine M, El-Bay B, Samia B, Salim C (2020) A study of LoRaWAN protocol performance for IoT applications in smart agriculture. Comput Commun 164:148–157. ISSN 0140–3664 8. Fanyu B, Xin W (2019) A smart agriculture IoT system based on deep reinforcement learning. Fut Gen Comput Syst 99:500–507. ISSN 0167–739X 9. El-Sayed AMA, Rida SZ, Gaber YA (2020) Dynamical of curative and preventive treatments in a two-stage plant disease model of fractional order. Chaos Sol Fractals 137:109879. ISSN 0960–0779 10. Vijai S, Namita S, Shikha S (2020) A review of imaging techniques for plant disease detection. Artif Intell Agric 4:229–242. ISSN 2589–7217 11. Sujeet V, Tarun D (2016) A novel approach for the detection of plant diseases. Int J Comput Sci Mob Comput 5(7):44–54. ISSN: 2320–088X 12. Britannica. https://www.britannica.com/science/plant-disease/Epiphytotics. Accessed 19 Dec 2020 13. Saleem MH, Potgieter J, Mahmood Arif K (2019) Plant disease detection and classification by deep learning, Plants 8(11): 468

Analysis and Classification of Plant Diseases Based on Deep Learning

133

14. Ohioline. https://ohioline.osu.edu/factsheet/plpath-gen-5. Michigan State University Extension. Accessed 19 Dec 2020 15. LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86:2278–2324 16. Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 1–9 17. Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. ICLR 75:398–406 18. Szegedy C, Liu W, Jia Y, et al (2015) Going deeper with convolutions. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 1–9 19 Szegedy C, Vanhoucke V, Ioffe S, et al (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition. IEEE, pp 2818–2826 20. Szegedy C, Ioffe S, Vanhoucke V (2016) Inception-v4, Inception-ResNet and the impact of residual connections on learning. arXiv Prepr arXiv160207261v2 131:262–263 21. He K, Zhang X, Ren S, Sun J (2015) Deep residual learning for image recognition. Multimed Tools Appl 77:10437–10453 22. Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC (2018) MobileNetV2: inverted residuals and linear bottlenecks. In: 2018 IEEE/CVF conference on computer vision and pattern recognition. IEEE, pp 4510–4520 23. Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L-C (2019) MobileNetV2: inverted residuals and linear bottlenecks. arXiv:1801.04381 24. Github. https://github.com/spMohanty/PlantVillage-Dataset. Accessed 19 Dec 2020 25. Kafi M, Maleki M, Davoodian N (2015) Functional histology of the ovarian follicles as determined by follicular fluid concentrations of steroids and IGF-1 in Camelus dromedarius. Res Vet Sci 99:37–40 26. Shin H-CC, Roth HR, Gao M et al (2016) Deep convolutional neural networks for computer aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging 35:1285–1298

A Novel Approach to Data Augmentation for Document Image Classification Using Deep Convolutional Generative Adversarial Networks Aissam Jadli, Mustapha Hain, and Abderrahman Jaize

Abstract Data augmentation is a procedure where new samples are generated from the training dataset by applying various techniques and algorithms to improve machine and deep learning models’ accuracy and generalization ability. The recent advances in deep learning and computer vision techniques have made scanned document classification a painless and straightforward process. However, such approaches require a lot of labeled data before training and validating the classifiers, which can be done by augmenting the existing dataset by different means. In this contribution, we explored using a system based on deep convolutional adversarial networks (DCGAN) to generate fake document images using an existing scanned documents dataset. Moreover, we compared conventional data augmentation techniques (rotation, zoom, random cropping, etc.) to DCGAN-based augmentation in a document classification context. The newly generated data could then be used along with the labeled data to train a Convolutional Neural Network (CNN) to classify scanned document images. This experiment compared the performances of a system trained on different datasets (original labeled data, GAN-augmented dataset, hybrid augmented dataset). Its results revealed the effectiveness of the proposed approach. Keywords Document classification · Data augmentation · Image classification · Deep learning · DCGAN

1 Introduction Enterprise Resource Planning (ERP) is a modularized software designed and implemented to manage various internal and external processes of a company, including Client relationship management (CRM), Supplier relationship management (SRM), finances and accounting, procurement, manufacturing, logistics, inventory, etc. The A. Jadli (B) · M. Hain SISM Team, I2SI2E Laboratory, ENSAM Casablanca, University Hassan II, Casablanca, Morocco A. Jaize LAVET Laboratory, FST Settat, University Hassan I, Settat, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_13

135

136

A. Jadli et al.

daily workflow of an organization usually generates a large volume of documents covering various transactions. Moreover, most popular ERP softwares have a document management module able to index, retrieve, store, and process saved documents. Within such a module, document classification is a useful feature seeing that different documents may need other processing pipelines. In this context, limited labeled data can make building accurate classifiers a problematic task due to insufficient training samples. Consequently, many approaches were thoughtfully researched and explored to solve the lack of labeled data issues in document classification, such as transfer learning, data augmentation, etc. [1]. Additionally, recent advances in generative modeling introduced new ways to generate new realistic images that plausibly look like they come from the same dataset and can be used for performance optimization. This paper examined using a Deep Convolutional Generative Adversarial Networks (DCGAN) approach to augment real-world scanned documents. Generated data is then used alongside original images to solve a document classification task by training the classifier used on different datasets and assessing the classification performances. This paper will be structured as follows: the second section goes through this paper’s context and the addressed problematic; the third section defines DCGANs concepts and technical details, while the fourth section details proposed experiments, and the fifth section presents the experiment results and draws conclusions.

2 Paper Context This section introduces the basic concepts of this paper, namely, explaining the ERP ecosystem and features, determining our problematic boundaries, and finally exploring related works.

2.1 Enterprise Resources Planning (ERP) ERP is a modularized and packaged software designed around the company’s vital processes (e.g. Client Relationship Management, finance and accounting, manufacturing, etc.) using a central database to manage efficiently and effectively resources usage and information processing. Additionally, different other modules exist to support ERP architecture, such as messaging, user support, and document management, etc.

A Novel Approach to Data Augmentation …

137

2.2 Problematic Document management is handled using a central document management module to store, index, and process scanned document images in most modern ERP solutions. Usually, these documents are processed using a technique called Optical Character Recognition (OCR) to extract useful pieces of information from them. This being said, to speed up the process, a preliminary document classification step can be interesting to optimize recognition results. Usually, a document classification task is a supervised learning task that needs sufficient labeled data to build accurate and performant models [4], meaning that the training dataset is ordinarily correlated to the model’s performance and stability. The problem of insufficient datasets is commonly encountered and can be tackled using different solutions (i.e. data augmentation, transfer learning, etc.). Generative modeling through DCGANs can help extend existing dataset with newly generated samples, improving classification models using existing images.

2.3 Related Works Document image classification uses different approaches and can be based on supervised or unsupervised feature learning, which usually requires a large amount of labeled training data. For example, Sherif et al. [2] presented an Exemplar-based unsupervised learning technique to analyze document layout to improve the document image classification efficiency without the need to expand the volume of annotated data, Jun et al. [3] studied the use of Faster RCNN (fast regional convolution for object detection) architecture to automatically classify documents’ internal structure, Kosaraju et al. [4] proposed a novel structure-based convolutional neural network for document layout analysis, called DoT-Net. DoT-Net is a multiclass classifier that can effectively identify document regions to solve document classification tasks. Similarly, The recent advances in Generative adversarial Networks (GAN) led research to explore extensive use for such a concept in different domains and various purposes [5]. Mansourifar et al. [6] introduced a new concept called Virtual Big Data to address the problem of insufficiency of training data in many research areas, Xu et al. [7] compared using GAN-based data augmentation for underwater image classification with other data-augmentation techniques, Lee et al. [8] proposed an iris image augmentation based on a conditional generative adversarial network (cGAN) and a method for improving recognition performance that uses this augmentation method. Meanwhile, Wang et al. [9] propose a GAN-based solution to augment training data for improved performance of palmprint recognition.

138

A. Jadli et al.

3 DCGAN-Based Data Augmentation 3.1 Data Augmentation Data augmentation is a technique widely used in deep learning because overfitting is more likely to occur in training that is based on supervised learning using inadequate or insu cient dataset. Efficient data augmentation methods may increase inter-class distances, lessen intraclass distances, and improve performance [9]. Standard data augmentation techniques that have been implemented in different deep learning frameworks include random geometric translations, rotations, horizontal and vertical flips, the application of Gaussian noise, random image cropping, horizontal and vertical shifting, and in/out zooming [10]. These geometric transform-based data augmentation methods result in insufficient data variation. Consequently, they are not fit for solving performance declines due to the input scarcity issue, including the overfitting problem.

3.2 Deep Convolutional Generative Adversarial Networks The Generative Adversarial Network architecture and the first empirical demonstration of the approach were proposed in 2014 by Goodfellow et al. [11], describing an architecture summarily involving a generator model that takes points from a latent space (random noise) and generates an image, and a discriminator model that classifies images as either real (authentic) or fake (output by the generator) (Fig. 1). The two models are arranged in a competition (in a game theory perception) where the generator model (Fig. 2) seeks to fool the discriminator model (Fig. 1), and the discriminator is provided with both examples of real and generated samples and seeks to distinguish between fake and real images.

Fig. 1 Example of a 64 x 64 discriminator architecture

A Novel Approach to Data Augmentation …

139

Fig. 2 Example of a 64 × 64 generator architecture [12]

4 Our Contribution The proposed framework is a DCGAN-based system to generate document images from existing real-world documents. Generated images are used to augment an existing dataset for a document classification task using a Convolutional Neural Network (CNN) classifier. The goal is to assess if the generated (fake) document images hold enough features and variance to effectively augment the training dataset.

Fig. 3 Overview of conducted experiment

140

A. Jadli et al.

4.1 Overview The experiment discussed in this paper (Fig. 3) can be divided into two different parts: • Using DCGAN to generate images of the scanned documents; • construct a model for visual document classification using generated and real data. A generator model (G) is trained on images of real-world company’s scanned documents. The process starts from a latent vector (usually a normal distributed random vector). It follows with the generator generating an image based on real vector distribution. The generated image is forwarded alongside the authentic images to the discriminator model, trying to tell the fake pictures from the real ones. The calculated loss helps back-propagate the errors improving performance over iterations until the discriminator cannot tell the real from the generated document images. Therefore, the previously trained generator is used to generate a subset of fake documents to create a new dataset, which is made of 50% of real documents and 50% of dummy document images. A Convolutional Neural Network (CNN) algorithm is used in the next step to create classification models out from each dataset and compare them based on models’ accuracy to assess if we can train classification models using data generated by DCGANs.

4.2 Materials and Methods Dataset Images used in these experiments are scanned real-world documents provided by different organizations obtained from a sponsoring company archive. Additionally, these documents cover various pipelines and business processes. We selected samples of different documents classes and labeled them manually into four categories: • • • •

Electronic Invoices (EI): Electronic documents generated by software; Handwritten Invoices (HI): Documents filled manually by an employee; Checks (CH): Bank Checks; Receipts (RT).

The distribution of sample classes of the experiment’s dataset is summarized in Table 1. For experiment results generalization purposes on different situations, the scanned document images were taken from various geometric conditions (angles, distances, rotations, skewness, etc.) and in different lighting conditions (shadows, brightness, etc.) using a standard smartphone camera. Figure 4 presents samples of different classes.

A Novel Approach to Data Augmentation … Table 1 Distribution of samples classes and the number of samples

141

Document class

Sample count

E.I

180

HI

109

CH

106

RT

103

Total

498

Fig. 4 Sample documents from dataset

The objective of this paper is to assess the impact of augmenting the existing training dataset on the accuracy of the classifier in a document classification task. The classifier used is a Convolutional Neural Network (CNN) using a simple architecture with an image input size of (64, 64) with a single channel (grayscale images). Different techniques can be used to tackle the model’s over-fitting issue, such as max norm constrains, L2/L1 regularization, and dropout. After comparing these methods, dropout was simpler to use and very efficient. It was used with a rate of 0.5 despite roughly doubling the number of iterations required to converge[10].

5 Results and Discussion 5.1 Data Generation Despite the small size of the dataset (498 images distributed on four classes), the generator showed good performances at generating fake document images that looked plausibly real (Fig. 5). The generator performed in EI and HI classes better than CH and RT classes because of the difference between the samples count in each category (EI case) and the simplicity of standard layouts (HI case),

142

A. Jadli et al.

Fig. 5 Sample images generated using DCGAN

5.2 Data Augmentation and Performance Comparison The second part of the experiment was using a CNN algorithm to classify the documents on 3 distinct datasets: • D1: contains only real authentic document images; • D2: contains original real dataset augmented with classical data augmentation techniques; • D3: contains original real dataset alongside images generated in part 1 (using DCGAN); • D4: contains original real dataset using the conventional and DCGAN data augmentation simultaneously. The models built were standard CNN using the same architecture over the three datasets. Table 2 shows the model’s performance based on accuracy. Based on overall results, we can assess that model trained on the conventional augmented dataset (83% accuracy) performed better than the model trained on the original dataset (77%), and DCGAN augmented dataset (79%). Due to low variance in geometric data augmentation techniques, this can lead to model overfitting. Combining the two approaches helped improve the model accuracy (89%), which is a considerable performance boost even without hyper-parameter tuning or optimization. Figures 6 and 7 show the confusion matrix of the model trained using the DCGAN augmented dataset (CNN3) and the model trained using the combination of conventional techniques and DCGAN (CNN4). Table 2 Performance comparison between constructed models

Augmentation technique

Accuracy (%)

Raw Dataset

77.1

Classical Augmentation

83.3

DCGAN-based Augmentation

79.2

Conventional + DCGAN Augmentation

89.6

A Novel Approach to Data Augmentation …

143

Fig. 6 Confusion Matrix of CNN3

Fig. 7 Confusion Matrix of CNN4

6 Conclusion and Perspectives This paper investigates the use of machine-generated data to substitute real data in training machine learning algorithms. The experimental results show that despite a small dataset size (498 documents), the use of DCGAN based data augmentation can be a good approach in the document classification context. We can recommend this approach to be used in practice in a lack of labeled data situation for document image

144

A. Jadli et al.

classification in an ERP ecosystem (i.e. Odoo). We plan to explore new state-of-theart DCGAN architectures and more specialized machine learning algorithms in our future work and investigate new ways to improve document image classification.

References 1. Jadli A, Hain M (2020) Automatic document classification using deep feature selection and knowledge transfer, April 2020. https://doi.org/10.1109/IRASET48871.2020.9092256 2. Abuelwafa S, Pedersoli M, Cheriet M (2019) Unsupervised exemplar-based learning for improved document image classification. IEEE Access 7:133738–133748. https://doi.org/10. 1109/ACCESS.2019.2940884 3. Jun C, Suhua Y, Shaofeng J (2019) Automatic classification and recognition of complex documents based on Faster RCNN. In: 2019 14th IEEE international conference on electronic measurement and instruments, ICEMI 2019, November 2019, pp 573–577. https://doi.org/10. 1109/ICEMI46757.2019.9101847 4. Kosaraju SC, et al (2019) DoT-Net: document layout classification using texture-based CNN. In: Proceedings of the international conference on document analysis and recognition, ICDAR, September 2019, pp 1029–1034. https://doi.org/10.1109/ICDAR.2019.00168 5. Jadli A, Hain M (2020) Toward a deep smart waste management system based on pattern recognition and transfer learning. In: 3rd international conference on advanced communication technologies and networking, CommNet 2020, September 2020, pp 1–5. https://doi.org/10. 1109/CommNet49926.2020.9199615 6. Mansourifar H, Chen L, Shi W (2019) Virtual big data for GAN based data augmentation. In: Proceedings - 2019 IEEE international conference on big data, big data 2019, December 2019, pp 1478–1487. https://doi.org/10.1109/BigData47090.2019.9006268 7. Xu Y, Zhang Y, Wang H, Liu X (2017) Underwater image classification using deep convolutional neural networks and data augmentation. In: 2017 IEEE international conference on signal processing, communications and computing, ICSPCC 2017, December 2017, vol 2017–January, pp 1–5. https://doi.org/10.1109/ICSPCC.2017.8242527 8. Lee MB, Kim YH, Park KR (2019) Conditional generative adversarial network-based data augmentation for enhancement of iris recognition accuracy. IEEE Access 7:122134–122152. https://doi.org/10.1109/ACCESS.2019.2937809 9. Wang G, Kang W, Wu Q, Wang Z, Gao J (2019) Generative Adversarial Network (GAN) based data augmentation for palmprint recognition, January 2019. https://doi.org/10.1109/DICTA. 2018.8615782 10. Antoniou A, Storkey A, Edwards H (2017) Data augmentation generative adversarial networks. arXiv. arXiv, 12 November 2017. https://arxiv.org/abs/1711.04340v3. Accessed 17 Jan 2021 11. Goodfellow I, et al (202) Generative adversarial networks. Commun ACM 63(11): 139–144. https://doi.org/10.1145/3422622 12. Radford A, Metz L, Chintala S (2016) Unsupervised representation learning with deep convolutional generative adversarial networks

Knowledge Driven Paradigm for Anomaly Detection from Tweets Using Gated Recurrent Units S. Manaswini, Gerard Deepak, and A. Santhanavijayan

Abstract Online social media has become of great importance in the recent past. It has proven to be a medium for connectivity as well as effortless publicity. Given the vast scope of activities that can be performed with the help of social media, it can also be misused. An anomaly has no concrete definition but the best description of an anomaly would be to identify it as the one that exhibits abnormal behavior. In this paper two events have been taken into consideration, namely black lives matter and demonetization, that took great popularity over social media platforms in recent years and received mixed emotions and behavioural patterns from the public. The response to these events have been observed on twitter and an anomaly in the trend has been predicted in the course of a selected time frame using a proposed system model to accomplish the process efficiently. The deep learning model uses a Gated Recurrent Unit classifier along with a domain ontology to enhance the process of prediction. A comparative graph of other unsupervised training models have also been included to display the efficiency accomplished through the system designed. The overall accuracy of the trained system for demonetization is observed to be 95.43% and black lives matter is seen to be 96.18%. Keywords Anomaly Detection · Deep learning · Domain ontology · Gated recurrent Unit · Online social media · Twitter

1 Introduction Technology has come to play a vital role in the connectivity of the global community. With its speeding advancements in the discovery of new software environments to S. Manaswini Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, India G. Deepak (B) · A. Santhanavijayan Department of Computer Science and Engineering, National Institute of Technology, Tiruchirappalli, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_14

145

146

S. Manaswini et al.

bind people across the globe, there are so many platforms that are available online today, that gives people a chance to encourage thoughts, opinions, talent promotion, news, forum for publicity of various on-going events, awareness propagation, business and so on. To list a few of the former- Facebook, Instagram, Twitter top the charts for maximum networking with regard to such content. Whereas anomalies are those that exhibit irregularity in a sequence of data. They are often categorized as illegal, sarcastic, undesired or counter-productive to the ideology. Anomalous behavior has both positive and negative impacts. Such data can be helpful in the prediction of natural disasters, disease out-breaks, emergency detection but on the contrary it can also be used to promote malevolent activities such as terrorist attacks, fraudulence using social media. It also takes into consideration, controversial standing of events and provides a platform to express political opinions for influencing the mob and trending topics. On a broad spectrum, social media anomalies are driven by either single individual—‘point anomaly’ or groups of people—group or tribe anomaly where latter generally refers to malicious behavior of hidden groups of people. These anomalies can be described as a change in interaction patterns, change in method of interaction and therefore can be classified into three kinds, based on the pattern on input of the anomaly namely- content, structural and behavioral. This paper shall be identifying two such scenarios- ‘Demonetization’ and ‘Black lives matter’ over selective time frames. Tweets from specific time frames extracted during the peak of both the issues have been analysed to detect anomalies in content. Motivation: Any political action or global issue implies uncalled for public attention wherein the entire cause of implication is twisted by individualistic opinions and the original motive is often lost. The issues considered above impacted in ways that have resulted in contrasting opinions. And it is interesting to determine and classify tweets that stand against/out of topic or those that have exhibited anomalous behaviour over the given time. The black lives matter protests have been observed over the time frame of two months- May 20 to July 10, 2020. The mentioned time frame witnessed a peak in the number of tweets regarding the issue. Demonetization was observed over five months- November 10 2016 to April 20 2017. Contribution: This paper proposes a methodology that makes use of Gated Recurrent Units (GRU), a kind of Recurrent Neural network (RNN) with a gating mechanism. A domain ontology [1, 2] derived from an RDF knowledge base has further been compared to the former using an integration of cosine similarity and Web Pointwise Mutual Information (Web PMI) that helps determine the prediction error or the dissimilarity in comparison which will in turn denote the anomaly in the crawled dataset. Twitter datasets have been specifically chosen due to the high political usage and media attention which is crucial to the topic addressed and tweets can be conveniently made distinct during pre-processing for easier classification. GRU being an improvised take on the RNN, have been observed to resolve a preliminary drawback of the standard RNNs. GRU has resolved the dependency issue of the loss of information in recurrent networks and has regulation over the input using structures called ‘gates’ to produce improved prediction performance over the constructed time series model. The proposed approach has been improvised with an overall F-measure of 96.8% in black lives matter and 95.47% in demonetization.

Knowledge Driven Paradigm for Anomaly Detection …

147

Organization: The flow of the rest of the paper is as follows -: Sect. 2 consists of related work to the field of study and experimentation. Section 3 drafts the proposed architecture for the system of anomaly detection. Section 4 consists of the implementation and experimental observation. Section 5 includes conclusion and references.

2 Related Work Sujatha et al. [3] has elaborately explained about various OSM anomalies. Behavioral anomalies have been observed over a twitter data set on demonetization policy using Self-Supervised learning. 86% of the public opinion was seen to support demonetization. Guo et al. [4] have put forth a Gated Recurrent Unit based Gaussian Mixture VAE system for detection of outliers on n-dimensional time-series data called GGMVAE and they claim that their scheme exceeds in performance when compared to the state-of-the-art abnormality detection model and has produced up to 5.7, 7.2% increased accuracy accuracy and F1 score respectively, in comparison to existing schemes. Liu et al. [5] have put together a literature review on the anomaly detection methodologies based on social media which includes an overview of the data types and social media data attributes, network data, temporal data anomaly detection and also includes description of state-of-the-art technique on massive social media data. Cho et al. [6] proposed a neural network model called RNN Encoder–Decoder that contains a double RNN. Both were jointly trained to maximize the conditional probability of a sequence target. This paper denotes the origin of GRU. Watson et al. [7] developed an automated outlier detection model built over supervised Long ShortTerm Memory [LSTM] and statistical analysis. Zhang et al. [8] used deep learning methodologies to social media data for detection of traffic accidents. Deep Belief Network (DBN) and LSTM were implemented on the extracted features and DBN showcased an accuracy of 85%. Results displayed superiority of DBN over Support Vector Machines (SVM) and supervised Latent Dirichlet allocation (sLDA). In [9– 20] several semantic and ontological approaches in support of the proposed work has been discussed.

3 Proposed System Architecture The proposed architecture as shown in Fig. 1 consists of three notable segments. The first segment includes data pre-processing and classification using GRU. The second segment involves the formulation of a domain ontology using an RDF knowledge base that has been filtered out of heterogeneous fact resources. The third segment compares the knowledge base to the tweets using an integrated cosine similarity and Web PMI module. The very aim of this system design is to verify that the key

148

S. Manaswini et al.

indices derived from the RDF knowledge base, drawn into an ontology, is compared to the tweets classified by GRU in order to point out an outlier in the extracted data sequence. The first phase of the process involves scraping of the tweets through ‘Twint’, an API that facilitates scrapping of twitter. Data pre-processing is a very vital step as the data extracted needs to be transformed into a workable format. Twitter data consists of multiple symbols like ‘@’ ‘#’, abbreviations, etc. that are difficult for the machine to contextually comprehend in language processing applications. Therefore, all text was converted to lowercase and tokenization was performed on the sequence of strings. Stemming and stop words were removed and lemmatization is carried out to retain words but at the same time preserve contextual meaning. On completion of the pre-processing of tweets, it is passed into the Term Frequency- Inverse Document Frequency [TF-IDF] [21]. Prediction models are highly dependent on the feature selection process. In text classification, features are a subset of the words in a given string. In order to classify the ‘importance’ of a word in a document TF-IDF is used. An anomaly can only be arbitrarily defined. In order to capture abnormal behaviour the data sequence first needs to be in the form of a computable input in order to be analysed. TFIDF is computed using the product of ‘the number of occurrences of a term’ and the ‘inverse document frequency of a word across a set of documents’. In common terms the latter computes how less valuable a term is in the set of documents. It is seen to approach 0 when a specific term is seen to occur frequently and otherwise approaches 1. Equations (2) and (3) shows how Eq. (1), the final TF-IDF equation is derived. TF-IDF = Term Frequency × Inverse DocumentFrequency

Fig. 1 System architecture

(1)

Knowledge Driven Paradigm for Anomaly Detection …

TF(t, d) =

149

Nt Nd

IDF(t, D) = loge

ND Dt

(2) (3)

The next step is to test and classify the pre-processed tweets and dataset using the GRU [4] classifier. The output of the classifier would be the top 20–25% of the categorized tweet data words based on the term classes initially derived from tweets. Further categorization, classification and comparison will be based on the top categories. The gated recurrent neural network is an improvised RNN architecture. It consists of two vectors or gates- reset gate and update gate. These two gates are responsible for the regulation of information that passes on to the output gate. GRU has been chosen as it conveniently addresses the vanishing gradient problem or the issue of retention of necessary information in more common terms. The update gate decides how much of the information from the preceding steps are carried onto to the succeeding steps. The reset gate decides how much of the information is eliminated or forgotten. The main activation functions involved in the adopted model of GRU includes Sigmoid and tanh. Assuming a new input ‘x’ is inserted to the network at time ‘t’, and a variable ‘y’ stores information until t-1 units, then the product of ‘x’ and its weight is summed with the product of y and its weight and the summation is passed into the sigmoid function which acts as the activation function and is used to contain the value between 0 and 1. Similar to the process of the update gate (1), reset gate has the exact same procedure but the way in which the weights and gates will be used varies in terms of function and design. When the important part of information is nearing the reset gate is assigned a value close to 0 in order to forget the unnecessary information from the traversal until the current point. The current memory content is then determined by the product of the input ‘x’ and its weight that is summed with the Hadamard product of the reset gate and ‘U.y’ passed into the nonlinear tanh activation function. The final memory at time step ‘t’ is determined by the Hadamard product of the update gate and ‘y’ summed with the product of (1−z) and the calculated current memory content ‘Ct’ and is passed through tanh function. From (6) the final memory content at step ‘t’ is given in (7). Equation (6) makes use of (5), Eq. (7) makes use of (66) and (4). Update gate(zt ) = σ(x.Wz + y.Uz )

(4)

Reset gate(Rt ) = σ(x.Wr + y.Ur )

(5)

  Current memory content C1t = tanh(x.W + Rt  U.y)

(6)

Final memory content(Ct ) = tanh(Zt  y + (1 − Zt )C1t

(7)

150

S. Manaswini et al.

The second phase involves various fact sources like prevalent news daily, Google news API, telegraph, RSS news feed and other verified resources that have been crawled over a specified time line and tokenized to collect facts or specific terms required for the formulation of the knowledge base. The RDF knowledge base [22] lays on the Subject-Predicate-Object format. The source of facts is the predicate, fact pretext is the subject and the fact context and summarized facts form the object. From the heterogeneous sources, all the ‘keywords’ are crawled and gathered to form an indexed fact base that leads to depiction of an in-depth domain ontology containing all the indexed terms from the fact base. This is then compared to the categorized tweets using the cosine similarity combined with Web PMI. The third phase helps compare the categorized terms with the domain ontology to help in the prediction of anomalies. The similarity function implemented is the cosine similarity [23] along with Web PMI. Irrespective of the size of the document cosine similarity is used to compare them. Cosine similarity is the cosine of the angle between two vectors in a multi-dimensional space. The smaller the angle the more the similarity.  Web PMI (A, B) =

0,

H A∩B ) log2 H (A)( −NH (B) N N

H (A ∩ B) ≤ C , other wise

(8)

Web PMI just like every other PMI is a metric to measure the correlation between two terms. It is observed to be directly proportional to the frequency of occurrence of the two terms or their similarity and is defined between 0 and 1. Increasing similarity implies that the value tends towards 1. For the purpose of anomaly prediction, the terms from the categories classified by the GRU and those indices from the domain ontology are compared. Because we aim on prediction of the outliers, we set the threshold to 0.25, and any value 0.25 implies its commonality in occurrence. Web PMI can be numerically defined as shown in Eq. (8), where, A and B are the terms for comparison and N is the total number of occurrence of synonymous term pairs and page count of the term-pair along with the page count for individual terms is denoted by H.

4 Implementation For the detection of anomalies the proposed algorithm has been run on a Windows 10 Operating System supported by an Intel Core i7 8th gen processor with 16 GB RAM. The code has been trained over python version 3.9.0 using Google Collaboratory, which has been stacked on multiple GPUs and hosted by Jupyter notebook. The library files included for the implementation of the proposed design are textblob, pandas, mathplotlib, scikit learn, and NLTK. Two events have been considered for the experiment—Black lives matter and Demonetization. Five other

Knowledge Driven Paradigm for Anomaly Detection …

151

training approaches apart from the proposed system have been adopted for observational purposes namely, self supervised learning, SVM, logistic regression, and RNN. Precision (%), Recall (%), Accuracy (%), F-measure (%) and False Negative Rate (FNR) has been calculated from the selected dataset. From Fig. 2, it is evident that the proposed model has achieved an accuracy of 96.18% showing an increase of 6.04% from the adopted baseline, self supervised learning model. The experimental precision is observed to be 98.89%, denoting a 6.71% increase. In consideration of the baseline model [1], even though selfsupervised models have shown improved accuracy when compared to most of the conventional unsupervised models, labelling of data is still an issue and has to be manually annotated. Random Forest produces an accuracy of 86.43% and has a precision of 87.15%, which clearly denotes an under-fitting model. This could be because of the absence of discrete labels, large data sets and large memory space and processing time that is caused by the complexity of the ensemble of the decision trees that tests the significance of the individual variables, leading to undesired accuracy and precision. Similarly, SVM does not perform well with large datasets and is seen to underperform in cases that have overlapping target classes and therefore shows an accuracy of 84.12% and a precision of 86.77%. The Linear Regression model is seen to have an accuracy of 91.19% and a precision of 93.89%. Though there is a significant increase when compared to Random Forest, this model assumes a linear relationship between dependent and independent attributes that highly affects the outliers as the boundaries are tight and it might falter in an inconsistent dataset, therefore the recall rate dipping to 86.76%. RNN shows an accuracy rate of 91.79% but has a precision of 94.33%. One of the main problems of a standard RNN is the retention of information through the succeeding steps and computation could be slow but with GRU the vanishing gradient problem stands resolved and further integration with cosine similarity and Web PMI improves performance hence proving to be the best suitable model in comparison. Similarly, for the event demonetization as shown in Fig. 3, the accuracy measure of the proposed model

Black lives maer Proposed Knowledge Centric Approach… RNN

98.89 94.33

94.81 89.41

96.18 91.79

96.8 91.8

Logisc Regression

93.89

SVM

86.77

82.12

84.12

84.38

Random Forest

87.15

85.33

86.43

85.22

Self-Supervised Learning [Basepaper]

92.18

89.32

0 Precision %

Recall %

86.76

91.19

90.14

90.18

90.72

50 100 150 200 250 300 350 400 450 Accuracy %

F-Measure %

Fig. 2 Performance comparison of the proposed approach for black lives matter

152

S. Manaswini et al.

Demonezaon Proposed Knowledge Centric Approach…

96.15

RNN

89.12

94.81

95.43

LogiscRegression

88.16

SVM

80.81

82.18

Random Forest

84.33

87.43

85.81

Self-Supervised Learning [Basepaper]

86.69

90.43

88.11

0 Precision

Recall

95.47

91.14

92.44 90.12

90.75

90.01 81.18

89.13 81.49 85.85 88.52

50 100 150 200 250 300 350 400 450 Accuracy

F-Measure

Fig. 3 Performance comparison of the proposed approach for demonetization

FNR:BLACK LIVES vs DEMONETISATION Proposed Knowledge Centric Approach… 0.05190.0519 RNN LogiscRegression SVM Random Forest Self-Supervised Learning [Basepaper] 0 FNR1

0.1059 0.1324

0.075 0.0988

0.1782 0.1467 0.1068

0.1782 0.1257 0.0957

0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 FNR2

Fig. 4 FNR—Black lives matter Vs Demonetization

is observed to be 95.43% and precision to be 96.15% indicating improved performance when compared to the other self-supervised and unsupervised methodologies. Figure 4 depicts the FNR of both the events.

5 Conclusions On an experimental basis it is interesting to know the scope of outliers in a given dataset. Especially, when real life events of influence are taken into consideration the identification of anomalies can help serve various political causes such as identifying protests and public opinions. The GRU classifier in the system design has proved more effective than most of the unsupervised models exhibiting an accuracy of 96.18% in the event ‘black lives matter’ and 95.43% in the event ‘demonetization’. The proposed model shows enhancement in detecting anomaly by the inclusion of a cosine similarity and Web PMI that determines the dissimilarity providing us with improved precision in the results of prediction.

Knowledge Driven Paradigm for Anomaly Detection …

153

References 1. Deepak G, Santhanavijayan A (2020) OntoBestFit: a Best-Fit Occurrence Estimation strategy for RDF driven faceted semantic search. Comput Commun 160:284–298 2. Pushpa CN, Deepak G, Kumar A, Thriveni J, Venugopal KR (2020) OntoDisco: improving web service discovery by hybridization of ontology focused concept clustering and interface semantics. In: 2020 IEEE international conference on electronics, computing and communication technologies (CONECCT). IEEE, July 2020, pp 1–5 3. Kokatnoor SA, Krishnan B (2020) Self-supervised learning based anomaly detection in online social media. Int J Intell Eng Syst (INASS) 4. Guo Y, Liao W, Wang Q, Yu L, Ji T, Li P (2018) Multidimensional time series anomaly detection: a GRU-based gaussian mixture variational auto encoder approach. Asian conference on machine learning (ACML) (2018) 5. Liu Y, Chawala S (2017) Social media anomaly detection: challenges and solutions. In: 10th ACM international conference (2017) 6. Cho K, van Merrienboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using RNN encoder–decoder for statistical machine translation, EMNLP 7. Jia W, Shukla RM, Sengupta S (2019) Anomaly detection using supervised learning and multiple statistical methods. In: 18th IEEE international conference on machine learning and applications (ICMLA) 8. Zhanga Z, He Q, Gaod J, Nic M (2017) A deep learning approach for detecting traffic accidents from social media data 9. Kumar A, Deepak G, Santhanavijayan A (2020) HeTOnto: a novel approach for conceptualization, modeling, visualization, and formalization of domain centric ontologies for heat transfer. In: 2020 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT). IEEE, July 2020, pp 1–6 10. Deepak G, Kasaraneni D (2019) OntoCommerce: an ontology focused semantic framework for personalised product recommendation for user targeted e-commerce. Int J Comput Aided Eng Technol 11(4–5):449–466 11. Gulzar Z, Anny Leema A, Deepak G (2018) Pcrs: personalized course recommender system based on hybrid approach. Procedia Comput Sci 125(2018):518–524 12. Deepak G, Teja V, Santhanavijayan A (2020) A novel firefly driven scheme for resume parsing and matching based on entity linking paradigm. J Discrete Math Sci Cryptograp 23(1):157–165 13. Haribabu S, Sai Kumar PS, Padhy S, Deepak G, Santhanavijayan A, N. Kumar D (2019) A novel approach for ontology focused inter- domain personalized search based on semantic set expansion. In: 2019 Fifteenth International Conference on Information Processing (ICINPRO), Bengaluru, India, pp 1–5. https://doi.org/10.1109/ICInPro47689.2019.9092155 14. Deepak G, Kumar N, VSN Sai Yashaswea Bharadwaj G, Santhanavijayan A (2019) OntoQuest: an ontological strategy for automatic question generation for e-assessment using static and dynamic knowledge. In: 2019 fifteenth international conference on information processing (ICINPRO). IEEE, pp 1–6 15. Santhanavijayan A, Kumar DN, Deepak G. A semantic-aware strategy for automatic speech recognition incorporating deep learning models. In: Intelligent system design. Springer, Singapore, pp 247–254 16. Deepak G, et al (2019) Design and evaluation of conceptual ontologies for electrochemistry as a domain. In: 2019 IEEE international WIE conference on electrical and computer engineering (WIECON-ECE). IEEE (2019) 17. Deepak G, Priyadarshini JS (2018) Personalized and Enhanced Hybridized Semantic Algorithm for web image retrieval incorporating ontology classification, strategic query expansion, and content-based analysis. Comput Electr Eng 72:14–25 18. Deepak G, Priyadarshini JS, Babu MH (2016). A differential semantic algorithm for query relevant web page recommendation. In: 2016 IEEE International Conference on Advances in Computer Applications (ICACA). IEEE, October 2016, pp 44–49

154

S. Manaswini et al.

19. Kaushik IS, Deepak G, Santhanavijayan A (2020) QuantQueryEXP: a novel strategic approach for query expansion based on quantum computing principles. J Discrete Math Sci Cryptography 23(2):573–584 20. Santhanavijayan A, Kumar DN, Deepak G. A novel hybridized strategy for machine translation of indian languages. Soft Comput Signal Process 363 21. Qaiser S, Ali R (2018) Text mining: use of TF-IDF to examine the relevance of words to documents. Int J Comput Appl (0975 – 8887) 181(1) 22. Stoermer H, Bouquet P, Palmisano I, Redavid D (2017) A context-based architecture for RDF knowledge bases: approach, implementation and preliminary results. In: Springer international conference on web reasoning and rule systems 23. Otsuka Y (1936) The faunal character of the Japanese Pleistocene marine Mollusca, as evidence of the climate having become colder during the Pleistocene in Japan. Bull Biogeograph Soc Jpn 6(16):165–170. AGCJ.

Stacked Deep Learning LSTM Model for Daily Solar Power Time Series Forecasting Soufiane Gaizen, Ouafia Fadi , and Ahmed Abbou

Abstract Several researches have been concentrated on predicting issues in many appl cation areas. In machine learning field, recurrent neural networks have been successfully employed. Such models were suggested to tackle time- dependent learning problems. The goal of this paper is to afford a credible forecasting methodology for solar power, which will reduce the inaccuracies involved in forecasting the near future generation. In this proposed methodology, the stacked LSTM models with different configurations are applied to track the dynamic performance of the solar energy. The second objective of this analysis is to assess the effect of the stacked LSTM hidden layers by comparing the predicted outcomes of each model. We evaluate the proposed approach with three forecasting algorithms. The simulation results show that the value of the MAPE, using stacked LSTM model, starts from 10.5% (one hidden layer) and decreases to 0.442% when four hidden layers are applied. Thus, the use of Stacked-LSTM with multiple layers provides a further decrease in the fore- casting error relative to other approaches. Keywords Solar power · Prediction · Machine learning · RNN · LSTM · Stacked-LSTM

1 Introduction Nowadays, solar power is the most important sources for electrical power generation [1, 2]. Solar photovoltaic produces energy from sunlight by converting solar radiance into electricity [3]. The efficiency of PV installations is influenced by climate parameters such as temperature [4]. photovoltaic energy relies on weather conditions and solar irradiance, which add to photovoltaic generation heterogeneity and instability. As stated by the IEA (International Energy Agency), The total power of renewable electricity is projected to increase by more than 1TW during the period starting from 2018 to 2023 [5]. Solar photovoltaic constitutes more than ½ of this S. Gaizen (B) · O. Fadi · A. Abbou Mohammadia School of Engineers, Mohammed 5 University, P.B. 765, Rabat, Morocco e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_15

155

156

S. Gaizen et al.

extension and dominates the increase of renewable capacity. Taking into account the ongoing development of solar PV sector, the electrical grid is facing the challenge of the aforementioned heterogeneity and instability in power production. It exists many alternatives to resolve these problems as energy storage installations to mitigate the discontinuity of photovoltaic production [6]. Moreover, knowing how much photovoltaic energy would be generated, may extremely decrease power plants operating costs and increase the resilience of the grid, thus, predicting PV power production is a challenging task because it relies considerably on external circumstances like PV radiation and climate conditions. Solar power prediction strategies are primarily parted into 3 groups in the literature: physical, statistical, and machine learning configurations [7–15]. Machine learning configurations are capable of managing complex problems [16]. It contains 3 structures: mentioned in these references [17, 18], one of these structures is the Artificial Neural Network [18]. This structure is currently the most widely employed strategies for time series prediction. Most traditional PV power prediction models are confined to expose the correlation between the data, but these models are not capable of discovering a deep correlation, uncovering hidden pattern and reveal significant data. For the tremendous data from the recent generating stations, the application of traditional methods isn’t conducive to ensuring reliable forecasting. Over a decade, deep learning strategies have appeared as effective machine learning technology [19]. They are becoming increasingly prevalent, thanks to their strong ability to explain dependencies in time series data. Several deep learning configurations have been recommended in these references [20–22], One of these configurations, is the Recurrent Neural Networks [22] that takes advantage of the sequential nature of input data. RNNs are employed to model time-dependent in timeseries data. In the time series forecasting, RNNs provide good results, which have been proven in several researches [23–25]. The LSTM (Long Short-Term Memory) networks is a kind of RNNs that can accommodate information retrieval for longer time periods [26]. Furthermore, it is largely employed for time series forecasting applications, which is ideally tailored to problems with solar PV generation and wind power forecasting [27]. Long short-term memory permits the memorization of the weights that are forward and back-propagated across layers [28]. LSTM-based RNNs are an appealing alternative for modeling consecutive data such as time series since they integrate qualitative knowledge from past inputs. LST memory has promising operations in solar power forecasting as a novel machine learning algorithm [6, 29]. By preserving the structure of memory cell, LSTM could sustain essential features that should be recognized throughout the learning procedure. Thus, applying LSTM to forecast solar power permits not only to achieve the correlation over uninterrupted hours but also to derive its long-term behavior patterns [6]. To forecast photovoltaic power, Abdel-Nasser [30] suggests the application of LST memory to reliably estimate the generated power for solar systems. The results have exposed that the use of LSTM provides a decrease in the forecasting error relative to other techniques. Qing [6] suggested a new method of forecasting hourly day-to-day solar power using LSTM-based weather forecasting. The suggested algorithm utilizes the hourly weather as the intrant parameter, and the hourly solar power values of the similar

Stacked Deep Learning LSTM Model …

157

scheduled day as the output parameter. Due to the assumption that time dependency is considered, the results reveal that the suggested learning algorithm is more precise than other three models. Srivastava [29] analyzed the ability of LSTM to forecast solar power time series, his study demonstrated the robustness of LSTM, also this model outperforms GBR and FFNN for day-to-day solar power time series forecasting. Liu [31] suggested a new deep learning approach using wavelet analysis to forecast wind speed. LST memory is applied to forecast the low frequencies of sublayer, and the high frequencies are forecasted by Elman-neural-network. The mixed configuration has satisfactory performance compared to 11 traditional approaches. Under complex weather conditions, Yuet [32] suggested an LST memory-based approach to forecast short-term global horizontal irradiance, the study demonstrated that LST memory surpasses ARIMA, SVR, and NN configurations, especially on cloudy weather. Despite the mentioned LST memory techniques don’t thoroughly analyzed experimentally the impact of various variables and structures, but these variables would influence the precision of prediction. Our article is provided to assess the impact of the depth of the LSTM networks namely Stacked-LSTM networks. The original time sets are separated into training and testing series. In addition, all the outcomes of the forecasts obtained are applied to the persistence comparison model for a proper benchmark evaluation. As a result, the Stacked-LST Memory configuration with multiple hidden layers show high performances in the daily solar energy forecasting.

2 Predictive Patterns 2.1 Database The solar radiation data used in this work are from the NASA prediction of worldwide energy resource website for the region of Fes/Morocco. The solar radiation data is recorded within the date range from 01/01/2017 to 01/01/2019. Every 30 min, the solar power data is registered. this recorded value was converted to daily average data. Averaging these values would make the signal smoother, and the algorithms are easier to learn. We observed that the forecast of solar radiation on a daily average basis is more reliable than the prediction of solar radiation of 30 min ahead. The data on solar radiation is standardized and the outliers and incomplete values are excluded.

2.2 Recurrent Neural Network Pattern For series prediction issues, RNNs are the most widely used Neural Network architecture. It also achieved particular attention in the natural language process domain.

158

S. Gaizen et al.

Fig. 1 Basic example for RNN.

Alike to Artificial neural networks, RNNs are considered universal approximators [33]. Unlike ANNs, the feedback loops of the recurrent neural network implicitly handle the temporal order, as well as the time dependency of the sequences [33]. The RNN is a type of ANNs, where the links between nodes creates a directed graph along a temporal sequence. And this permits it to display a temporal dynamic attitude. Figure 1 illustrates the fundamental concept of the RNNs. Xt represents the intrant layer at the time-step ‘t’, ht is the hidden state at ‘t’. It should be remembered that the hidden state is often referred to be the network memory. It is determined on the basis of the previously hidden state and the intrant layer at the same step. Equation 1 [33] represents a general formula to determine the hidden state ht .  ht =

0, t =0 δ(τxt , xt ), other wise

(1)

δ is a non-linear function. The actualized hidden state is achieved as follows: h t = F(τxt , uh t−1 )

(2)

F is a “tanh” function (hyperbolic tangent). Generally, when using recurrent neural networks patterns, it is not straight forward to catch long-term time-series dependencies. To overcome this restriction, LST Memory Networks models were designed.

2.2.1

LST Memory Recurrent Neural Network Pattern

The LST Memory Neural Network was suggested by [34] to prevent long-term dependency difficulties through a targeted architecture. Unlike typical NNs, LSTM hold a memory block to store temporary input data information. Figure 2 illustrates the hidden layer of the LSTM, that is recognized as the LST Memory cell. Applying a gate structure, the LST Memory cell binds the intrant layer Xt , to the output layer ht . There are three gates in the LST Memory cell: Intrant gate It , Output gate Ot , and Forget gate ft . The input cell state Cˇ t , the current cell output state Ct, and the output state of the previous irritation, are used in the training procedure. Using the relations given by [30], the input state, the output state and the gates can be defined. The architecture of the LSTM can be defined as follows [30]:

Stacked Deep Learning LSTM Model …

159

Fig. 2. Architecture of LSTM

    f t = σ w f × h t−1 , xt + b f

(3)

    i t = σ wi × h t−1 , xt + bi

(4)

    ct = tanh wc × h t−1 , xt + bc

(5)

ct = f t × ct−1 + i t × ct

(6)

    ot = σ wo × h t−1 , xt + bo

(7)

h t = ot × tanh(ct )

(8)

Where wf , wi , wc and wo are weight matrices. bf , bi , bc and bo present bias vectors. Cˇ t is a current state that is given by xt and ht−1 via the tanh layer. σ () is the function of activation.

2.3 Evaluation Metrics Three error metrics were suggested to measure the efficiency of the predicting model. The Root mean square error calculates the discrepancy between the real and the forecasted variables. Lower RMSE indicates better forecasting outcome, and is described as [35]:   N 1

 2 X f − Xa RMSE = N i=1

(9)

However, the Mean Absolute Percentage Error (MAPE) represents the ratio of error to the real value [35].   N 1 X f − Xa M AP E = N i=1 |X a |

(10)

160

S. Gaizen et al.

Where |X a | presents the mean of the actual solar power data. R-Square (R2 ): R2 measures the proportion of the total variance described by the fitting regression model, R2 is included in the interval [0, 1]. If R2 equal to 0, the configuration fits weakly; if R2 equal to 1, the configuration holds zero errors. R2 is determined as follow [35]: N  R =1− 2

i=1 X f N  i=1 X a

− Xa − Xa

2 2

(11)

3 Proposed Approach The suggested approach is designed to forecast solar power time series using deep stacked LSTM. Figure 3 outlines the essential steps of the suggested approach. The suggested approach comprises four primary points: 1.

Collect Solar power data from the NASA prediction of universal energy resource website for the region of Fes/Morocco. The solar power series are assembled into training and testing period.

Start

Data collection

Data Pre-processing

Data standardization Improve the performance and the depth of the model.

Training and validation data Test Data

Build and train LSTM Model Good Model yes

Test and validate Model End

Fig. 3 Flowchart of the proposed forecasting method

No

Stacked Deep Learning LSTM Model …

2.

3. 4.

161

Pre-process and clean up the data by eliminating outliers and imputing incomplete values. There are several essential steps in the preprocessing of data, such as data cleaning, data conversion, and the selection. Data cleaning and conversion are techniques used to eliminate outliers and standardize data in such a way that they take a shape that could be conveniently used to construct a model. Normalize the solar power initial data. Train, check and verify the Stacked-LST memory configuration. Several statistical metrics are employed to measure the precision of the configuration. If the precision is not good, the stacked LSTM layer number is increasing to assess the performance and the depth of the stacked-LST memory model.

4 Results and Discussion The future PV power is derived from the historical solar time series using the StackedLST memory configuration. MAPE, RMSE and R2 are the performance assessors for the suggested technique. In this analysis, 80% of data is applied to adjust the configurations and the remainder is applied for assessing purposes. In addition, the efficacy of the evolved configuration has been matched to the persistence reference configuration. The persistence reference configuration efficiency in the daily solar forecast was described in Fig. 4 as 1.027 for the RMSE, 27.7% for the MAPE and 0.668 for the R2 [35]. Using the stacked LST Memory configuration with four hidden layers, Fig. 5 shows the predicted solar energy values. As seen, as the depth of learning is increased, it is apparent that a strong match between the expected and real value exists. For the stacked LST Memory configuration, the precision of the forecast was increased by 98.7% over the persistence reference configuration. Figure 6 demonstrates the scattering plot of the expected solar energy using the Stacked LST Memory configuration over the real values. As shown in this figure, the traced data points generally correlated closer towards the fitting line.

Fig. 4 Forecasted solar energy values of the Persistence Reference configuration vs. the initial dataset [35]

162

S. Gaizen et al.

Fig. 5 Stacked LST Memory forecasting performance with 4 hidden layers

Fig. 6 Plot scattering of the expected results of the Stacked LST Memory configuration with four hidden layers compared to the initial test data.

Table 1 Solar energy forecasting outcomes of the Stacked LST Memory per day

N˚ of hidden layer

RMSE

MAPE

R2

Persistence model

1.027

27.7%

0.668

ANN

0.24

6.8%

0.943

SARIMA

0.29

7.4%

0.935

1

0.38

10.5%

0.921

2

0.16

4.42%

0.956

3

0.09

2.486%

0.979

4

0.016

0.442%

0.997

Table 1 displays the daily solar energy forecast results for the Stacked LST Memory configuration. When evaluating the error values in this table, the RMSE of 0.38, the MAPE of 10.5% and an R2 of 0.921 were found for the LSTM model with one hidden layer. RMSE of 0.16, MAPE of 4.42% and R2 of 0.956 were LSTM model with two hidden layers. RMSE of 0.09, MAPE of 2.486% and R2 of 0.979 were found for LSTM model with three hidden layers. RMSE of 0.016, MAPE of 0.442% and R2 of 0.997 were found for LSTM configuration with four hidden layers.

Stacked Deep Learning LSTM Model …

163

5 Conclusion Our work provides evaluation analysis of various hybrid configuration to predict solar energy. The precision of the forecasting is highly enhanced over to the persistencereference configuration. Our analysis reveals that the inclusion of hidden layers of the LST Memory configuration improves the precision of prediction. This is illustrated by the MAPE value beginning from 10.5% (one hidden layer) and dropping to 0.442% when four hidden layers are included. The suggested analysis is intended to alleviate the core problem of PV energy forecasting.

References 1. Sobri S, Koohi-Kamali S, Rahim NA (2018) Solar photovoltaic generation forecasting methods: a review. Energy Conv Manage 156:459–497 (2018) 2. Harrou F, Taghezouit B, Sun Y (2019) Improved $ k $ NN-based monitoring schemes for detecting faults in PV systems. IEEE J Photovolt 9(3):811–821 3. Lianos P (2017) Review of recent trends in photoelectrocatalytic conversion of solar energy to electricity and hydrogen. Appl Catal B 210:235–254 4. Gürtürk M, Benli H, Ertürk NK (2020) Determination of the effects of temperature changes on solar glass used in photovoltaic modules. Renew Energy 145:711–724 5. Renewables 2018. Power. 8 October 2018. https://www.iea.org/renewables2018/power/. Accessed 16 Sept 2020 6. Qing X, Niu Y (2018) Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM. Energy 148:461–468 7. Sobri S, Koohi-Kamali S, Rahim NA (2018) Solar photovoltaic generation forecasting methods: a review. Energy Conv Manage 156 8. Lopes FM, et al (2018) Short-term forecasts of GHI and DNI for solar energy systems operation: assessment of the ECMWF integrated forecasting system in southern Portugal. Solar Energy 170:14–30 (2018) 9. Alonso-Montesinos J, Batlles FJ, Portillo C (2015) Solar irradiance forecasting at one-minute intervals for different sky conditions using sky camera images. Energy Convers Manage 105:1166–1177 10. Miller SD, et al (2018) Short-term solar irradiance forecasting via satellite/model coupling. Solar Energy 168:102–117 (2018) 11. Halabi LM, Mekhilef S, Hossain M (2018) Performance evaluation of hybrid adaptive neurofuzzy inference system models for predicting monthly global solar radiation. Appl Energy 213:247–261 12. Hou W, Xiao J, Niu LY (2016) Analysis of power generation capacity of photovoltaic power generation system in electric vehicle charging station. Electr Eng 4:53–58 (2016) 13. Miao S, et al (2018) Markov Chain model for solar farm generation and its application to generation performance evaluation. J Clean Prod 186:905–917 (2018) 14. Agoua XG, Girard R, Kariniotakis G (2017) Short-term spatiotemporal forecasting of photovoltaic power production. IEEE Trans Sustain Energy 9(2):538–546 (2017) 15. Massidda L, Marrocu M (2017) Use of multilinear adaptive regression splines and numerical weather prediction to forecast the power output of a PV plant in Borkum, Germany. Sol Energy 146:141–149 16. Hossain M, et al (2017) Application of extreme learning machine for short term output power forecasting of three grid-connected PV systems. J Clean Prod 167:395–405

164

S. Gaizen et al.

17. Lin G-Q, et al (2020) An improved moth-flame optimization algorithm for support vector machine prediction of photovoltaic power generation. J Cleaner Prod 253:119966 18. Lu H, Ma X (2020) Hybrid decision tree-based machine learning models for short-term water quality prediction. Chemosphere 249:126169 19. Hinton GE, Osindero S, Teh Y-W (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554 20. Ogawa S, Mori H (2020) PV output forecasting by deep Boltzmann machines with SS-PPBSO. Electrical Eng Jpn 21. Zang H, et al (2020) Application of functional deep belief network for estimating daily global solar radiation: a case study in China. Energy 191:116502 (2020) 22. Jung Y, et al (2020) Long short-term memory recurrent neural network for modeling temporal patterns in long-term power forecasting for solar PV facilities: Case study of South Korea. J Cleaner Prod 250:119476 23. Kumar D, et al (2020) Forecasting of solar and wind power using LSTM RNN for load frequency control in isolated microgrid. Int J Model Simul 1–13 24. Park N, Ahn HK (2019) Multi-layer RNN-based short-term photovoltaic power forecasting using IoT dataset. In: 2019 AEIT International Annual Conference (AEIT). IEEE 25. Srivastava T, Vedanshu, Tripathi MM (2020) Predictive analysis of RNN, GBM and LSTM network for short-term wind power forecasting. J Stat Manage Syst 23(1):33–47 (2020) 26. Zhao F, et al (2020) Time-sequenced flow field prediction in an optical spark-ignition directinjection engine using bidirectional recurrent neural network (bi-RNN) with long short-term memory. Appl Thermal Eng 115253 27. Liu S (2019) Wind/solar power forecasting using improved LSTM neural networks. Diss 28. Hochreiter S, Schmidhuber J (1996) LSTM can solve hard long-time lag problems. Adv Neural Inf Process Syst 9:473–479 29. Srivastava S, Lessmann S (2018) A comparative study of LSTM neural networks in forecasting day-ahead global horizontal irradiance with satellite data. Sol Energy 162:232–247 30. Abdel-Nasser M, Mahmoud K (2019) Accurate photovoltaic power forecasting models using deep LSTM-RNN. Neural Comput Appl 31(7):2727–2740 31. Liu H, Mi X-W, Li Y-F (2018) Wind speed forecasting method based on deep learning strategy using empirical wavelet transform, long short-term memory neural network and Elman neural network. Energy Convers Manage 156:498–514 32. Yu Y, Cao J, Zhu J (2019) An LSTM short-term solar irradiance forecasting under complicated weather conditions. IEEE Access 7:145651–145666 33. Schäfer AM, Zimmermann HG (2006) Recurrent neural networks are universal approximators. In: International conference on artificial neural networks. Springer, Berlin, Heidelberg, September 2006, pp 632–640 34. Hochreiter S (1997) JA1 4 rgen Schmidhuber. Long Short-Term Memory. Neural Computat 9(8) 35. Gaizen S, Fadi O, Abbou A (2020) Solar power time series prediction using wavelet analysis. Int J Renew Energy Res (IJRER) 10(4):1764–1773

A Comparison Study of Machine Learning Methods for Energy Consumption Forecasting in Industry Mouad Bahij, Moussa Labbadi, Mohamed Cherkaoui, Chakib Chatri, and Soufian Lakrit

Abstract The rapid expansion in the industrial sector requires good control of energy consumption, whereas the prediction of the consumption is the most important factor for energy management as well as environmental protection. This paper presents a model for predicting energy consumption using ML machine learning models such as Linear Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Artificial Neural Networks (ANN), and Recurrent Neural Networks (RNN) are used to predict energy consumption based various attributes as inputs. In order to evaluate the effectiveness of the proposed approaches, a simulation is carried out using the Python software. Finally, according to simulation results, the Linear Regression (LR) method is efficient and give better performances. Keywords Energy consumption forecasting · Machine learning · SVM · LR · DT · ANN · RNN

1 Introduction Energy forecasting is increasingly important to protect the environment, in order to ensure the efficiency of the energy management and the reduction of greenhouse gas emissions. In the context, the energy forecasting used in many areas such as industrial sectors, buildings, and transport. MOROCCO focuses on enhancing their energy transition to green growth. This transition based on several factors such as energy efficiency, which presents an optimal solution in the planning and reducing

M. Bahij (B) · M. Labbadi · M. Cherkaoui · C. Chatri · S. Lakrit Engineering for Smart and Sustainable Systems Research Center, Mohammed V University, Mohammadia School of Engineers (EMI), Rabat, Morocco M. Labbadi e-mail: [email protected] M. Cherkaoui e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_16

165

166

M. Bahij et al.

costs consumption as well as minimizing energy consumption in the industrial sector [1]. The Machine Learning presents good results for the time series and regression problems. In order to improve the energy efficiency, it is necessary to conduct the statistics, to an analysis of historical energy data, and to predict future energy consumption [2]. These machine learning algorithms are able to process a large volume of data in the aim to solve the problem devoted to energy prediction [3]. In industry sector, the evolution of energy is composed according to the following steps: technical calculation, comparative analysis based on simulation models and statistical modeling and Machine Learning. Indeed, the ML process operates in accordance with the principle of the black box, which find the relationship between the various input data characteristics and the output objectives using data historical processing. The ML algorithm shows great potential for the energy modeling and the evaluation of the production in the industrial sector, for example, the ANN is used for predictions based on a mapping between data inputs and data outputs in the system [4]. For overcoming the problems of precision on prediction in the ANN, The RNN shows a better adaptability in the processing of energy consumption data, the structure of which consists of three layers; the input layer, and the hidden layer as well as the output layer [5]. The algorithm DT is used a mechanism to partition the data into groups according to a diagram in order to train the model [6]. The SVM model presents a robust solution to the problems of the regression and the classification [7]. The RL is a technique which combines one or several variables from the historical data base to predict a result according to other variables [8]. This paper proposes a comparative study with five approaches based on Machine Learning for estimation and analysis energy consumption in industry. The remainder of this paper is organized as follows. In Sect. 2, the related work is described. In Sect. 3, the background is developed. The simulation results are discussed in Sect. 4. Finally, Sect. 5 is devoted to conclusions.

2 Related Work Generally, the ML algorithm became more used for training prediction models from existing data compared with conventional methods. This section discusses the papers linked to this study. The authors in [9] develop a model for energy consumption forecasting according to three statistical learning methods included regression trees, RL and multivariate adaptive regression (MARS). In paper [10], the authors propose an algorithm based on the ANN for prediction of consumption load in the building from a set of hourly energy data measures of consumption of 93 households for 2 months. For the forecasting of energy consumption in the building or industry, multiple linear regression methods, the ANN, and the genetic algorithms are used to process for data, in order to generate the desirable

A Comparison Study of Machine Learning Methods …

167

prediction model [11]. In article [12], the ANN used to predict the consumption in the building for the cooling, the heat and the consumption and emission of carbon dioxide. In [13], the SVM method is compared with others for a tunnel settlement prediction problem. This approach is most used in prediction problems [14]. The authors in [6] used the improved TD as a predictive tool, where she found that this technique had good results for a small to medium-sized dataset. The DT presents an interpretable architecture, efficient for solving the problems the regression and the classification, which the DT does not require a large number of parameters and the results obtained are easy to in the classification model. From this brief rated work, a model for energy consumption forecasting in industrial depends on historical data, which comparison between the different algorithms in order to evaluate the most efficacious methods in this study.

3 Background 3.1 Recurrent Neural Networks The RNN is a new technique recently used in the processing of time series of energy consumption, where their structure is developed by the ANN network as it combines contextual information from previous entries. According to the memory mechanism the information is transmitted to the network one by one and the network nodes store their state at a time stage and use it to inform the next stage [5]. The {x1, x2,…, xn} input sequence is modeled at the RNN level using Eq. 1 looping: h i = f (h i−1 , xi )

(1)

According to the three layers are RNN, the calculation of each layer is described in detail below: a

Input layer

Input layer: The word sequence of the text at the input layer is of the sequence {x1 , x2 . . . . . . .xn },where the current input vector mapping is {x1∧ , x2∧ . . . . . . .xn∧ }. b

Hidden layer

∧ At the input layer the vector of the sequence xn−1 , as well as h t−1 describe the hidden state at the previous moment of the output vector. The output of the cache state is expressed according to Eq. 2:

  ∧ + W hh hn−1 + bh h = σ W hx xn−1

(2)

168

M. Bahij et al.

Where σ means the activation function of each element, and bh presents the vector bias of the hidden layer. c

Output layer

The output layer compute is based on the current cache layer memory state expression as input. Equation 3 describes the calculation process:   yt = so f tmax U y h t + by ∈ R V  P(xn /x1 , x2 . . . . . . .xn−1 )

(3)

From where yt presents the probable output displayed, and U y h t defines the weight matrix between the hidden state and the output layer.

3.2 Artificial Neural Networks In the ML field, neural networks are widely known as an energy estimation tool in building and industry. According to their strong learning ability the ANN are more used to solve the non-linear problem [4]. The ANN composed of the following layers; the input layer contains 4 neurons reserved for attributes, and the hidden layer is composed of 10 neurons, finally, 1 neuron in the output that expresses the prediction of energy. Figure 1 shows the Model ANN. The construction of the ANN algorithm according to six steps; • • • •

Preparation of the systems data base. Initialization of the weights. From the input value of previous neurons calculates the desired output value. Improve the output value by acting on the average square root factor between the output components. • Data training phase using back-propagation algorithm. • Forecasting model formulation.

Data input

be

Energy Consumption forecasting

bs Output layer Input layer

Fig. 1 Structure of ANN

Hidden layer

A Comparison Study of Machine Learning Methods …

169

3.3 Support Vector Machine In regression and classification problems, the SVM presents a strong robustness to solve non-linear system problems with good solutions by a few data samples. The SVM seeks to minimize the learning error value and a higher level of confidence in value [2]. In this study, this algorithm is used to predict the energy consumption of an industrial entity according to a training process based on historical data. The SVM makes the relation between the inputs of the system according to Eq. 3: F(xi ) = w, ϕ(xi ) + b   1 Minimise = ω2 + C ξ + ξi∗ 2 i=1 l

(4)

where ∗, ∗, w, ϕ(xi), and b ∈ R, describe the dot product, the weight, the nonlinear mapping function, and the bias.

3.4 Regression Linear Regression analysis is a traditional technique used to model predictions [7]. A multiple linear regression model can be expressed according to Eq. 5: Y = ϕ0 + ϕ1 b1 + ϕ2 b2 + ϕ3 b3 . . . . . . . + ϕ P b p + θ

(5)

where the function Y presents the output variable, ϕi for the regression parameters, bi presents inputs values, and θ is the random error.

3.5 Decision Tree Decision trees are widely proposed as the optimal solution for classification problems, or the architecture of the decision tree provide good flexibility, efficiency and precision for the generation of the prediction model [6]. In fact, their mechanism allows data to be partitioned using a flow chart to predict desirable consumption. For a large amount of data, the DT is suitable to discover hidden rules, but in classifiers it may be unstable in relation to the noise present in the learning data. When learning the model, the data processing undergone a process has a root node and 2 branch nodes, in order divided into the other sub-nodes so that the final result

170

M. Bahij et al.

of the DT approach is shows the terminal node. In the paper, this method is used to predict the outcome from the database provided by following certain decision rules.

4 Simulations Results and Discussion 4.1 Data Analysis In this work, the database established during training of the prediction model consists of the assigned disputes including temperature, humidity, lighting and consumption at the production level. The overall diagram proposed system is described in Fig. 2. The data set established in the model formation consists of two parts, one for training and the rest for testing, this database undergoes such a process of normalization and standardization described in Eq. 6: x=

x−μ σ

(6)

Pre-processing and Feature extraction

Where μ describes the mean of characteristic vectors, σ this standard deviation, x is the original characteristic vector and x is the vector of characteristics after normalization. Based on the difference between the expected results and the actual data, the efficacy of the approach used is determined.

Historical data of energy consumption in industrial

Data input analysis

Dataset training

ML Approche

Dataset testing

Model

Estimation

Fig. 2 Forecasting model

A Comparison Study of Machine Learning Methods …

171

4.2 Simulation Results In this part, the simulation and analysis results of RNN, ANN, SVM, DT and RL algorithms are expressed. The approaches are designed in the Python environment with the PyTorch tensor library. Then, to examine actual and predicted energy consumption using ANN, RNN, DT, SVM, and LR approaches are shown in Figs. 3, 4, 5, 6 and 7 figures respectively. The performance of these algorithms used in the theme of precision are of average absolute percentage Error (MAPE) described in Eq. 6:  n  100%  y − yi∧  M AP E = n I =0  yi 

(6)

The energy consumption profile is defined by the correlation between the actual and predicted energy consumption presented in Fig. 3. At the level of the simulation using the ANN approach, we can see that the forecast curve accurately follows the energy consumption compared to the real consumption. Then, the forecasting error is less than 0.2. In the RNN, the prediction error is of order 0.9, which means that this algorithm is less efficient than the ANN. Figure 4 expresses the behaviour of the energy consumption between the measured real and the profile estimated by the model. The performance of the SVM is demonstrated in Fig. 5, where the error value does not exceed 0.05 of the references, that expresses that the SVM has a strong training capacity. In Fig. 5, it is well defined that the estimated energy consumption is close to the curve of the real consumption. For DT, the error is 0.5, which the variation in the energy consumption profile between the actual consumption and the estimated is shown in Fig. 6

Fig. 3 Energy profile estimated by ANN

172

Fig. 4 Energy profile estimated by RNN

Fig. 5 Energy profile estimated by SVR

Fig. 6 Energy profile estimated by DT

M. Bahij et al.

A Comparison Study of Machine Learning Methods …

173

Figure 7 expresses the forecast of the energy consumption profile which is similar to the actual consumption. The error obtained is of reference value 0.6 × 10−3 in the LR approach. The performance criteria used in the comparison of the five approaches are the following: the absolute percentage of error MAPE, the training score and the prediction score are describing in Table 1. Moreover, The LR training score is 100% implies that higher performance algorithm. The score is 99.999% in the SVM and the same in DT method. But those are less inferior to the first RL algorithm. The RNN has a value of 99.708% and the ANN has a score of 99.506%, which presents the least efficient algorithm in the prediction of the model. In Similarly, the value of the mean square error is 4.202 for ANN, which is lower than the RNN approach of the order of 1.56. The error is 5.75 10–2 in DT method. Then, the SVM is the value of 5.05 10−4 which expresses that the ANN and RNN algorithms perform less than the two methods preceding. The LR most effective approach in model training with a lower order error 3.45 10−28 than all approaches proposed.

Fig. 7 Energy profile estimated by LR

Table 1 The performance parameters for the five approaches

Approaches

Training Score (%)

Predict Score (%)

MAPE

RL

100.00

100.00

3.45 10 −28

SVM

99.999

99.999

5.05 10–4

DT

99.999

99.990

5.75 10–2

RNN

99.970

99.970

1.5600

ANN

99.506

99.270

4.2026

174

M. Bahij et al.

5 Conclusion This study focuses on the development of ML approaches for the prediction of energy consumption in the Morocco industrial sector. The different ML methods, including the linear regression (LR), artificial neural network (ANN), recurrent neural network (RNN), support vector machine (SVM), and the decision tree (DT) have been described and modelled. Moreover, the training of the consumption forecasting model depends on the database of the attributes which influences consumption in the industry. The simulation results demonstrate that the linear regression offers better performance compared to other methods. However, the SVM is less than the LR but, it is better compared to the DT, ANN, and RNN.

References 1. Rapport annual. https://www.cese.ma/rapport-annuel-2019. Accessed 21 Nov 2020 2. Zhao HX, Magoulès F (2012) A review on the prediction of building energy consumption. Renew Sustain Energy Rev 16(6):3586–3592 3. Wei Y, Zhang X, Shi Y, Xia L, Pan S, Wu J, Han M, Zhao X (2018) A review of data-driven approaches for prediction and classification of building energy consumption. Renew Sustain Energy Rev 82:1027–1047 4. Ahmad AS, Hassan MY, Abdullah MP, Rahman HA, Hussin F, Abdullah H, Saidur R (2014) A review on applications of ANN and SVM for building electrical energy consumption forecasting. Renew Sustain Energy Rev 33:102–109 5. Chakib C, Mohammed O (2018) Sensorless control of the PMSG in WECS using artificial neural network and sliding mode observer. In: International symposium on advanced electrical and communication technologies (ISAECT). Rabat, Morocco, pp 1–6 6. Patil S, Kulkarni U (2019) Accuracy prediction for distributed decision tree using machine learning approach. In: 2019 3rd international conference on trends in electronics and informatics (ICOEI). IEEE. Tirunelveli, India, April 2019, pp 1365–1371 7. Kavitha S, Varuna S, Ramya R (2016) A comparative analysis on linear regression and support vector regression. In: 2016 online international conference on green engineering and technologies (IC-GET). IEEE. Coimbatore, India, November 2016, pp 1–5 8. Salam A, El Hibaoui A (2018) Comparison of machine learning algorithms for the power consumption prediction: case study of Tetouan city. In: 2018 6th International Renewable and Sustainable Energy Conference (IRSEC). IEEE. Rabat, Morocco, December 2018, pp 1–5 9. Williams KT, Gomez JD (2016) Predicting future monthly residential energy consumption using building characteristics and climate data: a statistical learning approach. Energy Build 128:1–11 10. Rodrigues F, Cardeira C, Calado JMF (2014) The daily and hourly energy consumption and load forecasting using artificial neural network method: a case study using a set of 93 households in Portugal. Energy Procedia 62:220–229 11. Jeong G, Park S, Lee J, Hwang G (2018) Energy trading system in microgrids with future forecasting and forecasting errors. IEEE Access 6:44094–44106 12. Sossan F, Namor E, Cherkaoui R, Paolone M (2016) Achieving the dispatchability of distribution feeders through prosumers data driven forecasting and model predictive control of electrochemical storage. IEEE Trans Sustain Energy 7(4):1762–1777

A Comparison Study of Machine Learning Methods …

175

13. Hu M, Li W, Yan K, Ji Z, Hu H (2019) Modern machine learning techniques for univariate tunnel settlement forecasting: a comparative study. Math Prob Eng 14. Pai PF, Hong WC (2005) Support vector machines with simulated annealing algorithms in electricity load forecasting. Energy Convers Manage 46(17):2669–2688

The Review of Objectives, Methods, Tools, and Algorithms for Educational Data Mining Mohamed Timmi, Adil Jeghal, Said EL Garouani, and Ali Yahyaouy

Abstract The remarkable growth in online learning resources, educational software, the usage of internet in the educational context, and the creation of national student information databases have produced extensive educational data repositories. Today, virtual educational systems have grown drastically and have led us to store a huge amount of potential data from a myriad of sources, with various formats and distinct levels of complexity. Particularly, lock-downs have established the fundamental necessity of online learning tools. For this reason, each educational aim has a range of specificities and characteristics. In fact, it goes along with numerous educational features requiring a specific formula to the extraction, processing and analysis of data. This is reflected in the variety and specificity of Educational Data (EDM) Mining tactics that are put in place to solve a definite educational assignment. The pivotal intention of this paper is to put forth a detailed scrutiny on the previously conducted studies related to educational data mining. Keywords Educational data mining · EDM · Classification · Clustering and prediction

M. Timmi (B) · S. EL Garouani · A. Yahyaouy LISAC Laboratory, Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fez, Morocco e-mail: [email protected] S. EL Garouani e-mail: [email protected] A. Yahyaouy e-mail: [email protected] A. Jeghal LISAC Laboratory, National School of Applied Sciences, Sidi Mohamed Ben Abdellah University, Fez, Morocco e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_17

177

178

M. Timmi et al.

1 Preliminaries This article discusses a study related to the field of data mining and how it can be diligently implemented within the educational field. Besides, it expands upon the central features of educational data mining processes and the objectives of these operations, along with some rudiments that pave the way for an effective application of data mining algorithms. Historically, data mining is not novel, because it mainly relies on statistics that date back to the middle of the 18th century. The current state of data mining is the result of substantial efforts that have been accumulated for many years [1], yet it is worth noting that this area has witnessed a remarkable development recently. Returning to the roots of educational data mining, we find that research in this area is relatively new. Although research and analysis of data, resulting from educational software, started a long time ago, knowledge exploration in educational data mining (EDM) has been referred to, in various conferences and scientific journals, as a separate field itself in recent times. This claim has also been advocated in a wide range of reputable conferences on educational technologies, namely the international conferences revolving around the following matters: educational data mining/artificial intelligence in education/intelligent systems/user modeling, adaptation and personalization (UMAP).

2 Educational Data Mining Educational data mining is defined in a variety of ways. However, the one that is put forward in the Journal of Educational Data Mining is fundamentally insightful. In this sense, the journal regards EDM as the process whereby methods are developed to explore idiosyncratic data that come from educational environments [2], and the use of these methods to deepen our own understanding of learners, and to reflect them on the environment in which they operate. Correspondingly, another definition considers EDM as the use of information mining techniques for specific types of data extracted from educational environments to either deal with or solve important education related issues. As far as we can tell, both definitions focus on extracting knowledge from educational data to improve education systems [3]. The exploration of knowledge in the field of education can occur by means of piecing the following elements together: computer science, education and statistics [4]. With that in mind, the intersection of the previous three areas forms other sub-areas, such as learning analytics, e-learning and data mining [5] (Fig. 1). Educational data mining is a multidisciplinary arena, for it uses various methods and techniques, namely statistics, data mining, information retrieval systems, suggestion systems, machine learning, science education, teaching principles and others. Notwithstanding, the process of selecting these techniques depends predominantly on the studied educational problem [6].

The Review of Objectives, Methods, Tools, and Algorithms …

179

Fig. 1 Areas in relation with EDM

Fig. 2 Educational data mining process

The stages through which the process of exploration in educational systems goes can be diagrammed as follows[7]: (Fig. 2). It is also worth mentioning that there are other areas where data mining is applied, such as criminal investigations, industrial engineering, fraud detection and customer relationship management.

3 Selection of Features The selection of features consists in choosing a small subset of features that are prerequisite to describe the target concept by means of prediction, classification, regression or clustering …. (It depends on the method used to explore the educational data). The selected subset is chosen with the assumption that the input data contains a range of irrelevant features, and its removal would not affect in any way the accuracy of the classification model (of the method used), as a matter of example. Obviously, this is advantageous in the sense that it allowed a remarkable reduction in computational complexity, better interpretability of the model and lessened data fit

180

M. Timmi et al.

by improving generalization. A wide spectrum of machine learning algorithms can be utilized for this purpose [8], and the best feature propositions are listed below for reference: Student’s gender, physically disabled, family size, student’s living milieu, number of brothers and sisters, student’s family situation, ownership of a vehicle, notes/notes obtained in high school, grade, section, type of school, location of school, home study, the parents’ education/occupation and monthly income.

4 Objectives of Educational Data Mining (EDM) Exploration objectives can be categorized in educational data according to the types of end users who benefit from the knowledge derived from exploration as follows [2, 9]:

4.1 Learners Providing notes and advice to students, meeting the needs of the students, working to increase the students’ productivity and academic level, etc.

4.2 Teachers Expand their understanding of the students’ nature, the progress of the educational process, and get them to update their teaching methods and performance.

4.3 Researchers Develop and compare data extraction techniques to intensify their focus on optimal suggestions for each problem or specific pedagogical tasks, assess the effectiveness of learning when using different methods, environments. etc.

4.4 Administrators Come up with alternative canals to arrange resources (human and material) and assess their educational establishments.

The Review of Objectives, Methods, Tools, and Algorithms …

181

It is also important to admit that the process of involving all stakeholders and providing them with the results of the analysis is a critical task that should be conscientiously managed. Actually, some of these interests may end up taking an exclusively different turn. Hypothetically, the use of learning analytics (LA) by personnel managers to introduce technology into teaching methods might be misperceived, as teachers would regard that as an attempt to evaluate or judge their teaching methods. Additionally, the same goes for students who may feel uncertain to use personal data for their assessment and classification. As far as we can see, this can produce an unexpected consequence; teachers and students alike will be unwilling to use these new technologies, or to partake in the various stages of the analysis. Previously, the objectives were classified based on the types of end users. However, it is challenging to categorize the objectives of knowledge exploration integrally in education systems using one criterion, particularly, when all the objectives are linked to several users. In this regard, another trustworthy research has described the classification of exploration objectives in educational data as follows [9]: • Student modeling: user modeling in the education system requires providing a detailed account about the characteristics of students as well as their situation. It is primordial to have an all-inclusive understanding of their knowledge, skills, experiences and motivations. On the other hand, one needs to examine certain types of problems, which can negatively derail the educational process, namely the use of ineffective educational resources. Consequently, the objective is to create or improve the student’s model based on the available information. • Observe the students’ level and their results: the aim is to anticipate the outcomes of every educational procedure by measuring how active the interactions in the classroom are. • Search for new learning suggestions: the aim is to suggest new content and better tasks that are in conformity with the students’ current level/situation. • A profound exploration of the learner’s behavior: This objective can actually vary. Based on the previously delivered answers, the classification of students has to make recourse to numerous factors, namely their personal profiles. • Communicate with stakeholders: the objective is to assist teachers and get managers to analyze the activities accredited to students. • The analysis of the terrain structure: the goal is to define the terrain structure to know the optimal content that can be added to the material and choose the best possible sequence for the educational process. This can be achieved by developing the skill of predicting the student’s performance and determine the ideal educational environment that would guarantee a fulfilled learning experience. • The Improvement of educational content: it is closely linked to the two previous objectives. The aim here is to decide on how to improve the educational material (content, activities, etc.) using student information. • Study the effects of educational support that is put forward by education systems. It is noteworthy that the previously named objectives of educational data mining are meant to improve and computerize education systems. More significantly, it can

182

M. Timmi et al.

be proclaimed that student modeling is the main key to achieve many objectives and tasks through the exploration of educational data [7].

5 Methods Used for Educational Data Mining (EDM) Attaining the objectives of educational data mining tends to call on a plenty of traditional methods of data mining (clustering, classification, association analysis, etc.) They are supposed to be classified as follows [2, 7, 10–12].

5.1 Prediction The goal of this method is to develop a model that allows the inference of a particular aspect of this data (called a predicate variable) in a set of aspects within the same data reservoir (called predictive variables). The applications of educational data mining using this method attempt to anticipate the student’s academic success by reviewing their behavior in the classification-based virtual environment. Besides, going about predicting the results of students who have completed the same teaching unite is manifestly useful to back up those who may fail to fulfill it. The KNN algorithm (K nearest neighbors) is among the techniques that are highly recommended in this respect. It should be pointed out that the algorithms used in this area, are virtually expressed in the interactions of students with the available sources in the form of multidimensional rays [12, 13].

5.2 Clustering This method implies, in the fewest possible terms, that the classified samples which encompass data are naturally similar to each other. A cluster is widely defined as a group of identical objects that differ from those found in other clusters. It is broadly agreed upon that the efficiency of the cluster upsurges suggestively depending on how similar or different it is from the elements contained in other clusters. This mechanism is postulated to discover similar features between different behavioral patterns manifested by students [13].

5.3 Relationship Mining It refers to the examination of variables that constitute a data sample. This connection can come in different forms, such as association rules mining, sequential pattern

The Review of Objectives, Methods, Tools, and Algorithms …

183

mining, correlation mining, and casual data mining. In the realm of educational data mining, “relationship mining” is manipulated to determine the possible connections between students’ online activities and their grades [13].

5.4 Distillation This method includes portraying data in a manner that allows the user to rapidly detect and categorize the data features. This technology chiefly depends on data Summarization, visualization, and the use of Interactive Interfaces to demonstrate useful information as well as supporting decision-making [13].

5.5 Outliers Detection This method ruminates on the discovery of certain data segments with a marked difference, compared to other casual genres of samples. It is predominantly applied to explore educational data in order to reveal common behaviors displayed by the involved stakeholders. Besides, it has been proven a practical medium to spot students with learning anomalies [13].

5.6 Social Network Analysis The structural analysis of this method is looking forward to studying the relationship between individuals rather than considering their behavioral characteristics. From an empirical viewpoint, social relationships are merely transcribed contractually to establish multiple social links. This elementary segment can manifest itself, in terms of educational data exploration, to interpret and analyze relationships in collaborative assignments [9, 12, 13].

5.7 Process Mining This particular procedure devotes itself to the extraction of knowledge-related processes, using the system’s recorded information to create an obvious visual depiction of the whole operation. In fact, it entails three well-calibrated sub-fields: conformance, checking and model discovery. In the Learning Data Syndicate, the exploration process can be put forth to reflect on the students’ behavior and its probable impacts on their studies.

184

M. Timmi et al.

5.8 Text Mining This method sheds light on extrapolating useful styles, patterns, and rules from unstructured texts, namely HTML files, discussion messages, and emails. The Tasks comprise text categorization, text compilation, analyzing the emotional insights within a text, synthesizing documents and modeling relationships between diverse objects. Essentially, the main reason behind using text mining is to examine the content of discussions, forums, chats, web pages, documents… etc. [12, 13].

6 Algorithms Used for Educational Data Mining (EDM) Educational data mining algorithms derive from a number of sciences. It is more or less an amalgamation of several scientifically oriented disciplines, such as statistics, mathematics, logic, science learning, artificial intelligence / expert systems, pattern recognition, and machine science, long with intelligent and non-traditional sciences. Educational data mining phase has attracted a considerable amount of attention in the research community over the past decade, with the intention of developing scalable algorithms and adapting to increasing quantities of educational data in research for significant cognitive models. Below are the most commonly used data mining algorithms [11, 14]:

6.1 K-means “K-means” is a reiterative algorithm that attempts to divide the dataset into K predetermined and non-overlapping subgroups (clusters). Here, every data point is only affiliated to one group [15]. The pseudocode of K-means algorithm as follows [16]:

The Review of Objectives, Methods, Tools, and Algorithms …

185

6.2 Apriori Apriori algorithm is given by R. Agrawal and R. Srikant in 1994 for finding frequent set of items in a dataset for Boolean association rule. Surprisingly enough, this significant appellation was given to the algorithm for its reliance on prior knowledge of frequent itemset properties [15, 17].

6.3 EM Algorithm EM algorithm is a combination of two distinctive features, expectation and maximization. It is an iterative method intended to find as much similarities as possible in statistical models, where these latter depend on unobserved latent variables [18].

6.4 PageRank Algorithm Google Search famously adopts PageRank algorithm to categorize web pages in their search engine results [19].

6.5 C4.5 Algorithm C4.5 algorithm is specially designed to generate a decision tree[15]. The pseudocode of Tree C4.5 is shown as follow [20]:

186

M. Timmi et al.

7 The Available Tools for Educational Data Mining (EDM) The applications of educational data mining require a considerable number of tools. In truth, the choice of the appropriate tool depends on the nature of the studied data, their size and the precise objective. In this scope, the most frequently used tools are the following [14]:

7.1 Rapid Miner Rapid Miner is widely known for being a readymade, open source, no-coding required software, which helps in giving advanced analytics [11].

7.2 Weka Weka is a range of machine learning algorithms for data mining tasks [11, 15].

7.3 Orange Orange-This tool comes with visually programmed environments. It deals with tools of data importing, data dragging and dropping widgets, as well as providing links to connect dissimilar widgets together so that the workflow can be completed.

7.4 R R- Is widely defined as a commonly used free software environment for statistical computing and graphics.

7.5 Knime-Primarily Knime-Primarily used for data preprocessing. It is a powerful tool with GUI that shows the network of data nodes.

The Review of Objectives, Methods, Tools, and Algorithms …

187

7.6 Tanagra Tanagra is a free open source of data mining software put in place for mere academic and research purposes.

7.7 XL Miner XL Miner-It is the only comprehensive data mining add-in for Excel, with neural nets, classification and regression trees, logistic regression, linear regression, Bayes classifier, K-nearest neighbors, discriminant analysis, association rules, clustering, principal components, and a lot more.

7.8 Microsoft Excel Microsoft Excel also provides maximum methods for statistical analysis.

8 Conclusion Educational data mining (EDM) has shown itself as an enormously fertile research area, considering its remarkable potentials to contribute to a deeper understanding of teaching, learning and motivational processes in individual and collaborative teaching environments. Currently, EDM’s main contributions are focused on two main axes: • Data analysis and creation of models to sharpen the collective understanding of the learning process. • Developing more effective and innovative methods to support learning and enhance the qualitative dimension of using educational web applications (ELearning, M-Learning). Today, around the world, many intelligent tutoring systems use EDM techniques to provide more personalized and better learning.

References 1. Sahin ¸ M, Yurdugül H (2020) Educational data mining and learning analytics: past. Present Future 9:121–131

188

M. Timmi et al.

2. Baker RS, Yacef K (2009) The state of educational data mining in 2009: a review and future visions. JEDM| J Educ Data Min 1(1), 3–17 3. Romero C, Ventura S (2017) Educational data science in massive open online courses. Wiley Interdisciplinary Rev Data Min Knowl Discov 7(1):e1187 4. Romero C, Ventura S (2020) Educational data mining and learning analytics: an updated survey. WIREs Data Min Knowl Disc. https://doi.org/10.1002/widm.1355 5. Salloum SA, Alshurideh M, Elnagar A, Shaalan K (2020) Mining in educational data: review and future directions. In: Joint European-US workshop on applications of invariance in computer vision. Springer, pp 92–102 6. Romero C, Ventura S (2020) Educational data mining and learning analytics: an updated survey. WIREs Data Min Knowl Disc 10(3):e1355. https://doi.org/10.1002/widm.1355 7. Anjum N, Badugu S (2020) A study of different techniques in educational data mining. In: Advances in decision sciences, image processing, security and computer vision. Springer, Cham, pp 562–571 8. Acharya A, Sinha D (2014) Application of feature selection methods in educational data mining. Int J Comput Appl 103(2) 9. Mitrofanova YS, Sherstobitova AA, Filippova OA (2019) Modeling smart learning processes based on educational data mining tools. In: Smart education and e-learning. Springer, pp 561– 571 10. Peña-Ayala A (2014) Educational data mining: applications and trends 11. Kumar AD, Selvam RP, Kumar KS (2018) Review on prediction algorithms in educational data mining. Int J Pure Appl Math 118(8):531–537 12. Bakhshinategh B, Zaïane O, Elatia S, Ipperciel D (2017) Educational data mining applications and tasks: a survey of the last 10 years. Educ Inf Technol 23. https://doi.org/10.1007/s10639017-9616-z 13. Aldowah H, Al-Samarraie H, Fauzy WM (2019) Educational data mining and learning analytics for 21st century higher education: a review and synthesis. Telematics Inform 37:13–49. https:// doi.org/10.1016/j.tele.2019.01.007 14. Shende A, Thakare MS, Byagar S, Joshi MAN (2020) The review of different data mining tools, techniques and algorithms for the data mining in education. Our Heritage 68(27):290–296 15. Dhingra K, Sardana KD (2017) Educational data mining: a review to its future vision. Int J Technol Transfer Commer 15(3):309–321 16. Olukanmi P, Nelwamondo F, Marwala T (2019) Rethinking k-means clustering in the age of massive datasets: a constant-time approach. Neural Comput Appl 1–23 17. Bhandari A, Gupta A, Das D (2015) Improvised apriori algorithm using frequent pattern tree for real time applications in data mining. Procedia Comput Sci 46:644–651. https://doi.org/10. 1016/j.procs.2015.02.115 18. Kurban H, Jenne M, Dalkilic MM (2017) Using data to build a better EM: EM* for big data. Int J Data Sci Anal 4(2):83–97 19. Agryzkov T, Curado M, Pedroche F, Tortosa L, Vicent JF (2019) Extending the adapted pagerank algorithm centrality to multiplex networks with data using the Pagerank two-layer approach. Symmetry 11(2):284 20. Wati, M, Budiman E, Kridalaksana AH, Haviluddin H, Dengen N, Purnawansyah P (2018) Performance of decision tree C4. 5 algorithm in student academic evaluation

Brain-Computer Interface: A Novel EEG Classification for Baseline Eye States Using LGBM Algorithm Said Abenna, Mohammed Nahid, and Abderrahim Bajit

Abstract This work has been carried out in the development of the best BCI systems to recognize EEG visual system signal helps tired drivers, the elderly, patients, and the disabled to drive on good safety condition. The method used in this article based on an application of rhythms (delta, theta and alpha) on EEG acquisition channels to increase the accuracy of the prediction system, after using delta rhythms on selected electrodes using ExtraTrees algorithm, we notice an increase in accuracy from 95.46 to 99.97%, also this system has a good prediction speed of 12560 samples per second during testing. Therefore, instead of minimizing the number of acquisition electrodes, these bad electrodes can be used to increase the performance of the BCI system using rhythms (delta, theta and alpha). Keywords Brain-computer interface (BCI) · Electroencephalogram (EEG) · Visual system · Feature extraction · Classification

1 Introduction The brain computer interface systems were launched in 1924 with the first acquisition of EEG signals by Hans Berger. Each organ connects to a specific area in the brain via the neural system, the distribution of electrical activity in the brain refers to a sensation or movement of an organ, so these signals transmit through a neurons network between organ and its analysis area in the brain [10]. The aim is to provide a direct link between observed human intentions in the brain activities and computer control, an important feature of current BCI systems is the high complexity of their functional extracts compared to their simple classifications (usually linear) [8]. In the human brain, there are billions of neurons and these neurons communicate with each other using weak electrical signals, using these electrical signals S. Abenna (B) · M. Nahid Faculty of Science and Technology, Hassan II University, Mohammedia, Morocco A. Bajit National School of Applied Sciences, Ibn Toufail University, Kenitra, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_18

189

190

S. Abenna et al.

brain activity is measured and these signals are called EEG signals [2], the main purpose of using EEG signals is to diagnose brain diseases [3], most medical applications are based on BCI systems, because it can be used to prevent harmful effects of smoking, alcohol consumption, it also helps detect and diagnose the brain disorders, sleep disorders and tumours, the BCI can also be used to restore the health of people with a cerebral vascular accident, disability and mental disorders [7]. The EEG signals is recorded by several electrodes placed on specific areas from scalp, the EEG signals have a distinctive high resolution of up to one millisecond, which is still not possible with the latest imaging technologies such as computerized tomography (CT) or magnetic resonance imaging (MRI) [13]. The EEG technology has been progressively used in the auxiliary diagnosis of diseases such as schizophrenia, mild cognitive impairment, epilepsy and Alzheimer’s disease. Above all, a close relationship has been found between brain and depression, a cognitive ability of depressed patients changes with mood, and these changes affect EEG [4], the difficulties required in EEG signals cannot be controlled because many activities take place in the brain at same time, the use of adaptive factors ap-pears to be beneficial for biological symptoms such as electroencephalogram [11], there have been many EEG classification studies in recent years, these studies used different classification techniques, collected their performance and evaluated different collections of characteristics, among these categories, k-nearest neighbor (k-NN), linear discriminant algorithm (LDA), support vector machine (SVM), artificial neural network (ANN) have become popular [6, 11]. In this paper, we have been interested in developing a prediction system using EEG signals for eye states (open or closed), such that this system is based on three main steps as a sequel, the selection of electrodes that allows to determine the most important electrodes for classification and extraction of characteristics based in the application of delta rhythms, theta and alpha on selected bad electrodes, the final step is the use of the LGBM algorithm to classify EEG signals. As a maximum accuracy value obtained in this work is in the order of 99.97%. The rest of this article is organized as follows, Sect. 2 present the data used, feature extraction and classifier proposed for system, the results obtained and discussed are presented in Sect. 3, while Sect. 4 provides conclusions and an overview of future work.

2 Methods 2.1 Dataset This paper uses statistical data from an acquisition of EEG signals for eye states (open or closed) using 14 non-invasive electrodes (Fig. 1a) from UCI Database [15]. The signals acquired using Emotive EEG Neuroheadset device use the labels ‘0’ to indicates when eyes are open and ‘1’ to indicates when eyes are closed, the total

Brain-Computer Interface: A Novel EEG Classification …

191

Fig. 1 Electrodes position (a) and the visual system (b)

Fig. 2 Proposed general architecture for baseline eye states recognition system

number of data is 14980 samples, such as training data use 80% of the total data and the rest 20% is used as testing data (Fig. 2).

2.2 Feature Extraction Electroencephalography signals are noisier, so they have poor accuracy during a direct classification, for this one using some algorithms for feature extraction. In this work, we used the rhythms Delta (0.5 to 4 Hz), Theta (4 to 7 Hz) and Alpha (7 to 13 Hz) to minimize the noise generated by the electrodes before classification, to get good results, we applied three types of filter passes the band in parallel, a first filter uses delta rhythms, the second filter merge the two rhythms delta and theta (0.5 to 7 Hz), and the third uses alpha rhythms, such as delta and theta rhythms are applied to the wrong electrodes and alpha on the selected medium.

192

S. Abenna et al.

2.3 Feature Selection This step is very important in determining the electrodes that are weak system recognition accuracy. There are many feature selection algorithms, in this work we used ExtraTrees (ET) [12] based on SelectKBest and chi2 algorithms to select important channels.

2.4 Classification The classification of EEG signals is a very important step to resume the quality of the BCI system, from many algorithms, the LGBM algorithm was chosen because of its effectiveness. Like LGBM (Light-Gray Box Model) is a gradient-enhancing tool that uses leaf tree growth for learning, which speeds up the formation and accuracy of better results, even when you use less memory [9].

2.5 Evaluation Metrics The principle measures are used to evaluate the performance of prediction in this paper are: With TP, FP, TN and FN are calculated from the confusion matrix of prediction during testing (Fig. 3b): -TP (True Positive): refers to a case where an alarm is raised when eyes open. -TN (True Negative): refers to a case where an alarm is not raised when eyes closed. -FN (False Negative): refers to a case where an alarm is not raised when eyes open. -FP (False Positive): refers to a case where an alarm is raised when eyes closed.

Fig. 3 Evaluation metrics (a) and confusion matrix for binary class (b)

Brain-Computer Interface: A Novel EEG Classification …

193

Fig. 4 Channels selection (a), correlation matrix between electrodes (b)

3 Results 3.1 Feature Selection Results The results of this paper were obtained using python3 on a core i-5 computer with 4-CPU and 6-Gb in RAM. Figure 4a illustrates a classification of electrodes from the bad electrode used (P8) to the best electrode (O1), the Fig. 4b shows the correlation values between the electrodes. we notice the existence of links between different electrodes of each group (AF3, P8, F8), (F7, F3), (FC5, O1), (P7, AF4) and (O2, T8), compared to Fig. 1b, each set of electrodes belongs to the same nerve which implies the same signal characteristics and ‘1’ as correlation value between the electrodes, which shows the good quality of the signals acquired by Emotive EEG Neuroheadset device.

3.2 Classification Results The classification of EEG data is the most important step in evaluating results. For this, a set of metric values such as accuracy (ACC), sensitivity (SEN), specificity (SP), precision (PR), kappa-score (k), F1-score (F1), mathews correlation coefficient (MCC), mutual information (MI), zero on loss (ZOL), pretreatment time (Ta), classification time (Tc) and prediction time (Tp) were used. Table 1 illustrates the classification results using the LGBM algorithm and rhythms on the selection channels, this table shows the increase in classification performance when applying the rhythms on several electrodes, so the classification time remains constant, in such a way the accuracy values increased from 95.46% when not using

AF3, F3, FC5, P8, FC6, AF4

AF3, F3, FC5, P8, FC6, AF4

F3, FC5, T7, FC6, F4, AF4

F3, FC5, FC6, F4, AF4

FC5, P8, FC6, F8, AF4

AF3, FC5, P7, F8, AF4

AF3, FC5, T7, FC6, AF4

F3, O2, FC6, AF4

AF3, P8, T8, FC6

AF3, P8, FC6, AF4 Nan

T7, FC6, F4, AF4

FC5, F4, AF4

6

6

6

5

5

5

5

4

4

4

4

3

Nan

AF3, F3, FC5, P8, FC6, AF4

Nan

Nan

Nan

Nan

Nan

Nan

Nan

Nan

Nan

Nan

Nan

Nan

All

6

99.63

99.63

99.67

99.73

99.73

99.67

99.70

99.70

99.70

99.73

99.73

99.77

99.80

99.97

ACC (%)

99.63

99.63

99.66

99.73

99.73

99.67

99.69

99.69

99.72

99.72

99.74

99.77

99.82

99.96

SE (%)

Classifier performances

Alpha

Feature selection

Delta + Theta

All

NEEG

Table 1 Classification results with LGBM

99.63

99.56

99.71

99.78

99.78

99.56

99.78

99.71

99.56

99.85

99.64

99.71

99.56

100.0

SP (%)

99.63

99.63

99.67

99.74

99.74

99.66

99.71

99.70

99.69

99.74

99.73

99.76

99.78

99.97

PR (%)

99.63

99.63

99.66

99.73

99.73

99.66

99.70

99.70

99.70

99.73

99.73

99.76

99.80

99.97

F1 (%)

99.26

99.26

99.33

99.46

99.46

99.33

99.39

99.39

99.39

99.46

99.46

99.53

99.60

99.93

K (%)

99.26

99.26

99.33

99.46

99.46

99.33

99.39

99.39

99.39

99.46

99.46

99.53

99.60

99.93

MCC (%)

0.67

0.67

0.67

0.67

0.67

0.67

0.67

0.67

0.67

0.67

0.67

0.67

0.68

0.69

MI

11

11

10

8

8

10

9

9

9

8

8

7

6

1

ZOL

1.53

0.96

1.02

0.86

0.90

0.85

1.02

0.96

0.96

0.86

0.97

1.02

0.84

0.90

Ta (s)

2.49

1.68

1.73

2.24

1.55

1.67

1.55

1.63

1.63

1.68

1.54

1.63

1.56

1.47

Tc (s)

(continued)

0.04

0.04

0.04

0.04

0.04

0.04

0.04

0.04

0.04

0.04

0.04

0.04

0.04

0.04

Tp (s)

194 S. Abenna et al.

F3, O2, FC6

FC5, P7, FC6

FC5, P7, AF4

FC6, F4

P7

FC6

P7, F8

F3

P7

T8

FC6

Nan

3

3

2

2

2

2

1

1

1

1

0

Nan

Nan

Nan

Nan

Nan

Nan

F8

F8

Nan

Nan

Nan

Nan

95.46

99.17

99.17

99.20

99.33

99.37

99.43

99.43

99.47

99.47

99.50

99.53

ACC (%)

95.44

99.15

99.14

99.20

99.33

99.38

99.44

99.41

99.46

99.47

99.49

99.52

SE (%)

Classifier performances

Alpha

Feature selection

Delta + Theta

3

NEEG

Table 1 (continued)

94.84

99.19

99.27

99.05

99.20

99.13

99.27

99.56

99.42

99.27

99.49

99.56

SP (%)

95.41

99.17

99.17

99.19

99.32

99.35

99.42

99.44

99.46

99.45

99.50

99.53

PR (%)

95.43

99.16

99.16

99.19

99.33

99.36

99.43

99.43

99.46

99.45

99.50

99.53

F1 (%)

90.86

98.32

98.32

98.39

98.66

98.72

98.86

98.86

98.92

98.92

98.99

99.06

K (%)

90.86

98.32

98.32

98.39

98.66

98.72

98.86

98.86

98.92

98.92

98.99

99.06

MCC (%)

0.51

0.64

0.64

0.64

0.65

0.65

0.65

0.65

0.66

0.66

0.66

0.66

MI

136

25

25

24

20

19

17

17

16

16

15

14

ZOL

0.79

0.83

0.93

1.47

0.81

0.84

0.84

0.85

0.81

1.49

1.43

1.44

Ta (s)

3.56

1.87

2.65

2.09

1.86

1.67

1.97

2.04

1.73

1.94

2.55

2.36

Tc (s)

011

0.05

0.11

0.06

0.05

0.05

0.05

0.05

0.05

0.04

0.05

0.04

Tp (s)

Brain-Computer Interface: A Novel EEG Classification … 195

196

S. Abenna et al.

Table 2 Comparative results with literature Method

NEEG

Results ACC (%)

SE (%)

SP (%)

Zhou et al. (2019) [15]

Delta-RFC

All

91.9

Nan

Nan

Zhou et al. (2019) [15]

Delta-RFC

5

99.8

Nan

Nan

Zhou et al. (2019) [15]

Delta-RFC

4

99.6

Nan

Nan

Zhou et al. (2019) [15]

Delta-RFC

3

99.6

Nan

Nan

Hamilton et al. (2015) [5]

K* + RRF

All

97.40

96.4

98.3

Hamilton et al. (2015) [5]

K*

All

97.30

96.5

98.0

Hamilton et al. (2015) [5]

ada

All

97.20

96.3

97.9

Hamilton et al. (2015) [5]

RRF

All

95.10

92.5

97.1

Reddy et al. (2016) [14]

MLN

All

94.92

Nan

Nan

Saghafi et al. (2017) [1]

Delta-Theta-LR

All

88.2

Nan

Nan

Saghafi et al. (2017) [1]

Delta-Theta-ANN

All

82.4

Nan

Nan

Saghafi et al. (2017) [1]

Delta-Theta-SVM

All

70.6

Nan

Nan

This work

Delta-Theta-LGBM

All

99.97

99.96

100.0

Delta-Alpha-LGBM

5

99.83

99.83

99.85

Delta-Theta-LGBM

4

99.73

99.73

99.78

Delta-Theta-LGBM

3

99.63

99.63

99.63

Delta-Theta-LGBM

2

99.47

99.46

99.42

Delta-Theta-LGBM

1

99.33

99.33

99.20

feature extraction to 99.97% when using feature extraction. Compared to the results with literature in Table 2, we notice that the wrong method was used by [1] using the Delta-Theta-SVM method with an accuracy value of 70.6%, on the other hand at our work, the accuracy value takes 99.97% and the specificity takes the ideal value (100%). So to calculate the speed of prediction of the system during testing, one divides the total number of test data over the duration of pretreatment and prediction, to calculate the total number of test data from Table 1, we divide the value of the ZOL on the error rate (100-ACC (%)), also the preparation time and prediction can be calculated by a simple addition between 20% of Ta and Tp (0.2 * Ta + Tp). After calculating the average speed of system prediction, it is found that its value takes 12560 samples per second. Every literature work suffering some technical deficiency to not find good results, the Zhou et al. [15] method use Delta rhythms only in the stage of feature extraction of EEG data as in our work, but these rhythms can delete an important set of data for classification, so we must add the rhythms Theta bandwidth as in the method of Seghafi et al. [1] so the filter passes band uses bandwidth between 0.5 and 7 Hz to add more character to the training data, with the possible minimum of noise, we also notice the possibility of improving the quality of classification when merging two algorithms as in the case of the Hamilton et al. [5] method, such that we notice that

Brain-Computer Interface: A Novel EEG Classification …

197

the accuracy value increases by 95.10% and 97.30 to 97.40% for this UCI dataset classification, so we notice that RRF and LGBM finds the same accuracy value without feature extraction algorithm, which shows that this work makes a good correction of the different methods of literature.

4 Conclusion In conclusion, this work improves the recognition of eye states, such as accuracy takes the value 99.97% using the LGBM algorithm and low rhythms of EEG signals from bad electrodes selected for the feature extraction, and compared to other methods, we notice that our system is reliable with a high average prediction speed of 12560 samples per second. We hope that this study helps other researchers develop good BCI systems to help and improve human life. For the future work, we will now improve a new method for the feature extraction and classification steps of motor movement-imagery recognition.

References 1. Saghafi A, Tsokos CP, Goudarzi M, Farhidzadeh H (2017) Random eye state change detection in real-time using EEG signals. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2016.12.010 2. Khosla A, Khandnor P, Chand T (2020) A comparative analysis of signal processing and classification methods for different applications based on EEG signals. Biocybernet Biomed Eng 5–6. https://doi.org/10.1016/j.bbe.2020.02.002 3. Emrah A, Turker T, Sengul D (2020) A Tunable-Q wavelet transform and quadruple symmetric pattern based EEG signal classification method. Medical Hypotheses 1–2. https://doi.org/10. 1016/j.mehy.2019.109519 4. Cai H, Qu Z, Li Z, Zhang Y, Hu X, Hu B (2020) Featurelevel fusion approaches based on multimodal EEG data for depression recognition. Inf Fusion 1–2. https://doi.org/10.1016/j.inf fus.2020.01.00 5. Hamilton CR, Shahryari S, Rasheed KM (2015) Eye state prediction from EEG data using boosted rotational forests. In: 2015 IEEE 14th international conference on machine learning and applications. https://doi.org/10.1109/ICMLA.2015.89 6. Nicolae C, Nirvana P, Marius L (2020) Reading into the mind’s eye: Boosting automatic visual recognition with EEG signals. Neurocomputing. https://doi.org/10.1016/j.neucom.2019. 12.076 7. Edla DR, Ansari MdF, Chaudhary N, Dodia S (2018) Classification of facial expressions from EEG signals using Wavelet Packet Transform and SVM for wheelchair control operations. Procedia Comput Sci 1–2. https://doi.org/10.1016/j.procs.2018.05.081 8. Farquhar J (2009) A linear feature space for simultaneous learning of spatio-spectral filters in BCI. Neural Networks J. https://doi.org/10.1016/j.neunet.2009.06.035 9. Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, Ye Q, Liu TY (2017) LightGBM: a highly efficient gradient boosting decision tree. In: 31st conference on neural information processing systems, Long Beach, CA, USA 10. Eitan N, Alex F, Dan F (2020) Real-time EEG classification via coresets for BCI applications. Eng Appl Artif Intell. https://doi.org/10.1016/j.engappai.2019.103455

198

S. Abenna et al.

11. Parvinnia E, Sabeti M, Zolghadri Jahromi M, Boostani R (2014) Classification of EEG Signals using adaptive weighted distance nearest neighbor algorithm. Comput Inf Sci 1–2. https://doi. org/10.1016/j.jksuci.2013.01.001 12. scikit-learn user guide, Release 0.20.4, 2019, pp 255–257 13. Amin SU, Alsulaiman M, Muhammad G, Mekhtiche MA, Shamim Hossain M (2019) Deep Learning for EEG motor imagery classification based on multi-layer CNNs feature fusion. Future Gen Comput Syst 1–2. https://doi.org/10.1016/j.future.2019.06.027 14. Reddy TK, Behera L (2016) Online eye state recognition from EEG data using deep architectures. In: 2016 IEEE international conference on systems, man, and cybernetics. https://doi. org/10.1109/SMC.2016.7844325 15. Zhou Z, Li P, Liu J, Dong W (2019) A novel RealTime EEG based eye state recognition system. Springer. https://doi.org/10.1007/978-3-030-06161-6 17

A Light Arabic POS Tagger Using a Hybrid Approach Khalid Tnaji, Karim Bouzoubaa, and Si Lhoussain Aouragh

Abstract Part Of Speech (POS) tagging is the ability to computationally determine which POS of a word is activated by its use in a particular context. It is a useful preprocessing tool in many natural languages processing (NLP) applications. In this paper, we expose a new Arabic POS Tagger based on the combination of two main modules: the 1st order Markov and a decision tree models. These two modules allow improving existing POS Taggers with the possibility of tagging unknown words. The tag set used for this POS is an elementary tag set composed of 4 tags {noun, verb, particle, punctuation} that are sufficient for some NLP applications but greatly help increasing the accuracy. The POS tagger has been trained with the NEMLAR corpus. The experiment results demonstrate its efficiency with an overall accuracy of 98% for the full system. Keywords Natural Language Processing · Arabic text processing · POS tagging · Machine learning · Decision trees

1 Introduction The world of the Internet has known a huge and continuous growth regarding the Arabic content in terms of texts, videos, images, etc. Statistics show1 that this content has increased by 9348% between the year of 2000 and 2020. As a result, developing Arabic Natural Language Processing (ANLP) tools is required to process this huge K. Tnaji (B) · K. Bouzoubaa Mohammadia School of Engineers, Mohammed V University in Rabat, Rabat, Morocco e-mail: [email protected] K. Bouzoubaa e-mail: [email protected] S. L. Aouragh Faculty of Law, Economics and Social Sciences, Salé, Mohammed V University in Rabat, Rabat, Morocco 1 https://www.internetworldstats.com

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_19

199

200

K. Tnaji et al.

and growing content. In fact, there are many applications that have been already developed for Arabic such as Part-of-speech (POS) taggers. This latter is an essential tool in many NLP applications such as word sense disambiguation [18], question answering [11] and information retrieval [9]. However, the number of Arabic POS taggers that can be used as rapid and free tools is still low [1, 7, 8]. In addition, they suffer from three major problems. The first and foremost problem concerns words having more than one possible tag when they are used in different contexts. The second problem concerns words not found in the annotated training set since the majority of the POS taggers can tag only words that actually exist in the training set. Therefore, these unknown words lead to a major problem in any tagging system and always decrease its performance. Finally, as will be detailed in the literature review, Arabic researchers use different and non-standard tag sets such as [4, 10, 14]. This situation leads to the difficulty of comparing or combining these POS taggers. Therefore, our objective is to develop a new tool for Arabic POS tagging fixing the above-mentioned drawbacks. This POS tagger is developed using a hybrid approach combining a statistical one to deal with ambiguous words and a symbolic one to cope with unknown words. Finally, instead of specifying a full and very detailed tag set, we limit our tag set to four generic tags (noun, verb particle and punctuation) since they are enough in many kinds of applications. We consider then our new POS tagger as a light Arabic POS tagger. The rest of this paper is organized as follows. The next section presents related works concerning POS tagging techniques for unknown and ambiguous words. Section 3 provides the steps for developing our POS tagger. The results of experiments are described and commented in Sect. 4. The exploitation of this system is described in Sect. 5. Finally, some conclusions and future works are given in Sect. 6.

2 Related Works Research on POS tagging has a long history where numerous techniques have been successfully applied. Some of them focus on the use of linguistic rules. Others consider POS tagging as a classification problem: the tags are the classes and a machine learning algorithm is used to assign each occurrence of a word to one class based on the evidence from the context. Different Arabic taggers have recently been proposed. Some of them use supervised machine learning approaches such as [1] while others use hybrid approaches, combining the rule-based approach and the statistical one such as [2]. Some of these POS taggers handle unknown words such as [13]. A detailed review about the most recent works related to Arabic POS taggers can be found in [16]. The major problem in all these Arabic POS taggers is the absence of a standard tag set. For example, the number of tags varies between 6 tags in the case of Columbia Arabic Treebank POS tag set [10], and 2000 tags in the case of Penn Arabic Treebank (PATB) [14]. This situation makes it impossible to compare or combine these existent

A Light Arabic POS Tagger Using a Hybrid Approach

201

systems. A comparison about some of the existing Arabic tag sets can be found in [5] where we can notice the big difference between the tag sets because each one is developed for a specified application. Some studies have been done to develop an Arabic standard tag set such as [5, 12] that aims to design detailed hierarchical levels of the Arabic tag set categories and their relationships. Relating to the rapidity of the Arabic POS tagging systems, few works report on this measure despite its importance. Among the systems that have a known execution time, we find farasa POS tagger [7] which is able to process 2000 words per second.

3 Our Approach Let us remind that our objective is to develop a POS tagger with unknown words handling. To avoid the non-standardization of the tag sets and to improve the POS execution time, one solution consists in reducing the tag set containing the basics {noun, Verb, Particle, Punctuation}. The development of this POS tagger is based on a supervised machine learning approach exploiting an annotated corpus. This system is developed using the Hidden Markov Model (HMM) combined with a decision tree guessing model based on word substring information.

3.1 Data Set The data set used for this POS tagger is the NEMLAR corpus. This corpus was produced within the NEMLAR project2 [3] and consists of about 500,000 words of Arabic text from 13 different categories, aiming to achieve a well-balanced corpus that offers a representation of the variety in morphology, syntactic, semantic and pragmatic features of modern Arabic language such as political news, political debate or Arabic literature. The tag set existing in the NEMLAR corpus is detailed, and since we typically need only the 4 elementary tags, we performed a simple modification on this corpus to map the used NEMLAR tag set to our four basic tags. For instance, adjectives, nouns and proper nouns are all mapped to the basic noun tag.

2 https://www.nemlar.org

202

K. Tnaji et al.

3.2 Processing A POS tagging system provides the most likely tag for each word of the sentence. Our system is based on the hidden Markov model and the Viterbi algorithm. The HMM [15] is a sequence model. A sequence model or sequence classifier is a model whose job is to assign a tag or class to each unit in a sentence, thus mapping a sequence of words to a sequence of tags. An HMM is a probabilistic sequence model: given a sequence of words, it computes a probability distribution over possible sequences of POS tags and chooses the best tag sequence by using the Viterbi algorithm. However, HMM can only tag the words existing in the training data. To tag the unknown words we have to pursue another technique. Indeed, our approach is to exploit the morphology of words (word substring information) to associate with each one its most likely tag. For instance, in the Arabic word. “‫( ”متفاهمون‬mtfAhmwn)3 [6], the three characters (‘‫( ’ف‬f), ‘‫( ’ه‬h)) and (‘‫( ’م‬m)) are root radical characters. Therefore, they can be replaced by other three Arabic characters such as {‘‫( ’ق‬q), ‘‫( ’د‬d), ‘‫( ’ص‬S)} to produce the other Arabic word “‫( ”متصادقون‬mtSAdqwn) which have different meaning but both words are nouns. The other characters at the beginning and the end of the word represent the augmented characters. Linguistically speaking each augmented character can be only one of these Arabic ten characters {‘ ‫( ’ا‬A), ‘‫( ’ه‬h), ‘‫( ’ی‬y), ‘‫( ’ن‬n), ‘‫( ’و‬w), ‘‫( ’م‬m), ‘‫( ’ت‬t), ‘‫( ’ل‬l), ‘‫( ’أ‬O), ‘‫( ’س‬s)}. The augmented characters, also known as affixes, associated with their position in the word play a critical role in determining the possible POS tag of the word. For example, Arabic words “‫( ”یتصالح‬ytSAlH), “‫( ”یتصاعد‬ytSAEd), “‫( ”یتعامل‬ytEAml) all have three augmented characters {‘‫( ’ی‬y), ‘‫( ’ت‬t)} and {‘‫( ’ا‬A)} in the first, second and the fourth position and all these words are verbs. Therefore, our objective is to extract automatically these classification rules that allow classifying a word from its augmented which represents the affixes4 . The extraction of these rules is done by using a decision tree algorithm. To build our decision tree algorithm. The idea is simple and consists in building a decision tree by learning all over the corpus and using as an attribute vector containing the prefix (first and second letters), the suffix (last and before last letter) and the size of the word which replaces the infix to avoid the complexity of extracting it. Consequently, the non-terminal nodes in our decision tree are the attributes (prefix, suffixes or word’s size). The edges represent the possible values for each attribute: Arabic letters or a number which represent the size of the word. Finally, the terminal nodes are the POS tags.

A Light Arabic POS Tagger Using a Hybrid Approach

203

3.3 Combining the Two Approaches The Objective is tagging a sentence composed of several known and/or unknown words: • If all the words are known, we can use the Viterbi algorithm to estimate the most likely tag sequence by using the emission and transition probabilities. • If there is one or more unknown words, this leads to zero probabilities being assigned to unseen words in the emission matrix, causing the probability of the whole sequence to be set to zero. To eliminate these zeroes in the emission matrix, we use the distribution of probability given by our decision tree-guessing model. The POS Tagging procedure is done as indicated in (Fig. 1). We must indicate that on a reduced tag set and a large corpus the transition probabilities will not cause a problem because virtually there will be no transitions with null probability. For instance, let us consider the following sentence: “‫( ”یأکل الولد من الطیبات‬yOkl Alwld mn AlTybAt). In the first step, the emission matrix of the input sentence is constructed, that means assigning a probability of words having each tag. Therefore, we have to extract this probability for each word of the sentence. If the word is

Fig. 1 Procedure of sentences tagging 3 Transliterated

using Buckwalter [6].

4 Affixes represent the prefixes, infixes and suffixes indicate substrings that come respectively at the

beginning middle and at the end of a word.

204 Table 1 Test on the ambiguity problem.

K. Tnaji et al. Test

Result

Total number of tested words

101991

Words correctly classified

100737 (99%)

Words incorrectly classified

1254 (1%)

present in the vocabulary, the vector of word/tag emission probability is obtained directly. Otherwise, we apply our decision tree guessing model that receives the prefix, suffix and the length of the unknown word as input. For example, if the word “‫( ”الطیبات‬AlTybAt) is unknown the input parameters for the prediction model are the prefix “‫( ”ال‬Al), the suffix “‫( ”ات‬At) and the word’s size (7). The model generates a probability distribution of this word on all tags and this distribution is used as an emission probability vector to complete the emission probability matrix of the sentence.

4 Experiments The test is subdivided into four evaluation series where the first three use the Nemlar corpus. In the first one, we test the efficiency of our system on the ambiguous words using only HMM. In the second, we test our decision tree guessing model and for the third series of evaluation, we test the full POS tagging system. Finally, we test the POS tagging system using another corpus.

4.1 Evaluation 1 To evaluate the efficiency of our system on ambiguous words, we test the POS tagger which depends only on the hidden Markov model. Therefore, it is necessary to use a test data with no unknown words. Statistics and results are presented in Table 1. Concerning words, the precision of the system is close to perfection. A precision of 99% is very high, but it is justifiable. First, all the words exist in the vocabulary and we are using a reduced tag set. In addition to that we can neglect the punctuation that can never be misclassified. Therefore, the remaining 1% error is practically between the verbs, nouns and the particles.

4.2 Evaluation 2 For this experiment, we performed a test by using 5-fold-cross-validation. Table 2 below represents the evaluation results.

A Light Arabic POS Tagger Using a Hybrid Approach Table 2 Decision tree cross validation evaluation

Table 3 Total evaluation of the POS tagger

205

Test

Result

Total number of tested words

539,465

Words correctly classified

527,290 (97.74%)

Words incorrectly classified

12,175 (2.26%)

Test

Result

Total number of Words

85,492

Words incorrectly classified

1,362 (1.6%)

Number of unknowns Words

908

Unknowns words incorrectly classified

292 (31%)

Execution time

4,473 words/second

Despite that the POS tag of some words depends on its context in the sentence and/or on the diacritics, this model provides an excellent accuracy.

4.3 Evaluation 3 To evaluate the full POS tagging system, we process with the train-test split technique. In the first step we divide the full dataset into sentences. In the second step, we take 80% of the sentences for training and we keep 20% for the test. Table 3 summarizes the statistics and results. As we notice the number of misclassified words is extremely insignificant. This is due to the very good quality of our model but also to the elementary tag set used. Concerning unknown words, the number of misclassified words represents 31% of the total unknown words that seems relatively high but technically it’s caused by the hidden Markov model and/or the decision tree guessing model. Finally, relating to the rapidity of this POS Tagger note that our model is extremely rapid and could process about 4,473 words per second (with i3 processor and 4 GB/RAM) in comparison with farasa POS tagger [7] which can process about 2,000 word/second (with i7 processor and 16 GB/RAM).

4.4 Evaluation 4 In this series of evaluation, we tested our system on the WikiNews5 corpus. It contains manually-segmented and POS tagged words from articles on seven domains: politics, 5 Corpus

available at https://github.com/kdarwish/Farasa.

206

K. Tnaji et al.

Table 4 Evaluation of the POS tagger using WikiNews corpus Test

Result

Total number of tested words

9,940

Number of unknown words

1758 (17.68%)

Words correctly classified

9,548 (96.05%)

Words incorrectly classified

392 (3.95%)

Execution time

4,000 words/second

Fig. 2 POS Tagging results interface

economics, health, science and technology, sports, arts, and culture. We started with a preprocessing step that transforms the 16 used tags into our reduced tag set. Table 4 summarizes the statistics and results. For instance, the WikiNews tags noun, adjective are mapped into the noun tag. Once again, we notice the good accuracy and rapidity of this POS tagging system, despite the considerable number of unknown words.

5 Exploitation This system has been developed in java and has been integrated into the Safar framework6 . This framework integrates several tools to process the Arabic language at the morphological, syntactic and semantic levels containing parses, lemmatizers or segmenters [17]. The framework is available both as a web interface7 and as an API. Figure 2 shows the use of the POS tagger web interface. After selecting “Safar light POS Tagger” from the input “Select Tagger”, the user can either tag a text by entering it to the target zone or by directly importing a text file from the machine. The tagging 6 http://arabic.emi.ac.ma/safar/ 7 http://arabic.emi.ac.ma:8080/SafarWeb/

A Light Arabic POS Tagger Using a Hybrid Approach

207

Fig. 3 POS Tagging program example

results can be visualized directly or they can be generated as an Excel, CSV or XML file. An example of using the POS tagger via SAFAR API is illustrated in Fig. 3. To do so, we import the package “safar-modern_standard_arabic-basic-syntax-posTagger”. The following code tags a text with only two instructions and then outputs the POS for every word in the input text.

6 Conclusions and Future Works This work was done in the perspective of having a rapid and efficient ANLP tool. Indeed, we have developed a POS tagger based on a hybrid approach containing two main modules: a statistical module based on HMM, and the second one based on decision trees. For the tag set we chose an elementary tag set to ensure accuracy and rapidity which allowed us to develop a very rapid and accurate POS tagging system with unknown words handling. The evaluation of each module gives satisfying results (accuracy of 99% for HMM and 97% for the tree-guessing model) and after the assembly of the two modules for the complete POS tagger the results obtained are encouraging and the execution time is very low. As perspectives for the improvement of this work, we plan to enlarge the vocabulary and increase the order of the hidden Markov model. It is also worth focusing on the disparity of the POS tag set of the Arabic language and propose a standardized version of both the tag set and the corresponding system.

References 1. Al Shamsi FG (2006) A hidden Markov model-based POS tagger for Arabic. In: Proceeding of the 8th international conference on the statistical analysis of textual data, France, pp 31–42 2. Albared MO (2009) Arabic part of speech disambiguation, pp 517–532

208

K. Tnaji et al.

3. Attia MM (2005) Specifications of the Arabic Written Corpus produced within th NEMLAR project 4. Atwell ES (2008) Development of tag sets for part-of-speech tagging 5. Atwell MS (2013) A standard tag set expounding traditional morphological features for Arabic language part-of-speech tagging. Edinburgh University Press 6. Buckwalter Arabic Transliteration. (n.d.). https://www.qamus.org/transliteration.htm. 20 Oct 2020 7. Darwish K, Mubarak H, Abdelali A, Eldesouki M (2017) Arabic POS tagging: don’t abandon feature engineering just yet. In: Proceedings of the third arabic natural language processing workshop, pp 130–137. https://doi.org/10.18653/v1/W17-1316 8. Diab M, Hacioglu K, Jurafsky D (2004) Automatic tagging of Arabic text: from raw text to base phrase chunks. In: Proceedings of HLT-NAACL 2004: short papers. association for computational linguistics, pp 149–152 9. Dinçer BT, Karao˘glan B (2004) He effect of part-of-speech tagging on IR performance for Turkish. In: Aykanat C, Dayar T, Körpeo˘glu ˙I (eds.), Computer and Information Sciences— ISCIS 2004, Springer, pp 771–778. https://doi.org/10.1007/978-3-540-30182-0_77 10. Habash NF (2009) Syntactic annotation in Columbia Arabic Treebank. In: 2nd International Conference on Arabic Language Resources & Tools MEDAR. Cairo 11. Hammo B, Abu-Salem H, Lytinen SL, Evens M (2002) QARAB: A: question answering system to support the Arabic language. In: Proceedings of the ACL-02 workshop on Computational approaches to semitic languages. July 2002 12. Imad Zeroual AL (2017) Towards a standard Part of Speech tagset for the Arabic language. J King Saud Univ Comput Inf Sci 171–178 13. Albared M, T-M O-S-A (2005) probabilistic Arabic part of speech tagger with unknown words handling. J Theor Appl Inf Technol 14. Maamouri MA (2004) Developing an Arabic treebank: methods, guidelines, procedures, and tools. In: Proceedings of the 20th international conference on computational linguistics 15. Rabiner L, Juang B (1986) An introduction to hidden Markov models. IEEE ASSP Mag 3(1):4– 16 16. Salameh S (2018) A review of part of speech tagger for Arabic Language. International Journal of Computation and Applied Sciences IJOCAAS, 4, 4–5, June 2018. Darwish K, Mubarak H (n.d.). Farasa: A New Fast and Accurate Arabic Word Segmenter. 5 17. Jaafar Y, Bouzoubaa K (2015) Arabic natural language processing from software engineering to complex pipelines. In: Cicling 2015, Cairo, Egypt, April 2015 18. Zouaghi A, Merhbene L, Zrigui M (2012) Combination of information retrieval methods with LESK algorithm for Arabic word sense disambiguation. Artif Intell Rev 38:257–269. https:// doi.org/10.1007/s10462-011-9249-3

RSEPUA: A Recommender System for Early Predicting University Admission Inssaf El Guabassi, Zakaria Bousalem, Rim Marah, and Aimad Qazdar

Abstract Machine Learning allows us to reduce the human error probability by providing very strong recommendations, predictions, and decisions based on only the input data. For that reason, it has become one of the most important and common aspects of the digital world. Different application areas adapt and adopt Machine Learning techniques in their systems such as medicine, finance, marketing, business intelligence, healthcare, etc. In our case, we aim to design a recommender system based on Machine Learning techniques in the field of Education. Therefore, the purpose of this research work is to provide a recommender system for early predicting university admission based on four Machine Learning algorithms namely Linear Regression, Decision Tree, Support Vector Regression, and Random Forest Regression. The experimental results showed that the Random Forest Regression is the most suitable Machine Learning algorithm for predicting university admission. Also, the Cumulative Grade Point Average (CGPA) is the most important parameter that influences the chance of admission. Keywords Linear regression · Decision Tree · Support vector regression · Random forest regression · Predictive model · Educational data mining

1 Introduction Machine Learning is a subset of artificial intelligence (AI) that enable computers to automatically improve through experience, today it is becoming an increasing part of our daily lives because its applications are extending to different areas like agriculture, finance, electronic commerce, logistics, marketing, security, shopping, I. El Guabassi (B) · R. Marah Faculty of Sciences, Tetouan, Morocco Z. Bousalem Faculty of Sciences and Technology, Settat, Morocco A. Qazdar ISI Laboratory FS Semlalia UCA, Marrakech, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_20

209

210

I. El Guabassi et al.

etc. Moreover, Machine Learning enables an increasing number of applications that were not possible before. In the area of education, the adoption of Machine Learning is also accelerating. In recent decades, several researchers and scientists show their interest in the application of Machine Learning in an educational context [1–3]. Therefore, some examples of the topics that are studied by different researchers are: • • • • • • • • • • •

Dropout prediction in e-learning courses Early prediction of student outcomes Prediction of academic performance associated with internet usage behaviors Predicting at-risk university students in a virtual learning environment Mental stress detection in university students Dropout early warning systems for high school students Analyze and support mediation of student e-discussions Predicting MOOC dropout over weeks Identifying students’ inquiry planning Detection of learner learning style Etc.

In this sense, early predicting university admission is also considered an important topic for not only the new graduate students but also for the university. Unfortunately, newly graduate students usually are not aware of admission requirements to a postgraduate program at the university. Thus, they, therefore, spend their precious time and money focusing on things that won’t increase their chances of admission to graduate programs. The real and major issues of this topic are: Firstly, which the best Machine Learning algorithm for predicting university admission? Secondly, what most important parameters can affect the chance of admission? The main aim of this paper is to provide a Recommender System for Early Predicting University Admission based on Machine Learning algorithms. Hence, the purposes are threefold. The first is to apply several supervised Machine Learning algorithms (i.e., Linear Regression, Support Vector Regression, Decision Tree Regression, and Random Forest Regression) on our dataset. The second purpose is to compare and evaluate algorithms used to create a predictive model based on various evaluation metrics. The last purpose is to determine the most important parameters that influence the chance of admission. It should be noted that the present research work uses the dataset for graduate students at the University of California in Los Angeles. The outline of the present paper is as follows: Sect. 2 presents recent studies regarding the specified area. The settings and basic definitions are briefly described in Sect. 3. Section 4 concentrates on the proposed methodology. In Sect. 5 our proposed recommender system is presented. Section 6 contains results and discussion. Finally, Sect. 7 presents the main conclusions considering some future research directions.

RSEPUA …

211

2 Related Work Due to the rapidly growing interest in the field of education [4–6], there are several research studies have been conducted on predicting university admission based on several factors using supervised or unsupervised Machine Learning algorithms. Xiaojun Wu & Jing Wu [7] conducted a study on predicting students’ selection criteria in non-native language MBA admission based on Ridge regression, SVM, Random forest, GBDT. Al Ghamdi et al. [8] applied three classification methods which are linear regression, decision tree, and logistic regression model to automatically predict postgraduate admission. Nandal [9] developed Student Admission Predictor (SAP) based on Deep Learning techniques namely Decision Tree, Support Vector Machine, Gaussian Naive Bayes, Linear Regression, Random Forest, and Deep Neural Networks. Zhao et al. [10] explained their study based on a quantitative Machine Learning approach to predict master students’ admission in professional institutions. They used standard SVM, S3VM, and SVM+, as well as their dataset, which is collected from Northeastern University’s MS in Computer Science (MSCS) program.

3 Settings and Basic Definitions In this section we will discuss the three main elements to achieve our purpose, these elements are Recommender Systems, Algorithms used, and evaluation methods.

3.1 Recommender Systems Simply put, a recommender system is an application intended to offer user items that may be of interest to him according to his profile. Recommender systems are used in particular on online sales websites. They allow e-commerce merchants to automatically highlight products likely to interest visitors. Recommender algorithms can be divided into three categories which are: • Content-Based Filtering [11]: This type of recommender system is based on profiles. In fact, we build profiles for users as well as for products. • Collaborative Filtering [12]: This method tries to find a group of users who have the same tastes and preferences as the target user. • Knowledge-based systems [13]: This type of recommender system recommends items based on the knowledge user.

212

I. El Guabassi et al.

3.2 Algorithms There are many algorithms for predictive modeling Machine Learning. In the next sections, we will present the algorithms used to build predictive models. Linear Regression [14] is the most important algorithm in the field of Machine Learning, especially supervised learning. It is a way to model a relationship between a dependent variable and one or more independent variables. It consists of finding a regression line straight line through the points. Decision Tree [15] is the most widely used classification and prediction technique. It is a tree structure, where each internal node with outgoing edges indicates a condition on an attribute, each branch is an outcome of the test, and each leaf terminal node represents a class label. Support Vector Regression (SVR) [16] is also a very popular Machine Learning technique used in both classification and regression. It is similar to Linear Regression with only a few minor differences. SVR allows defining how much error is acceptable in our predictive model and will find an appropriate line to fit the data.

3.3 Evaluation Methods Evaluating a model is a core part of building an effective Machine Learning model. There are many methods of evaluation that can be used. In the following, we will discuss the three main metrics which we will use in our evaluation. R-Squared (R2 or the coefficient of determination) [18] is an indicator that allows judging the quality of simple linear regression. It measures the fit between the model and the observed data or how well the regression equation is to describe the distribution of points. Mean Square Error (MSE) [18] is the arithmetic mean of the squares of the predictions between the model and the observations. This is the value to be minimized in the context of a single or multiple regressions. Root Mean Square Error (RMSE) is a standard way to measure the error in model evaluation studies. It is the square root of the mean of the square of all of the errors. After briefly determining the settings and describing basic definitions, in the next section, we will present the methodology used to develop our recommender System RSEPUA.

RSEPUA …

213

Machine Learning Algorithms

Dataset

Data Preparation

Modeling

Evaluation

Deployment & Integration

Fig. 1 Methodology used to our recommender System RSEPUA

4 Proposed Methodology Admission to graduate schools is the most difficult and important step. The major problem students often fall into when applying to graduate schools is they are not aware of admission requirements. The purpose of this research work is to provide a recommender system for early predicting university admission based on Machine Learning algorithms. The methodology used in this work is composed of six steps. Figure 1 presents an overview of this methodology. Dataset: The data used in this research work is collected from a second version of the data set named “Graduate Admission 2” [19]. It is inspired by the University of California in Los Angeles Graduate Dataset. This data is available publicly even on Kaggle which offers free datasets for training and evaluation. It is comprised of 500 rows with 8 features. The dataset contains various features which are considered important during the enrollment and admission for master’s programs. Table 1 represents an overview of dataset features used for training and testing.

214

I. El Guabassi et al.

Table 1 Dataset features Feature

Description

Type

GRE score

Graduate Record Examinations ( i.e., from 260 to 340)

Quantitative

TOEFL score

Test of English as a Foreign Language ( i.e., from 0 to 120)

Quantitative

University rating

University Rating ( i.e., from 1 to 5)

Quantitative

SOP

Statement of Purpose ( i.e., from 1 to 5)

Quantitative

LOR

Letter of Recommendation ( i.e., from 1 to 5)

Quantitative

CGPA

Cumulative Grade Point Average ( i.e., from 0 to 10)

Quantitative

Research

Research Experience ( i.e., 0 or 1)

Quantitative

Chance of admit

Probability of getting admitted ( i.e., from 0 to 1)

Quantitative

Data Preparation: It is referred to as “data preprocessing”. It represents one of the most crucial steps in all Machine Learning projects because it involves data collection, formatting data, Improving data quality, feature engineering, and labeling. Modeling: This step involves the conception of different Machine Learning algorithms (e.g., regression, classification, clustering, etc.) that can be used for predicting university admission. Machine Learning Algorithms: It represents the algorithms used to build our predictive model, which are Linear Regression (LR), Decision Tree (DT), Support Vector Regression (SVR), and Random Forest Regression (RFR). Evaluation: This step is a core part of building our Machine Learning model. There are different metrics of evaluations that can be used. The evaluation metrics used in this research work are Mean Square Error, Root Mean Square Error, and R-squared. Deployment and Integration: It is all of the tasks that make our recommender system RSEPUA available for use.

5 Proposed Recommender System The recommender system proposed in this paper is articulated in two parts as shown in Fig. 2. The first part is the User-based datamining that contains sample data. The second part focuses on Machine Learning algorithms, that use different regression algorithms (i.e., Linear Regression, Decision Tree, Support Vector Regression, and Random Forest Regression) to Build a predictive model in order to predict students’ admission for higher education. It should be noted that the parameters used in this study are GRE Score, TOEFL Score, University Rating, SOP, LOR, CGPA, and Research Experience. After presenting the proposed recommender system RSEPUA, in the next section we will present the discussion of the obtained results.

RSEPUA …

215

User Model User-based datamining

Machine Learning Algorithm Modeling

Evaluation

Predictive Model Prediction of Graduate Admission

GRE Score TOEFL Score University Rating SOP LOR CGPA Research

Fig. 2 Proposed recommender system RSEPUA

6 Results and Discussion In this present work, we used Supervised Machine Learning algorithms to build a predictive model for early predicting university admission. Hence, the prediction model is based on four regression algorithms which are Linear Regression (LR), Decision Tree (DT), Support Vector Regression (SVR), and Random Forest Regression (RFR). All those algorithms implementation is available in the XLSTAT environment [20].

6.1 Correlation Between the Parameters Profile and the Chance of Admission We used the Pearson Correlation Coefficient (PCC) [21] to evaluate the linear correlation between the variables in our dataset. Indeed, this coefficient allows us to reduce the number of features and determine the relationship between different parameters profile (i.e., GRE Score, TOEFL Score, University Rating, SOP, LOR, CGPA, and Research Experience) and the chance of admission. Table 2 illustrates the results obtained for the Pearson Correlation Coefficients. Figure 3 illustrates a correlation

216

I. El Guabassi et al.

Table 2 Correlation matrix (Pearson) Variables

GRE score TOEFL University SOP score rating

LOR

CGPA Research Chance of admit

GRE score 1

0,827

0,635

0,613 0,525 0,826

0,563

0,810

TOEFL score

1

0,650

0,644 0,542 0,811

0,467

0,792

University 0,635 rating

0,650

1

0,728 0,609 0,705

0,427

0,690

SOP

0,613

0,644

0,728

1

0,408

0,684

LOR

0,525

0,542

0,609

0,664 1

CGPA

0,826

0,811

0,705

Research

0,563

0,467

Chance of admit

0,810

0,792

0,827

0,664 0,712

0,373

0,645

0,712 0,637 1

0,501

0,882

0,427

0,408 0,373 0,501

1

0,546

0,690

0,684 0,645 0,882

0,546

1

Table 3 Evaluation of the results

0,637

MSE

RMSE

R2

LR

0.003322947

0.057645010

0.82190074

DT

0.003992939

0.063189710

0.784975338

SVR

0.003636979

0.060307369

0.821388466

RFR

0.003004176

0.065606216

0.885856381

map of the table values. The results obtained demonstrate a high correlation between CGPA, GRE score, and TOEFL score and the chance of admission.

6.2 Comparison of Machine Learning Algorithms for Predicting University Admission Table 3 represents a comparison of four Machine Learning algorithms namely Linear Regression (LR), Decision Tree (DT), Support Vector Regression (SVR), and Random Forest Regression (RFR) for predicting university admission. The evaluation metrics used in this comparison are Mean Square Error (MSE), Root Mean Square Error (RMSE), and R-squared (R2 ). As Table 2 shown, it is clear that Random Forest Regression (RFR) provides better performance because it has a low MSE, low RMSE, and high R2 score.

RSEPUA …

217

40

Mean increase error

35 30 25 20 15 10 5 0

GRE Score

University RaƟng

LOR

Research

Variables

Fig. 3 Parameters influence the chance of admission

6.3 Most Important Parameters Influence the Chance of Admission After identifying the most suitable Machine Learning algorithm for predicting university admission, it is therefore evident to ask which parameters have the most predictive power. Indeed, the parameters with high importance are the engines of the prediction and their values have a strong and significant impact on the outcome values. For that reason, after training and validating a random forest, the variable importance (VIMP) is calculated based on Mean increase error. The normalized variable importance measure for each variable is represented in Fig. 3. As we can see, the most important parameter is the Cumulative Grade Point Average (CGPA). Hence, it is clear that there is a very strong link between the CGPA and the chance of admission.

7 Conclusion and Future Work In the 21st century, University education is becoming a key pillar of social and economic life. In fact, it plays a major role not only in the educational process but also in ensuring two important things which are the prosperous career and financial security. However, predicting university admission can be especially difficult because the students are not aware of admission requirements. For that reason, the

218

I. El Guabassi et al.

main objective of this research work is to build a predictive model for early predicting university admission. Thus, the contributions were threefold: The first was to apply several Supervised Machine Learning algorithms (i.e., Linear Regression, Support Vector Regression, Decision Tree Regression, and Random Forest Regression) on our dataset. The second purpose was to compare and evaluate algorithms used to create a predictive model based on various evaluation metrics. The last purpose was to determine the most important parameters that influence the chance of admission. The experimental results showed that the Random Forest Regression is the most suitable Machine Learning algorithm for predicting university admission. Also, the undergraduate GPA (CGPA) is the most important parameter that influences the chance of admission. The major directions for future work are: (i) Applying techniques such as clustering and artificial neural networks to have better predicting. (ii) Utilizing dataset with massive size and diverse features to tackle the issue of scalability. (iii) Exploiting few hybrid feature selection algorithms.

References 1. Chui KT, Fung DCL, Lytras MD, Lam TM (2020) Predicting at-risk university students in a virtual learning environment via a machine learning algorithm. Comput Hum Behav 107:105584 2. Qazdar A, Er-Raha B, Cherkaoui C, Mammass D (2019) A machine learning algorithm framework for predicting students performance: a case study of baccalaureate students in Morocco. Educ Inf Technol 24(6):3577–3589 3. El Guabassi I, Al Achhab M, Jellouli I, El Mohajir BE (2016) Recommender system for ubiquitous learning based on decision tree. In: 2016 4th IEEE International Colloquium on Information Science and Technology (CiSt), pp 535–540. IEEE 4. Guabassi IE, Achhab MA, Jellouli I, Mohajir BEE (2016) Towards adaptive ubiquitous learning systems. Int J Knowl Learn 11(1):3–23 5. El Guabassi I, Al Achhab M, Jellouli I, Mohajir BEE (2018) Personalized ubiquitous learning via an adaptive engine. Int J Emerg Technol Learn (iJET) 13(12):177–190 6. Bousalem Z, El Guabassi I, Cherti I (2018) Toward adaptive and reusable learning content using XML dynamic labeling schemes and relational databases. In: International Conference on Advanced Intelligent Systems for Sustainable Development, pp 787–799. Springer, Cham. 7. Wu X, Wu J (2019) Criteria evaluation and selection in non-native language MBA students admission based on machine learning methods. J Ambient Intell Hum Comput 1–13 8. AlGhamdi A, Barsheed A, AlMshjary H, AlGhamdi H (2020) a machine learning approach for graduate admission prediction. In Proceedings of the 2020 2nd international conference on image, video and signal processing, pp 155–158 9. Nandal P (2020) Deep learning in diverse computing and network applications student admission predictor using deep learning. SSRN 3562976 10. Zhao Y, Lackaye B, Dy JG, Brodley CE (2020) A quantitative machine learning approach to master students admission for professional institutions. International Educational Data Mining Society 11. Di Noia T, Mirizzi R, Ostuni VC, Romito D, Zanker M (2012) Linked open data to support content-based recommender systems. In: Proceedings of the 8th international conference on semantic systems, pp 1–8 12. Herlocker JL, Konstan JA, Terveen LG, Riedl JT (2004) Evaluating collaborative filtering recommender systems. ACM Trans Inf Syst (TOIS) 22(1):5–53

RSEPUA …

219

13. Burke R (2000) Knowledge-based recommender systems. Encyclopedia of library and information systems 69(Supplement 32):175–186 14. Montgomery DC, Peck EA, Vining GG (2012) Introduction to linear regression analysis, vol 821. Wiley, Hoboken 15. Safavian SR, Landgrebe D (1991) A survey of decision tree classifier methodology. IEEE Trans Syst Man Cybern 21(3):660–674 16. Smola AJ, Schölkopf B (2004) A tutorial on support vector regression. Stat Comput 14(3):199– 222 17. Liaw A, Wiener M (2002) Classification and regression by randomForest. R news 2(3):18–22 18. Miles J R squared, adjusted R squared. Wiley StatsRef: Statistics Reference Online (2014) 19. Willmott CJ, Matsuura K (2005) Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Res 30(1):79–82 20. Acharya MS, Armaan A, Antony AS (2019) A comparison of regression models for prediction of graduate admissions. In: 2019 international conference on computational intelligence in data science (ICCIDS), pp. 1–5. IEEE 21. Addinsoft X (2015) Data analysis and statistics with MS Excel. Addinsoft, NY, USA. xlstat available at https://www.xlstat.com/en/home 22. Benesty J, Chen J, Huang Y, Cohen I (2009) Pearson correlation coefficient. In: Noise reduction in speech processing, pp 1–4. Springer, Heidelberg

Forecasting Students’ Academic Performance Using Different Regression Algorithms Inssaf El Guabassi, Zakaria Bousalem, Rim Marah, and Aimad Qazdar

Abstract In recent years, predicting student’s academic performance is the main objective of all educational institutions. Numerous research works show that machine learning can be a highly efficient technology to meet this objective. In this research work, our first purpose is to compare several machine learning algorithms for predicting students’ academic performance. The machine learning algorithms used for comparison are ANCOVA, Logistic Regression, Support Vector Regression, Loglinear Regression, Decision Tree Regression, Random Forest Regression, and Partial Least Squares Regression. Our second purpose is to evaluate the seven algorithms used using the various evaluation metrics. The experimental results showed that the Log-linear Regression provides a better prediction, closely followed by ANCOVA. Keywords Predictive model · Academic performance · Log-linear regression · Machine learning · Regression · Educational data mining

1 Introduction In the real world, with a remarkable growth within the universe of measured data warehouse sizes, analyzing the data in order to extract useful information is becoming a necessity and a rich subject for several research projects [1]. Many application areas adopt machine learning methods in their systems namely telecommunication, shopping platforms, restaurants, economy, marketing, tourist targets, and medicine. Over the last two decades, machine learning has entered the e-learning space as well [2–5]. Thus, several machine learning algorithms have been exploited by researchers to predict hidden patterns from educational settings [6–8]. The early prediction of I. El Guabassi (B) · R. Marah Faculty of Sciences, Tetouan, Morocco Z. Bousalem Faculty of Sciences and Technology, Settat, Morocco A. Qazdar ISI Laboratory FS Semlalia UCA, Marrakech, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_21

221

222

I. El Guabassi et al.

students at risk for academic failure is a crucial step in the learning process. Thus, it must be made as soon as possible during the period of learning. The prediction of student performance is necessary for: • • • •

Providing a good quality education for students as well as for tutors; Reducing the number of dropouts; Increasing the rate of school completion; Improving educational outcomes for all students.

However, the real and major problems are: How to identify the “weak” students who will need additional help to improve their performance? Also, which the suitable machine learning algorithm (i.e. predictive model) for predicting students’ academic performance? This research work evaluates and compares the effectiveness of different machine learning algorithms. While there are many regression algorithms for creating predictive models, this work concentrates on seven of them, which are (1) ANCOVA, (2) Logistic Regression, (3) Support Vector Regression, (4) Loglinear Regression, (5) Decision Tree Regression, (6) Random Forest Regression, and (7) Partial Least Squares Regression. The outline of the present paper is as follows: Sect. 2 contains recent studies regarding the specified area. The dataset used is briefly described in Sect. 3. Section 4 concentrates on our methodology. Section 5 presents the results and discussion. Finally, Sect. 6 presents the main conclusions considering some future research directions.

2 Related Literature In recent decades, many studies on predicting the performance of students by using machine learning algorithms have been proposed. Bravo-Agapito et al. [13] explained their study based on the prediction of 802 undergraduate student’s academic performance in e-learning. They used three machine learning techniques, namely factor analysis, cluster analysis, and multiple linear regressions. They concluded the “age” is a factor that affects the academic achievement of the student. Gray and Perkins [14] conducted a study on predicting student outcomes in the fourth week of the fall semester using several machine learning methods. Hamsa et al. [15] applied two classification techniques which are decision tree and fuzzy genetic algorithm to predict the student’s performance both for the Bachelor and Master degree students. Their study focused on two disciplines which are Computer Science, and Electronics and Communication. Hussain et al. [16] described a performance study on predicting student difficulties during learning. They have also used machine learning algorithms such as artificial neural networks, support vector machines, logistic regression, Naïve Bayes classifiers, and decision trees. Their results show that artificial neural networks and support vector machines are the most suitable algorithms to predict the performance of a student. Karthikeyan et al. [17] investigated the performance of the students by developing a hybrid educational data mining model called HEDM. Their model combines two

Forecasting Students’ Academic Performance …

223

techniques which are the J48 Classifier and Naive Baye’s classification. Their results show that HEDM outperforms the results obtained in EDM. In summary, many existing studies have made significant results in educational data mining. However, most of them use classification methods for predicting students’ academic performance. Moreover, there was very little focus on interactional and parental involvement features.

3 Dataset The data used for this work’s experimentation is collected from a first version of the dataset named “Students’ Academic Performance Dataset (xAPI-Edu-Data)” [18, 19]. It is, therefore, an open-source dataset available publicly on the Kaggle dataset repository for academic and research purposes. The primary source of this dataset used is from Elaf Abu Amrieh, Thair Hamtini, and Ibrahim Aljarah. There are three main categories of the dataset features named “Demographic characteristics”, “Academic background characteristics”, and “Behavioral characteristics”. Table 1 contains an overview of dataset features used for training and testing. It contains three fields: feature, description, and type. It should be noted that there are two major feature types, named “Nominal” and “Numeric”.

4 Methodology Increasingly, E-learning has become an important tool for teaching and learning around the world. Further, Learners have the opportunity to switch to distance learning in various scientific areas anytime and anywhere [9, 20]. It is therefore evident that many researchers work on the various aspects of e-learning [10–12]. The identification of the “weak” students and the factors affecting students’ academic performance is a crucial step for successful learning. Hence, in the present paper, our aim is evaluating student’s academic performance and identifying the factors that influence academic performance using supervised machine learning algorithms. This research work focuses on the following steps: • Applying several machine learning algorithms which are ANCOVA, Logistic Regression, Support Vector Regression, Log-linear Regression, Decision Tree Regression, Random Forest Regression, and Partial Least Squares Regression. • Comparing and evaluating machine learning algorithms for identifying which are most suitable by using several evaluation metrics which are Mean Square Error (MSE), Root Mean Square Error (RMSE), and R-squared (R2 ). Many experiments were conducted in seven major steps depending on the regression methods namely ANCOVA, Logistic Regression (Logit-R), Support Vector Regression (SVR), Log-linear Regression (Log-LR), Decision Tree Regression

224

I. El Guabassi et al.

Table 1. An overview of dataset features Demographic features

Academic background features

Behavioral features

Feature

Description

Type

Gender

Gender of Student (i.e., Male or Female)

Nominal

Nationality

Nationality of Student (e.g., Morocco, Kuwait, Lebanon, Jordan, Egypt, USA, etc.)

Nominal

Place of birth

Country of birth for the student (e.g., Morocco, Kuwait, Lebanon, Jordan, Egypt, USA, etc.)

Nominal

Educational Stages

The educational level of the Nominal student (i.e., Lowerlevel, MiddleSchool or HighSchool)

Performance Levels

The performance level of the student

Section ID

Classroom of the student (i.e., Nominal A, B or C)

Topic

Course topic (i.e., English, Nominal Spanish, French, IT, Maths, Chemistry, Biology, Quran, or Geology)

Semester

Semester of the year (i.e., First or Second)

Nominal

Parent responsible

Parent responsible for student (i.e., mother or father)

Nominal

Raised hand

Number of times the student raised hand in the classroom (i.e., from 0 to 100)

Numeric

Visited resources

Number of times the student visited a course content (i.e., from 0 to 100)

Numeric

Viewing announcements

Number of times the student Numeric checked the new announcements (i.e., from 0 to 100)

Discussion groups

Number of times the student participated in discussion groups (i.e., from 0 to 100)

Numeric

Parent Answering Survey

Parent answered the surveys which are provided from school or not (i.e., Yes or No)

Nominal

Parent School Satisfaction

The degree of parent satisfaction for the school (i.e., Yes or No)

Nominal

Student Absence Days

The number of absence days for each student (i.e., above-7or under-7)

Nominal

Nominal

Forecasting Students’ Academic Performance … Table 2. Evaluation of the ANCOVA algorithm

225 Value

Mean Square Error (MSE)

0.157256464

Root Mean Square Error (RMSE)

0.396555752

R-squared (R2 )

0.71890384

Table 3. Evaluation of the Logit-R algorithm

Value Mean Square Error (MSE)

0.156250000

Root Mean Square Error (RMSE)

0.395284708

R-squared (R2 )

0.73799242

(DTR), Random Forest Regression (RFR), and Partial Least Squares Regression (PLS-R). These regression methods were applied using the XLSTAT environment [28]. In the following, the experimental result of each algorithm is presented.

4.1 Ancova ANCOVA (ANalysis of COVAriance) [21] is used to compare the means of a variableresponse between two or more groups taking into account the variability of other variables, called covariates. Thus, we decompose the total variance of the sample into two partial variances, the inter-class variance, and the residual variance, and we compare these two variances. Table 2 represents the evaluation metrics obtained after applying the ANCOVA Algorithm on our dataset. In basic terms, the R-Squared is the proportion of variability obtained by the mathematical model compared to the total variability observed.

4.2 Logistic Regression (Logit-R) Logistic Regression or Logit Regression (Logit-R) [22] is a statistical approach that can be used to assess and characterize the relationships between a binary response variable (e.g., wine/lose, alive/dead, pass/fail), and one, or more, explanatory variables, which can be categorical (e.g., nationality), or continuous numeric (e.g., note). We evaluate our dataset using the Logistic Regression Algorithm. Table 3 represents an overview of the results of the evaluation. If R2 tends towards 1, it means that the cloud of points narrows around the regression line.

226

I. El Guabassi et al.

Table 4. Evaluation of the SVR algorithm

Value Mean Square Error (MSE)

0.212447120

Root Mean Square Error (RMSE)

0.460919863

R-squared (R2 )

0.6271547

Table 5. Evaluation of the Log-LR algorithm

Value Mean Square Error (MSE)

0.158611894

Root Mean Square Error (RMSE)

0.398261088

R-squared (R2 )

0.71667276

4.3 Support Vector Regression (SVR) Support Vector Regression (SVR) [23] is a binary classification algorithm. Just like the Logistic Regression. If we take the image above, we have two classes (e.g., suppose these are e-mails, and Spam mails are in red and non-spam emails are in blue). The table above (Table 4) shows the evaluation metrics obtained after evaluating the Support Vector Regression Algorithm. Mean Square Error (MSE) is the arithmetic mean of the squares of the predictions between the model and the observations. This is the value to be minimized in the context of a single or multiple regressions.

4.4 Log-Linear Regression (Log-LR) The log-linear regression (Log-LR) [24] is a prediction model that applies when the target variable Y is a counting variable (number of occurrence of events during a lap of time). Table 5 contains the evaluation metrics obtained after applying the Log-linear regression Algorithm. Root Mean Square Error (RMSE) represents a standard way to measure the error in the predictive model. It is the square root of the mean of the square of all of the errors.

4.5 Decision Tree Regression (DTR) The decision trees (DTR) [25] are classification rules that base their decision on a suite of tests associated with attributes, the tests being organized in a tree structure. We evaluate our dataset using the Decision Tree Algorithm. Table 6 represents an overview of the results of the evaluation.

Forecasting Students’ Academic Performance … Table 6. Evaluation of the DTR algorithm

227 Value

Mean Square Error (MSE)

0.195293449

Root Mean Square Error (RMSE)

0.441920184

R-squared (R2 )

0.65025193

Table 7. Evaluation of the RFR algorithm

Value Mean Square Error (MSE)

0.171994444

Root Mean Square Error (RMSE)

0.414722128

R-squared (R2 )

0.69480482

Table 8. Evaluation of the PLS-R algorithm

Value Mean Square Error (MSE)

0.205659323

Root Mean Square Error (RMSE)

0.453496773

R-squared (R2 )

0.63238366

4.6 Random Forest Regression (RFR) Random Forest Regression (RFR) [26] is a supervised learning algorithm that combines multiple predictions to make a more accurate prediction than a single model (Table 7). The table above represents the evaluation metrics obtained after applying the Random Forest Regression Algorithm.

4.7 Partial Least Squares Regression(PLS-R) Partial Least Squares Regression (PLS-R) [27] is a flexible statistical technique applicable to any form of data. It allows to model the relationships between inputs and outputs, even when the inputs are correlated and noisy, the outputs multiple and the inputs more numerous than the observations. Our predictive model based on Decision Trees is evaluated using several evaluation metrics such as Mean Square Error, Root Mean Square Error, and R-squared (see Table 8).

228

I. El Guabassi et al.

Table 9. Experimental results MSE

RMSE

R2

ANCOVA

0.157256464

0.396555752

0.71890384

Logit-R

0.156250000

0.395284708

0.73799242

SVR

0.212447120

0.460919863

0.6271547

Log-LR

0.158611894

0.398261088

0.71667276

DTR

0.195293449

0.441920184

0.65025193

RFR

0.171994444

0.414722128

0.69480482

PLS-R

0.205659323

0.453496773

0.63238366

5 Results and Discussion The table below, therefore, represents summary results for the seven algorithms used in this research work. The evaluation metrics used in this experiment are Mean Square Error (MSE), Root Mean Square Error (RMSE), and R-squared (R2 ). It should be noted that RMSE is just the square root of the MSE (Table 9). After rigorously evaluating all the seven algorithms on the 480 students of our dataset, we compare the performances in order to determine the most suitable predictive model. According to the experimental results, it is clear that Log-linear Regression (Log-LR) provides better performance because it has a low MSE, low RMSE, and high R2 score. On the other hand, we observed that Support Vector Regression (SVR) isn’t suitable for predicting students’ academic performance because it has a high MSE, high RMSE, and low R2 score. Given the R2 = 73% of the variability of the dependent variable, Class is explained by the 16 explanatory variables. The remainder of the variability is due to other explanatory variables that have not been considered during the present experiment research. The chart below (see Fig. 1) indicates the predicted values versus the observed values. Also, Confidence intervals for the mean allow the detection of potential outliers. The histogram below (see Fig. 2) represents the standardized residuals versus the performance. It indicates that the residuals grow with the Performance. As we can see in Fig. 2 the residuals bar chart allows to quickly showing the residuals that are out of the range [2, 2].

Forecasting Students’ Academic Performance …

229

Pred(Performance) - Performance 5

4

Performance

3

2

1

0 -1

0

1

-1

2

3

4

5

Pred(Performance)

Fig. 1. Predicted values versus the observed values

Standardized resudials/Performance

Observations

Obs457 Obs433 Obs409 Obs385 Obs361 Obs337 Obs313 Obs289 Obs265 Obs241 Obs217 Obs193 Obs169 Obs145 Obs121 Obs97 Obs73 Obs49 Obs25 Obs1

-4

-2

0

Standardized resudials

Fig. 2. Standardized residuals versus the performance

2

4

230

I. El Guabassi et al.

6 Conclusion and Future Work In recent years, the world’s population is increasingly demanding to predict the future with certainty, predicting the right information in any area is becoming a necessity. One of the ways to predict the future with certainty is to determine the possible futures. In this sense, machine learning is a way to analyze huge datasets in order to make strong predictions or decisions. In this research work, our main purpose was to compare several machine learning algorithms for predicting student’s academic performance. Certainly, this research work has some limitations. That’s why the major directions for future work could focus on the following: Firstly, applying techniques such as clustering and artificial neural networks to have better predicting. Secondly, utilizing dataset with massive size and diverse features to tackle the issue of scalability. The final area that can be improved is exploiting few hybrid feature selection algorithms.

References 1. Kalaivani S, Priyadharshini B, Nalini BS (2017) Analyzing student’s academic performance based on data mining approach. Int J Innov Res Comput Sci Technol 5(1):194–197 2. Moubayed A, Injadat M, Shami A, Lutfiyya H (2020) Student engagement level in e-learning environment: clustering using k-means. Am J Distance Educ 1–20 3. Alenezi HS, Faisal MH (2020) Utilizing crowdsourcing and machine learning in education: literature review. Educ Inf Technol 1–16 4. El Guabassi I, Al Achhab M, Jellouli I, El Mohajir BE (2016, October) Recommender system for ubiquitous learning based on decision tree. In: 2016 4th IEEE international colloquium on information science and technology (CiSt), pp 535–540. IEEE 5. Hew KF, Hu X, Qiao C, Tang Y (2020) What predicts student satisfaction with MOOCs: a gradient boosting trees supervised machine learning and sentiment analysis approach. Comput Educ 145:103724 6. Qazdar A, Er-Raha B, Cherkaoui C, Mammass D (2019) A machine learning algorithm framework for predicting students performance: a case study of baccalaureate students in Morocco. Educ Infn Technol 24(6):3577–3589 7. Huang AY, Lu OH, Huang JC, Yin CJ, Yang SJ (2020) Predicting students’ academic performance by using educational big data and learning analytics: evaluation of classification methods and learning logs. Interact Learn Environ 28(2):206–230 8. Waheed H, Hassan SU, Aljohani NR, Hardman J, Alelyani S, Nawaz R (2020) Predicting academic performance of students from VLE big data using deep learning models. Comput Hum Behav 104:106189 9. El Guabassi I, Al Achhab M, Jellouli I, Mohajir BEE (2018) Personalized ubiquitous learning via an adaptive engine. Int J Emerg Technol Learn (iJET) 13(12):177–190 10. Syed AM, Ahmad S, Alaraifi A, Rafi W (2020) Identification of operational risks impeding the implementation of eLearning in higher education system. Educ Inf Technol 1–17 11. Bousalem Z, El Guabassi I, Cherti I (2018, July) Toward adaptive and reusable learning content using XML dynamic labeling schemes and relational databases. In: International conference on advanced intelligent systems for sustainable development, pp 787–799. Springer, Cham 12. El Guabassi I, Bousalem Z, Al Achhab M, EL Mohajir BE (2019) Identifying learning style through eye tracking technology in adaptive learning systems. Int J Electr Comput Eng (2088– 8708):9

Forecasting Students’ Academic Performance …

231

13. Bravo-Agapito J, Romero SJ, Pamplona S (2020) Early prediction of undergraduate student’s academic performance in completely online learning: a five-year study. Comput Human Behav 106595 14. Gray CC, Perkins D (2019) Utilizing early engagement and machine learning to predict student outcomes. Comput Educ 131:22–32 15. Hamsa H, Indiradevi S, Kizhakkethottam JJ (2016) Student academic performance prediction model using decision tree and fuzzy genetic algorithm. Proc Technol 25:326–332 16. Hussain M, Zhu W, Zhang W, Abidi SMR, Ali S (2019) Using machine learning to predict student difficulties from learning session data. Artif Intell Rev 52(1):381–407 17. Karthikeyan VG, Thangaraj P, Karthik S (2020) Towards developing hybrid educational data mining model (HEDM) for efficient and accurate student performance evaluation. Soft Comput 1–11 18. Amrieh EA, Hamtini T, Aljarah I (2016) Mining educational data to predict student’s academic performance using ensemble methods. Int J Database Theory Appl 9(8):119–136 19. Amrieh EA, Hamtini T, Aljarah I (2015, November) Preprocessing and analyzing educational data set using X-API for improving student’s performance. In: 2015 IEEE Jordan conference on applied electrical engineering and computing technologies (AEECT), pp. 1–5. IEEE 20. El Guabassi I, Bousalem Z, Al Achhab M, Jellouli I, Mohajir BEE (2018) Personalized adaptive content system for context-aware ubiquitous learning. Proc Comput Sci 127:444-453 21. Rutherford A (2001) Introducing ANOVA and ANCOVA: a GLM approach Sage 22. Kleinbaum DG, Dietz K, Gail M, Klein M, Klein M (2002) Logistic regression. Springer, New York 23. Smola AJ, Schölkopf B (2004) A tutorial on support vector regression. Stat Comput 14(3):199– 222 24. Heien DM (1968) A note on log-linear regression. J Am Stat Assoc 63(323):1034–1038 25. Safavian SR, Landgrebe D (1991) A survey of decision tree classifier methodology. IEEE Trans Syst Man Cybern 21(3):660–674 26. Liaw A, Wiener M (2002) Classification and regression by randomForest. R news 2(3):18–22 27. Geladi P, Kowalski BR (1986) Partial least-squares regression: a tutorial. Anal Chim Acta 185:1–17 28. Addinsoft X (2015) Data analysis and statistics with MS Excel. Addinsoft, NY, USA. xlstat available at https://www.xlstat.com/en/home

Computational Analysis of Human Navigation Trajectories in a Spatial Memory Locomotor Task Ihababdelbasset Annaki, Mohammed Rahmoune, Mohammed Bourhaleb, Jamal Berrich, Mohamed Zaoui, Alexander Castilla Ferro, and Alain Berthoz Abstract In this paper, we use computational tools (Cipresso P, Matic, A, Giakoumis D, Ostrovsky Y (2015) Advances in computational psychometrics. Comput Math Methods Med. Article ID 418683. https://doi.org/10.1155/2015/418683.5) to explore human navigation through an example of a visuomotor spatial memory locomotor task, the Walking Corsi task (WCT) variant from a well-known table test known as the Corsi Block Tapping task [(CBT) [2] and [15]. This variant was performed using the “Virtual Carpet” ™ experimental setup. The subjects had to memorize a succession of the position of targets projected on the ground and reproduce sequences of 2 to 9 targets by walking to each. The trajectory of the head was recorded and processed from a kinematic point of view. Generic tools that computational data analytics provides and through computer simulations by replicating visually this data allowed categorization of the different features of the behavior of the subjects providing a new powerful tool for both normal and pathological behavior characterization. Keywords Data analytics · Computer simulation · Virtual reality · Artificial intelligence · Human navigation · Corsi block tapping test

I. Annaki (B) · M. Rahmoune · M. Bourhaleb · J. Berrich Embedded Systems, Electronics, and IT Team, Research Laboratory in Applied Sciences, National School of Applied Sciences, PB 669, 60000 Oujda, Morocco e-mail: [email protected] M. Rahmoune e-mail: [email protected] M. Zaoui · A. C. Ferro · A. Berthoz Collège de France, CIRB, 11, Place Marcelin-Berthelot, 75231 Paris Cedex 05, France Institut de Médecine Expérimentale (IME), Paris, France © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_22

233

234

I. Annaki et al.

1 Introduction Among the most fundamental human tasks is to navigate from an initial position to a destination. This process is performed several times a day by each of us. In fact, navigation is characterized as organized and goal-oriented environmental movements [3]. This includes the physical act of action and the cognitive dimensions of decisionmaking and an itinerary follow-up. These two navigational elements are known as locomotion, spatial orientation, and navigation [4]. To assess individuals’ efficiency at the highest level of locomotive control, i.e., navigation [5]. Neuropsychologists use a locomotor version of a table memory task called the Corsi Block Tapping task (CBT) [15]. They use the Walking Corsi Task (WCT) and its variants [2] and [6], which is closely related to navigation. The performance data resulting from the WCT has been analyzed previously in studies concerning the pathological deficits in adults and children, but the present work brings new methods for a Kinematic analysis of the trajectories and mental manipulation of viewpoint changes using [7]. The previous analysis of trajectories that a human may take to reach a distant position and turn in a given direction revealed that the formation of trajectories during goal-oriented locomotion in humans is, in practice, stereometric and kinematically stereotypical [8, 9]. Furthermore, robotics engineers and researchers are more concerned with implementing pathfinding algorithms that mimic human behavior and can also contribute to optimizations based on various parameters, such as time and space [10]. In neurosciences however classical statistical approaches have been used to test differences in behavior and navigation strategies between individuals and groups. This has prompted our attempt to develop more effective methods to help clinicians in sorting out normal subjects and patients or for the study of the development of sensory-motor and cognitive abilities [11]. From this perspective, the present paper aims to explore the extracted data outcomes of the WCT test from a kinematic point of view with data analytics using the potential of python and computer simulations developed with Unity. It represents, a new effort toward integrating in-depth artificial intelligence, computer vision, and deep learning approaches, and stepping into computer simulations by taking advantage of the capacity of virtual reality. It is a specific contribution to the wider developing field using information contained in experimental data and combining information from different sources and different nature and coping with a large amount of data (Big-Data Problem) [12]. Also, the effort here to generate a synthetic data analysis based on real samples provided from clinical trials using data augmentation models [13]. It also is applied to the virtual environment for simulations or enhancements of the neuropsychological assessment [14].

Computational Analysis of Human Navigation Trajectories …

235

2 Materials and Method 2.1 Neuropsychological Assessments The Corsi visuospatial memory protocol was used in a series of studies using different technologies. These protocols were all inspired by the Corsi Table Block Tapping test (CBT). A locomotor version of this table test was developed using simple targets. Then two new technologies were developed the “Magic Carpet” and the “Virtual Carpet”. They allowed more complex measurements of the behavior and more flexibility in the presentation of the targets and kinematic measurements of the trajectories and behavior of the subjects. Corsi Block-Tapping Test (CBT) The CBT [15], is a table spatial memory visuo-manual test. it consists of nine blocks (4.5 × 4.5 cm) mounted to the baseboard (30 × 25 cm) in a dispersed series. Both short-and long-term visual-spatial memory has been tested. The experimenter taps with a finger a series of blocks at a rate of one block per 2 s, in which the subject must tap the block sequence in the same order as it was introduced. Block sequences progressively increase in length (starting with a 2-block series). The score is the number of blocks correctly recalled in the most extended sequence (“block span”). The Walking Corsi Test (WCT) The WCT [16, 17], a locomotor version of the CBT (3 × 2.5 m; scale 1:10 of the CBT) developed by C. Guariglia and Laura Piccardi in Rome. It was mounted in an empty room. In this test, the participant must walk to visit various locations. The WCT consists of nine squares placed on a carpet. Both the examiner and the participants began from the same origin. The experimenter demonstrated the series by walking on the carpet and stopping for 2 s on each rectangle. The subject had to repeat the same pattern as the experimenter by walking and stopping on the squares in the way. The Magic Carpet (E-WCT) An electronic version of the WCT was created in Paris by the Berthoz Lab [18]. The protocol was the same as in the WCT, but the technology differed. The equipment was called the “Magic Carpet”. It has the same dimensions as the WCT (2.50 × 3 m). But nine electronically controlled plastic white tiles (30 × 30 cm) were mounted on a carpet and constituted targets for the Corsi protocol. It was named the “Magic Carpet Test” (MCT). Every tile was 10 mm thick and has had a luminous white surface of 75 mm at the top and six pressure sensors, placed under the surface. The tiles were connected to an electronic computer-controlled system that turned the luminosity of the tiles on or off and sensed the fact that a person was walking on the tiles. The computer managed each sequence at a rate of 2 s per tile and record the participant’s

236

I. Annaki et al.

output—specifically, the number of correct tiles achieved in the right order. The series to be memorized is presented by illuminating the tiles in series consecutively at one tile per 2 s (each tile lights up for 2 s and then shuts off before the next tile is lit). At the end of the experiment, the auditory alarm signals to the participant that the pattern detected while walking must be repeated. This Magic Carpet Corsi protocol was used for several studies on normal and pathological adults and children and the processing of correct target acquisitions, delays, and several kinds of errors were computed. The Virtual Carpet™ A new version of the carpet was designed by Mohamed Zaoui and used by the Berthoz lab. [19] for the application of the Walking Corsi protocol. Projection of Virtual Tiles Instead of real tiles on the ground virtual white tiles (10 cm square) were projected on the ground by a video projector. This was done using a PowerPoint image program allowing great flexibility in the organization of the series of tiles illuminated. This created a virtual environment using the Unity Game Engine and replicating the WCT with 2 to 9 target sequences. Programs of control of the image sequential appearance were built with opensource software. Blender. The protocol repeated the WCT protocol. They were designed in the Blender Game Engine to control motions and capture experimental data. created a virtual environment using the Unity Game Engine. Motion Capture of Head Trajectories The trajectories of the head during locomotion to the target were recorded using a virtual reality device: The HTC VIVE VR-Headset fitted with the SteamVR tracking system. These included a sensor placed on the head of the subject to measure head position, direction, and velocity in the horizontal plane. The experimental station consists of two synchronized computers for the generation of images and data acquisition. The protocol included a calibration of the target’s positions and the subject’s motions with precision around 1 cm.

2.2 Experimental Data The batch contains a calibration file that sets the coordinates of the targets relative to the current subject under focus, and each corresponding folder contains the derived data sets relative to the trajectory and locomotion.

Computational Analysis of Human Navigation Trajectories …

237

2.3 Participants The data collected were provided without further detail on the subjects, from gender and age to actual status so that the analysis would not have any a priori bias. However, this sample was subject to the usual protocols, and participants were made aware that their data would be analyzed, and they could have access to the results. Besides, the details have been obscured to make the data more accessible and to examine it extensively without any background restrictions.

2.4 Visual Replication Using Matplotlib, a robust library for developing static, interactive, and immersive Python visualizations and based on our theoretical claims. We visually repeated the sequence on which our findings were based. The reproduction of the trajectory and the targets with animation allowed us to understand the kinematic behavior of the subjects during each session (Fig. 1). This plot represents the experimental array seen from above each target is shown as a square. The departure point is on the right. The subject had to go to two targets and return. Head trajectory is shown, and the blue circles indicate that the subject stayed successfully on both targets.

Fig. 1 Example of reproduction of subject trajectory

238

I. Annaki et al.

2.5 Target Detection Algorithm Several computations have been carried out on the data to establish a firm interpretation and find criteria to help us draw statements about the various subjects. We developed an algorithm to detect the various targets visited. This was done by computing the head positions during the trajectory and from this set of numbers calculate a walking velocity along the trajectory (tangential velocity). To obtain the information that the subject had reached a target we compare the position data with a target calibration field in which the X and Y components of the nine targets were given. This allowed us to also compute the duration in seconds during which the subject had stopped on the target. This file implements nine target positions, as each dataset in the user folder reflects a series of scenarios. Modern techniques sallow computational approaches of these data [19] and [20]. Using “Pandas” a data analysis and manipulation tool library, in particular the concept of DataFrame, each file is stored as a data frame to avoid any conflict between the file types. Each target is also flagged to be able to settle on the series assigned to each subject. The crucial step in the algorithm is to compare the distance between the positions of the subject on the trajectory and the location of the target extracted from the calibration file, to be able to determine whether the target was visited by the subject during the session, and to track the kinematic behavior until the subject is close to the target, or until the entire session is over. This potentially can also indicate if the subject missed a target. Using all these data we could classify the different subjects according to the values of velocity, and time on various targets. We also distinguished the behavior when the trajectory included only 2 targets or more up to Nine which is a much more difficult task for instance in the Table Block test a span of 7 is generally the maximum that most normal subjects can memorize.

3 Results In this section, we provide a summary of the production classifications, concentrating, as an example, on one batch the others have been processed and evaluated similarly.

3.1 Classification of Subjects Based on the Length of the Sequence We took as a criterion that a subject had visited a target if his position was within a confidence circle of 30 × 30 cm, around the value given by the calibration file. A velocity slowdown was also taken as a criterion of target reaching. A first numerical calculation has allowed us to identify a classification of experiments according to

Computational Analysis of Human Navigation Trajectories …

239

the number of targets visited by the subjects. The result is a classification of the data according to the sequence’s length (number of visited targets). It indicates four classes. These classes referred to reflect the distribution of subjects according to the series correctly replicated by the subject. Specifically, we find classes in which the participant repeats situations of series lengths of 2, 3, 4, and 5. As a side effect, more classes were recognized, however, the number of subjects in these classes does not allow us to extract significant information.

3.2 Overall General Classification Based on Average Speed and Time Spent Within the Target We aggregate the subject’s data to retrieve their average speed along the entire trajectory and the time, they remain on a target before leaving the confidence area as mentioned in the target detection algorithm. The global analysis allows us to evaluate the performance of a subject throughout the whole process and determine aggregates related to speed. These aggregates are a comparison threshold allowing us to classify the subjects into 3 categories: – The fastest subjects. – The moderately fast subjects. – The slowest subjects. The aggregate of the global average time that the subject spends within a target allows us to compare the coherence between speed and time and allows us to identify anomalies and classify the subjects into 2 categories: – Subjects spending important time within a target. – Subjects moderately spending below or closer time to the global average of time spent within a target (Fig. 2).

3.3 A Detailed Classification Based on Average Speed and Time Spent Within the Target for Each Class Sequence Length The same approach is used for general analysis. For each class, we aggregate the data of the subject to extract its average speed and the time spent in the target. Every class helps us to calculate the class’s speed and time spent within the target aggregates to extract the same categories as in the general classification. The detailed analysis allows us to identify subject participation and attendance at the level of each class and to refine the overall analysis carried out (Fig. 3).

240

I. Annaki et al.

Fig. 2 General Combination Chart representation of average speed as a red line chart and time spent on the target as a blue-sky bar chart. The green reference line represents the overall average speed (All subjects), the red one is the average speed of slow subjects (the qualification of slow subjects is based on the subject’s speed below the overall average speed) and the blue line identifies the average speed of fast subjects (as cited for the slow subjects, the fast ones are those who got a speed over the overall average speed)

Fig. 3 Example: Class 3 combination chart representation of average speed as a red line chart and time spent on the target as a blue-sky bar chart. The green reference line represents the class average speed (All subjects attending the scenarios with 3 targets visited), the red is the average speed of slow subjects (the qualification of slow subjects is based on the subject’s speed below the class average speed) and the blue identifies the average speed of fast subjects (as cited for the slow subjects, the fast ones are those who got a speed over the class average speed)

Computational Analysis of Human Navigation Trajectories …

241

4 Discussion The purpose of this research was to identify the different behaviors of subjects who performed the Walking Corsi Protocol on the Virtual Carpet set up using a kinematic approach instead of basing the research on pre-established scoring strategies in the literature. Commonly, scoring is based on the maximum length of the series that the subject can achieve correctly. Here we characterized the behavior and performance used the subject using average speed to complete the sequences and the time spent on the targets before heading to another goal. Moreover, we were able to identify some incoherence between the subject’s pace and the spent time. Further studies are needed to generalize this approach and analyze its efficiency and accuracy. This is a very global approach that will complement other data analysis of the behavior, but it can be very useful to provide criteria that give us insights about the subjects who can repeat the sequence easily, and other subjects who find it challenging, particularly when the sequence is getting longer or requires mental rotations, etc. None of the existing publications on these tests has brought this tool and it is worth trying to use it in young children or with patients or elderly persons and eventually it may be very useful to measure quantitatively learning or functional recuperation during rehabilitation.

5 Conclusion The current paper provides the first attempt at characterizing the behavior of human subjects in a complex visuospatial task used to test memory and navigation capabilities. The aim is to integrate emerging technology into the psychometric methods that will give neuropsychologists greater insight into new technology, such as big data, machine learning, and computer vision [20]. Knowing that psychometric clinical trials require a significant amount of time and planning, it can take months to years to collect data. Then data science can offer new approaches to generate synthetic data based on the samples extracted from the psychometric tests. Neuropsychological assessments are valuable instruments for telling cognitive impairment diagnosis. However, decoding neuropsychological assessments requires experts and is often time-consuming. To streamline the use of psychometric evaluations in clinical environments, machine learning algorithms are being built and tested using multi-center neuropsychological data [21]. Acknowledgments We thank Dr. Bernard Cohen, Paris France, to let us use the preliminary test data obtained in cooperation with him to test the present method to evaluate its validity for the use of patient assessments.

242

I. Annaki et al.

References: 1. Cipresso P, Matic, A, Giakoumis D, Ostrovsky Y (2015) Advances in computational psychometrics. Comput Math Methods Med. Article ID 418683. https://doi.org/10.1155/2015/418 683.5 2. Tedesco M, Bianchini F, Piccardi L, Clausi S, Berthoz A, Molinari M, Guariglia C, Maria L (2017) Does the cerebellum contribute to human navigation by processing sequential information? Neuropsychology 31. https://doi.org/10.1037/neu0000354 3. Montello D, Sas C (2006) Human factors of wayfinding in navigation. International encyclopedia of ergonomics and human factors. https://doi.org/10.1201/9780849375477.ch394 4. Irmischer I, Clarke K (2018) Measuring and modeling the speed of human navigation. Cartogrph Geogr Inf Sci 45:177–186. https://doi.org/10.1080/15230406.2017.1292150 5. Belmonti V, Cioni G, Berthoz A (2016) Anticipatory control and spatial cognition in locomotion and navigation through typical development and in cerebral palsy. Dev Med Child Neurol 58(Suppl 4):22–27. https://doi.org/10.1111/dmcn.13044 6. Corsi PM (1998) Human memory and the medial temporal region of the brain (Ph.D.) McGill University (1972). Berch DB, Krikorian R, Huha EM (1998) The Corsi block-tapping task: methodological and theoretical considerations. Brain Cogn 38(3):317–338. https://doi.org/10. 1006/brcg.1998.1039 7. Meilinger T, Berthoz A, Wiener JM (2011) The integration of spatial information across different viewpoints. Memory Cogn 39:1042–1054. https://doi.org/10.3758/s134210110088-x 8. Hicheur H, Pham QC, Arechavaleta G, Laumond JP, Berthoz A (2007) The formation of trajectories during goal-oriented locomotion in humans. I. Stereotyped behavior. Eur J Neurosci 26(8):2376–2390. https://doi.org/10.1111/j.14609568.2007.05836.x 9. Pham QC, Hicheur H, Arechavaleta G, Laumond JP, Berthoz A (2007) The formation of trajectories during goal-oriented locomotion in humans. II. A maximum smoothness model. Eur J Neurosci 26(8):2391–2403. https://doi.org/10.1111/j.14609568.2007.05835.x 10. Pham Q (2014) A general, fast, and robust implementation of the time-optimal path parameterization algorithm. In: IEEE Trans Robot 30(6):1533–1540. https://doi.org/10.1109/TRO. 2014.2351113 11. Kang MJ, Kim SY, Na DL et al. (2019) Prediction of cognitive impairment via deep learning trained with multi-center neuropsychological test data. BMC Med Inform Decis Mak 19:231. https://doi.org/10.1186/s12911-019-0974-x 12. Gorriz J, Ramírez J, Ortiz A, Martínez-Murcia, F, Segovia F, Suckling J, Leming M, Zhang Y-D, Álvarez-Sánchez J, Bologna G, Bonomini M, Casado F, Charte D, Charte F, Contreras R, Cuesta-Infante A, Duro R, Fernández-Caballero A, Fernandez E, Ferrández J (2020) Artificial intelligence within the interplay between natural and artificial Computation: advances in data science, trends and applications. Neurocomputing. https://doi.org/10.1016/j.neucom. 2020.05.078 13. What Is Synthetic Data? | Unite.AI. https://www.unite.ai/what-is-synthetic-data/ 14. Cipresso P, Serino S, Riva G (2016) Psychometric assessment and behavioral experiments using a free virtual reality platform and computational science. BMC medical informatics and decision making 16:37. https://doi.org/10.1186/s12911-016-0276-5 15. Corsi PM (1972) Human memory and the medial temporal region of the brain (Ph.D.). McGill University 16. Piccardi L, Iaria G, Ricci M, Bianchini F, Zompanti L, Guariglia C (2008) Walking in the Corsi test: which type of memory do you need? Neurosci Lett 432:127–131. https://doi.org/10.1016/ j.neulet.12.044 17. Piccardi L, Leonzi M, D’Amico S, Marano A, Guariglia C (2014) Development of navigational working memory: evidence from 6- to 10-year-old children. Br J Dev Psychol 32:205–217. https://doi.org/10.1111/bjdp.12036 18. Perrochon A, Kemoun G, Dugué B, Berthoz A(2014) Cognitive impairment assessment through visuospatial memory can be performed with a modified walking Corsi test using the ‘Magic Carpet’. Dementia Geriatric Cogn Disord Extra 4:1–13. https://doi.org/10.1159/000356727

Computational Analysis of Human Navigation Trajectories …

243

19. Berthoz A, Zaou, M (2015) New paradigms and tests for evaluating and remediating visuospatial deficits in children. Dev Med Child Neurol 57(Suppl 2):15–20. https://doi.org/10.1111/ dmcn.12690 20. Elgendy N, Elragal A (2016) Big data analytics in support of the decision-making process. Procedia Comput Sci 100:1071–1084. https://doi.org/10.1016/j.procs.2016.09.251 21. Kang MJ, Kim SY, Na DL et al. (2019) Prediction of cognitive impairment via deep learning trained with multi-center neuropsychological test data. BMC Med Inform Decis Mak 19:231. https://doi.org/10.1186/s12911-019-0974-x.

An Electrocardiogram Data Compression-Decompression Technique Based on the Integration Filtering of Peaks’ Amplitude Signal Skander Bensegueni

Abstract In this paper, a novel electrocardiogram data compression/decompression technique is presented, based on transforming the electrocardiogram record to peaks’ amplitude signal. The proposed method reduces the number of non-zero amplitude of the signal and thus reduce the data dimension. At the decompression step and in order to recover the signal, an integration filtering is applied in aim to link the non-zero amplitudes together and reconstruct the signal. This technique was applied on the 48 records of the MIT-BIH arrhythmia database and its performance was evaluated through calculation of metrics such as the compression ratio and the mean-squareerror difference percentage. The average values of these last two metrics were equal respectively to 17.83 and 0.62, indicating that the proposed method reduces the dimension of the signal well by preserving its waveforms and segments. Keywords Electrocardiogram data · Compression-decompression · Integration filter · Peak’ amplitude signal

1 Introduction The electrocardiogram (ECG) data are the result of the electric potential recording of the myocardium. It’s used to monitor the heart’s health. The need for long duration recording or permanent monitoring of the electrocardiogram signal is gaining more and more attention for its ability to record pathological events appearing suddenly. The compression of ECG signal is very important in health monitoring, medical diagnosis aid and decision systems for patients with cardiovascular diseases. By reducing the size of the cardiac activity record, it will be easy to store and transmit data, which allow the analysis and processing of this data distantly and by automatic tools. The construction of an efficient ECG telemonitoring system can be achieved by reducing the amount of transmitted data and minimizing the computational

S. Bensegueni (B) National Polytechnic School, BP 75, A, Nouvelle Ville RP, Constantine, Algeria © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_23

245

246

S. Bensegueni

complexity [1]. In the paper of Polanía et al. [2], a reducing dimensionality technique was proposed by exploiting the representational power of restricted Boltzmann machines (RBMs) to model the probability distribution of the sparsity pattern of transmitted ECG signals. Several ECG data compression techniques have also been proposed in the literature. Techniques using wavelets give good performances; such as the dimensionality reduction approach applied in [3] by selecting elementary components from redundant wavelet dictionaries via a greedy strategy. The statistical methods also were used to compress ECG data as in [4] by varying discrete cosine transform thresholds applied on different principal directions of the ECG data. A sparse representation-based compression algorithm was proposed by Grossi et al. [5] through exploiting the repetition of common patterns of ECG segments as natural basis. The proposed compression algorithm is based on the application of transformation of the ECG signal into peaks’ amplitudes signal which will contain a reduced number of non-zeros amplitudes to be transmitted. This paper is divided into several sections; starting from the development of the new theoretical basics as: the slop change coefficients and the integrating filter, going through the application of these concepts to compress and decompress the electrocardiogram signal, and finishing by results and conclusions.

2 Theory 2.1 Slop Change Coefficients We calculate the slop change (SC) coefficients by using (1) in order to decompose the signal x(t) into coefficients characterized by their sensitivity to steepness changes where lower slopes have coefficients’ absolute values lower than those of sharper slopes. The SC coefficients are constructed by calculating the integral of the combination of two scale signals with opposite signs [6]. k

k+1/2

k−1/2

k

SC(k) = − ∫ x(t − k)dt + ∫ x(t − k)dt

(1)

However, the sign of the coefficients is inversely dependent on slopes’ direction. A minimum of the signal is represented by a positive-negative couple of SC coefficients, and a maximum is represented by a negative-positive couple of SC coefficients (see Fig. 1.b). The SC coefficients should be sub-sampled by 2. So, in order to recover the size of samples and to detect the optimal values of the signal (represented as showing before by SC coefficients couples of min.-max. or max.-min), we apply a high-pass interpolation filter g on the SC coefficients as following:

An Electrocardiogram Data Compression-Decompression Technique …

y(k) =

+∞ k=−∞

SC(l)g(l − 2k)

247

(2)

where the high-pass filter g(k) is given by: g(k) =

√

2δ(k − 1) −

√ 2δ(k − 2)

(3)

so, the resulting signal will be calculating by: l

y(k) = − ∫

+∞

l−1/2

l+1/2 +∞

+ ∫ l

k=−∞

l=−∞

g(l − 2k)x(t − l)dt (4)

g(l − 2k)x(t − l)dt

The resulting discrete signal y(k) will detect, by exploiting the proprieties of high-pass filter, the min.-max. (resp. max-min.) couples of SC coefficients and will interpolate the samples by 2. The maxima (resp. minima) of the signal x(k) are represented by two successive positive (resp. negative) y(k) samples (Fig. 1.c). In a final step, we calculate the peaks’ amplitude (PA) of the signal, by the following procedure: 1) 2)

If y(k) and y(k + 1) have the same sign, then: P A(k + 1) = 1 If y(k) and y(k + 1) have opposite signs, then: P A(k + 1) = 0. (a)

(b)

(c)

(d)

Fig. 1 Changing in slopes detection: (a) double triangular signal, (b) SC coefficients, (c) high-pass interpolation filter and (d) PA signal

248

S. Bensegueni

The PA signal will detect all the samples positions of the changes of slopes’ directions (i.e. position of optima values of the signal).

2.2 Integrator Filter The purpose of the integrator filter is to merge the amplitudes of separate non-zero samples by connecting them with regressive or progressive lines. In practice, this integrating filter is calculated using a moving window defined by the following equation: y(k) =

1  N −1 x(k − l) l=0 N

(5)

where N is the width of the integration window. The width N of this low-pass filter is calculated so as to connect the desired points of the signal without missing other desired samples by using a so large filter width.

3 Compression Stage The compression algorithm is based on the application of transformation of the ECG signal into peaks’ amplitudes signal which will contain a reduced number of nonzeros amplitudes to be transmitted. This peaks’ amplitudes signal represents the change of slopes while non-zero peaks are representative of the existent details of the ECG signal. However, before applying this transformation, we should minimize the high-frequencies of the signal because they are characterized by fast variation of slopes and may affect the high performance of our procedure. So, to reduce them, we use wavelets based high frequency filter as in [6]. The following sub-sections represent the steps of the compression algorithm:

3.1 Slop Change Coefficients of ECG Signal The slop change coefficients of the ECG signal are generated by applying (1), in order to detect all the changes of the directions of this signal. The resulting coefficients will represent peaks by two successive coefficients with the same sign, and the other samples of the signal will be represented by a sequence of coefficients having alternating opposite signs.

An Electrocardiogram Data Compression-Decompression Technique …

249

Fig. 2 Detecting of changing in direction of the ECG signal. All signal peaks are represented by two successive coefficients with the same sign

3.2 Signal Time Scale Recovery After calculating the SC coefficients, the time scale of this resulting signal, will be reduced to half the time size of the original signal. Then, to recover the initial signal size, we apply a high-pass interpolation filter g on SC coefficients by using (4). The resulting signal will detect the min.-max. (resp. max.-min.) couples of SC coefficients and will interpolate the samples by 2 (Fig. 1.d). The local optima values of the ECG signal will be represented by two successive positive (resp. negative) samples.

3.3 Generation of Null Samples The final step of the proposed compression method is to generate a peaks’ amplitudes (PA) signal which produces a signal with amplitudes equal to those of the ECG signal for the filtered SC min.-max. and max.-min. couples (i.e. at the peak positions of the ECG signal) and zero amplitudes for the other samples. Figure 3.b shows the PA of an electrocardiogram signal. We note that the samples of the ECG signal with changes of slopes’ directions are detected and replaced at peaks positions by pulses having the same amplitudes values. The remaining samples are replaced by zeros. The non-zeros PA coefficients coded on 32 bits (simple precision) and the null coefficients coded on one 1 bit, will be stocked or transmitted to the decompression bloc as shown in Fig. 4.

4 Decompression Stage In this stage, the received coefficients will be used to reconstruct the ECG signal by using an integrator filter in order to merge the amplitudes of separate non-zero samples and connecting them with regressive or progressive lines by using (5) and

250

S. Bensegueni

replacing the x(k) samples by the received PA coefficients. But before that, we should convert the one-bit zero samples to 32 bits (simple precision) samples. In general, the choice of the width of the integrator filter window should be approximately the same as the smallest ECG characteristic wave. Actually, the width of the window is determined empirically, and for a sample rate of 360 Hz, the window is 7 samples width (approximately equal to 19 ms).

4.1 Electrocardiogram Database In this paper, I use the MIT-BIH arrhythmia database, which is composed of 48 records of 30 min each. Few records (from 100 to 124) have been selected randomly from selected patients and the other (from 200 to 234) contain plots with major arrhythmic events. Several records were obtained from the modified derivation DII by placing the electrodes on the chest. The other ones were obtained from precordial leads V 1, V 2 and V 5 [7].

4.2 Evaluation Criteria To evaluate the performance of ECG data compression/decompression algorithm, I have chosen the following criteria [3, 8]: Percentage Root-Mean-Square Difference. The (PRD) evaluates the quality of the reconstructed signal compared to the original and is calculated as follows:  P RD =

n i=1 (ecg(i)



− ecgr (i))2

n 2 i=1 (ecg(i))

(6)

where ecg is the original signal and ecgr is the reconstructed signal from the decompression part; Compression Ratio. The (CR) is one of the most used measures for evaluating compression/decompression techniques performances. It is defined by: CR =

N umber o f bits in the original dataset N umber o f bits in the r econstr ucted dataset

(7)

Quality Score. The (QS) is used to evaluate the trade-off between the compression ratio and the PRD by using the following ratio: QS =

CR P RD

(8)

An Electrocardiogram Data Compression-Decompression Technique …

251

(a)

(b)

(c)

Fig. 3 Changing in slopes detection: a the ECG signal, b the PA signal and c in red, the reconstruction of the ECG signal by using the high-pass interpolation filter

Computational Speed. The (CS) is defined as the speed at which the computer execute the code and process data. It’s calculated by: CS =

N umber o f coded samples Computation time

The CS is usually defined in millions of instructions per second (MIPS).

(9)

252

S. Bensegueni

Original ECG signal

Slop Change Coefficients of ECG Signal

Signal Time Scale Recovery

Compression steps

Generation of Null Samples

Decompression steps 7 samples wide integrator filter

Reconstructed ECG signal

Fig. 4 Compression/Decompression bloc diagram

The previously defined criteria were applied on data from the MIT-BIH database, using the Matlab environment installed on a SSD drive of an i5 3rd generation laptop with 2.60 GHz 2 cores and 4 threads processor, and equipped with a 8 GB of DDR3.

4.3 Results of Compression/Decompression Technique The proposed method was applied on all the 30 min ECG records of the MIT-BIH database, starting by calculating the slop change coefficients of these ECG signals and by recovering their time scales. In a final step, the generation of null samples is achieved in order to reduce the dimension of the records. Table 1 shows the evaluation criteria values of the compression/decompression method of the 48 MIT-BIH records. In Fig. 5, we can see the subplots of the reconstructed and the original signal of segments of records: 100, 221 and 223. These figures show that the reconstructed and the original signals are almost identical even in segments with sudden changes in slope.From the previous results, we can verify that the mean value of the PRD is equal to 0.62. This value shows that the reconstructed signals are acceptable and the existent distortions are negligible and unimportant for many biomedical uses, especially if we compare this value with the CR mean value equal to 17.83 which represents a good ratio of signal compression.

An Electrocardiogram Data Compression-Decompression Technique …

253

Table 1 Evaluation criteria values of the compression/decompression method. Records

PRD

CR

QS

CS (MIPS)

100

0.49

15.88

32.40

2.00

101

0.68

18.02

26.50

1.37

102

0.46

17.88

38.87

1.46

103

0.67

19.62

29.28

1.80

104

0.69

18.60

26.96

1.74

105

0.68

18.46

27.15

1.80

106

0.74

19.42

26.24

1.77

107

0.98

27.16

27.71

1.74

108

0.44

14.60

33.18

1.73

109

0.77

19.62

25.48

1.72

111

0.51

19.16

37.59

1.87

112

0.28

16.88

60.29

1.80

113

0.71

19.28

27.15

1.59

114

0.40

15.44

38.60

1.65

115

0.53

18.44

34.79

1.84

116

0.53

17.54

33.09

1.76

117

0.36

15.40

42.78

1.79

118

0.67

15.04

22.45

1.78

119

0.60

17.34

28.90

1.73

121

0.36

13.64

37.89

1.43

122

0.47

16.84

35.83

2.00

123

0.42

15.82

37.67

1.37

124

0.52

16.56

31.85

1.46

200

0.84

15.70

18.69

1.82

201

0.61

18.16

29.77

1.70

202

0.70

18.28

26.11

1.74

203

0.75

16.50

22.00

1.62

205

0.71

16.08

22.65

1.53

207

0.59

20.06

34.00

1.35

208

0.69

18.12

26.26

1.61

209

0.56

15.44

27.57

1.14

210

0.64

17.78

27.78

1.27

212

0.61

15.46

25.34

1.19

213

0.74

19.62

26.51

1.18

214

0.83

19.64

23.66

1.04

215

0.69

16.04

23.25

1.82 (continued)

254

S. Bensegueni

Table 1 (continued) Records

PRD

CR

QS

CS (MIPS)

217

0.88

23.66

26.89

2.04

219

0.56

19.10

34.11

1.94

220

0.60

20.98

34.97

2.01

221

0.65

17.98

27.66

2.09

222

0.42

15.82

37.66

2.03

223

0.78

21.92

28.10

2.10

228

0.59

16.00

27.12

1.28

230

0.73

19.40

26.57

1.26

231

0.68

18.48

27.18

1.39

232

0.47

14.22

30.26

1.36

233

0.76

19.24

25.32

1.39

234

0.55

15.34

27.89

1.30

(a)

(b)

(c)

Fig. 5 Original and reconstructed signals of MIT-BIH records segments: a 100, b 221 and c 223

An Electrocardiogram Data Compression-Decompression Technique …

255

5 Conclusion In this paper, an electrocardiogram data compression-decompression technique was proposed, by applying a transformation of the ECG to a peaks’ amplitude signal, and hence, reducing the number of non-zero amplitude of the signal. At the decompression step an integrator filter was applied in order to link the non-zero amplitudes and reconstruct the signal. This compression method was very efficient in term of reducing the dimension of the signal by a CR average value equal to 17.83 and without too much distorting the signal with a PRD average value equal to 0.62.

References 1. Lalos AS, Alonso L, Verikoukis C (2014) Model based compressed sensing reconstruction algorithms for ECG telemonitoring in WBANs. Digital Signal Process 35:105–116 2. Polanía LF, Plaza RI (2018) Compressed sensing ECG using restricted Boltzmann machines. Biomed Signal Process Control 45:237–245 3. Rebollo-Neiraa L, Cerná D (2019) Wavelet based dictionaries for dimensionality reduction of ECG signals. Biomed Signal Process Control 54:1–9 4. Bensegueni S, Bennia A (2016) ECG signal compression using a sinusoidal transformation of principal components. Int J Softw Eng Appl 10:59–68 5. Grossi G, Lanzarotti R, Lin J (2015) High-rate compression of ECG signals by an accuracydriven sparsity model relying on natural basis. Digital Signal Process 45:96–106 6. Bensegueni S A new method for electrocardiogram features extraction using slope change coefficients (under review) 7. Moody GB (1992) ECG database applications guide. Harvard University 8. Tiwari A, Falk TH (2019) Lossless electrocardiogram signal compression: a review of existing methods. Biomed Signal Process Control 51:338–346

Internet of Things, Embedded Systems, Blockchain and Security

Narrowband IoT Evolution Towards 5G Massive MTC: Complete Performance Evaluation Adil Abou El Hassan , Abdelmalek El Mehdi , and Mohammed Saber

Abstract As the emergence of the 5G wireless network is expected to significantly revolutionize the domain of communication, its design should consider the Internet of Things (IoT) among the principal orientations to support the massive Machine-Type Communication (mMTC). The emerging IoT applications supporting mMTC, need new requirements other than throughput such as coverage, latency, power consumption and connection density. To this purpose, the third Generation Partnership Project (3GPP) has introduced a novel cellular IoT technology known as Narrowband IoT (NB-IoT). This paper aims to determine the NB-IoT system configuration and deployment required to fully meet the 5G mMTC requirements. A complete performance evaluation of NB-IoT against the 5G mMTC requirements is presented. It is shown through the simulation results that these requirements can be met but under certain conditions relating to the system configuration and its deployment. Keywords NB-IoT · 5G · mMTC · Performance evaluation · 3GPP

1 Introduction The Internet of Things (IoT) market for the 5G ecosystem is based on innovative technologies that efficiently support massive Machine-Type Communications (mMTC). The 5G system is a next-generation wireless network that is based on New Radio (NR) technology and is also designed to provide connectivity to a massive number

A. Abou El Hassan (B) · A. El Mehdi · M. Saber Mohammed First University Oujda, National School of Applied Sciences, SmartICT Lab, Oujda, Morocco e-mail: [email protected] A. El Mehdi e-mail: [email protected] M. Saber e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_24

259

260

A. Abou El Hassan et al.

of devices. The aim of the mMTC deployment is to connect up to billions of devices, such as remote outdoor and indoor sensors through a cloud-based system. The purpose of the 5G system design is to support three use case areas: enhanced Mobile Broadband (eMBB), massive Machine Type Communication (mMTC), as well as ultra Reliable Low Latency Communication (uRLLC) which is designed to fulfill the requirements of critical Machine Type Communication (cMTC) [1]. The advantage of the 5G system is the flexibility of its structure, which makes it possible to cover many use cases with a single integrated system, by using a new feature which is network slicing based on two new technologies that are Network Function Virtualization (NFV) and Software-Defined Networking (SDN) [2]. The third Generation Partnership Project (3GPP) has introduced in Release 13 (Rel-13) specifications, a new licensed low power wide area (LPWA) technology known as Narrowband IoT (NB-IoT) [3]. It is built from the existing Long Term Evolution (LTE) base system, and supports IoT applications that do not require high throughput like smart metering, smart parking and smart electric vehicle charging system. The 3GPP Rel-13 core specifications for NB-IoT were finalized in June 2016 [3]. About the Rel-14 and Rel-15 enhancements, they were completed in June 2017 and June 2018 respectively [3], whereas Rel-16 improvements are underway and scheduled for completion in 2020 [1]. The 3GPP design aims for Rel-13 were low complexity and low cost devices, long battery life, and support of a large number of devices. In addition the coverage enhancement to reach devices in poor coverage conditions. To this purpose, two power saving techniques have been implemented to reduce the power consumption: Power Saving Mode (PSM) and extended Discontinuous Reception (eDRX) introduced in Rel-12 and Rel-13 respectively [4, 5]. This paper aims to determine the NB-IoT system configuration and deployment required to fully meet the 5G mMTC requirements based on the complete performance evaluation of NB-IoT. In Sect. 2, an overview of NB-IoT is presented. It is followed in Sect. 3, by a complete performance evaluation against the 5G mMTC requirements in terms of coverage, throughput, latency, battery lifetime and connection density. It is shown through the simulation results that these requirements can be met but under certain conditions regarding the system configuration and its deployment. The enhancements provided by the recent 3GPP releases are also discussed. Finally, Sect. 4 concludes the paper.

2 NB-IoT Overview 2.1 3GPP Specifications and Numerology of NB-IoT To ensure deeper coverage, the bandwidth occupied by the NB-IoT carrier is 180 kHz corresponding to an one Physical Resource Block (PRB) of 12 subcarriers in an LTE system [4]. There are three operation modes to deploy NB-IoT: as a stand-alone

Narrowband IoT Evolution Towards 5G Massive MTC …

261

carrier, in guard-band of an LTE carrier and in-band within an LTE carrier, which offers operating mode flexibility of NB-IoT [6]. In order to coexist with LTE system, NB-IoT uses orthogonal frequency division multiple access (OFDMA) in downlink with the identical subcarrier spacing of 15 kHz and frame structure as LTE [4]. Whereas NB-IoT uses in uplink singlecarrier frequency division multiple access (SC-FDMA) and two transmission modes which are the multi-tone and single-tone transmissions to ensure both high capacity and maximum coverage for NB-IoT device with a single antenna [4, 6]. Multi-tone transmission uses the same 15 kHz subcarrier spacing and 0.5 ms slot duration as LTE. While single-tone transmission supports two numerologies which use 15 kHz and 3.75 kHz subcarrier spacings with 0.5 ms and 2 ms slot durations respectively [7]. Also, the restricted QPSK and BPSK modulation schemes are used in downlink and uplink [6]. Two device categories Cat-NB1 and Cat-NB2 are defined by NB-IoT which correspond to the device categories introduced in Rel-13 and Rel-14 respectively. The maximum transport block size (TBS) supported in uplink by Cat-NB1 is only 1000 bits compared to 2536 bits for Cat-NB2. For downlink, the maximum TBS supported by Cat-NB1 is only 680 bits compared to 2536 bits for Cat-NB2 [3].

2.2 Signals and Physical Channels of NB-IoT The signals and channels used in downlink (DL) are as follows: Narrowband Primary Synchronization Signal (NPSS), Narrowband Secondary Synchronization Signal (NSSS), Narrowband Reference Signal (NRS), Narrowband Physical Broadcast Channel (NPBCH), Narrowband Physical Downlink Shared Channel (NPDSCH) and Narrowband Physical Downlink Control Channel (NPDCCH) [4]. Since NB-IoT uses only one PRB in a slot, the downlink signals and channels are therefore multiplexed only in the time domain. The NB-IoT downlink frame is structured as follows: subframe #0 of every frame carries NPBCH, subframe #1 to #4, and subframe #6 to #8 of every frame carry NPDCCH or NPDSCH. NPSS is carried by subframe #5 of every frame, whereas NSSS is carried by subframe #9 of every even numbered frame. While subframe #9 of every odd numbered frame carries NPDCCH or NPDSCH [7]. In the uplink (UL), only one signal and two channels are used: Demodulation Reference Signal (DMRS), Narrowband Physical Uplink Shared Channel (NPUSCH) and Narrowband Physical Random Access Channel (NPRACH) [4]. Two formats are used for NPUSCH which are: Format 1 (F1) and Format 2 (F2). NPUSCH F1 is used by user equipment (UE) to carry uplink user’s data to the evolved Node B (eNB), and it supports both single-tone and multi-tone transmissions [5, 7]. Whereas, NPUSCH F2 is used to carry uplink control information (UCI), such as Hybrid Automated Repeat reQuest-Acknowledgement (HARQ-ACK), and it supports only single-tone transmission [5, 7].

262

A. Abou El Hassan et al.

Table 1. Simulation and system model parameters.

Parameter

Value

System bandwidth

10 MHz

Channel model

Tapped Delay Line (TDL-iii/NLOS)

Doppler spread

2 Hz

NB-IoT mode of operation

Guard-band

eNB Rx/Tx

4/2 and 4/4 only for NPSS/NSSS transmissions

Device Rx/Tx

1/1

For cell access, the UE must first synchronize with the eNB using NPSS and NSSS signals to achieve time and frequency synchronization with the network and cell identification. Then, it receives Narrowband Master Information Block (MIBNB) and System Information Block 1 (SIB1-NB) carried by NPBCH and NPDSCH respectively from eNB to access the system. Thereafter the UE starts random access procedure by using single-tone transmission of NPRACH with 3.75 kHz subcarrier spacing, and waits for the scheduling uplink grant on NPDCCH which is used to convey Downlink Control Information (DCI) for uplink, downlink and paging scheduling [4, 5].

3 Performance Evaluation Against 5G mMTC Requirements In Rel-15, 3GPP has defined five targets for 5G mMTC corresponding to the following performances: coverage defined by the maximum coupling loss (MCL), throughput, latency, battery life and connection density [8]. In this section, a complete evaluation of these five performances is presented to verify whether the 5G mMTC targets are fulfilled by NB-IoT. In addition, the enhancements of performance provided by the recent 3GPP releases are also discussed.

3.1 Coverage The MCL is a common measure to define the level of coverage a system can support. It is depending on the maximum transmitter power (PTX ), the required signal-tointerference-and-noise ratio (SINR), the receiver noise figure (NF) and the signal bandwidth (BW) [9]: MCL = PTX − (SINR + NF + N0 + 10 log10 (BW)) N0 is the thermal noise density, which is a constant equal −174 dBm/Hz.

(1)

Narrowband IoT Evolution Towards 5G Massive MTC …

263

Table 2. Downlink and uplink coverage of NB-IoT. Assumptions for simulation

Downlink physical channel

Uplink physical channel

NPBCH

NPDCCH

NPDSCH

NPRACH

NPUSCH F1

NPUSCH F2

TBS [Bits]

24

23

680

–

1000

1

Acquisition time [ms]

1280

512

1280

205

2048

32

BLER

10%

1%

10%

1%

10%

1%

Max transmit power [dBm]

46

46

46

23

23

23

Transmit power/carrier [dBm]

35

35

35

23

23

23

Noise figure NF [dB]

7

7

7

5

5

5

Channel bandwidth [kHz]

180

180

180

3.75

15

15

SINR [dB]

−14.5

−16.7

−14.7

−8.5

−13.8

−13.8

MCL [dB]

163.95

166.15

164.15

164.76

164

164

Based on the simulation assumptions given in Table 1 according to [10] and using (1) to calculate MCL, Table 2 provides the NB-IoT channels coverage to achieve the MCL of 164 dB which corresponds to the 5G mMTC coverage requirement to be supported [8]. Table 2 also shows the required acquisition time and block error rate (BLER) associated with each channel to achieve the targeted MCL of 164 dB. From Table 2, we note that to reach the MCL of 164 dB at the appropriate BLER, it is necessary to use the time repetition technique of the simulated channels. However, NPUSCH F1 is considered as the limiting channel, since it uses the longest acquisition time to achieve the MCL of 164 dB. Whereas NPDCCH must be configured with 512 repetitions to achieve the targeted BLER of 1%, knowing that the maximum configurable repetition number for NPDCCH is 2048 [5]. Therefore, NB-IoT can support device operations in extreme coverage.

3.2 Throughput The downlink and uplink throughputs are obtained according to the NPDSCH and NPUSCH F1 scheduling cycles respectively and based on the acquisition times shown in Table 2.

264

A. Abou El Hassan et al.

Fig. 1. NPDSCH scheduling cycle (Rmax = 512; G = 4) at the MCL

Fig. 2. NPUSCH F1 scheduling cycle (Rmax = 512; G = 1.5) at the MCL

Figure 1 depicts the NPDSCH scheduling cycle according to [10], where the NPDCCH user-specific search space is configured with a maximum repetition factor Rmax of 512 and a relative starting subframe periodicity G of 4. Based on BLER and TBS given in Table 2 and using an overhead (OH) of 5 bytes corresponding to the radio protocol stack, a MAC-layer throughput (THP) in downlink of 281 bps is achieved with the following formula: THP =

(1 − B L E R)(T B S − O H ) N P DCC H Period

(2)

The NPUSCH F1 scheduling cycle depicted in Fig. 2 corresponds to scheduling of NPUSCH F1 transmission once every fourth scheduling cycle according to [10], which ensures a MAC-layer THP in uplink of 281 bps according to the formula (2) using the BLER and TBS given in Table 2 and OH of 5 bytes. As part of 3GPP Rel-15, 5G mMTC requires that downlink and uplink throughputs supported at the MCL of 164 dB must be at least 160 bps [8]. As can be seen, the MAC-layer throughput meets the 5G mMTC requirement in both downlink and uplink. It should be noted that the BLER targets associated with each channel require the acquisition times shown in Table 2. Therefore, by choosing to operate with NPDSCH and NPUSCH F1 BLER levels above 10% and the use of new Cat-NB2 device introduced in 3GPP Rel-14 which supports a larger TBS in both downlink and uplink with two simultaneous HARQ processes [5], should further enhance the throughput levels.

Narrowband IoT Evolution Towards 5G Massive MTC …

265

3.3 Latency The latency should be evaluated for two cases: the first case is the use of the Radio Resource Control (RRC) Resume procedure depicted in Fig. 3, while the second case consists in use of new Early Data Transmission (EDT) procedure that has been introduced in Rel-15 and depicted in Fig. 4. The EDT procedure allows the device to terminate the transmission of small data packets earlier in RRC-idle mode without switching to RRC-connected mode. The latency evaluation is based on the same radio related assumptions and the system model given in Table 1 by using the data and signaling flows corresponding to RRC Resume and EDT procedures. Table 3 shows the used packet sizes and the evaluated latency at the 164 dB MCL according to [10]. When the RRC Resume procedure is used, we obtain a latency of 9 s, compared to 5.8 s when the EDT procedure is used. As can be seen the 5G mMTC target of Fig. 3. NB-IoT RRC Resume procedure

Fig. 4. NB-IoT EDT procedure

266

A. Abou El Hassan et al.

Table 3. Packet sizes and results of latency evaluation RRC resume procedure Random Access Response: Msg2

EDT procedure 7 bytes

Random Access Response: Msg2

7 bytes

RRC Conn. Resume Request: 11 bytes Msg3

RRC Conn. Resume Request: 11 + 105 bytes Msg3 + UL report

RRC Conn. Resume: Msg4

RRC Conn. Release: Msg4

24 bytes

Latency

5.8 s

19 bytes

RRC Conn. Resume 22 + 200 bytes Complete: Msg5 + RLC Ack Msg4 + UL report RRC Conn. Release

17 bytes

Latency

9s

10 s latency at the MCL of 164 dB defined in 3GPP Rel-15 [8] is met, for both the RRC Resume and EDT procedure. However, the best latency of 5.8 s is obtained by using the EDT procedure, through multiplexing the user’s data with Message 3 on NPUSCH F1 channel as shown in Fig. 4.

3.4 Battery Life The RRC resume procedure is used in battery life evaluation instead of the EDT procedure, since EDT procedure does not support uplink TBS larger than 1000 bits which requires long transmission times. The packet flow used to evaluate battery life is the same as shown in Fig. 3 where DL data corresponds to application acknowledgment regarding receipt of UL report by the eNB. Four levels of device power consumption are defined, including transmission, reception, Idle-Light sleep corresponding to device in RRC-Idle mode or RRC-Connected mode but not actively receiving or transmitting, whereas Idle-Deep sleep corresponds to power saving mode. The device power consumption for each level and assumed traffic model according to Rel-14 scenario are given in Table 4. In addition, an Active Timer of 20 s is included after connection release where the device is in Idle-Light sleep, to monitor the control channel NPDCCH according to [11]. The Rayleigh fading Extended Typical Urban (ETU) channel model with a Doppler shift of 1Hz is used to evaluate the battery life according to [11]. The battery life in years is calculated using the following formula according to [9]:   Batter y energy capacit y Battery life years = E day 365 × 3600

(3)

Narrowband IoT Evolution Towards 5G Massive MTC … Table 4. Assumptions of NB-IoT battery life evaluation

267

Message format UL report

200 bytes

DL Application Acknowledgment

20 bytes

Report periodicity

Once every 24 h

Device power consumption Transmission power (PTx )

500 mW

Reception power (PRx )

80 mW

Idle - Light sleep power (PILS )

3 mW

Idle - Deep sleep power (PIDS )

0.015 mW

Knowing that Eday is the device’s energy consumed per day, expressed in Joule and calculated as follows:   E day = (PT x × TT x + PRx × TRx + PI L S × TI L S ) × Nr ep + (PI DS × 3600 × 24) (4) Where TTx , TRx and TILS correspond to overall times given in seconds for transmission, reception and Idle-Light sleep respectively and obtained from the assumed transmission times according to [11] and the packet flow shown in Fig. 3, while Nrep corresponds to the number of reports per day. A device of 23-dBm power class with an 5 Wh battery and a base station of 46-dBm power class are used for guard-band and in-band operation modes, while for stand-alone operation mode the power class of the base station is only 43 dBm according to [11]. Based on the assumed transmission times given in [11] and using the formulas (3) and (4), the evaluated battery lifes to achieve MCL of 164 dB in in-band, guard-band and stand-alone operation modes are 11.4, 11.6 and 11.8 years respectively. Knowing that the 5G mMTC requires battery life beyond 10 years at the MCL of 164 dB, supposing an energy storage capacity of 5Wh [8]. Therefore, NB-IoT achieves the targeted battery life in all operation modes. Furthermore, to increase battery life further, the Narrowband Wake-Up Signal (NWUS) that was introduced in 3GPP Rel-15 can be implemented, since it allows the UE to remain in idle mode until informed to decode NPDCCH channel for a paging occasion, thereby achieving energy saving.

3.5 Connection Density The 5G mMTC target on connection density that is also part of the International Mobile Telecommunication targets for 2020 and beyond (IMT-2020), requires the support of one million devices per square kilometer in four different urban macro scenarios [8]. These scenarios are based on two channel models (UMA A) and (UMA

268 Table 5. System level simulation assumptions of urban macro scenarios

A. Abou El Hassan et al. Parameter

Value

Frequency band

700 MHz

LTE system bandwidth

10 MHz

Cell structure

Hexagonal grid with 3 sectors per size

NB-IoT operation mode

In-band

Pathloss model

UMA A, UMA B

eNB power and antennas configuration

46 dBm–2Tx/2Rx

UE power and antennas configuration

23 dBm–1Tx/1Rx

B) and two distances of 500 and 1732 m between adjacent cell sites denoted by ISD (inter-site distance) [12]. Based on the simulation assumptions given in Table 5 and the non-full buffer system level simulation to evaluate connection density according to [13], Fig. 5 shows the latency required at 99% reliability to deliver 32 bytes payload as a function of the connection attempts intensity (CAI) to be supported, corresponding to the number of connection attempts per second, cell and PRB. It should be noted that the latency shown in Fig. 5 includes the idle mode time to synchronize to the cell and read the MIB-NB and SIB1-NB by the device. Knowing that each device must submit a connection attempt to the system periodically, we can calculate the connection density to be supported (CDS) per cell area using the following formula: C DS =

C AI · C A P A

(5)

Where CAP is the periodicity of device’s connection attempts given in seconds √ and the hexagonal cell area A is calculated by A = I S D 2 · 3/6. To evaluate the connection density per PRB and square kilometer depicted in Fig. 6, and corresponding to the overall number of devices that successfully transmit a payload of 32 bytes accumulated over two hours with the required latency, the CDS values are obtained from formula (5) using the CAI values of Fig. 5 and a periodicity of connection attempts of two hours. As can be seen from Fig. 6, in the two scenarios corresponding to 500 m ISD, more than 1.2 million devices per PRB and square kilometer can be supported by an NB-IoT carrier with a maximum 10 s latency. However, only 94000 and 68000 devices per PRB and square kilometer can be supported in the scenarios using the (UMA B) and (UMA A) channel models respectively with an ISD of 1732 m within the 10-s latency limit. Since, in the case of 1732 m ISD, the density of base stations is 12 times lower than with 500 m ISD. Therefore, this difference in base station density results in

Narrowband IoT Evolution Towards 5G Massive MTC …

269

Fig. 5. Intensity of connection attempts in relation to latency

Fig. 6. Connection density in relation to latency

differences of up to 18 times between the connection densities relating to the scenarios of 500 and 1732 meters ISD.

4 Conclusion To conclude, this paper shows that the five targets of 5G mMTC are fulfilled by NBIoT. However, the evaluation results show that the performance achieved is highly dependent on certain conditions relating to system configuration and deployment, such as the number of repetitions configured for channels transmission and the density of base stations, which is significantly dependent on the inter-site distance ISD. Since the connection density evaluation shows that the target of 5G mMTC is only achieved if ISD is 500 m. In addition, 3GPP Rel-15 allows 5G NR to coexist with carriers when

270

A. Abou El Hassan et al.

NB-IoT devices are already deployed in massive IoT applications. Thus, NB-IoT can be considered as a key technology of the 5G system for massive IoT applications, such as smart metering systems allowing measurement of water, gas and electricity consumption.

References 1. Ghosh A, Maeder A, Baker M, Chandramouli D (2019) 5G evolution: a view on 5G cellular technology beyond 3GPP release 15. IEEE Access 7:127639–127651. https://doi.org/10.1109/ ACCESS.2019.2939938 2. Barakabitze AA, Ahmad A, Mijumbi R, Hines A (2020) 5G network slicing using SDN and NFV: a survey of taxonomy, architectures and future challenges. Comput Netw 167:106984. https://doi.org/10.1016/j.comnet.2019.106984 3. Ratasuk R, Mangalvedhe N, Xiong Z, Robert M, Bhatoolaul D (2017) Enhancements of narrowband IoT in 3GPP Rel-14 and Rel-15. In: 2017 IEEE conference on standards for communications and networking (CSCN), pp 60–65. IEEE. https://doi.org/10.1109/CSCN.2017.808 8599 4. Feltrin L, Tsoukaneri G, Condoluci M, Buratti C, Mahmoodi T, Dohler M, Verdone R (2019) Narrowband IoT: a survey on downlink and uplink perspectives. IEEE Wirel Commun 26(1):78–86. https://doi.org/10.1109/MWC.2019.1800020 5. Rastogi E, Saxena N, Roy A, Shin DR (2020) Narrowband internet of things: a comprehensive study. Comput Netw 173:107209. https://doi.org/10.1016/j.comnet.2020.107209 6. Mahmood A, Zafar S (2019) Performance analysis of narrowband internet of things (NB-IoT) deployment modes. In: 2019 22nd international multitopic conference (INMIC), pp 1–8. IEEE. https://doi.org/10.1109/INMIC48123.2019.9022748 7. Wang YPE, Lin X, Adhikary A, Grovlen A, Sui Y, Blankenship Y, Bergman J, Razaghi HS (2017) A primer on 3GPP narrowband internet of things. IEEE Commun Mag 55(3):117–123. https://doi.org/10.1109/MCOM.2017.1600510CM 8. 3GPP (2018): TR 38.913, 5G: study on scenarios and requirements for next generation access technologies Release 15, version 15.0.0. Technical report, ETSI, September. https://www.etsi. org/deliver/etsi_tr/138900_138999/138913/15.00.00_60/tr_138913v150000p.pdf. Accessed 21 Jan 2021 9. 3GPP (2015): TR 45.820 v13.1.0: Cellular system support for ultra-low complexity and low throughput Internet of Things (CIoT) Release 13. Technical report, 3GPP Organizational Partners (ARIB, ATIS, CCSA, ETSI, TTA, TTC), November. https://www.3gpp.org/ftp/Specs/arc hive/45_series/45.820/45820-d10.zip. Accessed 21 Jan 2021 10. Ericsson (2019): R1–1907398, IMT-2020 self evaluation: mMTC coverage, data rate, latency & battery life. Technical report, 3GPP TSG-RAN WG1 Meeting #97, May. https://www.3gpp. org/ftp/TSG_RAN/WG1_RL1/TSGR1_97/Docs/R1-1907398.zip. Accessed 21 Jan 2021 11. Ericsson (2017): R1–1705189, Early data transmission for NB-IoT. Technical report, 3GPP TSG RAN1 Meeting #88bis, April. https://www.3gpp.org/ftp/TSG_RAN/WG1_RL1/TSGR1_ 88b/Docs/R1-1705189.zip. Accessed 21 Jan 2021 12. ITU-R (2017): M.2412-0, Guidelines for evaluation of radio interface technologies for IMT2020. Technical report, International Telecommunication Union (ITU), October. https://www. itu.int/dms_pub/itu-r/opb/rep/R-REP-M.2412-2017-PDF-E.pdf. Accessed 21 Jan 2021 13. Ericsson (2019): R1-1907399, IMT-2020 self evaluation: mMTC non-full buffer connection density for LTE-MTC and NB-IoT. Technical report, 3GPP TSG-RAN WG1 Meeting #97, May. https://www.3gpp.org/ftp/TSG_RAN/WG1_RL1/TSGR1_97/Docs/R1-1907399. zip. Accessed 21 Jan 2021

A Fuzzy Ontology Driven Integrated IoT Approach for Home Automation Levin Varghese, Gerard Deepak, and A. Santhanavijayan

Abstract Major advancements and development, in the field of Internet and other technologies is now prevalent in many modern areas and dominating the way of life. This in turn has allowed us to connect everyday objects, such as sensing and actuating devices, to the digital world. These intelligent devices empower the dawn of state-of-the-art services and applications for a multitude of various domains and help in improving the complete utilization of the given resources. In this paper, an interoperable, low cost, energy efficient Internet of Things platform using ontologies for a smart home is being implemented. The said system promises to controls the home appliances from anywhere i.e. offers remote operability and also provides the collected data to Third party programs if need be, so that the Third parties can creates tailor- made applications and services that cater to the need of the consumer. The system promises to reduce human intervention, enable automation and provide access to devices remotely from any place. Also, it allows for the collected data to be sent to third parties for generation of third- party services, and also for the analysis, and overall improvement of the system. Keywords Fuzzy ontologies · Intelligent systems · Internet of Things · Smart home

1 Introduction The term “smart-home” refers to a residence which is equipped with a number of devices that help in sensing and automating tasks which are normally handled by humans. A growing trend sees people trying to add voice commands or artificial intelligence into these designs, so that the tasks which are required to be carried out, are done with much greater ease, and if possible, in the absence of a human being, i.e. certain decisions which need to be made are done automatically. Turning L. Varghese · G. Deepak (B) · A. Santhanavijayan Department of Computer Science and Engineering, National Institute of Technology Tiruchirappalli, Tiruchirappalli, Tamil Nadu, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_25

271

272

L. Varghese et al.

a house into a smart home, for some, could mean buying a wireless speaker, while it can mean different for others, such as connecting several various products for creating a so called smart environment or home. Home automation aims to enhance the comfort, energy consumption efficiency and security in domestic scenarios [6]. We aim to reduce the human intervention, secure access control to home devices from anywhere, provide smart homes data for application services as well as for analysis, and improve the utilization of resources [5]. Motivation: As our resources such as electricity, drinking water, food and other natural resources are getting scarcer and more expensive, there is a need to keep the utilization of these resources in check. In order to reduce the wastage of these resources and to ensure the safety of the people within a household, there is a great scope and need for technologies, such as smart homes, which help to monitor and save the resources and also try to keep the household secure from many mishaps, such as fires, theft, etc. Contribution: This paper is being done in two phases, in the first phase, identification of the topic, research work on the topic and a simulation is being carried out. In the second phase the implementation and ontology part of the paper is carried out. The overall system can further be used in various other applications, with addition of various other appliances. The system monitors room temperature, detects smoke (mainly carbon dioxide), it also detects fire and tries to prevent any damages using other appliances such as water sprinkler, alarm, etc. Further, all these devices can be remotely controlled through cloud and are secured using an ID and password. Organization: This paper is organized in the following way, in Sect. 2 literature review is done. In Sect. 3, the System Architecture is proposed. Implementation, Performance Evaluation and Results are discussed in Sect. 4 and finally, conclusion and future works is discussed in Sect. 5.

2 Related Work In [1], the authors, have integrated the semantic characteristics into the smart home system paradigm and have created an ontology based semantic service model which they call OSSM and that is what acts as the middleware for their system. In [2], the authors use a structure based on an ontological model to facilitate the mindless arrangement of fitting uses, instead of traditionally programming each household. In [3], the authors have successfully provided an answer for securing households by taking decisions dynamically with help of intrusive devices. In [4], the authors suggest a smart home, using Raspberry Pi and IoT, it is done by connecting cameras and sensing devices into a web app. In [5], the authors aim to reduce the involvement of humans, ensure secure access to home appliances from any place, also provide analytical services and help improve the system. In [6], the authors propose an interface through which context awareness and decision making is carried out using

A Fuzzy Ontology Driven Integrated IoT Approach …

273

distance based fuzzy algorithm. In [7], the authors have used a fuzzy logic approach for real- time checking and analysis of the data collected from temperature sensor, heart beat sensor and gas sensor. Data collected is used to evaluate if dangers exist.

3 Proposed System Architecture This IoT based smart home is built using a powerful tool, namely Raspberry Pi 3 Model B+ microcomputer. It is very cooperative when it comes to minimizing the required system components. This system uses a multitude of sensors such as motion detector, fire sensor, smoke sensor, etc. All these sensing appliances are interfaced using the GPIO pins present on Raspberry Pi board. The general architecture of the overall system can be seen in Fig. 1 below. A simulation of this said architecture has been carried out in Cisco Packet Tracer tool.

4 Implementation The application is completed using Python as the language for programming in Jupyter notebook. Fuzzy ontology is used for the ontology part of the project and the decision-making part is carried out with the help of triangular membership functions. The general algorithm can be seen in Table 1 below. After running the algorithm mentioned in Table 1 below, we get the output seen in Fig. 2 below. The simulation worked flawlessly and also helped us to understand the potential of the system in a very nice and informative manner. For the implementation part of the project, we used a DHT22 sensor, which is sensor to sense humidity and temperature, and an air quality sensor, CCS811. The sensors and actuators worked instantly when

Fig. 1 Architecture of the system

274

L. Varghese et al.

certain events were triggered, ensuring the minimization of any materialistic loss, as well as, any loss of life. We ran the fuzzy ontology for the scenario when there is a gas leak and or a fire in the house and plotted the graph for the same, only one example is shown in Fig. 2 below, but this was carried out for a dataset of values for temperature and smoke, and great results were achieved. Table 1 General algorithm for Fuzzy ontology

Input: Crisp input values (values read from sensors) Output: Crisp output values (values sent to actuators based on decision made) Begin Step 1: Initialization of terms and linguistic variables. temperature = ctrl.Antecedent(np.arange(10, 60, 10), 'temperature') Step 2: Construct MF (Membership Function or Functions) temperature['Cool'] = fuzz.trimf(op_sense.universe, [10, 20, 30]) smoke['Acceptable'] = fuzz.trimf(op_sense.universe, [10, 20, 30]) Step 3: Initialization of rule/rules (if-then conditions) rule1 = ctrl.Rule(temperature['Cool'] & smoke['Acceptable'], op_sense['sec1']) Step 4: Create control system (CS) and control system simulation (CSS) object Step 5: Using membership functions convert crisp input to fuzzy input OPIot.input['temperature']= 35 OPIot.input['smoke']= 35 Step 6: Compute the fuzzy output by feeding the fuzzy input to the CSS OPIot.compute() Step 7: Convert the fuzzy output and then convert to crisp output. print (OPIot.output['op_sense']) op_sense.view(sim=OPIot) End Fig. 2 Fuzzy Ontology output

A Fuzzy Ontology Driven Integrated IoT Approach …

275

The value 27.377, which we got in Fig. 2 above, indicates that our environment is relatively in a safe zone, but at the same time, caution is to be taken as the environment is still slightly above the acceptable environment (i.e. 23.5). So a prompt is sent to the user to ensure if things are alright or not. The value 21.60 which we got in Fig. 3 below, indicates that our environment is in a very safe zone, though a little caution is to be expected as you can see, sector 2 is also slightly shaded. Although the environment is stable, just some minor supervision is required. This is only the case during the early stages of deployment. Once the system learns the patterns of the consumer, it will make decisions accordingly. The value 33.50 which we got in Fig. 4 below, indicates that our environment is in the cautionary zone as well as in the danger zone. As you can see, sector 2 is now shaded almost 75% and sector 3 is shaded around 25%. Also, after seeing the inputs and checking the rules which were created by the user, code can be found in Appendix A.1, we can infer that the temperature is more than what the user has deemed cautionary, and it is this value of temperature which has caused the system to be in sector 2 as well as sector 3. The amount of smoke is within the cautionary limit. So, both parameters need to be monitored, and until the system is resolved, preventive measures need to be taken i.e. triggering alarm, opening window/s etc. Similarly, many other values were read, and he graphs for them were plotted and inferred from. Accuracy was used as a metric for the system. Equation 1 below, shows how the accuracy is calculated. Accuracy =

Fig. 3 System output when temp value is in cautionary zone

Correct Outcomes Total no. of outcomes

(1)

276

L. Varghese et al.

Fig. 4 System output when temp and smoke value are slightly above normal

The system was made to run a total of 2046 trials (Total no. of outcomes), out of which 1957 trials were perfect (Correct Outcomes) while others were wrong, due to wear and tear of the sensors. These achieved values imply that our system has about 96% accuracy, which is very good.

5 Conclusions In this paper we have simulated one feasible solution for smart home system using IoT and fuzzy ontology. One of our primary targets was to find an affordable and practical solution for a real-time smart home system, and we were able to achieve it by using the sensing devices along with the Raspberry Pi. Certain events were created to simulate mishaps which could potentially cause damage to property and life, but we were successfully able to negate any damage by designing a fast, responsive and interactive smart home system. The fuzzy ontology part also yielded great results, which has allowed us to complete the project successfully. This paper can be easily scaled up or down by addition or deletion of sensors. Furthermore, a more visual and informative, GUI (Graphical User Interface) can be used, based on the users requirements, to help understand the system in a better and easy way.

References 1. Yang Y, Wang Z, Yang Y, Li M, Yang Y (2012) An ontology based semantic service model in smart home environment. In: proceedings of 2010 3rd international conference on computer

A Fuzzy Ontology Driven Integrated IoT Approach …

277

and electrical engineering (ICCEE 2010 no 1) 2. Xu J, Lee YH, Tsai WT, Li W, Son YS, Park JH, Moon KD (2009) Ontology-based smart home solution and service composition. In: 2009 International Conference on Embedded Software and Systems, pp 297–304. IEEE, May 3. Javale D, Mohsin M, Nandanwar S, Shingate M (2013) Home automation and security system using android ADK. International J Electron Commun Comput Technol (IJECCT) 3:382–385 4. Ganta Dr, Vinod A, Priyanka N, Ch. K (2019) iot based web controlled home automation using Raspberry PI. Int J Sci Res Sci Eng Technol 229–234. https://doi.org/10.32628/IJSRSET19 6246 5. Iqbal A (2018) Interoperable internet-of-things platform for smart home system using webofobjects and cloud. Sustain Cities Soc 38:636–646 6. Suryaprakash S, Mathankumar M, Ramachandran R (2017) IOT based home automation system through adaptive decision making fuzzy algorithm. Res J Eng Technol 8(3):268–272 7. Abdulrazak B, Chikhaoui B, Gouin-Vallerand C, Fraikin B (2010) A standard ontology for smart spaces. Int J Web Grid Serv 6(3):244–268 8. Deepak G, Teja V Santhanavijayan A (2020) A novel firefly driven scheme for resume parsing and matching based on entity linking paradigm. J Discrete Math Sci Cryptograph 23(1):157– 165 9. Deepak G, Santhanavijayan A (2020) Onto best fit: a best-fit occurrence estimation strategy for RDF driven faceted semantic search. Comput Commun 10. Kumar N, Deepak G, Santhanavijayan A (2020) A novel semantic approach for intelligent response generation using emotion detection incorporating NPMI Measure. Procedia Comput Sci 167:571–579 11. Deepak G, Kumar N, Santhanavijayan A (2020) A semantic approach for entity linking by diverse knowledge integration incorporating role-based chunking. Procedia Comput Sci 167:737–746 12. Haribabu S, Kumar PSS, Padhy S, Deepak G, Santhanavijayan A, Kumar N (2019) A novel approach for ontology focused inter- domain personalized search based on semantic set expansion. In 2019 fifteenth international conference on information processing (ICINPRO), pp 1–5. IEEE, December 13. Deepak G, Kumar N, Bharadwaj GVSY, Santhanavijayan A (2019) OntoQuest: an ontological strategy for automatic question generation for e-assessment using static and dynamic knowledge. In: 2019 fifteenth international conference on information processing (ICINPRO), pp 1–6. IEEE 14. Kaushik IS, Deepak G, Santhanavijayan A (2020) QuantQueryEXP: a novel strategic approach for query expansion based on quantum computing principles. J Discrete Math Sci Cryptograph 23(2):573–584 15. Varghese L, Deepak G, Santhanavijayan A (2019) An IoT analytics approach for weather forecasting using Raspberry Pi 3 Model B+. In: 2019 fifteenth international conference on information processing (ICINPRO), pp 1–5. IEEE, December 16. Deepak G, Priyadarshini S (2016) A hybrid framework for social tag recommendation using context driven social information. Int J Soc Comput Cyber-Phys Syst 1(4):312–325

Towards an IoT/Big-Data Platform for Data Measurements, Collection and Processing in Micro-grid Systems Abdellatif Elmouatamid , Youssef Alidrissi , Radouane Ouladsine , Mohamed Bakhouya , Najib Elkamoun, Mohammed Khaidar, and Khalid Zine-Dine Abstract The resent infrastructure of the buildings is becoming socio-technical systems that integrate different heterogeneous entities (e.g., sensors, actuators, lighting, HVAC (Heating, Ventilation, and Air Conditioning), occupants, renewable energy, storage systems), which could interact dynamically and in a collective manner to balance between energy efficiency, occupants’ comfort, sustainability, and adaptability. This new infrastructure is known as the “Micro-grid” (MG) system concept. However, the main challenge for this infrastructure is real-time monitoring and data processing, which requires the use of new information and communication technologies. In addition, incorporating mechanisms and techniques are required in order to have buildings more energy-efficient while ensuring occupants’ comfort by allowing entities interaction for suitable actions (e.g., turning On/Off HVAC and lighting, balancing the fluctuation between power production and consumption). In this work, a new holistic architecture of smart buildings is presented by improving the main layers of MG systems. This architecture is proposed in order to integrate all buildings’ aspects with the main trade-off is to efficiently manage the building while maintaining a suitable occupants’ comfort. In fact, an MG system is structured into three layers following the proposed holistic architecture. More precisely, we shed more light on the MG system’s layer by integrating recent IoT/Big-Data technologies for data gathering, processing, and control. A set of sensors are installed for power measurement in the MG system while the measured data is gathered and transmitted to an IoT/Big-Data platform for analysis, processing, and storing. The main aim is to store a historical of data (e.g., power production/consumption, weather conditions) from the installed MG system with open-access tools. A. Elmouatamid (B) · Y. Alidrissi · R. Ouladsine · M. Bakhouya College of Engineering and Architecture, LERMA Lab, International University of Rabat, 11100 Sala ElJadida, Morocco e-mail: [email protected] A. Elmouatamid · N. Elkamoun · M. Khaidar Faculty of Sciences, STIC Laboratory, CUR”EnR&SIE, Chouaïb Doukkali University, 24000 El Jadida, Morocco K. Zine-Dine Faculty of Sciences, Mohammed V University, 10000 Rabat, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_26

279

280

A. Elmouatamid et al.

Keywords Micro-grid · Smart buildings · IoT/Big-data platform · Information and communication technologies · Renewable energy sources · Energy management

1 Introduction The integration of RESs (renewable energy sources) for large-scale of electrical energy production has recently accelerated because of evident climate change, insufficiency of fossil resources, and greenhouse gas emissions problem. RES are clean and eco-friendly sources and their abundance and renewable nature are among the most important factors for their integration into SG (smart grid) networks. Mainly, the simple consumer of electricity is becoming a producer of electricity due to the use of these RESs with the possibility to store and consume locally the electricity. However, these green energy sources come with new challenges, mainly their seamless integration with existing electrical networks. In addition, another important challenge for this new electricity infrastructure is real-time monitoring and data processing, which requires new ICT (information and communication technologies) based infrastructures (Fig. 1). The main aim is to ensure sustainable and reliable renewable energy generation systems [1]. Therefore, this integration of ICTs, energy distribution systems, as well as distributed energy generation systems (e.g., RESs), creates what is commonly named “Smart Grid”. In fact, SG represents the new smart electrical network, since it brings the flexibility to integrate new electrical services, such as electrical vehicles, and enables consumers to be energy producers by integrating RESs using a bidirectional communication network [2]. In fact, due to the development of IoT (internet of things) infrastructures and its related intelligent services, the electricity grid has new capabilities to monitor, manage, and control its components and then takes advantage of sophisticated bidirectional interactions.

Fig. 1 Global architecture: from SG to smart MG

Towards an IoT/Big-Data Platform for Data Measurements …

281

Moreover, the ICTs integration enables various smart and automatic services, such as smart metering infrastructure, smart control and management for D/R (demand/response) balance, advanced electricity marketing, and intelligent energy storage for electrical vehicles integration. However, despite this progress, some research work stated that the SG is experiencing new challenges. Mainly, the SG is based on the actual infrastructures of power distribution grids, which are limited by the unidirectional exchange of electricity [3]. Therefore, face these challenges; other concepts have been developed together with the revolution of SG, such as the IoE (internet of energy), the IoT, and the IoS (internet of services) [4]. Especially, the development and the emergence of smart MG systems could resolve some of the abovementioned SG challenges. MG could simplify the management of electrical energy, from centralized to distribute EM. In addition, in MG systems, different types of energy can be managed locally with the possibility to interconnect different MGs in a distributed manner. In this work, a new holistic architecture of smart buildings is presented by improving the main layers of MG. The proposed MG system presents a smart and active building that combines ICTs/Big-data infrastructure, RESs/storage systems, EM/control strategies, and electrical power grids. Mainly, the main component of the energetic system is presented, which is installed in our deployed MG system. We focused on the modeling of each component of MG layers, especially for sensing and measurement using our ICTs/Big-data infrastructure. The main contribution of this work is to propose open-access tools to build an IoT/Big-Data platform for researches in MG system. The installed platform collects the data from MG system (e.g., power flow, temperature, humidity, wind speed) using a set of installed sensors. The gathered data is used to develop predictive control approaches for energy management in MG system using the new ICTs methods (e.g., machine learning algorithms, IoT).

2 MG Layers Structure for Energy Efficient Buildings The MG system is structured into three horizontal layers: passive building layer (e.g., building envelope and insulation, architecture design), active building systems layer (e.g., HVAC system, Lighting), and RESs system layer (e.g., PV, wind, storage). These layers are monitored by one vertical layer for communication and ICTs integration. This vertical layer integrates mainly an IoT/Big-Data platform in order to measure, analyze, predict, and forecast actions depending on the actual and predicted context. In particular, our MG system is a smart and active building that combines ICTs/Big-data infrastructure, RESs/storage systems, EM/control strategies, and electrical power grids. This new concept of a building is more interactive for both consumers and energy producers. In fact, consumers will reduce the cost of their energy consumption based on the used control approaches, which take into account the real-time cost of the power and the predictive power generation, for efficient D/R management [4, 5]. Moreover, this MG structure offers the possibility to integrate new buildings’ services, such as electrical vehicles, which can be used as a storage

282

A. Elmouatamid et al.

device to compensate the energy in the building by integrating the “Grid-to-Vehicle & Vehicle-to-Grid” techniques.

2.1 Communication Technology Used for MG System The ICTs have a fundamental role in the performance of MG systems operation and its practical deployment. In an MG system, a large number of distributed components, such as the end-users, generators, and energy storage systems, require a reliable, sophisticated, and fast communication infrastructure to reach the mandatory of such systems. Therefore, effective communication systems should be normalized to enable real-time exchange of data in MG systems in order to collect the necessary information used for the EM (energy management) of the system. Mainly, the communication technologies that are used actually in MG systems can be classified into two categories, wired and wireless technologies. Table 1 presents the different communication technologies, which can be used in the MG system with the specified international standards [6, 7]. Table 1 Communication technologies for MG building systems WIRED

Type

Technology

Power line

PLC

ISO/ IEC 14908-3

Serial

RS-422, RS-485, RS-232

IEC-61968 IEC-61970

Ethernet

Ethernet IEEE 802.3

Bus-based

CANBus

Standard

Modbus

WIRELESS

Profibus

IEC61850

SCADA

PLC RS Ethernet IEEE 802.3

IEEE STD. 1815- 2012, IEEE 60870

WLAN

Wifi

IEC61850

Cellular networks

Lifi

IEC61850

Bluetooth

IEEE 802.15.1

ZigBee

IEC-61968 IEC-61970

WiMax

IEEE 802.16j/m

3G

IEC61850

4G 5G

Towards an IoT/Big-Data Platform for Data Measurements …

283

2.2 Data Sensing and Communication The building’s transition to a smart MG system requires a distributed communication as well as a distributed system for the management. For instance, the IEEE Standards Association permitted the OpenFog reference architecture as an official standard that is improved by the OpenFog Consortium incorporating Microsoft, Intel, Princeton University, Dell, Cisco Systems, and ARM Holdings, under the name of IEEE 1934 [8], for IoT communication in MG systems. The standard classifies the interaction of interconnected units for streaming and real applications into three classes: i) Machine-to-Machine: any component can send and receive data or command to and from any other one using the capabilities of IoT, ii) People-to-Machine: the users can control each machine, which can be adapted depending on the occupants’ comfort, and iii) People-to-People: the users can exchange data through a cooperation mechanism. In our studies, sensors are used to collect contextual data, which are submitted to our remote platform for storage or further analytics. Mainly, current and voltage sensors are installed to gather data from RESs, storage devices, and load consumption. Generally, three types of sensors outputs can be specified, current loop output, voltage output, and digital output. The sensors based on current loop output are calibrated over a specified range against various engineering units to convert the real value, such as the temperature, pressure, and current, to an equivalent current value measured by the deployed microcontroller. The output of the individual calibration certificate is compared to a mathematical manipulation improved in order to obtain the absolute value. The operation of the loop is straightforward characterized by the conversation of the sensors’ output signal to a proportional current, with the less current (generally on mA) presents the sensors’ zero-level output and the high current represents the sensor’s full-scale output. Therefore, the voltage output sensors can have various voltage outputs (5 V, 10 V, from −5 V to +5 V and more) that are calibrated over this range against various units. The output is compared to the individual calibration certificate for the specified measurement range for which the sensor is selected. Generally, two types of voltage output signals are considered, single-ended measurements and differential. Single-ended measurement considers the voltage difference between a wire and the ground and the noise is only on the positive wire, therefore, it is still measured along the output voltage of the sensor. The differential voltage signal is known as “floating”, in which no reference to the ground is considered. The measured signal is taken as the voltage difference between the two wires. The noise elimination is considered as a main benefit of this method because it is added to both wires and can then be filtered out by the common-mode rejection of the data acquisition system. However, other types of sensors use digital signal rather than analog signals of current and voltage because it is more robust face to the noises and the measurement perturbs. The two most common digital communications are RS-232 and RS-485. The RS-485 does not recommend any communications protocol, it only specifies the electrical characteristics of the generator and the receiver. The main examples of communication protocols employing the RS-485 are Canbus, Modbus, Profibus, and SDI-12.

284

A. Elmouatamid et al.

Fig. 2 Monitoring platform architecture

Therefore, the main advantage of the RS-485 system is the flexibility to transport the signal. It mains that each sensor is defined by its individual address offering the possibility to be attached to one cable. Multiple sensors use one port on the data logger saving significantly the installation cable and data logging costs. Mainly, our deployed MG system is equipped with component for measuring and collecting both power consumption and production. In fact, a set of current and voltage sensors are installed for power measurement as shown in Fig. 2. The system is equipped by an Arduino, which allows collecting the data from different sensors and send it to a micro-computer (Raspberry pi) connected directly to the IoT/Big-Data platform. In fact, sensors transmit analog signals to the microcontroller, which converts them into numerical data. The next section introduces our MG system’s architecture. In particular, we highlighted the necessity of integrating recent ICTs and IoT/Big-Data technologies for gathering external and internal data, which have been used to generate predictive actions (e.g., regulating the room temperature by forecasting the building’s occupancy, ventilation speed variation according to the forecasted CO2 , intelligent and predictive control of energy flows management using forecasted power production, consumption, and battery SoC).

2.3 IoT/Big-Data Platform for Data Collection The aim was to develop a research testsite integrating the different components of an MG system. As shown in Fig. 2, the system integrates PV panels, WT, batteries, and the utility grid connected together in order to supply electricity to the loads in

Towards an IoT/Big-Data Platform for Data Measurements …

285

the building according to actual contexts. The system is monitored by an IoT/BigData platform, which is used to collect, analyze, and store the data for EM and control strategies development. Moreover, several scenarios are deployed in order to develop a research platform that considers the concept of MG systems with the different components of the different layers. Real-time and context awareness information could be exploited for developing predictive and adaptive context-driven control approaches using recent IoT/Big-data technologies together with real-time and ML (Machine-learning) algorithms [9, 10]. The communication platform is composed of four main layers: i) sensors/actuators layer, ii) data acquisition layer, iii) data processing layer, iv) data visualization/storage layer. These layers are installed together with further services and applications for context-driven control (Fig. 3). The MG is mainly equipped with a component for measuring the different necessary parameters (e.g., current, voltage, temperature, wind speed), for interacting with the passive and the active equipment, for regulating the comfort for the occupancy, and for managing the power production and consumption. In fact, a set of sensors is installed depending on the desired scenarios. Regarding the data acquisition layer, a Kaa application is developed (i.e., IoT technique) [11] which is used to receive data from deployed sensors. We have also used the MQTT that is a publish-subscribe-based protocol for IoT applications. For data processing and storage, Storm services are used [12]. Mainly a topology composed of Spouts and Bolts was designed and developed to allow receiving and processing streaming data from sensors. The spouts receive the data from the Kaa application, and then transmit it to the Bolts for processing and storage into the database (e.g., MongoDB) for further in-depth analysis. The services layer includes real-time visualization and storage together with the control of active equipment and RESs power production and consumption monitoring and management. The platform was used for data gathering and processing of internal and external building’s context. For instance, it was used to have occupant information (e.g., number, presence, behavior, activities) in the building, since is a key input for control approaches in energy-efficient buildings (e.g., active systems control)

Fig. 3 IoT/Big-Data platform architecture

286

A. Elmouatamid et al.

[9]. In fact, comprehensive occupancy information could be integrated to improve the performance of occupancy-driven control of HVAC, lighting, and ventilation systems.

3 Experimental Platform of MG Systems Approaches developed in the frame of this work are deployed and tested in real-sitting scenarios in two MG systems. The first, named EEBLab (Energy Efficient Building Laboratory) is used as a testbed for testing scenarios and validation. The second MG system is a real house, which is deployed during Solar Decathlon Africa competition, and it is deployed in Green Energy Park in Benguerir, Morocco. Both MG systems are used actually as a research platform for MG system development. The main aim is to measure, collect, analyze, and store the different data in the deployed MG systems. Mainly, the collected data are, especially, the indoor/outdoor temperature, the indoor CO2 variation, the weather conditions, and the power production/consumption. Therefore, the measured data are used to develop different scenarios as the occupant’s detection, the comfort amelioration, and the EM/control for the deployed RESs/storage systems. In this part, we focus on the different measurements realized for the energetic system. Regarding the external context, we have deployed a weather station. In fact, for several scenarios, we need to collect internal and external context data. We have built a weather station near the WT and PVs in order to have as precisely as possible the data for wind speed, direction, irradiation, temperature, and humidity. The weather station was deployed and used to collect the data for real-time visualization and processing for further usage by other building’s services and applications. Moreover, D/R control strategies are therefore required for an efficient management of energy flows by considering the intermittent RES generation and the delay might occur between RESs power generation and the actual building’s demand. The main aim is to develop a control card to test the different studied control strategies for EM. Unlike existing systems, which are used as a black-box to collect and manage the energy in MG, the deployed control card allows us to measure, monitor, manage, and deploy our algorithms [13]. Therefore, to develop an optimal and reliable control strategy, multiple objective functions should be taken into account, like the continuity of the power generated to the loads, the electrical energy cost, the smoothing of RESs production„ and the C/D cycle of the batteries [13]. For that, a control strategy should be deployed to satisfy the constraints designed by the optimization functions. The communication infrastructure is employed for total electrical energy measurement and management. The installed infrastructure provides the autonomous operation with the required measurements, decisions, and command by gathering data through the sensors and producing the commands for the Hardwar/Software (Hw/Sw) card presented in Fig. 4, which is connected to the control switches used in the hybrid system [10]. For the different scenarios, the proposed IoT/Big-Data platform could be used to measure different parameters in the MG system. Depending on the studied scenario,

Towards an IoT/Big-Data Platform for Data Measurements …

287

Fig. 4 The deployed Hw/Sw control card

Fig. 5 Power measurement scenario in our deployed MG system platform

suitable sensors are selected and can be connected to this platform for data collection, monitoring, and processing. Mainly, the measured parameter is used to control efficiently the deployed MG system. Moreover, a case study is presented for a largescale installation in which the developed control card and the IoT/Big-Data platform are used to measure and store the data collected by the deployed current and voltage sensors for the total installed RESs, battery storage system, and load consumption. As shown in Fig. 5, the green curve presents the power generated from the eight PV panels for 24 h. This power is calculated by measuring the PV current and voltage variability during the day, which depends on the weather conditions (e.g., temperature, irradiance) changeability. At the same time, the battery SoC is calculated using our battery characterization system installed in the MG system. Moreover, the power consumption is measured and stored for the same period. These parameters are the main input for the EM strategy. However, due to the limited number of pages of this work, the obtained results are summarized in Table 2 for the different measurements realized in the deployed MG system focusing on that are used for EM in the deployed hybrid RESs system. Table 2 presented the obtained results for 10 days of the test. Actually, the house

288

A. Elmouatamid et al.

Table 2 Energy balance in the deployed MG system

is used as a research platform and it is planned to deploy the test control scenarios, tested in a medium-scale, for large and real rural MG systems.

4 Conclusions The main aim of this work is to introduce an MG platform by focusing on communication and data monitoring concepts. The MG platform connects the building’s components using sensing/actuating, IoT/Big-Data technologies in order to leverage real-time gathering and data processing for RESs production and loads’ consumption. The platform was deployed and several scenarios have been tested and evaluated. The proposed data gathering techniques can be an interesting reference for the researches to have their proper platform for data collection that can be used in the research works while the deployed mechanisms are open sources. In the future works, several scenarios will be developed using the deployed IoT/Big-Data platform, especially, that concerns EM and control, the active/passive equipment control, and the occupant’s comfort management. In addition, machine-learning algorithms will be developed and tested in real scenarios using real data to train the proposed algorithms. Acknowledgments This work is supported by MIGRID project (grant 5-398, 2017–2019), which is funded by USAID under the PEER program. It is also partially supported by HOLSYS project, which is funded by IRESEN (2020–2022).

References 1. Kabalci E, Kabalci Y (2019) From smart grid to internet of energy. Academic Press 2. Asaad M, Ahmad F, Alam MS, Sarfraz M (2019). Smart grid and Indian experience: a review. Resour Pol 101499

Towards an IoT/Big-Data Platform for Data Measurements …

289

3. Wang K, Hu X, Li H, Li P, Zeng D, Guo S (2017) A survey on energy internet communications for sustainability. IEEE Trans Sustain Comput 2(3):231–254 4. Tsiatsis V, Karnouskos S, Holler J, Boyle D, Mulligan C (2018) Internet of Things: technologies and applications for a new age of intelligence. Academic Press, San Francisco 5. Elmouatamid A, NaitMalek Y, Bakhouya M, Ouladsine R, Elkamoun N, Zine-Dine K, Khaidar M (2019) An energy management platform for micro-grid systems using Internet of Things and Big-data technologies. Proc Inst Mech Eng Part I J Syst Control Eng 233(7):904–917 6. Dede A, Della Giustina D, Massa G, Cremaschini L (2016) Toward a new standard for secondary substations: the viewpoint of a distribution utility. IEEE Trans Power Delivery 32(2):1123–1132 7. Parhizi S, Lotfi H, Khodaei A, Bahramirad S (2015) State of the art in research on microgrids: a review. IEEE Access 3:890–925 8. Chiang M, Zhang T (2016) Fog and IoT: An overview of research opportunities. IEEE Internet Things J 3(6):854–864 9. Elkhoukhi H, NaitMalek Y, Bakhouya M, Berouine A, Kharbouch A, Lachhab F, Essaaidi M (2019) A platform architecture for occupancy detection using stream processing and machine learning approaches. Concur Comput Pract Exp e5651 10. Elmouatamid A, NaitMalek Y, Ouladsine R, Bakhouya M, Elkamoun N, Khaidar M, ZineDine K (2020a) A micro-grid system infrastructure implementing IoT/big-data technologies for efficient energy management in buildings. Submitted to ATSPES’1 (Advanced Technologies for Solar Photovoltaics Energy Systems), Book Chapters (Springer Book). 11. Kaa (2020) IoT technique. https://www.kaaproject.org/. Accessed 10 Oct 2020 12. Storm (2020) Apache Storm. https://storm.apache.org/. Accessed 10 Oct 2020 13. Elmouatamid A (2020) MAPCAST: an adaptive control approach using predictive analytics for energy balance in micro-grid systems. Int J Renew Energy Res (IJRER) 10(2):945–954 14. Elmouatamid A, Ouladsine R, Bakhouya M, El Kamoun N, Khaidar M, Zine-Dine K (2021) Review of control and energy management approaches in micro-grid systems. Energies 14(1):168

Smart Hospitals and Cyber Security Attacks Yassine Chahid, Mohammed Benabdellah, and Nabil Kannouf

Abstract Until now, the Covid-19 pandemic was infected more than 37,167,291 people around the world in 212 countries and territories, killed 1,071,181 people since its appearance in December in Wuhan, of which more than 7,876,942 people in the United States, 6,979,434 people in the India and 5,057,958 people in Brazil (Novel Coronavirus (2019-nCoV) situation reports - World Health Organization (WHO), 2019 Novel Coronavirus (2019-nCoV) in the U.S -. U.S. Centers for Disease Control and Prevention (CDC), symptoms of Novel Coronavirus (2019-nCoV) – CDC, china Travel Advisory - U.S. State Department, accessed January 31, 2020.). This large number of infected people caused saturation in hospitals and put a big pressure on the intensive care units and hospital beds. While the medical staff are fighting to save the lives of these patients, others are playing with these lives by making cyber-attacks on research centers and health institutions, recently case was in September 2020, a woman in Germany died during a ransomware attack on the Duesseldorf University Hospital. Smart hospitals are one of the most affected because they use its technology (Smart hospitals) that generates a lot of personal and critical data, these hospitals are equipped with “things” that are used for patient monitoring, maintenance, remote operation and control like wearables, smart pills, smart beds, remote monitoring systems, RTHS (Real-time Health Systems), biosensors, glucose measurement devices, robots, equipment monitoring devices, and more. In this paper, we will propose a new secured system model to keep these hospital systems safe against cyber-attacks by using verification and classification of treatment requests and controlling the patients’ health using “sensors” to ensure the safety of the requested operations. Keywords Coronavirus · Internet of Things · Security · Cyber-attacks · Hospitals

Y. Chahid (B) · M. Benabdellah Faculty of Sciences, ACSA Laboratory, Mohamed First University, Oujda, Morocco N. Kannouf ENSA Al-Hoceima, DSCI, LSA Laboratory, University of Abdelmalek Essaadi, Tétouan, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_27

291

292

Y. Chahid et al.

1 Introduction The Internet of things continues to enter our lives slowly every day and affects many areas [2]. Healthcare is one of the richest domains that contain the biggest connected objects, thermometers, ultrasounds, automatic respirators, glucose monitors, digestible sensors, smartwatch, electrocardiograms and others, have become connected and letting patients track their health and facilitate the doctor’s work by automating some tasks like operations, drugs injections or insulin control [3]. Several hospitals have started to use smart beds, which can be equipped with sensors like injectors or blood collectors, insulin pens, automatic respirators and others, in order to facilitate the patient treatment operation and provide great support without the need for a doctor or nurse intervene [4]. The internet of things in the field of healthcare can also help patients to take their medicament with the right doses directly from their homes without having to stay in the hospital, this can also guarantee doctors better monitoring their patients and having an alert if ever there is the slightest concern [5]. IoT market in healthcare is predicted to exceed $10 billion by 2024 [6], which means billions of critical and personal data that will circulate on networks. In 2016, 73 million healthcare devices were connected worldwide. In 2020, they will be 161 million [7]. Growth will mainly be driven by 3 trends: the rising average age of the world’s population, the prevalence in some countries of diseases requiring regular monitoring (such as diabetes) and the growing demand for quantifiable fitness solutions. This paper is organized as follows: in the first section, we give a state of the art of the MIMT attack and the related work already done by other research to improve security against this attack, in the second section we will present a new model that can help us to improve security inside of smart hospitals, the last section will discuss the results obtained.

1.1 Internet of Things and COVID-19 The large amount of data generated by the IoT in the healthcare field forces us to fear, because this information attracts attackers and make this data accessible everywhere in the world if we do not apply strong security against these attacks. According to [8] Large numbers of domain names related to Covid-19 have been registered in recent weeks, more than 50% of which are thought to be part of “lure and decoy” operations. Phishing attempts to have soared by over 600% since the end of February, including traditional impersonation scams but also business email compromise (BEC) and extortion attacks [9]. Other e-mail campaigns are asking users to transfer money to sites claiming to be collecting charitable donations or to fake e-commerce sites offering personal protective equipment.

Smart Hospitals and Cyber Security Attacks

293

1.2 Cyber-Attack Examples The healthcare domain is not immune to these attacks, below are some events examples made during the COVID-19: 1. 2. 3.

4. 5.

May 2020, the FBI issues Flash Alert MI-000124-MW, covering specific indicators for COVID-19 phishing email campaigns. April 2020, Attacks/campaigns using COVID-themed lures has continued to increase over the last week. April 2020, several Android-focused campaigns were observed spreading the Anubis and Cerberus banking trojan to victims seeking additional information on Coronavirus in their area. March 2020, a fake email using CORONAVIRUS information disguised as an alert from the “Department of Health” was propagated. April 2020, fake charity and donation have become more and more frequent since the onset of the pandemic.

2 Related Work Many efforts have been performed to develop a secured IoT healthcare system. In this section, we cite some works that are reviewed and summarized. Fathi Ibrahim Salih et al. have proposed an IoT security risk management model for healthcare industry [10], as a solution, this paper proposes an enhanced IoT risk management model for healthcare with consideration of three risk categories: Secured Technology, Human Privacy and Trustable Process and Data. Bahar Farahani et al. discuss applicability of IoT in healthcare field by presenting an architecture of IoT eHealth ecosystem [11]. Behailu Negash et al. [12] presents a chapter focuses on a smart e-health gateway implementation for use in the Fog computing layer, also, connecting a network of such gateways, both in home and in hospital use. Simple healthcare scenarios are presented. The features of the gateway in the Fog implementation are discussed and evaluated. Nasser S. Abouzakhar et al. [13] have published a paper that presents different risks and security threats in the IoT, some security measures and solution models as well as a vulnerability assessment approach for the IoT in the healthcare field. Florian Kammüller [14] show in his paper, how to derive formal specifications of secure IoT systems by presenting a process that uses the risk assessment strategy of attack trees on infrastructure models, his proposed process in the paper uses two features to refine a system specification until expected security and privacy properties can be proved. He illustrates, in the final part of his paper, the stepwise application of the proposed process in the Isabelle Insider framework on the case study of an IoT healthcare system. Maria Almulhim and Noor Zaman [15] have published research that proposes a secure group-based lightweight authentication scheme for IoT based E-health applications, the proposed model provides mutual

294

Y. Chahid et al.

authentication and energy efficient, and computation for healthcare IoT based applications, which will use elliptic curve cryptography (ECC) principles that provide mentioned featured of suggested model.

3 The Proposed System The most dangerous attack in healthcare is that which relies on the use of sensors (insulin pens, automatic respirators, drugs injection…etc.) to inject higher/lower doses according to the patient’s health condition, stop automatic respirators or modify the volume of oxygen transmitted, or even the attack at the same time of remote surgery, these attacks can cause immediate patient death. To cope with this type of attacks, we are going to propose a secure system model which is based on the classification of the requested operation types for patients and to control access or any treatment according to these categories. In this table (Table 1), we give an example of attack which is based on the use of Man-In-The-Middle (MITM) which is very well-known in the attack’s types, MITM is an attack type when an attacker positions himself in the middle of the conversation and precisely between a user and an application to imitate one of the parties, making it appear as if a normal exchange of information is established.

3.1 Man in the Middle Attack The goal of this attack is to steal personal data: login credentials, credit card numbers or account details. Victims are generally the financial applications, e-commerce websites or smart hospitals. The MITM attack come in many types [16]. Table 1 Healthcare cyberattack workflow Heading level

Example

1

The attacker tries to connect to the network

2

It creates a Man-In-The-Middle (MITM) attack

3

It launches the injection action of a mortal dose of chloroquine to the patient using the drugs injector

4

The mortal dose is injected directly without any verification

5

The patient died immediately

Smart Hospitals and Cyber Security Attacks

295

3.2 The Proposed System The proposed model will be positioned between the hospital’s external server and the client, which in our case is the hospital (Fig. 1). The system will aim to retrieve requests and be able to process them according to their severity, the system will play the orchestrator role between any patient’s equipment like electrocardiogram (ECG), Electromyography (EMG), Electrogastrogram (EGG), Insulin pens…etc.) using tags [17, 18] (Fig. 2). To explain this algorithm, we will analyze each step (Table 2):

4 Implementation and Results Analysis Before continuing reading this article, it should be noted that this paper is a computer science paper and does not encourage the use of any medication type against COVID19 (We used the chloroquine just as an example).

4.1 The Prerequisites The proposed system is implemented using Python Web APIs and MySQL as database for storing logs and processed requests. We simulate a smart hospital, patients, and we regulated the categories as follows:

Fig. 1 Proposed secured system model

296

Y. Chahid et al.

BEGIN needed_value 0 isSafe false levels empty_list values empty_table_params levelList Get_category_levels // The system verify the criticity of the operation critic verify_criticity(RequestedOperationType, values) tag_level_action IF isSafe do_operation ELSE PRINT "Potential critical action” PRINT "Lanching internal checks” // Get chloroquine, glucose, tension values of the patient values Get_values(RequestedOperationType) IF level 2 needed_value calculate_needed_value do_action(needed_value) ELSE reject_action send_report_to_doctor save_operation_logs END IF END IF END

Fig. 2 Proposed system algorithm Table 2 Healthcare cyberattack workflow Step

Example

1

The attacker tries to connect to the network

2

It launches the injection action of a mortal dose of chloroquine to the patient using the drugs injector

3

The system retrieves the list of categories and tries to classify this request according to his criteria: nature of the request (injection of chloroquine dose, oxygen increasing, blood recovery…), the parameters of the request (dose value, value for increasing the oxygen rate…)

4

The system checks if the request is considered as safe or not

5

If the demand is considered as critic, the system launches the internal verification using sensors (Make an ECG test for example, calculate the blood pressure…etc.)

6

Based on these checks, the system can accept or reject the demand

7

The system logs and send a report to a doctor

Smart Hospitals and Cyber Security Attacks Table 3 Patient’s status with injected dose values without system implementation

1.

2.

297

Case

Requested chloroquine dose

Patient’s health

1

1000

Died

2

5000

Critic

3

5000

Critic

4

120

5

3000

Died

6

430

Critic

7

1000

Died

8

1000

Died

9

100

10

2670

Normal

Normal Died

If the action is of the injection type then automatically it is considered as level 2, if the value of certain parameters such as glucose exceeds 500 ml then the action goes to level 3. like oxygen for example, the action in this case is considered level 3. If the action is not an injection or stop, it is considered level 1.

All these rules can be marked and stored in a simple text file rules.txt that it must be encrypted and be a Read Only to prevent its modification from any intruder. The calls in this system are Asynchronous calls, because if ever the actions are considered as critical, the system will launch an internal check (glucose check, tension check…etc.) which will take a little time before completing the action. We have modeled 5 cases of attacks, in each case we put different information about the action type and the value of the demand and below the summary table (Table 3): As we can see in the table of results, we simulated a chloroquine injection test on 10 patients, each patient has an initial dose on his body. We have patients who needs chloroquine injection and others do not need that. Without implementation of the system, the attacker injected into the 10 patient’s blood an important and fatal dose, these doses were immediately killed most patients (5/10), worsening the situation of the (3/10) others while (2/10) were able to survive. Below a graph which shows the graph of the evolution of the patients after the attack (Fig. 3): The table (Table 4) shows the results of the patient’s health after the implementation of the system. As we can see, the system can prevent the injection of most of the doses by classifying the requests by category and degree of severity, then triggered the internal reverification of chloroquine for most patients before authorizing the execution of the injection. The system was able to save these patients by refusing 7 requests (Fig. 4). As we see, the system rejects 80% of the requests because they are considered like attacks. The result now provides evidence to use the proposed model in order to enhance smart hospitals security, these results confirm that this a good choice for using an internal system that can check MIMT sent requests before injecting these doses.

298

Y. Chahid et al.

Fig. 3 Patient’s status graphic with injected dose values without system implementation

Table 4 Patient’s status with injected dose values in case of system implementation Case

Requested chloroquine dose

Patient’s health

1

1000

Normal

2

5000

Normal

3

5000

Normal

4

120

Normal

5

3000

Normal

6

430

7

1000

Normal

8

1000

Normal

9

100

Normal

10

2670

Normal

Critic

When comparing our results to those of older studies, it must be pointed out that our proposed model can be integrated with any third system, also, the model can improve the security of smart hospitals against MITM attacks because he is the last who check requests and it is the closest to patient and this can guarantee that no one can modify data after the check.

Smart Hospitals and Cyber Security Attacks

299

Fig. 4 Patient’s status graphic with injected dose values with system implementation

5 Conclusion In summary, based on this experimental and theoretical study of this cyberattack, we can say that the proposed system is used to increase the level of security against IoT attacks, even if it will be difficult to launch a MITM attack, we must work on its security. It must be known that this solution can be applied also in other IoT automated fields like transport or industry and it can be combined with other algorithms to guarantee the three pillars of IoT that are integrity availability and confidentiality of data.

References 1. Novel Coronavirus (2019-nCoV) situation reports - World Health Organization (WHO), 2019 Novel Coronavirus (2019-nCoV) in the U.S -. U.S. Centers for Disease Control and Prevention (CDC), symptoms of Novel Coronavirus (2019-nCoV) – CDC, china Travel Advisory - U.S. State Department. Accessed 31 Jan 2020 2. Chahid Y, Benabdellah M, Azizi A (2017) Internet of things security. In: International conference on wireless technologies, embedded and intelligent systems (WITS). https://doi.org/10. 1109/WITS.2017.7934655 3. Al-Refaie A, Chen T, Judeh M (2018) Optimal operating room scheduling for normal and unexpected events in a smart hospital. Oper Res Int J 18:579–602. https://doi.org/10.1007/s12 351-016-0244-y 4. Hurysz T, Romisher A, Campbell D, Ottenstein Maia (2020) Utilizing the smart rooms device to improve patient experience in hospitals. Phase 1. Paper 12. https://jdc.jefferson.edu/si_dh_ 2022_phase1/12 5. Gandhi DA, Ghosal M (2018) Second international conference on inventive communication and computational technologies (ICICCT). https://doi.org/10.1109/ICICCT.2018.8473026

300

Y. Chahid et al.

6. Internet of Things (IoT) in Utility - Global Market Research and Forecast, 2015–2025 Report ID: HeyReport6861 | Number of pages: 80 | Publish Date: Feb 2019 | Publisher: HeyReport | Category: ICT and Media 7. Internet of Things (IoT) in Healthcare Market Size, Share & Trends Analysis Report By Component (Service, System & Software), By Connectivity Technology (Satellite, Cellular), By End Use (CRO, Hospital & Clinic), By Application, and Segment Forecasts, 2019–2025, Report ID: 978-1-68038-857-2 8. https://blog.checkpoint.com/2020/03/19/covid-19-impact-as-retailers-close-their-doors-hac kers-open-for-business/ 9. Phil Muncaster UK / EMEA News Reporter, Infosecurity Magazine https://www.infosecuritymagazine.com/news/covid19-drive-phishing-emails-667 10. Salih FI et al. (2019) IoT security risk management model for healthcare industry. Malaysian J Comput Sci [S.l.] 131–144. ISSN 0127-9084. https://doi.org/10.22452/mjcs.sp2019no3.9. Accessed 10 May 2020 11. Farahani B, Firouzi F, Badaroglu VCM, Constant N, Mankodiya K (2017) Towards fog-driven IoT eHealth: promises and challenges of IoT in medicine and healthcare. Elsevier. https://doi. org/10.1016/j.future.2017.04.036 12. Negash B et al. (2018) Leveraging fog computing for healthcare IoT. In: Rahmani A, Liljeberg P, Preden JS, Jantsch A (eds) Fog computing in the internet of things. Springer, Cham. https:// doi.org/10.1007/978-3-319-57639-8_8 13. Abouzakhar NS, Jones A, Angelopoulou O (2017) Internet of things security: a review of risks and threats to healthcare sector. In: IEEE international conference on internet of things (iThings) and IEEE green computing and communications (GreenCom) and IEEE cyber, physical and social computing (CPSCom) and IEEE smart data (SmartData). https://doi.org/10.1109/iTh ings-GreenCom-CPSCom-SmartData.2017.62 14. Kammüller F (2019) Combining secure system design with risk assessment for IoT healthcare systems. In: IEEE international conference on pervasive computing and communications workshops (PerCom workshops). https://doi.org/10.1109/PERCOMW.2019.8730776 15. Almulhim M, Zaman N (2018) International conference on advanced communication technology (ICACT). https://doi.org/10.23919/ICACT.2018.8323802 16. Chahid Y, Benabdellah M, Azizi A (2017) Internet of things protocols comparison, architecture, vulnerabilities and security: State of the art. In: ACM international conference proceeding series, a65 17. Kannouf N, Labbi M, Benabdellah M, Azizi A (2018) Security of information exchange between readers and tags. In: Maleh Y, Ezzati A, Belaissaoui M (eds.) Security and privacy in smart sensor networks, pp 368–396. IGI Global, Hershey. https://doi.org/10.4018/978-1-52255736-4.ch016 18. Kannouf N, Douzi Y, Benabdellah M, Azizi A (2015) Security on RFID technology. In: 2015 international conference on cloud technologies and applications (CloudTech), Marrakech, pp 1–5. https://doi.org/10.1109/CloudTech.2015.7336997 19. Eddy M, Perlroth N (2020) Cyber Attack Suspected in German Woman’s Death, New York Times, 18 September 2020

Design of an Automatic Control and IoT Monitoring System for Dual Axis Solar Tracker Fatima Cheddadi, Hafsa Cheddadi, Youssef Cheddadi, Fatima Errahimi, and Ikram Saber

Abstract This paper develops a combination of the implementation of a dual-axis solar tracker and real-time monitoring system based on the use of the Internet of Things. To obtain maximum efficiency, the photovoltaic panel must be constantly oriented perpendicular to the sun’s rays. However, the position of the sun varies continuously during the day from east to west along the azimuthal and during the seasons along a zenithal axis. For this reason, we have designed and implemented a dual-axis solar tracker that allows real-time tracking of the apparent movement of the sun to maximize the power extraction. The proposed sun tracker is equipped with a system of monitoring to supervise and evaluate its performances, by collecting the electrical parameters from the PV panels, and the meteorological parameters from its surface. Both the control and the monitoring is established using the ESP32 embedded board, and the designed support of solar tracker is made using a 3D printer. The dual-axis solar tracker performances are compared with a fixed system. Keywords Photovoltaic system · Internet of Things · ESP32 · Monitoring · Dual axis solar tracker

1 Introduction Due to the high demand of energy and the ever-increasing use of fossil resources, the whole world has led to the development of the alternative solution and have moved towards using renewable energy such as solar, wind, biomasses and hydraulic energy to decrease the uses of fossil resources. In addition to meet the growing energy demand, another advantage of the use of renewable energy is to reduce CO2 emissions and the earth warming. F. Cheddadi (B) · H. Cheddadi · Y. Cheddadi · F. Errahimi SIGER, USMBA, Fez, Morocco e-mail: [email protected] I. Saber TSI, USMBA, Fez, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_28

301

302

F. Cheddadi et al.

In this way, the photovoltaic (PV) energy is one of the cleanest energy resources and the most ecological type of energy to use [1]. It is directly converted into electrical energy by the use of PV modules. However, the harvested power mainly depends on climatic variations namely the temperature and irradiance, where the need to track the maximum power point (MPP). For this purpose, there are two strategies to extract the maximum power. The first one is an electronic strategy based on the called MPPT methods that try to find optimal operation points of the PV generators without any movement. The second is based on electro-mechanical trackers that track the sun movement during the day. In the literature, several researchers have developed and implemented different types of maximum power point tracking controller. These controllers differ from each other in terms of many parameters, namely the number of required sensors, convergence rate, rapidity of tracking the MPP when the climatic condition suddenly change, complexity, and the cost of implementation [2]. Some of the MPPT strategies are based on adjusting the duty cycle of the DC-DC converter [2–8]; the duty cycle plays the role of an adaptation stage between the load resistance seen by the PV system and the internal PV module. The other strategies are based on the implementation of solar trackers [9, 10], where there are two main families of solar trackers: the passive ones and the actives that include the single-axis and double-axis trackers. The energy supplied by the PV panel is highly dependent on the amount of sunlight absorbed by the panel. This amount depends on the orientation of the panel in relation to the sun. In order to collect the maximum energy, the PV panel should be constantly oriented perpendicular to the sunlight [11]. Every day this angle decreases or increases, the surface area (m2 ) of the panel exposed to the rays decreases and therefore the power produced decreases, hence the importance of the orientation of the panels in relation to the position of the sun [12]. Another factor that influences the performance of the PV panel is the tilt angle, which is the angle formed by the plane of the solar panel in relation to the horizontal. The purpose of PV monitoring systems is to offer continuously a clear information to improve energy efficiency, many researchers have proposed a monitoring system of PV station, which help also on fault detection [13–15]. Authors in [16] have presented a real-time monitoring system, which contain an alert system to a remote user, when there is a deviation of solar power. The main use of the system of monitoring in this work is to highlight the effectiveness of the dual axis solar tracker by comparing the measured power from the system of tracking with a fixed system. The paper is organized as follows: Sect. 2 presents the proposed tracker. Section 3 presents the PV monitoring system. Results and a discussion are described in Sect. 4. The paper is concluded in Sect. 5.

Design of an Automatic Control and IoT Monitoring System …

303

2 Dual Axis Solar Tracker Based on Light Sensors In order to collect the maximum amount of energy, the PV panel must be constantly oriented perpendicularly to the sun’s rays; this can be ensured by a solar tracking system allowing to follow the sun throughout the day. This method is based on light sensors or photo-resistors to determine the position of the sun at any time, allowing instantaneous tracking, and including optimal panel orientation. In this type of techniques, tracking is carried out independently of the zone in which the system will be located. The two types of solar trackers that exist are either single-axis or double-axis, which are either are based on the same principle of pursuit illustrated in Fig. 1. The technique used for this study is based on the difference in incident illumination on photo-resistance or LDR (Light-Dependent-Resistor) light sensors separated by opaque walls and placed on the PV panel. The control and command circuit compares the signals emitted by the sensors by calculating their differences and then sends pulses back to the motor to reposition the panel perpendicular to the sunlight.

LDR1 LDR2 LDR3

System of control (ESP32)

LDR4

M1 Vertical

M2 Horizontal

Fig. 1 General principle of tracking control LDR Top left

LDR bottom left

Fig. 2 Position of the four LDRs

LDR top right

LDR bottom right

304

F. Cheddadi et al.

Start

The initial position of the sun

TopRight=analogRead (LDR TopRight) TopLeft=analogRead (LDR TopLeft) BottRight =analogRead (LDR BottRight)

Read the analog value of each LDR sensor

AvTop= (TopRight + TopLeft) / 2 AvBott= (BottRight + BottLeft) / 2 AvRight= (TopRight + BottRight) / 2 AvLeft= (TopLeft + BottLeft) / 2

Calculate average values

Calculate the differences (azimuth && elevation)

| DiffAzim | NO

DiffElev= diffVerti= AvTop - AvBott DiffAzim=diffHoriz= AvRight AvLeft

Yes

Yes

DiffAzim > 0

Stop the left-right servomotor

The left-right servomotor moves the panel to the right.

NO Left-right servomotor moves the panel to the left NO

Yes | DiffElev | 0

The Up-down servomotor moves the panel downwards

Fig. 3 Flowchart of the double-axis tracking method

Up-down servomotor moves the panel upwards

Design of an Automatic Control and IoT Monitoring System …

305

Fig. 4 Solar tracker prototype

Starting from the previous general principle, to ensure a complete tracking of the solar path, 4 LDRs distributed in the 4 corners are used. The technique used consists in checking the equality of illumination of four light sensors distributed in the four quadrants represented in Fig. 2. The operating principle is based on the tracking algorithm described in the following flowchart Fig. 3. In order to prototype our solar tracker Fig. 4, we use the 3D printing, which is recognized as one of the biggest technological revolutions in the world today. It represents a very important new technique for the manufacturing processes of three-dimensional solid objects.

3 Photovoltaic Monitoring System In this part, we will present the components necessary for the realization of the prototype. Some components collect data (INA219 as a current/voltage sensor, BH1750 as an illumination sensor, DHT11 as a temperature sensor). Other components act as microcontrollers that process and analyze the data and provide wireless connectivity (dual core ESP32), and for monitoring PV production, a web server is embedded to store the daily collected data using the sensors to follow the metrics of our monitoring system (ThingSpeak). In this proposed conceptual system, the ESP32 DEVKITV1 board acts as a microcontroller that processes incoming data from various sensors. The ESP32 is a series of low-cost, low-power systems with Wi-Fi and Bluetooth integrated in a single chip. This card is chosen because it reduces the cost of the monitoring system. The ESP32 card is based on the microcontroller Xtensa LX6 dual core 32-bit processor Tensilica, it has built-in Wi-Fi and Bluetooth, it runs 32-bit programs. The clock frequency can go up to 240 MHz and has a RAM of 512 KB. It also offers a wide variety of peripherals, such as: CAN, CNA, UART, SPI, I2C, it is equipped with an

306

F. Cheddadi et al.

Current Current (mA)

100 80 60 40 20 0 13:26

13:33

13:40

13:48

13:55

14:02

13:48

13:55

14:02

13:48

13:55

14:02

Time

Voltage (V)

Voltage 14 12 10 8 6 4 2 0 13:26

13:33

13:40

Time

Power (mW)

Power 1200 1000 800 600 400 200 0 -20013:26

13:33

13:40

Time

Fig. 5 Current, voltage and power monitored

integrated Hall effect sensor and an integrated temperature sensor.so, it can support monitoring and tracking programs. The developed monitoring system is capable to measure in real time the energy produced by a photovoltaic station composed of 3 polycrystalline PV panels placed

Design of an Automatic Control and IoT Monitoring System …

307

in parallel capable to generate 1.6 W of power and 12 V of voltage for each one at the STC. To follow up and verify their correct operation and to analyze and have an overview of the entire system, the figures above present the results of the monitoring on 28/10/2020 in Fez Morocco. Three parameters namely Power, current, and voltage, are monitored using the experimental prototype Fig. 5. This latter is connected using WIFI and visualized via ThingSpeak platform. Between 13:50 and 13h55 the panels was exposed to the shadow which results in a decrease in the amount of irradiation measured and consequently a decrease in voltage, current and power have been detected.

4 Results and Discussion The main objective of this work is to highlight the effectiveness of the dual axis solar tracker by comparing the measured power from the tracking system with the fixed system. We have imitated the behavior and the movement of the sun, which is emulated by a lamp in five positions as shown in the Fig. 6, for both systems (fixed and with tracking system) at the temperature 21 °C. We have taken measurements in the darkness. Figure 7 shows the electrical measurements in real time, namely the current, voltage and power. Figure 7 displays the dashboard with different widgets to present and visualize the results of data collected using the solar tracker (blue curve) and compare them with fixed system (red curve). As it is mentioned, the system of control integrates an active method of tracking to follow and track in real time the sun position in order to maximize the generated power from the panel. As it is shown in Fig. 7 and Table 1, even if the position of the sun changes, the solar tracker is able to follow its position while extracting and keeping the maximum power. While in case of using the fixed station, the power is maximum only when the sun is in position 3 which corresponds to the midday productivity. The increase of energy extracted by the solar tracker could be calculated by the Eq. 1:

Fig. 6 Scenario of the sun position adopted during the experimentation

308

F. Cheddadi et al.

Power with solar tracker

Power with fixed system

25

Power (mW)

20 15 10 5 0 20:02 20:09 20:16 20:24 20:31 20:38 20:45 20:52 21:00 21:07 21:14 -5

Time 6

Voltge (V)

5 4 3 2 1 0 20:02 20:09 20:16 20:24 20:31 20:38 20:45 20:52 21:00 21:07 21:14

Time Voltage with solar tracker

Voltage with fixed system

6

Current (mW)

5 4 3 2 1 0 20:02 20:09 20:16 20:24 20:31 20:38 20:45 20:52 21:00 21:07 21:14

Time Current with solar tracker

Current with fixed system

Fig. 7 Comparison between current, voltage and power monitored using solar tracker and fixed system

Design of an Automatic Control and IoT Monitoring System …

309

Table 1 Power monitored from fixed system and using the proposed solar tracker Position

POS 1

POS 2

POS 3

POS 4

POS 5

Total

Fixed system (P1)

0 mW

4 mW

22 mW

4 mW

0 mW

30 mW

Solar tracker (P2)

20 mW

22 mW

22 mW

21 mW

18 mW

103 mW

 e f f iciency =

  P1 − P2  × 100 = 70.87% P1

(1)

We found the sum of power extracted by the fixed-axis by the three panels is equal to 30 mW, and the sum of power generated using the proposed dual axis sun tracker is equal to 103 mW, which means that the average efficiency of the used tracker is equal to 70.87%.

5 Conclusion The objective of this work was to present the results of the implementation hardware of the active tracking method, based on the difference in illuminance of the light collectors. The proposed sun tracker ensure a continuous maximum of power extraction. We implemented in parallel a real-time monitoring system to supervise remotely the performance of the prototyped sun tracker and to validate the use of solar tracker. This latter shows good results in comparison to the fixed-axis PV panels with an important increase in average of 70.87%.

References 1. Youssef C, Fatima E (2018) A technological review on electric vehicle DC charging stations using photovoltaic sources. In: IOP conference series: materials science and engineering, vol 353, no 1, pp 012014, 10. https://doi.org/10.1088/1757-899X/353/1/012014 2. Cheddadi Y, Errahimi F, Es-sbai N (2018) Design and verification of photovoltaic MPPT algorithm as an automotive-based embedded software. Solar Energy 171:414-425. https://doi. org/10.1016/j.solener.2018.06.085 3. Cheddadi Y, Cheddadi F, Errahimi F, Es-Sbai N (2017) Extremum Seeking Control-based Global maximum power point tracking algorithm for PV array under partial shading conditions. In: 2017 international conference on wireless technologies, embedded and intelligent systems (WITS), Fez, Morocco, pp 1–6. https://doi.org/10.1109/WITS.2017.7934653. 4. Abdel-Salam M, El-Mohandes M-T, Goda M (2018) An improved perturb-and-observe based MPPT method for PV systems under varying irradiation levels. Solar Energy 171:547–561. https://doi.org/10.1016/j.solener.2018.06.080 5. Al-Dhaifallah M, Nassef AM, Rezk H, Nisar KS (2018) Optimal parameter design of fractional order control based INC-MPPT for PV system. Solar Energy 159:650-664. https://doi.org/10. 1016/j.solener.2017.11.040.

310

F. Cheddadi et al.

6. Bayod-Rújula Á-A, Cebollero-Abián J-A (2014) A novel MPPT method for PV systems with irradiance measurement. Solar Energy 109:95–104. https://doi.org/10.1016/j.solener.2014. 08.017 7. Bukar AL, Tan CW (2019) A review on stand-alone photovoltaic-wind energy system with fuel cell: system optimization and energy management strategy. J Clean Prod. 221:73-88. https:// doi.org/10.1016/j.jclepro.2019.02.228 8. Bradai R et al. (2017) Experimental assessment of new fast MPPT algorithm for PV systems under non-uniform irradiance conditions. Appl Energy 199:416-429. https://doi.org/10.1016/ j.apenergy.2017.05.045 9. Bounechba H, Bouzid A, Snani H, Lashab A (2016) Real time simulation of MPPT algorithms for PV energy system. Int J Electr Power Energy Syst 83:67–78. https://doi.org/10.1016/j.ije pes.2016.03.041 10. Awasthi A, et al. (2020) Review on sun tracking technology in solar PV system. Energy Rep 6:392-405. https://doi.org/10.1016/j.egyr.2020.02.004 11. Hafez AZ, Yousef AM, Harag NM (2018) Solar tracking systems: technologies and trackers drive types – a review. Renew Sustain Energy Rev 91:754–782. https://doi.org/10.1016/j.rser. 2018.03.094 12. Mousazadeh H, Keyhani A, Javadi A, Mobli H, Abrinia K, Sharifi A (2009) A review of principle and sun-tracking methods for maximizing solar systems output. Renew Sustain Energy Rev 13(8):1800–1818. https://doi.org/10.1016/j.rser.2009.01.022 13. Adhya S, Saha D, Das A, Jana J, Saha H (2016) An IoT based smart solar photovoltaic remote monitoring and control unit, p 5 14. Lopez-Vargas A, Fuentes M, Vivar M (2019) IoT application for real-time monitoring of solar home systems based on ArduinoTM with 3G connectivity. IEEE Sensors J. 19(2):679–691. https://doi.org/10.1109/JSEN.2018.2876635 15. Madeti SR, Singh SN (2017) Monitoring system for photovoltaic plants: a review. Renew Sustain Energy Rev 67:1180–1207. https://doi.org/10.1016/j.rser.2016.09.088 16. Cheddadi Y, Cheddadi H, Cheddadi F, Errahimi F, Es-sbai N (2020) Design and implementation of an intelligent low-cost IoT solution for energy monitoring of photovoltaic stations. SN Appl Sci 2(7):1165. https://doi.org/10.1007/s42452-020-2997-4

Analysis Jamming Attack Against the Protocol S-MAC in IoT Networks Imane Kerrakchou , Sara Chadli , Mohamed Emharraf , and Mohammed Saber

Abstract The Internet of Things (IoT) is one of the growing developments of the recent period that has to a great extent pulled in industry and the scholarly community. Life sans IoT is indispensable. In order to disperse any questions about its far-reaching reception, IoT positively requires both actually and consistently right answers to guarantee fundamental security and protection. This paper gives a brief description of IoT and the different technologies utilized, for example, WSN, and inspects known attacks against remote sensor systems like a Jamming attack of DoS type. Among the current MAC protocols, the S-MAC protocol is proposed to analyze the performances of the WSN in case of a Jamming attack using the OMNeT++ test system. Distinctive application scenarios have been assessed. Performance parameters, for example, consumed energy, lifetime, system loading, and packet reception rate are the principal factors taken in our investigation. Keywords Internet of Things (IoT) · Attack · Jamming · DoS · S-MAC · Wireless sensor network (WSN)

I. Kerrakchou (B) · S. Chadli · M. Emharraf · M. Saber Mohammed First University Oujda, ENSA Oujda, SmartICT Lab, Oujda, Morocco e-mail: [email protected] S. Chadli e-mail: [email protected] M. Emharraf e-mail: [email protected] M. Saber e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_29

311

312

I. Kerrakchou et al.

1 Introduction The Internet of Things (IoT) is an evolving technique of immense social, technological, and economic value. Present forecasts about the effects of IoT are very remarkable. It is expected that 100 billion linked IoT devices will be utilized by 2025. It would also have a world economic effect of over $11 trillion [1]. The Internet of Things (IoT) could make every aspect of our lives easier. The prospect of a more comfortable daily life, safer road traffic, a more environmentally energy supply, and a healthier lifestyle promote its development. Possible applications cover a wide range of areas of life [2]. The primary thought of IoT is to empower programmed correspondence between objects of regular daily existence (things that have been given capacities for recognizing, transmitting, and handling data) using the Internet and to connect a local network of objects to a global network utilizing standardized protocols. Intelligent objects (for example things) can be communicating using different varied network technologies, for example, Wireless Sensor Networks (WSN), Radio Frequency Identification (RFID), Wireless Local Area Networks (WLAN), and cell systems (3G, 4G, LTE, and 5G) [3]. IoT makes things and individuals equipped for associating with anybody and anything, anywhere, utilizing any kind of media and service. IoT system faces various dangers and vulnerabilities [4], including the communication canal, which permits the attacker to mess with information in travel. The detection device has limited computing power and low storage capacity. Sensor nodes are not furnished with sealed equipment because of cost requirements. On the off chance that a huge level of system nodes, even a couple of basic nodes, are assaulted, the life of the network might be decreased to a few days. The main cause of energy waste in these sensor systems is the radio subsystem. The data link layer, particularly the MAC (Media Access Control) protocol, is liable for dealing with the radio. In this manner, the MAC protocol should maintain the radio in a non-powered standby state as far as could reasonably be expected. With this in mind, many searches in the field of sensor node energy conserving concentrate on MAC protocols. For this reason, this paper will examine the sensor MAC (S-MAC) protocol [5]. Two scenarios are proposed in order to compare network performances with and without Jamming attack. The rest of this document is presented as follows. Section 2 defines the security objective of IoT and the classification of attacks. Section 3 provides an overview of the functioning of the S-MAC protocol and the attack targeting this protocol. Section 4 explores the impact of Jamming attacks against the MAC protocol presented in Sect. 3 by describing their implementation on the simulator. Finally, Sect. 5 presents a conclusion.

Analysis Jamming Attack Against the Protocol S-MAC in IoT Networks

313

2 IoT Security 2.1 IoT Security Objective The general objective is to protect the whole system that represents an IoT installation against a wide range of attacks. The most granular security prerequisites frequently alluded to as security characteristics [6], are: – Confidentiality: A concept to ensure that information can only be read by authorized persons. – Availability: the objective is to guarantee the continuity of network services against DoS attacks. – Integrity: Set of means and techniques to limit the modification of data to authorized persons. – Privacy: the objective is to prevent the disclosure of confidential information to malicious entities and the illegal collection of sensitive information about entities.

2.2 IoT Attacks Classification In the course of the most recent times, the IoT network was confronted with various attacks that made the makers and consumers aware of the need to utilizing IoT equipment with more caution. This section explains the various types of attacks in IoT, which are physical and cyber-attacks where this last comprise of active and passive attacks (see Fig. 1). Cyber-attack is a danger that aims at diverse IoT equipment in a wireless system by breaking the network for manipulation of the user’s information (i.e., delete, destroy, steal, corrupt). Furthermore, physical attacks allude to the attacks that interrupt the service and physically harm the IoT equipment. The following paragraph presents the two kinds of cyber-attacks according to the gravity in IoT system: Fig. 1 The different types and surfaces of attack in IoT

314

I. Kerrakchou et al.

– Active attack: in order to interrupt certain services and manipulate the configuration of the system, an active attack happen by an intruder that gets to the network and its relating information. There are various methods of attacking the security of IoT devices, including disruptions, modifications, and interventions. Among active attacks, we can cite Jamming, Sybil, DoS, MITM, Spoofing, etc. – Passive attack: this attack attempts to accumulate user information with no his assent, using this data to break the security of his private information. Traffic analysis and Eavesdropping are the two fundamental elements to play out a passive attack using an IoT system. This subsection presents the generic IoT architecture, which consists primarily of three layers (see Fig. 1), in order to describe the possible attacks according to each layer. The IoT surface attacks are classified as follows [7]: – Perception surface attacks: the attackers may easily penetrate the physical layer of the IoT system, because the Micro-controllers, Sensors, actuators are a few pieces of the physical system that are utilized for communication, identification, and collecting information, and these elements are very susceptible to Jamming, radio interference, and DoS. Furthermore, physical attacks are the most disturbing for perception surfaces [8]. – Network surface attacks: In IoT systems, the physical devices are associated through network services, including wireless networks. This wireless sensor network is a primary objective for various forms of attacks because user information moves overtly with no solid secure protocol. Attacks on the surface of networks are likely to occur through MITM, DoS, Sybil, Routing, Spoofing, and so on. – Application surface attacks: The application layer manages various types of applications, which allow remote control, and access to IoT devices. This IoT equipment is linked to the network with clouds and servers. They utilize softwarebased applications that allow malicious developers to dispatch distinctive malware attacks in order to get to IoT devices with or without user authorization. Therefore, attacks targeting this layer are DoS, Data corruption, etc.

3 Jamming Attack on S-MAC Protocol 3.1 The Functioning of S-MAC Protocol For wireless sensor networks, the MAC protocols in which nodes have a sequence of active and inactive periods are called activity rate protocols. The activity rate represents the percentage of time spent in the period of activity. Protocols with activity rate may be classified into two main types. The first type is that of synchronous MAC protocols, in which all nodes have their period of activity simultaneously or planned. The second category is that of asynchronous MAC protocols, in which the nodes have activity periods independent. There are many MAC protocols, among which

Analysis Jamming Attack Against the Protocol S-MAC in IoT Networks

315

Fig. 2 Sequence of activity and inactivity periods in S-MAC

we can cite: S-MAC, T-MAC, B-MAC, etc. In this document, we will talk about the S-MAC, which is the synchronous protocol. Nodes switch between periods of activity and periods of inactivity by a fixed action rate. During the active period, a node can send and get data. The inactive period corresponding to the deactivation of the radio transmitter/recipient to spare energy. The active period is itself divided into a synchronization period utilizing a SYNC frame, and a data trade period. The nodes keep up a table with the wake-up times of their neighbors. At the point when a node is active, it listens to the channel for a while. If it hears a SYNC frame, it synchronizes to the schedule of the node transmitting this SYNC frame. If it hears no frame after this time, it embraces its schedule and communicates it to its neighborhood. All nodes that follow this schedule form a virtual cluster. At the point when a node has a frame to transmit, it utilizes a reservation mechanism such as RTS/CTS (for Request To Send/ Clear To Send) and ACK (Acknowledgement) based on the IEEE 802.11 norm (see Fig. 2) [9].

3.2 Jamming Attack Jamming is a type of attack that was planned to intentionally intervene with the typical functioning of the wireless device, at the perception layer, all the more exactly in the MAC layer, by saturating the canal through the injection and persistent transmission of data or control package, causing abnormalities in the transmission and reception of data. What’s more, this attack reduces the performance of the system by increasing power consumption, memory, bandwidth, etc. Generally speaking, this sort of attack is associated with Denial of Service attacks (DoS) [10]. However, the Jamming attack could be classified according to the level of knowledge of the protocol used to apply it. Without knowledge of the protocol utilized in the network, attacks are very restricted in their effects on network performances. On the other hand, knowing the communication protocol utilized in the system can be destructive to a WSN. With complete information of the MAC protocol, an attacker can create big traffic to increase the most extreme effect from Jamming attacks.

316

I. Kerrakchou et al.

4 Jamming Attack Implementation and Results In our situation, we are going to launch two types of attacks. First, we assume that the attacker does not know the protocol utilized. For this reason, he will use DATA packets to launch this attack. Therefore, he starts replaying these data packets throughout the network aiming at the sink. Secondly, we suppose that the attacker knows the protocol used in the network. He analyzes the traffic to determine the protocol utilized in the system, which is the S-MAC. Then, he starts replaying explicit data messages by targeting the sink. In this case, the attacker utilizes retransmission of DATA packets correctly programmed in the S-MAC protocol in order to waste more energy from the network. Moreover, even in the case of Jamming, as indicated by the MAC protocol, the lifetime of a WSN can be long due to the idle cycle that disables the nodes, forcing the attacker to scrambling the system for all simulation time.

4.1 Simulation OMNeT++ First of all, the simulator used in our work is OMNeT++. It is a powerful network simulator and especially useful when designing and validating new protocols, and exploring new or exotic scenarios. In this simulation experience, two scenarios are recreated that have almost the same qualities with the difference that the first scenario (see Fig. 3) is a classic situation with no malicious nodes or attacks on the network. We talk about the normal case. The second scenario (see Fig. 4) represents the attack of the jammer on the nodes. This scenario contains one jammer that injects unauthorized traffic into the network and affects the WSN, which has no particular mechanism for detecting or forestalling Jamming attacks. In this scenario, we simulate the two types of attack that we explained in the paragraph above. In the first type (no knowledge of the protocol), the attacker continuously transmits DATA packets in the transmission channel. In the Fig. 3 Scenario 1—normal case

Analysis Jamming Attack Against the Protocol S-MAC in IoT Networks

317

Fig. 4 Scenario 2—network with Jamming attack

second type (knowledge of the protocol), the attacker synchronizes with all the nodes and continuously transmits DATA packets during the activity period. The jammer used in this scenario is a no-mobile jammer that allows DATA messages to be transmitted to the sink in order to inject a specific number of packets into the network and subsequently cause it to fail. These Jamming attacks are one of the most effective types of Jamming because they reduce the life of the network. The jammer parameters are shown in Table 1 below: The simulation model examined utilizes a star topology, where communication takes place between the nodes and the sink. The three simulations consist of 11 fixed nodes. Every node is equipped with two AA batteries, enough for a long working span. The duration of the simulation is set at 100 s. So as to identify the effect of the attack, the simulation time stays consistent as well as the total number of nodes for both scenarios. The simulation parameters are shown in Table 2 below: The principal objective for simulating scenario 1 is to determine the current state of the network under ordinary circumstances, which will allow us to make comparisons with scenario 2 and distinguish the effect of a Jamming attack on the network. However, the goal of the two attack simulations is to determine the degree of the Jamming attack effect on the network performances. Table 1 Simulation parameters for Jammer node

Parameters

Values

No. of jammers

1

Transmit Power (mW)

57.42

Trajectory

Fixed

Data rate (pps)

50

Data packet size (bytes)

250

End time (s)

End of simulation

318 Table 2 Simulation parameters

I. Kerrakchou et al. Parameters

Values

Simulation time (s)

100

Simulation area (m)

40 × 40

Topology

Star

No. of nodes

11

Mobility model

No mobility

Transmit power (mW)

46.2

Data rate (pps)

5

Data packet size (bytes)

100

End Time

End of simulation

Protocol

S-MAC

SYNC packets size (bytes)

11

Frame time (ms)

610

Contention period (ms)

10

4.2 Results and Discussion In this section, network performance behavior is analyzed with and without Jamming attacks. For the second scenario, both attack simulations used a higher packet size, packet rate, and transmission power compared to scenario 1 where no attack is implemented. Based on the results of the simulation experiences, the following outcomes are obtained. Figure 5 presents the consumed energy by all the nodes, for the three situations. For the normal case, we notice that the S-MAC has better performance. The energy consumption is normal, which means that the network is functioning properly. At the point when the network is subject to a Jamming attack, with or without knowledge of the protocol used in the network, the system has a much higher energy consumption compared to the result obtained in scenario 1. In our example, the role of the sink (node 0) is to receive and collect packets from the entire network, so it does not transmit data packets. What explains its high energy consumption in the case of the no knowledge of the protocol compared to the normal case, is the reception of the traffic generated by the attacker. On the other hand, we notice that in the case of the knowledge of the protocol used, the consumption is much higher because of the reception and processing of a large number of fake packets sent. The knowledge of the protocol used in the network allows the attacker to behave like an ordinary node, which allows him to deceive the sink and let it process its fake packets. Moreover, the continuous sending of these data packets allows an attacker to force other nodes to send more frames than expected since a communication failure is usually followed by several more attempts. With each new attempt, the nodes consume even more power.

Analysis Jamming Attack Against the Protocol S-MAC in IoT Networks

319

Fig. 5 Energy consumption per node (J)

Once the power consumption is high, network life will decrease. The life of a network is defined as the average time between the deployment of the network and the moment when the power supply to the nodes is exhausted. Figure 6 shows the estimated lifetime of the network for the three situations. In the case of the attack, the life of the network is much shorter than in the ideal case. This is due to the Jamming attack, which increased traffic, resulting in wasted energy and therefore reduced network life. Figure 7 represents the number of DATA packets sent by all the network sensors and received by the Sink. We can see that sending and receiving packets work correctly in the normal case. Once the attack is implemented, the number of packets sent and received decreases. This decrease in the number of packets sent is due to the high throughput broadcast in the network by the malicious node. The attacker increased traffic and made the transmission channel busy, which prevented other nodes in the network from sending their packets properly and communicating with the Sink. As far as packet reception is concerned, we can see that the Sink can no longer correctly receive the packets sent to it by the legitimate nodes due to its occupation with receiving the fake packet and,

Fig. 6 Estimated network lifetime (days)

320

I. Kerrakchou et al.

Fig. 7 Number of sent and received packets in the network

Fig. 8 Packets reception rate per node

above all, with processing the fake packets in the case of knowledge of the protocol used. Which explains the gross decrease in the reception rate. Figure 8 shows the decrease in packet reception rate by each node. From the results obtained, it is deduced that both types of attacks harm network performance. On the other hand, the attack with knowledge of the protocol used in the system is much more effective compared to an attack without knowledge, because the attacker, who already knows the protocol used, diffuses the traffic in the network following all the rules of the MAC protocol used for collision avoidance, synchronization, etc.

5 Conclusion Most current IoT security research is concerned with confidentiality and integrity data, to a great extent disregarding data availability. Without the capacity to secure the physical medium in which communication happens, sensor networks are susceptible

Analysis Jamming Attack Against the Protocol S-MAC in IoT Networks

321

to a progression of potential attacks that focus on quickly depleting the batteries of sensor nodes and rendering the network unusable. This paper provides an overview of IoT, specifically WSN, with a focus on Jamming attacks and the power efficiency of wireless systems. The four parameters analyzed to determine network performances are power consumption, lifetime, the network load, and packet reception rate. The effects on these performances using the S-MAC protocol and the impacts of Jamming attacks on wireless communication networks were studied using the OMNET++ simulator. Future work will investigate a defensive system and discover ways to apply it to currently available detection devices so as to develop specific mechanisms to secure them against this type of attack and to plan new energy-efficient MAC protocols.

References 1. Manzoor A, Braeken A, Kanhere SS, Ylianttila M, Liyanage M (2020) Proxy re-encryption enabled secure and anonymous IoT data sharing platform based on blockchain. J Netw Comput Appl 176:102917 2. Hassija V, Chamola V, Saxena V, Jain D, Goyal P, Sikdar B (2019) A survey on IoT security: application areas, security threats, and solution architectures. IEEE Access 7:82721–82743 3. Khattak HA, Shah M, Khan S, Ali I (2019) Perception layer security in the Internet of Things. Future Gener Comput Syst 100:28 4. Sengupta J, Ruj S, Bit SD (2020) A comprehensive survey on attacks, security issues and blockchain solutions for IoT and IIoT. J Netw Comput Appl 149:102481 5. Jagriti, Lobiyal DK (2018) Energy consumption reduction in S-MAC protocol for wireless sensor network. Procedia Comput Sci 143:757–764 6. Tahsien SM, Karimipour H, Spachos P (2020) Machine learning based solutions for security of Internet of Things (IoT): a survey. J Netw Comput Appl 161:102630 7. Malik V, Singh S (2020) Evolutionary computing environments: implementing security risks management and benchmarking. Procedia Comput Sci 167:1171–1180 8. Mihajlov B, Bogdanoski M (2014) Analysis of the WSN MAC protocols under jamming DoS attack. Int J Netw Secur 16:516–524 9. Maitra T, Roy S (2016) A comparative study on popular MAC protocols for mixed wireless sensor networks: from implementation viewpoint. Comput Sci Rev 22:107–134 10. López M, Peinado A, Ortiz A (2019) An extensive validation of a SIR epidemic model to study the propagation of jamming attacks against IoT wireless networks. Comput Netw 165:106945

Simulation and Analysis of Jamming Attack in IoT Networks Imane Kerrakchou , Sara Chadli , Amina Kharbach , and Mohammed Saber

Abstract The Internet of Things (IoT) is one of the latest increasing technologies that has largely been drawn to the industry and the academic world. Life sans IoT is completely imperative. In order to dispel any concerns about its far-reaching acceptance, IoT needs both real and reliable responses to ensure essential security and defense. This paper provides a brief overview of IoT and the various technologies used, such as WSN, and discusses attacks targeting wireless sensor networks, such as DoS style Jamming attacks. The S-MAC protocol is suggested among the existing MAC protocols to evaluate the WSN performance in the event of a Jamming attack utilizing the OMNeT++ test system. Distinctive scenarios have been tested. The main parameters taken into account in our survey, to analyze network performance, are the number of packets delivered, energy consumed, network lifetime, and the rate of lost packets. Keywords Internet of Things (IoT) · Attack · Jamming · DoS · S-MAC · Wireless sensor network (WSN)

1 Introduction Kevin Ashton proposed the term Internet of Things for the first time in 1999. It didn’t get much consideration after that for about 10 years. However, massive interest was I. Kerrakchou (B) · S. Chadli · A. Kharbach · M. Saber Mohammed First University Oujda, ENSA Oujda, SmartICT Lab, Oujda, Morocco e-mail: [email protected] S. Chadli e-mail: [email protected] A. Kharbach e-mail: [email protected] M. Saber e-mail: [email protected]

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_30

323

324

I. Kerrakchou et al.

paid to IoT technologies in 2014 [1]. Any part of our lives could be made simpler by the Internet of Things (IoT). Its development is motivated by the promise of a more convenient everyday life, safer road traffic, a more environmentally sustainable source of energy, and a healthy lifestyle. A broad number of areas of life [2] are protected by future implementations. The key concept of IoT is to empower structured communication between objects of normal life (things that have been granted data identification, transfer, and handling capabilities) utilizing the Internet and to connect a local object network using uniform protocols to a global network. Intelligent objects (things) can be interacted utilizing different network technology, like Wireless Local Area Networks (WLAN), Radio Frequency Identification (RFID), Wireless Sensor Networks (WSN), and Cellular Services (3G, 4G, LTE, and 5G) [3]. IoT allows things and people equipped to use any sort of media and service, to communicate with anybody and anything, everywhere. There are numerous dangers and vulnerabilities facing the IoT device [4], including the communication canal, which helps the attacker to manipulate the transmitted data. There are minimal processing power and poor storage space for the detection device. Due to price specifications, sensor nodes are not furnished with sealed facilities. If a large number of machine nodes, or a few simple nodes, are targeted, the life of the network could be limited to a few days. The radio subsystem is the primary source of energy waste in these sensor systems. For interacting with the radio, the data link layer, in particular, the MAC (Media Access Control) protocol is responsible. In this case, the MAC protocol can retain the radio in a non-powered standby state. Much work in the field of sensor node energy conservation focuses on MAC protocols. The sensor MAC protocol (S-MAC) will be investigated for this purpose in this article. In order to compare network performance with and without the Jamming attack, two cases are proposed. The remainder of this paper is presented as followed. The security objective of IoT and the classification of attacks are presented in Sect. 2. A description of the S-MAC protocol functioning and the attack targeting this protocol are given in Sect. 3. Through explaining their implementation on the simulator, Sect. 4 discusses the effect of Jamming attacks against the MAC protocol used in this paper. Finally, a conclusion is presented in Sect. 5.

2 IoT Security 2.1 IoT Security Objective Mechanisms to ensure the security, availability, and confidentiality of services delivered by IoT must be provided by software, protocols, and infrastructures of this new technology [5]. On the basis of the following requirements, each IoT application can include facilities for protecting confidential information.

Simulation and Analysis of Jamming Attack in IoT Networks

325

– Authenticity: For communication between any two nodes, only allowed nodes can be involved. – Confidentiality: The leakage of any unauthorized recipient of classified information shall be excluded. – Integrity: When the information is transmitted to IoT devices, data integrity guarantees that the originality of the information is not rewritten, copied, or substituted by the attacker. – Privacy: In order to preserve privacy, the identity of an individual user should be covered by a secure IoT device. – Availability: Approved users can use different services provided by the protocols of the IoT network to facilitate access to services.

2.2 IoT Attacks Classification Different IoT systems are separated into different layers: Perception Layer, Network Layer, and Application layer [6]. Every layer has a feature of its own. To form a full IoT system, these layers work together. In this section (see Fig. 1), we briefly discuss the functioning of the IoT layers with the most common security attacks targeting each of them [7]. – Perception layer: the sensors are used in this layer to detect and acquire information about the environment in which they are implemented. The protocols related to communication between IoT network devices are included in this layer. In terms of battery energy, storage resources, and computing capability, IoT devices are resource-restricted. Among the attacks targeting this layer, we can find Eavesdropping, Jamming attack, physical attacks, etc. Fig. 1 Layered classification of IoT attacks

326

I. Kerrakchou et al.

– Network layer: This layer is linked to the provision of network connectivity. The data gathered from the perception layer is delivered through this layer to the specific device for processing. It is the duty of the network layer protocols to decide the best way to route data packets from one host to another on the network. This layer is also susceptible to attacks such as Sybil attack, Wormhole attack, Man In The Middle, etc. – Application layer: this layer forms the applicable section of the whole network with the implementation of the service provided for users. It allows the utilization of different protocols to efficiently provide the users with the requested service. The layer of the application acts as an interface between the users and the IoT network. Therefore, it is vulnerable to threats that can impact nodes or application systems such as attacks on stability, malicious code injection attacks, etc.

3 Jamming Attack on S-MAC Protocol 3.1 The Functioning of S-MAC Protocol S-MAC is a protocol based on contention, where time is fragmented into large frames. Two phases are composed of each frame (see Fig. 2): – Active period: where the node can connect with neighboring nodes, uses control packets like SYNC (synchronize), RTS (Request To Send), CTS (Clear To Send) and, ACK (Acknowledgment) and sends DATA messages. – Sleeping period: of which the node radio is switched off to prevent loss of energy. S-MAC applies what is referred to as virtual clustering in order to achieve some coordination between nodes, in which these last regularly transmit special SYNC packets at the beginning of every frame to ensure that the node and its neighbors wake up simultaneously. Virtual clusters are created by neighboring nodes and a shared sleep schedule is set up [8].

Fig. 2 Frame for S-MAC protocol

Simulation and Analysis of Jamming Attack in IoT Networks

327

3.2 Jamming Attack Jamming is a form of Denial-of-Service attack which may occur at the level of the perception layer. This attack stops the channel from being used via nodes to connect, by occupying it through injection and persistent data or control packages transmission. The key objective of a DoS attack is to deplete node resources, like bandwidth, battery life, and storage, and stop sensor data transmitted from reaching the destination, thus, impacting its long term availability. In a WSN, a Jamming attack is catastrophic as it does not require the realization of any specific hardware device or software [9].

4 Attack Implementation and Results 4.1 Proposed System The architecture of the system we have proposed consists of a base station (SINK), which is used to collect and process data centrally, and a set of nodes that allows packets to be sent. The system will present a Jamming type attack. In our case, it is assumed that the attacker knows the protocol used in the network, which is the SMAC. Once the malicious node is implemented in the network, the attacker analyzes the traffic to determine the protocol used. Then, he will continuously transmit a large number of RTS (Request To Send) control packets in the transmission canal. Here, two situations will be studied. In the first situation, the attacker will target an ordinary node other than the SINK. As soon as the malicious node is installed in the network, it will look for the node closest to it and which belongs to this system. Once detected, the Jammer node will start attacking this closest node by sending a lot of control packets generating high traffic. In the second situation, the malicious node will attack only the SINK. As a result, the flow of packets sent by the attacker will be transmitted to the base station.

4.2 Simulation The simulator used in this document is the OMNeT++ simulator. We use a set of 19 fixed nodes, where node 0 is the Sink. The simulation period is set to 200 s. We use a star architecture in which all the nodes send their packets to the base station. The initial energy of each node is 18720 J. The protocol used is SMAC. The transmission power is fixed at 36.3 mW and the number of packets sent equals five packets per second. The table below represents the simulation parameters (Table 1).

328 Table 1 Simulation parameters.

I. Kerrakchou et al. Parameters

Values

Simulation time (s)

200

Simulation area (m)

60 × 60

Topology

Star

No. of nodes

19

Mobility model

No mobility

Transmit power (mW)

36.3

Packet rate (pps)

5

Data packet size (bytes)

100

End time

End of simulation

Protocol

S-MAC

SYNC packets size (bytes)

11

RTS packets size (bytes)

13

Frame time (ms)

610

Contention period (ms)

10

Fig. 3 Scenario 1—Normal case

Fig. 4 Scenario 2– Network with Jamming attack_NODE

In our simulation, three scenarios are proposed. The first scenario (see Fig. 3) represents a network without attack, i.e. no malicious node is implemented in the system. The nodes synchronize with each other and then transfer data packets to the base station.

Simulation and Analysis of Jamming Attack in IoT Networks

329

Fig. 5 Scenario 3—Network with Jamming attack_SINK

Table 2 Simulation parameters for Jammer node

Parameters

Values

No. of jammers

1

Transmit power (mW)

57.42

Trajectory

Fixed

Packet rate (pps)

30

Protocol

SMAC

SYNC packets size (bytes)

11

RTS packets size (bytes)

200

Frame time (ms)

610

Contention period (ms)

10

End time (s)

End of simulation

The second scenario (see Fig. 4) represents the Jamming attack by implementing a malicious node, which is node 19, in the network. In this scenario, we will simulate the attack that targets an ordinary node, which is node 18, of the network as explained in Sect. 4.1. In our experiment, we added a third scenario (see Fig. 5) which also represents the Jamming attack in the network with the difference that in this third scenario, the attacker targets the base station. The second and third scenarios have the same values of the parameters of the attacker node (jammer node), in order to study the difference in the impact of each of these attacks. The Jamming used in our simulation is represented by a fixed node, which allows the sending of a large number of control packets (RTS) with a sending rate equals to 30 packets per second. The transmission power of 57.42 mW and a control packet size (RTS) equals to 200 bytes. The table below shows the parameters of the jammer node (Table 2):

330

I. Kerrakchou et al.

Fig. 6 Number of packets sent per node

4.3 Results and Discussion This section describes the performance analysis of the three proposed scenarios. The objective of the simulation of scenario 1 (normal case) is to analyze the behavior of the network in the absence of undesirable effects, and finally to compare it to a network under attack. Then, we proposed in scenario 2, the attack of a network node, and in scenario 3, the attack of the base station in order to compare the severity of these attacks on the network. The results obtained are as follows: Figure 6 shows the number of packets sent by each node, in the network, for the three scenarios. In the normal case (scenario 1) all nodes send almost the same number of packets. Therefore, the system is working properly. In scenario 2, where one node in the network (node 18) was attacked, we notice that the whole network works in the same way as the normal case, except the attacked node that sends only a very small number of packets. The reason is that the attacker continuously sends a set of false packets to the attacked node and prevents it from communicating with the rest of the network. For scenario 3, we notice that the attack has infected the whole network, which pushed all the nodes to send a very large number of packets. The reason for this is that the attacker has prevented the sink from responding to the other nodes, and when the other nodes do not receive any response from the base station, they keep sending the packets back until they get a response or their batteries run out. Figure 7 shows the packet loss rate by each node, on the network, for the three simulations. For Scenario 1, it can be seen that the packet loss rate is approximately zero, meaning that all packets that have been sent by the network nodes reach the base station. Therefore, the network is functioning properly. For Scenario 2, we notice that the packet loss rate is a bit high compared to Scenario 1 due to the high traffic generated by the attacker in the transmission canal, which prevented the routing of some packets. For the attacked node (node 18), almost half of the sent packets were not routed to the SINK because of the Jamming attack that interrupted the normal operation of the packet transmission process with huge unnecessary packets. On the

Simulation and Analysis of Jamming Attack in IoT Networks

331

Fig. 7 Packet loss rate per node

Fig. 8 Energy consumption per node (J)

other hand, in scenario 3, almost all the packets sent by all the nodes did not reach the base station because this attack left the sink occupied by the fake packets sent by the attacker and made it unable to receive the packets sent by the other nodes. The graph in Fig. 8 shows the battery energy consumption of each node in Joule. It can be seen that the energy consumed in scenarios 2 and 3 is higher than that consumed in the normal case for the entire network because of the attacker, which generated high traffic in the transmission canal and led the nodes to waste their energy in unnecessary retransmissions. We notice that the energy consumption of the attacked node (node 18) for scenario 2, and the base station (node 0) for scenario 3, is also very high because of the successive sending of large RTS messages generated by the attacker. The estimation of the energy consumed in the network, more specifically of the nodes, also allows estimating the lifetime of the system, which is an essential parameter of WSN. Thus, the high energy consumption of each node automatically reduces the life of its battery and then that of the network as shown in Fig. 9.

332

I. Kerrakchou et al.

Fig. 9 Estimated network lifetime (days)

As mentioned above, the objective of the simulation of scenarios 2 and 3 is to analyze the effect of each of these two attacks. According to the results obtained, we notice that the attack that targets the base station is much more efficient compared to the one that targets an ordinary node and has the greatest impact on the network. An attack that targets the SINK affects almost all the nodes that make up the system in a very significant way and degrades network performance, such as the very high energy consumption of all the nodes, the loss of packets transmitted to the base station, etc. The thing that destroys the whole system.

5 Conclusion Most recent IoT security studies are concerned with knowledge on integrity and confidentiality, generally without regard to the availability of data. Sensor networks are vulnerable to a progression of possible attacks that concentrate on gradually depleting the batteries of sensor nodes and making the network unusable in the case of impossibility to protect the physical canal where contact takes place. A summary of IoT, specifically WSN, with an emphasis on Jamming attacks and the power efficiency of wireless systems is given in this article. For evaluating network efficiency, four parameters were utilized: the number of packets delivered, energy consumption, the lifetime of the network, and the loss rate of packets. Using the OMNET++ simulator, the impacts on these performances and the effects of Jamming attacks on wireless communication networks were analyzed using the SMAC protocol. Future research will analyze a defense framework and find ways to adapt it to currently existing detection devices in order to build unique methods to defend them against this form of attack and to prepare new energy-efficient MAC protocols.

Simulation and Analysis of Jamming Attack in IoT Networks

333

References 1. Jha RK, Kour H, Kumar M, Jain S (2020) Layer Based Security in Narrow Band Internet of Things (NB-IoT). Comput Netw 185:107592 2. Anand S, Sharma A (2020) Assessment of security threats on IoT based applications. Materials today: proceedings 3. Khattak HA, Shah M, Khan S, Ali I (2019) Perception layer security in the Internet of Things. Future Gener Comput Syst 100:28 4. Yugha R, Chithra S (2020) A survey on technologies and security protocols: reference for future generation IoT. J Netw Comput Appl 169:102763 5. Tahsien SM, Karimipour H, Spachos P (2020) Machine learning based solutions for security of Internet of Things (IoT): a survey. J Netw Comput Appl 161:102630 6. Malik V, Singh S (2020) Evolutionary computing environments: implementing security risks management and benchmarking. Procedia Comput Sci 167:1171–1180 7. Hassija V, Chamola V, Saxena V, Jain D, Goyal P, Sikdar B (2019) A survey on IoT security: application areas, security threats, and solution architectures. IEEE Access 7:82721–82743 8. Jagriti, Lobiyal DK (2018) Energy consumption reduction in S-MAC protocol for wireless sensor network. Procedia Comput Sci 143:757–764 9. Osanaiye O, Alfa AS, Hancke GP (2018) A statistical approach to detect jamming attacks in wireless sensor networks. Sensors 18:1691

A Brief Survey on Internet of Things (IoT) Fatima Zahra Fagroud, Lahbib Ajallouda, El Habib Ben Lahmar, Hicham Toumi, Ahmed Zellou, and Sanaa El Filali

Abstract Today it be a necessity to setting off past the thought that IT as a cost center and seeing it as a benefit center, on backing of the improvement and method of the organization. Internet of things can be defined as a propelled standard which make straightforward gadget a shrewdly gadgets which allow transferring simple device an intelligent devices capable of exchanging data over a network without human interaction. It represent a technology that interest research and industry, also serve on transform the way to think, and work. This paper present an overview on Internet of things (IoT), its application areas and its challenges in the aim to give a guideline to future researchers. Keywords IoT · Internet of Things · Application · Challenges

1 Introduction Internet of Things is a new revolution of the Internet. Objects make themselves recognizable and they obtain intelligence by making or enabling context related decisions thanks to the fact that they can communicate information about themselves. They can access information that has been aggregated by other things, or they can be components of complex services. This transformation is concomitant with the emergence of cloud computing capabilities and the transition of the Internet towards IPv6 with an almost unlimited addressing capacity [1]. F. Z. Fagroud (B) · E. H. B. Lahmar · S. El Filali Laboratory of Information Technology and Modeling, Faculty of Sciences Ben M’sick, Hassan II University of Casablanca, BP 7955 Sidi Othman Casablanca, Morocco H. Toumi Higher School of Technology - Sidi Bennour, Chouaïb Doukkali University, El Jadida, Morocco e-mail: [email protected] L. Ajallouda · A. Zellou ENSIAS Mohammed V University in Rabat, Rabat, Morocco e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_31

335

336

F. Z. Fagroud et al.

The IoT infrastructure is in view of 3 fundamental components: park of connected objects (fixed or mobile, geographically distributed), network (can be wired or wireless short or long-range, mobile or low-speed) and application (Most often developed in a web technologies, which collects data from the object network to provide aggregated and reprocessed information). The majority of the data gathered from the connected objects permits making a decision, permanent follow, failure prediction. Three significant improvements have permitted IoT to appear: Internet for its communication capacity, increment of storage capacity limit that permit expanded the amount of recorded data, lower access costs. According to study of GSMA the number of IoT connections in world attained 25,2 billion connections and the global market of IoT rise to 1100 billion dollars excluding hardware (devices, modules and chips) by 2025. Internet of things represent one of the emerging and evolving domains that interest research and industry. By studying this topic several questions arise, among these questions: In which area can be applied it? What are the IoT challenges? What is the number published in this field? The rest of this paper is structured as following: in Sect. 2 a presentation of Internet of things. After we present its application areas. In Sect. 4 we discuss some IoT challenges, afterward we give an overview on research publications in this topic. At the latest a conclusion in Sect. 6.

2 Internet of Things Internet of things have many definitions, until now there is no standardized definition. IoT mean system that groups IT devices, mechanic and digital equipments, objects, animals or people which are interconnected and can communicate data via a network in a self-sufficient manner, in real time and without any human interaction. It represent a dynamic network infrastructure which include physical and virtual “things” which possess unique identifiers, physical attributes, and virtual characteristics into the information network. Auto-configuration, self-organization, transmit information and data, share resources, react and act face to divers situations and changes in the environment represent the IoT capabilities. According to the International Telecommunication Union “The Internet of Things is a global infrastructure for the information society, which provides advanced services by interconnecting objects (physical or virtual) through interoperable information and communication technologies existing or in evolution”. The concept of “objects” is intended to pose no limit as to the feasible possibilities of implementing technological tools in everyday elements. The Cluster of European Internet of Things Research Projects (CERP-IoT) defines the Internet of Things as: “a dynamic infrastructure of a global network. This global network has auto-configuration capabilities based on standards and interoperable

A Brief Survey on Internet of Things (IoT)

337

communication protocols. In this network, physical and virtual objects have identities, physical attributes, virtual personalities, and intelligent interfaces, and they are seamlessly integrated into the network.” [CLU 2010]. The development of a system where objects are identifiable in a unique way, communicating with each other and capable of collecting data without any human intervention is only possible through integrating different technologies. Various technologies are deployed for the implementation of IoT including: RFID (Radio Frequency Identification), Sigfox, WSN (Wireless Sensor Networks), Cloud Computing, NFC (Near Field Communication), and Artificial intelligence. IoT work in the following way: • Collect of data: collect of data from the physical world by using sensors • Communication: transfer of data collected through different types of networks • Storage and analysis of data: based on many technologies for example Cloud Computing to storage data collected • Visualization: Present information to users in a comprehensible way. IoT is characterized by: • Heterogeneity: diversity of platforms, hardware and networks used for creating of internet of things devices. These devices can interact with other devices or service platforms by various communications technologies. • Connectivity: allows accessibility and network compatibility. Accessibility is over a network while compatibility furnish the common ability to consume and generate data. • Intelligence: allows objects to respond intelligently to a well-specified situation and help them to carry out precise tasks • Dynamic change: about number and state of device • Interconnectivity: everything will be able to be interconnected with the global information and communication infrastructure • Huge scale: the number of devices to manage and communicate between them will be high compared to devices connected to the Internet • Detection: the information detected (information of the physical world) allows the understanding of our complex world • Security: must be guaranteed to protect the privacy of users.

3 Application Area IoT is present in different area like health, energy and agriculture which affirm field of the IoT application is almost unlimited. Several IoT applications have been developed in different areas since its appearance, for example: Ji-chun Zhao et al. [2] offer a remote greenhouse monitoring system, which allows automatic control over the ambient temperature, moisture factors. Tejaswinee A. Shinde and Dr. Jayashree R. Prasad have developed a system based on IoT, which allows monitoring of animal health. The user can supervise

338

F. Z. Fagroud et al.

Fig. 1 IoT application areas

the health of the animal from a distance and be alerted by his mobile phone in the event of any changes in the health status of the animal [3]. Tuan Nguyen Gia et al. [4] present a system that permit remote control of glucose in real-time and continuously. The presented system help doctors and caregivers to check their patients anytime, anywhere per a browser or a mobile application. Security is one of the main concerns, as all health data is considered private personal data. To mitigate the risks of these applications, strong security mechanisms are needed. Jorge Gomez et al. [5] have proposed a system which enable students to interact with physical objects in their environments associated virtually with a learning subject. Khaleel Mershad and Pilar Wakim [6] have developed a framework for a future LMS (Learning Management System: tool for creation, distribution, monitoring and management of various types of educational and training material) enhanced by IoT capabilities, several LMS elements will be affected by IoT. Academics, researchers and students are in a same position to carry on the exploration and advancement of IoT systems, devices, applications, and services. The deployment of these solutions in our environment will transform our environment into intelligent environment and conducive to entire human activity.

A Brief Survey on Internet of Things (IoT)

339

4 IoT Challenges 4.1 Security Security is an important aspect of IoT and must be guaranteed in the various layers of the IoT architecture. They are several challenges related to security in internet of things for example control access, data exchange security, protect the privacy. Traditional security mechanisms cannot be applied in internet of things which make necessary the creation of a news paradigms. To solve this issue we can use Machine Learning, Blockchain, Gateway, Software Defined Networking, symmetric cryptography, biometric modalities, wireless device radio fingerprinting to boost the security and privacy of IoT systems. Several research are proposed like: FLAUZAC Olivier et al. [7] propose a new security architecture for IoT (Internet of Things) based on SDN (Software defined Network), the latter allows to achieve network security in a more efficient and flexible way. Hui Suo et al. [8] present an analysis of the characteristics and security requirements from the IoT architecture based on four layers (perceptual layer, network layer, support layer and application layer). An Autonomic Agent Model Trust is a model that serves to reduce security issues, raise reliability and credibility, and afford information in IoT environments. It consists of installing Trustworthy Agent Execution Chip (TAEC), which adduce a profitable software and hardware platform for secured operation of the agent, on each sensor node, providing a hardware execution environment for agents which is secured and autonomous [9]. The work of Markowsky et al. [10] demonstrate that the search for vulnerable devices in the IoT is possible by using a different methods: Shodan (find vulnerable Cayman DSL routers), Masscan (quickly search a large address space for devices vulnerable to the Heartbleed Bug) and Nmap with PFT (find and connect to vulnerable networked printers).

4.2 IoT Architecture With the high expansion of internet of things it become a necessity to determine a standard architecture of the generic IoT or by domain, several architectures have been proposed since the appearance of this domain but the proposed architectures is generally adapted to the need for a specific application. Among the proposed architectures we present: IoT-Academia Architecture is an IoT architectural model that covers almost every vital division of the academic forum [11]. Ivan Ganchev et al. [12] proposed a high-level generic IoT architecture particularly appropriate to the foundation of smart cities. Weigong LV et al. [13] defined a unified IoT system architecture to determine a standard for building and implementing the IoT system and diminish the hardness of building an IoT system. Ioan Ungurean et al. present an architecture dedicated to IoT that is based on OPC.NET specifications able to

340

F. Z. Fagroud et al.

be deployed in industrial environments and in intelligent construction [14]. Michele Nitti et al. developed an architecture for sustainable tourism application in a Smart City scenario. It is based on a specific whole of exigencies and may be implemented using a cloud-based IoT platform [15].

4.3 Interoperability in IoT Most of IoT devices proposed until now are note interoperable which increase data consolidation costs. There are 4 types of interoperability: • Technical Interoperability: related to hardware/software components, systems and platforms • Syntactic Interoperability: structures and formats of data communicated • Semantic Interoperability: same content are comprehended by different means • Organizational Interoperability: organizations may communicate and transfer data even they use a diversity of information systems on much different infrastructures, perhaps at different geographic regions and cultures. To solve the issue of interoperability in IoT many techniques and methodologies can be used like: Ontology, RDF, SPARQL, IoT HUB. For example: Agarwal et al. [16] propose a holistic and a light-weight ontology that aims to attain semantic interoperability between divers fragmented testbeds (that store data in their proprietary format) in IoT domain.

4.4 Data Storage and Analysis IoT devices yield a broad deal of data in real-time and unstructured, which requires a decentralized structure to store and analyze this huge data. Integration of Cloud Computing and IoT represent a valid approach that will serve to solve this issue. Different IoT cloud platforms have been developed to solve various service areas such as application development, administration of devices and systems, heterogeneity management, data management, analysis tools and deployment [17]. Cloud4IoT is a platform for self-acting deployment, arrangement and dynamic configuration of software components for IoT support and data processing and analysis applications, enabling plug-and-play integration of novel sensors and dynamic scalability [18]. CloudThings Architecture is a cloud-based IoT platform that supports IaaS, PaaS, and SaaS to accelerate the application development and administration of the Internet of Things [19]. This integration faces a set of challenges: Resource assignment, Protocol Support, Identity Management, Energy effectiveness, Quality of Service attainment, Data Storage Location, Security and Privacy.

A Brief Survey on Internet of Things (IoT)

341

5 Research Evolution The opportunities of internet of things are diverse and unlimited which make then an important research topic that occupies latterly a great importance by researchers. The amount of research works interested in the IoT thematic increases from year to year, to affirm this we present an overview on the number of publications included in two scientific databases (IEEE and dblp). We carried out this study based on two scenarios: • First scenario: search for the number of publications including the chosen keyword – Keywords used: Internet of Things, IoT, Internet of Things challenge, IoT challenge, internet of things application, IoT application – Results: • Figure 2: show the number of journal papers published in dblp and IEEE and respond to our request • Figure 3: show the number of conference and workshop papers published in dblp and IEEE and respond to our request. • Second scenario: search for the number of publications interested to a specific challenge or application area and published in 2019 – Keywords used: Internet of Things architecture, IoT architecture, Internet of Things security, IoT security, internet of things in education, IoT in education, internet of things in healthcare, IoT in healthcare – Results: • Figure 4: show the number of works published in journals, conference and workshop proceedings especially in IEEE database, in 2019 and respond to our request

Fig. 2 Number of publication in IEEE and dblp databases: Journal Papers

342

F. Z. Fagroud et al.

Fig. 3 Number of publication in IEEE and dblp databases: Conference and Workshop Papers

Fig. 4 Number of publication in IEEE database published in 2019: Journal, Conference and Workshop Papers

Fig. 5 Number of publication in dblp database published in 2019: Journal, Conference and Workshop Papers

A Brief Survey on Internet of Things (IoT)

343

• Figure 5: show the number of conference, workshop and journal papers published in dblp in 2019 and respond to our request.

6 Conclusion Internet of Things present one of the trends in the field of new technologies and innovation, which have a big behoove in research. It has several advantages for its users (companies and civil users), despite these benefits many challenges are present which leads to think about developing of a new solutions to solve these. The novelty that IoT present is the use of internet and their protocols for daily objects. In this article we have presented a brief study of Internet of Things, its fields of applications and issues and a brief view on the number of research works, which will prove that IoT is an area of great importance and multidisciplinary. In the future work we wish to develop some internet of things applications/solutions and choice an issue of this field in the aim to propose a new solution.

References 1. Madakam S, Ramaswamy R, Tripathi S (2015) Internet of Things (IoT): a literature review. J Comput Commun 3(05):164 2. Zhao J, Zhang J, Feng Y, et al (2010) The study and application of the IOT technology in agriculture. In: 2010 3rd international conference on computer science and information technology. IEEE, pp 462–465 3. Shinde TA, Prasad JR (2017) IoT based animal health monitoring and crop monitoring system 4. Gia TN, Ali M, Dhaou IB et al (2017) IoT-based continuous glucose monitoring system: a feasibility study. Procedia Comput Sci 109:327–334 5. Gómez J, Huete JF, Hoyos O et al (2013) Interaction system based on internet of things as support for education. Procedia Comput Sci 21:132–139 6. Mershad K, Wakim P (2018) A learning management system enhanced with Internet of Things applications. J Educ Learn 7(3):23–40 7. Olivier F, Carlos G, Florent N (2015) New security architecture for IoT network. Procedia Comput Sci 52:1028–1033 8. Suo H, Wan J, Zou C, et al (2012) Security in the Internet of Things: a review. In: 2012 international conference on computer science and electronics engineering. IEEE, pp 648–651 9. Xu X, Bessis N, Cao J (2013) An autonomic agent trust model for IoT systems. Procedia Comput Sci 21:107–113 10. Markowsky L, Markowsky G (2015) Scanning for vulnerable devices in the Internet of Things. In: 2015 IEEE 8th international conference on intelligent data acquisition and advanced computing systems: technology and applications (IDAACS). IEEE, pp 463–467 11. Elyamany HF, Alkhairi AH (2015) IoT-academia architecture: a profound approach. In: 2015 IEEE/ACIS 16th international conference on software engineering, artificial intelligence, networking and parallel/distributed computing (SNPD). IEEE, pp 1–5 12. Ganchev I, Ji Z, O’droma M (2014) A generic IoT architecture for smart cities 13. Lv W, Meng F, Zhang C, et al (2017) A general architecture of IoT system. In: 2017 IEEE international conference on computational science and engineering (CSE) and IEEE international conference on embedded and ubiquitous computing (EUC). IEEE, pp 659–664

344

F. Z. Fagroud et al.

14. Ungurean I, Gaitan N, Gaitan VG (2014) An IoT architecture for things from industrial environment. In: 2014 10th international conference on communications (COMM). IEEE, pp 1–4 15. Nitti M, Pilloni V, Giusto D et al (2017) IoT Architecture for a sustainable tourism application in a smart city environment. Mob Inf Syst 2017:1–9 16. Agarwal R, Fernandez DG, Elsaleh T, et al (2016) Unified IoT ontology to enable interoperability and federation of testbeds. In: 2016 IEEE 3rd world forum on Internet of Things (WF-IoT). IEEE, pp 70–75 17. Ray PP (2016) A survey of IoT cloud platforms. Future Comput Inform J 1(1–2):35–46 18. Pizzolli D, Cossu G, Santoro D, et al (2016) Cloud4iot: a heterogeneous, distributed and autonomic cloud platform for the IoT. In: 2016 IEEE international conference on cloud computing technology and science (CloudCom). IEEE, pp 476–479 19. Zhou J, Leppanen T, Harjula E, et al (2013) Cloudthings: a common architecture for integrating the Internet of Things with cloud computing. In: Proceedings of the 2013 IEEE 17th international conference on computer supported cooperative work in design (CSCWD). IEEE, pp 651–657 20. Stergiou C, Psannis KE, Kim B et al (2018) Secure integration of IoT and cloud computing. Future Gener Comput Syst 78:964–975 21. Fagroud F, Ben Lahmar E, El Filali S (2019) Internet Of Things: statistical study on research evolution. Int J Adv Electron Comput Sci 6(5) 22. Arnaert M, Bertrand Y, Boudaoud K (2016) Modeling vulnerable Internet of Things on SHODAN and CENSYS: an ontology for cyber security. In: Proceedings of the tenth international conference on emerging security information, systems and technologies (SECUREWARE 2016), Nice, France, pp 24–28 23. Aldowah H, Rehman SU, Ghazal S, et al (2017) Internet of Things in higher education: a study on future learning. In: Journal of physics: conference series, p 012017 24. Xiao L, Wan X, Lu X et al (2018) IoT security techniques based on machine learning: how do IoT devices use AI to enhance security? IEEE Signal Process Mag 35(5):41–49 25. Yasuda S, Miyazaki S (2017) Fatigue crack detection system based on IoT and statistical analysis. Procedia CIRP 61:785–789 26. Prajapati H, Mishra AK (2018) A review on baggage tracing and handling system using sensor networks and IoT 27. Fraga-Lamas P, Fernández-Caramés TM, Suárez-Albela M, Castedo L, González-López M (2016) A review on Internet of Things for defense and public safety. Sensors 16(10):1644 28. Suresh P, Daniel JV, Parthasarathy V, et al (2014) A state of the art review on the Internet of Things (IoT) history, technology and fields of deployment. In: 2014 international conference on science engineering and management research (ICSEMR). IEEE, pp 1–8 29. Alansari Z, Soomro S, Belgaum MR, et al (2018) The rise of Internet of Things (IoT) in big healthcare data: review and open research issues. In: Progress in advanced computing and intelligent engineering. Springer, Singapore, pp 675–685 30. Anggorojati B, Prasad R (2018) Securing communication in the IoT-based health care systems. Jurnal Ilmu Komputer dan Informasi 11(1):1–9 31. Banerjee M, Lee J, Choo KKR (2018) A blockchain future for Internet of Things security: a position paper. Digit Commun Netw 4(3):149–160 32. Burhanuddin MA, Mohammed AAJ, Ismail R, Basiron H (2017) Internet of Things architecture: current challenges and future direction of research. Int J Appl Eng Res 12(21):11055–11061 33. Zhou C, Damiano N, Whisner B, Reyes M (2017) Industrial Internet of Things: (IIoT) applications in underground coal mines. Min Eng 69(12):50 34. Asim M, Iqbal W (2016) Iot operating systems and security challenges. Int J Comput Sci Inf Secur 14(7):314

Design and Implementation of Smart Irrigation System Based on the IoT Architecture Asmae Hafian, Mohammed Benbrahim, and Mohammed Nabil Kabbaj

Abstract Water is an indispensable resource in agriculture, and with the likely increase in restrictions on the volumes of water allocated to agriculture, irrigation is required to meet the needs of plants. In this sense, this article proposes the design and implementation of an intelligent irrigation system based on the IoT architecture. These systems are designed especially for urban areas where remote IoT devices don’t have direct access to the internet or power grids and where wireless communications are difficult. The proposed system is composed of different technologies to exploit their advantages to increase both the efficiency of water use and the efficiency of agricultural yields. Keywords Smart irrigation · IoT · ZigBee · LoRa · Wi-Fi · WSN

1 Introduction Irrigation is the operation of artificially bringing water to cultivated plants [1]. It is important to know that agriculture is by far the largest consumer of water whose irrigation is a necessary practice in several agricultural cropping systems in arid and semi-arid areas [13]. The development of a field distributed system based on irrigation sensors offers the potential to increase yield and quality while saving water [2], in which wireless sensor network technology can provide significant support that will enable accurate resource management due to their cost-effective nature A. Hafian (B) · M. Benbrahim · M. N. Kabbaj Engineering, Modeling and Analysis of Systems Laboratory, Sidi Mohamed Ben Abdellah University, Fez, Morocco e-mail: [email protected] M. Benbrahim e-mail: [email protected] M. N. Kabbaj e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_32

345

346

A. Hafian et al.

and deployment flexibility. Furthermore, the wireless sensor network possesses low cost and highly reliable sensors, and have a transmission radius, the latter measure autonomously the humidity, temperature parameters and communicate via wireless technology with a central data collection station. This allows the farmer to use this technology that provides the opportunity for producers to access remotely via wireless radio communication to see the field conditions and the irrigation operation, at home or the office. The search for an innovative and adequate material architecture (cost and performance) for the achievement of the objectives of our work led us to do a bibliographic search at the level of existing systems and related fields. For example, [3] which presented a network of wireless sensors controlled by the broker. The latter is in constant communication with an online server that uses the Message Queuing Telemetry Transport (MQTT) protocol via a Wi-Fi connection that communicates with the broker using a peer-to-peer LoRa connection. Another interesting work is proposed by [4], which presented the fundamental design and implementation of a wireless sensor network featuring a Zigbee protocol. This work describes the use of a high transmission power with a relatively low energy consumption based on a Zigbee wireless sensor network for the monitoring system of the water irrigation control. Also, [5] represents the design and implementation of an intelligent irrigation system based on LoRa technology. This article presents the energy consumption results, which take into account the different operating modes of the terminal nodes. In this sense, to ensure adequate access to digital agriculture, we have proposed an intelligent irrigation system with a diverse architecture using ZigBee, LoRa, and WiFi protocols based on sensor networks and wireless connectivity to all levels of the system. The use of these different protocols in our proposed architecture allowed us to take advantage of the different types of communication such as WLAN (wireless local area network), WPAN (wireless personal area network), LPWAN (Low Power Wide Area Network), which offers the possibility of remote access and at any time to our irrigation field. The proposed architecture far exceeds existing architectures in terms of low cost and sophisticated performance. Indeed, it is an architecture making it possible to benefit, on the one hand, from IoT and Cloud Computing and, on the other hand, from hardware or software evolutions at the level of each part of the system.

2 Design of the System 2.1 System Architecture Figure 1 represents the proposed system architecture which consists of five parts: IoT nodes, local gateways, main gateway, Cloud server, and application.

Design and Implementation of Smart Irrigation System …

347

IoT Nodes are responsible for collecting data from the sensor layer components. They are responsible for exchanging information with the local gateway, and they receive remote irrigation commands from the gateways or the cloud. IoT Nodes send the information obtained via the ZigBee protocol. This information from the various sensors will be processed locally by a local gateway. Subsequently, the main gateway performs the essential functions of collecting data from local gateways via the LoRa protocol. Also, third-party services such as weather forecasting can be used when deciding on irrigation programs, as well as programming, monitoring, and diagnosis of the main gateway [6]. Using Message Queuing Telemetry Transport (MQTT), the main gateway controls the network, which is in constant communication with the Cloud via a Wi-Fi connection. This communication is responsible for the exchange of messages between the Cloud and the multiple local gateways of the network, which is, in turn, communicate with the broker via the Lora connection. The cloud server is mainly responsible for storing data [15] and implementing the MQTT interface for the LoRa server and applications. For various applications can be offered: mobile applications that developed on both Android or iOS platforms or web applications. These applications allow users to get the status of IoT nodes as they can also control and supervise the irrigation system by sending commands.

2.2 Wireless Communication Protocols Devices in the IoT network use different communication protocols with each other. The wireless communication protocol is a set of rules that enable different electronic devices to communicate with each other wirelessly. The communication standard chosen to have an innovative and adequate hardware architecture in terms of cost and performance for our proposed system are three protocols: ZigBee, LoRa, Wi-Fi. This connectivity meets the following constraints: • Wi-Fi (Wireless Fidelity): Local connectivity without taking into account energy consumption (WLAN). It is the common popular IoT communication protocol for wireless local area networks (WLAN), which uses the IEEE 802.11 standard [14] via 2.4 GHz UHF and 5 GHz ISM frequencies. Wi-Fi provides Internet access to devices located approximately 20 to 40 m from the source. It has a data rate of 600 Mbps maximum, depending on the frequency of the channel used and the number of antennas, • ZigBee: Local connectivity with consideration of energy consumption. ZigBee is a wireless standard based on IEEE 802.15.4 [7]. It is a technology for robust Wireless Personal Area Network (WPAN) communication. This technology is now used in many equipment, very generally embedded, which imposes a very low consumption, and are enough of a very low data rate and it ranges up to

348

Fig. 1 Architecture of proposed system

A. Hafian et al.

Design and Implementation of Smart Irrigation System … Table 1 Characteristics of different wireless communication protocols

349

Feature

ZigBee

LoRa

Wi-Fi

Data rate [kbps]

250

110

11 * 102

Range [m]

10–100

2000

1–100

Frequency [GHz]

2.4

0.868

2.4

Power consumption [mA]

1–10

1–10

100–350

100 m [8]. The Zigbee protocol has a large number of applications in machine to machine & IoT technologies. It offers a range of approximately 10 to 100 m maximum, and its data transfer rate is approximately 250 Kbps, • LoRa: Remote connectivity with low energy consumption (LPWAN). LoRa wireless technology is a long-range, low-power platform that uses unlicensed radio spectrum [3] widely used in IoT applications. Low energy consumption, it offers LPWAN connectivity between wireless sensors and the Cloud [9]. To ensure reliable monitoring, LoRa-based sensors can be installed in smaller devices. It aims to eliminate repeaters, reduce the cost of devices, increase the battery life of devices, improve network capacity and support a large number of devices [3]. Table 1 presents the characteristics of different wireless communication protocols used in our proposed architecture [3].

3 System Implementation To design an intelligent irrigation system based on our IoT architecture proposed in Fig. 1, this section presents the implementation of the different components of the system. The different sensors used: • YL-69, a soil moisture sensor, a simple rupture, consisting of two buffers that function as probes. It uses the conductivity between these two buffers to measure moisture in soil and similar materials. • DHT22, a basic digital sensor, at low cost making it possible to effectively measure the temperature and humidity of the ambient air thanks to its two-in-one combination of a capacitive humidity sensor and a thermistor. The temperature reading range is: −40 to +80 °C and for humidity is: 0 to 100%, with an accuracy of 0.5 °C and 2–5% for temperature and humidity respectively [3]; This data is collected using a microcontroller, which is represented by an Arduino ADK. This latter is a microcontroller board based on the ATmega2560. It has a USB host interface to connect with Android-based phones, 54 digital input/output pins (of which 15 can be used as PWM outputs), 16 analog inputs, 4 UARTs (hardware serial ports), 16 MHz crystal oscillator, USB connection, a power jack, ICSP header, and a reset button. It contains everything needed to support the microcontroller; it can

350

A. Hafian et al.

be powered via the USB connection or with an external power supply. The power source is selected automatically [10]. The expected output is then used to send the control signal via serial communication to Arduino to control the water pump to water the field accordingly. This action can be commanded via the local gateway or remotely via applications. For the wireless communication between IoT nodes and the local gateway, we used two of XBee S2C Pro, which is using a 2.4 GHz wireless transceiver to communicate with another XBee module. Each local gateway is composed of two main elements: ESP32 (dual-core 32bit MCU with built-in Wi-Fi and BLE), with LoRa node chip SX1278; The LoRa radio transceiver module capable of creating a multipoint network, with addresses of individual nodes and encryption, within a range of about 2000 m [3]. About the main gateway we chose Raspberry Pi; it is an open-source, Linuxbased, credit card-sized computer board. It has USB ports to which you can connect a keyboard and mouse, and a High-Definition Multimedia Interface (HDMI) video output to which you can connect a TV or monitor. The Raspberry Pi is based on a Broadcom BCM2835 chip. It does not feature a built-in hard disk or solid-state drive, instead relying on an SD card for booting and long-term storage [11]. The main gateway uses the Wi-Fi connection to collect data via the LoRa protocol. This layer also ensures that the collected signals are filtered and are ready to perform the physical unit conversion. For the cloud, we linked the raspberry to an MQTT broker, which will allow us to control the irrigation system remotely. MQTT is a common protocol used in IoT systems to connect low-level devices and sensors [12]. MQTT is used to pass short messages to and from a broker.

4 Results and Discussion The test of the different configurations was done through a miniaturized prototype similar in terms of components to an agricultural gateway of real dimensions (Fig. 2). We chose a site with an irrigation system to test our proposed solution. The site is located at the Dhar ElMahraz Faculty of Sciences in Fez, Morocco (Fig. 3). The miniaturized prototype comprises a network of 2 sensor nodes, plus the main gateway. Every 5 min, the sensor data is acquired in the local gateway via the XBee protocol (Fig. 4). This data will then be sent to the main gateway via the LoRa protocol. Depending on the water needs of the crop and the climatic conditions, the main gateway decides to act on the local gateway to operate the pump. The loss of data is low in the case of the mobility of the nodes. The number of retransmissions is similarly low. For remote monitoring (application), we used the LabVIEW software to control and supervise our system remotely, with a simplified operator interface dialoguing via TCP/IP (Transmission Control Protocol/Internet Protocol) serial digital communication with the broker. TCP/IP is used for the transfer of data on the Internet, it

Design and Implementation of Smart Irrigation System …

351

Fig. 2 Miniaturized prototype of the proposed system

Fig. 3 Satellite image of the test site

represents in a certain way the set of communication rules on the Internet and is based on the notion of IP addressing, that is to say, the fact of providing an IP address to each machine of the network to be able to convey data packets. As Fig. 5 shows, the monitoring system was used with a LabVIEW interface; it represents the curves for soil moisture and temperature monitoring as well as the unit of the pump.

352

Fig. 4 Sensor data acquired at the local gateway

Fig. 5 LabVIEW interface in the remote computer

A. Hafian et al.

Design and Implementation of Smart Irrigation System …

353

5 Conclusion and Future Work In this article, we presented the design and implementation of a prototype of a new generic wireless sensor network platform for an intelligent irrigation system. The proposed design represented the combination of several protocols to ensure adequate access to digital farming. The use of these protocols improves the reliability and efficiency of the installation of soil humidity and temperature sensors and allowed us to remotely control the parameter of the soil water content in real-time. Server-side monitoring can present data logs from each sensor; Also, the power consumption of wireless network devices is lower, and the system performs a long-term function. To further test the system, we will, in the future, implement it in a real garden with several irrigation areas. Also, an application for agriculture, whose objective is to calculate not only when to bring the water, but also the amount of water that the plants need, and automatically adjusts the operating time according to the stage of growth and seasonal fluctuations.

References 1. Belkacem I (2014) L’irrigation intelligente. In: 15th annual global development conference, Ghana 2. Kim Y, William M (2008) Remote sensing and control of an irrigation system using a distributed wireless sensor network. IEEE Trans Instrum Meas 57(7):1379–1387 3. Gloria A, Dionisio C, Simoes G, Sebastiao P, Souto N (2019) WSN application for sustainable water management in irrigation systems. In: 2019 IEEE 5th world forum on Internet of Things (WF-IoT), Limerick, Ireland, pp 833–836 4. Rasin Z, Hamzah H, Mohd Aras MS (2009) Application and evaluation of high power Zigbee based wireless sensor network in water irrigation control monitoring system. In: IEEE symposium on industrial electronics applications, ISIEA 2009, vol 2 5. Zhao W, Lin S, Han J, Xu R, Hou L (2017) Design and implementation of smart irrigation system based on LoRa. In: 2017 IEEE Globecom workshops (GC Wkshps), Singapore, pp 1–6 6. Lamas PF, Echarri MC, Azpilicueta L, Iturri PL, Falcone F, Caramés T (2020) Design and empirical validation of a LoRaWAN IoT smart irrigation system. In: Proceedings, vol 42, p 62 7. IEEE 802.15.4TM (2011) IEEE standard for local and metropolitan area networks. Part 15.4: low-rate wireless personnal area networks (LR-WPANs) 8. Jackson F, Ferial V, Jiamin P, Yan Xin P, Thierry V (2013) ZigBee, de la théorie à la pratique : création d’un réseau ZigBee avec transmission de données. 3EI 71:1–18 9. Ayaz M, Ammad-Uddin M, Sharif Z, Mansour A, Aggoune EM (2019) Internet-of-Things (IoT)-based smart agriculture: toward making the fields talk. IEEE Access 7:129551–129583 10. Arduino store. https://store.arduino.cc/ 11. Kumar Sahu C, Behera P (2015) A low-cost smart irrigation control system. In: IEEE sponsored 2nd international conference on electronics and communication system 12. Kodali RK, Kuthada MS, Yogi Borra YK (2018) LoRa based smart irrigation system. In: 4th international conference on computing communication and automation (ICCCA), Greater Noida, India, pp 1–5 13. Hafian A, Benbrahim M, Kabbaj MN, Bouazi A, Abouabdillah A (2020) A new approach for optimal sizing of photovoltaic pumping systems for irrigation. In: 5th international conference on renewable energies for developing countries, Marrakech, Morocco, pp 1–6

354

A. Hafian et al.

14. Chong C, Kumar S (2003) Sensor networks: evolution, opportunities and challenges. Proc IEEE 91(8):1247–1256 15. Roopaei M, Rad P, Choo K (2017) Cloud of things in smart agriculture: intelligent irrigation monitoring by thermal imaging. IEEE Cloud Comput 4:10–15

Lightweight Key Exchange Algorithms for MQTT Protocol in Its Environment Anwar D. Alhejaili

and Omar H. Alhazmi

Abstract One of the most important challenges in the use of the Message Queue Telemetry Transport (MQTT) protocol in the Internet of Thing (IoT) environment is data confidentiality and user authentication. Moreover, it is easy to manipulate the default security provided by MQTT during user authentication through username and password, and it does not provide any type of encryption of the data to ensure confidentiality or integrity. In this paper, we propose a key exchange algorithm to provide a secret key that can be used for data encryption using permuted congruential generator PCG-Rand as well as to ensure data integrity using Spongent lightweight hash functions. Keywords IoT · MQTT · Key exchange algorithm · PCG-Rand · Spongent light-weight hash functions

1 Introduction In the last few centuries, the world has witnessed significant advancement in terms of technology and communication. An individual’s daily life has become dependent on the Internet of Thing (IoT), which includes most of the things or objects that are connected to each other remotely. In IoT environments, everything becomes smarter and is able to process data, manage and communicate with the other surrounding environments. Moreover, it provides many services and has created a significant impact in several fields, such as industry, smart homes, smart cities, healthcare, etc. All these different fields collect various types of data and use a variety of techniques and protocols. Currently, IoT security is one of the most popular topics in the field of research. IoT security may include a number of different aspects, such as how to provide confidentiality and maintain data integrity. Moreover, it provides authentication and A. D. Alhejaili (B) · O. H. Alhazmi Department of Computer Science, Taibah University, Madinah, Saudi Arabia O. H. Alhazmi e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_33

355

356

A. D. Alhejaili and O. H. Alhazmi

authorization to access smart devices, which are increasingly becoming a part of our everyday lives. However, there are different types of protocols that can be adapted to the IoT environment, such as Advanced Message Queuing Protocol (AMQP), Constrained Application Protocol (CoAP), Message Queue Telemetry Transport (MQTT), and Data Distribution Service (DDS). Each of these protocols has its own advantages and disadvantages, and the most commonly used protocol is the MQTT protocol. The Message Queue Telemetry Transport (MQTT) is one of the application layer protocols that works on Transmission Control Protocol/Internet Protocol (TCP/IP), and it is very stable for IoT devices with limitation. According to [1] MQTT is extremely suitable for communication that contains various types of data-link layer protocols such as Wi-Fi. MQTT is a lightweight protocol for IoT devices, and it is known as a publisher/subscriber protocol. The MQTT protocol contains three main layers: the Physical layer, the TCP/IP stacks, and the MQTT application layer [2]. Moreover, it contains the following components: the publisher, the subscribers, and the MQTT Broker (or MQTT server). Every IoT device can be a publisher; it can connect with the MQTT Broker and publish data on a specific topic. Every subscriber in this topic will receive the message “publish by publisher”. Figure 1 illustrates a simple scenario to explain the MQTT publisher/subscriber concept. Indeed, an increase in the amount of data generated by IoT devices takes place every day, and it then becomes big data. This data is prone to various types of attacks, which aim to obtain user information, breach privacy, and lead to unwanted communication from the nodes due to spamming [3] and lead to Distributed Denial of Service (DDoS). According to Vaccari et al. in [4] the SlowITe attack, which is a type of denial of service (DoS) attack, targets the MQTT protocol as well as all protocols that can work over TCP. Furthermore, the MQTT protocol provides the service of user authentication through username and password, and it does not provide encryption to ensure data confidentiality. This paper focuses on the MQTT protocol because it is the most popular one. The contribution of this paper is the proposed lightweight algorithm that can provide a secret key between the publisher

Subscribers

Publisher MQTT Broker Fig. 1 Scenario that explains the MQTT publisher/subscriber concept

Subscribers

Lightweight Key Exchange Algorithms for MQTT Protocol in Its Environment

357

and subscriber over an insecure channel based on the MQTT protocol in order to ensure and achieve data confidentiality and authentication. The rest of this paper is organized as follows: Sect. 2 mentions some related work on MQTT lightweight algorithms. Section 3 provides an overview of the exploited technologies along with a discussion of the main concepts and technologies that we used. Section 4 discusses the proposed key exchange algorithm. The last section includes the conclusion and a discussion on future research.

2 Related Work MQTT protocol is one of the most popular lightweight protocols that is used in IoT devices. The main drawback of the MQTT protocol is that it does offer security functions by default and it is based on SSL/TLS, which uses username and password for authentication. Nowadays, there are various types of attacks that target the MQTT protocols, such as the DoS and the SlowITe attack. This creates the need for robust security algorithms to overcome most types of attacks. Indeed, there is a various methods that can be used to make the MQTT protocol more secure, such as encryption/decryption (by symmetric or asymmetric techniques) or digital signatures. In the following section, we have discussed certain resources and studies that proposed a different way of fulfilling this objective. Symmetric block cipher or secret key encryption is one of the techniques that can be used to create lightweight algorithms. One of the most suitable encryption algorithms for the publisher/subscriber concept is Attribute-Based Encryption (ABE); it provides access control over the data and confidentiality. The authors in [5] proposed that the hybrid security algorithm passes through two main phases: The first phase is the setup phase where the key authority generates a public key and a master key by running the Attribute Based Encryption (KP-ABE) algorithm, and it distributes the public key among all users. The second phase is the encryption where the publisher encrypts the message by using the Dynamic S-box AES algorithm, followed by encryption by KP-ABE with both the S-box AES Dynamic and public key as input. As a result, the proposed algorithm provides confidentiality and access control over data. Additionally, the study by Iyer et al. [6] proposed the use of a symmetric cipher with 64 bits and Spongnt hash function for achieving integrity and authenticity. The authors tried different types of lightweight block cipher, such as Speck, Present, and Simon; the best result was obtained by using the Speck block cipher. Another study where Bashir and Hussain in [7] proposed an algorithm to prevent unauthorized users from accessing the data. This algorithm is based on the following: generating ID for each node, generating a set of random numbers as block seed, generate a random integer number used as block selector, and generating a random integer for choosing a block, and then using all the previous numbers with the ID for encryption through the chosen random block. Additionally, the author in [8] proposed MQTTAuth as a security solution for the MQTT protocol. It was based on the augmented passwordauthenticated key exchange (AugPAKE) algorithm, which creates a secure session

358

A. D. Alhejaili and O. H. Alhazmi

key to provide authorization and ensure the confidentiality of the transmitted data. On the other hand, in the case of asymmetric encryption, the Elliptic-curve cryptography (ECC) algorithm is the most popular and efficient for IoT devices. Amnalou et al. in [9] proposed a lightweight algorithm based on the use of Elliptic Curve Diffie Hellman Ephemeral (ECDHE) and a pre-shared key. The relevant previous work may offer methods that show better performance, but they consume the power of the devices due to the computational cost or make it difficult to adapt them to all devices. Therefore, we proposed a lightweight key exchange algorithm that can be used with the different types of a cryptography algorithm to make it suitable for IoT devices.

3 Overview of the Exploited Technologies This section discusses the main technologies used in the proposed algorithms.

3.1 Key Exchange Algorithms There are various types of key exchange algorithms, such as Diffie-Hellman, Elliptic curve Diffie-Hellman (ECDH), and ElGamal encryption. All previous of algorithm serve the same purpose, i.e., to exchange a secret key over an insecure channel. However, the benefit of using an elliptic curve algorithm is that it can be used to minimize the length of the key. The secret key can be used with various types of encryption. According to some studies that make a comparison between Diffie-Hellman (DH), Rivest–Shamir–Adleman (RSA) and ECDH [10], the ECDH algorithm showed the best result.

3.2 Spongent Hash Function Spongent is lightweight hash function. It has different sizes of input (such as 88, 128, 160, 224, and 256 bits). The sponge is composed of three stages. The first stage is the initialization where the message is padded by 0 as the initial value and cut into the r block. The second stage is the absorbing, and it implements the XOR operation with the first r-bit and the permutation of b and so on to the last r block in the data. The third stage is the squeezing, and it produces the output in bits based on the user’s choice [11].

Lightweight Key Exchange Algorithms for MQTT Protocol in Its Environment

359

3.3 PCG-Rand Random Number Generator The PCG stands for permuted congruential generator. It is an algorithm that is used to generate random numbers in cryptographic algorithms. The PCG family for random number generation is simple, easy to use, statistically efficient, and fast. Additionally, it has a high rate of predictability of difficulty and a small code size. According to O’Neill [12], the PCG-Rand algorithm is applicable to a range of 64 and 128-bit streams that can provides 32-and 64-bit outputs.

4 The Proposed Key Exchange Algorithm The following sections discuss the proposed generate/exchange secret key algorithm. Our algorithm is related to the Elliptic-curve Diffie–Hellman (ECDH) concept but with a reduced overhead of computation. It uses PCG Rand random number and lightweight Spongent hash (ESK-PCG-Spongent) to ensure data integrity. Our proposed algorithms are based on the assumption of the trusted system. The trusted system can be a secure bootstrapping that generates a prime number (PN) for publishers and subscriber, after making sure they are authorized to be enter according to their ID. Moreover, the trusted system is trusted by every node in the environment. However, to ensure security, the PN will be generated for each new session. The PN is same for the publisher and the subscriber for every session.

4.1 Generate and Exchange Stage In this stage and after the trusted system ensure publisher and subscriber are authorized to access, will generate PN as bootstrapping. 1. 2. 3. 4.

Send the prime number that generated by trusted system to MQTT broker to be distributed on publisher and subscriber. The publisher and subscriber will have their own private key (PKp and PKs, respectively). The private key is generated by using PCG Rand random number. Calculate the shared key for the publisher SHp = (PKp * PN) and the key for the subscriber, SHs = (PKs * PN). Then, after exchanging the key, the secret key will be calculated, which will be same value for both. The secret key for the publisher will be S = (SHs * PKp) and that for subscriber S = (SHp * PKS). All these symbols are summarized in Table 1. Figure 2 illustrates all these steps.

360

A. D. Alhejaili and O. H. Alhazmi

Table 1 Symbol used in the proposed algorithm and their meaning Symbol

Description

PN

Prime number for publisher and subscriber

PKp

PCG Rand random number as a publisher private key

PKs

PCG Rand random number as subscriber private key

SHp

The publisher shared key, which equal (PKp * PN)

SHs

The subscriber shared key, which equal (PKs * PN)

S

The secret key for publisher and subscriber

4.2 Encryption MQTT Payload Scenario Stage Now, after generating and exchanging the secret key. The publisher and subscriber connect with Broker through CONNECT and get CONNECTACK as response, and subscriber subscribes to specific topic as illustrated in Fig. 2. We will use the secret key to encrypt the MQTT payload. Then we will have the following scenario: • Using the simple logical operation XOR to reduce the overhead and power consumption. Also, it will use the Spongent hash to calculate the hash value of the payload before encrypting for payload integrity. Figure 3. Illustrates XOR encryption scenario.

4.3 Security Analysis In this section will discuss some attack may effect on the proposed algorithms. • Brute-force Attack: In this type of attack the attacker exploits the length of a key and tries to find all possible values to find the key. We assume the secret key size is equal to 128 bit. The number of possible values to get the secret key will be as illustrates in Eq. (1). Possible number of key = 2128

(1)

Possible number of key = 3.40 × 1038 In addition, the average of possible number of tries to get the correct key will be less than or equal the possible number as illustrated in Eq. (2). Average = 3.40 × 1038 /2

(2)

Lightweight Key Exchange Algorithms for MQTT Protocol in Its Environment

Trust System

Publisher

361

Subscriber

MQTT Broker

ID ID ACK ID ID ACK Prime number: PN PN

PN Shared key SHs = PKs * PN

Shared key SHp = PKp * PN

Publisher shared key

Subscriber shared key Subscriber shared key

Publisher shared key Secret key S = SHp * PKs

Secret key S = SHs * PKp

CONNE

CONNE

CONNECTAC CONNECTAC Subscriber SUBACK

Fig. 2 The generate and exchange secret key phase Fig. 3 The scenario encryption by using XOR

Publisher

MQTT Broker

Hash value + [E (payload)] XOR

362

A. D. Alhejaili and O. H. Alhazmi

5 Discussion This section will mention the improvements that have been added by our work into the key exchange algorithm that can be used on MQTT protocol. In our algorithm, the trusted system is the responsible for verifying the authorization. The trusted system will perform the authorization using the publisher and subscriber ID’s. Also, we reduce the overhead computation on the algorithm by using one prime number for publisher and subscriber, which then sent out to MQTT broker to distribute between publisher and subscriber. That means, we make the MQTT broker is responsible for the process of distributing and exchanging keys, because we might have more than MQTT broker on the system. Also, we use key length 128 bit, which takes a long time to be break. In addition, to ensure protection even in case the prime number is discovered, it will be generated for every session. Furthermore, we use a Spongent hash for the payload before encrypting it for integrity to be sent with the payload encryption.

6 Conclusion In the IoT environment, one of the most important factors is the security of the data. There are various types of protocols that can be used in the IoT environment. The MQTT protocol the most popular protocol for several reasons, and it follows the publisher/subscriber concept. In this paper, we proposed an algorithm for creating a secret key over an insecure channel. The proposed algorithm provides a secret key by length 128 bit that can be used with various types of encryption algorithms. Additionally, we encrypt the payload by using XOR operation. For provide integrity, we proposed the use of Spongent lightweight hash functions. Also, in security analysis for the algorithm, we implemented brute-force attack on the key length and it take many years to break it. Finally we mention to the most important improvements that have been proposed on the algorithm. In future work, we will implement the algorithm in the IoT environment to measure its performance on the batteries of devices. Furthermore, we will test the performance during different attack scenarios, such as MITM, spoofing attack, and SlowITe attack. We will replace the XOR operation Scenario with other lightweight encryptions, such as the HIGHT block cipher and the PRESENT block cipher to identify the best result.

References 1. Mrabet H, Belguith S, Alhomoud A, Jemai A (2020) A survey of IoT security based on a layered architecture of sensing and data analysis. Sensors 20(13):3625

Lightweight Key Exchange Algorithms for MQTT Protocol in Its Environment

363

2. Vaccari I, Maurizio A, Cambiaso E (2020) SlowITe, a novel denial of service attack affecting MQTT. Sensors 20(10):2932 3. Ajmal M, Bag S, Tabassum S, Hao F (2017) Privy: privacy preserving collaboration across multiple service providers to combat telecoms spam. IEEE Trans Emerg Top Comput 8:313– 327 4. Vaccari I, Aiello M, Cambiaso E (2020) SlowITe, a novel denial of service attack affecting MQTT. Sensors 20(10):2932 5. Bisne L, Parmar M (April 2017) Composite secure MQTT for Internet of Things using ABE and dynamic S-box AES. In: 2017 innovations in power and advanced computing technologies (i-PACT), pp 1–5. IEEE 6. Iyer S, Bansod GV, Naidu P, Garg S (December 2018) Implementation and evaluation of lightweight ciphers in MQTT environment. In: 2018 international conference on electrical, electronics, communication, computer, and optimization techniques 7. Bashir A, Hussain Mir A (2018) Securing communication in MQTT enabled Internet of Things with lightweight security protocol. EAI Endorsed Trans Internet Things 3(12):154390 8. Calabretta M, Pecori R, Vecchio M, Veltri L (2018) MQTT-Auth: a token-based solution to endow MQTT with authentication and authorization capabilities. J Commun Softw Syst 14:320–331 9. Amnalou S, Bakar KAA (2019) Lightweight security mechanism over MQTT protocol for IoT devices 10. Goyal TK, Sahula V (September 2016) Lightweight security algorithm for low power IoT devices. In: 2016 international conference on advances in computing, communications and informatics (ICACCI), pp 1725–1729. IEEE 11. Bogdanov A, Kneževi´c M, Leander G, Toz D, Varıcı K, Verbauwhede I (September 2011) SPONGENT: a lightweight hash function. In: International workshop on cryptographic hardware and embedded systems. Springer, Heidelberg, pp 312–325 12. O’Neill ME (2014) PCG: a family of simple fast space-efficient statistically good algorithms for random number generation. ACM Trans Math Softw 13. Hong D, Sung J, Hong S, Lim J, Lee S, Koo B-S, Lee C, Chang D, Lee J, Jeong K, Kim H, Kim J, Chee S (October 2006) HIGHT: a new block cipher suitable for low-resource device. In: International workshop on cryptographic hardware and embedded systems. Springer, Heidelberg, pp 46–59

IoT Design and Realization of a Supervision Device for Photovoltaic Panels Using an Approach Based on Radiofrequency Technology Zaidan Didi

and Ikram El Azami

Abstract In this paper, we present an innovative method that allows to measure, calculate and transmit the parameters of a photovoltaic panel based on radiofrequency communication. Our method focuses on the integration of a current sensor and a voltage divider to determine the power as well as the energy, these quantities are transferred in real time by a radio frequency-based approach. Note that our method can operate in real time and the quantities received are displayed on the serial monitor and on an LCD screen. In comparison with other approaches which point to the same objectives, our method is proven to work reliably even in the absence of internet because it is based on the 433 MHz radio frequency for data transfer. Our realization has been implemented and tested, the results show that our project is successful in calculating, measuring and transmitting the characteristics of a photovoltaic panel in real time. Keywords Radiofrequency · Photovoltaic panel · Solar energy · Current sensor · IoT

1 Introduction Photovoltaic energy is now experiencing vigorous development and is now becoming a grid-connected energy source, hence the latest PSC power conversion efficiency report (PCE, the ratio of the energy of incident solar photons to the electrical energy) exceeded 25% [1, 2]. But like any industrial evolution, this system must be competent to notice the anomalies of a photovoltaic installation and to process the measured parameters and, this is called a monitoring system [3]. A photovoltaic system can be subjected during its operation to various anomalies leading to a reduction in performance of the system [4]. Z. Didi (B) · I. El Azami Computer Science Research Laboratory (LaRI), Faculty of Sciences, Ibn Tofail University, Kenitra, Morocco e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_34

365

366

Z. Didi and I. El Azami

Many interesting studies point to the measurement of different parameters of photovoltaic panels based on the Arduino hardware platform [5], other projects use the ACS712 current sensor based on the hall effect [6]. In the same design, other achievements have integrated the Wi-Fi module to send the characteristics of a photovoltaic panel [7]. In this paper we propose a design and construction project for a monitoring system for the current, voltage and energy quantities of solar panels in real time. This IoT project will be a menu of connectivity allowing the user to transfer the parameters of photovoltaic solar panels by an RF radio frequency link.

2 Materials and Methods The hardware design uses an Arduino Uno board and esp32 microcontroller, a voltage divider to measure the voltage across the photovoltaic panel and an ACS712-30A sensor to measure the current, a 433 MHz (FS1000A) RF transmitter and the peer receiver to perform the radio frequency communication, at the end two 16 × 02 LCD displays for the transmitter and receiver.

2.1 Measure Voltage and Current Measuring electrical current and voltage and very useful for calculating energy and power in electrical systems. In this paper, we used the current sensor ACS71230A (see Fig. 1) which is based on the Hall Effect magnetic field to measure the electric current produced by the PVs, this sensor produces at output a direct voltage proportional to the current at a rate of 0.066 V/A [8]. As you can see, this sensor is absolutely simple and contains only a few parts, including the ASC712, passive components and connectors. There are three variants of the ACS712 sensor depending on the current detection interval ±5 A, ±20 A and ±30 A [8], the output sensitivity varies as follows (Table 1): Fig. 1 The ACS712 current sensor

IoT, Design and Realization of a Supervision Device for Photovoltaic Panels … Table 1 The sensitivity of the current sensor ASC712

367

Model ACS712

Optimized current

Output sensitivity

ACS712 ELC-05

±5 A

185 mV/A

ACS712 ELC-20

±20 A

100 mV/A

ACS712 ELC-30

±30 A

66 mV/A

Fig. 2 Hall effect of the ASC712-30A sensor

As mentioned before, the ASC 712 is based on the Hall effect (see Fig. 2). A copper strip joins the pins IP+ and IP−. The generated magnetic field is detected by the sensor to transform it into an adequate voltage. Then we are going to make a voltage divider in order to measure the voltage at the terminals of the photovoltaic panel so as not to damage the Esp32 microcontroller. To do this, we will integrate a voltage divider because the voltage across the Esp32 must not exceed 5 V. We take R1 = 1 K and R2 = 12 K, the output voltage is measured at the terminals of R2 when a voltage from the photovoltaic panel is applied to the input of these two resistors (see Fig. 3). Based on mesh law and then Ohm’s law with the voltage at pin34 of esp32, we can retain the authentic value of the voltage at the terminals of the photovoltaic panel. N.B: we can easily calculate the power P consumed as well as the energy dissipated because we have measured the two quantities, the current (I) and the voltage (V).

2.2 Radio Frequency Communication In this part we are going to carry out a radio frequency communication of 433 MHZ to transmit the values Voltage, Current, Energy and Power previously determined. We integrate the FS1000A transmitter with an Esp32 card and the proprietary receiver with an Arduino Uno card. Hardware Description and Specifications of the 433 MHz Module [9] • One-way wireless (RF) transmitter and receiver. • Transmission distance: 3 m (without antenna) to 40 m (Convenient)

368

Z. Didi and I. El Azami

Fig. 3 Circuit diagram (current sensor and voltage divider)

• • • •

Modulation technique: ASK (amplitude modulation) Transmitter operating current: 9 mA to 40 mA. Operating frequency: 433 MHz Data transmission speed: 10 Kbps.

The FS1000A RF Transmitter The FS1000A transmitter includes three pins, Vcc, Data, and GND, (see Fig. 4). The Vcc pin has an input voltage of 3 V to 12 V. GND is ground (0 V). The DATA pin is the data pin to which the signal to be transmitted is sent. After this signal is modulated by ASK (Amplitude Shift Keying) [10] then transferred to the waves with a frequency of 433 MHz. The wiring of the transmitter is very simple, it has only three pins. The VCC pin (5 V), the GND (0 V) on the ESP32. The Data-In pin is connected to pin n° 12 of esp32. Figure 5 shows the wiring of the transmitter. Fig. 4 Image of the FS1000A transmitter

IoT, Design and Realization of a Supervision Device for Photovoltaic Panels …

369

Fig. 5 Connection of the FS1000A transmitter with the Esp32

The RF Receiver The receiver associated with the FS1000A transmitter has four pins: Vcc, DATA and the two middle pins are attached together, and the GND, (see Fig. 6). Unlike the transmitter, the Vcc pin, the receiver must be powered by a regulated 5 V supply. GND is a ground pin (0 V). Wiring the receiver is as easy as the transmitter. Noted that the output pin of the receiver should be connected to pin 11 of the Arduino (see Fig. 7). Organization Charts • Radiofrequency transmitter. Figure 8 shows the block diagram of the FS1000A transmitter. • Radiofrequency Receiver Figure 9 represents the block diagram of a radiofrequency receiver. For the code, we must install the RadioHead library in our Arduino IDE, to ensure data transfer between the transmitter and the receiver. Fig. 6 The Radiofrequency receiver

370

Z. Didi and I. El Azami

Fig. 7 Arduino UNO assembly and receiver

Fig. 8 Schematic diagram—RF transmitter

Fig. 9 Schematic diagram—RF receiver

Then we generate an ASK object called “rf_driver”. In the loop, we define a buffer for the size of the transmitted message. You have to normalize this parameter to match the message size, you have to include spaces and punctuation marks because they evaluate as characters. We then examine whether we have obtained a valid package. Finally, we use the strtok() function to capture the values: Current, Voltage, Power and Energy [11]. Main Organization Chart Figure 10 shows the main flowchart of operation of the assembly.

3 Results and Discussion Figures 11, 12, 13 and 14 show the real-time results of our project following a 433 MHZ radio frequency link to transmit the two measured and calculated quantities

IoT, Design and Realization of a Supervision Device for Photovoltaic Panels …

371

Fig. 10 Main flowchart

(Voltage, Current, Power and Energy) while using the FS1000A transmitter with a card Esp32 and the receiver associated with an Arduino Uno board. It should be noted that in this experiment we chose the simplest solution, we soldered an antenna on the ANT pin, with this trick we increased the distance between the transmitter and the receiver to 100 m. unfortunately this type of antenna is omnidirectional. This method presents an inexpensive solution for the transmission of data in real time based on radio frequency, particularly in isolated “no internet” environments, so it is a realization of a remote supervision arrangement of photovoltaic panels. The images which represent our project are given as follows, Fig. 11 represents the box of the RF transmitter and Fig. 12 represents the box of the RF receiver.

372

Z. Didi and I. El Azami

Fig. 11 Photo of the RF transmitter box

Figure 13 shows the data captured on the Arduino Uno serial monitor at the receiver following a radio frequency link with a length of 30 m between transmitter and receiver. These data represent current, voltage, power and total energy (Current, Ts, Power, Energy/Min and Energy/T). Code Test We can finally display the total energy variation per unit of time (see Fig. 14) on the free platform ThingSpeak (Open-Source application) for the Internet of Things, it is a service carried by MathWorks allowing to store and collect data from.

IoT, Design and Realization of a Supervision Device for Photovoltaic Panels …

Fig. 12 Photo of the RF receiver box

Fig. 13 Testing the code on the Arduino serial monitor

373

374

Z. Didi and I. El Azami

Fig. 14 Variations in total energy

4 Conclusion As you can see, adding a wireless link to Arduino implementations is very easy and inexpensive to send/receive data one way, with 433 MHz transmit and receive modules. These RF modules are absolutely matched to the job at hand and the price is affordable. For an efficient and robust operation of the photovoltaic solar energy system, a vigilance system is mandatory to identify difficulties and system failures before any major failure. By taking advantage of the different measured data, we can simply draw particular data over time intervals, which leads to a commonly traceable pattern for detecting operating failures. Unfortunately, this type of antenna is omnidirectional so there is no point in radiating in the opposite direction and to the sides, which is not useful it is necessary to better guide the radiation in a desired direction. In addition, the development of the protection system in the solar photovoltaic energy system increases the reliability of the system and ensures its proper functioning. An effective monitoring system can help the user of that energy system keep the system running smoothly for years to come.

References 1. NREL: Best research-cell efficiency chart. https://www.nrel.gov/pv/assets/pdfs/best-researchcell-efficiencies.20190802.pdf. Accessed Sep 2019

IoT, Design and Realization of a Supervision Device for Photovoltaic Panels …

375

2. Chen B, Yu Z, Liu K, Zheng X, Liu Y, Shi J, Spronk D, Rudd PN, Holman Z, Huang J (2019) Grain engineering for perovskite/silicon monolithic tandem solar cells with efficiency of 25.4%. Joule 3:177–190. https://doi.org/10.1016/j.joule.2018.10.003 3. Daliento S, Chouder A, Guerriero P, Pavan AM, Mellit A, Moeini R, Tricoli P (2017) Monitoring, diagnosis, and power forecasting for photovoltaic fields: a review. Int J Photoenergy. https://doi.org/10.1155/2017/1356851 4. Chaibi Y, Malvoni M, Chouder A, Boussetta M, Salhi M (2019) Simple and efficient approach to detect and diagnose electrical faults and partial shading in photovoltaic systems. Energy Convers Manag 196:330–343. https://doi.org/10.1016/j.enconman.2019.05.086 5. Vestenicky M, Matuska S, Hudec R (2019) Simple method of photovoltaic panel power characteristic measurement based on Arduino hardware platform. Transp Res Procedia 40:579–585. https://doi.org/10.1016/j.trpro.2019.07.083 6. Yatchev I, Sen M, Balabozov I, Kostov I (2018) Modelling of a hall effect-based current sensor with an open core magnetic concentrator. Sensors 18(4):1260. https://doi.org/10.3390/ s18041260 7. de Arquer Fernandez P, Fernandez MAF, Cand JLC, Arboleya PA (2021) An IoT open source platform for photovoltaic plants supervision. Int J Electr Power Energy Syst 125:106540. https://doi.org/10.1016/j.ijepes.2020.106540 8. Allegro Microsystem. ACS712: fully integrated, hall effect-based linear current sensor with 2.1 kVRMS voltage isolation and a low-resistance current conductor (2017), ACS712-DS, Rev. 7. Northeast Cutoff Worcester, Massachusetts 01615-0036 U.S.A, https://www.sparkfun.com/ datasheets/BreakoutBoards/0712.pdf. Accessed 25 Aug 2017 9. Smith DW (2013) Chapter 12 - radio transmitters and receivers. PIC projects and applications using C. A project-based approach, 3rd edn., pp 129–134. https://doi.org/10.1016/B978-0-08097151-3.00012-X 10. Binh LN (2008) Multi-amplitude minimum shift keying modulation format for optical communications. Opt Commun 281:4245–4253. https://doi.org/10.1016/j.optcom.2008.04.041 11. Attaway S (2016) Matlab, 4th edn. (Cover date: 2017), pp 237–266. https://doi.org/10.1016/ B978-0-12-815479-3.00007-6

ESP8266 Wireless Indoor Positioning System using Fingerprinting and Trilateration Techniques Safae El Abkari, Jamal El Mhamdi, Abdelilah Jilbab, and El Hassan El Abkari

Abstract Location-based systems have come under the spotlight in the last decade. They became significant with the bloom of applications that have tracking services. Indoors, wireless technologies can be an alternative for precise positioning in small or large areas where GPS signals are too weak and inaccurate. In this paper, we present a Wi-Fi Received Signal Strength based system using fingerprinting and trilateration techniques. We constructed radio- maps by collecting signals with ESP8266 modules in the offline phase. Then, we looked for the high number of matches in real-time by comparing collected signals to those stored in database. Simultaneously, we use trilateration to find positions with three nearby APs in the online phase. We achieved an accuracy of 98% by combining fingerprinting and trilateration locating techniques. We also analyzed the error probability for position calculation at different time of the day and demonstrated high stability of our system with a low standard deviation. Keywords Wireless · Indoor · Position · Localization · RSS · Fingerprinting · Trilateration · ESP8266

1 Introduction The wide use of location services has increased with the growth and the popularity of locating computation applications in indoor environments, such as airports, schools, malls… etc. [1]. Consequently, tracking and positioning system industries have emerged and developed smart locating technologies. Because of the consistent emergence of location-based systems, [2] estimates that the general annual revenues of location-based system will approximately reach 25$ billion by 2022. In fact, Indoor localization offers an improvement of healthcare monitoring, network management, security and personal navigation [3, 4]. Multiple indoor locating technologies such as RFID [5], Wi-Fi [6] and Bluetooth [7] have been utilized for indoor locating systems. Most of those technologies require massive hardware and infrastructure S. El Abkari (B) · J. El Mhamdi · A. Jilbab · E. H. El Abkari E2SN, ENSAM, ST2I, Mohammed V University in Rabat, Rabat, Morocco e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_35

377

378

S. El Abkari et al.

deployment, which leads to high complexity and cost maintenance. Even though the main functionality of Wi-Fi is not locating service, Wi-Fi seems to be a promising technology and potentially a counterpart of Global Positioning System (GPS) [8] in indoor environment. Therefore, Wi-Fi localization using fingerprinting technique has been adopted by many researchers because of its availability in most indoor environments (no extra hardware needed). Wi-Fi based locating system using fingerprinting is employed to overcome the path-loss model issue which consists of variation of the received signal strength (RSS) intensity at certain positions caused by multi-path fading effect in indoor environments [9]. Thus, the knowledge of the received signal properties such as the RSS distribution, maximum, minimum, median, mean and standard deviation are important for the correct modeling of fingerprinting locating system. In fact, the knowledge of necessary data number to achieve high accuracy and number of necessary APs for precise location identification can be provided by studying the nature of RSS and how it is affected by the environment. In this paper, we present a Wi-Fi based positioning system using fingerprinting and trilateration techniques. In the first stage which is the offline phase, we collect the RSS data to construct radio-maps. In the second stage, we look for the high number of matches while we calculate the position using trilateration. Results demonstrate high positioning accuracy in presence of indoor constraints (fluctuations of RSS signals).

2 Wireless Positioning Systems 2.1 Related Works In this section, we discuss the existing literature about the wireless localization systems (Table 1).

2.2 Our Contribution We propose a Wi-Fi based locating system using fingerprinting and trilateration techniques. In the first stage which is the offline phase, we collected RSS values from APs to construct positioning radio-maps. In the second stage, we started the location process by reading RSS value in real-time. We determined positions by looking for the high number of matches while calculating positions using trilateration. We also studied the error probability for position calculations at different time of the day and analyzed the statistical values of error in different experiment scenarios. Our system can be adapted to various applications and easily be deployed because of the Wi-Fi technology popularity.

ESP8266 Wireless Indoor Positioning System ...

379

Table 1 Related works Ref. Description

Pros and cons

[10] They presented variable trilateration algorithms for indoor navigation. They created an android-based application that measures RSS of nearby Wi-Fi APs. The server calculates positions when RSS data is received and positions are returned to a custom-built radio-map. They reached an average margin of error of 1.99 m

Six positions which is a small number were tested on a single floor User configurations are not discussed in the paper

[11] They provided an indoor positioning system Accuracy is not disclosed using Wi-Fi trilateration Stopped providing their positioning system when they rebranded to MazeMap [12] They proposed a commercial indoor positioning system [12]. They combine Wi-Fi and BLE to determine positions

User configurations are not fully discussed

[13] It provided a positioning system that Methodology is not dicussed estimates positions of the user with an error Complex process of two to three meters. They combine accelerometer, compass and Wi-Fi and BLE fingerprinting to achieve this accuracy

2.3 Positioning Wireless Technologies This section presents a survey of the most three prominent Radio Frequency technologies used for indoor positioning as illustrated in table below. • RFID: is a process of automatic identification using Radio Frequency communication between RFID readers and tags for Indoor locating. It determines the position and the orientation of people or assets carrying the tag. It permits Real-Time positioning, with less complex programming and a very high response time. • Wi-Fi: is a wireless technology that works at high frequency waves to ensure connectivity and communication between the network nodes in indoor locating environment. It is suitable for locating in presence of multipath. Thus, there is no need of extra hardware due to the availability and the popularity of Wi-Fi infrastructure. • Bluetooth: is a technology that uses the client-server architecture. By con-trolling Bluetooth devices specifications such as communication parameters and emission power, we can enable the communication between the nodes of the network and start the positioning process. It permits high data speeds but has mobility limitations.

380

S. El Abkari et al.

Table 2 Review and comparison of indoor radio frequency technologies Technology

Technique

Algorithm

Complexity

Accuracy

Real-time positioning

RFID

Fingerprinting

RSS

Medium

Low

Yes

Wi-Fi

Trilateration fingerprinting

RSS TDOA

Medium

Medium

Yes

Bluetooth

Trilateration fingerprinting

RSS TDOA

Medium

Low – Medium

Yes

2.4 Positioning Methods • Received Signal Strength: this method is only possible for radio signals [14]. It estimates the distance of an unknown tag to a reference access point by using the attenuation of emitted signals strength. Basically, the smaller the RSS values the farthest the tag is from the reference access point (AP). • Time Of Arrival (TOA): It is a method based on the arrival time synchronization of an emitted and received signal [14]. We calculate distance between the emitter and the receiver using the following equation: distance = time ∗ speed o f the signal

(1)

2.5 Positioning Techniques Multiple techniques (Table 3) are utilized in wireless based locating system. In this section, we present two techniques used for Wi-Fi locating which are: • Fingerprinting: it is a method consisting of two phases [15]: – The Offline phase: which is also called calibration or training phase. At this phase, we set up radio-maps by empirical measurements with the definition of a reference node. The radio-maps are databases of radio signal characteristics such as the SSID/ RSS and coordinates. – The Online phase: wherein the process of locating is operational. The tag carried by the user measures SSID and RSS values of the signals at an unknown position. This value is compared then to those in the radio-map to find the higher number of matches. The main disadvantages of this technique are the time consumption in the offline phase and its sensitivity to changes in the locating environments. Therefore, the process of calibration must be repeated from time to time to guarantee a good accuracy.

ESP8266 Wireless Indoor Positioning System ...

381

Table 3 Comparison between trilateration and fingerprinting techniques Technique

Type of measurement

Affected by multipath

Coverage

Accuracy

Trilateration

Difference of arrival time

Yes

Good (in case of line-of-sight signals)

High

Fingerprinting

Received signal Strength

No

Good

High

Table 4 ESP8266 hardware specifications

Module

Type

Single board microcontroller

CPU

ESP8266

Storage

4MBytes

Memory

128kBytes

Power supply

3V, 5V

IDE

Arduino IDE

• Trilateration: it is a method consisting of the calculation of positions using measurements of distances [16]. This technique is based on the Received Signal Strength (RSS) or the propagation time (TOA…). Position of a person or an asset is determined from collected measurements using at least three nearby access points (Table 3).

2.6 ESP8266 Module In this work, we used ESP8266 NodeMCU module as a locating node and as a mobile tag. ESP8266 is Wi-Fi microchip with microcontroller capability and TCP/IP stack. It is cost effective and highly integrated for various IoT applications. Table 4 illustrates the hardware specifications of ESP8266 module.

3 Experimental Work and Results Our experimental work is divided into two main parts: • The first one uses fingerprinting technique wherein we constructed radio-maps (offline phase) in an indoor environment (8x8 meters) divided into 12 grid cells of same sizes. We then defined a calibration point (CP) at the center of each cell.

382

S. El Abkari et al.

We collected RSS values using an ESP8266 Module [17] at the 12 grids at a fixed orientation. We obtained different RSS samples at a unique cell vector and different sampling times. We constructed two radio-maps, with 100 samples for the first one and 300 samples for the second one. Using a laptop connected to an ESP8266 module, we retrieved RSS/SSID information and we stored all values in dBm. • The second experimental work uses trilateration to find positions in 2D using three access points (APs). We placed APs in a way that minimizes the effect of the multipath effects. We divided then the area of location into coordinates (x, y). The access points used for the experiment are ESP8266 modules [18, 19] that supports 802.11 n band operating at 2.4 GHz only. At full strength, ESP8266 nodes transmit at a maximum power of 25 mW which corresponds to 14 dBm. Collected RSS values from same APs fluctuate and vary because of walls, door… etc. We handle this issue and reduce it by correcting RSS signals. When part effects of its fluctuations are eliminated, collected RSS signal become more stable.

3.1 Locating Using Fingerprinting 3.1.1

Radio Map

Radio-map is constructed using collected signals in an area of 8 × 8 m using algorithm below. We divided the locating area into 12 grid cells with equal surfaces. We placed the calibration point in each cell grid center. We use 3 APs (ESP8266 Module) to collect RSS values. To achieve a suitable radio-map and a better experiment performance, we constructed two radio-maps with 100 and 300 samples of RSS. The laptop is then used to process received data and retrieve SSID/RSS information. Note that corrected RSS signals guarantee a best performance of both collection and matching processes.

3.1.2

RSS Measurements in Offline and Online Phases

• Mean and standard deviation of collected signals Devices working under 802.11 standards such as ESP8266 use Clear Channel Assessment protocol to prevent collision between two signals transmitted on the same channel [20]. This method checks if any other SSID than the one emitting is transmitting on the channel before sending data (frames). Table below shows the statistical values of our three APs (ESP8266 modules) at two different locations of the user tag (used for collection). One of the positions is near to APs while the second one is far from the APs. This table shows that RSS mean values and standard deviation of each ESP are similar at both position P1 and P2 (Table 5).

ESP8266 Wireless Indoor Positioning System ...

383

Table 5 Statistical values of RSS at two positions Position

Position P1

SSID

ESP1

ESP2

ESP3

ESP1

ESP2

ESP3

RSS mean value (dBm)

−32

−33

−34

−74

−72

−69

Standard deviation

4.5

5.4

5.1

2.2

2.5

2.3

Table 6 Distance measurement at three positions

Table 7 Statistical values of RSS at two positions

Position P2

SSID

P1 = 1m

P2 = 4m

P3 = 8m

ESP1

1.72

2.56

1.84

ESP2

1.9

2.14

2.23

ESP3

1.51

2.97

1.96

Method

Average (m)

Standard deviation

Precision

Fingerprinting only 1.124

1.658

75%

Fingerprinting + trilateration

1.433

89%

0.697

• Path-loss of ESP8266 RSS-signals According to the path-loss model [21], RSS values decrease with the increasing of the distance. Table 6 shows that when distance increases, the standard deviation decreases. Thus, measurements of ESP mean standard deviation are most comparable to the real path-loss model than the individual ones.

3.2 Locating Using Fingerprinting/Trilateration To guarantee a qualitative performance of the positioning process, we place the APs (EPS8266 modules) at defined positions to minimize geometrical errors that maximize position errors [22–24] when locating using trilateration. Our next experiment is carried out to evaluate performances of our proposed method which demonstrates a high positioning quality. In fact, with our proposed method we reach 89% of precision while 75% when using only fingerprinting for positioning at P = 0.6 m (Table 7). The presence of the experimental environment constraints such as doors, walls, presence of other signals… etc., positioning error increases with the increasing of distance. Therefore, we study the performance of our method by comparing the median, mean and maximum errors in different locating environment scenarios. Thus, our method demonstrates high stability with a low standard deviation compared to traditional location techniques (Table 8).

384

S. El Abkari et al.

Table 8 Statistical values of error in different experiment scenarios Position

Median error (m)

Mean error (m)

Maximum error (m)

RSS Positioning

1.4971

1.7847

4.1052

RSS positioning + Wi-Fi APs

2.4563

2.7645

6.4882

RSS positioning + fingerprinting + other Wi-Fi APs

0.86

0.4677

2.79

Table 9 Comparison of existing methods and our proposed locating technique Method

Description

Accuracy

RedPin [25]

Indoor map Positioning by the user

90% (Room Level)

FreeLoc [26]

Comparison of relative RSS

0

||r || > 0

(6)

if

fi = 0

||r || = 0

(7)

Wireless Sensor Network Fault Detection …

763

Fault Isolation. Based on the generated residual and on the parity matrix (ω) which its columns define n distinct fault signature directions, we can isolate the faulty sensor by multiplying transpose of each column of the parity matrix by the residual. F Ii = ωiT ∗ r

i = 1, 2, . . . n.

(8)

This function measure the correlation of the residual vector with fault signature directions. So, if the fault is detected, the largest value of F I i corresponds to the faulty sensor [10].

4 Simulation Results In our simulation, we have a WSN consisting of 3 sensor nodes scattered in different zones, each node contains one sensor and all of them measure the same quantity x(k). Those sensor nodes send wirelessly their measurements to the base station. Figure 2, illustrates the measurements recorded by the sensor nodes. These measurements form a model of the network in the base station as follow: ⎤ ⎡ y1 (k) (9) y(k) = ⎣ y2 (k) ⎦ y3 (k) Where y1 (k), y2 (k) and y3 (k) are the measurements recorded by sensor node 1, 2 and 3 respectively. Based on this model, an implemented algorithm in the base station generates a global residual and analyses it as explained in Sect. 3 for detecting and isolating sensor fault. In this paper, we examined only a single fault. So each time we inject a fault in one sensor node, our algorithm detects and isolates it. Below, the simulation results demonstrates the work that we did. Based on the measurements recorded by the WSN, our algorithm generates a residual norm as it is shown in Fig. 3. At t = 30 s a fault of type ‘abrupt’ is injected into sensor node 1. As can be seen from Fig. 3, the generated residual norm is sensitive to the fault and deviates from zero at t = 30 s, which means that from this instant, there is a fault detected in the WSN. After fault detection in WSN by the residual norm. To isolate the faulty sensor node, our algorithm automatically calculates the correlation of the residual vector with each column of the parity matrix (ω) as explained in Sect. 3.

764

N. Jihani et al.

Fig. 2 Measurements recorded by the wireless sensor nodes

Fig. 3 Network residual norm evolution

In our test, we have 3 fault signature directions defined by the column of the parity matrix; the first, second and the third column of ω correspond to the fault signature direction of the sensor 1, 2 and 3 respectively, the algorithm calculates the correlation of the residual vector with each column of the parity matrix (ω) (Eq. 8) and the largest value of F I i corresponds to the faulty sensor. As can be seen from Fig. 4, F I 1 has the largest magnitude which means the faulty sensor is the senor 1.

Wireless Sensor Network Fault Detection …

765

Fig. 4 Fault isolation process

Fig. 5 Network residual norm evolution

At t = 30 s, the fault is injected into sensor node 2, the Fig. 5 shows that the global residual norm of the network starts to react when the fault is present. And the Fig. 6 shows that the F I 2 has the largest magnitude and that is mean, the faulty sensor is sensor 2. The fault is inserted into sensor 3 at t = 30 s and Fig. 7 shows that the residual norm becomes different from zero from this instant.

766

N. Jihani et al.

Fig. 6 Fault isolation process

Fig. 7 Network residual norm evolution

The fault isolation process shows that F I 3 has the largest magnitude in Fig. 8. So the faulty sensor is sensor 3.

Wireless Sensor Network Fault Detection …

767

Fig. 8 Fault isolation process

5 Conclusion In this research, we developed an algorithm for detecting and isolating measurement faults in a WSN for smart irrigation. In designing this algorithm, we took several factors into consideration; first, we used analytical redundancy instead of hardware redundancy which is much expensive in terms of cost. Also, we adopted a centralized architecture rather than a distributed one to minimize the load on the sensor nodes which have a limited capacity at the level of computation and energy source. Finally, our approach does not require a model of the monitored system or a prior knowledge about the behavior of the monitored system. The simulation results obtained in this study demonstrated that the parity space approach detects and isolates the fault in WSN in a centralized manner; when we inject the fault into any sensor node, our algorithm detects and isolates it. This paper examines the problem of the measurement faults caused by the malfunctions of sensors or sensor nodes. In a WSN, a crash faults can be caused by the failure of the communication modules of the nodes, a crash faulty sensor node cannot participate in the network activities, and the future work may be conducted on this subject.

768

N. Jihani et al.

References 1. Riquelme JL, Soto F, Suardíaz J, Sánchez P, Iborra A, Vera JA (2009) Wireless sensor networks for precision horticulture in Southern Spain. Comput Electron Agric 68(1):25–35 2. Ojha T, Misra S, Raghuwanshi NS (2015) Wireless sensor networks for agriculture: the stateof-the-art in practice and future challenges. Comput Electron Agric 118:66–84 3. Abd El-kader SM, El-Basioni BMM (2013) Precision farming solution in Egypt using the wireless sensor network technology. Egyptian Inform J 14(3):221–233 4. Kassim MRM, Mat I, Harun AN (2014) Wireless Sensor Network in precision agriculture application. In: 2014 international conference on computer, information and telecommunication systems (CITS), pp 1–5. IEEE, July 5. Staroswiecki M, Hoblos G, Aitouche A (2004) Sensor network design for fault tolerant estimation. Int J Adapt Control Signal Process 18(1):55–72 6. Isermann R (2011) Fault-diagnosis applications: model-based condition monitoring: actuators, drives, machinery, plants, sensors, and fault-tolerant systems. Springer 7. Peng Y, Qiao W, Qu L, Wang J (2017) Sensor fault detection and isolation for a wireless sensor network-based remote wind turbine condition monitoring system. IEEE Trans Ind Appl 54(2):1072–1079 8. Hao J, Kinnaert M (2017) Sensor fault detection and isolation over wireless sensor network based on hardware redundancy. J Phys Conf Ser 783(1):012006. IOP Publishing 9. Smarsly K, Law KH (2014) Decentralized fault detection and isolation in wireless structural health monitoring systems using analytical redundancy. Adv Eng Softw 73:1–10 10. Patton RJ, Chen J (1991) A review of parity space approaches to fault diagnosis. IFAC Proc Vol 24(6):65–81

Multi-resolution Texture Analysis for Osteoporosis Classification Meriem Mebarkia, Abdallah Meraoumia, Lotfi Houam, Seddik Khemaissia, and Rachid Jennane

Abstract Osteoporosis is a systemic skeletal disease characterized by low bone mineral density (BMD) and micro-architectural deterioration of bone tissue, leading to bone fragility and increased fracture risk. Texture analysis applied to trabecular bone images offers an ability to exploit the information present on conventional radiographs. In this paper, a feature extraction scheme is proposed to analyze different bone X-ray images of two different populations in order to extract a precise and lightweight feature. The proposed scheme uses two efficient variants of Local Binary Patterns (LBP) and combines the information extracted from each level to improve the classification rate. The experimental results prove the efficiency of the proposed method in terms of classification speed and system accuracy. Keywords Local binary pattern · Fisher score · Data fusion · Osteoporosis

1 Introduction OSTEOPOROSIS is a skeletal disease characterized by low bone mass density (BMD), and deterioration of bone microarchitecture and can lead to compromised bone strength and an increased risk of fractures [1]. It is a silent disease until fractures occur, resulting in significant secondary health problems and even death [2]. M. Mebarkia · S. Khemaissia Laboratoire de Génie Electrique (LABGET), Larbi Tebessi University, Tebessa, Algeria e-mail: [email protected] A. Meraoumia (B) · L. Houam LAMIS Laboratory, Larbi Tebessi University, Tebessa, Algeria e-mail: [email protected] L. Houam e-mail: [email protected] R. Jennane I3MTO Laboratory, University of Orleans, Orléans, France e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_70

769

770

M. Mebarkia et al.

Several treatments have been shown to reduce the risk of osteoporotic fractures by following the necessary steps that can be taken to diagnose osteoporosis and thus prevent bone fracture [3, 4]. An early diagnosis of osteoporosis helps in preventing bone fractures. The diagnosis of osteoporosis relies primarily on measuring BMD (expressed as a T-score) using dual-energy X-ray absorptiometry (DXA). DXA is considered as the reference-standard examination for BMD assessment. Unfortunately, the high-cost, low-availability, and inconsistency of BMD measurements do not make it a promising tool for the diagnosis of osteoporosis [5, 6]. In addition, BMD does not cover the whole process of osteoporosis diagnosis, skeletal factors and micro-architecture features are also of great significance in the early diagnosis of osteoporosis. In accordance with many studies [7–9], the particular texture analysis of trabecular bone radiographs is a promising approach to assess and recognize this disease effectively. Image feature information of trabecular bone has been known to be correlated closely to bone density changes and therefore can be used for the evaluation of osteoporosis. Many algorithms have been used for the extraction of features of trabecular pattern, such as Gabor filter, wavelet transform and fractal models [10–12]. Song et al. have presented another approach which again used the analysis of bone textures by the usage of Fisher encoding of local descriptors algorithm with SVM for different classes [13], whereas Nasser et al. have proposed another approach to assess osteoporosis based on the sparse autoencoder and SVM classifier [14]. Zheng et al. have presented a concept based on a calculation of sparse representations, to classify bone texture image patterns [15]. Recently, with the advancements of Machine learning, deep CNNs have been recognized as effective tools for image classification [16]. For instance, Ran Su et al. combined CNN features with hand-crafted features for osteoporosis classification [17]. However, previous approaches relied mainly on handcrafted categorized feature indexes [14, 18], and machine learning (ML) algorithms, such as Support Vector Machine (SVM) and fuzzy classifiers [9]. In addition, most of the previous studies require tedious process and manual operations, such as extensive preprocessing, image normalization, and region of interest (ROI) extraction, which can drastically affect the reproducibility of the classification method [17]. The purpose of the present work is to validate the improved methods known as Three Patches Local Binary Patterns (TP-LBP), and Four Patches Local Binary Patterns (FP-LBP), suggested in [19]. The approach is composed in four stages procedure. First, a discrete wavelet transform (DWT) is performed to the original images of bone radiographs. In second step, TP-LBP/-FP-LBP methods are performed on all resulting DWT sub-bands at each decomposition level, to construct the feature vectors as shown in Fig. 2. The third step, the Fisher score algorithm is applied to the feature vector as supervised feature selection method. Finally, the SVM classifier is used with the linear kernel for classification of two different populations composed of osteoporotic (OP) and control subjects CS).

Multi-resolution Texture Analysis for Osteoporosis Classification

771

The rest of this paper is organized as follows: Sect. 2 presents and discusses in details the proposed feature extraction process. Section 3 deals with the experimental results in which unimodal and multimodal systems are discussed. Finally, the conclusion and future work are presented in Sect. 4.

2 Feature Extraction Process The performance of all pattern recognition systems depends mainly on the distinction of feature vectors extracted from the data. In fact, the role of the feature extraction process is to represent the images with a reduced and distinct representation, so that the system can discriminate the different patterns within a reasonable time. In our proposal, we used the texture analysis method with a multi-resolution analysis technique (e.g. DWT).

2.1 Local Binary Pattern (LBP) Local Binary Patterns (LBP) is an image-processing algorithm to extract texture properties for classification and segmentation purposes. LBP is computed in its simplest form by comparing each pixel with its surrounding neighbors. The original lbp operator is defined in 3 × 3-sized rectangular neighborhoods. Each pixel in a cell is compared to each of its 8 neighbors leading to an 8-digit binary number. Finally, an histogram is created for all the decimal values to be used as texture description for the image [20–22]. Due to the efficiency of this method, different variants have emerged; such as Three-Patches LBP (TP-LBP) and Four-Patches LBP (FP-LBP) [19], as shown in Fig. 1. Three-Path LBP (TP-LBP). The TP-LBP is an extended method of LBP; it compares the values of three patches with (w × w) size to produce a single bit value for each pixel. For a parameter α, pairs of patches are taken apart along the circle and compared to the central patch, S additional patches distributed uniformly in a ring of radius r around it [19]. The resulting code has S bits per pixel (Fig. 1(a)). The TP-LBP operator is given by the following equation: TP-LBPr,S,w,α (p) =

S−1      f d Cp, Ci − d Cp , Ci+αmod(S) 2i i=0

 f (x) =

1 if x ≥ τ 0 if x < τ

(1)

(2)

772

M. Mebarkia et al.

Fig. 1 Extended local binary pattern. a Three-Path Local Binary Pattern (TP-LBP) and b Four-Path Local Binary Pattern (FP-LBP) [19]

Fig. 2 Block diagram of the proposed multi-resolution bone texture analysis

Where d denotes the distance between patches, C is the patch, and f is a threshold function and τ is the threshold of comparison. Four-Path LBP (TP-LBP). The FP-LBP is another extended method of LBP. It uses two rings of radius r 1 and r 2 centered in the pixel, and S patches distributed around these two rings of (w × w) size (see Fig. 1.(b)) [19]. The FP-LBP is formed by the comparison between two center symmetric patches in the inner circle with two center symmetric patches in the outer circle by taking one bit in each pixel into accounts according to the similarity of the two patches. The FP-LBP operator is given by the following equation: FP-LBPr 1,r 2,S,w,α =

 S −1 2

i=0

  f (d C1,i , C2,i+αmod(S) − d(C1,i+ S , C 2,i+ S +αmod(S) ))2i 2

(3)

2

2.2 Proposed Feature Extraction Architecture Diagram The structure of the proposed feature extraction method is presented in Fig. 2. The image is first transformed using the DWT, then the features are extracted using

Multi-resolution Texture Analysis for Osteoporosis Classification

773

the LBP algorithm and finally, the extracted information is combined into the two system’s levels (feature extraction and feature matching levels). The input image (Ao ) of size (H × W ) is first transformed using the DWT into several decomposition levels, as follows: 

o FDWT (Ao ) = Ao ∈ R H ×W H W i FDWT (Ai−1 ) = [Ai , Hi , Vi , Di ] ∈ R 2i × 2i

i = 1, 2, . . . .

(4)

i Where F DW T denotes the DWT transform at i level and Ai , Hi , Vi , Di are the resulting decomposition bands at level i. Then, distinctive features of each band are extracted separately using the TP(FP)-LBP technique.

Vψi = F TP(FP)-LBP (ψ), ψ ≡ {Ai , Hi , Vi , Di }

(5)

Where FTP(FP)-LBP denotes the TP-LBP (FP-LBP) technique and one of the resulting decomposition bands (A: Approximation, H: Horizontal, V: Vertical, D: Diagonal). After extraction, the system can: i) either, combine (fuse) all the extracted feature vectors into one single vector (FF: Fusion at feature extraction level) then carry on the classification process, ii) or, continue the classification process for each vector alone till the end and then combine the classification results for all the vectors into a single result (FS: Fusion at the matching score level). Fusion at Feature Extraction Level. For its simplicity, the concatenation method is used in this study. Let V Ai , VHi , VVi , VDi be the feature vectors of the Ai , Hi , Vi , Di bands at level i, the combined vector is obtained as follows: V Ai = FCONCAT (V iA , VHi , VVi , VDi ) = [V Ai , VHi , VVi , VDi ]

(6)

Where FCONCAT combines the vectors by placing them in front of each other. Fusion at Matching Scores Level. Here, each band works independently, and their results are combined to obtain a scalar score that will be used later for decision making. In our study, the sum of weighted scores defined as follows is used:

dF =

n i=1

wi di , wi =

Ei

1 n

1 j=1 Ei

(7)

Where d F denotes the fused score, di is the score obtained by the subsystem i, n the number of subsystems, Ei represents nthe system error and wi is the weight wi = 1 and the weights are inversely associated with the subsystem i. Note that i=1 proportional to the corresponding errors and are therefore larger for the most precise systems.

774

M. Mebarkia et al.

3 Experimental Results This section highlights the performance of the proposed method by being integrated into an osteoporosis detection system. Thus, to evaluate the effectiveness of the proposed approach, we used a dataset of 174 images acquired at the hospital of Orleans (France) from postmenopausal women, using X-rays on the calcaneus site [23]. The dataset comprised 87 Osteoporosis Patients OP (with fractures) and 87 Control Subjects (CS).

3.1 Tests Protocol In this experiment, the 5-fold cross-validation procedure is adopted. The dataset is divided into 5 equivalent folds containing 34 samples, half (17) were from healthy subjects and the other half were osteoporotic subjects. It is worth noting that before the feature extraction process, each image was preprocessed (normalization and histogram equalization). The Support Vector Machine (SVM) technique is used for the classification step. Finally, the DWT application is limited to two levels of decomposition. The feature vector is extracted at each level for each band separately. Fisher’s feature selection technique is used to improve the system performance. Accordingly, (25%, 50%, 75%, 100%) of the resulting vector components are tested for each band.

3.2 Performance Tests Results Several experiments are conducted within two main parts. The first part of the experiments determines the optimal parameters for each feature extraction methods (TPLBP and FP-LBP). The second part focuses on the performance evaluation of the proposed osteoporosis detection system. This part is also divided into two sub-parts, the first concerns the study of the unimodal system (each DWT band is treated separately), while the second part focuses on the effect of the data fusion on the performance of the system (multi-modal system). Experimental Setup. Several experiments are conducted to select the appropriate combination in order to get the best performance. The patch size (w × w) is fixed among three predefined values (3×3, 5×5 or 7×7). The remaining parameters for the feature extraction methods (TP-LBP and FP-LBP) are taken from the predetermined values (r = 3, S = 6, α = 2) and (r1 = 2, r2 = 6, S = 12, α = 1), respectively. It should be noted that all the experiments are carried out on the whole image with 25% of the components of the vector (using Fisher feature selection). In the case of TPLBP, the system performance is very acceptable for almost any patch size value. For example, when using w size 3, 5 or 7 patches; our system operates at an Area Under

Multi-resolution Texture Analysis for Osteoporosis Classification

775

the Curve (AUC) equal to 96.83% (Accuracy (ACC) = 91.76%), 97.22% (99.18%) and 96.07% (90.59%), respectively. Compared to TP-LBP, the system performance is very efficient for all patch sizes with FP-LBP. Thus, under the different patch sizes, the AUC reaches 99.02% (ACC = 97.06%), 99.97% (99.41%) and 99.57% (98.24%) for w equal to 3, 5, 7, respectively. It can be noted that excellent performance exceeding 96.00% for both TP-LBP and FP-LBP are achieved. In conclusion, it is clear that for each feature extraction method, the value of 5 achieves the best performance. System Performance Evaluation. The performance of the proposed osteoporosis detection system, was evaluated for the selected parameter (w = 5 for both feature extraction methods). Unimodal System (Uni-Band Evaluation). In this series of tests, the performance of the osteoporosis detection system is evaluated using the information provided by each band extracted by the DWT (at 1 and 2 decomposition levels). Hence, Table 1 illustrates the results of all bands prior to DWT application under 1 and 2 levels of decomposition and feature selection rates (using Fisher’s criterion) equal to 25%, 50%, 75% and 100% of vector components. As can be seen on Table 1 for the whole i mage (with a size of 400 × 400 pixels), the AUC is 99.22 and 99.97%, and the processing time is estimated at 0.73395 and 1.79805 s for TP-LBP and FP-LBP, respectively. Actually, the smaller the image size, the smaller the processing time is, so that the DWT-band processing time for each analysis level is less than the total time. For DWT-bands of level 1, the processing time is 0.52297 and 0.74726 s, respectively for TP-LBP and FP-LBP. The time decreases as far as the analysis levels increase (the processing time for the DWT-bands of level 2 is 0.43189 and 0.48367 s for TP-LBP and FP-LBP, respectively). Figure 3 depicts the system performance for all decomposition bands (with 25% feature vector’s components) for both methods (TP-LBP and FP-LBP). The comparison of TP-LBP with the whole image is shown in Fig. 3(a), where the system operates Table 1 Unimodal osteoporosis detection system performance (AUC %)

TP-LBP

FP-LBP

DWT Bands

25%

50%

75%

100%

25%

50%

75%

100%

cA1

87.14

65.19

58.02

58.02

94.63

82.70

54.12

54.09

cH1

88.71

62.27

47.63

47.63

90.34

73.85

45.83

45.72

cV1

84.58

64.91

54.46

54.46

87.10

71.50

42.26

42.26

cD1

87.31

58.55

47.21

47.21

93.11

81.77

59.57

59.54

cA2

60.19

49.51

49.66

49.40

75.82

61.83

56.98

56.87

cH2

52.64

44.04

43.99

43.86

61.66

53.11

47.14

47.18

cV2

58.85

48.79

48.75

48.72

68.46

52.58

48.53

48.54

cD2

60.87

52.61

52.65

52.30

81.11

60.55

53.18

53.40

Whole image

97.22

91.28

68.65

68.65

99.97

99.40

91.61

54.01

Levels

L1

L2

776

M. Mebarkia et al.

Fig. 3 Performance of the unimodal osteoporosis detection system. a, c AUC for TP-LBP and FP-LBP, respectively and b, d ACC when using TP-LBP and FP-LBP, respectively

with an AUC of 88.71% (ACC = 80.02%), i.e. a degradation of 8.75% in the case of the horizontal detail band (cH). In this configuration, the best accuracy is obtained using the detail band (cD) with an ACC equal to 82.94% (a degradation of 9.04%), as in Fig. 3(b). In the case of the second method (FP-LBP), the system generally remains slightly unchanged with respect to the first method (see Fig. 3 (c) and (d). In this case the system works well (best AUC = 94.63% with ACC = 87.65%) with the cA band, the degradation rates in AUC and ACC are 5.34% and 11.83%, respectively. Therefore, the obtained results in all decomposition bands are suitable. In the next subpart, the osteoporosis detection system will be examined for all the decomposition bands when the first and second decomposition levels (L1 and L2 ) are fused. The fusion will be done at the feature extraction level and at the matching score level, then a comparison of the performance of the system will be addressed for the whole image. Multimodal System (Multi-band Evaluation). In general, a unimodal system can be affected by certain problems such as noise, variation within the same class (low intra-class correlation), and similarity between classes (high inter-class correlation). These problems cause the system to operate with certain errors which results in low

Multi-resolution Texture Analysis for Osteoporosis Classification

777

classification rates. Data fusion can reduce system errors and thus improve their efficiency. Therefore, this subpart aims to examine whether the system performance can be improved by using a combination of information extracted from different DWT-bands. Thus, the feature vectors extracted from these bands are combined to make the system more efficient than a unimodal system. Fusion at the feature extraction Level (FF) makes it possible to combine different vectors from different processing and analysis phases to form a single feature vector. Therefore, after decomposing the image using DWT and obtaining the four bands, features are extracted for each band separately and then the system combines (concatenates) the extracted vectors to get a single feature vector. Afterward, the system continues until the final decision is made (classification). In our experiments, all the DWT bands from each level (L1 and L2 ) are combined and the result (as AUC) is obtained using the whole image. Results are reported in Table 2. In fusion at matching score level (FS), the individual scores are combined to form a single score which is then used to make the final decision. It is the most widely used type of fusion because it can be applied to all types of systems in a simple and efficient way. In our multimodal system, each DWT-band has its own subsystem, which means that the overall system contains four subsystems that operate in parallel. Then the results of the subsystems are combined for the final decision. As previously, we also combined the DWT bands from each level of decomposition to examine the possibility of improving the performance of the system. The results obtained are shown in Table 2. As can be seen, it is clear that although data fusion did not improve the classification result, the results obtained are very satisfactory in both feature extraction methods. In the first method (TP-LBP), the system operates with an AUC equal to 97.19% (ACC = 93.53%), while the second method (FP-LBP) remains more efficient with an AUC = 98.69% (ACC = 94.12%). Figure 4 illustrates a comparison between unimodal and multimodal systems. Figure 4 shows that data fusion can give effective and promising results, especially when considering parallel processing. Table 2 Multimodal osteoporosis detection system performance (AUC %)

Levels L1 L2

TP-LBP

FP-LBP

Fusion Level

25%

50%

75%

100%

25%

50%

75%

100%

FF

98.80

73.66

57.13

57.13

98.01

86.13

50.02

50.05

FS

97.19

71.45

57.25

57.25

98.69

88.35

56.06

56.00

FF

62.88

46.63

46.67

46.64

90.85

62.09

52.30

52.36

FS

66.06

51.90

51.82

51.67

89.44

66.55

55.88

56.04

97.22

91.28

68.65

68.65

99.97

99.40

91.61

54.01

Whole image

778

M. Mebarkia et al.

Fig. 4 Osteoporosis detection performance under one level of DWT decomposition. a Performance comparison using TP-LBP and b Performance comparison using FP-LBP method

4 Conclusion and Further Works The assessment of osteoporotic disease from X-ray images is a challenging behavior for pattern recognition and medical applications. Textured images from the bone micro-architecture of osteoporotic and healthy subjects show a high degree of similarity, greatly increasing the difficulty of classifying these textures. In this work, we have proposed an osteoporosis detection system based on texture analysis. In this study, two improved variants of LBP were used in conjunction with the Discrete Wavelet Transform method to speed up the classification process. The results obtained are very promising and can be considered as a basic cell in the development of an effective medical diagnostic system. In our future work, we plan to fuse all first and second level DWT-bands using other fusion rules. In addition, we will examine the system using the principle of pyramid analysis.

References 1. NIH (2001) Osteoporosis prevention, diagnosis, and therapy. JAMA 285:785–95 2. Cauley JA (2013) Public health impact of osteoporosis. J Gerontol A Biol Sci Med Sci 68(10):1243-1251. https://doi.org/10.1093/gerona/glt093. Accessed 31 Jul 2013 3. Bliuc D, Nguyen ND, Nguyen TV, Eisman JA, Center JR (2013) Compound risk of high mortality following osteoporotic fracture and refracture in elderly women and men. J Bone Miner Res. 28(11):2317–2324. https://doi.org/10.1002/jbmr.1968 4. Melton LJ, Atkinson IEJ, O’Connor MK, O’Fallon WM, Riggs BL (1998) Bone density and fracture risk in men, vol 13, pp 1915–1923 5. Sozen T, Ozisik L, Basaran NC (2017) An overview and management of osteoporosis. Eur J Rheumatol 4:46–56 6. Tosteson AN, Melton LJ 3rd, Dawson-Hughes B, Baim S, Favus MJ, Khosla S, Lindsay RL (2008) Cost-effective osteoporosis treatment thresholds: The United States perspective. Osteoporos Int 19:437–447

Multi-resolution Texture Analysis for Osteoporosis Classification

779

7. Park C, Took CC, Seong J-K (2018) Machine learning in biomedical engineering. Biomed Eng Lett 8:1–3 8. Kavitha MS, Asano A, Taguchi A, Kurita T, Sanada M (2012) Diagnosis of osteoporosis from dental panoramic radiographs using the support vector machine method in a computer-aided system. BMC Med Imaging 12:1 9. Muthu Subash K, Pugalendhi Ganesh K, Soon-Yong P, Kyung-Hoe H, Min-Suk H, Takio K, Akira A, Seo-Yong A, Sung-Il C (2016) Automatic detection of osteoporosis based on hybrid genetic swarm fuzzy classifier approaches. vol 45, p 20160076 10. Houam L, Hafiane A, Boukrouche A, Lespessailles E, Jennane R (2012) Texture characterization using local binary pattern and wavelets. Application to bone radiographs. In: 2012 3rd International Conference on Image Processing Theory, Tools and Applications (IPTA), pp 371–376 11. Taleb-Ahmed A, Dubois P, Duquenoy E (2003) Analysis methods of CT-scan images for the characterization of the bone texture: First results. Pattern Recogn Lett 24:1971–1982 12. Oulhaj H, Rziza M, Amine A, Toumi H, Lespessailles E, Hassouni ME, Jennane R (2017) Anisotropic discrete dual-tree wavelet transform for improved classification of trabecular bone. IEEE Trans Med Imaging 36:2077–2086 13. Song Y, Cai W, Zhang F, Huang H, Zhou Y, Feng DD (2015) Bone texture characterization with fisher encoding of local descriptors. In: 2015 IEEE 12th international symposium on biomedical imaging (ISBI), pp 5–8 14. Nasser Y, Hassouni ME, Brahim A, Toumi H, Lespessailles E, Jennane R (2017) Diagnosis of osteoporosis disease from bone X-ray images with stacked sparse autoencoder and SVM classifier. In: 2017 international conference on advanced technologies for signal and image processing (ATSIP), pp 1–5 15. Zheng K, Jennane R, Makrogiannis S (2019) Ensembles of sparse classifiers for osteoporosis characterization in digital radiographs. Med Imag Comput Aid Diag 1095024 16. Singh A, Dutta MK, Jennane R, Lespessailles E (2017) Classification of the trabecular bone structure of osteoporotic patients using machine vision. Comput Biol Med 91:148–158 17. Su R, Liu T, Sun C, Jin Q, Jennane R, Wei L (2020) Fusing convolutional neural network features with hand-crafted features for osteoporosis diagnoses. Neurocomputing 385:300–309 18. Oulhaj H, Rziza M, Amine A, Toumi H, Lespessailles E, Jennane R, El Hassouni M (2017) Trabecular bone characterization using circular parametric models. Biomed Signal Process Control 33:411–421 19. Wolf L, Hassner T, Taigman Y (2008) Descriptor based methods in the wild. In: European conference on computer vision workshop on faces in real-life images (ECCV), Marseille, France 20. Ojala T, Pietikäinen M, Harwood D (1996) A comparative study of texture measures with classification based on featured distributions. Pattern Recogn 29:51–59 21. Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24:971– 987 22. Houam L, Hafiane A, Boukrouche A, Lespessailles E, Jennane R (2014) One dimensional local binary pattern for bone texture characterization. Pattern Anal Appl 17:179–193 23. Lespessailles E, Gadois C, Kousignian I, Neveu JP, Fardellone P, Kolta S, Roux C, Do-Huu JP, Benhamou CL (2008) Clinical interest of bone texture analysis in osteoporosis: a case control multicenter study. Osteoporosis Int 19:1019–1028

High Performance Decoding by Combination of the Hartmann Rudolph Decoder and Soft Decision Decoding by Hash Techniques Hamza Faham, My Seddiq El Kasmi Alaoui, Saïd Nouh, and Mohamed Azzouazi Abstract Decoding algorithms are used to recover information after its transmission through an unreliable radio channel. Soft decision decoding is very performant in concatenation solutions that exploit many decoding stages. Therefore, we carry out a combination of a symbol-by-symbol decoder, which is Hartmann Rudolph (HR) algorithm, and the Soft Decision Decoder by Hash Techniques (SDHT) as a word-toword decoder. In this paper, we introduce a concatenation of HR Partially exploited (PHR) and the SDHT algorithm to decode Bose-Chaudhuri-Hocquenghem (BCH) codes. This scheme enables at first to employ Hartmann Rudolph algorithm using only a part of the dual codewords, then the output of PHR is treated by the SDHT. Our proposed serial concatenation guarantees very good performances although we use very low percentage of the dual code space. As instance for the BCH(31, 21, 5) code, the obtained good results are based only on 21.29% of the dual codewords. Keywords Error correcting codes · Symbol-by-symbol decoder · Word-to-word decoder · HR · PHR · SDHT

1 Introduction Recently, there has been an expanding demand for perfect and reliable digital data transmission and backup systems. This demand has grown by the emersion of extensive, high throughput data networks for the exchange, treatment, and backup of binary information in different sectors and domains. A considerable concern of standards organizations, which develop protocols for mobile telephony, is the check of errors so that authentic reproduction of data can be achieved. Wherefore, error correction codes have been developed. Error correction coding is achieved by joining redundancy to the transferred message using an algorithm. Several correction codes are implemented in various equipment like smartphones, CDs, DVDs, storage media or packets transmitted through Internet and cellular networks. H. Faham (B) · M. S. El Kasmi Alaoui · S. Nouh · M. Azzouazi Information Technology and Modeling Lab, Ben M’sick Sciences Faculty, Hassan II University, Casablanca, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_71

781

782

H. Faham et al.

There are two types of decoders employed in communication systems: soft decision and hard decision decoders. Soft decision algorithms exploit directly the received symbols and they employ principally the Euclidian distance as a measure. While the hard decision decoders measure is usually the Hamming distance. This type of algorithms treat binary inputs. The rest of the work is arranged in this way. In the second section, some decoders are presented as cognate works. The third section presents the suggested serial concatenation of PHR and SDHT. The fourth section gives detail about our proposed algorithm experiments. Finally, we give a conclusion through the fifth section.

2 Related Works Researchers have investigated soft-decision decoding by diverse methods. The earliest solutions were based on probabilistic and algebraic approaches. For example, Chase-2 and HR algorithms. Chase-2 [1] is a decoder that employs a list of most probable error patterns. HR [2] is a soft decision decoder that uses a symbol by symbol decoding. In the last decades, metaheuristics approaches were suggested. For instance the use of genetic algorithms, compact genetic algorithms, neural networks and algorithm A*, which is an algorithm that employs a strategy priority-first search. In [3] the authors have used this algorithm to approach the decoding problem. GADEC [4] is a Genetic Algorithm for Decoding. This decoder presents a significant advantage over [3], indeed, GADEC is appropriate to be implemented on several parallel architectures. The authors of [5] have introduced evolutionary decoders. These decoders were obtained thanks to an appropriate chromosome representation and some special mutations and crossover operations. HDGA [6] is a Hard Decoder conceived by Genetic Algorithms. In addition, [6] authors have introduced a novel soft decoder at the base of HDGA and Chase decoder (SDGA). DDGA [7] is a Dual Domain decoding Genetic Algorithm. The authors of [8] have proposed two dual domain soft decision decoders at the base of compact Genetic Algorithm (cGA). The results have shown the benefit of using larger tournament size. CGAD is a Compact Genetic Algorithm Decoder described in [9]. To examine their decoder effectiveness, the authors have deployed it for BCH, Quadratic Residue (QR) and Reed-Solomon (RS) codes over Additive White Gaussian Noise (AWGN) channel. RNN [10] is a Recurrent Neural Network architecture for decoding linear block codes. This solution provides analogous Bit Error Rate (BER) results as regards the feed-forward neural network with notably less parameters. The same authors of [10] have presented in [11] an architecture of recurrent neural decoding at the base of successive relaxation technique. This method can achieve important performances by employing only a single learnable parameter. In [12] neural net decoding algorithms for nonbinary codes were considered. The authors have suggested analytic methods for synapse weight coefficients calculating.

High Performance Decoding by Combination of the Hartmann Rudolph Decoder …

783

SDHT [13] is a Soft decision Decoder at the base of Hash Techniques and syndrome decoding. The originators of [13] have introduced two hard decision decoders named HSDec [14] and HWDec [14], and Chase-HSDec decoding algorithm [15], which exploits HSDec in the Chase-2 decoder with the aim of minimizing its temporal complexity. In [16], it was proved that off-the-shelf Deep Learning decoding algorithms can achieve the maximum a posteriori (MAP) decoding performance for short length codes. In [17] a performance comparison of novel Chien search and syndrome blocks designs is given for BCH and Reed-Solomon codes.

3 The Proposed Serial Concatenation 3.1 HR Algorithm Hartmann & Rudolph algorithm is a symbol by symbol decoding algorithm. It employs a probabilistic approach for deciding if the bit rj of the received sequence r is equal to 0 or 1. In fact, HR employs the totality of 2n−k dual codewords. Formula (1) represents how HR decides if the mth bit of the decoded word c is equal to 1 or 0.  2n−k n 1−φl c 1 ⊕δml jl cm = 0 i f >0 j=1 i=1 ( 1+φl ) (1)  cm = 1 other wise 

1 if i = j m |1) and φm = Pr(r (rm |0) 0 other wise bit of the j th dual codeword.

Where δi j = c⊥jl is the l th

3.2 Soft Decision Decoding by Hash Techniques (SDHT) Let us note the linear code by C(n, k, d), k is the code dimension, n is its length and d is the  minimum distance between all its distinct vectors. Moreover, let us note the error correcting capability of C. The Soft Decision Decoding by Hash t = d−1 2 Techniques operates as following:

784

H. Faham et al.

3.3 Our Proposed Concatenation Algorithm HR has proposed to exploit all the dual codewords. This makes the temporal complexity of this algorithm very high especially for codes whose of parity bits number is relatively large. With the aim of overcoming this constraint, Nouh et al. [18] have suggested exploiting just M dual codewords, and then the HR is used only in some symbols of the received sequence. Therefore, the algorithm will be named Partial Hartman-Rudolph (PHR). In [19] we have applied the same idea. The formula (1) becomes (2). 

 M n 1−φl c 1 ⊕δml jl >0 cm = 0 i f j=1 i=1 ( 1+φl )  cm = 1 other wise

(2)

The fact of reducing the used dual codewords number influences the decoding quality. It is why we propose to reprocess the sequence returned by HR by a wordby-word decoder, which is the SDHT algorithm. The PHR-SDHT decoder works as follows:

High Performance Decoding by Combination of the Hartmann Rudolph Decoder …

785

4 Simulation Results and Discussions With the intention of showing our suggested scheme efficacy, we provide in this section the PHR-SDHT simulation results for some BCH codes. Some comparisons of PHR-SDHT performances with other competitors are also given. Table 1 describes the employed experiment parameters. The performances of decoding algorithms are traced by means of BER for each Signal to Noise Ratio (SNR = Eb /N0 ). Coding gain comparisons are achieved referencing the case where the transmission is performed without encoding on the transmitter side and without decoding on the receiver side. In this case, Bit Error Rate achieves 10–5 according to an SNR = 9.6 decibels through AWGN channel. This last is regarded like binary channel exploiting BPSK modulation. In Fig. 1(a), (b) and (c), the PHR-SDHT decoding performances are presented for certain BCH codes with lengths from 31 to 127. These figures show the presence of a significant coding gain. For instance, the coding gain is superior to 4 dB for BCH(31, 16, 7), nearly 4 dB for BCH(63, 45, 7) and nearly 3 dB for BCH(127, 113, 5). In Fig. 2(a) and (b), results comparison between PHR-SDHT, Chase-2 [1] and Chase-HSDec [15] decoding algorithms is presented for BCH(31, 16, 7) and BCH(31, 21, 5). From these figures, we deduce that our proposed decoding algorithm guarantees better performances than the two other decoders do. Table 1 Experiment parameters

Channel

Additive White Gaussian Noise

Modulation

Binary Phase Shift Keying (BPSK)

Minimum transferred blocks

1000

Minimum residual errors

200

786

H. Faham et al.

Fig. 1 PHR-SDHT results for certain BCH codes with length: a 31, b 63 and c 127

10

-1

BCH(31,26,3);s=3 BCH(31,21,5);s=5 BCH(31,16,7);s=7

BER

10

10

10

10

-2

-3

-4

-5

1

2

3

4 SNR

5

6

7

(a) 10

-1

BCH(63,57,3);s=3 BCH(63,51,5);s=5 BCH(63,45,7);s=4

BER

10

10

10

10

-2

-3

-4

-5

1

2

3

4 SNR

5

6

7

(b) 10

BER

10

10

10

10

-1

BCH(127,120,3);s=2 BCH(127,113,5);s=3

-2

-3

-4

-5

1

2

3

4 SNR

(c)

5

6

7

High Performance Decoding by Combination of the Hartmann Rudolph Decoder …

787

In Fig. 3, results comparison between PHR-SDHT, SD1 [20], GADEC [4] and PHR-ARDecGA [18] decoders is made for BCH(63, 51, 5). The comparison results confirm that PHR-SDHT exceeds its competitors. With the aim of showing our serial concatenation effectiveness, a comparison of the suggested scheme PHR-SDHT and Hartmann Rudolph algorithm is presented by means of Fig. 4 for BCH(31, 21, 5) code. This comparison indicates that the proposed combination of Hartmann Rudolph partially exploited and SDHT guarantees great performances using a small part of the dual codewords. The obtained good results are based on just 218 dual codewords, which means that we have employed just 21.29% from dual code space. Table 2 presents the reduction rate of used dual codewords. Fig. 2 Results comparison between PHR-SDHT, Chase-2 and Chase-HSDec for: a BCH(31, 16, 7) and b BCH(31, 21, 5)

10

BER

10

10

10

10

-1

Chase-2 Chase-HSDec PHR-SDHT;s=7 -2

-3

-4

-5

1

2

3

4 SNR

5

6

7

(a) 10

-1

Chase-2 Chase-HSDec PHR-SDHT;s=5

BER

10

10

10

10

-2

-3

-4

-5

1

2

3

4 SNR

(b)

5

6

7

788

H. Faham et al.

10

BER

10

10

10

10

SD1 GADEC PHR-ARDecGA PHR-SDHT;s=5

-1

-2

-3

-4

-5

7

6

5

4 SNR

3

2

1

Fig. 3 Results comparison between PHR-SDHT, SD1, GADEC and PHR-ARDecGA for BCH(63, 51, 5) code

10

-1

HR PHR-SDHT

BER

10

10

10

10

-2

-3

-4

-5

1

1.5

2

2.5

3

3.5 SNR

4

4.5

5

5.5

6

Fig. 4 Results comparison between HR and PHR-SDHT for BCH(31, 21, 5)

Table 2 Reduction rate of used dual codewords Code BCH(31, 21, 5)

Number of the used dual codewords Hartmann Rudolph

PHR-SDHT

Reduction rate of used dual codewords

231–21 = 210 = 1024

218

78,71%

To make sure of the temporal efficiency of our proposed decoder, we have plotted in the Fig. 5 the ratio of the required run time of PHR-SDHT to the one of HR algorithm for BCH(31, 16, 7). The reduction in run time is between 80 and 84%. We deduce that PHR-SDHT has allowed us to minimize in a very large way the run time of the HR algorithm. This proves the efficiency and the rapidity of our concatenation scheme.

High Performance Decoding by Combination of the Hartmann Rudolph Decoder … 0,20

BCH(31,16,7) Run Time of PHR-SDHT / Run time of HR

Fig. 5 PHR-SDHT to Hartmann Rudolph required runtimes ratio for BCH(31,16,7)

789

0,19

0,18

0,17

0,16 1

1.5

2

2.5

3

3.5 SNR

4

4.5

5

5.5

6

5 Conclusions We have introduced a powerful decoder at the base of a serial concatenation between the Hartmann Rudolph algorithm partially exploited and the SDHT algorithm in order to decode Bose-Chaudhuri-Hocquenghem codes. Performances analysis have shown that the suggested decoding algorithm offers very satisfying BER results with regard to the separate use of combined decoders. In addition, the partial use of the HR algorithm has reduced considerably its temporal complexity. For instance, we have used just 21.29% of the dual space for the BCH(31, 21, 5) code while maintaining good decoding quality.

References 1. Chase D (1972) A class of algorithms for decoding block codes with channel measurement information. IEEE Trans Inf Theory 18:170–181 2. Hartmann CRP, Rudolph LD (1976) An optimum symbol-by-symbol decoding rule for linear codes. IEEE Trans Inf Theory 22:514–517 3. Han YS, Hartmann CRP, Chen CC (1991) Efficient maximum likelihood soft-decision decoding of linear block codes using algorithm A*, Technical Report SU-CIS-91-42, School of Computer and Information Science, Syracuse University, Syracuse, NY 13244, December 4. Maini H, Mehrotra K, Mohan C (1994) Soft-decision decoding of linear block codes using genetic algorithms. In: Proceedings of the IEEE international symposium on information theory, Trondheim, Norway, p 397 5. Cardoso FA, Arantes DS (1999) Genetic decoding of linear block codes. In: Proceedings of international conference on telecommunications congress on evolutionary computation, Washington, DC, USA, pp 2302–2309 6. Azouaoui A, Chana I, Belkasmi M (2012) Efficient information set decoding based on genetic algorithms. Int J Commun Netw Syst Sci 5(7):423–429 7. Azouaoui A, Belkasmi M, Farchane A (2012) Efficient dual domain decoding of linear block codes using genetic algorithms. J Electr Comput Eng. Article ID 503834

790

H. Faham et al.

8. Berkani A, Azouaoui A, Belkasmi M, Aylaj B (2017) Improved decoding of linear block codes using compact genetic algorithms with larger tournament size. Int J Comput Sci Issue 14(1):15–24 9. Azouaoui A, Berkani A, Belkasmi M (2012) An efficient soft decoder of block codes based on compact genetic algorithm. Int J Comput Sci Issue 9(5), No 2:431–438 10. Nachmani E, Marciano E, Burshtein D, Béery Y (2017) RNN Decoding of linear block codes. arXiv:1702.07560v1 [cs.IT] 11. Nachmani E, Marciano E, Lugosch L, Gross WJ, Burshtein D, Béery Y (2018) Deep learning methods for improved decoding of linear codes. IEEE J Sel Topics Signal Process 12(1) 12. Butov VV, Dumachev VN, Fedyaeva SE (2020) Neural net decoders of nonbinary codes. J Phys Conf Ser 1479:012089 13. El Kasmi Alaoui MS, Nouh S, Marzak A (2019) High speed soft decision decoding of linear codes based on hash and syndrome decoding. Int J Intell Eng Syst 12(1) 14. El Kasmi Alaoui MS, Nouh S, Marzak A (2019) Two new fast and efficient hard decision decoders based on hash techniques for real time communication systems. In: lecture notes in real-time intelligent systems, RTIS 2017. Advances in intelligent systems and computing, vol 756. Springer, Cham 15. El Kasmi Alaoui MS, Nouh S, Marzak A (2017) A low complexity soft decision decoder for linear block codes. In: Proceedings of the first international conference on intelligent computing in data sciences 16. Gross WJ, Doan N, Ngomseu Mambou E, Ali Hashemi, S (2020) Deep learning techniques for decoding polar codes. In: F-L Luo (ed.) Machine learning for future wireless communications 17. Elghayyaty M et al (2020) Performance comparison of new designs of Chien search and syndrome blocks for BCH and Reed Solomon codes. Int J Commun Netw Inf Secur 12(2) 18. Nouh S, Aylaj B (2018) efficient serial concatenation of symbol by symbol and word by word decoders. Int J Innov Comput Inf Control 14 19. Faham H, El Kasmi Alaoui MS, Nouh S, Azzouazi M (2018) An efficient combination between Berlekamp-Massey and Hartmann Rudolph algorithms to decode BCH codes. Periodic Eng Nat Sci 6(2):365–372 20. Jung B, Kim T, Lee H (2016) Low-complexity non-iterative soft-decision BCH decoder architecture for WBAN applications. J Semicond Technol Sci 16(4)

Automated Assessment of Question Quality on Online Community Forums Harish Rithish, Gerard Deepak, and A. Santhanavijayan

Abstract People around the world often rely on online community forums for answers to their queries. These forums have become hugely popular in the last decade, leading to a spurt in the number of users and questions. For a better user experience, quality monitoring is essential. However, manual moderation of millions of questions is infeasible. Prior works mostly rely on handcrafted features which is ineffective or use community feedback as part of learning which makes them unsuitable for monitoring during question creation. In this work, we use recent deep learning techniques to assess the quality of questions in online community forums at creation time. We evaluate our model on the StackOverflow dataset that contains 60000 questions across three qualities. Our model achieves an F1 score of 0.92 on this dataset. Keywords Question quality prediction · Deep learning · Community Q&A · StackOverflow

1 Introduction Online community forums allow for the exchange of knowledge between people across the world. Over the past decade, an exponential rise in smartphones and Internet penetration has made these online forums accessible to people across the world. Question & Answer forums allow people to get answers directed specifically to their question, rather than having to search haphazardly from a myriad of resources. Moreover, users are able to get answers from experts in their field at free of cost. These advantages of online forums have resulted in their substantial growth, with an enormous number of users across the world relying on these sites for solutions [1] (Fig. 1). The rise in users of online forums has naturally led to a tremendous increase in the number of questions being raised on these forums. To maintain the standard of these questions, they are either checked manually for their quality through moderators or H. Rithish (B) · G. Deepak · A. Santhanavijayan Department of Computer Science and Engineering, National Institute of Technology, Tiruchirappalli, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_72

791

792

H. Rithish et al.

Fig. 1 In this paper, we are interested in classifying user-generated questions on online forums automatically, based on its quality

is left to the community to flag questions of poor quality. However, such manual moderation on millions of questions is ineffective and infeasible. Questions that are of low quality often result in users spending more time understanding them, either by asking clarification or by editing the question. Further, questions with low-quality have been observed to receive answers of poorer quality [2]. In contrast, high-quality questions receive more constructive answers which also result in long-term value with users being able to refer them in the future. Overall, low-quality questions reduce the productivity of users on the site. Thus, we need an automated solution that can flag potentially low-quality questions. Such a system can help analyze the quality of the question during creation, and suggest the user to improve the quality before posting them. They can also be used as input to ranking mechanisms during question search. Forums such as Quora, Reddit, Stack Exchange, Yahoo Answers and Wiki Answers serve the general audience and contain discussions on a variety of domains. Along with such forums, there co-exists another spectrum of online forums that are dedicated to a particular community, such as Brainly (academic), Avvo (legal), Sharecare (health), and MadSci Network (Science). Here, we focus on questions from the StackOverflow website, which is a dedicated forum to discuss queries in programming. StackOverflow have tried maintaining the quality of these forums by asking users to follow certain guidelines1 . Despite that, Correa and Sureka [3] estimate around 293 K questions from the period 2009 to 2013 were deleted due to being off-topic or containing low quality content. In this work, we automatically assess the quality of StackOverflow questions through recent deep learning techniques. We work with a dataset of 60000 questions collected between 2016 and 2020 and released by the StackOverflow platform. We develop an end-to-end pipeline that pre-processes the input question, extracts relevant features and classifies them into one of three categories. We focus only on inputs that an user has access to at creation time, allowing for instant flagging and suggestions.

1 http://stackoverflow.com/help/how-to-ask.

Automated Assessment of Question Quality …

793

In the remaining part of the paper, Sect. 2 discusses prior works on text classification and question quality assessment, Sect. 3 provides information on architecture, Sect. 4 elaborates on dataset used and implementation details, Sect. 5 shows experiments conducted and their corresponding results, and Sect. 6 concludes our work.

2 Related Work 2.1 Text Classification Question classification takes its root in text classification. This domain of text classification began when IBM stared investigating into techniques for information retrieval in the 1950’s. One of the earliest works in this domain were by Maron and Kuhns [4], where they relied on keywords to classify text. In the following decades, researchers designed classification methods that only worked for a specific field. These methods were designed with the knowledge of experts in the field by defining field-specific rules. Naturally, these approaches did not generalize well to other fields and started losing popularity when machine learning methods gained prominence in the 1990’s. Machine learning was used to extract features instead of relying on rules and then classify the text into relevant categories, based on these features. Some of the earlier machine learning techniques used for text classification include Naive Bayes [3], Nearest Neighbour [6], Linear Discriminant Analysis [7], Decision Trees [8], Logistic Regression [8] and Support Vector Machines [9]. Techniques in classical machine learning for text classification evolved over the next two decades, leading to practical deployment of these systems for a limited number of use-cases. However, these methods did not scale well to large datasets or content with complex semantics. With the rapid growth of deep learning in this past decade, however, text classification systems have matured significantly and are nowadays being deployed widely. Specifically, various improvements in vanilla Recurrent Neural Networks have facilitated this progress in text classification [11– 13]. While all the above methods discuss several approaches for text classification, we specifically focus on the task of assessing questions based on its quality.

2.2 Question Quality Assessment Agichtein et al. [14] consider inputs from both the question and their answers. They establish that there is a strong relationship between the quality of a question raised and their feedback from users. Shah et al. [15] approach this task differently, by first categorizing a question and then extract category-specific features for estimating the quality of a question. Ponzanelli et al. [16] work on identifying good quality

794

H. Rithish et al.

questions that have been mistakenly flagged for review. All the above approaches, however, rely on user answer information or community feedback for estimating question quality. Tausczik and Pennebaker [17] show that a user’s reputation influences the quality of a question. They design their model using votes received by a question as a feature to determine question quality. Ravi et al. [18] give importance to the length of the question content and title. They extend [17] by normalizing votes received by a question with the topic’s popularity. In both these approaches, they use information outside of what is available at question creation time, which makes them ineffective to provide immediate feedback while a user drafts a question. In contrast to all the above approaches, we use recent techniques in natural language processing along with inputs (title, body text, tags) that are available at creation time to assess the quality of a question.

3 Automated Quality Assessment of Community Questions See Fig. 2.

Fig. 2 We start by concatenating multiple components of our question into a single input. Our combined input is then passed through a set of preprocessing steps. Word embeddings are obtained for the processed text and then fed to a two-layer Bidirectional LSTM. The extracted features are then passed to a Fully Connected layer. Finally, we assess the quality of the input question by passing through a Softmax layer

Automated Assessment of Question Quality …

795

3.1 Task Definition In this section, we define the quality assessment of community questions task. Given an input title H, body text B and a set of tags T = , our goal is to classify the question into one of C qualities.

3.2 Data Preprocessing We start by concatenating multiple components of our question, viz., title, body text and associated tags into a single input, making it easier for the model to process further. We then filter out special characters and punctuation present in the input. We follow this by tokenizing the input, based on the top K most frequent words in the training set. We finally limit the size of our question to a maximum of L words, by truncating if the question is longer than L words or by padding if the question is lesser than L words.

3.3 Word Embeddings Each word in the question is first converted to its corresponding word embedding. Embeddings are a representation of text, with each word being encoded as a feature vector. We employ Global Vectors for Word Representation (GloVe) [19] for this purpose. GloVe is an unsupervised learning algorithm for obtaining vector representations for words [19]. GloVe does not rely just on the local context, but also incorporates global statistics to obtain word embeddings. The word embeddings are trained on aggregated global word-word co-occurrence statistics from a corpus. In this work, we pre-train the model on the Twitter corpus and obtain a 25-d feature vector for each word.

3.4 Model We extract features from a set of word embeddings by passing them through a series of layers. We start by constructing a 2-layer deep Bi-Directional Long ShortTerm Memory (Bi-LSTM) networks. LSTMs are a special kind of Recurrent Neural Networks that are capable of learning long-term dependencies. This allows the model to encode very-long questions, while retaining contextual information from all parts of the text. However, a unidirectional LSTM only has past contextual information since the only inputs it has seen are only from the past. To overcome this, we use Bi-Directional LSTMs which runs the questions in two ways, one from the beginning

796

H. Rithish et al.

to the end and another from the end to the beginning. Through this, we obtain two hidden states, one with past context and another with future context, which we then combine through concatenation. We use a hidden state of 128 units and thus, after concatenation, we obtain a 256-d feature vector. Additionally, we incorporate dropout and recurrent dropout to minimize overfitting. The sequence of L outputs from the earlier computation, are passed through another layer of Bi-LSTM to increase the complexity of the model function. We retain the same structure for the second layer, and only tweak hyper-parameter values. We pass the output of the 2-layer Bi-LSTM through a Fully Connected layer, whose output forms the final feature vector for our input question. The Fully Connected layer transforms the 256-d feature vector into a |C|-d vector, with each unit representing the unnormalized log value of a its classification label. Finally, we obtain the classification labels for the question by passing the unnormalized log values through a Softmax layer.

3.5 Loss Function Since our task is classification, we employ the standard Cross Entropy loss.

4 Datasets and Implementation Details 4.1 Datasets Stack Overflow is a widely used question and answering site for programming questions. The site is a community-based platform, where users can raise new questions or provide answers to existing questions of other users. Recently, Stack Overflow has collected a dataset of 60000 questions between 2016 and 2020 and released the data publicly. A question consists of a title, body and one or more tags. The task involves classifying a question into one of the three categories: • HQ: High-quality posts with 30+ score and without a single edit. • LQ (Close): Low-quality posts that were closed by the community without a single edit. • LQ (Open): Low-quality posts with a negative score and with multiple community edits. However, they still remain open after the edits. The dataset is balanced, with 60000 questions being evenly distributed between the three categories. The training and validation set contain 45000 and 15000 questions respectively, with the questions from each category being sampled in a stratified manner (Fig. 3).

Automated Assessment of Question Quality …

(a) HQ

797

(b) LQ (Close)

(c) LQ (Open)

Fig. 3 Word clouds for different question categories

(a) Loss vs Epoch graph

(b) Accuracy vs Epoch graph

Fig. 4 Training plots on the Stack Overflow dataset

4.2 Implementation Details For tokenization, we set the number of words K = 10000. We limit the number of words in each question, L to 300. For the Bi-LSTM module, we set dropout and recurrent dropout to 0.4 for the first layer and decrease the value to 0.2 for the second layer. We use Adam for optimization, with an initial learning rate of 10−2 and reduce the learning rate by a factor of 0.4 whenever the loss plateaus. We train the model on an NVIDIA-1080 Ti GPU for 15 epochs, with a batch-size of 256. We show the training plots of our model in Fig. 4.

798

H. Rithish et al.

5 Experiments and Results 5.1 Quantitative Results We present the quantitative results of the model on the StackOverflow dataset in Table 1. However, on low-quality questions that are closed as well as high quality questions, the model performance drops to an F1-score of 0.87 and 0.84, respectively. We observe that the model performs extremely well on low-quality questions that are still open with an F1-score of 0.99. We now present the confusion matrix for the predictions in Fig. 5. Validating the results seen in the table, we observe that the model performs well in identifying low-quality questions that are still open. Interestingly, the model confuses high-quality questions with low-quality questions that are closed, and vice-versa. Table 1 Quantitative results on the Stack Overflow validation set

Fig. 5 Confusion matrix for predictions on the StackOverflow validation set

Class

Precision

Recall

F1-score

HQ

0.88

0.80

0.84

LQ (Close)

0.88

0.87

0.87

LQ (Open)

0.99

0.99

0.99

Overall

0.92

0.92

0.92

Automated Assessment of Question Quality …

799

Fig. 6 Qualitative examples of HQ questions that are correctly predicted by our model

Fig. 7 Qualitative examples of LQ (Close) questions that are correctly predicted by our model

Fig. 8 Qualitative examples of LQ (Open) questions that are correctly predicted by our model

5.2 Qualitative Results We show qualitative examples of correct model predictions on the StackOverflow validation set, with high-quality questions in Fig. 6, low-quality questions that are closed in Fig. 7 and low-quality questions that are still open in Fig. 8.

6 Conclusion There has been a rapid growth in users and their queries in online community forums, necessitating an automated quality control mechanism during question creation time. In this paper, we design a model that takes title, body text and tags associated with a question as input and predicts the quality of a question. Our model preprocesses the inputs, embeds them into word vectors using GloVe, extracts features using a 2-layer deep bi-directional LSTM and finally uses a Softmax layer for classification. We evaluate our model on the StackOverflow dataset that consists of 60000 questions

800

H. Rithish et al.

and 3 categories of quality. Our model achieves an F1-score of 0.92, indicating that our system can be deployed for practical use.

References 1. Agichtein E, Castillo C, Donato D, Gionis A, Mishne G (2008) Finding high-quality content in social media. In: Proceedings of the 2008 international conference on web search and data mining, pp 183–194 2. Movshovitz-Attias D, Movshovitz-Attias Y, Steenkiste P, Faloutsos C (2013) Analysis of the reputation system and user contributions on a question answering website: Stackoverflow. In: International conference on advances in social networks analysis and mining, pp 886–893 3. Correa D, Sureka A (2014) Characterization and modeling of deleted questions on stack overflow 4. Maron ME, Kuhns JL (1960) On relevance, probabilistic indexing and information retrieval. J ACM (JACM) 7(3):216–244 5. Li YH, Jain AK (1998) Classification of text documents. Comput J 41(8):537–546 6. Weiss S, Kasif S, Brill E (1996) Text classification in use net newsgroups: a progress report. In: Proceedings of the AAAI spring symposium on machine learning in information access, pp 125–127 7. Hull D, Pedersen J, Schutze H (1996) Document routing as statistical classification. In: AAAI spring symposium on machine learning in information access, vol 12, pp 49–54 8. Lewis DD, Ringuette M (1994) A comparison of two learning algorithms for text categorization. In: Third annual symposium on document analysis and information retrieval, vol 33, pp 81–93 9. Schutze H, Hull DA, Pedersen JO (1995) A comparison of classifiers and document representations for the routing problem. In: Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval, pp 229–237 10. Zhang D, Lee WS (2003) Question classification using support vector machines. In: Proceedings of the 26th annual international ACM SIGIR conference on Research and development in information retrieval, pp 26–32 11. Chen T, Xu R, He Y, Wang X (2017) Improving sentiment analysis via sentence type classification using BiLSTM-CRF and Cnn. Expert Syst Appl 72:221–230 12. Ranjan MNM, Ghorpade Y, Kanthale G, Ghorpade A, Dubey A (2017) Document classification using LSTM neural network. J Data Mining Manage 2(2):1–9 13. Zhou C, Sun C, Liu Z, Lau F (2015) A C-LSTM neural network for text classification. arXiv preprint arXiv:1511.08630 14. Srba I, Bielikova M (2016) A comprehensive survey and classification of approaches for community question answering. ACM Trans Web (TWEB) 10(3):1–63 15. Shah C, Kitzie V, Choi E (2014) Questioning the question–addressing the answer-ability of questions in community question-answering. In: 2014 47th Hawaii international conference on system sciences, pp 1386–1395. IEEE 16. Ponzanelli L, Mocci A, Bacchelli A, Lanza M, Fullerton D (2014) Improving low quality stack overflow post detection. In: 2014 IEEE international conference on software maintenance and evolution, pp 541–544. IEEE 17. Tausczik YR, Pennebaker JW (2011) Predicting the perceived quality of online mathematics contributions from users’ reputations. In: Proceedings of the SIGCHI conference on human factors in computing systems, pp 1885–1888 18. Ravi S, Pang B, Rastogi V, Kumar R (2014) Great question! Question quality in community Q&A 19. Pennington J, Socher R, Manning CD (2014) Glove: global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp 1532–1543

QFRDBF: Query Facet Recommendation Using Knowledge Centric DBSCAN and Firefly Optimization Deepak Surya, Gerard Deepak, and A. Santhanavijayan

Abstract The Internet contains approximately 40 trillion gigabytes of data. A vast amount of information is presented to the user for their query. With such enormous search results, it becomes impossible for the user to refine the results to find the contents that match their interest. Almost 92% of search traffic clicks of Google are confined only to the first page. Also, Google identifies that about 16% to 20% of searches are new each year. Hence, a method that enables the users to filter the search results based on some attributes to obtain the web content that matches their intention is vital. One such method is query facets. Query facets enable the user to filter the web results, making it easy for them to seek their desired results. This paper presents a novel approach to extract query facets. Query words are obtained from the queries upon query preprocessing. TF-IDF is applied to the preprocessed dataset and the query words to reorder the terms based on the frequency. The reordered terms are sorted based on concept similarity, and a knowledge centric DBSCAN algorithm is employed on the sorted items to generate facets. Keywords Domain knowledge · DBSCAN · Facet recommendation · Knowledge centric approach

1 Introduction Query facets categorize web searches based on some attributes. Faceting technology facilitates the user to narrow down the web searches based on their interest. It is widely used in e-commerce websites, online libraries, and social networks. Facets are generated based on the web content and they describe the properties of the information. Most recent systems [1] have used query facets in order to achieve feasible and promising recommendation results in the era of Semantic Web. The web information for the search undergoes text analysis to identify the latent patterns. D. Surya · G. Deepak (B) · A. Santhanavijayan Department of Computer Science and Engineering, National Institute of Technology, Tiruchirappalli, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_73

801

802

D. Surya et al.

For example, in an e-commerce website, facets are generated based on customer reviews, ratings, and product descriptions. The faceted search enables the user to classify the search content based on brands, size, average customer rating, packaging option, condition, seller, discount, availability, price range, etc. An item can belong to multiple faceted categories, and the user can set multiple facet classifications to refine the search results. There is no predefined order to set the facets; instead, facet classifications are set according to the user’s convenience to navigate the search results. Motivation: Queries that capture the user intention are collected. Upon preprocessing the compiled queries, query words are obtained. Each query word’s significance in the query collection is determined by employing the TF-IDF measure to the query collection. Upon evaluation, the query terms are reordered based on their frequency. The reordered items are classified based on concept similarity. Finally, a knowledge centric DBSCAN clustering algorithm is employed to the reordered items, and Firefly with concept similarity is applied to the domain knowledge to generate query facets. Contribution: This paper proposes a novel approach for the automatic generation of facets from query words. The approach encompasses tags, keywords, and data labels along with domain knowledge for query facet generation. A combination of concept similarity and DBSCAN clustering algorithm is employed. Finally, the query facets enrichment is performed based on the firefly optimization computation with concept similarity. An overall F-Measure of 91.1% is achieved for the User Q dataset. Organization: The remainder of the paper is organized in following manner. Section 2 of the paper presents the Related Works. Section 3 consists of the Proposed System Architecture. The Implementation of the work is highlighted in Sect. 4 discusses and the Results and Performance Evaluation are described in Sect. 5. Finally, Sect. 6 concludes the paper.

2 Related Work Jiang et al. [1] used knowledge bases to mine query facets. Their approach covers a wide range of information to generate query facets rather than only the top results of the web search. Ramya et al. [2] put forth a method to extract facets for user queries automatically. The lists are classified to generate the user query facets based on HTML tags, free-text patterns, and repeat regions. Cosine Similarity is employed to evaluate the similarity between the lists. The High-Quality Clustering algorithm classifies the items, and the best items in each cluster are recommended to the user

QFRDBF: Query Facet Recommendation Using Knowledge …

803

as the facets. Vandic et al. [3] put forth a framework dynamic facet ordering in e-commerce web applications. An automated algorithm ranks facets based on specificity and dispersion measures. Some specific issues to e-commerce, such as copious numerical facets, classification of facets by their mutual attributes, and the chance of multiple clicks, are also addressed by their work. Hervey et al. [4] study the extraction and ranking of facets in geospatial data portals. Their study proposes methods of alternative interfaces and query processing pipelines to enhance the search for geospatial data. Babu et al. [5] present a method to improve the user search experience by incorporating sub-queries and facets. Galán et al. [6] presented evaluation approaches to compare region query for the DBSCAN algorithm and a novel region query strategy. The overall number of region queries and the nearest neighbor search’s complexity for each region query is minimized. The query strategies in each region are comparatively assessed based on clustering effectiveness and efficiency. Cherrat et al. [7] proposed an technique to advance the quality of segmenting the fingerprint images by employing K-means and DBSCAN algorithm. The quality of the fingerprint images are enhanced by the application of Soble and TopHat filtering algorithms. K-means clustering algorithm is used to obtain the precise segmentation of the foreground and background region for each block in the image. The computing time is reduced by using the local variance thresholding technique. Upon implementing Kmeans clustering, DBSCAN clustering is applied to overcome the flaws of the former one. Xue et al. [8] proposed a technique to match biomedical ontologies using the firefly algorithm. Their work introduced the compact firefly algorithm (CFA), which outperforms the existing state of art swarm intelligent algorithms. Kaushal et al. [9] proposed a segmentation technique based on Firefly optimization to segment medical images of breast cancer irrespective of the image type or modality. Their approach is compared with the results obtained from the current advanced techniques, and the results obtained certifies the performance of the proposed method. Xie et al. [10] addresses the two main problems presented by the K-means clustering model: the initialization sensitivity problem and local optima traps. These problems are fixed by the methods proposed by the author in this paper Iwendi et al. [11] proposed a TF-IDF algorithm with the temporal Louvain method to analyze the text from various intelligent sensing systems. The proposed work enables analysts to make reliable decisions by classing documents into hierarchical structures and presenting the relationships among the variables in the document. The results obtained validate the proposed work’s accuracy and shorter execution time over other existing approaches. In [12–26] several semantic and ontological approaches in support of the existing literature are depicted.

804

D. Surya et al.

3 Proposed System Architecture Queries that represent the user intention are obtained. The collected queries undergo query preprocessing to generate query words. The pre-processed dataset and query words are assessed using TF-IDF and reordered based on the frequency. The TF-IDF is a statistical measure used to determine how much each word in a document is related to it. It is implemented in information retrieval, keyword extraction, and automated text analysis. The term frequency and inverse document frequency convey a particular word’s frequency in one document and a body of documents, respectively. Most of the machine learning algorithms do not work with text but works with numbers. The TF-IDF is used to vectorize the text. Text Vectorization process converts a text document to a vector or array of numbers. It can represent a document as a vector of numbers to be fed to a machine-learning algorithm. The term frequency and inverse term frequency are computed as shown in the Figs. 1 and 2, respectively. The value of TF-IDF is computed by estimating the product of the TF (term frequency) and IDF (inverse document frequency). (1) (2) (3) The rearranged items are grouped based on concept similarity, and a knowledge centric DBSCAN algorithm is employed to generate facets. DBSCAN is an agglomerative clustering algorithm which can create automatic categories and determine the outliers. Two parameters are required to run the algorithm : the radius (maximum) of a local neighborhood, the number of points (minimum) needed to grow the cluster. Minimum domain knowledge is adequate to set the hyperparameters. The number of clusters formed is not determined before training the dataset. It is very efficient on large data sets as it works excellent with arbitrary clustering, cut noise, and label outliers. The samples are mutually exclusive and non-exhaustive. Also, the domain knowledge is fed to the firefly algorithm and concept similarity algorithm to create facets.

QFRDBF: Query Facet Recommendation Using Knowledge …

805

Fig. 1 Proposed Architecture for QFRDBF Framework

4 Implementation The dataset used in this work is the User Q dataset. The User Q dataset contains 90 queries in XML format, and each of those queries contains 100 documents. The id, title, URL, description, rank, and text of the document, repeat region, converted HTML list queries are found in each document. The implementation is performed in Google’s Collaboratory environment in a computer with i7 processor and 16 GB RAM. Experiments were conducted for News Domains, and Ontologies were modelled for the Domains of various News. OntoCollab is used for modeling and visualization of Ontology. The OntoCollab dynamically models ontologies and Customized Web Crawlers are used to extract metadata for Ontology Modeling. Hermit Reasoner is used for reasoning of Ontologies. NLTK framework is used to perform the Natural Language Processing tasks. The concept similarity (CS) has been employed to identify the semantically similar concepts in different Ontologies and measure the similarity between concepts in an Ontology. The neighborhood set

806

D. Surya et al.

Fig. 2 F1 Score vs No. of Recommendations graph

and the feature set of a concept are used to determine its similarity with another concept. The most related concepts to a user query can be obtained using the CS measure. Concept maps of the ontologies can be exploited to extract the concepts. Mostly, experts design the concept map with the help of the ontology developed by the domain experts. The concept maps can also be automatically derived from the ontologies that are used for comparison. An XML file is created for some specific classes that comprise the representative and characteristic set of some classes in Ontology. The characteristics and representatives obtained from the previous step are utilized to calculate the concept similarity measure value. The obtained concept similarity score is exploited to determine the most related concept to the user query.

QFRDBF: Query Facet Recommendation Using Knowledge …

807

Algorithm 1: The proposed algorithm for Query Facet Recommendation Input: Multiword user query, dataset, and domain knowledge Output: Query centric user relevant query facets. Step 1: The queries obtained are synonymized using WordNet Step 2: Real world knowledge is obtained from DBpedia. Step 3: Jaccard between the queries, synonymized terms and the real-world knowledge is calculated. Step 4: Only the datapoints that are semantically similar are considered, i.e., A datapoint in the data set is considered if Jaccard value is greater than 0.5. Step 5: The database is modified to store only the semantically similar terms. Step 6: DBSCAN clustering is employed on the modified database. DBSCAN (MDB, jaccardFunc, epsilon, simPts) clusterCount := 0 for each item I in the modified database DB if label(I) ≠ undefined then continue Neighbor Points NP := QueryRange(DB, jaccardFunc, I, epsilon) if |NP| < simPts then label(I) := Noise continue clusterCount := clusterCount + 1 label(I) := clusterCount SeedSet S := NP \ {I} for each point J in S if label(J) = Noise then label(J) := clusterCount if label(J) ≠ undefined then continue label(J) := clusterCount Neighbors NP := QueryRange(MDB, jaccardFunc, J, epsilon) if |NP| ≥ simPts then S := S ∪ NP

Step 7: The result obtained from the previous step is optimized using the Firefly algorithm. while (t < Max_Generation) for u = 1 : N (Total no of fireflies N) for v = 1 : u (N fireflies) if (Uv > Uu) : Vary attractiveness with distance Ω via log(sim(r,Ω)); Vary attractiveness with Pearson’s coefficient C via exp(α – 0.25*C); Push firefly u towards v; Evaluate the latest solutions obtained, and accordingly, update the intensity of light ; end if end for v end for u Rank the fireflies to determine the best; end while Results post-processing; Results visualization; end

The queries collected are synonymized using WordNet 3.0. WordNet 3.0 is used to obtain semantically similar terms to the query, such as synonyms, hyponyms, and meronyms. DBpedia is exploited to obtain real-world knowledge. Jaccard similarity

808

D. Surya et al.

between the queries, synonymized terms, and real-world knowledge is calculated. Only the semantically similar data points are considered, i.e., A data point in the data set is considered if the Jaccard value is greater than 0.5. The database is modified to collect the semantically similar data points that are identified in the last step. The DBSCAN clustering algorithm is employed on the modified database. The DBSCAN clustered result is optimized by applying the firefly algorithm, where the objective function is WebPMI.

5 Performance Evaluation and Results The clusters obtained are evaluated using PRF, wPRF, Purity, R1, F1, F5, and NMI to determine its quality and efficacy. Normalized Mutual Information (NMI) is an external measure used to determine the quality of the clusters. Two step clustering with different numbers of clusters can be compared using NMI values as it is a normalized score. The NMI score for a cluster CL with respect to a class label CA is calculated as depicted in Eq. 4. The similarity between two clusters is measured by Random Index (RI). RI is a statistical measure used widely in clustering to determine the accuracy. RI value can be calculated, as shown in Eq. 5. TRP, FLP, FLN, TRN represents the number of True Positives, False Positives, False Negatives, and True Negatives, respectively. The F-measure estimates the clusters’ accuracy describing the class labels from the test’s harmonic mean of the precision and recall. The Fmeasure of the cluster CL with respect to a class CA is computed as depicted in Eq. 6. (4) (5) The weighted total of maximum F-measure score for a cluster C gives the overall F-Measure value. The overall F-measure value can be computed as shown in Eq. 7. Of all the generated facets, some are useful than the other. Normalized Discounted Cumulative Gain (nDCG) is employed to identify and extract the most useful facets from a collection of facets. The nDCG method can be applied to the facet collection to rank the facets and identify the useful ones. It is applied as depicted in Eq. 8. Here, DCGk represents the cumulative gain obtained by correct ordering, and iDCGk depicts the discounted gain of the ith facet. The nDCG measure can be segregated into two kinds oF-Measure s: first, purity nDCG (fp−nDCG) and second, recall purity nDCG (rp − nDCG). The former is based on each cluster’s original representation, while the latter is based on overall facets. The product of DGi with the correctly assigned item percentage yields the purity of the ith facet. The weights associated with each item in fp-nDCG and rp-nDCG are illustrated in the Eq. 9 and 10, respectively.

QFRDBF: Query Facet Recommendation Using Knowledge …

809

(6)

(7)

n DC G k =

(8)

|C L| ∩ f i fi

(9)

|C L ∩ f i | |C L ∩ f i | fi CL

(10)

wi = wi =

DC G k n DC G i

Table 1 compares the results obtained among the proposed approach QFRDBF, QDMiner, and AEFTM (Automatic Extraction of Facets for user Query in Text Mining) frameworks on the user Q dataset. The nDCG score of the QFRDBF is higher than the nDCG score of AEFTM and QDMiner. The DCG score of QFRDBF, AEFTM, QDMiner are 0.691, 0.738, and 0.938. The PRF and wPRF score of QFRDBF is much greater than the other baseline models. The PRF score of the proposed QFRDBF approach is 0.78 while that of QDMiner and AEFTM are 0.392 and 0.436, respectively. The wPRF scores of QDMiner, AEFTM, QFRDBF are 0.392, 0.436, and 0.78. The purity value of QFRDBF is greater than AEFTM and QDMiner, as the similarity of the items found in clusters obtained from the proposed approach is higher than the items belonging to the clusters generated from AEFTM and QDMiner. The purity values of QDMiner, AEFTM, QFRDBF are 0.912, 0.932, and 0.972. The proposed approach QFRDBF has a better R1 measure value than AEFTM and QDMiner. The R1 measure value of QFRDBF is 0.967, while the R1 scores of AEFTM and QDMiner are 0.937 and 0.923. The F1 and F5 measure of FRDBH are 0.941 and 0.882. FRDBH has higher scores than QDMiner and AEFTM as 0.735, 0.689 are the scores of the former and 0.792, 0.59 are the scores of the latter. Also, FRDBH has the highest NMI scores among the other baseline methods, such as AEFTM and QDMiner. The NMI scores of FRDBH, AEFTM, and QDMiner are 0.984, 0.819, and 0.878. Table 1 Measures Indicating the Quality of Query Facets Recommended nDCG

PRF

wPRF

Purity

R1

F1

F5

NMI

QDMiner

0.691

0.384

0.392

0.912

0.923

0.735

0.689

0.819

AEFTM

0.738

0.470

0.436

0.932

0.937

0.792

0.590

0.878

Proposed

0.938

0.720

0.780

0.972

0.967

0.941

0.882

0.984

810

D. Surya et al.

6 Conclusions The queries that capture the user’s interest are collected, and query words are obtained by preprocessing them. The query words are synonymized using WordNet 3.0, and the real-world knowledge is obtained from DBpedia. The Jaccard similarity among the query words, synonymized terms, and real-world knowledge are computed. Based on the results, the semantically similar terms are identified and collected in a modified database. The DBSCAN clustering algorithm is employed on the modified database, and the firefly optimization technique is applied to the result obtained to generate query facets. The results achieved validates the efficacy and the performance of the proposed framework, i.e., Facet Recommendation Using KnowledgeCentric DBSCAN and Firefly (QFRDBF). It can be inferred from the results that the proposed framework functions better than the other baseline frameworks. QFRDBF has the highest PRF, wPRF, Purity, R1, F1, F5, and NMI scores among QDMiner and AEFTM. A F1 and F5 measure of 91.1% and 88.2% are achieved.

References 1. Jiang Z, Dou Z, Wen JR (2016) Generating query facets using knowledge bases. IEEE Trans Knowl Data Eng 29(2):315–329 2. Ramya RS, Raju N, Sejal N, Venugopal KR, Iyengar SS, Patnaik LM (2019) Automatic extraction of facets for user queries [AEFUQ]. In: 2019 Fifteenth International Conference on Information Processing (ICINPRO), pp 1–6. IEEE 3. Vandic D, Aanen S, Frasincar F, Kaymak U (2017) Dynamic facet ordering for faceted product search engines. IEEE Trans Knowl Data Eng 29(5):1004–1016 4. Hervey T, Lafia S, Kuhn W (2020) Search facets and ranking in geospatial dataset search. In: 11th International Conference on Geographic Information Science (GIScience 2021)-Part I. Schloss Dagstuhl-Leibniz-Zentrum für Informatik 5. Babu D: Web search using automatically generated facets (2019). In: 2019 2nd International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT), vol 1, pp 37–40 6. Galán SF (2019) Comparative evaluation of region query strategies for DBSCAN clustering. Inf Sci 502:76–90 7. Cherrat EM, Alaoui R, Bouzahir H (2019) Improving of fingerprint segmentation images based on K-means and DBSCAN clustering. Int J Electric Comput Eng 2088–8708:9 8. Xue X (2020) A compact firefly algorithm for matching biomedical ontologies. Knowledge and Information Systems, pp 1–17 9. Kaushal C, Kaushal K, Singla A (2020) Firefly optimization-based segmentation technique to analyse medical images of breast cancer. Int. J. Comput. Math. 1–16 10. Xie H, Zhang L, Lim CP, Yu Y, Liu C, Liu H, Walters J (2019) Improving K-means clustering with enhanced Firefly Algorithms. Appl Soft Comput 84: 11. Iwendi C, Ponnan S, Munirathinam R, Srinivasan K, Chang CY (2019) An efficient and unique TF/IDF algorithmic model-based data analysis for handling applications with big data streaming. Electronics 8(11):1331 12. Deepak G, Santhanavijayan A (2020) OntoBestFit: A Best-Fit Occurrence Estimation strategy for RDF driven faceted semantic search. Computer Communications, vol 160, 284–298

QFRDBF: Query Facet Recommendation Using Knowledge …

811

13. Pushpa CN, Deepak G, Kumar A, Thriveni J, Venugopal KR (2020) OntoDisco: improving web service discovery by hybridization of ontology focused concept clustering and interface semantics. In: 2020 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), pp 1–5. IEEE 14. Kumar A, Deepak G, Santhanavijayan A (2020) HeTOnto: a novel approach for conceptualization, modeling, visualization, and formalization of domain centric ontologies for heat transfer. In: 2020 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), pp 1–6. IEEE 15. Deepak G, Kasaraneni D (2019) OntoCommerce: an ontology focused semantic framework for personalised product recommendation for user targeted e-commerce. Int J Comput Aided Eng Technol 11(4–5):449–466 16. Gulzar ZA, Anny L, Gerard D: Pcrs: personalized course recommender system based on hybrid approach. Procedia Comput Sci 125, 518–524 (2018) 17. Deepak G, Teja V, Santhanavijayan A (2020) A novel firefly driven scheme for resume parsing and matching based on entity linking paradigm. J Discrete Math Sci Cryptography 23(1):157– 165 18. Haribabu S, Sai Kumar PS, Padhy S, Deepak G, Santhanavijayan A, Kumar N (2019) A novel approach for ontology focused inter- domain personalized search based on semantic set expansion. In: 2019 Fifteenth International Conference on Information Processing (ICINPRO), Bengaluru, India, 2019, pp 1–5. https://doi.org/10.1109/icinpro47689.2019.9092155 19. Deepak G, Naresh Kumar G, Sai Yashaswea Bharadwaj VSN, Santhanavijayan A (2019) OntoQuest: an ontological strategy for automatic question generation for e-assessment using static and dynamic knowledge. In: 2019 Fifteenth International Conference on Information Processing (ICINPRO), pp 1–6. IEEE 20. Santhanavijayan A, Naresh Kumar D, Deepak G (2010) A semantic-aware strategy for automatic speech recognition incorporating deep learning models. In: Intelligent System Design, pp 247–254. Springer, Singapore 21. Deepak G, et al. (2019) Design and evaluation of conceptual ontologies for electrochemistry as a domain. In: 2019 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE), IEEE 22. Deepak G, Priyadarshini JS (2018) Personalized and enhanced hybridized semantic algorithm for web image retrieval incorporating ontology classification, strategic query expansion, and content-based analysis. Comput Electric Eng 72:14–25 23. Kaushik IS, Deepak G, Santhanavijayan A (2020) QuantQueryEXP: a novel strategic approach for query expansion based on quantum computing principles. J Discrete Math Sci Cryptography 23(2):573–584 24. Varghese L, Deepak G, Santhanavijayan A: an IoT analytics approach for weather forecasting using Raspberry Pi 3 Model B+. In: 2019 Fifteenth International Conference on Information Processing (ICINPRO), pp 1–5. IEEE (2019) 25. Shreyas K, Deepak G, Santhanavijayan A (2020) GenMOnto: A strategic domain ontology modelling approach for conceptualisation and evaluation of collective knowledge for mapping genomes. J Stat Manage Syst 23(2):445–452 26. Deepak P, Zakir M (2016) Enhanced neighborhood normalized pointwise mutual information algorithm for constraint aware data clustering. ICTACT J Soft Comput 6(4)

SentiFusion (SF): Sentiment Analysis of Twitter Using Fusion Techniques Sampurn Anand, Chaya Vijaykumar, Gerard Deepak, and A Santhana Vijayan

Abstract The Internet has become a domain for learning, idea exchange, feedback, sharing thoughts or opinions, and many more. In the current world, Twitter has been the most liable platform where people express their views and ideas about a various range of products. In this paper, different feature extraction methods are used to get more accurate sentiment analysis results. Firstly, tweet pre-processing is performed using Euclidean Vector Length Similarity and other dictionaries. The feature extraction is performed using (Jaccard Similarity and Trigrams) and (Kmeans and KL-divergence). Then using tf-idf and Shannon Entropy, feature fusion and weighting are done. At last, before final Sentiment Computation, Bagging is accomplished using SVM classifiers and Decision Trees. Keywords Tf-idf · K.L. Divergence · Sentiment analysis · SentiFusion · Trigrams

1 Introduction In a layman’s language, Sentiment analysis is the analysis of emotions of different texts. Technically it is the process of recognizing and classifying different opinions using different computational algorithms expressed on a topic to determine the author’s feelings towards it. Social media is a platform that generates a variety of data-rich sentiments, which include tweets, blogs, comments, feedback, product reviews, and many more. It also provides an opportunity for businesses by connecting with their customers for advertising and improvising their products. One of the foremost and versatile social media platforms is Twitter. Amongst all the social media platforms, Twitter is one of the many famous microblogging sites. Twitter Social Feature Extraction [1] is quite important and has played a primary role for Feature S. Anand · G. Deepak (B) · A. Santhana Vijayan Department of Computer Science and Engineering, National Institute of Technology Tiruchirappalli, Tiruchirappalli, Tamil Nadu, India C. Vijaykumar Department of Computer Science and Engineering, Bangalore Institute of Technology, VV Puram, Bengaluru, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_74

813

814

S. Anand et al.

Extraction. It has more than 150 users with 2,400 tweets per day. Analyzing Twitter is the best option because it contains various opinions, feedback, and reviews. Extracting fascinating information and non-trivial patterns from unstructured data is the primary concern of Opinion Mining and Sentiment analysis which are the branches of Texting mining. Opinion mining (analyzing people’s opinions) is the automated process of identifying and extracting subjective information from a given document. The main aim of doing opinion mining is to classify the tweets as per their sentiment values. The sentiment values will help in making the decision-making process for different businesses and other applications. In SentiFusion, we propose a combination of several methods, namely the Jaccard Similarity, Trigrams, K-means Clustering with Silhouette coefficient, and Kullback Leibler Divergence, which help in extracting the right features and makes the data well-structured for classification. Following the feature extraction, the tf-idf has been encompassed to act as a feature weighting scheme. Bagging is done using the two most vital algorithms, which are Support-vector classifiers and Decision Trees. The experiments have been conducted on Sentiment140 dataset models and have been baselined with other models to show the proposed SentiFusion approach outperforms different approaches. SentiFusion has achieved better performance measures in terms of Precision, Recall, Accuracy, and F-Score. In Sect. 2, Related Work has been described. Section 3 presents the proposed system Sect. 4 consists of a description of Sentiment Computation. Implementation with experimental results and Performance evaluation is discussed in Sect. 5. The paper is concluded in Sect. 6.

2 Related Work In [1], (Savitha et al. 2018) have proposed measures to extract sentiment variation’s reasons on Twitter. The author used two approaches, Cosine Similarity and n-gram similarity with Latent Semantic Analysis, to notice the similarities between tweets and then concluded the reasons for sentiment variation by getting the highest similarity value. This approach leads to an overall increase in the time complexity of the system, which can be reduced. In [2], (Amit and Deshmukh 2013) used Pointwise Mutual Information (PMI) and Chi-Square (X2) for feature selection. Further, for sentiment classification, Naive Bayes (NB), Support Vector Machine (SVM), and Maximum Entropy (ME) were used. The approach was mainly Lexicon-based. This system lagged in the overall accuracy of judgment. POS was introduced as specific polarity features in [3] by (Apoorv et al. 2011). They used tree kernels for feature extraction. This model had outperformed the general state-of-the-art baseline but had lower F-Scores. (Öztürk and Ayvaz 2018), in [4] performed comparative sentiment analysis of the tweets retrieved and analyzed tweets in Turkish Language. For this purpose, they used the Turkish sentiment analysis lexicon then simply classified data using term frequencies. It had lower F-scores even if only the analysis of Tweets is concerned. The effects of pre-processing were ignored in [5]. In it, the main focus was

SentiFusion (SF): Sentiment Analysis of Twitter …

815

on classification. (Jianqiang and Xiaolin 2017) tried to depict the six pre-processing methods’ performances while using two feature models and four classifiers on five different Twitter datasets. In this paper, the accuracy and F1-measure proposed methods using classification classifiers were improved by expanding acronyms and replacing negation. It doesn’t change the URLs, numbers, or stop words, which was why it increased the time complexity of the system. The authors showed that Logistic Regression and SVM classifiers are less sensitive than Naive Bayes and Random Forest classifiers when various pre-processing methods were applied. Textual information of Twitter messages and sentiment diffusion patterns were fused to perform better [6]. But apart from the classifications, other sections of the system decreased the overall F-scores. (Wang et al. 2020) used sentiment reversal phenomenon. They proposed an iterative algorithm, which was named SentiDiff, to predict polarities of the sentiment of Tweets. The PR-AUC improvement (Precision-Recall Area Under Curve) between 5.09 and 8.38% was obtained using this model on Twitter sentiment classification tasks. Common sense knowledge (Domain Specific Ontology), a very innovative sentiment analysis method, was used in [7] by (Ramanathan and Meyyappan 2019). They created their Ontology based on ConceptNet. The combined sentiment lexicon approach finally analyzed sentiments. In the final step, concerning the domains, semantic orientations of domain-specific features were incorporated. The only problem with this system was it led to an increase in the time complexity because of the new Ontology. In [10–21] several ontological supporting strategies have been discussed.

3 Proposed System Architecture The architecture of the proposed system is depicted in Fig. 1. It can be interpreted as a probabilistic model wherein the concepts of association rule mining, trigram models, and statistical analysis have been integrated. It is an aspect level text classification. Our proposed system will obtain a decimal value as a sentiment value of a particular search option. This decimal value can describe the sentiments related to the search better as it can be put on a linear scale of sentiments and then interpreted. This way, the accuracy of the system can be increased. The first and foremost step is gathering the datasets. Tokenization and preprocessing Twitter data (Normalization using Euclidean Vector Length Similarity) are performed next. Feature extraction is implemented using two methods explained further. By combining the scores of all the tokens and keywords, feature-fusion is accomplished. For feature weighting scheme Shannon Entropy is carried out. This feature weighting method is used to compute the entropy of a probability distribution of the appearance of different clusters in different sentiments. Lastly, Bagging using SVM classifiers and Decision trees is conducted. All these steps rely on each other. The output of one step corresponds to the input of the very next step. Implementation of the methods mentioned here is done using the chosen data-set and different Python

816

S. Anand et al.

Fig. 1 Proposed system architecture

libraries for text mining. For reference purposes, the proposed system architecture will be called SentiFusion or SF in further mentions.

3.1 Data Preparation and Pre-processing The gathered data set is fed to the next stage of pre-processing the data. In Tokenization, tweets are broken down into words that are termed as ‘tokens’. Tokens are normalized to standard form (all lower cases) using Euclidean Vector Length Similarity. Acronyms are converted to their full forms using the Normalization process. Using the stop word dictionary, each word is tagged as ST (Stop-words) or NST (Non-Stop-words) for their Parts-of-Speech. Using the emoticon dictionary, emoticons are removed and taken in as input to their sentiment values for each tweet. The URLs are removed from the tweets. The inflected forms of words are grouped to find out the base term using the procedure of Lemmatization. Each token is now classified as (word, POS tag, English-word, Stop-word, Sentiment values). Directly, these tokens with their classifications are fed as inputs to the next step of the process. Dataset Description: In SentiFusion, the Sentiment140 data set is being used. This data set has already been extracted using Twitter API after selecting the default language as “English”. It contains 1,600,000 tweets. The six fields used from the dataset here in the proposed model are the target, ids, date, flag, user, and text. The

SentiFusion (SF): Sentiment Analysis of Twitter …

817

link for the data set is https://www.kaggle.com/kazanova/sentiment140. The Target field contains the tweet’s polarity, which won’t be utilized in this proposed system architecture as the classification will be done in the fifth step. The other fields are also not used in our approach. We are using the dataset for the tweets mainly, which is pre-processed and used further for classification.

3.2 Feature Extraction In this step for Extracting Features, two methods are adopted. In the First Method, Jaccard Similarity and Trigrams are being used. In the Second Method, K-means Clustering with the Silhouette coefficient and Kullback Leibler divergence is used. Tf-Idf is common to both the methods for Classification. Data from the previous step is fed to Method 1, where the initial level of feature extraction is done. The data is provided to Method 2, which refines the feature. This step aims to extract and summarize as many features from the tweets as possible, which will be fused and classified in the next steps. Using two methods of feature extraction ensures better accuracy. Jaccard Similarity Jaccard Similarity is the method of grouping the same data from two different data sets. This algorithm is used to increase the accuracy of the proposed system and shorten the given data sets. The pre-processed data from the previous step is compared with different tweets and assigned different weights. The whole file is read for various tweets to make comparisons between them for similar words. Every tweet will be assigned a Jaccard Similarity ratio given by dividing the size of the intersection of the two sets by the size of its union. Since we have already normalized our tweet in the first step, repeating a word in a single tweet is avoided without changing the results, a distinct feature of SentiFusion. So, by now, for a detailed analysis, all the tokenized words of tweets will be grouped in arrays with an ID number representing the number of appearances of the word present in the whole document. Each array is termed a cluster [8]. Trigrams An n-gram is collected from a text corpus, a contiguous set of n items from a given sample of text. Trigrams are a type of n-grams with n = 3. N-grams are used in text analysis for grouping up different letters of a word. For example, for the string “abcdefg” trigrams (n = 3) will be efg, def, cde, and so on. These language models are used in Natural Language Processing Models to reduce the algorithm’s overall time complexity. In SentiFusion, trigrams are used to group different elements of the arrays obtained from the previous step. Each array element will be converted into chunks of 3 and assigned a weight as per the array’s ID number. Then all the chunks with their weights will be inserted into a stack. This step is done so that there will be the least number of comparisons in the next step.

818

S. Anand et al.

K-means Clustering with Silhouette Analysis Technique The stacks obtained from the previous step are converted into clusters using K-means Clustering. With the assistance of SentiWordNet, a score is assigned to each cluster by its aggregated scored tweets within the next steps. A score of every term within the tweet will be reclaimed with its tag (i.e., noun, adjective, verb, and adverb). The results of K-means clustering are validated and interpreted using Silhouette analysis. This analysis gives a mean score of the clusters. If the typical score is 1, then we will say that the info point is well assigned to the primary cluster, whereas a score on the brink of −1 means data is aligned to the second cluster. Kullback–Leibler Divergence Kullback-Leibler divergence (relative entropy), in short, called KL divergence, measures the difference between any two probability distributions. Given two discrete probability distributions, p(x) and q(x), D(p||q), the relative entropy between p and q is defined in Eq. (1). D( p||q) = p(x) ∗ log( p(x)/q(x))

(1)

From Eq. (1) it can be conclude that, D(p||q) is always non-negative and if p = q then D(p||q) = 0. It shows the variation of p(x) with respect to q(x). If KL divergence is small, there is a high similarity between the distributions of two variables, and the converse of it is also True. This probability distribution is used to assign more precise values to the classified clusters obtained from the previous step. Every token of the clusters is now assigned a decimal value (KL—scores) of their related entropy, which gives a more precise analysis of Sentiments. TF-IDF We implement Term frequency-inverse document frequency, a numerical statistic for keyword extraction. TF-IDF is related to a bag of words (Bagging). The main idea behind using TF-IDF as a numeric feature generator is that—more importance should be given to the word that appears more in one text document and less in another, which eventually becomes useful for classification. In SentiFusion, this model is used for emphasizing the tokens, which are frequently repeated. The tokens in SentiFusion are terms, and similarly, each cluster is considered as a document here. For every token and cluster, a tf value and idf value are calculated, respectively. When these values are multiplied together, we get a score that is highest for tokens that frequently appear in the clusters and low for tokens that occur less often in every cluster, allowing us to find tokens necessary in the cluster. Hence, if the token is very common, then the value will approach 0; else, it nears 1. Now, the “tf-idf score” is assigned to all the tokens, and a distinction is made between normal tokens and keywords for all the clusters. This tf-idf score is multiplied with the KL-scores from the previous step. This clustered data is now fed to machine learning algorithms for further feature fusion and classification.

SentiFusion (SF): Sentiment Analysis of Twitter …

819

3.3 Feature Fusion This step is for feature fusion by combining the scores of all the tokens and keywords obtained in the previous step of Feature Extraction. The sentiment scores are multiplied with the scores obtained from the earlier steps. A final decimal sentiment score is obtained by the end of this step. The keywords will naturally have higher scores as they already have a higher combined tf-idf and KL scores. Let these scores of all the tokens be called “Senti-scores” for future references. All the clusters are fed to the next step of feature weighting and bagging to summarize the results of sentiment analysis of the queried topic.

3.4 Feature Weighting Using Shannon Entropy Shannon and Weaver proposed this feature weighting method in 1963. It acts as a criterion for measuring the degree of uncertainty represented by a discrete probability distribution. Entropy here measures existent contrasts between different sentiment datasets. Let p(x) or pij be the probability mass function of the random variable x or xij . Steps for feature weighting through Shannon Entropy are: 1.

Normalizing the arrays of performance indices to obtain the outcome pij based on Eq. (2) xi j p(x) = pi j = m

i=1 x i j

2.

For Computing entropy measure, Eq. (3) is used E j = −k ∗

3.

(2)

m i=1

pi j ln( pi j )

(3)

1 Where k = ln(m) Eq. (4) defines the weight based on Shannon Entropy

(1 − E j ) w j = n j=1 (1 − E j )

(4)

This feature weighting method is used to compute the entropy of a probability distribution of the appearance of different clusters in different sentiments. Applying the concepts of Shannon Entropy, we will be using the following formula: N entr opy(g) = H ( p(S|g)) = − p(Si |g)logp(Si |g) i=1

(5)

820

S. Anand et al.

where N is the number of sentiments (in SentiFusion, N = 3 as trigrams were used in feature extraction). A higher value of a cluster’s entropy in different sentiment datasets indicates that a distribution of the appearance of that cluster is close to uniform. Therefore, such clusters would not contribute much to the classification. On the other hand, a low value of the entropy will indicate that the cluster appears in some of the datasets more frequently than in other datasets. So it highlights a proper sentiment (or objectivity). Hence, to increase the overall accuracy the clusters with low entropy values are only passed on to the next step. This is achieved by setting a threshold value and then filtering out all clusters with entropy. As the number of features used is reduced, this step lowers the recall. Nevertheless still the primary attention is on high accuracy, as the dataset used is enormous.

3.5 Bagging Support Vector Machines or SVMs are supervised learning models. For the classification of data and then performing Regression analysis as per the need, SVMs analyze the data and recognize the patterns. In this step, using the SVM classifiers, we are creating a supervised learning model of three classes—strong sentiments, neutral sentiments, and Weak sentiments. This classification is done for each token based on Senti-sores (product of tf idf and KL divergence scores). This model is now trained to accept and classify any new tokens based on Senti-scores. For each of the three classes, the tokens are placed and interlinked in space in the form of Decision trees.

4 Sentiment Computation Using SentiWordNet SentiWordNet [9], derived from the WordNet database, is a lexical resource used for opinion mining. It assigns to each synset (a word or a term which are interchangeable synonyms) three sentiment scores: positivity (+1), negativity (−1), and objectivity(0). In this final step after feature selection, a ranking of the selected tokens is done using the SentiWordNet dictionary. The selected tokens include both positive and negative rankings. These score values of the products are inputted to SVC and Decision Tree classifiers in the three classes. The polarity of the word signifies the characteristic of a given tweet.

SentiFusion (SF): Sentiment Analysis of Twitter …

821

5 Implementation and Performance Evaluation The whole program was run on a system with an i7 Processor/16 GB RAM. It was connected with the DCX1 server of supercomputing to supercomputers. Stanford’s Python Natural Language Toolkit has been used for carrying out NLP as well as Preprocessing Tasks. Pre-processing of blank space and special character tokenizers were customized. Similarly, a customized algorithm was used for stemming or lemmatization and stop-word removals. The performance of the proposed SentiFusion has been computed using Precision, Recall, Accuracy, and F-Score as the desired matrix. In Table 1, a comparison between CSNM paper and SentiFusion paper is carried out. The standard formulae of the respective terms are used to calculate the performance measure. Table1 depicts a comparison between the existing method-CNSM and the proposed method—SentiFusion. In total, 2500 data are compared, of which the 500 datasets of CNSM yield 86.87% of Precision, 88.02% Recall, 87.445% Accuracy, and an F-score of nearly 87.44%. In contrast, we can see a Precision of 94.14%, 97.18% of Recall, 95.66%, and 95.64% of Accuracy and F-Score, respectively in SentiFusion. We can observe a considerable difference rate in Accuracy and F-Score of both the models. With the more reliable F-Score, we can conclude that the proposed system has higher performance measures. The SentiFusion method performed better than the CSNM method. This happened mainly because of the Bi-way verification system in SentiFusion. In the CNSM method, the author employs n-gram similarity matching and Cosine similarity using Latent Semantic Analysis methods. This made the model a bit slower in terms of Time complexity. Topic modeling is used with ranking for feature selection. There is no robust classifier included in the existing system of CNSM. The proposed SentiFusion has a concrete Bi-Classifier and Ensemble Classifier with SVM that harvests boundary features, granular and sub-granular features, unlike LSA. Whereas in feature extraction of the SentiFusion model, two different methods are used, which Table 1 Comparison of CNSM and SentiFusion No. of Data Points

Performance measures

Precision %

Recall %

Accuracy %

F-Score in %

CNSM

SF

CNSM

SF

CNSM

SF

CNSM

SF

500

86.87

94.14

88.02

97.18

87.44

95.66

87.44

95.63

1000

85.54

93.89

87.45

96.85

86.49

95.37

86.48

95.35

1500

84.44

93.07

87.01

95.18

85.72

94.12

85.7

94.11

2000

83.17

92.74

85.45

94.85

84.31

93.79

84.29

93.78

2500

82.21

91.45

84.39

93.17

83.3

92.31

83.28

92.3

822

S. Anand et al.

give a higher accuracy rate. The time complexity of the SentiFusion model is also less as the comparisons are less because of Shannon Entropy.

6 Conclusions A SentiFusion model was trained which can now be used to analyze sentiments for any topic properly. The model was trained using two different methods. The first method consisted of Jaccard Similarity and Trigrams while the second method had K-means Clustering and KL divergence implemented in it. Then feature fusion and weighting were facilitated by tf-idf and Shannon Entropy. Bagging in the last step was done using SVM Classifiers. This model was found to give an accuracy of 92% in extensive datasets whereas 95% accuracy was easily obtained for smaller datasets. The average F-score of our model is 94.23 which is higher than the others. The proposed model has 95% F-score for smaller datasets and 92% for extensive datasets. This model also has very high precision value. Hence, it is concluded that a two-way Feature Extraction combining two different methods provides better results.

References 1. Shirbhate AG, Deshmuk SN (2013) Feature extraction for sentiment classification on twitter data. Int J Sci Res (IJSR), pp 2184–2185 2. Mathapati S, Anil D, Tanuja R, Manjula SH, Venugopal KR (2018) CNSM: cosine and n-gram similarity measure to extract reasons for sentiment variation on Twitter. Int J Comput Eng Technol (IJCET) 9:150–161 3. Agarwal A, Xie B, Vovsha I, Rambow O, Passonneau R (2011) Sentiment analysis of Twitter data. In: Proceedings of the workshop on languages in social media, LSm 2011, pp 30–38 4. Öztürk N, Ayvaz S (2018) Sentiment analysis on Twitter: a text mining approach to the Syrian refugee crisis. Telemat. Inform. 35(1), 136–147 5. Jianqiang Z, Xiaolin G (2017) Comparison research on text pre-processing methods on Twitter sentiment analysis. IEEE Access 5:2870–2879 6. Wang L, Niu J, Yu S (2020) SentiDiff: combining textual information and sentiment diffusion patterns for Twitter sentiment analysis. IEEE Trans Knowl Data Eng 32(10):2026–2039 7. Ramanathan V, Meyyappan T (2019) Twitter text mining for sentiment analysis on people’s feedback about Oman tourism. In: 2019 4th MEC international conference on big data and smart city (ICBDSC), Muscat, Oman, pp 1–5 8. https://python.gotrained.com/nltk-edit-distance-jaccard-distance/#Example_1_Character_ Level 9. https://github.com/aesuli/SentiWordNet 10. Deepak G, Santhanavijayan A (2020) OntoBestFit: a best-fit occurrence estimation strategy for RDF driven faceted semantic search. Comput Commun 160:284–298 (2020) 11. Pushpa CN, Deepak G, Kumar A, Thriveni J, Venugopal KR (2020) OntoDisco: improving web service discovery by hybridization of ontology focused concept clustering and interface semantics. In 2020 IEEE international conference on electronics, computing and communication technologies, pp 1–5, July 2020

SentiFusion (SF): Sentiment Analysis of Twitter …

823

12. Deepak G, Kasaraneni D (2019) OntoCommerce: an ontology focused semantic framework for personalised product recommendation for user targeted e-commerce. Int J Comput Aided Eng Technol 11(4–5):449–466 13. Gulzar Z, Anny Leema A, Deepak G (2018) Pcrs: personalized course recommender system based on hybrid approach. Procedia Comput Sci 125:518–524 14. Deepak G, Teja V, Santhanavijayan A (2020) A novel firefly driven scheme for resume parsing and matching based on entity linking paradigm. J Discrete Math Sci Cryptography 23(1):157– 165 15. Haribabu S, Sai Kumar PS, Padhy S, Deepak G, Santhanavijayan A, Kumar DN (2019) A novel approach for ontology focused inter- domain personalized search based on semantic set expansion. In: 2019 fifteenth international conference on information processing 16. Deepak G, Kumar N, VSN Sai Yashaswea Bharadwaj G, Santhanavijayan A (2019) OntoQuest: an ontological strategy for automatic question generation for e-assessment using static and dynamic knowledge. In: 2019 fifteenth international conference on information processing (ICINPRO), pp 1–6 (2019) 17. Santhanavijayan A, Kumar DN, Deepak G (2020) A semantic-aware strategy for automatic speech recognition incorporating deep learning models. In: Intelligent system design. Springer, pp 247–254 18. Deepak G, et al (2019) Design and evaluation of conceptual ontologies for electrochemistry as a domain. In: 2019 IEEE international WIE conference on electrical and computer engineering 19. Deepak G, Priyadarshini JS (2018) Personalized and Enhanced Hybridized Semantic Algorithm for web image retrieval incorporating ontology classification, strategic query expansion, and content-based analysis. Comput Electr Eng 72:14–25 20. Kaushik IS, Deepak G, Santhanavijayan A (2020) QuantQueryEXP: a novel strategic approach for query expansion based on quantum computing principles. J Discr Math Sci Cryptography 23(2):573–584 21. Deepak G, Kumar DN, Santhanavijayan A (2020) A semantic approach for entity linking by diverse knowledge integration incorporating role-based chunking. Procedia Comput Sci 167:737–746

Using Internet of Things to Increase Efficient Collaboration in PLM Narjiss Tilioua, Fatima Bennouna, and Zakaria Chalh

Abstract Nowadays, the automotive sector needs more and more digitalized solutions, and this is especially with regard to PLM software and its effectiveness in communicating information related to new products under development, even if this is the main role of PLM but to increase its effectiveness and efficiency of collaboration between automotive companies, their subcontractors and suppliers. Because effective collaboration leads directly to the rapid success of product lifecycle management. In this paper we will show the need to better digitalize the communication between the different actors of the PLM wheel and we will also mention three principles that will help us to better collaborate in the context of product lifecycle management. Keywords PLM · Supplier · References · Industry 4.0 · Collaboration · IOT Things · Automotive

1 Introduction In a world where the only constant thing is change, the platform Product Lifecycle Management PLM has been created and designed over the past two decades to manage the entire product lifecycle efficiently and cost-effectively from ideation, design, manufacture and then service to disposal. In fact, the huge need of an innovative solution as PLM systems comes from the rapid evolution of the production worldwide. One reason that pushes organization to adapt their strategies to the context and the new challenges such as the international concurrence, the segmented market, the big flow of information and data, the intensive resources difficult to manage and control, etc.

N. Tilioua (B) · F. Bennouna · Z. Chalh LISA Laboratory, Sidi Mohamed Ben Abdellah University, Fes, Morocco e-mail: [email protected] Z. Chalh e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_75

825

826

N. Tilioua et al.

As a solution to all of this, PLM systems consist of integrating products, processes and people into a single system, enabling the co-engineering and the collaboration between internals, suppliers and customers in order to enable enterprises to drive innovation, increase productivity, reduce costs, improve quality, accelerate growth, ensure compliance and most importantly shorten the time to market. However, putting all the actors into the single system is not sufficient to get to the optimal results. As a matter of example, we know that PLM systems contain all the product references and their presentation of products in 3D format. So when engineers or technicians in the supply chain management desire to order products, they search for the latest updates of the products with their respective references on the PLM platform. But, the information flow of product datais often interrupted during the various stages of product testing at OEMs. As a consequence, these stages are not yet processed in some companies by PLM systems at the time of search and order. The rest of this document is structured as follows: Section 1: assesses the status and challenges of PLM systems and strategy implementations according to three main principles. In Sect. 2, we present the discussion on the artificial innovation to be added to increase the performance of automotive companies while improving the use of PLM.

2 Literature Review This paper is inspired by several scientific publications, a literature review of several sectors that use PLM, and our objective is to compare the solutions already communicated by other authors and to propose the best solution that will be based on digital technology to improve lifecycle management efficiency in the automotive sector. Knowing that the life cycle of automotive products is composed of several interrelated stages, roughly speaking we can mention three stages: Beginning of Life (BOL), Middle of Life (MOL) and End of Life (EOL). And to link the data from these three phases, the software and PLM solution has been created as a single information space that allows all actors in the value chain of the product under development to be involved in the research and development phase integrated in the first stage (BOL) discussed in this document. Our document therefore goes in the direction of optimizing the procurement time of manufactured products that are in the testing phase with the new references launched by suppliers. At the research and development stage, digital solutions can be implemented in the form of communication and information exchange on technical changes in products that is more interactive and fast, an example: connecting the phones or laptops of the players in this value chain to PLM software in order to alert them about new products that interest them in real time.

Using Internet of Things …

827

It is thanks to the rapid and advanced evolution of information technology that the automotive sector has become more competitive in recent years. Internet technology has contributed to better linking and virtually connecting processes even with distant actors and to the development of new strategies for the development of products that meet customer requirements and aim to better satisfy them. And as Mr. Sharma lied in his article, the real challenge of technology lies in its ability to link people, processes and information [1]. The current state of PLM software is based mainly on the BOL and MOL phases, but according to existing research, the future vision of PLM will also give importance to the EOL phase. The literature sets up several challenges and opportunities of PLM, including data and information management issues [2] and requirements management [3]. The definition of PLM presented by Corallo [4] groups together the information and collaboration management of products throughout their life cycle as well as the inherent aspects of managing. However, it should be noted that providing and sharing information will enable effective PLM processes, both within an organization and with partners and stakeholders across the enterprise. The evolution of PLM also addresses the increasing complexity of companies, which is mainly due to the following factors increasing organizational growth (collaboration, decentralization) increasingly complex products [5]. Product lifecycle management systems play an important role in the integration and process-based organization of information about a series of development and evolution phases of products, processes and resources. New information technologies, such as Digital Twin, are encouraging industrial digital transformation and effective lifecycle management that must now take into account all phases of the project, where The use of the 3D model in some of them is no longer the best solution [6]. Digital Twin (DT) has a great capacity to realize Industry 4.0. to simulate working conditions in real time and make intelligent decisions, when a cost-effective solution can be easily provided. Knowing that the term PLM allows an application of the Digital Twin as companies are moving towards green manufacturing with the Digital Twin which enablesmaximizing production quality throughout a complete loop cycle [7]. Thanks to the simulations of the Digital Twin tool and the opportunity it gives to the actors of the value chain to make decisions relevant to the topics concerning the life cycle of products, based on this opportunity, they canmake intelligent products with self-awareness [8].

828

N. Tilioua et al.

3 Internet of Things in PLM Tool Towards Efficiency in Management Data Flows The Automotive products lifecycle begins with the Research and Development stage. At the first stage, it is necessary for the vehicle company’s engineers and technicians to search for the composition of the products,and we know that suppliers develop new references of these products after any EOM negative return or any innovation of that product, so the idea is to link and facilitate the references searching to OEM engineers and technicians commands directly with suppliers by using PLM tool connected with their personal computers, as well as the future manufacturing technology. At this stage, applications are filed within patent offices to protect the intellectual property created during the drug development. It will also require the organization of pilot production and procurement since at the development stages, sample batches of automotive products are always required in those tests. It is also necessary to conduct a search and optimal supplier name selection for the product being developed with the subsequent necessary measures implementation for registering a command. Procurement stage is followed by banc tests of parts required. Once the idea research stage of a new part project is completed, a feedback is made to OEMs and suppliers. At this stage of the product life cycle, a future study protocol is created, and after its approval, research and development studies are conducted in accordance with the requirements of good practice. Component data of product samples from the research and development stage. This stage is followed by the registration of the product in the PLM, during which the actors are responsible for their modifications. After the evaluation, the engineers issue a marketing authorization for the automotive product. The process of collecting the documents in the file is complicated and requires specialists with a high technical level. At this stage, it is necessary to ensure that all the data generated by the previous steps are integrated. The production phase normally takes place after registration, but its preparation takes a considerable amount of time. The organization of the production is usually done in parallel with the previous steps. The production of automotive parts is controlled by testing, and the main component of this system is quality assurance at all stages of production, from the purchase of raw materials and materials and their control through to the final product. To remain competitive in today’s digital age, knowledge sharing and knowledge reuse between different companies in internal or external collaboration needs to be strengthened, and the knowledge needs of the different phases of the product lifecycle need to be identified and detailed in a clear way. We will compare three articles in this sense, the first article chose to deal with the two BOL phases of the R&D companies because this phase is well known as the most important stage on which the entire product life cycle is based, and the MOL phase with logistics companies and it was noticed that the staff in both phases insist on the need for more advanced integration of the suppliers this is shown in the article by the following sentence: The R&D staff focused on new materials and innovations from

Using Internet of Things …

829

suppliers to apply them more quickly to their own product development (M2), while the logistics staff focused on the transport capacity of suppliers because sometimes their own fleet cannot meet the customers’ requirements. And for the same article the author focused his vision on expertise as a type of knowledge to be shared to better streamline and help manage product lifecycles, saying that R&D expertise was mainly related to design, development, technology and manufacturing process. For the logistics people, it was mainly related to import and export, and export, insurance, and political and legal aspects. This by insisting in the side of logistics companies that the expertise acquired from competitors is more useful because it is more relevant to them unlike R&D companies which have confidentiality in their expertise compared to their competitors. And the latter can only obtain knowledge through other channels, especially through person-to-person communication. However, logistics personnel expect the government to organize special meetings to obtain faster updates and among the things that show the need for supplier integration in logistics are hygiene, safety and temperature in transport, limiting the weight and length of aircraft. And an example was given in this article that knowledge sharing is done in a hierarchical way from the internal base of the company to the external side by specifying reference persons and insisting that Knowledge must be shared within the department, within the company, with the branch, with the supplier, with the customer and even with the competitor. In the other article called: A Green PLM approach, which focuses on product lifecycle management from an environmental perspective, his contribution suggests a framework for sustainable product development that takes into account the entire product lifecycle and will enable companies to be more resource efficient. For him the Factory of the Future is a set of competent regional small and medium sized companies that could compete but, at the same time, be available to collaborate when needed by sharing knowledge and resources. with customers, stakeholders and suppliers and thus enable sustainability through green products and processes (Green Factory certified). The author in this article emphasizes a special attention to green manufacturing which must be considered critical as it is the most polluting stage of the product life cycle due to greenhouse gas emissions and liquid and solid waste that are not sufficiently controlled. In the 3rd article: Convergence of IoT and product lifecycle management in medical health care, the author highlights the technological and artificial side to better develop the role of PLM while saying that with the modern cloud and IOT Intelligent connected devices improve collaboration and flexibility throughout the value chain, Greg Cline [9] presents that things already generate more data than people or applications. The main contribution of this paper is to raise the importance of IoMT integration with Product Lifecycle Management (PLM), information and data sharing, and collaboration from medical experts to patients and between devices.

830

N. Tilioua et al.

The author thus insists on the link between the Internet of Things (IoT) and PLM in the medical market because with Information Communication Technologies (ICT) and sensors that are driving a dynamic change in industry and academia. As research on PLM and IoT has been conducted for a long time. But these feature-based devices consume more energy and a majority of small nodes are battery-powered, which is a critical and interesting challenge. The e-Cloud with IoT-enabled devices is being developed; in which devices can communicate with each other while sharing and exchanging very important information and the power and life cycle of these devices will be managed from the beginning to the end of the medical treatment process thus keeping in mind the ability to gather knowledge and information. Finally, I think it is important to combine these three ideas of IOT and lean manufacturing in green manufacturing by ensuring supplier integration and effective collaboration between all the collaborators in the value chain with the control of environmental experts and industrial managers through the use of intelligent sensors and connecting them directly to the devices of all these people each on their own side of the collaboration. Giving the importance to collaboration in the purpose of having the effective management of products throughout their life cycle. The literature on the efficiency of integration and communication between suppliers and stakeholders and OEMs its objective in this article is facilitating the selection of the latest references that can be adapted to the context of product life cycle management. In Table 1, we summarize their relevance and importance for decision support in the context of LCM. And for the problem of efficient supplier integration and communication between test engineers on new vehicle projects and suppliers of the latest 3D part modification or creation, I propose to integrate one more step in the PLM tool process which is the effective communication to order parts directly without the need to switch to another system to order them, and these steps are outlined as follows (Fig. 1): As a result of this proposition there will be an efficiency in rapid exchange of references flows between suppliers and OEMs, and also in logistics. to approve that we. The hyper-connected world of products, human actors, and operating environments, enabled by IoT technologies [13], creates a potential for explosive growth in the generation of product—related data streams. It is not sufficient anymore to seek to integrate such information centrally. Instead, part of the information integration is best performed at the point of data consumption. For such integration to produce meaningful results, the complexity of relevant product information needs to be managed. Directly relevant to establishing efficient architectures, indexing, and big data management capabilities for IoT—generated data is the key concept of context, adopted in context aware computing [13].

Example C.Vila, An approach to Sustainable Product Lifecycle Management (Green PLM), Procedia Engineering 132 (2015) 585–592 [10]

YanXin, Dealing with Knowledge Management Practices in Different Product Lifecycle Phases within Product-service Systems, Volume 83, 2019, Pages 111–117 [11]

ALIHASSANSODHRO, Convergence of IoT and product lifecycle management in medical healthcare, 2018 [12]

Issues

Current PLM systems must enable collaboration, mandatory consultations, and capture inputs, contributions, and changes in all system interactions

A number of participants can contribute to PLM decisions—groups, deparments, and otherrelevant stakeholders within the organizational structures and hierarchies of different

Problems using PLM manifest themselves in many forms different

Table 1 Collaboration support issues and principles

See Principle 3 below To improve the integration of actors and minimize training expenses, and also to ensure the integration of suppliers who are a different organization by adding a training page in the PLM tool according to the user’s role

See Principle 2 below In the stages of an automotive part Development process, involve the relevant participants in these stages and give each participant his or her role and the opportunity to send messages and order emails directly to the suppliers of these parts. Assign a participant as a key decision-maker

See Principle 1 below Wherever possible,relevant communications should be captured at an appropriate level of detail; it can be used to enable better knowledge retrieval. Collaboration 4.0 concepts can provide a context for knowledge reuse throughout the product lifecycle, and time and money optimization

Principles

Using Internet of Things … 831

832

N. Tilioua et al.

Fig. 1 Proposition of collaboration support solution

4 Conclusion This work contributes to provide the collaboration in PLM. Time optimization and all actors’ integration in automotive company’s value chain are essential conditions to have an efficient product management. The paper highlights how supporting concepts in-group collaboration and integration facilities can help to meet the requirements of choice and reuse of PLM decisions and decision-making processes, thus contributing to organizational knowledge accumulation and organizational learning. The principles presented to support decision-making in a PLM context provide a foundation for effective PLM decision support with significant potential for integration with existing PLM systems and for exploiting emerging and future PLM ontologies, which may have broad implications for organizations’ PLM processes and protocols in thefuture.

References 1. Sharma (2005) Collaborative product innovation: integrating elements of CPI via PLM framework 37(13), 1425–1434 2. Marra M (2012) Supply chain knowledge management: a literature review 39(5): 6103–6110 3. Silventoinen A (2014) Challenges of information reuse in customer-oriented engineering networks 34(6):720–73

Using Internet of Things …

833

4. Corallo A (2013) Defining product lifecycle management: a journey across features. Def Concepts, Volume 2013 |Article ID 170812 5. Meier U (2017) Twenty Years of PLM–the good, the bad and the ugly. Product Lifecycle Manage Ind. Fut, pp 69–77 6. Tchana Y (2019) Designing a unique Digital Twin for linear infrastructures lifecycle management. Procedia CIRP84, pp 545–549 7. Papinniemi J, Hannola L, Maletz M (2014) Challenges in integrating requirements management with PLM. Int J Prod Res 52(15):4412–4423 8. Mabkhot M (2018) Requirements of the smart factory system: a survey and perspective 9. Cline G (2017) Industry 4.0 and industrial IoT in manufacturing: a sneak peek 10. Vila C (2015) An approach to sustainable product lifecycle management (Green PLM). Procedia Eng 132:585–592 11. Xin Y (2019) Dealing with knowledge management practices in different product lifecycle phases within product-service systems 83:111–117 12. Alihassansodhro. Convergence of IoT and product lifecycle management in medical healthcare (2018) 13. Koshechkin K. Implementation of digital technologies in pharmaceutical products lifecycle 14. Leal AG, Santiago A (2014) Integrated environment for testing IoT and RFID technologies applied on intelligent transportation system in Brazilian scenarios

ECC Image Encryption Using Matlab Simulink Blockset Sara Chillali

and Lahcen Oughdir

Abstract To implement hardware operations on various Xilinx FPGAs, we provide a method of software simulation, providing models (set of Simulink blocks) and presenting the concept of software simulation using Matleb Simulink for processing and encryption of images. This work presents an efficient architecture of various image algorithms, encryption, decryption and Keys generate using an elliptic curve, for all kinds of color and grayscale images with a minimum number of generator blocks possible. Example and illustration of these architecture implemented were presented. Keywords Matlab Simulink · Image processing · ECC · Encryption · Decryption

1 Introduction Cryptography is a science that ensures the security of sharing and storage of data by individuals in the presence of adversaries. This is done through techniques that ensure the confidentiality, authenticity and integrity of the data. As a science of secrecy, cryptology is divided into two; cryptography which aims to build and prove Cryptosystems and cryptanalysis whose goal is to "break" the systems built by cryptographers. For a long time and in all previous eras, secret codes were the basis of cryptography, until 1970, the development of an encryption and signature system was an essential objective of cryptography [3, 9, 10], therefore cryptanalysts wage their wars in the shadows by controlling communication networks and information transformation. S. Chillali (B) Sidi Mohamed Ben Abdellah University, FP, LSI, Taza, Morocco e-mail: [email protected] L. Oughdir Sidi Mohamed Ben Abdellah University, ENSAF, Fez, Morocco e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_76

835

836

S. Chillali and L. Oughdir

Fig. 1 Encryption and decryption protocol

The emergence of digital information sent through insecure channels like the Internet has facilitated communication between different parties, in fact, there are security holes in networks, which facilitates access to information. Mathematics with difficult inverse problems and a support of computer science and applied physics gave a power to cryptography which became a science attached to mathematics and computer science; it is a science of encrypting messages, other than cryptanalysis, which is used to decrypt them. [7, 8]. Our contribution in this work is the encrypted of different types of images of different sizes, using a mathematical object called elliptic curve, or we can give several definitions, studied in algebraic geometry, [4]. For more than forty years Diffie-Hellman, [1] gave the appearance of the key exchange protocol which was developed at the end of the 1980s: ECC, Elliptic Curve Cryptography. These cryptographic protocols use group structures whose security is ensured by the difficulty of the discrete logarithm problem on these groups, (DLP), which shows the success of these elliptic curves in public key cryptographic systems, hence the interest of the arithmetic study of these geometric curves. Image processing is certainly the most innovative that man has known, [5, 6] motivated by his needs in various fields, including imaging and the following, generates sensitive problems solved by the various image processing techniques. The digital image is represented by a matrix of points called pixels (Picture Element), each having as characteristic a gray level or a color coming from the corresponding location in the real image or calculated from an internal description of the scene to represent [2]. We can represent an image by a matrix M(mi,j ); mi,j ∈ {0, 1, 2, . . . , 255}.

ECC Image Encryption Using Matlab Simulink Blockset

837

In [11], the authors gave a new image encryption algorithm based on a secure variant of Hill Cipher (HC) and three enhanced one dimensional chaotic maps (1D). Our approach is to propose an encryption scheme based on a safer concept, namely discrete logarithm problem on elliptic curves. Thus, the simulation results carried out on a database of images in colors and in grayscale give very effective security results, this is the subject of an article already submitted. In the following, thanks to the discrete logarithm problem of this elliptic curve, a new method of image encryption will be constructed, using the Diffie-Hellman protocol on such a curve, the security of this type of encryption is proven because the resolution of this problem is difficult on these curves (Fig. 1).

2 Elliptic Curve on F P The definition of an elliptical curve is formal and uses mathematics, supported by algebra and geometry to define the group law on such a curve. In this part we will define an elliptic curve on the finite field FP , p ≥ 5 is a prime number [4]. In the following, we can define an elliptic curve on the finite field FP by the equation of the form: y 2 = x 3 + ax + b,

(1)

where (a, b) ∈ F2p and  = −16(4a3 + 27b2 ) is invertible in FP . Group law: We denote this elliptic curve by: Ea,b (p), we can write:   E a,b ( p) = (x, y) ∈ F2p /y 2 = x 3 + ax + b ∪ {[0 : 1 : 0]}

(2)

We define the law + on Ea,b (p) by: Let P(x1 , y1 ) and Q(x2 , y2 ) be two points on the curve, then P + Q = R(x3 , y3 ) such that: • R = [0 : 1 : 0], for x1 = x2 ,y2 = −y1 ; (P is the opposite of Q, even for Q) • R(x3 , y3 ); x3 = t 2 − x1 − x2 and y3 = −tx3 − s, with:  y −y  x y −x y 2 1 1 2 2 1 , i f P = Q , i f P = Q x2 −x1 x2 −x1 t = 3x12 +a and s = ax1 −x13 ,if P = Q ,if P = Q 2y1 2y1 A series of k consecutive additions of point P is called multiple point of order k is defined as follow: kP = P · · · + P  +P+ k times

(3)

838

S. Chillali and L. Oughdir

Fig. 2 Affine points over y 2 = x 3 − x on F89



Ea,b (p), + is an abelian group, [0:1:0] is the point at infinity it is the neutral element for the + law. Let Q also be an element of a cyclic subgroup G =< P >. An integer k that solves the equation kP = Q is called a discrete logarithm of Q in the base P. One writes k = logP Q. Let P and Q, find x satisfying xP = Q, is called discret logaritm problem (DLP) (Fig. 2).

3 Construction of a Secret Key Let p be a large prime number, Ea,b (p) is an elliptic curve on the field FP defined by equation (1), whose discrete logarithm problem is difficult in this curve.

3.1 Diffie-Hellman Key Exchange Aicha and Omar publish a point P which generates a subgroup of known order r of an elliptic curve Ea,b (p). • Aicha chooses a private number t ∈ {1, 2, . . . , r − 1} and calculates tP. • Omar chooses a private number s ∈ {1, 2, . . . , r − 1} and calculates sP. • Aicha sends tP to Omar.

ECC Image Encryption Using Matlab Simulink Blockset

839

• Omar sends sP to Aicha. At the end, Aicha and Omar build their shared secret key: K = tsP = stP.

3.2 Secret Encryption Matrix Each for him, the two entities build the secret encryption matrix T by the following two steps: • Step1: Build a matrix A = (ai,j )i=1,...,f;j=1,2,3 . For 1 ≤ i ≤ f , if i K = (xi , yi ), then ai,1 = i, ai,2 = xi and ai,3 = yi , i.e.: ⎛

1 ⎜2 ⎜ ⎜ ⎜: A=⎜ ⎜i ⎜ ⎝: f

x1 x2 : xi : xf

⎞ y1 y2 ⎟ ⎟ ⎟ : ⎟ ⎟ yi ⎟ ⎟ : ⎠ yf

• Step2: Build a Key matrix T that is the same size as the image. One of the algorithms at this work is to find the key matrix T from matrix A. Let [n, m, p] the size of the image to be encrypted, then T (Fig. 3). • Flowchart Algorithm:

Fig. 3 Flowchart algorithm

840

S. Chillali and L. Oughdir

4 Encryption Algorithm We represent our software simulation approach to show the multiple image encryption, see Figs. 4, 5, 6 and 7. Figure 8, we use the available Simulink generator block set. We explore the important aspects related to the hardware implementation with a MATLAB Simulink combination, we obtain an architecture that offers an alternative through a graphical user interface. Aicha wants to send a confidential image “image” to Omar, she will construct Embedded functions and follows the structure of the algorithm described by the following steps: • Setp1: Aicha transforms the image, ‘image’ to the matrix M(mi,j ); mi,j ∈ {0, 1, 2, . . . , 255}. • Step2: Aicha transforms M to matrix Mt and T to matrix Tm using two Embedded functions: This function is composed of an entry which represents the original image to be encrypted and three exits which represent the three make up of the matrix Mt. • This function is composed of an entry which represents the original image to be encrypted, another entry which represents the ECC key A and three exits which represent the three make up the matrix Tm. • The sizes of the dies are:size(Mt) = size (Tm) = [n ∗ m ∗ 8, 1, 3]. • Setp3: Aicha calculates the column vector, R = mod(Tm + Mt, 2). • Setp4: Aicha transforms R into a matrix Rt and then encrypt the image “encryptimage” by the following encryption system: Fig. 4 Flowchart Embedded function1

ECC Image Encryption Using Matlab Simulink Blockset

841

Fig. 5 Flowchart Embedded function2

Fig. 6 Flowchart encryption system

Encrypt System is composed of 7 inputs which represent the image, which we want to encrypt, the components of the matrix Mt, the components of the matrix Tm and two outputs which represent the image encrypt and its transform into matrix.

5 Decryption Algorithm Omar receive, "encryptimage" sent by Aicha, he has the private key which allows him to calculate the decryption function and to find the matrix M, which can transform it into the image, “image”, see Fig. 9. We can summarize the encryption and decryption method by a flowchart see Fig. 10.

842

S. Chillali and L. Oughdir

Fig. 7 Component of encryption system

Fig. 8 Block diagram approach for dual image encryption

6 Example and Illustration In this section, we assume that; p = 2543, a = 1758 and b = 254. E 1758,254 (2543) : y 2 = x 3 + 1758x + 254 So;   E 1758,254 (2543) = (x, y) ∈ F22543 /y 2 = x 3 + 1758x + 254 ∪ {[0 : 1 : 0]}

ECC Image Encryption Using Matlab Simulink Blockset

843

Fig. 9 Block Diagram Approach for dual image decryption

Fig. 10 Flowchart encrypt and decrypt system

Let K = (146, 2377) ∈ E1758,254 (2543), is Diffie-Hellman’s secret key between Aicha and Omar, these two entities count iK = (xi , yi ), i ∈ {1, 2, . . . , 256} and determine their key matrix:

844

S. Chillali and L. Oughdir

⎛

1 ⎜ 2 ⎜ ⎜ ⎜ : A=⎜ ⎜ i ⎜ ⎝ : 256

146 460 : xi : 867

⎞ 2377 144 ⎟ ⎟ ⎟ : ⎟ ⎟ yi ⎟ ⎟ : ⎠ 401

Figure 11 shows input images respective encrypted and decrypted images. The histograms of the encrypted image are uniform distributed in all gray levels, therefore they are random, one can observe in Fig. 12, the histograms of the original and encrypted images which are flat and are derived from the encrypted images. To assess the effectiveness of image encryption algorithms, a study should be made on the following points: • Differential Analysis, • Statistical Analysis, • Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR)

Fig. 11 Results obtained from the images

ECC Image Encryption Using Matlab Simulink Blockset

845

Fig. 12 Histograms of input and encrypted images

• Image Entropy. This study is a very important task. Such an evaluation of these encryption algorithms proposed in this work was carried in an article entitled: “Image encryption algorithm based on elliptic curves” and submitted for publication in a journal Soft Computing.

7 Conclusion Using Matlab Simulink System Generator, we performed an ECC image encryption approach, this study presents a software simulation for encryption and decryption, we noticed that the histograms of the encrypted image are uniformly distributed in all grayscale, the efficiency of this type of encryption was studied in another article submitted in a journal Soft Computing. Acknowledgments The authors thank the reviewers and everyone who contributed to this work.

References 1. Diffie W, Hellman M (1976) New directions in cryptography. IEEE Trans Inf Theory 2. Chillali S, Oughdir L (2019) A diagram of confidentiality of information during a traffic offence. AIP Conf Proc 2074:020028 3. Zeriouh M, Chillali A, Boua A (2019) Cryptography based on the matrices. Bol Soc Paran Mat 37(3):75–83

846

S. Chillali and L. Oughdir

4. Boulbot A, Chillali A, Mouhib A (2020) Elliptic curves over the ring R. Bol Soc Paran Mat 38(3):193–201 5. Hua ZY, Zhou YC, Pun CM, Chen CLP (2015) 2D sine logistic modulation map for image encryption. Inf Sci 297:80–94 6. Zhang Y (2018) The unified image encryption algorithm based on chaos and cubic. S-Box Inf Sci 450:361–377 7. Zhang L, Zhang F (2009) A new certificate less aggregate signature scheme. Comput Commun 32(6):1079–1085 8. Xiong H, Guan Z, Chen Z, Li F (2014) An efficient certificate less aggregate signature with constant pairing computation. Inform Sci 219:225–235 9. He D, Chen J, Zhang R (2012) An efficient and provably-secure certificate less signature scheme without bilinear pairings. Int J Commun Syst 25:1432–1442 10. Karati A, Islam SKH, Biswas GP (2018) A pairing-free and provably secure certificate less signature scheme. Inf Sci 450:378–391 (2018) 11. Essaid M, Akharraz I, Saaidi A, Mouhib A (2019) Image encryption scheme based on a new secure variant of Hill cipher and 1D chaotic maps. J Inf Secur Appl 47:173–187

Development of Large Chaotic S-boxes for Image Encryption Younes Qobbi, Abdeltif Jarjar, Mohamed Essaid, and Abdelhamid Benazzi

Abstract In this work, we suggest a novel algorithm for construction of large chaotic confusion and diffusion S-boxes for gray scale and color image scrambling. The modification of the value of a pixel by these S-boxes, depend not only on its gray level intensity but also on its position in the original image. In order to raise the impact of avalanche effect and to make our method robust against differential attack, we have developed a strong link between the ciphered pixel and the next original. The simulations carried out by our system on a large number of randomly selected images show the effectiveness of this crypto system against any known attack. Keywords S-box · Substitution · Diffusion · Image encryption · Image decryption

1 Introduction With the rapid development of information and communication technology, securing data transmission in particular digital images trough communication networks becomes a very important hot topic. Digital images are characterized by a large amount of information and also by a strong correlation between adjacent pixels. Image encryption based on the use of chaotic dynamic systems is an effective ways to ensure the security of this transmission [5]. Chaotic systems obtained by deterministic equations have crucial advantages in the generation of pseudorandom numbers; these Y. Qobbi (B) · A. Benazzi Mohamed First University, HSTO, AMSPCS Laboratory, Oujda, Morocco e-mail: [email protected] A. Benazzi e-mail: [email protected] A. Jarjar High School Moulay Rachid, Taza, Morocco M. Essaid Sidi Mohamed Ben Abdellah University, LSI, Taza, Morocco e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_77

847

848

Y. Qobbi et al.

systems are characterized by extreme sensitivity to initial conditions and to control parameters. These systems are widely used in image encryption systems [1]. Several chaos-based crypto systems are proposed. Fridrich [2] propose an image encryption system based on complex permutations created by using two-dimensional chaotic maps, and a simple diffusion mechanism. In ref [3], authors proposed an encryption method using the bit-level and on the use of a new hyper chaotic map. In ref [4] authors presented a new image encryption algorithm based on the improvement of several chaotic maps (logistics map, sine map and Chebyshev map) and also on the use of a new version of Hill cipher, which is deemed more secure. In ref [5], authors proposed an enhancement of the classical Hill algorithm, this improvement is due to the use of an invertible matrix (3 × 3) and of a vector of dynamic translation chaotic. Through to the important cryptographic characteristics of the substitution tables, S-boxes (Substitution boxes) have attracted the attention of designers of crypto systems. Several approaches for construction S-boxes based on chaos are proposed. In ref [6], authors have proposed a new method for construction an S-box with high performances. In ref [7], authors propose an image encryption system based on the use of dynamic S-boxes, so that S-boxes are generated according to the size of the original image. This algorithm is based on a method for scrambling the pixels of the plain image and another method for substitution of the pixels by using the previous generated S-box. In ref [8], authors are used a new chaotic system Sine-Tent noted (STS) for construction of double S-boxes. In ref [1], the author presents a new image crypto system using the same encryption and decryption mechanism. This crypto system named Unified image encryption system. This system is based on the use of the cubic S-box. In ref [9], author presents an encryption algorithm based on the use of multi-parameter chaotic systems for the construction an S-box. In ref [10], authors propose an algorithm of image encryption using permutation and substitution, in this algorithm a new chaotic system is used to generate an S-box of high performances. In ref [11], authors propose an efficient technique to construct S-boxes based on the use of a cubic polynomial map. In ref [12], authors propose an encryption scheme based on permutation-substitution. In the first phase the plain image is permuted row- and column-wise. In the second phase an encryption of pixels is established by using the dynamics S-boxes generated by a novel technique based on the logistic map. In this work, a novel technique is developed to create three S-boxes of dynamic size. Our approach based on Substitution-Diffusion. Pixel’s substitution established by using previously generated S-boxes and a new substitution formula. The three S-boxes changed their places to establish the Diffusion process. The rest of this article is organized as follows. In the next Sect, the technique of construction of three S-boxes is described in detail. In the Sect. 3, the encryption and decryption processes are presented. The performance analysis and simulation are described in Sect. 4. At the end, conclusion of the present contribution is drawn in Sect. 5.

Development of Large Chaotic S-boxes for Image Encryption

849

Fig. 1 Preparation of plain image

2 Proposed Method Our technique is based on the use of three large chaotic S-boxes generated from the chaotic maps to establish substitution and diffusion. The substitution of a pixel depends not only on its value but also on its position. This technique is based on the following steps: • • • • •

Preparation of Plain Image. Generation of Chaotic Sequences. Encryption Settings. Encryption Process. Decryption Process.

2.1 Preparation of Plain Image After loading of the plain image of size h × w: where h and w present the height and width of plain image respectively. This image is subdivided into three vectors VR, VG and VB of size 1 × t : where t = h × w. which are represents the red, green and blue channels respectively. This transformation is illustrated in the Fig. 1.

2.2 Chaotic Sequences Generation For the generation of encryption keys, our method is based on the use of chaotic dynamic systems. Indeed we use two chaotic maps. Logistic Map. Is a chaotic dynamic system most used for the generation of pseudorandom number sequences. It is given by the following expression: {xn+1 = µ1 xn (1 − xn ) : µ1 ∈ [3.57, 4] and x0 ∈ [0.5, 1[

(1)

850

Y. Qobbi et al.

Tent Map. It is the simple chaotic dynamic system used in image encryption. It is described by the recursion relation:  yn+1 =

µ2 yn i f yn < 0.5 : µ2 ∈ [0, 2] and y0 ∈ ]0, 1]. µ2 (1 − yn )Other wise

(2)

2.3 Encryption Settings Our approach is based on the use of three S-boxes (SR, SG and SB) of size t × 256. Where t = h × w. Which are used to encrypt the channels (Red, Green and Blue). Generating technique of three s-boxes is similar. In the next section, we present the method of generating the one of the three S-boxes dented SX. Where SX represents SR, SG or SB. Method of Generating S-box (SX). The proposed method to generate the SX is described by steps below: Step 1: the initial conditions and the control parameters are set for the logistic and tent maps. Step2: the logistic and tent maps are iterated for 3 × 256 times to generate two random sequences X and Y of size (3 × 256). Step 3: The two sequences X and Y are combined to generate three chaotic vectors P, Q and R of size (1, 256) by using the following algorithm: P = (int)((max(X(i),Y(i)) × 1012 ) i = 0 to 255 Q = (int)((max(X(i),Y(i)) × 1012 ) i = 256 to 2 × 256–1 R = (int)((max(X(i),Y(i)) × 1012 ) i = 512 to 3 × 256–1 Step 4: By sorting in ascending order the elements of the vectors P, Q and R, three permutation vectors U, V and W of size (1, 256) are obtained. Step 5: the elements of the vectors U, V and W, which contain the values in [[0, 255]] are used to initialize the three first rows of SX. The other rows are generated by the following algorithm: SX(i,j) = SX(i-2 , SX(i-1,j)) i = 3 to t-1 and j = 0 to 255 Steps 1 to 5 are repeated three times to generate SR, SG and SB.

Development of Large Chaotic S-boxes for Image Encryption

851

Initialization vector. In order to increase the performance of our algorithm, a diffusion phase is proposed. Indeed, an initialization vector Iv of size (1, 3) is generated from three vectors VR, VG and VB by using the following algorithm: Iv(0) = Iv(0) ⊕ VR(i) i = 1 to t-1 Iv(1) = Iv(1) ⊕ VG(i) i = 1 to t-1 Iv(2) = Iv(2) ⊕ VB(i) i = 1 to t-1

2.4 Encryption Process After decomposing the Plain image into three vectors VR, VG and VB and generation the encryption parameters, the proposed encryption process is applied according to the following steps: Step 1: Modification of the first pixel of each vector VR, VG, and VB by using the initialization vector (Iv) to start the diffusion process. Step 2: The three S-boxes (SR), (SG) and (SB) are used to encrypt the pixels of three color vectors VR, VG and VB. This encryption of a pixel depends not only on its value but also on its position in the original image, using the following algorithm: VR(i) = SR (i,VR(i)) where i = 0 to t-1 VG(i) = SG (i,VG(i)) where i = 0 to t-1 VB(i) = SB (i,VB(i)) where i = 0 to t-1 Step 3: In order to establish a diffusion process, the encrypted pixel is linked to the next original pixel, by using the strong expression as follows: VR(i + 1) = SB(i, VR(i)) ⊕ VR(i + 1) where i = 0 to t-2 VG(i + 1) = SR(i, VG(i)) ⊕ VG(i + 1) where i = 0 to t-2 VB(i + 1) = SG(i, VB(i)) ⊕ VB(i +1) where i =0 to t-2 Where ⊕ is the Xor operation. The diagram below (Fig. 2) represents the encryption mechanism of the suggested scheme.

2.5 Decryption Process Our approach is based on diffusion process, which requires starting the decryption process from the last pixel to the first. This process goes through the following steps: Step 1: split the cipher image of size (h, w) into three vectors VR, VG and VB of size (1, t) where t = h × w.

852

Y. Qobbi et al.

Fig. 2 Encryption process

Step 2: Construction the inverse S-boxes (RS, G S and B S) by using the following algorithm:

Step 3: The three large invers S-boxes (RS, G S and B S) are used to find the original pixel by using the following algorithm:

Development of Large Chaotic S-boxes for Image Encryption

853

3 Experimental Results To prove the effectiveness of our scheme against any known attack. We present several simulation results obtained by application of our crypto system on Lena (256 × 256), Peppers (512 × 512), and Baboon (512 × 512), The results are given in the following diagram (Fig. 3).

4 Security Analysis The main objective of any encryption system is to secure the information from any known attack. Some security analysis has been applied on the proposed algorithm which includes correlation, histogram, key space, sensitivity and entropy analysis. The result analysis of our system is described as follows.

4.1 Correlation Analysis The adjacent pixels in data image are generally highly correlated. So an efficient scheme is the one that allows removing this correlation to resist against statistical attacks. The correlation between adjacent pixels is given by the following equation: Corr x y =

E((x − E(x))(y − E(y))) √ D(x) × D(y)

(3)

854

Y. Qobbi et al.

Fig. 3 Plain, Ciphered and decrypted images

Table 1 Correlation analysis

Horizontal Vertical Plain Lena (256 × 256) Cipher Lena (256 × 256) Plain Peppers (512 × 512)

0.8397

Cipher Peppers (512 × 512)

0.0123

0.9576

0.8964

−0.0059 −0.0063 0.7195

0.6985

−0.0052 −0.0023

1 N 1 N xi and E(y) = yi 1 1 N N

(4)

1 N 1 N (xi − E(x))2 and D(y) = (yi − E(y))2 1 1 N N

(5)

E(x) = D(x) =

0.9354 −0.0107

Diagonal

Where E is the expected value operator and D is the variance of variable. The results prove that the values of these coefficients are very close to zero. So our algorithm is robust against statistical attacks. These results are shown in Table 1.

Development of Large Chaotic S-boxes for Image Encryption

855

Fig. 4 Histogram analysis

4.2 Histogram Analysis An image histogram is a graphical representation of the distribution of pixels according to their grayscale values in that image. The histograms of all the images encrypted by our crypto system are uniform as shown in the figure (Fig. 4). Which prove that our approach is robust against attacks by histogram.

4.3 Entropy Analysis Entropy information indicates the level of randomness in a source of information. It is given by the following expression: H(m) = −

255 i=1

Pr(mi ) × logPr(mi )

(6)

Where Pr(mi ) is the probability of random variable m of ith index.The ideal entropy for 256 symbols representing the different gray levels in an 8-bit image is H(m) = 8.

856

Y. Qobbi et al.

Table 2 Entropy analysis

Cipher Image

Proposed algorithm

Ref [1]

Ref [12]

Lena (256 × 256)

7.9990

–

7.9046

Baboon (512 × 512)

7.9998

7.999285

–

Peppers (512 × 512)

7.9998

7.999314

–

The entropy of the different ciphered images obtained by our scheme is near to 8. That prove our scheme can resist against the attacks by entropy. The information entropy of our algorithm and some references are listed in the Table 2 below.

4.4 Brute Force Attack Key Space A good encryption system should have a key space large enough to remain strong against brute force attacks. Indeed the key space must be greater than 2100 [13].The key space of our method is composed of two initial conditions x0 and y0 and two control parameters µ1 and µ2 . These parameters are the float (32 bit). Then the key space of our system is 2128 . This makes our system safe to any brute force attack.

4.5 Differential Attacks A strong encryption system is one that is sensitive to a small change in the original image such as a single pixel change in the original image produces entirely different ciphered image. This sensitivity is evaluated through a Number of Pixels Change Rate (NPCR) and Unified Average Changing Intensity (UACI). Their definitions are presented as follows. w  h

1 Di j × 100% w∗h     1 i j I C1i j − I C2i j U AC I = × 100% w×h 255

N PC R =

1

(7)

(8)

IC1 = [IC1i j ] and IC2 = [IC2i j ] express two ciphered images corresponding to two plain images with a tiny difference, and Di j is calculated by  Di j =

1 i f I C1i j = I C2i j 0 else

(9)

Development of Large Chaotic S-boxes for Image Encryption

857

Table 3 NPCR and UACI analysis Image

Proposed algorithm

Ref [1]

NPCR

UACI

NPCR

UACI

NPCR

Ref [12] UACI

Baboon (512 × 512)

100

33.6019

99.6100

33.4643

---------

--------

Peppers (512 × 512)

100

33.5819

99.6089

33.4689

--------

-------

Lena (256 × 256)

100

33.5862

----------

---------

98.9182

32.7865

The following Table 3 below shows the measurement of NPCR and UACI between two cipher images of the Babon (512 × 512) Lena (256 × 256) and Peppers (512 × 512). When making a slight modification in the original image.

5 Conclusion In this article, a novel technique is developed to create three chaotic S-boxes. Which are used to perform two main processes of any image encryption system: Substitution and diffusion. The main contributions of our approach are: First, the modification of a value of a pixel depends on two parameters which are: its value and its position in the plain image. Second, our algorithm uses three large chaotic S-boxes of dynamic size. The simulation results and security analysis presented by a correlation coefficient close to zero, a flat histogram of the ciphered image and an entropy value near to 8, prove that our algorithm is strong against any known attack.

References 1. Zhang Y (2018) The unified image encryption algorithm based on chaos and cubic S-Box. Inf Sci 450:361–377 2. Fridrich J (1998) Symmetric ciphers based on two-dimensional chaotic maps. Int J Bifurcat Chaos 8(06):1259–1284 3. Cao C, Sun K, Liu W (2018) A novel bit-level image encryption algorithm based on 2D-LICM hyperchaotic map. Signal Process 143:122–133 4. Essaid M, Akharraz I, Saaidi A (2019) Image encryption scheme based on a new secure variant of Hill cipher and 1D chaotic maps. J Inf Secur Appl 47:173–187 5. Hraoui S, Gmira F, Abbou MF, Oulidi AJ, Jarjar A (2019) A new cryptosystem of color image using a dynamic-chaos hill cipher algorithm. Procedia Comput Sci 148:399–408 6. Belazi A, Abd El-Latif AA (2017) A simple yet efficient S-box method based on chaotic sine map. Optik 130:1438–1444 7. Wang X, Wang Q (2014) A novel image encryption algorithm based on dynamic S-boxes constructed by chaos. Nonlinear Dyn 75(3):567–576 8. Zhu S, Wang G, Zhu C (2019) A secure and fast image encryption scheme based on double chaotic s-boxes. Entropy 21(8):790 9. Khan M (2015) A novel image encryption scheme based on multiple chaotic S-boxes. Nonlinear Dyn 82(1–2):527–533

858

Y. Qobbi et al.

10. Lu Q, Zhu C, Deng X (2020) An efficient image encryption scheme based on the LSS chaotic map and single S-box. IEEE Access 8:25664–25678 11. Zahid AH, Arshad MJ (2019) An innovative design of substitution-boxes using cubic polynomial mapping. Symmetry 11(3):437 12. Rehman AU, Khan JS, Ahmad J, Hwang SO (2016) A new image encryption scheme based on dynamic s-boxes and chaotic maps. 3D Res 7(1), 7. 13. Wu X, Zhu B, Hu Y, Ran Y (2017) A novel color image encryption scheme using rectangular transform-enhanced chaotic tent maps. IEEE Access 5:6429–6436

Evaluation of Feature Extraction Methods Combined with Support Vector Machines for Powerline Component Recognition in Aerial Images Jamila Garfaf, Lamyae Fahmani, and Hicham Medromi

Abstract As part of its efforts to maintain the stability and sustainability of power generation for its consumers, the Moroccan National Office of Electricity and Drinking Water (ONEE) conducts regular inspections of its transmission and distribution networks. Compared to other inspection methods, UAV inspection enables regular inspection with minimized effort and lower cost and provides more detailed images of network components safely and securely. However, human analysis of the amount of data produced by the UAV may not be efficient. Therefore, the image analysis process’s automation should be implemented by developing advanced algorithms to detect components and their defects. In this work, we focused on object recognition, which we consider a key step in the development of more complex tasks. We evaluated HOG, LBP, and SIFT extractors’ recognition performance in the identification of power line components, by combining each of these extractors with the SVM classifier. The results showed that combining the HOG and SVM can give the best results compared to the LBP-SVM and SIFT-SVM methods with an accuracy rate of 97.98% and a shorter acquisition time for image processing. These results indicate that this combination can meet electric companies’ technical requirements related to accuracy and speed. Keywords Object recognition · SVM · Feature extraction · HOG · LBP · SIFT · Unmanned Aerial Vehicles (UAV) · Powerline equipment

1 Introduction In order to maintain the reliability, stability, and sustainability of electricity production for its consumers, the National Office of Electricity and Drinking Water (ONEE), J. Garfaf (B) · L. Fahmani · H. Medromi Research Foundation for Development and Innovation in Science and Engineering (FRDISI), 16 469 Casablanca, Morocco Engineering Research Laboratory (LRI), System Architecture Team (EAS), National and High School of Electricity and Mechanic (ENSEM) Hassan II University, 8118 Casablanca, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_78

859

860

J. Garfaf et al.

the pillar of the energy strategy and the armed arm of the state in the water and sanitation sector in Morocco [1], conducts regular diagnoses and visual inspection of their transmission and distribution networks. These inspection missions within the ONEE were until recently carried out by patrols on the ground and by helicopters. However, these traditional methods have several drawbacks. In addition to the fact that these methods are expensive and too slow, they jeopardize the safety of individuals given that most components are located in hard-to-reach places and that these inspection operations are often carried out under more difficult meteorological conditions [2, 3]. Accordingly, the ONEE devotes significant investment and effort to carry out these types of operations. To ensure inspection and maintenance operations with the lowest possible cost and effort as well as ensure the safety of agents and operators, the ONEE has resorted to the implementation of UAV technology at its transmission and production units. Compared to other inspection methods based on foot patrols and helicopter-assisted inspections, operating costs are lower, and the UAVs can fly safely as close as possible to the power line element, allowing more detailed photos of the components to be taken [3–6]. However, given the enormous amount of data produced by the UAV, manual human analysis can be ineffective. This explains the use of artificial intelligence as an engineering solution to replace humans and optimize results, as is the case for many other engineering problems [7]. Indeed, it is necessary to develop efficient and advanced algorithms to automate the process of identifying the presence of anomalies or damage on inspected power lines by exploiting the aerial vector of photos and videos taken and produced remotely by the UAV. Numerous studies are being conducted on the monitoring and inspection of power transmission systems. In this work, we are focusing on the detection and recognition of power network components as an essential part of the development of complex algorithms. In [8], a supervised learning approach has been proposed to detect and classify pylons. This approach uses Histogram of Oriented Gradients (HOGs) to train two MLP (Multilayer Perceptron) neural networks. The first classification separates the foreground from the background, and the second multilayer MLP distinguishes the four different types of electrical pylons described in the paper. The same feature extractor has been used in [9] for the detection of molten insulators by the local binary model (LBP). The extracted features were transferred to the SVM (Support Vector Machine) classifier. An evaluation of the proposed method was performed on 500 images, which gave a detection rate of 89.1%. Using the infrared images introduced by the UAV, ZHOU et al. [10] designed a system for fault diagnosis and remote isolator detection. Each isolator is located using the Scale-invariant Feature Transform (SIFT) method. The obtained results showed that the choice of SIFT allows the identification and localization of isolators with a high accuracy. This boost is thanks to the invariance of scale, and to the invariance to variations in rotation angle and image brightness. Because of its performance, the features of SIFT are also used by [11]. They are mainly used to detect the tilt angle of the electric pylon during video surveillance in the substation. Among the most prominent traditional object extractors, are: Scale-Invariant Features Transform (SIFT) [12], Histogram of Oriented Gradients (HOG) [13], Local

Evaluation of Feature Extraction Methods …

861

Binary Patterns (LBP) [14] which have been used in many works, we can cite among others [15–17]. In order to evaluate their recognition performance in the identification of power line components, each of these extractors will be combined with the famous SVM classifier which has proven in many works its performance in computer vision applications. The experiments were conducted on a set of images of power line components captured from videos taken by the UAV. We organized this paper as follows: the second section describes in detail the methodology adopted and the algorithms studied and used. The results are presented and discussed in the third section. The last section concludes the paper while highlighting future research directions.

2 Methodology In the computer vision domain, object recognition is a crucial and challenging task. It consists of determining the predefined category to which an object in a given image belongs [18]. The recognition process consists of two main steps: the feature extraction step, followed by the classification step [19]. Indeed, before the received image is fed into the recognition process, a pre-processing phase is necessary to improve the quality of the input images so that the relevant features can be better extracted from the image. This preprocessing phase consists firstly of converting the images produced by the UAV, which are generally in RGB colors, into grayscale [20, 21] and secondly of resizing them to a smaller fixed size of 256 * 256 so that no important information is missing. This allows to prevent the overloading and saturation of the network and to reduce the computing time. Then, a feature extractor is placed whose objective is to represent the recovered image as a descriptive vector based on the extracted features. This vector is afterwards passed to the classifier to recognize the class to which the image belongs (see Fig 1). For an image recognition system to be effective, it must have a well-defined and highly targeted feature extraction step. To achieve this, the features must be as distinctive and informative as possible in order to obtain reliable identification. In fact, if the features are not well selected, this could significantly impair the performance and efficiency of the recognition system. Among the most popular traditional object extractors are SIFT, HOG, LBP, which have been used in many works. These extractors will be the main subject of this paper. An evaluation of their recognition performance will be performed in our paper by

Fig. 1 Block diagram of the recognition process

862

J. Garfaf et al.

combining them with the famous SVM classifier, which is becoming more and more popular [22, 23] due to its performance in the classification of noisy and highdimensional data [24]. The experiments are conducted on a data set constructed from aerial images of power line components. A detailed description of the HOG, LBP and SIFT extractors and the SVM classifier is given in the following sections.

2.1 Histogram of Oriented Gradients Extractor The gradient histogram was proposed initially by Dalal and Triggs [13] for human detection. The gradient-oriented histogram is used to extract features using information about the orientation of the gradient from the appearance and shape of the local object. The calculation of HOG descriptors is based on four key operations: calculation of the amplitude and orientation of the image gradient, generation of cell histograms, histogram normalization and descriptor generation. First, a detection window of 64 * 128 pixels is divided by cells of 8 * 8 pixels, forming 8 * 16 = 128 cells. For each pixel (x, y), the gradient magnitude m(x, y) and the orientation θ(x, y) can be calculated using Eqs. (1) and (2), respectively: m(x, y) =



G x (x, y)2 + G y (x, y)2 

(x, y) = arctan

G y (x, y) G x (x, y)

(1)

 (2)

where G x (x, y)et G y (x, y) represent the gradient of the image along the two directions x and y successively as given below: G x (x, y) = I (x + 1, y) − I (x − 1, y)

(3)

G y (x, y) = I (x, y + 1) − I (x, y − 1)

(4)

With I (x, y) represent the intensity of the image in the pixel (x,y). Then, a histogram is generated for each cell containing nine boxes. The amplitude of each cell in the histogram is calculated from the accumulation of the amplitudes of the pixels with directions belonging to the interval of the cell. A 9 × 1 vector is then obtained for each cell and presented as a histogram. The blocks are normalized to give a 36 * 1 vector in order to have a better invariance to changes in illumination and contrast. The process is repeated by passing through all the pixels of the image. In the end, all 36 × 1 normalized vector is grouped into a single vector corresponding to the HOG descriptor.

Evaluation of Feature Extraction Methods …

863

Fig. 2 LBP Operator calculation example

2.2 Local Binary Pattern Extractor Local Binary Pattern (LBP), originally proposed by Ojala in 1996 [14], allows characterizing the spatial structure of the image texture using a 3 * 3 neighborhood around a central pixel. Based on a 3 * 3 neighborhood, the LBP compares the gray values of the 8 neighboring pixels to the central pixel, which is taken into account as a threshold. Let gc , g0 ,…, g7 denote respectively the grey levels of the central pixel and its eight neighboring pixels. The LBP code of the central pixel characterized by its coordinates (xc , yc ) is calculated as follows: L B P(xc , yc ) =

7 

S(gc − gi ) × 2 p

(5)

p=0

With S(z) is the sign function defined as follows:  S(z) =

1x ≥0 0x 0

(9)

3 Experiments and Results Following a detailed study and presentation of feature extraction algorithms in the classical approach, namely SIFT, HOG and LBP and the famous SVM classifier, we compare in this section and evaluate their performance and recognition capabilities by combining each of the three extractors with the SVM classifier. The three models that have been designed, namely the HOG-SVM model, the LBP-SVM model and the SIFT-SVM model have been trained and tested using aerial images of power line components. Initially, we built the database from videos and images produced by the UAV during inspection missions carried out on two transmission lines at ONEE stations. We have also used the data set designed by [27] and [28], which is available for research purposes. By using data augmentation methods such as flipping (horizontally and vertically) and cropping, we collected 5850 aerial images, divided into three categories: towers, insulators and conductors. These images were grouped into two sets: a training set and a test set. Details of the data set are presented in Tables 1 and 2. Some samples of these images are shown in Fig 3. In order to evaluate and compare the recognition and classification capabilities of the three models studied in our paper, we used the following statistical parameters: Table 1 Details of the electrical components DataSet Type

Tower

Insulator

Conductor

Total

Numbers of images

1899

1845

2106

5850

Table 2 Details of the training and the testing DataSet

DataSet

Training DataSet

Testing DataSet

Training DataSet

3919

1931

866

J. Garfaf et al.

Fig. 3 Samples of electrical power line equipment aerial images

Fig. 4 Accuracy for the three models

120

97.98

80 45.78

32.37

40

Accuracy(%)

0 HOG-SVM

SIFT-SVM

LBP-SVM

precision, recall, F1-score, and accuracy which are defined as follows [29]: Pr ecision = Recall = F1 − scor e = Accuracy =

TP T P + FP

TP T P + FN

(10) (11)

2T P 2T P + T N + F P + F N

(12)

TP +TN T P + T N + FP + FN

(13)

Where TP, FP, TN and FN are the numbers of true positives, false positives, true negatives, and false negatives in the detection results, respectively. As shown in Fig 4, it can be seen that the HOG-SVM model achieves the best performance in power line recognition with 97.98% accuracy, compared to SIFTSVM and LBP-SVM, which achieved an accuracy of 45.78% and 32.37%, respectively. In addition, Table 3 shows the other evaluation parameters related to the

Evaluation of Feature Extraction Methods … Table 3 Performance results of the three models

867

Models

Precision

Recall

F1-score

HOG-SVM

97.99%

97.96%

97.97%

SIFT-SVM

46.26%

44.34%

35.32%

LBP-SVM

10.79%

33.33%

16.30%

Table 4 Image computation time Computation time

HOG-SVM

SIFT-SVM

LBP-SVM

0.349 s

2.885 s

0.392 s

accuracy, recall and f1 scores. It shows that the combination of HOG and SVM presents a good compromise between accuracy and recall. Recall reaches 97.96%, indicating that most components are correctly detected. Besides, given that the solution will be implemented in real-time applications [30], the image processing time for each model must also be calculated and compared. As shown in Table 4, the image processing time for the HOG-SVM model is 0.349 s, which is less than the other two models. Both the SIFT-SVM model and the LBPSVM are very time-consuming, resulting from the large amount of data and the high resolution of the collected images. Experimental results, based on the comparative study and evaluation of models designed using different evaluation measures, show that the method in which HOG is the extractor combined with SVM gives the best results in terms of accuracy and computing time. These results indicate that this combination allows utilities to meet their technical requirements for accuracy and speed when performing off-line analysis for transmission line inspection.

4 Conclusion In this paper, we have focused on component recognition, which we see as an essential step towards the overall goal of developing an intelligent system for the identification of power line component dysfunctions. We have evaluated HOG, LBP, and SIFT extractors’ recognition performance in the identification of power line components, combining each of these extractors with the famous SVM classifier. We aim to study and evaluate the deep approach with convolutional neural network architectures on larger power line equipment datasets in our future work. This is in order to study in more detail the efficient model that could be implemented in future algorithms for detection and identification of malfunctions and anomalies and to collect and design large power line equipment datasets that we want to make available for research purposes. Acknowledgement This work was supported by the National Center for Scientific and Technical Research (CNRST, Morocco) and the National Office for Electricity and Drinking Water (ONEE,

868

J. Garfaf et al.

Morocco). We want to express our gratitude to the ONEE teams for their collaboration and support for successfully conducting our research project.

References 1. The official Website of the ONEE - Electricity sector (2020). http://www.one.org.ma/. Accessed 26 Oct 2020 2. Nguyen VN, Jenssen R, Roverso D (2018) Automatic autonomous vision-based power line inspection: a review of current status and the potential role of deep learning. Int J Electric Power Energy Syst 99: 107–120 3. Fahmani L, Garfaf J, Boukhdir K, Benhadou S, Medromi H (2020) Modelling of very high voltage transmission lines inspection’s quadrotor. SN Appl Sci 2: 1425 4. Liu X, Miao X, Jiang H, Chen J (2020) Review of data analysis in vision inspection of power lines with an in-depth discussion of deep learning technology. arXiv:2003.09802[cs,eess] 5. Jalil B, Leone G, Martinelli M, Moroni D, Pascali M, Berton A (2019) Fault detection in power equipment via an unmanned aerial system using multi modal data. Sensors 19: 3014 6. Fahmani L, Garfaf J, Boukhdir K, Benhadou S, Medromi H (2020) Unmanned aerial vehicles inspection for overhead high voltage transmission lines. In: 2020 1st International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET). pp 1–7. IEEE, Meknes, Morocco 7. Chergui M, Chakir A, Medromi H (2019) Smart IT governance, risk and compliance semantic model: business driven architecture. In: 2019 Third World Conference on Smart Trends in Systems Security and Sustainablity (WorldS4), pp 297–301. IEEE, London, United Kingdom 8. Sampedro C, Martinez C, Chauhan A, Campoy P (2014) A supervised approach to electric tower detection and classification for power line inspection. In: 2014 International Joint Conference on Neural Networks (IJCNN), pp 1970–1977. IEEE, Beijing, China 9. Tiantian Y, Guodong Y, Junzhi Y (2017) Feature fusion based insulator detection for aerial inspection. In: 2017 36th Chinese Control Conference (CCC), pp 10972–10977. IEEE, Dalian, China 10. Shen-pei Z, Xi L, Bing-chen Q, Hui H (2017) Research on insulator fault diagnosis and remote monitoring system based on infrared images. Procedia Comput Sci 109: 1194–1199 11. Yu P, Dong BG, Xue YJ (2012) Electric power tower inclination angle detection method based on SIFT feature matching. AMM. 236–237: 759–764 12. Lowe DG (1999) Object recognition from local scale-invariant features. In: Proceedings of the Seventh IEEE International Conference on Computer Vision, pp 1150–1157, vol 2. IEEE, Kerkyra, Greece. https://doi.org/10.1109/ICCV.1999.790410 13. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), pp 886–893. IEEE, San Diego, CA, USA 14. Ojala T, Pietikäinen M, Harwood D (1996) A comparative study of texture measures with classification based on featured distributions. Pattern Recogn 29: 51–59 15. Jones MJ, Viola P (2010) Robust Real-time Object Detection, 30 16. Ta¸skiran M, Çam ZG (2017) Offline signature identification via HOG features and artificial neural networks. In: 2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI), pp 000083–000086 17. Zhang H, Qu Z, Yuan L, Li G (2017) A face recognition method based on LBP feature for CNN. In: 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), pp 544–547 18. Liu L, Ouyang W, Wang X, Fieguth P, Chen J, Liu X, Pietikäinen M (2019) Deep Learning for Generic Object Detection: A Survey. arXiv:1809.02165[cs]

Evaluation of Feature Extraction Methods …

869

19. Yao G, Lei T, Zhong J (2019) A review of Convolutional-Neural-Network-based action recognition. Pattern Recogn Lett 118: 14–22 20. RGB to grayscale — skimage v0.19.0.dev0 docs. https://scikit-image.org/docs/dev/auto_exam ples/color_exposure/plot_rgb_to_gray.html 21. Poynton C (2015) Frequently asked questions about color. 24 22. Malathi V, Marimuthu NS (2010) Wavelet transform and support vector machine approach for fault location in power transmission line. Int J Electric Electron Eng 6: 512–513 23. Ray P, Mishra DP (2016) Support vector machine based fault classification and location of a long transmission line. Eng Sci Technol Int J 19: 1368–1380 24. Niu X-X, Suen CY (2012) A novel hybrid CNN–SVM classifier for recognizing handwritten digits. Pattern Recogn 45: 1318–1325 25. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60: 91–110 26. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20: 273–297 27. Lee SJ, Yun JP, Choi H, Kwon W, Koo G, Kim SW (2017) Weakly supervised learning with convolutional neural networks for power line localization. In: 2017 IEEE Symposium Series on Computational Intelligence (SSCI), pp 1–8. IEEE, Honolulu, HI 28. Tao X, Zhang D, Wang Z, Liu X, Zhang H, Xu D (2020) Detection of power line insulator defects using aerial images analyzed with convolutional neural networks. IEEE Trans Syst Man Cybern, Syst 50: 1486–1498 29. Powers DM (2011) Evaluation: from Precision, Recall and F-measure to ROC, Informedness, Markedness and Correlation 30. Chergui M, Chakir A, Medromi H, Radoui M (2017) A new approach for modeling strategic IT governance workflow. In: El-Azouzi R, Menasche DS, Sabir E, De Pellegrini F, Benjillali M (eds.) Advances in Ubiquitous Networking, vol 2, pp 285–298. Springer Singapore, Singapore

A Novel Brain Tumor Detection Approach Based on Fuzzy C-means and Marker Watershed Algorithm Hanae Moussaoui, Mohamed Benslimane, and Nabil El Akkad

Abstract Segmentation has a very essential role in medical imaging. Image segmentation can be defined as the process that gives the ability to separate a random image into parts or objects that make up the image, which means separating objects from the background, so we can later analyze each of these components separately. In this work we’ll propose a method based on the combination between fuzzy c-means clustering and marker watershed algorithm. The first thing to do is to apply the fuzzy c-means clustering which is an unsupervised clustering method that is usually used in medical imaging. Then the first output image will be going through the second algorithm which is the marker watershed that uses concepts from edge detection and mathematical morphology. The proposed method was applied and tested on several medical images to detect anomalies such as tumors, and according to the results, we can say that it gives a satisfactory and good results of detection. To evaluate the performance of our proposed method, we used a set of metrics such as the “Dice coefficient”, “Sensitivity” and “Specificity”; which all have shown satisfactory results. Keywords Image segmentation · Medical imaging · Fuzzy c-means · Watershed algorithm · Thresholding

1 Introduction When we talk about Medical imaging we’ll be definitely looking for a technique or a process of making the interior of the body visually represented. Medical imaging is used for clinical analysis and medical intervention, such as revealing any structure hidden by bones or skin. It’s also very helpful when doctors try to diagnose and treat H. Moussaoui (B) · N. El Akkad LISA, Engineering, Systems and Applications Laboratory, ENSA of Fez, Fez, Morocco e-mail: [email protected] M. Benslimane LTI Laboratories, EST, Sidi Mohamed Ben Abdellah University, Fez, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_79

871

872

H. Moussaoui et al.

a disease. Image processing is used in several areas of computer vision, in particular, image segmentation [1–4], 3D reconstruction [5, 6], camera Self-calibration [7–13] and cryptography [14–16]. Image segmentation has many approaches to segment an image such as thresholding, clustering based methods, edge detection and region growing methods. In this paper we’re going to present a method based on watershed algorithm which takes into account the gradient of the desired image. The second algorithm is the fuzzy c-means clustering method which is an unsupervised segmentation technique which allows pixels to be in more than one cluster. In this paper we will talk in details about the proposed method. Starting by explaining the fuzzy c-means algorithm, followed by marker controlled watershed and thresholding; after that we’ll present our proposed method and results, in the end the paper will end up with a conclusion.

2 Related Works In the past 10 years many brain tumor detection methods have been proposed, we can start by fuzzy c-means algorithm which plays a very important role in medical image segmentation, nevertheless it has many drawbacks. By using fuzzy c-means algorithm we need first to set the number of clusters which is considered as a disadvantage, and also it’s known with it sensitiveness about noise and other artifacts. M. Sreerangappa, M. Suresh and D. Jayadevappa [17] have proposed a modified fuzzy c-means algorithm known as a spatial FCM clustering to avoid all the drawbacks listed above. For this methodology they started by pre-processing the MRI image by applying the Median Filter followed by Wavelets Transform and Spatial FCM. Nilesh Bhaskarrao Bahadure, Arun Kumar Ray and Har Pal Thethi [18] have proposed a combination between Berkeley Wavelet Transformation (BWT) and SVM that helps to classify MRI images to healthy or infected tumor. P. Kumar and B. Vijayakumar [19] have proposed a brain tumor segmentation and classification methodology, based on Principal Component Analysis (PCA) and Radial Basis Function (RBF).

3 Fuzzy C-means Algorithm Fuzzy c-means is an extension of k-means clustering is considered as a soft computing technique, which means that each pixel in the image can belong to multiple clusters. Fuzzy c-means is widely used in medical imaging [20–22], especially for brain tumor detection. Fuzzy c-means clustering works in five steps: Step (1): Initialize randomly the membership matrix using the following equation: c  j=1

uj (xi ) = 1 i = 1, 2, . . . , k

(1)

A Novel Brain Tumor Detection Approach Based on Fuzzy C-means …

873

Step (2): Calculate the centroid using the equation: m   i u j (x i ) x i Cj =   m i u j (x i )

(2)

Step (3): Calculate dissimilarity between the data points and centroid using the Euclidean distance:  Di = (x2 − x1 )2 (y2 − y1 )2 (3) Step (4): Upgrade the new membership matrix. For this we use the equation bellow:  u j (xi ) =

1 d ji

1/ m−1



c k=1

1 dki

(4)

1/ m−1

Here m is a fuzzification parameter. The range m is always [1.25, 2]. Step (5): Repeat the process from step 2, unless the centroids keep the same value (Fig. 1).

Fig. 1 Fuzzy c-means diagram

Start

Initialize randomly the membership matrix

Calculate the centroid Calculate”Euclidean distance” ‘”Update the new membership matrix”

Yes

Centroids changement No Output

874

H. Moussaoui et al.

4 Marker Controlled Watershed Algorithm Marker watershed is a robust morphological gradient-based segmentation technique that is used especially in the case when we have to segment objects that are so close and touching one another. Marker controlled Watershed algorithm is highly recommended for medical image segmentation [23–25]. The watershed algorithm works by finding catchment basins and also watershed ridge lines in the input image by treating it as a topographic surface, where the light pixels are high and the low pixels are low. Watershed algorithm works in five steps: Step (1): Transform the input image to Grayscale. Step (2): Apply the Gradient Magnitude. Step (3): Mark the main objects of the foreground. Step (4): Calculate the background markers. Step (5): Determine the watershed transform. The main disadvantage of watershed algorithm is that it generally results over segmentation. For this, we usually de-noise the image before applying watershed algorithm [26].

5 Thresholding Image thresholding is a simple form of image segmentation; it’s a subset of image segmentation [20]. Threshold segmentation is a way to create a binary image based on the histogram or the threshold value on the pixel intensity of the original image [28]. Image binarization is the process of separating pixels’ values into two groups, black for background and white for objects or foreground. Image thresholding can be grouped in local and global thresholding (Fig. 2). In this paper will be using Otsu’s thresholding for our image segmentation. Otsu’s thresholding is based on finding that one threshold that minimizes the weighted within class variance as well as maximizing the between class variance. Frequency Formula: ω=

T 

P(i)

when

P(i) = n i /N

(5)

i=0

N is the total pixel number. n i is the number of pixels in level i. Mean Formula: μ=

T  i  =0

i P(i)/ω

(6)

A Novel Brain Tumor Detection Approach Based on Fuzzy C-means …

875

Threshold segmentation

”Bernsen” ”Chow and Kanelo” ”Eikvil” ”Mardia and Hainsworth” ”Niblack” ”Yanowitz and Bruckstein” ”Tr Singh” ”Sauvola”

Local thresholding

Traditional (Otsu)

Global thresholding

Multistage (Quadratic ratio technique)

Iterative (iterative triclass thresholding)

Fig. 2 Thresholding diagram

Total Variance Formula: σt2

T  = (i − μ)2 P(i)

(7)

i=0

6 The Proposed Method In this section we’ll present our proposed method which is made of two stages. First of all we’ll start by applying fuzzy c-means clustering on our input image which is supposed to be a MRI image, and then the first output image will be going through the second algorithm which is the marker controlled watershed. The reason behind choosing fuzzy c-means clustering wasn’t arbitrary; but, because of it robust characteristics for ambiguity. This segmentation method is highly recommended when we’re dealing with medical imaging, also, it’s a soft clustering method that assigns a membership of every pixel to each cluster. The second stage is to apply the watershed algorithm to the segmented image. The proposed method gives good detection results. As we can see that the final result shows clearly the tumor area well segmented and surrounded by a marker which defines the borders. The flow chart of the proposed method is as follow:

876

H. Moussaoui et al.

Input image First Stage

Applying fuzzy c-means segmentation

The first segmented image

Second Stage

De-noise the image with Median

Otsu’s thresholding

Watershed algorithm Generate Markers Final segmentation

Fig. 3 Flowchart of the proposed method

7 Experimental Results and Discussion In this experiment part we’ll be using brain web images that have a tumor. As we can see in the flow chart in the top (Fig. 3), the proposed method is divided into two stages. In the first stage we’ll apply fuzzy c-means algorithm to the input image, here, we tried many values of k and the conclusion we can draw is that by choosing a small value of k (k = 3) we will have good results at the end. After applying fuzzy c-means we de-noise the first output image using Median filter. In the second stage of the proposed algorithm, we’ll apply both thresholding and marker watershed algorithm to identify and segment the tumor using markers in the brain image. As we can see bellow in (Fig. 4), the proposed algorithm gives a good segmentation and identification of tumors in different locations. To evaluate the performance of the proposed method, we used a set of metrics such as “Dice coefficient”, “Sensitivity” and “Specificity”. Dice Similarity: 2 ∗ GT ∗ CS /|GT| + |CS|

(8)

CS: represents the tumor segmentation and GT is the ground truth. Sensitivity: is an evaluating metric that indicates the true positivity, which means that sensitivity gives us the probability that a segmented pixel belongs to the brain tumor. Sensitivity = TP/(TP + TN)

(9)

A Novel Brain Tumor Detection Approach Based on Fuzzy C-means …

Original image

Fuzzy c-means

watershed

After using Otsu threshold

877

Final segmentation

Fig. 4 A table showing the obtained results using the proposed method

Dice

Sensitivity

Specificity

The proposed method FCM

0.7906

0.9813

0.8664

0.7501

0.9630

0.3317

watershed

0.7626

0.9979

0.6623

Fig. 5 A table showing the obtained results for the first image

Specificity: an evaluating metric that indicates the true negativity, which means that sensitivity gives us the probability that a segmented pixel doesn’t belong to the brain tumor area, but belongs to the background. Specificity = TN/(FP + TN)

(10)

TP: indicates the true positivity, which refers to the number of pixels that belong to the tumor area. TN: indicates the true negativity, which refers to the number of pixels that don’t belong to the tumor area. FP: The false positive, which indicates the number of pixels that were wrongly detected as brain tumor pixels (Figs. 5 and 6).

878 Fig. 6 The graphic representation for the first table

H. Moussaoui et al. 1.2 1 0.8 0.6

Dice Coefficient

0.4

Sensitivity

0.2

Specificity

0 The proposed method

Fuzzy cmeans

Watershed

8 Conclusion In this paper we’ve been talking about our proposed method which is based on fuzzy c-means clustering and marker controlled watershed algorithm. The results obtained show satisfactory abnormalities detection. We’ve used several metrics to evaluate the performance for the proposed method like “Dice coefficient”, “Sensitivity” and “Specificity”.

References 1. Khrissi L, Elakkad N, Satori H, Satori K (2019) Image segmentation based on kmeans and genetic algorithms. In: Proceedings of ESAI 2019, embedded systems and artificial intelligence, pp 489–497 (2019) 2. Zheng X, Lei Q, Yao R, Gong Y, Yin Q (2018) Image segmentation based on adaptive K means algorithm. EURASIP J Image Video Process 2018:1–10. https://doi.org/10.1186/s13640-0180309-3 Article number: 68 3. Zaitoun NM, Aqel MJ (2015) Survey on image segmentation techniques. In: International conference on communication, management and information technology (ICCMIT 2015). ScienceDirect 4. Ghosh S, Das N, Das I, Maulik U (2019) Understanding deep learning techniques for image segmentation. ACM Comput Surv 52(4):1–35. https://doi.org/10.1145/3329784 5. El Akkad N, El Hazzat S, Saaidi A, Satori K (2016) Reconstruction of 3D scenes by camera self-calibration and using genetic algorithms. 3D Res 6(7):1–17 6. Merras M, Saaidi A, El Akkad N, Satori K (2016) Multi-view 3D reconstruction and modeling of the unknown 3D scenes using genetic algorithms. Soft Comput 22(19):6271–6289 7. El Akkad N, Merras M, Baataoui A, Saaidi A, Satori K (2017) Camera Self-calibration having the varying parameters and based on homography of the plane at infinity. Multimed Tools Appl 77(11):14055–14075 8. El Akkad N, Merras M, Saaidi A, Satori K (2014) Camera self-calibration with varying intrinsic parameters by an unknown three-dimensional scene. Vis Comput 30(5):519–530 9. El Akkad N, Merras M, Baataoui A, Saaidi A, Satori K (2018) Camera self-calibration having the varying parameters and based on homography of the plane at infinity. Multimed Tools Appl 77(11):14055–14075 10. Akkad NE, Merras M, Saaidi A, Satori K (2013) Robust method for self-calibration of cameras having the varying intrinsic parameters. J Theor Appl Inf Technol 50(1):57–67 11. Akkad NE, Merras M, Saaidi A, Satori K (2013) Camera self-calibration with varying parameters from two views. WSEAS Trans Inf Sci Appl 10(11):356–367

A Novel Brain Tumor Detection Approach Based on Fuzzy C-means …

879

12. El Akkad N, Saaidi A, Satori K (2012) Self-calibration based on a circle of the cameras having the varying intrinsic parameters. In: Proceedings of 2012 international conference on multimedia computing and systems, ICMCS. pp 161–166 13. Merras M, El Akkad N, Saaidi A, Nazih AG, Satori K (2014) Camera calibration with varying parameters based on improved genetic algorithm. WSEAS Trans Comput 13:129–137 14. Es-sabry M, El Akkad N, Merras M, Saaidi A, Satori K (2019) A novel color image encryption approach based on random numbers generation of two matrices and bit-shift operators. Soft Comput 24(5):3829–3848 15. Es-Sabry M, El Akkad N, Merras M, Saaidi A, Satori K (2018) A novel text encryption algorithm based on the two-square cipher and caesar cipher. Commun Comput Inf Sci 872:78– 88 16. Es-Sabry M, El Akkad N, Merras M, Saaidi A (2018) Grayscale image encryption using shift bits operations. In: 2018 international conference on intelligent systems and computer vision, ISCV, pp 1–7 17. Sreerangappa M, Suresh M, Jayadevappa D (2019) Segmentation of brain tumor and performance evaluation using spatial FCM and level set evolution. Open Biomed Eng J 13(1):M6. https://doi.org/10.2174/1874120701913010134 18. Bahadure NB, Ray AK, Thethi HP (2017) Image analysis for MRI based brain tumor detection and feature extraction using biologically inspired BWT and SVM. Int J Biomed Imaging 2017:12. Article ID 9749108. https://doi.org/10.1155/2017/9749108 19. Kumar P, Vijayakumar B (2015) Brain tumour Mr image segmentation and classification using by PCA and RBF kernel based support vector machine. Middle-East J Sci Res 23(9):2106–2116 20. Maolood I, Al-Salhi Y, Lu S (2018) Thresholding for medical image segmentation for cancer using fuzzy entropy with level set algorithm. Open Med J 13(1):374–383. https://doi.org/10. 1515/med-2018-0056 21. Jobin Christ MC, Parvathi RMS (2011) Fuzzy c-means algorithm for medical image segmentation. https://doi.org/10.1109/ICECTECH.2011.5941851 22. Zhou H, Schaefer G, Shi C (2009) Fuzzy c-means techniques for medical image segmentation. In: Jin Y, Wang L (eds) Fuzzy systems in bioinformatics and computational biology. Studies in fuzziness and soft computing, vol 242. Springer, Heidelberg. https://doi.org/10.1007/978-3540-89968-6_13 23. Grau V, Mewes AUJ, Alcaniz M, Kikinis R, Warfield SK (2004) Improved watershed transform for medical image segmentation using prior information. IEEE Trans Med Imaging 23(4):447– 458. https://doi.org/10.1109/TMI.2004.824224 24. Zhanpeng H, Qi Z, Shizhong J, Guohua C (2016) Medical image segmentation based on the watersheds and regions merging. In: 2016 3rd international conference on information science and control engineering (ICISCE), Beijing, pp 1011–1014. https://doi.org/10.1109/ICISCE. 2016.218 25. Lu Y, Jiang Z, Zhou T, Fu S (2019) An improved watershed segmentation algorithm of medical tumor image. In: IOP conference series: materials science and engineering, vol 677, no 4 26. Ravi S, Khan AM (2014) Bio-medical image segmentation using marker controlled watershed algorithm: a case study. Int J Res Eng Technol 3:26–30. eISSN: 2319-1163, pISSN: 2321-7308 27. Sandhya G, Kande GB, Savithri S (2017) A novel approach for the detection of tumor in MR images of the brain and its classification via independent component analysis and kernel support vector machine. Imaging Med 9(3):33–44 ISSN: 1755-5191 28. Senthilkumaran N, Vaithegi S (2016) Image segmentation by using Thresholding techniques for medical images. Comput Sci Eng: Int J (CSEIJ) 6(1):1–13

Image Retrieval Based on MPEG-7 Feature Selection Using Meta-heuristic Algorithms Naoufal Machhour

and M’barek Nasri

Abstract The continuous growth of the internet and its diversified domains and tools; and the rapid development of new technologies have enormously increased the amount of digital images and consequently the dimension of the image data sets. Therefore, finding the relevant images to a query image in such database is a challenging task. Content-based image retrieval is the powerful method so far which use the visual characteristics of the image. The choice of these visual descriptors is a decisive phase in this area. This work presents a method which extracts the three image features which are: color, texture and shape based on the MPEG-7 standards. Then, the genetic algorithm is implemented for feature selection to provide the best extracted features to avoid unnecessary ones and reduce calculations and retrieval time. Meanwhile, the k-nearest neighbors algorithm is used to search for the relevant images. Finally, we will apply our method on two different image data-sets which contain images belonging to different domains to show the efficiency of selecting the correct features using meta-heuristic algorithms. Keywords Content-based image retrieval · Feature selection · Genetic algorithm · K-NN algorithm · MPEG-7 standards

1 Introduction The image bases are becoming very wide and diversified. In order to well exploit these large databases and extract the relevant images, content-based image retrieval (CBIR) is the powerful method so far. This method which used the visual descriptors of the image is able to find the closest images to a query image (QI) in wide image data-set. All CBIR systems follow two steps as represented in Fig. 1. In the offline phase, the CBIR system extract the visual features of the database images, then store MATSI Laboratory, ESTO, University of Mohammed Premier, Oujda, Morocco N. Machhour (B) · M. Nasri Laboratory MATSI, ESTO, University of Mohammed Premier, Oujda, Morocco e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_80

881

882

N. Machhour and M. Nasri

Fig. 1 CBIR system diagram

them in a new features database. In the next online phase, the CBIR system calculates the similarity measures between the extracted features of all data-set images and the QI ones to determine the similar images to the inputted QI. Therefore, color descriptor which is used widely in CBIR systems provide good results. Yue et al. [1] demonstrated that color features are introduced in pretty nearly all CBIR systems due to their advantages, such as color histogram [2] and color moments such as the mean, variance and third moments. Also, texture is an essential descriptor in CBIR systems. The gray level co-occurrence matrix (GLCM) which give more details about the arrangements of colors [3] can provide some important informations such as: energy, entropy, contrast and homogeneity. Moreover, shape features are used to describe particular regions in image. Thus, many techniques are developed such as: edge detection, filters, Fourier transform or Hu moment. There are many works developing CBIR systems which use a single image descriptor such as color, as developed in the work of Sharma et al. [4] which used the conventional color histogram (CCH) and Lei et al. [5] which applied a quantization method on the HSV color space into 36 non-uniform bins, etc. These studies give encouraging results but ignore other descriptors which can enhance the retrieval precision. On another hand, several works merged a lot of features simultaneously, these methods can increase the search time and provide inefficient results by using unnecessary descriptors. The work in hand used an efficient method based on the feature selection using meta-heuristic algorithms applied to MPEG-7 standards which increasingly become a general framework for content description. The aim of this study is to enhance the precision rate and decrease the query time by maintaining intelligently the relevant features from the MPEG-7 standards to avoid the unnecessary descriptors which can influence the accuracy of the retrieved relevant images and increase the response time by performing unnecessary calculations. For this, the proposed CBIR system extracts the image features based on the MPEG-7 standards. Then applied the genetic algorithms (GAs) for feature selection to retain the relevant descriptors. Finally, the k-nearest neighbors algorithm (k-NN) is applied for image retrieval.

Image Retrieval Based on MPEG-7 Feature Selection …

883

2 Related Works Multiple and various methods have been implemented in this domain. These studies combined different visual features and used several search techniques. Alsmadi [6] proposed a CBIR system using a technique based on the neutrosophic clustering algorithm. They extract color descriptor based on the YCbCr using Canny edge histogram (CEH) and discrete wavelet transform (DWT). The CEH is utilized to describe shape characteristics and the GLCM to extract texture features. Machhour et al. [7], established a method using the color string coding (CSC) technique combined to genetic algorithm in the first work. Then, in an extended work, the authors in [8] improved the color string coding technique by grouping each color series separately and developing a segmentation process based on the HSV color space. The GLCM is used for extracting the texture characteristics. Meanwhile, they performed a comparison between the simulated annealing (SA) and GAs in term of precision and response time for image retrieval. In Ashraf et al. [9], the representation and image retrieval are based on the artificial neural network (ANN) and the bandelet transform. ElAlami [10] extracted the texture and color signature using the Gabor filter and the 3-D color histogram. Then, they used the GAs to provide the optimal boundaries of the numerical domains ranges with discrete values represented by nominal features. Finally, they extracted the relevant features using 2 consecutive operations: the preliminary and deeply reduction. In the aim of maximizing the precision and simplifying the computation of the image search, the study in Lin et al. [11] is based on the feature selection using the sequential forward selection technique which is developed on 3 characteristics: color co-occurrence matrix, pixels variance of scan pattern and color histogram for k-mean.

3 Materials and Methods The efficiency of a CBIR system is linked to several factors such as the features extracted, retrieval method, response time, etc. As we know, many of these features that are extracted from the image are irrelevant, which influence the quality of the retrieved images and increase the calculation and response time. In the proposed CBIR system as shown in Fig. 2, we implement a technique which is based on the MPEG-7 standards to extract 6 descriptors representing the color, texture and shape features of the image. Therefore, our technique is based on two phases. In the first one which is done offline, we extract the image features which are: the dominant color descriptor (DCD), color structure descriptor (CSD) and color layout descriptor (CLD) for color features; texture edge histogram descriptor (EHD) and 2D-discrete wavelet transform (2D-DWT) for texture features; and the curvature scale space (CSS) for contour-shape descriptor. Then, we implement the genetic algorithms for feature selection to avoid the unnecessary descriptors and retain the relevant ones.

884

N. Machhour and M. Nasri

Fig. 2 Proposed CBIR system diagram

In the second phase which is done online, we extract the query image features, and apply the k-nearest neighbors algorithm (k-NN) to find the relevant images.

3.1 Features Extraction Color Features Extraction Dominant Color Descriptor (DCD). This descriptor allows to limit the number of representative values of color characteristics by regrouping them into a small number of colors as depicted by Manjunath et al. [12]: F = {(ci , pi , vi ), s}, (wher e i = 1, . . . , N )

(1)

N , represents the number of dominant color ci ; pi , vi and s are the percentages, color variances and the optional spatial coherency of ci respectively. Manjunath et al. [12] proved that eight dominant colors are sufficient to describe an image. The value of the percentage pi is between 0 and 1. The pi value is the fraction of image elements which correspond to color ci where: N i=1

pi = 1

(2)

The color variance vi depicts the variation of the values of image elements assigned to the class of the dominant color ci ; while s which represents the spatial coherency shows the homogeneity of this dominant color ci . Color Structure Descriptor (CSD). This descriptor is used to represent an image by the color distribution [13, 14]. According to Messing et al. [14], this feature can be defined as a generalization of the color histogram which can be represented by the color structure histogram described by the formula below: h(m), (wher e m = 1, . . . , M)

(3)

Image Retrieval Based on MPEG-7 Feature Selection …

885

Where, M is the quantized color cm (M ∈ {256, 128, 64, 32}), and h(m) is the bin value representing the number of the structuring elements having pixels with color cm . Hence, the CSD is based on the information of the spatial structure of the colors and their frequency occurrence. Messing et al. [14] proved that the performance of this descriptor can be enhanced based on the non-uniformly quantization histogram amplitudes using the nonlinear HMMD color space. Color Layout Descriptor (CLD). The discrete cosine transform (DCT) is developed to extract this descriptor to describe the spatial distribution of colors. For this, Manjunath et al. [12] specified four steps which are: image partitioning technique, representative color detection method, discrete cosine transform, and then, the nonlinear quantization of the zigzag-scanned coefficients. Texture Features Extraction Edge Histogram Descriptor (EHD). This descriptor describes a range of 5 types of edges in each part of image which is divided into n × n blocks. The 5 types of these edges developed by Won et al. [15] and shown in Fig. 3 are: vertical (a), horizontal (b), 45-degree diagonal (c), 135-degree diagonal (d), and non-directional edges (e). Therefore, every local histogram contains 5 bins which correspond to one of 5 edge types. Won et al. [15] demonstrated that dividing the image into 16 blocks provide a total of 80 histogram bins. Therefore, to extract these features, the image is converted to gray level, then dividing this image into sub-blocks. Finally, these descriptors are provided using the directional and non-directional edge features. 2D-Discrete Wavelet Transform (2D-DWT). This descriptor is very efficient in many domains including image processing. This technique can decompose an image in various frequency sub-bands. Also, 2D-DWT depicts the frequency and spatial range and can be used efficiently for non-stationary signals. The technique is based on dividing the image into rows and columns by applying the average intensity of the image (which is illustrated by low pass) and high temporal resolution describing the edges of image (high pass). Figure 4 illustrate an example of two-level decomposition of the DWT. This method utilizes filters such as Haar, Daubechies, etc. In this study, we used the low-pass (L) and the high-pass (H ) filters based on the Haar Wavelet Descriptor as shown below: L = [1, 1]

Fig. 3 Directional and non-directional types of edges

(4)

886

N. Machhour and M. Nasri

Fig. 4 Two-level decomposition of DWT

H = [−1, 1]

(5)

Shape Features Extraction. In this study, the shape features extraction is based on the MPEG-7 standards. Thus, we utilize the curvature scale space (CSS) for contourshape descriptor. This latter is characterized by its robustness against noise, scale and change in translation and orientation. Firstly, we select N -samples equidistant point on the contour, next we choose randomly a point and follow the contour clockwise. Then, we create two series (x) and (y) to group and store separately for each selected point two coordinates (x and y coordinates) respectively. Consequently, we get an arc-length parameterization depicted by the formula below where (u) is an arbitrary parameter: r (u) = (x(u), y(y))

(6)

Finally, this curve parametric representation is undergoing the convolution process with the Gaussian function to compute the CSS representation. In order to extract the curvature zero-crossing points of these curves, Mokhtarian et al. [16] demonstrated that the standard deviation of the Gaussian varies from small to large value. Below, the formula for calculating the curvature function as developed by Mokhtarian et al. [16]: k(u) =

x(u) ˙ y¨ (u) − x(u) ˙ y˙ (x)  3 x˙ 2 (u) + y˙ 2 (u) /2

(7)

3.2 Feature Selection This important machine learning technique which is feature selection is a powerful and efficient tool for selecting a subset of relevant characteristics and features. The choice of using this method is based on several reasons: elimination of the unnecessary features, simplification and reducing the complexity of the retrieval method, decreasing the search time by diminishing the calculations, avoiding the problem of dimensionality and improving the accuracy of relevant images by choosing the right subset of features. In this area, various techniques and methods were implemented

Image Retrieval Based on MPEG-7 Feature Selection …

887

and classified in 3 categories of algorithms: wrappers, filters and embedded methods. In the work in hand, we implement the wrappers technique which is based on the search method using the genetic algorithms. Genetic Algorithms (GAs). This popular and efficient meta-heuristic algorithm which is based on a random search for the optimal solution in a large population was introduced by Holland in 1962 [17]. Therefore, the GA is based on the evolutionary processes using natural operations which are: evaluation, selection, crossover, mutation and the best survival individual [17]. The steps followed by the GAs are: – Generate randomly the initial population – Repeat until termination criteria: • • • •

Evaluate population Generate the new population Apply the genetic operations (crossover and mutation) Get the population offspring.

– Get the best chromosome. Population and Chromosome Representation. The population which is generated randomly is represented by the database features which contains vectors where each one contains 6 MPEG-7 features of each image int the data-set. This population is composed of a set of chromosomes, each one consists of a number of genes containing the image features. The GA begins the optimization process by calculating the fitness value for each chromosome to select the best ones to generate the next population. Therefore, the fitness function used to evaluate each individual in the population is the standard deviation (σ ) as illustrated below:  σ =

1 N (xi − μ)2 i=1 N

(8)

Where, μ represents the chromosome mean, xi the gene value and N the size of the chromosome. Natural Operations of the GAs. The roulette wheel technique is used in the selection phase to generate randomly an array (Ri) containing values between 0 and 1. Then, we calculate the selection probability (Pi) for each chromosome to provide the cumulative probabilities (Qi). The comparison between (Ri) and (Qi) select the chromosomes to yield the new generation. In the next phase, the population is exposed to the crossover operation. The GA chooses 2 chromosomes to exchange their genes between them from a crossing point chosen randomly. The probability of the crossover operation (Pc) is 0.75, that means that on average 75% of the population will undergo this operation. The mutation operation is an essential process, because it makes random changes in the population. Thus, all individuals in the population have the same chance to be chosen and the mutation probability is 0.01. Then, the gene in a position chosen

888

N. Machhour and M. Nasri

randomly of the offspring chromosome is replaced by another gene chosen randomly from all the population. The application of these genetic operations provides a better fitness value which is higher than the previous one of the old generations. Therefore, we can observe that the new best chromosome has a better evaluation than the best one in the old population. Thus, we store for each population generated the fittest chromosome. This latter contains the relevant features for image retrieval.

3.3 Proposed Image Retrieval Algorithm In CBIR systems, many retrieval methods have been developed such as traditional techniques or new approaches using artificial intelligence techniques or metaheuristic algorithms. Machine learning methods have shown their effectiveness in this type of problems. In this study, we develop the k-nearest neighbors algorithm for image retrieval. This non-parametric and supervised machine learning algorithm was introduced in 1967 by Thomas Cover et al. [18] and developed for regression and classification problems. The k-NN algorithm is among the simplest methods in machine learning; and it is used massively in several domains because of its ease of implementation and use and low execution and calculation time. The steps followed by the k-NN algorithm are: – Load the database of selected features. – Initialize the value of k. – For each image in the data-set. • Compute the distance value of the current image and the QI. • Store this distance in the distance data-set. – Sort the distance data-set. – Select the first k images from the distance data-set.

4 Results and Discussion 4.1 Image Data-Set and Evaluation In this work, we apply our method on two different image bases which are: Corel and Caltech image data-sets. These large image bases contain hundreds of images belonging to different domains; and allow us to evaluate and test the performance of the feature selection method and compare the results obtained in this study with other methods. For this, we calculate two measures: precision (P) and recall (R).

Image Retrieval Based on MPEG-7 Feature Selection …

889

Table 1 The average recall and precision values using Corel image data-set Classes Africa Food Horses Elephant Flower Beach Building Nature Bus Dinosaur (P)

0.76

0.76

0.84

0.84

0.88

0.76

0.76

0.76

0.88 0.96

(R)

0.19

0.19

0.21

0.21

0.22

0.19

0.19

0.19

0.22 0.24

Table 2 The average recall and precision sample using Caltech image data-set Classes Minaret Tick Flamingo Bike Okapi Chandelier Ball Rhino Rooster Panda (P)

0.92

0.84 0.80

0.80 0.76

0.72

0.96 0.72

0.80

0.80

(R)

0.30

0.42 0.29

0.32 0.48

0.16

0.37 0.30

0.40

0.52

Table 3 The recall and precision rates compared to other works Methods

Yue et al. [1]

Lin et al. [11]

ElAlami [10]

Machhour et al. [19]

Ashraf et al. [9]

Proposed method

Precision

0.650

0.722

0.735

0.78

0.82

0.82

Recall

0.129

0.144

0.146

0.187

0.164

0.205

N umber o f Per tinent I mages Found T otal I mages Retrieved

(9)

N umber o f Per tinent I mages Found N umber o f Per tinent I mages in Data − Set

(10)

P= R=

The average precision and recall computed in this paper are: 0.82 and 0.205 for Corel image data-set and 0.812 and 0.356 for Caltech image data-set respectively. Table 1 and Table 2 represent the precision and recall rates computed for Corel and Caltech data-sets respectively. Furthermore, Table 3 depicts a comparison of the results found in this study and other CBIR systems using the Corel image data-set. As evidenced by the results, the proposed method provides good recall and precision rates which shows the efficiency of feature selection technique based on GAs. Furthermore, applying several times the GAs for feature selection, we notice that the EHD descriptor is efficient in images including distinct objects and CSD descriptor provide pertinent results in images containing principal colors. However, combining the 3 best selected features in this study which are: CSD for color features, EHD for texture descriptor and CSS for contour-shape descriptor, provide relevant results for almost any type of images as shown in results above. Moreover, the use of the feature selection reduces the number of descriptors which decreases enormously the calculations, computations of image retrieval and execution time. Furthermore, the k-NN algorithm is a relevant choice as image retrieval method because it provides pertinent and satisfactory results in low execution and response time.

890

N. Machhour and M. Nasri

5 Conclusion In the work in hand, we have developed an efficient CBIR system based on feature selection technique. Once the QI is entered, the proposed system extracts 6 MPEG7 features for color, texture and shape signature. Meanwhile, these descriptors are subject to natural operations of the genetic algorithm to retain the relevant features and remove the unnecessary ones. Finally, the k-NN algorithm is applied for image retrieval. The experiments are done using the Corel and Caltech image data-sets. As illustrated in the previous section, the results obtained have demonstrated that the GAs are powerful and intelligent method for selecting the correct and relevant image features. Furthermore, removing the needless image characteristics has improved enormously the retrieval time by avoiding additional calculations and increased the accuracy of retrieved images. Also, the results found by the k-NN algorithm show the efficiency of this machine learning technique. As depicted by the retrieval results, especially the average recall and precision rates which are 0.205 and 0.82 respectively, combining the MPEG-7 standards for image features extraction, GAs for feature selection and the k-NN method for image search provide satisfactory results which demonstrates and proves the efficiency of the proposed method which also exceeds other proposed CBIR systems. In the prospective works, other techniques and optimization methods will be utilized for feature selection; and combined to other image descriptors while using different retrieval methods to increase the accuracy of relevant images in content-based image retrieval system.

References 1. Yue J, Li Z, Liu L, Fu Z (2011) Content-based image retrieval using color and texture fused features. Math Comput Model 54(3–4):1121–1127 2. Swain MJ, Ballard DH (1991) Color indexing. Int J Comput Vis 7(1):11–32 3. Shapiro LG, Stockman GC (2001) Computer vision. Prentice-Hall, Upper Saddle River 4. Sharma NS, Rawat PS, Singh JS (2011) Efficient CBIR using color histogram processing. Signal Image Process 2(1):94 5. Lei Z, Fuzong L, Bo Z (1999) A CBIR method based on color-spatial feature. In: Proceedings of IEEE. IEEE Region 10 conference. TENCON 99. Multimedia technology for Asia-Pacific information infrastructure (Cat No 99CH37030), vol 1. IEEE, pp 166–169 6. Alsmadi MK (2020) Content-based image retrieval using color, shape and texture descriptors and features. Arab J Sci Eng 45:3317–3330 7. Machhour N, M’Barek N (2020) Content based image retrieval based on color string coding and genetic algorithm. In: 2020 1st international conference on innovative research in applied science, engineering and technology (IRASET). IEEE, pp 1–5 8. Machhour N, Nasri M (2020) A novel content-based image retrieval based on a new approach of color string coding and meta-heuristic algorithms. Int J Adv Trends Comput Sci Eng 9(1.5):128–137 9. Ashraf R, Bashir K, Irtaza A, Mahmood MT (2015) Content based image retrieval using embedded neural networks with bandletized regions. Entropy. 17(6):3552–3580 10. ElAlami ME (2011) A novel image retrieval model based on the most relevant features. KnowlBased Syst 24(1):23–32

Image Retrieval Based on MPEG-7 Feature Selection …

891

11. Lin CH, Chen RT, Chan YK (2009) A smart content-based image retrieval system based on color and texture feature. Image Vis Comput 27(6):658–665 12. Manjunath BS, Salembier P, Sikora T (2002) Introduction to MPEG-7: multimedia content description interface. Wiley 13. Manjunath BS, Ohm J-R, Vasudevan VV, Yamada A (2001) Color and texture descriptors. IEEE Trans circuits Syst Video Technol 11(6):703–715 14. Messing DS, Van Beek P, Errico JH (2001) The mpeg-7 colour structure descriptor: Image description using colour and local spatial information. In: Proceedings 2001 international conference on image processing (Cat No 01CH37205), vol 1. IEEE, pp 670–673 15. Won CS, Park DK, Park S (2002) Efficient use of MPEG-7 edge histogram descriptor. ETRI J 24(1):23–30 16. Mokhtarian F, Abbasi S, Kittler J (1996) Robust and efficient shape indexing through curvature scale space. In: In British machine vision conference. Citeseer 17. Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor 18. Cover T, Hart P (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13(1):21– 27 19. Machhour N, Nasri M (2020) New color and texture features coding method combined to the simulated annealing algorithm for content based image retrieval. In: 2020 fourth international conference on intelligent computing in data sciences (ICDS). IEEE, pp 1–8

A Powerful and Efficient Method of Image Segmentation Based on Random Forest Algorithm Zahra Faska, Lahbib Khrissi, Khalid Haddouch, and Nabil EL Akkad

Abstract Segmentation is the heart of an automatic image analysis system. There are several segmentation techniques. The contour segmentation approach aims to separate regions of different gray levels and relatively homogeneous. The region segmentation approach consists in grouping the adjacent pixels of the image into distinct regions. The cooperative approach is used in order to improve the result of segmentation and the segmentation based on classification methods; in this case, classes are defined by the maximum sets of related pixels belonging to the same class. In this work by studying the dual problem, we develop a simple but efficient cooperative approach between a Random forest classification method and a set of contour detection methods as Canny, Prewitt and Sobel. Firstly, original image is initially segmented by hybridization of Canny, Prewitt, and Sobel’s algorithms for edge detection. Then, we will use the output image obtained by another supervised classification segmentation process, which is random forest. To compare between the results obtained by the different methods, we used a several evaluation metrics such us: entropy, MI, IoU and DSC. The advantage and robustness of the proposed method is demonstrated by in-depth experimentation on a set of images. Keywords Segmentation · Random forest · Edge detection · Canny · Prewitt · Sobel · Cooperation

1 Introduction In an image processing system, the most important step is image segmentation. A good segmentation leads to good image analysis because it is from the segmented Z. Faska (B) · K. Haddouch · N. EL Akkad Laboratory of Engineering, Systems and Applications, ENSA, Sidi Mohamed Ben Abdellah University, Fez, Morocco e-mail: [email protected] L. Khrissi LIIAN, Department of Computer Science, Faculty of Science, Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fez, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_81

893

894

Z. Faska et al.

image that the measurements are carried out for the extraction of the discriminating parameters with a view to the classification or interpretation. Therefore, image segmentation aims to find homogeneous regions and contours. A region is a set of adjacent pixels, which are grouped into distinct and homogenous regions according to pre-defined criteria (texture, color, etc.).We can summarize the segmentation task as follows given an image; the objective of segmentation is to establish a compact description that is representative of its information content, which is more useful than all of its points. This involves extracting relevant visual indices (primitives), sufficiently correlated with the entities that make up the scene from which the image is taken. Moreover, image segmentation is a vast subject of study. In the last years, several works dealing with this subject have been published in different fields as: camera self-calibration [1–5], 3D reconstruction [6, 7] and Cryptography [8, 9],… etc. We can distinguish the following four different segmentation techniques that exist in the literature (Fig. 1): In the contour segmentation approach, we consider that the primitives to be extracted are the lines of contrasts separating regions of different and relatively homogeneous gray levels, or else regions of different texture. In practice, it is a question of recognizing the transition zones and of locating the border between the regions as well as possible. In the contour-based segmentation approach, there are three categories of methods are derivative, analytical, and deformable methods [10, 11]. In the region approach a grouping, the process is repeated until all pixels in the image are included in regions. This approach, therefore, aims to segment the image based on the intrinsic properties of the regions. We can distinguish the following methods: region growing, merge, the division into regions, and split/merge [12, 13]. The segmentation by region-contour cooperation arouses great interest. It combines the two segmentation techniques (region-based, contour-based), takes the

Segmentation techniques

Contour segmentation

Region segmentation

Cooperation region-contour

Classification

• Derivative methods

• Region growing

• Sequential cooperation.

• SVM

• Deformable methods

• Merge region

• Mutual cooperation.

• Analytical methods

• Division into regions. • Split and merge

Fig. 1 Classification of segmentation approaches

• Cooperation results

• KNN • Random Forest • … Etc.

A Powerful and Efficient Method of Image Segmentation ...

895

benefits of the complementary nature of region and contour information. Thus, a segmentation by region-contour cooperation is a mutual aid between these two techniques to obtain a better final segmentation result. This approach can be a solution to overcome the disadvantages of both techniques. We can cite three forms of region-contour cooperation: sequential, mutual, results cooperation [14–16]. Segmentation based on classification methods, in this section we consider pixels of the image as objects to be classified. The techniques of segmentation by classification aims to provide a partitioning of the image, where each class contains pixels that have similar gray levels. We can cite supervised methods (KNN [17], SVM [18], Random Forest [19]), and unsupervised methods (Kmeans [20–22] [23], Fuzzy C -means (FCM) [24]). There is no one-classification method that can be applied to any type of image and that can provide optimal and most natural partitioning. This explains the great diversity of classification methods that exist in the literature. Previous work often used simple local features for node testing; such as pixel differences or differences between the sums of accumulated filter responses on local rectangular regions [25–27]. In contrast, [28, 29] present a dataset containing over 20K images (each pixel has a semantic tag) with a large and unlimited open vocabulary. Figure 2 shows examples of this dataset. This paper focuses on a cooperative segmentation between a segmentation by Canny, Prewitt, Sobel contour detection models, and a segmentation that treat the problem of segmentation based on classification methods, which the discriminant classifier used is Random forest. In the second section, we present the segmentation procedure by the proposed method as well as how each of these algorithms works. The third section will focus on the experimental and interpretations of the results. In the last section, we will present the conclusion. (a) (b)

(c)

Fig. 2 Examples of images in the ADE20K dataset. The a row shows the sample images, b row shows the annotation of objects, and the c row show the annotation of object parts

896

Z. Faska et al.

2 Proposed Approach In this section, we will treat our proposed method that is used to combine two complementary processes. The first process to make the segmentation of the contours by combining the canny algorithm, Prewitt and Sobel’s filters. The second process is a supervised classification segmentation by using the results of the first process, which is Random forest. For this, we worked with the ADE20K Dataset [28, 29]. The flowchart of the method proposed is illustrated as follows (Fig. 3):

2.1 Canny Method The Canny filter (or Canny detector) [10] is used in image processing for edge detection. The algorithm was designed by John Canny in 1986 to be optimal according to three clearly explained criteria: • Good detection: the operator must detect as precisely as possible all the contours of an image. • Good localization: optimization of the precision with which the contour is detected. • Uniqueness: the contour should elicit a unique operator response. These three criteria are expressed in the joint optimization of three functions for defining the optimal linear filter for the detection of a stair-step under the assumption of an independent additive noise signal.

Edge detection with Canny’s algorithm Edge detection with Prewitt and Sobel’s filters Contour Extraction Segmentation by classification using the model RandomForestClassifier Image segmented

Fig. 3 Procedure for implementing image segmentation by the proposed method

Combination of methods

Initial contour segmentation

Color image

A Powerful and Efficient Method of Image Segmentation ...

897

If we consider that the filter has an impulse response ψ(x), these functionalities are written: ∞  ψ(x)d x Good detection : =  0 (1) ∞ 2 (x)d x ψ −∞    ψ (0) Good localization :  =  (2) ∞  −∞ ψ 2(x)d x Uniqueness :



   ψ (0)

∞ −∞

ψ 2 (x)d x

0 = k −∞

∞ −∞

ψ(x)d x

(3)

ψ 2 (x)d x

The solution that verifies these three criteria, proposed by Canny is as follows: x

x

ψ(x) = a1 e σ sin ωx + a2 e σ cos ωx + a3 e

−x σ

sin ωx + a4 e

−x σ

cos ωx

(4)

Where the coefficients ai and ω are determined from the size of the filter. The parameter is a parameter of great importance that we find in all other filters derived from the approach of Canny. This scale parameter indicates how far short of two parallel contours will be merged into one. Canny shows that the derivative of a Gaussian is a good approximation of its filter. Starting from other initial conditions than those of Canny, Deriche’s detector meets exactly the same quality criteria as Canny’s, but which has an infinite impulse. It could therefore be synthesized in a particularly efficient recursive manner [11]. The Deriche filter has a general expression of the form: 

ψ(x) = −C X e

−α|X |

1 − e−α With c = e−α

2 (5)

2.2 Sobel and Prewitt Operators Noise, interference between adjacent edges, and precision errors due to the discreet character of the image definition grid are among the factors that can corrupt the performance of edge detection in real images. To detect intensity transitions by numerical differentiation, derivative methods are the most used. These local methods scan the image with a mask defining the area of interest. At each position, an operator is applied to detect significant transitions at the level of the chosen discontinuity attribute.

898

Z. Faska et al.

Numerous contour extraction techniques exist in the literature. They can be classified into the following categories: Algorithms based on the gradient (or firstorder operators), Algorithms based on the Laplacian (or second-order operators), Multi-scale algorithms, and Algorithms using sophisticated filters. In the case of gradient-based algorithms (or first-order operators), the gradient of an image gives the rates of change in gray level per unit distance in the directions of the coordinate axes. It is defined as a vector characterized by its amplitude (related to the amount of local variation in gray levels), and direction (orthogonal to the border, which passes at the point considered. We can therefore obtain a perfect knowledge of the image, which is calculated as follows: ∇x =

∂ I (x, y) ∂ I (x, y) Et ∇ y = ∂x ∂y

(6)

Thus, at each point of the image (x, y), we define two partial derivatives, along x and y. The direction of the gradient vector maximizes the directional derivative, and its norm is the value of this derivative. The various existing variants, including the Roberts operator [30], provide improvements to the discrete estimation of the gradient (derivative) and to the consideration of the orientation of the contour. The Prewitt operator [31] and the very popular Sobel operator [32] allow local estimation of the norm of the two-dimensional spatial gradient of a gray-level image. They amplify regions of strong local variations in intensity corresponding to the contours. These operators consist of a pair of 3 × 3 convolutional masks. Applying each of the masks separately gives an estimate of the horizontal and vertical components of the gradient by simple linear filtering to moderate the effects of noise.

2.3 Random Forest Algorithm The Random Forest classifier is a powerful machine-learning tool for solving classification and regression problems. Uses adaptive (boosting) or random (bagging) strategies. It consists of using a combination or an aggregation of a large number of models while avoiding overfitting. Bagging is an ensemble method introduced by Breiman in 1996 [33]. The word Bagging is the contraction of the words Bootstrap and Aggregating. Bootstrap is a principle of statistical resampling [34] traditionally used for the estimation of quantities or statistical properties. The idea of bootstrap is to use multiple sets of data sampled from all observed data using a random draw with replacement. Thus, each elementary classifier of the set will be trained on one of the bootstrap samples so that they are all trained on a different training set. Aggregating these classifiers allows for a more efficient predictor.

A Powerful and Efficient Method of Image Segmentation ...

899

A Random Forest is a predictor made up of a set of elementary classifiers of the decision tree type. In the specific cases of CART models (binary trees) [33, 35], for building trained Random Forest model, two steps of randomness are used: • Step1: Create randomly from the dataset b samples with replacement Z i , i = 1…, B. (each sample having n points). • Step2: Create randomly q attribute among the existing p and we build the CART tree G i (x) on these attributes. • Aggregation by average in regression. G(x) =

1 B G i (x) i=1 B

(7)

• Aggregation by vote in classification. G(x) = Ma jorit y V ote(G 1 (x), G 2 (x), . . . , G B (x)

(8)

3 Results and Interpretations The method we have proposed is a combination of the segmentation of contours by the Canny, Sobel, and Prewitt’s algorithm and the segmentation by classification. We demonstrated our approach by experimenting on a set of the existing labeled images using the Random Forest algorithm. The advantage and robustness of the proposed method is demonstrated by in-depth experimentation on a set of images. From the images in Fig. 4, we can see that the combination of the Random Forest algorithm with other filters leads to a further increase in performance. The quality and performance of the segmented image are measured with four metrics as IoU, DSC, Entropy, and MI presented in the expressions (9), (10), (11) and (12). • Intersection over Union (IoU): to compare the similarity between two arbitrary shapes (volumes) A, B ⊆ S e R n I oU =

|A ∩ B| |A ∪ B|

(9)

The Intersection over Union (IoU) also called Jaccard index, is an evaluation metric used to measure the accuracy of an object detector on a particular dataset. It measures the degree of similarity between the target mask and our prediction output. • The Dice similarity coefficient (DSC), also called the Sorensen - Dice index or simply the Dice coefficient, is a statistical tool that measures the similarity between two sets of data. This index has undoubtedly become the most widely used tool in the validation of image segmentation algorithms, A, B ⊆ S e R n it is expressed by:

900

Z. Faska et al.

Original Image

Edge Detection using Canny, Sobel, and Prewitt’s algorithms

Segmentation by classification using Random Forest algorithm

The proposed method

Fig. 4 Image segmentation results obtained by the different methods

DSC =

2|A ∩ B| |A| + |B|

(10)

Here, | A | is the number of elements of A. The index can vary from 0 (when A and B are disjoint) to 1 (when A and B are equal). • Entropy: was introduced by Shannon (1948), is a mesure of image information content, which is interpreted as the average uncertainty of information source, were the higher value of entropy means more detailed information [35]. Entr opy = −

 L−1 i=0

p f (i)log2 p f (i)

(11)

With p f the ratio of the number of pixels to the value of the gray level over the total number of pixels. • Mutual information (MI): is a quantity that measures a relationship between the original image and image segmented, were the higher value of MI means the relationship between the images exists. MI =

 af

PAF (a, f )log

PAF (a, f ) PA (a)PF ( f )

(12)

With PAF (a, f ) the joint histogram of the merged image F and the source image. In order to show the quality of our approach on a set of images, which takes the same size of the original image in all methods:

A Powerful and Efficient Method of Image Segmentation ...

901

Table 1 Comparison of three segmentation methods on the living room image Metrics

IoU

DSC

MI

Entropy

Canny, Sobel and Prewitt

0.6016

0.7512

0.2158

5.3829

Random forest

0.7123

0.8320

0.5818

5.9333

Our approach

0.7440

0.8532

0.3617

6.2322

Table 2 Comparison of three segmentation methods on seaside image Metrics

IoU

DSC

MI

Entropy

Canny, Sobel and Prewitt

0.4834

0.6518

0.2253

4.4726

Random forest

0.8188

0.9004

0.5482

6.6097

Our approach

0.8526

0.9204

0.4591

6.6271

Table 3 Comparison of three segmentation methods on the museum image Metrics

IoU

DSC

MI

Entropy

Canny, Sobel and Prewitt

0.6612

Random forest

0.6990

0.7960

0.2663

5.8720

0.8228

0.4592

Our approach

0.8970

5.3751

0.9457

0.6026

5.9905

– The living room image is 256 * 256 in TrueColor, the Seaside Image is 225 * 300 in TrueColor, and the Museum image is 256 * 256 in TrueColor. The best result of segmentation is where the values of IoU and dice are close to the value 1, while the values of MI and Entropy are high. The metric values of some images presented in Fig. 4 are given in the following Tables 1, 2, and 3: Tables 1, 2, and 3 presented the results of the different methods obtained from the four measurements IoU, Dice similarity coefficient, MI, and Entropy, where the value in Italic in each of the rows shows the best segmentation result obtained by the three methods. We have found that our proposed segmentation cooperative approach gives the best segmentation result compared to other methods. From these tables, we notice that the values of IoU and DSC obtained by our approach are better than the other techniques, as well as the value of entropy. Therefore, from the comparison between different methods, the proposed cooperative approach provides an improvement in the image segmentation results compared to other methods.

902

Z. Faska et al.

4 Conclusion In this work, we have proposed a cooperative approach between several methods widely used in the field of image segmentation. To date, many researchers have developed different segmentation algorithms, and to respond to the limits and inadequacies of these proposed methods, the tendency is to make a combination between two or more methods. In this point, we proposed an approach combining the contours segmentation by the Canny’s algorithm, Prewitt, and Sobel’s derivatives methods, and the segmentation by classification with the Random Forest algorithm. We tested our method on an existing labeled image dataset. From analysis and a comparison of the results obtained by the different methods, we can say that our proposed approach gives better segmentation results and leads to a greater increase in performance.

References 1. El Akkad N, Merras M, Baataoui A, Saaidi A, Satori K (2018) Camera self-calibration having the varying parameters and based on homography of the plane at infinity. Multimed Tools Appl 77(11):14055–14075 2. Akkad NE, Merras M, Saaidi A, Satori K (2013) Robust method for self-calibration of cameras having the varying intrinsic parameters. J Theor Appl Inf Technol 50(1):57–67 3. Akkad NE, Merras M, Saaidi A, Satori K (2013) Camera self-Calibration with Varying Parameters from Two views. WSEAS Trans Inf Sci Appl 10(11):356–367 4. El Akkad N, Saaidi A, Satori K (2012) Self-calibration based on a circle of the cameras having the varying intrinsic parameters. In: Proceedings of 2012 international conference on multimedia computing and systems, ICMCS, pp 161–166 5. Merras M, El Akkad N, Saaidi A, Nazih AG, Satori K (2014) Camera calibration with varying parameters based on improved genetic algorithm. WSEAS Trans Comput 13:129–137 6. El Akkad N, El Hazzat S, Saaidi A, Satori K (2016) Reconstruction of 3D scenes by camera self-calibration and using genetic algorithms. 3D Res 7(1):6 7. Merras M, Saaidi A, El Akkad N, Satori K (2018) Multi-view 3D reconstruction and modeling of the unknown 3D scenes using genetic algorithms. Soft Comput 22(19):6271–6289 8. Es-Sabry M, El Akkad N, Merras M, Saaidi A, Satori K (2018) Grayscale image encryption using shift bits operations. In: 2018 International conference on intelligent systems and computer vision, ISCV, pp 1–7 9. Es-Sabry M, Akkad NE, Merras M, Saaidi A, Satori K (2018) A novel text encryption algorithm based on the two-square cipher and caesar ciphe. Commun Comput Inf Sci 872:78–88 10. Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 8(6):679–698 11. Deriche R (1987) Using Canny’s criteria to derive a recursively implemented optimal edge detector. Int J Comput Vis 1:167–187 12. Muñoz X, Freixenet J, Cufi X, Marti J (2003) Strategies for image segmentation combiningregion and boundary information. Pattern Recogn Lett 24:375–392 13. Fan J, Yau DKY, Elmagarmid AK, Aref WG (2001) Automatic image segmentation by integrating color-edge extraction and seeded region growing. IEEE Trans Image Process 10:1454–1466 14. Sebari I (2007) Les approches de segmentation d’image par coopération régioncontour 15. Muñoz X, Cufi X (2000) A new approach to segmentation based on fusing circumscribed contours, region growing and clustering. In: IEEE International conference on image processing, pp 800–803

A Powerful and Efficient Method of Image Segmentation ...

903

16. Saber EA (1998) Integration of color, edge, shape, and texture features for 94 automatic regionbased image annotation and retrieval. J Electron Imaging 7(3):684–700 17. Wazarkar S, Keshavamurthy BN, Hussain A (2018) Region-based segmentation of social images using soft KNN algorithm 18. Yang HY, Wang XY, Wang QY, Zhang XJ (2012) LS-SVMbased image segmentation usingcolor and textureinformation. J Vis Commun Image Represent 23:1095–1112 19. Breiman L (2001) Random forests. Mach Learn 45:5–32 20. Dhanachandra N, Manglem K, ChanuImage YJ (2015) Segmentation using K-means clustering algorithm and subtractive clustering algorithm 21. Khan SS, Ahmad A (2004) Cluster Centre Initialization Algorithm for K-means Cluster. Pattern Recognit Lett 25:1293–1302 22. Khrissi L, El Akkad N, Satori H, Satori K (2020) Image Segmentation based on k-means and genetic algorithms. In: Embedded Systems and Artificial Intelligence. Springer, Singapore, pp 489–497 23. Khrissi L, Akkad NE, Satori H, Satori K (2019) Color image segmentation based on hybridization between Canny and k-means. In: 2019 7th Mediterranean congress of telecommunications (CMT). IEEE, pp 1–4 24. Khrissi L, El Akkad N, Satori H, Satori K (2020) Simple and efficient clustering approach based on cuckoo search algorithm. In: 2020 fourth international conference on intelligent computing in data sciences (ICDS). IEEE, pp 1–6 25. Shotton J, Winn J, Rother C, Criminisi A (2006) TextonBoost: joint appearance, shape and context modeling for multi-class object recognition and segmentation. In: Proceedings of ECCV, pp 1–15 26. Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features. In: Proceedings of CVPR, pp 511–518 27. Winn J, Criminisi A (2006) Object class recognition at a glance. In: Proceedings of CVPR 28. Zhou B, Zhao H, Puig X, Fidler S, Barriuso A, Torralba A (2017) Scene parsing through ADE20K dataset. In: Computer vision and pattern recognition (CVPR) 29. Roberts LG (1965) Machine Perception of Three Dimensional Solids. In: Tippet JT (ed) Optical and electrooptical information processing. MIT Press, pp 159–197 30. Prewitt JMS (1970) Object enhancement and extraction. In: Lipkin BS, Rosenfeld A (eds) Picture processing and psychopictorics. Academic Press 31. Sobel (1990) An isotropic image gradient operator. In: Freeman H (ed) Machine vision for three-dimensional scenes, pp 376–379. Academic Press 32. Breiman L (1996) Bagging predictors. Mach Learn 24(2):123–140 33. Efron B, Tibshirani RJ (1993) An introduction to the bootstrap. Chapman & Hall, New York 34. Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. Chapman and Hall, New York 35. Yang L, Guo BL, Ni W (2008) Multimodality medical image fusion based on multiscale geometric analysis of contourlet transform. Neurocomputing 72:203–211

Digital Technologies in Battle Against COVID-19

Towards Overlapped Objects Recognition Using Capsule Network Merieme Mansouri, Samia Benabdellah Chaouni, and Said Jai Andaloussi

Abstract Many approaches have proposed successful systems to recognize objects from an image. However, Overlapped objects recognition is still a main challenge for all kinds of objects. It is difficult for a machine to recognize two or more objects that partly hide each other. On the other hand, the new artificial neural network capsule network (CapsNet) has shown promising results in recognizing overlapped digits from multi-MNIST dataset. However, CapsNet has achieved low performance when it recognizes an object from an image with complex background. In this paper, we propose a contribution containing GrabCut to extract foreground from background, and then use CapsNet to recognize the overlapped foreground. As a result, we will address the overlapped objects recognition challenge and the complex background limit of CapsNet. Keywords Object recognition · Overlapped objects · Capsule network

1 Introduction Artificial intelligence is making anything artificial not natural like machines have the intelligence of humans, the ability to understand, think and learn. Machine learning is a subset of artificial intelligence. Number of definitions of machine learning have been presented based on everyone’s vision. The most powerful one is Arthur Samuel’s definition in 1959: “machine learning: field of study that gives computers the ability to learn without being explicitly programmed”. [1] Learning is a specific human property, having machines with human properties will make our life much easier. Machine learning has different types of algorithms. Two most important ones are: supervised learning and unsupervised learning. Supervised learning is teaching the computer how to do a task while unsupervised learning learns to do it but itself [2]. Machine learning serves many fields: Data Mining [3], Natural Language Processing

M. Mansouri (B) · S. B. Chaouni · S. Jai Andaloussi Faculty of Science Ain Chock, Hassan II University, Casablanca, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_82

907

908

M. Mansouri et al.

(NLP) [4], and so on. One of the most powerful fields of AI is Computer Vision; it is training machines to interpret the visual world like humans using images and videos. We are interested in object recognition from images, an essential domain in computer vision that recognizes objects from an image based on several tasks such as: detection, segmentation and classification. Two types of approaches were developed to recognize objects: conventional approaches and deep learning based approaches. Conventional or traditional approaches are any machine learning methods or approach before the appearance of Deep learning-based approaches. Deep learning is a subset of machine learning inspired by the construction of the human brain. The difference between these two concepts is that classical machine learning takes the input data and parses it to determine its features, then trains the machine learning with that data, eventually the machine will apply what it has learned to make a decision. While deep learning has come to ensure end-to-end learning, it means from the input image to the output, it handles the both feature extraction and decision phases. Object recognition has a major role in different life domains: security [5], space exploration [6], medical diagnosis [7] and so on. Some works recognize food to help people who suffer from obesity and diabetes to control their nutrition, by calculating for example number of calories of the recognized food to control the taken quantity by day [8–11] while others try to recognize animals and give all information you need about them [12, 13]. There are researches in the traffic monitoring environment to recognize traffic signs [14, 15]. And others to detect and recognize general objects [16–18]. The common challenge in object recognition is the overlapped objects. All those mentioned works have shown impressive results in recognizing simple objects or multiple non-overlapped objects in an image, but it is very challenging to define two or more objects that partially hide each other. The missing parts of objects will make it unfamiliar for machines. There are methods that arrive to detect and classify overlapped objects, those methods may ensure state of the art accuracy but they work either on one type of object shape [19] or require specific equipment [20]. Among overlapped objects recognition methods, the new artificial neural network Capsule network (CapsNet) has shown great results on overlapped digits dataset multi-MNIST [21], a special version of Modified National Institute of Standards and Technology (MNIST) dataset. CapsNet has improved high accuracy compared to convolutional neural networks (CNN). It has come to solve some CNN limits, but nothing is perfect. CapsNet requires more computational resources and it does not show high performance when it recognizes objects from an image with complex background. In this paper, we propose the use of capsule networks to extract features and classify six categories of overlapped fruits. In order to overcome CapsNet limits that consist in complex background, we propose extracting the foreground (object) from the background using GrabCut [22] segmentation algorithm. Our contribution will overcome the challenge of overlapped objects recognition and CapsNet limit. This paper can be summarized as follows: Sect. 2 contains related works, Sect. 3 describes our proposed system, and conclusion and future works are presented in Sect. 4.

Towards Overlapped Objects Recognition …

909

2 Related Works In this section, several existing works have been chosen based on specific comparison criteria, there are works on general object recognition and others on specific objects such as food, traffic signs, animals, and so on. The existing works of our problematic: “overlapped object recognition” are presented while some approaches of capsule network have been studied as well. The summary of those works are collected and presented in a synthesis table giving a brief explanation of their limitations according to our problematic and contribution.

2.1 Different Objects Recognition Yu et al. [8] propose a classifier to learn incrementally the user’s data and each user has 300 new types of food. The system used convolutional neural network (CNN) to extract features and the combination of nearest class mean (NCM) classifier and nearest neighbor (NN) classifier for classification, trained and tested with FoodLog dataset achieving with that the state of the art accuracy. Zhou et al. [16] discuss the detection of the main object in an image contains different objects, the authors propose region convolutional neural network (RCNN) to recognize the multi-objects in an image, based on four steps, search selective for detection, CNN for feature extraction, support vector machine (SVM) for classification and Nonmaximal suppression(NMS) to remove redundant candidate region and find the best position of object detection. To obtain the main object from all detected objects, they put a system based on the ratio of object volume and the image, and the rarity of all the objects to mark the main object. The experiment was trained and tested with ImageNet2012 and Flickr8k datasets and achieves 90% accuracy. Hayat et al. [17] constructed a deep learning framework based on CNN to extract feature and recognize nine general objects, they worked with Caltech-101 dataset for training and testing, their work contains also a comparison of the proposed system and variant approaches of Bag of Words (BOW) based on SVM, the results show that the proposed method is much better with an accuracy of 90.12%. Liu et al. [9] propose an improved CNN to extract features and recognize food using two datasets UEC-256 and Food-101 and achieving 76.3 and 77.4% consecutively. Huang et al. [12] present a deep learning system for recognizing 27 kinds of birds from images. The authors used the Internet of Birds (IoB) dataset and preprocessed the images by flipping, rotating, translating, and noise removing, and then feature extraction and recognition have done with CNN achieving 95.3% accuracy. Vennelakanti et al. [14] worked on traffic signs detection and recognition. First, they preprocess their images by converting RGB image to HSV image, then detect the signs using color and shape based detection, and finally extract features and recognize signs using CNN, for training and testing, they used Belgium and German data sets achieving 98% accuracy. Sadeq et al. [10] present the problem of size estimation in food recognition. Authors propose a system

910

M. Mansouri et al.

that employs capturing solid and liquid food items with fixed posture and estimate the volume with simple geometric information, the system detects the food item with ellipse detector, and segment it with color based K-means clustering, Otsu thresholding, and Canny edge Detector, then uses CNN for features extraction and classification. The used dataset was FooDD reaching 96% for recognition and 4.46% error for size estimation. Taheri et al. [13] propose a system that classifies 19 classes of animals using score-level fusion: fusion of two classifiers, one based on features extracted from CNN and second based on appearance and shape-based features, to make a decision and give final results, authors used nearest neighbor (NN) classifier, and preprocessed images with resizing, converting from RGB to grayscale and histogram equalization. For training and testing they employed LHI-Animal-Faces dataset and achieved 95.31% accuracy. Ege et al. [11] present a review of five size estimation methods of food; we are going to focus just on the two last new methods. The first method used the two backside cameras in the iPhone to obtain depth information. Authors used the convolutional neural network U-NET for extraction and segmentation, and UECFood-100 for training, the second method used grains of boiled rice as a reference object to estimate the real size based on CNN and its own dataset trained. The approach achieves 10% estimation error.

2.2 Overlapped Objects Recognition Yukitoh et al. [20] present the difficulty of overlapping objects, the upper object changes its angle and the lower object is partially hidden which leads to difficulty in object recognition. To solve that, authors used two RGB-D sensors to recognize different kinds of overlapped bread from own dataset with simple background, the opposite viewpoints of the sensors were concatenated by iterative closest point (ICP) algorithm, to recognize the upper object, authors used posture adjustment and the two sensors and achieve 96.9% accuracy, for lower object, they used the two sensors according to the degree of hidden, they reach 84.4% accuracy for 50% hidden object. Changhui et al. [19] focused on overlapped citrus recognition for automatic harvesting; they used threshold segmentation in HSV space, gift-wrapping convex hull algorithm and transform method for citrus fruit localization and reconstruction. They used their own dataset, as a result, the positioning and reconstruction error rate were 6.51 and 8.84% respectively. Zhou et al. [23] present a novel segmentation method for overlapped fish images, authors captured images in real time using camera attached to a computer, then preprocessed image by extracting background and filtered the image from noise, then segmented overlapped fish applying several methods: determine overlapped area using a shape factor, detect corners using the curvature scale space algorithm then extract skeleton Zhang-Suen algorithm to obtain as a conclusion the intersection points and the segmented overlapped fish. Authors obtained 90% in accuracy as results. Song et al. [24] propose an improved version of non-maximum suppression algorithm (NMS) in order to detect overlapped objects. Authors grouped detected boxes into clusters and selected the center cluster with the

Towards Overlapped Objects Recognition …

911

maximum score. A predefined threshold is used to specify the degree of overlapping, any box exceeding the threshold will be incorporated into the corresponding cluster finally they applied soft-NMS to remove unclear boxes. Authors combined this method with two object detection networks Faster R-CNNs and R-FCNs to demonstrate its effectiveness using MS COCO dataset.

2.3 Object Recognition Using Capsule Networks Kumar et al. [15] propose a system that detects and recognizes traffic signs with Capsule Network; they preprocessed their input images by enhancing brightness and contrast and augmented the dataset by rotation and flipping. They used Germany traffic signs dataset and achieved 97.6% accuracy. Ahmed et al. [18] present a 3D capsule network for 3D object recognition, they proposed four 3D capsule network architecture, containing different numbers of convolution layers, a dynamic routing algorithm which is used between a primary capsule layer and class layer, squash function was added and a decoder for object reconstruction. They used ModelNet10 and ModelNet-40 dataset which contain general objects 3D models, achieving 91.37 and 89.66% accuracy respectively. In order to reduce computational time and improve classification, Hoogi et al. [25] propose a novel Capsule Network architecture that contains self-attention mechanism which select the salient regions, and CapsNet analyze only those regions, the proposed system was applied on three medical datasets, MNIST and SVHN and achieve 90 94 92% accuracy on liver, brain, lung datasets respectively and 99.5% on MNIST and improve the baseline CapsNet accuracy in SVM with 2.4%.

2.4 Comparison Criteria In our search of related works to our thesis, we chose several approaches, mentioned in the previous section, based on different comparison criteria; as a result, we built a table of synthesis as described in Sect. 4.5. The comparison criteria proposed are: Object Type (OT). The type of the recognized object in the approach. Detection Method (DM). Methods used for detection of the object from an image. Segmentation Method (SM). Methods used for segmentation of the object from an image. Feature Extraction Method (FEM). The method used to extract features. Classification Method (CM). The method used for classification. Overlapped Objects (OvO). The existing or not of detecting and classifying overlapped objects: Method Used (MU). The method used to recognize the overlapped objects. Background (B). Define if the background is complex or simple. Datasets (D). The used datasets.

912

M. Mansouri et al.

Accuracy/Error (A/E). The average accuracy or test error results of the experiment in each approach.

2.5 Table of Synthesis As shown in Table 1, we synthesized and presented the related approaches in the table of synthesis based on the comparison criteria mentioned in the previous section. 16 selected approaches of different types of object recognition have been analyzed based on the previous mentioned comparison criteria. As we can see, some used conventional methods, and most of them decided to work with deep learning to reduce time and effort from designing features separately [9, 10, 14], others used the new deep learning network CapsNet [15, 18, 25] except that these contributions cannot be applied in the context of overlapped objects. However, only four approaches have worked on the overlapped objects. Yukitoh et al. [20] presented the difficulty of overlapped objects, but the system requires specific equipment which conducts to a practical limitation in real-world conditions. In addition, Changhui et al. [19] and Zhou et al. [23] recognize overlapped objects based on one specific shape or type. While Song et al. [24] consider each overlapped two objects or more should be from the same class. In the following section, we will present the proposed approach that overcomes these limitations.

3 Proposed Approach To recognize overlapped objects, we propose to segment the object and separate it from the background based on GrabCut, then extract features and classify them using capsule networks. In this way, we address the overlapped objects recognition challenge and CapsNet limit in recognizing objects with complex background. In Fig. 1, we present the architecture of the proposed system giving an example of the supposed input and output of the system.

3.1 Used Methods This paragraph defines the main used methods in our system. GrabCut. Is a semi-automatic algorithm to extract foreground from background, User has to draw a bounding box around the desired object, the pixels outside the box are defined as background and the pixels inside are defined as unknown pixels. A Gaussian Mixture Model (GMM) is used to estimate the background and the foreground based on color distribution, a graph is generated containing nodes (pixels)

OT

Food

General objects

General objects

Food

Birds

Traffic Sign

Food

Animal

Food

Breads

Traffic sign

General objects

Ref

[8]

[16]

[17]

[9]

[12]

[14]

[10]

[13]

[11]

[20]

[15]

[18]

---

---

---

Edge detection Selective search

---

Ellipse detector

Color based detection Shape based detection

---

Bounding box

---

Selective search

---

DM

Table 1 Table of synthesis

---

---

---

K-means clustering GrabCut U-Net

---

K-means clustering Otsu thresholding Canny edge Detector

---

---

---

---

---

---

SM

CapsNet

CapsNet

---

CNN

KFA CNN

CNN

CNN

CNN

CNN

CNN

CNN

CNN

FEM

CapsNet

CapsNet

---

CNN

NN

CNN

CNN

CNN

CNN

CNN

SVM

NCM NN

CM

---

---

Two RGB-D sensors

----

---

---

---

---

---

---

---

---

MU

OvO

---

---

Simple

---

---

---

---

---

---

---

---

---

B

91.37% 89.66%

97.6%

*Upper object: 96.9% *Lower object: 84.4%(for half hidden)

10% error for calorie estimation

95.31%

96% for recognition 4,46% error for size estimation

98%

95.37%

76.3% 77.4%

90.12%

90%

---

A/E

(continued)

ModelNet-10 ModelNet-40

German traffic sign

Own dataset

UECFood-100

LHI-Animal-Faces

FooDD

Belgium German

Internet of Birds(IoB)

UEC-256 Food-101

Caltch-101

ImageNet2012 Flickr8k

FoodLog

D

Towards Overlapped Objects Recognition … 913

OT

Medical objects Digits

Citrus fruit

Fish

General objects

Ref

[25]

[19]

[23]

[24]

Table 1 (continued)

---

---

---

---

DM

---

---

HSV space threshold

---

SM

---

---

---

CapsNet Self-attention mechanism

FEM

FastR-CNNs R-FCNs

---

---

CapsNet

CM

NMS Soft-NMS

Shape factor Curvature scale space Zhang-Suen

Gift-wrapping Convex hull Distance transform method

---

MU

OvO

Complex

Simple

Simple

---

B

---

90%

6.51% 8.84%

90% 94% 92% 99.5%

A/E

MS COCO

---

Own dataset

Liver, Brain, Lung datasets MNIST SVHM

D

914 M. Mansouri et al.

Towards Overlapped Objects Recognition …

915

Fig. 1 Proposed system architecture. The images are examples of the supposed input and output of the system

and edges, the weight of edges is defined based on the probable definition of each pixel, a graph-cut is run to find a new classification of the pixels, after several iterations, the unknown pixels are classified either background or foreground. Users can correct results by marking the missing parts for optimization [22]. Capsule Network (CapsNet). Convolutional neural network(CNN) has shown great results in different types of object recognition [12, 14, 17], however, it still have some limits, it doesn’t give importance to spatial hierarchical between features due to Max Pooling layer of CNN which loses those information, the model can be able to recognize object in different orientation but it needs a lot of training examples [26]. In order to overcome those limits, Jeffery Hinton et al. [21] proposed Capsule Networks, It is a new artificial neural network works with capsules instead of scalar features, the length of vectors represents the probability of existing, and their orientation represent a specific parameter of the object (position, size..), CapsNet uses dynamic routing between capsules instead of max pooling, it takes low features and routing it to high features, if there is an agreement, the weight will be augmented and the high capsule will be activated. Another property for CapsNet is the squashing function, it is an activation function similar to ReLu function but it is suitable for vectors. Although Capsule Network has some limits; even it needs much less samples for training than CNN and has simpler architecture, CapsNet requires more computational resources and it does not show high performance when it recognizes objects from images that contain complex background like CIFAR dataset [26].

3.2 Used Dataset Our main dataset is fruit-360 dataset [27], it is a vegetables and fruits dataset containing 120 categories, 61488 images for training and 20622 for testing of simple objects with simple background. We chose just six categories as a start: apple, avocado, banana, orange, lemon and strawberry. In order to avoid overfitting in our network where the model learns the training set too well so it fails in predicting other data of the same object, we expanded the principal dataset with a

916

M. Mansouri et al.

Fig. 2 Extraction foreground from complex background using GrabCut

maid dataset composed of collected images from the internet. Moreover, the second dataset contains overlapped objects to enrich the training phase. We applied GrabCut on the second dataset to extract only the foreground and put it on a simple background as shown in Fig. 2. In order to train CapsNet we combined images from the two datasets, each training class contains 100 images, and each testing class contains 25 images. All images have been resized to 100 × 100.

3.3 Segmentation and Recognition In the training phase, we preprocess our dataset by extracting foreground from background using GrabCut, and then feed our CapsNet for training. The CapsNet model contains three layers, and a decoder, first layer is a convolution layer with ReLu activation function, second layer is a primary layer contain a squash and reshape function, the feature maps from convolution layer output will be reshaped to vectors, and then normalized their length between 0 and 1 with squash function. Third layer is capsule layer, here we use routing between capsules algorithm, the capsules in primary layer will predict the output of the capsule layer, if there is an agreement, the capsule predicted will be activated, and the capsule weight will be augmented, this stage will be repeated after a number of iterations. The decoder is based on three fully connected layers with two ReLu activation functions and one sigmoid function to reconstruct the input segmented object. Figure 3 represents the used architecture

Fig. 3 Capsule network architecture

Towards Overlapped Objects Recognition …

917

of Capsule Network. To test a new image, we extract foreground from background using GrabCut, then we use CapsNet to predict the objects classes.

4 Conclusion and Future Works In this paper, we present a contribution in order to overcome the overlapped object recognition challenge and handle the complex background limit of capsule networks. Our proposed approach uses GrabCut to separate objects from the background, and then feed it to the capsule network in purpose to extract features and give the predicted class for each object in an image. Our next work consists of continuing the realization phase and recording the results obtained using the machine learning platform Tensorflow-GPU and running on top of it the neural network interface Keras written in python, GrabCut has applied with the help of OpenCV. In addition we will perform a comparison with the most common object recognition networks. We intend to apply this contribution on overlapped food items on plates and extract nutrition information such as number of calories in order to help people who suffer from obesity and diabetes to control their nutrition.

References 1. Samuel A (1959) Some studies in machine learning using the game of checkers. IBM J Res Dev 3(3):210–229 2. COURSERA Homepage https://www.coursera.org/learn/machine-learning/home/welcome 3. Zhao L, Kong W, Wang C (2020) Electricity corpus construction based on data mining and machine learning algorithm. In: 2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC), pp 1478–1481. IEEE 4. Lansley M, Kapetanakis S, Polatidis N (2020) SEADer++ v2: detecting social engineering attacks using natural language processing and machine learning. In: 2020 International Conference on Innovations in Intelligent Systems and Applications (INISTA), pp 1–6. IEEE 5. Liu B, Wu M, Tao M, Wang Q, He L, Shen G, Yan J (2020) Video content analysis for compliance audit in finance and security industry. IEEE Access 8:117888–117899 6. Yang X, Nan X, Song B (2020) D2N4: a discriminative deep nearest neighbor neural network for few-shot space target recognition. IEEE Trans Geosci Remote Sens 58(5):3667–3676 7. Szymkowski M, Saeed E, Omieljanowicz M, Omieljanowicz A, Saeed K, Mariak Z (2020) A novelty approach to retina diagnosing using biometric techniques with SVM and clustering algorithms. IEEE Access 8:125849–125862 8. Yu Q, Anzawa M, Amano S, Ogawa M, Aizawa K (2018) Food image recognition by personalized classifier. In: 25th International Conference on Image Processing (ICIP), pp 171–175. IEEE, Greece (2018) 9. Liu C, Cao Y, Luo Y, Chen G, Vokkarane V, Ma Y (2016) Deepfood: deep learning-based food image recognition for computer-aided dietary assessment. In: 14th International Conference on Smart Homes and Health Telematics, pp 37–48. Springer, Cham (2016) 10. Sadeq N, Rahat FR, Rahman A, Ahamed SI, Hasan MK (2018) Smartphone-based calorie estimation from food image using distance information. In 5th International Conference on Networking, Systems and Security (NSysS), pp 1–8. IEEE, Bangladesh

918

M. Mansouri et al.

11. Ege T, Ando Y, Tanno R, Shimoda W, Yanai K (2019) Image-based estimation of real food size for accurate food calorie estimation. In: Conference on Multimedia Information Processing and Retrieval (MIPR), pp 274–279. IEEE, USA 12. Huang YP, Basanta H (2019) Bird image retrieval and recognition using a deep learning platform. IEEE Access 13. Taheri S, Toygar Ö (2018) Animal classification using facial images with score-level fusion. IET Comput Vision 12(5):679–685 14. Vennelakanti A, Shreya S, Rajendran R, Sarkar D, Muddegowda D, Hanagal P (2019) Traffic sign detection and recognition using a CNN ensemble. In: International Conference on Consumer Electronics (ICCE), pp 1–4. IEEE, USA 15. Kumar AD (2018) Novel deep learning model for traffic sign detection using capsule networks. arXiv preprint arXiv:1805.04424 16. Yu L, Chen X, Zhou S (2018) Research of image main objects detection algorithm based on deep learning. In: 3rd International Conference on Image, Vision and Computing (ICIVC) pp 70–75. IEEE, China 17. Hayat S, Kun S, Tengtao Z, Yu Y, Tu T, Du Y (2018) A deep learning framework using convolutional neural network for multi-class object recognition. In: 3rd International Conference on Image, Vision and Computing (ICIVC), pp 194–198. IEEE, China 18. Ahmad A, Kakillioglu B, Velipasalar S (2018) 3D capsule networks for object classification from 3D model data. In: 5fnd Asilomar Conference on Signals, Systems, and Computers, pp 2225–2229. IEEE, USA 19. Changhui Y, Youcheng H, Lin H, Sa L, Yanping L (2017) Overlapped fruit recognition for citrus harvesting robot in natural scenes. In: 2nd International Conference on Robotics and Automation Engineering (ICRAE), pp 398–402. IEEE, China 20. Yukitoh M, Oka T, Morimoto M (2017) Recognition of overlapped objects using RGB-D sensor. In: 6th International Conference on Informatics, Electronics and Vision & 2017 7th International Symposium in Computational Medical and Health Technology (ICIEV-ISCMHT), pp 1–4. IEEE, Japan 21. Sabour S, Frosst N, Hinton GE (2017) Dynamic routing between capsules. In: Advances in Neural Information Processing Systems, pp 3856–3866 22. Rother C, Kolmogorov V, Blake A (2004) Grabcut: interactive foreground extraction using iterated graph cuts. ACM Trans Graph (TOG) 23(3):309–314 23. Zhou C, Lin K, Xu D, Liu J, Zhang S, Sun C, Yang X (2019) Method for segmentation of overlapping fish images in aquaculture. Int J Agric Biol Eng 12(6):135–142 24. Song Y, Li X, Gao L (2020) Improved non-maximum suppression for detecting overlapping objects. In: Twelfth International Conference on Machine Vision (ICMV 2019), v ol. 11433, p. 114330F). International Society for Optics and Photonics (2020). 25. Hoogi A, Wilcox B, Gupta Y, Rubin DL (2019) Self-Attention Capsule Networks for Image Classification. arXiv preprint arXiv:1904.12483 26. Mukhometzianov R, Carrillo J (2018) CapsNet comparative performance evaluation for image classification. arXiv preprint arXiv:1805.11195 27. Mure¸san H, Oltean M (2018) Fruit recognition from images using deep learning. Acta Universitatis Sapientiae, Informatica 10(1):26–42

The Spread of the Corona Virus Disease (Covid-19) and the Launch of 5G Technology in China: What Relationship Abdelhakim Moutaouakil, Younes Jabrane, Abdelati Reha, and Abdelaziz Koumina

Abstract In this paper, we highlight the technology of the fifth generation of mobile communications (5G), its impact on human health and its association with the emergence of the Corona virus in China. We have found through this study, that this technology uses frequencies below 6 GHz used also in other previous generations of mobile networks and they do not lead to the emergence of dangerous diseases like cancer or the corona virus, like what some people claim, and it turned out that this virus comes from the pangolin, which transmitted the infection to humans in Wuhan, China, in late 2019. Keywords 5G · Millimeter waves · Antenna · COVID-19

1 Introduction The last 20 years have witnessed important changes in mobile phone networks with a profound transformation from the second generation (2G) (which enables us to communicate with voice), to the third generation (3G) (which was distinguished in addition to voice communication data transmission), and then to the fourth generation (4G) (Which has been known to provide and transfer large-scale communications) [1]. In fact, from a phone initially designed to conduct a voice conversation between two users without providing any service other than sending/receiving SMS, today’s modern smartphone has become a true mobile data center that provides access to several services and applications (browser, camera, internet and games). This generalization of smartphone internet access and video call access are driving a higher A. Moutaouakil (B) · Y. Jabrane Modelisation of Complex Systems Laboratory, Cadi Ayyad University, Marrakech, Morocco A. Reha Laboratory of Innovation in Management and Engineering for the Enterprise, ISGA, Marrakech, Morocco A. Koumina Nanostructures Physics Laboratory, Cadi Ayyad University Marrakech, Marrakech, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_83

919

920

A. Moutaouakil et al.

data rate. That is why the mobile phone industry has chosen to develop the fifthgeneration network (5G) to provide a technical solution to the problem facing 4G today [2, 3]. But as soon as China announced the launch of this technique, it received a series of accusations, according to which it would have caused a group of diseases, such as cancer, heart diseases, the most recent of which is the Corona virus, in reason for using this technique for strong millimeter wave. For some, it has developed into burning 5G towers and assaulting telecom worker [4]. Today, we are witnessing a wide spread of the Corona virus (Covid-19), which has caused the death of one million of people around the world, young and old, and has made countries confused and Sacrificing their economy by closing the borders in the absence of a vaccine so far. The disease first appeared in November 2019 in Wuhan, central China and It is the city where 5G was launched in the same month. The World Health Organization (WHO) declared this epidemic a public health emergency of international concern on January 30, 2020 [5]. The thing that made everyone wonder about the source of this epidemic, how it spreads, how to prevent it and how to treat it, and whether it has anything to do with the launch of 5G technology. In this paper, we will study the fifth generation (5G) and how it works and what damage it does, especially for human health, and then compare it with the symptoms caused by the Corona virus and determine if there is a connection between them or not.

2 5G Technology In telecommunications, 5G is the fifth generation of mobile communications, since it will allow subscribers to take advantage of ultra-high speed, while limiting the energy consumption of smart phones. Its maximum speed should be 1 Gbit/s for downloads, and 500 Mbit/s for uploads. Today, the 5G network is still under test, even if it is used during military operations [6]. The 5G will provide wireless connectivity for a wide range of new applications as illustrated in Fig. 1 such as: Smart Home Systems, road safety and autonomous Fig. 1 Targeted applications for the fifth-generation network

The Spread of the Corona Virus Disease (Covid-19) …

921

Fig. 2 5G Spectrum

car, E health, Smart Cities, industrial process control, entertainment, and of course the Internet of Things (IoT). In order to provide and support the ever-increasing traffic and ever-increasing demand for bandwidth, 5G will need to expand its frequency range beyond the bands normally used in mobile communications. This includes both the frequency bands below 6 GHz and the millimeter frequencies. As shown in the following figure, the lower part of this frequency range, below 30 GHz, is preferred from the point of view of propagation properties. In addition, it is important to understand that high frequencies, especially above 10 GHz, can serve as a complement to the lower frequency bands, and will be primarily used to provide additional system capacity and very wide band transmission for higher data rates. Millimeter frequencies can be used for indoor applications to provide very high-speed connections [7]. The two basic types are referred to as “sub-6 GHz” (which just uses frequencies below 6 GHz) and “millimeter waves”, which are 24 GHz and higher (this name comes from the fact that the lengths of individual radio waves can be measured in millimeters). According to [8], the only types of 5G radio signals currently used in 5G networks in China are the less than 6 GHz variants]. To ensure, at millimeter frequencies or the frequencies sub-6 GHz, a high-speed connection and the ubiquity of the urban network, 5G will use "massive MIMO" (Multiple Input Multiple Output) technology using multi-antenna networks [9]. The design of a new antenna for 5G wireless devices is more demanding at MMW frequencies where the flexibility of the system architecture, efficiency, reliability and compatibility with MIMO systems are the main considerations for achieving efficiency. High spectral frequency, as well as reduced multipath fading. The most important requirements for 5G MIMO antenna are greater bandwidth to allow simultaneous operation of many system services, high gain to cope with high frequency atmospheric attenuation and absorption. The following figure shows examples of antennas used for 5G. The spread of this kind of antennas elicited a cry from many scientists. Over 200 scientists and physicians demanded a 5G moratorium. These scientists highlight in particular the risks of cancer, genetic damage and neurological disturbances due to the use of this type of antenna which sends and receives millimeter waves, but the

922

A. Moutaouakil et al.

Fig. 3 Examples of 5G antennas

Fig. 4 Corona virus (Covid-19) [11]

truth is that the energy associated with these waves is too weak to cause cell damage or break the weak energy bonds that maintain molecular clusters among living things. As well that we have been using wireless signals less than 6 GHz around the world for years, which China is currently using for fifth generation as we have said in a large number of applications. For example, all existing 4G cellular networks use signals in this range, as do WI-Fi (operating at 2.4 GHz and 5 GHz) and a home microwave oven [10]. So, if these signals could cause a kind of metabolic change in animals or people, as some say, then we all have had their effect long ago. It did not happen because due to the signal strength used by all of these systems, there was no measurable negative connection between these signals and our health, and there was no way that could lead to something that would have created the corona virus.

3 Covid-19 In late December of 2019, some similar symptoms appeared in many people in the Chinese city of Wuhan, which made doctors believe that it was a virus that infected the city and research began to show that later we are really facing a global epidemic that is spreading rapidly as the World Health Organization said. Covid-19 is an infectious disease caused by a strain of the corona virus, called SARS-CoV-22, contagious by human-to-human transmission via respiratory droplets or by touching contaminated surfaces and then touching one’s face. The most common symptoms are fever, cough and difficulty breathing, but they can cause acute respiratory distress which can lead to death [11].

The Spread of the Corona Virus Disease (Covid-19) …

923

Covid-19 can be identified by this ring of small pointed proteins - called spicules - which surrounds it. Once the viral particles enter the mouth, nose or eyes, they will move to the back of the nasal passages, but also to the mucous membranes located at the back of the throat. Thanks to their small spikes, the particles will cling to the cell membranes of the respiratory tract, which produce a protein called ACE2. Once hooked, they will release genetic material similar to DNA, called RNA, which will enter the human cell. This genetic material will then "divert" cells from their usual functions to give them a new role: to create and release millions of copies of the virus. The investigation into the animal origins of SARS-CoV 2, the etiological agent of Covid-19, is still current. Bats, notably the species Rhinolophus affinis, and the Pangolin are the only two animal species harboring corona viruses very close to SARS-CoV-2. While the bat is the most likely source of the virus, although no formal evidence exists, the role of Pangolin in the chain of transmission remains to be determined [12]. A study, the publication of which has been accelerated in Nature, analyzes the composition of the firm in several samples of pangolins seized during anti-poaching operations. Despite its protected species status, the Pangolin is the mammal that suffers the most from illegal trade. Its flesh is consumed in Asia and its scales are used in Chinese medicine. Genetic analysis of the Pangolin samples identified six strains of corona viruses, all of which belong to the same phylo-genetic group as SARS-CoV-2, beta-corona viruses. At the genomic level, the genes of the six strains of penguins are organized in the same way as those of SARS-CoV-2 [13, 14]. Scientists are therefore now certain of the appearance of Covid-19, a process which has absolutely nothing to do with the installation of antennas in 5G phase because the epidemic comes from an animal.

4 Conclusions The world now knows a host of challenges, the most important of which is how to tackle this epidemic, which is spreading wildly and killing people. As health concerns become so frightening, it is easy to fall into the trap of thinking that everything is a potential threat. Fortunately, the scientific development that the world knows answers us about these fears, thanks to which we have learned that the source of this epidemic is some animals, and there is no connection to launching 5G in its spread, and we have also confirmed that this technology does not cause any diseases currently and in particular they only use waves which frequency is below 6 GHz used by 4G and other recent applications.

924

A. Moutaouakil et al.

References 1. Rappaport TS, Sun S, Mayzus R et al (2013) Millimeter wave mobile communications for 5G cellular: It will work. IEEE Access 1:335–349 2. Khan F, Pi Z (2011) An introduction to millimeter wave mobile broadband systems. IEEE Commun Mag 49:101–107 3. Haraz OM, Ali MMM, Alshebeili S, Sebak AR (2015) Design of a 28/38 GHz dual band printed slot antenna for the future 5G Mobile communication networks. Paper presented at: IEEE International Symposium on Antenna and Propagation & USNC/URSI National Radio Science Meeting 10:1532–1533 4. Wang C-X, Haider F, Gao X et al (2015) Cellular architecture and key technologies for 5G wireless communication networks. IEEE Commun Mag 52: 122–130 5. WHO: Statement on the second meeting of the International Health Regulations 2005 Emergency Committee regarding the outbreak of novel coronavirus (2019-nCoV) (2020) 6. Wen-Shyang C, Chun-Kun W, Kin-Lu W (1999) Square-ring microstrip antenna with a cross strap for compact circular polarization operation. IEEE Trans Antennas Propag, vol 47, no 10, pp 1566–1568 7. Cheng X, Tang C, Zhang Z (2019) Accurate channel estimation for millimeter-Wave MIMO systems. IEEE Trans Veh Technol 68:5159–5163 8. Seker C, Güneser MT, Ozturk T (2018) A review of millimeter wave communication for 5G. In: Proceedings of the 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), Ankara, Turkey, 19–21 October 9. Li B, Fei Z, Zhang Y (2019) UAV Communications for 5G and beyond: Recent advances and future trends. IEEE Internet Things J 6:2241–2263 10. Huang J, Wang C-X, Feng R, Sun J, Zhang W, Yang Y (2017) Multi-frequency mmWave massive MIMO channel measurements and characterization for 5G wireless communication systems. IEEE J Sel Areas Commun 35:1591–1605 11. Li Q, Guan X, Wu P et al (2020) Early Transmission dynamics in Wuhan, China, of novel corona virus infected pneumonia N Engl J Med 12. Zhu N, Zhang D, Wang W et al (2020) A novel coronavirus from patients with pneumonia in China, 2019 N Engl J Med, 382 2020, pp 727–733 13. Lai C-C, Shih T-P, Ko W-C, Tang HJ, Hsueh PR (2020) Severe acute respiratory syndrome coronavirus2 (SARS-CoV-2) and corona virus disease-2019 (COVID-19): the epidemic and the challenges International Journal of Antimicrobial Agents, 105924 14. Parry J (2020) China coronavirus: cases surge as official admits human to human transmission. British Medical Journal Publishing Group

Analysis of Covid-19 Impact on Gender (Male and Female) in South Asian Country Pakistan Saba Malik , Mubbra Habib, Mehmood Ahmed Husnain Hashmi, Muhammad Tariq Saeed, Anwaar ul Huda, Syeda Anam Zahra, Muhammad Nisar , Muhammad Sajid, Shahbaz Ahmad Shahzad, Saddam Hussain , and Muhammad Sakandar Majid Abstract The impact of Coronavirus (COVID-19) disease has been reported for different countries. Males are at high risk of death due to COVID-19 as compared to females. There is no data available for Pakistan, the South Asian region. We are therefore undertaking this analysis to determine the gender effect on the outcomes of COVID-19 in the South Asian nation of Pakistan. An online survey was performed across Pakistan (Punjab, Sindh, Khyber Pakhtunkhwa, Balochistan) and national figures from the Pakistani government. In total, N = 113 individuals were included (92 males and 21 females). The descriptive statistics, association tests, and Pie chart indicate that males are more impacted by COVID-19 as compared to females. The doctor’s response indicates that 81.4% of males and 18.6% females are COVID-19 effective in Pakistan. Information from Pakistani national government statistics indicates that the number of male cases are 78%. Therefore, from the entire studies, we can infer that COVID-19 attacks more on males rather than females in the South Asian nation Pakistan. However, more quantitative studies are recommended to measure S. Malik (B) · M. Habib · M. A. H. Hashmi · M. S. Majid (B) Department of Zoology, Wildlife, and Fisheries, University of Agriculture Faisalabad, Faisalabad, Pakistan M. T. Saeed · S. A. Zahra Department of Diet and Nutritional Sciences, University of Lahore Islamabad Campus, Islamabad, Pakistan A. ul Huda College of Pharmacy, University of Sargodha, Sargodha, Pakistan M. Nisar Pakistan Studies Islamia College, Peshawar, Pakistan M. Sajid Department of Psychology, Institute of Southern Punjab Multan, Multan, Pakistan S. A. Shahzad Department of International Relation, Iqra University, Islamabad, Pakistan S. Hussain Department of Irrigation and Drainage, University of Agriculture Faisalabad, Faisalabad, Pakistan © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_84

925

926

S. Malik et al.

the extent to which sex would result in COVID-19 outcomes among Pakistan’s South Asian countries. Keywords COVID-19 cases · Global gender deference · Sex-disaggregated data · Gender

1 Introduction In December 2019, the source of severe pandemic of respiratory disease in Wuhan City, China, was confirmed to be a modern bacterial β-coronavirus, now recognized as Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV-2) [1]. SARS-COV2 is responsible for the most significant lethal complication and is also known as the 2019 coronavirus (COVID-19). Since its emergence, SARS-CoV-2 has been extended in 196 countries and yet was declared a pandemic in the World Health Organization (WHO) by 11 March 2020 [2, 3], respectively. This contributed to approximately 2 million reported illnesses and approximately 130,000 fatalities in global cases, including 2/3 in Europe (as of 15 April 2020). There are currently no recorded SARS-CoV-2 antiviral drugs, although several therapeutic substances such as redeliver, lopinavir-ritonavir, an IL-6 receptor hybrid protease inhibitor, colchicine, and tocilizumab are known to be investigated [3, 4]. The fatality of COVID-19 is more than the global level of seasonal flu by 3.4% worldwide. The state of acute respiratory distress (ARDS), coagulopathy, septic shock, and metabolic acidosis is a consequence of the occurrence of COVID-19. In 7–17% of hospital patients, COVID-19 cardiac complications included arrhythmia, acute heart illness, and shockwaves [5]. The estimated death rate in Italy was 7.2% [6], in South Korea [3] it was 0.9% and in China it was 2.3%. Case pregnancy is higher in individuals > 80 years of age (14.8% in China, 20.2% in Italy) and cancer. Moreover, all comorbidities, the most prevalent heart condition in the aged was this high-risk population, which has a fatality incidence of 10.5%, correlated to negatives [8]. The first findings from China revealed a gender gap concerning observed cases and the COVID-19 patient fatality rate [7, 8]. Up till now, however, only a small number of publications discussed gender differences in COVID-19 occurrence and course of the disease and a detailed study of the root factors is incomplete now [9, 10]. There are a total of 260,000 confirmed positive cases in the country with 184,000 rescued and 5,475 deaths up till Friday, July 17, 2020, according to the Ministry of Health, Government of Pakistan. The largest cases were reported in the province of Sindh (109,000), followed by Punjab (88,539), Khyber Pakhtunkhwa (31,217), Balochistan (11,322), Gilgit Baltistan (1775), Federal (82), and Azad Jammu & Kashmir (1,808) confirmed cases [11]. Outcomes are seen in the Fig. 1. To date, the largest number of deaths in Punjab has been at 2,059, followed by Sindh (1,922) KPK (1,124), Islamabad (157) Balochistan (128), AJK (46), and Gilgit Baltistan (39). In the province of Sindh, a total of (74,076) infected persons

Analysis of Covid-19 Impact on Gender …

927

Fig. 1 COVID-19 incident number in all provinces of Pakistan [11]

were recovered, followed by Punjab (64,815), KPK (22,613), Islamabad (11,599), Balochistan (8,161), and AJK (1,077) recovered cases to date as summarized in Table 1 [11]. Statistics from several countries throughout the globe propose males and females will receive equal COVID-19 while males would be at high risk of severe illness & death [12]. Male had worse results than females in previous coronavirus results or serious respiratory syndromes (SARS) [13] (Fig. 2). COVID-19 Case Fatality Rate (CFR) for males and females across 38 regions with sex-disaggregated data on COVID-19 deaths are shown in Fig. 3. The CFR was calculated by the following formula: [33]

The male CFR is higher than the female CFR in 37 of the 38 nations, with the average male CFR 1.7 times higher than the average female CFR (P < 0.0001, Wilcoxon signed the ranking test) [21] Denmark and Greece. Table 1 COVID-19 case all over the South Asian Country Pakistan as per July 17, 2020 [11] Sr. No

Province

Confirm cases

Active cases

Deaths

Recoveries

1

Sindh

1099,000

34,070

1,922

74,076

2

Punjab

88,539

22,149

2,059

64,815

3

KPK

31,217

7,749

1,124

22,613

4

Balochistan

11,322

3,096

128

8,161

5

Gilgit Baltistan

1,775

340

391

396

6

AJK

1,808

685

46

1,077

928

S. Malik et al.

Fig. 2 Sex-disaggregated data of confirmed COVID-19 across the whole world [14]

Fig. 3 Comparative analyses of COVID-19 impact on gender whole world [21]

In older people, the proportion of Male/Females mortality proportion continues to raise it is generally over two in people 60 years of age and beyond, and more than three in Italy for males and females aged 70–79 [14]. A lot of work has been done on COVID-19 gender influence; they show that males have more cases of COVID-19 than females. In China, Italy, Germany Switzerland, and Spain, France, the recorded case fatality rates are relatively homogeneous and vary from 1.7 to 1.8. It supports

Analysis of Covid-19 Impact on Gender …

929

the belief that there is a strong biological pattern, responsible for the higher rate of deaths in humans, irrespective of country-specific demographics and approaches to science (Fig. 3) [12, 16–18]. The COVID-19 ratio of males and females in the table below indicates the gender equality trend. In Fig. 4 hospitalizations, Intensive Care Unit (ICU) deaths, and CFR are listed in multiple nations. However exact numbers are given. Throughout all indicators, sexually disaggregated data was not accessible [18] (Fig. 5).

Fig. 4 Gender difference of COVID-19 in Europe (Italy, Germany Switzerland, Spain, France) and East Asia (China)

Fig. 5 The male vs female data varies significantly by East Asian countries (China & North Korea), European countries (Italy, Spain, Germany, Sweden), Oceania countries (Australia), Middle East country (Iran), and North American country (Canada) [14]

930

S. Malik et al.

Fig. 6 The sex-disaggregated data at global level

During past coronavirus epidemics, the outcome of the outbreak was worse for men than for women: men had worse outcomes with Serious Acute Respiratory Syndrome (SARS) and a higher risk of dying from Middle East Respiratory Syndrome (MERS) [15]. Pakistan is unique in this collection of results, there are concerns around the accuracy of COVID-19 coverage of females. Overall, though more studies, the finding that COVID-19 is just like widespread for males and females all over the world would need to be confirmed. People diagnosed with COVID-19 are more likely to die in all states that record rates of death by gender. The data in Fig. 6 showed that for every 10 females worldwide there are more cases, death, hospitalization, and ICU admissions of males. Male had worse results than females in coronavirus or serious respiratory syndromes (SARS) attacks. Statistics from several countries throughout the globe propose that males would be at high risk of death due to COVID-19. East Asian countries (China & North Korea), European countries (Italy, Spain, Germany, Sweden, Switzerland, and France), Oceania countries (Australia), Middle East country (Iran), and North American country (Canada) has a higher number of male cases than female attacked by COVID-19 [18, 19]. But there is no data available for the South Asian country Pakistan. Hence, we conduct this study to evaluate the sex gender impact on COVID-19 results in the South Asian country Pakistan.

2 Methodology We have collected data from different sources for this analysis. Firstly, we obtained data on 23rd march from doctor’s doing jobs in hospitals (n = 113) all over Pakistan (Punjab, Sindh, Khyber Pakhtunkhwa, Balochistan). All the doctors associated with private and government hospitals are included in the study. We excluded nurses, healthcare workers, and the doctors, which are doing house jobs. We obtained data

Analysis of Covid-19 Impact on Gender …

931

from the doctors with the help of a Google form-based questionnaire. In this data we apply descriptive statistics first and then association tests to achieve the goal of gender and sex influence on COVID-19 outcomes in Pakistan. Secondly, the data on sex-specific percentages for Pakistan was collected directly from the national government statistics of 25 April 2020 and African population of health center 25 January, 2021, which are added to the aggregated data issued by the Government of Pakistan on 27 May 2020.

3 Results In Table 2 expresses the results, which are obtained from our first source from the doctors. The Table 2 describes the distribution of frequencies for which COVID-19 is more influenced by gender. Here, 81.4% of men respondents were impacted and COVID-19 impacted 18.6% of women’s respondents in the South Asian country Pakistan. The variable’s mean and standard deviation is (1.1858) and (.39071), respectively. The below pie chart diagram explains our results. As shown in Fig. 6 shows that more males are affected by COVID-19 than females in the South Asian country Pakistan (Fig. 7). Secondly, the data from the national government statistics of 25 April 2020 and data issued by the Government of Pakistan on 27 May 2020 was analyzed. Our analyses indicate that approximately 70% of all COVID-19 cases in the South Asian country of Pakistan are male, with very close statistics for sex-specific hospital admission, mortality, and post-infection recovery as shown in Table 2. Although the number of male cases in Pakistan are 78%. From across South Asian nation Pakistan, with sex-specific statistics on the number of all COVID-19 related deaths, the rate of male deaths ranged from 65–76%. In the South Asian nation of Pakistan, the fatality pattern indicates that the male-to-female event fatality ratio is 0.9 (1.9% for males vs. 2.1% for females). In the below table Case Fatality represents the percentage of positive cases that die afterward (Figs. 8, 9, 10 and 11). Table 2 Descriptive analysis of gender affected to COVID-19 Gender

Frequency

Percent

Mean

Standard deviation

Male Female

92

81.4

1.1858

.39071

21

18.6

Total

113

100.0

932

S. Malik et al.

Fig. 7 Pie chart of gender affected by COVID-19 in South Asian country Pakistan

Fig. 8 COVID-19 cases and deaths rates per 100,000 in men/women in Pakistan

4 Discussion Researcher [31] mentioned in his research that worldwide, medical statistics warn us that males suffer more from diseases of the respiratory system than females (25%), particularly those caused by acute viral infections. Females are less vulnerable to infectious diseases than Men, relying on distinct innate immunity, gonad development of steroid hormones, and sex chromosome related factors. Researcher [32] discusses in his study that there was increased predisposition and susceptibility to heart disease in clinical trials where testosterone levels were

Analysis of Covid-19 Impact on Gender …

933

Fig. 9 COVID-19 Cases by age and sex (rates per 100,000) in Pakistan

Fig. 10 COVID-19 Total cases (% male/female) in Pakistan

high, which is more common in men who are more prone to COVID-19. Those devoid of COVID-19 cardiac disease have a better prognosis associated with patients with heart failure and hypertension [32]. Discussion in his paper that the sex and gender inequalities found in COVID-19 prevalence highlight the need to consider the effect of gender and sex on the occurrence of the disease and death, and to adapt gender and sex care to fit. Experiences from previous diseases and pandemics have strongly illustrated the value of integrating a gender and sex study into health policy preparedness and response efforts. Nevertheless, the gendered effects of illness rise in, epidemics or diseases have not yet been discussed by legislation and public health initiatives. Such countries do not disaggregate data by ethnicity and age as other countries do. Also, policymakers will disaggregate and evaluate data for gender and age gaps in all countries. In fact,

934

S. Malik et al.

Fig. 11 COVID-19 deaths (% male/female) in Pakistan

as prophylactic and clinical trials proceed. Many studies identify the autoimmunity effect of sex hormones. In addition, data from many studies also shows that male COVID-19 patients and other acute viral infections have greater vulnerability to mortality than females. In men compared with women, it is considered to be more prevalent. Researchers [21] explain in his study that there are many reasons behind this variance, like behavioral, genetic, hormonal, as well as immunological variables. The mortality rates for men are two to three times higher than for women, but tend to fluctuate in feature of population area and demography. Social variables linked to ethnicity, immune differences, hormonal variations and lifestyle behaviors including smoking and drunkenness will play a role. Researcher [22] illustrates in his research that male populations can in any way be vulnerable to the COVID-19 pandemic as a result of genetic polymorphisms identified by gender. Researcher [23] stated in his paper that beyond the biological danger the disease’s epidemiological evidence of transmission indicates that the area is not similar and that its effect on multiple populations is markedly interdependent. Researcher [24] justify the occurrence of COVID-19 more on males, researcher stated based on its socio-economic status the risk of males contracting and dying from SARS-CoV-2 is significantly increased. In England and Wales, males throughout the deprived areas have a mortality rate of 77.6 per 100,000 (44.3 per 100,00 for females) rising to 172 per 100,000 in its most deprived areas (97 per 100,000 for females). Data shared by a globally chosen company Global Health 50/50 encouraging balance between men and women in health, found a higher share of deaths in men than in women in virtually every region. The details in the Italy Integrated Monitoring Newsletter (April 23, 2020 update reveals that men’s deaths are about twice as many as women’s deaths (17.1 vs. 9.3%). Researcher [25] found similar results in Greece, the Netherlands, Denmark, Belgium, Spain, China and the Philippines. A sample of 4,880 respiratory patients or near contact with Covid-19 patients in a hospital in

Analysis of Covid-19 Impact on Gender …

935

Wuhan showed that positive rates of SARS-CoV-2 in men and older populations were substantially higher (>70 years) but only age is defined as a risk factor (>70 years). Likewise, a recent retrospective observational analysis of researcher [26] showed that 67% of critically ill patients with SARS-CoV-2 were men. Furthermore, a study of researcher [27] results for 1.099 Covid-19 patients found 58.1% to be males of 173 extreme incidents, 57.8% have affected this population. Furthermore, reported information from survival research [28] found that male deaths are considerably higher and symptoms greater than female deaths. Finally, the researcher [29] recently recorded a 1.7-fold higher case fatality rate for males than females (P < 00001). However, researchers [30] recommend a walk-through gate for mitigating the COVID-19 effect.

5 Conclusion We conduct this study to evaluate the sex gender impact on COVID-19 results in the South Asian country Pakistan. Mixed methodology was used in this study, primary data from doctors doing jobs all over Pakistan (Punjab, Sindh, Khyber Pakhtunkhwa, Balochistan) and secondary data from national Pakistani government statistics. The doctor’s response shows that 81.4% of males and 18.6 females are affected by COVID-19 in Pakistan. Descriptive analysis between gender and COVID-19 results in a mean of 1.1858 and a standard deviation of .39071. The data from national Pakistani government statistics show that the number of male cases in Pakistan is 78%, the rate of COVID-19 related male deaths ranged from 65–76%. From the whole analysis, we can conclude that COVID-19 attacks more on males rather than females in the South Asian country Pakistan. East Asian countries (China & North Korea), European countries (Italy, Spain, Germany, Sweden, Switzerland, and France), Oceania countries (Australia), Middle East country (Iran), South Asian country (Pakistan) and North American country (Canada) has a higher number of male cases than female attacked by COVID-19. However, we recommend further comprehensive studies to measure the degree to which sex will lead to COVID-19 results among South Asian country Pakistan. Limitations of the Study The key limitation of this study applies to the comparatively low number of samples carried out in the field, indicating that the overall number of tests and figures can be significantly higher than what we report. However, we do not expect the central point of this study to change considerably. Acknowledgment The authors were very thankful to Dr. Imran Hussain, Dr. Wajid Yaqoob Bajwa, Dr. Sheerbaz, Dr. Ali Hassan Shah, and Dr. Saqib Ishaq for providing data for our research. Conflict of Interest Statements Author Saba Malik (Corresponding author), Mubbra Habib, Mehmood Ahmed Husnain Hashmi, Muhammad Tariq saeed, Anwaar ul huda, Syeda Anam zahra, Muhammad Nisar, Muhammad Sajid, Shahbaz Ahmad Shahzad, Saddam Hussain, and Muhammad Sakandar Majid declares that they have no conflict of interest.

936

S. Malik et al.

References 1. Guan WJ, Ni ZY, Hu Y, Liang WH, Ou CQ, He JX, et al (2020) Clinical characteristics of coronavirus disease 2019 in China. N Engl J Med. https://doi.org/10.1056/NEJMoa2002032 2. Who director-generals opening remarks at the media briefing on COVID-19 - 11 March 2020. World Health Organisation (2020). https://www.who.int/dg/speeches/detail/who-directorgenerals-opening-remarks-at-the-media-briefing-on-COVID-19%2D%2D-11-march-2020. Accessed 22 Mar 2020 3. Coronarvirus Resource Center, Johns Hopkins University Medicine. 2020. Johns Hopkins University Medicine. Medicine JHU. https://coronavirus.jhu.edu/map.html. Accessed 22 Mar 2020 4. Wu C, Chen X, Cai Y, Xia J, Zhou X, Xu S, et al (2020) Risk factors associated with acute respiratory distress syndrome and death in patients with coronavirus disease 2019 pneumonia in Wuhan, China. JAMA Intern Med. https://doi.org/10.1001/jamainternmed.2020.0994 5. Wang D, Hu B, Hu C, Zhu F, Liu X, Zhang J, et al (2020) Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China. Jama. https://doi.org/10.1001/jama.2020.1585 6. Grasselli G, Pesenti A, Cecconi M (2020) Critical Care Utilization for the COVID-19 Outbreak in Lombardy, Italy: Early Experience and Forecast During an Emergency Response. Jama. https://doi.org/10.1001/jama.2020.4031 7. Zhao S, Cao P, Chong MK, Gao D, Lou Y, Ran J, et al (2020) The time-varying serial interval of the coronavirus disease (COVID-19) and its gender-specific difference: a data-driven analysis using public surveillance data in Hong Kong and Shenzhen, China from January 10 to February 15, 2020. Infect Control Hosp Epidemiol.https://doi.org/10.1017/ice.2020.64:1-8 8. Mo P, Xing Y, Xiao Y, Deng L, Zhao Q, Wang H et al (2020) Clinical characteristics of refractory COVID-19 pneumonia in Wuhan. China Clin Infect Dis. https://doi.org/10.1093/cid/ciaa270 9. Conti P, Younes A (2020) Coronavirus Cov-19/Sars-Cov-2 affects women less than men: clinical response to viral infection. J Biol Regul Homeost Agents 34(2) 10. Cai H (2020) Sex difference and smoking predisposition in patients with COVID-19. Lancet Respir Med. https://doi.org/10.1016/s2213-2600(20)30117-x 11. World Health Organization. WHO Coronavirus Disease (COVID-19) Dashboard, 17 July 2020 https://covid19.who.int/. Accessed 17 July 2020 12. The epidemiological characteristics of an outbreak of 2019 novel coronavirus diseases (COVID19)—China, 2020. 2020. The Novel Coronavirus Pneumonia Emergency Response Epidemiology Team, China CDC Weekly. https://www.ne.jp/asahi/kishimoto/clinic/cash/COVID19. pdf. Accessed 03 Apr 2020 13. Globalhealth. (2021). The COVID-19 Sex-Disaggregated Data Tracker | Global Health 50/50. https://globalhealth5050.org/the-sex-gender-and-covid-19-project/the-data-tracker/. Accessed 18 April 2021 14. Karlberg J, Chong DS, Lai WY (2004) Do men have a higher case fatality rate of severe acute respiratory syndrome than women do? Am J Epidemiol 159(3):229–231. https://doi.org/10. 1093/aje/kwh056 15. Chen X, Chughtai AA, Dyda A, MacIntyre CR (2017) Comparative epidemiology of Middle East respiratory syndrome coronavirus (MERS-CoV) in Saudi Arabia and South Korea. Emerg Microbes Infect 6(6):e51. https://doi.org/10.1038/emi.2017.40 16. Epidemia COVID-19 (2020) Aggiornamento Nazionale. Dipartimento Malattie Infettive e Servizio di Informatica, Istituto Superiore di Sanità. https://www.epicentro.iss.it/coronavirus/ bollettino/Bollettino-sorveglianza-integrata-COVID19_30-marzo-2020.pdf. Accessed 30 Mar 2020 17. Coronavirus disease 2019 (COVID-19) daily situation report of the Robert Koch Institute 2020. Robert Koch Institute. Institute RK. https://www.rki.de/DE/Content/InfAZ/N/Neuartiges_C oronavirus/Situationsberichte/2020-04-01-en.pdf?__blob=publicationFile. Accessed 03 Apr 2020

Analysis of Covid-19 Impact on Gender …

937

18. Gebhard C, Regitz-Zagrosek V, Neuhauser HK et al (2020) Impact of sex and gender on COVID-19 outcomes in Europe. Biol Sex Differ 11:29 https://doi.org/10.1186/s13293-02000304-9. 19. Pennell LM, Galligan CL, Fish EN (2012) Sex affects immunity. J Autoimmun 38(2–3):J282– J291. https://doi.org/10.1016/j.jaut.2011.11.013 20. Haque A, Akçe¸sme FB, Pant AB (2018) A review of Zika virus: hurdles toward vaccine development and the way forward. Antivir Ther 23(4):285–293 21. Scully EP, Haverfield J, Ursin RL et al (2020) Considering how biological sex impacts immune responses and COVID-19 outcomes. Nat Rev Immunol 20:442–447. https://doi.org/10.1038/ s41577-020-0348-8 22. Agrawal H, Das N, Nathani S, Saha S, Saini S, Kakar SS, Roy P (2020) An assessment on impact of COVID-19 infection in a gender specific manner. Stem Cell Reviews Reports 1–19. https://doi.org/10.1007/s12015-020-10048-z 23. Wenham C, Smith J, Davies S, et al (2020) Women are most affected by pandemics—lessons from past outbreaks. Nature 583(7815):194–198 24. Bowleg L (2020) We’re not all in this together: on COVID-19, intersectionality, and structural inequality. Am J Public Health 110(7):917 25. ONS (2020) Deaths involving COVID-19 by local area and socioeconomic deprivation: deaths occurring between March and May 2020. Accessed 24 Jul 2020 26. Liu R, Han H, Liu F, Lv Z, Wu K, Liu Y, et al (2020) Positive rate of RT-PCR detection of SARS-CoV-2 infection in 4880 cases from one hospital in Wuhan, China, from Jan to Feb 2020. Clin Chim Acta 505:172–175. https://doi.org/10.1016/j.cca.2020.03.009 27. Xie J, Tong Z, Guan X, Du B, Qiu H (2020) Clinical characteristics of patients who died of coronavirus Disease 2019 in China. JAMA New Open 3:e205619. https://doi.org/10.1001/jam anetworkopen.2020.5619 28. Jin JM, Bai P, He W, Wu F, Liu XF, Han DM et al (2020) Gender differences in patients with COVID-19: focus on severity and mortality. Front Public Health 8:152. https://doi.org/10.3389/ fpubh.2020.00152 29. Scully EP, Haverfield J, Ursin RL, Tannenbaum C, Klein SL (2020) Considering how biological sex impacts immune responses and COVID-19 outcomes. Nat Rev Immunol 20:442–447. https://doi.org/10.1038/s41577-020-0348-8 30. Hussain S, Cheema MJM, Motahhir S, Iqbal MM, Arshad A, Waqas MS, Malik S (2020) Proposed Design of Walk-Through Gate (WTG): Mitigating the Effect of COVID-19. Appl Syst Innov 3(3):41. https://doi.org/10.3390/asi3030041 31. Fung TS, Liu DX (2014) Coronavirus infection, ER stress, apoptosis and innate immunity. Front Microbiol 5:296. https://doi.org/10.3389/fmicb.2014.00296 32. Gebhard C, Regitz-Zagrosek V, Neuhauser HK et al (2020) Impact of sex and gender on COVID-19 outcomes in Europe. Biol Sex Differ 11:29. https://doi.org/10.1186/s13293-02000304-9

Deep Residual Convolutional Neural Network Based Detection of Covid-19 from Chest-X-Ray Images Valaparla Rohini, M. Sobhana, Ch. Smitha Chowdary, Mukesh Chinta, and Deepa Venna

Abstract The Coronavirus or 2019-nCoV (COVID-19) is a contagious disease. This new strain outbreak pressuring the health community in the world because this virus not identified early and also spreading to a large number of countries and territories, here some of them are on the edge of failing to control the spread of this virus. This virus mostly affects the respiratory system. So, it is possible to diagnose the virus, infected in the respiratory system using CT scan and chest-x-ray imaging approaches. Chest-X-ray images are amply available and swift imaging time than CT scan. The coronavirus pneumonia infected chest-x-rays has taken as the input data. Convolutional Neural Network (CNN) pre-trained ResNet-50 model is the proposed method, and it uses cross-validation for analysis. However, this model is employed to get high performance with more accurate result. Keywords Corona virus · Chest-X-ray · Diagnosis · ResNet-50

1 Introduction COVID-19 has been an epidemic disease caused by the foremost recently discovered Severe Acute Respiratory Syndrome CoronaVirus2 (SARS-CoV2) [1]. There are different diseases find by coronaviruses. Including a Severe Acute Respiratory Syndrome (SARS), discovered from musk cats to humans in 2003, the Middle East Respiratory Syndrome (MERS) discovered from dromedary to humans in 2012, and COVID-19 was caused by bats [2]. Coronavirus first unveiled in Wuhan, December 2019. COVID-19 is presently an epidemic affecting many nations globally. With the information of the World Health Organization (WHO) approximately in ten months, V. Rohini (B) · M. Sobhana · M. Chinta · D. Venna Department of CSE, V R Siddhartha Engineering College, Vijayawada 520007, Andhra Pradesh, India Ch. Smitha Chowdary Department of CSE, Koneru Lakshmaiah Education Foundation, Guntur 522502, Andhra Pradesh, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_85

939

940

V. Rohini et al.

there are more than 35.4 million people infected across 214 nations and territories. Coronavirus caused more than 1.04 million deaths, 24.6 million individuals have recovered. The number of individuals infected by this Coronavirus disease is increasing, and it turns to be increasingly hard to end the diagnostic process within the constrained time. Currently, many efforts and more attention paid to identifying the COVID-19 affected patients. The COVID-19 most common signs and symptoms are dry coughs, fever, tiredness and less common signs and symptoms are aches and pains, a headache, sore throat, diarrhea, conjunctivitis, lack of taste or smell, a rash on the skin or discoloration of palms or toes [3]. Signs and symptoms are difficulty respiration, chest pain, and lack of speech or movement. This virus is unfolding among people at some point of contact, commonly through tiny droplets through coughing, sneezing and talking. This contamination can also additionally attain to pneumonia. Lungs was effected by the pneumonia infection and also in the human body lungs was mostly effected by the coronavirus. So that CT scan and Chest-x-ray images are used to diagnose the pneumonia. Therefore, it requires expertise and necessitates for different algorithms to analyze. Consistent with this context, Machine Learning algorithms have obtained better performance in detection of pneumonia, and provides accurate detection rate. By using machine learning algorithms CT scan method is used to diagnose the chest pneumonia [4]. But it is the time taken to process and cost-effective. And CT scanner may not available in many regions as well. For that purpose, the contribution of our paper used chest-x-ray images because chest-x-ray images are widely available and take less time than the CT scan [5, 7]. So that, our paper follows penalized tuned versions of Convolution Neural Network-based models InceptionV3, Resnet50.

1.1 Deep Convolutional Neural Network A deep convolutional neural network used to examine the visual images and also for classifying images. Convolutional Neural Networks (CNNs) is the used weights for each pixel are fully connected. The layers in the convolutional neural network are fully connected, pooling and Convolutional layers. These layers are used to perform a different tasks on the input data. When deep transfer learning [6] techniques applied to medical data, meaningful results are come out and these techniques [7] are used for classification, segmentation and infection detection of medical data.

2 Related Work Here some related research work for COVID-19 disease. S. Sai Thejeshwar et al. [8] presented a KE Sieve Neural Network using the transfer learning for features which extracted and weights on the VGG-19 pre-trained CNN model. This algorithm is not capable of some specified dataset.

Deep Residual Convolutional Neural Network …

941

Al-Bawi, Ali, et al. [9] the classical VGG neural network performed with the convolutional COVID block, and it obtained 95.51% accuracy for covid-19, normal, pneumonia chest x-ray images. It was time taking process, and training deep neural network was difficult. Fang Y. et al. [10] proposed an RT-PCR technique used to identify the viral RNA and serology testing will identify the visible representation of antibodies. In this study when observing the patient samples, there is a difference in the detection rate for initial CT (50/51 [98%]) patients greater than first RT-PCR (36/51 [71%]) patients. Collected annotated data is very expensive or difficult validation CT datasets collected in one system developed to quantify infections only. Zhang, Jianpeng, et al. [11] Rapid and accurate detection of the virus used pervasive prevention and large-scale screening. Without fine-tuning, anomaly three modules achieved an accuracy of 83.61%. It is cost-effective. Shan F, Gao Y, Wang J, et al. [12] Deep learning-based method used for segmentation and quantification of the respiratory system using chest CT scans [4]. This system used a “VB-Net” neural network obtained dice similarity coefficients 91.6% ± 10.0% and with accuracy 86.7% on CT images. It is less sensitive to detect virus from the throat. Mohamed Loey et al. [13] GAN (Generate Adversarial Network) is for preprocessing, deep transfer model for training, testing and validation. GAN has Image Generation network and discriminator network. The Image Generation network generates false data. Discriminator network describes the difference between real and fake data. The algorithm takes some set of transfer models. The considered dataset was 30 times larger than the original dataset, so it creates the fake dataset images.

3 Proposed Methodology In this method, the deep convolutional neural network (DCNN) based pre-trained models have used for the categorization of chest-x-ray images. By using the Deep Transfer learning method, it controls inadequate data and training time. By using the pre-trained models of the CNN i.e., ResNet50 [14] and InceptionV3 [15] the coronavirus was detected from chest-x-ray images. ResNet50 is the best technique [16] of CNN because it uses shortcuts and also saves the time of training. It intercepts the distortion of a system which is deeper and complex. In the ResNet-50 model, bottleneck blocks used for faster training [17]. ResNet50 contain 50-layer deep neural network and it has trained on the Image Net database [18]. The database has contained 14 million images created for image recognition competitions which are belonging to more than 20000 categories [19]. InceptionV3 is CNN pre-trained model. It consists of several convolutional steps and maximum pooling steps. A Fully connected convolution network has connected at the final stage. The InceptionV3 pre-trained model used to extract the features of images [20] (Fig. 1).

942 Fig. 1 Flow diagram for prediction of Covid-19

V. Rohini et al.

Clinical Symptom

Chest-x-ray images

Load pre-trained models

Cross-Validation

Training dataset Validation dataset

Estimated Probability

3.1 Data Set The input dataset has taken from open source Kaggle dataset repository [21]. The repository contains covid-19 and regular chest-x-ray images. It is an unbalanced dataset. Here the output is a probability of accuracy (Figs. 2 and 3). Fig. 2 Covid-19 positive chest-x-ray images

Deep Residual Convolutional Neural Network …

943

Fig. 3 Normal Patient’s chest-x-ray images

Table 1 5-Fold Cross-validation Training and Testing Data. Iteration1

Iteration2

Iteration3

Iteration4

Iteration5

Test fold1

Train fold1

Train fold1

Train fold1

Train fold1

Train fold2

Test fold2

Train fold2

Train fold2

Train fold2

Train fold3

Train fold3

Test fold3

Train fold3

Train fold3

Train fold4

Train fold4

Train fold4

Test fold4

Train fold4

Train fold5

Train fold5

Train fold5

Train fold5

Test fold5

3.2 Performance Evaluation The introduced method using python3 programming language with Ubuntu 16.04 operating system. The considered dataset contains chest x-ray images. The 30 epochs carried out to over fitting pre-trained models. The dataset differentiated into 2 parts 80% training and 20% testing for performance evaluation. The 5-fold cross-validation the method is chosen k value from 1 to 5 (Table 1). 1 performancek 5 k=1 5

performance =

Specificity = True − Negative/(True − Negative + False − Positive)

(1)

Precision = True − Positive, /(True − Positive + False − Positive)

(2)

Recall = True − Positive/(True − Positive + False − Negative)

(3)

F1 − Score = 2x((Precision x Recall)/(Precision + Recall))

(4)

Accuracy =(True − Negative + True − Positive)/(True − Negative + True − Positive + False − Negative + False − Positive)

(5)

944

V. Rohini et al.

Here True-Positive correctly labelled as Covid-19, False-Positive represent normal but mislabeled positive, True-Negative correctly labelled normal, but FalseNegative mislabeled negative by the proposed model. The above equations defined for obtaining the performance.

4 Results and Discussion In this proposed work, data is trained and tested on the chest-x-ray images. Training accuracy and loss values of pre-trained ResNet50, InceptionV3 for fold-3 shown in Figs. 4 and 5. To avoid the over-fitting 30epochs are taken for the training. When compared to InceptionV3, Resnet50 obtained the highest training accuracy. The ResNet50 gives a fast training process than InceptionV3. The loss values are decreased in ResNet50 and InceptionV3 at the training stage. ResNet50 pre-trained model reduces the training loss values quickly than the InceptionV3. Fig. 4 Fold-3 performance of training accuracy for pre-trained models

Fig. 5 Fold-3 performance of training loss values for pre-trained models

Deep Residual Convolutional Neural Network …

945

Table 2. Confusion Matrix Performance results Models/Fold

Inception V3

Confusion matrix and performance results (%) TP

TN

FP

FN

Acc

Rec

Spe

pre

F1

Fold 1

7

10

0

3

85

70

100

100

82

Fold 2

10

10

0

0

100

100

100

100

100

Fold 3

10

10

0

0

100

100

100

100

100

Fold 4

10

10

0

0

100

100

100

100

100

Fold 5

10

10

0

0

100

100

100

100

100

97

94

100

100

96.4

Performance ResNet50

Fold 1

8

10

0

2

90

80

100

100

89

Fold 2

10

10

0

0

100

100

100

100

100

Fold 3

10

10

0

0

100

100

100

100

100

Fold 4

10

10

0

0

100

100

100

100

100

Fold 5

10

10

0

0

100

100

100

100

100

98.2

96

100

100

97.8

Performance

The other detailed way of given test data shown in Table 2. The highest accuracy of 98.2%. In training and testing, ResNet50 provides superiority over the InceptionV3. Here when observe the given table confusion matrix results are described from the pre-trained models fold-1 to 5.

5 Conclusion For the detection of coronavirus, the proposed method deep convolutional neural network based approach using chest-x-ray images has obtained a good result on the considered dataset automatically. ResNet-50 is the one of best algorithm in deep learning having a good result. The performance of ResNet50 pre-trained model attains with the highest accuracy 98.2%. It is an accurate, rapid and effective clinical support system for covid-19 detection. The training dataset is limited for proposed work. Further, will try to work on different CNN models can be tested by using the more number of Covid-19 and chest-x-ray images in the dataset.

References 1. Yan L, Zhang HT, Xiao Y, Wang M, Guo Y, Sun C, Tang X, Jing L, Li S, Zhang M, Xiao Y, Cao H, Chen Y, Ren T, Jin J, Wang F, Xiao Y, Huang S, Tan X, Huang N, Jiao B, Zhang Y, Luo A, Cao Z, Xu H, Yuan Y (2020) Prediction of criticality in patients with severe covid-19 infection using three clinical features: a machine learning-based prognostic model with clinical data in Wuhan. MedRxiv 2020.02.27.20028027 2. Costa S, Gomes, V, Moreli ML, Saivish, MV (2020) The emergence of SARS, MERS and novel SARS-2 coronaviruses in the 21st century

946

V. Rohini et al.

3. Bouaziz JD, Duong T, Jachiet M, Velter C, Lestang P, Cassius C, Arsouze A et al (2020) Vascular skin symptoms in COVID-19: a French observational study. J Eur Acad Dermatol Venereol 4. Shuai W, Bo K, Jinlu M, Xianjun Z, Mingming X, Jia G, Mengjiao C, Jingyi Y, Yaodong L, Xiangfei M, Bo X (2020) A deep learning algorithm using CT images to screen for corona virus disease (covid-19), medRxiv 5. Zhang J, Xie Y, Li Y. Shen C, Xia Y (2020) COVID-19 screening on chest X-ray images using deep learning based anomaly detection, arXiv 6. Chuanqi T, Fuchun S, Tao K, Wenchang Z, Chao Y, Chunfang L (2018) A survey on deep transfer learning In: The 27th international conference on artificial neural networks (ICANN). Lecture Notes in Computer Science. Springer 7. Oh Y, Park S, Ye JC (2020) Deep learning covid-19 features on cxr using limited training data sets. IEEE Trans Med Imaging 8. Sai Thejeshwar S, Chokkareddy, C, Eswaran K (2020) Precise prediction of covid-19 in chest x-ray images using ke sieve algorithm. ResearchGate 9. Al-Bawi A, Al-Kaabi KA, Jeryo, M, Al-Fatlawi A (2020) CCBlock: an effective use of deep learning for automatic diagnosis of covid-19 using x-ray images. arXiv preprint arXiv: 2009.10141 10. Fang Y, Zhang H, Xie J, Lin M, Ying L, Pang P, Ji W (2020) Sensitivity of chest CT for COVID-19:comparison to RT-PCR. Radiology 11. Zhang J, Xie, Y, Liao Z, Pang G, Verjans J, Li, W, Sun Z et al (2020) Viral pneumonia screening on chest x-ray images using confidence-aware anomaly detection. arXiv 12. Fei S, Gao Y, Wang J, Shi W, Shi N, Han M, Xue Z, Shi Y (2020) Lung infection quantification of covid-19 in ct images with deep learning. arXiv preprint arXiv: 2003.04655 13. Loey M, Smarandache F, Khalifa NEM (2020) Within the lack of chest covid-19 x-ray dataset: a novel detection model based on gan and deep transfer learning. Symmetry 12(4):651 14. Wen L, Li X, Gao L (2019) A transfer convolutional neural network for fault diagnosis based on ResNet-50. Neural Comput Appl. Springer 15. Wang C, Chen D, Hao L, Liu, X, Zeng Y, Chen J, Zhang G (2019) Pulmonary image classification based on inception-v3 transfer learning model. IEEE Access 7 16. Wu Z, Shen C, Van Den Hengel A (2019) Wider or deeper: Revisiting the resnet model for visual recognition. Elsevier 17. Yamazaki M, Kasagi A, Tabuchi, A, Honda T, Miwa M, Fukumoto N, Tabaru T, Ike A, Nakashima K (2019) Yet another accelerated SGD: Resnet-50 training on imagenet in 74.7 seconds. arXiv: 1903.12650 18. Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg AC, Fei-Fei L (2015) Imagenet large scale visual recognition challenge. Int J Comput Vis 115:211–252 19. Krizhevsky A, Sutskever I, Hinton GE (2017) ImageNet classification with deep convolutional neural networks. Commun ACM 60(6):84–90 20. Dong N, Zhao L, Wu, CH, Chang JF (2020) Inception v3 based cervical cell classification combined with artificially extracted features. Elsevier (2020) 21. Kaggle home page (2018) https://www.kaggle.com/paultimothymooney/chest-xray-pne umonia

A New Optimized Approach to Resolve a Combinatorial Problem: CoronaVirus Optimization Algorithm and Self-organizing Maps Omayma EL Majdoubi, Farah Abdoun, and Otman Abdoun

Abstract The optimization provides resolutions to complex combinatorial problems that generally deal within large data size and expensive operating processes. Metaheuristics target the promoting of new applied algorithms to resolve NP-Hard problems in order to improve resolutions requiring enhanced search strategies. Travelling Salesman Problem (TSP) is a common combinatorial problem, applied on several benchmark networks, namely the transportation networks and the routing vehicle problem in order to establish new intelligent computing methods as well as, to prove studies on their performances and efficiencies. Collective intelligence has proven satisfactory resolutions wherewith metaheuristics, although their algorithms complexity. The aim of this work is to solve the Euclidean TSP, classified as a NPhard problem by means of Self-Organizing Maps (SOM) which is a Kohonen-type network. The resolution is also computed corresponding to a new bio-inspired evolutionary strategy so-called coronavirus optimization algorithm combined with SOM algorithm. The present approach is combining an unsupervised learning strategy within the new coronavirus optimization algorithm to replicate iteratively new infected individuals and to generate diversification on the search space. This new hybrid method presents a good approximative resolutions, proved by applying tests for TSPLIB instances wherein the exact optimum is defined corresponding to each TSP data. However, the present resolutions are complex specifically for large scale by means of increasing the size of input data or size parameters. Keywords Travelling salesman problem · CoronaVirus optimization algorithm · Kohonen network · Neural network · Self-organizing maps · Parallel computing · Combinatorial optimization problem · Adaptation · TSPLIB

O. EL Majdoubi (B) · O. Abdoun Computer Sciences Department, FPL, Abdelmalek Essaâdi University, Larache, Morocco F. Abdoun Research Center STIS, M2CS, ENSAM, Mohammed V University, Rabat, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_86

947

948

O. El Majdoubi et al.

1 Introduction The optimization problem is defined as a maximization or minimization problem admitting a set of input design variables, the set of admitted solutions and their definitions via an associated computed measurement [1]. Many fields of operations research and artificial intelligence emphasize on presenting resolution of combinatorial optimization problems, in order to define a solution vector depending on the given objective function and within the respect of predefined constraints [2]. Various techniques are used to solve exactly the combinatorial optimization problems and to determine the optimum. These methods perform an implicit research among the set of space of solutions. Yet, the optimum solutions are proved only for small or medium scale of problem instances and then many interesting problems are not computed by exact methods. Therefore, the aim of the optimization is based on developing heuristics or metaheuristics in order to find good eligible approximations that are not necessary the optimal solution [3]. Collective intelligence, perceived on biological cycle of social insects, is inspiring researchers to develop metaheuristics algorithms in the context area of swarm intelligence (SI) and conducting efficient resolutions of various optimization problems [4]. The paper is structured as following: The first section concerns a literature review on combinatorial optimization problems and TSP resolutions. The second section is a review related to Kohonen-type network. The third section is a review related to coronavirus optimization algorithm. The next section is related to discuss and analyze the numerical results. Finally, a conclusion and future work are presented.

2 Combinatorial Optimization Problems The theory of computing complexity reveals that several problems are complex and proven to be not efficiently processed by sequential or parallel deterministic algorithm [5]. The interest of the researchers surrounds the proposal of methods to solve complex problems in order to reach optimal or sub-optimal solutions within a reasonable run time. The exact methods are dedicated for small problems. Thus, the approximate methods including heuristics and metaheuristics achieve solutions within a reasonable execution time [6]. Commonly, there is no efficient method to attain optimality in a quick polynomial run time for resolving NP-Hard problems, including graphic coloring, Knapsack problem, integer programming, TSP and its variants. It is imperative to speed up the execution time of proposed resolving methods, in particular for large scale problems [7]. The embedding of parallel processing techniques is recommended and possibly depending on parallel architecture of the machine, for resolving expensive algorithms and providing feasible solutions [8]. TSP is a classical optimization problem, applied to many area contexts of computing, logistics and industrial engineering. For its various applications, the researchers solved this problem through branch and bound method, linear programming and dynamic

A New Optimized Approach to Resolve a Combinatorial Problem …

949

programming, as well as, the genetic algorithms, ant colony optimization and artificial bee colony optimization wherein the input scale is increasing [9–14]. Furthermore, the aforementioned problem is a complex combinatorial optimization problem among the NP-Hard problems, that requires exploring new resolution methods, specifically the bio-inspired metaheuristics. The Cat Swarm Optimization inspired from real cat behaviors, reveals to be efficient for the TSP resolution [6]. Spider Monkey Optimization is a new method among SI algorithms, is proved to solve the TSP with good performances [4]. Indeed, the bio-inspired methods used to solve TSP, for instance genetic algorithms, ant colony optimization, particle swarm optimization and bat algorithm optimization, are not necessary providing the best solutions [15]. Differential evolution is an evolutionary algorithm, usually used to solve continuous optimization problems. A discrete version of the differential evolving algorithm is proposed to improve resolving of complex discrete TSP [16]. The TSP problem is described as a salesman who is required to choose an itinerary of minimum distance to carry out the distribution of its merchandises. The salesman must travel from the depot, visit each city once and return to the starting point [17]. The enumeration of all tours for a given problem is not a possibly appropriate method. Corresponding to an n-city TSP, the possible permutations is equal to (n − 1)! Routes [18]. In addition, the exact methods are requested to constitute a search space of all possible permutations of cities, then to find the corresponding optimal solutions. The resulting run time is a polynomial factor of O(n!), as n is the length of the route. The exact resolutions of large size problems are inoperable [7]. As the genetic algorithm is well performing in achieving good solutions for TSP, this genetic approach is also inconvenient in case of premature convergence, expensive evolving routines and non-performance of local search techniques. Thus, an immune based genetic algorithm is attested to exceed these inconvenient and improving TSP solutions [15]. Admittedly, the TSP is formulated mathematically as following [19]: n n ci j xi j Minimize : z = i=1 j=1 n Subject to : xi j = 1, j = 1, 2, . . . .n, i=1 n xi j = 1, i = 1, 2, . . . .n, j=1

xi j ∈ {0, 1}, i, j = 1, 2, .......n.  x f or ms a H amiltonien cycle

(1)

Where cij and xij are the costs and the decision variables respectively linking position i to position j. Vector x˜ has the whole sequence of the resulting route which is a solution for the TSP [20]. In what follows, a literature review is achieved for studying a Kohonen-type neural network in order to investigate its performances in resolving TSP.

950

O. El Majdoubi et al.

3 Self-organizing Maps: SOM The Artificial Neural Network (ANN) is described as a processing information system, bio-inspired based on the properties of biological nervous systems [21]. Also, the neural network is designed as the model of brain information processing, consisted of samples of communicating computing units within a connected network [22]. In fact, ANN simulate biological networks system of neurons, in order to permit computers to learn similarly as human-reinforcement learning. The neural network is basically compound of perceptron that is consisted of inputs, processor and single resulting output [23]. The SOM algorithm introduced by Kohonen, is described as an unsupervised learning method for algorithms improvement associated to a specific input data [24]. SOM algorithm is a class of competitive neural network aiming to explore network topologies and distribution strategies of input patterns [25]. Particularly, the SOM are derived from neural network fundaments and are structured in two-layers of competitive learning network [26]. Generally, the SOM are distinguished by its abilities of recognition via an unsupervised method, are a full monolayered connected network [27], where the output nodes are in contest explained by the update of the winner node and its neighbors [28]. Variants of SOM are commonly able to evaluate input patterns of the network via various strategies of auto-organization based on the learning approaches. Generally, the self-organizing learning is based on frequent update of synaptic weights of the considered network and in accordance with the input patterns by means of learning strategies [29]. The Fig. 1 is a representation of SOM network. The SOM algorithms are used to solve TSP especially by integrating efficient initialization methods and adequate adaptation parameters to better explore solutions also, to enhance fast convergence [29]. Applying this unsupervised learning method to TSP, leads to define an input vectors including the coordinates for the given cities and the corresponding weights of network nodes. The winner neuron is conquering the output nodes for a given city. Then, the neighboring routine is applied to find the closer node to the winner node by updating neurons weights. This SOM procedure is applied till up attaining a given max error or a predefined maximum of evolvement iterations [30]. SOM algorithms are efficient in solving planning path problems argued by its performances of adaptation procedures and on exploring iteratively winner nodes and their corresponding neighboring [30]. Figure 2 illustrates SOM algorithm applied to the TSP [30]. Fig. 1. Self-organizing map network on n input pattern

A New Optimized Approach to Resolve a Combinatorial Problem …

951

Fig. 2. SOM algorithm applied to TSP [30]

The geometric tour is modeled as a stretching ring of nodes while the network is evolving. The input data are presented randomly in the network. The neuron network competition is based on considering Euclidean distance between nodes of the network. The winner node J is obtained with the minimum distance to the present city as formulated below [29]:    J = Argminj xi − yj 2

(2)

Where (xi, yi ) is the coordinate of city i and ||.|| is the Euclidean distance [29]. The SOM adaptation procedure is applying firstly via an inhibition mechanism in order to explore a winner node corresponding to each city up to represent all the cities of the network once in one vector. The winner node is selected when the Euclidean distance connecting the given city and the candidate node is minimum. Furthermore, the adaptation procedures result in moving the winner node and its neighbors towards the presenting city as following [30]:   vj∗ = vj + μf(., .) c − vj

(3)

Where μ is the corresponding learning rate. The neighboring function is defined as: f (G, d) =

  ex p −d 2 /G 2 , d < 0.2 m 0, else

(4)

Where G is the gain parameter and d is the cardinal distance measured along the ring consisted of m nodes. The gain parameter is initialized by G0 = 0.06 + 12.41 m and updated for each iteration. The predefined values of learning and decreasing rates are μ = 0.6 and α = 0.1 [30]. The competitive training algorithm is used to allow the winning neuron to learn as well as the neurons located within a predefined radius from the winning neuron and to update the related adaptation measures simultaneously [29]. The cardinal distance measured along the ring between nodes j and J, where m

952

O. El Majdoubi et al.

is the number of the neurons is obtained as following [29]: d = min{||j − J||, m − ||j − J||}

(5)

Importantly, the neighborhood function has an impact on planning for the shortest path by exploring closer nodes to each other’s [29]. It is important to develop heuristics for parameters adaptation and adjustment in order to reach a faster convergence. The main adaptation parameters are updated as described on Eqs. 6 and 7. The learning rate is related to the number of iterations. Furthermore, the neighborhood function variance is defined depending to an appropriate initialization equal to 10 [29]. 1 αk = √ 4 k

(6)

σk = σk−1 (1 − 0.01 × k), σ0 = 10

(7)

The resolution of Euclidean TSP is also made via hybrid methods, for instance, by combining SOM procedure within an evolutionary dynamic algorithm, by the integration of a mapping operator, a fitness evaluation and a selection procedure. This method is interesting justified by the efficiency and the performances relative to computed solutions and run time [31]. The hybridization of evolutionary algorithms is a powerful method for the resolution improvement. The recombination of algorithms is possibly made by implementing unrelated methods to previous information of parents, evolutionary process, or designing interaction models within the set of solutions [32].

4 Coronavirus Optimization Algorithm: CVOA Recently, the world faced a new respiratory virus so-called coronavirus (COVID19). At the event of the outbreak, a novel bio-inspired metaheuristic is developed, modeling the spread of coronavirus. Since an initial infected individual called PatientZero (PZ), the coronavirus infects new individuals according to a specific rate and create a search space of infected individuals. Each individual belongs to infected population, is possibly constrained to die, infect or recover. The main properties of CVOA are as follows: The probabilities and parameters are defined also updated by scientific community, the exploration of search space is handled as long as the infected population is not null and the high rate of expansion ensures better use of search space leading to the intensification of the resolution. Also, the concept of parallel strains reformulated as applying the processing algorithms in different processors in order to generate diversification of the resolutions. The Coronavirus optimization algorithm begins by generating a random initial population since the individual PZ. The next step concerns the exploration of dead individual depending on die probability(P_DIE)

A New Optimized Approach to Resolve a Combinatorial Problem …

953

and make their removal from the infected population. This step so-called intensification is also handled for the detection of recovered individual who possibly can infect others, depending on probabilities of spreading (P_SUPERSPREADER). Also, for ensuring diversifications of individuals, it is appropriate to define an immigration probability (P_TRAVEL) for detecting individuals enabling the spread of coronavirus in other solutions. The update of populations is ensured for each iteration. Each dead individual is appended in dead population to avoid reusing it. After each iteration, the infected population are sent to recovered population. The recovered individual can be reinfected if the probability of reinfection (P_REINFECTION) is acquired. Thus, each individual is subjected to distancing or isolation measures if the probability of isolation is achieved. Finally, each infected individual is sent to new infected population. The stop criterion is expected if there is no evolution of the infection, explained by the decreasing size of infected population and new infected population, or if the individuals in the new infected population cannot transmit anymore the virus. Generally, the stop criterion is achieved throughout a specific duration called PANDEMIC_DURATION [33]. The main CVOA algorithm [33] is presented as following (Fig. 3):

Fig. 3. Main CVOA procedure [33]

954

O. El Majdoubi et al.

5 Results and Discussions This work consists on applying a new method hybridizing Coronavirus Optimization Algorithm and SOM algorithm to solve the Euclidean TSP. The conceived approach is based on considering subsequently a random path of n city as an input pattern. Corresponding to each generated tour, the adaptation parameters are calculated to select the winner node of which the distance is minimum to the located point of the tour. The adaptation procedure is then applied to detect the neighbors in order to create a new candidate solution. The implementation of the proposed method is an evolutionary algorithm based on probabilistic procedures, that requires a definition of the replicate function, which is suggested to insert new individuals to be subjected to CVOA evolution. The replicate function is thus conceived to generate candidate solution since SOM algorithm. The results are obtained by programming in Matlab using the TSPLIB instances. The Table 1 presents the numerical results of SOM algorithm by applying adaptation functions. The following section is presenting the obtained results of TSP resolution using the proposed approach combining the CVOA and SOM algorithms. Corresponding to primary executions, the implementation requires an expensive run time, especially when the size of the tour is increasing or the number of iterations since SOM implementation(it_Max) as well the number of iterations since the present hybrid algorithm (PANDEMIC_DURATION). Hence, the multi-core parallelism via SPMD is applied. The obtained results also are obtained by considering: it_Max = 10, number of cores = 4, PANDEMIC_DURATION = 250, and by considering a predefined learning parameter: μ = 0.6 and α = 0.1. The results as shown in Table 2 are better improved compared to SOM implementation. The following section is presenting the aforementioned implementation by considering the adaptation functions of learning parameters. The presented solutions are also better improved by considering the adaptation of learning parameters. The evolution of the convergence related to the considered approach is described in Table 3 and Fig. 4. The convergence evolution corresponding to small and medium data size (Burma14, Ulysses16, Ulysses22) is similar as the average deviation are 0.09, 0.08 Table 1. The Numerical results of SOM algorithm applied to TSP

TSPLIB instance

Present solution

Exact optimum

Numbers of iterations

Burma14

33.6037

30.8785

3000

Berlin52

20985

7542

2500

Ulysses16

74.2578

74.1087

3000

Ulysses22

99.7789

75.5975

3000

Att48

108250

10628

2500

Rat99

6738. 3

1211

2500

Pr76

467350

108159

2500

A New Optimized Approach to Resolve a Combinatorial Problem …

955

and 0.06 related to the Euclidean TSPLIB instance Burma14 and corresponding to results of Tables 1, 2 and 3 respectively. Also, the average deviation related to the TSPLIB instance Ulysses16 are 0.002, 0.06 and 0.017 corresponding to SOM resolution and to hybrid proposed algorithm considering a predefined and adapted learning parameter respectively. The calculated average deviation related to the TSPLIB instance Ulysses22 are 0.3, 0.4 and 0.3 resulting since the three considered implementation tests respectively. The considered approaches presented in this work are efficient to resolve the TSP corresponding to small and medium data scale. Indeed, corresponding to the TSPLIB instances namely Berlin52, Att48, Rat99 and Pr76, the SOM resolution is not good performing. Thus, the results of the aforementioned instances are better improved by the implementation of the proposed hybrid method especially, whereas the learning parameters are adapted. The obtained average deviation is 0.3 and 0.5 corresponding to Pr76 and Rat99 respectively, resolved by the present combined algorithm and where the learning parameters are adapted. Therefore, it is important to improve the present proposed resolution by tuning of the considered parameters or experiment the successful neighborhood procedures. Importantly, it is necessary to apply the techniques of parallel computing in order to achieve fast convergence and to improve resolutions. Table 2. The Numerical results of hybrid CVOA-SOM algorithm applied to TSP

Table 3. The Numerical results of adapted hybrid CVOA-SOM algorithm applied to TSP

TSPLIB instance

Present solution

Exact optimum

Burma14

33.5524

30.8785

Berlin52

20624

7542

Ulysses16

79.1248

74.1087

Ulysses22

107.8736

75.5975

Att48

108920

10628

Rat99

1915

1211

Pr76

147060

108159

TSPLIB instance

Present solution

Exact optimum

Burma14

32.8596

30.8785

Berlin52

20985

7542

Ulysses16

75.3930

74.1087

Ulysses22

100.4639

75.5975

Att48

24246

10628

Rat99

1915

1211

Pr76

147060

108159

956

O. El Majdoubi et al.

Fig. 4. The convergence evolution of the proposed algorithm to solve TSP

6 Conclusion and Future Work The present work is emphasizing on the importance of hybridizing bio-inspired methods within learning strategies. The proposed approach is requiring parallel processing especially when the complexity of the algorithm is increasing. Also, the improvement of this hybrid approach depends on the improvement of SOM algorithm, on the selection and search strategies as well as on tuning parameters. Hence, the improvement of the proposed hybrid metaheuristic is the prospect for future research.

References 1. Bovet DP, Clementi A, Crescenzi P, Silvestri R (1996) Parallel approximation of optimization problems. In: Ferreira A, Pardalos PM (eds) Solving combinatorial optimization problems in parallel. LNCS, vol 1054, pp 248–274. Springer, Berlin 2. Lüling R, Monien B, Reinefeld A, Tschöke S (1996) Mapping tree-structured combinatorial optimization problems onto parallel computers. In: Ferreira A, Pardalos PM (eds) Solving combinatorial optimization problems in parallel. LNCS, vol. 1054. Springer, Berlin, Heidelberg 3. Laursen PS (1996) Parallel heuristic search – introductions and a new approach. In: Ferreira A, Pardalos PM (eds) Solving combinatorial optimization problems in parallel, LNCS, vol 1054. Springer, Berlin, Heidelberg, pp 248–274 4. Akhand MAH, Ayon SI, Shahriyar SA, Siddique N, Adeli H (2020) Discrete spider monkey optimization for TSP. Appl Soft Comput vol 86, p 105887 5. Clementi A, Rolim JDP, Urland E (1996) Randomized parallel algorithms. In: Ferreira A, Pardalos PM (eds) Solving combinatorial optimization problems in parallel. LNCS, vol 1054. Springer, Heidelberg 6. Bouzidi A, Riffi ME (2019) Improved CSO to solve the TSP. In: Ezziyyani M (eds) AI2SD Conference 2018. Advances in Intelligent Systems and Computing, vol 714. Springer, Singapore 7. Othman A, Haimoudi E, Mouhssine R, Ezziyyani M (2019) An effective parallel approach to solve multiple TSP. In: Ezziyyani M (eds) AI2SD Conference 2018. Advances in Intelligent Systems and Computing, vol 714. Springer 8. Ferreira A, Pardalos PM (1996) SCOOP: Solving combinatorial optimization problems in parallel. In: Ferreira A, Pardalos PM (eds) Solving combinatorial optimization problems in parallel. LNCS, vol 1054. Springer, Berlin

A New Optimized Approach to Resolve a Combinatorial Problem …

957

9. Durbin R, Willshaw D (1987) An analogue approach to the travelling salesman problem using an elastic net method. Nature 326:689–691 10. Wang J, Ersoy OK, He M, Wang F (2016) Multi-offspring genetic algorithm and its application to TSP. Appl Soft Comput 30:484–490 11. Ezugwu AES, Adewumi A, Frîncu M (2017) Simulated annealing based symbiotic organisms search optimization algorithm for traveling salesman problem. Expert Syst Appl 77:189–210 12. Choong SS, Wong LP, Lim CP (2019) An artificial bee colony algorithm with a modified choice function for the traveling salesman problem. Swarm Evol Comput 44:22–635 13. Chen WL, Dai SG (2006) Survey of algorithms for traveling salesman problem. J Chuzhou Univ 8(3):1–6 14. Guo JY (2006) An overview of traveling salesman problem. Pop Sci Technol 8:229–230 15. Lahjouji A, Tajani C, Krkri I, Fakhouri H (2019) Immune based genetic algorithm to solve a combinatorial optimization problem: Application to traveling salesman problem. In: Ezziyyani M (eds) AI2SD Conference 2018, Advances in intelligent systems and computing, vol 915. Springer 16. Ismail MA, Daryl E, Kathryn K (2020) A novel design of differential evolution for solving discrete TSP. Swarm Evol Comput 52:100607 17. Hoffman KL, Padberg M, Rinaldi G (2013) Traveling salesman problem. In: Gass S I, Fu MC (eds) Encyclopedia of operations research and management science. Springer, Boston 18. Gilmore PC, Gomory RE (1964) A solvable case of the traveling salesman problem. Proc Natl Acad Sci 51:178–181 19. Ahuja RK, Magnanti TL, Orlin JB (1993) Network flows. Prentice-Hall 20. Bazaraa MS, Jarvis JJ, Sherali HD (1990) Linear programming and network flows. Wiley, New York 21. Koprinkova HP, Mladenov V, Kasabov NK (2015) Artificial neural networks. Springer Series in Bio-/Neuroinformatics 22. Trick M (2008) Oper Res Lett 36(2):276–277 23. Khourdifi Y, Bahaj M (2020) Analyzing social media opinions using hybrid machine learning model based on artificial neural network optimized by particle swarm optimization. In: Ezziyyani M (eds) AI2SD Conference 2019. Advances in Intelligent Systems and Computing, vol 714. Springer, Singapore 24. Kohonen T (1990) The self-organizing map. Proc IEEE 78(9):1464–1480 25. Kohonen T (2001) Self-organizing maps. Springer , Heidelberg 26. Somhom S, Modares A, Enkawa T (1997) A self-organising model for TSP problem. J Oper Res Soc 48(9):919–928 27. Kohonen T, Kaski S, Lagus K, Salojarvi J, Honkela J, Paatero V, Saarela A (2000) Self organization of a massive document collection. IEEE Trans Neural Netw 11(3):574–585 28. Moukhafi M, Yassini KE, Bri S, Oufaska K (2019) Artificial neural network optimized by genetic algorithm for intrusion detection system. In: Ezziyyani M (eds) AI2SD Conference 2018. Advances in Intelligent Systems and Computing, vol 714. Springer, Singapore 29. Yanping B, Wendong Z, Zhen J (2006) A new self-organizing maps strategy for solving TSP. Chaos Solitons Fractals 28(4):1082–1089 30. Jan F, Miroslav K, Vojtˇech V, Libor P (2011) An application of the self-organizing map in the non-euclidean TSP. Neurocomputing 74(5):671–679 31. Créput JC, Koukam A (2009) A memetic neural network for the euclidean TSP. Neurocomputing 72(4–6):1250–1264 32. Tinós R (2020) Artificial neural network based crossover for evolutionary algorithms. Appl Soft Comput 95:106512 33. Martínez-Álvarez F, Asencio-Cortés G, Torres JF, Gutiérrez-Avilés D, Melgar-García L, PérezChacón R, Rubio-Escudero C, Riquelme JC, Troncoso A (2020) Coronavirus optimization algorithm: a bioinspired metaheuristic based on the COVID-19 propagation model. Big data 8(4):308–322

Study of a Dual-Function Intelligent Electronic Pin to Help Compliance with Security Measures Related to Covid-19 Laince Pierre Moulebe, Abdelwahed Touati, Eric Obar Akpoviroro, and Nabila Rabbah

Abstract COVID-19 is a virus that developed in December 2019, the disease has spread around the world and there are many cases per day. The latest information reports that there are more than sixty-nine million cases of infection worldwide according to an official diagnosis. The new coronavirus pandemic has killed at least one million six peoples worldwide since its emergence, according to a report by AFP (Agence France Presse), but the actual number estimate higher, knowing that some countries are exceeded by the number of cases in hospitals and that others do not have the resources to test the majority of their population. Several treatment methods to slow down and eliminate Covid-19 exist to date. Indeed, following the appearance of the new Corona Virus, the world scientific community has carried out several research and development for eliminated the virus, there are smart masks, respirators, gels, mobile applications, vaccines that are gradually developing and much more research. However, according to studies, transmission is mainly by direct contact, including contact of uninfected hands with the face, non-compliance with distancing measures. And, according to psychologists, one of the most effective methods to fight against the Covid-19 is to prevent these frequent forget fulnesses. On the other hand, research shows that hand-to-face contact is a frequent human behavior. Hence, the main objective of this article is to propose a prevention technique through the study of an electronic device that allows users to keep their safety distance, while reminding them of the approach of a hand towards their face. It is an electronic pin featuring PIR sensors, with lenses designed to detect at distances 1.5 m. Containing a battery for power, a button, which turn on or off the pin, depending on whether you are in a bus for example or in an airport. Besides, this simple electronic component designed, pin will have suitable cost for all social categories. Keywords Covid-19 · Prevention · Pin electronic · Distancing measure · Hand detection · PIR sensor

L. P. Moulebe (B) · A. Touati · E. O. Akpoviroro · N. Rabbah Laboratory of Structural Engineering, Intelligent, Systems & Electrical Energy ENSAM, Hassan II University, Casablanca, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_87

959

960

L. P. Moulebe et al.

1 Introduction Covid-19 was declared by the World Health Organization on March 11, 2020 as a pandemic following the number of deaths which has multiplied 13 times more than that recorded in China [1]. Covid-19 continues to increase today, with more than sixty-nine million cases positive infection that have been officially diagnosed in 208 countries and territories since the start of the epidemic. According to the AFP, the actual number of contaminations is much higher than this value, knowing that a large number of countries now only testing cases requiring hospital treatment. Among these cases, at least forty-four million are now considered cured. Covid-19 is usually spread through close contact (shaking hands, by the airway, coughing and sneezing). Several researches are established in order to fight this pandemic [2], through some suggestions for health workers because they are less exposed and their availability is essential [3, 4], moreover according to [5] it is important to take an interest in the behavior of people working in hospitals, on their knowledge about the epidemic. Despite the containment including about 4.5 billion of the population, awareness of behaviors and habits, respect for hygiene, we note that the number of cases continues to increase. Practical innovative systems such as automatic disinfectants suitable for entering large spaces [6], smart bracelets allowing hands to be disinfected at any time [7], it is clear that innovations are continually emerging. All these systems aim to fight against the pandemic which affects more than 300 people every minute [8]. To date several countries have developed vaccines which are being distributed [9–11], however populations remain hesitant about these vaccines [12, 13], because some of the first vaccines were not effective from the start. This distrust of the population shows that it remains important to offer solutions in addition to vaccines which are the subject of speculation, this is why in this article we propose another innovation in the prevention of Covid-19. According to [14], touching the face is a typically human behavior that expresses self-awareness; however, it is one of the best ways to prevent coronavirus spread. Several studies on self-contact have been established over the time [15–19], showing that, it is a behavior that is repeated frequently, but further research shows that in self-contact the contact of hand to face is the most frequent [20]. In [21] a study shows the behavior concerting the contact of the hands with the face to perform on 60 peoples. We see that it is indeed one of the most frequent human behaviors, but the best way to fight covid-19 is to limit it, even avoid contact of non-disinfected hands with the face because each forget (Fig. 1) may be likely to cause multiple casualties. Hence, to limit the errors and forgetting when faced with Covid-19 concerning touching the face, in this article we propose the study of an electronic device that would allow social distancing (Fig. 2) while allowing users to always clean their hand before touching their face and keep the distancing. The electronic system studied mainly consists of a PIR sensor, a lens, an on/off button, a power supply battery, a microcontroller and a sound. The studied device will be worn like a pin, its size is around 50 mm.

Study of a Dual-Function Intelligent Electronic Pin …

961

Fig. 1 Example of behavior

This work is divided into three parts, the first paragraph presenting the description of the system, then in the second the proposed technical solution followed by a conclusion.

2 Description Studied Devices used to remind humans to perform tasks are found a lot in our environment, in the health sector we use connected objects such as Medi’Rappel, Medisafe, to remind to take these drugs or to respect the dosage, warning lights used in cars, control systems in industry. The Covid-19 is mainly transmitted between people by respiratory droplets and contact [21–25],In an analysis of 75,465 cases of Covid-19 in China, no transmission by air was reported [26]. The device is designed to detect peoples over a distance of 0 to 1.5 m, the device mainly consists of a PIR (Fig. 3) detector which is ideal motion sensors for humans and animals [27], a battery, a microcontroller allowing the sounder to be put on and standby for 5 after detection of the approach of a hand towards the face or of a person within a radius of 1.5 m according to the simple algorithm (Fig. 4) and a microphone (Fig. 5 b). Note that these are miniaturized devices with the smallest sizes. The detection distance is an important parameter who is calibrate with the lens of the PIR sensor. Through the work established by [27, 28], we can evaluate the characteristics (Fig. 5 a) of the battery for a detection distance between 0–1,5 m which is around 5 V. The electronic pin is designed to allow users to keep their safe distance. Therefore, the user wearing the pin is automatically alerted by a song in the event of presence of a person within 1.5 m, but also when a hand approaches the face, knowing that the hand passes through the field of the lens, this sensor can be shut down if we are in a bus or public transport where compliance with the measures is difficult. In this flowchart we show the system that puts the sensor on stand-by, (1) the sensor is active and detects a hand, then the alert is issued and (2) the user cleans his hands. Subsequently, through a microcontroller, the sensor is put on stand-by for 5 min (3). And finally the controller activates the sensor after 5 min (4).

962

L. P. Moulebe et al.

Fig. 2 Safety distance

Fig. 3 PIR sensor

3 Technical Solution In this part we present an electronic diagram of the proposed solution to perform on easyEDA Fig. 6. The proposed circuit is similar to a basic motion detection circuit with some modification, in fact the proposed circuit mainly consists of a PIR sensor (IRA), a push button (KEY1) to turn on/off the pin, a power supply battery (BAT), a sounder to emit the alert song (BUZZER) and the components necessary for the operation of the usual motion sensor circuit. One of the particularity of this system is the function of being a pin placed on a garment, and the lens which must limit the detection to 110°/1.5 m.

Study of a Dual-Function Intelligent Electronic Pin …

sensor on

1

963

hand/human detected

Alert 2

Hand clean/distance taken Microcontroller desactiveted sensor

3

sensor off

Delay 5 min

Microcontroller activeted sensor 4

sensor on

Fig. 4 Algorithm of microcontroller

Fig. 5 a Output of a PIR sensor in case of passages at different distances, b sounder

As mentioned in the introduction, the device is of the order of a few centimeters, in order to have a shape similar to the common pins, the 3D solution appears as shown in Fig. 7.

964

L. P. Moulebe et al.

Fig. 6 Electronic circuit of pin

Fig. 7 3D pins model

4 Conclusion In this work we were able to highlight the fact that COVID-19 is mainly spread by contact, and that despite several restriction such as containment, awareness to clean hands and safety distance, the number of cases continues to increase, then it has been shown that touching the face is a typically human behavior. This allowed us to show the need for a simple system allowing users to have a simple and easily portable system that reminds them to clean their hands before touching their face but also to respect social distancing measures. Thus, omissions of safety measures in supermarkets, in front of banks or any other space will be limited as well for contact of the hands with the face. In definitive this solution will help to reduce the propagation of the Covid-19.

Study of a Dual-Function Intelligent Electronic Pin …

965

References 1. Ho LTF, Chan KKH, Chung VCH, Leung TH (2020) Eur J Integr Med 101116. https://doi.org/ 10.1016/j.eujim.2020.101116 2. Yen M, Schwartz J, Chen S, King C, Yang G, Hsueh P (2020) ScienceDirect Interrupting COVID-19 transmission by implementing enhanced traffic control bundling: implications for global prevention and control efforts. J Microbiol Immunol Infect. 3. https://doi.org/10.1016/ j.jmii.2020.03.011 3. Xu K, Lai X, Zheng L (2020) Suggestions on the prevention of COVID-19 for health care workers in department of otorhinolaryngology head and neck surgery. World J Otorhinolaryngol Neck Surg. https://doi.org/10.1016/j.wjorl.2020.03.002 4. Tebala GD, Bond-Smith G ( 2020) Guidelines and recommendations during the COVID-19 pandemic: a word of caution. Am J Surg 220(6):1526–1527. https://doi.org/10.1016/j.amjsurg. 2020.06.005 5. Shi Y et al (2020) Brain, Behavior, & Immunity - Health Knowledge and attitudes of medical staff in Chinese psychiatric hospitals regarding COVID-19. Brain Behav Immun Heal 4:100064. https://doi.org/10.1016/j.bbih.2020.100064 6. Hussain S et al (2020) Proposed design of walk-through gate (WTG): Mitigating the effect of COVID-19. Appl Syst Innov 3(3): 1–7. https://doi.org/10.3390/asi3030041 7. El Majid B, Motahhir S, El Hammoumi A, Lebbadi A, El Ghzizal A Preliminary design of a smart wristband disinfectant to help in Covid-19 Fight. https://doi.org/10.3390/inventions50 30032 8. CovInfo - Suivez l’évolution du Coronavirus dans le monde avec notre bulletin quotidien. https://www.covinfo.fr/monde. Accessed 06 Dec 2020 9. Reddy KA, Srinivas K, Ayyappan GS (2015) Simulation of power management circuit in a standalone solar photovoltaic-fuel cell-battery hybrid power system. In 2015 IEEE IAS Joint Industrial and Commercial Power/Petchem Product Industries ICPSPCIC, 168–173. https:// doi.org/10.1109/CICPS.2015.7974070 10. “Covid-19 vaccine: who are countries prioritising for first doses? | World news | The Guardian.” https://www.theguardian.com/world/2020/nov/18/covid-19-vaccine-who-are-cou ntries-prioritising-for-first-doses. Accessed 11 Dec 2020 11. Vaccins contre le Covid-19 : quels sont les pays qui ont déjà passé commande ? - L’Express.” https://www.lexpress.fr/actualite/monde/vaccins-contre-le-covid-19-quels-sontles-pays-qui-ont-deja-passe-commande_2138952.html. Accessed 11 Dec 2020 12. On Kwok K et al (2020) Journal Pre-proof Influenza vaccine uptake, COVID-19 vaccination intention and vaccine hesitancy among nurses: a survey TITLE Influenza vaccine uptake, COVID-19 vaccination intention and vaccine hesitancy among nurses: a survey. Int J Nurs Stud (2020). https://doi.org/10.1016/j.ijnurstu.2020.103854 13. Vaccine hesitancy in the University of Malta Faculties of Health Sciences, Dentistry and Medicine vis-à-vis influenza and novel COVID-19 vaccination | Elsevier Enhanced Reader. https://reader.elsevier.com/reader/sd/pii/S0378378220307623?token=22F51A1FB4FEFC9 15C562BAC3928A46C9FB5C26CBB714C789EB335A44A2423C702966C5AD8E77760B 4D65FC3F6264393. Accessed 07 Dec 2020 14. Why you can’t stop touching your face, even though it’s one of the best ways to prevent coronavirus spread. https://www.businessinsider.fr/us/psychological-reason-why-you-cant-stop-tou ching-face-coronavirus-2020-3?utm_source=feedburner&utm_medium=referral. Accessed 09 Apr 2020 15. Ekman P, Friesen WV (1974) Nonverbal behavior and psychopathology. In: Kats MM, Friedman RJ (eds) The psychology of depression. Contemporary theory and research, New York, pp 203–224 16. Barroso F, Freedman N, Grand S (1980) Self-touching, performance, and attentional processes. Percept Mot Skills 50(3):1083–1089 17. Hampson E, Kimura D (1984) Hand movement asymmetries during verbal and nonverbal tasks. Canadian J Psychol 38(1):102–125 PMID: 6713294

966

L. P. Moulebe et al.

18. Harrigan JA (1982) (1985) Self-touching as an indicator of underlying affect and language processes. Soc Sci Med 20(11):1161–1168 19. Leavens DA, Aureli F, Hopkins WD, Hyatt CW (2001) Effects of cognitive challenge on selfdirected behaviors by chimpanzees (Pan troglodytes). Am J Primatol 55(1):1–14. https://doi. org/10.1002/ajp.1034. PMID: 11536312 20. Goldberg S, Rosenthal R (1986) Self-touching behavior in the job interview: antecedents and conse- quences. J Nonverbal Behav 10(1):65–80 21. Mueller SM, Martin S, Grunwald M (2019) Self-touch: contact durations and point of touch of spontaneous facial self-touches differ depending on cognitive and emotional load. PLoS One 14:3. https://doi.org/10.1371/journal.pone.0213677 22. Liu J, et al. (2020) Community Transmission of Severe Acute Respiratory Syndrome Coronavirus 2, Shenzhen, China, 2020. Emerg Infect Dis 26(6). https://doi.org/10.3201/eid2606. 200239 23. Modes of transmission of virus causing COVID-19: implications for IPC precaution recommendations https://www.who.int/news-room/commentaries/detail/modes-of-transmission-ofvirus-causing-covid-19-implications-for-ipc-precaution-recommendations. Accessed 11 Apr 2020 24. Li Q et al (2020) Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia. N. Engl J Med 382(13):1199–1207. https://doi.org/10.1056/NEJMoa2001316 25. Chan JFW et al ( 2020) A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster. Lancet 395(10223):514–523. https://doi.org/10.1016/S0140-6736(20)30154-9 26. Frieden TR, Lee CT (2020) Identifying and Interrupting Superspreading Events—Implications for Control of Severe Acute Respiratory Syndrome Coronavirus 2. Emerg Infect Dis. 26(6) https://doi.org/10.3201/eid2606.200495 27. Gami H (2017) Movement direction and distance classification using a single PIR sensor. IEEE Sensors Lett. 2(1):1–4. https://doi.org/10.1109/lsens.2017.2782179 28. Zappi P, Farella E, Benini L (2010) Tracking motion direction and distance with pyroelectric IR sensors. IEEE Sens J 10(9):1486–1494. https://doi.org/10.1109/JSEN.2009.2039792

DenTcov: Deep Transfer Learning-Based Automatic Detection of Coronavirus Disease (COVID-19) Using Chest X-ray Images Youssra El Idrissi El-Bouzaidi and Otman Abdoun

Abstract On 31 December 2019, COVID-19, a novel coronavirus, appeared for the first time in the Chinese city of Wuhan, to act as a preliminary warning and affected a wider human being in the world. This virus, declared a pandemic by the auspices of the World Health Organization (WHO), given its high rate of transmissibility. The protocol most often used to detect the virus is PCR. It is a time-consuming and less sensitive procedure with high false-negative results. These problems are solved through radiographic imaging techniques to detect radioactive symptoms related to COVID-19. Furthermore, significant time is required to complete the analytical task, and mistakes can occur, meaning that automation is necessary. The use of advanced Artificial intelligence tools can significantly accelerate both the time and quality of the analysis. We suggest DenTcov, a computer-aided approach to detect COVID-19 infection via chest X-ray images. Our model is a two-phase process: Phase (1) Pre-Processing and data augmentation, Phase (2) COVID-19 detection based DensNet121, a pre-trained model, then trained with the dataset prepared by us. During the experimental phase of DenTcov, we measure the performances of the architecture by calculating a set of common metrics, both 2-class and 3-class classification. The experimental assessment confirms the DenTcov model offers a 96.52 and 99% higher classification accuracy for three and two classes, respectively, compared to other proposed methodologies. Keywords Artificial intelligence · Deep learning · CNNs · Transfer learning · DenseNet121 · COVID-19

1 Introduction Recently, coronavirus (COVID-19) caused a veritable worldwide panic on all the continents, disrupting lives and causing high mortality. After initial screening, the virus has spread to almost every country, killing more than 1,104,061 citizens out of Y. El Idrissi El-Bouzaidi (B) · O. Abdoun Laboratory of Advanced Sciences and Technologies, Polydisciplinary Faculty, Abdelmalek Essaadi University, Larache, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_88

967

968

Y. El Idrissi El-Bouzaidi and O. Abdoun

an estimated 39,164,496 confirmed cases as of October 16, 2020, according to the statistics published on the World Health Organization’s website [1]. A rapid expansion of the virus was expected. Therefore, it is necessary for prevention, quarantine, and treatment procedures to identify early and effectively any infected person. Many developed countries have seen their health infrastructures deteriorate, the lack of equipment and kits (PCR) has become a problem. To diagnose this virus, one of the following tests is considered justified: radiographic imaging, or X-rays, and computed tomography or CT images. Chest radiography is preferable to computed tomography because CT imaging generally takes much longer than x-ray images, making it expensive, the availability of needs-based CT scanners is restricted to industrialized countries. On the other hand, radiographic images remain largely the most common and accessible method of diagnostic imaging for the most practical and clinical needs [2]. One problem with the X-ray images is that sometimes the characteristics that describe the existence of COVID-19 are mixed with other respiratory diseases such as pneumonia, and therefore, radiologists find it difficult to diagnose this disease. Deep learning techniques help to solve these problems, and their predictive accuracy is the same, and sometimes better than that of a radiologist [3]. Diagnostic using Artificial Intelligence (AI) solutions are getting more and more successful in the modern medical field [4]. In particular, image classification by convolutional neural networks is the most interesting tool of deep learning [5]. Image-based diagnostics using deep learning and computer vision techniques have proven their great value in providing a rapid and precise disease diagnosis consistent with a radiologist’s reliable accuracy [3]. Advanced learning methods are not currently being replaced by qualified medical diagnostic clinicians and are intended to complement strategic decision-making. DenTcov Model based on deep learning, more precisely convolutional neural network-based an advanced deep-transfer learning algorithm to automatically extract features from the radiographic images that describe the appearance of the disease and indicate whether it is a COVID-19 infection, which is very useful in helping healthcare clinicians screen, identity, and track positive cases. The paper was organized into the following sections: The proposed model is outlined and detailed below starting Sect. 3. The parameters relating to the experimental model with performance measures appear in Sect. 4. For more discussion of the proposed DenTcov model and related results, see Sect. 4.2 and 4.3, respectively. See Sect. 5 to conclude.

2 Related Work Since computer-assisted image and deep learning technologies have proven their superiority for medical image analysis, with the reliability to detect COVID-19 into chest X-ray image [6, 7], researchers use advances of these techniques as a tool to diagnose COVID-19. Applying artificial intelligence offered significant advantages in detecting the disease and measuring infection rates, with promising results.

DenTcov: Deep Transfer Learning-Based Automatic Detection ..

969

Farooq and Hafeez [8] proposed by successive stages based on the architecture of ResNet-50 a model, called COVIDResNet, using a collection of radiographic image datasets that classifies patients as normal, bacterial, viral Pneumonia, and COVID-19 positive infections. An accuracy of 96.23% was achieved. In another Paper [9], transfer learning techniques applied with the concatenation of pre-trained models, ResNet50V2 and Xception, the proposed approach was compared to Xception Res-Net50V2 models as a means of classifying chest X-ray images according to the normal, COVID-19, and non-COVID pneumonia classes. They used five-fold cross-validation as part of their classification testing process. This technique resulted in approximately an accuracy of 91.4%. Narin et al. [10] showcased several deep learning approaches, including three distinct deep learning architectures, ResNet-50, Inception-ResNetV2, and InceptionV3, The binary classification is used to discriminate COVID-19 distinct to the healthy image. The evaluation indicates that ResNet50 had the highest classification efficiency with an accuracy of 98.0%, recall of 96%, and accuracy of 100%, against the accuracy of 97.0 and 87% for InceptionV3 and Inception-ResNetV2, respectively. Chowdhury et al. [7] demonstrate some techniques with a transfer technique used to detect an infection caused by COVID-19 on X-ray images of the lungs. They demonstrate using pre-trained architecture such as AlexNet, DenseNet-201, ResNet18, and SqueezeNet, employed as classifiers. A classification accuracy of 98.3% was achieved using the SqueezeNet network, with a specificity of 99%. Asif and Wenhui [11] propose a system to automatically diagnose COVID-19, the Transfer Learning Based Inception V3 was used to diagnose the patient’s chest infection. This model was tested against normal radiographic imaging, pneumonia, and COVID-19. Classification accuracy of 96% was achieved. Khan et al. [12] have come with a classifier of chest X-ray images called CoroNet, classifying images as Normal, Bacterial, Viral Pneumonia, and COVID-19 based on the Xception architecture. As a result, they obtained an overall performance accuracy of up to 89.6%. Sethy and Behera [13], a number of various CNN models for extraction features were trained using radiographic imagery of different architectures together in conjunction with the Support Vector Machine for detecting COVID-19 positive cases, thus achieving the accuracy of 95,38%.

3 Proposed COVID-19 Disease Detection Model In this section, the DenTcov model is illustrated and described in detail. The architecture of the different parts is illustrated (see Fig. 1). The model was defined in two phases. Phase I consists of (1) pre-processing and (2) data augmentation. In phase II, (1) a deep convolutional network is specified as the first step, (2) the best deep transfer learning model has been defined in the second step. a background discussion on the process for each part of the model is given.

970

Y. El Idrissi El-Bouzaidi and O. Abdoun

3.1 Model Architecture Overview An advantage offered by an automated diagnostic solution based on the radiographic image using deep learning approaches, helping to significantly reduce examination processing time and cost-efficiency, such solutions allow network weights to be trained from large datasets. Only a limited amount of positive radiographic image data relating to COVID-19 are available at present. An effective way to achieve significant results in classification problems with a lack of data in a given domain is to exploit existing knowledge from a similar domain, a technique known as transfer learning [14], which is processed by employing a pre-trained CNNs and fine-tuning weights on small datasets. Given a model founded on transfer learning, with a limited training dataset, overfitting was a major issue. Therefore, the technique of augmenting the data on a limited set of training data both solved the overfitting challenge and improved the generalizability of the model. Train Set Validation Set Test Set Update

Pre-Processing Augmentation

Train Loss

Input X-ray images

Automatic Learning rate

Dense block

AdaptiveMaxPool

Covid-19

Conv + ReLu

AdaptiveAvgPool

Normal

MaxPool

BatchNorm2d

Transition

Softmax

Pneumonia

Decision-Making of the proposed DenTcov

Phase I Fig. 1 DenTcov architecture

Phase II

(COVID-19 Probabilities)

Best Model Obtained

DenTcov: Deep Transfer Learning-Based Automatic Detection ..

971

Fig. 2 A Representative chest x-ray images after performing the Augmentation techniques

Table 1 Augmentation and Pre-processing parameters used in the proposed model.

Data augmentation

Pre-processing

Random flip

Resizing (to 224 × 224 × 3)

Random rotation (maximum angle was 10)

Normalization (mean and standard deviation)

Random zoom (5%) Change in brightness and contrast

3.2 Data Augmentation Data augmentation has occurred in the creation of new data using modifications made to the dataset. Basically, with reasonable modifications and by applying different transformations randomly to our training set, we create new augmented data (see Fig. 2). The following Table 1 summarizes the transformations we used in this work.

3.3 Network Architecture For the purpose of this paper, for feature extraction, the knowledge from an existing set of Convolutional Neural Network architectures pre-trained was used, with excellent results achieved by using a variety of classification tasks as alternatives to proposing our own architecture from scratch. For this reason, The DensNet121 architecture [17] has been used as an effective way for feature extraction by using its weights that are pre-trained in the ImageNet dataset [15] using reduced parameters. Another reason to use this network is to take advantage of the network’s condensed structure, offering easy training, especially due to the reuse of features by other layers, which increases the variety of its content between layers and improves the network’s performance and generalization. we also adopt advanced high-speed techniques introduce in Fastai [16], allowing us to improve the accuracy of our model through every few epochs. The subsequent section provides a short summary of the DenseNet121 architecture. DensNet121. Transmits the characteristics to all the next layers in each dense block. In its architecture, DensNet121 is defined as follows: there are four dense blocks,

972

Y. El Idrissi El-Bouzaidi and O. Abdoun

each dense block has a different number of layers (6, 12, 24, 16 consecutive layers). Between each dense layer, there are transition layers, which allow reducing the size of the features that are generated by the concatenation. The classification layers contain a Global Average-Pooling plus a Fully Connected layer. For details please see [17]. Pre-trained Model. Our DenTcov has two components: the first consists essentially of exploiting the capabilities offered by the DensNet121 for the transformation of visual elements into feature vectors, while the second component consists of a fully connected layer whose primary objective is to predict classification. As part of this work, we have added a sequence of layers to the early portion of a typical DensNet121, namely an adaptive Average/Max pooling, Batch normalization, and drop out according to [16]. The second part of the DensNet121 is replaced with a fully connected network so that the probabilities are calculated according to our classes, Normal, Pneumonia, and COVID-19. As a predictable result, between each of these classes, is the one having the highest probability. During the training phase, the modified parts of the model were fine-tuned with pre-trained weights, leaving the original part untrained. We obtain the best convolutional neural network architecture for transfer learning and its hyperparameter by training the model and selecting an optimal learning rate using the cyclic learning rate technique proposed in [18]. We configure the training process of our model with the parameters given in the following Table 2.

Table 2 Hyperparameters details used in DenTcov model.

Parameters

Value

Batch size

64

Optimizer

Adam, SGD

Momentum

Betas = (0.9, 0.99)

Classes

3 Classes (Normal, Pneumonia, Covid-19)

Loss function

Crossentropy. Useful in multi-classification problems

Training epochs

15

Dropout

0.3

Classifier

Softmax (layer outputs the probability distribution)

Activation function

ReLu

DenTcov: Deep Transfer Learning-Based Automatic Detection ..

973

4 Experimentation and Discussion 4.1 Experiments Dataset. The dataset was obtained from GitHub, the public source repository that Dr. Joseph et al. has shared [19] which contains chest X-rays of patients infected by COVID-19, also X-rays belonging to other COVID 19 families, such as ARDS, SARS, and MERS. In our dataset, we only included radiographic images with COVID-19, representing a total of 190 images, 264 normal x-ray images, and 275 bacterial and viral pneumonia infections, obtained from the Kaggle “Chest XRay Images (Pneumonia)” [20]. Pre-processing. During the pre-processing phase, the images are distributed randomly, 80% for training, and 20% of the data to validate. At the input of the DenseNet121, the images are reduced to 224 × 224 × 3 pixels, and the model is tuned. Training. During the training, only the newly added layers of the network and last layers group; Fully connected layers, are trained for 5 epochs while preserving the weights of ImageNet for the rest of the network. We use the cyclic learning rate technique introduced in [18], to help select the optimal learning rate. Using these discriminatory learning rates, which keep a low learning rate for the initial layers because they need less tuning and progressively increase the learning rate on the later layers that need more tuning, especially those that are fully connected. By using the learning rate (Max_lr) in the range 1e−4, 1e−3, all the network is fine-tuned, as first layers with 1e−4 and the last layers with 1e−3. Each intermediate layer has been trained with learning rates between these values for 10 epochs. Evaluation Metrics. We have provided the four evaluation matrices to judge the achievement of the results obtained from the DenTcov model. The true positives, true negatives, false positives, and false negatives correspond to T.P, T.N, F.P, and F.N, respectively, which are as follows in Table 3.

Table 3 Hyperparameters details for the DenseNet121 pre-trained model

Metric

Value

Accuracy

(1)

Precision

(2)

Metric Recall F1

Value

(3) (4)

974

Y. El Idrissi El-Bouzaidi and O. Abdoun

4.2 Results

Loss

The DenTcov was evaluated for two scenarios. The first is the classification of the radiographic images into three classes: pneumonia, COVID-19, and Normal. The second scenario is the classification of both COVID-19 and Pneumonia. Figures 3, 4 illustrates the curves representing the loss convergence relating to the training and the validation phases for 3-class (see Fig. 3) and 2-class (see Fig. 4), respectively, the loss function going into 70 epochs and the highest classification achieved an Accuracy of 96.52% (see Fig. 5). Figure 6 gives an overview of the confusion matrix of the COVID-19 class compared to the other classes. We show how the Pneumonia class is not classified well (see Fig. 6), This problem may be due to the fact that the class group two types of pneumonia: Viral and Bacterial. Moreover, people who develop Pneumonia can have different levels of progression. It is also notable that the classes COVID-19 achieves a perfect sensitivity, and Class Normal already have a good level of correct classification. An excellent compromise between the true and predicted values by DenTcov; which made it possible to verify the effectiveness of the model, these results provide an encouraging

Batches processed

Loss

Fig. 3 Convergence graph of training and validation losses for class 3

Batches processed

Fig. 4 Convergence graph of training and validation losses for 2-class

975

Accuracy

Error_rate

DenTcov: Deep Transfer Learning-Based Automatic Detection ..

Batches processed

Batches processed

Fig. 5 Accuracy and Error rate convergence graph for 3-class

Covid-19 Actual

Actual

Covid-19

Normal

Pneumonia

Predicted

Pneumonia

Covid-19

Pneumonia

Normal

Covid-19

Pneumonia

Predicted

Fig. 6 The confusion matrix of DenTcov for 3-class & 2-class classification

Table 4 Precision, Recall, and F1-score corresponding to different classes.

Metrics

COVID-19 (%)

Normal (%)

Pneumonia (%)

Recall

100.0

92.0

89.09

Precision

97.29

93.24

89.09

F1-Score

98.62

92.61

89.09

as a tool of reducing stress on the health care system. Table 4 details the precision, the recall, and the F1 score, respectively, for each class.

4.3 Discussion Our current work focuses on automated detection from X-ray images of the characteristics that could be significantly related to COVID-19 disease compared to other respiratory diseases, in order, to classify it effectively, the use of the DensNet121 architecture was effective in this task, due to the high accuracy that has been achieved, 96.52 and 99% for three and two classes, respectively. Another key advantage, getting a higher recall value signifies fewer False Negative, which is a significant result in

976

Y. El Idrissi El-Bouzaidi and O. Abdoun

Table 5 A comparison of State-of-the-art and DenTcov according to different criteria State-of-the-art approaches

Accuracy 3-Class (%) Accuracy 2-Class (%) Param (Million)

VGG-19 [6]

93.48

98.75

143

Xception + ResNet50V2 [9] 91.40

NA

NA

Xception [12]

89.60

99.00

33

DarkNet [21]

87.02

98.08

1.1

DenTcov

96.52

99.00

8

medical applications. As a result, our model is a guiding decision whose performance is comparable (sometimes even superior) to human experts, allowing for a timely diagnosis. The model DenTcov proposed is higher compared with other references (see Table 5).

5 Conclusion COVID-19 grew worldwide in a short period of time resulting in a significant number of patients who eventually died. The WHO reports that this can be prevented through prompt diagnosis. DenTcov automates the detection of COVID-19 in chest X-rays using Artificial intelligence techniques, involving complex structures with fewer parameters that require less computing power with greater results. A combination of transfer learning and data augmentation techniques are used to address the overfitting issue. In the experiments, an accuracy of 96.52 and 99% for 3-class and 2-class, respectively, were achieved with a high F1 of 98.62%, confirming the effectiveness of the proposed DenTcov model. Although several methodologies were developed to operate, the proposed model achieved the best results.

References 1. WHO Coronavirus Disease (COVID-19) Dashboard. https://covid19.who.int. Accessed 16 Oct 2020 2. Cherian T et al (2005) Standardized interpretation of paediatric chest radiographs for the diagnosis of pneumonia in epidemiological studies. Bull World Health Organ 83:353–359 3. Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL (2018) Artificial intelligence in radiology. Nat Rev Cancer 18:500–510 4. Kallianos K et al (2019) How far have we come? Artificial intelligence for chest radiograph interpretation. Clin Radiol 74:338–345 5. Shin H-C et al (2016) Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging 35:1285– 1298 6. Apostolopoulos ID, Aznaouridis S, Tzani M (2020) Extracting possibly representative COVID19 Biomarkers from X-Ray images with deep learning approach and image data related to pulmonary diseases. J Med Biol Eng 40:462–469

DenTcov: Deep Transfer Learning-Based Automatic Detection ..

977

7. Chowdhury MEH et al (2020) Can AI help in screening Viral and COVID-19 pneumonia? IEEE Access 8:132665–132676 8. Farooq M, Hafeez A (2020) COVID-ResNet: A Deep Learning Framework for Screening of COVID19 from Radiographs. arXiv:2003.14395 [cs, eess] 9. Rahimzadeh M, Attar A (2020) A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of Xception and ResNet50V2. Inform Med Unlocked 19:100360 10. Narin A, Kaya C, Pamuk Z (2020) Automatic Detection of Coronavirus Disease (COVID-19) Using X-ray Images and Deep Convolutional Neural Networks. arXiv:2003.10849 [cs, eess] 11. Asif S, Wenhui Y, Jin H, Tao Y, Jinhai S (2020) Classification of COVID-19 from Chest X-ray images using Deep Convolutional Neural Networks. medRxiv 2020.05.01.20088211. https:// doi.org/10.1101/2020.05.01.20088211 12. Khan AI, Shah JL, Bhat M (2020) CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images. Comput Methods Programs Biomed 196:105581 13. Sethy PK, Behera SK, Ratha PK, Biswas P (2020) Detection of Coronavirus Disease (COVID19) Based on Deep Features and Support Vector Machine 14. Du SS, Koushik J, Singh A, Poczos B (2017) Hypothesis transfer learning via transformation functions. In Guyon I, et al (eds) Advances in Neural Information Processing Systems, vol 30, pp 574–584. Curran Associates, Inc. 15. Deng J, et al (2009) ImageNet: A large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition 248–255 (2009). https://doi.org/10. 1109/CVPR.2009.5206848 16. Howard J, Gugger S (2020) Fastai: a layered API for deep learning. Information 11:108 17. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely Connected Convolutional Networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 2261–2269. https://doi.org/10.1109/CVPR.2017.243 18. Smith LN (2017) Cyclical Learning Rates for Training Neural Networks. arXiv:1506.01186 [cs] 19. Cohen JP (2020) ieee8023/covid-chestxray-dataset (2020). Accessed 02 Oct 2020 20. Chest X-Ray Images (Pneumonia) | Kaggle. https://www.kaggle.com/paultimothymooney/ chest-xray-pneumonia. Accessed 02 Oct 2020 21. Ozturk T et al (2020) Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput Biol Med 121:103792

Network Technology

Explainable Deep Learning Model for COVID-19 Screening in Chest CT Images Mostafa El Habib Daho , Amin Khouani, Mohammed El Amine Lazouni, and Sidi Ahmed Mahmoudi

Abstract In this work, we proposed an Explainable model based on Deep Learning for fast COVID-19 screening in chest CT images. We first collected a database of 360 COVID and Non-COVID images at the Tlemcen hospital in Algeria. This database was merged with two other public datasets (the first one has been collected from several articles published on medRxiv, bioRxiv, NEJM, JAMA, Lancet, etc. The second one was obtained from infected cases in hospitals in Sao Paulo, Brazil). We also conducted a comparative study between Deep Learning classification models that are widely used in the state of the art such as VGG16, VGG19, Inception v3, ResNet50, and DenseNet121. We also proposed an interpretable architecture based on the ResNet50 model and the GradCam explanation algorithm. Experimentations showed promising results and prove that the introduced model can be very useful for the diagnosis and follow-up of patients with COVID-19. Keywords COVID-19 · GradCam · Deep learning · Classification · Chest CT-scan

1 Introduction COVID-19 is an infectious and contagious virus and, given its global magnitude, the World Health Organization (WHO) has announced it as a pandemic. The declaration of a pandemic also highlighted the deep concern about the dangerous speed of spread and severity of COVID-19. To date (mid-November), the number of patients has exceeded 54 million worldwide. Worldwide, the most effective screening technique is Reverse Transcription Polymerase Chain Reaction (RT-PCR). Nevertheless, the poor sensitivity of RT-PCR and the lack of test kits in pandemic regions increases the workload of screening, M. El Habib Daho (B) · A. Khouani · M. E. A. Lazouni Biomedical Engineering Laboratory, University of Tlemcen, Tlemcen, Algeria e-mail: [email protected] S. A. Mahmoudi University of Mons, 20 Parc Sq., B7000 Mons, Belgium © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_89

981

982

M. El Habib Daho et al.

so numerous sick persons are not immediately confined [14] and this accelerates the spread of COVID-19 as many infected subjects cannot obtain immediate medication. In this circumstance, it is important to use other diagnostic techniques to better control the pandemic. Healthcare departments recommend the use of medical imaging (computed tomography (CT) and chest X-Ray) as a complement to RTPCR. Recent studies have shown that CT has a high diagnostic and prognostic value for COVID-19. Computed Tomography is an easy exam and quick to obtain at no additional cost and has shown a much higher sensitivity than RT-PCR for the investigation of COVID-19 [9]. The CT-scan image can distinguish potential high-risk subjects that are predisposed to become critical and require instant medical support. With advances in artificial intelligence, machine learning (ML), and computer vision, the build of smart prediction and diagnostic models has grown at an accelerated velocity [11]. Improving these intelligent models for disease prediction and diagnosis requires the use of ML techniques [9, 14]. However, to get better ML models, efficient and effective feature extraction techniques are needed. Deep learning (DL), as an ML method, has given encouraging performances in facilitating the diagnosis of lung diseases using CT-scan images of the chest [2, 13]. Deep learning, unlike the classic ML, can extract relevant characteristics automatically without expert feature engineering. In this work, we introduce a diagnostic support system for COVID-19 by classifying CT-scan images. Since the appearance of the COVID-19 pandemic, several works on the classification and analysis of scanner images have been carried out. Among these works, researchers such as [16] and [1] have opted to use classic machine learning techniques. In Barstugan et al. [1], the authors proposed several feature extraction techniques based on the Grey Level (GL Co-occurrence Matrix, GL Run Length Matrix, GL Size Zone Matrix), Discrete Wavelet Transform, and Local Directional Pattern. The classification task was performed using SVM. Other researchers have used deep learning techniques for this task. Among these works, we find the work of Jin et al. [7], Xu et al. [22] and Wang et al. [21]. They have used CNN models (Convolutional Neural Networks) as DL techniques for the screening of COVID-19 using CT-scan images. Pathak et al. [12], Ying et al. [19] and Li et al. [10] have performed a transfer learning on the ResNet-50 architecture. Zheng et al. [24] and Jin et al. [8] have used U-Net and CNN as deep learning models to achieve good performances. Works cited above focus on classifying COVID-19 images using either machine or deep learning techniques trained on public datasets. This work aims to propose an explainable deep learning model for the classification of COVID-19 CT-scan images. For that, we have collected a new CT-scan dataset for the CHU of Tlemcen in Algeria. We have also proposed an explainable deep learning classifier based on the Resnet50 architecture and the GradCam algorithm.

Explainable Deep Learning Model for COVID-19 Screening in Chest CT Images

983

2 Methods Deep learning methods have proven their effectiveness in several fields such as Computer Vision (CV) and Natural Language Processing (NLP). Deep Convolutional Neural Networks, or CNN for short, are the most popular networks for various computer vision tasks. These models are reputed to be very good for visual recognition since once a segment in a particular area of an image is learned, the CNN can recognize that segment anywhere else in the image. Among the commonly used CNN architectures for the classification of images, we can find VGG16, Resnet50, InceptionV3, and Densenet. We have chosen to use these architectures in this work since they have been very successful in the challenges of ImageNet and it was proven that using these pre-trained models in other classification problems (transfer learning) gives good results.

2.1 VGG Simonyan and Zisserman [17] proposed the VGG architecture. This model is simple; it uses only 3 × 3 stacked convolutional layers. The reduction of the convolution matrix dimension is achieved by a pooling operation (Max Pooling). The fully connected layers are the two last layers, containing 4,096 neurons each, followed by a softmax function. VGG16 and VGG19 are the two variants of this architecture. The “16” and “19” represent the number of layers in the model.

2.2 ResNet As image recognition becomes more complex, Deep Learning training becomes very difficult, since extra deep layers are needed to calculate and enhance the accuracy of the model. Residual training is a concept created to solve this issue and the resulting model is known as ResNet [5]. ResNet consists of several residual modules where each module represents a layer. The depth of a ResNet can vary considerably, the most used are Resnet50 [5] and the one developed by Microsoft which contained 152 layers [5]. Such depth was infeasible before the appearance of ResNet because of the problem of vanishing gradients. Therefore, ResNet solves this problem by providing skip connections, applied to each layer before the ReLu activation function, allowing to preserve the gradient [4].

984

M. El Habib Daho et al.

2.3 Inception The first version of this model was named GoogLeNet [20], but later variants were simply denominated Inception. Google published successively new versions of this architecture. This model uses some convolution filters (1 × 1, 3 × 3, and 5 × 5) in the same inception module to extract multi-level features.

2.4 DenseNet DenseNet [6] is composed of blocks called “dense”. In these blocks, each layer is connected with all the previous layers. To summarize, the DenseNet architecture makes maximum use of the residual mechanism by making each layer (in the same dense block) connect to its following layers.

2.5 Proposed Method The proposed model used the ResNet50 architecture as shown in the Fig. 1. We have performed a transfer learning of the ResNet50 model using the ImageNet weights. Then we added a GlobalAveragePooling layer and two Dense layers of size 1024 and 512. The last classification layer contains two neurons and uses the sigmoid activation function. For better interpretability of the results, we used the GradCam algorithm to explain the decision of the model.

Fig. 1 Our proposed method

Explainable Deep Learning Model for COVID-19 Screening in Chest CT Images

985

3 Results and Discussion 3.1 Dataset In this study, we have used three different datasets; two of them are available for free access on the internet and the third one we collected from the Hospital of Tlemcen (CHU Tlemcen). Yang et al. [23] published the first dataset; it contains 349 COVID and 397 nonCOVID images. This database has been collected from several articles published on medRxiv, bioRxiv, NEJM, JAMA, Lancet, etc. The second one is also public and has been published by Soares et al. [18], it contains 1252 COVID and 1230 non-COVID images. This dataset is gathered from cases inside hospitals in Sao Paulo, Brazil. We have collected the third dataset from the Hospital of Tlemcen in Algeria; it contains 180 COVID and 180 non-COVID images. For training and testing, we used the three datasets with data augmentation. During the test, we validated the different models on 355 COVID-19 and 363 Non-COVID images. Figure 2 shows examples of images with and without the presence of COVID19 in the three datasets. On the figure, we can see that the three datasets present variations in color and texture, which will allow the model to be more robust to the difference in quality and type of the image presented during the test phase.

(a) Dataset 1_COVID

(b) Dataset 2_COVID

(c) Dataset 3_COVID

(d) Dataset 1_ NON-COVID

(e) Dataset 2_NON-COVID

(f) Dataset 3_ NON-COVID

Fig. 2 Images from different datasets

986

M. El Habib Daho et al.

Fig. 3 Block diagram of this work

3.2 Experiments The experiments were performed on a local machine with a 4820k i7 processor, 56 GB RAM, an 8 GB GTX1070 GPU and Windows 10 operating system. The block diagram of this work is presented in Fig. 3. First, the database is pre-processed, it contains images of different sizes, and we have readjusted it so that all images will be 224 × 224 in size. For a better generalization, we made a data augmentation by applying random transformations on the input images, several transformations have been performed such as rotation and zoom with a probability of 0 to 1 to ensure the diversity of the data. During the acquisition process, the input images can suffer important variations on the color due to the sensor quality (Fig. 2). To solve this problem we have performed color normalization, this process can eliminate variations in luminosity and contrast. After the pre-processing step, a training process is carried out on each model, and then an evaluation is made on a test set. We have trained all models using batches of size 32 for 50 epochs using the Stochastic Gradient Descent method to reduce the loss function. In Fig. 6, the curves show that all models have learned well from the dataset and suffer neither from over- nor under-fitting. This can be explained by the fact that SGD, as explained in [3], contains the error, stays stable, and prevents overfitting even without any regularization term. From the confusion matrix in Fig. 4, we can evaluate the quality of each model by measuring the accuracy, the sensitivity, the specificity and the F-score on the validation set using the following equations:

Explainable Deep Learning Model for COVID-19 Screening in Chest CT Images

987

With: True positive (TP): Prediction is COVID and the image is COVID. True negative (TN): Prediction is Non COVID and the patient is healthy. False positive (FP): Prediction is COVID and the patient is healthy. False negative (FN): Prediction is Non COVID and the image is COVID. Table 1 shows the obtained performances of different models. We can notice that our approach gives better results compared to the other architectures on accuracy, specificity, and F-score. For sensitivity, our model is ranked second after Inception v3, which is good since it is first on the other metrics. In medical diagnostic aid in general and decision support for COVID-19 in particular this metric is very important. Indeed, high sensitivity is equivalent to a very low false-negative rate. False negatives are patients who have been diagnosed as Non-COVID by the system but are positive. Such an error can cause the death of the patient and does not have the

(a) VGG16

(b) VGG19

(c) DenseNet121

(d) Inception V3

(e) ResNet50

(f) Our Method

Fig. 4 Confusion matrix of different models

Table 1 Results VGG 16

Accuracy %

Sensitivity %

Specificity %

F-score %

96.10

96.90

95.31

96.08

VGG 19

95.96

94.92

96.96

95.87

DenseNet 121

97.35

97.18

97.52

97.32

Inception V3

97.49

97.74

97.24

97.47

ResNet 50

96.23

96.33

96.14

96.20

Our work

97.63

97.46

97.79

97.60

988

M. El Habib Daho et al.

Fig. 5 Explanation with heatmaps on COVID-19 images using Grad-CAM

(a) VGG16

(b) VGG19

(c) DenseNet121

(d) Inception V3

(e) ResNet50

(f) Our Method

Fig. 6 Train and validation accuracy of different models

same impact as a false positive error that can be quickly corrected with additional tests. We can therefore deduce that our model and Inception V3 are the best in terms of performance.

Explainable Deep Learning Model for COVID-19 Screening in Chest CT Images

989

To enhance the explainability of our architecture and to highlight the relevant areas driving the decision of our model, we have adopted the Grad-CAM [15]. Without extra manual annotation, this heatmap is entirely produced by the DL model. Network predictions are interpreted by generating heatmaps to visualize the most representative areas of the image using gradient-weighted class activation maps. Figure 5 presents the heatmap of the suspect regions for the COVID examples. These heatmaps illustrate the fact that our algorithm focuses on infected areas while neglecting regular regions as demonstrated in the three images in Fig. 5.

4 Conclusion In this study, we introduced a benchmarking analysis and study on different models for the classification of CT-scan images of COVID-19. Such a tool is necessary for the management of the COVID-19 crisis given the worldwide shortage of RT-PCR screening kits. In this work, we have exploited the power of feature extraction in deep learningbased methods. The lack of all deep learning models is the absence of explainability. To overcome this problem, we used a heatmap, generated by the GradCam algorithm, to visualize relevant regions in the images driving the final decision of the model. The obtained results by our explainable model outperform those achieved by the other techniques used in this work and prove that a machine learning model using the CNN architecture allows us to properly classify COVID-19 images.

References 1. Barstugan M, Ozkaya U, Ozturk S (2020) Coronavirus (covid-19) classification using CT images by machine learning methods 2. Hagerty JR, Stanley RJ, Almubarak HA, Lama N, Kasmi R, Guo P, Drugge RJ, Rabinovitz HS, Oliviero M, Stoecker WV (2019) Deep learning and handcrafted method fusion: higher diagnostic accuracy for melanoma dermoscopy images. IEEE J Biomed. Health Inf 23(4):1385– 1391 3. Hardt M, Recht B, Singer Y (2016) Train faster, generalize better: Stability of stochas- tic gradient descent. In: International conference on machine learning, pp 1225–1234 4. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 770–778 5. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778 6. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700–4708 7. Jin C, Chen W, Cao Y, Xu Z, Tan Z, Zhang X, Deng L, Zheng C, Zhou J, Shi H, Feng J (2020) Development and evaluation of an AI system for covid-19 diagnosis. medRxiv. https://doi.org/ 10.1101/2020.03.20.20039834

990

M. El Habib Daho et al.

8. Jin S, Wang B, Xu H, Luo C, Wei L, Zhao W, Hou X, Ma W, Xu Z, Zheng Z, Sun W, Lan L, Zhang W, Mu X, Shi C, Wang Z, Lee J, Jin Z, Lin M, Jin H, Zhang L, Guo J, Zhao B, Ren Z, Wang S, You Z, Dong J, Wang X, Wang J, Xu W (2020) Ai-assisted CT imaging analysis for covid-19 screening: Building and deploying a medical AI system in four weeks. medRxiv. https://doi.org/10.1101/2020.03.19.20039354. 9. Kaur M, Gianey HK, Singh D, Sabharwal M (2019) Multi-objective differential evolution based random forest for e-health applications. Mod Phys Lett B 33(05):1950022 10. Li L, Qin L, Xu Z, Yin Y, Wang X, Kong B, Bai J, Lu Y, Fang Z, Song Q, Cao K, Liu D, Wang G, Xu Q, Fang X, Zhang S, Xia J, Xia J (2020) Artificial intelligence distinguishes covid-19 from community acquired pneumonia on chest CT. Radiology 296(2) (2020). https://doi.org/ 10.1148/radiol.2020200905 11. Mei X, et al (2020) Artificial intelligence–enabled rapid diagnosis of patients with covid-19. Nat Med. https://doi.org/10.1038/s41591-020-0931-3 12. Pathak Y, Shukla P, Tiwari A, Stalin S, Singh S, Shukla P (2020) Deep transfer learning based classification model for covid-19 disease. Ingenierie Rec. Biomed. https://doi.org/10.1016/j. irbm.2020.05.003, h 13. Pezeshk A, Hamidian S, Petrick N, Sahiner B (2019) 3-D convolutional neural networks for automatic detection of pulmonary nodules in chest CT. IEEE J Biomed Health Inf 23(5):2080– 2090 14. Kumar Shukla P, Kumar Shukla P, Sharma P, Rawat P, Samar J, Moriwal R, Kaur M (2020) Efficient prediction of drug–drug interaction using deep learning models. IET Syst Biol 14(4):211– 216. https://doi.org/10.1049/iet-syb.2019.0116. IET Digital Library, https://digital-library.the iet.org/content/journals/10.1049/iet-syb.2019.0116 15. Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) Grad-CAM: visual explanations from deep networks via gradient-based localization. In: 2017 IEEE international conference on computer vision (ICCV), pp 618–626 16. Shi F, Xia L, Shan F, Wu D, Wei Y, Yuan H, Jiang H, Gao Y, Sui H, Shen D (2020) Largescale screening of covid-19 from community acquired pneumonia using infection size-aware classification 17. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 18. Soares E, Angelov P, Biaso S, Higa Froes M, Kanda Abe D (2020) SARS-CoV-2 ct- scan dataset: a large dataset of real patients CT scans for SARS-CoV-2 identification. https://doi. org/10.1101/2020.04.24.20078584 19. Song Y, Zheng S, Li L, Zhang X, Zhang X, Huang Z, Chen J, Zhao H, Jie Y, Wang R, Chong Y, Shen J, Zha Y, Yang Y (2020) Deep learning enables accurate diagnosis of novel coronavirus (covid-19) with CT images. medRxiv. https://doi.org/10.1101/2020.02.23.20026930 20. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1–9 21. Wang S, Kang B, Ma J, Zeng X, Xiao M, Guo J, Cai M, Yang J, Li Y, Meng X, Xu B (2020) A deep learning algorithm using CT images to screen for corona virus disease (covid-19). medRxiv. https://doi.org/10.1101/2020.02.14.20023028 22. Xu X, Jiang X, Ma C, Du P, Li X, Lv S, Yu L, Chen Y, Su J, Lang G, Li Y, Zhao H, Xu K, Ruan L, Wu W (2020) Deep learning system to screen coronavirus disease 2019 pneumonia 23. Yang X, He X, Zhao J, Zhang Y, Zhang S, Xie P (2020) Covid-ct-dataset: a CT scan dataset about covid-19 24. Zheng C, Deng X, Fu Q, Zhou Q, Feng J, Ma H, Liu W, Wang X (2020) Deep learning-based detection for covid-19 from chest Ct using weak label. medRxiv. https://doi.org/10.1101/2020. 03.12.20027185

A Novel Tool for Automating the Trace File Analysis in Vehicular Ad Hoc Networks with Multi-agents Method Sanaa Achchab, Souad El Houssaini, Amal Tmiri, Souad Ajjaj, and Mohammed-Alamine El Houssaini

Abstract Vehicular Ad hoc Networks (VANETs) are a nascent area of research that has gained considerable attention in recent years due to their role in the design of Intelligent Transportation Systems (ITS). NS-3 (Network Simulator 3) is one of the most widely used discrete event simulators for the VANET Simulation. Trace files, which capture events occurring in these networks, can be used to study the performance. This article introduces a new tool for automating the analysis of trace files in VANETs using the concept of a multi-agents system. This tool allows to calculate the following performance metrics: throughput, packet delivery rate, loss of packets, packets sent and packets received in a very short time for all different file sizes. In addition, it facilitates and speeds up the entire task of analyzing a high number of network simulations along with offering the possibility of storing all the trace files and all the measurements generated. Keywords VANET · Trace file · Multi-agents system · NS-3

1 Introduction Vehicular Ad hoc Networks are a subclass of MANETS, and they are considered as one of the components of Intelligent Transport Systems (ITS). These systems aim to incorporate new information and communication technologies in the (transportation field) to improve the safety and the comfort of the road users. A VANET is an ad hoc type network. It can opportunistically use communications with infrastructures, thus S. Achchab (B) · S. El Houssaini · A. Tmiri Department of Computer Science, Faculty of Sciences, Chouaib Doukkali University, El Jadida, Morocco S. Ajjaj ENSAM, Hassan II University, Casablanca, Morocco M.-A. El Houssaini ESEF, Chouaib Doukkali University, El Jadida, Morocco e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_90

991

992

S. Achchab et al.

allowing access to other networks and also to the Internet. The various infrastructures used within the framework of the VANETs are called “Roadside Units” (RSU). These units can be traffic lights, parking meters, or any other terminal placed at the edge of the road. Network simulation [1, 2] in telecommunication and computer networks is a technique in which the behavior of a real network is modeled using a simulator. Network simulation tools [1, 2] are used to model real networks with great ease. There are several network simulators (open source or commercial), which have different functions [3], such as NS-3 [4], NS-2 [5], OPNET, QualNet, OMNeT, J-Sim [6] and NetSim. The NS-3 is one of the most used and popular simulators. The main objective of this NS-3 project is to give network researchers an open simulation environment to test their network protocols. After simulating a network using a network simulator such as NS-3, a trace file is created which contains the events that occurred during the simulation and based on these events, users study the network performance. There are different types of files: trace file.tr and file. Pcap. In this article, we have developed a new tool for automating the analysis of the NS-3 trace file.tr in VANETs using multi-agent systems that automatically calculates VANET performance parameters such as throughput, packet delivery rate, packet loss, packets sent and packets received, it can also draw a graph. This tool calls Ns-3 Analyzer using Multi-Agents System (NsAMAS). Our article is structured as follows Sect. 2 presents previous work. Section 3 presents the concept of agent and multi-agents system. Section 4 introduces NsAMAS, presents its concepts and explains its architecture. In Sect. 5 we highlight the most important and unique characteristics of NsAMAS. Section 6 explains the uses and the obtained results. Finally, a conclusion and the future work are given in Sect. 7.

2 Related Work There are several analysis tools that have been developed to generate statistics on network simulation performance metrics or parameters [7, 8]. For example, NS-2 Trace Analyzer JTrana, and Trace Graph. They use NS-2 trace files. JTrana [9] is a NS-2 trace analyzer built from scratch in Java used to study wireless network simulation using a graphical user interface. Its input is an NS-2 wireless trace, and its output consists of many graphs or data sets of the network. Trace Graph [10, 11] is another NS-2 trace file analyzer. This software offers different analysis options. It is developed in MATLAB 6.0 [12]. This software also allows the user to retrieve useful statistics from a specific NS-2 trace file. NS-2 Trace Analyzer [13, 14] is a tool written in the C/C++ language. Similar to the precedent tools. This tool does not generate graphs related to the recovered statistical results.

A Novel Tool for Automating the Trace File Analysis ...

993

All the software mentioned above are designed for the analysis of the NS-2 trace file. In addition, it does not allow the user to store the results of each trace file analyzed in a database locally, so that they can reuse it without having to repeat the analysis. To extract statistics from another previously analyzed trace file, the user must download the trace file again. This task may be acceptable for a small trace file, but this process can take a long time for simulations that generate large trace files. In this article, we will try to resolve these issues.

3 Multi-Agents System (MAS) The topic of multi-agents system (MAS), although not new, is currently a very active research area. It is a specialty which is interested in collective behavior evoked by the interaction between several autonomous and flexible entities called agents, these interactions are manifested on cooperation, competition or coexistence between these agents. We are going to present the key concepts needed by agents and multi-agents system (MAS) to design a practical architecture for analyzing the NS-3 trace file.

3.1 Agent The concept of agent is a whole concept born decades ago. In the literature, many works have focused on agents and multi-agents system. Definitions differ from author to author and from field of application to another. We can cite the most consensual definitions that we have found [15] as follows: – An agent is an autonomous entity or simply an independent entity, abstract or real, able to act for himself and his environment and can communicate with other agents in a multi-agents universe. – An agent is an entity which presents the following characteristics: independence, reactivity and anticipation and has a specific social capacity.

3.2 Multi-Agents System Multi-Agents System (MAS) [16] is a system made up of agents of the same type or not, which cooperate and interact in a coordinated manner in order to achieve a common goal or objective in an environment of which they have the same perception and point of view. A multi-agents system (or MAS) is a system that composed of the following components [16]: – An environment E, is a space which generally has a criterion.

994

S. Achchab et al.

– A set of objects O. These objects are localized. For each one it is possible to assign a position in E, at a precise moment. These objects can be perceived, destroyed and modified by agents. – A set A of agents, is specific objects which contain the active entities of the system. – A set of relations R that bring things together. – A set of Op operations allow O objects to be interpreted, produced, consumed, transformed and manipulated by agents.

4 NsAMAS Overview NsAMAS is a new tool for automating the analysis of NS-3 trace file.tr in VANETS using multi-agents system. The objective of NsAMAS is to perform a practical and automatic analysis of the .tr trace file produced by a simulation using graphical interfaces.

4.1 Architecture This software is built on three layers: a presentation layer, a business layer and a database access layer, as shown in Fig. 1. • Presentation Layer This layer presents a graphical interface which allows the user to interact with the software. This layer contains the master agent to read the file, extract the fields and transfer the data to another agent. User requests move from the presentation layer to the actual processing layer. It also provides data in table and graph form to study the results. • Treatment Layer This layer processes the data received from the source and stores the results in the database. It contains two agents, one for calculating metrics and the other for drawing graphs. The primary processor is responsible for analyzing the trace files and storing the results, which is one of the basic software processes. It is also responsible for studying general simulation information’s and each simulation node. The Charts agent is responsible for plotting all types of graphics. • Database Access Layer This layer is used to store analysis results in the local database or to retrieve requested data. It also interacts with the agent which calculates the performance parameters to store the results, and with the master agent to retrieve the data and store the information for each trace file.tr. The database used in NsAMAS is MySQL and contains a table for each trace file entered by the user.

A Novel Tool for Automating the Trace File Analysis ...

995

Fig. 1 NsAMAS architecture

4.2 Issues with the Design One of the main objectives of NsAMAS is to facilitate the analysis of trace files. Currently, NsAMAS focuses on NS-3.tr trace files. The trace file in ASCII code contains 12 fields [2]. each file line begins with an event (+, -, d, r) descriptor followed by the simulation time (in seconds) of that event, and the from and to node. Then we have the type, the size of the packet (in bytes) and Flags (ignore). The next field is the packet class, which can be used to identify a particular TCP connection. The next two fields are the source address and the destination address. Finally, the sequence number of the protocol and the identifier. When a trace file.tr is sent as input, the agent reads the file, extracts the 12 fields mentioned above for use in the processing phase, and finally stores the information about this file in the database.

5 Trace File Analysis In this part, we identify the most important characteristics of NsAMAS and we also present the architecture and the use of this software.

996

S. Achchab et al.

Fig. 2 Simulation execution steps

5.1 Simulation Execution The first step of the analysis is to download the NS-3 simulation.tr trace file. Then the file will be read and the information processing will start using an agent. The data on the produced documents will be recorded in the database at the end of the processing and analysis step. The general idea of the system, for analyzing, processing and storing the file information, is shown in Fig. 2.

5.2 Identification and Storage In this step our software starts to extract 12 fields from the trace file which were provided as input. Once the trace file has been selected, an agent first tries to extract the fields, then another agent uses these fields to process and analyze the data. Once the processing phase of the trace file is completed, the results must be stored in the database. Each trace file has its own table.

A Novel Tool for Automating the Trace File Analysis ...

997

5.3 Statistics Calculation In the trace file processing step, the NsAMAS can automatically calculate the general simulation metrics for the.tr trace file using an agent and without any user intervention and it calculates other information regarding the metrics for each node. General Simulation Information The general simulation data contains the file path and the general simulation time, information on packets transferred or dropped during simulation, the recorded information is the number of packets sent, the number of packets received and the number of packets dropped. Node Specific Information After processing of the.tr trace file, the user of the simulation data can obtain information corresponding to a particular node. This information contains statistics such as TX Packets, RX Packets, Total dropped packet, Data received, Data sent, Throughput, Goodput and Packets Delivery Ratio (PDR).

6 NsAMAS Usages and Results In this section, we explain in detail the different functions of NsAMAS and also describe the information that NsAMAS can retrieve such as general simulation information, general information about each node and graphics. The Table 1 provides the simulation parameters that we used in our experiment.

6.1 Simulation Performance Metrics Extraction To measure the network performance metrics, using our NsAMAS trace file analyzer. An agent calculates various performance parameters: the general simulation parameters and the specific parameters to each node (Fig. 3). As an example of these Table 1 Simulation parameters

Parameters

Values

Version of simulator

NS-3.27

Routing protocol

DSR

Number of vehicles

50

Simulation time

120 s

Physical and MAC layer

802.11p

Packet size

1000 bytes

Mobility model

Manhattan Grid

998

S. Achchab et al.

metrics: throughput, packet drop rate, good throughput, packet delivery rate (PDR), packet loss, packet loss rate (PLR), normalized routing load (NRL) and concentration activity. These metrics are the most common requested by researchers and the most useful for evaluating the performance of VANETs. User can also retrieve the results of previously scanned trace files from a local database as shown in Fig. 4.

Fig. 3 Specific node metrics

Fig. 4 Selection of a file in the previously analyzed database

A Novel Tool for Automating the Trace File Analysis ...

999

6.2 Chart Plotting At this point, the user can draw various graphs in the form of a curve or a histogram as shown in Figs. 5 and 6. Once all parameters are set, graphics can be drawn for general simulation or for a particular node. The graphics provided by this tool are

Fig. 5 Chart plotting

Fig. 6 Bar chart

1000

S. Achchab et al.

Fig. 7 Processing time result for the trace file.tr

Fig. 8 File processing time comparison

the packet delivery rate and the bit rate (bits/s) versus time. Finally, it is also possible to export graphics as an image.

6.3 NsAMAS Results In this part, we provide synchronization results related to the basic steps described in Sect. 5, which contains the reading of the trace file, analyzing and finally storing the data in the local database. We have proved that the software not only handles trace file.tr, but it does so extremely quickly. We present the results in Fig. 7 regarding the above processes. Based on the above results, it is clear that our NsAMAS software operates even with large trace files to reduce the processing time. And we notice that the analysis time increases with the size of the file. We also provide a comparison of NsAMAS with other popular simulation analysis tools. These tools are illustrated in Sect. 2 and are some of the best-known tools dealing with simulated trace files. One of their main drawbacks is the time spent in analyzing the trace file. The comparison shows that our NsAMAS software gives good results in processing time Fig 8.

A Novel Tool for Automating the Trace File Analysis ...

1001

7 Conclusion and Future Work In this work, we presented a new tool labeled NsAMAS to automate the analysis of VANETs simulation trace files, performed with NS-3 using the multi-agents system concept. The main objectives of this tool are: offering a simple interface to facilitate the processing of.tr trace files in a very short time for all file sizes. Furthermore, displaying the results automatically in the form of tables and graphs to help students and researchers studying the performance of VANETs under NS-3. Results can be stored in the local database to reuse them without the re-analysis phase. Our NsAMAS software provides good results in terms of processing time and analysis compared to other popular simulation analysis tools. Building on the success of this software, we intend to upgrade it to calculate other metrics, such as transmission capacity and power consumption. We also intend to use other big data tools to analyze the file and draw other types of diagrams.

References 1. Weingärtner E, vom Lehn H, Wehrle K (2009) A performance comparison of recent network simulators. In: Proceedings of the IEEE international conference on communications 2009 (ICC 2009). IEEE, Dresden 2. Bouras C, Charalambides S, Drakoulelis M, Kioumourtzis G (2013) Simulation design and execution the case of TRAFIL. In: International conference on simulation and modeling methodologies, technologies and application 3. Siraj S, Gupta A, Badgujar R (2012) Network simulation tools survey. Int J Adv Res Comput Commun Eng 1:199–206 4. https://www.nsnam.org/. Accessed 11 July 2020 5. Simulator Network 2, Ns-2. https://www.isi.edu/nsnam/ns. Accessed 4 Oct 2020 6. Dorathy I, Chandrasekaran M (2018) Simulation tools for mobile ad hoc networks: a survey. J Appl Res Technol 16(5):437–445 7. https://www.nsnam.org/docs/manual/html/tracing.html. Accessed 10 July 2020 8. https://www.nsnam.org/docs/release/3.9/tutorial/tutorial_23.html. Accessed 10 July 2020 9. Qian H, Fang W (2008) Jtrana: a java-based NS2 wireless trace analyzer 10. Malek J, Nowak K (2003) Trace graph-data presentation system for network simulator NS. In: Proceedings of the information systems – concepts, tools and applications (ISAT 2003), Poland 11. Cicconetti C, Mingozzi E, Vallati C (2009) A 2 k· r factorial analysis tool for ns2measure. In: Proceedings of the fourth international ICST conference on performance evaluation methodologies and tools. ICST (Institute for Computer Sciences, Social-Informatics, p 4 12. The Mathworks, Inc. (2012) 3 Apple Hill Drive, Natick, MA 01760-2098, USA 13. Salleh AU, Ishak Z, Din NM, Jamaludin MZ (2006) Trace analyzer for NS-2. In: Proceedings of the 4th student conference on research and development (SCOReD 2006), Shah Alam, Selangor, Malaysia, 27–28 June 2008 14. Pujeri UR, Palaniswamy V (2015) Trace analyzer for NS3 15. Xie J, Liu C-C (2017) Multi-agent systems and their applications, 14 July 2017 16. Dorri A, Kanhere SS, Jurdak R (2018) Multi-agent systems: a survey

A Fuzzy On/Off Switching Strategy for Green Cellular Networks Soufiane Dahmani , Mohammed Gabli, Abdelhafid Serghini, and El Bekkaye Mermri

Abstract In the current generation of cellular networks, practitioners and researchers have shown a keen interest in green wireless communication due to its capability to create eco-efficient networks. To adapt to the increase in traffic and services for all mobile subscribers, Base stations (BSs ) and relay stations (RSs ) must be deployed more and more in order to meet the growth in this demand. However, increasing the number of BSs or RSs can increase energy consumption and reduce efficiency as it is responsible for large carbon dioxide (CO2 ) emissions. In this paper, we introduce a new approach to the management of the base stations and the relay stations using a fuzzy dynamic distribution of the BSs and RSs in the center according to the distribution of users. We use, secondly, a sleep mode activation of these stations which is seen as the key to reducing grid power consumption. The performance and the effectiveness of the proposed approach are clarified by a simulation example that reveals the capacity of our strategy in reducing energy consumption. Keywords Green optimization · Fuzzy logic · Base station and relay station deployment · Energy consumption · On-off switching strategy

S. Dahmani (B) · A. Serghini ANAA Research Team, ESTO, FSO, LANO Laboratory, University Mohammed Premier, Oujda, Morocco e-mail: [email protected] A. Serghini e-mail: [email protected] M. Gabli FSO, Department of Computer Science, University Mohammed Premier, Oujda, Morocco e-mail: [email protected] E. B. Mermri FSO, Department of Mathematics, University Mohammed Premier, Oujda, Morocco e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_91

1003

1004

S. Dahmani et al.

1 Introduction In recent years, mobile data traffic is growing rapidly and yields very high productivity. Wireless networks have become a challenge for operators in current and future years. That’s why researchers are trying to develop the network every time and everywhere. A basic requirement for green wireless communications is low energy consumption and low air pollution. As the number of mobile stations has swelled and seen the needs of users, mobile operators have to increase the number of Base Stations (BSs ) or Relay Stations (RSs ), which implies an increase of the energy consumer in the wireless cellular network. According to [1], the annual number of BSs has enhanced by two million for the period from 2007 to 2012, which conducts to uncontrolled production of carbon dioxide. The BSs used to cover remote areas are mainly operated by diesel generators. The annual operating cost of these power grids is often very high. As pointed by [2], in 2002, the production of CO2 related to information and communication technologies (ICT) reached 151 metric tons of carbon dioxide (MtCO2 ), nearly half of this value is due to the wireless connection. CO2 emissions are also expected to attain 349 metric tons in 2020. The authors in [3] detect that information and communication technology (ICT) consumes approximately 600 Terawatt-hour (TWh) of electrical energy or three percent of the total worldwide electrical energy, this number is expected to grow to 1700 TWh in 2030. These statistics convince researchers around the world to develop new ways to reduce energy consumption and reduce its negative impacts on the environment. In the telecommunication system, the RSs are distributed with BSs to increase the connectivity rate of these networks as shown in Ref. [4, 5]. Therefore the mobile stations (MSs) can send their data directly to the base station (BS) or by relay stations (RSs) to relay their data to the BS as shown in Fig. 1. This technique is called a multi-hop relay network. The CO2 emissions and energy consumption by RS are lower than those by BS. Moreover, the installation is easier and faster and the maintenance costs are lower. Unlike BS, RS does not require a direct connection to the core network [6]. The use of RS in a cellular network in parallel with the use of BS clearly has its own unique characteristics. However, incorrect deployment in unsuitable locations can lead to lower energy efficiency, throughput, transmission delay, transmission loss, power consumption, and increased cost of deployment expenses. In order to properly utilize the advantages of RS and BS, an effective strategy is required to choose the method and how to deploy BS and RS to green cellular networks, to achieve the desired target and throughput, with a reasonable deployment budget, less energy wastage, and most of all, minimal environmental pollution. Regarding the deployment of base stations in an environment, there is a set of deployment strategies such as randomly, or at the edges of cells as indicated in [7, 8].

A Fuzzy On/Off Switching Strategy …

1005

Fig. 1 Example of the multi-hop relay networks model

Fig. 2 BS or RS deployment in area divided into grids

To install BS, the central position is chosen because it reduces the number of stations to be installed and at the same time it covers a large number of mobile stations with less disturbance [9]. Figure 2 shows an example of a base stations and relay stations deployment site in area divided into grids.

1006

S. Dahmani et al.

Our first challenge, in this paper, is to propose an efficient placement scheme for the deployment of BSs and RSs on a selected site taking into account the uncertainty and imprecision of the coverage rate and traffic demand. The second challenge is to minimize the power consumption of these stations by using the on-off switching strategy while maintaining the network coverage as used in our paper [10]. In Sect. 2 we describe the system model and problem formulation. Section 3 presents our proposed approach based on fuzzy logic and on-off switching strategy. Section 4 shows the simulation Results and application. The conclusion is drawn in the final Section.

2 System Description and Problem Formulation We consider the network of base stations or relay stations in a space or position of any size, with the random distribution of the users and variable traffic in a day. To find the publishing site, the area is divided into networks of similar dimensions. Step 1: We divide the area into divers’ grids, the publishing site is already chosen as a focal point. The decision to install a BS or a RS in each site depends on the number of users deployed in each network, as shown in Fig. 3.

Fig. 3 BS or RS deployment in an area divided into grids depending on the number of mobile stations (MSs ) in each grid.

A Fuzzy On/Off Switching Strategy …

1007

Fig. 4 Binary logic to decide to install a base station or a relay station.

In the literature, this deployment mainly depends on the threshold value of the number of users which is calculated as follows: Tr =

T NU T NG

(1)

where Tr is the threshold value, TNU is the total number of users and TNG is the total number of grids. The authors in [9] relied directly on this threshold to define the type of station that will be installed. If the number of users (TNU) on each grid is higher than the threshold value, BS is published. On the other hand, if this number is lower than the threshold, the RS is displayed, as shown in Fig. 4. In the existing model to define the number of users, the decision maker monitored the traffic for a defined period (e.g. one month) and calculated the average value. In our view, the average does not accurately reflect the user traffic. Therefore, we consider that this value is an imprecise and uncertain value and not a constant one. Step 2: In the classic case, after the final publication of base stations and relay stations, an installed station remains active all the time, even for periods when the traffic is low, which leads to a loss of energy. The next step (challenge) of our work is the implementation of the on-off switching approach for these stations to reduce energy consumption (see [10]).

1008

S. Dahmani et al.

3 Proposed Approach Based on Fuzzy Logic and On-Off Switching Strategy 3.1 Fuzzy Threshold As we mentioned earlier, traffic is an ambiguous and uncertain value. To address this situation, we provide a fuzzy logic model that considers each threshold value as an imprecise one. In this case, the threshold value is given by:  r = T N U T T NG

(2)

 where, T N U is a fuzzy number with the following membership function: 

 max0, 1 − μT (t) NU max 0, 1 −



T NU −t α i f t−T NU if β

t ≤ T NU t > T NU

where the two values α and β are positive fixed values signifying respectively the left and right deviations of the fuzzy numbers. The membership function μT NU (t) is represented in Fig. 5.

 Fig. 5 Illustration that represents the membership function of the fuzzy number T NU.

A Fuzzy On/Off Switching Strategy …

1009

3.2 Base Station and Relay Station On/Off Switching Strategy Each BS or RS station has two main powers, the basic and transmitting energy. The basic energy is small energy related to these stations when they are in an idle state, while the transmission power is related to them when sending information to the users and thus it is in an active state, this means that the power consumption can be reduced by controlling the BS or RS mode. When BS or RS is not fully used or not connected to the maximum number of users, it can be kept in standby or idle mode to minimize power consumption. We will rely on the same strategy used in our previous article [8], which is based on the concept of traffic states, the reactive approach and the proactive approach. Traffic States: Suppose that in a given area and for one day, the traffic is distributed as follows (see Fig. 6). Between 00 am and 10 am, the traffic does not exceed 450 GBytes; Between 10 am and 2 pm, the traffic is between 450 and 900 GBytes; Between 2 pm and 8 pm, the traffic exceeds 900 GBytes; Between 8 pm and 00 am, the traffic is between 450 and 900 GBytes; The maximum traffic during the day is 1200 GBytes. From this data, traffic variations can be divided into three different states s1 , s2 and s3 where s1 = [0; 450], s2 = [450; 900], and s3 = [900; 1200]. Reactive Approach: We consider the highest traffic state. Next, we determine the optimal set of BS for that state. As the traffic conditions decrease, we need to deactivate a number of BS while still meeting the coverage requirements.

4 Simulation Results and Application 4.1 Data Description and Computational Results In order to carry out our simulation study, we have considered a large network area to be covered. The size of this area is 10 × 10 km2 divided into 40 grids. The number of mobile stations (MS) to be covered is 1200. Fixed power consumption (P) at the BS and RS is set, respectively, to 68.8 W and 20 W (see [11] for instance). For traffic reports, we randomly generated this traffic over a day spanning one month and we calculated the average traffic per each hour. We obtained three traffic states as follows: s1 = [0, 450], s2 = [450, 900], and s3 = [900, 1200].

1010

S. Dahmani et al.

Fig. 6 Representation of traffic variations over a day.

In the classical model, Tr = 1200 = 30. Therefore, if the number of users in a 40 grid is greater than 30, then a BS must be installed. Otherwise, we must install a RS. Using our fuzzy approach with α = 1 and β = 1, we found that 32 BSs and 8 RSs needed to be installed. In the reactive approach, the spread of BSs or RSs is initially implemented based on the highest traffic state s3 . Therefore, the 32 BSs and 8 RSs which define the largest possible group of stations can be active at any time. Then, when the traffic demand decreases, some base stations and relay stations of the primary stations are deactivated. The output results for traffic states s2 are 20 BSs and 6 RSs and the output results for s1 are 11 BSs and 5 RSs .

4.2 Analysis of CO2 Emissions Base stations and relay stations consume the majority of energy in the cellular network. Therefore, activating and deactivating these components will lead to significant energy savings and reduced CO2 emissions. Electrical energy is derived from fuel oil, where each one kilowatt hour represents 620 g of carbon dioxide (see [12]). The expression of carbon dioxide emissions for each traffic state is defined by: C O 2 emissions[kg/day](si ) =

Nsi × PB S/R S × Tsi × 620 106

(4)

A Fuzzy On/Off Switching Strategy …

1011

where, CO2 (si ): The amount of CO2 produced by the traffic state si ; Nsi : The number of BSs and RSs started from the traffic state si ; PBS (W): The energy consumption of the BS; PRS (W): The energy consumption of the RS; Tsi (H): The duration of the traffic state. In the classic approach, the decision makers decide to keep all the 40 BSs active throughout the day, and then the total CO2 emission, in KgCO2 /Day, is 40 × 68.8 × 24 × 620 = 40.94976 106 When we use the switching on-off strategy considering only the BSs, the CO2 emissions for each traffic state is (Fig. 6). C O 2 (s3 ) =

40 × 68.8 × 6 × 620 = 10.2374 106

C O 2 (s2 ) =

26 × 68.8 × 8 × 620 = 8.87244 106

C O 2 (s1 ) =

16 × 68.8 × 10 × 620 = 6.82496 106

CO2 (Day) = 10.23744 + 8.872448 + 6.82496 = 25.934848 In our approach, after the fuzzy installation of relay stations in parallel with base stations and the application of the reactive approach during the day, the total CO2 emission is: 32 × 68.8 × 6 × 620

8 × 20 × 6 × 620 + = 8.189952 + 0.5952 = 8.785152 106 106 20 × 68.8 × 8 × 620 6 × 20 × 8 × 620 C O 2 (s2 ) = + = 6.82496 + 0.5952 = 7.42016 106 106 11 × 68.8 × 10 × 620 5 × 68.8 × 10 × 620 C O 2 (s1 ) = + = 4.69216 + 0.62 = 5.31216 106 106

C O 2 (s3 ) =

CO2 (Day) = 8.785152 + 7.42016 + 5.31216 = 21.517472 By comparing these results, we see that the CO2 emission is reduced by 17%, which shows the efficiency of our approach.

1012

S. Dahmani et al.

5 Conclusion In view of the notable environmental pollution in recent years, we have looked in this article, at the issue of environmental protection which is a basic problem that arises in cellular network systems. The main challenge here concerns improving energy efficiency in order to reduce the emission of CO2 . Indeed, we have developed a system that takes into account both BSs and RSs deployment using the possibility theory concerns as well as the on-off switching strategy. The effectiveness of our approach has been assessed taking into account energy consumption and CO2 emissions. The results show that the fuzzy deployment of BSs and RSs and the on-off switching strategy reduced the total power consumption in the cellular network and reduced the CO2 emissions. Future research in this area may focus on (i) including other objectives like reducing the total cost of installing base stations, and (ii) to apply our approach to realistic test data generated by a real case study.

References 1. Mclaughlin S, Grant PM, Thompson JS, Haas H, Laurenson DI, Khirallah C, Hou Y, Wang R (2011) Techniques for improving cellular radio base station energy efficiency. IEEE Wirel Commun 18:10–17 2. Alsharif MH, Nordin R, Ismail M (2013) Survey of green radio communications networks: techniques and recent advances. J Comput Netw Commun 3. Humar I, Ge X, Xiang L, Jo M, Chen M, Zhang J (2011) Rethinking energy efficiency models of cellular networks with embodied energy. IEEE Netw 25:40–49 4. Yang Y, Hu H, Xu J, Mao G (2009) Relay technologies for WiMAX and LTE-advanced mobile systems. IEEE Commun Mag 47:100–105 5. Chen L, Huang Y, Xie F, Gao Y, Chu L, He H, Li Y, Liang F, Yuan Y (2013) Mobile relay in LTE-advanced systems. IEEE Commun Mag 51:144–151 6. Arthi M, Arulmozhivarman P (2017) Power-aware fuzzy based joint base station and relay station deployment scheme for green radio communication. Sustain Comput Informatics Syst 13:1–14 7. Son K, Oh E, Krishnamachari B (2011) Energy-aware hierarchical cell configuration: from deployment to operation. In: 2011 IEEE conference on computer communications workshops (infocom wkshps), pp 289–294 (2011) 8. Mohammed G, Soufiane D, El Bekkaye M, Abdelhafid S (2019) Optimization of multiobjective and green LTE RNP problem. In: 2019 International Conference on Wireless Technologies, Embedded and Intelligent Systems, WITS 2019 (2019) https://doi.org/10.1109/ WITS.2019.8723811. 9. Ameer R, Rolly RM, Jacob J (2018) Dynamic base station or relay station deployment and small cell On/Off strategy for green ultra dense networks. Procedia Comput Sci 143:827–834 10. Dahmani S, Gabli M, Mermri EB, Serghini A (2020) Optimization of green RNP problem for LTE networks using possibility theory. Neural Comput Appl 32. https://doi.org/10.1007/s00 521-018-3943-x 11. Ratheesh R, Vetrivelan P (2019) Energy efficiency based on relay station deployment and sleep mode activation of eNBs for 4G LTE-A network. Automatika 60:322–331 12. Koutitas G (2010) Low carbon network planning. In: 2010 European Wireless Conference (EW), pp 411–417

Multi-band Planar-Inverted-F-Antenna Design for WIFI WIMAX and WLAN Applications Asma Khabba, Layla Wakrim, Saida Ibnyaich, and Moha M’Rabet Hassani

Abstract In this paper we propose a new triple band Planar-Inverted-F-Antenna (PIFA) to be employed for WIFI/WIMAX/WLAN applications. The multi-band operation is obtained by etching three L-shaped slots from the radiating patch. The first band is centered at 2.47 GHz, the second band is centered at 3.51 GHz and the third band is centered at 5.23 GHz. The radiating patch is mounted on the top of low cost FR4-epoxy substrate acting as ground plane. The patch is connected to the ground plane using a short circuit and powered with a feeding plate. The overall design has an area of 41.66 × 50 mm2 . The proposed antenna is studied in terms of reflection coefficient, impedance matching, radiation pattern and distribution current. While the evolution steps has been studied to find the optimized version of the proposed design. Keywords PIFA · L-shaped slots · Multiband

1 Introduction With the rapid development of modern wireless communication technology, multiband antennas have become one of the most important circuit elements and have attracted great interest [1–3]. Meanwhile, the need of different services in mobile communication terminals, has turned the antennas design towards multi-band structures as well as miniature structures [4]. Due to its high performance, simple structure and low manufacturing costs, PIFA antenna is frequently used for mobile terminals.

A. Khabba (B) · L. Wakrim · S. Ibnyaich · M. M. Hassani Instrumentation, Signals and Physical Systems (I2SP) Team, Faculty of Sciences Semlalia, Cadi Ayyad University, Marrakesh, Morocco S. Ibnyaich e-mail: [email protected] M. M. Hassani e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_92

1013

1014

A. Khabba et al.

The multiband PIFA antenna has attracted great interest for the employment in wireless devices, in addition its miniature dimensions are compatible with mobile terminals and warrants versatile functions of a compact mobile handset [5]. Several techniques are used to achieve multi-band performance such as slot insertion [6–8], the addition of a parasitic element [9], the addition of a capacitive load [10]. In this manuscript, a novel triple-band PIFA antenna using L-shaped slots is designed and studied using CST Microwave Studio software. The Proposed antenna is designed in such manner to accommodate the three applications WIFI, WIMAX and WLAN in single antenna. The antenna is marked with high gain at three resonant frequencies. While the antenna evolution process is carried out to investigate the effect of the etched slots on the resonant frequencies. In addition, the simulated results concerning current distribution and radiation patterns are discussed. The rest of this paper is organized as follows: Sect. 2 presents the antenna structure and geometry. The antenna operating mechanism is discussed in Sect. 3. While Sect. 4 describes the simulating results including reflection coefficient, input impedance and distribution current. The gain and radiation patterns are presented in Sect. 5. Finally, Sect. 6 concludes the study.

2 Antenna Configuration and Geometry The antenna geometry is presented in Fig. 1. The suggested design consists of a radiating plate which is connected to the ground plane by a short circuit of width 2.5 mm and powered with a plate of width 4.363 mm. The ground plane is made up using the low cost FR4 epoxy substrate with dielectric constant of 4.3, loss tangent of tanδ = 0.018 and thickness of 1.2 mm. The dimension of the radiating plate, ground plane, shorting plate and the powered plate is displayed in Fig. 1(a). While the dimensions of the L-shaped slots is showed in Table 1.

3 Evolution Process of the Proposed Antenna Figure 2 presents the stepwise geometries through which the proposed design has been obtained. While the reflection coefficient of the antennas at the four stages is presented in Fig. 3. As can be seen the design is started with a conventional PIFA antenna which exhibits a single band operation where the resonant frequency is obtained at 4.75 GHz with 850 MHz of bandwidth. The second step is came out with the target to create a novel operating band. The second antenna in Fig. 2(b) is obtained by inserting an L-shaped slot of size 19.365 × 5 mm2 in the radiating plate. As can be seen in Fig. 3 the loaded slot leads to create a novel resonant frequency at 2.8 GHz, however the new resonant mode does not have an appropriate impedance matching (S11 = −8 dB). The third step is performed by inserting a second L-shaped slot of size 22.8 × 5 mm2 in the radiating plate (Fig. 2(c)). As remarked from Fig. 3,

Multi-band Planar-Inverted-F-Antenna Design …

1015

Fig. 1 Configuration of the proposed PIFA antenna

Table 1 Slots dimensions

Parameters

Values (mm)

Parameters

Values (mm)

X1

1.671

X4

5

Y1

9.457

Y4

1

X2

8

X5

1

Y2

1.671

Y5

19.365

X3

1

X6

5

Y3

22.8

Y6

1

the effect of the second slot is presented in the creation of the third resonance at 2.3 GHz while the impedance matching does not reach an appropriate value. Finally, the last step is done with the cutting of another L-shaped slot of size 9.457 × 8 mm2 . It is clearly remarked that this cutting slot has a crucial role to adjust and improve the impedance matching of the three resonant modes. Which makes it ready to be employed for WIFI, WIMAX and WLAN standards. The details about the three resonant modes is described in the next subsection.

1016

A. Khabba et al.

Fig. 2 Configuration of the proposed PIFA antenna a conventional antenna, b antenna 2 c Antenna 3, d proposed antenna

Fig. 3 Reflection coefficients of the stepwise geometries

4 Simulation Results The reflection coefficient of the proposed PIFA antenna is presented in Fig. 4. As depicted, the antenna exhibits three resonant frequencies. The first resonant frequency

Multi-band Planar-Inverted-F-Antenna Design …

1017

is obtained at 2.47 GHz (S11 = −16.08 dB) which corresponds to WIFI standard, with 120 MHz of bandwidth from 2.41 GHz to 2.53 GHz. While, the second resonant frequency is obtained at 3.51 GHz (S11 = −21.77 dB) which corresponds to WIMAX standard with 40 MHz of bandwidth from 3.49 GHz to 3.53 GHz and the third resonant frequency is obtained at 5.23 GHz (S11 = −32.11 dB) which corresponds to WLAN standard with 390 MHz of bandwidth from 5.04 GHz to 5.43 GHz. The input impedance of an antenna is expressed as Z in = Rin + j X in , where Z in is the input impedance, Rin is the resistance (real part) and X in is the reactance (imaginary part). In order to obtain a good impedance matching at the resonant frequency the input impedance should be close to 50  which means the input resistance Rin should be close to 50  and the input reactance X in should be close to 0. The input impedance of the proposed PIFA antenna is presented in Fig. 5.

Fig. 4 Simulated reflection coefficient of the Suggested design

Fig. 5 Simulated input impedance Zin of the Suggested design

1018

A. Khabba et al.

Fig. 6 Current distribution at the resonant frequencies, a 2.47 GHz, b 3.51 GHz, c 5.23 GHz

As can be seen, the input resistance at the three resonant frequency is Rin is 39.15 , 54.74  and 48.01  respectively and the input reactance X in is −8.96 , 7.12  and −1.4  respectively. It should be noted that the best impedance matching is reached at the third resonance frequency where Rin is close to 50  and X in is close to 0  which generates a low reflection coefficient of −32.11 dB. To better understand the antenna behavior, the distribution of current at the three resonant frequencies is depicted in Fig. 6. As depicted in Fig. 6(a) at the first resonant frequency the maximum of current is concentrated at the horizontal part of the second slot and a slight current intensity is concentrated in the horizontal part of the third slots. While at the second and third resonant frequencies (Fig. 6b, c), it is remarked that the maximum of current is intensified around the vertical and horizontal parts of the second and third slots). Therefore, it can be concluded that the second and the third slots are responsible to generate the resonant frequencies while the first slot is responsible to enhance the impedance matching as well as tuning the resonance frequencies values. The 3D radiation patterns at 2.47 GHz, 3.51 GHz and 5.23GHz are presented respectively in Fig. 7(a), (b) and (c), while the 2D radiation patterns at the two cutting plan phi = 0° and phi = 90° are presented in Fig. 7(d), (e) and (f). As can be seen the proposed antenna provides high radiation performance in term of gain where the max gain value obtained at 2.47 GHz, 3.51 GHz and 5.23 GHz are respectively 4.09 dB, 5.45 dB and 8.5 dB while the maximum radiation is oriented toward broadside direction (phi = 0°, theta = 0°) for the first and third resonant frequencies and toward the positive part of x axis for the second resonance.

Multi-band Planar-Inverted-F-Antenna Design …

1019

5 Radiation Patterns 6 Conclusion A triple band PIFA antenna covering WIFI/WIMAX/WLAN standards has been presented for mobile communications. The antenna is characterized with an appropriate area which can be easily fitted inside wireless devices. The three slots position and dimensions are chosen carefully to operate at the desired frequencies. In addition the antenna proves high radiation performance in terms of 3D far-field radiation pattern at the three resonances while the 2D radiation pattern is computed to show the directional radiation behavior.

Fig. 7 Current distribution at the resonant frequencies, a 2.47 GHz, b 3.51 GHz, c 5.23 GHz

1020

A. Khabba et al.

References 1. EL bakouchi RJ, Ghammaz A (2015) A quad-band compact PIFA operating in the GSM1800/GSM1900/UMTS/LTE2300/LTE2500/2.4-GHz WLAN bands for mobile terminals. In: The proceeding of the 2015 Third World Conference on Complex Systems (WCCS), pp 1–4. IEEE, Piscataway 2. Mondal T, Samanta S, Ghatak R, Bhadra Chaudhuri SR (2015) A novel tri-band hexagonal microstrip patch antenna using modified sierpinski fractal for vehicular communication. Progress in Electromagnetics Research C, vol 57, 25–34 3. Nashaat D, Elsadek, H, Ghali, H (2003) Dual-band reduced size PIFA antenna with U-slot for Bluetooth and WLAN applications. In : IEEE Antennas and Propagation Society International Symposium. Digest. Held in conjunction with: USNC/CNC/URSI North American Radio Sci Meeting (Cat No 03CH37450), pp 962–965. IEEE 4. Manouare AZ, Ibnyaich S, Idrissi AE et al (2016) Miniaturized triple wideband CPW-fed patch antenna with a defected ground structure for WLAN/WiMAX applications. J Microwaves Optoelectron Electromagn Appl 15(3):157–169 5. Ben Ahmed M, Bouhorma M, Elouaai F, Mamouni A (2011) Low SAR planar antenna for multi standard cellular phones. Eur Phys J Appl Phys 53:33604 6. Wakrim L, Ibnyaich S, Hassani Moha M (2017) Multiband operation and performance enhancement of the PIFA antenna by using particle swarm optimization and overlapping method. Appl Comput Intell Soft Comput 7. Shagar AC, Wahidabanu SD (2011) Novel wideband slot antenna having notch-band function for 2.4 GHz WLAN and UWB applications. Int J Microwave Wirel Technol 3:451–458 8. Fregoli L, Peixeiro C (2004) Small Multi-band Planar Inverted-F Antenna for mobile communication systems and WLAN/WPAN applications. URSI EMTS 9. Meshram MK, Animeh RK, Pimpale AT, Nikolova NK (2012) A novel quad-band diversity antenna for LTE and W-Fi applications with high isolation. IEEE Trans Antennas Propag 60:4360–4371 10. Ibnyaich S, Ghammaz A, Hassani MM (2012) Planar inverted-F antenna with J-shaped slot and parasitic element for ultra-wide band application. Int. J Microwave Wireless Technol 4:613–621

A New Approach for Evaluating the Performance of AODV Routing Protocol in VANETs Using the Plackett-Burman Method Souad Ajjaj, Souad El Houssaini, Mustapha Hain, and Mohammed-Alamine El Houssaini

Abstract In order to evaluate the performance of routing protocols in Vehicular Ad hoc Networks (VANETs), the common method is to simulate different scenarios by varying one factor at a time. Hence, the number of simulations increases as the number of factors rises. This method attains useful results, but it is demanding and time consuming. In this paper, we present a new approach to address performance analysis tasks in VANET routing protocols, by applying the experimental plan methodology particularly the Plackett-Burman Method (PBM). The main objective of our work is to build mathematical models that allow studying the effect of multiple factors simultaneously with a minimum number of experiments. To implement our work, we select NS-3 for the Network simulation and SUMO for generating the mobility model. Additionally, we opt for AODV as a VANET routing protocol. We involve four factors, which are the Black Hole Attack, the Worm Hole Attack, the Node Density and the Number of Connections. The outcomes of our study show the relevancy of our method in the performance analysis of VANET routing protocols. Keywords Vehicular Ad hoc Network (VANET) · Experimental plan · Performance analysis · AODV · Plackett-Burman Method (PBM)

1 Introduction Vehicular Ad hoc Networks (VANETs) are a special case of Mobile Ad hoc Networks (MANETs), where the mobile nodes are intelligent vehicles. VANETs have particular S. Ajjaj (B) · M. Hain ENSAM, Hassan II University, Casablanca, Morocco e-mail: [email protected] S. El Houssaini Department of Computer Science, Faculty of Sciences, Chouaib Doukkali University, El Jadida, Morocco M.-A. El Houssaini ESEF, Chouaib Doukkali University, El Jadida, Morocco e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_93

1021

1022

S. Ajjaj et al.

characteristics such as the high mobility, the dynamic topology, the delay constraints and the intermittent connectivity [1]. Thus, the routing in VANETs must be as efficient and reliable as possible. In this context, the present work introduces a new approach for studying the effect of factors on the performance of the routing process in VANETs. Our proposed method will offer the possibility to simultaneously assess the effect of numerous factors with fewer experiences, by applying the PlackettBurman method [2]. The aim of our new approach is to model mathematically the effect of factors on variables called responses. To perform our model, we conduct eight experiments integrating four independent factors namely the Black Hole Attack, the Worm Hole Attack, the Node Density and the Number of Connections. Accordingly, we select Throughput, Packet Loss Ratio, Average End to End delay and Routing Overhead as response variables. To implement our work, experiences are built using Network Simulator NS-3, SUMO and AODV routing protocol [3]. The remainder of this paper is structured as follows. In Sect. 2, we present AODV routing protocol. In Sect. 3, we will discuss the related work. Section 4 then introduces the Plackett-Burman method. Section 5 will deal with the details of our proposed work. Analysis of the acquired results is detailed in Sect. 6. Finally, we conclude with synthesis of the work achieved and future research directions.

2 Overview of AODV Ad hoc On Demand Distance Vector (AODV) is a reactive protocol in which routes are established only when required. The [3] details the working of AODV. AODV uses the concept of sequence numbers in order to maintain newer and fresher routes. When a source node needs to send data to a destination, two mechanisms are invoked: the route discovery and the route maintenance. In the route discovery process, the source node seeks in its routing table if a valid route exists for this destination, then it sends a RREP (Route Reply) message; otherwise, it broadcasts a RREQ (Route Request) message to all its neighboring nodes. Each intermediate node rebroadcasts the RREQ message until the message reaches the destination or an intermediate node that has a valid route to the destination. Hence a RREP message is sent in the reverse path. If the source node does not receive a response within a specified timeout, it retransmits another RREQ message. This procedure is repeated RREQ-RETRIES times before delivering an error message (RERR).In Route maintenance, AODV maintains routes as long as they are active, a route is active if data packets periodically transit from source to the destination. AODV uses the HELLO messages to check the activity of the routes.

A New Approach for Evaluating the Performance of AODV Routing Protocol …

1023

3 Related Work Multiple researches have dealt with the performance analysis of AODV in VANETs. For instance, the authors in [4] evaluated the performance of three VANET routing protocols, AODV, DSR and OLSR under two scenarios: urban and highway, in terms of Packet Delivery Ratio and End to End delay in different node density. They conclude that AODV has better performances regarding to packet delivery ratio in both scenarios, while DSR has better delay, whereas OLSR showed average performances in both scenarios. In [5], the authors studied the effect of node density on Throughput, Packet Delivery Ratio (PDR), End to End delay and Packet Loss Ratio using DSDV, OLSR and AODV. The comparison shows that AODV gives better results. [6], compared throughput and delay of AODV and DSR. The results show that AODV has better Throughput than DSR but more delay. In [7], the researchers simulated a VANET using NS2 and VanetMobiSim. They compared three routing protocols DSDV, AOMDV and AODV in terms of Packet Delivery Ratio and End to End Delay. They stated that reactive routing protocols provide good results in low and middle node densities. In [8] a modified protocol of AODV for multi-path was proposed to overcome the problem of AODV high routing overhead. The authors of paper [9] studied the effect of Black Hole attack on the performance of AODV and ZRP, in terms of average Throughput, Packet Delivery Ratio, End-to-End Delay, Normalized Routing Load and Average Path Length. The findings show that the performance of AODV is decreased when Black Hole attack is launched. [10], illustrates the vulnerability of AODV to Black Hole Attack in VANETs and propose a new algorithm to secure it. In this paper [11], authors evaluated the impact of Worm Hole Attack on AODV, in terms of Average Number of Hopes per Route, Average Delay, Average Route Discovery time, Average Throughput, Average Retransmission Rate, Average Data Dropped, and Average Traffic received. They emphasize the degradation of AODV under Worm Hole Attack.

4 The Plackett-Burman Method (PBM) Experimental plan methodology is used to plan and structure the experiments in order to extract maximum information with a minimum number of experiences. It is widely employed in research and industries. The goal is to study the effect of variables called factors on other variables called responses. In this paper, we adopt the Plackett-Burman Method (PBM). PBM [2] allows studying up to N-1 factor in a number N of experiments, for example, an 8-experiment plan can handle up to 7 Factors. The number N of experiments is a multiple of 4 but not a power of 2, i.e., 8, 12, 16, 20, etc.… To implement our work using PBM, the steps listed below are required: 1.

Identify the Factors and the Responses of the experimental Plan, for each Factor two levels are defined: the low level coded (–1) and the high level coded (+1).

1024

2.

3.

S. Ajjaj et al.

Build the PB matrix of experiments: the table where columns represent the Factors and rows indicate the number of experiments. The matrix contains only +1 and −1. Perform the matrix of effects denoted X. It is based on the matrices of HADAMARD [12]. We build it by adding a column containing only +1 to the left of the matrix of experiments. This matrix is then used to calculate the coefficients of the mathematical model. X is such that: XT X = nIn

4.

(1)

with In : identity matrix of order n, and X T is the transposed matrix of X. Calculate the coefficients of the model mathematically expressed in the form: yˆ = b0 + b1 x1 + b2 x2 + b3 x3 + · · · + bk xk

(2)

Where yˆ is the response, b0 is the constant of the model, b1 , b2 , .. bk are the coefficients of the variables x1 , x2 … xk This equation is written in a matrix form as follows: Y = BX

(3)

Y is the matrix of experimental responses, B is the vector of coefficients, and X is the matrix of effects. The coefficients are then determined by the method of least squares [13]: −1

B = (X T X ) (X T Y)

4.

(4)

X T : is the transposed matrix of X. Calculate the main effect of each Factor, that is the difference between the average of the response values where factor A is set at the high level (+1) and the average of the response values where factor A is set at the low level (−1): EA = 2 × [

N 

i =1 A = +1

(yi) −

N 

(yi)]/N

(5)

i =1 A = −1

Where N is the number of experiments and y represent the values of the response.

A New Approach for Evaluating the Performance of AODV Routing Protocol …

1025

5 Proposed Work One of the main constrictions in VANETs is security wherein the vulnerability mainly originates from the wireless communication and the lack of infrastructure [1]. Therefore, various types of attacks can be launched in the network, e.g., the Black hole Attack and the Wormhole Attack. In the Black hole Attack, the malicious vehicle falsely pretends to have a short and fresh route to the destination and sends a fake route reply with a high sequence number to the source node. The source node begins the data transmission, believing that the optimal path is through this malicious node. The malicious node then captures all routing packets and drops them. Worm Hole Attack is carried out in a cooperative way, where two colluding nodes create a virtual tunnel between them (at remote location) and let other nodes think that the routing is faster via them. Therefore, fake routes are created resulting in a virtual traffic in the network. The goal is to change the network topology and confuse the routing process. Other constraints influence the routing in VANETs. The findings in [7] and [14] show that the routing performances vary considerably in terms of density and the number of connections. In our study we choose to study the effect of four factors: the Black Hole Attack, the Worm Hole Attack, the Node Density and the Number of Connections. For each factor, we define the levels low (−1) and high (+1) (Table 1). For Black Hole and Worm Hole attacks, we use logical values (0 for level −1 and 1 for level +1). Node Density varies from 50 to 150 vehicles, whereas Number of Connections varies from 4% of the total number of vehicles in level −1, and 20% in level +1. In our study, we select Throughput (y1), Packet Loss Ratio (y2), Average End to End Delay (y3) and Routing Overhead (y4) as response variables. 1. 2. 3. 4.

Throughput (Th) is the total number of bits successfully received during simulation time, it is measured in kilobits per second (kbps). Packet Loss Ratio (PLR) represents the ratio of the number of lost packets to the total number of sent packets. End to End Delay (E2E delay) is the average time needed to transmit data from source to destination, it is measured in (ms). Routing overhead (RO) is the total number of control packets generated by AODV routing protocol during route discovery and route maintenance.

Table 1 The values for levels (+) and (–) of all factors N

Factor

High level (+1)

Low Level (−1)

Factor code

1

Black hole attack

1

0

X1

2

Wormhole attack

1

0

X2

3

Node density

150

50

X3

4

Number of connections

20%

4%

X4

1026

S. Ajjaj et al.

Table 2 Simulation parameters in NS3 N

Parameter

Value

1

Network simulator

NS3.29

2

Mobility simulator

SUMO-0.32.0

3

Propagation model

TwoRayGroundPropagationLossModel

4

Wifi channel

YansWifi

5

Mac and physic layer

IEEE 802.11p

6

Simulation time

200 s

7

Packet size

1024

8

Routing protocol

AODV

Table 3 The matrix of the effects with experiment responses N°

X0

X1

X2

X3

X4

Y1(TH)

Y2(PLR)

Y3(delay)

Y4(Overhead)

1

+1

+1

−1

– 1

+1

22,459

2

+1

+1

+1

−1

−1

18,970

55,115

72,678

26227,000

62,090

5,090

13046,000

3

+1

+1

+1

+1

−1

10,993

4

+1

−1

+1

+1

+1

17,282

78,033

39,931

1082724,000

65,465

126,736

5

+1

+1

−1

+1

+1

1428075,000

15,964

68,093

76,912

541073,000

6

+1

−1

+1

−1

7

+1

−1

−1

+1

+1

19,666

60,697

121,866

81667,000

−1

25,474

49,085

58,955

259989,000

8

+1

−1

−1

−1

−1

30,886

38,279

41,685

11356,000

In this study, A simulation map of El Jadida city Morocco is imported from Open Street Map (OSM). Simulation parameters are described in (Table 2).

6 Experimental Results 6.1 The Matrix of the Effects 6.2 Coefficients of the Mathematical Model The coefficients of the mathematical model are calculated by applying the method of least squares. The calculations are done using a spreadsheet. The resulting model for each Response yˆ is presented below: yˆ 1 (throughput) = 34,4312 − 6,2302x1 − 6,9679x2 − 0,0557x3 − 17,1105x4 (6)

A New Approach for Evaluating the Performance of AODV Routing Protocol …

1027

yˆ 2 (PLR) = 31,1904 + 12,4515x1 + 13,9283x2 + 0,1112x3 + 34,1914x4

(7)

yˆ 3 (EED) = 19,2327 − 38,6577x1 + 10,8485x2 + 0,1530x3 + 394,5790x4

(8)

yˆ 4 (RO) = −703589,188 − 29504,25x1 + 441716,75x2 + 7948,9125x3 + 1109260,94x4

(9) where: x1, x2, x3 and x4 are respectively the factors of Black Hole Attack (BKH), Worm Hole Attack (WMH), Node Density (ND), and Number of Connections (NC).

6.3 Graphical Representation of the Results Plots of Effects Figure 1 illustrates the effect of all Factors on the AODV throughput. We notice that the throughput decreases in + l level of all Factors. For Worm Hole and Black Hole Attacks, this decrease is due to the presence of malicious nodes that try either to drop packets in case of Black Hole Attack or capture and tunnel the packets to a distant location in case of Worm Hole Attack. As a result, the number of successfully received packets reduces causing the degradation of the throughput. AODV Throughput also cuts down with the increase in Node Density and Number of Connections. According to Placket Burman Method, we can rank these factors based on the average effects. The effect of Worm Hole Attack comes first, then the Black Hole Attack, the Node Density and finally the Number of Connections. The results Fig. 1 Plot of main effects on throughput

Fig. 2 Plot of main effects on PLR

1028

S. Ajjaj et al.

achieved using PBM agree with several works in literature. For example, the authors in [5] reported that the throughput decreases under the effect of Node Density. The same conclusion is achieved in [14] regarding to the Number of Connections. Furthermore, the study carried out in [10] confirmed that AODV throughput is better in scenarios free of routing security attacks. Examining Fig. 2 related to the effects on AODV Packet Loss Ratio. It is clear that the four Factors have a positive effect in their +1 level. Meaning that, the Packet Loss Ratio increases in the presence of Black Hole and Worm Hole Attacks as well as when the network is dense or the Number of Connections is high. According to PBM, the most influencing factors are respectively Worm Hole and Black Hole Attacks. Node Density comes in third position and Number of Connections in the last rank. The outcomes revealed by Plackett-Burman Method are in line with other researcher’s study. For instance, the results in paper [5] emphasize that the number of lost packets grows proportionally with the number of nodes. Additionally, [15] stated that the Worm Hole Attack is responsible for rising AODV packet loss. In [9] the conclusion is that AODV performance significantly degrades under Black Hole. Figure 3 indicates the effect on End To End delay. We notice that the effect of Black Hole Attack is negative unlike the three other factors with positive effect. In other words, the End To End delay decreases in the level +1 of Black Hole Attack, while it increases in the level +1 of the other factors. This rise is very important for Number of Connections, while it slightly grows for Worm Hole Attack and Node Density. The reason is that AODV uses the mechanism of route discovery before sending data packets. As the number of connections increases, the route search time will also Fig. 3 Plot of Main Effects on E2E delay

Fig. 4 Plot of Main Effects on Overhead

A New Approach for Evaluating the Performance of AODV Routing Protocol …

1029

increase leading to high values of delay. Additionally, in Worm Hole Attack, packets are tunneled through the malicious nodes that are located at remote positions; the End To End Delay then becomes higher. The results of PBM show that AODV performance decrease in the presence of Worm Hole Attack, also in high dense networks and increased Number of Connections. The exception found is that the End To End Delay for Black Hole Attack is reduced when the attack is launched. This is explained by the behavior of Black Hole node that sends fake and fast Route Reply (RREP) to source node, this significantly decreases both the route discovery process and the delay. This conclusion is compatible with [9] where it is concluded that delay under Black Hole is better than in a normal scenario. [4] Confirms the increase of AODV End To End delay with the rise in the number of nodes and number of connections. The authors in [16] studied the Worm Hole delay and proposed an improved AODV to preserve the network from this attack. The results of the effect on Routing Overhead are shown in Fig. 4. We observe that the Routing Overhead increases in +1 level of all the Factors except for Black Hole Attack that shows insignificant effect. This result copes with the study in [17], which proved that the Routing Overhead decreases under Black Hole Attack because malicious node sends a fake RREP as fast as possible. Consequently the Overhead decreases because the route discovery packets are reduced. We also note that Node Density has the most significant effect, followed by Worm Hole Attack and finally the Number of Connections. This rise in Overhead is caused by the large number of Route Request messages generated by AODV, mainly in VANET environment qualified by high mobility and frequent connections and disconnections of nodes. The outcomes of our study match with several previous studies. [8] stated that the Routing Overhead of On-Demand routing protocols rises significantly when the number of nodes increases. In [18] the researchers emphasized the importance of implementing new mechanisms to reduce AODV control messages overhead. The authors developed two improved versions of AODV protocol (AODV-LAR and AODVLine) to overcome the issue of high routing over-head. The authors in [16] confirm the negative impact of Worm Hole Attack on AODV overhead. They also presented a new approach to reduce AODV Overhead, and secure it against Worm Hole Attack. Plots for Model Validation The plots below are used to check whether the experiment values of the responses fit with the estimated responses (from the mathematical model). X-axis represents the experimental values and Y-axis shows the estimated values of the responses (Figs. 5, 6, 7 and 8). From the above figures, we can say that the values of the responses estimated by our model fit perfectly with those experimentally obtained for: Throughput, Packet Loss Ratio and End To End delay. Otherwise, the Routing Overhead shows acceptable correlation between experimental and estimated results.

1030 Fig. 5 Experiment Vs Estimated throughput

Fig. 6 Experiment Vs Estimated PLR

Fig. 7 Experiment Vs Estimated E2E delay

Fig. 8 Experiment Vs Estimated overhead

S. Ajjaj et al.

A New Approach for Evaluating the Performance of AODV Routing Protocol …

1031

7 Conclusion In this paper, we presented a new approach for the performance analysis of routing protocols in Vehicular Ad hoc networks. As per our humble knowledge, our proposed work unprecedentedly applied the Plackett-Burman Method, in which the findings eventually cope with previous studies in the same area. This method allows to simultaneously evaluate the effect of variables called factors on other variables called responses through a few number of experiences. Moreover we performed a mathematical model that describes the relationship between each response and the coefficients of the factors. In this study, we investigated the effect of four factors namely the Black Hole Attack, the Worm Hole Attack, the Node Density and the Number of Connections on the following performance metrics of AODV routing protocol: Throughput, Packet Loss Ratio, End To End Delay and Routing Overhead. The achieved results demonstrate the degradation of AODV performances when the attacks are launched in the networks, as well as when the network is dense with high number of communications. The present work can be extended to appraise the effect of more factors on the performance of other VANET routing protocols.

References 1. Arif M, Wang G, Zakirul Alam Bhuiyan M, Wang T, Chen J (2019) A survey on security attacks in VANETs: Communication, applications and challenges. Veh Commun 19:100179 2. Plackett RL, Burman JP (1946) The design of optimum multifactorial experiments. Biometrika 33(4):305–325. https://doi.org/10.1093/biomet/33.4.305lastaccessed2020/10/26 3. Perkins C, Belding-Royer E, Das S (2003) Ad hoc On-Demand Distance Vector (AODV) Routing. RFC 3561, RFC Editor https://www.rfc-editor.org/rfc/rfc3561.txt. Accessed 26 Oct 2020 4. Pranav K, Kapang L, Tuithung T (2011) Simulation based analysis of Adhoc routing protocol in urban and highway scenario of VANET. Int J Comput Appl 12(10):42–49 5. Gupta P, Chaba Y (2014) Performance analysis of routing protocols in vehicular ad hoc networks for CBR applications over UDP connections. Int J Eng Comput Sci 3(6) 6. Ghori MR, Sadiq AS, Ghani A (2018) VANET routing protocols: review, implementation and analysis. J Phys Conf Ser 1049:012064 7. Vidhale B, Dorle SS (2011) Performance analysis of routing protocols in realistic environment for vehicular ad hoc networks. In: 2011 21st International Conference on Systems Engineering, pp 267–272 8. Hu S, Jia Y, She C (2017) Performance analysis of VANET routing protocols and implementation of a VANET terminal. In: 2017 International Conference on Computer Technology, Electronics and Communication (ICCTEC), pp 1248–1252 9. Purohit KC, Dimri SC, Jasola S (2017) Mitigation and performance analysis of routing protocols under black-hole attack in vehicular ad-hoc network (VANET). Wireless PersCommun 97:5099–5114 10. Tyagi P, Dembla D (2017) Performance analysis and implementation of proposed mechanism for detection and prevention of security attacks in routing protocols of vehicular ad-hoc network (VANET). Egyptian Inf J 18:133–139

1032

S. Ajjaj et al.

11. Ojha M, Kushwah RS (2014) Impact and performance analysis of WORMHOLE attack on AODV in MANET using NS2. Int J Sci Res (IJSR) 3(6):1823–1826 12. Kharaghani H, Tayfeh-rezaie B (2005) A Hadamard matrix of order 428. J Comb Des 13 13. Dodge Y, Rousson V (1993) Alternative Methods of Regression. A Wiley-Interscience Publication 14. Spaho E, Ikeda M, Barolli L, Xhafa F (2013) Performance comparison of OLSR and AODV protocols in a VANET crossroad scenario. In Park JJ, Jong H, Barolli L, Xhafa F, Jeong HY (eds) Information Technology Convergence, Lecture Notes in Electrical Engineering. Springer Netherlands, Dordrecht, pp 37–45 (2013) 15. Narayanan SS, Murugaboopathi G (2018) Modified secure AODV protocol to prevent wormhole attack in MANET. Concurr Comput Pract Exp 32 16. Safi SM, Movaghar A, Mohammadizadeh M (2009) A novel approach for avoiding wormhole attacks in VANET. In: 2009 First Asian Himalayas International Conference on Internet, pp 1–6 17. Chavan AA, Kurule DS, Dere PU (2016) Performance Analysis of AODV and DSDV Routing Protocol in MANET and Modifications in AODV against Black Hole Attack. Procedia Comput Sci 79:835–844. Proceedings of International Conference on Communication, Computing and Virtualization (ICCCV) 18. Ayash M, Mikki M, Yim K (2012) Improved AODV routing protocol to cope with high overhead in high mobility MANETs. In 2012 Sixth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, pp 244–251

Design and Simulation of a New Dual Band Fractal Antenna for TM Applications Abdelhakim Moutaouakil, Younes Jabrane, Abdelati Reha, Abdelaziz Koumina, and Nadia El Makoussi

Abstract In this paper, we present the three first iterations design of a new dual-band antenna based on U fractal geometry feeding by microstrip inset feed. The proposed antenna is printed on FR4 substrate with a dielectric constant of 4.4. At the third iteration, the studied antenna has a dual-band behavior with tow resonant frequencies: 2.25 and 3.29 GHz with a good impedance matching. The simulated results performed by CADFEKO a Method of Moments (MoM) based Solver. The antenna offers low profile, low coast and a very light weight which satisfy the requirement of MicroSat Telemetry (TM) applications. Keywords Fractal antenna · Dual-band · TM

1 Introduction Interest in multi-band antennas is growing, in particular with the aim of reducing the number of onboard and ground antennas and by combining several applications on the same antenna. In particular, the increasing diffusion of radio navigation systems such as GPS (Global Positioning System) has made it possible to flood the world market with a large number of new receivers combining good performance, small size and reduced consumption [1–3]. In addition, the deployment of the new radio navigation system European Galileo (planned for 2013) and its combination with GPS will allow the implementation of more reliable, high-precision applications. Thus, the development of new antenna architectures combining two or more radio navigation A. Moutaouakil (B) · Y. Jabrane · N. El Makoussi Modelisation of Complex Systems Laboratory, Cadi Ayyad University, Marrakech, Morocco A. Reha Laboratory of Innovation in Management and Engineering for the Enterprise, ISGA, Marrakesh, Morocco A. Koumina Nanostructures Physics Laboratory, ENS, Cadi Ayyad University Marrakech, Marrakech, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_94

1033

1034

A. Moutaouakil et al.

systems is in full swing. Indeed, most satellite communications systems, whether geostationary, in low or elliptical orbits, require circularly polarized antennas. As part of the new Galileo satellite navigation system, the CNES Antennas service offered to analyze the possibility of combining the GPS, Galileo and Telemetry (TM) applications of the Microsoft family of satellites [4]. In this paper, an original topology of compact multi-band antennas is presented the form of a U-shaped fractal. Fractal antennas elements are currently beneficial to antenna designers & researchers. The concept of fractal geometry was introduced for the first time by Mandelbrot in [5]. Generally, using fractal geometries in antennas tends to miniaturize their physical sizes and produce multiband response.

2 Antenna Design Initially, we designed a rectangular patch antenna fed by a microstrip line by adding at the level of the ground plane a rectangular slot of width Wp+10 and length Lp+8, while respecting the different parameters for each antenna elements. The patch antenna is designed using FEKO Solver. Here, we use FR4 epoxy as the substrate (εr = 4.4), The parameters characterizing our patch antenna (Fig. 1) are presented in Table 1 [6, 7]: The previous rectangular patch antenna Fig. 1 will be modified based on the addition of a fractal U-shape. Applying fractal theory for antenna design is an interesting method of improving antenna performance thanks to the property of self-similarity which characterizes fractal geometries [8–10]. As shown in Fig. 2, the first iteration replaces the horizontal segment by 5 segments reduced to two thirds of the initial length, which means that the fractal dimension is: [11]. d=

ln(5) ln(N )   = = 1.464 ln(3) ln 1 R

Fig. 1 Geometry of the patch antenna. a Top view b Bottom view

Design and Simulation of a New Dual Band … Table 1 Design parameters & corresponding values

1035

Design parameters

Value (mm)

Length of substrate Ls

56.02

Width of substrate Ws

72.44

Length of patch Lp

28.01

Width of patch Wp

36.22

Thickness of the substrate h

1.6

Width of supply line Wf

2.8

Length of inset point

1

Fig. 2 Iteration steps to get Carpet geometry

3 Simulation Results The following figures represent a comparison of the reflection coefficient versus frequencies for different iteration numbers in the band [1–4 GHz]. We note that when the number of iterations increases the first band becomes larger with good adaptation (Fig. 3). It is worth noting from the simulation that the resonant frequency increases with the number of iterations increases. We also notice a widening of the bandwidth in the third iteration equal to 499 MHz. The adaptation to the second band is improved at the second iteration, but decrease at the third iteration. The performances of a linearly polarized antenna are described in terms of its main E and H plans. The E-plan contains the electric field vector while the H-plan contains the magnetic field vector in the direction of the maximum radiation [9]. Figure 4 shows the simulated radiation pattern of the third iteration antenna its resonance frequencies. From the simulation, we see that our antenna has a bi-directional radiation pattern for the two resonant frequencies. The Table 2 below summarizes some results on the fractal antenna performance.

1036

A. Moutaouakil et al.

Fig. 3 Simulated reflected coefficient (S11) versus frequencies for three first iterations

Fig. 4 Simulated radiation pattern in E-plan and H-plan at: a 2.25 GHz b 3.29 GHz Table 2 Summary of results

Iterations Resonance Gain (dB) Bandwidth (MHz) frequency’s (GHZ) 0 1 2 3

2.01

3.7

[1830–2201 MHz]

3.29

1.20

[3125–3485 MHz]

2.02

3.78

[1814–2206 MHz]

3.27

1.72

[3135–3409 MHz]

2.03

4.69

[1834–2228 MHz]

3.14

2.54

[2992–3406 MHz]

2.25

7.08

[1887–2386 MHz]

3.29

3.8

[3013–3486 MHz]

Design and Simulation of a New Dual Band …

1037

4 Conclusions In this work, the setup of U-shape fractal on the patch antenna allows a behavior multiband. The simulated results performed by CADFEKO a MoM based Solver are encouraging. We see that with the development of this antenna in terms of form, it can be used for the MicroSat Telemetry (TM) application, which is characterized by a resonance frequency equal to 2.245 GHz, and a bandwidth wide [2200–2290] MHz.

References 1. Kumar D, Singh RB, Kaur R (2019) Global positioning system. In: Spatial Information Technology for Sustainable Development Goals. Springer, Cham 2. Hadj Abderrahmane L, Brahimi A A new design of dual band fractal antenna for LEO applications. The Eighth International Conference on Systems and Networks Communications. ISBN 978-1-61208-305-6 3. Wibisono G, Wildan M, Wahyudi J, et al (2020) Co-design structure of dual-band LNA and dual-band BPF for radio navigation aid application. Wireless Pers Commun 4. Hebib S, Aubert H, Pascal O, Fonseca N, Marc Lopez J (2007) Antennes multi-bandes pour application GPS/Galileo/TéléMesure MicroSat. 15èmes Journées Natio ales Microondes, May 2007, Toulouse, France, pp 1–4 5. Mandelbrot BB (1983) The Fractal Geometry of Nature, San Francisco, pp 152–180 6. Constantine Balanis A (1997) Antenna Theory Analysis and Design. Wiley, Hoboken 7. Moutaouakil A, Jabrane Y, Reha A, Koumina MA (2019) Effect study of a ring on the performances of a patch antenna. In 2019 International Conference of Computer Science and Renewable Energies (ICCSRE), Agadir, Morocco, pp 1–3 8. Chen WL, Wang GM (2008) Small size edge-fed Sierpinski carpet microstrip patch antenna. PIERS C, vol. 3 9. Gupta S, Kshirsagar P, Mukherjee B (2018) Sierpinski fractal inspired inverted pyramidal DRA for wide band applications. Electromagn 38(2):103–112 10. Moutaouakil A, Jabrane Y, Reha A, Koumina MA (2020) Design and simulation of a fractal multiband antenna printed on a circular patch. Int J Adv Sci Technol 29(06), 5290–5296 11. Boutejdar A, Halim BI (2019) Design and manufacturing of a dual patch ring antenna using parasitic ring resonators and partial ground plane for multiband applications. In: 2019 IEEE International Symposium on Phased Array Systems and Technology

Design and Simulation of Three Bands Planar Inverted F Antenna Array for 5G Communication Systems Sara Arsalane, Nabil Arsalane, and Mounir Rifi

Abstract The fifth generation of wireless mobile communication is one of the most recent technologies in the field of wireless communications; this advance should provide a very great service in terms of speed and diversity. The old phones had an exterior antennas as an antenna dipole, but now most of them had an integrated antennas, these antennas are PIFA (Patch Inverted F antenna), a patch short—circuited in a corner (to decrease its size) and a power supply beside it. This paper is focused on this type of antenna that is frequently used in the field of mobile communication, and it aims to present a design and simulation of a threeband planar inverted F antenna array, which will be used as equipment under test for electromagnetic compatibility tests in the fifth generation context. Keywords Wireless network · PIFA antenna · PIFA array antenna · 5G · CEM

1 Introduction A wireless network is a network within—at least—two devices can communicate without a cables, through networks wireless, a user has the capability to stay connected while moving around a geographic perimeter that is why occasionally we hear about “mobility” [1]. Wireless networks are based on a link make use of radio waves in place of the usual cables. There are some technologies depending on various factors like the transmission frequency used as a well as the throughput and range of transmissions. Wireless networks simplified the connection between equipment for ten meters and S. Arsalane (B) Labo RITM, University Hassan II, Casablanca, Morocco N. Arsalane Labo LREA, Institut Superieur de genie applique, Casablanca, Morocco M. Rifi Labo RITM, University Hassan II, Casablanca, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_95

1039

1040

S. Arsalane et al.

a few kilometers. either, the installation of such networks does not need a heavy upgrading of existing infrastructure as is the case of wired networks (digging trenches to route cables, trucking and connectors…), which guide to a rapid development of this type of technologies. On the other hand, there is a problem of regulation relating to radio transmissions. Indeed, radio electric transmissions save for a large applications (military, amateurs…), however they are sensitive to interference, that’s why regulation is necessary in each country in order to define the frequency ranges and the powers at which it is possible to transmit for each category of use [2]. Mobile telephony or cellular telephony is a way of telecommunication more accurately of radio communication. The transmission of voice and data using radio waves (frequencies in the UHF bands ranging from 700 to 2600 MHz) between a base stations that can cover an area of several tens of kilometers in radius and the user’s mobile phone [3]. Antennas are a cornerstone of wireless telecommunications systems, these elements allows accessing radio, television telephony wherever they are, and they are present at both ends of a link. Wireless transmission cannot be performed without the use of transmitting and receiving antennas. On transmission, it is the only interface capable of ensuring signal transfer between a guided propagation medium and free space. At the reception, it takes care of the interfacing between the free space and the guided environment [4]. Among the most used antennas, it is PIFA antenna (inverted F antenna) which is increasingly used in the mobile phone market, and it is resonant at a quarter wavelength (thus reducing the required space needed on the phone) [5]. This paper aims to study the PIFA antenna characteristics and simulation of a three band PIFA antenna array for 5G communications systems that will be used as equipment under test for electromagnetic compatibility tests in incoming paper. This article is organized in three parts: • Presentation of PIFA antenna and its characteristics. • Implementation of a model of the PIFA antenna and PIFA array antenna. • Results and discussion.

2 Presentation of Technical Details of a PIFA Antenna In Recent years, the development of mobile telephony has growing rapidly, new bands have been added, and the market is demanding small mobile phones with more services, which requires the use of miniature antennas, multiband and which minimize the risk of parasitic radiation to the human body [6]. PIFA antennas (Planar Inverted F antennas) are today the most used in mobile telephony, due to their simple and flexible structure: reduced volume, low manufacturing cost, facility of integration; however, their major disadvantage is having a narrow bandwidth [7].

Design and Simulation of Three Bands Planar Inverted F Antenna Array …

1041

Fig. 1 Typical inverted F antenna (left) and typical PIFA antenna (right)

2.1 Antenna Description The PIFA antenna is the consequence of the modification of the IFA antenna (inverted F antenna) from a horizontal wire element to a planar structure in order to rectify its losses mismatch and improve its radiation characteristics [8] (Fig. 1). This structure consists of a metallic radiating element parallel to the plane of mass. One of the edges of the patch is connected to the ground by a short circuit plane which constitutes a particularities of this antenna. The upper element is usually excited by a probe coaxial that penetrates the dielectric substrate [9]. The variation of its dimensions, the power supply position, the plane of short circuit, the substrate height alter the performance of PIFA. These antennas have a several advantages like: • Their compactness, low manufacturing cost. • Their performance can be improved by adding loads judiciously. • In research carried out on mobile telephony: PIFA represent relatively large losses in the hand, but week in the head.

2.2 Implementation of a Model of the PIFA Antenna This paragraph aims to choose the model of the PIFA antenna and its parameters. The structure of antenna consists to choose the metal of the plate which induce a short circuit between the radiating patch and the ground plane, as in the Fig. 2, also the metal of (substrate, patch, the ground, the feed rod, the coaxial cable) [10] (Table 1). And to improve the performance of the PIFA antenna as well as to solve the problem of having a narrow bandwidth there are several techniques: • • • • • •

Increase the height of the substrate to increase the volume of the antenna. Reduce the size of the ground plane. Inserts slots in the round plane. Use of material of low relative permittivity as substrate. Adding a parallel plane between the patch and the ground plane (load capacitive). Inserting slots into the patch.

1042

S. Arsalane et al.

Fig. 2 The inverted F antenna

Table 1 Different material of such layer of the antenna

Component

Material

Mue

Epsilon

Feed rod

Copper (pure)

1

–

Coaxial cable

Copper (pure)

1

–

Ground

Copper (pure)

1

–

Patch

Copper (pure)

1

–

Substrate

FR4

1

4.3

Short

Copper (pure)

1

–

For the antenna presented in this paper we chose to insert slots in the patch to have a multiband antenna. And as the dimensions of the antenna had an impact on the directivity of the antenna, so it is important to choose it carefully (Table 2). After determination of all the factors that will be used to simulate the antenna and also the dimensions of PIFA, so this paragraph will be concentrated on the conception and simulation of the antenna in the chosen frequency range (2 GHz–6 GHz). The calculations are made in far field (Fraunnhofer area) (Fig. 3). Concerning the adaptation of the antenna, the following figure shows the magnitude of the reflection coefficient in the frequency range (2 GHz–6 GHz) (Fig. 4). The measurements are made in the frequency range (2 GHz−6 GHz) which includes the second frequency range of the 5G technology. The magnitude of the reflections coefficient is in decibel with a frequency up to 6 GHz. As we can notice that the magnitude of reflection coefficient S (1, 1) is less than (−10 dB) for three frequencies (2.4 GHz, 3.4 GHz and 5.4 GHz) which means that our antenna is a tri band antenna, and it is adapted for this three frequencies and as a consequence we have the transmission of the maximum of electromagnetic radiation. For the radiation pattern that it obtained by the calculation of the electric field in two plane E and H. that it represent the energy radiated by an antenna. It’s a

Design and Simulation of Three Bands Planar Inverted F Antenna Array … Table 2 Dimension of the PIFA antenna

Name

Description

Value

Lg

Ground length

90

Wg

Ground width

60

R

Radius of outer cylinder

5

ri

Inner cylinder radius

2

Hs

Substrate height

2

Hg

Ground height

2

Hc

Cylinder height

10

Hpole

Monopole height

9

Icc

Short circuit length

5

Wcc

Short circuit width

10

Lpatch

Patch length

50

Wpatch

Patch width

50

Ws

Substrate width

55

Ls

Substrate length

65

Wslot1

Slot1width

40

Wslot2

Slot2 width

30

Lslot1

Slot1lengh

39

Lslot2

Slot 2 length

35

1043 Expression

Icc+Hs+Hg

Fig. 3 PIFA antenna

diagrammatical representation of the transportation of radiated energy into space, according to the energy radiation [11] (Fig. 5). This shape represent a three dimensional radiation pattern, for our PIFA antenna the large section of the radiated field, that covers a huge area, is the main lobe or major lobe. This is the part where the maximum radiated energy exists. The direction of this lobe stipulates the directivity of the antenna. The angle of aperture of an antenna is described as the area, aligned perpendicular to the direction of an incoming electromagnetic waves [12] (Fig. 6).

1044

S. Arsalane et al.

Fig. 4 Reflection coefficient magnitude of PIFA antenna

Fig. 5 The 3D PIFA antenna radiation pattern

Fig. 6 The 2D PIFA antenna radiation pattern

For the opening angle it fluctuate between 60 to 120 theta but it is intensive from 50 to 100, so the opening angle is 50 theta in the far field. After the simulation of our antenna and obtaining the different results, we will approach the periodization of this antenna in order to obtain an antenna array, which will be functioning in the 5G frequency band and used subsequently in tests as an equipment under tests.

Design and Simulation of Three Bands Planar Inverted F Antenna Array …

1045

2.3 Implementation of a Model of the PIFA Array PIFA or formed beam antennas are used for several purposes, an antenna array is a collection of separate antennas and fed synchronously, the electromagnetic field produced by an antenna array is the vector sum of the fields produced by each of the elements. In addition these antennas are generally not very cumbersome in comparison with horn type aperture antennas or reflector antenna which would fulfill the same mission [13]. Principle Antenna array are the result of the judicious association of several antennas to synthesize a radiant opening [14]. Antenna array are made up of N radiating sources (dipoles, patches, PIFA…) distributed in space. The electrical quantities (signals) injected or collected on the access of each of these source are weighted in amplitude and (or) in phase before to be summoned among themselves. A beam splitter is used to perform coherent and weighted summations of the signals collected by a receiving systems, and the splitter will be injected into each source of antenna. Due to this weighting, the antenna arrays can produce diagrams of radiations having the desired shape [15]. In Particular, it will be possible to create several lobes simultaneously or a lobe in the direction of the incident signal and a zero in the direction interference. Array antennas can have several geometric configurations that can be grouped as follows: • • • •

Linear Arrays: alignment of sources on a straight line. Planar Arrays: the sources are arranged on a plane. Circular Arrays: the sources are arranged on a circle. Volume Arrays: the sources are distributed in a volume. Several factors contribute to the formation of the antenna radiation pattern:

• • • •

The geometric configuration (linear, circular, planar, volume). The distance between the sources. The amplitude of excitation for each element. The polarization phase for each element.

For our case we choose to implement a linear array, it is the most frequently used geometry in the design of antenna arrays because it is easy to implement, it is made up of N sources equidistant with a lattice patched. These sources are supplied with the same amplitude and with a progressive phase gradient for a point M located in the far radiation zone, the total radiated field is given by the sum of each elementary field [16]. Therefor this paragraph present the case of PIFA array in a linear geometric. The linear array composed of four elements (PIFA antenna) distributed on an axis and separated by a distance d (pitch of the array) (Fig. 7).

1046

S. Arsalane et al.

Fig. 7 PIFA array

• The mutual coupling between the antenna elements: The principle of an antenna is to change electromagnetic energy into an induced voltage or current to be measured and vice versa. Nevertheless, the voltage calculated in each antenna piece will depend on the incident field, and also on the tensions of other elements. However, the voltage received on each element will induce a current that affects the neighboring element that in turn radiates [17]. • The distance between the antenna elements: Concerning the design of the antenna array, there is a major factor to consider in the calculation. This factor is the spacing between the elements of the array that acts on the radiation pattern and gain. Noting that when we elevate the number of radiating elements, the gain is increased by 3 dB but this without taking into account losses [18].The maximum gain is obtained when the spacing is between 0.4 and 0.9 λ, if the elements are too close to each other, a coupling phenomenon reduces the value of the gain and when they are too far apart, secondary lobes appear and therefore reduce the directivity. Implementation of a Model of PIFA Array This part aims to present the results of calculation of the PIFA array. The measurements are made in far field in the frequencies band (2 GHz–6 GHz) (Fig. 8). This figure present the reflection coefficient S(1, 1) given in decibel with a frequency up to 6 GHz, and as we can observe that the amplitude is less than 3 dB for three frequencies (2.4 GHz, 3.4 GHz, 5.3 GHz), so our PIFA array is adapted for this three frequencies. And as a result we managed to correct the major disadvantage of is type of antenna which is the narrow bandwidth. For the radiation pattern we chose to present it for the frequency 3.4 GHz (Fig. 9). By definition the angle of aperture of an antenna is the angular size for which the radiated power is half of the radiated power in the most favorable direction and for our case it varies from 65 to 110, but concentrated from 70 to 100 (Fig. 10).

Design and Simulation of Three Bands Planar Inverted F Antenna Array …

1047

Fig. 8 Reflection coefficient magnitude of PIFA array

Fig. 9 The 2D PIFA array radiation pattern

Fig. 10 The 3D PIFA array radiation pattern

And for the 3D presentation it shows the concentration of the energy in a specific direction, that represent to the width of the major lobe which is determinate by the angular width of each boundary of the lobe whose intensity decreases by half, a decrease of 3 decibel.

1048 Table 3 The energy balance of each frequency

S. Arsalane et al. Frequencies in GHz

Energy in dB

2.4

54,13 e−6

3.4

65,22 e−6

5.3

84,26 e−6

For the energy balance of the antenna which means the radiated power carried by the electromagnetic wave for a specified frequency. The energy of each frequency it presented in this table (Table 3): The radiated electric energy depends on the frequency, and that what we observe in the table, the energy balance increases with the frequency.

3 Conclusion Innovation in communication systems, required an advanced studies in the field of antennas. In this context, we propose a design of PIFA antenna array (4 * 1) operate in three frequency bands (2.4 GHz, 3.4 GHz, 5.3 GHz), therefore we remedy to a main drawback of the PIFA antenna that is the narrow bandwidth by inserting slots into the patch. And as a perspective of this work we have: – The implementation of this antenna array. – Use of this antenna as an equipment under test for the electromagnetic compatibility tests.

References 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11.

Imrich chlamtac (2019) Wireless networks. J Mobile Commun Comput Inf Muscatello J, Martin J (2005) Wireless networks security, 20 April Vasseur, JP, Dunkels A (2010) what are smart objects Dai HN, Wu MY, Mingluli (2013) An overview of using directional antennas in wireless networks, April Firoozy N, shirazi M (2011) Planar inverted F antenna (PIFA) design dissection for cellular communication application, January Preethi V, Annapurna Devi S (2018) PIFA antenna for wireless communications, June Kumar N, Thakur A, der shama J (2017) Study of planar inverted F (PIFA) for mobile devices, June IEEE (2017) Design of multi resonant PIFA antenna for mobile telecommunication networks, March Agarwal M, Singh R, Meshram MK (2013) Dual band linearly polarized planar inverted F antenna (PIFA) for GPS/WIMAX applications Mohamed G (2016) Conception d’une antenne PIFA multibandes pour applications sans fils, 27 June Vallozi L, Rogier H (2016) Latest developments in the field of textile antenna

Design and Simulation of Three Bands Planar Inverted F Antenna Array …

1049

12. Luo Y, Zhang Z, Chen H, Huangfu J, Ran L (2009) High directivity antenna with small aperture, November 13. Sayidmarie KH, Maewan NJ, Aabd R, See C (2019) Antennas for emerging 5G systems 14. Reckeweg M, Rohner C (2015) Antenna Basics 15. Kumar ST, Susila M (2017) Design of multi resonant PIFA for mobile telecommunication networks 16. Rhee E (2020) Miniaturized PIFA for 5g communication networks, 15 February 17. Singh H, Sneha HL, Jha RM (2013) Mutual coupling in phased arrays, April 18. Abimbola RA (2017) Effects of spacing and number of elements on the fields radiated by linear arrays of equally spaced elements, October

RFID System for Hospital Monitoring and Medication Tracking Using Digital Signature Safae El Abkari, Soufiane Kaissari, Jamal El Mhamdi, Abdelilah Jilbab, and El Hassan El Abkari

Abstract The enhancement of patient safety and the improvement of medical care services remain one the main concerns of the World Health Organization. Thus, given the healthcare critical impact on patients, there is a growing need for high-risk and expensive medicine management. For this purpose, we propose an innovative system prototype for the reduction of medication error possibility from prescription, validation to its preparation. We present a Radio Frequency IDentification monitoring system for hospital and pharmacy using digital signature. Our designed system is constituted of RFID readers, ESP8266 modules, and tags to ensure an automated medication management and a good inventory control. Keywords Tracking · Location · Medication · Monitoring · Management · RFID · Wi-Fi · Hospital

1 Introduction Nowadays, healthcare sector can be described as one of the fast-growing domains. Therefore, there is a consistent need of innovative solutions and applications [1–3] to improve the medical care and the clinical process quality [4]. In healthcare intuitions, medication errors’ percentage is 56% for ordering, 34% for administration, 6% for transcribing, and 4% for dispensing [5]. While statistical data of hospitals show that computerized data is utilized in most healthcare institutions, the Institute of Medicine (IOM) records that there is an occurrence of many medication errors during the clinical process [6]. Hospital monitoring and management systems can be a solution to efficiently speed up data flow in hospitals, reduce errors and im-prove the inventory control of high risk or expensive medications [7]. For this purpose, the following actions are necessary to develop to ensure patient and medication safety: S. El Abkari (B) · S. Kaissari · J. El Mhamdi · A. Jilbab · E. H. El Abkari E2SN, ENSAM, ST2I, Mohammed V University in Rabat, Rabat, Morocco e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_96

1051

1052

S. El Abkari et al.

• Prevent and identify adverse events to reduce medication errors. • In case of errors, fix and evaluate the medication process. Traditionally, most medical institutions manage medications in warehouses [8]. Thus, Radio Frequency IDentification (RFID) comes as an innovative solution for medications, equipment or medical staff identification and tracking. In fact, it is a technology for an automatic and flexible monitoring system in healthcare due to the following advantages: • Direct access to data in the system in a short time which reduce human error risks. • Automatic non-touch object and people identification within various sensing ranges. • Information data can be coded and encrypted which permits high secured data system. • High storage capacity (up to 1 Mbits). Our main contributions are summarized as follows: • A pharmacy and a hospital medication monitoring system using digital signature and RFID technology. • Organization and fast implementation of the medication process. • Reduction of manpower with a digitalized and efficient monitoring-management system. • Material and information flow speed up. • Medication safety improvement with an automated and an intelligent medication monitoring protocol. The rest of the paper is organized as follows. Section 2 presents related works. We introduce our proposed RFID system in Sect. 3. Finally, Sect. 4 concludes our paper.

2 Wireless Monitoring Systems Many studies on medication monitoring systems have evolved and achieved high performances by utilizing different wireless technologies. Table 1 below present different wireless identification technology related management systems.

3 Methods and Results 3.1 RFID Technology (Radio Frequency Identification) RFID or Radio Frequency IDentification (electronic label) is a wireless technology for automatic identification of people and object using radio waves. RFID system is

RFID System for Hospital Monitoring and Medication Tracking …

1053

Table 1 Wireless Identification Technology related management systems Ref.

Technology

Proposition

Description

[9]

Barcode

Medication identification

Reads barcodes (Identifiers) using smartphone cameras

[6]

Wi-Fi

Medication Management

Presents frameworks for personalized medication treatment management It could also be applied for existing personalized care system

[10]

NFC

Medication tracking

Tracks medications using NFC tags and a microcontroller Transfers data to smartphone

[11]

RFID

Medication tracking

Evaluates an RFID application in tracing medication and patient in an intensive care unit

[12]

RFID/NFC

Medication tracking

Uses RFID tags (Identifiers) and NFC smartphones as readers

[13]

RFID

Medication stocks’ monitoring system

Proposes an automated system for medication stocks’ monitoring Uses two sensors’ types which ensure a wide range of medical product management (medical equipment and medicines)

[14]

RFID

Healthcare moni-toring system

Creates monitoring cycles and monitors health status of patients using mi-crocontrollers, sensors and Internet of Thing (IoT)

Fig. 1 The process of RFID identification

constituted of a tag, a reader and a software application [15]. RFID tags include chips for data storage and antennas while RFID readers (responders) read and can write information of multiple tags simultaneously without line-of-sight communication [16, 17]. RFID technology offers great advantages such as its reusability, ease of data transmission, and security [18]. Figure below illustrates the RFID identification process which consists of reading an RFID tag attached to a person or an object without any physical contact (Fig. 1).

1054

S. El Abkari et al.

We classify RFID tags depending on their features into different categories: • Frequency bands – – – –

Microwave: 2.4–5.8 GHz. Ultra-High Frequency: 865–954 MHz. High Frequency: 13.6 MHz. Low Frequency: 125–134 kHz.

• Power Source – Passive: wherein tag and reader possess same energy source. – Active: wherein tag and reader do not possess same energy source. – Semi-active: wherein tag energy source is used for chip power supply and reader energy source is used for data transmission. • Storage Capacity: Kbits, Megabits … RFID technology has a significant potential for reducing medical errors and improve medical service quality in the healthcare field [19]. Thus, RFID can be highly effective for rapid tracking of medical staff, medications or equipment.

3.2 System Components In order to design an efficient medicine monitoring system prototype, it is necessary to select the right components that will properly operate during the medication process (ordering, administration, transcribing, and dispensing). We used the following com-ponents (Table 2):

3.3 Methodology Our RFID management system consists of four main parts (Fig. 3): • • • •

Pharmacy system (Fig. 2. b) placed in the pharmacy, Nurse/doctor modules (Fig. 2. a) carried by the medical staff, Tracking modules placed at each room door, Monitoring system which collects and processes data collected using RFID and Wi-Fi devices.

For the pharmacy system, we establish communication between RFID module and Arduino Uno board with Serial Peripheral Interface (SPI) using SS, clock, MISO, and MOSI. ESP8266 module and Arduino Uno board [20] communicate with TX and RX pins. Thus, ESP8266 modules are utilized to send RFID IDs to pharmacy and other system components to track medications.

RFID System for Hospital Monitoring and Medication Tracking …

1055

Table 2 Functions of used hardware Item

Function

Arduino Uno card 8-bit microprocessor 32k Flash Memory 2K RAM -16 MHz Clock Speed 3.3–5 V 14 Digital + 6 Analog I/O Pins

Temporary medication data processing

13.56 MHz RFID Reader (NFC –PN532 Module) Passive RFID tags 13.56 Mhz

Medication management

ESP8266 module (Wi-Fi module)

Nurses/Doctors locating Wireless network communication

Computer (Toshiba satellite skull-candy l50- b- 11g)

Medication data processing

Computer (HP elite 8200 – Windows 10)

Locating data processing Medication data storage

Fig. 2 a RFID nurse/doctor module, b Pharmacy monitoring system.

Medications (Fig. 4), patients, nurses, and doctors have their respective tags and IDs (Fig. 5). Medical staff and medications are tracked and monitored by PN532NFC and ESP8266 modules. Each patient, doctor, and nurse have a unique digital signature (unique identification code (ID)). Traditionally, prescriptions are handwritten, nurses verbally inform the pharmacist about a patient medication. In addition, records of token medication are done manually by the pharmacist (Table 3). It means that it is mainly based on human resources which leads to many errors during the medication process. Our system attempts to locate, manage, and monitor medications by improving the traditional practices through an automated monitoring system. Tables 3 and 4 show traditional and proposed medication monitoring processes. The medication process is constituted of two main phases: Prescription phase and medicine dispensing-tracking phase. • Prescription phase

1056

Fig. 3 RFID monitoring system.

Fig. 4 Medication RFID IDs

Fig. 5 Nurse, doctor and patient RFID IDs

S. El Abkari et al.

RFID System for Hospital Monitoring and Medication Tracking …

1057

Table 3 Traditional medication monitoring system Step

Description After diagnosing a patient, the doctor prescribes medicines and gives prescription to nurse in charge.

The pharmacist prepares medications. Nurse receives medications.

The patient receives his medications.

Table 4 RFID medication monitoring system

Step

Description After diagnosing a patient, the doctor prescribes medicines and gives prescription to nurse in charge. If needed, doctor changes or updates prescriptions. The pharmacist prepares medications.

When medications are ready for pickup, the nurse receives a notification via her/his mobile module.

The nurse checks for any errors or changes in the prescription, confirms medication reception.

The patient receives his medications.

In this phase, the doctor prescribes medications after diagnosing a patient. The pharma-cist then receives prescriptions signed with doctors’ IDs, verify IDs, prepares medications, and sends a notification to the nurse in charge (Fig. 6.). If there is an error in the prescription, the doctor receives a query to update it.

1058

S. El Abkari et al.

Fig. 6 Prescription received at the pharmacy system level

Fig. 7 Prescription information received by the nurse

• Phase of medication dispensing-tracking The nurse receives a notification from the pharmacist (Fig. 7.), verifies if the prescription has no errors (patient, medications, dosage and time are correct) and alerts the pharmacist by his/her module. When the nurse picks up medications, she/he confirms the reception. Once the nurse receives medications prepared by the pharmacist, the monitoring system starts tracking those medications. Tracking modules send then notifications of medication locations with correspondent nurse ID to the monitoring system. ESP8266 modules are also placed in different places in the hospital to permit communication and data transfer within the network. Our proposed system shows an improvement in patients’ safety in hospitals and the medication process. Table 5 below illustrates features of our proposed system compared to existing medication monitoring systems.

RFID System for Hospital Monitoring and Medication Tracking …

1059

Table 5 Comparison of our system to existing monitoring systems

Ref. Medication prescription (list, dose, instruction) Medication identification

[22]

[23]

[24]

[9]

[25]

Our system

Medication locating Handling of medication errors Medication validation Medication updates

4 Conclusion In this paper, RFID technology is implemented in a monitoring system for the real-time medication tracking, reducing medical errors during medical or clinical processes, and enhance the pharmacy inventory control. The proposed system constituted of ESP8266 and RFID modules allows the information flow speed up between the medical staff, thus improves the quality service of patient care and work performances. In addition, we designed the proposed RFID system according to the recent healthcare trend requirements and we experimentally demonstrated its effectiveness for the medical process improvement. For future works, we aim to integrate Internet of Things (IoT) features to our doctor/ nurse modules which are an early stage. We will also focus on developing the tracking of people within the hospital (medical staff and patients).

References 1. Husar J, Iakovets A (2019) Proposal for monitoring the movement of biological samples in hospitals. In: Automation and Management in Theory and Practice. In: 13th Anniversary Conference of Universities, High Schools and Practice, Stará Lesná, SR 2. Balog M, Husár J, Iakovets A (2020) RFID System for Effective Managing of Medical Facilities. New Approaches in Management of Smart Manufacturing Systems, pp 1–23. Springer, Cham 3. Mhamdi J, El Abkari S (2015) Contriving an RFID system for Alzheimer patients tracking. In: 3rd International Workshop on RFID and Adaptive Wireless Sensor Networks, pp 23–28. IEEE, Agadir 4. Bates DW, Cullen DJ, Laird N, Petersen LA, Small SD, Servi D, Vander Vliet M (1995) Incidence of adverse drug events and potential adverse drug events: implications for prevention. JAMA 274(1):29–34 5. Omotosho A, Ayegba P (2019) Medication adherence: a review and lessons for developing countries. Int J Online Biomed Eng (iJOE) 15(11):104–123 6. Koutkias VG, Chouvarda I, Triantafyllidis A, Malousi A, Giaglis GD, Maglaveras N (2009) A personalized framework for medication treatment management in chronic care. IEEE Trans Inf Technol Biomed 14(2):464–472

1060

S. El Abkari et al.

7. Tejesh BSS, Neeraja SJAEJ (2018) Warehouse inventory management system using IoT and open-source framework. Alexandria Eng J 57(4):3817–3823 8. Balog M, Matiskova D, Semanco P (2017) System of Workpiece Automation with RFID Identifier Published Patent Application SK 50063–2016 Darina Matisková, A., Michal Ba-log, Pavol Semanˇco Banská Bystrica: IPO SR 7 9. Sarzynski E, Decker B, Thul A, Weismantel D, Melaragni R, Cholakis E, Tewari M, Beckholt K, Zaroukian M, Kennedy AC (2017) Beta testing a novel smartphone applica-tion to improve medication adherence. Telemed e-Health 23:339–348 10. Morak J, Schwarz M, Hayn D, Schreier G (2012) Feasibility of mHealth and Near Field Communication technology-based medication adherence monitoring. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp 272–275. IEEE 11. Yang J, Feng Z, Ma X, Zhang X (2018) Indoor positioning method based on wireless signal. Int J Online Biomed Eng (iJOE) 14(10):53–67 12. Schreier G, Schwarz M, Modre-Osprian R, Kastner P, Scherr D, Fruhwald F (2013) Design and evaluation of a multimodal mHealth based medication management system for patient selfadministration. In: 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp 7270–7273 13. Mirea A, Albu A (2018) Acquisition of physical data in an automated system for monitoring medication stocks. In: 12th International Symposium on Applied Computational Intelligence and Informatics (SACI), pp 000179–000182 14. Khan SF (2017) Health care monitoring system in Internet of Things (IoT) by using RFID. In: International Conference on Industrial Technology and Management (ICITM), pp 198–204 15. McCarthy JF, Nguyen DH, Rashid AM, Soroczak S (2002) Proactive displays and the experience UbiComp project. ACM SIGGROUP Bull 23(3):38–41 16. RFID Journal, 2002–2007, “referred 6.8.2007” https://www.rfidjournal.com. Accessed 25 July 2020 17. El Abkari S, Jilbab A, El Mhamdi J (2018) Real time positioning over WSN and RFID network integration. In: 4th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), pp 1–5. IEEE, Tunisia 18. Cheng-Ju L, Li L, Shi-Zong C, Chi Chen W, Chun-Huang H, Xin-Mei C (2014) Mobile healthcare service system using rfid. In: International Conference on Networking, Sensing and Control, vol 2, pp 1014–1019 19. Castro L, Lefebvre E, Lefebvre LA (2013) Adding intelligence to mobile asset management in hospitals: the true value of RFID. J Med Syst 37:1–17 20. El Abkari S, Jilbab A, El Mhamdi J (2020) Wireless Indoor localization using fingerprinting and Trilateration. J Adv Res Dyn Control Syst 12(7):2597–2602 21. Devos P, Jou AM, De Waele G, Petrovic M (2015) Design for personalized mobile health applications for enhanced older people participation. Eur Geriatr Med 6:593–597 22. Abbey B, Alipour A, Gilmour L, Camp C, Hofer C, Lederer R, Rasmussen G, Liu L, Nikolaidis I, Stroulia E (2012) A remotely programmable smart pillbox for enhancing med-ication adherence. In: Computer-Based Medical Systems (CBMS). In: 25th International Sym-posium, pp 1–4 23. Neubeck L, Coorey G, Peiris D, Mulley J, Heeley E, Hersch F, Redfern J (2016) Devel-opment of an integrated e-health tool for people with, or at high risk of, cardiovascular dis-ease: the consumer navigation of electronic cardiovascular tools (CONNECT) web application. Int J Med Informatics 96:4–37 24. Ferreira F, Almeida N, Rosa AF, Oliveira A, Teixeira A, Pereira JC (2013) Multimodal and adaptable medication assistant for the elderly: a prototype for interaction and usability in smartphones. In: Information Systems and Technologies (CISTI), pp 1–6

Building a Domain Ontology for the Construction Industry: Towards Knowledge Sharing Hayat El Asri, Fatine Jebbor, and Laila Benhlima

Abstract As the construction industry has crucial interplays with many related fields, all in a constant state of flux, the use of a domain ontology to capture various relevant entries from related subdomains has become of paramount importance. The purpose of this article is to conceptualize the utility of ontologies in the construction industry as a solution for inter-sectorial transactions. Thereafter, upon presenting the process of ontology development, a real-life company was taken as a case study to implement the developed ontology. The purpose behind this study is to fully automate the generation of a domain ontology from Excel to OWL files. The methodology and process followed are thoroughly explained in the paper. The process is meant to be generic in such a way it could be reused in other projects for other industries, provided that the data is hierarchical and presented under Excel. Keywords Domain ontology · Construction · Knowledge sharing

1 Introduction The construction industry is constantly evolving, and new technologies are being implemented and used on a regular basis. One of those trendy technologies is ontologies. In fact, because the construction industry generally implicates stakeholders from different backgrounds and disciplines, having a “common representation of concepts” is crucial and has proven to be effective and time-saving. The use of ontologies in the construction industry is gaining popularity. Indeed, their ability to describe relationships and their high interconnectedness make them H. El Asri (B) · F. Jebbor · L. Benhlima Mohammadia School of Engineering, Mohammed V University, Rabat, Morocco e-mail: [email protected] F. Jebbor e-mail: [email protected] L. Benhlima e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_97

1061

1062

H. El Asri et al.

the bases for modeling high-quality, linked and coherent data [1]. Research in this area has been going on since the 2000s; however, it was not as advanced as it is today. In fact, before 2000, research was focused on Artificial Intelligence (AI) and software requirements. After the 2000s, AI was still in vogue, but several technologies emerged like ontologies that became more popular with the rise of knowledge management. Further, with the emergence of BIM (Building Information Modeling) technologies [2], the use of ontologies has become even more important. However, the discrepancy in file and item labelling is a seeming problem that the solution proposed in this article tries to solve. This paper is structured as follows. Section 2 discusses key concepts in ontologies. Section 3 discusses some relevant previous works in the field of construction. Section 4 presents the case study for which a general ontology was elaborated. Section 5 discusses the domain ontology implementation with a focus on the mapping rules and the implementation process. Last but not least, Sect. 6 presents the future works.

2 Ontologies: Key Concepts 2.1 Definition The definition of ontologies evolved tremendously over the past decades. In 1995, Gruber defined ontology as an “explicit specification of a conceptualization”. A few years later, in 1998, [1] added that ontologies can be used in many applications to “capture relationships, boost knowledge management, and make knowledge assets intelligently accessible to people in organizations”. In 2001, the definition evolved and started encompassing conceptual models and implementations. [3] defined ontologies as the implementation of a conceptual model that satisfies the engineering trade-offs of a running application. In the past few years, ontologies have started being linked to knowledge management. [4] said that “Ontologies play an important role in knowledge management and representation. In practice, ontology means a hierarchy of concepts with a set of properties and relations that represent a domain. Properties of each class describe the features and attributes of the class and their restrictions.” This is, however, not always true as it is not a hierarchy. What is interesting about ontologies is the inference process. In a nutshell, ontologies can be seen as a “unifying structure” that enables researchers to represent information in such a way that it provides common understanding and representation [5]. Furthermore, ontologies can represent any information. They are often associated with taxonomic hierarchies of classes, although they are not limited to this only.

Building a Domain Ontology for the Construction Industry ...

1063

2.2 Types and Uses Different types of ontologies exist and each is used to serve a specific purpose. The following are the well-known types. General ontologies are known to contain and provide general knowledge rather than domain-specific knowledge. Domain ontologies that are focused and applicable to a specific application domain, which helps in defining how a group of users conceptualize and visualize some specific phenomenon. Core reference ontologies that are usually linked to a specific domain; yet, integrates different viewpoints related to groups of users. It is considered to be the result of numerous domain ontologies. Information ontologies focus on concepts, instances, and their relationships with the aim of proposing a general overview. Last but not least, linguistic or terminological ontologies that can be glossaries, dictionaries, controlled vocabularies, taxonomies, folksonomies, thesaurus, or lexical databases. According to [6], “the roles of linguistic ontologies are twofold: The first one is to present and define the vocabulary used. Second, linguistic ontology is the result of a terminology agreement between a users’ community. This agreement defines which term is used to represent a concept in order to avoid ambiguity.” It is worth noting that an ontology and a thesaurus are completely different in several aspects. On the one hand, ontologies are multidimensional and flexible. They provide “controlled vocabulary” to describe a specific domain in a hierarchical manner. Furthermore, ontologies do not only provide definitions, but also relationships, axioms, and restrictions. On the other hand, a thesaurus is rigid and single-dimensional. It does not provide definitions, but rather relationships. It should be noted that there exist three types of relationships only: BT (Broader Term), RT (Related Term), and NT (Narrower Term).

2.3 Tools Several languages, mechanisms, and tools are available for developing, constructing, and implementing ontologies. The main languages used are: XML, RDF, RDFS, and OWL. Many tools based on the aforementioned languages have been developed and widely used like Protégé, WebOnto, WebODE, Swoop, and OWLGrEd, to mention but a few.

1064

H. El Asri et al.

Table 1 below presents a broad comparison of the commonly-used tools:

2.4 General Ontology Development According to [8], there are 5 main phases for building and implementing an ontology: methodology, pre-development, development, implementation, and postdevelopment. The first phase, the methodology, is all about the extraction of the domain-specific terminology from the different documents available and from the web. In the pre-development stage, a bottom-up conceptualization is used to construct the core ontology, which will define the semantics of the referential ontology. In this step, a conceptual graph is also formalized. In the development phase, all requirement specifications are analyzed and the taxonomic structure is elaborated. Representing the ontology using a specific language, OWL for example, is carried out and the conceptual model is officially elaborated. Then, the implementation process starts and is focused on two main aspects: the operations and the exploitation. In the operationalization process, making the core ontology context-specific is key. Then, in the exploitation process, the focus shifts towards integrating the referential ontology into a knowledge based-system that includes a fact base and a rule base. Maintenance happens in the post-development phase. Last but not least, determining the need for change after implementation happens in the integration process. For this project, we are interested in developing and implementing a semiautomatic process that would enable the generation of domain-specific ontologies for the construction industry.

3 Previous Works In the past two decades, researchers got interested in ontologies and have started to investigate their uses and how they can help the construction industry move forward. Table 1 Comparison of ontology tools (adapted from [7]) Base Language Storage Protégé

OKBC+ CLOS

DBMS and

Availability

Library Yes

Files WebOnto

OCML

WebODE

HTML + Java

Files only

Yes

DBMS only

No

Swoop

OWL

HTML models

No

OWLGrEd

OWL

Files only

Yes

Building a Domain Ontology for the Construction Industry ...

1065

For instance, [9] proposed a systematic domain ontology for requirements change management process in the global software development environment. A hybrid method combining methontology, which is a well-known approach, and the 101 method was used for its development, the Web Ontology Language (OWL) for its representation, and Protégé for its implementation. According to the researchers, there are several benefits for building such ontology; for example, it can be used to mitigate miscommunication and misunderstanding issues that may arise within organizations. Besides, [10] presented a method for developing an ontology of web-pages mapped to a domain knowledge. The researchers focused on solutions of semiautomatic ontology construction using web usage data; an experiment of Microsoft Web data was implemented and evaluated. The goal behind this study, that was successfully achieved, was to show the efficiency of page recommendations based on a domain ontology. Similarly, [11] have also conducted a study on ontologies as a crucial element in the semantic web. An ontology for the health tourism industry in Thailand was implemented. The final results have shown that the ontology was efficient and further works are getting implemented. From another perspective, [12] presented a comprehensive analysis of the alignments between one domain ontology from the OAEI Conference track and three well known top-level ontologies, namely: DOLCE, GFO and SUMO. The researchers mentioned that the reuse of the knowledge available in top-level ontologies along with that of domain ontologies reduces the modelling time and complexity, hence the heterogeneity issue of knowledge representation. The problem of matching domain and top-level ontologies has been addressed and a viable solution was proposed. In addition, [13] investigated the conversion from EXPRESS, a schema that defines the standard IFC (Industry Foundation Classes), into OWL for the construction industry. Although this has previously been investigated by researchers, no results turned into a referential yet. [13] analyzed the available research and came up with recommendations in order to get a usable ontology. The result of this study is a list of criteria and requirements to be followed in order to generate a referential ontology. Furthermore, [14] investigated an approach for building domain ontologies for guiding requirements’ elicitation and extracting semantic graphs from textual technical standards to generate compatible baseline domain ontologies. To achieve that, three main steps were followed: term extraction, term clustering, and taxonomy building. The researchers identified three core properties of domain ontologies that they called “explicit relational expression, qualified relation identification, and explicit temporal and spatial expressions.” The results of the paper suggest that the methodology/approach presented in the paper could help reduce the hassle that comes with building a domain ontology from scratch. The number of research papers available that discuss ontologies in the construction industry are not that numerous. Those that investigated automation processes are limited. What we are proposing in this article is to provide an automated process that could be reused in other domains where data is hierarchical in Excel files.

1066

H. El Asri et al.

4 Case Study A Rabat-based construction company was taken as a case study. This company uses many documents (mainly in excel, word, and pdf formats) that are shared among several collaborators. As an example, Fig. 1 presents a snapshot of one of the numerous documents used by the company, which is in Excel format: the “bordereau”. It is one of the main documents used in all the works that the company does. The column “designation des ouvrages” (2nd column) represents the different classes and subclasses while the column “N° des prix” (1st column) provides the identification of each class/subclass. For instance, the class “Gros oeuvre – Charpente Métallique” is a main class that contains 2 direct sub-classes, namely: “terrassements et remblais” (identifier 100.1) and “bétons et acier en infrastructure” (identifier 100.2). Each of these sub-classes has several sub-classes of its own. This document is the basis of an important process in the company and the problem that can occur is that collaborators sometimes use different names for the same concept. Therefore, a domain ontology is of great help. Our objective was then to develop a domain ontology based on several documents provided.

5 Building-Domain Ontology Development The main objective behind developing this domain ontology for the construction industry is to provide a common and unified terminology to enable the experts who work on the same project(s) to use the same terminology.

N° DES PRIX

DESIGNATION DES OUVRAGES EN TOUTES LETTRES

100 GROS-ŒUVRE - CHARPENTE MÉTALLIQUE 100.1 TERRASSEMENTS ET REMBLAIS 100.1.1 TERRASSEMENTS 100.1.1.1 FOUILLES EN PLEIN MASSE TOUT TERRAINS 100.1.1.2 FOUILLES EN PUITS ET EN RIGOLE TOUT TERRAINS 100.1.2 REMBLAIS 100.1.2.1 REMBLAI D'APPORT EN TOUT VENANT COMPACTE 100.1.2.2 REMBLAIS EN MATERIAUX PROVENANT DES FOUILLES 100.1.2.3 ÉVACUATION DES DÉBLAIS SOUS-TOTAL HT DE TERRASSEMENTS ET REMBLAIS 100.2 BÉTONS ET ACIERS EN INFRASTRUCTURE 100.2.1 BÉTON DE PROPRETÉ 100.2.2 GROS BÉTON 100.2.3 BÉTON ARMÉ EN INFRASTRUCTURE 100.2.3.1 BÉTON POUR SEMELLES ISOLÉES, FILANTES ET RADIERS

Fig. 1 Snapshot of an excerpt of the “bordereau” of the structural works

UNITE DES MESURES

m³ m³ m³ m³ m³ m³ m³ m³

Building a Domain Ontology for the Construction Industry ...

1067

5.1 Building-Domain Ontology Development Methodology The ontology development was divided into two main phases: the specification phase, in which the domain knowledge is acquired, and the conceptualization phase, in which the knowledge is structured and organized. Figure 2 presents the general methodology followed to build this ontology. The process started with the gathering of the user requirements from the company. A thorough analysis was conducted and a pre-building phase started. To make sure the pre-building phase was heading in the right direction, several interviews and document checks were carried out as part of the user requirements gathering. After making sure that all the specifications were taken into account, the ontology development and implementation was carried out. Last but not least, an ontology evaluation was conducted. Because the majority of the company’s data is available in Excel files, our goal was to automate the domain ontology implementation process to make it easier, timesaving, and reliable to have access to the needed data/information. The first step was to find the mapping rules.

5.2 Mapping Rules The following are the mapping rules used. 1. 2.

concepts -> classes: each designation is a concept. This latter is gotten by reading each element of the column “designation des ouvrages”. relationships between concepts - > object properties: there are two types of relation-ships, namely ISA and ISAD: “C1 ISA C2” if C1 is a sub-concept of C2. “C1 ISAD C2” if C1 is a direct sub-concept of C2.

Fig. 2 General process of the proposed approach

1068

H. El Asri et al.

We are interested in ISAD relationships because if a concept C1 is a direct subconcept of C2, then C1 is automatically a sub-concept of C2. The algorithm in Fig. 4 was used to extract ISAD relationships from the “bordereau”.

5.3 Domain Ontology Implementation Process In this section, we are especially interested in the building-domain ontology implementation, based on the mapping rules to automatically extract concepts and relationships between these latter from the Excel files, and to generate an owl file. In order to map the “bordereau” into a conceptual model, three main steps were followed for each line in the Excel file. The general process of the proposed approach is explained in Fig. 3. The following are the steps followed, in a loop of iterations, in the proposed approach. Step 1: Concept extraction from the Excel file & integration in the OWL file. The function getDesignation() returns the designation of a specific price number. Step 2: ISAD relation extraction & concepts linking. After checking if the extraction concept is linked via an ISAD relation with other concepts, the identified relations are integrated in the OWL file. Step 3: ontology evaluation. The file obtained (DOnto4CI.owl) is checked and evaluated by a human expert. The result of the implementation process is presented in Fig. 5 and 6. Fig. 3 General process of the proposed approach

Building a Domain Ontology for the Construction Industry ...

1069

Algorithm 1: extraction of ISAD relations 1: Read num=N°_prix 2: Do 3: If num is an integer Then 4: nume->d->c->f->g) is modified to the route (a>b->c->d->e->f->g) by reversing the order of visit to tour e and c. More generally, reversing the running order of two tours need to reverse the running order of all the tours between them [11].

5 Formalization of the 2-Opt Algorithm Let a graph G = (V, E) and H be a Hamiltonian cycle in G provided with a cost function returning the cost of the weights of the paths composing the tour. We define a 2-permutation in the Hamiltonian cycle H as the replacement of two edges a1, a2 ∈ H by two edges a3, a4 ∈ E such that the resulting turn is always Hamiltonian in G. In our case of the Ad Hoc network problem, we consired the edges like a node and their diagonals like a path.

6 New Design Algorithm (GA-2opt) In this paper, our new GA-2opt approach aims to solve the routing problem in Ad Hoc network, between the source node and the destination node in a short time too [12], we have proposed a new algorithm design as follows: Begin with the constraint limits is set for the sp route GenerationSize = 1000; PopulationSize=40; Number of iterations = 100; CrossoverRate =0.8; MutationRate = 0.05; NodeSource = 3; NodeDistination = 35; Repeat GetBestCycleHamiltonian(GenerationSize, populationSize, NodeSource, NodeDestination, CrossoverRate, MutationRate); 6.1 function SubsectionSample 2-opt (G: graph, H: GetBestCycleHamiltonian) improvement: boolean: = true Please note that the first paragraph of a section or subsection is not indented. The first as long as improvement = real doing paragraphsimprovement: that follows a =table, falsefigure, equation etc. does not have an indent, either. Subsequent paragraphs, however, for any vertex xi of H do are indented. for any vertex xj of H, with j different from i-1 from i and from i + 1 do if disatance (xi, x (i + 1)) + distance (xj, x (j + 1))> distance (xi, xj) + distance (x (i + 1), x (j + 1)) then replace the edges (xi, x (i + 1)) and (xj, x (j + 1)) by (xi, xj) and (x (i + 1), x (j + 1)) in H improvement: = true end.

1078

H. Khankhour et al.

Table 1 The computational results between GA et GA-2opt TSPLIP

Optimal

GA

GA-2opt

Berlin52

7 542

7 885

7 600

Pr76

108 159

119 988

109 500

Rat99

1 211

1 400

1 220

Bier127

118 282

139 821

120 233

Eil51

426

430

426

Lin318

42 029

47 510

43 215

Fig. 3 Comparison result between GA and GA-2opt

7 Implementation and Discussion To see the effect of the 2-optimizer algorithm on the Ad Hoc network, we have built a program that uses the uniform (GA) as a heuristic, and in the crossing step we check both children by the “2- optimizer”, if there is an optimization, replace the child (route) with the new solution. Table 1 shows the great effect of this optimize. From the results in Fig. 3, we notice that the distance of the genetic algorithm 2opt is much less than the (GA) mostly for the big city like Bier127. it means that

Optimization of the Ad Hoc Network by Using Hybridization …

1079

Fig. 4 Topology with 20 nodes

in a big map network using 2-opt optimization we can find the shortest path in the shortest time too, from the source node to the destination node. After testing our algorithm with the BENCHMARK instances, we applied our algorithm to the ad hoc network using the topology Fig. 4, used in this article [9] after several execution of our program, from the above results we can say that the delay for our new design algorithm AG2OPT is good, we find the best path, the cost is 142, in short time too 0,020 s better than result in this article [9].

8 Conclusion This article proposes a new conception of the hybridization between the metaheuristic (GA) algorithm and two optimization algorithms, and we choose to further optimize our algorithm using the advantages of the 2 opt optimizer, for this purpose the goal is to find the shortest path from a source to a destination in quick time too, mainly for a large network of cardswhose goal that the nodes do not lose its energy, choosing the best path, to avoid shocks, collision and of course not to saturate the bandwidth in Ad Hoc network in a big map.

1080

H. Khankhour et al.

References 1. Alander JT (2001) An indexed bibliography of genetic algorithms in economics, Technical Report Report 2. Lerman I (1995) Algorithmes génétiques séquentiels et parallèles pour une représentation, Rapport de Recherche de l’INRIA Rennes 3. Abdoun O, Abouchabaka J, Tajani C (2012) Analyzing the performance of mutation operators to solve the travelling salesman problem. IJES, vol. 2(1) 4. Liu K, Xu H, Gao H, Peng X (2009) Research on confirmation of tension leveller basic technological parameters based on neural network and genetic algorithm, Le Yao in Computational Mechanics, pp 422 5. Dorigo MG (1997) Ant colony system: acopperative learning approach to the traveling salesman problem. IEEE Trans Evol Comput 6. Fausto A, Araújo R, Garrozi C (2010) MulRoGA: a multicast routing genetic algorithm approach considering multiple objectives. Appl Intell 32, 330–345 7. Ge X, Xue G (2019) An improved genetic algorithm for pro-active dynamic vehicle routing problem. In: Proceedings of the Twelfth International Conference on Management Science and Engineering Management 8. Khankhour H, Abouchabaka J, Abdoun O (2020) Genetic Algorithm for Shortest Path in Ad Hoc Networks, Lecture Notes in Networks and Systems, vol. 92, pp. 145–154 9. Chang WA, Ramakrishna RS (2002) A genetic algorithm for shortest path routing problem and the sizing of populations. IEEE Trans Evol Comput 6(6), 12 10. Okdem S, Karaboga D (2009) Routing in wireless sensor networks using an Ant Colony Optimization (ACO) router chip. Sensors, 9(2), 909–921. https://doi.org/10.3390/s90200909 11. Mavroidis I, Papaefstathiou I, Pnevmatikatos DN (2007) Hardware implementation of 2opt local search algorithm for the traveling salesman problem. In: 18th IEEE International Workshop on Rapid System Prototyping (RSP 2007), 28–30 May 2007, Porto Alegre, RS, Brazil 12. Kumar S, Mehfuz S (2016) Intelligent probabilistic broadcasting in mobile ad hoc network: a PSO approach. J. Reliable Intell. Environ. 2, 107–115

Design of a Microstrip Antenna Two-Slot for Fifth Generation Applications Operating at 27.5 GHz Salah-Eddine Didi, Imane Halkhams, Mohammed Fattah, Younes Balboul, Said Mazer, and Moulhime El Bekkali

Abstract Currently, industry and academia have been engaged in significant research and development activity in anticipation of the next generation of wireless micro and picocellure networks (5th generation). Microstrip antennas are widely employed for non-wired communications because of their affordability, reduced profile, and simplicity of realization. However, weak characteristics such as low bandwidth, a bad gain, etc. limit their possible uses. Since the future generation (5G) needs a high gain antenna, this study’s principal reason is to conceive a sensitive gain microstrip antenna. This article presents a patch antenna design with two slots for the 5G application and provides a bandwidth of around 27.5 GHz. The role of these slots improved the antenna’s performance. This antenna has been conceived on a polyimide substrate of relative permittivity of εr = 4.3 and a height of the substrate of (h = 0.15 mm). The metrics of this offered antenna are 27.91 GHz, −25.4052 dB, 0.96 GHz, 6.6 dB, and 6.9 dB are working frequency, reflection coefficient, bandwidth, gain and directivity respectively. Keywords 27.5 GHz · Millimeter wave · Microstrip patch antenna · 5G

1 Introduction The role of an antenna is to convert the electrical energy of a signal into electromagnetic energy, or conversely to convert electromagnetic energy into electrical energy. A transmitting antenna is a device that ensures the transmission of energy between a transmitter and the free space where this energy will propagate. Conversely, a S.-E. Didi (B) · Y. Balboul · S. Mazer · M. E. Bekkali IASSE Laboratory, Sidi Mohamed Ben Abdellah University, Fez, Morocco e-mail: [email protected] I. Halkhams SED Laboratory, Private University of Fez, Fez, Morocco M. Fattah IMAGE Laboratory, Moulay Ismail University, Meknes, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_99

1081

1082

S.-E. Didi et al.

receiving antenna is a device that transmits the energy of a wave propagating in space to a receiving device. The patch antenna is a flat antenna whose radiating element is generally a square conductive surface, separated from a conductive reflector plane by a dielectric substrate. Its realization resembles a double-sided printed circuit board, substrate, and is therefore suitable for industrial production. The use of patch antennas is almost generalized in all mobile communication systems. These antennas are light, compact, and inexpensive [1]. The 5th generation (5G) wireless technology network is scheduled to be deployed by 2020. It is the most ambitious wireless communication network project ever carried out and will have to meet the expectations of a mass market. The latest specification trends focus on network densification, increased data throughput (10 Gb/s, 10 times higher than 4G), increased network coverage, latency between 1 ms and 5 ms and reduced power consumption [2]. Frequency bands: It is planned to free up frequencies for 5G that are located practically all along the radio spectrum. The National Frequency Agency (ANFR) indicates that they are divided into two major classes. The low frequencies offer extensive coverage and excellent transmission in the indoor environment. For high frequencies, they are strong, but the transmission in residential environments is restricted. Therefore, the chosen technology strategy is to combine the new radio spectrum as well as frequencies currently available for 2G, 3G, and 4G mobile data transmission. Currently, Two new funding tranches will apply to 5G: the 3,5 GHz frequency band is between 3,4 GHz and 3,8 GHz and the 26 GHz frequency band is in the [24,25 GHz; 27,5 GHz] frequency range [3, 4]. In this article, we present a patch antenna with two slots for 5G. This proposed antenna must resonate at 27.5 GHz and has a size of 4.678 mm × 4.55 mm × 0.15 mm structure. In the first phase, we introduce the required introduction, in the second phase; we explain the design methodology of this antenna offer, in the third phase, we carry out the simulation of this created antenna through an HFSS (HighFrequency Structure Simulator) software tool. The details of these simulations will be discussed, and we thus compare the different results acquired from this new antenna with those already available. In the fourth phase, we finish this work by concluding with a conclusion.

2 Antenna Design and Performance In this paragraph, we present the most commonly used method for designing microstrip patch antennas, including the procedure for determining their parameters (width and length), and also the dimensions of the ground plane, and those of the feed lines, and the type of power supply for this design, as well as how to glue the patch antenna to the substrate of your choice. For simplified analyses and antenna performance predictions, we propose a rectangular patch antenna with two slots operating at 27.5 GHz as part of the fifth generation 5G application, as illustrated in Fig. 1.

Design of a Microstrip Antenna Two-Slot for Fifth Generation Applications …

1083

Fig. 1 The suggested shape of the Microstrip patch antenna

The proposed antenna has been printed on a substrate of permittivity εr = 4.3. Polyimides are commonly used for coating metal substrates, and use a microstrip line for their feed. Quarter Wave Converter is a microstrip cable employed to ensure proper impedance matching. To determine the width and length of this quarter-wavelength (WT, LT) and those of the micro-strip feed cable (Wf, Lf), and those of this offered antenna (Wp, Lp), we apply the formulas in [5, 6]. • by applying Eq. (1), it is possible to find the value of the width Wp:

W p = 0.5

2 c 1 ( )2 f r εr + 1

(1)

• by applying Eq. (2), it is possible to find the value of the width Lp:

L p = L e f f − 2L p = With : L p = 0.412 And : εe f f = • Ground plan dimensions:

c − 2L p √ 2 f r εe f f

(εe f f + 0.3)( WH sp + 0.264) (εe f f − 0.258)( WH sp + 0.8)

εr + 1 H s −1 εr + 1 + (1 + 12 )2 2 2 Wp

(2)

(3) (4)

1084

S.-E. Didi et al.

Table 1 Suggested antenna size in this work

Parameters

Values (mm)

LE

4.678

WE

4.55

Hs

0.15

Wp

2.578

Lp

3.35

LT

1.1

WT

0.5

Lf

0.9

Wf

0.2

S1

1.7 mm × 0.2 mm

S2

0.6 mm × 0.3 mm

WE = Wp + 6Hs et LE = Lp + 6Hs

(5)

where: the symbols Wp and Lp represent the width and length of the rectangular patch antenna respectively, fr represents the working frequency, c shows that the celerity of light is approximately equal to: 300000000 m/s, Lp is shown the stretch path, Hs is the height of the material substrate, εeff indicates the set value of the dielectric constant of the substrate material, WE is the latitude of the earth plane, LE is the longitude of the earth plane (Table 1).

3 The Results of the Simulation and the Debates In this part, we show how we performed simulations on the design of a two-slot patch antenna with S11 return loss, VSWR, gain and directivity. For this design, we have chosen HFSS (High-Frequency Structure Simulator) as the tool of choice. The latter it is a tool for simulating three-dimensional shapes that allows the creation of circuits with finished ground planes, any shape, various types of materials and insulators of different thicknesses. It applies the Finite Element Technique (FEM). This Finite Element Technique (FEM) enables to digitally perform the following partial differential equations.

3.1 Return Loss of This Offered Antenna The return loss S11 of an antenna makes it possible to quantify the quantity of the reflected signal in relation to the incident signal. This return loss is equal to − 10 of the fund value, indicating that about 10% of the received power is returned,

Design of a Microstrip Antenna Two-Slot for Fifth Generation Applications …

1085

Fig. 2 Reflection coefficient S11 of this offered antenna in this research

that is to say that 90% as much power is absorbed through the antenna, something that is believed to be very good for wireless mobile communication. the reflection coefficient at the antenna level, if there is a perfect match there is no reflection, that is to say that in this case the reflection coefficient will be equal to zero, if there is no match the reflection coefficient is different from zero, we can define the qualification at the match level of an antenna either by attributing its impedance characteristic, generally equal to 50 ohms, or by attributing to its level of reflection coefficient. This antenna functions at 26.91 GHz with a return loss of −25.4052 dB, a bandwidth of 0.96 GHz (27.35 GHz–26.39 GHz) as shown in Fig. 2 below.

3.2 VSWR (Stand-Wave Ratio) VSWR occurs due to the reflection of a large amount of the generated power. The value of this VSWR on the efficiency bandwidth must be between 1 and 2. In order for the transmission of the antenna power to be maximized, this ratio must tend towards 1. Figure 3 illustrates VSWR as a function of frequency. The VSWR value is 1.095 at the operating frequency of this antenna.

3.3 Gain The gain is equal to the ratio between the power density radiated by the antenna in a specific direction and the power density emitted by a reference antenna. The gain of this study for this new antenna is 6.6 decibel, as shown in Fig. 4 below. The gain of this antenna obtained is quite suitable and its use is the most appropriate for 5G.

1086

S.-E. Didi et al.

Fig. 3 Offered Antenna Standing Wave Ratio (VSWR) Table 2 Comparative analysis of the results of this antenna in relation to those of the existent results

Fig. 4 Three-dimensional radiation curve of this proposed antenna

Design of a Microstrip Antenna Two-Slot for Fifth Generation Applications …

1087

3.4 Directivity The directivity of an antenna in a specific sense is the density of power radiated by the antenna in this sense relative its average value in all space. Directivity is a concentration of energy in an antenna or the ability to concentrate it and each antenna has a specific directivity in a preferred direction. The value of the directivity of this antenna is equal to 6.9 dB as shown in Fig. 5 below: Table 2 contains the results of the few preceding studies concerning gain, directivity, bandwidth, S11, overall antenna size and operating frequency. The performances of the proposed antennas, such as gain, bandwidth and reflection coefficient S11, are preferable to those of antennas [6, 8, 9] and [11]. Thus, a second advantage is that the proposed antenna is smaller in size than antennas [8, 6, 9] and [11]. For the antenna [7] has a higher reflection coefficient compared to the proposed antenna, but the size of the latter is smaller than that of the antenna [7]. The gain of the antenna [3] is higher than that of the suggested antenna of shorter size. The antenna [10] has a better return loss than the proposed antenna, but the latter also has a higher gain than the antenna [10], so the proposed antenna has a very important feature, namely its smaller size. Consequently, the aim of this work is to compensate for the decrease in performance and the reduction in size of the microstrip patch antenna for 5G technology. Fig. 5 The three-dimensional directivity pattern of the offered microstrip antenna

1088

S.-E. Didi et al.

4 Conclusion In this article, I have shown the definition and characteristics of the antenna as well as some of the advantages of the fifth generation, these advantages being the date of its publication, the increase of the data transmission rate, the increase of the coverage area, the decrease of the power consumption, also the steps of the transition from the first to the fifth generation, He also mentioned the presentation of the fifth generation frequency bands. I specified the steps in the design of a patch antenna as well as the study of the simulations on the HFSS software and I compared the results obtained with those of some other antennas designed during work carried out in previous years. In this article, rectangular microstrip antenna is presented with two slots S1 and S2 that improve antenna performance. The improved bandwidth allows for increased user occupancy, indispensable for 5G portable communications. The global dimensions of the offered antenna are 4.55 mm × 4.678 mm × 0.115 mm. The antenna described in this document operates over 26,91 GHz and a reflection coefficient of − 25.4052 dB, a bandwidth of 0.96 GHz, a gain of 6.6 dB, and a directivity of 6.9 dB. Through this study, we have shown that the two-slot antenna obtains the best results in the relevant frequency band (24,25 GHz–27,5 GHz) on which it can work well with the fifth generation portable communication system. The proposed antenna would therefore be a good candidate for wireless applications at 27.5 GHz. For future work, we will use this antenna in the design of patch antenna arrays to improve the performance of patch antennas that meet the requirements of the fifth generation 5G.

References 1. Odile P (2009) Antennas Theory, design and application. French center for the exploitation of coupier law, Paris. https://www.dunod.com/ 2. Faisal MMA, Nabil M, Kamruzzaman Md (2018) Design and simulation of a single element high gain microstrip patch antenna for 5G wireless communication. In: Faisal MM (ed) International Conference on Innovations in Science. Engineering and Technology (ICISET). IEEE, Chittagong, pp 290–293 3. Iftikhar A, Sun H, Qasim A, Abdul S (2020) Design of umbrella shape single element patch antenna with high gain and high efficiency for 5G wireless communication in 28 GHz. In: 17th International Bhurban Conference on Applied Sciences and Technology (IBCAST), pp 710–713. IEEE, Islamabad, Pakistan 4. Muhammad Z, Sultan S, Yasar A, Yuriy V, Peter E (2020) Ultra-wideband circular antenna for future 5G. In: Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), pp 2280–2283. IEEE, St. Petersburg and Moscow, Russia 5. Ghanendra K, Chakresh K (2020) Design and analysis of u-slot mircostrip patch antenna for mobile communication at 60 GHZ. Bull Eng 6. Dennis NA-C, Jorge LA-C (2018) High Gain 4 × 4 rectangular patch antenna array at 28 GHz for future 5G applications. In: XXV International Conference on Electronics, Electrical Engineering and Computing (INTERCON), pp 1–4. IEEE. Lima, Peru

Design of a Microstrip Antenna Two-Slot for Fifth Generation Applications …

1089

7. Merlin Teresa P, Umamaheswari, G (2020) Compact slotted microstrip antenna for 5G applications operating at 28 GHz. IETE J Res 1–9 8. Omar D, Dominic B, Onyango K, Franklin M (2019) A 28 GHz rectangular microstrip patch antenna for 5G applications. Int J Eng Res Technol ISSN 0974–3154, vol 12, no 6, pp 854–857 9. Khraisat YSH (2018) Increasing microstrip patch antenna bandwidth by inserting ground slots. J Electromagn Ana Appl. ISSN: 1942-0730 10. Jyoti S, Agarwal SK (2017) T and L slotted patch antenna for future mobile and wireless communication. In : 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp 1–5. IEEE, Delhi, India 11. Safpbri J, Muhammad AJ, Shaifol II, Mohd NM, Norhafiza H (2018) 28 GHz microstrip patch antennas for future 5G. J Eng Sci Res 2(4): 01–06

Performance Improvement of Corner-Truncated Sierpinski Carpet Fractal Antenna Using U-tree Slots for Vehicular Communications and Other Bands Fatima Ez-Zaki , Abdelilah Ghammaz, and Hassan Belahrach Abstract This paper describes the design of a Corner-truncated Sierpinski Carpet Fractal Antenna which resonates at multiple frequencies (1.84, 4.09, 5.9, 7.18, 10.88, 13.17, 15.15, and 15.60 GHz). Different performance parameters like radiation pattern, Voltage Standing Wave Ratio (VSWR), return losses (S11) are observed at all the operating frequencies. The FR4 glass epoxy with relative permittivity of 4.4 and a thickness of 1.6 mm is used as substrate material. The proposed antenna has a simple structure. Antenna investigation is done between 0.5 and 16 GHz frequencies. The antenna is fed by 50 Ohms microstrip feed line and simulated using CST Microwave Studio. The established antenna can be used for multiple applications and bands such LTE, WIFI, and DSRC for vehicular communication, in addition to satellite, radar, and space communications. Keywords Multiband fractal antenna · Sierpinski carpet fractal · Defected ground plane · Vehicular communication

1 Introduction Nowadays, wireless technology requires antenna with wider bandwidth and smaller dimensions. Therefore characteristics such as light weight, low profile, low cost, easy to be manufactured and to be integrated with RF devices makes the Microstrip antenna very attractive research area [1, 2]. Besides self-similarity and space fillings, frequency independent, compact size make fractal antenna an excellent candidate to minimize the size of the antenna or to improve its characteristics [2]. Furthermore Fractal geometry is also combined with electromagnetic theory in order of investigating a new class of radiation, propagation, and scattering problems. Recent studies F. Ez-Zaki (B) · A. Ghammaz · H. Belahrach Electrical Systems, Energetic Efficiency and Telecommunication Laboratory, Faculty of Science and Technology, UCAM, Marrakech, Morocco H. Belahrach Royal School of Aeronautics, 40150 Marrakesh, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_100

1091

1092

F. Ez-Zaki et al.

on the microstrip fractal antenna have been recently reported in [3–11]. Different fractal geometries, such as Hilbert, or Sierpinski curves, have been used to bring the benefits of miniaturization [3], wide bandwidth [4–6], and multiband operation [5–10]. So far the bandwidth of the microstrip fractal antenna is reported to be narrow. Therefore multiple techniques are used to resolve this task such as using slots, notches, defected ground structure (DGS) [12–14]. In this paper, we propose a novel truncated antenna based on the Sierpinski carpet shape of the second order, and a slotted ground. The proposed antenna was simulated using CST Microwave Studio. Impact of fractal iterations and defected ground plane was analysed in details.

2 Antenna Design Procedure Analysis of the proposed structure is obtained by using CST Microwave Studio. A patch antenna with square shape is designed by using the following equations [15], the procedure assumes that the specified information includes the dielectric constant (εr ) of the substrate, fr which represents the resonant frequency and the thickness (h) of the substrate. The Sierpinski carpet is constructed using square geometry as figured in Fig. 1. Figures 2, 3, and 4 describe the process of construction for cornertruncated Sierpinski carpet fractal antenna. The antenna finale structure is depicted in Fig. 5 including the defected ground plane by inserting U-tree slots. Antenna 1: The design of desired antenna starts with eventually rectangular patch which operates at resonant frequency of 5.8 GHz. By using the following equations for the values of εr = 4.4, f r = 5.8 GHz, h = 1.6 mm and c = 3 × 108 m/s the rectangular patch dimensions are calculated as following. c  2 f r ε2r

(1)

  h 1/2 εr + 1 εr − 1 + 1+ = 2 2 W

(2)

W = Effective dielectric constant: εreff Patch effective length: Lef f =

c √ 2 f r εr e f f

(3)

Patch length extension because of the fringing effect: W L =

0.412h  Wh h

 + 0.264 (εreff + 0.3)  + 0.8 (εreff − 0.258)

(4)

Performance Improvement of Corner-Truncated Sierpinski Carpet Fractal Antenna …

1093

Patch length: L = L e f f + 2L

(5)

For the used frequency of 5.8 GHz the patch width and length are: W = 15 mm and L = 12 mm, the substrate is of 20 × 20 mm2 , the dimensions of the full ground plane are 20 × 20 mm2 . The thickness of substrate (h) is of 1.6 mm. Antenna 2: four semi-circle corners are cut to provide as a perturbation as shown in Fig. 2. Antenna 3: To get 1st iteration of the antenna, the base patch geometry is divided into 9 congruent sub-rectangles in a 3-by-3 grid, and remove the central rectangle as shown in Fig. 3. Antenna 4: the same procedure as step 3 each rectangle patch is divided of 1st iteration geometry into 9 congruent sub-rectangles, and remove the central one as illustrated in Fig. 4. Antenna 5: To get the finale geometry a U-tree slots in the ground plane as shown in Fig. 5. Fig. 1 Antenna 1 geometry

Fig. 2 Antenna 2 geometry

Fig. 3 Antenna 3 geometry

1094

F. Ez-Zaki et al.

Fig. 4 Antenna 4 geometry

Fig. 5 Antenna 5 geometry

3 S-Parameter and VSWR Characteristics of Various Antenna Design Stages 3.1 Various Antennas S11 and VSWR Response Simulated S11 and VSWR versus frequency plots of all the antenna construction stages are depicted in Figs. 6, and 7 respectively. The S11 for the eventual square patch are less than −10 dB at 0.82, 5.543, 9.10, and 15.08 GHz frequencies whereas the corner truncated patch antenna resonates at 0.84, 5.8, 9.39 GHz. The Sierpinski corner truncated patch first iteration works at 0.87, 5.36, 12.35 GHz similarly to the second iteration as it can be seen in Fig. 6 for antenna 3 and 4. So far the U-tree slots Sierpinski corner truncated antenna shows more improved performances and presents 7 resonant frequencies at 2.15, 4.37, 7.61, 11.3, 13.36, 15.743 GHz (Table 1).

3.2 Effect of Feed Line and DGS in the Investigated Antenna Response So further the effect of the feed line dimensions is studied. As can be noted clearly from the Fig. 8 and Fig. 9 the dimensions of the feed line have relatively smaller effects on the antenna’s response. As for the selection of the U-tree dimensions it is greatly dependent on the required operating bands and they mainly affect the directivity of the antenna’s radiation patterns. However, the U-tree dimensions are choose in order to get more improved

Performance Improvement of Corner-Truncated Sierpinski Carpet Fractal Antenna …

Fig. 6 S11 results of various Antennas

Fig. 7 VSWR results of various Antennas

1095

1096

F. Ez-Zaki et al.

Table 1 Various antennas results comparison Antenna 1 Resonant Frequencies (GHz)

Antenna 2

0.82

0.84

5.543

5.8

9.10

9.39

Antenna 3

Antenna 4

Antenna 5

0.87

0.87

2.15

5.36

5.35

4.37

12.35

12.39

15.08

7.61 11.3 13.36 15.743

S11(dB)

−14.34

−13.25

−13.77

−13.63

−18.79

−16.017

−13.32

−10.79

−10.19

−11.87

−32.93

−12.39

−21.30

−15.68

−14.34

−18.54 −39.65 −26.83 −22.06

VSWR

1.53

1.55

1.5

1.52

1.26

1.4

1.54

1.8

1.9

1.68

1.18

1.62

1.39

1.39

1.04

1.27 1.02 1.1 1.17

Fig. 8 VSWR results of various Antennas

Performance Improvement of Corner-Truncated Sierpinski Carpet Fractal Antenna …

1097

Fig. 9 VSWR results of various Antennas

antenna features and to attain the desired frequency band of vehicular communications at 5.9 GHz. In sum simulated results for S11 (dB) and VSWR of proposed antenna are analysed in next section.

4 Proposed Antenna Results and Discussion In this section, the simulation results of proposed antenna are analyzed in details.

4.1 S11 and VSWR of the Proposed Antenna Looking at Fig. 10, it is apparent that as we approach to antenna 5, the antenna reveals more multiband and wideband behavior due to the increase in paths and lengths of the antenna geometry by using the DGS technique. As it can be easily noticed from the graph of the VSWR results the proposed antenna presents a good impedance matching described by a VSWR closer to 1 within the operating bands.

1098

F. Ez-Zaki et al.

Fig. 10 Investigated antenna S11

Fig. 11 Investigated antenna VSWR

4.2 Directivity of Proposed Antenna The simulated radiation pattern of the proposed antenna at resonant frequencies are illustrated in Fig. 12. The designed antenna shows a nearly directional characteristics in Phi = 90 with a very good directivity of a peak value of 6.87 dBi at almost operating frequencies. As can clearly observed that the radiation patterns have multiple lobes at higher frequencies than lower frequencies which leads that the radiation pattern is distorted at high frequencies (Fig. 11).

Performance Improvement of Corner-Truncated Sierpinski Carpet Fractal Antenna …

1099

Fig. 12 Investigated antenna Directivity

5 Conclusion The current paper discussed the construction of corner-truncated Sierpinski carpet antenna steps, besides the effect of inserting U-tree slots on the ground plane

1100

F. Ez-Zaki et al.

has been analyzed. The key features of this antenna is compact size, simple in construction using Sierpinski fractal geometry. The investigated antenna works at multiple frequencies (1.84, 4.09, 5.9, 7.18, 10.88, 13.17, 15.15, and 15.60 GHz) and get improved performance for multi band (C, S, X and Ku) applications. Investigated antenna can be used for multiple applications and bands such LTE, WIFI, and DSRC for vehicular communication, in addition to satellite, radar, and space communications.

References 1. Werner DH, Ganguly S (2003) An overview of fractal antenna engineering research. IEEE Antennas Propag Mag 45(1):38–57 2. Anguera J, Andújar A, Jayasinghe J et al (2020) Fractal antennas: an historical perspective. Fractal Fractional 4(1):3 3. Singh K, Grewal V, Saxena R (2009) Fractal antennas: a novel miniaturization technique for wireless communications. Int J Recent Trends Eng 2(5):172 4. Bhatt S, Mankodi P, Desai A et al (2017) Analysis of ultra wideband fractal antenna designs and their applications for wireless communication: a survey. In: 2017 International conference on inventive systems and control (ICISC). IEEE, pp 1–6 5. Soni BK, Singhai R (2018) Design and analysis of minkowskized hybrid fractal like antenna for multiband operation. Prog Electromagnet Res 80:117–126 6. Ali JK, Abdulkareem S, Hammoodi A et al (2016) Cantor fractal-based printed slot antenna for dual-band wireless applications. Int J Microwave Wirel Technol 8(2):263–270 7. Bhutani P, Sagar S, Kumar A (2019) Performance analysis of sierpinski carpet fractal antenna for wireless communication. In: Applications of computing, automation and wireless systems in electrical engineering. Springer, Singapore, pp 749–758 8. Dhankhar A, Kaur J (2015) Design of sierpinski carpet fractal antenna for multiband applications 9. Radonic VK, Palmer GS, Crnojevic-Bengin V (2012) Flexible Sierpinski carpet fractal antenna on a Hilbert slot patterned ground. Int J Antennas Propag 11:7 p, article ID 980916 10. Sivia JS, Kaur G, Sarao AK (2017) A modified sierpinski carpet fractal antenna for multiband applications. Wireless Pers Commun 95(4):4269–4279 11. Wong S, Ooi BL (2001) Analysis and bandwidth enhancement of Sierpinski carpet antenna. Microwave Opt Technol Lett 31(1):13–18 12. Gupta A, Joshi HD, Khanna R (2017) An X-shaped fractal antenna with DGS for multiband applications. Int J Microwave Wirel Technol 9(5):1075 13. Arya AK, Kartikeyan MV, Patnaik A (2010) Defected ground structure in the perspective of microstrip antennas: a review. Frequenz 64(5–6):79–84 14. Garg C, Kaur M (2014) A review of defected ground structure (DGS) in microwave design. Int J Innovative Res Electr Electron Instrum Control Eng 2(3):1285–1290 15. Balanis CA (2005) Antenna Theory: Analysis and Design, 3rd edn. Wiley-Interscience, Hoboken

A Novel System Based V2V Communications to Prevent Road Accidents in Morocco Zakaria Sabir and Aouatif Amine

Abstract Due to the deaths and injuries caused by road accidents every year, road safety became an important field of research for many institutes. The development of smart solutions can remarkably enhance road safety. Vehicular networks are one of those solutions. It can bring advanced services, namely collision detection, traffic management, communication between vehicles, and so on. This paper introduces an implementation of a complete vehicle communication system. The prototypes are developed using Raspberry Pi boards and other sensors. The results demonstrate the efficiency of the presented safety scenarios in dealing with the dangers of the road. Keywords Connected vehicles · V2V · ITS · Prevent accidents · Road safety · Vehicular communications

1 Introduction Road safety has become a very important subject during the last years. Every year, millions of people die and thousands are injured due to road accidents. The World Health Organization (WHO) reported that 1.25 million people died in 2013 and more than one million accidents were recorded in 2014 in Europe, resulting in 1.4 million injuries and 25 900 deaths. In Morocco, 3593 persons were killed in 2016 against 3499 in 2017 [1], which represents a limited reduction of 2.6% year to the other. Even if the statistics show a minor decrease, a lot of worrying facts are hidden [2]. These statistics are so flagrant that the Ministry of Equipment, Transport, Logistics, and Water (METLE) dedicated a call for projects especially in the field of road safety, in cooperation with the National Center for Scientific and Technical Research (CNRST) and Moroccan universities. The aim of those projects is first Z. Sabir (B) · A. Amine ILM, ENSA, Ibn Tofail University, Kenitra, Morocco e-mail: [email protected] A. Amine e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_101

1101

1102

Z. Sabir and A. Amine

of all decreasing the number of accidents that occur every year in Morocco. Our project entitled “SafeRoad: Multiplatform for Road Safety (MRS)” is one of those. His main goal is to reduce the collision rate using the anticipation and the detection of accidents. And since the collection of statistics of the corporal accidents of road traffic carried out by the METLE in 2016 [3] reported that there were 80680 accidents caused by obstacles, we considered also working on the detection of obstacles using vehicular communications. The automation/semi-automation of vehicles can help humans avoiding errors that cause most road accidents. Constructors are developing smart vehicles that will be supplied by a variety of technologies and connected. They will assist drivers by alerts indicating the dangers on the road. Principally, they will be connected to a variety of sensors within the vehicle: warning lights, stability, diagnostic tools, airbags, and others. Thus, a vehicle is able to automatically disseminate information to others, like the presence of a slippery roadway, if it is down or in an accident. If the technologies of networking, electronics, and computing are well integrated, the roads will be safer and the impact on the environment will be minimized [4]. Recently, diverse definitions have been given to a “connected” vehicle. A description that involves all other connections of a smart vehicle is provided by the US Department of Transportation (USDOT): “Connected vehicle applications provide connectivity between vehicles to prevent accidents, between vehicles and infrastructure, in order to generate safety, mobility and environmental benefits; between vehicles, infrastructure and wireless devices to provide continuous real-time connectivity to all users of the system” [5]. The system of connected vehicles [6] will operate using Vehicle to Infrastructure (V2I) [7] and Vehicle to Vehicle (V2V) [8] communications. In this paper, we present our solution to the problem of road accidents, especially those caused by obstacles. We introduce an implementation of a vehicular communications testbed. We focus on V2V technology, and we carry out our experiments using mini cars based on Raspberry Pi boards. We add different sensors and features to develop our complete prototypes and we propose two road safety scenarios that can help to avoid accidents. The next sections of the paper will be as follow: Sect. 2 discusses the related work, Sect. 3 recalls some vehicular network characteristics. The proposed system is presented in Sect. 4, Sect. 5 Concludes the paper.

2 Related Work In the early 1980s, research that treaded inter-vehicular communications was conducted in Japan by the Association of Electronic Technology for Automobile Traffic and Driving [9]. In the 1990s, other projects, like the California PATH program [10] and the European “Chauffeur” [11], have deployed and considered automated platooning systems via the dissemination of data over vehicles. Since 2000, many research institute, private companies, and automobile manufacturers have supported worldwide projects concerning vehicular safety using wireless communications:

A Novel System Based V2V Communications …

1103

• FleetNet: This German project was initiated by six companies and several research institutes [12]. The project aimed to develop and demonstrate a wireless adhoc network for vehicular communications through a platform of cooperative driver-assistance systems, in order to ensure comfort and security for drivers and passengers. • The Car2Car communication consortium was inaugurated by European automobile manufacturers. The goal was to improve road safety and to manage effectively the traffic thanks to the use of inter-vehicle communications [13]. This project had several missions and intends to develop a V2V system prototype for road safety applications. • CarTalk2000 [14] is a European project which aimed to establish, test, and evaluate an organized and pure ad hoc wireless network and also to develop cooperative driver assistance systems, in order to enhance global traffic safety and to increase driving comfort and transport efficiency. • NoW (Network on Wheels) [15] is a German research project supported by the government and implemented by research institutes and universities, providers, and vehicle constructors. The goal is to develop a vehicular communication system dedicated to road safety, traffic efficiency and infotainment applications, radio, security, and privacy. • The Integrated Project PReVENT [16] is a European automotive industry activity, started in February 2004 with the goal of establishing and validating preventive safety applications and technologies. Co-funded by the European Commission, PReVENT stands for Preventive safety and act in accordance with the objective of the safety initiative to pull down accidents and traffic death in Europe. • MobiVIP is a research project of Predit 3 (Integration of the Communication and Information systems Group) [17]. Its objective is evaluating and showing the influence of the information and communication technologies on a new service formulated by small urban vehicles for mobility in town centers. In our work, different from those researches, we focus on the Moroccan case and we propose a system that we will be adapted to fit the Moroccan situation since a foreign system cannot be applied directly in Morocco. Many factors are to take into consideration such as the culture, the environment, the driver behavior, and the architecture of the cities. We succeeded in the first step using mini cars and we will move to real tests with real vehicles in the next step.

3 Vehicular Networks Characteristics Vehicular ad hoc networks represent a special case of mobile ad hoc networks. However, research works studied and accomplished in this area cannot be employed straight in the field of vehicular networks. There are several characteristics of vehicular networks that must be taken into consideration before applying the classical architectures and protocols of ad hoc networks. We present in the following some

1104

Z. Sabir and A. Amine

features and restrictions related to vehicular networks which differentiate them from ad hoc networks: • Neighborhood recognition: drivers can receive various information that will help them ameliorate their visibility and behave accordingly to any adjustments in the surroundings of the vehicle. This information could be the state of the road, weather conditions… etc. • Elevated processing capacity: vehicles can provide powerful communication, sensing, and computing capacity. Nowadays, they are capable of analyzing and interpreting the assembled data in order to help drivers to make good decisions. • Extensive scope: as opposed to other ad hoc networks that adopt generally a restricted network scope, vehicular networks are able to reach the whole route system and thus cover a lot of users. • Unlimited transmission power and adequate storage: vehicles have big storage and can provide stable power to computing and communication equipment. They can have also different communication interfaces such as Bluetooth, Wi-Fi, and others. • Changes of the topology: vehicular networks are distinguished by fast movements due to the vehicles’ speed that can reach 200 km/h. Therefore, nodes connect and disconnect quickly and the topology changes frequently. This points to poor connectivity and minimal road life. • Anticipated movement: usually, vehicles’ mobility is restricted by the roadways, they are often connected to Road Side Units (RSUs), roadside infrastructures (like panels and traffic lights), or highways. This makes the anticipation of their movement easier, in contrast to classic mobile ad hoc networks, where nodes move arbitrarily. • Diverse communication status: the two habitual communication status in vehicular networks are outside and inside the town. In the first one, the area is almost easy and the roads are usually straight (especially on highways). However, in the second one, the circumstances are quite difficult. Inside the town, obstacles such as buildings may isolate the paths. Consequently, communication is not always continuous.

4 Proposed System Today’s traveling manner can be transformed and improved through the use of connected vehicles. They will help to create a wireless communication network, safe and interoperable, which includes various components (trains, cars, buses, traffic light …). Connected vehicle technologies address some huge challenges in several fields such as environment, safety, transportation, and mobility. The aim of our work is part of the road safety field, particularly in the Moroccan case in order to resolve the problems mentioned above. We propose a new system which can be used to notify Moroccan drivers in various circumstances. It will contribute to reducing the number of accidents and avoiding the crash that frequently

A Novel System Based V2V Communications …

1105

Fig. 1 Example of V2V communication. The first vehicle stops and sends a warning to other vehicles after detecting an obstacle on the road

occurs between vehicles or between vehicles and bikes, animals, pedestrians, or other objects. Our system uses V2V communications and it is based on reel time information. Vehicles interchange important information that is valuable to predict a risky situation. Thanks to this system, the danger of unstructured roads in Morocco can be avoided by delivering the aforementioned information to drivers. Considering the statistics cited in [3], the first road safety scenario in our system can alert a driver of an obstacle in the road. An example is depicted in Fig. 1, where three vehicles are traveling in one lane and the first vehicle stopped because of a cow crossing the road. The second vehicle is blocking the vision; hence the third vehicle can’t see the first one. Thanks to V2V communication, the last vehicle is capable of receiving a warning about the hard braking and avoiding a possible accident. The second road safety scenario we propose aims to alert a driver about a specific traffic condition on the road. The driver will then reduce the speed and change his behavior regarding the situation (accident, road work, …). He may consider changing the route and taking a safer one. Due to V2V communication, the warning is transmitted by a vehicle ahead that has already discovered the problem. Figure 2 shows an explicative scheme of our system.

4.1 Experimental Setup In order to validate our proposition, we started by testing our system in prototypes before moving to real case testing. To develop our complete prototype, we used two mini cars from Sunfounder [18] (Fig. 3). Each one is based on a Raspberry Pi 3 B+ [19] and has the indispensable configuration to authorize the control of movements, communication, and sensors. The specifications of the used boards are as follow: they are based on an ARM Cortex-A53 quad-core processor which is operating at 1.4 GHz and have 4 USB port 2.0, an Ethernet port, a memory of 1 GB (RAM), a micro-SD slot, a camera interface, and an audio jack. A micro USB port is used for power (5V/2.5 A). Each car is equipped with a DC motor driver which power its movement, and since they have integrated Wi-Fi, they have the ability to communicate within the

1106

Z. Sabir and A. Amine

Fig. 2 Explicative scheme of V2V Communication

Fig. 3 Mini car from Sunfounder [18]

Fig. 4 Our car prototype

test zone using a local ad hoc network. They are also equipped with a step-down DCDC converter module, a USB camera, and a PCA9685-based servo controller. In order to build our complete prototype, we had to add some other sensors since the original kit didn’t come with all the necessary equipment. In Fig. 4 we can see the added sensors which are the infrared obstacle detector and the line follower sensors. This figure depicts also the location of each sensor. The infrared sensor will be used

A Novel System Based V2V Communications …

1107

to detect any obstacles on the road while the line follower sensors will help to keep the car on the road. The details of the sensors are provided in Table 1. Table 1 The specifications of the used sensors

Fig. 5 V2V communication in our testbed. V1 detects the obstacle on the path and sends the warning to V2

Fig. 6 V2V communication in our testbed. V1 detects the obstacle on the path and sends the warning to V2 which changes the path taken by V1 to get to the destination

Sensor name

Line follower sensor

Infrared obstacle detector

Reference

TCRT5000

FC-51

Detection distance

1 mm–25 mm

2 cm–30 cm, Angle: 35

Operating voltage

5V

3.3–5 V

Quantity

5

1

1108

Z. Sabir and A. Amine

4.2 Results and Discussion Using V2V communications, our road safety scenarios aim to disseminate an alert informing the drivers of the existence of an obstacle on the roadway. As a first try, we attempt to run the cars on a map, so they follow pre-programmed distances. The result was that the cars couldn’t move following a straight line, due to the little difference between the motors of each car. Therefore, we preferred to use line follower sensors to help cars following the road. The first road safety scenario is about the hard-braking warning. We put an obstacle on the road and ran the two cars at once. Since the first car (V1) is equipped with an infrared sensor (see Fig. 4), it detects the obstacle and sends the alert to the second car (V2) using the Wi-Fi interface. The moment it received the alert, V2 stops as well (see Fig. 5). Receiving the warning message depends on the Wi-Fi range (the maximum distance between V1 and V2). The second road safety scenario aims to send a warning to the drivers, so they can choose the safer road. Similarly, we put an obstacle on the path before the final destination and we ran V1 and V2 at the same time. Once the infrared sensor senses the obstacle, V1 stops instantly and sends the alert to V2 which changes the current path (taken by V1) and chooses the safer one. Figure 6 illustrates the second road safety scenario. Table 2 Comparison of our work with other studies Study Communication Implementation Tools mode

Scenario

Purpose

Special case

[20]

V2V

Real time

RPi; Blind spot Ultrasonic detection sensors; Audible Alarm; GPS Sensor; RF Module

Improving Not traffic specified congestion

[21]

V2V

Real time

OsmAnd; Android Devices; GRCBox

Warning Not about specified emergency vehicles

[22]

V2V

Real time

RPi; Autonomous Smooth Not Controller; driving traffic flow specified IR Sensor (level 3)

Our work

V2V

Real time

RPi mini cars; IR sensor; Line Followers

Displaying location of relevant vehicles

Obstacle detection

Preventing Moroccan accidents Case

A Novel System Based V2V Communications …

1109

We present a comparison of our proposed system with other studies in the literature in Table 2.

5 Conclusion We reached our target and succeeded in achieving the solution. The primary consideration of the paper was to introduce an implementation of a complete vehicle communication system using V2V technology in the context of the SAFEROAD project. Thanks to the partnership with the METEL, we obtain real information about road accidents in Morocco. Our solution is developed especially for the Moroccan case and it is the first of its kind in our country. The aim was to send an alert to the drivers in time, in order to help them avoiding road dangers and so decreasing the number of accidents. We conducted our experiments using mini cars based on Raspberry Pi as well as infrared obstacle detector and line follower sensors. The results affirm the usefulness of our system in the field of road safety. In future work, we will confirm the reality on the ground by applying this system to real cars and test it in different cities, especially those where a grand number of accidents occur. Acknowledgment This research work is supported by the “SafeRoad: Multiplatform for Road Safety (MRS)” Project under contract No: 24/2017, financed by the Ministry of Equipment, Transport, Logistics and Water (METLE), and the National Center for Scientific and Technical Research (CNRST).

References 1. World Health Organization (2018) Global status report on road safety 2018 (WHO). World Health Organization, Geneva 2. Leconomiste: Accidents routiers. https://www.leconomiste.com/flash-infos/accidents-routiersau-maroc-tue-presque-autant-qu-en-france-avec-7-fois-moins-de. Accessed 08 July 2019 3. Ministry of Equipment, Transport, Logistics, and Water, Direction of Roads (2017) Compendium of Statistics of Road Traffic in Personal Accidents 4. Guerrero-Ibáñez JA, Flores-Cortés C, Zeadally S (2013) Vehicular ad-hoc networks (VANETs): architecture, protocols, and applications. In: Chilamkurti N, Zeadally S, Chaouchi H (eds) Next-generation wireless technologies. Springer, London, pp 49–70 5. Brookes R, Pagani P (2014) What becomes a car. In: Proposed paper for: BIT 2014 conference workshop-technology enabled business models: platforms, analytics and performance, March 2014 6. Sabir Z, Amine A (2020) NDN vs TCP/IP: which one is the best suitable for connected vehicles? In: Proceedings of the first international conference on technology, engineering, and mathematics, Kenitra, Morocco, 26–27 March 2018. Springer, Cham, pp 151–159 7. Sabir Z, Dafrallah S, Amine A (2019) A novel solution to prevent accidents using V2I in Moroccan smart cities. In: 2019 international conference on computational intelligence and knowledge economy (ICCIKE), Dubai, UAE, pp 621–625

1110

Z. Sabir and A. Amine

8. Sabir Z, Amine A (2018) Connected vehicles using NDN for intelligent transportation systems. In: International conference on industrial engineering and operations management, Paris, France, pp 2433–2441 9. Tsugawa S (2005) Issues and recent trends in vehicle safety communication systems. IATSS Res 29:7–15 10. Hedrick JK, Tomizuka M, Varaiya P (1994) Control issues in automated highway systems. IEEE Control Syst Mag 14:21–32 11. Gehring O, Fritz H (1997) Practical results of a longitudinal control concept for truck platooning with vehicle to vehicle communication. In: Proceedings of conference on intelligent transportation systems, Boston, MA, USA. IEEE, pp 117–122 12. Enkelmann W (2003) FleetNet - applications for inter-vehicle communication. In: IEEE IV2003 intelligent vehicles symposium. Proceedings (Cat. No. 03TH8683), Columbus, OH, USA. IEEE, pp 162–167 13. C2C-CC: CAR 2 CAR Communication Consortium. https://www.car-2-car.org/. Accessed 24 June 2019 14. Reichardt D, Miglietta M, Moretti L, Morsink P, Schulz W (2002) CarTALK 2000: safe and comfortable driving based upon inter-vehicle-communication. In: IEEE intelligent vehicle symposium, 2002, vol 2, pp 545–550 15. Festag A, Noecker G, Strassberger M, Lübke A, Bochow B, Torrent-moreno M, Schnaufer S, Eigner R, Catrinescu C, Kunisch J (2008) ‘NoW – network on wheels’: project objectives, technology and achievements, Hamburg, Germany 16. Schulze M, Nöcker G, Böhm K (2005) PReVENT: a European program to improve active safety. In: Proceedings of 5th international conference on intelligent transportation systems telecommunications, France 17. Despeyroux T, Masseglia F, Rossi F, Senach B (1992) User-centered design, improvement and analysis of information systems. Bull Sociol Methodol/Bulletin de Méthodologie Sociologique 37:55–57 18. Sunfounder: Smart Video Car for Raspberry Pi. https://www.sunfounder.com/learn/category/ Smart-Video-Car-for-Raspberry-Pi.html. Accessed 15 July 2019 19. Buy a Raspberry Pi 3 Model B+ – Raspberry Pi. https://www.raspberrypi.org/products/raspbe rry-pi-3-model-b-plus/. Accessed 01 Aug 2019 20. Ghatwai NG, Harpale VK, Kale M (2016) Vehicle to vehicle communication for crash avoidance system. In: 2016 international conference on computing communication control and automation (ICCUBEA), pp 1–3 21. Hadiwardoyo SA, Patra S, Calafate CT, Cano J, Manzoni P (2017) An android ITS driving safety application based on vehicle-to-vehicle (V2V) communications. In: 2017 26th international conference on computer communication and networks (ICCCN), pp 1–6 22. Tayeb S, Pirouz M, Latifi S (2017) A raspberry-pi prototype of smart transportation. In: 2017 25th international conference on systems engineering (ICSEng), pp 176–182

Detection of Underground Cavities Using Electromagnetic GPR Method in Bhalil City (Morocco) Oussama Jabrane, Driss El Azzab, Mahjoub Himi, Mohammed Charroud, and Mohammed El Gettafi

Abstract The presence of underground cavities of natural or anthropogenic origin in urban areas presents a major risk, which causes severe problems for civil engineering and environmental management, damage to properties and infrastructures, and can trigger major building collapse. A geophysical survey was carried out, based on the sequential application of magnetic, low-frequency ground penetrating radar (GPR) to locate and detect underground cavities and voids below the surface which have a high relative dielectric permittivity (RDP). Ten profiles were carried out in a karstic location in Bhalil Medina listed as an environmentally sensitive area, giving 10 radargrams as results. The latter are processed to extract the maximum information and to be easily correlated with the geological study (geological section and the lithostratigraphic column). The observed RDP anomalies can be related to the presence of air-filled cavities within the shallow subsurface, small geological structures, or a type of formation of high reflection of the signal, and revealed the presence of a high degree of karstification in the inspected site. The presence of such features prevents the extension of urbanizations, and endangers the existing habitats, especially in the old town like the Medina of Bhalil. O. Jabrane (B) · D. El Azzab · M. Charroud Faculty of Science and Technology, SIGER Laboratory, Sidi Mohamed Ben Abdellah University, BP2202 Fez, Morocco e-mail: [email protected] D. El Azzab e-mail: [email protected] M. Charroud e-mail: [email protected] M. Himi Economic and Environmental Geology and Hydrology Group, University of Barcelona, Barcelona, Spain e-mail: [email protected] M. El Gettafi FPN, G-ES Laboratory, Mohammed Premier University, 62999 Nador, Morocco e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_102

1111

1112

O. Jabrane et al.

Keywords Ground penetrating radar · Cavities · Bhalil · Karst · Voids · Survey

1 Introduction Karst processes often result in the formation of underground cavities due to the effect of chemical erosion by the circulation of acidic water (dissolution) on carbonate rocks. The urban area of Bhalil continues to develop locally on more or less unstable grounds, in particular its old town, subjected to multiple constraints. These are linked to rapid population growth coupled with the fragile geological nature of the underlying formations. Another critical aspect of these cavities lies in the fact that they can provide a safe and consistent habitat for many people in this area. Underground cavities detection is one of the primary objectives of geophysical investigation techniques because of the danger that threatens the inhabitants causing soil instability, which can provoke dramatic events such as subsidence and even total collapse to the ground surface [6, 15]. The diagnosis of these buildings condition is carried out regularly by the authorities sometimes forces them to evacuate certain houses deemed to be in danger. For the subsurface identification and mapping of such karst features, the non-invasive and high-resolution geophysical techniques have appeared as an appropriate choice [12]. The present case study, applied Ground-penetrating radar (GPR) for the site characterization, which uses radar pulses to image the subsurface. It is a non-intrusive prospecting technique based on the propagation of electromagnetic waves in the underground to explore the presence of a medium RDP [13]. It has become one of the most important geophysical methods for cavities and voids detection, to delimit them laterally and in-depth according to its efficiency, high resolution, rapid execution and simultaneous result by imaging the subsurface. Mapping dangerous areas in the Old Medina are an asset in terms of future urban development in the city, protection of people’s properties, and especially their lives. Our investigation consists of two complementary parts: i/ Geological study to identify the origin of the observed voids and their consequence at the surface; ii/ prospecting and locating the underground cavities using indirect and non-destructive electromagnetic GPR method.

2 Geology of the Study Area 2.1 Site Description The study area is located in a small parcel around the old town of Bhalil, which is 28 km south of the city of Fez. The town of Bhalil is 950 m above sea level, it is a transition zone between the tabular Middle Atlas, to the south, and the Sais basin, to the north.

Detection of Underground Cavities Using Electromagnetic GPR Method …

1113

Karst features presence greatly increases the risk of underground cavities development by a cluster of inter-related processes, including bedrock dissolution, soil sapping, rock collapse, and soil collapse. The presence of underground cavities in the basement of an urban area can cause severe effects and threatens the lives of the inhabitants.

2.2 Geological Setting The geological deposits layers, tectonic and geomorphological settings, combined with the anthropic action, makes the old town of Bhalil and its surroundings a region at high risk of potentially destructive collapse because of its karstic nature. The Bhalil basin is limited to the east by an accident in the direction of N170 parallel to the P.20 road, to the west by the Bhalil-Sefrou fault, to the northwest by the Bhalil inlier, and limited to the south by Aggay River. The region knows a diverse lithostratigraphy (Fig. 1B) ranging from the Paleozoic deposits outcrops in 3 inliers (Kandar, Beni Mellala and Bhalil), then the Jurassic layers which appears in the South and the North, overcoming geological formations of Triassic age, composed of red gypsum-salt clays with intercalation of often altered doleritic basalts. These Paleozoic and Mesozoic terrains are drowned northward under recent formations of the Upper Miocene and Plio-Quaternary age. The geological formations of the Upper Miocene are mainly marine and composed at the base of a mollasse, made up of conglomerates and sandstone, which is first surmounted by pinkish to beige fossiliferous limestones then ocher silts and blue sandy marls [3, 4, 9, 11]. The old town of bhalil is built on recent geological formation (Fig. 1A), mainly formed by, Miocene age limestones with conglomeratic units, early quaternary

Fig. 1 A-Geological section showing the layout of the outcrops in the study area [7], BLithostratigraphic column showing the main formations described in the study area

1114

O. Jabrane et al.

(villafranchien) tufa and travertine deposits. The latter, contains many voids and cavities, due to the natural karst phenomenon, or to the human action consisting in digging into the rocks for underground habitats. A final recent layer consists of late quaternary (colluvial deposits that conceal the cavities).

3 Methodology 3.1 Basic Theory This study was conducted to detect subsurface cavities using GPR (Ground Penetrating Radar) designed by the Swedish company Mala geosciences. The advantage of this technique is its high-resolution results, faster operation, equipment portability and simultaneous picturing of sub-soil in terms of RDP. This technique is based on the use of two antennas, one for transmitting electromagnetic (EM) waves, and the other for receiving these waves after reflection on the RDP targets. The two antennas are connected to a console, itself connected to a laptop that allows data visualization during acquisition, or to configure the console. A simple automotive battery supplies the power, the transmitter sends electromagnetic waves, ranging between two frequencies from 10 MHz to 2 GHz. The signal is not a single sinusoid at the designated frequency, but a sum of sinusoids whose spectrum is centered on that frequency [2]. The investigation depth range using GPR method depends on the DPR which is related to the electrical conductivity of the underground layers, transmitted center frequency of antennas and the radiated power [8, 10], while the EM velocity is conditioned by the dielectric permittivity of the soils [1, 5, 14]. The dielectric permittivity can be expressed as: ε = ε = εr ε0

(1)

Where εr is the relative permittivity, and ε0 is the dielectric permittivity in space (ε0 = 8.854187817 × 10−12 F.m−1 ). The GPR method suffers from a major handicap as it is attenuated by various factor and especially the electrical conductivity. While the attenuation constant depends on the physical properties of the media. In general, the attenuation constant can be expressed as: 

με ∝= ω 2

1/ 2   σ 2 1/ 2 1+ −1 ωε

(2)

Where ε, μ and σ are absolute permittivity, magnetic permeability and electrical conductivity of the subsurface material respectively.

Detection of Underground Cavities Using Electromagnetic GPR Method …

1115

Radiowaves propagate through different materials at different speeds. The velocity of the radiowaves depends on the physical properties of the medium. In general, the velocity of radiowaves through a homogeneous material is given by: V =

⎤−1/2 ⎡   σ 2 1/2 2 ⎣ 1+ − 1⎦ με ωε

(3)

When σ is small as compared with ωε, the EM velocity can be expressed as: C V =√ εr

(4)

Where V is relative velocity of propagation of electromagnetic wave pulse in a solid medium (m/s), C is velocity of the electromagnetic wave in the air, equal to light 0,3 m/ns, and εr is relative dielectric constant of the material.

3.2 Field Investigations The measurements were carried out in a parcel of the Rquiba district (Fig. 2), using two antennas RAMAC GPR. For all ground-penetrating radar profiles, it was necessary to choose some characteristics of implementation in the field. After several tests to avoid sever attenuation and to get the best resolution, we opted for a recording

Fig. 2 A Satellite image showing the survey area, B map showing profiles arrangement in Rquiba district

1116

O. Jabrane et al.

time of 200 ns. It corresponds, in the context of the study area, to an investigation depth of about 10 m and bistatic type antennas with a central frequency of 200 MHz. The profiles have different lengths and different orientations, 6 profiles which are oriented NNE-SSW perpendicular to the length of the recognized cavities in the studied area, separated by a distance of 1.2 m, and 4 profiles perpendicular to the previous profiles separated by a distance of 3 m.

3.3 Processing The geophysical ground penetrating radar work carried out as part of this study enabled us to record a large amount of data. The radargrams collected systematically underwent the processing sequence. To improve the image provided by the radiograms, it is necessary to apply certain processing to the raw data from the ground penetrating radar, in order to increase the signal/noise ratio of the radar sections, and to make reading and interpretation easier. They help to attenuate the physical phenomena inherent in the propagation of electromagnetic waves in the ground so as to obtain an image that reflects the object to be identified. The processing applied in our work include in particular: • Time-zero correction for shifting the traces along the time axis. • Removing the low-frequency components (“wow” operation) of the GPR record; it reduces the data to a mean zero level. • Band-pass filtering (1D filter band pass frequency): Eliminate the high-frequency (noise). • Signal gain «energy decay»: Applying a gain to compensate the signal attenuation. The final sections obtained were then carefully analyzed in order to extract as much information as possible on the structure of the subsoil investigated and on the possible existence of underground cavities within the plot.

4 Results and Discussion The ground-penetrating radar profile shows areas of strong linear reflections and areas of very low amplitude. We associate these with the voids contained in the travertines layer, numbered A1, A2, and A3 (Fig. 3). We can follow spectacularly the first decimeters of travertines formation before encountering disturbances linked in our case to the presence of 3 anthropic cavities. Alongside the anomalies related to voids, discontinuities have been highlighted that we associate to small fractures, which are sloping towards the North East. Strong reflections continue up to 20 ns. We measured the thickness of the roof of the cavities related to a small subsurface travertine formation, which was about 0.5 m on average.

Detection of Underground Cavities Using Electromagnetic GPR Method …

1117

Fig. 3 GPR Profile 1 with depth scale, showing the arrangement of the 3 cavities

Fig. 4 GPR Profiles (a, b, c, d. and e.) showing all A1 anomalies, (a, and b. profiles) showing the second cavity anomaly A2

1118

O. Jabrane et al.

Fig. 5 Perpendicular GPR Profiles (f, j, h, and i.), radar-gram (f, and h.) showing the same anomaly A2

We can follow these voids in profiles 3, and 4 (Fig. 4), in which we note the absence of anomaly A3. As a result, the cavity linked to this anomaly is very limited in space. The cavities corresponding to anomalies A1 and A2 continue deeper to appear in profile 4, implying a length of about 6 m. From the fifth profile, we note that all kind of anomalies disappear, so that indicates a more or less homogeneous level of the same travertine formation. The profiles from 7 to 10 (Fig. 5), are perpendicular to the first ones, so they follow the cavities but present little disturbance. The profile 7 and 9 which are perpendicular to profile 1 and passes through the middle, show an anomaly appears close to the surface, attributing in our case to anomaly A2 of profile 1 which indicates the presence of an underground cavity in the same travertine formation.

5 Conclusion In this work we demonstrated the efficiency of the GPR application as a very powerful tool for mapping distribution of different anomalies related to cavities in urban area.

Detection of Underground Cavities Using Electromagnetic GPR Method …

1119

The results are very encouraging, as most of the cavities reported by the investigation have been confirmed. We were able to follow these voids limit in the three directions of space at the scale of the plot studied. It was therefore possible to select the largest cavities from the small ones thanks to the large number of profiles and their arrangements. The very fine spacing of the measurements (20 cm) also made it possible to highlight small fractures that affect the travertines, which are very limited in space (a few meters) and sloping NE direction, which is consistent with the relatively large structures mentioned in previous geological work in the area. In this work the description of danger zones (areas with potential risk evidenced by cracks and voids in-depth), requires additional GPR surveys at the western central limit of the urban area in the old Bhalil, to define the necessary preventive measures. A better geotechnical knowledge of the underlying layers would also be beneficial seeking to minimize the risk of the possible collapse and landslides. From a geotechnical point of view, all the new infrastructure and facilities must be located in a stable area to avoid construction disorder which represents a high financial impact for many owners. Funding This research was funded by AECI project: A/012172/07: Geophysical investigating for the delimitation of the unstable zones of the city of Fez and vicinity.

References 1. Annan AP (2003) Ground-penetrating radar principles, procedures & applications. Sensors and Software Inc, Mississauga 2. Bano M (1996) Constant dielectric losses of ground-penetrating radar waves. Geophys J Int 124(1):279–288 3. Charriere A (1990) Héritage hercynien et évolution géodynamique alpine d’une chaîne intracontinentale: le Moyen Atlas au SE de Fès (Maroc). Thèse, Doctorat d’Etat, Toulouse, p 589 4. Charroud M, Cherai B, Benabdelhadi M, Charroud A, El Moutaouakkil N, Falguères C, Lahrach A (2006) Sedimentary evolution of a fore-chain Sais basin during plio-quatrenary and modalities of tectonic inversion (Sais basin, Morocco). In: Geophysical research abstracts, European geosciences union, vol 8, p 10039 5. Daniels DJ (2004) Ground penetrating radar. The Institution of Electrical Engineers, 2nd edn. London 6. De Bruyn IA, Bell FG (2001) The occurrence of sinkholes and subsidence depressions in the far West Rand and Gauteng province, South Africa, and their engineering implications. J Environ Eng Geosci 7(3):281–295 7. Fassi D (1999) Les formations superficielles du Saïs de Fès et de Meknès. Du temps géologique à l’utilisation actuelle des sols. Notes et Mémoires du Service Géologique, Maroc, pp 389–527 8. Franke JC, Yelf RJ (2003) Applications of GPR to surface mining. In: Proceedings of the 2nd international workshop on advanced ground penetrating radar, University of Technology, Delft, The Netherlands, pp 115–119 9. Gourari L, Boushaba A, Ramon J, Akdim B (2000) Hydrochimie, processus, facteurs de précipitation des encrôutements travertineux et les causes géo-environnementales du ralentissement de leur formation actuelle dans la bassin karstique de l’Oued Aggai (Causse de Sefrou, Moyen-Atlas, Maroc). Comm Inst 115–133

1120

O. Jabrane et al.

10. Gregoire C, Halleux L (2002) Characterisation of fractures by GPR in a mining environment. First Break 20(7):467–471 11. Hinaje S (2004) Tectonique cassante et paléochamps de contraintes dans le Moyen et le haut Atlas (Midelt-Errachidia) depuis le Trias à l’Actuel. Thèse d’Etat Es-Sciences, Rabat, p 425 12. Pazzi V, Di Filippo M, Di Nezza M, Carlà T, Bardi F, Marini F, Fontanelli K, Intrieri E, Fanti R (2018) Integrated geophysical survey in a sinkhole-prone area: Microgravity, electrical resistivity tomographies, and seismic noise measurements to delimit its extension. Eng Geol 243:282–293 13. Pueyo-Anchuela O, Casas-Sainz AM, Soriano MA, Pocovi-Juan A (2010) A geophysical survey routine for the detection of doline areas in the surroundings of Zaragoza (NE Spain). Eng Geol 114(3–4):382–396 14. Vasudeo AD, Katpatal YB, Ingle RN (2009) Uses of dielectric constant reflection coefficients for determination of ground-penetrating radar. World Appl Sci J 6(10):1321–1325 15. Waltham T, Bell F, Culshaw M (2005) Sinkholes and Subsidence. Karst and Cavernous Rocks in Engineering and Construction. Springer, Berlin, p 382

An Adapted Routing Protocol for Mobile Edge Computing Ali Ouacha

Abstract In the current time, technological progress produces devices that have become smaller in size. Performing tasks by these devices is in most cases a delicate task due to their limited performances (such as processing capacity, storage and connectivity), especially when dealing with large data size and when having time constraints. However, Mobile Edge Computing (MEC) comes to overcome these weaknesses by delegating the execution of heavy tasks to other peripheral nodes with more powerful in computing capacity (Edge Server). In addition, these devices form a mobile network in which communication between them is based on routing protocols. Thus, in this paper, we take benefits from the control information exchanged between different nodes of the network to dissimulate information concerning the processing capacities of nodes, and to take advantage of physical links characteristics between nodes. All these information is combined into a metric allowing the selection of the most privileged neighbor node to perform various tasks of a particular node. Intensive simulations are done by the NS-3 simulator showing the effectiveness of this idea especially in runtime. Keywords Mobile Cloud Computing (MEC) · Computation offloading · Routing protocol · Mobility · Optimized Link State Routing Protocol (OLSR)

1 Introduction Technological progress have led to the birth of smaller equipment that generates large amount of data. This has created the need to work together and collaborate to achieve a number of goals. Indeed, in an environment made up of several devices, some of them have very limited computational performance. To be able to process a set of tasks within acceptable deadlines, it must rely them on another powerful equipment. It is the key concepts of Mobile Edge Computing (MEC) [1, 2]. In addition, in order A. Ouacha (B) Computer Science Department, Faculty of Sciences, Mohammed V University, Rabat, Morocco e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_103

1121

1122

A. Ouacha

to communicate, all these devices use a routing protocol. One of these protocols and most commonly used is the Optimized Link State Routing Proto-col (OLSR) [3, 4] It is classified under the proactive protocols category whose principle is based on the periodic exchange of information on the state of the links and on the neighborhood through several types of control messages. Thus, so that each node can learn about the performance of the other nodes of the network, several types of information are added to the exchanged control messages. So, the main objective of this work is to use these information that present the computation characteristics of each neighboring node and combine them with other information extracted from the characteristics of the physical link that connects the concerned node to other neighboring nodes in order to be able to choose the best node that is capable of perforing a task list. Moreover, since this is a mobile environment, the time required to process the tasks by a neighbor node must be taken into consideration. This is what the predicted time to quit the neighborhood is used for. It allows the node to avoid selecting a neighbor node which risks leaving the neighborhood without even finishing execution of tasks assigned to it. The whole article has several sections in addition to the introduction and the conclusion. The first section illustrates an overview of the OLSR protocol. The second section provides a state of art that summarizes the various works on the concept of treatment sharing. The third section presents the formulation of the problem as well as the proposed solution whereas the fourth section presents the various tests and simulations carried out as well as the obtained results.

2 OLSR Overview OLSR [3] is a proactive routing protocol, designed to operate in a Mobile Adhoc NETworks (MANETs) [5] without any central controlling entity. It represents an adaptation and optimization of the principle of links state routing for ad hoc networks. The optimization is that in this protocol, the nodes will only declare a sub-set of their neighborhood by using MPR (Multipoint Relay). The OLSR protocol defines an unique general format of the packet for all control messages circulating in the network. It defines four types of control messages. In addition to HELLO and Topology Control (TC) messages sent respectively every two and five seconds, the OLSR protocol offers two other messages: Multiple Interface Declaration (MID) and Host and Network Association (HNA). The multipoint relays concept of OLSR aims to reduce the number of unnecessary control messages during flooding in the network. The principle is based on the fact that each node chooses a minimal sub-set of its symmetrical neighbors with one hop, so as to be able to reach the whole neighborhood with two hops. As illustrated in Fig. 1, to reach all two hop neighbor nodes (nodes with blue color), the node N just needs to send its message to the subset of nodes selected as MPR (nodes with green color) among nodes of the first neighborhood. The MPR allow an optimized broadcast and minimize the used bandwidth by avoiding periodic sending of messages control.

An Adapted Routing Protocol for Mobile Edge Computing

1123

Fig. 1 Categories of nodes in OLSR protocol

3 Related Works The authors of the research made in [6] present an algorithm based on a task scheduling approach that is founded on dynamic voltage and frequency scaling (DVFS). It aims to achieve energy reduction by allocating tasks to the appropriate cores of mobile devices or offloaded it to the mobile cloud computing (MCC) environment. The authors of the research made in [7] propose an heuristic based approach named Edge Cloud Heuristic Offloading (ECHO) to find a tradeoff solution between application runtime, battery lifetime, and user cost according to the user’s preferences. In the research made in [8], the authors propose a solution based on game theory for offloading tasks among a swarm of Unmanned Aerial Vehicles (UAVs). They opt to reduce their energy consumption and guarantee QoS for users by considering the UAVs as aerial capacitated cloudlets pooling their computational resources to execute tasks from the ground internet of things (IoT) devices in their coverage. The authors of [9] propose an online energy efficient task assignment and computing offloading strategy in the aim to minimize the time average energy consumption of the multimedia services. The proposed strategy can adaptively decide the task assignment, coordinate and optimize the wireless and computation resource allocation by taking the dynamic wireless condition and service delay constraints into consideration. In [10] authors propose a framework to provide guaranteed services at minimum system cost. To address the challenges of configuring the edge optimization capability and determining the type and amount of clouds in which resource provision within the Cloud Assisted Mobile Edge (CAME) framework, the authors provide an Optimal Resource Provisioning (ORP) algorithm that is used for optimization of the capacity of adjacent hosts and provides dynamic cloud renting strategies. The authors of the research made in [11] propose a model that uses the Markov approximation method for full implementation of tasks and computations in MEC. This model provides a multi-server system in which only one mobile is brought close to

1124

A. Ouacha

the neighboring servers for complete computing. The use of the Markov approximation technique provides a common optimization of the allocation of computing tasks and the frequency of the processor’s scalability to minimize the runtime and mobile power consumption. The research made in [12] proposes a new MEC architecture based on intelligent computational offloading. Depending on the size of the data from the mobile user computing task and the performance characteristics of the periphery computing nodes, a task-predicting and task migration algorithm are proposed. In the work done in [13], the authors propose an Energy-aware Collaborative Routing (ECoR) scheme for optimally handling task offloading between source and destination UAVs in a grid-locked UAV swarm. The proposed ECoR acts on two objectives: routing path discovery and routing path selection. In the first one (routing path discovery), the scheme selects the most optimal path based on the maximization of residual energy. In the second one (routing path selection), the scheme ensures balanced energy utilization between members of the UAV swarm and enhances the overall path lifetime without incurring additional delays in doing so. Like these researches, our goal is to find for a mobile node (MN), whose computational performance is limited, the best neighbor node (NN) that presents the ideal environments to perform its tasks. But unlike all these works, we seek taking advantage of the normal operating of the OLSR protocol and insert in the control messages exchanged between nodes the characteristics relating to the processing capacity.

4 Problem and Resolution 4.1 System Model As shown in Fig. 2, the Mobile Node (MN) contains a list of N independent tasks, where each one is denoted τi with i = {1, 2, . . . , N }. Those tasks that are supposed to be computationally greedy and sensitive to delays should all be achieved. The execution time of the task list cannot exceed a maximum required time t max and each task τi can be processed remotely by an elected Neighbor Node (NN). Each data task is atomic (i.e. it cannot be divided into subtasks) and it is characterized by the following two parameters τi  di , λi . The first one denoted di [bits] represents the size of the task (quantity of input parameters and program codes) to be transferred from the MN in question to the NN. The second parameter, denoted λi [cycles], specifies the workload referring to the amount of computation necessary to accomplish the processing of this task. In addition, the execution time of a task τi also depends on the frequency f j [H er t z] of the N N j . Considering the constraint of a mobile environment, the NN should be chosen from nodes in the first neighboring so that it stays in the neighboring of the MN as long as possible. and for all the tasks to be executed on the N N j , we have:

An Adapted Routing Protocol for Mobile Edge Computing

1125

Fig. 2 Node with list of tasks offloaded to a Neighbor Node

t exec = j

N i=1

λi [Second] fj

(1)

Since the task τi is offloaded to the N N j , the time needed to transmit it is given by: = ti,comm j

di [second] rj

(2)

In this equation r j corresponds to the uplink data rate [14]. It is calculated by: r j = w × log(1 +

PT × gj ) σj

(3)

With: w is the bandwidth of the link between the MN and N N j , note that the MN and all nodes of the neighborhood use the same channel. P T represents the transmission power of the MN, σ j the noise of the channel to which the MN and the N N j are connected and g j the gain between the MN and the N N j . and for all the tasks to be offloaded on the neighbor node N N j , we have: = t comm j

 N di [Second] i=1 r j

(4)

1126

A. Ouacha

Thus, the completion time of the process of offloading and executing a single task τi , which will be called Time Metric (TimeMetric) is given by: ti, j = ticomm + tiexec

(5)

Consequently, for a single task τi we have: ti, j =

di λi + [Second] rj fj

and for all offloaded tasks, we have:   N  di λi + tj = [Second] i=1 r j fj

(6)

(7)

4.2 Problem Formulation In this section, we present our formulation of the optimization problem that aims at selecting the convenient neighbor node N N j whitch minimizes the time t j it takes for an offloaded task set to be executed. In other words, this is to select the node that  corresponds to the lowest value t j from the possible set of available values T t1 , t2 , . . . , tj , . . . , tM and that minimizes our TimeMetric. The obtained problem is formulated as follows: ⎧ ⎪ ⎪ ⎪ ⎪ ⎨ P1

⎪ ⎪ ⎪ ⎪ ⎩

ArgMin {T } { f j ,r j ,λi ,di } (C1 ) f j > F min (C2 ) t j > T min (C3 ) e M N > E min

(8)

In this work, each of the tasks is offloaded to a N N j so that it can be executed. Thus, each feasible offloading must meet the below constraints: • The constraint (C1) indicates that the frequency of the N N j f j must be greater than a minimum authorized value F min in order to avoid the Neighbor Node whose execution capacity is very limited. • The constraint (C2) shows that the execution time of all the offloaded tasks must be less than the given latency requirement T min . This constraint is necessary and especially when it comes to a mobile environment where the probability of the N N j to leave the neighbourhood of the MN is very greater. • The last constraint (C3) is important especially if the N N j battery is critical. It imposes that the total remaining energy e M N of the MN must be greater than a

An Adapted Routing Protocol for Mobile Edge Computing

1127

threshold E min in order to avoid the nodes that risk shutting down before achieving the execution of the tasks. The threshold E min for a single task is calculated by the Eq. 10 below.

E imin =

P T λi [J ] fj

(9)

and for all offloaded tasks, we have: E min =

N i=1

P T λi [J ] fj

(10)

4.3 Problem Resolution In the formulation part of the problem, Constraint (C2) specifies that the execution time of all the offloaded tasks must be less than threshold T min in order to avoid choosing the NN which risks leaving the neighbourhood of the MN because of mobility constraint. The time T min in question will be represented by the predicted Rest Time to Quit (RTTQ) calculated as illustrated in Fig. 3 [14]. It is estimated based on the cartesian coordinates exchanged through the HELLO messages received from NN. The TimeMetric is calculated when the MN has a list of tasks to transmit to be executed on the corresponding neighbor node N N j using algorithm 1. Note that the frequency f j , λi as well as the node coordinates x j and y j of each neighbor node are retrieved from the HELLO message sent and they are stored by the MN. Fig. 3 Predict remaining time estimation

1128

A. Ouacha

5 Simulations and Results Wherever Times is specified, Times Roman of Times New Roman may be used. If neither is available on your word processor, please use the font closest in appearance to Times. Avoid using bit-mapped fonts if possible. True-Type 1 fonts are preferred.

5.1 Simulations To test the solution presented in this work, we used as a simulator NS-3. It has the advantage of being more suitable for network environments. It contains an implementation of a very large number of protocols, most often used in various types of networks. In addition, it is a free and open source. The network architecture used in NS-3 is based on existing software and hardware in the real world. This facilitates the use of the simulator and allows easy integration of new models. The simulation environment as well as the simulation parameters, as shown in Table 1, consists of a network of 50 mobile nodes according to the Random Waypoint Mobility model [15]. All nodes have a single interface that uses OLSR as the routing protocol. A variable number of nodes between 5 and 25 with a step of 5 are chosen to send a set of tasks represented by a data packet of size 1000 Bytes. Each node sends 10 packets with a frequency of 1 packet for one (1) second.

An Adapted Routing Protocol for Mobile Edge Computing Table 1 Simulation parameters

1129

Parameter

Value

Network simulator

NS −3.29

Simulation time

100 s

Simulation area

1500 × 1500 m × m

Number of nodes

50

Transmission range

500 m

Mobility model

Random WayPoint (RWP)

Max speed

20 m/s

Max pause time

0s

Packet size

1000 bytes

WiFi

802.11b

To simulate the calculations performance, two numbers are assigned to each node. The first one represents the frequency f j of the N N j processor. It is initialized by a randomly generated value between 0.5 and 3.0 GHz. The second represents the number of cycles λi necessary to process a single task. It is initialized by an integer value generated randomly between 1 and 8. The simulations consist in choosing a number of source nodes; each one generates 10 data packets constituting the tasks to be executed. Before sending each set of tasks, each node researches through the nodes of its first neighborhood for the one that has the most minimal possible TimeMetric. Given the mobile environment and to avoid the choice of a neighboring node which risks leaving the neighborhood even before giving the result of the processing, only the one whose RTTQ (algorithm 1) is twice higher than the TimeMetric is chosen. In order to be able to calculate the communication time which is part of the TimeMetric, the information that will be needed is extracted from the physical layer of MN as well as from the frame received from the NN containing the hello message.

5.2 Results At the end of the simulations under NS-3, a set of results are obtained. Thus, the graph of Fig. 4 clearly shows that the average time necessary for the execution of the tasks by the selected NN always remains lower than the average time calculated at the level of all the other neighboring nodes necessary for the execution of the same set of tasks regardless of the number of source nodes. In addition, Fig. 5a shows that the TimeMetric has experienced a slight increase when the number of source nodes increases. Indeed, once the number of source nodes increases, the number of generated packets also increases which causes pressure on the elected NN. Thus several packets will be directed to this node which generates waiting times whether it is at the level of the reception of the packets by the lower

1130

A. Ouacha

Neighbor Node All Neighbor Nodes

Average Time Metric Average Time

3 2 1 0 5

10

15

20

25

Number of Source Nodes Fig. 4 Average time required for the execution of tasks by a neighbor node compared to the average time calculated at the level of other neighboring nodes

2.18

0.66 0.64 0.62 0.6 0.58

2.16 2.14 2.12 2.1 5

10

15

20

25

Number of Source Nodes a. Selected Sink node

5

10

15

20

25

Number of Source Nodes b. All Neighbor Nodes

Fig. 5 Average of the time required for the execution of tasks

layer or at the level of the order of execution of the tasks on the microprocessor. Figure 5b also shows a slight decrease in the average of the calculated time metric at all neighboring nodes as the number of source nodes increases. Despite the gain recorded in terms of the TimeMetric, this system has the disadvantage of an additional load that will be added to the control traffic generated by the OLSR protocol itself. Indeed, Fig. 6 shows a slight increase in the average throughput exhibited by the Modified OLSR version compared to the standard OLSR version. This is due to the fact that the HELLO control messages sent every two seconds are enriched by other information used for the calculation of the time metric and the RTTQ (current position of the sending node of the HELLO message, its calculation frequency, the number of cycles of its processor, …).

An Adapted Routing Protocol for Mobile Edge Computing

Average Throughput

Average Throughput

1131

OLSR Standart Vertion OSLR Modified Version

1

0.5

0 5

10

15

20

25

Number of Source Nodes Fig. 6 Average flow rate comparison between the standard version of OLSR Protocol and the modified version

6 Conclusion This article illustrates the case of a node whose computational performance is very limited (the case of a smart watch or a smart garment …) and that has a set of tasks to perform. To do this, it seeks among the nodes from the first neighbourhood the one which presents the smallest metric of time. Thus, a first solution consists in taking advantage of the control messages of the OLSR protocol to exchange the calculation performances of each node of the network. So each node has everything it needs to decide which node will run this task list. The results of the simulations carried out by NS3 show a gain in terms of the time metric against a price paid by a slight increase in the average flow. These results also show that (Disadvantages) in a cell, the neighbour node which has the smallest time metric will be elected by all the other neighbouring nodes. This presents a resulting pressure by sending multiple sets of spots to that same node. As Perspectives, a forthcoming study aims to integrate a mechanism to balance the loads so as to share the processing of tasks between several neighbouring nodes. A first proposition consists in finding the ideal node or nodes for the execution of the tasks in the set of nodes of the first and second neighbourhood. Another perspective this time is to also take advantage of other metrics such as energy performance and those of the main memory in the search for the execution node.

References 1. Cao J, Zhang Q, Shi W (2018) Edge Computing: A Primer, 1st edn. Springer, Cham 2. Nunna S, Ganesan K (2017) Mobile edge computing. In: Thuemmler C, Bai C (eds) Health 4.0: how virtualization and big data are revolutionizing healthcare. Springer, Cham, pp 187–203 3. Clausen T, Jacquet P (2003) RFC3626: Optimized Link State Routing Protocol (OLSR), vol RFC3626: RFC Editor

1132

A. Ouacha

4. Clausen T, Dearlove C, Jacquet P, Herberg U (2014) The Optimized Link State Routing Protocol Version 2. Internet Engineering Task Force (IETF), April 2014 5. Corson S, Macker J (1999) RFC2501: Mobile Ad hoc Networking (MANET): Routing Protocol Performance Issues and Evaluation Considerations: RFC Editor 6. Lin X, Wang Y, Xie Q, Pedram M (2015) Task scheduling with dynamic voltage and frequency scaling for energy minimization in the mobile cloud computing environment. IEEE Trans Serv Comput 8:175–186 7. De Maio V, Brandic I (2018) First hop mobile offloading of DAG computations. Presented at the 2018 18th IEEE/ACM international symposium on cluster, cloud and grid computing (CCGRID), Los Alamitos, CA, USA 8. Ma W, Liu X, Mashayekhy L (2019) A strategic game for task offloading among capacitated UAV-mounted cloudlets. In: 2019 IEEE international congress on internet of things (ICIOT), pp 61–68 9. Sun Y, Wei T, Li H, Zhang Y, Wu W (2020) Energy-efficient multimedia task assignment and computing offloading for mobile edge computing networks. IEEE Access 8:36702–36713 10. Ma X, Wang S, Zhang S, Yang P, Lin C, Shen XS (2019) Cost-efficient resource provisioning for dynamic requests in cloud assisted mobile edge computing. IEEE Trans Cloud Comput. https://doi.org/10.1109/TCC.2019.2903240 11. Zhou W, Fang W, Li Y, Yuan B, Li Y, Wang T (2019) Markov approximation for task offloading and computation scaling in mobile edge computing. Mob Inf Syst 2019:8172698 12. Miao Y, Wu G, Li M, Ghoneim A, Al-Rakhami M, Hossain MS (2020) Intelligent task prediction and computation offloading based on mobile-edge cloud computing. Future Gener Comput Syst 102:925–931 13. Mukherjee A, Misra S, Chandra VSP, Raghuwanshi NS (2020) ECoR: energy-aware collaborative routing for task offload in sustainable UAV swarms. IEEE Trans Sustainable Comput 5(4):514–525 14. Ouacha A, Lakki N, Abbadi JE, Habbani A, Bouamoud B, Elkoutbi M (2013) Reliable MPR selection based on link lifetime-prediction method. In: 2013 10th IEEE international conference on networking, sensing and control (ICNSC), pp 11–16 15. Roy RR (2011) Handbook of mobile ad hoc networks for mobility models, 1st edn. Springer, Heidelberg

Advanced Technologies in Energy and Electrical Engineering

Improved Geographic Routing Protocol for Wireless Sensor Networks Kenza Redjimi, Mohammed Redjimi , and Mehdi Boulaiche

Abstract Nowadays, several researches focus on Wireless Sensor Networks (WSNs) protocols. These small sensors are disseminated in a large geographical area, sense local data, and send them over multi-hop routes to the base station (BS). Furthermore, the BS processes data and sends results to remote centers. The most problem in the WSNs is the energy optimization of the sensor nodes for collect, process, and information broadcast. Each node has limited energy autonomy and most of the existing protocols focus on the optimal management of energy consumption aiming to maximize the network lifetime. We present, here, a geographic routing protocol dedicated to the WSNs. This approach exploits the neighboring node’s positions, which are in the direction of the BS to send the data packets with minimum energy dissipation. In order to forward packets efficiently around voids, new mechanisms are introduced. The shortest path between the sender node and the destination node is calculated by taking in account only the local knowledge of sensor nodes (neighborhood). Simulation results show that this protocol is very efficient and outperforms other geographical protocols in terms of energy management. Keywords Wireless sensor networks · Geographic routing protocols · GSM Localization · Energy consumption · Energy efficiency

1 Introduction Wireless Sensor Networks (WSNs) [1] are systems made up of several independently functioning elements, which are disseminated over large areas. These elements include limited computing and storage resources (processing units, memories, etc.) and are fitted with sensors specific to the environments in which they operate and K. Redjimi · M. Redjimi · M. Boulaiche (B) Department of Computer Science, LICUS Laboratory, University 20 August 1955, 21000 Skikda, Algeria e-mail: [email protected] K. Redjimi e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_104

1135

1136

K. Redjimi et al.

to data, which they must measure. They are also equipped with wireless means for sending and receiving data. The information collected by the various nodes of the network undergoes a first elementary processing and then is transmitted to the base station. These sensors can measure various ranges of quantities such as humidity, temperature, level, pressure, wind speed, objects detection, etc. Consequently, these sensor networks are useful in several fields and their applications are multiple, for example in monitoring, control and decision-making. Thus, they are used in medicine [2], military field, forest or flood fire monitoring, smart homes and cities [3, 4] and industrial automation among other fields [5]. Batteries supply these elements with energy and thus allow them certain autonomy of operation. Optimal management of the energetic charge of these batteries remains one of the most important problems to be resolved with regard to WSNs [6]. A sensor node expends a maximum of energy when it is in the phase of sending data. The energy expended during the reception is less. However, to optimize the energy consumption in a sensor node and, consequently, to increase the network lifetime, it is essential to consider all the energy expenses at all levels of the architecture of the WSN. Indeed, these sensors are sometimes inaccessible and recharging their batteries can be very restrictive especially when they are located in hostile areas. As a result, many researchers have carried out the implementation of protocols making it possible to better managing energy consumption in wireless sensor networks. Note, however, that several solutions provided are in fact improvements of other pre-existing solutions. Most of the developed routing protocols for WSNs [7] require node localization coordinates since events are directly related to the node location. The positions of two nodes make it possible to calculate the distance between them, which makes it possible to estimate the energy expended during the communication between these two nodes. This work exploits this idea to improve the energy consumption in WSN’s. Our protocol increases packets delivery ratio to the base station. The following sections are organized as follows: Sect. 2 briefly presents general notions concerning routing protocols in WSNs. Section 3 is devoted to present the proposed approach. In Sect. 4; the results and their discussion are presented and Sect. 5 concludes this article.

2 Backgrounds Generally, three classes of routing protocols can be distinguished: flat, hierarchical, and geographic routing protocols (also called location-based routing protocols). There are two categories in flat routing protocols; reactive (On-Demand) protocols such as Dynamic Source Routing (DSR) [8] and Ad hoc On-demand Distance Vector (AODV) [9] or proactive (Table-Driven) protocols such as Destination Sequenced Distance Vector (DSDV) [10]. In the hierarchical routing protocols, the nodes are regrouped into clusters and in each cluster, a cluster head (CH) is periodically elected. The cluster head plays specific roles such as the aggregation of data transmitted by

Improved Geographic Routing Protocol for Wireless Sensor Networks

1137

the cluster nodes and their routing to the base station. Low-energy adaptive clustering hierarchy (LEACH) [11] and its variants are typical examples of this class. In the case of location-based routing protocols, the sensor nodes are accessible thanks to their positions via the Global Positioning System (GPS), thanks to tables containing their coordinates or other specific tools. These positions allow the implementation of information routing. For example, Greedy Perimeter Stateless Routing (GPSR) [12] is a geographic routing protocol. In Geographic Routing, each node is concerned only with its one-hop neighbors. Therefore, in order to make forwarding decisions, geographic routing protocols, only require the position of the packet’s destination and the position of its one-hop neighbors. This property has made Geographic Routing very assuring. Geographic Routing protocols operate according to two functioning modes: i) the packets are routed from one node to its neighbor, which can be the closest node to destination or the node that has the highest energy, etc. ii) if there are no nodes with these prescriptions, the algorithm goes into recovery mode. Geographic Routing protocols give interesting and attractive solutions [13–18] for the WSNs because to forward information, a node needs only to know its own position, and its neighbors and the BS locations. The localization information can be obtained thanks to the Global Positioning System (GPS). This reduces the complexity of control management at the level of each node very significantly. The greedy protocols [18] use the shortest paths to distribute the packets between a source and a destination node. In the Nearest with Forward Progress (NFP) protocol [18], the algorithm chooses the nearest neighbor among the nodes that are situated in the forward direction of the destination to send the packets. In the Most Forward within the transmission Range (MFR) routing protocol [1], a node selects the neighbor that is nearest from the destination as the following routing node. In the case of the normalized advance (NADV) for geographic routing protocol, the neighboring nodes of the source node are selected taking into account their proximity as well as the cost of the link. The protocol proposed here is based on EEGR [19] and brings improvements to it: – The next neighbor is calculated each round rather than calculating it for each forwarded packet, – In order to forward packets around a void through the optimal path, a new mechanism is used, – In the proposed approach, the algorithm tries to find a global optimum path without any assumptions.

1138

K. Redjimi et al.

2.1 The Network Model A WSN is represented as a graph G = (E, V), where E is the set of edges that corresponds to the links between nodes and V the nodes. The proposed protocol adopts the model of Ivan Stojmenovic et al. [20, 21] that generalized the energy model of Rodoplu and Meng’s [22] by adjusting a linear factor for the transmission and the reception of a radio signal between two nodes separated by a distance d: u(d) = ad a + c

(1)

α is an attenuation factor (2 ≤ α ≤ 6). c is a constant (also related with other energies consumed for data processing at each node), and a is a linear factor.

3 The Proposed Approach In the proposed approach, the WSN is considered as a connected-graph. Therefore, to transmit a packet from a source node to the BS, the algorithm tries to find a global optimum path among the entire locally intermediate nodes. The aim of this operation is to minimize energy consumption. In a greedy mode, each concerned node uses its neighbor localization that is closest to the BS and builds iteratively the optimal path.

3.1 Approach Overview As explained above, in geographic protocols, a node considers a closest neighbor to the BS for a next hop. Although, this technique does not make it possible to guarantee energy optimization on the one hand and comes up against the so-called void problem [23], when there is no neighbor closer than the current node. The proposed approach tries to avoid these problems. In the proposed approach, at first a Minimal Spanning Tree (MST) calculation process is initiated. Each node builds a local sub-graph G = (E , V ), including the neighbors, which are closest to the BS than the source node and that are situated in the BS direction. (See Subsect. 3.2). This area is referred to as a routing area. Therefore, by using Prim’s algorithm [24], a minimal spanning tree is calculated using the local sub-graph G’ (Fig. 1). As a result, we obtain the paths that start from the current node and which traverse a set of nodes whose sum of weights is minimal (see Subsect. 3.2) is the hop neighbor.

Improved Geographic Routing Protocol for Wireless Sensor Networks

1139

Fig. 1 The routing and recovery areas

A node that encounters a void must expand its subgraph by δ degrees to include other neighbors; this zone is called the recovery area. The same algorithm is applied by using the source routing to route packets. According to the shortest path that is included in the packet’s header, the next hop transmits the packet.

3.2 Sub-graph Formation Algorithm In greedy routing techniques, each node participates for packet forwarding towards the BS. At the network setup time, the nodes select their next forwarding neighbor. This neighbor forwards all received packets. In order to balance the load of neighboring nodes, the selection is initialized at each round. The duration of a round depends on the intensity of the network traffic. Note that a node plays the rotation role if its next forwarding neighbor energy decreases. In each node, a table that contains the neighbor’s identifier, position and remaining energy is periodically updated. In addition, each node periodically broadcasts a message displaying its position and remaining energy. In the local sub-graph G = (E ,V ), V corresponds to the set of neighbors in ARouting_Area/ARecovery_Area, E to the set of edges between these neighbors. The distance between two nodes u and v is identified as dist(u, v), and r is the sensor node’s communication range.

1140

K. Redjimi et al.

3.3 Edge Weights To balance and to regularly distribute the consumption energy on all the neighbors, we add a new metric. The energy required for the transmission and the reception of data between two nodes is represented in Eq. (2): Edgeweight =

(ad a + c) R Eu

(2)

c represents the cost of transmission/reception between two nodes u, v?V  , REu is the remaining energy for the node u. Note that G is constructed only at the network setup since sensor nodes are static but the edge weights are recalculated at each round in order to balance the role of next forwarding neighbor among multiple neighbors.

3.4 The Routing Strategy In the WSN, data packets are forwarded from source nodes to the BS via multi-hop ways. In the proposed approach, we distinguish two modes for packets forwarding: In the normal mode, the packets are forwarded through the Routing Area whereas in the recovery mode, the packets are routed through the Recovery Area. A packet is forwarded in normal mode but when it encounters a void at a node p, it will be balanced to recovery mode to forward it around the void. When the packet gets to a node q having at least a neighbor closer to the BS than node p, the packet is returned back to greedy mode forwarding again. In that case, the packet is forwarded to that neighbor. To do that, a node p that balances a packet into recovery mode includes in the packet’s header its position, i.e. the position where the packet was balanced to recovery mode. The node p includes also in the header the minimum path through which the packet will be forwarded in recovery mode. Afterwards, each node q that receives the packet checks first whether it has at least a neighbor closer to the BS than node p using the position included in the packet’s header. If so, it balances the packet to normal mode and forwards the packet to that neighbor. Otherwise, it forwards the packet according to the minimum path included in the header. The algorithm 1 presented bellow summarizes the routing strategy of this approach.

Improved Geographic Routing Protocol for Wireless Sensor Networks

1141

Algorithm 1 : Route(Packet “P”); Si : the node that is handling packet P; N(Si ): neighbors that are closest to the BS; R(Si ): neighbors that are in the recovery area; H(Si ): the one hop neighbors; Then // No neighbor is closer to the BS; If N(Si If Si is the source of the packet Then Next_Neighbor := Nearest Neighbor in H(Si ); Else If (P.mode is in normal mode) Then Apply sub-graph G’ Formation Algorithm from R(Si ); Apply Prim’s Algorithm; Find the minimum path in theMST found; Next_Neighbor := First nod in the minimum path in the MST found; Include the minimum path inthe packet’s header; Else // P.mode is in recovery mode Check the address contained in the packet’s header; If there is at least a neighbor closer to BS than P.Rec_Addrr Then Balance the packet to normal mode; Next_Neighbor := That_Neighbor; Else Next_Neighbor := first node in the path included in packet’s header; EndIf EndIf EndIf Else // there is neighbors closer to the BS; Apply sub-graph G’ Formation Algorithm from N(Si ) ; Apply Prim’s Algorithm; Find the minimum path in the MST found; Next_Neighbor := First node in minimum path in the MST found; EndIf Transmit P to Next_Neighbor;

Table 1 Simulation parameters

Parameters

Values

Network nodes number

20 to 160

Generation mode

Randomized

Trigger nodes

05 trigger nodes

Size of the network

100 m × 100 m

Transmission range of nodes

30 m

Radio Transmission power

0.028 w

Radio Reception power

0.036 w

MAC sub-layer

MAC IEEE 802.11

Angle δ

2π/3

1142

K. Redjimi et al.

4 Results and Discussion Aiming to evaluate the proposed protocol performance, a set of network’s simulations was conducted by using the J-Sim simulation software [25]. We considered homogenous sensor nodes as, which have the same initial amount of energy, the same hardware resources, the same transmission range and the energy of the base station is considered as unlimited. These networks are considered deployed in an area of 100 m × 100 m creating topologies containing 20 to 160 nodes. The generation of the positions of nodes is generated randomly. The other factors used to calculate edge weights ‘α’, linear factor ‘a’, and the constant ‘c’ are set to 2, 1 and 1000 respectively. The Table 1 presents these parameters. The simulation results of our proposed approach called Improved Greedy Routing protocol (IGR) are compared with four geographic protocols: EGGR, NFP, GPSR and MFR as reported in [15]. The Figs. 2 and 3 represent the energy efficiency allowing evaluating these protocols. The Fig. 2 shows the average of energy consumption when we change the number of nodes in the network and the Fig. 3 depicts this average per time. From these figures, we can note that the proposed protocol (so-called IGR) gives best results when compared with the other protocols (EGGR, GPSR, NFP, and MFR). It improves average energy consumption in the network. The justification of this improvement lies at the fact that in IGR protocol, the packets are forwarded through the optimal paths, whereas in EGGR protocol, the calculation of the optimal path takes into account the assumption that the cost of the retransmission between each neighbor

Fig. 2 Average of the energy consumed compared to the number of nodes in the network

Improved Geographic Routing Protocol for Wireless Sensor Networks

1143

Fig. 3 Average energy consumed with time

and the BS is optimal, which is not the fact in the reality. In the other protocols (GPSR, NFP, and MFR), a neighbor closest to the BS is selected without regarding the energy consumption criteria. Therefore, the forwarded packets can be expensive in terms of energy consumption, especially in the case of voids (GPSR protocol applies recovery mechanism to get out of voids).

5 Conclusion The proposed approach (called IGR: Improved Greedy Routing geographic protocol) allows an efficient energy management for the geographic routing protocol. As a solution to resolve the local minima problem by creating an additive area called recovery area to forward packets. Mechanisms are introduced to elaborate the optimal way during each round. After realizing several simulations and analyzing the simulation results, we observe that the proposed protocol contributes to the reduction of the energy consumption at the sensor nodes and consequently, increase the network lifetime. In future works, we project to realize a series of simulations aiming to demonstrate the effectiveness and the efficiency of this protocol in increasing the packet’s delivery rate especially in the case of complex networks.

1144

K. Redjimi et al.

References 1. Obaidat MS, Misra S (2014) Principles of wireless sensor networks. Cambridge University Press, Cambridge 2. Dhobley A, Ghodichor NA, Golait SS (2015) An overview of wireless sensor networks for health monitoring in hospitals via mobile. Int J Adv Res Comput Commun Eng 4(1):169–171 3. Belghith A, Obaidat MS (2016) Wireless sensor networks applications to smart homes and cities. In: Smart cities and homes, pp 17–40. https://doi.org/10.1016/b978-0-12-803454-5.000 02-x 4. Lasek A, Cercone N, Saunders J (2016) Smart restaurants: survey on customer demand and sales forecasting. In: Smart cities and homes, pp 361–386. https://doi.org/10.1016/b978-0-12803454-5.00017-1 5. Kandris D, Nakas C, Vomvas D, Koulouras G (2020) Applications of wireless sensor networks: an up-to-date survey. Appl Syst Innov 3(1):14 6. Khan JA, Qureshi HK, Iqbal A (2015) Energy management in wireless sensor networks: a survey. Comput Electr Eng 41:159–176. https://doi.org/10.1016/j.compeleceng.2014.06.009 7. Bhushan B, Sahoo G (2018) Routing protocols in wireless sensor networks. Studies Computational Intelligence, pp 215–248. https://doi.org/10.1007/978-3-662-57277-1_10 8. Boukerche A, Turgut B, Aydin N, Ahmad MZ, Boloni L, Turgut D (2011) Routing protocols in ad hoc networks: a survey. Comput Netw 55:3032–3080. https://doi.org/10.1016/j.comnet. 2011.05.010 9. Mulert J, Welch I, Seah WKG (2012) Security threats and solutions in manets: a case study using aodv and saodv. J Netw Comput Appl 35(4):1249–1259. https://doi.org/10.1016/j.jnca. 2012.01.019 10. Ade SA, Tijare PA (2010) Performance comparison of AODV, DSDV, OLSR and DSR routing protocols in mobile ad hoc networks. Int J Inf Technol Knowl Manag 2(2):545–548 11. Tyagi S, Kumar N (2013) A systematic review on clustering and routing techniques based upon LEACH protocol for wireless sensor networks. J Netw Comput Appl 36(2):623–645. https:// doi.org/10.1016/j.jnca.2012.12.001 12. Karp B, Kung H (2000) GPSR: greedy perimeter stateless routing for wireless networks. In: Proceedings of the sixth annual international conference on mobile computing and networking. ACM Press, pp 243–254. https://doi.org/10.1145/345910.345953 13. Milocco RH, Costantini H, Boumerdassi S (2014) Improved geographic routing in sensor networks subjected to localization errors. Ad Hoc Netw 13:476–486. https://doi.org/10.1016/ j.adhoc.2013.10.001 14. Lee S, Bhattacharjee B, Banerjee S, Han B (2010) A general framework for efficient geographic routing in wireless networks. Comput Netw 54(5):844–861. https://doi.org/10.1016/j.comnet. 2009.09.013 15. Boulaiche M, Bouallouche-Medjkoune L (2015) EGGR: energy-aware and delivery guarantee geographic routing protocol. Wireless Netw 21(6):1765–1774. https://doi.org/10.1007/s11276014-0880-1 16. Tao S, Ananda AL, Chan MC (2010) Greedy face routing with face identification support wireless networks. Comput Netw 54(18):3431–3448. https://doi.org/10.1016/j.comnet.2010. 07.004 17. Kleerekoper A, Filer NP (2015) Perfect link routing for energy efficient forwarding in geographic routing. Ad Hoc Netw 30:46–62. https://doi.org/10.1016/j.adhoc.2015.03.001 18. Al-Shugran M, Ghazali O, Hassan S, Nisar K, Arif ASM (2013) A qualitative comparison evaluation of the greedy forwarding strategies in mobile ad hoc network. J Netw Comput Appl 36(2):887–897. https://doi.org/10.1016/j.jnca.2012.10.008 19. Zhang H, Shen H (2007) Eegr: energy-efficient geographic routing in wireless sensor networks. In: 2007 international conference on parallel processing (ICPP 2007). IEEE, p 67. https://doi. org/10.1109/ICPP.2007.37 20. Bose P, Morin P, Stojmenovi´c I, Urrutia J (2001) Routing with guaranteed delivery in ad hoc wireless networks. Wireless Netw 7(6):609–616. https://doi.org/10.1023/A:1012319418150

Improved Geographic Routing Protocol for Wireless Sensor Networks

1145

21. Stojmenovic I, Lin X (2001) Power-aware localized routing in wireless networks. IEEE Trans Parallel Distrib Syst 12(11):1122–1133. https://doi.org/10.1109/71.969123 22. Rodoplu V, Meng TH (1999) Minimum energy mobile wireless networks. IEEE J Sel Areas Commun 17(8):1333–1344. https://doi.org/10.1109/49.779917 23. Chen D, Varshney PK (2007) A survey of void handling techniques for geographic routing in wireless networks. IEEE Commun Surv Tutor 9(1):50–67. https://doi.org/10.1109/COMST. 2007.358971 24. Prim RC (1957) Shortest connection networks and some generalizations. Bell Syst Tech J 36(6):1389–1401 25. https://www.physiome.org/jsim/. Accessed 8 Dec 2020

Supervision and Monitoring of Photovoltaic Systems Using Siemens PLC and HMI Ahmed Bouraiou, Ammar Neçaibia, Saad Motahhir, Mohammed Salah Bouakkaz, Issam Attoui, Nadir Boutasseta, Rachid Dabou, and Abderrezzaq Ziane

Abstract Automation is the top priority in modern industries, the debate on this phenomenon is always controversial as the machine tends to replace man in several tasks, and he unfortunately cannot perform the tasks that the machine does in several fields. The supervision and monitoring of the industrial systems are two very important fields in industrial automation. For ensuring the reliability and stability of PV system operation, it is indispensable to install supervision and monitoring systems. The work in this paper presents a simple platform for supervision and control in realtime of pumping system parameters (current, voltage, irradiance, temperature and flow) based on programmable logic controller (PLC) and human-machine interface (HMI) of Siemens tools. Keywords PV system · Supervision · Monitoring · PLC · HMI

1 Introduction The production technology and the complexity of the operations to be performed, lead to the implementation of devices and systems for the automation of manufacturing or production workshops. Automation is the scientific and technological field that

A. Bouraiou (B) · A. Neçaibia · R. Dabou · A. Ziane Unité de Recherche en Energie Renouvelables en Milieu Saharien, URERMS, Centre de Développement Des Energies Renouvelables, CDER, 01000 Adrar, Algeria S. Motahhir Engineering, Systems and Applications Laboratory, ENSA, SMBA University, Fez, Morocco e-mail: [email protected] M. S. Bouakkaz Department of Electrical Engineering, Laboratoire D’électrotechnique de Skikda «LES», Université du 20 Août 1955, 21000 Skikda, Algeria I. Attoui · N. Boutasseta Research Center in Industrial Technologies CRTI, P.O. Box 64, Cheraga, Algeria © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_105

1147

1148

A. Bouraiou et al.

performs and controls technical tasks, by machines operating without human intervention, or with the aid of reduced intervention. The supervision and monitoring of the industrial systems are two very important fields in industrial automation. They are based on the application of different analysis of many signals and parameters like temperature, current, vibrations, voltage, etc. Many research works have been done on this area like [1–10]. On the other hand, the transition to renewable energy resources utilization has become an indispensable task due to the negative effect of conventional sources (fossil) in terms of environmental pollution and global warming [11, 12]. Many scientific papers have been published on this topic like [13–25]. Solar energy is one of the most popular renewable energies especially in countries that characterized by huge solar energy potential [26–28]. The supervision and control of the photovoltaic system is essential key for ensuring the reliability and stability of PV system operation [5]. The paper presents a simple platform for supervision and control of pumping system parameters (Current, voltage, irradiance, temperature and flow) based on a programmable logic controller (PLC) and human-machine interface (HMI) of Siemens tools. After this introductory section, the paper is organized as: The first section is ‘The used software’ which divided into 3 parts: Step 7 software, WinCC Flexible software and Project Creation and Hardware (HW) configuration. The second section concerns ‘The developed platform’. At the end of this paper the conclusion is drawn.

2 The Used Software The used software in this work is given below:

2.1 Step 7 Software STEP7 is the basic software package for configuring and programming of S7 300 and S7 400 programmable logic controller. It is part of the SIMATIC software industry.

2.2 WinCC Flexible Software WinCC Flexible is a software compatible with the STEP7 environment, it offers the configuration of various operator panels and SCADA system, moreover creating the graphical interface (HMI) and the variables means being able to read the process values via the PLC, display them so that the operator can supervise, control and adjust them.

Supervision and Monitoring of Photovoltaic Systems …

1149

Fig. 1 SIMATIC STEP7 project

2.3 Project Creation and Hardware (HW) Configuration The implementation of an automation solution with STEP7 requires the realization of following basic tasks: • Creation of the SIMATIC STEP7 project • Hardware (HW) configuration Figure 1 presents the picture of SIMATIC STEP7 project (PV system). The picture of the hardware part is illustrated in Fig. 2.

3 The Developed Platform Before giving the results of this work, it is more suitable to provide the following detail: The CPU 314C-2 PN/DP PLC (Siemens) is used for programming using Ladder language the process step, the main component of this PLC is given Table 1 below:

1150

A. Bouraiou et al.

Fig. 2 Hardware (HW) configuration

Table 1 PLC main component

Main component

Number

Specification

Digital input (DI)

24

24 DC

Digital output (DO)

16

24 DC

Analog input (AI)

5

10 V or 0–20 mA

Analog output (AO)

2

10 V or 0–20 mA

In this work, the five analog input is used for measurement of the parameters like current, voltage, irradiance, temperature and flow, the detail of these parameters via symbolic configuration in Step 7 software is presented in Fig. 3.

Supervision and Monitoring of Photovoltaic Systems …

1151

Fig. 3 Symbol of parameters

The simulation of the PLC behavior in Step 7 is realized by S7-PLCSIM, it can run the STEP 7 user program and test it in a simulated programmable controller, S7-PLCSIM also offers a simple interface to the STEP 7 user program used to view and modify various objects such as input and output variables. The simulation of the different analog input in S7-PLCSIM for our application is given Fig. 4. The scaling of these input parameters was done in step 7 using the FC 105 (see Fig. 5). The realization of the human-machine interface (HMI) is performed using WinCC Flexible. The principal interface (Home) contains the general information about the system, (see Fig. 6).

1152

A. Bouraiou et al.

Fig. 4 S7-PLCSIM interface

The second interface (system) allows the supervision of the system parameters’ values in real-time like current, voltage, irradiance, temperature and flow, as presented in Fig. 7. An example of real-time plotting curves is illustrated in Fig. 8.

Supervision and Monitoring of Photovoltaic Systems …

Fig. 5 Input parameters scaling

Fig. 6 Main interface

1153

1154

Fig. 7 Real-time system supervising

Fig. 8 Real-time system supervising

A. Bouraiou et al.

Supervision and Monitoring of Photovoltaic Systems …

1155

4 Conclusion The supervision and monitoring of the industrial systems are two very important fields in industrial automation. In this paper, a simple platform based on Siemens PLC and HMI was realized in order to supervise and monitor the solar pumping system parameters. The real realization of this platform requires the sensors signal converting to universal signals such as 4–20 mA or 0–10 V. This study shows that can use the PLC and HMI for resolving the supervision and monitoring issues in photovoltaic systems.

References 1. Atoui I, Meradi H, Boulkroune R, Saidi R, Grid A (2013) Fault detection and diagnosis in rotating machinery by vibration monitoring using FFT and Wavelet techniques. In: 2013 8th international workshop on systems, signal processing and their applications (WoSSPA). IEEE, pp 401–406. https://doi.org/10.1109/WoSSPA.2013.6602399 2. da Silva AM, Povinelli RJ, Demerdash NAO (2008) Induction machine broken bar and stator short-circuit fault diagnostics based on three-phase stator current envelopes. IEEE Trans Ind Electron 55:1310–1318. https://doi.org/10.1109/TIE.2007.909060 3. Attoui I, Boutasseta N, Fergani N, Oudjani B, Deliou A (2015) Vibration-based bearing fault diagnosis by an integrated DWT-FFT approach and an adaptive neuro-fuzzy inference system. In: 2015 3rd international conference on control, engineering & information technology (CEIT). IEEE, pp 1–6. https://doi.org/10.1109/CEIT.2015.7233098 4. Attoui I, Boudiaf A, Fergani N, Oudjani B, Boutasseta N, Deliou A (2015) Vibration-based gearbox fault diagnosis by DWPT and PCA approaches and an adaptive neuro-fuzzy inference system. In: 2015 16th international conference on sciences and techniques of automatic control and computer engineering (STA). IEEE, pp 234–239. https://doi.org/10.1109/STA.2015.750 5177 5. Madeti SR, Singh SN (2017) Monitoring system for photovoltaic plants: a review. https://doi. org/10.1016/j.rser.2016.09.088 6. Attoui I, Oudjani B, Fergani N, Bouakkaz S, Bouraiou A (2020) Novel predictive features using a wrapper model for rolling bearing fault diagnosis based on vibration signal analysis. Int J Adv Manuf Technol 106:3409–3435. https://doi.org/10.1007/s00170-019-04729-4 7. Attoui I, Fergani N, Boutasseta N, Oudjani B, Bouakkaz MS, Bouraiou A (2021) Multiclass support vector machine based bearing fault detection using vibration signal analysis. https:// doi.org/10.1007/978-981-15-6403-1_61 8. Attoui I, Boutasseta N, Fergani N (2021) Novel machinery monitoring strategy based on timefrequency domain similarity measurement with limited labeled data. IEEE Trans Instrum Meas 70:1–8. https://doi.org/10.1109/TIM.2020.3011874 9. Attoui I (2020) Novel fast and automatic condition monitoring strategy based on small amount of labeled data. IEEE Trans Syst Man Cybern Syst 1–10 (2020). https://doi.org/10.1109/TSMC. 2020.3018102 10. Boutasseta N, Ramdani M, Mekhilef S (2018) Fault-tolerant power extraction strategy for photovoltaic energy systems. Sol Energy 169:594–606. https://doi.org/10.1016/J.SOLENER. 2018.05.031 11. Bouraiou A, Necaibia A, Boutasseta N, Mekhilef S, Dabou R, Ziane A, Sahouane N, Attoui I, Mostefaoui M, Touaba O (2020) Status of renewable energy potential and utilization in Algeria. J Clean Prod 246:119011. https://doi.org/10.1016/J.JCLEPRO.2019.119011

1156

A. Bouraiou et al.

12. Touaba O, Ait Cheikh MS, Slimani ME-A, Bouraiou A, Ziane A, Necaibia A, Harmim A (2020) Experimental investigation of solar water heater equipped with a solar collector using waste oil as absorber and working fluid. Sol Energy 199:630–644. https://doi.org/10.1016/j.sol ener.2020.02.064 13. Attoui I, Omeiri A (2014) Modeling, control and fault diagnosis of an isolated wind energy conversion system with a self-excited induction generator subject to electrical faults. Energy Convers Manag 82:11–26. https://doi.org/10.1016/j.enconman.2014.02.068 14. Attoui I, Omeiri A (2014) Contribution to the fault diagnosis of a doubly fed induction generator for a closed-loop controlled wind turbine system associated with a two-level energy storage system. Electr Power Components Syst 42:1727–1742. https://doi.org/10.1080/153 25008.2014.950361 15. Attoui I, Omeiri A (2015) Fault diagnosis of an induction generator in a wind energy conversion system using signal processing techniques. Electr Power Components Syst 43:2262–2275. https://doi.org/10.1080/15325008.2015.1082161 16. Bouakkaz MS, Boukadoum A, Boudebbouz O, Bouraiou A, Boutasseta N, Attoui I (2020) ANN based MPPT algorithm design using real operating climatic condition. In: Proceedings of the 2020 international conference on mathematics and information technology, ICMIT 2020, pp 159–163. https://doi.org/10.1109/ICMIT47780.2020.9046972 17. Bouakkaz MS (2020) Fuzzy logic based adaptive step hill climbing MPPT algorithm for PV energy generation systems. In: 2020 International conference on computing and information technology (ICCIT-1441), pp 248–252 18. Fergani N, Charef A, Attoui I (2015) Desired closed-loop based self-tuning fractional PID controller for wind turbine speed control. In: 2015 4th international conference on systems and control, ICSC 2015. https://doi.org/10.1109/ICoSC.2015.7152771 19. Boutasseta N, Bouakkaz MS, Bouraiou A, Attoui I, Fergani N (2020) Practical implementation of computational algorithms for efficient power conversion in photovoltaic energy generation systems 20. Abderrezzaq Z, Mohammed M, Ammar N, Nordine S, Rachid D, Ahmed B (2017) Impact of dust accumulation on PV panel performance in the Saharan region. In: 2017 18th international conference on sciences and techniques of automatic control and computer engineering (STA), pp 471–475. https://doi.org/10.1109/STA.2017.8314896 21. Mostefaoui M, Ziane A, Bouraiou A, Khelifi S (2018) Effect of sand dust accumulation on photovoltaic performance in the Saharan environment: southern Algeria (Adrar). https://doi. org/10.1007/s11356-018-3496-7 22. Mostefaoui M, Necaibia A, Ziane A, Dabou R, Rouabhia A, Khelifi S, Bouraiou A, Sahouane N (2018) Importance cleaning of PV modules for grid-connected PV systems in a desert environment. In: 2018 4th International Conference on Optimization and Applications, pp 1–6. https://doi.org/10.1109/ICOA.2018.8370518 23. Dabou R, Sahouane N, Necaibia A, Mostefaoui M, Bouchafaa F, Rouabhia A, Ziane A, Bouraiou A (2017) Impact of partial shading and PV array power on the performance of grid connected PV station. In: 2017 18th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering, pp 476–481. https://doi.org/10.1109/STA. 2017.8314901 24. Motahhir S, Chouder A, Hammoumi AEl, Benyoucef AS, Ghzizal AEl, Kichou S, Kara K, Sanjeevikumar P, Silvestre S (2020) Optimal energy harvesting from a multistrings PV generator based on artificial bee colony algorithm. IEEE Syst J 1–8 (2020). https://doi.org/10.1109/ jsyst.2020.2997744 25. Motahhir S, El Hammoumi A, El Ghzizal A (2020) The most used MPPT algorithms: review and the suitable low-cost embedded board for each algorithm. https://doi.org/10.1016/j.jclepro. 2019.118983 26. Blal M, Khelifi S, Dabou R, Sahouane N, Slimani A, Rouabhia A, Ziane A, Neçaibia A, Bouraiou A, Tidjar B (2020) A prediction models for estimating global solar radiation and evaluation meteorological effect on solar radiation potential under several weather conditions at the surface of Adrar environment. Measurement 152:107348. https://doi.org/10.1016/J.MEA SUREMENT.2019.107348

Supervision and Monitoring of Photovoltaic Systems …

1157

27. Sahouane N, Dabou R, Ziane A, Neçaibia A, Bouraiou A, Rouabhia A, Mohammed B (2019) Energy and economic efficiency performance assessment of a 28 kWp photovoltaic gridconnected system under desertic weather conditions in Algerian Sahara. Renew Energy. https:// doi.org/10.1016/j.renene.2019.05.086 28. Bouakkaz MS, Boukadoum A, Boudebbouz O, Fergani N, Boutasseta N, Attoui I, Bouraiou A, Necaibia A (2020) Dynamic performance evaluation and improvement of PV energy generation systems using Moth Flame Optimization with combined fractional order PID and sliding mode controller. Sol Energy 199:411–424. https://doi.org/10.1016/j.solener.2020.02.055

Maximum Power Point Tracking Using SEPIC Converter and Double Diode Solar Cell Model Khalid Chennoufi and Mohammed Ferfra

Abstract In this paper a hybrid controller for tracking the maximum power point of photovoltaic modules is proposed. For an accurate modelling, the double diode model was adopted, and so as to decrease the current undulation the single-ended primaryinductor-converter was used. The proposed controller is developed by integrating sliding mode and Artificial Neural Network, the latter has been designed to delivers an optimal voltage, which corresponds to the voltage of the maximum power point, whereas the sliding mode was developed to track the signal of the reference voltage by computing the duty cycle of the converter. The simulation has been carried out using Simulink software under different environmental conditions, the results show that the MPPT controller tracks the reference voltage in 80 ms, and exhibits good performance at brusque variation of temperature and irradiation, in addition the accuracy of the proposed method was further justified against previous work. Keywords MPPT · SEPIC · Double diode model · ANN · Sliding mode

1 Introduction The photovoltaic energy is the principal source of renewable energy, which generates electricity by using photovoltaic modules that convert sun energy into electrical power. In fact solar power is more cost-effective than standard electricity. However, the power supply output depends on the load demand, which evokes researchers to propose several algorithms that force the photovoltaic panel to works at its maximum power. The most commonly employed is perturb and observe [1–3] due to its simplicity of implementation, but it’s unstable and suffers from oscillations around the maximum power point. So, to overcome this problem, the non-linear commands are employed such as sliding mode [4, 5], backstepping [6, 7], and fuzzy logic [8, 9]. So as to increase the accuracy of the tracking, authors in [10], propose a hybrid method which integrates perturb and observe (PO) loop in sliding mode K. Chennoufi (B) · M. Ferfra Mohammadia School of Engineers, Mohammed V University in Rabat, Rabat, Morocco e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_106

1159

1160

K. Chennoufi and M. Ferfra

controller (SMC), the latter was conceived to track reference voltage generated with PO algorithm. The proposed approach illustrates good results. However, there are still oscillations around the maximum power point. So as to surmount the fluctuations issue around the maximum power, the present paper proposes an MPPT controller which combines Artificial Neural Network (ANN) and sliding mode controller (SMC) using SEPIC converter. The ANN is conceived to estimate the optimum voltage that generates the maximum power and the sliding mode is designed to track the desired voltage by changing the duty cycle of the converter (Fig. 1). Unlike the previous works which use the one-diode model for PV modules and a traditional DC-DC converter, the present work adopts the double diode model for an accurate result, and a SEPIC converter, because it offers additional advantages than the traditional DC-DC converter. This paper is structured as follows. The extraction of the module parameters and the modelling of the converter are presented in the Sect. 2. The Sect. 3 is devoted to the MPPT controller design. The Sect. 4 is dedicated to the simulation results and discussions.

2 Photovoltaic System Design The solar power system considered in this work is a PV panel, coupled to a resistive load with a SEPIC as illustrated in the Fig. 1. The specifications of the PV panel KD 245 are illustrated in Table 1. Fig. 1 Proposed photovoltaic system

Maximum Power Point Tracking Using SEPIC Converter … Table 1 Polycrystalline PV panel specifications

1161

Parameter

Value

Isc (A)

8.91

Voc (V)

36.9

Im (A)

8.23

Vm (V)

29.8

Ns

60

Fig. 2 Equivalent circuit of the SEPIC converter, (a) switch is ON, (b) switch is OFF

2.1 Analyse and Modelling of the SEPIC The single ended primary inductor converter is used since it can reach maximum power with feeble current’s undulation [11], and it is able to work in a buck or a boost mode [12]. The mathematical modelling of the converter is carried out based on these states [13, 14] as illustrated in the Fig. 2. Figure 2-a shows the equivalent circuit of the SEPIC when the switch is on (u = 1). In this case the output is supplied from the capacitor C3 , and the amount of current stored in the inductors raises. The input and the capacitor C2 supply energy to the inductor L1 and to the inductor L2 respectively. By using capacitor and inductor equations the representation of state space is as follows: ⎡ ⎢ ⎢ ⎣

dIL1 dt dVC2 dt dIL2 dt dV0 dt

⎤⎡ ⎤ ⎡V pv 0 0 0 0 IL1 L1 1 ⎥ ⎢ ⎥ ⎢ ⎢ ⎥ ⎢0 0 C 0 ⎥⎢ V ⎥ ⎢ 0 ⎥ = ⎢ −1 2 ⎥ ⎢ c2 ⎥ + ⎢ ⎦ ⎣0 0 0 ⎦ ⎣ IL2 ⎦ ⎣ 0 L2 −1 Vo 0 0 0 RC 0 3 ⎤

⎡

⎤ ⎥ ⎥ ⎥ ⎦

Figure 2-b shows the equivalent circuit of the SEPIC when the switch is off (u = 0). In this case the inductor L1 and L2 are discharged, which raises the energy stored in the capacitors. By using capacitor and inductor equations the representation of state space is as follows:

1162

K. Chennoufi and M. Ferfra

⎡ ⎢ ⎢ ⎣

dIL1 dt dVC2 dt dIL2 dt dV0 dt

⎤⎡ ⎤ ⎡V pv 0 −1 IL1 L1 L1 ⎢ ⎢ ⎥ ⎢ ⎥ ⎥ ⎢ C1 0 0 0 ⎥ ⎢ V ⎥ ⎢ 0 ⎥=⎢ 2 ⎢ c2 ⎥ + ⎢ ⎦ ⎣ 0 0 0 1 ⎥ I ⎣ 0 L2 ⎦ ⎣ L2 ⎦ 1 −1 −1 V 0 0 C3 RC3 o C3 ⎡

⎤

0

−1 L1

⎤ ⎥ ⎥ ⎥ ⎦

The state space for u = 0 or u = 1 can be is expressed as follows: ⎡ ⎢ ⎢ ⎣

dIL1 dt dVC2 dt dIL2 dt dV0 dt

⎤

⎡

0

1−u ⎥ ⎢ C2 ⎥=⎢ ⎢ ⎦ ⎣ 0 1−u C3

u−1 L1

0 −u L2

0

0 u C2

0 u−1 C3

⎤ ⎡V pv IL1 L1 ⎥ ⎥ ⎢ ⎢ 0 ⎥ ⎢ V C2 ⎥ ⎢ 0 ⎥+⎢ 1−u ⎥ ⎢ I ⎣ 0 L2 ⎦ ⎣ L2 ⎦ −1 V 0 o RC u−1 L1

⎤⎡

⎤ ⎥ ⎥ ⎥ ⎦

3

Thus, the mathematical modelling can be expressed as follows: VPV dIL1 (u − 1)VC2 (u − 1)Vo = + + dt L1 L1 L1

(1)

dVC2 dIL2 (1 − u)IL1 = + dt C2 C2

(2)

dIL2 uVC2 (1 − u)Vo =− + dt L2 L2

(3)

dV0 VO (1 − u)IL1 (u − 1)IL2 = + − dt C3 C3 RC3

(4)

2.2 Photovoltaic Modelling The single diode model is widely used for the modelling of photovoltaic modules due to its simplicity. However, the accuracy of the model may suffer, as the absolute errors of current and voltage are relatively high [15]. Thus, in order to have an accurate modelling the double diode model is considered in this work, which is represented in Fig. 3. The output current of a solar cell can be represented by the following equation:     V + Rs I V + Rs I V + Rs I − 1 − I02 exp −1 − (5) I = Iph − I01 exp a1 V T a2 VT Rp Where I: Current of PV module, Iph : Photocurrent, I01 and I02 saturation current for diode 1 and diode 2 respectively, Rp : Cell parallel resistance, Rs : Cell series resistance, a1 and a2 ideality factor for diode 1 and diode 2 respectively, V: Voltage of PV module in voltage, VT : Thermal voltage.

Maximum Power Point Tracking Using SEPIC Converter …

1163

Fig. 3 Double-diode equivalent circuit

Table 2 Parameters of the module KD 245GH

Parameter

Value

a1

1.005879

a2

1.994120

Iph (A)

8.931164

I01 (A)

3.906668 × 10–10

I02 (A)

1.094485 × 10–06

Rs ()

0.286199

Rp ()

120.495147

The modelling of the PV module is carried out using mathematical operations using the three operating points, that are (VOC − 0), (0 − ISC ) and (Vm − Im ). The aim of this calculus is to make I01 , I02 , Rp and Iph depending only on Rs , a1 and a2 , these parameters are obtained by adjusting computed and experimental powers [16]. The obtained parameters for the module KD 245 are given in Table 2.

3 Proposed Controller In this section a sliding mode controller (SMC) combined with Artificial Neural Network (ANN) is designed as shown in Fig. 1. The ANN provides a voltage reference for any environmental condition that corresponds to maximum power voltage, while the SMC tracks this reference by computing the duty cycle of the converter.

3.1 Design of the ANN The Artificial Neural Network is a data analysis and computational algorithm which consists of a related group of nodes inspired from biological neuron [17]. The ANN is the most suitable algorithm for the nonlinear system, its architecture consists of three layers, the input layer collects the external information, the second layer is the

1164

K. Chennoufi and M. Ferfra

Fig. 4 Neural network regression

hidden layer, contains hidden neurons that receive information from the input layer and send them to the output layer [18]. In this paper, the input layer was designed with a database of 158 irradiation and temperature cases, the temperature has been increased by a step of 5 °C, from 5 °C to 60 °C, and the irradiance was increased with a step of 100 W/m2 for each temperature degrees, whereas the hidden layer has been conceived with 100 neurons. The output layer has been conceived with one neuron that corresponds to the maximum power voltage for every 158 irradiation and temperature cases. The ANN model fitting shows the outputs for the targets for training and test sets. The R-square value was 0.99999 for all training sets, as shown in Fig. 4, which illustrates an excellent relationship between the output and target sets.

3.2 Sliding Mode Controller The SMC is designed in order to tracks the maximum power voltage, Vpv and Vref are the photovoltaic and the reference voltages respectively. The tracking error (e) is expressed as follows: e = VPV − Vref

(6)

The time derivative of Eq. (6) is: ˙ ref ˙ PV − V e˙ = V

(7)

IPV = IC1 + IL1

(8)

The photovoltaic current is:

Maximum Power Point Tracking Using SEPIC Converter …

1165

The derivative of photovoltaic voltage is: ˙ pv = IPV − IL1 V C1

(9)

Thus: e˙ =

IPV − IL1 − Vref C1

(10)

The sliding surface is defined by: S = e − λ˙e

(11)

The following dynamic sliding surface is used: S˙ = AS − Bsign (S)

(12)

The time derivative of (11) with respect to time is: S˙ = e˙ − λ¨e

(13)

Where λ > 0. Second derivative of (10) is given by: e¨ =

˙IPV − ˙IL1 ¨ ref −V C1

(14)

By replacing Eq. (1) into (14) the Eq. (15) is obtained:

e¨ =

˙IPV −



(u−1)VC1 L1

+

(u−1)Vo L1

+

VPV L1

 ¨ ref −V

C1

(15)

The time derivative of the current can be expressed as: ˙IPV = dIPV dVPV dVPV dt

(16)

By introducing the Eq. (5) in the Eq. (16) the derivative of photovoltaic current can be expressed as: ⎡ ˙IPV = ⎣−

I01 exp

V+Rs IPV a1 VT

a1 VT

−

I02 exp

V+Rs IPV a2 VT

a2 VT

⎤ 1 ⎦˙ VPV − RP

(17)

1166

K. Chennoufi and M. Ferfra

By replacing Eq. (17) into (14) and combining (13) with (12) the command u(t) is obtained:        ˙ ref + V ¨ ref + ˙IPV ˙ PV − V u = 1 − VPV − L1 C1 (AS + Bsign(S)) − λ V

1 VC2 + V0

(18)

4 Simulation and Discussions The tracking accuracy was evaluated using KD 245 module connected to a 40  load via SEPIC converter which the parameters are shown in Table 3. Figure 5 shows the temperature and irradiation cases used in this simulation. In the first interval [0 s, 23 s], the photovoltaic panel operates under the maximum irradiation, whereas the temperature has been increased to 45 °C. In the next case [23 s, 48 s], the irradiation drops suddenly to 580 W/m2 , and the temperature drops in two successive levels to 25 °C. In the last case [48 s, 60 s], the temperature is maintained constant at 25 °C, while the irradiation has been increased to reach 1000 W/m2 . Table 3. Component of the converter Component

Value

Inductor 1 (L1 )

5.1 mH

Inductor 2 (L2 )

5.1 mH

Capacitor 1 (C1 )

4.5 × 103 uF

Capacitor 2 (C2 )

7.9 × 10 μF

Capacitor 3 (C3 )

2.4 × 103 μF

Constant (λ)

106

Constant (A)

106

Constant (B)

103

(a) Temperature (K)

Irradiance (W/m²)

(b)

Time (S)

Fig. 5 Scenarios of irradiation (a) and Scenarios of temperature (b)

Time (S)

Maximum Power Point Tracking Using SEPIC Converter …

1167

Vref (ANN) Present method Method proposed by [10] (a) Power (W)

Voltage (V)

(b)

Time (S)

Time (S)

Fig. 6 PV voltage (a) and PV power (b), at STC.

Figure 6 illustrates a comparative results in standard test conditions (irradiation of 1000 W/m2 and a temperature of 298 K), between the proposed method and sliding mode combined with P&O proposed by [10]. It can be seen in the figure (a) that the present controller reaches the desired voltage in 80 ms, which guaranty the maximum power output with slight oscillations that fade during the tracking, Figure (b). Whereas the method mentioned before arrives at the desired voltage at 140 ms with large oscillations which remain throughout the tracking. Therefore the proposed method provides good and fast reaction at any environmental condition. Figure 7 shows the voltage and the current of the PV module using the proposed method under the different weather scenarios. The results show that the Artificial Neural Network generates the reference voltage at any the temperature and the irradiance conditions, and the sliding mode tracks this voltage with high efficiency which ensure a smooth voltage and current curves. Figure 8 illustrates the current and voltage of the converter using the proposed method. It can be seen that the system reacts with good efficiency and that it is able to operate under all environmental conditions. In addition, the SEPIC operates in this case as a voltage boost, which is suitable for equipment that requires high voltage. (b)

Voltage (V)

Current (A)

(a)

Time (S)

Fig. 7 PV voltage (a) and PV current (b)

Time (S)

1168

K. Chennoufi and M. Ferfra (b) Current (A)

Voltage (V)

(a)

Time (S)

Time (S)

Fig. 8 Output voltage (a) and output current (b)

5 Conclusion This paper presents a sliding mode control combined to the Artificial Neural Network for Maximum Power Point Tracking of a PV system, which consists of a photovoltaic module, modelled with double diode model and a SEPIC converter connected to a resistive load. The assessment of the efficiency of the proposed method has been carried out by simulating the proposed controller in Matlab/Simulink software under different weather conditions. In one hand the simulation demonstrates its efficiency and stability against the previous work, in standard test condition. In the other hand, the proposed approach exhibits a fast reaction to track the reference voltage with good performance even when the environmental condition changes. In addition there is no oscillation in the obtained curves and the algorithm can easily implemented, which justify the interest behind its use.

References 1. Thakran S, Singh J, Garg R, Mahajan, P (2018) Implementation of P&O algorithm for MPPT in SPV system. In: International conference on power energy, environment and intelligent control (PEEIC), Greater Noida, India 2. Ishaque K, Salam Z, Lauss G (2014) The performance of perturb and observe and incremental conductance maximum power point tracking method under dynamic weather conditions. Appl Energy 119:228–236 3. Yong Y, Zhaob FP (2011) Adaptive perturb and observe MPPT technique for grid connected photovoltaic inverters. Procedia Eng 23:468–473 4. Islam S, Liu XP (2011) Robust sliding mode control for robot manipulators. IEEE Trans Ind Electron 58(6):2444–2453 5. Lachtar S, Bouraiou A, Djaafri O, Maouedj R (2019) Smooth sliding mode-based MPPT algorithm for photovoltaic applications. In: 1st global power, energy and communication conference (IEEE GPECOM 2019), Cappadocia, Turkey 6. Parsa A, Kalhor A, Atashgah MAA (2016) Backstepping control performance enhancement using close loop identification for quadrotor trajectory tracking. Modares Mech Eng 16(11):224–234 7. Zhang Y, Fidan B, Ioannou P (2003) Backstepping control of linear time-varying systems with known and unknown parameters. IEEE Trans Autom Control 48(11):1908–1925

Maximum Power Point Tracking Using SEPIC Converter …

1169

8. Belcher R, Fattahi J, Hinzer K, Schriemer H (2020) Fuzzy logic based module-level power electronics for mitigation of rapid cloud shading in photovoltaic systems. In: IEEE Applied Power Electronics Conference and Exposition (APEC), Hioki, USA 9. Ziane A, Necaibia A, Mostfaoui M, Bouraiou A, Sahouane N, Dabou R (2018) A fuzzy logic MPPT for three-phase grid-connected PV inverter. In: Twentieth international middle east power systems conference (MEPCON), Cairo, Egypt 10. Mostafa MR, Saad NH, El-sattar AA (2020) Tracking the maximum power point of PV array by sliding mode control method. Ain Shams Eng J 11:119–131. https://doi.org/10.1016/j.asej. 2019.09.003 11. Efendi MZ, Murdianto FD, Setiawan RE (2017) Modeling and simulation of MPPT sepic converter using modified PSO to overcome partial shading impact on DC microgrid system. In: International electronics symposium on engineering technology and applications, Surabaya, Indonesia 12. Duran E, Sidrach-de-Cardona M, Galan J, Andujar JM (2008) Comparative analysis of buckboost converters used to obtain I-V characteristic curves of photovoltaic modules. In: 2008 IEEE power electronics specialists conference, Rhodes, Greece 13. Kashyap AR, Ahmadi R, Kimball JW (2013) Input voltage control of sepic for maximum power point tracking. In: Power and energy conference at Illinois (PECI), pp. 30–35. IEEE 14. Erickson RW (2000) Fundamentals of power electronics, 2nd edn. Springer, Heidelberg, p 910 15. Chennoufi K, Ferfra M (2020) Parameters extraction of photovoltaic modules using a combined analytical - numerical method. In: International conference on cloud computing technologies and applications (CloudTech), Marrakech, Morocco 16. Chennoufi K, Ferfra M, Mokhlis M (2021) An accurate modelling of photovoltaic modules based on two-diode model. Renew Energy 167:294–305 17. Nambiar N, Palackal RS, Greeshma KV, Chitra A (2015) PV fed MLI with ANN based MPPT. In: 2015 international conference on computation of power, energy, information and communication (ICCPEIC), Chennai 18. Saadi A, Moussi A (2003) Neural network use in the MPPT of photovoltaic pumping system. Rev Renew Energies ICPWE 4:39–45

Global Maximum Power Point Tracking Using Genetic Algorithm Combined with PSO Tuned PID Controller Mohammed Salah Bouakkaz, Ahcene Boukadoum, Omar Boudebbouz, Abdel Djabar Bouchaala, Nadir Boutasseta, Issam Attoui, Ahmed Bouraiou, and Saad Motahhir

Abstract Several techniques have been proposed to track the global maximum power point (GMPP) in the presence of multiple local peaks introduced when the PV generator is affected by partial shading. Such methods include scanning methods and advanced optimization-based strategies. In this paper, a Genetic Algorithm based MPPT, combined with PID controller tuned using PSO, is proposed to track the MPP. The GA algorithm is applied to give the reference voltage and measured PV voltage to the PID controller that its parameters were tuned using PSO. The obtained results give a good tracking performance of the GMPP under STC conditions and partial shading scenarios. Keywords Maximum power point tracking (MPPT) · Genetic algorithm (GA) · PID controller · Particle Swarm Optimization (PSO)

1 Introduction Photovoltaic (PV) solar energy is one of the most important renewable energies for the following reasons: clean and non-polluting energy, economical and maintainable, the availability of solar radiation in most countries of the world [1, 2]. The photovoltaic solar system consists of a group of elements which include: photovoltaic solar panels, charge controller (including MPPT), storage batteries, and the power inverter (for AC loads). In general; solar photovoltaic energy in terms of load connection into two M. S. Bouakkaz (B) · A. Boukadoum · O. Boudebbouz · A. D. Bouchaala Department of Electrical Engineering, Laboratoire d’électrotechnique de Skikda «LES», Université du 20 Août 1955, 21000 Skikda, Algeria N. Boutasseta · I. Attoui Research Center in Industrial Technologies CRTI, P.O. Box 64, Cheraga, Algiers, Algeria A. Bouraiou Unité de Recherche en Energies Renouvelables en Milieu Saharien (URERMS), Centre de Développement des Energies Renouvelables (CDER), 01000 Adrar, Algeria S. Motahhir ENSA, SMBA University, Fez, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_107

1171

1172

M. S. Bouakkaz et al.

types: isolated from the grid (which is a simplified system consisting of solar PV that feeds a load), or connected to the grid (which is a complex system that includes solar PV, local load and grid) [3]. The operation of solar photovoltaic modules is dependent on climatic conditions lake solar radiation and temperature of the cell, which is also considered to have a non-linear current-voltage characteristic curve. The output power of PV arrays depends on the characteristic curves (I-V and P-V) which are nonlinear and change with environmental conditions [4]. The maximum power is calculated by the product of current and voltage, this product is known as the maximum power point (MPP) which is not constant. For this purpose, the maximum power point tracking technique (MPPT) is used to determine and extract the MPP value [5]. The strategy to approach MPPT varies from method to method which is classified into two classes, conventional MPPT and artificial intelligence-based approaches [6–8]. Conventional MPPT technique such as the incremental conductance technique (IC), perturb and observe (P&O) [9], open-circuit voltage (OCV), and short circuit current [10]. On the other hand, artificial intelligence techniques are different and developed using various algorithms such as fuzzy logic (FL) [11], Genetic algorithm (GA) [12], Adaptive Neuro-Fuzzy Inference System (ANFIS) [13–16], Artificial Neural Network (ANN) [17] and Particle Swarm Optimization (PSO) [18, 19]. In this paper, the proposed method is based on GA and PSO tuned PID controller, which combines two techniques of artificial intelligence approaches that are applied to MPPT to track global MPP in solar PV systems operating in different climatic conditions. Simulations in Matlab/Simulink are carried out to implement the proposed method through which results are obtained. In the following section, the PV energy generation system which combines the PV array and power converter are presented. The proposed MPPT strategy in this work is illustrated in Sect. 3. Section 4 presents simulation results and gives discussions. Conclusions are given in the end.

1.1 Photovoltaic Array Model The PV cell has been the subject of modeling studies resulting in the three known and most used models depending on the application. When Maximum Power Point Fig. 1 Bloc representation of a PV system

Global Maximum power Point Tracking Using Genetic Algorithm …

1173

Tracking is selected as a target application as in [20], the single-diode model is generally used for simulation purposes. The main characteristics of the single-diode model are the simpler calculations and acceptable accuracy when compared with the two and three diodes models which require advanced parameters identification techniques as in [21]. In the consequent work, the representations shown in Fig. 1 are used for the PV array energy generation system. The output current I pv of the PV array composed of N pp panels in parallel and N ss panels in the series, which is formulated as follows: I pv = I ph − Id −

v pv + I pv Rs N SS R p N SS

(1)

Where V t represents the thermal voltage of the PV array (V t = N s kT/q), a is a diode constant, with N s series cells, q is the electron charge, k the Boltzmann constant. Rsh is the shunt resistance. Rs is the series resistance that depends on the material applied to realize the PV cell.

1.2 Step-Up Converter A step-up converter is a converter that produces a high voltage at its output terminal than its input voltage. The boost DC-DC converter a type of switched-mode power supply consisting of a minimum of two switching devices and one energy storage element, a capacitor, an inductor, and a filter is generally inserted at the output to reduce the ripple of VR . The boost converter is illustrated in Fig. 2. Fig. 2 Step-up converter

1174

M. S. Bouakkaz et al.

Fig. 3 Photovoltaic system developed with GA-based MPPT and PSO tuned PID controller (proposed)

1.3 Association PV Array with the Step-Up Converter The association PV array/step-up converter shown in Fig. 3 is connected to a load and has three main inputs, namely: Temperature (T), Solar Irradiation (E), and PWM control signal of the Switch (S). The role of the MPPT technique is to generate adequate control signals to track the optimal operating point in the presence of variation in T and E.

2 Proposed MPPT Strategy The proposed MPPT strategy illustrated in Fig. 3 is applied to find the MPP under regular and PS weather conditions.

2.1 GA Based MPPT The GA is applied to find the GMPP of the PV system under different operating conditions. Figure 4 presented the flowchart of the consequent steps of the GA-based MPPT.

Global Maximum power Point Tracking Using Genetic Algorithm …

1175

Fig. 4 Flowchart of GA based MPPT

2.2 PID Controller Tuning Using the PSO Algorithm The PID is a commonly used controller in industrial control systems [22–24]. A PID controller gives continuously a correction value based on its PID terms on a calculated error between a reference voltage (V ref ) and a measured value (V PV ). In this work, a PID controller is used to track the reference operating point of the GMPP generated by the GA. The PID is characterized by the parameters (kc, Ti, Td) that are trained using a Particle Swarm Optimization (PSO) technique [25] as presented in Fig. 5. The PSO tuning algorithm uses reference voltage and measured PV voltage to tune the parameters of the PID that output the duty ratio, which will adjust the actual operating point using the switch S. The position update of the swarm is illustrated in Fig. 6.

Fig. 5 Tuning of PID controller parameters using PSO

1176

M. S. Bouakkaz et al.

Fig. 6 Particle Swarm Optimization position update representation

Fig. 7 Solar irradiation and temperature patterns for simulation scenarios

3 Simulation Results and Discussions The proposed GA combined with the PSO-PID controller is designed and tested under MATLAB/Simulink using the Kyocera KC200GT PV panel. In this work, changes in solar radiation and temperature are applied to verify and assess the robustness of the proposed controller. Irradiation and temperature patterns that are used in the consequent simulations are shown in Fig. 7. As demonstrated in Fig. 8, which PV tracking performance when using the genetic algorithm-based MPPT algorithm combined with PSO tuned PID controller, the

Global Maximum power Point Tracking Using Genetic Algorithm …

Fig. 8 The PV array’s response to a variable scenario: (a) STC, (b) P1, (c) P2, (d) P3

1177

1178

M. S. Bouakkaz et al.

proposed method tracks the global optimal operating point of the PV system in every scenario efficiently with less tracking time. It is also characterized by fast convergence speed, high PV tracking efficiency, and low power loss.

4 Conclusions In this paper, a global MPPT strategy to improve the efficiency of the PV system under regular and partial shading environments has been suggested. In this work, the impacts of various partial shading patterns on the output of the PV system is also investigated. The proposed strategy is developed to track the optimum operating point of the PV system. The controller is employed in the voltage control loop to accurately regulate the optimum operating voltage. The proposed MPPT control procedure provides excellent performances in the tracking of the generated reference from the genetic algorithm, which is verified by different simulation results presented in the paper. The present work may be extended by considering the integration of the proposed method with a PV inverter to build the solar photovoltaic grid-connected power generation system.

References 1. Bouraiou A, Necaibia A, Boutasseta N, Mekhilef S, Dabou R, Ziane A, Sahouane N, Attoui I, Mostefaoui M, Touaba O (2020) Status of renewable energy potential and utilization in Algeria. J Clean Prod 246:119011. https://doi.org/10.1016/j.jclepro.2019.119011 2. Blal M, Khelifi S, Dabou R, Sahouane N, Slimani A, Rouabhia A, Ziane A, Neçaibia A, Bouraiou A, Tidjar B (2020) A prediction models for estimating global solar radiation and evaluation meteorological effect on solar radiation potential under several weather conditions at the surface of Adrar environment. Measurement 152:107348. https://doi.org/10.1016/J.MEA SUREMENT.2019.107348 3. Sahouane N, Dabou R, Ziane A, Neçaibia A, Bouraiou A, Rouabhia A, Mohammed B (2019) Energy and economic efficiency performance assessment of a 28 kWp photovoltaic grid-connected system under desertic weather conditions in Algerian Sahara. Renew Energy 143:1318–1330. https://doi.org/10.1016/j.renene.2019.05.086 4. Bouraiou A, Hamouda M, Chaker A, Mostefaoui M, Lachtar S, Sadok M, Boutasseta N, Othmani M, Issam A (2015) Analysis and evaluation of the impact of climatic conditions on the photovoltaic modules performance in the desert environment. Energy Convers Manag 106:1345–1355. https://doi.org/10.1016/j.enconman.2015.10.073 5. Lachtar S, Bouraiou A, Diaafri O, Maouedj R (2019) Smooth sliding mode-based MPPT algorithm for photovoltaic applications. https://doi.org/10.1109/gpecom.2019.8778484 6. Motahhir S, El Hammoumi A, El Ghzizal A (2020) The most used MPPT algorithms: review and the suitable low-cost embedded board for each algorithm. https://doi.org/10.1016/j.jclepro. 2019.118983 7. Motahhir S, Chouder A, El Hammoumi A, Benyoucef AS, El Ghzizal A, Kichou S, Kara K, Sanjeevikumar P, Silvestre S (2020) Optimal energy harvesting from a multistrings PV generator based on artificial bee colony algorithm. IEEE Syst J 1–8. https://doi.org/10.1109/ jsyst.2020.2997744

Global Maximum power Point Tracking Using Genetic Algorithm …

1179

8. Mirza AF, Mansoor M, Ling Q (2020) A novel MPPT technique based on Henry gas solubility optimization. Energy Convers Manag 225:113409. https://doi.org/10.1016/j.enconman.2020. 113409 9. Motahhir S, Chalh A, El Ghzizal A, Derouich A (2018) Development of a low-cost PV system using an improved INC algorithm and a PV panel proteus model. J Clean Prod 204:355–365. https://doi.org/10.1016/j.jclepro.2018.08.246 10. Motahhir S, El Hammoumi A, El Ghzizal A (2018) Photovoltaic system with quantitative comparative between an improved MPPT and existing INC and P&O methods under fast varying of solar irradiation. Energy Rep 4:341–350. https://doi.org/10.1016/j.egyr.2018.04.003 11. Bouakkaz MS, Boukadoum A, Boudebbouz O, Attoui I, Boutasseta N, Bouraiou A (2020) Fuzzy logic based adaptive step hill climbing MPPT algorithm for PV energy generation systems. In: 2020 International conference on computing and information technology (ICCIT1441), pp 1–5. IEEE. https://doi.org/10.1109/ICCIT-144147971.2020.9213737 12. Daraban S, Petreus D, Morel C (2014) A novel MPPT (maximum power point tracking) algorithm based on a modified genetic algorithm specialized on tracking the global maximum power point in photovoltaic systems affected by partial shading. Energy 74:374–388. https://doi.org/ 10.1016/j.energy.2014.07.001 13. Attoui I, Boudiaf A, Fergani N, Oudjani B, Boutasseta N, Deliou A (2015) Vibration-based gearbox fault diagnosis by DWPT and PCA approaches and an adaptive neuro-fuzzy inference system. In: 2015 16th international conference on sciences and techniques of automatic control and computer engineering (STA), pp 234–239. IEEE. https://doi.org/10.1109/STA.2015.750 5177 14. Attoui I, Oudjani B, Boutasseta N, Fergani N, Bouakkaz M-S, Bouraiou A (2020) Novel predictive features using a wrapper model for rolling bearing fault diagnosis based on vibration signal analysis. Int J Adv Manuf Technol 106:3409–3435. https://doi.org/10.1007/s00170-01904729-4 15. Attoui I, Boutasseta N, Fergani N, Oudjani B, Deliou A (2015) Vibration-based bearing fault diagnosis by an integrated DWT-FFT approach and an adaptive neuro-fuzzy inference system. In: 3rd international conference on control, engineering and information technology, CEIT 2015. https://doi.org/10.1109/CEIT.2015.7233098 16. Lasheen M, Abdel-Salam M (2018) Maximum power point tracking using hill climbing and ANFIS techniques for PV applications: a review and a novel hybrid approach. Energy Convers Manag 171:1002–1019. https://doi.org/10.1016/j.enconman.2018.06.003 17. Bouakkaz MS, Boukadoum A, Boudebbouz O, Bouraiou A, Boutasseta N, Attoui I (2020) ANN based MPPT algorithm design using real operating climatic condition. In: 2020 2nd international conference on mathematics and information technology (ICMIT), pp 159–163. IEEE. https://doi.org/10.1109/ICMIT47780.2020.9046972 18. Boutasseta N, Ramdani M, Mekhilef S (2018) Fault-tolerant power extraction strategy for photovoltaic energy systems. Solar Energy 169:594–606. https://doi.org/10.1016/j.solener. 2018.05.031 19. Boutasseta N, Bouakkaz MS, Bouraiou A, Necaibia A, Attoui I, Fergani N (2020) Practical implementation of computational algorithms for efficient power conversion in photovoltaic energy generation systems. In: 2020 international conference on computing and information technology (ICCIT-1441), pp 1–5. IEEE. https://doi.org/10.1109/ICCIT-144147971.2020.921 3761 20. Shams I, Saad M, Kok Soon T (2020) Improved team game optimization algorithm based solar MPPT with fast convergence speed and fast response to load variations. IEEE Trans Ind Electron 1. https://doi.org/10.1109/tie.2020.3001798 21. Qais MH, Hasanien HM, Alghuwainem S (2020) Parameters extraction of three-diode photovoltaic model using computation and Harris Hawks optimization. Energy 195:117040. https:// doi.org/10.1016/j.energy.2020.117040 22. Al-Dhaifallah M, Nassef AM, Rezk H, Nisar KS (2018) Optimal parameter design of fractional order control based INC-MPPT for PV system. Solar Energy 159:650–664. https://doi.org/10. 1016/j.solener.2017.11.040

1180

M. S. Bouakkaz et al.

23. Attoui I, Omeiri A (2014) Contribution to the fault diagnosis of a doubly fed induction generator for a closed-loop controlled wind turbine system associated with a two-level energy storage system. Electr Power Compon Syst 42:1727–1742. https://doi.org/10.1080/15325008.2014. 950361 24. Attoui I, Omeiri A (2015) Fault diagnosis of an induction generator in a wind energy conversion system using signal processing techniques. Electr Power Compon Syst 43:2262–2275. https:// doi.org/10.1080/15325008.2015.1082161 25. Bouakkaz MS, Boukadoum A, Boudebbouz O, Fergani N, Boutasseta N, Attoui I, Bouraiou A, Necaibia A (2020) Dynamic performance evaluation and improvement of PV energy generation systems using moth flame optimization with combined fractional order PID and sliding mode controller. Solar Energy 199:411–424. https://doi.org/10.1016/j.solener.2020.02.055

Experimental Validation of a Neuro-Fuzzy Model of Open Circuit Voltage and Maximum Power Voltage Case of Amorphous Technology Brahim Bouachrine, Mustapha Kourchi, Driss Yousfi, Kaoutar Dahmane, Mhand Oubella, and Mohamed Ajaamoum Abstract The objective of this work is to develop an adaptive neuro-fuzzy inference system (ANFIS) for the determination of the open circuit voltage and the maximum power voltage of a photovoltaic generator of the Amorphous/Microcrystalline type. In order to evaluate the performance of the proposed ANFIS system, we compared the results obtained using the ANFIS system to the results obtained using a system of analytical equations developed by smoothing the experimental measurements. For the experimental validation of our research work, we used an experimental database from the station located at Green Energie Park in Bengrire Morocco. The comparison results show that the proposed ANFIS model is more accurate than the analytical model and allows to better emulate the electrical characteristics of the studied photovoltaic generator. Keywords ANFIS model · Photovoltaic emulator · Analytical model · Amorphous/microcrystalline

1 Introduction The photovoltaic (PV) emulator is an electronic system able to reproduce the currentvoltage output characteristics of photovoltaic generators. It facilitates laboratory testing and operational evaluation of a photovoltaic system by applying the same real-world conditions such as radiation and shading to achieve results similar to reality [1]. The proper functioning of these emulators depends on the quality of B. Bouachrine (B) · M. Kourchi · K. Dahmane · M. Oubella · M. Ajaamoum LASIME, National School of Applied Sciences, Ibn Zohr University, Agadir, Morocco e-mail: [email protected] M. Kourchi e-mail: [email protected] D. Yousfi Department of Electrical Engineering, National School of Applied Sciences, Mohammed First University, Oujda, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_108

1181

1182

B. Bouachrine et al.

the mathematical models used to generate photovoltaic characteristics. Indeed, the choice of these models directly affects the static and dynamic performance of the photovoltaic emulator [2, 2]. The aim of this work is to develop an adaptive neuro-fuzzy inference system (ANFIS) for the determination of open-circuit voltage (V oc ) and maximum power voltage (V mp ). This system should allow us to better emulate the electrical characteristics of the studied photovoltaic generator. In order to evaluate the quality of the proposed ANFIS model, the results obtained using the proposed ANFIS system will be compared with the results generated by exploiting a system of analytical equations obtained by smoothing the experimental measurements. For the experimental validation of our research work, we have used an experimental database from the Green Energie Park in Bengrire. The technology used in this experiment is the amorphous/microcrystalline technology (case of the solar module SHARP NS-F135G5) [4]. This paper is organized as follows: after the introduction, in the second part, we present the studied mathematical model of the PV module. In this part, we also present the systems of analytical equations developed for the calculation of the I(V ) characteristic at the points of open circuit V oc , maximum power (V mp , I mp ), and short circuit I sc . In the third section, we describe the adaptive neuro-fuzzy inference system ANFIS elaborated to compute the V mp and V oc voltages which allows increasing the accuracy of the photovoltaic model. In the fourth part, we will present the experimentally validated results followed by discussions and comparisons. Finally, a conclusion that encompasses the work done in this paper.

2 Proposed Photovoltaic Model 2.1 Mathematical Model for the Calculation of the Characteristic I(V) The proposed model is obtained from the semiconductors theory. It represents the electrical behavior of a PV module by a mathematical model with a one exponential [5]:    I = Isc 1 − C1 exp

V C2 Voc



 −1

(1)

This model allows an explicit computation of the electrical characteristic I(V ) of the PV module regardless of the lighting and temperature conditions. The two parameters C 1 and C 2 are determined as a function of the currents I sc , I mp and the voltages V mp , V oc for a given illumination and temperature:

Experimental Validation of a Neuro-Fuzzy Model …

    − Vmp Imp exp C1 = 1 − Isc C2 Voc C2 =

Vmp Voc

−1   I log 1 − Imp sc

1183

(2)

(3)

2.2 Analytical Model for the Calculation of Currents Isc, Imp For the computation of the currents Isc, Imp as a function of the illuminance G, and the temperature T of the PV source we use the system of analytical Eqs. (4). The system of equations is obtained by smoothing the experimental measurements using the Levenberg-Marquardt algorithm [6, 7]. ⎧   ⎨ Isc = Iscn −0.00534 + 0.9768 G + 0.0002441 (T − Tn ) G n   ⎩ Imp = Impn −0.005973 + 0.9712 G + 0.000476 (T − Tn ) Gn

(4)

With: Tn : Temperature (°C) under standard conditions. Gn : Sunlight (W/m2 ) under standard conditions.

2.3 Analytical Model for the Calculation of Voltages Vmp and Voc The system of analytical Eqs. (5) allows for the calculation of the voltages Vmp, Voc as a function of the illumination G, and the temperature T of the PV source. This system of equations is obtained by smoothing the experimental measurements using the Levenberg-Marquardt algorithm [6]. ⎧   ⎨ Voc = Vocn + 1.067 Vt log G − 0.3017 (T − Tn ) Gn  ⎩ Vmp = Vmpn + 0.5284 Vt log G − 0.222 (T − Tn ) Gn With: Vocn : Open circuit voltage in (V) under standard conditions. Vmpn : Voltage at maximum power in (V) in standard conditions. V t = N s kT /q: Thermodynamic potential of Ns cells connected in series.

(5)

1184

B. Bouachrine et al.

3 ANFIS Model for Calculating Voltages Vmp et Voc In order to improve the accuracy of the photovoltaic model, we have opted for the neuro-fuzzy approach for the identification of Voc and Vmp. Neuro-fuzzy systems that combine fuzzy logic and neural networks have proven their efficiency in a variety of industrial problems. They are useful for the identification of non-linear, complex, and difficult to model systems [7, 8]. We have consequently chosen to use adaptive neuro-fuzzy inference systems (Anfis), which does not require a particular form of modeling. This is a Perceptrontype neural network with 5 layers, where each layer corresponds to the realization of one step of a fuzzy inference system (SIF) of the Takagi Sugeno type. A Matlab code is developed for the generation of the ANFIS model according to the flowchart in Fig. 1. After several learning tests, we have come up with the simplest and most efficient architecture of the ANFIS model illustrated in Fig. 2. It has two inputs and a polynomial output of order 1, each input is represented by two fuzzy sets of the Gaussian type. The final output is calculated as the average of the outputs of each rule weighted by the degrees of activation. Inputs x and y are expressed as a function of the irradiance G and temperature T of the PV module according to the system of Eq. (6). Fig. 1 The flowchart of the developed code

Database : Inputs : x, y Output : Voc or Vmp

Generating the initial SIF: genfis1

Stopping Criterion: Max. number of iterations Tolerance

Learning SIF : Anfis Hybrid optimization

Validate the results

Experimental Validation of a Neuro-Fuzzy Model …

1185 x y

w1

A1 x

A2

y

B1

w2 w3 w4

B

N N

Voc or Vmp

N N

Fig. 2 ANFIS architecture used



x=

G Gn

+ T − Tn

y = (T + 273.15) log



G (T +273.15) G n



(6)

4 Results and Experimental Validation 4.1 Experimental Bench For the validation of the proposed ANFIS model, we used an experimental database from Green Energy Park in Bengrire [6]. The database consists of experimental I(V) electrical characteristics captured with a sampling pitch of 10 min along the day. In this work, we used a set of measurement samples with 195 I(V) characteristics. The PV technology studied is of the Amorphous/Microcrystalline type (case of the SHARP solar module NS-F135G5 [3]). Its technical specifications are described in Table 1. To evaluate the accuracy of the estimated parameters and the quality of the smoothing, we have calculated the coefficient of determination Rd and the root mean square error RMSE [6]. To simulate the different models, we chose the Matlab 2016a environment. In order to estimate the speed of execution of each of the models studied, we evaluated the average calculation time tCPU, necessary for the calculation of the I(V) characteristics of the set of measures considered. For the processing carried out Table 1 Electrical parameters of the solar modules used PV Module

Pmax Ns Isc (W) (A)

SHARP 135 NS-F135G5 Amorphe/ Microcrystalline

–

Imp Vmp Voc (A) (V) (V)

3.41 2.88 47

Ksc

Koc

Kmp (%/°C)

NOCT (°C)

61.3 0.07%/°C −0.3%/°C −0.24%/°C –

1186

B. Bouachrine et al.

during our investigations, we used a PC-type computer equipped with the Intel(R) Core (TM) i5-4310U CPU @ 2.00 GHz, 2601 MHz, 2 core(s), 4 logic processor(s), with a RAM memory capacity of 4 GB.

4.2 Results of the Calculation of the Voltages Voc and Vmp In Fig. 3 the values predicted by the ANFIS model and the analytical model (5) are compared with the corresponding experimental values of the Amorphous/Microcrystalline technology studied. Amorphous Technology

Fig. 3. Comparison of simulated and measured voltages for the studied technologies.

Table 2 Comparison of the ANFIS model and the analytical model Technology

Parameter

PV model with ANFIS

PV model with analytic (5)

Rd

RMSE

Rd

RMSE

SHARP NS-F135G5

Voc

0.9825

0.1709

0

1.4509

Amorphous

Vmp

0.9670

0.2010

0.0390

1.0854

Experimental Validation of a Neuro-Fuzzy Model …

1187

Table 2 summarizes the calculated values of the determination coefficient Rd and the root mean square error RMSE corresponding to the calculation of the voltages Voc and Vmp by the ANFIS model and the analytical model (5). Examination of Fig. 3 shows that the results obtained by the ANFIS model are satisfying for all the measures, however, it can be seen that the Analytic model is not consistent with much of the experimental data set. The different comparisons (see Table 2) confirm the performance and the good concordance between the measurement and the modeling by the ANFIS neuro-fuzzy system. This shows us the efficiency and identification capacity of this model.

4.3 Calculation Results for Characteristic I(V) This part presents some comparison results of the experimental and simulated I(V) characteristics obtained in the two cases where the voltages Voc and Vmp are computed by the Analytical model (5) and by the ANFIS model. Figure 4 shows an example of application results of the studied PV model for the studied technology Microcrystalline amorphous, and for the following sunlight G and temperature T values: • G (W/m2 ) = [566, 600, 635, 669, 701] • T (°C) = [36.1, 35.9, 35.8, 36.6, 36.9] The Fig. 5 illustrate an example of application results of the studied PV model for the studied technology Microcrystalline amorphous, and for the following irradiation G and temperature T values:

Fig. 4 Experimental and simulated I(V) characteristics

1188

B. Bouachrine et al.

Fig. 5 Experimental and simulated I(V) characteristics

• G(W/m2 ) = [516, 564, 607, 648, 73] • T (°C) = [19.4, 22.6, 23.2, 24.1, 20.3] The Fig. 4 demonstrates that the first sample of measurements is consistent with the values simulated by both the ANFIS and analytical models. Although, it can be seen (Fig. 5) that the simulation results do not agree with the second sample of measurements, especially in the proximity of the open circuit point. We note that the use of the analytical model, for the amorphous microcrystalline technology, does not make it possible to reproduce the experimental measurements. This is predictable because the precision of the analytical model is not sufficient for the estimation of the voltages Vmp and Voc for part of the experimental data set.

4.4 Results Summary For the experimental database as a whole, results comparing the performance of the models used in this study are grouped in Table 3. Analysis of the results in Table 2 shows that the ANFIS model achieves a very good trade-off in terms of speed of execution. Table 3 Summary of performance and computing time tCPU

Technology

Criteria

PV model with ANFIS

PV model with analytic (5)

SHARP

Rd

0.9974

0.9407

NS-F135G5

RMSE

0.0472

0.2247

Amorphous

t CPU (s)

0.0316

0.0082

Experimental Validation of a Neuro-Fuzzy Model …

1189

5 Conclusion During our investigations, we look for a model that reproduces the characteristics of the PV panels as well as possible. To achieve this, we compared two types of models, namely the neuro-fuzzy ANFIS model and an analytical model. For the calculation of the Voc and Vmp voltages we successfully used the ANFIS neuro-fuzzy system. The study results show that the proposed model is more accurate than the analytical model and allows to better emulate the electrical characteristics of the studied photovoltaic generator. Acknowledgement This work is funded and supported by the Moroccan Research Institute for Solar Energy and New Energies (IRESEN).

References 1. Chen C-C, Chang H-C, Kuo C-C, Lin C-C (2013) Programmable energy source emulator for photovoltaic panels considering partial shadow effect. Energy 54:174–183 2. Ayop R, Tan CW (2017) A comprehensive review on photovoltaic emulator. Renew Sustain Energy Rev 80:430–452 3. Ram JP, Manghani H, Pillai DS, Babu TS, Miyatake M, Rajasekar N (2018) Analysis on solar PV emulators: a review. Renew Sustain Energy Rev 81:149–160 4. Fiche technique de 135W/ 125W NS-F135G5/NS-F125G5 Amorphous Silicon/Microcrystalline Silicon SHARP MIDDLE EAST FZE (SMEF) 5. Rekioua D, Matagne E (2012) Optimization of photovoltaic power systems, modelization, simulation and control. Springer, Heidelberg. ISBN 978-1-4471-2348-4 6. Idadoub H, Kourchi M, Ajaamoum M, Yousfi D, Rachdy A (2019) Comparison and experimental validation of three photovoltaic models of four technology types. Int J Tech Phys Probl Eng 11(41):1–10 7. Shabaan S, El-Sebah MIA, Bekhit P (2018) Maximum power point tracking for photovoltaic solar pump based on ANFIS tuning system. J Electr Syst Inf Technol 5:11–22 8. Kharb RK, Shimi SL, Chatterji S, Ansari MF (2014) Modeling of solar PV module and maximum power point tracking using ANFIS. Renew Sustain Energy Rev 33:602–612

Simulation of Natural Ventilation on Building with Solar Chimney Under Climatic Conditions of Errachidia Morocco Zone Herouane Aboubakr, Thami Ait Taleb, and Mourad Taha Janan

Abstract In this work we intend to present a numerical simulation by the ANSYS FLUENT software of a solar chimney used for natural ventilation, and therefore the cooling of a cubic room, under the actual climatic conditions of Errachidia Morocco zone. The study focuses on the variation of the diameter of the chimney with a fixed length, compared to a square window. The air velocity and turbulence of kinetic energy in different zones of the room were simulated for the three considered cases of the solar chimney. The results of the study clearly show the important role of the solar chimney in favoring natural ventilation in building, as well as the optimum width of the solar chimney recommended in the climatic zone of Errachidia Morocco. Keywords Natural ventilation · Solar chimney · Energy efficiency

Nomenclature Ui X, Y, Z ν λ Cp ρ k g T

Velocity magnitude (m/s) Cartesian coordinates (m) Kinematic viscosity (m2 /s) Thermal conductivity (W · m−1 · K−1 ) Heat capacity (J · kg−1 · K−1 ) Air density (kg/m3 ) Pressure loss coefficient Gravitational acceleration (m/s2 ) Temperature (°C)

H. Aboubakr (B) · T. A. Taleb ERME, Department of Physics-Chemistry, Poly Disciplinary Faculty of Ouarzazate, Ibn Zohr University, Agadir, Morocco M. T. Janan Advanced Technical Teachers Training School, Mohammed V University, Rabat, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_109

1191

1192

P Pm β K δ Z

H. Aboubakr et al.

Pressure (Pa) Pressure in reference state of the fluid (ρ0 , T0 ) (Pa) Coefficient of thermal expansion (K− 1 ) Thermal diffusivity of the fluid (m2 · s−1 ) Kronecker symbol Height (m)

1 Introduction The building sector is the largest energy consumer in the world with a 34% share, ahead of industry (27%) and transports (28%). It is also responsible for about a third of greenhouse gas emissions. The electricity consumed in the building sector represent for 53% of the total electricity consumption in the world [1]. In Morocco the building sector has a share of about 36% of the country’s total energy consumption, with 29% reserved for the residential sector and the rest for the tertiary sector, with an increase of 41% in 8 years [2]. To reduce energy consumption and CO2 emissions in the building sector, several countries are adopting increasingly strict thermal regulations. In France, for example, by 2020, all new buildings must be positive energy [3]. The Thermal Regulation of Buildings in Morocco (RTBM) aims essentially to improve the following thermal performances [2]: • • • •

Reduce the heating and air conditioning needs of buildings; Improve the comfort of non-air-conditioned buildings; Reduce the power of heating and air conditioning equipment to be installed; Encouraging architects, engineers and supervisors to use efficient thermal design approaches to the building envelope; • Provide builders, public decision-makers and funders with a tool to improve the productivity of their investments; • Assist in the realization of energy diagnoses of existing buildings. To improve thermal comfort in buildings and reduce environmental impact, it’s necessary to use passive solutions. So integrating natural ventilation into the building can be an efficient way to reverse the national energy trend in the building. According to ADEREE, the zoning map of Morocco includes six climatic zones that are represented by the following cities: Zone 1: Agadir; Zone 2: Tangier; Zone 3: Fez; Zone 4: Ifrane; Zone 5: Marrakech; Zone 6: Errachidia.

Simulation of Natural Ventilation …

1193

This work presents a simulation study of the effect of the natural ventilation of a room using a solar chimney with different widths under the climatic conditions of the climatic zone of Errachidia Morocco. The overall goal is to simulate the air velocity and turbulence of kinetic energy in each room position relative to each width of the solar chimney, in order to determine the optimum width of the solar chimney to be used in this climatic zone.

2 Review on Natural Ventilation with Solar Chimney In a study presented by Sajjad et al. [4] the authors have simulated by ANSYS software the effect of the solar chimney in the natural ventilation of buildings. They examined the effect of the inclination of the chimney with different angles (30°, 45°, 60°, 90°) and different widths (0.2 m, 0.4 m, 0.6 m, 0.8 m) and the height of the chimney. The results of this study show that the optimum chimney width to maximize natural ventilation should be 0.4 m and the length should be greater than 2 m or equal to half of the length of the room, this for different inclinations except for 90°, Thus the width of the window must be more than 1 m. By their study on the thermo-aeraulic phenomena in a solar chimney, Saifi et al. [5] showed that the temperature gradient between the absorber and the glass varies according to the incident solar flux. The design adopted has resulted in relatively high air flows at the outlet, which are favorable for use in natural ventilation. The variation in the thickness of the air gap (chimney width) plays a very important role and significantly increases the volume flow rate of the air. For optimal thermal draft, the angle of inclination must be 45°. Kumar et al. [6] carried out a comparative study between a vertical and horizontal solar chimney, their results show that vertical chimney enhanced ventilation rate much better in comparison to Horizontal chimney as much as 275% enhancement, and vertical Solar Chimney enhanced air flow stream velocity up to 22 times. Sudheer et al. [7] studied the dynamic and thermal behaviour inside a solar chimney for three different chimney widths (0.1 m, 0.2 m, 0.3 m). The study showed that for a width of 0.1 m, the air velocity and temperature inside the chimney is higher than the other two cases. Thus in case 1 (0.3 m), some amount of stagnant air is observed at the air inlet near the absorber. By decreasing the air gap from 0.3 m to 0.1 m nearly 44.18% of outlet velocity increase has been observed. So they concluded that when the chimney width increases there is a decrease in air velocity. In their study on passive cooling by a solar chimney in southern Algeria, Belfuguais et al. [8] determined that the solar ray is the most important parameter influencing solar chimney performance. Thus an optimal design between the chimney width and the absorber must be taken in consideration to increase the air flow rate in the room. Charvat et al. [9] simultaneously installed two solar chimneys: one with an absorber to store heat, the other without absorber, to compare their performances. The study showed that the air velocity inside the solar chimney containing the absorber

1194

H. Aboubakr et al.

is 25% higher than that of the chimney without absorber this is because the absorber gives up its heat to overheat the air in order to escape more quickly to the outside. Kasim et al. [10] investigated the effect of different opening positions on natural ventilation. The study is based on nine different positions of two openings located on two opposite walls, the result of this study shows that the air flow rate is maximum when both openings are located at the top of the air flow level. Mekkawi et al. [11] simulated natural ventilation through a solar chimney in Egypt. Their study showed that the installation of the solar chimney of size of (0.4 × 1.5 m) can decrease the temperature from 29.45 °C to 28.64 °C in the room and increase the air velocity with 50%. Long Shi [12] carried out a study of the impacts of wind on solar chimney performance in a building, his study shows that a higher wind velocity does not represent a better solar chimney performance, and the effects from the window area keep decreasing under a higher wind velocity. Based on the results of all these studies, it can be concluded that there are several parameters that can affect the air velocity at the outlet of the solar chimney, such as the intensity of the solar radiation, the width of the chimney, the type of glass used in the chimney, the type of absorber used, the entrance surface of the chimney, the high of the chimney and the inclination of the solar chimney.

3 Methodology 3.1 Principle The principle of operation of the solar chimney, as schematized in Fig. 1, is to increase the air temperature in the chimney so that it escapes to the outward. Under the effect of the density difference, the hot air rises upwards unlike the cold air, so to create this

Fig. 1 Diagram of the solar chimney in a room

Simulation of Natural Ventilation …

1195

density difference we use a chimney with glass faces. The glass allows transmitting the solar rays to the air that passes from the chimney, the air heats up and rejects to the outside, the air of the room replaces the air of the chimney and the cycle continues. With this method the room will refresh in a natural and continuous way and without any energy consumption or noise, as opposed to mechanical cooling systems.

3.2 Physical Model The simulations were performed using ANSYS FLUENT commercial software. The first step on Ansys fluent is the creation of the geometry, and then generates the mesh and finally the configuration for which we choose the model, the materials, the boundary conditions, and the type of solution, then start of calculation. The physical model considered is shown in Fig. 2. It is a room with sides equal to 4 m, with a solar chimney of 1 m of high and variable width (0.3 m, 0.4 m, and 0.5 m). The mesh model used is shown in Fig. 3. For the simplification of the problem, the following assumptions are taken a count: • • • • • •

The fluid is Newtonian and incompressible. The flow is unsteady. The inlet pressure equal to the atmospheric pressure The outside temperature is constant at 313 k. The air flow is turbulent. Normal direct solar irradiation is 1423 W/m2

Fig. 2 Physical model of geometry

1196

H. Aboubakr et al.

Fig. 3 Mesh of the model

3.3 Mathematical Model • Conservation of mass (Continuity): ∂ ui =0 ∂ xi

(1)

Where ui are velocity components in x, y, z directions • Momentum equation (Navier – Stokes): Considering the linear variation of the density of the Boussinesq approximation, we obtain the following momentum equation:      ∂ ui u j ∂u j ∂ ui ∂ ui 1 ∂ pm ∂ v + gβ(T − T 0 )δ i z (2) + =− + + ∂t ∂x j ρ0 ∂ x i ∂x j ∂x j ∂ xi With: • Pm = p + ρ0 gz: the pressure corresponding to the reference state of the fluid (ρ0 , T0 ), ν the kinematic viscosity and g the gravitational field. β: the coefficient of thermal expansion at constant pressure. It describes the change of volume at constant pressure as a function of temperature. Energy equation: This equation is deduced from the first principle of thermodynamics by neglecting the effect of pressure in front of temperature variation. It is then expressed by:

Simulation of Natural Ventilation …

     ∂T ∂(T ) ∂ u j T ∂ k = + ∂t ∂x j ∂x j ∂x j

1197

(3)

Where: k = ρCλ is the thermal diffusivity of the fluid (m2 ·s−1 ), Cp is the heat capacity p

(J.kg−1 ·K−1 ) and λ is the thermal conductivity (W·m−1 ·K−1 ) [13]. In this study, we used a tetrahedron-type mesh with a high number of mesh nodes, access to 98,344 nodes, as it is shown in Fig. 2. After having carried out several attempts and mesh tests in order to choose the optimal mesh which should give a good precision on the speed of the air and on the flow of air, we opted for an optimal choice which is composed of 556,478 elements.

4 Results and Discussion Air velocities and turbulence of kinetic energy in different plane of the room for the 0.3 m width of the solar chimney (Figs. 4, 5, 6 and 7). • Air velocities and turbulence of kinetic energy in different plane of the room for the 0.4 m width of the solar chimney. • Air velocities and turbulence of kinetic energy in different plane of the room for the 0.5 m width of the solar chimney • The results of the study show that the air velocity in the room is higher in the chimney of 0.5 m of width (Fig. 14), 2,74 m/s in the middle of the plane taken at 0.2 m (Fig. 13), and this velocity decreases as we move up and down the level

Fig. 4 Air velocity in the 0.15 m plane

1198

H. Aboubakr et al.

Fig. 5 Air velocity in the 2 m plane

Fig. 6 Air velocity in the room

of the window plane. The lowest velocity is for the chimney of 0.4 m in width, (Fig. 10), 2.12 m/s in the middle of the plane taken at 0.2 m, (Fig. 9). For the distribution of the air velocity in different zones of the room, it is noted that it is more important in the case of the chimney of 0.5 m of width (Fig. 14). Thus the ventilation is present in all zones of the room for the three cases studied (Figs. 6, 10 and 14), this clearly shows the importance of the solar chimney in the natural ventilation. The results also show that the air velocity in the room is not uniform, it is higher in the middle of the room for the two chimneys with a width of 0.4 m (Fig. 9) and 0.5 m (Fig. 14), while for the chimney of 0.3 m of width the air velocity is higher at the solar chimney (Fig. 4), this is because the air heats up

Simulation of Natural Ventilation …

1199

Fig. 7 Turbulence of kinetic energy in the 2 m plane

Fig. 8 Air velocity in the 0.2 m plane

faster and flows out into this chimney. For the turbulence of the kinetic energy it is higher in the plane taken at 2 m according to the variations of the wind speed in the room for the three chimneys, (Figs. 7, 11 and 15); and it decreases by going up and down of the window plane. In all three cases, the absence of a reverse flow of air in the solar chimney was reported; this refers to the phenomenon of the difference in density between inside and outside air (Figs. 4, 8 and 12).

1200

Fig. 9 Air velocity in the 2 m plane

Fig. 10 Air velocity in the room

H. Aboubakr et al.

Simulation of Natural Ventilation …

Fig. 11 Turbulence of kinetic energy in the 2 m plane

Fig. 12 Air velocity in the 0.25 plane

1201

1202

Fig. 13 Air velocity in the 2 m plane

Fig. 14 Air velocity in the room

H. Aboubakr et al.

Simulation of Natural Ventilation …

1203

Fig. 15 Turbulence of kinetic energy in the 2 m plane

5 Conclusion This work presents a numerical modeling and simulation of natural ventilation by a solar chimney. The three-dimensional simulation of a room with a solar chimney was carried out using the Ansys Fluent commercial software. The results of the study show the importance of the solar chimney in natural ventilation by increasing the air velocity in the room. For the three types of the solar chimney simulated, significant air velocities are observed in the different room positions and in the chimney. Thus there is a large distribution of air through in all zones of the room, which allows for continuous passive cooling. The air flow is normal, as it passes through the opening (inlet) and out through the chimney (outlet), no reverse air flow through the chimney. For the climatic conditions of Errachidia Morocco zone, according to this study, the optimum chimney to be installed in the buildings is that of 0.5 m of width.

References 1. International Energy Agency (2013) Key world energy statistics 2. ADEREE (2011) Les éléments techniques du projet de la réglementation thermique du bâtiment 3. Faggianelli GA (2014) Rafraichissement par la ventilation naturelle traversante des bâtiments en climat méditerranéen. Thèse de doctorat en Génie des procédés. Université PascalPaoli 4. Sajjad A, Badshah S, Chohan GY (2014) Modeling and simulation of natural ventilation of building using solar chimney. World Appl Sci J 32(5):741–746 5. Saifi N, Settou N, Dokkar B, Negrou B, Chennouf N (2012) Experimental study and simulation of air flow in solar chimneys. Energy Procedia 18:1289–1298

1204

H. Aboubakr et al.

6. Kumar J, Raj A, Sharma HM (2017) Enhancement of natural ventilation using SolarChimney; a numerical investigation. Int J Adv Eng Res Sci (IJAERS) 4(3):237105 7. Sudheer V, Sridhar B (2017) Computational fluid dynamics analysis of a building ventilation solar chimney. Int J Adv Eng Res Sci (IJAERS) 2(2) 8. Belfuguais B, Larbi S (2011) Passive ventilation system analysis using solar chimney in South of Algeria. Int J Mech Aerosp Ind Mech Manufact Eng 5(10) 9. Charvat P, Jicha M, Stetina J (2004) Solar chimneys for ventilation and passive cooling. World Renewable Energy Congress VIII, Denver, Colorado, USA 10. Kasim NFM, Zaki SA, Ali MSM, Ikegaya N, Razak AA (2016) Computational study on the influence of different opening position on wind- induced natural ventilation in urban building of cubical array. Procedia Eng 169:256–263 11. Mekkawi G, Elgendy R (2016) Solar chimney for enhanced natural ventilation based on CFDsimulation for a housing prototype in Alexandria, Egypt. Int J Advances Mech Civil Eng 3(5). ISSN 2394-2827 12. Long S (2019) Impacts of wind on solar chimney performance in a building. Energy 185:55–67 13. Brangeon B (2012) Contribution à l’étude numérique de la ventilation naturelle dans des cavités ouvertes par la simulation des grandes échelles, Application au rafraichissement passif des bâtiments. PhD thesis in Mechanics and Building Sciences, University of Reunion

Investigating the Effect of the Air Gap of a Solar Air Heater Intended for an Indirect Drying System Mourad Salhi, Dounia Chaatouf, Benyounes Raillani, Samir Amraqui, and Ahmed Mezrhab

Abstract As the world’s population increases, the demand for vegetarian products increases, therefore, in order to preserve the food products throughout the year, it was necessary to find a way that can preserve almost all the nutritional and vitamin value. In this term, we find that the most efficient method is indirect solar drying. This latter is used for drying, because of its many advantages, such as being covered from direct sunlight, which protects the enzymes in the product, while preserving the original color and reduce the drying time. However, with all these advantages, this method had problems with air uniformity along the trays. Therefore, in this work, we will study the dimensional parameters of the system in order to improve the air and temperature distribution inside the drying chamber, and to be more precise the study is mainly based on the optimization of the air inlet which is the air gap of the solar air heater for a newly designed drying chamber. The ANSYS software is used to simulate the system, which usually consists of a drying chamber and a solar collector under the meteorological data of Oujda city. As a result, we have found that the air distribution in the drying chamber depends directly on the size of the air gap of the solar air heater. Keywords Inlet · Solar air heater · Solar dryer

1 Introduction Solar energy is one of the most important clean and inexhaustible energies on earth, as it represents an energy that can be exploited without limit as long as the sun is present. This energy can be converted into different forms that can be used in daily life such as electricity, heating, and cooling. Since there is an enormous increase in the world’s population it becomes necessary to satisfy the need for energy and food, therefore solar energy has been exploited in other systems related to the conservation and storage of the alimentary product, such as solar drying, Which is an ancient and M. Salhi · D. Chaatouf · B. Raillani · S. Amraqui (B) · A. Mezrhab Laboratory of Mechanics and Energetics, Faculty of Sciences, Mohammed First University, 60000 Oujda, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_110

1205

1206

M. Salhi et al.

widely used method of food conservation, as a preventive against the growth of microorganisms and bacteria and fermentation that lead to food spoilage [1]. Currently, different drying methods are available, such as conventional drying, which is a traditional method that has been used for a very long time to preserve the alimentary product, where the products are dried by direct exposure to both sun and wind. This drying method is not that effective because of the time required to dry the product and because of the disadvantage of direct exposure to weather conditions, especially aerosol particles, rain, and insects. This is why other drying systems were invented, intending to improve the quality of the dried product through a clean and safe method. In this term, there are three main and best-known ways to rely on the energy of the sun as a source for the drying process, direct, indirect, and mixed mode. The indirect solar dryer is one of the most efficient ways to dry the product and improves its quality by avoiding some of the disadvantages of direct and traditional solar dryers. This system is mainly composed of a solar collector where the air is heated by direct contact with the absorber, as well as by the greenhouse effect created by the glass. Afterward, the air rises into the drying chamber to dry the products that are distributed on the trays before exiting from the outlet. In recent years, several works and studies have been carried out to improve the performance of the indirect solar dryer. Among these researches is the use of a system composed of two double-pass solar air heaters [2] the result that has been found has proved that the efficiency of this system in terms of color, flavor and drying time of the product, compared to an open solar drying system. In another study, we have found that they have completely changed the previous concept of air heating, where they use a heat exchanger inside the drying chamber [3], and as a result, they showed that it can improve and increase the temperature of the drying chamber. Research has not only focused on the solar air heater. We also found researchers who put their expertise into developing geometry to distribute the air evenly throughout the chamber and to improve the flow of air in and out, by studying the flow path. In this regard, [4] have studied the chimney effect by inducing a thermosiphon to improve the efficiency of the drying, via accelerating the process of evacuating the humid air from the drying chamber, the results show that the humidity level decreases from 86% to 8.12% in 9 h and 20 min. All this research studies are focused on general studies and does not take into consideration the optimization of certain internal parameters to improve the efficiency of the drying system. For this purpose and to improve the air and temperature distribution inside the drying chamber, we will study the dimensional parameters of the air gap of the solar air heater which is the inlet of the drying chamber for a newly designed drying chamber using ANSYS FLUENT under the meteorological conditions of Oujda city (eastern of Morocco).

Investigating the Effect of the Air Gap of a Solar Air Heater …

1207

Air outlet

Solar radiation

The door of the chamber

Air inlet Fig. 1 Schematic diagram of the solar dryer studied

2 Methodology 2.1 Geometry and Basic Governing Equations The indirect solar dryer studied shown in Fig. 1 composed of a 1.5 m2 solar air heater that composed of an aluminum absorber painted in black and a simple glazing, the chamber is actually composed of two parts the first one contains three trays where the products are distributed and the second one is equipped with solar air heater to make the air pass horizontally in the trays, so the air distribution will be more uniform [5]. The governing equations are based on the Navier-Stokes equations, while the turbulent pattern inside the solar dryer is modeled using the K-epsilon model, which characterized with two equations, the turbulence kinetic energy k (1) and the dissipation rate of turbulent kinetic ε (2). ρ( ρ(

∂(ku) ∂(kv) μt ∂ 2 k ∂ 2k ∂k + + ) = (μ + )( 2 + 2 ) + G k − ρε ∂t ∂x ∂y σk ∂ x ∂y

μt ∂ 2 ε ∂ε ∂(εu) ∂(εv) ∂ 2ε ε + + ) = (μ + )( 2 + 2 ) + (c1 G k − c2 ρε) ∂t ∂x ∂y σε ∂ x ∂y k

(1)

(2)

2.2 Climate Data and Boundary Conditions The simulation was made under the metrological conditions of Oujda city, the data used in the simulation where collected from the metrological station installed in university Mohammed first in Oujda presented in figure below. The climate data of the chosen day which are the ambient temperature and the solar radiation where transformed into equations as presented in Eqs. (3) and (4) before inserting it in ANSYS FLUENT as a UDF subroutine (Fig. 2).

1208

M. Salhi et al.

Fig. 2 Meteorological station used to collect the data

Tamb (t) = 25 + 6 cos( G sun (t) = 965 sin(

π (t − 14)) 12

t −6 π ), 14

6 < t < 20

(3) (4)

The studied solar dryer is working under naturel convection and for that the bossinessq approximation is used, and the inlet as well the outlet where pressure type, regarding the walls we have convective heat losses in all walls including the solar air heater while in the glass cover we have a mixed mode of convective and radiation losses. The trays are modeled as a porous medium with 50% porosity, in order to model the airflow resistance created by the trays.

2.3 Validation Result In order to make sure that the simulations made by ANSYS FLUENT is right, two validations with experimental and numerical results where made the first article [6] studied a cabinet dryer equipped with a chimney and a solar air heater, the outlet temperature of this latter was investigated for different lengths, the comparison between our results and the authors is presented in Fig. 3.a, the other validation concerns a wooden chamber equipped with a solar chimney [7] the air velocity at the outlet is presented in Fig. 3.b in those two validations our results showed a better values than the numerical ones found by the same authors.

Investigating the Effect of the Air Gap of a Solar Air Heater …

1209

Fig. 3 Comparison between our results and the reference [] (a) and [] (b)

3 Results Before going ahead to the simulations, a mesh independency test is necessary to make sure that the results are independent with the mesh density, and for that, four different grids, 5532, 12130, 16606, and 23820 triangular cells were studied, and the results show that a mesh grid with 12130 cells is quite enough to study the geometry. In this part, we studied the effect the inlet size of the drying chamber, which is the air gap of the solar collector on the air behavior inside the drying chamber. For this purpose, we have changed the size of the inlet into different values and analyzed its effect on the temperature distribution and the air velocity inside the drying chamber. The inlet size values studied were ranged between 1 cm to 14 cm, which is presented in Fig. 4 in term of the mean air velocity and temperature at the inlet of the chamber, specifically, at the outlet of the solar collector. The results show that in terms of temperature, the optimal size value of the inlet, where we get the highest temperature which is 373 k is 3 cm. From this value, the temperature started to decrease with the increase of the inlet size. In terms of air velocity, the graph shows that velocity value increases with the increase in the inlet size until reaching the highest value which is 0.1 m/s at 4 cm and then it started to decrease with the increase of inlet size. Among all the studied inlet sizes, the three and four centimeters sizes showed a better performance for the air temperature and velocity respectively. But, finding the optimal air gap size to get a high temperature and high velocity at the inlet of our drying system is not enough in this case, because we are more interested in the distribution of air flow in the drying trays where the products are placed, That’s why we have plotted in Fig. 5, the curves that shows the average air temperature in the drying chamber and the mass flow rate at the inlet of the chamber for different air gap of the solar air heater. And they both increase with the increase in the air gap, the temperature increase in the chamber due to the increase in the mass flow rate which increased the heat gained by the flowing air through the solar air heater but the temperature remains almost stable beyond 7 cm.

1210

M. Salhi et al.

Fig. 4 The mean air velocity and temperature at the inlet of the chamber for different inlet size at 1:00 pm

Fig. 5. a The average air temperature in the drying chamber, and b the mass flow rate at the inlet of the chamber in function of the air gap size at 1:00 pm.

To see more clearly the air velocity and temperature along the three trays, we have presented in Fig. 6 the average air velocity and temperature in the trays for different air gap sizes, and the results show that the air flow distribution along the trays is better for an inlet size equal to 7 cm. although for this size, the velocity and temperature at the inlet is low compared to 4 cm. This is due to the amount of the airflow entering the drying chamber which becomes high for the inlet size 7 cm. From Figs. 4, 5 and 6, it can be concluded that the increase of the inlet size provokes an increase in the air mass flowrate as a results the efficiency of the solar drying system.

Investigating the Effect of the Air Gap of a Solar Air Heater …

1211

Fig. 6. The mean air velocity (a) and temperature (b) in the three trays for an inlet size from 3 to 7 cm at 1:00 pm.

4 Conclusion Food preservation is a necessity nowadays, which is why it has become a priority to develop ecological systems that offer a very good drying quality. For this reason, an indirect solar dryer represents the best choice; in this study we investigate the effect of the air gap of solar air heater which is the inlet of the chamber as well. In order to improve the distribution of the airflow along the trays by making it more uniform in terms of temperature and velocity. The results obtained showed that the size of the air gap of solar air heater that gives a high air velocity and temperature at the outlet is 4 and 3 cm respectively. In terms of the drying system, the mass flow rate and the temperature inside the drying chamber increase with the increase in the air gap but it remains almost stable beyond 7 cm.

References 1. Bourdoux S, Li D, Rajkovic A, Devlieghere F, Uyttendaele M (2016) Performance of drying technologies to ensure microbial safety of dried fruits and vegetables. Compr Rev Food Sci Food Saf 15(6):1056–1066 2. Sharma A, Sharma N (2012) Construction and performance analysis of an indirect solar dryer integrated with solar air heater. Procedia Eng 38:3260–3269 3. Reddy RM, Reddy ES, Maheswari CU, Reddy KK (June 2018) CFD and experimental analysis of solar crop dryer with waste heat recovery system of exhaust gas from diesel engine. In: IOP conference series: earth and environmental science, vol 164, no 1. IOP Publishing, p 012010 4. Musembi MN, Kiptoo KS, Yuichi N (2016) Design and analysis of solar dryer for mid-latitude region. Energy Procedia 100(1):98–110 5. Chaatouf D, Salhi M, Raillani B, Amraqui S, Mezrhab A (2021) Applying CFD for the optimization of the drying chamber of an indirect solar dryer. In: Proceedings of the 6th international conference on wireless technologies, embedded and intelligent systems (wits-2020)

1212

M. Salhi et al.

6. Ghaffari A, Mehdi R (2015) Modeling and improving the performance of cabinet solar dryer using computational fluid dynamics. Int J Food Eng 11(2):157–172 7. Jyotirmay M, Sanjay MA (2006) Summer performance of inclined roof solar chimney for natural ventilation. Energy Build 38:1156–1163

Experimental Investigation of Efficiency and Dynamic Losses for a Constructed Solar Boost Converter Weam El Merrassi, Abdelouahed Abounada, and Mohamed Ramzi

Abstract Power converters are used in numerous industrial applications; especially for renewable energy, they are vital. However, the use of a conventional power converter involves high switching losses, high Electro-Magnetic Interference (EMI), and increase junction temperature of the switches. Therefore, to overwhelm the previously mentioned drawbacks, soft techniques are required to enhance the performance of the power converters. Among several varieties of snubber cells, passive snubbers are epitome due to their easiness and vigorousness. In this paper, a soft switching solar DC-DC static Boost converter is presented using RID and RCD passive snubbers. The proposed snubber cell intends not only to provide zero soft-switching and stabilize the temperature of the main switch but also to enhance the power conversion efficiency of the converter. The effectiveness of the model is evidenced via experimental results using a 1.6 kW prototype to validate the proposal. Keywords Boost converter · Renewable energy sources · Passive Snubber · Zero current switching (ZCS) · Zero voltage switching (ZVS)

1 Introduction Up to now, power converters have been ushering in a new kind of industrial revolution. They have been extensively indispensable in manifold industrial applications, notably for the progress of renewable energy resources [1, 2]. Therefore, the DC-DC converter has been spotlighted in multiples studies for diverse intentions. It provides good operability, simple and affordable design to implement. However, the widespread shortcoming of these converters is the switching losses that arise in the hard switching operating mode [3]. Above and beyond, higher switching frequency gets the higher switching losses along with electromagnetic interface. Yet, renewable energy applications are one of the principal W. El Merrassi (B) · A. Abounada · M. Ramzi Laboratory of Automatic, Energy Conversion and Microelectronics (LACEM), Department of Electrical Engineering, Faculty of Science and Technology, University of Sultan Moulay Slimane, Beni Mellal, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_111

1213

1214

W. El Merrassi et al.

applications that rely on the converter, however these class of resources known by their significantly lower yield. Accordingly, this type of applications involve specific design criteria to be adopted such as diminished EMI, minimized power losses, and stabilized junction temperature. In order to break this bottleneck, and enhance the efficiency of the converter, soft switching strategies are incited as an alternative to hard-switching operating mode. Several snubbers have indorsed for different converter to eschew the high losses, and limited the rate change in voltage/current and overvoltage during the turn on/off. The snubbers presented in the literature can be classified into active and passive snubbers. As presented in [4, 5] the active snubber are used to reduce the turn-on switching losses. The auxiliary circuit proposed for the converters use an extra switch that includes an intermediate control circuit. Some of the problems that persist for the active snubber is the synchronicity issue of the control for the different switches involved in the converter, which rise the complexity of the control procedure, the extend size of the circuit, and the cost [6]. Therefore, the passive snubber have been a worthwhile solution that offer a low cost, easiness of control and implementation. Yet, the passive snubber presented in the literature has unveiled some downsides highlighted under the inability to promise a perfect turn-off zero voltage switching, regardless of the switching turn-on at zero current [7]. Accordingly in [2, 6, 8], the both models was not able to eradicate the switching losses. In addition, in [9–11] the increase number of passif components in snubber cells engender extra conduction losses, intricated applications, and expensive. Furthermore, certain passive snubbers are not qualified for the family of fundamental DC-DC converter, they are notably provided for their own converter structure [12]. Consequently, the original idea for proposing new snubbers and highperformant solar converters primarily originates from fixing the shortcomings and improving the efficiency of previous approaches snubbers and converters. Consequently, the snubbers circuits and soft-switching converter ought to achieve an equilibrium amid the reliability and easiness of integration. In this treatise, the dynamical losses for a conventional solar converter is explored, to promote the system’s performance by lessening the total losses of the solar DC-DC boost converter. A novel approach to design RID and RCD snubber cells had been proposed. The snubber assured a zero voltage-switching in the switch-on periods side to a zero currentswitching in switch-off periods, which affords a perfect soft-switching for the main interruptor in the both transition periods. The proposed snubber cells entail merely few passive components; it has a simple structure with a minor size, easiness of drive and low cost. The structure of this paper presented as follow. In the first hand, the fundamental operational principles of the suggested snubber are proved in depth, furthermore the experimental test is provided to verify the results’s efficiency above to a comparison of the effictiveness of the system face to other proposed models in literature.

Experimental Investigation of Efficiency and Dynamic Losses ...

1215

2 Power Losses Analysis in Boost Converter 2.1 Conduction Losses The power losses in nearly all converters are leading out of two major bases: Conduction losses and dynamic losses. The conduction losses in a boost converter take place when the switch is entirely in conduction. The resistance elements persisted in the different component of the boost converter such as diode, inductors, capacitors, and switch are the origin of these losses. The conduction losses is outlined as [13]: 2 2 2 2 2 + Resl IL.eff + Ron. ITr.eff + Rd Id.e Pcond = Rgen IL.eff f f + Vd Id.av + R esr.c IC.e f f (1)

Where, Rgen is the generator resistor, Rgsl is the induction resistor, Ron is the switch resistor, Rd is the diode resistor, Resrc is the capacitor resitor, IL.eff is the effective current that cross the generator and inductance, I.Tr.eff is the effective current that cross the main switch, I.d.eff is the effective current that cross the diode, I.d.av is the average current of the diode, I.c.eff is the effective current that cross the output capacitor of the converter.

2.2 Dynamic Losses Dynamic losses in the boost converter include three sources of losses represented as: choke core, Switch device, and diode. However, the losses in Switch depends majorlyon losses in gate, output capacitance and switch losses during transition period. The mathematical expression of these losses are introduced below: Psg = Csin Vg2 f sw Psc =

1 Cs V 2 f sw 2 out ds

(2) (3)

Psw = A.(tr .Imax + t f .Imin ).Vout f sw

(4)

Pdiode = Vd .(trr .Imin + Q r d ). f sw

(5)

i Pcor = a.B j Vcor f sw

(6)

Where, Psg , Psc are the losses in the switch, Psw is losses in switching periods, Pdiode is the losses produced by the diode, Pcor is the losses produced by the choke core.

1216

W. El Merrassi et al.

Fig. 1 The voltage and current variation during hard switching transients

The total dynamic losses are equavilent to the summation of individual switching losses of converter elements and they can be described as follows: Pdynamic = Pcor + Pdiode + Psw + Psc + Psg

(7)

In spite of everything, the power losses in the core choke, diode, gate control and the output capacitance can be marginal against the losses occur the switching periods. Which link directly the dynamic losses in the converter to the switching losses in the switch. Therefore,in the boost converter, the Semiconductors represent the most element presentinglosses in the circuit. Theswitchon/off periodsoftheswitch is in the order of a few tens of nanoseconds to multifold microseconds. At these switching transitions moment, very large power losses can occur in the semiconductor device. Despite, the switching time of semiconductor elements is very brief, the average power loss can be substantive. Dynamic losses are less depending on power load but instantly depending on switching frequency as shown in the Eq. (2)–(4). As Fig. 1 unveils, the dynamic losses in the switch are predominant compared to the conduction losses. For this reason, reducing the switch losses will gain less power loss by the circuit.

3 Proposed Converter System Configuration The suggested passive snubber is served to conserve the main switch’s high performance of the converter as shown in Fig. 2. The snubber contains two cells, the first one RID is composed with a diode and resistor with a parallel inductor implemented in series to the switch, while the second RCD cell comprises a parallel diode and resistor in series with a capacitor [13]. The capacitor C and the inductor L delay the time at which the switch reaches the final value of voltage and current. In return, the value of the capacitor and the inductor usually optimized in order to minimize the power dissipated in the switch. However, it may not be the best value of the snubber elements from the reliability

Experimental Investigation of Efficiency and Dynamic Losses ...

1217

Fig. 2 Solar converter with the proposed snubber

point of view. The stress in switch reduced but the losses persist. Therefore, in the proposed snubber circuit, the elements are selected based on maximum reliability considerations for the set of the switch and its snubber. To ensure as results a softswitching and a minimum power loss for the switch. As shown in Fig. 2b, in this circuit when the interruptor proceeds turning-off, load current charges the capacitor C up to source voltage V. Consequently, it delays the time at which the capacitor (alsoswitch) voltage waveform reaches the final value V, the snubber capacitor allows the voltage to rise and reach full value V after the switch current falls to zero. Besides, the same scenario achieved by the snubber inductor that allows the current to rise and reach full value I after the switch voltage falls to zero.

4 Hardware Implementation Results The performance characteristics of both the conventional and the proposed boost converter are verified by the prototype of 1.6 kw and 20 Khz switching operation using the same components and the same conditions [13]. The prototype itself is summarized, with the aforementioned design considerations. The converter operated with a source simulating PV as a source.

4.1 Galvanic-Isolation Circuit The signal gate is given to the devices through an intermediate interference circuit that provides isolation from the hardware power circuit and the control circuit, as shown in Fig. 3(b). This circuit integrated a 6N137 Optocoupler to isolate, and an IR2112 Driver, which had as role amplifying the output signals, delivered from the control card. From a low voltage to reach the minimum gate threshold voltage of

1218

W. El Merrassi et al.

Fig. 3 Galvanic-isolation circuit a Hardware set-up of the boost converter with the galvanicisolation circuit. b the switch signal in the output of the 6N137 Optocoupler and IR2112 driver

the MOSFET. The Fig. 3(b) Channel 2 Shows the digital output waveform of gate pulse for the boost converter Mosfet switch generated by the DSP, which offers a signal variation of 0–3.3 V, the switching frequency is 20 kHz equals to the imposed frequency 20 kHz and the duty cycle is about 50%. The output signal of Optocoupler is amplified from 3 V to 12 V and the others parameters of switching frequency and duty cycle remain the same as before as it can be seen in channel 1 in the Fig. 3(b) that illustrates the output of IR2112 Driver for boost converter [13].

4.2 Experimental Results In the proposed implementation, we tested our prototype using a low power supply. Prior to experiments, the Fig. 4, 5 and 6 present the experimental results of the active switch’s performance for a solar boost converter operated in both mode hardswitching and soft-switching conditions. As illustrated in Fig. 4(a)–(c) the drainsource voltage in the active switch for the conventional circuit endows a high voltage and current values along with high spike during the device’s turn-off. And, that is linked to the fast changing of the voltage dv/dt. The proposed circuit achieved a soft switching with a zero voltage switch at turn-off. Due to the control of Rs Cs that improved theperformance significantly by reducing the changingrate dv/dt, which raised the switch’s voltage gradually from0 toV0, as shows in Fig. 4(b)–(d). The overlapped area between the voltage and current switch is almost omitted. As results, the main switch in the proposed circuit had zero switching point at the turn off and turn on. Consequently, it exhibits zero power losses during turn-on and turn-off. On this ground, the switching loss of the advanced circuit is reduced to a lower loss compared to the one in the conventional boost converter with hard switching operating mode, owing to the deduction of the dv/dt and di/dt. The lessen losses reach 65.55%

Experimental Investigation of Efficiency and Dynamic Losses ...

1219

Fig. 4 The voltage and current waveforms for the active switch in a, c the hard switching operating mode, b, d the soft switching operating mode

Temperature (°C)

i

i

60

i

50

i

i

27.0 24.0

40 i

i

i

i

21.0

30 i

20 10

18.0

i

i

83

i

166

i

i

245

Time (s)

i

332

(a)

i

415

i

498

15.0 i

100

i

200

i

i

300

Time (s)

i

400

i

500

(b)

Fig. 5 The Switch junction temperatures measured for a the conventional boost and, b the proposed boost

in the switch. From the Fig. 5(a), it can be observed that hard-switching operating mode increases the temperature in the Switch up to 58 °C due to the presence of the spikes, ripples as was seen in Fig. 4(c) and the high-power losses, which impact the switch’s performance and accelerates aging of the device. Comparing to that, the proposed circuit has unveiled a advanced dynamic behavior of the temperature by

1220

W. El Merrassi et al.

reducing it into 44.44% as given in Fig. 5(b). Owing to the fewer power losses and the good waveforms of the voltage and current above the switch. To analyze further the effectiveness of the proposed converter, and examine this issue with the previous proposed models, an electrical characteristics analysis of the principal components is adopted for the different converters. As seen in Table 1, the major drawback of zero voltage switching of boost converter in [9] is the persistent of the ripple and peak in the main switch current. This issue would raises the turn-off capacitance losses, which enlarge the global losses in the system. Further, to the need of an additional control circuit for the snubber circuit that expand the size and the cost of the converter. Alongside in [6, 11] the zero voltage transition converter includes a high number of extra component, which diminished the efficiency, complicated the implementation and increased the cost. In addition, the high voltage rate of power main switch enlarge the on-state resistance Rds(on). As result, in the converter [14] the conduction losses of the main switch get increased permanently. However In [15], the current and voltage stress of the main switch is substantially decreased. On the ground of this, in this converter, the switch-on capacitive losses are atomic. Compared totheseconverters,theproposed model provided a good voltage and current quality waveform, by assuring less of stresson the switch, higher power efficiency, ease of control and implementation and a very low cost. Table 1 Comparativeresultsforthedifferentconverter Converter Converter Converter Converter Converter Converter Proposed [9] [6] [14] [15] [11] [10] converter Number of extra component

6

9

5

5

7

5

5

Type of snubber

Active

Passive

Passive

Passive

Passive

Active

Passive

Voltage stress of the main switch

VH

1.6VH

1.2 VH

VH

VH

1.3 VH

VH

The main switch peak voltage (V)

0

1.5Vds

0.2 Vds

0

0

5.5 Vds

0

The main switch peak current (A)

2 Ids,rms

0

5 Ids,rms

0

1.2 Ids,rms 1.8 Ids,rms 0

Additional control circuit

Yes

No

No

No

No

Yes

No

Efficiency (%)

95%

92.8%

95.6%

94.2%

94%

93.3%

97.3%

Cost

High

High

Medium

Medium

High

Medium

Low

Most complex

Simple

Complex

Complex

Simple

Simplest

Implementation Most complexity complex

Experimental Investigation of Efficiency and Dynamic Losses ...

1221

Efficiency (%)

97

Ref[15] 92

Proposed

87

Ref[14]

82

Ref[11]

77 20

40

60

Output Power (%)

80

100

Ref[9] Conventional

Fig. 6 Efficiency curves of the proposed converter under hard switching and soft switching, compared with the soft converter proposed by [9, 11, 14, 15]

The efficiency curves of the conventional solar hard-switching boost converter and the proposed soft boost converter are illustrated in Fig. 6. In total, the soft switching converter presented an extremely high power efficiency than that of the hard switching converter for the different power range. Overall, the value of the efficiency attained is 97.03%, while the conventional converter reached 90.17%. Therefore, the proposed circuit enhanced, and increased the efficiency up to 7%. On another hand, the figure presented as well the power efficiency for the previous model converter presented in the literature [9, 11]. These models presented different value of the power efficiency. For the [6] model the efficiency gained for full load is 95.3%, while in [9, 11, 15] it ranges among 92% and 94.2%. Compared to these results, the proposed converter still achieves higher efficiency, the improvement reached over 6.83% for light load and more than 4.51% for heavy load. As advantage, the converter assured high efficiency for light loads as for heavy one.

5 Conclusion The power conversion efficiency can be increased by achieving soft-switching strategy in DC-DC static converters. In this paper, a dynamic losses investigation was treated for a solar boost behaviour under hard switching and soft-switching operating mode. The proposed snubber model validates the effectiveness using experimental test. According to these results, the main switch assured a zero voltage switching and a zero current switching, beside the power losses was diminished by 65%, which entitled a stabilization of the temperature junction and assured a reduction up to 44.5%. It also reveals that the proposed circuit has no extra current/voltage stress on the switch. Further, the efficiency attained 97%. As results, it is proven that the Solar DC-DC static boost converter with the proposed passive snubber provides better performance than the conventional boost for the renewable energy applications [16].

1222

W. El Merrassi et al.

References 1. Solatialkaran D, Zare F, Saha TK, Sharma R (2020) A Novel approach in filter design for gridconnected inverters used in renewable energy systems. IEEE Trans Sustain Energy 11:154–164 2. Shan Y, Hu J, Chan KW, Fu Q, Guerrero JM (2019) Model predictive control of bidirectional DC–DC converters and AC/DC interlinking converters—a new control method for PV-windbattery microgrids. IEEE Trans Sustain Energy 10:1823–1833 3. EL Merrassi W, Abounada A, Ramzi M (2019) Design of a PWM sliding mode voltage controller of a DC-DC boost converter in CCM at variable conditions. In: Advances in smart technologies applications and case studies. Springer, Cham, pp 263–270 4. Jabbari M, Mokhtari M (2020) High-Frequency Resonant ZVS boost converter with grounded switches and continuous input current. IEEE Trans Industr Electron 67:1059–1067 5. Mohammadi MR, Peyman H, Yazdani MR, Mirtalaei SMM (2020) A ZVT bidirectional converter with coupled-filter-inductor and elimination of input current notches. IEEE Trans Industr Electron 67:7461–7469 6. Zhang Z, Yang X, Zheng TQ, Zhang J (2020) A Passive soft-switching snubber with energy active recovery circuit for PWM inverters. IEEE Access 8:100031–100043 7. Chen Y-T, Li Z-M, Liang R-H (2018) A Novel Soft-Switching interleaved coupled-inductor boost converter with only single auxiliary circuit. IEEE Trans Power Electron 33:2267–2281 8. Lin H, Hu B, Li F, Chen J, Si L, Zhou X, Li Y, Chen J, Yan M, Dong Y (2018) A fault-tolerant two-permanent magnet synchronous motor drive with field-oriented control scheme. In: 2018 2nd IEEE advanced information management,communicates,electronic and automation control conference (IMCEC), pp 1029–1033 9. Zhang Y, Cheng X-F, Yin C, Cheng S (2018) Analysis and research of a soft-switching bidirectional DC–DC converter without auxiliary switches. IEEE Trans Industr Electron 65:1196–1204 10. Muchina EG, Masike L, Njoroge Gitau M (2019) High boost-ratio bidirectional converter for interfacing low-voltage battery energy storage system to a DC bus. IET Power Electron 12:2372–2381 11. Do H-L (2011) Nonisolated bidirectional zero-voltage-switching DC–DC converter. IEEE Trans Power Electron 26:2563–2569 12. Meng T, Ben H, Li C (2019) An input-series flyback converter with the coupled-inductor-based passive snubber for high-input voltage multiple-output applications. IEEE Trans Ind Electron 66:4344–4355 13. El Merrassi W, Abounada A, Ramzi M (2020) Switching losses analysis of a constructed solar DC-DC static boost converter. Adv Electr Electron Eng 18:132–141 14. Lin J-Y, Lee S-Y, Ting C-Y, Syu F-C (2019) Active-clamp forward converter with losslesssnubber on secondary-side. IEEE Trans Power Electron 34:7650–7661 15. Zhang Y, Cheng X-F, Yin C, Cheng S (2018) A soft-switching bidirectional DC–DC converter for the battery super-capacitor hybrid energy storage system. IEEE Trans Industr Electron 65:7856–7865 16. El Merrassi W, Abounada A, Ramzi M (2021) Advanced speed sensorless control strategy for induction machine based on neuro-MRAS observer. Mater Today: Proc

Improvement of the Non-linear Control Strategy of a Wind Turbine by a High-Gain Observer Azeddine Loulijat, Najib Ababssi, and Mohamed Makhad

Abstract The production of a wind turbine depends mainly on the energy of the wind, the power graph of the turbine, and the response of the generator to changes in the wind. This article suggests a robust sliding mode of second-order nonlinear control technique based on the concept of the super-twisting algorithm for the doublyfed induction generator (DFIG) of a wind turbine that provides optimal power production (efficiency). For this task, an observer with a high-gain was added for Torque estimation delivered from a wind turbine with a sliding mode of second-order non-linear control. The suggested overall method was evaluated using MATLAB/SIMULINK software with a 2 MW three-blade wind turbine. Keywords Wind turbine · DFIG · Non-linear control · Sliding mode of second-order · High-gain observer · Super-twisting algorithm

1 Introduction Much other research on wind turbines has already been achieved. As result wind turbines operate with variable speeds and have pitch control in recent models. We are capable of modifying the speed and angle of pitch of each blade, Which makes it possible for us to increase wind turbine production [1]. However, it must introduce more intelligence into the operation of wind turbines. The main issues of this article are proposed control of the g enerator which can improve the production of the wind turbine, including energy production quality and energy efficiency. But also Mechanical exhaustion reduction, which would create it viable to construction lighter aero-turbines, Thus enhances productivity [1]. Therefore, control must take the system’s behavior as a global system into account. Also, wind disturbances must be taken into account. The studied wind turbine is a three-blade wind turbine with A. Loulijat (B) · N. Ababssi IMII Laboratory FST, Hassan 1 University, Settat, Morocco M. Makhad ERERA Laboratory ENSET, Mohammed V University, Rabat, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_112

1223

1224

A. Loulijat et al.

Fig. 1 Global synoptic of the system

a horizontal axis and pitch regulation operating a doubly-fed induction generator (DFIG). Indeed, Cost reduction is the biggest objective, the power circulating in This design of the converters is lower compared to a cage generator [2, 3] or synchronous generator [2–4]. The proposed control strategy schematically illustrated in Fig. 1, is divided into two main blocks:1) This block represents the high-gain observer and sliding mode of second-order control (SMSOC) defining the torque reference that the generator must offer to optimize performance compared to proportional-integral control (PIC) [5] and sensorless vector control (SVC) [6], which use considerations and approximations to define the torque reference as a result, the performance is low, and 2) the block controls by a sliding mode of second-order (SMSOC) that controls the DFIG through the converters so that the measurements tend to their references promptly.

2 Mathematical Model of the Wind Turbine The turbine modeling is inspired from [1–4, 7]. The disposable power of the wind passing through an Sv surface is defined by: Pwind =

1 ρ Sv v 3 2

(1)

The wind turbine then aims to capture power following: Pa =

1 ρπ R 2 C p (λ)v 3 2

(2)

Improvement of the Non-linear Control Strategy …

1225

The power coefficient is determined by the power ratio of the wind turbine: Cp =

Pcap Pwind

(3)

C p is a function defined by the specific speed: λ=

Rω v

(4)

The power captured by the wind turbine can be expressed by [1–4, 7]: Pcap = Ta ω

(5)

From the above equations, The aerodynamic torque expression can be described by: Ta =

C p (λ) 1 ρπ R 3 Cq (λ)v 2 with Cq (λ) = 2 λ

(6)

The dynamic equation of the turbine is written by [1–4, 7, 8]: J

dω = Ta − f ω − Tem dt

(7)

3 Optimization of the Produced Power The torque of reference generated by the block (1) has to answer two problems: Maximizing wind turbine power and areas operational management [7]. The ratio among the powers obtained from the wind and the available total power presents a maximum known by the Betz limit. This maximum has never really occurred, And the power coefficient of each turbine is determined by its relative speed, representing the ratio of the speed from the tip of the turbine blades to the wind speed. Figure 2 gives an example of a power coefficient with a fixed pitch angle β for a wind turbine changing on the tip speed ratio λ [9, 10]. More efficiently extract the wind power while keep the regime safe,the wind turbine also must be usable following the three zones, linking wind speed, maximum rotor speed acceptable, and the power like to see as shown in Fig. 3. In zone I, the wind turbine is out of service because the wind is not intense to make energy production viable about operating energy [10, 11]. In zone II, The partially loaded wind generator works.Here the main goal is to optimize performance energy. So, the rotation speed varies with wind speed to maintain itself at the point of operation with maximum efficiency of aerodynamic. The aim is to ensure an always optimum

1226

A. Loulijat et al.

Fig. 2 Power coefficient curves

Fig. 3 Typical wind turbine power curves derived by wind speed

power coefficient [10, 11]. Zone III is a full loading activity (Intense wind). The power must limit in order not to deteriorate the system [10, 11]. The maximum efficiency of the turbine is ensured by keeping the maximum power coefficient [10–12]. From Eq. (6), we can write the expression of the aerodynamic torque: Ta = αω2 & α =

C p max 1 ρπ R 5 3 2 λopt

(8)

Where the λopt specific speed helps us to maximize the power extracted [8]. The aim of The approach used in the following for Ta to converge on Topt while Tg = Topt is required by normal laws. This facilitation consists of ignoring The impact of mechanical power transmission causing a power loss. A high-gain observer [13–15] is used for estimating the aerodynamic torque for that the control is appropriate. Compared to an observer in sliding mode [13], it allows us to limit the chattering phenomenon in practice [14].

Improvement of the Non-linear Control Strategy …

1227

3.1 Observer Synthesis From Eq. (7), we can write: dω Ta fω Tem = − − dt J J J

(9)

By choosing the following state variables:   Ta T x = [x1 x2 ] = ω ; Y = x1 ; U = Tem J T

So the state model is written:  X˙ = AX + ϕ(X, U ) + ε(t) Y = CX

(10)

The state model of a high-gain observer [13–18] candidate to the system written by the state Eq. (10) is: 

−1 −1 ˆ X˙ˆ = A Xˆ + ϕ( Xˆ , U ) − θ −1 θ S C C( X − X ) ˆ ˆ Y = CX

(11)

Where θ be the diagonal matrix and S is the only solution of Lyapunov algebraic Eq. (12) [13–18]: S + AT S + S A − C T C = 0

(12)

By defining the error of observation by [19]: x˜ = θ (xˆ − x)

(13)

ˆ − ϕ(x)) − θ ε(t) x˙ˆ = θ (A − S −1 C T C)x˜ + θ (ϕ(x)

(14)

Deriving from it, we have:

3.2 Determination of the Conditions for Convergence. We consider the following function Lyapunov in order to discover the convergence [18]:

1228

A. Loulijat et al.

V (x) ˜ = x˜ T S x˜

(15)

V˙ (x) ˜ = 2 x˜ T S x˙˜ = −θ V − x˜ T C T C x˜ + 2 x˜ T S θ (ϕ(x) ˆ − ϕ(x)) − 2 x˜ T S θ ε˜ (t) (16) By Eq. (12), therefore:   ˆ − ϕ(x)) +  θ ε˜ (t)) V˙ (x) ≤ −θ V + 2xσ ˜ max (S)( θ (ϕ(x)

(17)

Now we can assume (ϕ is a function considered lipchitzian and is bounded) that:    θ (ϕ(x) ˜ &  f (t) ≤ ρ ˆ − ϕ(x)) ≤ τ x Where τ =

k J

and ρ = sup t≥0 ε(t). So we have: ρ ˜ max (S) V˙ (x) ˜ ≤ −θ V + 2x ˜ 2 σmax (S)τ + 2xσ θ

(18)

From the result of the mathematical Lemma 2.1. Assume that system (10) satisfies the above assumptions. Then [18]: We have x = T a ⇒ T = J x , conclude that: 





2

J

a



2

˜ + θ ρ] T˜a = Tˆa − Ta = J (xˆ2 − x2 ) ≤ J [θ σ e(−ηθ t) x(0)

(19)

θ is decreasing when θ is increasing, allowing a practical estimate of the aerodynamic torque [8].

3.3 Determination of the Law of Control The sliding mode of second-order is the control law used in this part is founded on the algorithm of super-twisting so that the estimated aerodynamic torque converges to the optimal torque in the short amount of time [9]. Pursuit error is defined by: eT = Topt − Tˆa

(20)

The dynamics of this error is giving by its derivative for time. e˙T = 2αopt ω (Tˆa − f ω − Tem ) − T˙ˆa

(21)

Improvement of the Non-linear Control Strategy …

1229

We develop the last expression: e˙T = −2αopt ωTem + [2αopt ω(Tˆa − f ω) − T˙ˆa ]

(22)

We pose: W = 2αopt ω (Tˆa − f ω) − T˙ˆa . Then: e¨T = −2αopt ω T˙em + W˙

(23)

The control law is given by [20–22]: Tem = U + β1 |eT |0.5 sgn(eT ) with U˙ = β2 sgn(eT )

(24)

The constants β1 and β2 satisfy the following inequalities. 

β1 ≥ β22 ≥

2 μm 42 μ M (l1 +1 ) μ3m (l1 −1 )

&

  W˙  < 2

0 < μm ≤ μ M

Thus, we can say that there is a finite time tc for that: t ≥ tc ⇒ Ta = Topt . The equivalent control law can obtain by annulling the Eq. (22): Tem eq =

1 2αopt ω

[2αopt ω(Tˆa − f ω) − T˙ˆa ]

(25)

4 Electric Generator Model A doubly-feed induction generator model is written in [13–23] in the d-q frame of park. Moreover, the various article assumes that stator resistance is ignored since the DFIG used in a wind energy device still reflects a reasonable assumption. From these considerations, we arrive at a system of Eq. (26). ⎧ dI 1 M dφsd rd ⎪ ⎨ dt = σ L r (Vr d − Rr Ir d + gωs σ L r Irq − L s dt ) d Irq = σ 1L (Vrq − Rr Irq − gωs σ L r Ir d − gωs LMs φsd ) dt ⎪ ⎩ T = − pr M φ I em L s sd rq

(26)

Where σ = 1 − LMs L r and g = ωsω−ω . s The reactive power reference value will be kept at zero to maintain a unity power factor on the stator side [24]. Within d-q coordinates, the reactive power is defined by: 2

Q s = Vsq Isd − Vsd Isq

(27)

1230

A. Loulijat et al.

Adapting this equation to our assumptions simplifiers gives: Qs =

Vs φs Vs M − Ir d Ls Ls

(28)

The desire reactive power is: Q s = 0 Using these equations, we get the reference value. Idr _r e f =

Vs Vs ; φs = ωs M ωs

(29)

The proposed control strategy also uses the algorithm that says super-twisting. In this perspective, we consider the following errors. 

s Ir d = Ir d − Ir d_r e f sTem = Tem − Tem_r e f

(30)

The dynamics of these errors is expressing by: ⎧ ⎨ s˙I = rd ⎩ s˙T

em

 Vr d − Rr Ir d + gωs σ L r Irq − LMs dφdtsd − I˙r d_r e f

 = − p σ L1s L r φs Vrq − Rr Irq − gωs σ L r Ir d − gωs LMs φsd − T˙em_r e f 1 σ Lr

(31)

By posing G 1 and G 2 such that: s¨ Ir d =

1 ˙ 1 M M Vr d + G˙ 1 − Rr Ir d & s¨Tem = − p φs V˙rq + G˙ 2 + p φs Rr Irq σ Lr σ Lr σ L s Lr σ L s Lr

(32)

Let’s now consider the following control [19–21]: 

 0.5 Vr d = z 1 − β3 s Ir d  Sgn(s Ir d ) + σ 1L r Rr Ir d with z˙ 1 = −β4 Sgn(s Ir d )  0.5 Vrq = z 2 + β5 sTem  Sgn(sTem ) − p σML r φs Rr Irq with z˙ 2 = β6 Sgn(sTem ) (33)

With positive constants β3 , β4 , β5 , β6 , 3 and 4 that satisfy the following inequalities.   3 (β3 +3 ) G˙ < 3 ; β3 > 3 & β 2 ≥ 4 4 σ Lr σ 2 L r2 (β3 −3 )  1 M 4 (β5 +4 ) G˙ 2 < 4 ; β5 > p φs 4 & β 2 ≥ 4 2 2 σ Ls Lr

6

σ L r (β5 −5 )

Improvement of the Non-linear Control Strategy …

1231

5 Discussion of Simulation Results The control strategy suggested in this article was validated on the 2-MW DFIG-based WT using the MATLAB/Simulink environment. The following table illustrates the values of some technical parameters used in the simulation (Table 1). As can be seen in Fig. 4, we can estimate the aerodynamic torque precisely, which allows us to control it. As concerns, the DFIG control, the performances obtained are very satisfactory as they show the trajectory tracking of the desired electromagnetic torque (Fig. 5a). Also, Fig. 5a shows that the torque on the shaft is not subject to the phenomenon of chatter, thus limiting the mechanical stress on the entire transmission of the wind turbine and Fig. 5b shows a very rapid convergence to the rotor reference current. To demonstrate the interest of the proposed strategy, its performances had been comparing with control strategies such as proportional-integral control (PIC) [5]. The PIC uses active power as a reference. This presumes that the active power conforms to the electromagnetic power. Ps = Pr e f ⇒ Irq_r e f = −

Ls Pr e˙ f Vs M

(34)

Table 1 The wind turbine Parameter

Value

Number of blades

3

Length of blade (m) Swept area Sv (m2 )

39 5027

Wind speed at nominal speed and at Cp-max : v(m/s)

Fig. 4 Torque estimated (green), Torque optimal (blue), Torque real (red)

11

1232

A. Loulijat et al.

Fig. 5 Electromagnetic torque with SMSOC (a), Present efficiency of Ird tracking (b)

Fig. 6 Electromagnetic torque with PIC (a) and SVC (b)

This approximation causes problems of convergence towards the desired couple (Fig. 6a). A second SVC is to use the following equation [6]: Irq_r e f = −

Ls Tem_r e f pMφs

(35)

The many simplifications made to obtain the expression of the electromagnetic torque of (35) and also the fact that the stator flux is considered constant do not allow the desired torque to be followed precisely (Fig. 6b). Compared to these two controls, the strategy proposed allows a very good pursuit of the reference. This undoubtedly improves performance. The simulations were realized with the following gains: θ = 30, β1 = β5 = 1.5, β2 = β6 = 50, β3 = 200, β4 = 1000, αopt = 161240, 1 = 3 = 5.103 , 2 = 4 = 5 = 1.105 .

Improvement of the Non-linear Control Strategy …

1233

6 Conclusion A linear control method such as PIC and SVC is standard practice for A linear control method such as PIC and SVC is standard practice for dealing with generator control in wind turbines. However, these control methods come at the costs of decreased system performances awaited due to random operation and process uncertainties considered. As a consequence, a strong non-linear control method should be used to address these issues. The method has been provided and discussed in this article. Firstly a high-gain observer to define the reference torque in combination with the sliding mode of second-order control, secondly a sliding mode of second-order is selected based on a super- twisting algorithm to control the doubly-fed induction generator, achieves the desired goals. It is strong and so durable, increases energy efficiency, and limits mechanical stress on the drive shaft thanks to the low chatter it produces. In future work, this strategy will be combined with the method of passive protection in case of network disturbance in the form of a voltage dip.

References 1. Baala Y, Bri S (2020) DFIG-based wind turbine control using high-gain observer. In: 2020 1st Interanational Conference Innovation Research Application Science Enginnering Technology, IRASET 2020 (2) https://doi.org/10.1109/IRASET48871.2020.9092280 2. Goudarzi N, Zhu WD (2013) A review on the development of wind turbine generators across the world. Int J Dyn Control 1(2):192–202. https://doi.org/10.1007/s40435-013-0016-y 3. Lin FJ, Tan KH, Fang DY (2015) Squirrel-cage induction generator system using hybrid wavelet fuzzy neural network control for wind power applications. Neural Comput Appl 26(4):911–928. https://doi.org/10.1007/s00521-014-1759-x 4. Mustafaev RI, Gasanova LG (2015) An investigation of the dynamics of a wind power unit equipped with synchronous generators with permanent magnets. Russ Electr Eng 86(5):258– 263. https://doi.org/10.3103/S1068371215050077 5. Poitiers F, Bouaouiche T, Machmoum M (2009) Advanced control of a doubly-fed induction generator for wind energy conversion. Electr Power Syst Res 79(7):1085–1096. https://doi.org/ 10.1016/j.epsr.2009.01.007 6. Cárdenas R, Peña R (2004) Sensorless vector control of induction machines for variable-speed wind energy applications. IEEE Trans Energy Convers 19(1):196–205. https://doi.org/10.1109/ TEC.2003.821863 7. Beltran B, Ahmed-ali T, El M, Benbouzid H, Member S (2008) Sliding mode power control of variable-speed wind energy conversion systems 23(2):551–558 (2008) 8. Touati A, Abdelmounim E, Aboulfatah M, Moutabir A, Majdoul R (2015) Improved strategy of an MPPT based on the torque estimator for variable speed turbines. Int Rev Model Simul 8(6):620–631. https://doi.org/10.15866/iremos.v8i6.7122 9. Abd I, Abdelmounim E, Aboulfatah M, Majdoul R, Moutabir A (2015) Design of an Mppt based on the torque estimator for variable speed turbines (1):166–171 10. Beltran B, Benbouzid MEH, Ahmed-Ali T (2020) A combined high gain observer and highorder sliding mode controller for a DFIG-based wind turbine. In: 2010 IEEE International Energy Conference Exhibilish EnergyCon 2010 (1):322–327 (2010). https://doi.org/10.1109/ ENERGYCON.2010.5771699

1234

A. Loulijat et al.

11. Zamzoum O, Derouich A, Motahhir S, El Mourabit Y, El Ghzizal A (2020) Performance analysis of a robust adaptive fuzzy logic controller for wind turbine power limitation. J Clean Prod 265:121659. https://doi.org/10.1016/j.jclepro.2020.121659 12. Adapa H, Pushpak V, Kumar MV (2020) Nakandhrakumar RS: Modeling and simulation of pitch controlled FRP material based horizontal axis wind turbine system to extract maximum power. Mater. Today Proceeding 2020. https://doi.org/10.1016/j.matpr.2020.06.131 13. Deshpande AS, Patil SL (2020) Robust observer-based sliding mode control for maximum power point tracking. JControl Autom Electr Syst. https://doi.org/10.1007/s40313-020-006 17-5 14. Khalil HK (2008) High-gain observers in nonlinear feedback control. In: 2008 International Conference Control Automation System, ICCAS 2008 (2008). https://doi.org/10.1109/ICCAS. 2008.4694705 15. Adekanle OS, Guisser M, Abdelmounim E, Aboulfatah M (2018) Nonlinear controller with rotor crowbar and DC-chopper fault ride through technique for grid-connected doubly-fed induction generator. Int Rev Autom Control 11(6):281–292. https://doi.org/10.15866/ireaco. v11i6.13496 16. Zareian M, Shafiei MH (2020) A modification in the structure of low-power high-gain observers to improve the performance in the presence of disturbances and measurement noise. Eur J Controlhttps://doi.org/10.1016/j.ejcon.2020.07.009 17. Kadrine A, Tir Z, Malik OP, Hamida MA, Reatti A, Houari A (2020) Adaptive non-linear high gain observer based sensorless speed estimation of an induction motor. J Franklin Inst.https:// doi.org/10.1016/j.jfranklin.2020.06.013 18. Farza M, M’Saad M, Rossignol L (2004) Observer design for a class of MIMO nonlinear systems. Automatica 40(1):135–143. https://doi.org/10.1016/j.automatica.2003.08.008 19. Ahmed-Ali T, Cherrier E, M’Saad M (2009) Cascade high gain observers for nonlinear systems with delayed output measurement. Proc IEEE Conf Decis Control 4:8226–8231. https://doi. org/10.1109/CDC.2009.5400398 20. Levant A, Alelishvili L (2007) Integral high-order sliding modes. IEEE Trans Automat Contr 52(7):1278–1282. https://doi.org/10.1109/TAC.2007.900830 21. Moussa O, Abdessemed R, Benaggoune S (2019) Super-twisting sliding mode control for brushless doubly fed induction generator based on WECS. Int J Syst Assur Eng Manag 10(5):1145–1157. https://doi.org/10.1007/s13198-019-00844-3 22. Zhang C, Gutierrez SV, Plestan F, De León-Morales J (2019) Adaptive super-twisting control of floating wind turbines with collective blade pitch control. IFAC-PapersOnLine 52(4):117–122. https://doi.org/10.1016/j.ifacol.2019.08.165 23. Beltra B (2010) Contribution à la commande robuste des éoliennes à base de génératrices asynchrones double alimentation: Du mode glissant classique au mode glissant d’ordre supérieur 24. Zouggar EO, Chaouch S, Abdeslam DO, Abdelhamid AL (2019) Sliding control with fuzzy type-2 controller of wind energy system based on doubly fed induction generator. Instrum. Mes. Metrol. 18(2):137–146. https://doi.org/10.18280/i2m.180207

Optimization of DFIG Wind Turbine Power Quality Through Adaptive Fuzzy Control Hamid Chojaa, Aziz Derouich, Mohammed Taoussi, Othmane Zamzoum, and Mourad Yessef

Abstract The control algorithms to optimize power extraction in wind energy conversion systems are often designed by making some approximations and taking standard parameter values into consideration. This approach has proven inefficient as parameter variations and disturbances often degrade the robustness and power quality of the system. This paper proposes a simple-to-implement adaptive techniques to upgrade the efficiency and power quality of the system. Simulation results obtained on a 1.5 MW Doubly Fed Induction Generator (DFIG) are given and comparison is made against the performance of a PI-controller. Keywords Doubly Fed Induction Generator · Adaptive control · Power quality · Maximum power point tracking (MPPT) · Fuzzy logic · Disturbance compensation

1 Introduction Due to the fact that the real nature of wind speed is stochastic, the grid-connected DFIG is provided with automatic control strategies for optimal power extraction and efficient exchange of energy between the turbine and the grid. The aim of the MPPT strategy, when the turbine is on partial load is to continually adjust the operating point of the wind turbine in order to convert the maximum power possible. A technique using relative velocity [1] has been proposed in the literature. Meanwhile, wind speed acquisition from a single anemometer leads to erroneous measure of wind speed which does not the effective wind speed incident on the blades. Thus, a faulty measurement of the incident wind speed can cause the deterioration of the extracted power. Wind speed estimation with two different Kalman filter versions were proposed and analyzed by the authors in [7]. The complexity of Kalman H. Chojaa (B) · A. Derouich · M. Taoussi · O. Zamzoum Laboratory of Technologies and Industrial Services, Higher School of Technology, SMBA University, Fez, Morocco e-mail: [email protected] M. Yessef Laboratory of Engineering, Modeling and Systems Analysis, SMBA University, Fez, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_113

1235

1236

H. Chojaa et al.

filters and the high number of tuning parameters required are the major drawbacks of this method. A simple method based on Fuzzy Logic were synthesized in [3] to estimate wind speed and track the DFIG variables. Easy to implement Perturbation and Observation technique usually employed in photovoltaic systems is proposed in [5]. It can be implemented easily, not expensive and does not require accurate parameters of the turbine and wind speed information. However, a compromise between convergence speed and performance is needed. Neural Network, Gradient methods have been proposed to give very good performance but are yet to be easy to implement [1]. The disadvantage of the Optimal Torque Control technique presented in [9] is that characterization tests are required off-line to design the look-up table. This paper proposes a scheme based on Fuzzy Logic, which is unrestrained by wind speed measurement, knowledge of turbine parameters and characterization experiment to search the maximum power point. It indirectly compensates for the different power loss and requires only the acquisition of DFIG speed and output power signals. Vector control schemes and PI-controllers have been employed hand-in-hand in the literature to regulate DFIG velocity, power, and currents. However, grid voltage dips, model incorrectness and unexpected conditions like temperature increase deteriorate the effectiveness of this control technique. To improve on these inconveniences, several other controllers like Sliding Mode [9, 10], Backstepping [2, 8], Feedback Linearization [6], Neural Predictive Control [4] have been studied and evaluated in the literature. The drawbacks of these approaches range from chattering to implementation complexity. In this paper, we design an adaptive control technique in line with Lyapunov stability theorem to regulate speed and current. Estimating and compensating uncertainty in real time, it is characterized by fast response time, good precision, better robustness against disturbances.

2 System Modelling Under Stator Voltage Orientation Figure 1 illustrates the set-up of grid-connected turbine comprising a DFIG connected directly to the grid through its stator and indirectly via the rotor windings with backto-back power converters as intermediary. The converters are denoted Rotor Side Power Converter (RSC) and Grid Side Power Converter (GSC). This section presents the mathematical model of all the components of the turbine system needed for the control and observation of the system. For the independent control of active and reactive power to be achieved, Stator Voltage Orientation Scheme is employed to align the d-axis of the reference frame to the stator voltage position. Thus, it is directly obtained that vsq ≡ 0 and vsd equals to the magnitude of the grid voltage. RSC mathematical models can then become (1)–(4). This consequently means that direct ir d and quadrature irq components of rotor current control generator speed ωg and stator reactive power Q s respectively [10].

Optimization of DFIG Wind Turbine Power Quality …

1237

Fig. 1 DFIG connection to the grid

L m ωr ϕsq vr d dir d Rr ir d = − + ωr irq + + θr d dt σ Lr σ Lr σ Lr Ls

(1)

dirq Rr irq vrq − − ωr ir d + θrq = dt σ Lr σ Lr

(2)

J

dωg 3 Lm = Tm − f v ωg − p ϕsq ir d + θω dt 2 Ls  Ps = − 23 LLms vsd ir d   Q s = − 23 vLsds ϕsq − L m irq

(3)

(4)

3 Controller Design Three controllers are designed in for the work presented in this paper: pitch angle, RSC and GSC controllers. The pitch angle controller has for objective to mechanically prevent blade rotation speed from exceeding nominal power and speed. The aim of the RSC controller is to adaptively drive and set the system on the maximum power point and track desired stator reactive power. The PI corrector, designed but not presented in this paper to control the GSC, is aimed at maintaining DC voltage constant and annulling filter reactive power.

1238

H. Chojaa et al.

Fig. 2 Turbine power variation in function of generator speed for different wind speed

3.1 Fuzzy Logic MPPT Figure 2 presents the curve of mechanical power in function of generator speed for different wind speed values. Popt represents the maximum power point (MPP) of the turbine system. Since the wind speed is random and not fix, to obtain the optimum output power of the wind turbine, an adaptive controller is needed to track the optimal power curve through the MPPT strategy. The MPPT strategy in this paper adopts the fuzzy technique described in Fig. 2(b), well known as an adaptive and robust tool for complex nonlinear systems. It is employed in this paper because it neither needs the information of turbine parameters nor wind speed signal acquisition. The FLC perturbs the generator speed and observes the direction of variation of the output power and adjusts the speed accordingly in other to place the turbine at the operating point where energy extracted is maximal. The output is the optimal generator speed (ωopt ) that ensures the extraction of maximum power from the turbine blades. The speed reference is then fed to a fast controller which converges the real generator speed to the reference. The calculation of output power from stator and filter measurement is more efficient because it avoids the complex determination of power loss in the gearbox, the converters and the filter. The fuzzy logic controller synoptic diagram is shown in Fig. 3, where K1, K2 and K3 are adaptive gains. The FLC searches the maximum point of the power-speed curve of the generator using the rules presented in Table 1: with P and ωg as inputs, while ωgr e f represent the output. These variables are represented by NB, NM, NS, Z, PS, PM, PB denoting Negative Big, Negative Medium, Negative Small, Zero, Positive Small, Positive Medium, and Positive Big respectively. The triangular membership functions of input and output are presented in Fig. 4 and have seven fuzzy subsets. The fuzzy inference is carried out by using Sugeno’s method and the defuzzification uses the center of gravity to determine the output of this FLC.

3.2 PI Controller (PIC) Design Figure 5 represents the PI controller synthesized to track reference stator power factor and reference machine velocity. Controller parameters are deduced using the

Optimization of DFIG Wind Turbine Power Quality …

1239

Fig. 3 Fuzzy Logic MPPT controller Table 1 MPPT fuzzy rules ωg

P NB

NM

NS

Z

PS

PM

PB

NB

PB

PB

PM

Z

NM

NB

NB

NM

PB

PM

PS

Z

NS

NM

NB

NS

PM

PS

PS

Z

NS

NS

NM

Z

NB

NM

NS

Z

PS

PM

PB

PS

NM

NS

NS

Z

PS

PS

PM

PM

NB

NM

NS

Z

PS

PM

PB

PB

NB

NB

NM

PM

PB

PB

Fig. 4 Degree of membership of change in speed, power and output

Fig. 5 PI regulator for RSC control

1240

H. Chojaa et al.

pole-zero cancellation method. F(s) = 1/(σ L r s + Rr ) is the transfer function and L ω ϕ frq = −ωr ir d and fr d = ωr irq + σmL rrL ssq are the coupling terms.

3.3 Adaptive Backstepping Controller (ABC) Design The most important feature of the ABC is that, unlike the PIC, it takes eventual uncertainty (θω , θr d , θrq ) into account in the modelling, estimates it and sends commands to compensates for it in real time. This is essential to mitigate the effect of model incorrectness, parameter variation, modelling approximations, and saturation. Current reference value which guarantees the convergence of generator speed to its optimum is derived in step 1. An ABC is designed to tracks this reference current in step 2 to give the final control variable vr d . Step 1: Eqs. 5 and (6) show speed tracking error and its integral respectively. εω = ωg − ωopt ε˙ ω =

(5)

f v ωg 3 Lm Tm − − p ϕsq ir d + θω − ω˙ opt J J 2 J Ls

(6)

Lyapunov quadratic function V1 to ensure that the speed error εω convergences and remains stable is chosen as and shown in (7). Its derivative V˙1 is given in (8), and to guarantee that it is strictly negative, the equality in (8) has to be fulfilled. 2

1 1 θω V1 = εω2 + 2 2 mω

(7)

  f v ωg Tm 3 Lm − − p V˙1 = εω ϕsq ir d + θ ω − ω˙ opt = −K ω εω J J 2 J Ls 

(8)

The current output ir∗d is the reference input for step 2 and is derived from (8) and presented in (9). Equation 10 represents the estimate of the uncertainty computed in real time.    f 2J L s Tm kω εω + − + θ ω − ω˙ opt = αr d (9) ir∗d = 3 pL m ϕsq J J 

˙ θ ω = εω m ω



(10)

Step 2: The control of stator reactive power will be included to this step. The ∗ = α2 ): error between the actual and the desired current can be defined as (irq

Optimization of DFIG Wind Turbine Power Quality …

 

1241

εr d = ir d − αr d εrq = irq − αrq

(11)

L ω ϕ

ε˙ r d = σvrLdr − RσrLirrd + ωr irq + σmL rrL ssq + θr d − α˙ r d v R i ε˙ rq = σ rqL r − σrLrqr − ωr ir d + θrq − α˙ rq ⎧ 2 ⎨ Vr d = Vω + 1 ε2 + 1 θ r d 2 rd 2 mr d 2 ⎩ V = 1 ε2 + 1 θ rq rq

2 rq

(12)

(13)

2 m rq

Second step Lyapunov functions taking into account uncertainty parameters and their derivatives, are presented as (13) and (14) respectively. 

V˙ω = −K ω εω2 − K r d εr2d 2 V˙rq = −K rq εrq

(14)

Derivatives of the estimates of the uncertainties and the control variables deduced and given as (15) and (16) ensure that the derivative of the Lyapunov function is strictly negative. 

˙ θ r d = εr d m r d ˙ θ rq = m r d εr d 

(15)



⎧

⎨ vr d = σ L r −K r d εr d + 3 p εω L m ϕsq + Rr ir d + ωr irq − ωr L m ϕsq − θ r d + α˙ r d 2 J L σ L σ L L s r r s

R i ⎩ vrq = σ L r −K rq εrq + σrLrqr + ωr ir d − θ rq + α˙ rq 



(16) The control (K ω , K r d , K rq ) and adaptation (m ω , m r d , m rq ) parameters are chosen strictly positive.

4 Simulation and Discussion The DFIG model studied in this work is rated 1.5 MW. It is designed under Matlab/Simulink to examine the performance of the ABC in comparison to that of the PI-controller taking the stochastic wind speed profile presented in Fig. 6(a) as input. Figure 6(b) shows that the PIC maintains the DC bus voltage constant for effective power conversion. Particular interest is placed on the convergence time, precision, and power quality under pitch angle control activation. Figure 6(c) proves that actual speeds converge to corresponding reference generator speeds under the ABC and PIC with good precision and robustness. We however observe discrepancies between speed evolution under both controllers. This is due

1242

H. Chojaa et al.

(a)

(c)

(e)

(g)

(b)

(d)

(f)

(h)

Fig. 6 a Wind speed b DC-link voltage c Generator speed d Power coefficient e Electromagnetic Torque f Active and reactive power at the point of common coupling g Stator current under ABC h Stator current under PIC

principally to the better precision and faster convergence of the ABC. This can be explained by the higher power coefficient obtained under the ABC as illustrated in Fig. 6(d). The rapidity of the ABC enables the FLC to take optimum decision more quickly, leading to better precision and more extracted power. It can also be noticed on

Optimization of DFIG Wind Turbine Power Quality …

1243

Fig. 6(e) that the electromagnetic torque is less noisy under the ABC. This consequently implies less mechanical vibration and better power quality. Figure 6(f) shows that the powers transmitted to the grid under both controllers possess smooth evolution and the reactive power converges to zero with more power extracted under the ABC without chattering. The stator current waveforms under ABC in Fig. 6(g) takes a better sinusoidal form than that of the PIC given in et Fig. 6(h). This consequently demonstrates the better performance of the ABC. Due to the limitation of the reference speed below 1.2 p.u, the pitch angle remains close to zero for both controllers.

5 Conclusion Optimizing energy conversion in DFIG wind turbine requires to, first, determine the Maximum Power Point (MPP) and second, control rotor converters to set the system on the MPP. Due to uncertainties and model imperfection, a Fuzzy Logic adaptive controller is employed in this paper to track the optimum generator speed. Comparative studies and simulation results show that the Adaptive Backstepping rotor converter controller proved more power conversion, better robustness, faster response time and higher precision than the conventional PI controller.

References 1. Abdullah MA, Yatim AHM, Tan CW, Saidur R (2012) A review of maximum power point tracking algorithms for wind energy systems. Renew Sustain Energy Rev 16:3220–3227. https://doi.org/10.1016/j.rser.2012.02.016 2. Adekanle OS, Guisser M, Abdelmounim E, Aboulfatah M (2017) Robust integral backstepping control of doubly fed induction generator under parameter variation. Int Rev Autom Control 10. https://doi.org/10.15866/ireaco.v10i2.11163 3. Belmokhtar K, Doumbia ML, Agbossou K (2014) Novel fuzzy logic based sensorless maximum power point tracking strategy for wind turbine systems driven DFIG (doubly-fed induction generator). Energy 76:679–693. https://doi.org/10.1016/j.energy.2014.08.066 4. Douiri MR, Essadki A, Cherkaoui M (2018) Neural networks for stable control of nonlinear DFIG in wind power systems. Procedia Comput Sci 127:454–463. https://doi.org/10.1016/j. procs.2018.01.143 5. Hong C-M, Ou T-C, Lu K-H (2013) Development of intelligent MPPT (maximum power point tracking) control for a grid-connected hybrid power generation system. Energy 50:270–279. https://doi.org/10.1016/j.energy.2012.12.017 6. Li P, Xiong L, Wu F, Ma M, Wang J (2019) Sliding mode controller based on feedback linearization for damping of sub-synchronous control interaction in DFIG-based wind power plants. Int J Electr Power Energy Syst 107:239–250. https://doi.org/10.1016/j.ijepes.2018.11.020 7. Song D, Yang J, Dong M, Joo YH (2017) Kalman filter-based wind speed estimation for wind turbine control. Int J Control Autom Syst 15:1089–1096. https://doi.org/10.1007/s12555-0160537-1 8. Youness EM, Aziz D, Abdelaziz EG, Jamal B, Najib EO, Othmane Z, Khalid M, Bossoufi B (2019) Implementation and validation of backstepping control for PMSG wind turbine

1244

H. Chojaa et al.

using dSPACE controller board. Energy Reports 5:807–821. https://doi.org/10.1016/j.egyr. 2019.06.015 9. Zamzoum O, El Y, Errouha M, Derouich A, El A (2019) Active and Reactive Power Control of Wind Turbine based on Doubly Fed Induction Generator using Adaptive Sliding Mode Approach. Int J Adv Comput Sci Appl 10. doi: https://doi.org/10.14569/IJACSA.2019.010 0252 10. Hamid C, Derouich A, Taoussi M, Zamzoum O, Hanafi A (2020) An improved performance variable speed wind turbine driving a doubly fed induction generator using sliding mode strategy. In: 2020 IEEE 2nd International Conference on Electronics, Control, Optimization and Computer Science (ICECOCS). https://doi.org/10.1109/ICECOCS50124.2020.9314629

Use of a Doubly-Fed Induction Generator for the Conversion of Wind Energy Wahabi Aicha, Elmoudden Abdelhadi, and Senhaji Rhazi Kaoutar

Abstract Wind energy is considered one of the most important and promising renewable energy sources in the world. We begin this study with a description of the current state of the world energy situation and in Morocco in particular. We then described a medium power wind power conversion chain that we modeled and simulated. The chain consists of the turbine, the multiplier and the dual power induction generator (DFIG). The adopted chain model is developed using Matlab/Simulink/Sim-Power-Systems software. We found fairly correct simulation results which are presented and analyzed at the end of this study. Keywords Wind energy · DFIG · Conversion chain

1 Introduction 1.1 Increase in Energy Demand in the World Electric power is a crucial element for any socio-economic development. Given the scale of industrialization in recent decades and the proliferation of household appliances that consume more and more electrical energy, the demand for electrical energy has become very important. Unfortunately, most of this growth in demand has been met by fossil fuel energy which, in addition to its harmful effect on the ozone layer, has a reserve which is of course limited. Faced with this situation, several countries have resorted to nuclear power plants. However, the risks of a nuclear accident (Chernobyl, Fukushima) have slowed down the development of nuclear

W. Aicha (B) · S. R. Kaoutar Network, IT, Telecommunications and Multimedia Research Laboratory in the Superior School of Technology, University Hassan 2, Casablanca, Morocco E. Abdelhadi Computer Science, Renewable Systems and Energies Laboratory, The National School of Electricity and Mechanics, University Hassan 2, Casablanca, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_114

1245

1246

W. Aicha et al.

energy, which encouraged several countries like Morocco to develop other forms of energy: renewable energies.

1.2 Renewable Energy in Morocco In Morocco, the development of renewable energies is a national priority which calls for a diversification of energy sources by 2020 with 42% of the total installed electrical energy supplied by green energies [6]. The energy strategy of our country is based on the development of energy efficiency and renewable energies: the objective is to save 12% of energy consumption by 2020 and 15% by 2030. With this in mind, action plans for energy efficiency were implemented in all key sectors, particularly transport, industry and construction [6]. Figure 1 shows the installed power in Morocco in 2018. The city of Casablanca, which is the economic capital of morocco, has a fairly large wind farm. Figure 2 gives an idea of the average wind speeds for each month. Before the realization of any project, stages of study and design on computer are essential. A wind farm project goes through several stages. The modeling and simulation of the project constitutes a very important step for the success of the project. The study we are going to carry out concerns a medium power aero-generator project.

2 Description of a Wind Energy Conversion System Piloted by a DFIG In the conversion of wind energy, the DFIG is widely used because it has several ad-vantages: the generation of energy at variable speed (±30% around the speed of

Fig. 1 Power installed in Morocco (MW) in 2018

Use of a Doubly-Fed Induction Generator …

1247

Fig. 2 The wind speed averages for Casablanca

Fig. 3 Conversion of wind energy

synchronism) which allows to obtain the maximum possible power for each wind speed: MPPT. The wind chain, as shown in the following figure, includes the turbine, the multiplier, the DFIG whose stator circuit is directly connected to the electrical network. A second circuit placed at the rotor is also connected to the network, but via power converters. The machine side power converter is called “Rotor Side Converter” (RSC) and the line side power converter is called “Grid Side Converter” (GSC). The machine-side power converter is used to control the active and reactive power produced by the machine. The mains side converter controls the DC bus voltage and the mains side power factor (Fig. 3).

1248

W. Aicha et al.

3 Modeling of the Asynchronous Generator We remember that the asynchronous generator DFIG is modeled in the reference of Park according to the following equations [4, 5]: d∅ds − ωs ∅qs dt

(1)

d∅qs ωs ∅ds dt

(2)

vdr = Rr idr +

d∅dr − ωr ∅qr dt

(3)

vqr = Rr iqr +

d∅qr + ωr ∅dr dt

(4)

vds = Rs ids +

vqs = Rs iqs +

⎧ ∅ds ⎪ ⎪ ⎨ ∅qs ⎪ ∅dr ⎪ ⎩ ∅qr

= Ls ids + Midr = Ls iqs + Miqr = Lr idr + Mids = Lr iqr + Miqs

(5)

With: ωr = ωs − p and: Ls = ls – Ms; Lr = lr + Mr. Active and reactive power are written P = vds ids + vqs iqs

(6)

Q = vqs ids − vds iqs

(7)

4 Modeling and Simulation of the Complete Conversion Chain 4.1 Schematic Diagram After having studied each element separately, we will now be interested in the system in its totality: model it and simulate it. The complete open loop system can be represented by the following block diagram (Fig. 4).

Use of a Doubly-Fed Induction Generator …

1249

Fig. 4 Diagram of the complete green-loop system (without regulation)

4.2 Results and Comments In Fig. 5, we show the voltage across the DC bus. We note that the voltage across the capacitor is (apart from fluctuations) continuous and equal to ≈ 86.4 V. The small voltage variations (±1 V), are probably due to the fact that we worked in open loop. The Fig. 6 shows the stator voltage delivered by the generator. The phase-to-phase voltage across the generator stator is sinusoidal, with an effective value ≈ 380 V and a period T ≈ 0.02 s: the frequency is 50 hz, we can say that the machine operates in optimal conditions (nominal V = 380 V, nominal f = 50 hz). The following figure shows the three currents feeding the balanced three-phase resistive load (Fig. 7).

1250

W. Aicha et al.

Fig. 5 Voltage at DC bus terminals

Fig. 6 Stator voltage supplied by the generator

Note that the three-phase system of currents (is1, is2, is3) is balanced with the same pulsation as the voltages. Imax ≈ 6 A. The Fig. 8 shows the total active power supplied by the generator.

Use of a Doubly-Fed Induction Generator …

1251

Fig. 7 Stator currents (A)

Fig. 8 Total active power supplied by the wind chain to the load

It can be seen that the wind chain provides a fairly high active power: average P = 2000 W. (The minus sign indicates of course that it is a supplied power). We note the presence of fluctuations (of the same frequency as the voltage) on the power plot which are due to the use of power converters and to the fact that we worked without power regulation. The last figure shows the total reactive power supplied by the chain (Fig. 9). The average value of the reactive power is zero since the load we have placed is of a resistive nature.

1252

W. Aicha et al.

Fig. 9 Reactive power Q in VAR

5 Conclusion and Prospects In the light of the results given previously, we can conclude that the studied conversion chain was modeled correctly. The simulation of the design carried out using the software are quite valid: the currents, the voltages as well as the delivered powers correspond to the desired results. In order to minimize the observed parasitic fluctuations, we plan to insert filters at the terminals of the load.

References 1. Boyette A (2006) Control command of a dual power asynchronous generator with storage system. Doctoral thesis from Poincaré University, Nancy 2. Wahabi A, El Moudden A, Bounifli F (2014) Modeling and Simulation of a Dual Power Asynchronous Generator for Wind Energy Control. Moroccan thermal association (2014) 3. Belmokhtar J, Doumbia K, Agbossou K (2010) Modeling and control of a wind power system based on a dual power asynchronous machine for the supply of power to the electricity grid. J Sci Res 2 4. Khojet el khil S (2006) Vector control of a double-fed asynchronous machine (MADA)/Optimization of losses in converters 2006

Use of a Doubly-Fed Induction Generator …

1253

5. Wahabi A, El Moudden A, Bounifli F, Aarib A (2015) Improved performance of a wind energy conversion chain driven by an asynchronous generator doubly-fed. Int J Electron Commun Comput Eng 6(2): 255–262. ISSN 2249–071X 6. Azeroual M, El Makrini M, El Moussaoui H, El Markhi H (2018) Renewable energy potential and available capacity for wind and solar power in morocco towards 2030. J Eng Sci Technol. Rev 11(1): 189–198

Improvement of the Wind Power System Based on the PMSG Btissam Majout, Badre Bossoufi, Mohammed Karim, and Chakib El Bekkali

Abstract Given the role of PMSG in the wind power system, several commands are proposed to improve the quality of energies supplied to the electrical grid. In this work, two control strategies are presented and applied to the static converter in order to control the electromagnetic torque and the active and reactive power exchanged between the PMSG and grid. Firstly, in order to extract a maximum power from the wind a speed control strategy (MPPT) has been presented, followed by a PMSG model in reference d-q. Then, the conception of the Field-Oriented Control (FOC) and the sliding mode control (SMC) are developed and tested with Matlab/Similink, where these controls make it possible to operate the system in the best performances in dynamic regimes. And finally, we ended by a discussion about the interpretation of the simulation results in order to compare the advantages and the performances of each control. Keywords Wind energy conversion system (WECS) · Permanent magnet synchronous generator (PMSG) · Field-Oriented Control (FOC) · Sliding mode control (SMC)

1 Introduction Recently, the development and the exploitation of renewable energy have experienced a strong progress, due to its different advantages (technical, economical and environmental) compared to conventional energies, including fossil energies (coal, gas, petrol). There are many types of renewable energy resources (wind, solar, hydro, geothermal and biomass) [1–3]. Where the wind energy occupies a good place, it is one of the cleanest, the safest and fastest sectors to implement, it is supposed to be the best in terms of quality and price [4–6]. There are several research studies about the wind turbine. In particular, those equipped with Permanent magnet synchronous machine. This type of machine is B. Majout (B) · B. Bossoufi · M. Karim · C. El Bekkali Laboratory of Engineering Modeling and Systems Analysis, SMBA University Fez, Fez, Morocco e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_115

1255

1256

B. Majout et al.

Fig. 1 General Model of WECS

characterized by reliable operation without gearboxes, lower losses and a reduced size compared to a doubly-fed induction generator (DFIG) of the same power [7]. The non-linear nature of the wind system requires robust controllers to mitigate effects of internal parametric variations and external disturbances [7–10]. In order to control this machine, two control techniques proposed in this paper include both classical vector control based on PI regulators and non-linear Sliding mode control based on Lyapunov theory [16]. Recently, many researches are turned towards the nonlinear techniques SMC to increase the robustness and the precision of the systems, where this control is characterized by its high precision, rapid dynamic response, stability, simplicity [16], compared to linear command FOC which is characterized by its sensitivity to parametric variations [7–16]. This paper is organized as follows: in Sect. 2 a Wind System Model is presented; Sect. 3 is dedicated to the control Synthesis of SMC and FOC; the simulation results and discussion are presented in Sect. 4; the paper finishes with a conclusion in Sect. 5. All simulations performed in the MATLAB/Simulink environment. The general structure of the wind system is given by (Fig. 1) [7].

2 Wind System Modelling 2.1 Wind Turbine Modeling The turbine model is presented by the following equations [7–11]: Pwind =

1 .ρ.S.vw3 2

Pturb = Cp (A, β).Pwind =

1 .ϕ.S.Cp (A, β).v3 2

(1) (2)

Improvement of the Wind Power System Based on the PMSG Table 1 Wind turbine aerodynamic constants

Parameters

Coefficients

C1

0.5176

C2

116

C3

0.4

C4

5

C5

21

C6

0.0068

λ= Cmax p (λ, β) = 

1257

t .R v

(3)

16 = 0.5930 27

(4)

  −C5 C p (A, β) = C1 CA2 − C3 β − C4 e A + C6 .λ 1 1 0.035 = λ+0.08.β − 1+β 3 A Ttur b =

Ptur b 1 1 = .ϕ.S.C p (A, β).v 3 . t 2 t Jtot = Jtur b + Jg

Jtot

dmec = Tmec = Ttur b − Tem − f.mec dt

(5)

(6) (7) (8)

The parameters C1, C2, C3, C4, C5 and C6 are constants dependent on the turbine geometry (Table 1) [7]. According to Fig. 2, the plot of the Cp follows the variation of the Beta angle inversely and reaches its maximum 0.426, for λopt = 8 and B = 0°. Given that the wind turbine can’t convert more than 59% of the kinetic energy contained in the wind into mechanical energy according to Betz’s Law [7–9]. Fig. 2 Curve of Cp

1258

B. Majout et al.

Fig. 3 MPPT strategy without speed control

2.2 Extraction of Maximum Power (MPPT Control) The MPPT control strategy consists in controlling the electromagnetic torque in order to adjust the mechanical speed in a way to maximize the power of the incident energy of the wind turbine (see Fig. 3) [8–11]. Because of erroneous measures of the wind speed delivered by the anemometer, most wind turbines are controlled without speed control. This technique is based on the hypothesis that the wind speed varies very slowly in permanent regime compared to the electrical time constant of the system [9]. In this case, the electromagnetic torque control is deduced from the dynamic equation of the turbine [7–9]. By neglecting the torque due to viscous friction (f.mec ) the equation of the reference electromagnetic torque becomes: Tem_r e f =

  ρ.π.R 5 .C p_max λopt .2mec Ptur b−r e f = t 2.λ3opt

(9)

2.3 PMSG Model System The electrical equations of the PMSG in q-d reference are as follows (see Fig. 4) [10, 11]: V sd = Rs .i sd +

dsd − wr .sq dt

(10)

V sq = Rs .i sq +

dsq + wr .sd dt

(11)

sd = L d .i sd + ∅ f ; sq = L q .i sq

(12)

wr = p.mec

(13)

The electromagnetic torque expression can be described as follows [7–11].

Improvement of the Wind Power System Based on the PMSG

1259

Fig. 4 d-q model of PMSG in synchronous reference frame

Tem =

  3  . p L d − L q Isd .Isq + Isq .∅ f 2

(14)

The active and reactive power are expressed by: 

  Pgen = 23 Vsd.isd + Vsq.isq  Qgen = 23 Vsq.isd − Vsd.isq

(15)

3 Synthesis of Controls In order to guarantee a high performance of a wind energy system based on the PMSG with smooth poles, where the inductances Ld and Lq are equal L s = L d = L q , The frequency and the voltage of the generator connected to the distribution grid must be adjusted when the wind speed varies [7]. We use the following PMSG model [7–12].  1  d Isd =− Rs .i sd − p.mec .L q .i sq − V sd I˙ds = dt Ld  d Isq 1  I˙qs = =− Rs .i sq + p.mec .L d .i sd + p.mec .∅ f − V sq dt Lq ˙ mec = 

  f 1 3 p  .Ttur b − . . L d − L q Isd .Isq + Isq .∅ f − .mec Jtot 2 Jtot Jtot

(16) (17) (18)

The PMSG model represented by differential Eqs. (10) and (11) indicate that Id and Iq are coupled together by nonlinear terms mec .L d .i sd and mec .L dq .i sq [7]. To solve this problem, two controls have been developed in this paper in order to control the machine-side converter: 1) 2)

Field-Oriented Control (FOC). Sliding Mode Control (SMC).

1260

B. Majout et al.

3.1 Field Oriented Control (FOC) The basic principle of vector control was described by BALSCHKE and HASSE in the early ’70s [10–16]. the Vector control consists in eliminating the coupling between the inductor and the armature by decomposing the stator current into two parts (Isd, Isq), where the inductive current controls the magnetic flux and the armature current controls the electromagnetic torque. The objective of this command is to establish a simple model of PMSG which makes it similar to a DC machine with separate excitation [13, 14]. The vector control is generally based on Proportional-Integral regulators PI to regulate the error between the measured input and desired output. According to the compensation method, the expression of the reference voltages after removing the terms of compensation −wr .Lq .isq and wr (Ld .isd + ∅f ) become: [13–17]: 

V sdref = (Rs + S.Ld )isd V sqref = (Rs + S.Lq )isq

(19)

In order to decouple the electromagnetic torque and control it by Isq only, the vector control imposes Id_ref = 0. the expression of the electromagnetic torque becomes as follows [7, 13]. Tem =

3 p . .Isq .∅f 2 Jtot

(20)

The control strategy FOC is represented in the Fig. 5 below:

3.2 Sliding Mode control (SMC) The sliding mode control technique considered one of the special cases of the VSS variable structure system, which is characterized by a choice of the structure and the switching logic [16]. The evolution of this system depends only on properties

Fig. 5 Field Oriented Control of the PMSG.

Improvement of the Wind Power System Based on the PMSG

1261

of the sliding surface. The principle of this method consists in forcing the system state trajectory to attain a hyper surface in finite time and then stay there, where the behavior of results corresponds to the desired dynamics [14–18]. The implementation of this method of control requires three complementary steps concerning [16–18]: • Selecting the sliding surface. • Defining the convergence conditions based on Lyapunov functions. • Determining the strategy of the control. The Sliding Mode applied to PMSG Considering the sliding surface proposed by SLOTINE [16]:  S(X) =

d +δ dt

r−1 ∗ e(x)

(21)

With, e(x): the error in the output state e(x) = xref – x. δ: a positive coefficient. r: the rth order system. Where for r = 1, S(X) = e(x). For the order system r = 1, the sliding surface for the dq-axis current and mechanical speed generator can be presented as follows [7–18]: S(mec ) = e(mec ) = mecref − mec

(22)

S(Isd ) = e(Isd ) = Isdref − Isd

(23)

  S Isq = e(Isq ) = Isq ref − Isq

(24)

Where the derivative of the sliding surface is expressed by: f 3 p 1 ˙ mec ) =  ˙ mec−ref + .mec + . .Isq .∅ f − .Ttur b S( Jtot 2 Jtot Jtot

(25)

˙ sd ) = I˙sd −r e f + Rs .i sd − p.mec . L q .i sq − V sd S(I Ld Ld Ld

(26)

  ∅f Rs Ld V sq S˙ Isq = I˙sq −r e f + .i sq + p.mec . .i sd + p.mec . − Lq Lq Lq Lq

(27)

The Direct and Quadrature Stator Current Controller In order to obtain a switching around the surface and a good dynamic performance, the controller structure entails two parts: a discontinuous control VN according to the sign of the sliding surface and an equivalent control Veq [14–18].

1262

B. Majout et al.

Vsd,q _r e f = Vsd,q _eq + Vsd,q _N

(28)

˙ The sliding mode occurs on the sliding surface when S(x) = 0 and Vsd,q _N = 0 [16–18], We can deduce the expression of the equivalent command Veq:

Vsq _eq

 Lq Rs .i sd − p.mec . .i sq Vsd _eq = L d I˙sd −r e f + Ld Ld

 ∅f Rs Ld = L q I˙sq −r e f + .i sq + p.mec . .i sd + p.mec . Lq Lq Lq

(29) (30)

While the discontinuous control is given by the following equations: Vsd _N = K d .sat(S(Isd ))with K d > 0

(31)

   Vsq _N = K q .sat S Isq with K q > 0

(32)

Speed Regulator For the speed controller, the control variable becomes the reference current of the quadrature axis.by Following the same method used for controlling the d and q axes, the expression of Isq _ref is given as follows [7–18]. Isq _r e f = Isq _eq + Isq _N 

Jtot ˙ mec−r e f +  Isq _eq = − 23 . p.∅ f

f

.mec − Jtot

(33) 1 Jtot

.Ttur b

Isq _N = K mec .sat(S(mec )) with K mec > 0

(34)

4 Simulation and Results In order to evaluate the performance of both control strategies FOC and SMC, we tested the behavior of the wind system based on PMSG in a dynamic regime, by a test performed in MATLAB/Simulink, where the turbine is trained by a variable wind speed using the MPPT technique to approach reality (see Fig. 6). The wind system parameters (Turbine and PMSG) are given in Table 3. The following figure illustrate the results of the PMSG simulation in the Matlab /Simulink environment: From curves shown in Fig. 7, we can notice that the sliding mode control offers better results compared to vector control based on the classical PI regulators. On the

Improvement of the Wind Power System Based on the PMSG

1263

Fig. 6 Wind speed profile

other hand, the references fixed for both controls are well followed by the electromagnetic torque and the active and reactive powers as well as the stator currents. In addition, the reactive stator power is null which gives a unit power factor. The electromagnetic torque varies according to the variation of the mechanical torque. The torque ripple for SMC control is strongly reduced compared to the torque ripple for FOC control. The control of the rotation speed is well ensured. The Zoom 1 of the rotational speed shows clearly the quality of the SMC control compared to FOC on the mechanical rotational speed as well as the rapidity and precision. We note also that the static and dynamic errors are infinitely low in the case of SMC. The good quality of the stator currents in the rotating frame of reference generates sinusoidal three-phase currents in good quality. However, the frequency of the currents changes with the change of the mechanical rotation speed which is proportional to the variation of the wind speed. The ripple rate of the currents for SMC control is low than the rate found in the case of vector control. The Table 2 shows the time when each response starts to follow its reference.

5 Conclusion This paper presents a comparative analysis between SMC and FOC control applied to the machine-side static converter for a variable speed wind turbine based on the permanent magnet synchronous generator. In the first time, We presented the vector control then the non-linear SMC control in the second time. According to the results shown in Fig. 7 and Table 2, we notice that the vector control offers an acceptable result, whereas the SMC control offers a high static and dynamic performances. Therefore, the SMC control remains the most appropriate solution to be applied for the control of the three-phase rectifier.

1264

B. Majout et al.

FOC Technique

SMC Technique

Fig. 7 Simulation results of the Field-Oriented Control and Sliding mode control

Improvement of the Wind Power System Based on the PMSG Table 2 Summarization table of the response time SMC and FOC Controller

1265

FOC (Tr) (ms)

SMC (Tr) (ms)



20 ms

10 ms

Ps

20 ms

12 ms

Qs

22 ms

16 ms

Tem

50 ms

30 ms

Table 3 PMSG and Wind Turbine parameters PMSG Parameters

Symbol

Values

Turbine parameters

Symbol

Values

Power Generator

Pn

1,5 MW

Radius of the turbine blade

R

55 m

Pole number

P

75

Turbine + generator moment

Jtot

10000 N.m

Stator Resistance

Rs

6.25e−3 

Specific density of air ρ

d axis inductance

Ld

4.229e−3 H

Tip-speed ratio

λopt

8

q axis inductance

Lq

4.229e−3 H

Optimal power coefficient

Cpmax

0.426

Generator flux

Øf

11.1464 Wb

Generator flux

Øf

11.1464 Wb

1.22 kg/m3

Coefficient of friction f 0.0142 N.m.s/rad

Table 4 Nomenclature Nomenclature Pturb

Power captured by the wind turbine

S

Blade swept area

Vw

Wind speed

R

Radius of the turbine blade

mec

Mechanical generator speed

ωr

Electric pulsation

t

Mechanical turbine speed

Pgen

Active generator power

Tem

Electromagnetic generator torque

Qgen

Reactive generator power

Tem_ref

Reference generator torque

Vs

Stator voltage vector

Ttur

Turbine torque

Rs

Stator resistance

Cp(λ, β)

Power coefficient

L(d,q)

d-q axis inductance

λ

Tip-speed ratio

Vs(d,q)

d-q axis stator voltage

β

Pitch angle

is(d,q)

d-q axis stator current

ρ

Specific density of air

(d,q)

d-q axis flux

p

Number of poles pairs

∅f

Generator flux

References 1. Ackermann T, Soder L (2002) An overview of wind energy-status 2002. Renew Sustain Energy Rev 6(1e2):67e127

1266

B. Majout et al.

2. Allouhi A (2019) Energetic, exergetic, economic and environmental (4 E) assessment process of wind power generation. J Clean Prod 235:123–137 3. Jelavi M, Petrovi V, Peri N (2018) Estimation based individual pitch control of wind turbine. Automatika 51:181–192 4. Boualouch A, Essadki A, Nasser T, Boukhriss A, Frigui A (2015) Power control of DFIG in WECS using backstipping and sliding mode controller. Int J Power Electron Drive Syst (IJPEDS) 9:612–618 5. Dragomir G, Serban ¸ A, N˘astase G, Brezeanu AI (2016) Wind energy in Romania: a review from 2009 to 2016. Renew Sustain Energy Rev 64:129–143 6. Gasch R, Twele J (eds) (2011) Wind power plants: fundamentals, design, construction and operation. Springer, Heidelberg 7. El Mourabit Y, Derouich A, El ghzizal A, Bouchnaif J, El ouanjli N, Zamzoum O, Mezioui K, Bossoufi B (2019) Implementation and validation of backstepping control for PMSG wind turbine using dSPACE controller board. Energy Rep J 5: 807–821 8. BossoufiB, Ionita S, Alami Arroussi H, El Ghamrasni M, Ihedrane Y (2017) Managing voltage drops a variable speed wind turbine connected to the grid. IJAAC Int J Autom Control 11(1), 15–34 9. Bossoufi B, Karim M, Lagrioui A, Taoussi M, Derouich A (2015) Observer backstepping control of DFIG-generators for wind turbines variable-speed: FPGA-based implementation. Renew Energy 81:903–917 10. Seyoum D, Grantham C (2003) Terminal voltage control of a wind turbine driven isolated induction generator using stator oriented field control. IEEE Trans Ind Appl 846–852 11. Hong CM, Chen CH, Tu CS (2013) Maximum power point tracking-based control algorithm for PMSG wind generation system without mechanical sensors. Energy Convers Manage 69:58–67 12. El Hammouchi F, El Menzhi L, Saad A, Ihedrane Y, Bossoufi B (2019) Wind turbine doublyfed asynchronous machine diagnosis defects using stator and rotor currents lissajous curves. Int J Power Electron Drive Syst (IJPEDS) 10(2):961–971 13. Blaschke F (1972) The principle of field orientation as applied to the new transvector closedloop system for rotating-field machines. Siemens Rev 34(3):217–220 14. Taoussi M, Karim M, Hammoumi D, Elbakkali C, Bossoufi B, El Ouanjli N (2017) Comparative study between Backstepping adaptive and Field-oriented control of the DFIG applied to wind turbines. Process IEEE Xplore 23 15. Slotine JJ, Li W (1991) Applied nonlinear control. Prentice-Hall, Englewood Cliffs, NJ. 461, Google Scholar (1998) 16. Ihedrane Y, El-Bekkali C, Bossoufi B (2018) Improved performance of DFIG-generators for wind turbines variable-speed. Int J Power Electron Drive Syst (IJPEDS) 9(4):1875–1890 17. Fantino R, Solsona J, Busada C (2016) Nonlinear observer-based control for PMSG wind turbine. Energy 113:248–257 18. Majout B, Abrahmi D, Ihedrane YE, Bakkali C, Mohammed K, Bossoufi B (2020) Improvement of sliding mode power control applied to wind power generation system based on DFIG. Int J Power Electron Drive Syst (IJPEDS) 11(2):580–593

Control of Wind Water Pumping Using Input-Output Feedback Linearization Technique Atarsia Loubna, Toufouti Riad, and Meziane Salima

Abstract To solve the problem of water for irrigation in isolated regions and rural remote areas, where the expansion of the power network line is complex and she is more costly. In this paper a control of the wind pumping systems is presented. Classical control strategy of induction motors (IMs) is very sensitive to the variation of rotor resistance, to leads a strong coupling between the rotor flux and the electromagnetic torque. To resolve this difficulty, we propose in this paper, a nonlinear control of high performance applied of induction motor, to improve the tracking control, for guarantee stability, robustness to parameter variations, perfect decoupling between the flux and torque. The robustness of the proposed study is confirmed through software simulation with parameter variations. Keywords Wind energy · Induction motor · PMSG · Input output linearization · Centrifugal hydraulic pump · Water pumping system

1 Introduction Water supply, livestock and domestic necessities are main issues in the progress of isolated and Saharan regions, where the extension of the conventional power network remains very hard to do [1, 2]. Therefore, recently it is necessary to use inexhaustible resources to fulfill their energy requirements. [2, 3]. The demand for rural water for domestic supplies and irrigation is increasing, with the result that surface water become increasingly scarce in the future [4, 8]. For this, it seems that the only and good alternative to this problem is groundwater. but the water table also decreases, making manual pumping more challenging. However, the pumping of water mechanized will be the only dependable substitute for lifting water A. Loubna (B) Department of Electrical Engineering, Oum El Bouaghi University, Oum El Bouaghi, Algeria T. Riad · M. Salima Department of Electrical Engineering, Laboratory of Electrical and Renewable Energies LEER, Souk Ahras University, Souk Ahras, Algeria © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_116

1267

1268

A. Loubna et al.

from the ground. Even so, water pumping in the world usually depends on conventional electricity produced by electricity or diesel. A photovoltaic and wind pumping system has appeared as a satisfactory alternative to classical pumping systems used in isolated regions and rural remote areas. Pumping water through wind energy minimizes costly fuels, noise and air pollution [9]. The water pumping systems with wind turbines are generally composed of a wind turbine, an electric generator, one or more converters and a motor generally driven the centrifugal pump load [7]. A squirrel cage induction generator (SCIG), and DC Motor the Permanent Magnet Synchronous Generator (PMSG), were generally most used in the water pumping [7–10]. the permanent magnet synchronous generator (PMSG) has been gaining popularity due to its high mass torque efficiency with low inertia ratio, its simple of control, recently are good competitor of the induction motor in a wind energy conversion [11, 12]. For this, the research has oriented to other solutions involving less expensive and more robust actuators, namely the Induction machine, more reliable and less costly in terms of construction and maintenance [14, 15]. Induction machine is careful as a distinctive actuator in the all industries for variable speed applications since it has many advantages such as reliability, simplicity of design, high efficiency, good self-starting, the absence of the collector brooms system, low maintenance, and relatively reasonable budget. [2, 16], However, it dynamic model is nonlinear and there control is very complex, due the high coupling between the input variables, output and internal variables [16–19]. But the advancement of power electronics, made a fundamental revolution which its m main objective is to develop the recent control strategies for IM. Several control strategies have been developed and used for IM drives such as: Direct Torque and Flux Control (DTFC), Vector Control (VC) [13, 16]. However, this technique demands the use of a magnetic sensor based on Hall Effect, to measure the magnitude of the rotor flux, this sensor remains very sensitive to motor parameters variations and raises the cost of the drive equipment [16, 21, 22]. To solve this problem involved in a vector control, many researchers presented new control strategy using the rotor flux and speed estimation without any sensor in order to decrease the expenses and have the features of precision, elimination of disturbance and fast torque response [22]. One among nonlinear control methods is the nonlinear control with input output linearization, based on the transformation of the nonlinear model into several linear system; which makes the linear feedback control easy to be applied in respect to trajectory reference [16, 22, 23], The input output linearization control based on differential geometry theory is used successfully to eliminated coupling problem, resulting in a rapid transient response with decoupled flux and torque response [16]. In recent decades, the theory of feedback control of nonlinear seen significant developments in various industrial areas and has now the axis of several research status, it is based on theoretical concepts of differential geometry such that the Lie derivative and deffeomorphism, it allows diffeomorphic by a nonlinear transformation and an uncoupled state feedback to linearize the template and placed in the canonical form [6, 19]. In this background several research paper have been published in various research laboratory.

Control of Wind Water Pumping …

1269

In [25] the authors present a control technique of water pumping system based on wind turbine generator in an isolated area (in South of Algeria), Simulation results have confirmed satisfactory performance and verified the efficiency of the suggested pumping system. In [26] The authors provided control of a water pumping system based on a wind turbine generator in an isolated area by using the Maximum Power Tracking Point (MPPT), The wind energy conversion system is used to drive the permanent magnet synchronous generator (PMSG) to feed the isolated load which is consisted of DC motor drives a hydraulic centrifuge pump. In [27] The authors applied the vector control of a wind power conversion system based on the doubly fed induction machine (DFIM) connected to the grid. In [28] presented an experimental study on the efficient field oriented control with power losses for the optimization of the energy production of a wind energy conversion chain based on the six phase asynchronous generator. In [29] the authors used the scalar command for controlling a PV water pumping system, consists in driving the machine with a constant flow for variable voltages, the simulation results showed the effectiveness and feasibility of the suggested method. In [2] the authors presented an robust control technique based input-output linearization control of IM coupled with hydraulic pump powered by a PV panel. Simulation results have confirmed good performance of the proposed PV Water pumping system. To solve the problems of high coupling between the flux and torque in vector control strategy and to improve the tracking control, for guarantee stability, robustness to parameter variations, perfect decoupling between the flux and torque, we propose in this paper, a nonlinear control of high performance applied of (IM). In the proposed system, the (PMSG) is wind turbine driven. The centrifugal pump is driven by an IM and fed by a voltage-source inverter (VSI).This article is presented as follows; the description of wind pumping system is evolved in Sect. 2. In Sect. 3 feedback linearization control of IM will be presented. In Sect. 4 the performance of the proposed control of the water pumping system is verified by numerical simulation. Finally, some conclusions are given in Sect. 5. The robustness of the proposed control system is verified through numerical simulation with motor parameters variations (Figs. 2, 3, 4, 5, 6 and 7).

2 Description of Wind Pumping System 2.1 Wind Turbine The wind turbine captures the kinetic energy of the wind, passing through the section of the active surface swept by the turbine blades, and converts it into mechanical energy (torque) which turns the rotor of an electric generator [26, 30]. The mechanical power of the wind turbine is given by the following expression [4, 31]:

1270

A. Loubna et al.

Fig. 1 Representation of the wind pumping system

Ptur =

1 ρ SV 3 C p (λ) 2

(1)

Where: Ptur : is the extracted power, ρ: is the air density [kg/m3 ], V: is the wind speed [m/s], S: is the area swept by the rotor blades, C p : is called the power coefficient. λ is tip speed ratio of rotor blade which equals a tip speed to a wind speed is written as follows [32, 33]. λ=

t R V

(2)

R: is the radius, t : is the mechanical speed of the wind turbine rotor.

2.2 Permanent Magnet Synchronous Generation Model In the (d, q) axis the model of the PMSG used in the modeling model is given by the following equations [4, 11, 26]: 

      ψd id ψv Ld 0 + = iq 0 0 Lq ψq  Vd = Rs .i d + dtd ψd + ωr ψq Vq = Rs .i q + dtd ψq − ωr ψd

(3)

(4)

Control of Wind Water Pumping …

1271

The equation of mechanical speed is given by Tem − Tr = J

dr + f.r dt

(5)

The equation of electromagnetic torque is expressed by (6) [11]: Tem =

   3p  ψ f iq + L d − L q id iq 2

(6)

ψ f : Rotor Flux.

2.3 Modeling of Motor Pump System • Model of IM. The model of the IM in the stationary frame (α, β) is given by (7) [2, 21, 34]:

x = f (x) + g(x).u y1 = h 1 (x);

(7)

y2 = h 2 (x)

Where ⎡ ⎢ ⎢ ⎢ f (x) = ⎢ ⎢ ⎣

⎤ −γ Isα + TKr φr α + pK φrβ K ⎥   T −γ Isβ − pK φr α + T r φrβ ⎥ 1 g1 ⎥ M 1 = σ LS ⎥ [g] = T r Isα − T r φr α − pφrβ ⎥ 0 g2 M 1 ⎦ T r Isβ + pφr α − T r φrβ M 1 p J L r (φr α Isβ − φrβ Isα ) − J (TL + f )

0 000 1 σ LS 0 0 0

t

  2 x = [Isα Isβ φr α φrβ ]T : u = [u sα u sβ ]T , y =  (φr2α + φrβ ) Tr =

Lr M2 M Rs Rr M 2 ; σ =1− ;K = ;γ = + Rr Ls Lr σ Ls Lr σ Ls σ L s L r2

The expression of electromagnetic torque and dynamical speed can be expressed as follows [20]: Tem = J

PM (φr d Isq − φrq Ir d ) Lr

dr = Tem − TL − f r dt

(8) (9)

1272

A. Loubna et al.

Model of Centrifugal Pump. The water pump is an essential element in water pumping systems [2] centrifugal pump is mostly commonly used due there advantages such as: simple working, supple modular and high efficiency [5, 6, 34].

H pump = k0 + k1 Q + k2 Q 2

(10)

Where: k 0 , k 1 , k 2 : are the constant of the pump, H[m]: the head of the pump and Q [L/s]: the flow rate of the pump [35, 36].The IM is coupled to centrifugal pump, by the following mechanical equation [2, 2]: J

d = Tem − T pump dt

(11)

The torque- speed relation of this pump is given by. T pump = k T 0 + k T 1  + k T 2 2

(12)

Where, k T0 , k T1 , k T2 are the empirical constants of the pump which can be obtained from the measurements.

3 Input-Output Linearization Technique The difficult case for the principle of the linearization input-output decoupling is the choice of output variable yi. For the application of linearization methods state feedback to control the asynchronous machine, there are two cases where we can [15, 16]. The system to be controlled by a control law linearization must be square type. As a result, we chose to rotor speed and magnitude of rotor flux as outputs of the process [15].  y=

⎤  1 2  2 2  φ + φ 2 rβ ⎥ ⎢ 2 rα φr α + φrβ h 1 (x) ⎥ =⎢ ⎣ pM  ⎦ = h 2 (x) Ce Isβ φr α − Isα φrβ Lr      h 1 (x)  =  2 y= 2 h 2 (x) φr α + φrβ 

⎡

(13)

(14)

To obtain the optimal non-linear law control, we determine the relative degree of the output y1 and y2 , that is to say the number of times to derive the output to show the input Vsα Vsβ .changes in output are selected given by the Eq. (14) such that: [15, 15].

Control of Wind Water Pumping …

1273

⎧ ⎨ h 1 (x) = y1 =    2 ⎩ h 2 (x) = y2 = 1 φr2α + φrβ 2

(15)

Using the Lie derivatives to express the machine model by the relation input-output [2, 23]. • Rotor speed y1 =  ⎧ y1 = L 0f h 1 (x) = h 1 (x) =  ⎪ ⎪ ⎪ ⎨  Cr fr pM  φr α I Sβ − φrβ I Sα −  − y˙1 = L f h 1 (x) = ⎪ J Lr J J ⎪ ⎪ ⎩ y¨1 = L 2f h 1 (x) + L g1 L f h 1 (x).u sα + L g2 L f h 1 (x).u sβ

(16)

• Rotor flux y1 = φr2 ⎧ 2 y = L 0f h 2 (x) = h 2 (x) = φr2 = φr2α + φrβ ⎪ ⎨ 2 y˙2 = h˙ 2 (x) = L f h 2 (x) ⎪ ⎩ y¨2 = h¨ 2 (x) = L 2f h 2 (x) + L g1 L f h 2 (x).u sα + L g2 L f h 2 (x).u sβ

(17)

Where  ⎡   ⎤ Rs 1−σ 1 fr  − I Sβ .φr α − I Sα .φrβ − + + + 2 ⎥ pM ⎢ σ Ls σ Tr Tr J ⎢ ⎥ + f  + f Cr L 2f h 1 (x) =     ⎣ ⎦ 2 J Lr pM. J J2 2 + φ 2 − p I φ + I φ . φrβ  sα rβ sβ r α r α σ L s Lr

L g1 L f h 1 (X ) == −

PM φrβ ; J σ Ls Lr

L g2 L f h 1 (X ) = −

PM φr α J σ Ls Lr

      2M 2 Rs 1−σ 3M  4  2 2 2 Isα φrβ + Isβ φrα + M + + + 2 φrβ + φrα 2 Tr σ Ls σ Tr Tr σ Tr L s L r Tr   2M 2  2 2pM  2 + + Isβ Isα φrβ − Isβ φrα + 2 Isα Tr Tr

L 2f h 2 (x) = −

L g1 L f h 2 (x) =

2M φrβ σ L s Tr

;

L g2 L f h 2 (x) =

2M φr α σ L s Tr

The total relative degree r = r 1 + r 2 = 4 < degree system n = 5, a one not observable dynamics of order 1 is obtained. [15, 16] to solve this problem we choice the variable (Z5 ) of rotor angle flux corresponding to the same approach [24] for this the new coordinates and there derivative as follows:

1274

A. Loubna et al.

⎧ z 1 = y1 = h 1 (x) ⎪ ⎪ ⎪ ⎪ ⎪ z 2 = L f h 1 (x) ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎨ z 3 = y2 = h 2 (x)

⎧ z˙ 1 ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ z˙ 2 ⎪ ⎪ ⎨ Derivative ⇒ z˙ 3 z 4 = L f h 2 (x) ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ z˙ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ 4 ⎪ ⎪   ⎪ ⎩ ⎪ φ ⎪ rβ z˙ 5 −1 ⎩ z 5 = y3 = tan φrα

= y˙1 = L f h 1 (x) = y¨1 = L 2f h 1 (x) + L g L f h 1 (x)Vsα + L g L f h 1 (x)Vsβ = y˙2 = L f h 2 (x) = y¨2 = L 2f h 2 (x) + L g L f h 2 (x)Vsα + L g L f h 2 (x)Vsβ = y˙3 = L f h 3 (x)

(18)

Using equation system (18). Derived outputs are given by: 

y¨1 (x) y¨2 (x)



 =

L 2f h 1 (x) L 2f h 2 (x)



 + D(x).

Vsα



Vsβ

(19)

The determinant of D(x) is non-zero except when the engine is stopped; thus the matrix D(x) is reversible. The optimal control is given by Eq. (20) [16, 24]: 

   −L 2f h 1 (x) + v1 Vsα −1 = [D(x)] . Vsβ −L 2f h 2 (x) + v2

(20)

If the determinant of the decoupling matrix is not zero, the non-linear control law is defined by a relation that connects the new internal inputs (v1 , v2 ) to the physical inputs (us1 , us2 ) (usα , usβ ) [2, 18, 24]. In order to further a reference trajectory * and the flux φ*r 2 these variations v1 and v2 are calculated as follows: ⎧      Cr pM  ⎪ ∗ ⎪ ˙∗ ¨ ⎪ ⎨ v1 = −k11  −  − k12 J L φrα Isβ − φrβ Isα − J −  + ∗ r      ⎪   2 2  ⎪ 2 2 2 ⎪ M φrα Isβ + φrβ Isα − φrα − φr∗2 + φr∗2 + φrβ − φr∗2 + k22 + φrβ ⎩ v2 = −k21 φrα Tr

(21)

The coefficients k ij are chosen such that the two polynomials s2 + sk 11 + k 12 and s + sk 21 + k 12 have roots at negative real part. 2

4 Simulation Results and Discussions In order to confirm the performances of the proposed control, the numerical simulation was presented on a water pumping system described in section II, composed: Wind turbine, PMSG, Rectifier, Inverter and a centrifugal pump driven by IM. The induction motor is controlled by Input-Output Linearization Control (IOLC).the simulation results are presented by Figs. 2, 3, 4, 5, 6 and 7. From the simulation results we notice that the Fig. 2 shows the actual and reference rotor speed. The rotor speed tracks quickly the reference speed.

Control of Wind Water Pumping …

1275

Fig. 2 Rotor speed of IM

100

Wr Wref

Wr Wref [Rad/s]

50

0

-50

-100 0

0.5

1

1.5

2 Time [S]

2.5

3

3.5

4

1.4

Fig. 3 Rotor flux of IM

Phir Phiref

1.2

Phir Phiref [Wb]

1

0.8

0.6

0.4

0.2

0

0

0.5

1

1.5

2 Time [s]

2.5

3

3.5

4

30

Fig. 4 Torque pump and Torque of IM

Te Tpump 20

Te Tpump [N.m]

10

0

-10

-20

-30

0

0.5

1

1.5

2 Time [S]

2.5

3

3.5

4

0

0.5

1

1.5

2 Time [S]

2.5

3

3.5

4

40

Fig. 5 Stator current of IM

30

Isa [A]

20

10

0

-10

-20

1276

A. Loubna et al.

Fig. 6 Flow of pump

2.5

Flow [m3/s]

2

1.5

1

0.5

0

Fig. 7 Total monomer head speed

0

0.5

1

1.5

2 Time [s]

2.5

3

3.5

4

0

0.5

1

1.5

2 Time [s]

2.5

3

3.5

4

35

30

Head [m]

25

20

15

10

5

0

Figure 3 shows manifests that the quick response of the rotor flux in the transient state and in steady state the rotor flux is very tracking to the reference flux and is not affected by resistances variation. We show clearly that the decoupling control between torque and flux. Figure 4 shows good torques responses in steady state. It can be observed that the motor torque is not affected by the rotor speed change and is tracks with very fast dynamic the load torque of centrifugal pump. show the wave form that phase stator current is sinusoidal with in acceptable limits of nominal current of the IM, in transient state the stator current has the peak current due low values of speed. See Fig. 6. It is also noted that the flow of the pump are not disturbed by the variations of the wind speed. The monomeric height is well maintained for the variation of the speed rotor see Fig. 7. So from these results we find that the dynamics of flux and the torque is stably maintained for the different operating regimes, which show that the decoupling is perfect and assured by this technique. It can be concluded that this technique is robust and guarantee a good insensitivity to parametric variations of the machine. In addition, it has the advantage of being simple to implement in practice.

Control of Wind Water Pumping …

1277

5 Conclusion We have presented in this article, the application of control theory by input output linearization for a wind turbine water pumping system, shows that the good regulation of the rotor flux and speed of IM by eliminating system’s nonlinearity and coupling effect control. Simulation results have shown the excellent performances of the proposed IOLC controller. It can be inferred that this system is an ideal solution for water supply in sparsely populated, isolated and enclosed areas.

References 1. Chikh A, Chandra A (2009) Optimization and control of a photovoltaic powered water pumping system. In: IEEE Electrical Power & Energy Conference (EPEC), pp 1–6 (2009) 2. Meziane S, Atarsia L, Toufouti. R (2018) A global stability of linearizing control of induction motor for PV water pumping application. Int J Syst Dyn Appl (IJSDA), 7:31–56 3. Camocardi P, Battaiotto P, Mantz R (2008) Wind generator with double stator induction machine. Control strategy for a water pumping application.In: 43rd international universities power engineering conference, pp 1–5 (2008) 4. Rahrah K, Rekioua D (2015) Economic study of a solar/wind water pumping system with battery storage installed on the site of Bejaia. In: 3rd International renewable and sustainable energy conference (IRSEC), pp 1–6 5. Bouzeria H, Fetha C, Bahi T, Abadlia I, Layate Z, Lekhchine S (2015) Fuzzy logic space vector direct torque control of PMSM for photovoltaic water pumping system. Energy Procedia 74:760–771 6. Jaziri S, Jemli K (2013) Optimization of a photovoltaic powered water pumping system. In: International conference on control, decision and information technologies (CoDIT), pp 422– 428 7. Hammadi S, Hidouri N, Sbita L (2011) A DTC-PMSG-PMSM drive scheme for an isolated wind turbine water pumping system. Int J Res Rev Electr Comput Eng (IJRRECE) 1:1–6 8. Girma M, Molina M, Assefa A (2015) Feasibility study of a wind powered water pumping system for rural Ethiopia (2015) 9. Chandel S, Naik MN, Chandel R (2015) Review of solar photovoltaic water pumping system technology for irrigation and community drinking water supplies. Renew Sustain Energy Rev 49:1084–1099 10. Lebsir A, Bentounsi A, Benbouzid M, Mangel H (2015) Electric generators fitted to wind turbine systems: an up-to-date comparative study 11. Mahersi E, Kheder A (2016) Adaptive backstepping control applied to wind PMSG system. In: 7th International Renewable Energy Congress (IREC), pp 1–6 12. Tahour A, Aissaoui A, Abid M, Essounbouli N, Megherbi AC (2010) La commande de la puissance active et réactive d’une éolienne à génératrice synchrone. In: Revue des Energies Renouvelables SMEE 2010 Bou Ismail Tipaza, pp 327–335 13. Atarsia L, Oukaci A, Toufouti R, Meziane S (2015) Direct torque control of induction motor and regulation speed using the adaptive control. In: International conference on automatic control, telecommunications and signals (ICATS15), pp 1–7 14. Meziane S, Toufouti R, Benalla H (2008) Generalized nonlinear predictive control of induction motor. Int Rev Autom Control 1:65–71

1278

A. Loubna et al.

15. Toufouti R, Meziane S, Feddaoui O, Saidia M (2016) A robust decoupling control of induction motors based adaptive rotor flux observer. In: 4th International Conference on Control Engineering & Information Technology (CEIT 2016), Hammamet, Tunisia, 16–18 December 2016 16. Oukaci A, Toufouti R, Dib D, Atarsia L (2017) Comparison performance between sliding mode control and nonlinear control, application to induction motor. Electr Eng 99:33–45 17. Tarbouchi M, Le Huy H (1998) Nonlinear control of an induction motor in magnetic saturation. In: Conference record IEEE industry applications conference. Thirty-third IAS, pp 648–654 18. Pietrzak-David M, De Fornel B, Purwoadi M (1998) Nonlinear control for sensorless induction motor drives. In: International conference on power electronic drives and energy systems for industrial growth, Proceedings, pp 300–306 19. Bendaha Y, Mazari B (2007) Commande Adaptative Linéarisanted’un Moteur Asynchrone 20. Blaschke F (1972) The principle of field orientation as applied to the new transvector closedloop system for rotating-field machines. Siemens Rev 34:217–220 21. Casadei D, Profumo F, Serra G, Tani A (2002) FOC and DTC: two viable schemes for induction motors torque control. IEEE Trans Power Electron 17:779–787 22. Zhu J, Xu B, Wang X, Feng H, Xu X (2013) The research of sensorless vector control for permanent magnet linear synchronous motor. J Comput 8:1184–1191 23. Enev S (2007) Input-output decoupling control of induction motors with rotor resistance and load torque identification. In: Mediterranean conference on control & automation, pp 1–5 24. Marino R, Peresada S, Tomei P (1999) Global adaptive output feedback control of induction motors with uncertain rotor resistance. IEEE Trans Autom Control 44:967–983 25. Harrouz A, Dahbi A, Harrouz O, Benatiallah A (2014) Control of wind turbine based of PMSG connected to water pumping system in South of Algeria. In: 3rd international symposium on environmental friendly energies and applications, pp 1–4 (2014) 26. Dahbi A, Harrouz A. MPPT control of wind turbine for water pumping system. Relation 7:0.1 27. Charles CR, Vinod V, Jacob A (2015) Field oriented control of Doubly Fed Induction Generator in wind power system. In: IEEE international conference on computational intelligence and computing research (ICCIC), pp 1–5 28. Pantea A, Sivert A, Yazidi A, Betin F, Carriere S, Capolino G-A (2016) Efficient field oriented control with power losses optimisation of a six-phase induction generator for wind turbines. In: IECON 42nd annual conference of the IEEE industrial electronics society, pp 1912–1917 29. Rebei N, Benghanem B, Hmidet A, Hasnaoui O (2013) Study of photovoltaic water pumping system using scalar-DVC based control. In: International conference on electrical engineering and software applications, pp 1–8 30. Guettaf A, Bettka A, Bennis O, Benchabane F, Khaled Y, Ali A (2011) Direct field oriented control of induction motor fed by wind turbine generator under saturation effect 31. Toual B, Mokrani L, Kouzou A, Machmoum M (2018) Control and management of a solar-wind hybrid system for power quality improvement. J Eng Sci Technol 13:1420–1439 32. Guettaf A (2013) Optimisation floue-génétique d’un système de pompage éolien.Université Mohamed Khider–Biskra 33. Zhang J, Qi Z, Wen J (2017) Control of water pumping driven by wind turbine based on Quasi Z source inverter. In: Chinese automation congress (CAC), pp 7074–7077 34. Atasrsia L, Toufouti R, Meziane S (2020) Standalone photovoltaic array fed induction motor driven water pumping system. Int J Electr Comput Eng (2088–8708) 10 35. Belarbi M (2006) Modeling and simulation of PV water pumping system. Master thesis Oran university 36. Karampuri R, Jain S, Somasekhar V (2014) A single-stage solar PV power fed open-end winding induction motor pump drive with MPPT. In: IEEE international conference on power electronics, drives and energy systems (PEDES), pp 1–6

Improved Hybrid Control Strategy of the Doubly-Fed Induction Generator Under a Real Wind Profile Mourad Yessef, Badre Bossoufi, Mohammed Taoussi, Ahmed Lagrioui, and Hamid Chojaa

Abstract The design of a control technique of doubly-fed induction generator (DFIG) for providing a high quality of energy, without harmonics accumulation, to the electrical grid is a real challenge because of the nonlinearity of the wind energy conversion system (WECS). In this paper, an improved robust command has been developed and then tested in order to control the rotor side converter (RSC) of the WECS. The main contribution of this research work is to optimize the extraction of the wind energy in the real circumstances and to push the DFIG working properly, with the highest performance and robustness, in the different operation modes. Therefore, the dynamic model of the turbine has been presented in this research paper, also the hybrid Direct Power Control-Backstepping (DPC-BS) command is highlighted. Matlab/Simulink simulations analysis, with the real parameters of the turbine and the real wind profile of Dakhla-Morocco city, confirm the high accuracy, the high performance and the robustness of the proposed method, which based on the the combination between the Direct Power Control (DPC) and the Backstepping controller. Moreover, the obtained results prove, despite the variable wind speed, the validation of the developed command with a total harmonic distortion THD ~0.33% and a null overshoot for the grid currents with a frequency of 50 Hz, which gives the possibility of injection into the electrical grid. Keywords Backstepping command · DFIG · Direct Power Control · WECS

M. Yessef (B) · B. Bossoufi Laboratory of Engineering, Modeling and Systems Analysis, SMBA University, Fez, Morocco e-mail: [email protected] M. Taoussi · H. Chojaa Laboratory of Technologies and Industrial Services, Higher School of Technology, SMBA University, Fez, Morocco A. Lagrioui Department of Electrical and Computer Engineering, Higher National School of Arts and Trades, Moulay Ismail University, Meknes, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_117

1279

1280

M. Yessef et al.

1 Introduction All over the planet, the demand of electrical energy is increased more and more. However, the major energy resources of electricity generation are natural gas, fossil fuels, petroleum [1]. Nowadays, the excessive consumption of these fossil sources causes a serious environmental and economic problems; namely, on the environmental side, planet temperature increases and global warming, water pollution, the emission of CO2 and other toxic gases, and acid rain. For the economic impact, it consists of the increase in the price of petrol, oil and gas, which influences the electricity tariff and, subsequently, on all economic, agricultural and service activities. Generally, this huge use has a dangerous effects on humans and animals. Therefore, the renewable energy resources are revealed to be the serious alternatives to the polluting energies [2]. There are many kinds of renewable energy resources, that are exploited all over the world (e.g. solar, geothermal, biomass, hydropower and wind) [3]. In the last decades, the installation of wind farms has been developed and received a great support from the most governments, thanks to their profitability and cleanliness. Then, to face these urgent energy challenges, the wind energy becomes usual more and more in many countries with global power reaching over 600 GW and it target to be 1600 GW in 2030 [Word Wind Energy Association]. So, the optimization of the operation of the wind energy conversion system (WECS) has become essential, given their large share of the global energy production. Thereby, due to their several advantages, the DFIG (Doubly-Fed Induction Generator) technology with variable speed is presented to be the important way to develop turbine generators within other competing machines. The DFIG is characterized by its ability to work over a wide range of wind speeds and it performs an optimal conversion of aerodynamic power at a low cost [4]. The Kinetic power of the wind is transformed to mechanic power by the turbine and then delivered to the electrical machine by the gearbox in the transmission machine. The DFIG stator is directly linked to the electrical grid, at a time, the rotor is connected by an electronic power converter. Since the stator of the DFIG is connected directly to the electrical grid, when high peaks of stator currents occur at the stator windings, because of the establishment of a high voltage drop, overcurrents occur at the rotor windings and then on the Rotor Side Converter (RSC), due to the coupling between the stator and rotor [5, 6]. For this reason the DC bus voltage increases and the torque fluctuations would take place. Therefore, in the absence of the protection circuit, the high transient currents will damage the RSC and the DFIG [5, 7]. Parallel to the wind farms development, several algorithms and structures of control are then proposed to confident the stability, the performance and the robustness of aero-generator of WECS such as the PI (Proportional Integral) controllers and the adaptive PI [9, 10]. Other works focused on the other command types such as the scalar, the adaptive fuzzy logic controller, the direct power control (DPC) [5],

Improved Hybrid Control Strategy ...

1281

the sliding mode control (SMC) [11, 12], the predictive control and the linearization control. Unfortunately, the simulations with real wind and the testing of several commands on the same system in the same environment are lacking, in this research fields. In this work, an improved command has been developed, which combines between the Direct Power Control and the Backstepping command. The utilization of real wind conditions from a wind farm located in south of Morocco is another innovative aspect meted in this research paper. This research paper is presented as follow; firstly, after the introduction, the turbine is modeled and the dynamical model of the DFIG is presented, then the general structure of the hybrid DPC-BS command is approached. The last section for this paper will be reserved for obtained simulation results of performance and THD tests. All results are reported and have been analyzed to prove the validity, of the proposed command, to be implemented in a real scenario.

2 Methods 2.1 WECS General Model The Fig. 1 shows the structure of the studied WECS. This system is composed of a wind turbine driving a DFIG connected to a three-phase electrical grid. This model is characterized by the fact that the stator is directly coupled to the grid, whereas, in order to establish a power exchange, between the machine and the grid, in subsynchronous speed, two converters are installed between the electrical grid and the rotor. As the active and reactive powers of DFIG are controlled through the RSC, the GSC operates as a rectifier [5, 7].

Fig. 1 General model of the WECS based on DFIG

1282

M. Yessef et al.

2.2 Wind Turbine Model The wind energy is captured by the wind turbine and converted into a torque or speed. This process is modeled by the following equations [7]: ⎧ PRotor = Pwind .C p (λ, β) ⎧ ⎪ ⎪ 3 ⎪ ⎨ ⎨ PRotor = 0.5ρ.A.C p (λ, β).v 3 Pwind = 0.5ρ Av ⇒ 1 ⎪ ⎩ TRotor = .ρ.S.C p (λ, β).v 3 PRotor ⎪ ⎪ 2t ⎩ TRotor = t

(1)

⎧ ⎨ C (λ, β) = k .( k2 − k .β − k ).e( −kA5 ) + k .λ p 1 3 4 6 A where: ⎩ k1 = 0.5872, k2 = 116, k3 = 0.4, k4 = 5, k5 = 21, k6 = 0.0085 At the rotor of the turbine, the expression of the mechanical torque is [13]: ⎧ ⎨T

mec

⎩

Tmec

dmec dmec dt = Tg − Tem − f mec ⇒J dt = Tg − Tem − f mec =J

 where the gearbox equations are given by:

(2)

g = G.t TRotor = G.Tg

2.3 Dynamic Model of DFIG Wind Energy System In the WECS, the mechanical power is transferred to electrical power by the DFIG. In the (d,q) reference frame, the electrical equations of the stator and the rotor which model the DFIG are given by [7]: ⎧ ⎧ dϕsd dϕr d ⎪ ⎪ ⎪ ⎪ Vr d = Rr .Ir d + V − ω − ωr .ϕrq = R .I + .ϕ sd s sd s sq ⎪ ⎪ ⎪ ⎪ dt dt ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ dϕsq dϕrq ⎪ ⎪ ⎪ ⎪ + ωs .ϕsd + ωr .ϕr d ⎨ Vrq = Rr .Irq + ⎨ Vsq = Rs .Isq + dt dt and 1 Msr 1 Msr ⎪ ⎪ ⎪ ⎪ Isd = .ϕsd − .ϕr d Ir d = .ϕr d − .ϕsd ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ σ.L σ.L σ.L σ.L s r r r .L s ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ 1 Msr 1 Msr ⎪ ⎪ ⎩ Irq = ⎩ Isd = .ϕsd − .ϕr d .ϕrq − .ϕsq σ.L s σ.L s .L r σ.L r σ.L r .L s

(3)

The electromechanical torque, in the rotor, is modeled by the equation [7]: Tem = p(ϕr d .ϕsq − ϕrq .ϕsd )

(4)

Improved Hybrid Control Strategy ...

⎧ ϕr d ⎪ ⎪ ⎪ ⎨ϕ rq where: ⎪ ϕ sd ⎪ ⎪ ⎩ ϕsq

1283

= L r .Ir d + Msr .Isd = L r .Irq + Msr .Isq = L s .Isd + Msr .Ir d = L s .Isq + Msr .Irq

The equations which model the active and reactive powers are: 

Ps = Vsd Isd + Vsq Isq Q s = Vsq Isd − Vsd Isq

(5)

2.4 Hybrid Control Strategy of DFIG (DPC-BS) In 1991, Kokotovic proposed the Backstepping algorithm, which leads to a global and asymptotic stabilization of the system if and only if each Lyapunov function is developed to converge in a function of the Lyapunov stability theory [5]. The basic process of the Backstepping algorithm is the decomposition of the sophisticated nonlinear system into several lower order subsystems, bordering on the system orders. Then comes the stage of the elaboration of the Lyapunov functions of all the subsystems from the lowest one back until to the global system; as the functions are constructed, intermediate variables are often used to check that the subsystems are working correctly. The system could be guaranteed to be strictly stable if the development of the Lyapunov function of each subsystem satisfies the general conditions of Lyapunov stability theory. On the other hand, Neghouchi, in 1998, was the first scientific researcher who developed the DPC algorithm to control directly the active and reactive powers of the rectifiers. The presented approach in this research paper proposes a DPC-BS algorithm, which combines between the both algorithms DPC and Backstepping, to control the DFIG, whose the objective is to provide active and reactive powers which chase values without derivations [5, 7]. Therefore, after combining the both methods, the stator active and reactive powers equations become:   ⎧ K r Vs 1 ⎪ ⎪ ϕsq − ϕrq ⎨ Ps = σ L Kr s   ⎪ 1 K V ⎪ ⎩ Qs = r s ϕsd − ϕr d σ L s Kr The rotor voltage equations are given by [7]:

(6)

1284

M. Yessef et al.

⎧ Rr ϕr d dϕr d Rr K s ⎪ ⎪ + − ωr ϕrq − ϕsd ⎨ Vr d = dt σ Lr σ Lr Rr ϕrq ⎪ dϕrq Rr K s ⎪ ⎩ V rq = + − ωr ϕr d − ϕsq dt σ Lr σ Lr

(7)

The rotor voltage equations can be regenerated as follows [5]: ⎧ 1 d Qs 1 1 dϕsd 1 ⎪ ⎪ ⎨ Vr d = − μ dt − α Q s + μ ωr Ps + K dt + δϕsd − K ωr ϕsq r r dϕ ⎪ d P 1 1 1 1 s sq ⎪ ⎩ Vrq = − − α Ps − ωr Q s + + δϕsq + ωr ϕsd μ dt μ K r dt Kr where: μ =

kr Vs ,α σ Ls

=

Rr ,δ σ Lr μ

=

(8)

Rr (1−K r K s ) . σ L r Kr

Thus, the correct operation of the DFIG is expressed by the first three terms, in the voltage equations. Contrariwise, the last three terms model the variation of flux during the fault of the electrical grid. These equations can be written differently as follows [7]: ⎧ μωr ϕsq d Qs μ dϕsd ⎪ ⎪ = −μα Q s + ωr Ps + + μδϕsd − − μVr d ⎨ dt K r dt Kr ⎪ dP μ dϕsq μωr ϕsd ⎪ ⎩ s = −μα Ps − ωr Q s + + μδϕsq + − μVrq dt K r dt Kr

(9)

According to the latest equations, the rotor voltages will be the instructions to be given to the RSC to control the active and reactive stator powers. Thus the reactive stator power Qs will be controlled by the rotor voltage Vrd , and the active stator power Ps will be controlled by the rotor voltage Vrq . On the other hand, the powers errors eQ and eP , used in the proposed DPC-BS algorithm, are given by [5]: 

e Q = Q ∗s − Q s e P = Ps∗ − Ps

(10)

The derivatives from the above parameters are given by: ⎧ ∗ ∗ ⎪ μωr ϕsq μ ϕsd ∗ ⎪ ∗ ⎪ e = Q +μα Q − ω P − − μδϕsd + + μVr d s r s ⎨Q s Kr Kr ∗ ⎪ ∗ ⎪ μ ϕsq μωr ϕsq ⎪ ⎩ e∗P = Ps∗ +μα Ps − ωr Q s − − μδϕsq − + μVrq Kr Kr

(11)

To reduce these errors, we use the Lyapunov candidate function: v=

1 2 e Q + e2P 2

(12)

Improved Hybrid Control Strategy ...

1285

Fig. 2 Structure of the proposed hybrid DPC-BS command

The Lyapunov candidate function derivative should be negative, to ensure a good regulation of the stator powers. Therefore, using this function and its derivatives, the expressions of the control rotor voltages variables Vrd and Vrq can be given by [7]:     ∗ ⎧ 1  1 ∗ ∗ ∗ ⎪ ⎪ ⎨ Vr d = − μ Q s +k Q e Q − ωr Ps + α Qs − K ωr ϕsq + ϕsq + δϕsq r   ∗    ⎪ 1 1 ∗ ⎪ ⎩ Vrq∗ = − ωr ϕsq + ϕsq + δϕsq Ps∗ −ωr Ps + α Qs − μ Kr (13) The Backstepping controller intervenes when the voltage dips occur on the stator supply terminal, in order to calculate the stator power references to control the RSC and push the DFIG to operate correctly and satisfactory, by imposing the return of the voltage at its nominal value (Fig. 2).

3 Simulation Results and Discusion 3.1 Performance Test under the Real Wind Profile To ensure the satisfactory operation of the proposed hybrid command (DPC-BS) under the real wind profile, simulation tests based on Matlab/Simulink have been carried out with the 1.5 kW wind turbine driven the DFIG. Technically, to perform this test scenario, the reactive power set-point will be kept zero, to ensure a unity power factor, so as to optimize the quality of the injected energy into the eletrical grid. The chosen real wind profile is that of the Dakhla city, located in the south of Morocco (Fig. 3). This choice of site was based on the studies by researcher Allouhi

1286

M. Yessef et al.

[4]. The parameters of the used machine are described in the Tables 1 and 2 (see appendix). This test allows to evaluate the performance, the reliability of the WECS and the decoupling of the powers generated by the DFIG during the instantaneous variation of the power set-points (Fig. 4, 5, 6, 7). The analysis of the simulation results confirm, that the active power follows perfectly its reference value, which is deduced by the MPPT strategy, with a dynamic error of εd = 1.05%. It can be also seen, that the reactive stator power is kept null in order to have a unit power factor obtained after the end of its transient regime. Moreover the stator currents varies according to the wind variations, which confirms its proportional relationship with the power. Despite the wind fluctuations, this current

zoom

15

10

5

0 0

5

10

15

20

Zoom wind speed (m/s)

10.5 10 9.5 9 8.5 8 7.5

0

5

10

15

20

25

time (s)

Fig. 3 Wind profile of the Dakhla city

1500 Ps

-500

Ps*

zoom

-1000

-500 -1000

Zoom

Actice power (w)

0

-1500 -2000

-1500 -2000

-2500 -3000

0

5

10

15

20

25

-2500 14.5

time (s)

15 time (s)

15.5

Fig. 4 Active power

15 Reactive power (VAR)

Fig. 5 Reactive power

Qs

10

Qs*

5 0 -5 -10 -15

0

5

10

15 time (s)

20

25

Improved Hybrid Control Strategy ... Fig. 6 Stator currents

1287

Stator currents (A)

80

isa

isb

isc

40

0

-40 zoom(1)

Rotor currents (A)

-80

10 8 6 4 2 0 -2 -4 -6 -8 -10

0

ira

5

irb

10 15 time (s)

20

25

irc

zoom(1) 0

5

10 15 time (s)

20

25

Fig. 7 Rotor currents

remains sinusoidal with a frequency of 50 Hz, which is the frequency of the electrical grid. The rotor currents remain sinusoidal during the DFIG operation, its frequency varies proportionally to the mechanical speed. These currents reveal fewer ripples compared to other control techniques existing in literature.

3.2 Spectral Analysis Test In order to verify the effect of the hybrid command DPC-BS on the quality of the supplied power by the machine, a spectral analysis of the stator and rotor currents was carried out. Note that this measurement was carried out for 3 operation cycles with a harmonic order of 70. The Fig. 8 shows respectively the currents of the stator phase “isA ” and the rotor phase “ira ” supplied by the DFIG, as well as their harmonic spectra. These figures confirmed the influence of the control on the quality of the supplied energy. The rate of harmonic distortion is equal to 0.33% at the level of the rotor current, and 0.94% at the level of the stator current. From the depicted results in ira (3Hz) = 1.85 , THD= 0.33%

Fig. 8 THD of injected currents into the electrical grid

1288

M. Yessef et al.

these simulations, it can be noted that the harmonic distortion rate THD has a good quality for the both injected currents into the electrical grid and remains below the limit of 2% which imposed by the IEEE-519 standard [13]. This THD value (0.33%) obtained, in this work, by the proposed DPC-BS hybrid control is the lowest, compared to the value recently reached by the researchers [8] for WECS based on DFIG which is 1.67%.

4 Conclusion In this work, we have presented a contribution for the control of the wind energy conversion system. Therefore we applied the improved hybrid DPC-BS command to improve the performance of the doubly-fed induction generator. This approach is based on the combination of the Backstepping controller and the Direct Power Control. The simulation results analysis have shown that the hybrid DPC-BS command gives highest dynamic performance in the reference tracking, in a test scenario with a real wind profile, and it also ensures an optimal energy conversion. Decidedly, this command provides, a concrete solution to the problems of robustness and pursuit. Moreover, another essential point of this work is that, it confirmed the injecting possibility of the supplied energy, by the studied WECS, into the electrical grid. For the future works, a hardware-in-the-loop simulation on dSPACE will be realized to verify the proposed approach in the real time by experiments.

Appendix

Table 1 The DFIG parameters

Name

Values

Nominal stator voltage

220/380 V

Nominal rotor voltage

12 V

Nominal current

3.64/6.31A

Nominal power

Pn = 1,5 kW

Nominal frequency

fn = 50 Hz

Nominal torque

Cn = 10 N.m

Power factor

0.89

Number of pole pairs

P=2

Nominal speed

1440 tr/min

Stator resistance

Rs = 0.435 (continued)

Improved Hybrid Control Strategy ... Table 1 (continued)

Table 2 The wind turbine parameters

Name

1289 Values

Rotot resistance

Rr = 0.816

Stator inductance coefficient

Ls = 0.071H

Rotor inductance coefficient

Lr = 0,070H

Mutual inductance coefficient

M = 0,069H

Friction coefficient

f = 6,73.10−3 kg.m2

Moment of inertia

J = 0,3125 N.s/rad

Name

Value

Nominal power

Pn = 7,8 kW

Density of air

ρ = 1,225

Blade angle

β = 2°

Number of blades

3

Blade radius

R=3m

Gearbox ratio

G = 10,38

Conversion yield

η=1

Moment of inertia

J = 0,042 kg.m2

Viscous friction coefficient

f = 0,024 N.s/rad

References 1. Allouhi A (2019) Energetic, exergetic, economic and environmental (4 E) assessment process of wind power generation. J Clean Prod 235:123–137 2. Lacal Arantegui R (2015) Materials use in electricity generators in wind turbines – state-ofthe-art and future specifications. J Clean Prod 87:275–283 3. Allouhi A, Zamzoum O, Islam MR, Kousksou T, Jamil A, Saidur R, Derouich A (2017) Evaluation of wind energy potential in Morocco’s coastal regions. Renew Sustain Energy Rev 72:311–324 4. De Luca E, Nardi C, Giuffrida LG, Krug M, Di Nucci MR (2020) Explaining factors leading to community acceptance of wind energy. Results of an expert assessment. Energies. 13:2119 5. Mensou S, Essadki A, Nasser T et al (2020) A direct power control of a DFIG based-WECS during symmetrical voltage dips. Prot Control Mod Power Syst 5:5 6. Bossoufi B, Karim M, Lagrioui A, Taoussi M, Derouich A (2014) Modeling and backstepping control of DFIG generators for wide-range variable-speed wind turbines. J Electr Syst JES 10(3):317–330 7. Bossoufi B, Karim M, Taoussi M, Alami Aroussi H, Bouderbala M, Motahhir S, Camara MB (2020) DSPACE-based implementation for observer backstepping power control of DFIG wind turbine. IET Electric Power Appl 14(12):2395–2403 8. Zamzoum O, Derouich A, Motahhir S, El Mourabit Y, El Ghzizal A (2020) Performance analysis of a robust adaptive fuzzy logic controller for wind turbine power limitation. J Cleaner Prod 265:121659. https://doi.org/10.1016/j.jclepro.2020.121659 9. Taoussi M, Karim M, Lagrioui A, Bossoufi B, El Mahfoud M (2014) The fuzzy control for rotor flux orientation of the doubly-fed asynchronous generator drive. Int J Comput Technol 13(08):4707–4722

1290

M. Yessef et al.

10. Taoussi M, Karim M, Bossoufi B, Hammoumi D, Lagrioui A (2015) Speed backstepping control of the doubly-fed induction machine drive. J Theor Appl Inf Technol 74(2):189–199 11. Bossoufi B, Karim M, Lagrioui A, Taoussi M, Derouich A (2015) Observer backstepping control of DFIG-generators for wind turbines variable-speed: FPGA-based implementation. Renew Energy 81:903–917 12. Taoussi M, Karim M, Bossoufi B, Hammoumi D, Elbakkali C, El Ouanjli N (2017) Comparative study between Backstepping adaptive and Field-oriented control of the DFIG applied to wind turbines. In: Processing IEEE Xplore 13. Institute of Electrical and Electronics Engineers. Static Power Converter Committee (1981) IEEE Guide for Harmonic Control and Reactive Compensation of Static Power Converters

Flow-Oriented Control Design of Wind Power Generation System Based on Permanent Magnet Synchronous Generator Nada Zine Laabidine, Chakib El Bakkali, Karim Mohammed, and Badre Bossoufi

Abstract In this article we have studied the control of a high performance electric machine, more precisely a permanent magnet synchronous generator using the Flux Orientation Control (FOC), this allowing us to independently control the torque and the flux of our machine in the same way as a DC machine with separate excitation, where the inducing current controls the magnetic flux, and the induced current controls the electromagnetic torque. We transformed the stator instantaneous currents to two current components, one which controls the flux (along the d axis), and the other controls the torque (along the q axis). The goal of our article is based on the DFOC (direct) technique, which consists of neglecting the coupling terms, and setting up a proportional integral regulator (PI) in order to follow the parametric variations of our machine using two types of wind (variable and step) and independently control the active and reactive power. Keywords Wind power generator system · Permanent magnet synchronous generator (PMSG) · Flow-oriented control

1 Introduction In the last few years, the use of synchronous machines in industry has grown sharply because of their similar characteristics with DC machines [1, 2]. Two types of synchronous machines are present in the market, synchronous machines with permanent magnets and winding rotor machines, those with permanent magnets have superior performance compared to the other with excellent efficiency, high mass power and reduced maintenance [3, 4]. Several control strategies are present in the literature and are reserved to synchronous generator with permanent magnet, we can quotes for example: N. Z. Laabidine · C. El Bakkali · K. Mohammed · B. Bossoufi (B) LIMAS Laboratory, Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fez, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_118

1291

1292

N. Z. Laabidine et al.

direct torque control (DTC), vector oriented flow control (FOC) or the non-linear Back-stepping control. We present in this article the modeling of the synchronous permanent magnet machine using vector-oriented flow control [5] (FOC) applied to the static converter on the machine side [6], which allows us to control the electromagnetic torque, magnetic flux, active and reactive powers of a wind generator in order to obtain a simple and reliable model for numerical simulation.

2 Permanent Magnet Synchronous Generator Model (PMSG) The GSAP has a coiled stator but the rotor is replaced by magnets, which eliminates rhetoric losses and is considered a power source that can provide excitation current.

2.1 PMSG Electrical Equations The equations are related to the stator by [7]: ⎤ ⎡ ⎤ ⎤ ⎡ isa Vsa sa d ⎣ Vsb ⎦ = Rs ⎣ isb ⎦ + ⎣ sb ⎦ dt Vsc isc sc ⎡

(1)

With: • • • •

R s :The resistance of a statoric phase () V sa : Statoric phase voltage (V) i sa : Statoric current intensity (A) sa : Total flow through the stator coils (Wb) PMSG stator voltage equations in a system of axes (d-q) Vsd = −Rs i sd − sd − ωe sq

(2)

Vsq = −Rs i sq − sq + ωe sd

(3)

With: • ωe : The electric pulsation of voltages (rad.s−1 ) • sd : Direct component of the statoric flow (Wb) • sq : Quadratic component of the statoric flow (Wb)

Flow-Oriented Control Design of Wind Power Generation System ...

1293

Expressions of (sd , sd ) depending on the stator currents sd = L d · i sd +  f

(4)

sq = L q · i sq

(5)

With: L d L q are the direct and quadratic components of the statoric inductance (H). Then [8]:     d L d · i sd +  f − ωe L q · i sq Vsd = −Rs i sd − dt     d L q · i sq + ωe L d · i sd +  f Vsq = −Rs i sq − dt

(6)

(7)

Ignore the stator transients and obtain: d(i sd ) − ωe (L q · i sq ) dt     d i sq + ωe L d · i sd +  f − Lq · dt

Vsd = −Rs i sd − L d . Vsq = −Rs i sq

(8)

(9)

From (8) and (9) we can deduce the equations of statoric currents −1 d(i sd ) = (Vsd + Rs i sd + ωe (L q · i sq )) dt Ld

(10)

  d(i sq ) −1 = (Vsq + Rs i sq − ωe L d · i sd +  f ) dt Lq

(11)

Expression of electromagnetic torque −3  .p. sd .isq − sq· isd 2

(12)

−3  .p. (Ld − Lq )isd isq + f· isq 2

(13)

Tem = Tem = Tem−r e f =

−3 .p.f .i sq 2

(For a machine with plain poles(L d = L q )).

1294

N. Z. Laabidine et al.

Fig. 1 Equivalent circuit (d-q) for a PMSG

The expression of statoric voltages (using the laplace transformation) [9] Vsd = −(R s + S.L d ).I sd − ωe (L q · Isq )

(14)

  Vsq = −(R s + S.L q ).I sq + ωe L d .Isd +  f

(15)

−1 (Vsd + ωe (L q · i sq )) (R s + S.L q )

(16)

  −1 (Vsq − ωe L d .i sd +  f ) (R s + S.L q )

(17)

Isd = Isq =

Expression of active and reactive powers Pgen = Tem . = Q gen =

3 . Vsd .i sd + Vsq .i sq 2

3 . Vsd .i sd − Vsq .i sq 2

(18) (19)

Equivalent circuit (d-q) for a PMSG (Fig. 1)

3 Oriented Flow Vector Control (FOC) Vector control was first proposed by “Blaschke” in 1972 [1], after it was developed by “Hasse” in 1979 [2], also called flow-oriented control (FOC), is a method in which the three-phase statoric currents of an AC electric motor are transformed into two orthogonal components which can be considered as vectors, The first produces the electromagnetic torque and the second produces the magnetic flux. This allows us to operate similar to a DC motor [10]. An Integral Proportional Controller (PI) was used to maintain the current at its requested value (Id) (Fig. 2).

Flow-Oriented Control Design of Wind Power Generation System ...

1295

Fig. 2 Oriented Flow Vector Control (FOC)

4 Wind Turbine Model The kinetic energy of the wind is transformed into mechanical energy by the rotor of the turbine, and then converted into electrical energy by a generator; its formula is given by [11]: Ec =

1 .m.v 2 2

(20)

The kinetic power of the wind received by the turbine through its blades is given by: Pwind =

1 .ρ.π.R 2 .v 3 2

(21)

The aerodynamic power Ptur appearing at the turbine rotor is written: Ptur =

1 .C p (λ, β).ρ.π.R 2 .v 3 2

(22)

The expression of the aerodynamic torque Cp of the turbine is given by the following formula: Cp =

1 .C p (λ, β).ρ.π.R 2 .v 3 2.t

The rotor torque induced by the wind is determined by the equation:

Rω Ptur = ω.T  tur ; λ = V 2 Ttur = 21 ρπ R 3 V C P (λ, β)

(23)

1296

N. Z. Laabidine et al.

The coefficient of performance changes with different angles of inclination (β) as well as the speed ratio ((λ):

• • • • • • • • • • •

Ttur _opt = K ω2 K = 21 ρπ R 5 C P−max (λopt )

Ptur , The power captured by the wind turbine. Ttur _opt , The optimal turbine torque. Ttur , The turbine torque. C p , The power coefficient. S, The blade swept area. R, The radius of the turbine blade. ρ, the specific density of air. V, the wind speed. ω, the rotor radium. λ, tip-speed ratio; β: pitch angle. λopt The optimal tip speed ratio that maximizes the power extracted by the wind turbine.

5 MLI Model In order to have a constant frequency and amplitude voltage on the machine side and to optimize the power taken from our wind energy conversion system, we will use MLI power converters.

S=

⎧ ⎨ +1, S = −I ⎩

, S = a, b, c

(24)

−1, S = +I

The input voltages between phases of the rectifier are: ⎧ ⎡ ⎤ ⎤ ⎡ ⎤⎡ VA 2 −1 −1 V AO ⎨ U AB = V AO − VB O 1 U = VB O − VC O ; ⎣ VB ⎦ = ⎣ −1 2 −1 ⎦⎣ VB O ⎦ ⎩ BC 3 UC A = VC O − V AO VC VC O −1 −1 2 ⎤ ⎡ ⎡ ⎤ V AO S UCC ⎣ A ⎦ We have : ⎣ VB O ⎦ = SB 2 VC O SC ⎡ ⎤ ⎡ ⎤⎡ ⎤ vA 2 −1 −1 SA U CC ⎣ then : ⎣ v B ⎦ = −1 2 −1 ⎦⎣ S B ⎦ 6 vC SC −1 −1 2

(25)

(26)

(27)

Flow-Oriented Control Design of Wind Power Generation System ...

1297

Park Transformation Park’s transformation is also called direct-quadrature-zero (dq0) allows to transform the stator or rotor windings a, b, c into two windings d and q, We denote by “d” the direct axis and “q” the quadrature axis.

5.1 Direct Park Transformation 

    ⎤⎡ ⎤ ⎡ Cos() cos  − 2π cos  + 2π xA 3  3  2 ⎣ 2π ⎦ ⎣ ⎦ · −sin() −sin  − 2π . −sin  + x B 3 3 3 1 1 1 xC 2 2 2

(28)

5.2 Reverse Park Transformation ⎤  ⎡ ⎤⎡ ⎤ Cos() −sin() 1 xA xd     2 2π ⎣ xB ⎦ = ⎦.⎣ xq ⎦ .⎣ cos  − 2π −sin  − 1 3  3    3 −sin  + 2π 1 xC xo cos  + 2π 3 3 ⎡

(29)

6 PI Regulator In order to regulate the stator current values i sd eti sq , we are going to use a PI regulator, which also aims to provide better performance and robustness to our wind power k .s+K system. The equation of the PI regulator used to control the GSAP: p s i With: • k p : proportional gain of the corrector PI • K i : proportional gain of the corrector P

Vsd = −Rs Isd − L d . Vsq = −Rs i sq − L q .

d(i sd ) − ωe (L q · i sq ) dt

d(i sq ) + ωe (L d .i sd +  f ) dt

We put Aq = ωe (L q · i sq ); Ad = ωe (L d .i sd +  f )

1298

N. Z. Laabidine et al.

Then the transfer function of our GSAP becomes: Vsd = −Rs Isd − S.L d .I sd − Aq

(30)

Vsq = −Rs Isq − S.L q .I sq − Ad

(31)

FT(s) =

1 1 1 . = ; Rs + L s .s Rs 1 + Te .s

 with Te = L s Rs (Electric time constant) L d = L q (Smooth pole machine stator inductance) that implies that k p = ki (Fig. 3 and 4). We can simplify the control loop to be similar to that of a first order system (Fig. 5). The open-loop transfer function is given by:  FTBO(s) =

k p .s + K i s

Fig. 3 Direct stator current regulation loop

Fig. 4 Quadrature stator current regulation loop

Fig. 5 Simplified stator current regulation loop



1 Rs + L s .s

 (32)

Flow-Oriented Control Design of Wind Power Generation System ...

1299

The closed loop transfer function: FTBF(s) =

Ls 2 .s ki

+



1 Rs +k p ki

 = s+1

1 .s 2 ωn

1   + ω2ζn s + 1

(33)

• ωn : The natural pulsation of the system • ζ : The damping coefficient of the system.

ki = ωn 2 .L s ; k p = 2.ζ.ωn .L s − Rs

7 Modeling and Simulation Result The simulation results obtained after the introduction of models in MATLAB/SIMULINK are illustrated in the following figures: We have studied a mathematical model of the permanent magnet synchronous machine in Park’s benchmark to make the GSAP machine similar to the separately excited DC machine (Fig. 6 and 7). We used a variable wind speed to check the decoupling between the components of the stator current to control the GSAP then a step speed (Fig. 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 and 18).

Fig. 6 Model of our permanent magnet synchronous generator

1300

Fig. 7 The GSAP model with the SMC command

Fig. 8 Variable wind profile; Step wind profile

Fig. 9 Active power as a function of the wind

Fig. 10 The electromagnetic torque as a function of the wind

N. Z. Laabidine et al.

Flow-Oriented Control Design of Wind Power Generation System ...

Fig. 11 The direct stator current «i sd » as a function of the wind

Fig. 12 The quadrature stator current «i sq » as a function of the wind

Fig. 13. The stator currents produced by the PI regulator

Fig. 14 The active power as a function of the wind (scale)

Fig. 15 The active power as a function of the wind (Scale)

1301

1302

N. Z. Laabidine et al.

Fig. 16 The direct stator current «i sd » as a function of the wind (Scale)

Fig. 17 The quadrature stator current «i sq » as a function of the wind (Scale)

Fig. 18 The stator currents produced by the PI regulator as function of wind (Scale)

8 Conclusion We have studied a mathematical model of the synchronous machine with permanent magnets in Park’s landmark in order to make the GSAP machine similar to the DC machine with separate excitation. Two types of wind speed will be studied in this test, one variable and the other step in order to analyze the decoupling between the components of the stator current (to control the GSAP) as well as the operation of our PI regulator. The previous figures summarize the simulation results performed while applying the MPPT power maximization algorithm with mechanical rotation speed control. Following the results of the test we can notice that the instructions set for vector control based on PI regulators are followed for the electromagnetic torque, the active and reactive powers as well as the stator currents generated. On the other hand, the stator currents show ripples due to the switching of the electronic switches of the static converter on the machine side. The active power generated is a direct consequence of the electromagnetic torque of the machine. In other words, the active power is controlled by the stator current of the quadrature axis (q), while the reactive power is a direct consequence of the stator current of the direct axis (d).

Flow-Oriented Control Design of Wind Power Generation System ...

1303

References 1. EL Mourabit Y, Derouich A, EL Ghzizal A, Bouchnaif J, EL Ouanjli N, Bossoufi B (2019) Implementation and validation of backstepping control for PMSG wind turbine using dSPACE controller board. Energy Report J 5:807–821 2. EL Ouanjli N, Derouich A, EL Ghzizal A, Bouchnaif J, Taoussi M, Bossoufi B (2019) Realtime Implementation in dSPACE of DTC-Backstepping for doubly fed induction motor. Eur Phys J Plus 135(1):2–9 3. Bossoufi B, Karim M, Lagrioui A (2014) MATLAB & simulink simulation with FPGA Based Implementation adaptative and not adaptative backstepping nonlinear control of a permanent magnet synchronous machine drive. WSEAS Trans Syst Control 9:86–100 4. Bossoufi B, Karim M, Lagrioui A, Taoussi M, El Hafyani ML (2014) Backstepping control of DFIG generators for wide-range variable-speed wind turbines. Int J Autom Control 8(2):122– 140 5. Beltran B, Ahmed-Ali T, Benbouzid MEH (2009) High-order sliding-mode control of variablespeed wind turbines. IEEE Trans Industr Electron 56(9):3314–3321 6. Bossoufi B, Ionita S, Alami Arroussi H, EL Ghamrasni M, Ihedrane Y (2017) Managing voltage drops a variable speed wind turbine connected to the grid. IJAAC Int J Autom Control 11(1) 7. Ihedrane Y, El Bekkali C, EL Ghamrasni M, Mensou S, Bossoufi B (2019) Improved wind system using on-linear power control. Indonesian J Electr Eng Comput Sci 14(3):1148–1158 8. Bossoufi B, Karim M, Ionita S, Lagrioui A (2012) Nonlinear non adaptive backstepping with sliding-mode torque control approach for PMSM motor. J Electr Syst JES 8(2):236–248 9. Bossoufi B, Karim M, Lagrioui., (2015) Observer backstepping control of DFIG-generators for wind turbines variable-speed: FPGA-based implementation. Renew Energy 81:903–917 10. Yi Liu W, Ai BC, Chen K, Luo G (2016) Control design and experimental verification of the brushless doubly-fed machine for stand-alone power generation applications. IET Electr Power Appl 10(1):25–35 11. Bossoufi B, Taoussi M, Alami Aroussi H, Bouderbala M, Motahhir S, Camara MB (2020) DSPACE-based implementation for observer backstepping power control of DFIG wind turbine. IET Electr Power Appl 14(12):2395–2403

Households Energy Consumption Forecasting with Echo State Network Wadie Bendali, Mohammed Boussetta, Ikram Saber, and Youssef Mourad

Abstract Careful planning and forecasting of energy consumption not only influences a nation’s environmental and energy sustainability, as well as giving a useful basis for policy makers to make decisions. This paper presents the results of an appropriate deep learning model forecasting for consumption using echo state network (ESN). ESN is a new paradigm that offers an intuitive methodology using for time series prediction. Basically, it is a recurrent neural network (RNN) with a vaguely connected hidden layer, known as a reservoir, which functions in a strange way in the existence of time-series patterns. In this contest, three types of recurrent neural network used for comparison with ESN, aiming to evaluate the accuracy and performance of the model. To train and test the proposed model, we used the historical data of multivariate household consumption. ESN showed an improvement of 0.057% and 0.095% in terms of mean square error (MSE) and mean absolute error (MAE), and it is trained faster than other models, in very short-term energy consumption forecasting. Keywords ESN · Consumption energy · RNN models · Deep learning · Forecasting

1 Introduction The role of energy in human life is absolutely essential. In one way, the evolution of human society due to the rise of energy efficiency and the exploitation of new artificial intelligence techniques. In parallel with economic and population dynamics, global consumption of energy continues to increase, causing several challenges. There is enormous pressure on the energy sectors worldwide to ensure a stable and reliable energy supply. Over the years, the consumption of energy has grown remarkably. Based on the statistics published in 2019 by British Petroleum, there will be a 2.8% increase in primary energy consumption in 2018, which is higher than in the previous year 2017 [1]. Due to the rapid growth of intelligent measuring systems and the W. Bendali (B) · M. Boussetta · I. Saber · Y. Mourad Laboratory of Industrial Technologies and Services, Higher School of Technology (EST) of Fez, University of Sidi Mohammed Ben Abd Allah, Fez, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_119

1305

1306

W. Bendali et al.

“Green Button” project [2], electricity consumption power for household users is now measured, collected and presented. The use of Demand Response (DR) and Home Area Networks (HAN) has put a new emphasis on individual households [3]. While residential households contribute to DR programs as a willing-seller, it is difficult to plan and estimate energy for these individual households prior to the program without an energy forecast at the individual household level. Certain utilities publish utilization or billing forecasts for residential customers to provide an innovation in service, assisting households to plan their consumption and reduce their energy bills. As a result, there is a growing recognition of the potential for forecasting household energy consumption by governments and research institutes. Short- and very shortterm energy forecasting methods are very useful for managing residential energy demand, energy efficiency, and market design of electricity prices, in addition they are very powerful for planning in maintenance of large-scale, complex smart grids [4–6]. Furthermore, it offers the smart grid reliability, security and protection to help meet the increasing energy demand of residential homes. ESN represents a family of Reservoir computing (RC) models where the recurrent part is created randomly, and then kept fixed. Even with this strong simplification, the recurring part of the model (the reservoir) provides a rich set of dynamic characteristics that allow to solve a wide variety of problems. In deep learning, the echo state network (ESN) further belongs to the general class of the reservoir computing. The ESN has powerful non-linear time series modelling capabilities ESN has a dynamic reservoir with a number of connected units, which makes the calculation of the data very uncomplicated [7]. Among the specific features of the reservoir computing methods, they have the ability to train only the readout weights by using the method of linear regression throughout the training procedure. By condensing this characteristic, ESN can achieve a high velocity of convergence towards an excellent overall solution. This study aims to propose echo state network model with multivariable input for energy consumption prediction. The rest of this study is organized as follows. Section 2 introduces consumption data description and describes preprocessing of data. Section 3 represents the basic ESN architecture, his parameters and training process. Section 4, experimental application used, and presentation of results. Finally, conclusion.

2 Data Description 2.1 Household Power Consumption Dataset Generally, four time horizons can be distinguished, that are used to predict time series: very short, short, medium and long term [8]. Very Short-term prediction concentrates on minute time ranges, contributing to assure an uninterrupted power flow in the near future and supporting electricity trade and price management [8, 9]. Medium and long-term prediction concentrate over a duration of a few days to several months and

Households Energy Consumption Forecasting with Echo State Network

1307

assist in the installation and maintenance of production facilities [8, 10], transmission and distribution system development [11], and energy plan formulation [12]. In this work, very short term data used for train forecasting model, which is a multivariate time series of household electricity consumption. The data used are collected from a house located in Sceaux (7 km of Paris, France), during one year (2009), with observations of every minute [13]. These data made up of 7 features: Pa :: Global active household power consumption (KW). Pr : Overall reactive power required by the household (KW). V : Means voltage (volts). Ig : Average current intensity (amps). E a1 : Sub-measure of active energy for the kitchen (Wh). E a2 : Sub-measure of active energy for laundry (Wh). E a3 : Sub-measure of active energy for climate control systems (Wh). The data set contains the active power, voltage and intensity current averages and certain distribution of active energy in the household like kitchen, washroom and air-conditioning. In order to obtain greater accuracy, we also add the rest of the sub-measures of the active energy E a4 , subtracting the three sub-measures from the overall active energy, by this following equation (Eq. (1)). All features are shown in Fig.1: E a4 = (P a × 1000/60) − (E a1 + E a2 + E a3 )

(1)

2.2 DATA Preprocessing In this work, we use data of 1 year (2009), which contain timescale for each minute. This data are separated like this: 473040 sample used for model train, 52560 samples used for the test. Global active power data have strong nonlinear properties, this can cause negative impacts on the prediction model. Before the model is fed by train data, we pre-process input values, for raison to minimize the effect of the wide values scale. The pre-processing process that we choose, namely the linear normalization that changes the data in the interval between 0 and 1. Post-processing is necessary upstream or downstream of the analysis for the performance of the forecast model. In this case post-processing of the normalized values must be anti-normalizing to extract the expected actual global power active and analyze the model performance.

1308

Fig. 1 Model input variables

Fig. 2 ESN architecture

W. Bendali et al.

Households Energy Consumption Forecasting with Echo State Network

1309

3 Echo State Network 3.1 Architecture The architecture of ESN is like any neural network, has input, hidden, and output layers, which are also having a certain number of units (neurons). However, as shown in Fig. 2, ESN has a reservoir instead of simple hidden layer, made up of some number of recurrent or simple units. These units are interconnected in order to achieve high treatment capability and, consequently, obtain good forecasts [7]. Although the ESN has been applied in many areas since its introduction in 2001 [14], this includes time series prediction and data classification [7, 15]. Generally, ESN consists of k input neurons that represent features or variable numbers, n units in the hidden reservoir, which are responsible for creating internal information, and o units in the final layer, which gives forecast time series values. This study deals with prediction problems in a single step so we are used o = 1, in time step j, assume that the input array of the input state is given as Eq.(2) The array of the reservoir state is Eq. (3), and the array of the output state is Eq. (4): U ( j) = [U1 ( j), U2 ( j), U3 ( j), · · · , Uk ( j)]T

(2)

X (i) = [X 1 ( j), X 2 ( j), X 2 ( j), · · · , X n ( j)]T

(3)

Y ( j) = [Y1 ( j), Y2 ( j), Y2 ( j), · · · , Yo ( j)]T

(4)

Where j = 1, 2…N and N represent all training time steps. During processing, the reservoir and output states are updated as shown in Eqs. (5) and (6), accordingly: X ( j + 1) = F(win U ( j + 1) + w X ( j) + wback Y ( j))

(5)

Y ( j + 1) = G(wout X ( j + 1))

(6)

In ESN, four weight matrices exist, win represent the connection between input and reservoir state, w represent the weight between internal units of reservoir layer, wback gives the connection between neurons of output layer and reservoir layer, and wout as a matrix weight that link the internal units and output units. The activation functions of the reservoir and output layers is F and G respectively. These functions can represent a linear or nonlinear function depending on the nature of data.

1310

W. Bendali et al.

The weight matrix wout , which is from the reservoir to the output layer, can be updated throughout the training process, whereas win , w and wback are randomly chosen then stay unchanged [16, 17].

3.2 Hyperparametres of ESN ESN has three critical reservoir Hyperparametres, which have a major effect on the efficiency of the system and should be set at appropriate values. n: The number of units in hidden reservoir, that can effect in terms of model quality and accuracy, due to his role in dealing with large data size in the training phase. Generally, the choose of number of units according on the nature and size of data using for training. In this study, we use n = 60. α: The connectivity rate, which allow to choose the linking degree between units of hidden reservoir layers. That can aide to control the memory chance and deal with computing complexity [16]. In general, with reference to review of experience, α is chosen between 1%–5% [18]. ρ: The spectral radius, has a very important role in ESN model, his value represents the max of the absolute proper value of the weight matrix w. In other to make ESN has dependency only with input and output sample of training we concede that the range of ρ is between 0 and 1. It is unaffected by its first state after a few epochs if the entries are long enough. [19, 20].

3.3 The Training Algorithm of ESN In order to achieve accurate output compared with target values, which are collected from data training, we have to train models by using N time samples of input and target data. There are three steeps using for training models: Firstly, it must initialize the ESN parameter values, by choosing a pertinent number of reservoir units n, select the best values of connectivity rate α and spectral radius ρ, and initialize randomly weight values win ,wback , and w. After that, it is important to update the reservoir states using the actual input, the previous values of reservoir and output states as mentioned in Eq. (5). Finally, Computing wout by obtaining M the vector of hidden reservoir values, which has (N − I0 + 1) × n sample, and T the target values, which has (N − I0 + 1) × o as follows in Eq. (7). With I0 is the washout time step. wout = (M −1 T )

T

(7)

ESN has a special training algorithm, which can make it able to deal simply and quickly with data like consumption power of households [14, 21].

Households Energy Consumption Forecasting with Echo State Network

1311

4 Application and Results In this work, we create our model with Python environment by using KERAS as a deep learning library, which supports CPU and GPU [22]. We choose RNN and their extension Long Short Term Memory (LSTM), and Gated Recurrent Unit (GRU) as deep learning models to compare them with ESN. These models used input data that contains eight variables represent eight units, total active power(GAP), total reactive power, voltage, total intensity, and four Sub-measuring data of the household’s energy (Fig. 1). The row of this data contains 525600 samples of minute, these data are used as inputs of models training and testing. Data are split into 90% for training and 10% for testing. The output of the models has one unit, which represent, predict global active power consumed. For dealing with the effect of dimensionality problem of data, we normalize data values between 1 and 0 by using normalization as a preprocessing method. After obtaining predict values, we used anti-normalization for obtaining the forecast GAP. The hyperparameters of ESN that we choose the number of units n = 50, connectivity rate α = 0.02, and spectral radius ρ = 0.5. We choose these parameter values based on Trial and Error method, aiming to achieve the smaller error by testing various parameter values. This method is an efficient and standard method of parameterization. Activation functions using in ESN are F = Tanh and G = identity. Concerning the standard RNN and its extensions (LSTM, GRU), has two hidden layer’s neurons,each one of theme has 60 neurons or units with Tanh activation function for two hidden layers and Relu for the output layer. The error metrics compared in this paper was mean square error (MSE) (Eq.(8)), and mean absolute error (MAE) (Eq.(9)), as following equations: MSE =

M AE =

N 1  (P f or ecast (t) − Ptr ue (t))2 N i=1

(8)

N  1  P f or ecast (t) − Ptr ue (t) N i=1

(9)

With N is the number of samples, P f or ecast (t) is the forecasting of global active power at time t, and Ptr ue (t) is the true global active power at time t. In this work, we compared the accuracy of the proposed deep learning method ESN with simple RNN, LSTM and GRU. As we can see in Figs. 3, 4, 5, and 6, ESN model has more accuracy than standard RNN and his extension in consuming power. In Table 2, comparisons based on MAE and MSE of models. As shown in this table, the LSTM and GRU models have better performance than Simple RNN method, that is because LSTM and GRU methods characteristics of carrying initially learnt important information over a long distance [23, 24]. However, ESN still much better compared with them, because of his ability to deal with nonlinearity, and his special training algorithm. In terms of training speed, we can see from Table 1, ESN is faster

1312

W. Bendali et al.

Fig. 3 True values vs. ESN forecast values of global active power consumed in 1 day

Fig. 4 True values vs. GRU forecast values of global active power consumed in 1 day

Fig. 5 True values vs. simple RNN forecast values of global active power consumed in 1 day

than RNN, which is considerably faster than LSTM, and GRU, that is because in ESN we only train the output weight wout and it can speed up the training of neural network.

Households Energy Consumption Forecasting with Echo State Network

1313

Fig. 6 True values vs. LSTM forecast values of global active power consumed in 1 day

Table 1 Training time of models Models

Simple RNN

LSTM

GRU

ESN

Time training (s)

25.859375

53.703125

46.640625

15.890625

Table 2 MSE and MAE of models Simple RNN

LSTM

GRU

ESN

MSE

0.0011

0.00067

0.00063

0.000576

MAE

0.0203

0.0108

0.0103

0.009746

5 Conclusion The development of an appropriate energy prediction model is required to assure a secure and reliable power consumption. This study illustrates the efficiency and performance of the ESN model for forecasting energy consumption. Different deep learning methods are used to predict consumption power like RNN, which has a good accuracy compared with other machine learning methods. In this study, we compared ESN with simple RNN, LSTM, and GRU, to test the accuracy and the performance of ESN for consumption hold houses forecasting. LSTM and GRU are famous model that is used in many fields, they are good in term of efficiency specially for time series. However, these methods have slow and cumbersome calculation, because of their algorithm, which trait input, recurrent and output relations using gradient descent, and makes the learning disrupted. Unlike other methods, ESN has a loosely connected hidden layer, called “Reservoire”, which allows for rapid training, and can easily be implemented. As a result, ESN has outperformed all other methods of nonlinear dynamic modeling.

1314

W. Bendali et al.

References 1. World B (2018) BP Statistical Review of World Energy. https://www.bp.com/en/global/corpor ate/energy-economics/statistical-review-ofworld-energy.html 2. Zhang XM, Grolinger K, Capretz MA, Seewald L (2018) Forecasting residential energy consumption: single household perspective. In: 2018 17th IEEE international conference on machine learning and applications (ICMLA), Orlando, FL, pp 110–117. https://doi.org/10. 1109/ICMLA.2018.00024 3. Pipattanasomporn M, Kuzlu M, Rahman S (2012) Demand response implementation in a home area network: A conceptual hardware architecture. In: Proceedings of the IEEE innovative smart grid technologies (ISGT), pp 1–8 4. Guo P, Lam JC, Li VO (2019) Drivers of domestic electricity users price responsiveness: a novel machine learning approach’. Appl Energy 235:900–913 5. Mazzi N, Kazempour J, Pinson P (2018) Price-taker offering strategy in electricity pay-as-bid markets. IEEE Trans Power Syst 33(2):2175–2183 6. Wang W, Chen H, Lou B, Jin N, Lou X, Yan K (2018) Data-driven intelligent maintenance planning of smart meter reparations for largescale smart electric power grid. In: Proceedings IEEE smartworld, ubiquitous intelligent computer, advance trusted computer, scalable computer communication, cloud big data computer, internet people smart city innovation, pp 1929–1935 7. Zhong S, Xie X, Lin L et al (2017) Genetic algorithm optimized double-reservoir echo state network for multi-regime time series prediction. Neurocomputing 238:191–204 8. de Oliveira EM, Oliveira FLC (2018) Forecasting mid-long term electric energy consumption through bagging ARIMA and exponential smoothing methods. Energy 144:776–788 9. Arora S, Taylor JW (2018) Rule-based autoregressive moving average models for forecasting load on special days: a case study for France. Eur JOper Res 266(1):259–268. https://doi.org/ 10.1016/j.ejor.2017.08.056 10. Mason K, Duggan J, Howley E (2018) Forecasting energy demand, wind generation and carbon dioxide emissions in Ireland using evolutionary neural networks. Energy 155:705–720 11. Al-Hamadi HM, Soliman SA (2005) Long-term/mid-term electric load forecasting based on short-term correlation and annual growth. Electric Power Syst Res 74(3):353–361 12. Lima RM, Novais AQ, Conejo AJ (2015) Weekly self-scheduling, forward contracting, and pool involvement for an electricity producer. An adaptive robust optimization approach. Eur. J Oper Res 240(2):457–475 13. Berard A (2012) TELECOM ParisTech Master of Engineering Internship at EDF R&D, Clamart, France. https://archive.ics.uci.edu/ml/datasets/individual+household+electric+ power+consumption 14. Jaeger H (2001) The, “echo state” approach to analysing and training recurrent neural networkswith an erratum note. Bonn, Germany: German Nat Res Center Inf Technol GMD Technical Report 148(34):13 15. Lacy SE, Smith SL, Lones MA (2018) Using echo state networks for classification: a case study in Parkinson’s disease diagnosis. Artif Intell Med 86:53–59 16. Chouikhi N, Ammar B, Rokbani N, Alimi AM (2017) PSO-based analysis of echo state network parameters for time series forecasting. Appl Soft Comput 55:211–225 17. Ma Q, Shen L, Chen W et al (2016) Functional echo state network for time series classification. Inf Sci 373:1–20 18. Xu X, Niu D, Fu M et al (2015) A multi time scale wind power forecasting model of a chaotic echo state network based on a hybrid algorithm of particle swarm optimization and tabu search. Energies 8(11):12388–12408 19. Wang L, Lv SX, Zeng YR (2018) Effective sparse adaboost method with ESN and FOA for industrial electricity consumption forecasting in China. Energy 155:1013–1031 20. Maiorino E, Bianchi FM, Livi L et al (2017) Data-driven detrending of nonstationary fractal time series with echo state networks. Inf Sci 382:359–373

Households Energy Consumption Forecasting with Echo State Network

1315

21. Chen Q, Shi L, Na J et al (2018) Adaptive echo state network control for a class of pure-feedback systems with input and output constraints. Neurocomputing 275:1370–1382 22. Dahl GE, Sainath TN, Hinton GE (2013) Improving deep neural networks for LVCSR using rectified linear units and dropout. In: 2013 IEEE international conference on acoustics, speech and signal processing, vancouver, BC, p 8609−8613. https://ieeexplore.ieee.org/stamp/stamp. jsp?tp=&arnumber=6639346&isnumbe=6637585 23. Ruiz CW, Perapoch J, Castillo F, Salcedo S, Gratacós E (2006) Bessons monocoriònics afectes de transfusió fetofetal: conseqüències a curt i llarg termini. Pediatria Catalana 66(2):53–61 24. Cho K, van Merrienboer B, Bahdanau D, Bengio Y (2015) On the properties of neural machine translation: encoder–decoder approaches. pp 103–111. https://doi.org/10.3115/v1/w14-4012

Management of Multi-agents in a Smart Grid Network with the Python Using the Contract Net Protocol Saber Ikram, El Bachtiri Rachid, Bendali Wadie, Boussetta Mohammed, El hammoumi Karima, and Cheddadi Fatima

Abstract Smart grids are one of the best solutions for the integration of different distributed energy sources (DES), load, and storage elements. However, optimizing and managing these systems is a big challenge because it is only done in a distributed way. One of the best methods of managing a smart grid is the use of artificial intelligence (AI), especially multi-agent systems (MAS) approaches. This work constitutes a demonstration of one of those methods which is the contract net protocol (CNP). We will use the extended version of the contract net protocol(ECNP) to optimize the movement of MASs inside a smart grid network. Our example of a smart grid will be a grid constituted from nodes, and each node is a location of an element of the smart grid which could be a wind turbine, a customer…, in other words, a task or/and an agent. In the scenario each task it’s given a target which is also a node inside the grid, and our main objective is that the agent or agents could contribute to each task, depending on their priority order and may allow it to reach its target, which is another node in the network far from its initial location, using the shortest way as possible. Keywords Microgrid · Multi-agent systems · Contract net protocol

1 Introduction The great and fast development of renewable energy sources (RESs) [1, 2] encouraged researchers to begin forming different size units provides as microgrids [3]. They S. Ikram (B) · E. B. Rachid · B. Wadie · E. Karima Laboratory: Technologies et Services Industriels, University Sidi Mohammed Ben Abd Allah, High school of technology of fez (ESTF), Fez, Morocco e-mail: [email protected] B. Mohammed Laboratory: Technologies Innovantes, University Sidi Mohammed Ben Abd Allah, High school of technology fez (ESTF), Fez, Morocco C. Fatima Laboratory: Systèmes Intelligents, Géoressources et Énergies Renouvelables, Faculty of Science and Technologies Fez (FSTF), Fez, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_120

1317

1318

S. Ikram et al.

were presented as a solution for the reliable integration of distributed energy resources [4, 5], and resilience-related problems [6, 7] as shown in Fig. 1. Developing such outstanding units of energy has so many economic and environmental benefits [8, 9] as reliability because they improve consumers’ supply [10, 11], their average interruption frequency, duration between consumers and system, and resilience which is the capability of the system to minimize possible power outage and returning to stable status [12]. With the start-up of microgrids, different types of control architectures have been defended, starting with the very first ones as centralized and decentralized procedures. That kind of approach depends on how many components the microgrid is constituted of and its dimension, so their complexity increase despite it. Each architecture has a particular number of levels, and every level supports unique tasks, as maintaining stability or load sharing. However, the major challenge with microgrids is to provide efficient management techniques with a high level of optimization results for system efficiency, minimizing the use and processing of data, and, of course, independence in management [13, 14]. Yet, those architectures didn’t bring much satisfaction to their inventors, due to their high cost and management’s difficulties, the reason behind swishing into decentralized architectures. On account of the arguments mentioned above, added to the important number of DERs, loads, and storing devices implemented on MGS [12, 15, 16], shifting into distributed control methods has been motivated [17], as agent technology has given their many advantages like using local data exchanges between neighbors, especially the multi-agents systems (MASs) approaches [18–20], to optimize the operation of local power production, and improve performances of microgrid using decentralized control [11]. MASs have been defined in many ways in literature, as «a comprises of two or more independent agents with some information to achieve a set target» [21], or autonomous decision-making entities [22]. They are known for their capability of reaction in the environment, intercommunication inside the network, and high level of autonomy.

Fig. 1 Example of a modern distribution network

Management of Multi-agents in a Smart Grid Network ...

1319

Yet, to reach those advantages and gain from MASs qualities, we should overcome technical challenges and problems, which are quite numerous [21, 22], starting with MASs platform choices, communication languages, architecture, and design while respecting the Foundation for Intelligent Physical Agents standards [23] shown in Fig. 2. In other words, insuring compatibility, robustness, flexibility, extensibility plus the capability of interacting with each other irrespective of the platform they run on [22]. One of those techniques will be presented below. This article is organized as follows: in the second section we will present a definition of the contract net protocol, the technique used in our work to optimize the movement of the agents, after that, we will explicitly explain our work with different scenarios, tables, and simulation showing the movement inside the smart grid, at last in the third section we will end up with a conclusion and a discussion.

2 Contract Net Protocol 2.1 The Organizational Architecture Contract net protocol (CNP) is a technique invented to solve communication problems in distributed networks, it applies to microgrid of all types and it gave very good results on the cost side, management, and speed of data processing. It was defined as a high-level protocol for communication among the nodes in a distributed problem solver [24, 25]. Modified by the FIPA in 2000, to become a more satisfying, sophisticated protocol, by adding rejection and communication acts [26]. This protocol is applied essentially to perform negotiation tasks [27] while using exchange messages in a bidding process. In this protocol, agents can play manager or bidder role, divided into two agent types, the initiator and the participants: Initiator: is the manager who is occupied by commissioning a task or demanding a service. Participants: are the elements of the networks which can collect manager’s calls for proposals and make them propositions depending on the call nature. Explication: The first message sent from the initiator to the participants is a call for proposals, the answer to this call can be divided into two: – To refuse, by sending a not understood message. – To accept, by making a proposition. In this case, the initiator receives their propositions and chooses among them which to accept. After a lapse of time, the participant which made the accepted proposition informs the initiator of the result, it could be a collapse, and it means that the task is concluded, or that the service asked for is executed. The work presented in this paper constitutes an optimization of agents shifting inside a smart-grid, as it is known, agents have to move inside the smart-grid, being a

1320

S. Ikram et al.

software or a hardware agent, it has to move between smart-grid elements to receive tasks and provide solutions in the shape of bides. In the next paragraph, a very detailed explication will be provided through scenarios, the results of our program will be exposed in tables extracted from the program and simulations.

2.2 Different Scenarios Used in Our Work and Their Experiment Results Before explaining our first scenario, we will clarify our program configuration and the implementation structure: Program Configuration Our configuration is established of six major scripts: – Agent script: written as an index of vocabularies respectively introducing a set of attributes describing an agent (identification number, launching cell, priority order, speed, leaning time, and max capacity). – Tasks: written as an index of vocabularies respectively introducing a set of attributes describing a task (identification number, initiating moment, target cell). – Initiators: written as an index of vocabularies respectively introducing a set of attributes describing an initiator (identification number, task’s identification number) – Smart grid network: written as an index of vocabularies respectively introducing a set of attributes describing the smart grid network (quantity of columns rows in which tasks and agents are placed). – Simulation script: it contains the main instructions for all scripts simulation at once. – Experiment script: at this level, we use the scenarios’ arguments and conditions to simulate our scripts. The inputs of our system as shown in the scripts are the tasks and the outputs are the bides provides by the agents at each scenario. About Scenario_1_Part_AG: The first scenario is developed to test our version of ECNP for tasks and agent’s movement inside a smart grid network. We choose to use one agent and three tasks. Every task comes at a specific time identification(id), as known that our agent has a special order of priority, we spread tasks in the opposite order of that priority, the main goal of that is to show the capability of the agent to bid and contract for a task no matter its importance for him (Table 1). 1)

Task used in the first scenario:

Management of Multi-agents in a Smart Grid Network ...

1321

As shown in the table above, every target is introduced in a special time id, and each one has its aim that it should reach to complete its role inside the smart grid network (Table 2). 2)

The agent used in the first scenario:

The second table presents our agent’s attributes and characteristics. Depending on those qualities the agent can bid and contract for every task and offer the best path between the task first position and its aim position. 3)

Smart grid network used in the first scenario studied:

The grid extracted from our simulation, as shown in Fig. 2, illustrates the agent and task’s initial positions and their aims as cited in the table above. We choose a network from three rows and three columns, the results extracted from the first pdf document are outlined in the next table, and the simulation presents how the agent could bide for each task without failures (Table 3 and Fig. 3): 4)

About scenario_2_part_ag:

The second scenario is developed to test the use of two agents at the same time, with three tasks. Table 1 Task’s properties

Task identification(id)

Introduction time

Aim position

2 0 1

0 1 2

(2,2) (1,1) (0,0)

Table 2 Agent’s properties Agent (id)

Agent departure position

Agent priority

speed

Leaning time

Max capacity

0

(0,0)

[2, 1, 0]

1

1

3

Fig. 2 Smart grid network used in the first scenario

1322

S. Ikram et al.

Table 3 Agent’s bid for each task

Simulation steps

Tasks

Agent 0 provides

0

0

5

1 1

0 1

5 2

2 2 2

0 1 2

5 2 10

3 3 3

0 1 2

5 2 10

4 4 4

0 2 1

5 2 10

Fig. 3 Simulation of the agent bides using the CNP

The main goal of this scenario is to demonstrate the capability of the agents to bid and contract for the tasks, without interfering in each other job, and the contract came at a specific time, depending on that time, one of the agents took the task and provide bids, so it could be completed, depending on its priorities, rest time and speed (Table 4 and Table 5, Fig. 4). 5)

Task used in the SECOND scenario:

6)

The agent used in the SECOND scenario:

Table 4 Task’s properties

Task Identification(id)

Introduction time

Aim position

0

0

(3,0)

1

1

(3,4)

2

2

(2,2)

Management of Multi-agents in a Smart Grid Network ...

1323

Table 5. Agent’s properties. Agent (id)

Agent departure position

Agent priority

speed

Leaning time

Max capacity

0

(0,0)

[2,0,1]

1

1

2

1

(0,4)

[2,0,1]

1

1

2

Fig. 4 Smart grid network used in the second scenario

7)

Smart grid network used in the SECOND scenario studied:

The grid extracted from the second simulation of the second scenario is wider and more complicated than the first one. It is clear that in our first work, we choose to optimize only the movement of the agents inside the grid network, but it is known in every smart-grid, agents been hardware or software they need to move between the components of that grid, so came the importance of our work which could facilitate this job for the agents (Table 6, Fig. 5). Table 6 Agent’s bid for each task

Simulation steps Tasks Agent 0 provides Agent 1 provides 0 0

0 1

4 10

10 16

1 1 1

0 1 2

4 10 5

10 10 5

2 2 2

0 1 2

4 10 5

10 4 5

3 3 3

0 1 2

4 10 5

10 4 5

1324

S. Ikram et al.

Fig. 5 Simulation of the agent bides using the CNP

3 Discussion At is shown in the tables below, extracted as a simulation for the different bids, our agents have contracted for the three tasks in every step time of the first and second scenario, the ECNP allowed them to provide many movements for each task to help it to get to its target easily and without forgetting any of them. We chose these particular scenarios to show that MAS are perfectly capable of managing any size of smart grid starting with three rows and columns and ending with a much bigger number of elements. Using his priority, resting time, and capacity, which are variable and could be changed from one scenario to another, the agent has the capability of managing very well movement inside a smart grid network, without interfering in the other agent job, executing orders and tasks in the disposal that he has been given and the most important without forgetting any. The simulations show that the bides are very clear and separated from each other, which explain that the speed, capacity and rest time for every agent is respected in both scenarios, and every bides come at a specific time and don’t collapse with another, this property is very important of good work of a smart-grid.

4 Conclusion The application of the extended version of contract net protocol for the movement of agents in a smart grid of different sizes, and with a more challenging number of agents and elements, has shown the ability of agents to manage objectives and accomplish tasks, which can be used as a technique to forecast energy production inside a wind farm or a photovoltaic field. In other words, MAS are capable of achieving real-time management of distributed systems as a microgrid, creating from it a smart-grid with self-reflections every moment of the day, using the proprieties of MAS mentioned above, it can provide

Management of Multi-agents in a Smart Grid Network ...

1325

energy for a very large number of customers, also it can maintain its secure level of energy by using various types of RES it can acces to.

References 1. Zhou B, Lei Y, Li C, Fang B, Wu Q, Li L, Li Z (2019) Electrical LeaderRank method for node importance evaluation of power grids considering uncertainties of renewable energy. Int J Electr Power Energy Syst 106:45–55. https://doi.org/10.1016/j.ijepes.2018.09.034 2. Steffen B (2018) The importance of project finance for renewable energy projects. Energy Econ 69:280–294. https://doi.org/10.1016/j.eneco.2017.11.006 3. Olivares DE, Mehrizi-Sani A, Etemadi AH, Canizares CA, Iravani R, Kazerani M, Hajimiragha AH, Gomis-Bellmunt O, Saeedifard M, Palma-Behnke R, Jimenez-Estevez GA, Hatziargyriou ND (2014) Trends in microgrid control. IEEE Trans Smart Grid 5(4):1905–1919. https://doi. org/10.1109/TSG.2013.2295514 4. Martin-Martínez F, Sánchez-Miralles A, Rivier M (2016) A literature review of Microgrids: a functional layer based classification. Renew Sustain Energy Rev 62:1133–1153. https://doi. org/10.1016/j.rser.2016.05.025 5. Hirsch A, Parag Y, Guerrero J (2018) Microgrids: a review of technologies, key drivers, and outstanding issues. Renew Sustain Energy Rev 90(April):402–411. https://doi.org/10.1016/j. rser.2018.03.040 6. Jirdehi MA, Tabar VS, Ghassemzadeh S, Tohidi S (2020) Different aspects of microgrid management: a comprehensive review. J Energy Storage https://doi.org/10.1016/j.est.2020. 101457 7. Ruotolo MA (2018) A social cost-benefit analysis of community microgrid systems in New York State. https://udspace.udel.edu/handle/19716/23942 8. Adefarati T, Bansal RC (2019) Reliability, economic and environmental analysis of a microgrid system in the presence of renewable energy resources. Appl. Energy 236:1089–1114. https:// doi.org/10.1016/j.apenergy.2018.12.050 9. Wang S, Li Z, Wu L, Shahidehpour M, Li Z (2013) New metrics for assessing the reliability and economics of microgrids in distribution system. IEEE Trans Power Syst 28(3):2852–2861. https://doi.org/10.1109/TPWRS.2013.2249539 10. Khodaei A (2014) Resiliency-oriented microgrid optimal scheduling. IEEE Trans Smart Grid 5(4):1584–1591. https://doi.org/10.1109/TSG.2014.2311465 11. Logenthiran T, Srinivasan D, Khambadkone AM (2011) Multi-agent system for energy resource scheduling of integrated microgrids in a distributed system. Electric Power Syst Res 81(1):138– 148. https://doi.org/10.1016/j.epsr.2010.07.019 12. Logenthiran T, Srinivasan D, Khambadkone AM, Aung HN (2010) Scalable Multi-Agent System (MAS) for operation of a microgrid in islanded mode. In 2010 Joint International Conference on Power Electronics, Drives and Energy Systems, PEDES 2010 and 2010 Power India. https://doi.org/10.1109/PEDES.2010.5712459 13. Reilly JT (2019) From microgrids to aggregators of distributed energy resources. The microgrid controller and distributed energy management systems. Electricity J 32(5):30–34. https://doi. org/10.1016/j.tej.2019.05.007 14. Lasseter R, Akhil A, Marnay C, Stephens J, Dagle J, Guttromson R, Eto J et al (2002) The CERTS microgrid concept, white paper on integration of distributed energy resources. California Energy Commission, Office of Power Technologies-US Department of Energy, LBNL-50829, https://Certs. Lbl. Gov, 29 15. Hatziargyriou N, Jenkins N, Strbac G, Pecas Lopes JA, Ruela J, Engler A, Amorim A et al (2006) Microgrids - Large scale integration of microgeneration to low voltage grids. In 41st International Conference on Large High Voltage Electric Systems 2006, CIGRE 2006 (2018)

1326

S. Ikram et al.

16. Liu G, Jiang T, Ollis TB, Zhang X, Tomsovic K (2019) Distributed energy management for community microgrids considering network operational constraints and building thermal dynamics. Appl Energy 239:83–95. https://doi.org/10.1016/j.apenergy.2019.01.210 17. Sermakani S, Thangaraja M (n.d.) Power demand optimization in smart grid via wireless networks. J Electr Electron Eng (IOSR-JEEE), 18–23 18. Ghiani E, Serpi A, Pilloni V, Sias G, Simone M, Marcialis G, Armano G, Pegoraro P (2018) A multidisciplinary approach for the development of smart distribution networks. Energies 11(10):2530. https://doi.org/10.3390/en11102530 19. Worighi I, Maach A, Hafid A, Hegazy O, Van Mierlo J (2019) Integrating renewable energy in smart grid system: Architecture, virtualization and analysis. Sustain Energy Grids Netw 18:100226. https://doi.org/10.1016/j.segan.2019.100226 20. Kamdar R, Paliwal P, Kumar Y (2018) A state of art review on various aspects of multi-agent system. J Circ Syst Comput 27(11):1–5. https://doi.org/10.1142/S0218126618300064 21. FIPA standards, https://www.fipa.org/Smith R (1980) Communication and control in problem solver. IEEE Trans Comput 29(12):12. https://www.csupomona.edu/~ftang/courses/CS599DI/notes/papers/contractnetprotocol.pdf 22. PankiRaj JS, Yassine A, Choudhury S (2019) An auction mechanism for profit maximization of peer-to-peer energy trading in smart grids. Procedia Comput Sci 151(2018):361–368. https:// doi.org/10.1016/j.procs.2019.04.050 23. Smith RG (1988) The contract net protocol: high-level communication and control in a distributed problem solver. Morgan Kaufmann, pp 357–366 24. FIPA. Fipa contract net interaction protocol specification. Technical, Report SC00029H, FIPA (2000) 25. Bicocchi N, Cabri G, Leonardi L, Salierno G (2019) A survey of the use of software agents in digital factories. In 2019 IEEE 28th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE), 3–8 https://doi.org/10.1109/wetice. 2019.00010 26. Maˇrík V, Kadera P, Rzevski G, Zoitl A, Anderst-Kotsis G, Tjoa AM, Khalil I (Eds) (2019) Industrial applications of holonic and multi-agent systems. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-030-27878-6 27. Li K, Zhou T, Liu B, hai, & Li, H. (2018) A multi-agent system for sharing distributed manufacturing resources. Expert Syst Appl 99(32):43. https://doi.org/10.1016/j.eswa.2018. 01.027

Smart Grid Operation Using Hybrid AI Systems Chakib Alaoui and Hajar Saikouk

Abstract Battery storage system is an essential component for the depletion of the microgrid, and of the smart grid on a larger scale. It acts as an interface between the highly intermittent renewable energy resource such as solar photovoltaic or wind energy system, the load, such as residential homes and businesses, electric vehicle fleets and the electric power grid. In principle, battery storage is intended to store excess electricity production from the renewable resource. The energy stored is then typically discharged in the evening when demand is higher, or sold to the grid when the available electricity is higher than the demand. However, the implementation of such a system becomes more complicated when taking into account the stochastic nature of the renewables, the variations of the load demand and the electric vehicle availability. In this project, a fuzzy logic management system, optimized through genetic algorithm process, is proposed. It seeks to obtain a fully functional microgrid that minimizes electricity consumption from the utility grid, while offering ancillary services to the grid such as peak shaving. These objectives are accomplished by maximizing the energy input from the renewables and optimizing the utilization of the energy storage system. Finally, the microgrid’s control main objective is to maintain a constant voltage at the DC bus under all operating conditions. Initial simulations and experimental data show some promising results. Keywords Microgrid · Battery storage system · Smart grid · Artificial intelligence · Fuzzy logic · Genetic algorithm

1 Introduction The integration of renewable energy resources (RES) in a microgrid system constitute an effective solution to improve reliability, resilience and the economics of the smart grid systems [1]. However, the increasing development of RES with uncertain outputs and increasing the number of microgrids makes the operation and control of them a C. Alaoui (B) · H. Saikouk INSA EuroMediterannée, Euromed University, Fez, Morocco e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_121

1327

1328

C. Alaoui and H. Saikouk

major challenge, especially with the inclusion of electric vehicles into the microgrid [2–4]. The adjustment of a DC microgrid voltage with the use of classical controllers in such environment is a challenging work and requires new and appropriate control strategies [5, 6]. Moreover, the battery-based electric energy storage (EES) play a key role for an efficient smart grid [7, 8] or microgrid management, in addition to the integration of electric vehicles (EV) into the grid through an effective battery management [9, 10]. Recently, fuzzy logic (FL) based controllers have been developed and used in various industrial applications [11]. The performance of these controllers is influenced by fuzzy rules and control coefficients that determine their level of controllability [12]. Compared to other adaptive control methods such as artificial neural networks (ANN) [13], FL controllers have a special place in the industry and have been used in a variety of applications such as electric machine ripple minimization [12], energy management system in microgrid [14], automatic voltage regulator system [15], load frequency control [16] and others [17, 18]. Several methods have been used to provide a solution to efficiently manage the energy flow of a smart grid while reducing its overall costs. These methods vary from rule-based controllers through optimization techniques to machine learning methods with different objectives. The author of [19] proposed a rule-based controller to dispatch a battery system with a PV and a grid-connected residential house. The objective of the controller was to maximize the energy delivered from the PV. The results showed a significant reduction from the power taken from the grid. The author of [20] implemented Mixed Integer Linear Programming (MILP) for a similar system. They used a weighted multi-objective optimization to minimize energy consumption, costs and peak power demands. They achieved a reduced peak demand of 50% and a reduced cost of 20%. While the authors of [21] used a similar system with the objective of reducing the overall costs and CO2 emission. Genetic algorithms have been used by [22, 23]. They achieved higher system efficiency and higher selfconsumption from PV panels. Many universities and research centers, such as in [24, 25], have used machine learning techniques for microgrid control. Their objective was to maximize the usage of the battery bank and the energy input from a PV system. The authors of [26] used 2-step ahead Q-learning system to manage a grid-tied home having a battery pack and a wind turbine. The results of this study revealed that while the use of the battery and wind turbine to supply the load improved by several percent’s after the agent had learned, the economic benefit was less than 1%. Finally, the author of [27] used a Q-learning parametrized by neural networks in order to control a system consisting of PV, battery and hydrogen storage. They have been able to reduce the costs of imports from the grid. The aim of this project is to implement a fuzzy logic system, whose parameters are optimized with genetic algorithm, that can control the charge scheduling of a grid-tied battery, photovoltaic and wind energy residential system, with the objective of taking the minimum energy from the grid, while making the grid to benefit from ancillary services such as peak demand shaving. Two scenarios will be considered: an electric vehicle connected to the smart grid, and disconnected from the smart grid. Finally, the validation of the proposed algorithm was done in Morocco, where the

Smart Grid Operation Using Hybrid AI Systems

1329

Fig. 1 Block diagram of the proposed microgrid system

electricity tariff is not in hourly rate, but rather in monthly rate, where the electricity rates vary monthly from ‘low-power’, ‘high power’ and ‘peak power’ consumption periods [28].

2 System Description 2.1 Microgrid System The general block diagram of the microgrid is shown in Fig. 1. The main components of the proposed system are a PV system (PVS) with appropriate MPPT system, and a feedback controlled boost converter used to connect the PV system to the 750 V DC bus. In addition, a wind power system (WPS) equipped with a Permanent Magnet Synchronous Generator (PMSG) and appropriate converters and MPPT system. A battery based energy storage system (ESS) connected with a bidirectional DC-DC converter to the main DC bus. An electric vehicle (EV) connected to the DC bus through a battery charger/discharger. Finally, an AC load and a grid connection.

2.2 Genetic-fuzzy Control Fuzzy logic systems (FLS) possess many interesting features allowing it to manage the energy flow within a microgrid having stochastic units such as PVS and WES,

1330

C. Alaoui and H. Saikouk

Fig. 2 Fuzzy-genetic control system

in addition to a load that contains an EV. FLS is model free, rule-based approach incorporating linguistic description of a highly non-linear system. However, fine-tuning a FLS necessitates a trial-and-error process that may be long, expensive and tedious. In order to establish the rules, some prior knowledge or experience is needed. Sometimes, such experience or prior knowledge are unavailable. Genetic algorithms (GAs) are sometimes used to optimize the FLS rules. Depending on the available information and on the system’s settings, GA can be used to optimize the fuzzy rules and/or the fuzzy memberships. Fig. 2. shows the block diagram of such a system.

2.3 Photovoltaic System Conventional photovoltaic modules are typically rated between 200 and 300 W with an open circuit voltage range of 20–30 V. For the PV-grid charging system, the modules are arranged in series-parallel strings to attain the required working voltage, current and power. DC-DC converters are connected as an interface between the PV array and the DC bus. In order to transfer the maximum power to the DC bus at all times, two converters are used as shown in Fig. 3. The first converter, referred to as the boost DC-DC converter, tracks the MPPT of the PV panels in real time. At its output, the maximum power is transferred, but with varying output voltage value, which correspond to different irradiation and temperature values. This is shown in Fig. 4. In order to maintain a constant voltage value at the output of the PV system, a second DC-DC converter is added, and with appropriate feedback control, in order to maintain 750 V at the output and hence connect it to the DC Bus. The switching ON/OFF of the PV panels to the grid is decided by the second stage converter; if

Smart Grid Operation Using Hybrid AI Systems

1331

Fig. 3 PV panel connected to the DC bus through a first stage converter for the MPPT, and the second stage to maintain 750 VDC at the output

Fig. 4 PV panel output voltage vs. solar irradiation and ambient temperature

the voltage input to the second inverter is below a threshold V PV,th such that the 750 V cannot be established at the output DC-DC converter and the feedback controller, the fuzzy control system will disconnect the PV system from the DC bus.

2.4 Wind Power System The wind power system (WPS) consists of a Permanent Magnet Synchronous Generator (PMSG) rotated by a wind turbine which is interfaced to the DC microgrid through a proper AC-DC power converter. Eq. (1) shows the mechanical power PM available at the shaft of the turbine.

1332

C. Alaoui and H. Saikouk

Fig. 5 Typical Cp-λ curve for three bladed wind turbine

Fig. 6 PMSG – MPPT control diagram

PM = 0.5ρ AVW3 C P (β, λ)

(1)

where ρ is the air density (kg/m3 ), A is the cross-sectional area of the stream (m2 ) and V w is the free wind velocity (m/s). For a small wind turbine with fixed pith angle, C P is solely a function of the tip speed ratio λ expressed in (2). λ=

ωr ot R VW

(2)

where, R is the radius of the rotor (m) and ωrot is the rotational speed of the rotor (rad/s). A typical C p -λ curve is shown in Fig. 5. Incremental MPPT technique was selected for this wind turbine. Its main advantage is its independence from the wind turbine parameters. This algorithm solely requires power output measurement for operation. This can be readily achieved from the DC link current and voltage without requiring any speed or wind measurements. Fig. 6. shows the algorithm of the MPPT. It uses the perturb and observe principle to increase the delivered power by increased the duty cycle of the PWM signal sent to the boost converter of Fig. 7.

2.5 Battery-based Energy Storage System (BESS) The battery ESS can only be charged whenever there is a surplus power from the PVS or WTS. The state of charge (SOC) of the ESS is expressed in Eq. (3) as [26, 29].

Smart Grid Operation Using Hybrid AI Systems

1333

Fig. 7 MPPT algorithm for the wind turbine

Fig. 8 BESS connection to the DC bus block diagram

S OC(t) = S OC(0) + ηc

n  t=1

Pch,i(t) − ηd

n 

Pdis,i(t)

(3)

t=1

where ηc and ηd are the charging and discharging efficiencies of the battery pack, Pch,i(t) and Pdis,i(t) are the power for charging and discharging the batteries at time t. Fig. 8 shows the block diagram of the battery based energy storage system (ESS). The DC-DC converter maintains the voltage output at 750V as long as the SOC of the batteries allows it.

1334

C. Alaoui and H. Saikouk

Fig. 9 EV connection to the microgrid

2.6 V2G Block Diagram Figure 9 shows the bidirectional DC-DC converter that will act as a battery charger, or as a battery discharger that will transfer the battery pack energy into the grid through the DC bus.

3 Microgrid Operation In order to establish the appropriate rules for the operation and control of the proposed microgrid, the following guidelines were established: i. ii. iii. iv. v.

The ESS and the EV can be recharged according to two modes: ‘NORMAL’ or ‘URGENT’ Either the ESS or the EV can be charged in ‘URGENT’ mode, they cannot be operating in this mode simultaneously The fuzzy controller reads the voltage levels at each component in order to estimate its power and energy status The EV cannot be recharged from the ESS. This would incur important energy losses due to the cycling efficiencies of the batteries. The power demand from the load PL must be met at all times.

For the proposed microgrid, there are seven possible modes of operation, as described below and illustrated in Fig. 10. In each mode of operation, the fist equation expresses the condition necessary to pass to the mode of operation as in Eqs. (4, 6, 8, 10, 12, 14, 16), and the second equation shows the power and hence the energy transferred, this is shown in Eqs. (5, 7, 9, 11, 13, 15, 17). The DC link voltage is used as the criteria for switching between the seven modes of operation. Mode 1: The power generated by the renewable sources and the power available in storage devices BESS and EV is higher than the power demanded by the load (SOC EV > 50%). In addition, this mode of operation matches peak power demand from the grid. Satisfying the Grid peak demand and load demand, EV being connected and

Smart Grid Operation Using Hybrid AI Systems

1335

discharging. The condition to pass to this mode is expressed in (4), and the power received by the grid is (5). Ppv + Pwp + Pess + Pev > PL

(4)

PG = Ppv + Pwp + Pess + Pev − PL

(5)

Mode 2: This mode of operation is similar to mode 1, except that the EV needs to be recharged (SOC EV < 50%). Satisfying the Grid peak demand and load demand, EV being connected and charging. The grid receives power expressed in (7) as ancillary support. Ppv + Pwp + Pess > PL + Pev

(6)

PG = Ppv + Pwp + Pess − Pev − PL

(7)

Mode 3: This mode of operation is also similar to mode 1, except that the EV is disconnected from the microgrid. Satisfying the Grid peak demand and load demand, EV being disconnected Ppv + Pwp + Pess > PL

(8)

PG = Ppv + Pwp + Pess − PL

(9)

Mode 4: When enough power is received from the renewable sources and the BESS is below a threshold (SOC ESS < 50%), the available energy is used to recharge the BESS assuming that there is no peak demand from the grid. Moreover, the EV is not connected in this mode of operation. Recharging ESS and load demand, EV being disconnected PL < Ppv + Pwp

(10)

Pess = Ppv + Pwp − PL

(11)

Mode 5: When the BESS is almost fully discharged (SOC ESS < 10%), it must include the power from the grid in order to reach SOC ESS = 50%. Beyond 50% of recharge, the BESS is recharged in mode4. Recharging ESS in urgent mode, EV being disconnected PL > Ppv + Pwp

(12)

Pess = PG + Ppv + Pwp − PL

(13)

1336 Table 1 Microgrid modes of operation

C. Alaoui and H. Saikouk Modes

Services

1, 2, 3

Grid peak power demand

4, 5

Recharge of the ESS

6, 7

Recharge of EV

Fig. 10 Microgrid modes of operation

Mode 6 : Recharging EV in normal mode PL < Ppv + Pwp

(14)

Pev = Ppv + Pwp − PL

(15)

Mode 7: When the energy available in the EV is below a threshold (SOC EV < 10%) and very little to no energy input from the renewables, then the EV should be recharged in urgent mode by using power from the grid. Recharging EV in urgent mode PL > Ppv + Pwp

(16)

Pev = PG + Ppv + Pwp − PL

(17)

Table 1 recapitulates the operating modes of the microgrid.

4 Microgrid Simulation The system under study consists of a grid-connected house situated in the north of Morocco. It is equipped with photovoltaic panels (PV) rated at 7 KW, a wind

Smart Grid Operation Using Hybrid AI Systems

1337

turbine generator (WTG) of 5 KW and an electric storage system (ESS) of 14 KHh of capacity. Lastly, the house owner is supposed to possess an electric vehicle (EV) to be connected to the system under study when needed. In order to connect these components, it is necessary to add an inverter between the AC bus and the PV and the battery pack. The EV has its own on-board inverter, hence it can be charged directly from the AC bus. In order to charge the battery from the grid, it is necessary to add a rectifier circuit. Finally, all the inverter circuits must provide electricity at the same frequency as the grid. All conversions incur losses, however, the converters were modeled together and their inefficiencies were assumed to be 90% and were included in the models of the PV, wind turbine and battery systems. Figure 11 shows the household electricity demand for one whole year. It shows the overall trend and seasonal variations. This house is supposed to demand about 15000_KWh of electricity annually. Figure 12 shows the output of the PV panels used in this project. Each data point denotes the electricity provided by the panels over a ten-minute interval. Figure 13 shows the wind data used to simulate the wind power system. Figure 14 shows the simulation result of the DC bus voltage that was regulated to 750 V. At 0.75 and 1.5 s, switching between modes of operations happen, and spikes in output voltage values result as a consequence. Fig. 11 Electricity demand data points with trend line illustrating the seasonal variations

Fig. 12 Energy output from PV panels

1338

C. Alaoui and H. Saikouk

Fig. 13 Wind speed data

Fig. 14 DC bus voltage

5 Conclusion This paper has presented the initial results of a FLC-based design of an energy management system for smoothing the voltage level of a DC microgrid. It contains renewable resources such as PV and Wind power systems, an EV, an energy storage system and a grid-connected load. The proposed approach is implemented on a microgrid simulation model to validate the proposed methodology. Simulation of real-condition operation including experimental data has been performed. The preliminary results of this study has shown that it is possible to train an RL agent using a continuous action algorithm to control the charging scheduling of a battery in a residential setting. Initial results showed the feasibility of this agent, although the reward function needed further tuning.

Smart Grid Operation Using Hybrid AI Systems

1339

References 1. Teimourzadeh S, Aminifar F, Davarpanah M, Shahidehpour M (2018) Adaptive control of microgrid security. IEEE Trans Smart Grid 9:3909–3910 2. Sadabadi MS, Shafiee Q, Karimi A (2017) Plug-and-play voltage stabilization in inverterinterfaced microgrids via a robust control strategy. IEEE Trans Control Syst Technol 25:781– 791 3. Sassi HB, Errahimi F, Essbai N, Alaoui C (2019) V2G and Wireless V2G concepts: state of the art and current challenges. In: 2019 International Conference on Wireless Technologies, Embedded and Intelligent Systems (WITS) pp 1–5 IEEE 4. Diouri O et al (2019) Modeling and design of single-phase pv inverter with mppt algorithm applied to the boost converter using back-stepping control in standalone mode. Int J Photoenergy 2019 5. Amoateng DO, Hosani MA, Elmoursi MS, Turitsyn K, Kirtley JL (2018) Adaptive voltage and frequency control of islanded multi-microgrids. IEEE Trans Power Syst 33:4454–4465 6. Diouri O et al (2019) Control of single phase inverter using back-stepping in stand-alone mode. In: 2019 International Conference on Wireless Technologies, Embedded and Intelligent Systems (WITS). IEEE 7. Alaoui C (2017) Thermal management for energy storage system for smart grid. J Energy Storage 13:313–324 8. Chakib A, Mhamed Z, Saidou N (2017) Towards the implementation of refurbished EV lithiumion batteries for smart grid energy storage. In: 2017 Intelligent Systems and Computer Vision (ISCV), pp 1-5. IEEE 9. Alaoui C, Salameh Z (2007) Electric vehicle diagnostic and rejuvenation system (EVDRS). Int J Power Energy Syst 27(2):151 10. Alaoui C, Salameh ZM (2006) A novel system-on-chip system for diagnostic & rejuvenation for electric & hybrid vehicles. In: International Symposium on Power Electronics, Electrical Drives, Automation and Motion, 2006. SPEEDAM, pp 920–925. IEEE 11. Xiang X, Yu C, Lapierre L, Zhang J, Zhang Q (2018) Survey on fuzzy-logic-based guidance and control of marine surface vehicles and underwater vehicles. Int J Fuzzy Syst 20:572–586 12. Feng G, Lai C, Kar NC (2017) A closed-loop fuzzy-logic-based current controller for PMSM torque ripple minimization using the magnitude of speed harmonic as the feedback control signal. IEEE Trans Ind Electron 64:2642–2653 13. Chakib A (2019) Hybrid vehicle energy management using deep learning. In: 2019 International Conference on Intelligent Systems and Advanced Computing Sciences (ISACS), pp 1–5. IEEE 14. Arcos-Aviles D, Pascual J, Marroyo L, Sanchis P, Guinjoan F (2018) Fuzzy logic-based energy management system design for residential grid-connected microgrids. IEEE Trans. Smart Grid 9(2):530–543. https://doi.org/10.1109/TSG.2016.2555245 15. Shayeghi H, Younesi A, Hashemi Y (2015) Optimal design of a robust discrete parallel FP + FI + FD controller for the automatic voltage regulator system. Int J Electr Power Energy Syst 67:66–75 16. Shayeghi H, Younesi A (2017) A robust discrete FuzzyP + FuzzyI + FuzzyD load frequency controller for multi-source power system in restructuring environment. J Oper Autom Power Eng 5:61–74 17. Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM (2017) Salp swarm algorithm: A bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163– 191 18. Mahat P, Chen Z, Bak-Jensen B (2011) Control and operation of distributed generation in distribution systems. Electr Power Syst Res 81:495–502 19. Bozchalui MC, Hashmi SA, Hassen H, Canizares CA, Bhattacharya K (2012) Optimal operation of residential energy hubs in smart grids. IEEE Trans. Smart Grid 3(4):1755–1766. https:// doi.org/10.1109/TSG.2012.2212032 20. Ren H et al (2016) Optimal operation of a grid-connected hybrid PV/fuel cell/battery energy system for residential applications. Energy 113:702–712

1340

C. Alaoui and H. Saikouk

21. Pena-Bello A et al (2017) Optimizing PV and grid charging in combined applications to improve the profitability of residential batteries. J Energy Storage 13:58–72 22. Matallanas E et al (2012) Neural network controller for active demand side management with PV energy in the residential sector. Appl Energy 91(1):90–97 23. Glavic M, Fonteneau R, Ernst D (2017) Reinforcement learning for electric power system decision and control: past considerations and perspectives. IFAC-Papers On Line 50(1):6918– 6927 24. Leo R, Milton RS, Sibi S (2014) Reinforcement learning for optimal energy management of a solar microgrid. In: Global Humanitarian Technology Conference-South Asia Satellite (GHTC-SAS), pp 183–188. IEEE 25. Kuznetsova E et al (2013) Reinforcement learning for microgrid energy management. Energy 59:133–146 26. François-Lavet V, et al (2016) Deep reinforcement learning solutions for energy microgrids management. In: European Workshop on Reinforcement Learning (EWRL 2016) (2016) 27. Schaarschmidt M, Kuhnle A, Fricke K (2017) Tensor force: a tensor flow library for applied reinforcement learning. Web page https://github.com/reinforceio/tensorforce 28. Website: electricity tariff in morocco https://www.invest.gov.ma/index.php?Id=34503&lan g=en. Accessed 19 Jul 2020 29. Sassi HB, Errahimi F, Essbai N, Alaoui C (2018) A comparative study of ANN and Kalman Filtering-based observer for SOC estimation. In: IOP Conference Series: Earth and Environmental Science vol 161, p 012022

Economic Comparison Between Two Hybrid Systems (Wind-Hydrogen) and (Wind-Hydroelectric) for Electricity Production in Socotra, Yemen Saif Serag, Outhman Elbakkali, and Adil Echchelh

Abstract Renewable energy sources are one of the main sources of energy production, Therefore, all researchers interesting in these sources, and consider it as a primary source to cover a country’s needs, especially in developing countries such as Yemen, which suffer from a large deficit to cover region’s need for electric energy, including remote islands such as Socotra. In this paper, we will present an economic study for electricity production by wind turbines in Socotra Island, and an economic comparison between two means of energy storage, which is energy storage by hydrogen production or by hydroelectric, and we accurately estimate the energy unit price (kWh) to know the economic cost of two storage methods. Keywords Wind energy · Hydrogen · Hydroelectric · KWh cost

1 Introduction Renewable energy sources have grown significantly in recent times due to the problems that fossil fuels cause to environment such as air pollution, climate change, and other problems, which have made all researchers and scientists a trend to seriously study renewable energy and classify it as an energy that ensures a balance between energy security and economic development [1]. Globally, The annual growth rate of wind energy is about 30% annually, this is an indication that many countries are interested in this source type to energy production and reducing dependence on fossil energy [2]. In remote areas that are far from the power grid of main station, the demand for energy sources increases, especially wind energy, and since wind energy changes seasonally, this energy must be stored, one of the best storage methods is the method that clean, renewable, and hybrid to energy produce at need time, peak energy use, and use Part of the excess wind energy, especially in off-peak, to store it as hydroelectric energy, which has been greatly distinguished recently due to its high capacity and ease of use [3], or to produce hydrogen to be used when needed to S. Serag (B) · O. Elbakkali · A. Echchelh Laboratory of Energetic Engineering and Materials, Ibn Tofail University, Kenitra, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_122

1341

1342

S. Serag et al.

Fig. 1 Socotra Island

electricity production by fuel cells, which has proven high efficiency to produce electricity better than other types of fuel [4], and it will noted in this study the economic difference between these two methods according to the knowledge of energy unit price for each of storage methods separately [2]. Yemeni population is considered to have the least access to electrical energy, as the population rate benefiting from electricity is 51.7% of total population number [5], due to Yemen circumstances which have passed from 2011 until today, total capacity of energy production has decreased to 70%, which has negatively affected of Yemeni population significantly [6]. All this makes it necessary to plans study to energy production by renewable sources, specifically wind energy, as the average wind speed for coastal areas in Yemen is 8 m/s [7], which is sufficient to energy production with high efficiency. This study has been applied in Socotra island as a field of study, which is one of the largest Yemeni islands, and contains a population density of approximately 80,000 people, which increase their need for electricity energy recently [8], and it will check the economic cost of producing and storing the energy by renewable energy sources, and compare it in cost, which has great importance, as it gives a future vision of possibility of renewable energy using, storing it by a clean, environmentally friendly methods, and even at lowest costs, as the economic comparison enables to know the best methods of electricity production, least cost, and best in efficiency.

2 Site Data Socotra is Yemen island, and considered within the Hadramawt governorate, located 250 km east of the Africa Horn and 350 km south of Arabian Peninsula [8]. It has an area of 3,600 m3 and height above sea level is 257 m. All remote islands suffer from

Economic Comparison Between Two Hybrid Systems …

1343

a great shortage of generating electricity and depends on generating electricity there by diesel or gasoline generators. Because of high prices of these exporters, electricity prices are high in the region. On other hand, Socotra Island possesses wind energy averaging between 6–12 m/s, which enables it to generate electricity at a lower cost, more sustainable, and safer for environment [9] (Fig. 1).

3 Methodology 3.1 Wind Energy Cost Wind energy cost is calculated by knowing several factors, turbine type used, its capacity factor, tower height, annual amount of energy produced according to wind speed, its distribution during year, and lifetime for energy production, then applying the following equations [1, 10]:  P V C = Cinv + Comr ×

1+i r −i



     1+i t 1+i t − Cs × × 1− 1+r 1+r

(1)

Where Cinv investment cost, Comr Operation and maintenance costs, Cs Additional costs, i interest rate = 1.5%, t Turbine lifetime = 20 years, r inflation rate = 2%. (kWh cost) =

PV C . PE

(2)

Where PE = energy production in a rated lifetime. This study will be applied by turbines (Nordex 1500 kW) with height towers between 65–80 m and cost approximately 1220 $/kW [11].

3.2 Hydrogen Production Cost To calculate the cost of electrical energy production by hydrogen producing and storing, it must calculate the cost for each production stages, and hydrogen conversion. Figure 2 shows the stages for electricity production from hydrogen, and it note that the alternating current must be converted into continuous for use in water analysis to its basic components (hydrogen and oxygen), which is followed by hydrogen storage process, to be used at peak electricity demand to cover the shortage for this period or when wind speed is weak. LCOE. (Levelized cost of electricity generation) which is equal to total cost divided by total energy produced during the lifetime, for renewable energies calculated by formula [12–14]:

1344

S. Serag et al.

n

It +Mt +Ft (1+r )t Et t=1 (1+r )t

t=1

LC O E = n

(3)

Where It = investment expenditures in year t, Mt operations and maintenance expenditures in t, Ft = fuel expenditures, Et electricity generation in the year t, r = discount rate (wind = 6.5 to 10%), (Hydro = 3.75 to 10%) [15], and n = system economic life = 20 years. Now we should explain each of the stages shown in Fig. 2 to clarify the method that it uses in this economic study: Electrolysis. This will be studied with taking into account analyzing process of hydrogen production by proton exchange membrane [PEM], and it is one of best ways to analyze water into oxygen and hydrogen. It is noted that energy needed to produce 1 kg of hydrogen consumes 50 kWh of electrical energy. Table 1 shows the costs per kW in dollars with rate of operation, maintenance, and capacity factor [16]: Storage. There are several methods for hydrogen store according to hydrogen state (solid, liquid, gas) and each method has its advantages and disadvantages, but the best economic way is to store hydrogen in a gaseous state under a pressure of 700 bar, it is less expensive, simpler than other methods. We will note in Table 2 Storage cost per kg of hydrogen, percentage of costs allocated to compression, operation, and maintenance [17]. Electricity Production (Fuel Cell). When the amount of energy generated from turbines decreases due to peak use or low winds in region, hydrogen stock must be used to electricity production to cover the electricity need. Fuel cells are used to convert hydrogen into electricity in oxygen presence and the reaction result is electric current and water. Table 3 shows the price of 1 kilowatt of fuel cell, its efficiency, percentage of operation and maintenance during its lifetime [19]. Transport Cost. In event that hydrogen needs to be transported from the generation place to the consumption place, there are many ways to transport hydrogen, the best way is tube trucks, and the transportation costs will appear in Table 4, [20]. Other Cost. (Fuel expenditures, water treatment, management, taxes…) = 5% kWh.

Table 1 PEM analysis cost Description

Amount

Unit

Investment PEM

1000

$/kW

Capacity factor O&M Life time

50

%

3

%

20

Years

Economic Comparison Between Two Hybrid Systems …

1345

Fig. 2 Hydrogen production and storage stages for Using to electricity production

Table 2 Hydrogen storage cost (gas state)

Description

Amount

1kg storage cost

500

Compression cost 700 O&M Life period

Unit $/kg

20

%

6

%

20

Years

Note: 1 kg of hydrogen produces approximately 39.4 kWh [18]

Table 3 Fuel cell cost

Description

Amount

Unit

1 kW fuel cell cost

1400

$/kW

Capacity factor O&M Lifetime

Table 4 Truck transport cost

70

%

2

%

20

Years

Description

Amount

Unit

Cost of 180 kg transport (tube truck)

100,000

$

Lifetime

20

Years

3.3 Hydroelectric Cost Figure 3 shows the stages of electric power generation by hydroelectricity, where surplus energy from wind turbines is used to store a quantity of water by pumping it from the sea into an elevated reservoir, and then using stored water to generate electricity as fallen and rushing water from reservoir to drive a turbine to generate electricity at peak use. On the economic side, investment costs for hydroelectric

1346

S. Serag et al.

Fig. 3 Hydroelectric stages for electricity storage and production

Table 5 Hydroelectric cost Description

Amount

Unit

Hydroelectric installed with storage costs

5000

$/kW

O&M

3

%

Capacity factor

70

%

Lifetime

20

Years

stations with storage range from 1050 $ to 7650 $ per kW, depending on the efficiency and use period of the station. Table 5 shows the potential price of hydroelectric stations with storage, along with cost of maintenance, operation, and lifetime [14].

4 Results and Discussion 4.1 Wind Data Figure 4 shows the monthly average wind speeds for nine years from 2006 to 2014, and clearly shows a large amount of wind speed, especially in June, July, August, and September, while average of wind speed value was in January and December, slightly lower values in remaining months, the lowest value for that speed is in April 4.37 m/s and the largest value in July is 17.22 m/s. As for the mean of wind speed for 9 years as a whole, it reached 9.17 m/s, a value that emphasizes a large and sufficient amount to use for energy production. When using a simulation program such as ALWIN, and by adding wind speed data for area and type of turbine used (Nordex 80−1500 kW), the results are shown in Fig. 5.

20.00 10.00

13.99 8.37

1347

17.22 15.91 11.04 7.91 5.65 6.54

6.79 5.63 6.64 4.37

dec.

Nov.

Oct.

Sep.

Aug.

Jul.

Jun.

May.

Apr.

Mar.

Feb.

0.00 Jan.

Wind Speed

Economic Comparison Between Two Hybrid Systems …

Month Fig. 4 Monthly mean wind speed for 9 years

Fig. 5 AlWIN simulation application for (Nordex80-1500) turbine

It is noted that all variables related to wind speed, such as Weibull parameters (a = 13.55 m/s, c = 1.42), average speed at a height of 80 m (vm = 12.34 m/s), as well as the variables for turbine, which are average power (Pm = 635.6, 859.1 and 677.8) kW, annual energy generated by the turbine (5567.7, 7525.8, and 5937.7) MWh, and capacity factor (42, 56.9, and 45) %, were calculated in three cases: Measured values, Rayleigh distribution, Weibull distribution. By taking average annual energy for three values, measured, Weibull and Rayleigh will be 6343 MWh, knowing the island’s population, and amount of energy consumed by Yemeni citizen, we can know the number of turbines needed to cover the region’s need for electricity, as shown in Table 6. Thus, the number of turbines needed to cover island’s energy need is 3 turbines with an annual excess of energy of 1429 MWh, equivalent to 3915 kWh per day, and this excess energy contributes in storage process for use when needed.

1348 Table 6 Number of turbines needed for Socotra Island

S. Serag et al. Description

Amount

Unit

Turbine annual energy

6343

MWh

Energy per Capita

220*

kWh

Population

80000

Person

Total consumption

17600

MWh

Turbine no

3

Turbines

* data.worldbank.org

Table 7 Wind energy cost in a lifetime

Table 8 Costs for all hydrogen production stages

Description

Amount

Unit

PVS

3.24 ×

$

PE

3.8 × 108

kWh

kWh cost $

0.085

$/kWh

107

Input

Cost

Unit

Electrolysis cost

0.021

$/kWh

Storage cost

0.018

$/kWh

Fuel cell cost

0.012

$/kWh

Other cost

0.003

$/kWh

4.2 Wind Energy Cost By applying Eqs. (1), (2), calculating values of (Cinv , Comr., Cs ), and compensating for parameters ( i = 1.5%, t = 20 years, r = 2%), it get the results as in Table 7, where it was found that the total cost of energy producing by wind turbines is (3.24 × 107 )$ and energy produced from those turbines during their lifetime is (3.8 × 108 ) kWh, Thus, we can see the cost of kWh equal to (0.085) $.

4.3 Hydrogen Production Cost By knowing cost and values , which appear in Tables 1, 2, 3, and 4, and applying the equation of total cost to total energy production in a lifetime, we can get the results shown as follows: Table 8 shows the cost for each stage of hydrogen production in dollars per kWh, and Table 9 shows total costs with or without transportation (without transportation in the case of direct electricity production so that the hydrogen production plant and energy generator plant are adjacent and do not need to transport the hydrogen from one place to another), as it is shown that the largest costs are for the production

Economic Comparison Between Two Hybrid Systems … Table 9 Total cost in tow methods

Table 10 Hydroelectric cost

1349

Transport cost

0.014

$/kWh

Total cost without transport

0.054

$/kWh

Total cost with transport

0.068

$/kWh

Input

Cost

Unit

Investment costs

0.0408

$/kWh

O&M

0.0012

$/kWh

Other cost’s

0.0008

$/kWh

Total

0.0428

$/kWh

and storage of hydrogen because of the large energy used for production or storage under pressure of not less than 700 bar. While the cost of transportation occupies a relatively large percentage of 21% for same reason, (energy required for pressure, storage, and cost for truck tanks and pipes, which are used for transportation).

4.4 Hydroelectric Cost Table 10 shows the costs of generating electricity by hydroelectric stations, which with total 0.0428 $/kWh, and we note that the costs of electricity producing in it are less than cost of electricity producing by fuel cells because hydroelectric stations do not need pressure and great security, as a case in storing hydrogen, Hydroelectric just need a water pump, dam or tank, and a turbine to electricity generating, it is an easy and safer method than others.

4.5 Total Cost and Hybrid Systems (Wind—Hydrogen) and (Wind—Hydroelectric) After studying the costs of electric energy generating from wind turbines, storage methods, and generation of electricity by (hydrogen production and storage) or by (hydroelectric storage), we ought to study the total costs of hybrid systems that link (wind-hydrogen) or (wind-hydroelectric) energy, or even the use of three systems Wind, hydrogen and hydroelectric. Table 11 shows the total costs for (wind-hydrogen), (wind-hydroelectric), and (wind-hydrogen-hydroelectric). It is noticeable the total costs shown in previous tables are low compared to electricity prices resulting from oil derivatives, especially with high prices and even lack of in recent times in Yemen. Therefore, electricity production in these ways is lower in cost, safer, more sustainable, and it is recommended to be used.

1350

S. Serag et al.

Table 11 Total cost for hybrid system

Input

Cost

Unit

Wind-Hydrogen

0.14

$/kWh

Wind-Hydrogen (transport)

0.15

$/kWh

Wind-Hydroelectric

0.13

$/kWh

Wind-Hydrogen-Hydroelectric

0.18

$/kWh

5 Conclusion Renewable energy sources are the best and cheapest way to electricity production, including wind energy, but because of the instability of these sources, energy must be stored for use when needed, so this study was applied on the Socotra island, Yemen for electric energy production by wind energy and store this energy in two methods, first is produce, store hydrogen, and second is hydroelectric, these methods have been economically compared and we get the following: 1.

2.

3.

4.

5.

6.

Socotra Island has a mean wind speed of 9.17 m/s. This result of the average winds for nine years, which is a great speed that makes this island able to produce electricity by turbines with high efficiency. When using turbines Nordex 1500 kW with tower height equal 80 m, we find an annual average energy produced from this turbine is 6,343 MWh, since the annual consumption of the island is 17,600 MWh, Island needs three turbines to cover regions by electricity with energy excess approximately 1429 MWh. The unit cost of energy produced from wind turbines, according to the parameters concluded from wind distribution and its probability is approximately 0.085 $/kWh. Electricity producing process from hydrogen goes through several stages, the most important of which is hydrogen production and storage, cost has been calculated for all of those stages and it has been found that the total cost is equal to 0.054 $/kWh to electricity production from hydrogen without transportation and 0.068 $/kWh by transportation. Hydroelectricity is the easiest and safest method for electricity production, the costs of electricity producing by it are 0.043 $/kWh, which is less expensive compared to other storage and production methods. When applying a hybrid system (wind-hydrogen) and (wind-hydroelectric) or (wind—hydrogen and hydroelectric), it is found that the costs are ((0.14 or 0.15), 0.13, 0.18) $/kWh respectively, which are low costs compared to the methods of producing electricity with fossil fuels, especially in recent times when fuel value has increased.

Economic Comparison Between Two Hybrid Systems …

1351

References 1. Ohunakin OS, Akinnawonu OO (2012) Assessment of wind energy potential and the economics of wind power generation in Jos, Plateau State, Nigeria. Energy Sustain Dev 16(1):78–83 2. Shawon MJ, El Chaar L, Lamont LA (2013) Overview of wind energy and its cost in the Middle East. Sustain Energy Technol Assess 2:1–11 3. Kumar K, Ansari MA (2013) Design and development of hybrid wind-hydro power generation system. In: 2013 International Conference on Energy Efficient Technologies for Sustainability, Nagercoil 4. Garland NL, Papageorgopoulos DC, Stanford JM (2012) Hydrogen and fuel cell technology: progress, challenges, and future directions. Energy Procedia 5. Alkholidi AG (2013) renewable energy solution for electrical power sector in Yemen. Int J Renew Energy Res (IJRER), 9 6. Saleh Qasem AQ (2018) Applications of renewable energy in Yemen. J Fundam Renew Energy Appl 08(02) (2018) 7. Hashim Alkipsy EI, Raju V, Kumar H (2020) A review of the challenges of energy sector and prospects of renewable energy utilization in Yemen. Glob J Manag Bus 8. Fritz M, Okal EA (2008) Socotra Island, Yemen: field survey of the 2004 Indian Ocean tsunami. Nat Hazards 46(1):107–117 9. Saqqaf A, Alkaf, Muqbel MA, Bin SM (2010) World renewable energy congress XI cost analysis of renewable energy for power supply for remote area in Yemen: a case study for Socotra Island 10. Soulouknga MH, Oyedepo SO, Doka SY, Kofane TC (2020) Evaluation of the cost of producing wind-generated electricity in Chad. Int J Energy Environ Eng 11(2):275–287 11. ‘Renewable Energy Technologies Cost Analysis Series Wind Power’ International Renewable Energy Agency IRENA June 2012 12. Kornbluth K, Greenwood J, Jordan E, McCaffrey Z, Erickson PA (2012) Economic feasibility of hydrogen enrichment for reducing NOx emissions from landfill gas power generation alternatives: a comparison of the levelized cost of electricity with present strategies. Energy Policy 41:333–339. https://doi.org/10.1016/j.enpol.2011.10.054 13. Shea RP, Ramgolam YK (2019) Applied levelized cost of electricity for energy technologies in a small island developing state: a case study in Mauritius. Renew Energy 132:1415–1424. https://doi.org/10.1016/j.renene.2018.09.021 14. Gielen D (2012) Irena renewable energy technologies cost analysis series hydropower 15. A Grant Thornton and Clean Energy Pipeline initiative (2018) Renewable energy discount rate survey a grant thornton and clean energy pipeline initiative, p 36 16. Chan A, Hart D, Lehner F, Madden B, Standen E (2014) Fuel cells and hydrogen Joint undertaking, “Development of Water Electrolysis in the European Union”, February 2014 17. Riis T, Hagen EF, Preben, Vie DJS, Ulleberg Ø (2005) International energy agency hydrogen production and storage © OECDIE 18. Touili, S Alami Merrouni A, Azouzoute A, El Hassouani Y, Amrani A (2018) A technical and economical assessment of hydrogen production potential from solar energy in Morocco. Int J Hydrog Energy 43(51):22777–22796 19. Fuel Cell Technologies (2016) Office of energy efficiency and renewable energy - section 3.4 20. Amos WA (1999) Costs of Storing and Transporting Hydrogen NREL/TP-570–25106, ON: DE00006574, 6574

A New Hybrid System Used to Connect PV to a Microgrid Alexandru Dusa, Petru Livinti, Daniel Ciprian Balanuta, and Gelu Gurguiatu

Abstract This paper proposes a hybrid system by connection the photovoltaic panels to a microgrid through an active power filter (APF). This new hybrid system enables the limitation of the power generated by photovoltaic panels and follow the load demand without supplying power to the microgrid. This whole unified hybrid system will allow the attenuation of the harmonic injected to the microgrid and the compensation of the reactive power in the case of lack of power from the PV system. The Matlab/Simulink simulation environment is used to demonstrate the proper functioning of this hybrid system. A modified perturb and observe algorithm is used to control the boost converter and indirect method for the APF control. Keywords PV · Microgrid · Boost converter · Shunt active power filter · Power limitation

1 Introduction The increase of clean energy demand involves the need of using renewable sources like PV panels. The storage of energy through batteries for later uses leads to an increased investment costs, maintenance costs and chemical pollution associated with batteries, which makes this option less feasible. Injecting energy directly into the microgrid ensures the maximum use of clean power generated by renewable energy sources [1]. The integration of renewable energy systems is the most current trend in the field of energy research. The increase in renewable energy production and injection into electric its grids has begun to affect the security and stability of the operation of power systems [2]. Obtaining the maximum power from renewable sources without affecting the grid management and the attenuation of harmonics in A. Dusa (B) · D. C. Balanuta · G. Gurguiatu “Dunarea de Jos”, University of Galati, Galat, i, Romania e-mail: [email protected] P. Livinti “Vasile Alecsandri” University of Bacau, Bac˘au, Romania © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_123

1353

1354

A. Dusa et al.

the distribution grid is the main objectives of this paper. The maximum power point (MPP) principle and supply of nonlinear load from PV is presented and demonstrated in the papers [3–6]. In this paper it is proposed to interconnect the PVs to the low power grid through the shunt active power filter (APF) and injecting only the amount of power needed by the electrical loads. The APF consists of a three-phase inverter with three levels in Voltage Source Inverter (VSI) with two capacitors on the DC side, with the aim of attenuating the harmonics introduced in the grid from nonlinear tasks as well as compensating the power factor. The control algorithm of the APF is performed using the indirect control method. The connection of the PVs with APF is made through a boost converter. The connection between APF and boost converter is made on the DC side. Extraction of the maximum power from the PVs and its limitation is performed using a modified Perturb and Observe (P&O) control algorithm.

2 The Hybrid System Figure 1 presents in detail the proposed diagram for this new hybrid system that allows supply the nonlinear load, PVs output power limitation at load level and harmonics attenuation as well as correction of the power factor.

Fig. 1 Detailed electrical diagram of the hybrid system

A New Hybrid System Used to Connect PV to a Microgrid

1355

2.1 Boost Convertor The boost converter allows the output voltage to be raised. The wiring diagram of a boost converter is present in Fig. 2 For designing this type of converter it is necessary to know the following values: minimum input voltage U in_min = 345.4 V, output voltage U out = 750 V, intensity of the load current I out = 9.25 A, switching frequency F sw = 25000 Hz, power P = 7300 W, effectiveness η = 95%, the percentage from the output voltage for calculating the ripple voltage is equal to 1 [7]. The design elements of the boost converter are: The filling factor D for the PWM control signal for IGBT in the boost converter circuit is determined by:  D = 1−

  Uin_min · η Uout

%

(1)

The output current is calculated with the relation: Iout =

P Uout

(2)

The value of the input current I in results from the formula: P = I in · U in = I out · U out Iin =

P U1n

(3)

The value of the input current 1 is equal to the current rate in the coil. The ripple current of the inductance L will be equal to 10% of the input current. I L = Iin · 10%

(4)

The inductance L is determined through the relation: L=

Uin · D I L · Fsw

(5)

The value of the ripple voltage U out will be equal to 1% of the value of the output voltage: Fig. 2 Boost converter

1356

A. Dusa et al.

Uout = Uout · 1%

(6)

The determination of the output capacity is performed with the relation: C=

Iout · D Fsw · Uout

(7)

The load resistance is determined by the relation: R=

Uout Iout

(8)

2.2 Maximum Power Point Tracking Algorithm The modified P&O control method [8] allows the extraction of the maximum power from the PVs and then compares it with the load need. If the power value of the PVs is higher than the load need then the P&O control algorithm increases the duty cycle, otherwise is decreases the duty cycle. The diagram of the modified P&O control algorithm is present in Fig. 3.

Fig. 3 Diagram of the modified P&O control algorithm

A New Hybrid System Used to Connect PV to a Microgrid

1357

2.3 Active Power Filter Control Indirect control is a strategy that does not require knowledge of electrical current spectrum absorbed by the nonlinear load. This strategy imposes that the grid current to be sinusoidal and in phase with the voltage [9]. i sa (t) = i La (t) + i f a (t)

(9)

The electric current on phase a absorbed by the nonlinear load is: 1 i La (t) = i La (t) +



i Lak (t) + i Laq (t)

(10)

k 1 where: i La (t) - fundamental active component of load current;  i Lak (t) - harmonics sum of load current; k

i Laq (t) - reactive component of load current. The electric current through the APF on phase a will be: i f a (t) = i 1f a (t) + i˜ f a (t)

(11)

where: i 1f a (t) - fundamental active component of APF current; i˜ f a (t) - harmonics sum of APF current. The electric current absorbed by the grid must be sinusoidal and must have the same phase as the voltage. The component to be compensated by the APF is given by: ˜ = i˜ f a (t) + i(t)



i Lak (t) + i Laq (t)

(12)

k

From (1) ÷ (4) it can be writing: 1 + i 1f a + i˜ i sa = i La

(13)

The signal is generated for the charging input of the current regulator of phase a of the power supply: va i a∗ (t) = ε DC √ = ε DC sin ωt 2V

(14)

where: V is the effective value of the phase voltage of the grid and εDC the output of the DC voltage regulator. The above charge is compared with the measured value of the electric current absorbed from the isa grid, resulting for the control of the APF on phase a:

1358

A. Dusa et al.

   1 u ∗ca = k i a∗ − i sa = k i a∗ − i˜ − i La − i 1f a

(15)

where: k is the amplifier of the regulator. The regulator is linear, the sinusoidal components of the load and the APF are found, in the sinusoidal imposition of the regulator: 1 i a∗ = i La + i 1f a

(16)

This remark results in the imposition for phase a of the APF:

u ∗ca

= −k i˜ = −k i˜ f a (t) +



 i Lak (t) + i Laq (t)

(17)

k

proportional to the polluting component. If it is correctly designed in steady state, the controller cancels the steady state error

i˜ f a (t) = −



 i Lak (t) + i Laq (t)

(18)

k

so, the APF will generate the polluting component itself necessary for the nonlinear load. The assessment εDC from the regulator for charging capacitor C is converted to current reference, as follows: ⎧ ∗ i = ε · sin ωt ⎪ ⎨ a    i b∗ = ε · sin ωt − 2π 3 ⎪    ⎩ ∗ i c = ε · sin ωt − 4π 3

(19)

Sinusoidal signal in phase with fundamental are obtained with Phase-Locked Loop (PLL), which allows the synchronization of the compensation signal and the voltage of the power supply system. Figure 4 presents the control scheme of the APF with the indirect method. Where we can see the PLL block for determining the grid phase (ωt) and the DC regulator ∗ ). for maintaining the voltage at the set value (Vdc

3 Simulation Results The simulations were done in the Matlab/Simulink programming environment considering two hypotheses:

A New Hybrid System Used to Connect PV to a Microgrid

1359

Fig. 4 The control schemes

Table 1. Values used to stimulate the hybrid system Equipment

Data

Value

Microgrid

Line voltage (U L )

400

V

Apparent power (S)

25000

VA

Resistance (R)

21/96



Inductance (L)

0.001

H

Inductance (L)

0.0471

H

Capacitance (C)

2.5·10−5

F

Switching frequency IGBT (F sw )

25000

Hz

Peak Power (Pmax )

365

W

Open-circuit Voltage (V oc )

42.8

V

Voltage at Pmax (V mp )

36.7

V

Short-circuit current (I sc )

10.8

A

Current at Pmax (I mp )

9.95

A

Series/Parallel

10/2

Panels

Irradiances (Irr)

1000

W/m2

Nonlinear load Boost converter

Photovoltaic panel (LG365Q1C-A5)

Active power filter

Proportional and integral (PI) controllers

Unit

Temperature (T )

25

°C

Inductance (L)

0.0038

H

Capacitance (C)

2·0.001

F

Switching frequency IGBT (F sw )

30000

Hz

Continuous voltage of the APF (V dc )

750

V

The proportional coefficient (kp)

1

The integral coefficient (ki)

20

• PVs output power was less than the nonlinear load need and the deficit was supplemented from the microgrid. • PVs output power was limited at load consumption level. In Table 1 are presented the values used for the simulation of the proposed hybrid system.

1360

A. Dusa et al.

Fig. 5 Boost converter results a) power, b) voltage, c) current

Figure 5 presents (a) power, (b) voltage and (c) current from the boost converter, were: • The first hypothesis is presented in the range 0.22–0.25 s: – PVs produce the maximum power, PPV = 6700 W (due to losses on the boost converter and APF); – nonlinear load power PL = 13700 W; – the power deficit was supplemented from the microgrid. • The second hypothesis in the range 0.25–0.32 s: – the PVs output power was limited to the nonlinear load need (PL = 3000 W). In Figs. 6 and 7 is presented the voltage and current waveforms taking into account the two hypotheses. Here is presented for the first hypothesis the waveform of the microgrid voltage (Fig. 6(a)), the current generated by the PVs through the APF (Fig. 7(a)) and injected in the common connection point (CCP) to supply the nonlinear load (Fig. 7(b)) and attenuate the harmonics in the microgrid. And the current from the microgrid (Fig. 6(b)) supplements the supply of the nonlinear load. In the second hypothesis it is notice that the current from the microgrid is 0 (zero) (Fig. 6(b)). This is due to the limitation of the power generated by the PVs, injecting the power needed by the nonlinear load.

A New Hybrid System Used to Connect PV to a Microgrid

1361

Fig. 6 Current and voltage results a) microgrid voltage, b) microgrid current

Fig. 7 Current results a) PV + APF current, b) nonlinear load current

Also, here we can observe the transient regime at the moment t = 0.25 s, at the change of the power of the nonlinear load. Figure 8 present the Fast Fourier Transform (FFT) analysis for the source voltage waveforms on phase 1. Figure 8(a) presents the harmonic level of the source voltage for the first hypothesis, THDu = 4.01%. Figure 8(b) presents the harmonic level of the source voltage for hypothesis two, THDu = 4.83%. Figure 9 present the FFT analysis for current waveforms for phase 1. Figure 9(a) presents the harmonic level of the nonlinear load current, THDi = 29.24%. Figure 9(b) presents the harmonic level of the microgrid current, THDi = 4.88%.

1362

A. Dusa et al.

Fig. 8 FFT analysis of the voltage waveform a) in the first hypothesis, b) in hypothesis two

Fig. 9 FFT analysis of the current waveform a) the nonlinear load, b) the microgrid current

4 Conclusions The aim of this paper was to create a new hybrid system were to connect the PVs to the microgrid using a shunt APF. Which will allow the maximum power to be extracted from the PVs or to limit the output PVs power, without introducing the excess power into the microgrid. At the same time, this hybrid system allows the attenuation of the harmonic level in the microgrid and the compensation of the reactive power. For this paper, simulations were performed in Matlab/Simulink, which demonstrate the advantages of this proposed system.

A New Hybrid System Used to Connect PV to a Microgrid

1363

References 1. Ali Q, Al-Shetwi MA, Hannan KPJ, Mansur M, Mahlia TMI (2020) Grid-connected renewable energy sources: review of the recent integration requirements and control methods. J Clean Prod 253(119831):1–17 2. Sriranjani R, Jayalalitha S (2018) PV interconnected shunt active filter for power balancing and harmonic mitigation. Int J Pure Appl Math 118(18):2341–2354 3. Djeghader Y, Chelli Z, Rehaimia S (2018) Harmonic mitigation in electrical grid connected renewable energy sources. ACTA Electrotehnica 59(4):287–291 4. Manimegalai V, Senthilnathan N, Sabarimuthu M (2017) Grid interfaced renewable energy source to improve power quality using SAPF. Int Res J Eng Technol 4(4):834–839 5. Chaithanakulwat A (2019) Multi-functionality control, power filtering single-phase gridconnected photovoltaic system. Am J Electr Power Energy Syst 8(2):62–70 6. Bouzelata Y, Kurt E, Chenni R, Altın N (2015) Design and simulation of a unified power quality conditioner fed by solar energy. Int J Hydrogen Energy 40(44):15267–15277 7. Livinti P, Ghandour M (2020) Fuzzy logic system for controlling of DC/DC boost converter developed in MATLAB SIMULINK. Int Res J Eng Technol 7(9):1629–1636 8. Zala MP, Pandya MH, Odedra KN, Patel DP (2017) Active power control of PV system in MPPT and CPG mode. Kalpa Publ Eng 1:270–277 9. Gurguiatu G Munteanu T (2012) Indirect control in active power filters. In: International Conference on Optimization of Electrical and Electronic Equipment (OPTIM) 2012 - 13th, ResearchGate

Coordinated Control and Optimization Dispatch of a Hybrid Microgrid in Grid Connected Mode Marouane Lagouir, Abdelmajid Badri, and Yassine Sayouti

Abstract This paper presents a novel daily energy management system for coordinated control and optimization dispatch of a grid connected hybrid microgrid (HMG). The main objective is to minimize both operating cost and emissions level, taking into account ensuring continued power balance, maximize the generation of renewable energy sources, optimal charging/discharging of battery storage system, and the system constraints. The economic and emission dispatch problem has been solved using grey wolf optimizer (GWO) algorithm, under four case studies. Then a comparison with other heuristic algorithms is considered. The obtained results confirm the potential of GWO in terms of stability, robustness, and convergence speed. Therefore, it can be used for real time control and optimization dispatch of HMG. Keywords Energy management system · Optimization dispatch · Hybrid microgrid · Operating cost · Emissions level · Renewable energy sources · Grey wolf optimizer

1 Introduction In the last years, with the growing electricity demand and the development of distributed generation (DG) technology. The integration of microgrid (MG) into the existing electricity power system is considered as a promising alternative that provides more reliable operation, considerable cost saving and reduction in pollutant gas emissions due to excessive energy dependency on fossil fuel [1]. However, the intermittent generation of renewable energy sources (RESs) and difficulty of prediction of load demand caused a significant mismatch between the generated power and the required demand [2]. Therefore, an energy management system (EMS) plays a vital role in optimizing dispatch problem and coordinated control in MG system. Several studies have been conducted in area of MG energy management and control. M. Lagouir (B) · A. Badri · Y. Sayouti Department of Electrical Engineering, EEA&TI Laboratory, Faculty of Science and Technology (FSTM), Hassan II University of Casablanca, BP 146, 20650 Mohammedia, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_124

1365

1366

M. Lagouir et al.

In [3], the authors proposed a novel optimization model to minimize both operational and pollution cost in a MG, then an advanced dynamic programming (ADP) method is employed to solve the optimization dispatch problem. To evaluate the system performance two case studies were considered in the presence and without a battery storage system. In [4], the authors discuss the application of ant lion optimizer (ALO) algorithm for handling the optimal economic load dispatch problem. Considering challenging constraints such as spinning reserve constraint, ramp rate limits, prohibited operating zones of generating units. Three case studies have been investigated, then the results are compared with other heuristic methods to prove the stability and robustness of ALO method. Many heuristic methods are implemented for optimal energy management in MG, including particle swarm optimization (PSO) algorithm [5], antlion optimizer algorithm [4, 6], artificial immune system (AIS) [6], grey wolf optimizer (GWO) algorithm [7], and others. The main contribution of this work is the development of a novel energy management system, with the aim to minimize both operating cost function and emissions level of pollutant. While taking into account system power balance, high level penetration of RESs, optimal charging/discharging of battery storage system (BSS), monitoring state of charge (SoC) of the battery, and the system’s components power supply constraint. The grey wolf optimizer heuristic algorithm is used in order to solve the optimization dispatch problem. And a comparison from applying GWO algorithm and other known heuristic methods, including PSO, novel bat algorithm (NBA) [8], whale optimization algorithm (WOA) [9], has been performed, to show the validity and effectiveness of the proposed strategy. The rest of the paper is organized as follows: The system description is given in Sect. 2. Section 3 outlines the proposed energy management approach. The results and discussion are presented in Sect. 4. Finally, Sect. 5 concludes the work.

2 Hybrid Microgrid System The proposed hybrid microgrid (HMG) system is running under grid connected mode. Figure 1 shows the architecture of the studied HMG. The actual load demand is primarily supplied by renewable energy sources represented by photovoltaic panels (PV) and wind turbine (WT) unit. Three conventional energy sources (CESs) composed of diesel generator, fuel cell unit and microturbine unit (DG, FC and MT) are settled and used as backup to supply the load whenever the combination output power from RESs and the energy storage system (ESS) represented by battery is not sufficient to supply the required demand. Moreover, the HMG can exchange power with the main grid through purchasing or selling power. Finally, all the system’s components use various conversion devices for connection to the common bus AC. Thus they are connected to a centralized control system dedicated to dealing with the coordinated control and optimization dispatch problem in HMG.

Coordinated Control and Optimization Dispatch …

1367

Fig. 1. Studied hybrid microgrid architecture

3 Problem Statement In this work, two contradictory objective functions are considered for minimizing: The first objective function is the operating cost and the second objective function is the pollutant gas emissions of Nitrogen oxide, Sulfur dioxide and Carbon dioxide (NOx , SO2 and CO2 ), respectively. The problem can be mathematically formulated by [10]: min O FH M G = ω · C1 + (1 − ω) · C2

(1)

Where ω is weight coefficient, and it is always a positive value. Finally, C1 and C2 represent the operating cost and emissions objective function, respectively.

3.1 Operating Cost Function The first objective function is the operating cost of generating units, it combines four terms representing the fuel cost, operation and maintenance cost, start-up cost and the exchanged power cost with the main grid, expressed as [11, 12]:

1368

M. Lagouir et al.

C1 =

N 

{C Fi (Pi ) + O Mi (Pi ) + ST Ci + (C P E i − RS E i )}

(2)

i=1

The fuel or gas cost C Fi consumed by DG or FC, MT unit is expressed as a quadratic function of the produced power Pi [12, 13]: C Fi (Pi (t)) = αi + βi Pi (t) + γi Pi2 (t)

(3)

Where αi , βi, γi are cost coefficients of each dispatchable source i. The second term represents the operation and maintenance cost; it can be represented mathematically as [11, 13]: O Mi =

N 

K O Mi Pi

(4)

i=1

The start-up cost of dispatchable generator i is expressed in term of the unit has been off [11, 13]:    To f f,i ST Ci = σi + δi 1 − exp − τi

(5)

According to the RESs supply limitation and the actual required demand, the HMG may import or export power from or to the main grid. The exchanged power cost can be represented mathematically as [11]: C P E i = C p × max(PD − Pi , 0) RS E i = Cs × max(Pi − PD , 0)

(6)

Where C p and Cs represent the price of power purchased or sold to the main grid in [$/Kwh].

3.2 Pollutant Gas Emissions Cost The pollutant gas emissions cost of (NOx , SO2 and CO2 ) is expressed as a linear function of the generated power Pi from dispatchable source i and the power provided by main grid PGrid [12, 13]: C2 =

N  M  i=1 k=1

χk (E Fik Pi ) +

M  k=1

χk (E FGridk PGrid )

(7)

Coordinated Control and Optimization Dispatch …

1369

Where χk is externality costs of emission type k. E Fik and E FGridk represent the emission factors of generator i and main grid, respectively.

3.3 System Constraints In the operation of the HMG system, the power balance should be satisfied with each time step. It is expressed as follows: PD (t) = PGrid (t) + PC E Ss (t) + PE SS (t) + PR E Ss (t)

(8)

The limit of output power of each distributed generator i is given by: 0 ≤ PP V ≤ PP VM ppt 0 ≤ PW T ≤ PW TM ppt PE SSmin ≤ PE SS ≤ PE SSmax Pi Min ≤ Pi ≤ Pi Max PGrid Min ≤ PGrid ≤ PGrid Max

(9)

4 Energy Management System This work aims to design and implement a two-layer energy management system, for daily coordinated control and optimal dispatch of HMG system. The upper layer combines a fuzzy logic control system (FLS), used to define the optimal charging/discharging power of BSS, the decision to turn on or off the dispatchable sources and the interactive mode between the HMG and the main grid. On the other hand, the lower layer consisting of grey wolf optimizer algorithm dedicated to solve the economic and emission dispatch problem in case of using the CESs. Figure 2 shows the structure of the proposed energy management system. The following subsection briefly describes the mathematical models of GWO algorithm.

4.1 Overview of Grey Wolf Optimizer Algorithm The grey wolf optimizer algorithm was proposed by Mirjalili in 2014 [14]. The basic idea of this algorithm was inspired by hunting technique and social hierarchy of grey wolves. To simulate the leadership hierarchy, the grey wolves living in the same pack are divided into alpha, beta, delta and omega. The alpha is responsible for decisionmaking about hunting, sleeping place and so on. In the second level of hierarchy, we

1370

M. Lagouir et al.

Fig. 2. Proposed energy management system

found beta wolves with the mission of helping the alpha in decision-making or any other activities related to the pack. The third level in the grey wolves’ hierarchy is delta, in this level the wolves are responsible for watching boundaries, guarantee the safety and taking care for the weakest of the pack. And in the last level, there are the omega wolves that assumed to follow the wolves of the highest levels in GWO algorithm. Mathematically, the position of wolf is modeled as follows [7, 14]: → − → − →  − → − D =  C · X P (t) − X (t)

(10)

− → − → − → − → X (t + 1) = X P (t) − A · D

(11)

− → − → Where X and X p represent the position vector of a grey wolf and the prey, respectively. t is the current iteration. − → − → Finally, A and C indicate coefficient vectors, and they are calculated using the following formulas: − → → → → a A = 2− a ·− r1 − −

(12)

− → → C =2·− r2

(13)

Coordinated Control and Optimization Dispatch …

1371

Where, the components of a decreased linearly from 2 to 0. During iteration process, → → r2 are random vector in [0, 1]. and − r1 , − In the GWO algorithm, the α wolf is considered as fittest solution, following by β and δ as the second and the third best solutions, respectively. Finally, the rest of wolves, including the are supposed to move according to the position of wolves of higher levels. This is mathematically modelled as follows:        β = C2 · X β (t) − X (t), D  δ = C 3 · X δ (t) − X (t)  α = C1 · X α (t) − X (t), D D (14)  α , X 2 = X β − A2 · D  β , X 3 = X δ − A3 · D δ X 1 = X α − A1 · D

(15)

X 1 + X 2 + X 3 X (t + 1) = 3

(16)

Where X α (t), X β (t) and X δ (t) represent the position of α, β, and δ, respectively.

5 Simulation and Analysis Results First, it is assumed that PV and WT units generate power without any emissions, and with a generation limits equal to 25 kw and 20 kw, respectively. The maximum and minimum output powers of BSS are set to 30 kw and −30 kw, respectively. And the limit on power exchanged with the main grid is considered as 30 kw. Finally, the maximum capacity of each dispatchable generator is set to 30 kw. Figure 3 shows the actual output power from both RESs and optimal output power of the BSS based on a fuzzy logic control system. As it is observed, the combination output power of RESs and BSS is not enough to serve the load demand, therefore the fuzzy logic

Fig. 3. Output power of RESs and the battery storage system

1372

M. Lagouir et al.

Fig. 4. Optimal output power of conventional generators and main grid

Fig. 5. Contribution of system’s components in power management

controller incorporates the dispatchable sources (DG, FC, and MT) in the energy management, while permits importing power from the main grid. Figure 4 reveals optimal output power of CESs and main grid. And Fig. 5 shows the contribution rates of each power generation source. As we can see, the best choice to minimize both operating cost and emissions level is to make use of generated power of FC and MT unit, respectively. Hence, the DG is dispatched only when the demand becomes very high. Finally, the total system balance for a daily simulation time is shown in Fig. 6. It is to be noted that the previously described figures are plotted with a weight coefficient ω = 0.16, and a time step of 15 min. To evaluate the impact of the selected weight coefficient on the formulated objective function, the operating cost and emissions level are calculated for different values of ω, as is shown in Fig. 7. It is clearly seen that as long as ω is increased from 0 to 1, more the priority is given to minimizing the operating cost, therefore the emissions level becoming higher (Fig. 8). Similarly, when the value of ω is decreased, the emissions level are first considered to minimized. According, to the results better compromise between the two contradictory objective functions is achieved when the value of ω = 0.16. Finally, Table 1 summarizes the obtained results considering four

Coordinated Control and Optimization Dispatch …

1373

Fig. 6. Total system power balance

Fig. 7. Evolution of operating cost as a function of emissions level

Fig. 8. Evolution of the pollutant gas emission as a function of ω

case studies when ω = 1; ω = 0.5; ω = 0.16; and ω = 0.01, respectively, then a comparison of GWO algorithm with other heuristic methods has been evaluated. From the results, it is clear that the proposed GWO algorithm is able to achieve better solutions. In addition, the results show the advantage of GWO in terms of stability, and good convergence to the optimum solution in fewer iterations. Which confirms its potential to deal better with the optimization dispatch problem.

1374

M. Lagouir et al.

Table 1. Comparison of obtained simulation results under 4 different case studies Daily operating cost ($/h)

Daily power generated by DG (kwh)

Daily power generated by FC (kwh)

Case GWO 1864.6752 464.1969 2053.0748 1 PSO 1864.9310 461.4069 2065.4071

Daily power generated by MT (kwh)

Daily Daily exported emissions power cost ($/h) from main grid (kwh)

Daily emissions level (kg/h)

886.1505 1975.1393 79.9978

15.3247

933.3225 1918.4876 71.8993

15.1015

1868.2241 444.5014 2102.1870

968.4948 1863.3782 69.6505

14.7345

WOA 1868.6513 463.8059 2148.5225

918.3371 1847.8960 70.8982

14.8524

Case GWO 1877.5217 199.2115 2250.0304 1296.4861 1632.8335 46.2096 2 PSO 1879.2765 203.4923 2197.7221 1229.9017 1747.8690 48.3720

11.4882

NBA

NBA

11.9399

1885.3066 199.5290 1991.6969 1419.1773 1768.1582 48.6768

12.0558

WOA 1890.4623 198.9263 2141.8636 1347.8002 1689.9714 47.2162

11.7234

Case GWO 1919.3090 199.2432 2554.6371 1515.0981 1109.5831 37.8433 3 PSO 1919.7558 199.1626 2522.3023 1516.2146 1141.5502 38.3704

9.5989

NBA

9.7200

1922.2369 199.0307 2311.2919 1523.0586 1345.1803 41.7383

10.4924

WOA 1939.7665 199.0106 1976.3342 1776.4579 1426.7588 43.5054

10.9081

Case GWO 2317.5969 199.2226 2350.9518 1827.6136 1000.7734 36.5137 4 PSO 2390.9327 198.9273 1961.9595 1842.5563 1375.1183 42.7414

10.7396

NBA

9.3184

2385.0831 199.0805 2032.3525 1800.0990 1347.0365 42.2211

10.6168

WOA 2385.0619 199.0102 2000.2704 1832.5740 1346.7069 42.2600

10.6286

For all case studies, the population size is set to N = 30, and the maximum iteration I termax is considered as 100.

6 Conclusion In this paper, a novel approach based on combining the fuzzy logic control system with a GWO heuristic method, is introduced to deal with the control and optimization dispatch problem in hybrid microgrid system. Four case studies are considered, then the system’s performance is compared with other algorithms, including PSO, NBA, and WOA. Simulation results prove that GWO is most effective in determining the global optimal solution in less time, which confirms the validity of the proposed approach to efficiently manage the generated power of each component of the studied HMG system, considering technical, economic and environmental criteria.

Coordinated Control and Optimization Dispatch …

1375

References 1. Murty Vallem VVSN, Kumar A (2020) Optimal energy dispatch in microgrids with renewable energy sources and demand response. Int Trans Electr Energy Syst 30(5):1–27 2. Ren Sh, Wang J, Ma M (2019) Multi-objective optimal control of microgrid based on economic model predictive control. In: Chinese Control Conference (CCC), pp 1–6. IEEE, Guangzhou 3. Moradi H, Esfahanian M, Abtahi A, Zilouchian A (2018) Optimization and energy management of a standalone hybrid microgrid in the presence of battery storage system. Energy 147:226–238 4. Van TP, Snasel V, Nguyen TT (2020) Antlion optimization algorithm for optimal non-smooth economic load dispatch. Int J Electr Comput Eng 10(2):1187–1199 5. Radosavljevic J, Jevtic M, Klimenta D (2015) Energy and operation management of a microgrid using particle swarm optimization. Eng Opt 48(5):811–830 6. Hatata AY, Hafez AA (2019) Ant lion optimizer versus particle swarm and artificial immune system for economical and eco-friendly power system operation. Int Trans Electr Energy Syst 29(4) 7. Cherkaoui N, Belfqih A, El Mariami F, Boukherouaa J, Berdai A (2020) Active power output optimization for wind farms and thermal units by minimizing the operating cost and emissions. Int J Electr Comput Eng 10(4):3412–3422 8. Meng XB, Gao XZ, Liu Y, Zhang H (2015) A novel bat algorithm with habitat selection and doppler effect in echoes for optimization. Expert Syst Appl 42(17–18):6350–6364 9. Mirjalili S, Lewisa A (2016) The Whale optimization algorithm. Adv Eng Softw 95:51–67 10. Nwulu NI, Xia X (2015) Multi-objective dynamic economic emission dispatch of electric power generation integrated with game theory based demand response programs. Energy Convers Manag 89:947–963 11. Mohamed FA, Koivo HN (2012) Online management genetic algorithms of microgrid for residential application. Energy Convers Manag 64:562–568 12. Nemati M, Braun M, Tenbohlen S (2018) Optimization of unit commitment and economic dispatch in microgrids based on genetic algorithm and mixed integer linear programming. Appl Energy 210:944–963 13. Alvarado-Barrios L, Rodríguez del Nozal A, Tapia A, Martínez-Ramos JL, Reina DG (2019) An Evolutionary computational approach for the problem of unit commitment and economic dispatch in microgrids under several operation modes. Energies 12 14. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey Wolf Optimizer. Adv Eng Softw 69:46–61

Smart Energy Management System: SCIM Diagnosis and Failure Classification and Prediction Using Energy Consumption Data Oussama Laayati, Mostafa Bouzi, and Ahmed Chebak

Abstract This paper represents the impact of smart energy management system in industrial microgrid on improving the key performance indicators of the maintenance, the case study will be on the squirrel cage induction motors, the idea is to finding correlations between energy consumption data and mechanical failures of these machines, this work shows a first version of a test bench acquiring vibration, voltage, current and speed while the eccentricity fault is present, after test bench preparation, installation, configuration, gathering the data, monitoring it, and see the harmonics of current and vibration in frequential domain and see the first results, the idea is to store the data and trying to implement machine learning algorithms, which gives an accuracy between 0.71 and 0.96 between model features which are current voltage and vibration, this work is a primary work of a big idea that is already developed and mention by a lot of scientists and researchers, that can be implemented in a real case of study in mining industry. Keywords SCIM failure · Energy management · Machine learning

O. Laayati (B) · M. Bouzi Computer Science, Mechanical, Electronics and Telecommunication Laboratory, LMIET, FSTS, University Hassan 1, Settat, Morocco e-mail: [email protected] O. Laayati Innovation Lab for Operations, University Mohamed VI Polytechnic, Benguerir, Morocco A. Chebak Green Tech Institute, University Mohamed VI Polytechnic, Benguerir, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_125

1377

1378

O. Laayati et al.

1 Introduction 1.1 Problematic Description In smart micro grids energy consumption and production management is a necessary element that can improve the health and the performance of the grid and its components, sources, and loads [1]. Squirrel Cage Induction Machines (SCIM), invented by Nicolas Tesla with 3 phase and Galileo Ferraris with 2 phase, is one of the most popular load in industry in the whole world represents 90% of industrial motors in the world, and 50% of global electrical energy consumption is due to induction motors, Researchers proposed efficiency monitoring of SCIM motors for maintenance decision making and decrease the CO2 emission [1] others proposed analytical solutions to optimize the energy consumption for induction motors [2]. Mainly when an electrical load is in a faulty condition, this abnormal behavior increases the energy consumption, the purpose of this work is to know if we can detect the abnormal activities of SCIM motors using only it is energy consumption data [3]. There were a lot of methodologies that can identify and classify mechanical faults in the SCIM, and are based on vibration, bearing temperature, moisture [4]. The mining industry is one of the most industries that uses SCIM motors in conveyors, reclaimers, Stackers, draglines and jaw crushers because of its efficiency and high performance, the approach is detecting faulty loads in an industrial micro grid using only specific energy consumption of these loads.

1.2 Proposed Solution This work represents new methods for classifying SCIM faulty behaviors, by only using current and voltage data, that means specific energy consumption data which is acquired by a smart grid monitoring system, the goal is using new machine learning techniques and algorithms that help finding correlations between vibration data, and classifies failures [5], such as an unsupervised and general algorithm that can classify an anomaly while a voltage unbalance is detected, and supervising the oscillations of the current third harmonic.

2 Methodology 2.1 Introduction The idea is to prepare a test bench to test the data acquisition and monitoring system, installing and testing the current, voltage, speed and vibration sensors on the test

Smart Energy Management System …

1379

bench, monitoring the results using a data acquisition software, monitoring these data and store it then apply classification and prediction algorithms to know if there is some correlations between data. The next step is to implement an industrial solution in the mining industry to test the efficiency and measure the performance of the smart prediction and classification algorithms and control its impact on the maintenance and production.

2.2 Failure Types The main defects and failures of a SCIM showed in the state of art [6, 7] are: The rotor eccentricity which is the most common problem in the mining industry in conveyors and draglines, this fault is due to an offset between the axis of symmetry and the rotation axis, we can see a lot of eccentric bearing and eccentric armature motor. The breakage of rotor bars and end-bearings this type of failures is classified as critical failures in SCIM motors and can generates damage and more energy consumption due to friction and mal functioning of the bearing lubrication. The rotor bows this fault is due to the heating or cooling of the rotor, it causes a local thermal asymmetric distribution. And other failures after the damage of the rotor that can increase the energy consumption and impact voltage and current behavior.

2.3 Failures Analysis Techniques Using Energy Consumption Data There are many failures analysis techniques that helps to detect or to predict defects of SCIM motors, we mention here some popular one such as acoustic emission, Airgap torque, stator current, electromagnetic field monitoring, induced voltage, instantaneous angular speed, instantaneous power, motor circuit analysis, surge testing, vibration, and voltage. In this work we will focus on electrical energy consumption data, which is current, voltage, and time, and correlate these data with faulty SCIM motors vibration data regarding torque and speed. The voltage unbalance and electrical signature is a fault can be detected by current signal [8] it can be applicable in framework of 3 different signal domain, time, domain knowledge and hybrid time frequency data [9], the spectral analysis of the current of SCIM motor using the spectrogram background theory short time fourier transform, the auto winger distribution and the wavelet decomposition [10], for the detection of air gap eccentricity the most popular method is the stator current monitoring, by showing shape deviations in park’s vector which is a circular pattern normally [11], the comparing method by combining harmonics of electrical components, power

1380

O. Laayati et al.

supply frequency and harmonics of cage frequency which is the rotation of the rotor, getting spectral amplitude and comparing the slopes show very efficient results to classify and detect failures [12].

2.4 Machine Learning Algorithms Introducing machine learning and artificial intelligence to the classification of defects and finding some not explored correlations between energy consumption data and mechanical data, many researchers start implementing algorithms to classify defects by focusing only on an expert system and tried to make new defects predictions algorithms by using convolutional neural network (CNN), linear regression, support vector machine, k-means clustering for unsupervised SCIM failures classification. The idea is comparing healthy and faulty condition of the SCIM motor, gathering the energy consumption data, vibration, and rotation per minutes, in variable rotation speed and variable torque, these gathered data will feed the classification and prediction algorithm. This scheme Fig. 1 describes the data flow from vibration, current, voltage torque and speed sensors, from the SCIM under test, the database contain all different tests data, these data will be prepared to feed the clustering algorithm, the maintenance expert must analyze the result of the algorithm and give a score of the anomaly, the machine learning algorithm using reinforcement learning and visualize the result. The adaptative gradient optimizer (ADG) based on deep (dCNN) algorithms for faulty bearing with ball defect, with outer and inner race defect and rotor with one broken bar [3]. This algorithm shows some good results that can be enhanced and implemented in a mining industry machine for example reclaimer, dragline, or jaw crushers.

Fig. 1 General scheme of test data mining, preparation, clustering, and visualization

Smart Energy Management System …

1381

3 Case Study 3.1 Introduction This case study will discuss the feasibility of this idea and the general acquisition system architecture, with monitored data and results, we will propose the data mining steps data acquisition, introducing machine learning classification and prediction models and the key performance indicators that will be monitored.

3.2 General Schematic In this figure we are proposing a scheme to gather data using National Instruments Modules of vibration, current voltage and rotation speed, the gathered data using LABVIEW and stored into a PostgreSQL database, introducing python code to extract data from POSTGRESQL database to check correlations between data. This scheme is based on the intelligent power quality monitoring test bench using Internet of Things monitoring [11].

Fig. 2 Simulation hardware and software data flow

1382

O. Laayati et al.

Fig. 3 Simulation test vibration, STFT and RMS values

3.3 Simulation Steps This test bench has options to create and to simulate defects and failure, in this paper, we will focus on the eccentricity fault detection, the acquired data, of voltage of the stator in each line [13], the current, the root mean square value of each current line and its Short Time Fourier Transform STFT [6], the vibration and its FFT in 4 points axial and vertical, and the rotation per minutes. The data is being recorded into the database, using database connection toolbox for historical data with timestamp, extracted by python code, preprocessed, and recalculated using predefined machine learning algorithm to find and explore visual correlations to choose effectively good clustering, classification, and prediction algorithms. The Fig. 3 graph shows the RMS and the vibration temporal values with STFT, the current, signal, RMS and the STFT in the Fig. 4, we are using these data to see if there is any correlations and implementing a multilinear regression and see the score of each, the Fig. 5 shows the voltage signal to detect voltage unbalance.

3.4 Machine Learning Results As mentioned, to choose a good machine learning model for classification and prediction, first all the acquired signals are being plotted to show the correlations. The plots in Figs. 6 shows the correlation between STFT signals in a healthy condition, while the Fig. 7 shows the faulty condition with the eccentricity fault [14], here it is identified that there are some strong correlations between gathered data of STFT vibration and current, for the machine learning algorithm we can use in first step the multi Linear Regression algorithm with a gradient descent cost function the results shown in Fig. 8 that represent the accuracy of this machine learning multilinear regression between each feature measured data.

Smart Energy Management System …

1383

Fig. 4 Current data and STFT with RMS values

Fig. 5 Simulation Voltage values.

4 Conclusion This work identifies correlations between STFT Current and vibration data, in the test bench simulation and data gathering of a faulty and healthy conditions, there are some differences in the data plots shown in the test, the voltage unbalance is not well shown in this test bench simulation, because of the condition of the SCIM motor which is in a good health, the machine learning basic linear regression algorithms shows good results between STFT vibration and current of each current line, which

1384

O. Laayati et al.

Fig. 6 Correlations plots between STFT vibration and STFT current healthy condition.

Fig. 7 Correlations plots between STFT vibration and STFT Current faulty condition.

is a good indicator to continue working on this idea and implementing new machine learning algorithms, to classify and to predict other failures and SCIM electrical and mechanical defects and finding more correlations between all faults.

Smart Energy Management System …

1385

Fig. 8 Accuracy of linear regression model between STFT Signal of current and vibration.

This paper shows the importance of gathering energy consumption data in maintenance of SCIM motors, this idea can be implemented the smart micro grid in the mining industry to control every single SCIM with current and voltage and save a lot of time of maintenance and increase production and the profitability.

5 Future Work In the future work we propose to continue on gathering more data, and testing all mechanical and electrical defects of the SCIM motors in the hardware, in software we are proposing to implement machine learning algorithms, such as neural network, Support vector machine, dCCN, and trying to image data to better understand the behavior of the faulty and health SCIM machine, the next step is to develop an application and implement it in a real case study of jaw crusher in mining industry, gathering data, and classifying its faults and problems, while controlling the Maintenance KPIs.

References 1. Singh G, Anil Kumar TCh, Naikan VNA (2018) Efficiency monitoring as a strategy for cost effective maintenance of induction motors for minimizing carbon emission and energy consumption. Reliab Eng Syst Safety. https://doi.org/10.1016/j.ress.2018.02.015 2. Abdelati R, Mimouni MF (2011) Analytical solution of optimized energy consumption of induction motor operating in transient regime. Eur J Control 4:397–411 © 2011 EUCA. https:// doi.org/10.3166/EJC.17.397-411 3. Kumar P (2020) Ananda Shankar Hati: deep convolutional neural network based on adaptive gradient optimizer for fault detection in SCIM. ISA Trans. https://doi.org/10.1016/j.isatra.2020. 10.052 4. Raja Singh R, Yash SM, Shubham SC, Indragandhi V, Vijayakumar V, Saravanan PC, Subramaniyaswamy V (2020) IoT embedded cloud-based intelligent power quality monitoring system for industrial drive application. Future Gener Comput Syst 112:884–898 5. Dorrell DG, Thomson WT (1997) Analysis of airgap flux, current, and vibration signals as a function of the combination of static and dynamic airgap eccentricity in 3-phase induction motors. IEEE Trans Ind Appl 33:24–34

1386

O. Laayati et al.

6. Frosini L (2019) Novel diagnostic techniques for rotating electrical machines—a review. Electrical machines. Design, control and diagnosis (WEMDCD), Athens, Greece, 22–23 April 2019 7. Mehrjou MR, Mariun N, Marhaban MH, Misron N (2011) Rotor fault condition monitoring techniques for squirrel-cage induction machine—a review. Mech Syst Signal Process 25:2827– 2848 8. Ebrahimi BM, Roshtkhari MJ, Faiz J, Khatam SV (2014) Advanced eccentricity fault recognition in permanent magnet synchronous motors using stator current signature analysis. IEEE Trans Ind Electron 61:2041–2052 9. Hendrickx K, Meert W, Mollet Y, Gyselinck J, Gryllias BCK, Davis J (2020) A general anomaly detection framework for fleet-based condition monitoring of machines. Mech Syst Signal Process 139:106585 10. Burnett R, Watson JF, Elder S (1996) The application of modern signal processing techniques for use in rotor fault detection and location within three-phase induction motors. Signal Process 49:57–70 11. Faiz J, Moosavi SMM (2016) Eccentricity fault detection – from induction machines to DFIG— a review. Renew Sustain Energy Rev 55:169–179 12. Han Q, Ding Z, Xueping X, Wang T, Chu F (2019) Stator current model for detecting rolling bearing faults in induction motors using magnetic equivalent circuits. Mech Syst Signal Process 131:554–575 13. Nandi S, Lee SB (2011) Detection of eccentricity faults in induction machines based on nameplate parameters. IEEE Trans Ind Electron 58(5) 14. Park Y, Choi H, Shin J Park J, Lee SB, Jo H (2020) Airgap flux-based detection and classification of induction motor rotor and load defects during the starting transient. IEEE Trans Ind Electron 67:10075–10084

Intelligent Technique Proposed for Nonlinear Inductor Modelling for DC/DC Converters Rafika El idrissi, Ahmed Abbou, Abderrahim Taouni, and Mohcine Mokhlis

Abstract The inductor is one of the main components for converting energy within power converters. The inductance value does not always be constant as it decreases with the current, particularly in the saturation region where the current’s value increases. Thus, the inductor decrease affects the dynamic of the power converter. To exploit its nonlinear behavior and to achieve a precise dynamic behavior of the converter, an accurate inductor model must, therefore, be considered. Several models has been dedicated to analyze these characteristics, but they are complicated and difficult to be implemented. In this paper, the Adaptive Neuro-Fuzzy Inference system (ANFIS) model is used to solve the nonlinear behavior of the inductor. The proposed ANFIS model is a clever neuro-fuzzy technique, which has proved to model and control diverse processes that are nonlinear. The main feature of this model is that the evolution of the flux vs current curve behavioral analysis has been taken into account using an experimentally characterized set of data. The study is set up on the DC/DC buck converter, using Matlab/Simulink. The results obtained by simulation provide the inductor current and voltage prediction, which have good agreement with the experimental measurements with relative error about 25%. Keywords DC/DC converters · Nonlinear inductor modeling · Adaptive Neuro-Fuzzy Inference System (ANFIS) · Magnetic saturation

1 Introduction In many applications, energy converters are commonly used, particularly when energy produced from renewable energy sources [1, 2]. These converters stock energy in the inductor, which is driven by voltage source and able to transmit this energy R. El idrissi (B) · A. Abbou · M. Mokhlis Department of Electrical Engineering, Mohammadia School of Engineers, Mohammed V University, Rabat, Morocco A. Taouni Laboratory of Electrical Systems and Control Engineering, Faculty of Sciences Aïn-Chock, Hassan II University, Casablanca, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_126

1387

1388

R. El idrissi et al.

to the load maintained by a capacitor at constant voltage. Most of the theoretical studies suppose that the inductor can be perfectly described by linear characteristics, a magnetic flux dipole relative to the current. In fact, the inductance is characterized by a nonlinear behaviour (saturation, hysteresis and power losses) which must be included in circuit simulators to correctly simulate such power converters [3]. The induction saturation theory triggers a renewed interest nowadays, primarily because of its favorable effect on power density increase and cost reduction. In order to avoid the inductor saturation, the converter should be operated at a higher switching frequency. This leads to increased power converters and magnetic material switching losses. A strong ripple current results when the inductance is decreased. The current during off-on switching varies from low to maximum, so that the poor saturation area is integrated. In order to properly design the converters, an understanding of magnetic material behavior must be improved while the current changes. Thus, new models must be created. Many papers deal with the saturation of the inductance in literature [4–6]. In [7] a finite element analysis (FEA) to model a dynamic core is presented. Reference [8] proposes an analytical model of magnetically saturated inductance using lambert W function. The inductor model is described in [9] based on a polynomial curve fitting the variance in the inductance. In addition, some analytical nonlinear models are presented in [10, 11]. In this work, the technique suggested was extended to model an inductor, with a saturation effect of the magnetic core, using as simple and easy model based on the adaptive neuro fuzzy inference (ANFIS) model. The model was trained based on experimental flux vs current curve data. The ANFIS technique was chosen due to its potential for creating fuzzy models with strong prediction skills [12, 13]. In a basic buck converter, the nonlinear model of inductance reveals the accuracy of small ripple and large ripple inductor current waveforms prediction. The proposed model is simulated using Matlab/Simulink and the obtained results prove a high agreement between the simulation and experimental inductor current waveforms. Therefore, experimental results confirm the proposed model accuracy. The rest of the paper is systemically as follows: the model of the buck converter including the nonlinear inductor model is described in Sect. 2, Sect. 3 deals with the inductor characterization, Sect. 4 is devoted to the inductor modelling, Sect. 5 presents the experimental and the simulation results, and conclusion is presented in Sect. 6.

2 DC/DC Buck Converter with Nonlinear Inductor To test the proposed model for the nonlinear inductor taking into account the saturation, a buck converter, working in CCM, is used. The inductor is excited by a square waveform provided by the converter. The power inductor of all switching converters conducts DC and triangular ripple currents, in a similar manner. The buck converter is also a good way to check and illustrate how saturating conditions usually meet

Intelligent Technique Proposed for Nonlinear Inductor Modelling …

1389

in switched-inducer circuits, particularly because in point of load applications it is often operated at high currents. In CCM, when the switch is on the inductor undergoes a constant voltage and, if it is linear, its current increases with inclination of (Vin − Vout )/L, in contrast when the switch is off the current of the inductor moves through the diode with an inclination of −Vout /L (Fig. 1). If the average inductor current rises or the ripples rise, saturation is achieved; the maximum current is consequently higher when compared to the linear case and the form varies depending on the change in the induction Fig. 2 shows the details.

Fig. 1 Buck converter scheme with nonlinear inductor

Fig. 2 The buck converter inductor current vs time waveforms in case of a linear inductor b nonlinear inductor

1390

R. El idrissi et al.

3 The Inductor Characterization 3.1 Linear Behaviour The relation between magnetic flux and current is widely considered to characterize an inductor:  = Li

(1)

The current vs voltage ratio is obtained by the Faraday law for the linear inductor, which considers inductance to be constant: VL =

di d =L dt dt

(2)

3.2 Nonlinear Behaviour In most studies assume that the inductance L is constant. Indeed, in practice the inductance varies with respect to current in the inductor. In particular, the inductor core magnetic flux is a nonlinear function of the current as follow:  = (i)

(3)

Where (i) represents the inductor core’s magnetic characteristic. Figure 3a depicts this function. According to (3), the inductor voltage becomes as follow:

(a)

(b)

Fig. 3 Flux value against current (a) Inductance value against current (b)

Intelligent Technique Proposed for Nonlinear Inductor Modelling …

VL =

d(i L ) d(i L ) di L d di L = = = L(i L ) dt dt di L dt dt

1391

(4)

Where: L(i L ) =

∂(i L ) ∂i L

(5)

L (i L ) is a nonlinear function of the magnetic core saturation. Figure 3b demonstrates an example of such function characterized experimentally, the values relating to this are LR = 3.5 mH, L deepsat = 2.3 mH, L70% = 3.14 mH, L30% = 3.14 mH. An iron ring core’s inductor with an inductive rate of 2.5 mH and a current rated of 3A has been analyzed. It may be found that for low currents the highest value is shown. The greater the current, the lower the inductance. Three operating regions can be described in the inductance versus current curve: – The region of low saturation where inductance decreases slightly with L R to approximately 70% of L R . – The roll-off region in which the inductance ranges from near L R to a near saturation inductance value, the value is (Ldeepsat). – Deep saturation, where the inductance is well below 30% and the value of Ldeepsat is approaching.

4 The Inductor Modeling When using an inductor, some non-ideal characteristics can be encountered. The main one is the magnetic saturation. There exist different approaches to model the saturation behavior of an inductor. The method has been proposed here is based on the ANFIS model using the flux versus inductor current curve’s data. The advantage of this method that does not require any mathematic formula and using ANFIS model does not need a large amount of data to predict and compute the output variables. Figure 4 shows the structure of the nonlinear inductance. It includes a fuzzy logic controller block, which enforces a Simulink fuzzy inference system

Fig. 4 The proposed nonlinear model

1392

R. El idrissi et al.

(FIS), an integrator to get the flux, and an enforced current source, which transforms the input signal into a corresponding current source.

4.1 Anfis Structure A Neuro-Fuzzy architecture is includes a fuzzy logic and neural networks combination. It is a fuzzy sugeno paradigm, which has been developed as an integrated framework for learning facilitation and adaptation. The main advantages of ANFIS are to work with language phrases that human experts comprehend (if then) rules and the capacity to receive training with samples of input output data. In [12, 13] are given the ANFIS architecture and implementation examples for modeling a nonlinear function and dynamic system recognition. In this work, the ANFIS that is taken into consideration is based on an experimental data set of the flux and the current of an inductor core working saturation. The magnetic flux (i) is considered as input and the current i L as output. The data set of the curve of the (F vs I) in Fig. 3a was taken into consideration. Six membership functions were considered of Gaussian function type, which was found suitable in this work as it is more accumulated with modelling behaviour, were considered to generate the FIS model. The grid partition was chosen to define these membership functions. Figure 5a demonstrates the required ANFIS architecture. It consists of a five layers. Once the data is loaded and the FIS model is generated, the FIS training step comes. The hybrid algorithm was chosen, in this work, to train the input output data in 400 epochs and 0.0005 error tolerances. Figure 5b illustrates the curve of ANFIS training error. After the ANFIS is trained, the system can be tested to check the system accuracy. The following figures illustrate the results obtained using the learned ANFIS. The ANFIS generated surface is depicted in Fig. 6a. Figure 6b shows the ANFIS rule viewer. The first column displays the member function of the magnetic flux

(a) Fig. 5 ANFIS structure (a) ANFIS training error (b)

(b)

Intelligent Technique Proposed for Nonlinear Inductor Modelling …

(a)

1393

(b)

Fig. 6 ANFIS surface viewer (a) ANFIS rule viewer (b)

input parameter. The second column is the output variable’s membership function, the inductor current. The rule Viewer simultaneously interprets the complete fuzzy inference mechanism. Figure 7 validates the precision of the current-flux curve modelling using ANFIS model. It contrasts the real value of the inductor with the values expected by the ANFIS model. It is obvious from Fig. 7 that the ANFIS predicted values well represent the real values of the entire set of test data.

Fig. 7 Comparison of the inductor current values predicted by ANFIS models and the actual values of data set and the error between them

1394

R. El idrissi et al.

Fig. 8 Simulink model of ANFIS inductor current predictor

The ANFIS MATLAB/Simulink model of inductor current predictor is illustrated in Fig. 8. The developed ANFIS model has an extremely uncomplicated structure, a short workout time, rapid computational speed and robustness properties. It has proven that is suitable for nonlinear modelling of the inductor. The method is very simple and easy to implement.

5 Simulation Results The DC/DC buck converter was introduced in Matlab/Simulink taking into account the inductor’s parasite effects and not the use of a linear inductor; the proposed model in Fig. 5 was taken into account. In this simulation, the load has been changed at 0.008 s and the frequency has been lowered to 6 kHz. Figures 9a, b and c show the inductor current, the magnetic flux, and the variation of the inductance value in the step response. The step response is obtained by changing the load resistance from 70  to 13 . It is obvious that when the current increases, the inductance declines and the current ripples rise. Magnetic flux also increases according to the inductor current. Figure 9d shows the comparison of the proposed model with linear inductance model at load change. After load changes, the current in the proposed model, for the inductor core saturation effect, is rising at a much higher rate. Inductance value significantly decreases. Hence, the current ripples rise. In contrast, the linear model does not consider saturation. Figure 9d gives the difference between the nonlinear and linear inductor model.

6 Experimental Results The circuit was also hardware designed for testing the simulation efficiency. B82615B2302M EPCOS inductor is used. Figure 11 illustrates the experimental system photo (Fig. 10). The model is tested by comparing the current and the voltage of the inductor. Figure 11 reflects the current form and voltage that passes through the inductor while the frequency of the switch is high and low. In Fig. 11a, the switching frequency is 20 kHz with a supply voltage 30 V and a duty cycle equals to 50%. It can be observed

Intelligent Technique Proposed for Nonlinear Inductor Modelling …

(a)

(c)

1395

(b)

(d)

Fig. 9 The buck converter’s simulation results based on the proposed inductor model

Fig. 10 Tested buck converter model picture

from this figure that the shape of the current is almost triangular. In Fig. 11b, the switch frequency is decreased to 5 kHz and the voltage of supply is raised to 60 V while the duty cycle is kept at 50%; it can be noted from the figure that the current ripples are increased, the peaks on the current, and some fluctuations on the converter output voltage. Figure 12a displays the experimental and simulation current and Fig. 12b indicates the relative error. It is observed that in the in the worst case, the loss is smaller than 15% in a switching period. The simulation of the proposed method, the linear, and experimental waveforms of the current are illustrated in Fig. 12c. Figure 12d shows the zoom of the last figure.

1396

R. El idrissi et al.

(a)

(b)

Fig. 11 The buck converter assessed high frequency (a) and low frequency (b) experimental waveforms: output voltage (blue), the inductor current (red)

(a)

(b)

(c)

(d)

Fig. 12 The inductor current simulation and experimental details comparison

It is observed in Fig. 12c that the inductor current of experimental circuit and the proposed model are much closed and the difference is largest between the proposed and the linear models. Therefore, the proposed model performs well to keep up the current waveforms. Figure 13 shows waveforms of the inductor voltage obtained in experimental and simulation studies.

Intelligent Technique Proposed for Nonlinear Inductor Modelling …

1397

Fig. 13 Simulation and experimental inductive voltages

7 Conclusion A new analytical model, considering the nonlinear behaviour of inductor core, has been proposed in order to exploit the inductor saturation. The model is based on the ANFIS technique. The ANFIS model is trained using a separate collection of measured data of magnetic flux vs inductor current. The proposed technique is characterized by its simplicity and does not require a complicated mathematical equation. In a Buck converter, the nonlinear model for the inductor has been implemented in Matlab/Simulink and experimentally evaluated. The study of performances indicates a very close coordination between inductor current ripple waveforms of the proposed model and the experimental waveforms, which proves the proposed model prediction accuracy.

References 1. Palma L, Todorovic MH, Enjeti P (2006) Design considerations for a fuel cell powered DC-DC converter for portable applications. In: 21st IEEE APEC 2006, p 6 2. Vazquez MJV, Marquez JMA, Manzano FS (2008) A methodology for optimizing stand-alone PV-system size using parallel connected DC/DC converters. IEEE Trans Ind Elect 7:2664 3. Fadil HEl, Giri F, Magueri OEl, Chaoui FZ (2009) Control of DC–DC power converters in the presence of coil magnetic saturation. Cont Eng Pract 17:849 4. Salas RA, Pleite J (2011) Simulation of the saturation and air-gap effects in a POT ferrite core with a 2-D finite element model. IEEE Trans Magn 47:4135

1398

R. El idrissi et al.

5. Salas RA, Pleite J (2013) Equivalent Electrical model of a ferrite core inductor excited by a square waveform including saturation and power losses for circuit simulation. IEEE Trans Magn 49:4257 6. Stadler A, Stolzke T, Gulden C (2012) Nonlinear power inductors for large current crest factors. Proc PCIM 1548 7. Salas R, Pleite J, Olias E, Barrado A (2006) Nonlinear saturation modelling of magnetic components for circuit simulation. IEEE. INTERMAG, 993 8. Gurleyen H, Erkan M (2017) Analytical modeling of magnetically saturated inductance by lambert W function. J Magn 22:369 9. Lullo G, Scirè D, Vitale G (2017) Non-linear inductor modelling for a DC/DC buck converter. Ren En Pow Qual J 10. You BG, Lee SW, Choi GB, Yoo DW, Lee BK (2011) Comparison of simulation and experimental results for Mega Flux inductors in hybrid electric vehicles. In: 8th IPCE 11. You BG, Lee BK, Lee SW, Choi GB, Yoo DW (2011) Experimental comparison of mega-flux and JNEX inductors in high power DC-DC converter of hybrid electric vehicles. IEEE. VPPC 1 12. Susitra D, Paramasivam S (2014) Non-linear flux linkage modeling of switched reluctance machine using MVNLR and ANFIS. J Int Fuz Syst 26:759 13. Bassoui M, Ferfra M, Chraygane M (2016) ANFIS modeling of nonlinear inductance. In: 2nd ICEIT 14. Jang JSR, Sun CT (1995) Neuro-fuzzy modeling and control. Proc IEEE 83:378 15. Jang JSR (1993) ANFIS: adaptive-network-based fuzzy Inference Systems. IEEE Trans Syst 23:665

High Order Sliding Mode Power Control of Doubly Fed Induction Generator Based on Wind Turbine System Yahya Dbaghi, Sadik Farhat, Mohamed Mediouni, Hassan Essakhi, and Aicha Elmoudden

Abstract This paper discusses the control of the active and reactive stator power of a doubly fed induction generator (DFIG) based on a wind energy conversion system using super-twisting sliding mode control (STSMC) control. The objective of this work is to evaluate the robustness of STSMC control against the variation of internal parameters of the DFIG, and also to reduce the power ripple resulting from the chattering phenomenon which is the major drawback of the conventional SMC. The modeling of the DFIG and the implementation of the proposed control strategy are performed and tested using the numerical simulation environment Matlab/Simulink. Keywords DFIG · Sliding mode control (SMC) · Super-twisting · Wind turbine · MPPT · Tip speed ratio · Vector control

1 Introduction The use of renewable energies is nowadays becoming an indispensable reality. Whether onshore or offshore, today, wind energy is taking its rightful place in the market of electricity production. They are clean, safe, and less toxic and, above all, they are renewable. The overall studied wind energy system that is shown in Fig. 1 consists of the following devices: a wind turbine with a gearbox, a doubly fed induction generator, and two bi-directional static converters. The implementation of DFIG in wind energy systems has been the subject of several pieces of research, due to its ability to operate over a wide range of wind speeds in addition to the reduced sizing of the associated converters (about 30% of the nominal power) [1]. The control of the DFIG and its operation has been intensively addressed by the researchers. In the literature in recent years, several control approaches have been proposed, notably the linear PI control, the non-linear Y. Dbaghi (B) · S. Farhat · M. Mediouni · H. Essakhi · A. Elmoudden Laboratoire Des Sciences de L’Ingénieur et Management de L’Energie(LSIME), ESTA Ibn Zohr University, BP 33/S, 80000 Agadir, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_127

1399

1400

Y. Dbaghi et al.

Fig. 1 Synoptic diagram of the studied system

controls such as backstepping and sliding mode control, and the intelligent controllers based on fuzzy logic and neural networks. Boubzizi, S. et al. have demonstrated in [2] that the PI controller is limited in the linear functioning of the system otherwise it diverges. Kelkoul B. et al. have concluded that the use of the conventional sliding mode offers acceptable robustness and a fast response time except that it leads to an undesirable chattering phenomenon [3]. In our paper, the objective is to design a high order sliding mode controller that allows both the minimization of the chattering phenomenon and ensures high robustness and rapid response against unpredictable internal disturbances of the machine. This article is organized as follows: in Sect. 2 we present the modeling of the wind turbine. Section 3 will be devoted to the modeling and vector control of the DFIG. The design of the proposed control is described in Sect. 4. The 5th section presents the simulation results and their Interpretations.

2 Modeling of Wind Turbine The following formula presents the power produced by the wind turbine as [4]: Paer =

1 Cp (λ, β).ρ.π.R2 .V3 2

(1)

High Order Sliding Mode Power Control of Doubly Fed Induction Generator ...

1401

Where: ρ is the air density, R is the radius of the blades, V is the wind speed, β is the pitch angle. λ is the speed ratio defined by [5]: λ=

R.t V

(2)

t : The angular speed in the shaft. of turbine. Cp(λ, β) is the power coefficient represent the efficiency of the wind energy conversion system, which given by [6]: 

Cp (λ, β) = C1 (C2 × A − C3 β − C4 )e−c5 ×A + C6 λ 1 − 0.035 A = λ+0.08β β3 +1

(3)

With C1 = 0.5109, C2 = 116, C3 = 0.4, C4 = 5, C5 = 21, C6 = 0.0068. For a MPPT operating with β = 0◦ , the optimal value of power coefficient is C Pmax ≈ 0.475, and the optimal speed ration λopt ≈ 8.1 (Fig. 2). The rotary speed and torque at the generator shaft are defined as [7]: 

mec = G.t T g = TGaer

(4)

Where, mec is the mechanical speed of the generator, Tg is the generator torque, and Tem is the electromagnetic torque. Newton’s second law of motion on the generator shaft gives [8]:

Fig. 2 Power coefficient curves in terms of β and λ

1402

Y. Dbaghi et al.

Fig. 3 Block diagram model of wind turbine system with TSR MPPT algorithm



Tg − Tem − f  = J ddtmec J = GJt + Jg

(5)

With: J is the total inertia, Jg is the generator inertia and, f is the viscosity coefficient, and Jt is the turbine inertia. The MPPT algorithm used is based on TSR (Tip Speed Ratio).the optimal electromagnetic torque expression for the maximum power is defined as [8] (Fig. 3): Temopt =

C pmax (λ, β) ρ.π.R 5 2 × mec × 3 2 G3 λopt

(6)

3 Modeling and Vector Control of the DFIG The stator and rotor DFIG’s voltages in the park frame are expressed as [9]: ⎧ Vds ⎪ ⎪ ⎨ Vqs ⎪ V ⎪ ⎩ dr Vqr

= = = =

Rs Ids Rs Iqs Rr Idr Rr Iqr

+ + + +

d ϕ dt ds d ϕ dt qs d ϕ dt dr d ϕ dt qr

− ωs ϕqs + ωs ϕds − ωr ϕqr + ωr ϕdr

With: Rr and Rs are respectively the rotor and stator resistances per phase. The stator and rotor flux of DFIG are defined by:

(7)

High Order Sliding Mode Power Control of Doubly Fed Induction Generator ...

⎧ ϕds ⎪ ⎪ ⎨ ϕqs ⎪ ϕ ⎪ ⎩ dr ϕqr

= = = =

L s Ids L s Iqs L r Idr L r Iqr

+ M Idr + M Iqr + M Ids + M Iqs

1403

(8)

Where: Idr , Iqr , Ids , Iqs , are respectively the rotor and stator currents in dq-axis. Lr , Ls are the rotor and stator inductances. M is the DFIG mutual inductance. The expression of the stator active and reactive powers is defined by [10]: 

Ps = Vds Ids + Vqs Iqs Q s = Vqs Ids − Vds Iqs

(9)

Electromagnetic torque’s expression is given as: Tem =

pM (Idr ϕqs − Iqr ϕds ) Ls

(10)

p is the pair pole number. By orientation of the stator flux in d-axis direction [11] we obtain: ϕds = ϕs , ϕqs = 0

(11)

The value of the stator resistance is small as that can be neglected, then stator voltages can be rewritten as: 

Vds = 0 Vqs = Vs = ωs ϕs

(12)

From Eq. (8), Eq. (9) and Eq. (12), the stator active and reactive powers can be written as function of rotor currents as:  Ps = Vs LMs Iqr (13) 2 Qs = ωVssLs Vs LMs Idr By combining the previous equations, the rotor voltages (d, q) in terms of the rotor currents (d, q) are expressed as: ⎧  ⎨ Vdr = Rr Idr + Lr − M2 dIdr + eq Ls dt  ⎩ Vqr = Rr Iqr + Lr − M2 dIqr + ed Ls dt ⎧  2 ⎨ eq = −gω Lr − M Iqr L s s ⎩ ed = gωs Lr − M2 Idr + gMVs Ls Ls

(14)

(15)

1404

Y. Dbaghi et al.

Where: g is the slip, and ed , ed represent the coupling terms between the dq-rotor voltages.

4 Super-Twisting Sliding Mode Control of the DFIG The sliding mode is one of the most powerful nonlinear control techniques that is based on the stability of the Lyapunov. This technique was developed by V. I. Utkin in [12], its main operating principle lies in its ability to force a system’s state variables to slide in a specific area called the sliding surface around a desired state of equilibrium. This is achieved while respecting stability in the sense of Lyapunov. Its weak point is the phenomenon of chattering caused by the discontinuous part of the control, this phenomenon degrades and causes undesirable functioning of the system. The use of a super-twisting sliding mode control presented a solution to this problem by its ability to reduce this phenomenon to minimum as possible. The STSMC The control law is given by [12]: V(t) = VC (t) + Vst (t)

(16)

This control law consist of a continuous control VC (t), and a super-twisting control Vst (t) that is defined by [13]: Vst (t) = v1 (t) + v2 (t)

(17)

√ v1 (t) = −α |S|.sat (S) v2 (t) = −γ .sat (S)

(18)

With: 

Where α and γ are positive gains and are calculated as follows: 

ϕ α > τmin 4ϕ τmax (α+ϕ) γ ≥ τmin 2 τmin (α−ϕ) 2

(19)

With: ϕ, τmin , τmax are positive constants. The sliding surfaces are chosen as follows: 

S(Ps ) = Ps∗ − Ps S(Qs ) = Qs∗ − Qs

By deriving Eq. (20) and using Eqs. (13), and (14) we get:

(20)

High Order Sliding Mode Power Control of Doubly Fed Induction Generator ...



 With: σ = 1 −

 ˙ s ) = P˙ s∗ + Vs M Vqr − Rr Iqr − ed S(P σLs Lr

 ˙ s∗ + Vs M Vdr − Rr Idr − eq ˙ s) = Q S(Q σLs Lr

M2 Lr Ls

1405

(21)

.

˙ s , Qs ) = 0. Then we can express the In the sliding mode we have S(Ps , Qs ) = S(P continuous controls of the dq rotor voltages by: 

s Lr Vqr (C) = −P˙ s∗ σL + Rr Iqr + ed Vs M σL ∗ ˙ s . s Lr + Rr Idr + eq Vdr (C) = −Q Vs M

(22)

The super-twisting controls of the dq rotor voltages are given by: 

√ Vqr(st) = −α1 |S(Ps )|.sat[S(Ps )] − γ1 .sat[S(Ps )] √ Vdr(st) = −α2 |S(Qs )|.sat[S(Qs )] − γ2 .sat[S(Qs )]

(23)

To ensure the stability of system control we should respect the Lyapunov condi˙ s , Qs ) < 0, then we should have α1 , α2 , γ1 and γ2 as positive tion S(Ps , Qs ) × S(P constants.

5 Results and Discussion 5.1 Simulation of the MPPT Operating The results of this section show the MPPT operation of the wind turbine based on the DFIG. We applied a random wind profile (Fig. 4 (a)) of a mean value of 9 (m/s) to the wind turbine. The results obtained show a successful operation while ensuring an optimal tracking of the maximum power (Fig. 4 (b)). This is verified by the power coefficient maintained at its maximum value according to Fig. 5.

Fig. 4 (a) Wind turbine profile (b) MPPT stator active power response

1406

Y. Dbaghi et al.

Fig. 5 Power coefficient Cp response

5.2 Simulation Results in the Robustness Test In order to test and validate the performance of our proposed control method, we have attacked the rotor side converter of the DFIG with two profiles representing active and reactive power references such as illustrated by the blue traces in Fig. 6. The simulation results shown in Fig. 6 describe the responses of the active and reactive stator powers of the generator under two tests, a normal operation and an operation disturbed by an increase of the stator resistance and the stator inductance of the DFIG by 50% of their nominal values. The simulation results show a great tracking of the active and reactive power references and the chattering phenomenon is almost negligible for the normal test (red trace) and also for the robustness test (pink trace). We can also notice that the response time is quite fast: tr5% = 1.5(ms) in the normal test. The robustness test has only shown a degradation of the response time, which has increased to 5.2(ms) that remains just as fast. It is also observed that the decoupling of the q-axis and the d-axis has been perfectly achieved, allowing an independent control of the stator active and reactive power. Figure 7 shows the evolution of the stator and rotor currents of DFIG. It is clear that they vary according to the stator powers references. By comparing our results with others published in the literature, our approach shows a very fast response time with a negligible chattering phenomenon in comparison to the results obtained by the conventional SMC proposed by Y. Bekakra et al. In [14]. For the backstepping control proposed by L. Saihi et al. in [15], our method offers a perfect decoupling with respect to their controller, which presents a poor separation between the dq-axis of Park, which have caused of intense and undesirable peaks in the active and reactive power responses.

High Order Sliding Mode Power Control of Doubly Fed Induction Generator ...

1407

Fig. 6 (a) Stator active power response (b) Stator reactive power response

Fig. 7 (a) Stator currents response (b) Park frame rotor currents response

6 Conclusion This paper presents the control of the active and reactive stator power of the DFIG by Super-twisting SMC according to the DFIG model with stator flux orientation. The simulation results have largely satisfied the required objectives, they have shown a high robustness against variations of the machine’s internal parameters, a fast enough response time, and the chattering phenomenon has now been nearly neglected. From these results it can be concluded that the use of a high order sliding mode controller can significantly enhance the performance of a DFIG based wind energy system.

Appendix DFIG Nominal Parameters Pn = 7.5 kW, Vs = 380/220 V, f = 50 Hz, p = 2, Rr = 0.19 , Rs = 0.455 , Ls = 0.07H, Lr = 0.0213H, M = 0.034H. Wind Turbine Parameters Pt = 7.5 kW, ρ = 1.225 kg.m−3 , R = 3 m, G = 5.4, J = 0,042 kg.m−1 , f = 0,024 N.m.s.rad−1 .

1408

Y. Dbaghi et al.

References 1. Tahir A, EL-Mukhtar M, EL-Faituri A, Mohamed F (2020) Grid connected wind energy system through a back-to-back converter. Comput Electr Eng (85):106660 2. Boubzizi S, Abid H, El Hajjaji A, Chaabane M (2018) Comparative study of three types of controllers for DFIG in wind energy conversion system. Prot Control Mod Power Syst 3 3. Kelkoul B, Boumediene A (2019) Nonlinear sliding mode control of DFIG based on wind turbines. ICAIRES 2018, LNNS 62, pp 206–215 4. Mensou S, Essadki A, Nasser T, Bououlid Idrissi B, Ben TL (2020) Dspace DS1104 implementation of a robust nonlinear controller applied for DFIG driven by wind turbine. Renew Energy 147:1759–1771 5. Nahilia H, Boudour M, Cardenas A, Agbossou K, Doumbia M (2019) Doubly fed induction wind generators model and field orientation vector control design and implementation on FPGA. Int J Dyn Control. 7:1005–1014 6. Farhat S, Alaoui R, Kahaji A, Bouhouch L (2018) Wind turbine MPPT strategy with DFIG vector control. Int Rev Model Simul 11(6):406 7. Sabiri Z, Machkour N, Rabbah N, Nahid M, Kheddioui E (2017) Command of a doubly-fed induction generator with a backstepping controller for a wind turbine application. Int Rev Autom Control 10(1):56–62 8. Dbaghi Y, Farhat S, Mediouni M (2020) First order sliding mode and super-twisting sliding mode control of doubly fed induction generator driven by wind turbine system: a comparative study. J Adv Res Dyn Control Syst 12(04):1656–1667 9. Moradi H, Vossoughi G (2015) Robust control of the variable speed wind turbines in the presence of uncertainties: a comparison between H∞ and PID controllers. Energy 90:1508– 1521 10. El Azzaoui M, Mahmoudi H (2017) Fuzzy control of a doubly fed induction generator based wind power system : Int. J. Wseas Trans Power Syst (12) 11. Kiruthiga B (2015) Implementation of first order sliding mode control of active and reactive power for DFIG based wind turbine. Int J Inf Futur Res 2(8):2487–2497 12. Utkin VI (1973) Sliding modes in multidimensional systems with variable structure. In Conference on Decision and Control including the 12th Symposium on Adaptive Processes, pp 727–727, IEEE ,San Diego, CA, USA 13. Han Y, Ma R, Pan W, Wang C (2019) A novel super-twisting algorithm-based direct power control strategy for doubly fed induction generator. In 12th Asian Control Conference (ASCC), pp 1619–1624, Kitakyushu-shi, Japan 14. Chalanga A, Kamal S, Fridman LM, Bandyopadhyay B, Moreno JA (2016) Implementation of super-twisting control: super-twisting and higher order sliding-mode observer-based approaches. IEEE Trans Ind Electron 63(6):3677–3685 15. Bekakra Y, Ben Attous D, Bennadji H (2019) Sliding mode control of DFIG driven by wind turbine with SVM inverter. In Hatti M. (eds) Renewable Energy for Smart and Sustainable Cities. ICAIRES 2018. LNNS, (62). Springer, Cham 16. Saihi L, Bakou Y, Harrouz A, Colak I, Kayisli K, Bayindir R (2019) A comparative study between robust control sliding mode and backstepping of a dfig integrated to wind power system. In 7th International Conference on Smart Grid (icSmartGrid), IEEE

Robust Voltage Control for Four-Phase Interleaved DC-DC Boost Converter for Electric Vehicle Application Mohamed Barara, M. R. Barakat, Nabil Elhaj, and Ghania Belkacem

Abstract The electric vehicle and the quality of power electric are currently the most requested areas of research in power electronics. In this context this paper presents a robust control strategy for 4 phase interleaved bidirectional DC/DC for HEV/HV application. The interlaced topology is used to reduce the current and voltage ripples and to limit in current rating the inductor switch. Indeed, this paper aims to improve the dynamics of the output voltage of this converter, two control strategies are analyzed and conformed by simulation and experimental work. Advanced control technique based on RST controller and PI controller. The work presented in this article shows the advantage of the RST controller method over the PI controller, the measurements correspond well to the simulated results. Keywords Electric vehicles · Power quality · Four phase interleaved boost converter

1 Introduction The Electric Vehicles (EVs) have advantages over existing cars until now, these cars contribute to reducing CO2 emissions and earth warming and improving public health and reducing ecological damage, which slows down climate change. The advent of electric cars has reduced the intensive exploitation of natural resources as petroleum M. Barara (B) · G. Belkacem ESME, SUDRIA, Paris, France M. R. Barakat Faculty of Engineering at Helwan, Helwan University, 1 Sherif Street, Helwan, Cairo P.O. 11792, Egypt N. Elhaj Materials and Subatomic Physics Laboratory, Faculty of Sciences, Ibn Tofail University, Kénitra, Morocco M. Barara Lusac Laboratory, University of Caen-Base Normandie, Cherbourg-Octeville, France © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_128

1409

1410

M. Barara et al.

Fig. 1 Block diagram for the power system configuration

and gas. EVs have turned to other alternative sources of electrical energy which has improved the environment. The EVs are essentially based on the bidirectional DC-DC converter which connects the inverter to the energy storage system. During, the buck mode the energy is sent back to the energy storage system, while in boost mode power is supplied from the energy storage system to the drive system [1] as shown in Fig. 1. In the EV system, the input voltage of the DC-DC converter boost is the battery voltage and the current flowing through this system can flow in either direction depending on the mode of operation of the vehicle. This is the bidirectional converter. The boost converter is used in EVs to maintain the motor current at the desired value in order to ensure motor voltage regulation. Thus it allows the regulation of the speed of the engine and the torque and consequently of the vehicle in traction and also in electric braking. The bidirectional converter is a DC-DC converter that allows us to obtain from a fixed DC voltage source a DC voltage source with an adjustable value of upper or lower value (boost or buck of voltage). It essentially consists of power switches (transistors, diodes…) and passive components (inductors, capacitors …). The DC-DC converter provides power to the vehicle’s onboard network (lighting, computers, air conditioning…) and recharges the low voltage battery from the high voltage battery. Among the topologies of the DC-DC converters existing in the literature, the choice was based on the interlacing topology because it is very suitable for the system studied in this research work given the minimization of the nominal current of the inductance switch and reduction of current and voltage ripple, smaller component size, improved output impedance properties. Several control techniques have been proposed in the literature to control converters in order to solve the problem of the load variation. Among the classic converter control techniques there is the PI, PID, fuzzy logic controller or a mixed controller between them [1]. The interleaving technique allowed to reduce input/output voltage ripples, and minimize power loss to provide higher efficiency as well as reduce the weight and size of passive converter components while reduction can also be performed by increasing the switching frequency. In reference [2], the mode of operation of the converter with the input current ripple lowest compared to regular inductance, and then determine the appropriate

Robust Voltage Control for Four-Phase Interleaved DC-DC Boost Converter ...

1411

range of duty cycle. As the reference [3] proposes for the control of the DC-DC converter the controller in sliding mode in order to reduce the ripples in the output voltage of the converter and the input currents. Finally, reference [4] also proposes a digital control based on the synchronization and the phase shift operation. While this paper focuses on improving the dynamics of the output voltage of a DC-DC converter, within this framework two control strategies are analyzed in simulation and tested on an experimental step for one phase. In order to improve the regulation of the output voltage of the DC-DC converter influenced by any disturbance, we propose an advanced control technique based on RST controller which has proved its robustness in terms of stability, response fast and dynamic and also in terms of simplicity to implement. The RST controller is tested experimentally with a single-arm DC-DC converter using the dSPACE controller; the results once again confirm the robustness of the control technique proposed in this work. PI controller coefficients are designed to compare the behavior of this classic controller with advanced RST controller. The simulation results showed that the RST controller gave transient rapid response with less startup undulations stable than the PI controller. This paper is organized in 5 sections. The Sect. 2 is dedicated of the studied system description. The advanced RST controller is presented in the Sect 3. In the Sect 4 the design of the RST controller is presented. The last section is devoted to the results and discussions.

2 Studied System Description To ensure correct operation of the step-up converter, a synchronization circuit is required in order to generate the signal necessary for the control of this converter [4]. Figure 2 shows the digital control system used for the boost converter. A controller based on the error signal of the external voltage loop generates the reference current. The inductance current in each converter is compared to the reference current. The generation of the PWM signals can be accomplished by programming general purpose timers with a switching frequency period and initializing four different counters registers [5]. The control strategy of the proposed bidirectional DC-DC converter is shown in Fig. 2, its composed of a cascade voltage controller with current controllers, which are used to regulate the output voltage with a maximum preset current and minimal [6]. The Control technology based on RST controller essentially consists of a pole placement method based on zero gonpole cancellations and   aDiophantine    solving equation [8]. The RST controller consists of polynomials R z−1 , S z−1 and T z−1 , provides the control law u(k) with the following z-transform:

1412

Fig. 2 Control mechanism

Fig. 3 RST controlled discrete-time system

M. Barara et al.

Robust Voltage Control for Four-Phase Interleaved DC-DC Boost Converter ...

     −1  R z−1  −1   −1  T z−1 =  −1  Yr z −  −1  Y z U z S z S z

1413

(1)

Where Yr (k) is the reference input. The following figure shows the block diagram of the RST controller [7] (Fig. 3).

3 Simulation Results The simulation work has been carried using Matlab/Simulink. The model of the studied system presented in Fig. 1 is simulated by applying the parameters of the Table 1. In this section, the studied system has been simulated using a four arms converter. Figure 2 shows the control scheme that has been designed in the case where the DC-DC converter is powered by an ideal source on the input side, and a variable load on the output side. This scheme has been tested in several configurations. The parameters of the converter support are given in Table 1.

3.1 Variation of Load and Reference Voltage We varied the load (between 0 and 0.8 s and from 0.8 s to 1.4 s and the reference voltage between 1.4 s and 1.8 s) to see the response and the behavior of the studied system especially when we have applying two controls type on this system. Figure 4 shows the output voltage of the Four-phase converter with PI controller.

3.1.1

PI Controller

A smalls peaks in the output voltage is observed in Fig. 4 due to sudden variation of load and reference voltage, but it recovers quickly by the corrective action of the PI controller. It is observed that the value of the output voltage is maintained Table 1 Simulation parameters

Parameter

Value

Input voltage

200 V

L1,L2,L3 and L4

2 mH

C

26000 µF

R1

32

R2

16

F

20 kH

1414

M. Barara et al.

Fig. 4 Output voltage of four phase converter with PI controller

at a constant value of reference even with variations and variation of reference DC voltage. We can clearly see that after varying the load, the output voltage follows with delay and appearance of the ripples with different amplitudes, which proves the ineffectiveness of the PI controller.

3.1.2

Advanced Controller

Now, the RST controller is applied. We can see the improvement carried by the advanced controller by reducing the ripples of the output voltage also at the level of the pursuit of the reference voltage. Figure 5 shows the output voltage of the system studied after applying the advanced control. The output voltage precisely pursues its reference without ripples when the load is varied (between 0 and 0.8 s; from 0.8 s and 1.4 s and reference voltage between 1.4 s and 1.8 s), which proves the efficiency of the proposed controller.

Fig. 5 Output voltage of for phase converter with advanced controller

Robust Voltage Control for Four-Phase Interleaved DC-DC Boost Converter ...

1415

Finally, it’s clear that even if varied when the reference voltage and load the output voltage perfectly follows its reference. This result proves the effectiveness of the proposed method of control in the simulations works.

4 Experimental Results In this section we present the results of the experimental setup. We limited the experimental setup to one arm of the converter using a dSPACE card. Figure 6 shows the experimental setup of the studied system with the different devices used in work.

4.1 Variation of Reference Voltage Figure 7 show the experimental performance of the control scheme for the output voltage under variable reference voltage to prove the efficiency of the proposed control method, by applying the classic PI controller.

4.1.1

PI Controller

We varied the reference voltage while the PI controller is applying, it s clear that the output voltage follows its reference with a fairly long delay time (Fig. 7).

4.1.2

Advanced Controller

This time we applied the advanced RST controller, it is obvious that the output voltage follows precisely its reference with a faster response time, a reduced overshoot (Fig. 8). Fig. 6 Test stand of one phase interleaved boost converter

1416

M. Barara et al.

(a) Output voltage under sudden variation of reference voltage with PI controller

(b) Zoom of Output voltage under sudden variation of reference voltage with PI controller Fig. 7 Variation of reference voltage with PI controller

4.2 Variation of the Load 4.2.1

PI Controller

After varying the load, we can clearly see that we observe ripples and small peaks (Fig. 9).

4.2.2

Advanced Controller RST

This figure shows that the experimental work confirms the simulation results, after applying the advanced RST controller and even if when the load is changed the value of the output voltage remains stable and this voltage follows its reference perfectly without ripple and small peaks (Fig. 10).

Robust Voltage Control for Four-Phase Interleaved DC-DC Boost Converter ...

1417

(a) Output voltage under sudden variation of reference voltage with advanced controller

(b) Zoom of output voltage under sudden variation of reference voltage advanced controller Fig. 8 Output voltage after applied the advanced controller

5 Conclusion The study portrayed in this paper purposes to improve the quality both output of current and voltage of the DC-DC converter connecter to an electric vehicle. To overcome this problem, a boost converter was adopted introducing a new controller design for regulating a DC bus voltage. The proposed design of the controller is based on RST controller. Theoretical study of the RST approach was dressed and verified through computer simulations performed under MATLAB/Simulink software. The simulation results and experimental works are organized in subsections. The objective was to prove the effectiveness of the RST controller when compared to PI controller. The comparative study and analysis are demonstrated the superiority of the RST controller which it s presents a good regulation of voltage, a faster response time, a reduced overshoot, and a lower static error.

1418

M. Barara et al.

(a) Output voltage under sudden variation of load with PI controller

(b) Zoom of output voltage under sudden variation of load with PI controller Fig. 9 Output voltage with PI controller

Robust Voltage Control for Four-Phase Interleaved DC-DC Boost Converter ...

1419

(a) Output voltage under sudden variation of load with advanced controller

(b) Zoom of output voltage under sudden variation of load with advanced controller Fig. 10 Output voltage with controller REST

References 1. Schumacher D, Magne P, Preindl M, Bilgin B, Emadi A (2016) Closed loop control of a six phase interleaved bidirectional DC-DC boost converter for an EV/HEV application. In IEEE Transportation Electrification Conference and Expo (ITEC), pp 1–7. Dearborn 2. Kascak S, Prazenica M, Jarabicova M, Konarik R (2018) Four phase interleaved boost converteranalysis and verification. Acta Electrotechnica et Informatica 18(1) 3. Ayoubi Y, Elsied M, Oukaour A, Chaoui H, Slamani Y, Gualous H (2016) Four-phase interleaved DC-DC boost converter interfaces for super-capacitors in electric vehicle application based on advanced sliding mode control design. Electric Power Syst Res 134:186–196 4. Nagulapati K, Rangavalli V, Vanajakshi B (2014) Modelling, analysis and simulation of 4- phase boost converter. Int J Electr Comput Energ Electron Commun Eng 8(9) 5. Nisak K, Aida SA, Zainul AZ (2018) Interleaved 4-Phases DC-DC converter for electric scooter to improve efficiency. In: MATEC Web Conference Volume 225, 2018 UTP-UMP-VIT Symposium on Energy Systems

1420

M. Barara et al.

6. Elsied M, et al (2015) Efficient power-electronic converters for electric vehicle applications. In 2015 IEEE Vehicle Power and Propulsion Conference (VPPC), Montreal, QC, pp 1-6 7. Prasanth P, DRS (2016) Investigation of four phase interleaved boost converter under open loop and closed loop control schemes for battery charging applications. Int J Adv Mater Sci Eng (IJAMSE) 5(1) 8. Godoy E, Ostertag E (2006) RST-controller design : a rational teaching method based on two diophantine equations. IFAC Proc Volumes 39(6):541–546

MPPT and Pitch Angle Based on Neural Network Control of Wind Turbine Equipped with DFIG for All Operating Wind Speed Regions Moussa Reddak, Ayoub Nouaiti, Anass Gourma, and Abdelmajid Berdai

Abstract The wind turbine coupled with a doubly fed induction generator (DFIG) demonstrates good robustness and energy performances against the intermittent nature of the wind, which allows many control strategies driving this system to control it in the right conditions. Besides, the wind energy is completely depending on the wind flow which is variable and can overpass the rated value of the wind speed. In this case, the wind turbine should be sheltered versus load stress and the potential risk of destruction. In this paper, to profit from wide wind speed and exploiting the totality of wind energy, an artificial neural network is designed to implement a maximum power point tracking MPPT-pitch angle control strategy for controlling the wind turbine. This strategy lets the DFIG extract the maximum power and protects the wind turbine system from the overload of the electromagnetic torque when the wind speed is higher than its rated value. The simulation results are performed using the Matlab/Simulink environment and show well control performances under a wide wind speed range. Keywords Wind turbine · DFIG · Neural network · MPPT · Pitch angle control

1 Introduction Nowadays, to face the challenges of increased energy demand and climate changes, several renewable energy sources are committed to meeting the expectations of manufacturers and consumers. Among these renewable energy sources, wind energy is considered a vital energy source that relies on the kinetic energy extracted from the wind.

M. Reddak (B) · A. Nouaiti · A. Gourma · A. Berdai Laboratory of Energy and Electrical Systems ENSEM, Hassan II University, Casablanca, Morocco A. Nouaiti Laboratory of Computer Science, Applied Math and Electrical Engineering, Moulay Ismail University, IEVIA Team, EST, Meknès, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_129

1421

1422

M. Reddak et al.

Due to the intermittent nature of the wind, new wind turbines are equipped with a robust energy conversion system [1, 2] for achieving maximum energy efficiency. Among the components of this system, a generator is used for converting mechanical energy to electrical one. Currently, (DFIG) [3, 4] is extensively used as an electromechanical structure in wind energy conversion systems because of its advantages. To benefit of full range of the wind speed, control strategies are designed to reach the maximum power once the wind speed is average, and also to preserve the power of the wind turbine at its maximale value while the wind speed is upper relying on the pitch control [5]. In this context, several robust control strategies, for example, nonlinear controls (sliding mode, backstepping) [6] and even fuzzy logic control [7], are used to drive the wind turbine in different wind regions. Artificial neural network (ANN) control also meets this need since it has given good results in the areas of complex problem solving and robust control [8–10]. The objective of this paper is the control of the wind turbine in large wind speed by adopting the pitch control. The pitch angle is generated by a neural network while the wind speed is upper to the limit value. Also the system control strategy aims to warrant the taking out of the maximum power once the wind speed is less. This paper is planned as follow: in Sect. 2, the model of the system is developed. In Sect. 3, the pitch angle control using neural network is described. The strategy control of the system will be introduced in Sect. 4 and before conclusion, some simulation results will be discussed in Sect. 5.

2 Model of the Wind Turbine System The wind turbine equipped with the DFIG system, shown in Fig. 1, contains three elements: the generator (DFIG), the turbine and the power converters. These later are divided into two converters, a rotor side converters (RSC) which controls the powers delivered by the generator, and a grid side converter that controls the DC-bus. The control of the grid side converter is not concerned in this paper.

2.1 Turbine Model The wind speed profile could be characterized by Eq. 1 [11, 12]: Vw (t) = 12.8 + 0.869 sin(3t) + 0.74 sin(5t)− 0.624 sin(10t) + 0.24 sin(30t) + 0.49 sin(50t)+ 0.24 sin(100t)

(1)

MPPT and Pitch Angle Based on Neural Network Control of Wind Turbine Equipped ...

1423

Fig. 1 DFIG system piloted by wind turbine

The power taken from kinetic energy, according to Betz’s theory, is defined as follows: Pa =

1 ρπ R 2 v 3 C p (λ, β) 2

(2)

According to this theory, the extraction of the power produced by the wind flow through the wind turbine cannot be fully achieved. But this power is fractionated by the coincidence effect of the wind with the blades. To define this limit, the power coefficient C p that depends on this aerodynamic power is defined in Eq. 3: C p (λ, β) = 0.46(

150.9 − 18.4 − 0.579β − 0.00201β 2.14 − 13.21)(e λi ) λi

(3)

λi is expressed in Eq. 4: λi =

1 1 + 3 λ + 0.002β β +1

(4)

The ratio of tip speed is given by Eq. 5: λ=

Rt v

(5)

Figure 2 gives the curve of C p of different pitch angle values. And Eq. 6 gives the obtained aerodynamic torque: Ta =

Cp 1 ρπR3 v 2 2 λ

(6)

1424

M. Reddak et al.

Fig. 2 Power coefficient across λ for different pitch angle values

2.2 DFIG Model With simplified notation and by relating the Park reference to the stator field, the expression of the DFIG voltages and fluxes in dq reference are given by Eq. 7. ⎧ ⎪ v = Rs ids + dϕdtds − ωs ϕqs ⎪ ⎪ ds ⎨ dϕ vqs = Rs iqs + dtqs + ωs ϕds ⎪ vdr = Rr idr + dϕdtdr − ωr ϕqr ⎪ ⎪ ⎩ v = R i + dϕqr + ω ϕ qr r qr r dr dt  ϕds = L s i ds + Mi dr = ϕs ϕqs = L s i qs + Mi qr = 0 The stator and rotor current are related as follow:  i sd = Lϕss − LMs ir d i sq = − LMs irq

(7)

(8)

(9)

The expression of the powers is given in Eq. 10: 

Ps = −vs LMs irq Q s = vs Lϕss − vs LMs ir d

(10)

The equation of the electromagnetic torque is shown in Eq. 11: Tem = − pϕs

M irq = K T irq Ls

And the dynamic of the rotor currents is expressed in Eq. 12:

(11)

MPPT and Pitch Angle Based on Neural Network Control of Wind Turbine Equipped ...

 dirq dt

= σ 1L r (vr d − Rr ir d + σ L r ωr irq ) = σ 1L r (vrq − Rr irq − σ L r ωr ir d − ωr LMs ϕs ) di dr dt

1425

(12)

3 Neural Network A neural network is based on the working of biological neurons and materializes in a computer as an algorithm. The neural network can adjust itself according to the results of its actions, which tolerates learning and problem solving without an algorithm and conventional programming. The neuron mathematical model, in the lth layer, is assumed by the following equation: y lj = f (

nl 

wlji xi + bil )

(13)

i

With xi : Outputs of the previous neurons. wlji : The weight of synapses binding neurons. bil : Coefficient for adjusting the neural network. The function f can be presented by hyperbolic, threshold, sigmoid or radial functions. This ensemble of interconnected formal neurons allows the resolution of complex problems by adjusting the weight coefficients using a learning algorithm. In our case, the ANN bloc, that control the pitch angle, is a static multi-layer perceptron MLP. The architecture of this later is revealed in Fig. 3. According to Fig. 3, the neural network takes as input the ratio of the tip speed λ and C p , and as output the pitch angle β. The performance training of the proposed neural network is depicted in Fig. 4.

4 Wind Turbine Control Strategy Figure 5 illustrates the curve of the wind turbine power across the wind speed; there are three operation modes: In the first region, before the cut-in speed, there is no production of the power. Then in second region, that matches to the average wind speed, it is necessary to control the DFIG to benefit from the maximum power. In the last region, where the wind speed exceeds the passage value which is 12 m/s, it is necessary to ensure the control of the pitch angle to conserve the rated power.

1426

M. Reddak et al.

Fig. 3 The proposed MLP neural network for pitch angle control

Fig. 4 Performance training of the MLP neural network

Figure 6 shows the wind turbine coupled with the DFIG system and the used control strategy. The control system is separated to two parts: the control at the turbine side and the control at the DFIG side. The first part maintains the power captured at its nominal value by acting on the pitch angle control in region 3, it also ensures the speed control of the generator according to the measured speed of the wind by optimizing the power extracted in region 2 regarding this equation.

MPPT and Pitch Angle Based on Neural Network Control of Wind Turbine Equipped ...

1427

Fig. 5 Operating the wind turbine regions

Fig. 6 Control strategy of the wind turbine driven a DFIG using an ANN

Popt =

1 ρπ R 2 v 3 C p_max (λopt , βmin ) 2

(14)

Therefore, this part gives the reference torque necessary for the active power control by generating the rotor current reference irq r e f . On the side of DFIG control, the converter of the machine-side controls the generated active and reactive powers.

1428

M. Reddak et al.

Parameters of DFIG and PI controllers are given by [13].

5 Simulation Results To confirm the control strategy approach of the wind turbine equipped with a DFIG, a wind profile, described in Eq. 1, has been adopted and potted as depicted in Fig. 7. Indeed, it can be verified, in Fig. 8, that the pitch angle produced by the artificial neural network, varies according to this wind profile. It is at minimum value (0°) when the wind speed is under the transition value (12 m/s). Whereas in region 3, which reflects the wind speed exceeding the pass through value, the pitch angle takes the corresponding value other than the minimum one. With these strategies, the MPPT and pitch control has been achieved. Also it can be shown in Fig. 9 that the Cp is maintaining in the maximum value, that is around 0.45, when the wind speed reflects the region 2. Although, the Cp is less when the wind speed surpasses the transition value, which endorse the robustness of the control under neural network. The electromagnetic torque shown in Fig. 10 is limited to protect the system from damaging and load fatigue while the wind speed is upper to the pass trough value. Besides, as depicted in Fig. 11, the stator power is limited while the wind speed is higher than the pass through value to give the nominal value that is 2e6 W, and when the wind speed reveals the region 2, the regulator forces the DFIG to extract the maximum power.

Fig. 7 The used wind speed profile

MPPT and Pitch Angle Based on Neural Network Control of Wind Turbine Equipped ...

Fig. 8 Pitch angle given by the neural network

Fig. 9 The variation of the power coefficient

1429

1430

Fig. 10 The reference and response of the electromagnetic torque

Fig. 11 Electromagnetic power delivered by the DFIG

M. Reddak et al.

MPPT and Pitch Angle Based on Neural Network Control of Wind Turbine Equipped ...

1431

6 Conclusion An intelligent ANN control, which is used for the MPPT and the pitch angle strategy, has been suggested in this paper, to profit from the power delivered by a wind turbine equipped by DFIG under full range of wind speed. For the purpose of protecting the wind turbine from the fatigue load when the speed surpasses the rated value, the electromagnetic torque has been limited. In this case, the pitch angle, generating using an ANN control block, and power coefficient are set by producing a corresponding value. However, when the wind speed is under the pass through value, and the pitch angle given by ANN is at a minimum value to extract minimum value to ensure maximum power extracting. The control strategy potency using ANN of wind turbine equipped by DFIG has been shown in the simulation results under Matlab/Simulink environment .

Appendix (Table 1) Table 1 DFIG parameters

Nomenclature

Designation

Value

Ps

Stator power

2e6 W

Vs

Stator voltage

690 V

Rs

Stator resistor

2.6e–3 

Rr

Rotor resistor

2.9e–3 

Lm

Mutual inductance

2.5 mH

Ls

Stator inductance

2.587 mH

Lr

Rotor inductance

2.587 mH

1432

M. Reddak et al.

References 1. Tripathi SM, Tiwari AN, et Singh D (2016) Optimum design of proportional-integral controllers in grid-integrated PMSG-based wind energy conversion system: optimum design of PI controllers. Int Trans Electr Energy Syst 26(5):1006−1031 2. Singh M, Khadkikar V, Chandra A (2011) Grid synchronisation with harmonics and reactive power compensation capability of a permanent magnet synchronous generator-based variable speed wind energy conversion system. IET Power Electron 4(1):122. https://doi.org/10.1049/ iet-pel.2009.0132 3. Bekakra Y, Attous DB (2014) Optimal tuning of PI controller using PSO optimization for indirect power control for DFIG based wind turbine with MPPT. Int J Syst Assur Eng Manag 5(3):219−229 4. Boubzizi S, Abid H, El hajjaji A, Chaabane M (2018) Comparative study of three types of controllers for DFIG in wind energy conversion system. Prot Control Mod Power Syst 3(1):21 5. Reddak M, Berdai A, Nouaiti A, Vlasenko V (2018) Collaboration of nonlinear control strategy and pitch angle control of DFIG equipped wind turbine during all operating regions. Int J Comput Appl 179(25):16−21 6. Reddak M, Berdai A, Gourma A, Boukherouaa J, Belfiqih A (2016) Enhanced sliding mode MPPT and power control for wind turbine systems driven DFIG (doubly-fed induction generator). Int Rev Autom Control IREACO 9(4):207 7. Karimi-Davijani H, Sheikholeslami A, Livani H, Karimi-Davijani M (2009) Fuzzy logic control of doubly fed induction generator wind turbine. p 11 8. Zhang Z-Y, Wang K-S (2014) Wind turbine fault detection based on SCADA data analysis using ANN. Adv Manuf 2(1):70−78 9. Fazelpour F, Tarashkar N, Rosen MA (2016) Short-term wind speed forecasting using artificial neural networks for Tehran. Iran Int J Energy Environ Eng 7(4):377−390 10. Marugán AP, Márquez FPG, Perez JMP, Ruiz-Hernández D (2018) A survey of artificial neural network in wind energy systems. Appl Energy 228:1822−1836 11. Abdelli A, Sareni B, Roboam X (2007) Optimization of a small passive wind turbine generator with multiobjective genetic algorithms. Int J Appl Electromagn Mech 26(3−4):175−182 12. Mirecki A, Roboam X, Richardeau F (2007) Architecture complexity and energy efficiency of small wind turbines. IEEE Trans Ind Electron 54(1):660−670 13. Abad G, Iwanski G (2014) Properties and control of a doubly fed induction machine. In Power electronics for renewable energy systems, transportation and industrial applications, H. AbuRub, M. Malinowski, et K. Al-Haddad, Éd. Chichester, UK: John Wiley & Sons, Ltd, pp 270−318

Cairo Metro Power Analysis and Harmonic Mitigation Ahmed Abd Elsadek, O. A.Monem, Zaki Matar, Mokhtar Hussien, and Yasser Elsayed

Abstract In Egypt, Traction became one of the most important transportation means so many electrical projects related to the metro have been established. Metro electrical network is supplied by 12-pulse converter rectifier substations. These rectifier stations are of course nonlinear loads which result in several orders of harmonics to the electrical grid. Moreover, some problems occurred due to the negative effect of harmonics, for example, cables and transformers insulation failure due to fatigue resulted from the high frequency voltage harmonic content. This paper proposes a passive filter for this harmonic reduction. IEEE519-2014 recommendation sets the limits of current harmonics, and the resulted voltage distortion at the point of common coupling. This work uses the measured current and voltage for estimating the significant harmonic orders. A suitable filter can be designed, and implement according to these measured harmonics. The effect of transformer impedance and changing the configuration of motor traction from DC series motor into AC induction motor with speed drive is illustrated in the analysis section. To overcome the harmonic distortion problem and to define the proper mitigation method, recommendations have been concluded. Keywords Point of common coupling · Traction · Cairo metro · Total harmonic distortion · Nonlinear loads · 99th percentile · Rectifier station · High voltage station

A. Abd Elsadek (B) Egyptian Company for Cairo Metro Management and Operation, Cairo, Egypt O. A.Monem Higher Institute of Engineering and Technology Alobour, Cairo-Belbeis Desert Road, Cairo, Egypt Z. Matar · Y. Elsayed Electrical Power Department, ALAZhar University, Cairo, Egypt e-mail: [email protected] M. Hussien North Cairo of Electrical Distribution Company, Cairo, Egypt © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_130

1433

1434

A. A. Elsadek et al.

1 Introduction Rectifier substations (RS’s) are employed to convert AC to DC voltage to metro trains. The twelve-pulse converter has a non-linearity nature. This non-linearity distorts the drawn rectifier station source current. Distorted source current will have high levels of harmonic contents. These harmonics have a negative impact in different ways; like transformer heating, error in metering, false tripping of breakers, insulation damage, etc. Due to these problems, the need to assess and evaluate the harmonic level in Egypt metro network arises. The applicable recommended standard IEEE 519-2014 is the most common in power quality analysis [1]. Different researches use it to evaluate the power quality [2–4].

2 System Configurations Cairo metro network is composed of two lines, line 1 and line 2, two high voltage stations (HVS) that feed the two lines. HVS of Tura El Ballad 66/20 kV that supplies 17 rectifier stations in line 1. The second HVS is at Ramses 220 kV /20 kV which feeds the rest of line 1 rectifier stations, and all rectifier stations of line 2. In line 1, RS consists of three parts, two rectifier groups, each group has one power transformer with voltage 20 kV/1228 v and 2.6 MW connected ϒ/ − ϒ to achieve phase shift of 30° to cancel 5th and 7th harmonic order from source current. The third transformer is an auxiliary for feeding the rectifier lighting loads. The output DC voltage is 1500 V. On the other hand, the RS’s in line 2 have the same configuration, however, it contains one traction group, and the output dc voltage is 750 vdc. For line 1, there are two types of traction motors; the first one is induction motor which was working since 2015 and the second oldest one is DC series motors. Figure 1 describes the rectifier station configuration which represents the building block of metro electrical.

3 Measurements and Results The measurements were carried out using the power quality analyzer, Hioki 3196 used for recording source current, and voltage waveforms, harmonic content, power factor, and other power quality indices which are required for harmonic assessment. All data are collected and analyzed using statistical analysis to consider the full period of measurements in terms of 99th percentile and maximum values according to the recommendation of IEEE 519-2014 [5]. The first location of measurement is point “A” considering one group of traction loads. The second measurement has been taken aFt location “B” to consider the two

Cairo Metro Power Analysis and Harmonic Mitigation

1435

Fig. 1 Configuration system

groups of the substation and the auxiliary transformer which supply the substation loads (Lighting, air condition, etc.). The third measurement location is at point “C” which considers the effect of the cable length. On the other hand, the last measurement location is at point “D” which considers groups of rectifier substations. Measurements of point “B” are indicated in the following figures as a sample. Figure 2 indicates source voltage and current waveforms. The instantaneous value of voltage THDv and THDi versus time for 24 h period compared to the predefined limit are described in Fig. 3, and Fig. 4 respectively showing actual value exceeds the standard limit.

4 Measurement Summary Table 1 illustrates a summary of the power quality indices measured for THDv, and THDi at different locations of points A, B, C, and D. • It is noticed that the distortion rate increases in case of an increase of load current, conversely at the no-load condition the voltage distortion is under the critical

1436

Fig. 2 Source current and voltage waveform of point (B)

Fig. 3 Measured THDv of point (B)

A. A. Elsadek et al.

Cairo Metro Power Analysis and Harmonic Mitigation

1437

Fig. 4 Measured THDi of point (B)

Table 1 Measurement results of THDV and THDi

limit, a combination between the non-load period and the start of moving the first train indicates the difference in Fig. 5. • The voltage individuals of orders 11th , 13th appeared at all measured points as a result of the behavior of 12-pulse rectifiers in load condition, it’s called characteristic harmonics. • Despite of these results, an old study related to measurement analysis attempt only in line 1 of the metro network in 2010. It is showed that the current distortion is slightly out of specs, and consequently, it doesn’t have an effect on voltage distortion to exceed the limits. By comparing both analyses, resulted that the reason for increasing the harmonic distortion is related to adding new trains in 2015 to the operating system. These trains have induction motors driven by PWM drives, while the oldest trains have dc series motors controlled by resistances, also

1438

A. A. Elsadek et al.

Fig. 5 Instantaneous values of THDv at the first hour of operating

• •

• •

•

•

• •

changing impedance, power of the new AC trains are important factors for high harmonic value results. The distortion of line two is much more than line one, this is because of increasing the value of the impedance of the traction transformer of line two than line one [6], moreover, all trains are AC induction machine, and driven by PWM inverters. Common feeding of line 1 and line 2 through the point (D) has major effecting on each other through a distorted voltage supplied. Up till modernizing line 1 it is recommended to separate loads of line 2 on HVS Ramses from loads of line 1 to decrease the voltage distortion effect, moreover design proper filter on AC side to line 2. Auxiliary transformers of 20 kv/400 v, voltage transformers (VT), and current transformers (CT) are affected rapidly more than traction transformers 20 kv /1228 v, which may be due to different values of K-factor. Slightly impact of auxiliary transformer this is due to the light consumed loads, although having significant non-linear loads such as (led lamps, chargers and inverters …), this result got by connecting and disconnecting the auxiliary transformer on service at operating period and after the non-traction period. Reference to system configuration, it’s noticed that the resonant dc filter has a slight effect as the measurements on AC side, maybe it has an effective rule on dc side, this result was according to measuring at some (RSs) that have a filter on dc side and the others haven’t a filter. It is noted that sometimes the transformers overloaded with about 1.5 of its primary rate, due to changing some of the old trains with higher power airconditioned trains without increasing the power of the rectifier stations, and headway increasing. The harmonic currents associated with high losses and temperature will decrease the expected lifetime of transformers. Recommendation of resizing substation supplied power transformer depends on many factors included in reference [7]. In 2019, a new RS was put in service according to the extension plan of line 1; the dc filter needs to be checked according to proper design for new trains.

Cairo Metro Power Analysis and Harmonic Mitigation

1439

Fig. 6 PF measured at point D

• Also, the presence of audible noise in transformers is due to high frequencies current harmonics, it is an important indication of losses [8]. • Note that some of the individual’s currents orders decreased gradually from measured point “A” up to location “D”, it has high values, but it’s under the limit, maybe due to shifting cancellation and the effect of impedance of cable and network [9, 10]. • Due to harmonic distortion, poor power factor results at point D ranged between 0.66 to 0.88 according to the load current values, hence has high reactive power due to inductive load as shown in Fig. 6. • Practical measurements on 220 kV are not permissible, but it is expected that more voltage and current distortion and decreasing of power factor based on the following equation: Vpcc = Vs − Ls dls /dt

(1)

Where Vpcc : the voltage at the point of common coupling, Vs : the supply voltage, Ls : the source inductance and (Is ) is the source current. The harmonic current components multiplied by the source impedance at this frequency represent a voltage drop and cause a voltage distortion for the supply voltage affecting other consumers at the point of common coupling [11].

5 Filter Design and a Comparative Study As per the violation identified by the recorded current THDI , the voltage at the point of common coupling THDv, distortion factor, and total power factor, the need to mitigate the harmonics is arising. Few of the most prevailing methods used today

1440

A. A. Elsadek et al.

to reduce harmonics for example (Delta-Delta and Delta-Wye Transformers, Multipulse converter (12-pulse, 18-pulse, and pulse multiplication 36-pulse), Isolation Transformers, Use of Reactors, Passive Harmonic Filters (or Line Harmonic Filters), and Active filters. The selection of harmonic mitigation method for a specific application is an important decision, especially from cost point of view. In Research [12] co-phase traction transformer decreases the current distortion from 38.3 to 4.7%, although this is an effective solution, it is not easy to change 32 power transformers along the line from a cost and reliability point of view. On the other hand, using multi pulse techniques of 18 pulses as mentioned in [13] to decrease the voltage distortion from 8.9 to 6% a has high cost and is not applicable for unbalanced loads. Passive filters are cheaper, simple, and reliable. Moreover, it supports tackle specific orders harmonic, and plug and play.

5.1 Passive Filter Design A passive filter is combined of capacitors and inductors that are tuned to resonate at a single frequency, or through a band of frequencies. Passive filters work by exhibiting different impedance values at the resonant frequency. A filter connected in series should present high impedance to the harmonic frequency that needs to be blocked. Although a series configuration is possible, it is more common to connect filters in parallel. Such a shunt configuration diverts harmonic currents to ground, and simultaneously provide reactive power, which may be used to correct the power factor. As such, passive shunt filters are designed to be capacitive at the fundamental frequency. Passive filter design is conducted to mitigate 11th and 13th harmonic order [4, 14, 15]. According to IEEE design recommendation, filter design will be 10.5th instead of 11th order, and 12.5th instead of 13th order [16]. – Single Tuned for the 11th Order The value of filter components can be evaluated through the equations from (2) to (7) as the following: Xeff =

V2 (KV) Q rate of filter(MVAR)

(20)2 = 31746.03  0.0126    XC = h2 / h2 − 1 ∗ Xeff

(2)

Xeff =

 XC =

10.52 10.52 − 1

 ∗ 31746.03 = 32036.6 

(3)

Cairo Metro Power Analysis and Harmonic Mitigation

C11 = C11 =

1 2Π f XC

1441

(4)

1 = 0.099 μF 2π ∗ 50 ∗ 32036.6

Where Xeff : effective reactance of harmonic filter Qeff : effective reactive power of harmonic filter, this value from the measurement VLL : System line to line voltage XC : Capacitive reactance of harmonic filter capacitor at f tuned XL : Capacitive reactance of harmonic filter reactor at f tuned h: harmonic number. XL = L11 =

32036.6 Xc = = 290.58 h2 (10.5)2

(5)

290 XL = = 924 mH 2πF 2π ∗ 50

(6)

The resistance of filter value (R) is calculated according to the following equation by assuming the quality factor Q is 50 as in [11] R = R=

XL Q

(7)

290 = 5.8  50

– Single Tuned for the 13th Order Similarly, as the single tuned 11th equations from Eqs. (2) to (7), the final values of the 13th order filter can be designed as the following: C13 = 0.099 μF L13 = 651 mH R13 = 4.1 

5.2 PQI’s Improvement Achieved by Inserting Passive Filter • Some individual currents exceed the limits; moreover, it does not affect its voltage distortion, so there is no need for a high pass filter to eliminate from 23th to 50th as the most significant effect of current is at 11th and 13th as in Eqs. (8), and (9).  ( (I2)2 +(I3)2 + (I4)2 + . . . . . . . . . . . . In2 . THDi = I1

(8)

1442

A. A. Elsadek et al.

THDi at point (B) is 78% and by eliminating the value of 11th and 13th of individual current, the new value of THDi = 19% as considering filter compensation  ( (V2)2 +(V3)2 + (V4)2 + . . . . . . . . . . . . Vn2 . THDv = V1

(9)

THDv at point (B) is 8.2% and by eliminating the value of 11th and 13th of individual voltage, the new value of THDv = .07% as considering filter compensation. • The optimum filter location and best area space is at point (B) of the incoming of the rectifier substation.

6 Conclusion As the nature of time-varying of traction loads, and geographical location (from a station to another according to the population), the drawn load current and the associated harmonic currents amplitude and phase angle will be different accordingly. This load variation nature of traction is the reason behind the unsuitability of passive filter to mitigate the harmonics from the system in this load type. This is a clear drawback that can be added to the conventional drawbacks of passive filters. In the case of no-load, it will be a source of reactive power. On the other hand, the pulse multiplication technique is difficult to be applied due to the reliability factor which can’t be overcome due to the importance of the metro as sensitive and critical transportation means in Egypt that transports millions of people per day.

7 Future Work Reference to the previous argument, the recommended solution for metro harmonic mitigation is the active filter. The active filter has the advantage of not only mitigate the harmonics but also compensates the reactive power to improve both distortion factor and displacement power factor. The active filter becomes a common industrial solution; however, the design of it is a critical job. The location and the rating of the active filter should be carefully selected to achieve the mitigation target to meet the international standard with better harmonics handling performance and overall system efficiency. This will be discussed in future work as a part of the metro continual improvement strategy.

Cairo Metro Power Analysis and Harmonic Mitigation

1443

References 1. Smith JC, Hensley G, Ray L (2009) IEEE recommended practice for monitoring electric power quality. IEEE Std 1159–2009 (Revision of IEEE Std 1159–1995), pp c1–81 2. Gunavardhini N, Chandrasekaran M, Thirumoorthy A (2012) A study on harmonics in Indian railway traction. IOSR Journal of Electrical and Electronics Engineering 2:1–4 3. Navaneethakrishnan G, Muthial C (2017) Power quality in railway traction and compensation by combining shunt hybrid filter and TCR. Automatika 57:610–616 4. Popescu M, Bitoleanu A, Dobriceanu M (2008) Harmonic current reduction in railway systems. WSEAS Transactions on Systems 7:689–698 5. Sabin DD (2002) Analysis of harmonic measurement data. IEEE power engineering society summer meeting 2:941–945 6. Nhavira S, Polimac V (2007) Quality of supply issues arising from DC traction loads on a metro system 7. EN 50329 (2003) Railway applications – Fixed installations – Traction transformers 8. Lukic L, Djapic M, Lukic D, Petrovic A (2001) Aspects of design of power transformers for noise reduction. Parameters 60076:10 9. Elhenawy, AAE-M., Sayed, MM, Gilany MI (2018) Harmonic cancellation in residential buildings. In: 2018 twentieth international middle east power systems conference (MEPCON), pp 346–351 10. Zhongming Y, Pong M, Lo W, Yuen K (1999) Harmonic evaluation of traction system by Monte Carlo simulation. In: 30th annual IEEE power electronics specialists conference. Record (Cat. No. 99CH36321), pp 424–429 11. Monem O (2019) Harmonic mitigation for power rectifier using passive filter combination. In: IOP conference series: materials science and engineering, p 012013 12. Fathima F, Karthikeyan SP (2016) Harmonic analysis on various traction transformers in cophase traction system. Ain Shams Eng J 7:627–638 13. Persson J (2014) Comparing harmonics mitigation techniques. In: Comsys AB, Lund, p 6 14. Vasanthi V, Ashok S (2011) Harmonic filter for electric traction system. In: ISGT2011-India 15. Shah N (2013) Harmonics in power systems causes, effects and control. Whitepaper design engineering low-voltage drives 16. IEEE std 1531 (2003) IEEE guide for application and specifications of harmonic filter

Dual Fuzzy Direct Power Control for Doubly-Fed Induction Generator: Wind Power System Application Hala Alami Aroussi, El Mostafa Ziani, Manale Bouderbala, and Badre Bossoufi

Abstract In this work, a control strategy based on fuzzy logic controllers is developed for the control of an asynchronous machine. The proposed control consists of active and reactive powers estimation, two fuzzy controllers, and tracking based on the theory of direct torque control. The originality of our work has been in the implementation of the different switching states in different fuzzy controllers by rules that describe the operation of each magnitude controlled, ensured by the simplicity of implementation and the robustness offered by the fuzzy logic. As an approach, a fuzzy controller will be studied and implemented for the purpose of improving the static and dynamic performance of a dual classical direct power control applied to a wind power system. In this context, hysteresis controllers will be replaced by two fuzzy controllers and the simulation results of the generator-side and grid-side controls will be presented to prove the e of the control. Keywords DTC · Dual DPC · Fuzzy logic · Asynchronous machine · Hysteresis controllers

Nomenclature Symbols Vsα , Vsβ Vgα , Vgβ i sα , i sβ ir α , irβ

Parameters Stator voltages in (α, β) reference Grid voltages in (α, β) reference Stator currents in (α, β) reference Rotor currents in (α, β) reference

H. Alami Aroussi (B) · E. M. Ziani Laboratory of Electrical and Maintenance Engineering (LGEM), Ecole Supérieure de Technologie, Mohammed Premier University, Oujda, Morocco e-mail: [email protected] M. Bouderbala · B. Bossoufi LISTA Laboratory, Faculty of Sciences Dhar El Mahraz, Sidi Mohammed Ben Abdellah University, Fez, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_131

1445

1446

i gα , i gβ ψsα , ψsβ ψr α , ψrβ ωm Ps , Q s Pg , Q g S ρ v C p (λ, β)

H. Alami Aroussi et al.

Grid currents in (α, β) reference Stator fluxes in (α, β) reference Rotor fluxes in (α, β) reference Rotating speed Stator active & reactive powers Grid active & reactive powers Surface swept by the pales Air density Wind speed Power coefficient

1 Introduction In many countries, wind power has become a pillar of their energy systems since it is a reliable and inexpensive source of electricity. Indeed, the advancement of using clean energies in recent years lead researchers to develop wind energy controllers to exploit the maximum of wind energy extracted and to ensure its quality. Nowadays, the wind power system (WPS) based on a doubly-fed induction generator (DFIG) is the most implemented configuration. The latter has numerous advantages that can be summarized mainly in the fact that the machine can drive under variable speed, operates in both sub and hyper-synchronism modes, offers better energy capture, reduces mechanical stress, and costs fewer thanks to the emerged power converters [1–3]. The schematic of the wind power system configuration that we will use in the simulation part is illustrated in Fig. 1. The DC-Link is linked to the back-to-back converter. The rotor side converter (RSC) is connected to the rotor to control the stator active and reactive powers while the grid side converter (GSC) is connected to the grid to maintain the voltage of the DC-Link fixed [4]. As a first step, we propose a model of the wind power system, described before, including the dynamic model of both the machine and the wind turbine. Then, we choose the control that will be applied to run the whole system and will generate the results displayed using the software Matlab/Simulink. In our case, many techniques were developed by researchers, considering that the system is linear or not, such as Field Oriented Control, Direct Power Control, Backstepping, Sliding mode, Predictive Control… Indeed, Vector Control is the most implemented one in wind farms. However, its main drawback is the fact that this control is sensible to parameters variations of the system such as resistance and inductance variations [5]. To overcome this, the Direct Power Control, which is initially based on the Direct Torque Control, offers direct control of the powers using a predefined lookup table based on estimation and a perfect decoupling between reactive and active powers [6]. Though, the non-linear

Dual Fuzzy Direct Power Control for Doubly-Fed Induction Generator …

1447

Fig. 1 Dual DPC applied to DFIG-WPS synopsis

behaviours of the WPS parts and the non-constant switching frequency lead us to use non-linear techniques such as the Fussy Logic. As a result, the major emphasis of the work is targeted at the application of Fuzzy logic controllers as the replacement of the hysteresis controllers in the direct power control scheme (Control level 1). The dual direct power control applied to the backto-back converter is carried under variable speed and the results are displayed through simulations.

2 Dynamic Model of the System The dynamic model of the wind power system in the reference αβ can be expressed by [7]:

2.1 Stator and Rotor Voltages The voltages of the doubly-fed induction machine, in space vector notation, are written as:

1448

H. Alami Aroussi et al.

− → − → Vs = Rs i s + − → − → Vr = Rr ir +

d ψr dt

d ψ s dt

 ⇒

− → − jωm ψr ⇒



Vsα = Rs i sα + Vsβ = Rs i sβ +

Vr α = Rr ir α + Vrβ = Rr irβ +

dψsα dt dψsβ dt

dψr α dt dψrβ dt

+ ωm ψrβ − ωm ψr α

(1)

(2)

2.2 Stator and Grid Powers The stator and grid powers (active and reactive) are given by the following equations [4]: → − → − Ps = 23 Re{Vs , i s∗ } ⇒ Ps = 23 (Vsα i sα + Vsβ i sβ )

(3)

→ − → − Q s = 23 I m {Vs , i s∗ } ⇒ Q s = 23 (Vsβ i sα − Vsα i sβ )

(4)

→ −→ − Pg = 23 Re{Vg , i g∗ } ⇒ Pg = 23 (Vgα i gα + Vgβ i gβ )

(5)

→ −→ − Q g = 23 I m {Vg , i g∗ } ⇒ Q g = 23 (Vgβ i gα − Vgα i gβ )

(6)

2.3 Aerodynamic Power Theoretical wind power corresponding to a random wind crossing a surface S without a decrease in speed v is [8, 9]: Ptheor =

1 .ρ.S.v 3 2

(7)

Though, the aerodynamic power appearing on the shaft of the turbine is expressed as: Paeor = C p .Ptheor =

1 .ρ.S.C p (λ, β).v 3 2

(8)

The power coefficient C p (λ, β) will characterize the aerodynamic efficiency of the turbine. The previous equations will help us to elaborate a model of the main parts of our wind power system. In the next task, we will explain the direct power control and how to apply it to the machine part as well to the grid part.

Dual Fuzzy Direct Power Control for Doubly-Fed Induction Generator …

1449

3 Dual Fuzzy Direct Power Control The purpose of this technique (see Fig. 2) is to control directly the powers of our system by applying different voltages to the converters. These vectors determine their states [10, 11]. The two magnitudes controlled are the powers that are generally controlled by hysteresis regulators. The principle is to keep the magnitudes of the powers within these bands of hysteresis. The performance of these comparators defines the optimum voltage to be injected into the inverter for every switch [6]. The use of this type of regulator assumes the existence of a variable switching frequency that needs a very minor calculation step. Furthermore, it is preferable to operate with high frequency to decrease oscillations generated by this kind of comparator. Assuming that, in classic DPC, two types of power controllers are used: hysteresis controllers that have a property based on the command and its history or strip controllers where the command history does not play any role. They are based on the number of power error bands [12]. In this approach, we will replace the classic regulators with two fuzzy controllers. Here are all the rules describing the operating principle for this algorithm [13–15]: ⎧ ⎪ If ⎪ ⎪ ⎪ ⎪ I ⎨ f If ⎪ ⎪ ⎪If ⎪ ⎪ ⎩If

eQ s P eQ s N e Ps P e Ps Z e Ps N

then HQ I ncr ease then HQ Decr ease then H P I ncr ease then H P is kept then H P Decr ease

These fuzzy controllers (see Fig. 2) have the following properties:

Fig. 2 Synopsis of the fuzzy DPC applied to DFIG

(9)

1450

H. Alami Aroussi et al.

• The fuzzy controller is a Sugeno type. • Inputs fuzzification e Ps (stator active power error), eQ s (stator reactive power error) • The control strategy depends mainly on inferences (rules). The inference is based on Mamdani’s method that is associated with the min-max decision. To simplify the description of inferences, we use an inference table. • For defuzzification, the center of gravity is the method used. The same control will be applied to the grid side converter by changing the stator powers by grid powers.

4 Results and Simulations In this part, the dual fuzzy direct power control applied to a wind power system of 1.5 kW was tested under the Matlab/Simulink environment with the presence of a balanced three-phase load (Rload ) of 300 W. The system, shown in Fig. 1, was driven under subsynchronous and hypersynchronous speed (steady and transient state) regarding the following conditions: • The generator operates at variable speed (see wind profile); • The DC bus voltage: Vbus = 600 V. The simulation results of the power fuzzy logic applied to dual DPC are given by the following figures (Fig. 3): Under the circumstances mentioned previously, we observe that the stator active power follows its reference. It has a very fast dynamic and fewer harmonics. Indeed, the figure above shows a very good response of the stator active and reactive powers (around 0.28 s), where these follow their references perfectly with almost zero static error except for the peak that appears at 3.9 s. On the other hand, the grid active power presents a clear distortion beginning at t = 3 s, while the grid reactive power transient is almost not seen due to a reasonably decoupled control performance. It should also be remembered that the reactive power setpoint is imposed zero to obtain a unit power factor on both rotor and grid sides. Also, the patterns of the stator, rotor, and grid currents show small harmonic rates. These currents show also fewer ripples but keep their sinusoidal shapes. Figure 4 shows the spectrums of the stator (2.05%), rotor (3.43%), and grid (9.01%) currents. Notice that the Total Harmonic Distortion (THD) of the currents is less than 10% recommended by the standards IEC 61727 and IEEE 1547.

Dual Fuzzy Direct Power Control for Doubly-Fed Induction Generator … Wind Profile

Vbus Vbusref

10

600

8

Vbus (V)

V (m/s)

DC Voltage

800

12

1451

6 4

400 200

2 0

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

0

5

0

1

0.5

1.5

2

(a) Zoom Stator Currents (A)

Stator Currents (A)

2 0 -2 -4 0

0.5

1

1.5

2

2.5

3

4

3.5

4.5

5

(b)

Stator Currents

4

2.5

Time (s)

Time (s)

3

3.5

4

4.5

5

Zoom Stator Currents

3 2 1 0 -1 -2 -3 3.7

3.71 3.72 3.73 3.74 3.75 3.76 3.77 3.78 3.79 3.8

Time (s)

Time (s)

(c) 4 2 0 -2 -4 -6 0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

Zoom Rotor Currents

Zoom Rotor Currents (A)

Rotor Currents (A)

Rotor Currents 6

5

6 4 2 0 -2 -4 -6 2.6

2.8

3

3.2

Time (s)

3.4

3.6

4

3.8

4.2

Time (s)

500

Ps Psref

X: 0.2862 -1000

Y: -68.77

0

-1200 -1400

-500

3.8

4

4.2

-1000

Stator Reactive Power

2000

Qs Qsref

1000 X: 0.2998 Y: 0

0

-1000

-1500 -2000 0

Stator Reactive Power (Var)

Stator Active Power (W)

(d) Stator Active Power

1000

0.5

1

1.5

2

2.5

3

Time (s)

(e)

3.5

4

4.5

5

-2000 0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

Time (s)

(f)

Fig. 3 Simulation results: (a) wind profile (b) DC voltage (c) abc stator currents (d) abc rotor currents (e) stator active power (f) stator reactive power (g) abc grid currents (h) grid active power (i) grid reactive power

1452

H. Alami Aroussi et al. Grid Currents Zoom Grid Currents (A)

Grid Currents (A)

10 5 0 -5 -10 0

0.5

1

2

1.5

2.5

3

4

3.5

0

-5 3.7

5

4.5

Zoom Grid Currents

5

3.71 3.72 3.73 3.74 3.75 3.76 3.77 3.78 3.79 3.8

Time (s)

Time (s)

Grid Active Power (W)

4000

Pg Pgref

3000 2000 1000 0

-1000 -2000 0

0.5

1

1.5

2

2.5

3

4

3.5

4.5

5

Grid Reactive Power (Var)

(g) Grid Active Power

Grid Reactive Power

1500

Qg Qgref

1000 500 0 -500

-1000 0

0.5

1

1.5

Time (s)

2

2.5

3

3.5

4

4.5

5

Time (s)

(h)

(i)

Fig. 3 (continued)

a

b

Fundamental (50Hz) = 2.129 , THD= 2.05% Isa

0.4

c

Fundamental (5Hz) = 7.76 , THD= 3.43% Ira

5

5

4

0.3

4 3

3

0.2 2

0.1 0

Fundamental (50Hz) = 4.333 , THD= 9.01% 6 Iga

2

1

0

200

400

600

800

Frequency (Hz)

1000

0

1 0

200

400

600

800

1000

0

0

200

Frequency (Hz)

400

600

800

1000

Frequency (Hz)

Fig. 4 THD: (a) stator current Isa (b) rotor current Ira (c) grid current Iga

5 Conclusion The work performed is a digital simulation of the direct control of the powers of a wind power system based on the doubly-fed induction generator. We have proposed a method to improve the dynamic performance of direct power control by introducing a fuzzy type regulator while detailing its main modules such as Fuzzification, Inference, and Deffuzification.

Dual Fuzzy Direct Power Control for Doubly-Fed Induction Generator …

1453

Table 1 DFIG & WT parameters DFIG parameters

Value

WT parameters

Value

Nominal power

Pn = 1.5 KW

Rated power

1.5 KW

Stator voltage

Vs = 380V

No. of blades

3

Stator resistance

Rs = 1.18 

Radius

R=1m

Stator leakage

Ls = 0.4 H

Gearbox

G=2

Rotor resistance

Rr = 1.66 

Inertia

J = 1000 Kg.m2

Rotor leakage

Lr = 0.18 H

Friction

f = 0.007 N.m.s−1

No. of poles

P=2

The simulation results on the machine and grid sides showed acceptable robustness of these regulators, especially from the point of view of decoupling the active and reactive powers, and the speed variation. The next work will be a comparison between dual direct power control using a different type of comparators and its validation using a testbench.

Appendix (See Table 1).

References 1. Stiebler M (2008) Wind energy systems for electric power generation. Springer 2. Wu B, Lang Y, Zargari N, Kouro S (2011) Power conversion and control of wind energy systems. Wiley, Hoboken 3. Derbel N, Zhu Q (eds) (2019) Modeling, identification and control methods in renewable energy systems 4. Abad G, Lopez J, Rodríguez MÁ, Marroyo L, Iwanski G (2011) Doubly fed induction machine: modeling and control for wind energy generation. Wiley-IEEE Press 5. Boubzizi S, Abid H, El Hajjaji A et al (2018) Comparative study of three types of controllers for DFIG in wind energy conversion system. Prot Control Mod Power Syst 3:21 6. Alami Aroussi H, Ziani EM, Bouderbala M, Bossoufi B (2020) Improvement of the direct torque control of the doubly fed induction motor under variable speed. Int J Power Electron Drive Syst 11(1):97–106 7. Dehong X, Frede B, Wenjie C, Nan Z (2018) Advanced control of doubly fed induction generator for wind power systems. The Institute of Electrical and Electronics Engineers. Wiley 8. Aissaoui AG, Tahour A (eds) (2016) Wind turbines - design, control and applications. IntechOpen, no 4133 9. Bouderbala M, Bossoufi B, Alami Aroussi H, Taoussi M, Lagrioui A, Livinti P (2020) Deadbeat control applied to wind power system. In: Proceedings the 5th international conference on renewable energy in developing countries, IEEE-REDEC’2020, March 24–26, 2020, Marrakech, Morroco

1454

H. Alami Aroussi et al.

10. Alami Aroussi H, Ziani EM, Bossoufi B, Bouderbala M (2020) DPC & DNPC applied to wind energy converter system. In: Proceedings of the 5th international conference on renewable energy in developing countries, IEEE-REDEC’2020, March 24–26, 2020, Marrakech, Morroco 11. Lee Y-S, Martin HL (2018) Chow in power electronics handbook, 4th edn. Elsevier 12. Bossoufi B, Alami Aroussi H, Bouderbala M (2020) Direct power control of wind power systems based on DFIG-generator (WECS). In: Proceedings the 12th international conference on electronics, computers and artificial intelligence, IEEE-ECAI’2020, June 25–June 27, 2020 13. Sudheer H, Kodad SF, Sarvesh B (2018) Improvements in direct torque control of induction motor for wide range of speed operation using fuzzy logic. J Electr Syst Inf Technol 5(3):813– 828 14. Chikouche TM, Hartani K, Bouzar S, Bouarfa B (2020) New direct power control based on fuzzy logic for three-phase PWM rectifier. In: Hatti M (ed) Smart energy empowerment in smart and resilient cities. ICAIRES 2019. Lecture Notes in Networks and Systems, vol 102. Springer 15. Aissa O, Moulahoum S, Kabache N, Houassine H (2014) Fuzzy logic based direct power control for PWM three-phase rectifier. In: 22nd mediterranean conference on control and automation, Palermo, pp 79–84

DFIG-WECS Non Linear Power Controls Application Manale Bouderbala, Badre Bossoufi, Hala Alami Aroussi, Mohammed Taoussi, and Ahmed Lagriou

Abstract This study proposes two different controlling approaches of Wind Energy Conversion System (WECS). The first one deals with Field/Oriented/Control/ (FOC) and the second with Direct Power Control (DPC). Both approaches were applied on Doubly-Fed-Induction-Generator-(DFIG). DPC seems to give good tracking and better efficiency compare to FOC. In fact, by comparing Stator active and reactive powers, DPC gives much less oscillations. Regarding rotor and stator currents, DPC needs less time to achieve steady-state. Keywords DFIG · FOC · DPC · WECS

1 Introduction The field of electric machine control has known a great evolution in industrial sector. Used for several centuries (windmills), wind energy has been developed considerably. This significant development of this energy is going to be essential to attain the aims set for renewable energies. A wind system is usually composed of turbine, gearbox, and generator. There are two varieties of wind turbines: vertical axis wind turbines and horizontal. The latter is the most used because of its great efficiency. Regarding the generator, DFIG is often used thanks to its several advantages: the rotor power reaches around 30% of the total power. As result, the dimensions of converters among the conversion system is reduced [1]. M. Bouderbala (B) · B. Bossoufi · M. Taoussi LISTA Laboratory, Faculty of Sciences Dhar El Mahraz, Sidi Mohammed Ben Abdellah University, Fez, Morocco e-mail: [email protected] H. Alami Aroussi Laboratory of Electrical and Maintenance Engineering (LGEM), Ecole Supérieur de Technologie, Mohamed Premier University, Oujda, Morocco A. Lagriou Department of Electrical and Computer Engineering, The Higher National School of Arts and Trades, Moulay Ismail University, Meknes, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_132

1455

1456

M. Bouderbala et al.

For many years, power control has been the topic of many researches. Many control strategies have been proposed. Among these strategies, “vector controls” gives dynamic performance identical to that acquired by the-direct-current (DC) machine. Nowadays, the improvement of modern digital signal processing strategies has prompted extensively, to give a rise to “Direct Control of the Powers” [2]. Blaschke was the first one to develop the primary theory of the field-oriented techniques in 1970. Ten years later, its effective applications were adopted. Indeed, it requires calculations of Park transform, evaluation of trigonometric functions and regulations, which could not be done in an analogical approach. Vector control is designed to provide the same control performance as a separately excited DC machine, which is characterized by a natural decoupling of the variable flux and torque [3]. In the middle of the 1980s, TAKASHI initiated the Direct control methods of asynchronous machines as concurrent with classical controls depending on PulseWidth-Modulation (PWM) which uses field’s orientation to guarantee the decoupling between flux and torque. This study deals with direct power control (DPC) that lies on the principle of direct torque control (DTC) [4]. These two control strategies discussed previously both aims to control the/active/and/reactive/powers/separately. The comparison between FOC and DPC evaluate the difference between two different regulators: PI and hysteresis. However, each control lies on a different approach. Vector control is based on PI controllers, which are highly dependent on the machine parameters, while DPC control is based on hysteresis/comparators, which do not depend on the machine parameters. In order to compare between DPC and FOC control strategies, this work is organized as follows: First, In Sect. 2 the turbine and DFIG was modelled to model the WECS. The Field Oriented Control and Direct Power Control are described in Sect. 3 and Sect. 4 respectively. The results simulations are obtained using Matlab/Simulink then, an extensive results discussions are presented in (Sect. 5). Finally, the conclusion is presented.

2 The modeling of the WECS Figure 1 illustrates the synopsis of the studied system. The/wind/turbine drives the DFIG at a variable rotational speed through a multiplier. The DFIG stator is linked to the network directly. On the other hand, the rotor is linked to the network via a/back-to-back/converter and transformer [1]. First of all, it is necessary to model the system before proceeding to control.

DFIG-WECS Non Linear Power Controls Application

1457

Wind turbine

Grid 50Hz

Stator Power

DFIG Rotor

Controls

Fig. 1 WECS model

2.1 Turbine Model The aerodynamic/power (Paer ) is the product of power coefficient (Cp ) and wind power (Pv ). This power is calculated from wind speed (V), blades swept area (S), and air density (ρ). The power coefficient presents efficiency as a function of beta angle (β) and the lambda ratio (λ). The ratio depends on the wind speed (V), blade-radius (R), and turbine speed () t [2]. The following equations describe wind turbine system. Paer = Cp .(λ, β).Pv Pv = λ=

(1)

ρ.S.V3 2

(2)

R.t V

(3)

The multiplier is described by the equations cited below: Ct G

(4)

mec G

(5)

Cg = t =

With, Cg, mec and G are generator/torque, mechanical/speed of the generator, and multiplier/gain respectively. The dynamic equation of the turbine is given by:

1458

M. Bouderbala et al.

J

dmec = Cmec = C g − Cem − C f .mec dt

(6)

With, Cem, Cmec, Cf, and J: electromagnetic torque, mechanical torque, and viscous friction torque, total inertial respectively.

2.2 Doubly Fed Induction Generator Model There are three types of equations organize the dynamic modeling of DFIG in the park frame (d.q): Electrical equations, Flux equations and electromagnetic torque [5]. Electrical equations: V sd = Rs.I sd +

dϕsd − ϕsq.ωs dt

V sq = Rs.I sq +

dϕsq + ϕsd.ωs dt

V r d = Rr.I r d +

dϕr d − ϕrq.ωr dt

V rq = Rr.I rq +

dϕrq + ϕr d.ωr dt

(7)

Flux equations: ϕsd = Ls.I sd + Lm.I r d

(8)

ϕsq = Ls.I sq + Lm.I rq ϕr d = Lr.I r d + Lm.I sd ϕrq = Ls.I rq + Lm.I sq Electromagnetic torque: Cem = p.

 M  . ϕsq .Ir d − ϕsd .Irq Ls

(9)

Vs (d.q), ϕs (d.q), Is (d.q): voltages, flux, and currents statoric in PARK reference Vr (d.q), ϕr (d.q), Ir (d.q): voltages, flux, and currents rotoric in PARK reference Rs, Rr: stator and rotor resistances

DFIG-WECS Non Linear Power Controls Application

1459

Ls, Lr: cyclic stator and rotor Inductances M: mutual inductance. ωr = ωs − ω

(10)

ω = p.

(11)

p: machine’s pairs of pole ωs, ωr: stator and rotor angular speed.

3 Field Oriented Control (FOC) The direct current machine is utterly adjusted to variable speed applications. By decoupling the electromagnetic torque and the flux. Consequently, FOC command makes DFIG same as DC machine. This method lies on the transformation of the electrical variables of the machine to a reference frame that rotates with the flux vector. The flux may be oriented according to the stator (SFOC) or the rotor (RFOC), i.e. the dq frame can be fixed to the stator or the rotor. In this study the SFOC has been used. The following equation describe the orientation of the flux according to the axis d therefore: ϕsq = 0 ϕsd = ϕs

(12)

As result, the DFIG can easly be controlled. Active power is controlled separately from reactive power [6, 7]. It is assumed that the stator resistance is negligible as well as the flux is constant. Equations 7 becomes then: Vsd = 0

(13)

Vsq = ϕsd.ωs Injecting Eqs. 12 and 13, in Eqs. 8 and we obtain the following equations of stator currents and stator flux respectively: Isd =

ϕs M . .Ir d Ls Ls

Isq = −

M .Irq Ls

(14)

1460

M. Bouderbala et al.

ϕsd = ϕs = Ls.I sd + M.I r d

(15)

ϕsd = 0 = Ls.I sq + M.I rq The following equations are obtained using stator currents (Eqs. 14) and stator voltages (Eqs. 13). 3 M Ps = − . .V s.I rq 2 ϕsd   3 ϕsd M Qs = .V s − .I r d 2 Ls Ls M 2 d Ir d M2 ). ).Iqr − g.ωs .(L r − Ls dt Ls M 2 d Irq M2 M.Vs − g.ωs .(L r − + (L r − ). ).Idr + g. Ls dt Ls Ls

(16)

Vr d = Rr .Ir d + (L r − Vrq = Rr .Irq

(17)

The equations above shows clearly that: • Vr depends on Ir • Ps depends on Iqr • Qs depends only on Idr. In order to establish this command, the PI controllers will be used. Based on the Eqs. (17), the following Synopsis Fig. 2 presents the vector control modelling.

Fig. 2 FOC modeling

DFIG-WECS Non Linear Power Controls Application

1461

4 Direct Power Control (DPC) Based on direct torque control (DTC) approach, which is specifically designed to control torque and flux. Direct Power Control (DPC) has been developed to control directly machine’s power outputs [8–10]. As mentioned above, the DPC control is based on hysteresis controllers. However, the obtained errors between the reference and measured power values are inserted in hysteresis controllers. Those controllers define the switching state of the semiconductors, depending on the switching table and voltage vectors values [11, 12].

4.1 Switching Table The switching table presented in this section divides the plan into six sectors ((1)–(6)). Other tables divide the plan into twelve sectors to improve accuracy. When the voltage vector is aligned with the chosen rotating reference frame, the power is related to the direct component of the output current and the power instantaneous reactivation to the quadrature current [13]. The selection of the inverter switching mode is imposed by two hysteresis bands Hp, Hq in order to maintain the errors between the power reference values (Pref and Qref) and their measured values in these bands. To accomplish this, the errors of the instantaneous active and reactive powers are processed by the hysteresis comparators. Their outputs are set to 1 to increase the control variable (P or Q) and to 0 when the variables controlled are unchanged or must decrease [14, 15].

4.2 Hysteresis Comparators The hysteresis controllers are used for the purpose to adjust the active and reactive power settings. One is influenced by the error ep = Pref − P for active power. The other is influenced by the error eq = Qref − Q for reactive power. The hysteresis band widths affect the efficiency of the inverter, especially, the distortion of the harmonic current, and the frequency average switching time [16, 17]. The following Synopsis Fig. 3 presents the DPC modelling through the hysteresis comparators then, vector selection after that the pulse generation as indicated above:

1462

M. Bouderbala et al.

5 Results To approve these controls, Matlab/Simulink was used. The results identified with a DFIG of 2MW are given as follows. Fig. 3 DPC modeling

Stator Active Power ( w)

6

/

/

Stator Reactive Power ( Var)

10

2 1 0 -1 -2 1

2

3

4

5

10

2

6

1 0 -1 -2 2

1

4

3

5

times(s)

times(s)

.b Stator Reactive Power

a Stator Active Power

Stator Currents(A)

Rotor Currents (A)

4000

5000

0

-5000

2000 0 -2000 -4000 1

0 1

2

3

4

times(s)

4

3

2000 0 -2000 -4000 4.3

4.4

4.5

times(s)

d Stator Currents Fig. 4 a, b, c, d FOC control responses

5

4000

Stator Currents (A)

c Rotor Currents

2

times(s)

5

4.6

DFIG-WECS Non Linear Power Controls Application

Stator Active Power ( w)

6

Stator Reactive Power ( Var)

10

2 1 0 -1 -2 1

1463

2

4

3

5

10

2

6

1 0 -1 -2 2

1

4

3

times(s)

5

times(s)

b Stator Reactive Power

a Stator Active Power 5000

Stator Currents (A)

Rotor Currents(A)

4000

0

-5000

2000

0

-2000

-4000 1

2

3

4

5

2

1

Stator Currents(A)

c Rotor Currents

4

3

5

times(s)

times(s)

2000 0 -2000 4.3

4.35

4.4

4.45

4.5

4.55

times(s)

d Stator Currents Fig. 5 a, b, c, d DPC control responses

Both FOC and DPC control strategies are simulated, tested, and compared to their powers reference. Due to this reason, steps have been applied as references. The active power step varies from 0 to −2 MW at times t = 4 s; while the reactive power step varies also from 0 to −2 MVAR at times t = 25 s. The simulation results are presented in Fig. 4 for the FOC strategy and Fig. 5 for the DPC strategy; as follows (a): active power Ps, (b): reactive power Qs, (c): rotor current Ir, (d): stator current Is. Firstly, based on Fig. 4a, b and Fig. 5a, b we can clearly conclude that; active and reactive powers are decoupled. Also, the variables follow their references for both types of controls. At the instant t = 2 and t = 4, the currents Fig. 4c, d and Fig. 5c, d change values with the variations of powers Fig. 4a, b and Fig. 5a, b respectively. By comparing Stator active Fig. 4a and Fig. 5a, and reactive powers Fig. 4b and Fig. 5b, DPC gives much less oscillations. Regarding rotor and stator currents, DPC needs less time to achieve steady-state. From the results, it can be deduced that with hysteresis comparators, there are less oscillations even the response time is reduced compared to the PI controllers.

1464

M. Bouderbala et al.

Table 1 FOC & DPC differences

FOC

DPC

Coordinates reference frame

d-q reference frame

α-β reference frame

Controllers

PI controllers

Hysteresis controllers

Switching frequency

Constant

Variable

Active and reactive power control

Indirectly controlled by rotor currents

Directly controlled

PWM

Required

Not required

Robustness

Low

Medium

Transitory response

Medium

Good

The Table 1 summarizes the principal differences between the FOC and the DPC strategy.

6 Conclusion This work was dedicated to a comparative study between field-oriented control (FOC) and direct power control (DPC) applied to a wind energy conversion system (WECS). The configuration and principle of the FOC and the DPC has been detailed. The FOC is based on the PI regulator. This regulator has some drawbacks among them it depends greatly on machine parameters. Though, The DPC is based on hysteresis comparators and independent of parameter variations. In this way, the authors can attest that the utilization of the hysteresis comparators in DPC control has different focal points, for example, diminishing the exchanging recurrence of intensity switches and improving the waveforms of the yield factors of the machine, instead of the utilization of the corresponding fundamental regulator (PI) in the FOC. Thus, the authors can affirm that the use of the hysteresis comparators in DPC control has various advantages such as: decreasing the switching frequency of power switches and improving the waveforms of the output variables of the machine, rather than the use of the proportional-integral controller (PI) in the FOC. The results achieved prove the effectiveness of the DPC strategy employed for DFIG-based system control and attest to the expected performance. After obtaining these results in the Matlab software, as a perspective, we envisage applying these commands on the test bench. Funding The authors declare that they have funding for research from CNRST.

Conflict of Interest The authors declare that they have no conflict of interest.

DFIG-WECS Non Linear Power Controls Application

1465

References 1. Gonzalo A, Jesus L, Miguel R, Luis M, Grzegorz I (2011) Doubly fed induction machine: modeling and control for wind energy generation. Wiley, Hoboken 2. Bossoufi B, Aroussi H, Bouderbala M (2020) Direct power control of wind power systems based on DFIG-generator (WECS). In: Proceedings of the 12th international conference on electronics, computers and artificial intelligence, IEEE-ECAI’2020, June 25–June 27, 2020 3. Li S, Wang H, Tian Y, Aitouch A, Klein J (2016) Direct power control of DFIG wind turbine systems based on an intelligent proportional-integral sliding mode control. ISA Trans 64:431– 439 4. Alami Aroussi H, Ziani EM, Bouderbala M, Bossoufi B (2020) DPC & DNPC applied to wind energy converter system. In: Proceedings of the 5th IEEE-REDEC’2020, March 24–26, 2020, Marrakech, Morroco 5. Bouderbala M, Bossoufi B, Aroussi H, Lagrioui A, Taoussi M, Livinti P (2020) DEADBEAT control applied to wind power system. In: Proceedings of the 5th international conference on renewable energy in developing countries, IEEE-REDEC’2020, March 24–26, 2020, Marrakech, Morroco 6. Siegfried H (2006) Grid integration of wind energy conversion systems. Wiley, Chichester 7. Muller S, Deicke M, De Doncker RW (2003) Doudly fed induction genertor systems for wind turbines. IEEE Ind Appl Mag 8:26–33 8. Alami AH, Ziani EM, Bouderbala M, Bossoufi B (2020) Improvement of the direct torque control of the doubly fed induction motor under variable speed. Int J Power Electron Drive Syst 11(1):97–106 9. Noguchi T, Tomiki H, Kondo S, Takahashi I (1998) Direct power control of PWM converter without power-source voltage sensors. IEEE Trans Ind Appl 34(3):473–479. https://doi.org/ 10.1109/28.673716 10. Kendouli F, Boulahia A, Nabti K, Benalla H (2010) Modeling of a variable speed wind turbine driving PMSG using hysteresis band pulse width modulation (PWM). Int Rev Model Simul 3(6):1196–1201 11. Kumar DK, Ram Das GT (2018) Simulation and analysis of modified DTC of PMSM. Int J Electr Comput Eng 8(5):2894–2903 12. Alami AH, Ziani EM, Bouderbala M, Bossoufi B (2020) Enhancement of the direct power control applied to DFIG-WECS. Int J Electr Comput Eng (IJECE) 10(1):35–46 13. Tamalouzt S, Idjdarene K, Rekioua T, Abdessemed R (2016) Direct torque control of wind turbine driven doubly fed induction generator. Rev. Roum. Sci. Techn.– Électrotechn. et Énerg. 61(3):244–249 14. Daoud A, Derbel N (2019) Direct power control of DFIG using sliding mode control approach. In: Derbel N, Zhu Q (eds) Modeling, identification and control methods in renewable energy systems. Green Energy and Technology. Springer, Singapore 15. Kairous D, Belmadani B (2015) Robust fuzzy-second order sliding mode based direct power control for voltage source converter. Int J Adv Comput Sci Appl 6:167–175 16. Yousefi-Talouki A, Zalzar S, Pouresmaeil E (2019) Direct power control of matrix converter-fed DFIG with fixed switching frequency. Sustainability 11:2604 17. Monmasson E, Cirstea M (2007) FPGA design methodology for industrial control systems – a review. IEEE Trans Ind. Electron. 54(4):1824–1842

Behaviour of Concrete and Steel for the Construction of Storage Tanks: Comparative Study Salma El Aamery, El Hassan Achouyab, and Hassan Ouajji

Abstract The demographic growth and economic development are two main factors which create more demand for energy reserve production in petrochemical industry. Under such circumstances, an increase in the volume and quantity of storage tanks has become must due to the their importance in preventing harms and risks by storing large volumes of flammable, explosive, toxic and harmful materials, that is why many researchers are orientating themselves in research projects of materials for the construction of storage tanks with good quality and with the cheapest. In this article, we show the different thermo-mechanical behaviors of the two materials which are concrete and steel so that the constructors can make the most adequate choice for their needs (storage conditions, temperature, pressure, etc…), especially since each of these materials has specific characteristics that can influence the types of tanks as well as the storage modes. Keywords Storage tanks · Concrete · Steel · Material behaviour · Comparative study

1 Introduction In recent years, several events have led to the development of the construction of storage tanks, to store the various products encountered in the petrochemical and chemical industry, which is mainly carried out in metal tanks, which are welded and installed freely in the air (overhead tanks) [1]. Storage has been the subject of much attention because it offers the possibility of energy savings and efficient use [2]. For this reason, manufacturers use various types of storage tanks, for example, aboveground tanks, flat-bottomed tanks, concrete silos or steel for the storage of coal, S. El Aamery (B) · H. Ouajji Laboratory SSDIA, ENSET, Mohammedia, Hassan II University of Casablanca, Casablanca, Morocco E. H. Achouyab Laboratory of Condensed Matter Physics and Renewable Energies FST, Mohammedia, Hassan II University of Casablanca, Casablanca, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_133

1467

1468

S. El Aamery et al.

coke, grain, etc., tanks spherical (pressure vessels) for the storage of water and oil, of high-pressure liquefied water and underground tanks for the storage. This requires thinking about tanks with larger volumes [3], therefore, it is useful to do research, with the aim of comparing the properties, and the thermo-mechanical behaviour of the materials, in order to select the best to build the storage tanks, the most efficient at the lowest cost [4]. Numerous researchers are studying the physical and chemical properties of concrete, as concrete is a material widely used all over the world and in different fields [5]. It is composed of four basic ingredients: water, cement, gravel as coarse aggregate, and sand as fine aggregate. On the other hand, advanced energy storage systems are being actively pursued worldwide to respond to the rapid development of sustainable energy [6, 7]. Aggregates are the world’s most mined mineral resources, and the global construction market consumed the equivalent of $360 billion in 2018 alone [7]. In this context, this article also discusses concrete as a building material for storage tanks where it will be compared with steel. For a better presentation of the results and the different characteristics of these materials, our article is organized as follows: A review of the literature on tanks and storage methods is described in Sect. 2. Section 3 unveils a comparative study between concrete and steel. A discussion is presented in Sect. 4; finally, a conclusion will be presented in the last section.

2 Literature Review 2.1 Types of Tanks There are several structures in the industrial sector, among the best known of these structure, there are storage tanks, which are of different types: vertical cylindrical tanks, horizontal cylindrical tanks, spherical tanks and spheroidal tanks, each of these tanks has its own specific characteristics as they can be used for different purposes in industrial projects [8]. Storage tanks Liquid, as special structures, behave differently from ordinary structures in terms of dynamic characteristics [9]. They are most often used in distribution systems, industries and nuclear power plants for the storage of drinking water, toxic and flammable liquids and nuclear waste respectively. Because earthquake damage to liquid storage tanks can cause secondary hazards, they are strategically important structures [10], ich are built in two different shapes, either cylindrical or rectangular. Figure 1 shows the cylindrical tank with the chordal system (r; 8; z).

Behaviour of Concrete and Steel for the Construction of Storage Tanks …

1469

Fig. 1 The cylindrical tank and the coordinate system [9]

2.2 Storage Modes The dimensions of the tank and its material depend on the fluids to be stored and the type of storage. Oil storage generally consists of a steel structure for a shell and annular reinforced concrete beams that transfer the load to the ground. The storage mode can be classified according to the pressure and the operating temperature of the stored liquid, taking into account the relationship between these two parameters. Figure 2 shows the three storage modes for liquids with a normal boiling point (STP) below room temperature.

Fig. 2 The three storage modes for liquids with a normal boiling point (STP) below room temperature [1]

1470

S. El Aamery et al.

Fig. 3 Concrete composition

Aggregate [POURCENT AGE]

cement [POURCENT AGE]

Sand [POURCENT AGE]

water [POURCENT AGE]

2.3 Concrete Composition Concrete is a generic term for a composite construction material manufactured (see Fig. 3) from aggregates (sand, gravel) agglomerated by a binder (cement) [11]. Due to the characteristic difference between the aggregates (rigidity, strength, coefficient of expansion), the heterogeneity of concrete undergoes significant deformations and mechanical stresses that can actually locally exceed the elastic limit. The change in volume of the concrete would therefore be limited to a certain extent. Consequently, the internal tensile stress developed. Adding that cracking would occur when the tensile stress in the concrete exceeds the tensile strength, which can lead to a series of problems concerning the mechanical properties, durability and aesthetics of structural concrete [12]. Several experiments [13] show that concrete resists better to compression than to traction, its tensile behavior is strongly influenced by temperature, above 600 °C, most concretes show a low expansion and sometimes a slight shrinkage due to chemical decomposition of their various components [14].

2.4 Steel Composition Steel is a material consisting mainly of iron and some carbon, which are extracted from natural raw materials from underground (iron and coalmines). Carbon is only involved in the composition of the material for a very small part (generally less than 1%) [15]. In Table 1, we will show the symbols and values of the steel properties. On the other hand, electrical steels used for motors, transformers and generators are usually covered with an insulating coating to improve the performance of the material in terms of reduced power loss, punching and welding characteristics and corrosion resistance [16].

Behaviour of Concrete and Steel for the Construction of Storage Tanks … Table 1 Steel properties

Steel properties

Symbol

Value

Young’s module

Ea

200 × 106 KNm−2

Poisson Coefficient

νa

0.3

Elastic limit stress

σay

400000 KNm−2

Ultimate Constraint

σau

500000 KNm-2

Hardening module

Ha

10 × 106 KNm-2

Softening module

Sa

−30 × 106 KNm-2

1471

3 Comparative Study Between the Behaviour of Concrete and Steel Tanks 3.1 Behaviour of Concrete Tanks In general, the tensile strength and the flexural strength of concrete are respectively of the order of ~10 and ~15% of the compressive strength. In addition, compressive strength is often considered as a main quality of concrete because it is directly related to the structure of the hydrated cement paste [17]. On the other hand, concrete is classified in several strength classes based on compressive strength in most European standards [18]. As an example, fiber reinforced concrete (FRC) is a high performance material for the construction and reinforcement of structural elements exposed to impacts and other types of extreme loads. Many studies for prediction were a reference to the advantages of incorporating fibers in concretes exposed to explosions [19, 20], ballistic shocks [21], cyclic loads, and different types of impacts [22]. When fibers are homogeneously distributed and used in appropriate quantities inside the concrete, they reduce strength, toughness, ductility, durability and improve other mechanical properties [23].

3.2 Behaviour of Steel Tanks Several authors have studied the dynamic behavior of steel storage tanks and their contents using simplified models capable of predicting the seismic response of these facilities [24]. These tanks are generally used in petrochemical plants. Vertical cylindrical steel storage tanks are responsible facilities that are filled to their maximum capacity for most of their operational life. They often contain toxic, flammable, and/or expensive products and must therefore be treated with care and responsibility [25]. They can also be stored above ground using sandbags or tires as chocks [26]. Steel structures must have sufficient toughness to withstand various operating temperatures [27], that is why several tests were done to reveal its

1472

S. El Aamery et al.

Fig. 4 Diagrammed contrainte/deformation

mechanical characteristics, the most important one is the tensile test that leads to the following diagram (stress/strain) [28] (see Fig. 4). • OA: rectilinear zone where there is proportionality between the deformation and the applied stress. It is a reversible elastic zone • AA’: horizontal bearing, reflecting elongation under constant load. There is material flow, this is the plastic zone. • A’B: the load increases again with the elongations up to point B. If the specimen is unloaded in the plastic zone A’B a remanent elongation is observed; if it is reloaded, an elastic behavior up to the previous load is observed: the yield strength has been increased, the metal has been hardened. • BC: Elongation continues, although the load is decreasing, until point C where it breaks. In this phase, there is striction: the plastic deformation is localized in a small portion of the specimen and is therefore no longer homogeneous.

3.3 Comparative the Behaviour of Concrete and Steel Tanks When tensile tests were done on a steel beam and a concrete beam, with two different temperature conditions, to compare the displacements of these two materials, we find that: At a temperature of 100 °C: we notice that the displacements of these two materials increase (see Fig. 5), also the displacements of steel are a little bit larger than concrete of the order of 0.02, same thing we notice at a temperature of 500 °C (see Fig. 6). Thus, the displacements of either concrete or steel are higher at 500 °C than at 100 °C. Thus, we can conclude that the displacement increases with the increase in temperature.

Behaviour of Concrete and Steel for the Construction of Storage Tanks …

1473

0.6 0.5 0.4 0.3 0.2 0.1 0 0

100

200

300 steel

400

500

600

500

600

concrete

Fig. 5 Travel u(x) at temperature 100 °C 3 2.5 2 1.5 1 0.5 0 0

100

200

300 steel

400 concrete

Fig. 6 Travel u(x) at temperature 500 °C

4 Discussions In the world of energy storage, we are always looking for the evolution and use of materials and storage conditions, in this context that always researchers try and carry out studies for new materials and compare them in order to make the best choice in terms of price quality. In this part, we will compare two materials that are Steel S355 and Concrete B30, to know their thermo-mechanical behavior and study the advantages and disadvantages, to build storage tanks. Table 2 and Table 3 below show the different properties of strengths and stiffnesses in MP.

1474

S. El Aamery et al.

Table 2 Strength properties in MPa

Steel S355

Concrete 30

Flexion

270

18

Axial traction

270

2,4

Perpendicular traction

270

2,4

Axial compression

270

18

Perpendicular compression

270

18

Shear

213

1,8

Steel S355

Concrete 30

Average axial module

210000

11500

Mean Transversal Module

210000

11500

8100

4800

Table 3 Stiffness properties in MPa

Average shear modulus

Compared to concrete tanks, steel tanks have many advantages which are: Main Advantages • • • •

The high tensile strength of steel. The good resistance to earthquakes. Easy transportation due to the low weight which allows to transport far. Architectural possibilities, much more extensive than concrete [29].

On the other hand, this material also has a number of disadvantages, of which one finds. Main Drawbacks: • Lower compressive strength than concrete. • Poor fire resistance, requiring expensive protective measures. • Need for regular maintenance against corrosion.

5 Conclusion Based on this comparative study between concrete and steel, we can conclude that concrete is a good material, which resists compression unlike steel, which is more rigid in traction. Therefore, to better build a storage tank, we recommended using a composite material of concrete and steel, or using a high performance concrete, especially if it is used with high temperatures and high pressures, that is to say, physical properties that are particularly suitable for municipal and industrial applications. As a research perspective, we are thinking of modeling by the finite element method the storage tanks built by any material in order to have the thermo-mechanical behaviors.

Behaviour of Concrete and Steel for the Construction of Storage Tanks …

1475

References 1. Chamayou R (1997) Réservoirs métalliques: stockage des liquides. Généralités, p 7 2. Wu S, Yan T, Kuai Z, Pan W (2020) Thermal conductivity enhancement on phase change materials for thermal energy storage: a review. Energy Storage Mater 25:251–295. https://doi. org/10.1016/j.ensm.2019.10.010 3. Guo X, Ji J, Khan F, Ding L (2020) Fuzzy Bayesian network based on an improved similarity aggregation method for risk assessment of storage tank accident. Process Saf Environ Prot 144:242–252. https://doi.org/10.1016/j.psep.2020.07.030 4. Chen HC et al (2019) Synthesis of amorphous nickel–cobalt–manganese hydroxides for supercapacitor-battery hybrid energy storage system. Energy Storage Mater 17:194–203. https://doi.org/10.1016/j.ensm.2018.07.018 5. del Rey CE, Almesfer N, Saggi O, Ingham JM (2020) Light-weight concrete with artificial aggregate manufactured from plastic waste. Constr Build Mater 265:120199. https://doi.org/ 10.1016/j.conbuildmat.2020.120199 6. Zhang LL, Zhao XS (2009) Carbon-based materials as supercapacitor electrodes. Chem Soc Rev 38(9):2520–2531. https://doi.org/10.1039/B813846J 7. Zheng S et al (2017) Graphene-based materials for high-voltage and high-energy asymmetric supercapacitors. Energy Storage Mater 6:70–97. https://doi.org/10.1016/j.ensm.2016.10.003 8. Télécharger norme api 650 français pdf api 650 version francaise, Réservoirs de stockage : Méthodologie de - Revues et Congrès PDF. PdfCours.com. https://pdfcours.com/Exercices_ PDF_Corriges_Cours_1.php?Exercices_Cours_Corriges_Gratuit=7807&PDF_Corriges= norme_api_650_fran%C3%A7ais_pdf. Accessed 23 Sep 2020 9. Moradi R, Behnamfar F, Hashemi S (2018) Mechanical model for cylindrical flexible concrete tanks undergoing lateral excitation. Soil Dyn Earthq Eng 106:148–162. https://doi.org/10.1016/ j.soildyn.2017.12.008 10. Soni DP, Mistry BB, Panchal VR (2011) Double variable frequency pendulum isolator for seismic isolation of liquid storage tanks. Nucl Eng Des 241(3):700–713. https://doi.org/10. 1016/j.nucengdes.2011.01.012 11. Ec B (2005) Calcul des structures en béton, p 27 12. Igarashi SI, Bentur A, Kovler K (2000) Autogenous shrinkage and induced restraining stresses in high-strength concretes. Cem Concr Res 30(11):1701–1707. https://doi.org/10.1016/S00088846(00)00399-9 13. Hager I (2020) Comportement a haute temperature des betons a haute performance - evolution des principales proprietes mecaniques. https://www.academia.edu/18524207/Comportement_ a_haute_temperature_des_betons_a_haute_performance_evolution_des_principales_propri etes_mecaniques. Accessed 15 Sep 2020 14. Noumowe A (1995) Effet de hautes températures (20-600°C) sur le béton: cas particulier du béton a hautes performances. These de doctorat, Lyon, INSA 15. Zrak A, Koˇnár R, Jankejech P (2015) Influence of chemical composition in steel on laser cutting stability. Manuf Technol 15(4):748–752. https://doi.org/10.21062/ujep/x.2015/a/12132489/MT/15/4/748 16. Lindenmo M, Coombs A, Snell D (2000) Advantages, properties and types of coatings on non-oriented electrical steels. J Magn Magn Mater 215:79–82. https://doi.org/10.1016/S03048853(00)00071-8 17. Adenot F (1992) Durabilite du beton: caracterisation et modelisation des processus physiques et chimiques de degradation du ciment. These de doctorat, Orléans 18. NORME NF EN 206/CN: Présentation Générale. Infociments. https://www.infociments.fr/bet ons/norme-nf-en-206-cn-presentation-generale. Accessed 22 Sep 2020 ˘ 19. Drdlová M, Buchar J, Krátký J, Rídký R (2015) Blast resistance characteristics of concrete with different types of fibre reinforcement. Struct Concr 16(4):508–517 20. Luccioni B et al (2018) Experimental and numerical analysis of blast response of high strength fiber reinforced concrete slabs. Eng Struct 175:113–122. https://doi.org/10.1016/j.engstruct. 2018.08.016

1476

S. El Aamery et al.

21. Almusallam TH, Siddiqui NA, Iqbal RA, Abbas H (2013) Response of hybrid-fiber reinforced concrete slabs to hard projectile impact. Int J Impact Eng 58:17–30. https://doi.org/10.1016/j. ijimpeng.2013.02.005 22. Yahaghi J, Muda ZC, Beddu SB (2016) Impact resistance of oil palm shells concrete reinforced with polypropylene fibre. Constr Build Mater 123:394–403. https://doi.org/10.1016/j.conbui ldmat.2016.07.026 23. Mehta PK, Monteiro PJM (2001) Concrete microstructure, proprietis and materials, 20 Oct 2001 24. Caprinozzi S, Paolacci F, Dolšek M (2020) Seismic risk assessment of liquid overtopping in a steel storage tank equipped with a single deck floating roof. J Loss Prev Process Ind 67:104269. https://doi.org/10.1016/j.jlp.2020.104269 25. Elsevier Enhanced Reader. https://reader.elsevier.com/reader/sd/pii/S2452321620300913? token=BAA4AC4C9E8559789F2173771B34EB6C910AFAAD131CA7A1AB38CBBBD 7B9982BA183C696752D3A40375F3FBFE9D06392. Accessed 25 Sep 2020 26. Cheremisinoff NP, Cheremisinoff PN (1995) Steel and fiberglass construction for below ground storage tanks. In: Fiberglass reinforced plastics. Elsevier, pp 158–180 27. Diagnosis of the microstructural and mechanical properties of over century-old steel railway bridge components. Elsevier Enhanced Reader (2020). https://reader.elsevier.com/reader/ sd/pii/S1350630719311501?token=406588265883F448006A629FB71499E9200E0552B3 C37465EB2E1FAE3EBF2B8B895A70028F5D6F5C811518653F7C41AF. Accessed 08 Oct 2020 28. Les essais sur le matériau acier. https://notech.franceserv.com/materiau-acier-essais.html. Accessed 09 Oct 2020 29. Tomek R (2017) Advantages of precast concrete in highway infrastructure construction. Procedia Eng 196:176–180. https://doi.org/10.1016/j.proeng.2017.07.188

Sensorless Control of DC-DC Converter Using Integral State Feedback Controller and Luenberger Observer Djamel Taibi , Toufik Amieur , Mohcene Bechouat , Moussa Sedraoui , and Sami Kahla

Abstract This paper presents a design of linear state feedback control of DC-DC Boost converters, in order to achieve a particular desired behavior. To guarantee a zero steady state error, we introduce an integral action, which will work out this problem by assuring that the steady state error will end up to zero. If it is supposed that both the voltage and current are measured, so much more sensors are needed then and consequently causing a high cost, so that to estimate the voltage and current with a low cost and less complexity it is preferred to introduce a state observer. An observer or estimator is a dynamic system that uses the available information on a real system, according to the inputs and outputs of the real process and estimate the system state. Simulations results demonstrate the robustness and effectiveness of the proposed control scheme. Keywords Boost converter · Observer · Static error · Feedback control

D. Taibi (B) Département de Génie Électrique, Université de Kasdi Merbah, Ouargla, Algeria T. Amieur Département de Génie Électrique, Université de Larbi Tebessi, Tebessa, Algeria e-mail: [email protected] T. Amieur · M. Bechouat · M. Sedraoui Laboratoires des Télécommunications LT, Université 8 mai 1945, Guelma, Algeria e-mail: [email protected] M. Bechouat Faculté des Sciences et Technologie, Université de Ghardaïa, Noumirat BP 455, Route Ouargla Ghardaia, 47000 Ghardaia, Algeria S. Kahla Centre de Recherche en Technologies Industrielles CRTI, 16014 Cheraga Alger, Algeria © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_134

1477

1478

D. Taibi et al.

1 Introduction The development of semiconductors in recent years resulting from advances in physics led to the advent of a new discipline in electrical engineering. Under the name of power electronics, there is a part of electrical engineering that treats the static conversion of electrical energy from one form to another, adapted to the need of the user. The systems responsible for handling electrical energy are the converters that allow the electrical energy source to be adapted to a given receiver by converting between network and load. These transformations appear in four forms for which four types of converters are associated: rectifier, inverter, dimmer, chopper [1, 2]. The use of power converters is becoming more and more important. Recent technological applications require a high level of precision and performance, so DC-DC converters have a very important role in systems requiring conversion and adaptation of the energy level. In this work; we are interested in modelling of DC-DC converters and in developing control laws to allow ensuring stability and a certain level of performance by taking into account the problem and their application in an industrial environment. The objective is therefore: to improve the performance of the converters and to reduce the static error. For this reason, several control strategies are tested in this work in order to regulate the output voltage of DC-DC Boost converters. Finally, in order to economically reduce the number of sensors, we have introduced the estimation of the converter coil current by using an observer. The control technique consists of using a linearized model of the system with uncertainties proposed in [3]. The LQR controller used in [4–6], robust state feedback has been designed in [7] using passivity and using LMI in [8–11]. In this work, we investigate the application of linear state feedback control techniques to improve the dynamic behaviour of DC-DC converters. Section 2 is reserved for the modelling step of the boost. Sections 3.A and 3.B present the necessary theoretical elements for the use of these techniques, namely, the state feedback with integral action. Section 4, presents the state feedback with integral action and the state feedback with observer. Section 5, presents the application of these techniques to boost converters which is modelled in the second section. Finally, the whole of this paper is achieved by a conclusion.

2 Modeling of Boost Converter The boost converter has earned its name due to its ability of producing a DC output voltage greater in magnitude than the DC input voltage (step-up). The Circuit diagram of a Boost converter is as shown in Fig. 1. While the transistor is passing current, the diode is reverse biased. Hence, the source E is not connected to a load R. While the transistor is blocking current, the diode is forward biased and conductive, which makes the source E connected to a load R [12–14].

Sensorless Control of DC-DC Converter …

1479

Fig. 1 Circuit diagram of a Boost converter

Fig. 2 Boost converter switch turned ON

Fig. 3 Boost converter switch turned OFF

2.1 Switched Converter Model When the ideal switch is ON u = 1 as shown in the Fig. 2 The following dynamic is obtained: 

di L dt =E = − Rv C dv dt

(1)

When the switching function is u = 0 (Fig. 3): The following dynamic is obtained: 

di L dt = E −v = i − Rv C dv dt

(2)

1480

D. Taibi et al.

From the two Eqs. (1) and (2), we can obtain a single unified model, which is u ∈ {0, 1}. So the dynamic of the converter is as follows [12]: 

L di = E − v(1 − u) dt dv C dt = − Rv + i(1 − u)

(3)

2.2 Linearization The linearization of the average model is found to be given by [12]:  e˙ =

  E  1 DL 0 − DL e+ 1 1 D 2 eu − RC − RC DC

(4)



 i −i With e = and eu = u av − u av . v−v

3 State Feedback Controller with Integral Action 3.1 State Feedback Controller The State feedback controller consists in considering the model of the process described by the following equation [15]: 

x(t) ˙ = Ax(t) + Bu(t) y(t) = C x(t)

(5)

It assumes xi (t) of the state vector x(t) are accessible for measurement. A possible state-feedback control law can be given as [16]: u(t) = r − K c x(t)

(6)

Hence, the matrix K c of (1 × n) dimension is called the gain matrix. If the process is controllable, the choice of components of K c allows you to place n poles of the closed loop system as desired. The associated block diagram is given below (Fig. 4). Referring back to the precedent equations and schematic, we see that by employing a law u(t) = r − K c x(t), the state-space equations become the following [17]:

Sensorless Control of DC-DC Converter …

1481

Fig. 4 Closed loop system with u(t) = −K c x(t)

Fig. 5 State feedback with integral action



x(t) ˙ = (A − B K c )x(t) + Br (t) y(t) = C x(t)

(7)

3.2 State Feedback Controller with Integral Action As we have seen before, the status feedback command moderates the poles of the system in closed loop. However, the latter does not ensure a position error. One possibility to solve the problem is to add an integrator in the direct chain as shown in Fig. 5. We introduce an integral action: a noted state variable xi placed at the exit of the integrator with [18].  xi =

 (r (t) − y(t)) =

(r (t) − C x(t))

(8)

1482

D. Taibi et al.

Where v(t) is an external disturbance which acts on the states of the system through the matrix B0 . The command designed before the screen cannot cancel the effect of this disturbance on monitoring the reference which is given by: ⎧ ˙ = Ax(t) + Bu(t) + B0 v(t) ⎨ x(t) x˙i (t) = −C x(t) + r ⎩ y(t) = C x(t)

(9)

If we define the augmented state vector xaT = x xi , we can write the system in the following increased form [18]: 

x˙a (t) = Aa xa (t) + Ba u(t) + B0a v(t) y(t) = Ca xa (t)

(10)

With the matrices      

A 0 B B Aa = , Ba = , B0a = , Ca = C 0 −C 0 0 0 The control law is given by  

x(t) u(t) = −K a xa (t) = − K K i xi (t)

(11)

u(t) = −K x(t) − K i xi (t)

(12)

Which gives a closed loop equation as follows: x˙a (t) = [Aa − Ba K a ]xa (t) + Ba u(t) + B0a v(t)

(13)

Finally the gain vector is equal to the desired polynomial    s I − (A − B K ) B K i    |s I − (Aa − Ba K a )| =  C s 

(14)

Based on linearization (4) 

e˙1 e˙2





   e1 eu1 =A +B e2 eu2

The integral action introduced by e3 = The increased Eq. (15) becomes:



e2 therefore e˙3 = e2 .

(15)

Sensorless Control of DC-DC Converter …

1483

⎡

⎤ ⎡ ⎤ ⎡ ⎤ e˙1 e1 eu1 ⎣ e˙2 ⎦ = Aa ⎣ e2 ⎦ + Ba ⎣ eu2 ⎦ e˙3 e3 eu3

(16)

With  Aa =

A0 C0

 (17)

4 Luenberger Observer In this part, it is considered that all the state variables are available for the feedback. However, practically, some variables may not be available. Therefore, the estimate phase of those variables is definitely an essential step, means, that we must estimate the non-measurable state variables so as to elaborate the control signals. The observer is given by [19, 20]: 

  ˆ˙ = A x(t) x(t) ˆ + Bu(t) + L y − yˆ yˆ (t) = C x(t) ˆ

(18)



L: gain, L T = l1 l2 . . . ln . Error is defined as e(t) = x(t) − x(t) ˆ

(19)

Differentiating Eq. (19) gives ˙ˆ e(t) ˙ = x(t) ˙ − x(t)

(20)

and by substituting Eqs. (5), (18) into (20) we get  

e(t) ˙ = Ax(t) + Bu(t) − A x(t) ˆ + Bu(t) + L y(t) − yˆ (t)

(21)

    + L y(t) − yˆ (t) e(t) ˙ = A x(t) − x(t) ˆ

(22)

    e(t) ˙ = A x(t) − x(t) ˆ + LC x(t) − x(t) ˆ

(23)

e(t) ˙ = (A − LC)e(t)

(24)

With (5) and (18)

1484

D. Taibi et al.

Solution of Eq. (24) is given by e(t) = e( A−LC) e(0)

(25)

The eigenvalues of the matrix (A − LC) can be made arbitrary by appropriate of the choice gain L (Fig. 6).

5 Simulation and Results 5.1 Open Loop The converter is controlled by the PWM technique (carrier and modulating) linked to an open loop IGBT switch (Figs. 7, 8 and 9). Discussion – The controlled system in open loop is a blind system. – The open loop simulation gives a short rise time, but a long response time with oscillations and in addition a significant error and this is not good for the system (Figs. 10, 11, 12 and 13).

5.2 Integral State Feedback Controller Integral with Observer Discussion – State feedback improves system stability and eliminates overflow and static error of the system that appears in the open loop. – The integral action compensates the error that the variation of the load creates.

Fig. 6 Schematic diagram of the Luenberger observer

Sensorless Control of DC-DC Converter …

1485

Fig. 7 Open loop boost converter

Fig. 8 Current measurement in the open loop

1.4

Current Measurement

1.2 1 0.8 0.6 0.4 0.2 0 0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

0.045

0.05

0.035

0.04

0.045

0.05

Time (sec.)

Fig. 9 Voltage measurement in the open loop

30

Voltage Measurement

25 20 15 10 5 0 -5

0

0.005

0.01

0.015

0.02

0.025

0.03

Time (sec.)

– According to the gotten results from the above graphs, the convergence of the estimated current and voltage towards its true value is perfectly achieved.

1486

D. Taibi et al.

Fig. 10 Boost converter controlled by state feedback integral with an observer Fig. 11 Control signals

0.9 0.8

Control signals

0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

0.02

0.04

0.06

0.0 8

0.1

0.12

0.14

0.16

0.18

0.2

Time (sec.)

Fig. 12 Current

1 I Estimate

0.9

I Measurement

0.8 0.7

Current

0.6 0.5 0.4 0.3 0.2 0.1 0 0

0.02

0.04

0.06

0.08

0.1

0.12

Time (sec.)

0.14

0.16

0.18

0.2

Sensorless Control of DC-DC Converter … Fig. 13 Voltage

1487

25

20

Voltage

15

10

5 V Estimate V Measurement Reference

0

-5 0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

Time (sec.)

6 Conclusion This paper has demonstrated the control design methods for Boost converter. The state feedback control for with an integrator, underling the clear advantages of state feedback, that has a positive effect on response settling time, reducing the undesirable peak overshoots and serve having a less oscillated performance, referring that this approach doesn’t provide a zero static error, this latter has been solved by adding an integral action to state feedback control, that has proven its efficiency when working out the steady-state error then the use of state estimation technique with Luenberger observer was discussed and simulated, that comes out with the advantages of the observer in estimating those non measurable state variables.

References 1. Hsu CF, Lee TT, Wen YW, Ding FS (2006) Intelligent control for DC-DC power converter with recurrent fuzzy neural network approach. In: 2006 IEEE international conference on fuzzy systems, pp 457–462 2. Xia C, Geng Q, Gu X, Shi T, Song Z (2012) Input–output feedback linearization and speed control of a surface permanent-magnet synchronous wind generator with the boost-chopper converter. IEEE Trans Ind Electron 59(9):3489–3500 3. Ortiz-Lopez MG, Leyva-Ramos J, Carbajal-Gutierrez EE, Morales-Saldana JA (2008) Modelling and analysis of switch-mode cascade converters with a single active switch. IET Power Electron 1(4):478–487 4. Woo YJ, Le HP, Cho GH, Cho GH, Kim SI (2008) Load-independent control of switching DCDC converters with freewheeling current feedback. IEEE J Solid-State Circuits 43(12):2798– 2808 5. Jaen C, Pou J, Pindado R, Sala V, Zaragoza J (November 2006) A linear-quadratic regulator with integral action applied to pwm dc-dc converters. In: IECON 2006–32nd annual conference on IEEE industrial electronics. IEEE, pp 2280–2285

1488

D. Taibi et al.

6. Olalla C, Queinnec I, Leyva R, El Aroudi A (2011) Optimal state-feedback control of bilinear DC–DC converters with guaranteed regions of stability. IEEE Trans Ind Electron 59(10):3868– 3880 7. Linares-Flores J, Reger J, Sira-Ramérez H (2006) A time-varying linear state feedback tracking controller for a boost-converter driven dc motor. IFAC Proc Vol 39(16):926–931 8. Doliya D, Bhandari M (November 2017) An LMI approach for robust lqr control of PWM buck converter with parasitics. In: 2017 7th international conference on communication systems and network technologies (CSNT). IEEE, pp 103–108 9. Olalla C, Leyva R, El Aroudi A, Garces P, Queinnec I (2010) Lmi robust control design for boost pwm converters. IET Power Electron 3(1):75–85 10. Olalla C, Queinnec I, Leyva R, El Aroudi A (2011) Robust optimal control of bilinear DC–DC converters. Control Eng Pract 19(7):688–699 11. Olalla C, Leyva R, Queinnec I, Maksimovic D (2012) Robust gain-scheduled control of switched-mode dc–dc converters. IEEE Trans Power Electron 27(6):3006–3019 12. Nisso N, Raïdandi D, Djongyang N, Menga FD (2018) Modeling and analysis of boost converter in small-signals applied to the wind energy conversion system using Matlab/Simulink. Revue des Energies Renouvelables 21(4):635–649 13. Ounis F, Goléa N (May 2015) µ-synthesis based robust voltage control for cascade boost power converter. In: 2015 3rd international conference on control, engineering & information technology (CEIT). IEEE, pp 1–6 14. Yazdani A, Iravani R (2010) Voltage-sourced converters in power systems: modeling, control, and applications. Wiley, Hoboken 15. Shuai DX (November 2014) State feedback exact linearization control of Buck-Boost converter. In: 2014 international power electronics and application conference and exposition. IEEE, pp 1490–1494 16. Gkizas G, Amanatidis C, Yfoulis C, Stergiopoulos F, Giaouris D, Ziogou C, Voitekis S, Papadopoulou S (June 2016) State-feedback control of an interleaved DC-DC boost converter. In: 2016 24th mediterranean conference on control and automation (MED). IEEE, pp. 931–936 17. Shen M, Yang GH (2012) H 2 state feedback controller design for continuous Markov jump linear systems with partly known information. Int J Syst Sci 43(4):786–796 18. Chowdhury A, Debnath D (2013) Performance comparison between PID controller and statefeedback controller with integral action in position control of DC motor. In: Applied mechanics and materials, vol 367. Trans Tech Publications Ltd., pp 188–193 19. Vinodh KE, Jovitha J, Ayyappan S (2013) Comparison of four state observer design algorithms for MIMO system. Arch Control Sci 23:131–144 20. Kwon TS, Shin MH, Hyun DS (2005) Speed sensorless stator flux-oriented control of induction motor in the field weakening region using Luenberger observer. IEEE Trans Power Electron 20(4):864–869

Optimization and Improvement of the Efficiency of a Drying System Based on the Dimensional Parameters Dounia Chaatouf, Benyounes Raillani, Mourad Salhi, Samir Amraqui, and Ahmed Mezrhab

Abstract Nowadays, modern drying methods have replaced traditional drying that consists of exposing the product to direct sunlight. The indirect solar dryer is one of the best-known systems used since this revolt. For the latter, there are different concepts and equipment to build it, and although there are differences between them, they all have the same goal, which is preserving food by reducing its moisture content. These methods use heat under appropriate conditions in terms of temperature, relative humidity, and air velocity, which results in a decrease in the moisture content of the dried product, in the extent to prevent enzymatic activities as well as all biological organisms that can be the cause of a corruption of the vegetal products. Therefore, it is very important to ensure a good dried product, for that several parameter must be adjusted, the objective is to optimize the distribution of the airflow along the trays and to improve the efficiency of the system based on the dimensional parameters and the quantity of the product that can be dried, for that, the optimal number of trays and the distance between the trays is investigated using ANSYS FLUENT software under the oriental region climate of Morocco. The results show that a drying chamber with 3 trays found to be the optimal number with a distance of 5 cm between the trays. Keywords ANSYS FLUENT · Indirect solar dryer · Trays

1 Introduction There have been many ways of preserving organic products in different parts of the world thousands of years ago, and these methods differ depending on the product to be dried and the regional traditions. Some of the most knowns methods of preservation are drying, canning, freezing, processing (smoking or salting), and pickling. Morocco has considerable solar potential that present for most of the year, which makes the solar drying the most widely used method for preserving products. But, D. Chaatouf · B. Raillani · M. Salhi · S. Amraqui (B) · A. Mezrhab Laboratory of Mechanics and Energetics, Faculty of Sciences, Mohammed First University, 60000 Oujda, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_135

1489

1490

D. Chaatouf et al.

after the failure of traditional drying [1] that consists of drying the products in the open air, extensive research has been carried out to design and develop the performance of solar dryers, some of them are presented in this section. The general design of a drying system is mainly based on the dimensions and the construction concept of the most important components such as the drying chamber, the solar air heater, and other several parameters that influence the drying quality of the system. However, the chamber geometry and the solar air heater have not interested some developers who have chosen to study how to encourage airflow through intake fans driven by photo-voltaic (PV) solar panels [2, 3]. In addition, several studies were made on the hybridization of the indirect solar dryer, like the one made by S. Dhanushkodi et al. [4] in which they use biomass and solar energy as sources. The study of the dryer was also investigated on different modes, to idealize and analyze the behavior of the system. There are many modes of operation to evaluate the efficiency of the dryer, I. Montero et al. [5] conducts a comparison study between indirect, passive, mixed, active, forced convection, natural convection and hybrid modes of operation, and it has been revealed that the forced hybrid is the most efficient mode of operation, followed by passive and active modes. In the indirect natural convection solar dryer, the most common problem with it is the non-uniform distribution of air inside the chamber, which affects the quality of the dried products. Therefore, in this study, we will focus on the geometric study and optimization of the dimensional parameters specifically, the distance between the trays and the number of it which is a crucial parameter in terms of the uniformity of air [6]. The chosen geometry in this paper is an electric solar dryer studied by Y. Amanlou et al. [7] that characterize with a side mounted plenum chamber, which affects positively on the air distribution inside the chamber, but in our case which is natural convection solar dryer equipped with a solar air heater, the distribution of air is going to be totally different, that we need a new study to ensure good uniformity of air along the trays.

2 Methodology 2.1 Solar Dryer Description In this study, the drying system presents an indirect natural convection solar dryer (Fig. 1) consists with a drying chamber where the figs are distributed on trays as a product to be dried, equipped with a trapezoidal plenum chamber and a solar air heater of a 1.5 m2 that composed of an aluminium absorber and simple glazing.

Optimization and Improvement of the Efficiency of a Drying System … Fig. 1 Schematic diagram of the solar dryer studied

1491

Air outlet

Solar radiation

Air inlet

Fig. 2 Meteorological station used to collect the data

2.2 Boundary Conditions The meteorological data used in this paper are collected from the high precision station shown in Fig. 2 installed in the university Mohamed first Oujda. The main data provided for this study are the ambient temperature and the solar irradiation presented in Eqs. (1) and (2) respectively, that have been used in the subroutine UDF (User Defined Function) that we wrote to integrate it in ANSYS FLUENT so we can study the solar dryer more realistically. Tamb (t) = 25 + 6 cos

π

12   t −6 G sun (t) = 965 sin π , 14

(t − 14)



6 < t < 20

(1) (2)

1492

D. Chaatouf et al.

Fig. 3 Comparison of the average outlet temperature and velocity with those of [8]

The inlet boundary condition is a pressure type with atmospheric pressure because the dryer studied works under natural convection, as well as the outlet type. Regarding the convective losses in the walls a heat transfer coefficient is defined (3) h 0 = 2.8 + 3Vw

(3)

Where V w = 3 m.s−1 is the wind velocity. To simulate the resistance to the airflow by the trays that are filled with the fruit of the fig, we modeled them as a porous medium with 50% porosity using the power-law model.

3 Results 3.1 Validation To validate our work, we compare our results with certain experimental and theoretical results available in the literature as Jyotirmay et al. [8] that studied a cubical wooden chamber, similar to a cabinet dryer equipped with an inclined solar chimney for natural ventilation. Our results presented in Fig. 3 shows a good agreement. The difference rate for temperature and velocity does not exceed 1.47% and 16% respectively. Noting that our results are closer to the experimental results than the numerical ones calculated by the authors.

3.2 The Optimal Distance Between the Trays and Its Number In order to obtain an optimum drying chamber, that is capable of carrying out the drying process with high quality in a short period of time, the optimization of the dynamic behavior is necessary for the chosen geometry. For the mesh independency

Optimization and Improvement of the Efficiency of a Drying System …

1493

test, four different grids, 5532, 12130, 16606, and 23820 triangular cells were studied, and the results show that a mesh grid with 12130 cells is quite enough to study the geometry. We will study the effect of adding trays on the dynamic behavior of the existing ones. The cases studied are generally two to five trays, and the distance between the trays is also studied. The objective is to create a high-performance drying system with an optimal number of trays for high-quality drying. Concerning the distance between the trays, three different distances were studied between the two first trays and the results are presented in Fig. 4 in term of the average temperature of the trays in the sunshine hours, which shows that as long as the distance between the two trays increases, the temperature decreases along the lower tray, while the first one remains constant, this is due to the hot air that rises directly when entering the drying chamber after being heated by the solar air heater. Furthermore, the Fig. 5 shows the average temperature of the trays in the sunshine hours for 4 different cases, where the number of trays is the parameter on which the study is based. As you can see, the higher the number of trays, the more T between the additional tray and the first one. The big differences can affect the products, that’s why the temperature in the trays must be kept at the same level as uniform as possible. It has been shown in the figure that the temperature in the case of two trays is higher than the others; and the difference in temperature is very remarkable, after every addition of a tray compared to the initial case which has an important and significant effect on the temperature of the existing ones. In the cases of two and three trays, it can be noticed that the average temperature in the trays is relatively equal in each case, but when the number of trays is greater than three, the temperature differences between the plates of each case are getting bigger. In particular, by decreasing the average temperature in these trays. The difference can reach more than 10° in some cases, which is not good for the uniformity of drying and the product will take a longer time to get it dried, especially the product that was put in the lower trays where the temperature is lower compared to the high (first) one. In terms of these qualifications (drying uniformity and drying time), the two-trays case is better than the others, followed by the three-trays case, which is slightly better than the others, with a temperature difference of around 2°, besides this, adding a third tray will carry more product, and thus the optimal number of trays in our case is three trays (Fig. 6). Fig. 4 The average temperature of the two trays in the sunshine hours of the chosen day

TRAY 1 TRAY 2

316 315

T(K)

314 313 312 311 310 d= 5 cm d= 7,5 cm d= 10 cm

D. Chaatouf et al.

T(K)

1494 318 316 314 312 310 308 306 304 302 300 298 296

Tray 1 Tray 2 Tray 3 Tray 4 Tray 5

5 trays

4 trays

3 trays

2 trays

Fig. 5 The average temperature of trays in the 4 cases in the sunshine hours of the chosen day

V (m/s) 0.00 0.009 0.018 0.027 0.036 0.042 0.048 0.054 0.069 0.075 0.087 0.093 0.1

T(K) 290 296 301 310 316 321 327 333 338 347 353 358 364 370 378 385

(a)

(b) 10a.m

1p.m

5p.m

Fig. 6 Distribution of velocity (a) and temperature (b) inside the chamber at 3 different hours in the chosen day

The figure above shows the air distribution inside the optimal geometry of the drying chamber that we studied (the solar dryer was cut off in the pictures). Where we can see the drying chamber getting hot through the day and the temperature distribution become more uniform after 1 p.m. where the irradiation is at its maximum. The hot air rises directly when entering the drying chamber after being heated by the solar air heater and then moves horizontally in the trays before exiting from the outlet. The air with high velocity passes through the three trays which is not the case in more than three ones because it crosses just the high trays due to the lower density of the hot air.

Optimization and Improvement of the Efficiency of a Drying System …

1495

4 Conclusion In the present work, the optimization of an indirect solar dryer composed of a solar air heater and a wooden chamber is studied in 2D using ANSYS FLUENT software under the meteorological conditions of Oujda city (eastern of morocco), the distance between the trays and its number is a crucial parameter in term of the uniformity of air inside the drying chamber and according to the results, a drying chamber with 3 trays found to be the optimal number with a distance of 5 cm between the trays.

References 1. Essalhi H, Tadili R, Bargach MN (2017) Conception of a solar air collector for an indirect solar dryer. Pear drying test. Energy Procedia 141:29–33 2. Goud M, Reddy MVV, Chandramohan VP, Suresh S (2019) A novel indirect solar dryer with inlet fans powered by solar PV panels: drying kinetics of Capsicum Annum and Abelmoschus esculentus with dryer performance. Sol Energy 194:871–885 3. Saini V, Tiwari S, Tiwari GN (2017) Environ economic analysis of various types of photovoltaic technologies integrated with greenhouse solar drying system. J Clean Prod 156:30–40 4. Dhanushkodi S, Wilson VH, Sudhakar K (2017) Mathematical modeling of drying behavior of cashew in a solar biomass hybrid dryer. Resour-Effic Technol 3(4):359–364 5. Montero I, Blanco J, Miranda T, Rojas S, Celma AR (2010) Design, construction and performance testing of a solar dryer for agroindustrial by-products. Energy Convers Manag 51(7):1510–1521 6. Chaatouf D, et al (2021) Trays effect on the dynamic and thermal behavior of an indirect solar dryer using CFD method. In: Hajji B, Mellit A, Marco Tina G, Rabhi A, Launay J, Naimi S (eds) Proceedings of the 2nd international conference on electronic engineering and renewable energy systems, ICEERE 2020. Lecture Notes in Electrical Engineering, vol 681. Springer, Singapore. https://doi.org/10.1007/978-981-15-6259-4_72 7. Amanlou Y, Zomorodian A (2010) Applying CFD for designing a new fruit cabinet dryer. J Food Eng 101(1):8–15 8. Mathur J, Mathur S (2006) Summer-performance of inclined roof solar chimney for natural ventilation. Energy Build 38(10):1156–1163

Monitoring and Control System of a Hybrid Micro-CSP/Biomass Boiler System for Traditional Baths Said Lamghari, Hassan Hamdi, Mehdi Khaldoun, Mickael Benhaim, Fatima Ait Nouh, and Abdelkader Outzourhit

Abstract Traditional hammams are big consumers of wood-energy and water. This work presents a hybrid micro-csp/biomass system for space heating and hot water production in traditional baths that enables to offset the wood that is inefficient used in such baths. In addition, energy efficiency of these baths is enhanced through an optimal control system. Furthermore, the use of local agricultural residues instead of wood all contribute to reducing the stress on forests. The system to monitor and control the consumption of hot water and energy is presented. The data can be locally stored and transferred through internet, together with system faults. Preliminary test of the micro-csp showed that the maximum temperature achieved at the outlet of the parabolic trough system was 41.7 °C while at the bottom of the storage tank it was 34.4 °C corresponding to a 8.7 °C increase even though water was withdrawn by users. Space heating and hot water production are decoupled which allows to stabilize the hot water temperature to meet the needs when the boiler is off. Keywords Control system · Monitoring · Parabolic trough · Micro-CSP · Hybrid systems · Biomass boiler · Underfloor heating · Solar energy · Hot water · Traditional hammam

S. Lamghari (B) · A. Outzourhit LaMEE, Faculty of Science Semlalia, Cadi Ayyad University, Marrakech, Morocco e-mail: [email protected] H. Hamdi LMFE, Faculty of Science Semlalia, Cadi Ayyad University, Marrakech, Morocco H. Hamdi · F. Ait Nouh EnR2E, National Center for Studies and Research on Water and Energy (CNEREE), Cadi Ayyad University, Marrakech, Morocco M. Khaldoun BuildIT, Marrakech, Morocco M. Benhaim Water Solar Maroc, Marrakech, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_136

1497

1498

S. Lamghari et al.

1 Introduction Traditional baths (hammams) are a part of the Moroccan culture. Actually, there are about 10 000 hammams in Morocco, including 300 located in the city of Marrakech. Since, on average, a traditional hammam can consume about 1.5 tons of wood energy per day, they are regarded as one of the most important consumers of wood energy. This is mainly a consequence of using the traditional method of heating, where the water is heated by wood combustion and the energy of the combustion gases is used to heat the floors of the hammam rooms. Several studies were performed on traditional baths (hammams) in Egypt, Turkey, Morocco, Syria and Algeria [1]. They found that three-quarters of the total thermal energy consumption of a traditional baths was due to water heating. In addition, insulated roofs could reduce the demand for space heating by around 20%. Sibley and Sibely [2] evaluated the initiatives undertaken in Morocco to reduce wood consumption in hammams, in order to improve their energy efficiency and reduce deforestation. Sobhy et al. [3], on the other hand, performed a study on an individual hammam located in Marrakech. Temperatures between 30 °C and 37 °C can be reached for sunny days in the winter by using a solar heating system without thermal storage. Most of the studies that target the hammams focused on the different part used in the installed system. For example, Badran and Hamadan [4] compared the use of solar collectors and solar ponds as a supply of heat for an underfloor heating system. The found that the solar collectors are more efficient and break even in shorter time than solar ponds. For these systems to operate continuously (as in the case of hammam) and late in the evenings either large systems with storage are required. Another option is to couple the solar heating systems with another source (ex. Biomass) to remedy to the problem of the intermittence and to ensure a continuous supply of hot water and heat, which is the object of this work. The main objective of the present work is to present a hybrid system, which consists of parabolic troughs (micro-csp) and biomass boilers that was developed for heating water and space of a traditional hammam. We focus our attention of the control and monitoring system. Previous studies have focused on the modeling in the system in order to evaluate study the energetic performance. It was shown that there is no significant difference in the temperature of the rooms when copper or PEX tubing are used in floor heating [5]. In the same aspect there are several studies on the performance of the underfloor hating [4, 6–10]. An experimental study of the system, which is the subject of the present work, was carried out to test and improve the control system used.

Monitoring and Control System of a Hybrid Micro-CSP/Biomass Boiler System ...

1499

2 Description of the Hammam and the Hybrid System The hammam is located in Mhamid 4, Marrakech, (Morocco). The hammam is northoriented and consists of two bathhouses, one for women, and the other for men. Every hammam consists of four bathing rooms: a hot one, two warm ones and last one is not heated, in addition to a dressing room.

2.1 Description of the Studied Hybrid System Figure 1 represents the hybrid micro-cps/biomass heating system under investigation destined for space heating and the production of hot water. The Micro CSP plant using an external heat exchanger preheats the first hot water tank. When the set point temperature (60 °C) is not attained, the biomass boilers will automatically provide enough heat to the water in the other two tanks via external heat exchangers

Fig. 1 Schematic diagram of the hybrid biomass-micro-CSP system

1500

S. Lamghari et al.

by activating the corresponding pumps (circulators) installed to the supply manifold. Space heating; on the other hand, is performed by biomass boilers through underfloor heating systems and radiators, which are independently operated. In addition, the corresponding circulating pumps are installed on the supply manifold and are independently controlled by temperature controllers.

2.2 The Micro-CSP Plant The installation consists of six branches of collectors in parallel. Each branch has four parabolic troughs with a total of 8.24 m collector length and 8.25 m2 -aperture area yielding a peak power of 6 kW under a DNI of 1000 W/m2 . The total peak power of the solar field is therefore 36 kW. A single-axis east-west tracking systems was used in our case.

2.3 Biomass Boiler The boiler used is designed to heat water in a closed loop to a maximum temperature of 90 °C and uses only biomass feed in granular form. Local agricultural residues such as argan shells, olive pomace and nuts shell can be used as the biomass feed, thus allowing to avoid the extensive use of wood energy. The boiler is equipped with a controller allowing to control the biomass feed rate by adjusting the speed of rotation of a worm shaft and flow of the air leading to an efficiency as high as 90%.

2.4 The Monitoring and Controller System The role of the monitoring and control is to ensure the operation of the various parts of system within the specified temperature set points, the protection of personnel and material, and to help in detecting system faults. It is also designed to shut down a part of the system without affecting the other parts, which allows to perform preventative or corrective maintenance without stopping the whole system. A schematic diagram of the control/monitoring system is shown in Fig. 2: The main controller uses a simple algorithm. First, the temperatures are measured through the sensors located in various places of the hammam and the system components (boiler, storage reservoirs, rooms, inlet and outlet of the micro-csp plant…). The measured temperatures are then compared with the corresponding set points (TCSP, TBoiler…). When difference is higher than the 6 °C for a given sensor, the corresponding circulating pump is activated until the difference is lower than 4 °C.

Monitoring and Control System of a Hybrid Micro-CSP/Biomass Boiler System ...

1501

3 Results and Discussion The evolution of the outdoor air temperature and irradiance during the testing period of the hybrid micro-csp/biomass boiler system is given in Fig. 3. In addition, the temperature of the water at the outlet the micro-csp plant and the bottom of the solar tank were continuously monitored. Figure 4 shows typical temperatures profiles at these points. It should be noted that hot water is always drawn from the storage tanks. At night, the temperature of water in the bottom of the solar tank is the temperature of feed water from well which is around 25.7 °C. For a sunny day, the maximum temperature achieved at the outlet of micro-csp plant was 41.7 °C and in the bottom of the tank is 34.4 °C, corresponding to increase by 8.7 °C due to the fact that water is withdrawal by users from the tanks.

Fig. 2 Overall block diagram of the monitoring system

Fig. 3 The evolution of the outdoor air temperature and irradiation

1502

S. Lamghari et al.

Fig. 4 The evolution of the temperature of water in the bottom of the solar tank and at outlet of parabolic trough system

For a cloudy day, the maximum temperature achieved was 35.6 °C at the outlet of micro-csp plant and 28.5 °C in the bottom of the solar tank, corresponding to an increase of 2.8 °C as result of the water withdrawal by users from the tanks. The evolution of the temperatures of the biomass boiler and of the two tanks heated by the boiler is shown in Fig. 5, in addition to the control signals (on/off) of their respective circulator pumps. When the temperature is higher than the set-point temperature (60 °C), as clearly seen for example in the case of the second biomass-heated tank, the corresponding pump is stopped as it can be seen in Fig. 5a (red curves). However, it can be seen that the water temperature at the bottom of this tank keeps increasing beyond 60 °C as result of water coming from the top of the first tank since water is drawn from this the system. Figure 5b shows the evolution of the temperatures of the biomass boiler and of the two tanks heated by the boiler when the underfloor heating system is on. It can be seen that their respective pumps are stopped when the temperature of the boiler had dropped below or is within 4 °C of the temperatures of the tank. The pumps are activated when the temperature of the corresponding tank is less than 60 °C and the boiler temperature is 6 °C higher this value.

Monitoring and Control System of a Hybrid Micro-CSP/Biomass Boiler System ...

(a)

1503

(b)

Fig. 5 Control scenarios of the tanks heated by the biomass boiler (a) first one (b) second one

4 Conclusion In this work we described a hybrid solar-biomass system for space and water heating in a traditional Hammam. The focus was made on the system control. A simple algorithm was used to control the system and regulate the temperatures at various points. The results show that the control system enabled to avoid the cooling of the hot water when the water in the biomass boiler loop is colder than the water in storage tanks when the underfloor heating is activated. This work will allow us to develop an energy management system that will enable to optimize the energy consumption. Acknowledgment This study is carried out thanks to the funding from the Solar and New Energy Research Institute of Morocco (IRESEN) under the InnoTerm/InnoBiomass 2014 program.

References 1. Kristina Orehounig AM (2011) Energy performance of traditional bath buildings. Energy Build 43(9):2442–2448. https://doi.org/10.1016/J.ENBUILD.2011.05.032

1504

S. Lamghari et al.

2. Sibley M, Sibley M (2015) Hybrid transitions: combining biomass and solar energy for water heating in public bathhouses. Energy Procedia 83:525–532. https://doi.org/10.1016/j.egypro. 2015.12.172 3. Sobhy I, Brakez A, Benhamou B (2017) Energy performance and economic study of a solar floor heating system for a hammam. Energy Build 141:247–261. https://doi.org/10.1016/j.enb uild.2017.02.044 4. Badran AA, Hamdan MA (2004) Comparative study for under-floor heating using solar collectors or solar ponds. Appl Energy 77(1):107–117. https://doi.org/10.1016/S0306-2619(03)000 12-6 5. Krarouch M, Hamdi H, Lamghari S, Outzourhit (2018) A simulation of floor heating in a combined solar-biomass system integrated in a public bathhouse located in Marrakech. In: IOP conference series: materials science and engineering, vol 353, no 1. https://doi.org/10.1088/ 1757-899X/353/1/012005 6. Mokhtari A, Kazeoui H, Boukezzi Y, Achard G (1998) Utilisation d’un Circuit Hydraulique dans un Plancher pour le Chauffage et le Rafraîchissement des Locaux. Revue Energies renouvelables 1:17–27 7. Oudrane A, Aour B, Benhamou MHM (2016) Méthodologie pour la détermination de l’ écartement optimal de la chaîne tubulaire d’ une dalle chauffante. Revue Energies Renouvelables 19:11–19 8. Belkacem N, Loukarfi L, Khelil A, Naji H, Braikia M, Missoum M (2015) Simulation des charges thermiques dans une habitation pilote à plancher réversible. Nature Technologie 9. Amir M, Lacroix M, Galanis N (1999) Thermal analysis of electric heating floor panels with daily heat storage. Int J Therm Sci 38(2):121–131. https://doi.org/10.1016/S1290-0729(99)800 49-0 10. El Mays A et al (2017) Using phase change material in under floor heating. Energy Procedia 119:806–811. https://doi.org/10.1016/j.egypro.2017.07.101

Low Capacity Diffusion Absorption Refrigeration: Experimental Investigations and Thermal Characterization Ikram Saâfi, Ahmed Taieb, and Ahmed Bellagi

Abstract A low capacity commercial absorption-diffusion refrigerator (DAR) is tested under various operating conditions, in transient and steady-state mode. A series of twenty-three experiments in unsteady-state mode were performed to characterize the thermal exchange between refrigerator cabin and ambient air and inside the cabin, between its content and the evaporator. Steady-state tests are also realized by varying the electrical power supplied to the generator from 15 W to 63 W in order to identify the optimal operating conditions of the refrigerator. All the experiments are performed in an air-conditioned room at 26 °C. It is found that the optimal coefficient of performance is 0.15 for a cooling capacity of 7 W. Keywords Refrigeration · Absorption-diffusion · Experimental study · Steady-state and dynamic mode

1 Introduction The DAR, subject of our present investigations, was invented in 1928 by two Swedish engineers, von Platen and Munters [1]. It is a clean thermally-powered refrigeration technology that can readily be activated by low- to medium-grade renewable heat. It has been recognized as one of the most encouraging sustainable technologies for cold production. The machine’s cycle operates at constant total pressure. It uses ammonia as refrigerant, water as absorber and hydrogen (or less frequently helium) as a nonabsorbable inert gas, necessary to reduce the partial pressure of the refrigerant in the evaporator to allow the evaporation process to occur in the uniform pressure device.

I. Saâfi (B) · A. Taieb · A. Bellagi Thermal and Thermodynamic Research Laboratory of Industrial Processes, National Engineering School of Monastir, University of Monastir, Monastir, Tunisia A. Bellagi e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_137

1505

1506

I. Saâfi et al.

The main feature of this machine is its good reliability because it doesn’t have any moving parts. The circulation of the aqueous ammonia solution is driven by a bubble pump and that of the gases between absorber and evaporator by gravity. Numerous theoretical and experimental studies are reported in the literature dealing with the analysis of the performance of this machine, or part of it, operated with different energy sources and using various mixtures of working fluids [2–8]. Chen et al. [9] designed a modified generator including a heat exchanger that reuses the heat released in the rectifier to preheat the rich solution leaving the absorber. The new cycle configuration showed a significant improvement in cooling coefficient of performance, COP, up to 50% compared to the original cycle for the same cooling capacity. Taieb et al. [10, 11] proposed a simulation model for a DAR using ammonia/water/hydrogen as working fluids. The refrigerator was supposed cooled with ambient air and activated with solar hot water at 200 °C. An optimal result of COP of 0.126 associated to a cooling capacity of 22.3 W was found. Freeman et al. [11] established a thermodynamic model to simulate the performances of a DAR using ammonia-water-hydrogen as working fluid and integrating solar heat pipes as a source of heat under the climate conditions of Chennai, India. They found that for a charge pressure of 14 bar and 200 W heat input, a maximum COP of 0.25 was found for cooling temperature of 5 ºC. We report in this paper on our experimental investigations of a low-power commercial refrigerator with a capacity of 25 L, powered by an electrical resistance and operating according to the von Platen and Munters cycle. The refrigerator uses ammonia/water as refrigerant/absorbent pair and hydrogen, as pressure equalizer. All its elements are made of steel. The objective of the study is to assess the performance of the machine under various energy input conditions, in steady state as well as in transient operation mode. A key parameter for the evaluation of the cooling capacity of the refrigerator is the overall heat exchange coefficient that characterizes the thermal exchange between cabin and ambient air, i.e. that allows the estimation of heat infiltration from outside in the isolated cabin. This crucial parameter is generally determined by tests consisting of electric-resistance heating of the refrigerator cabin in steady-state mode. In the present study however we deduce this key parameter from a series of unsteady state temperature measurements during the startup of the refrigerator. To this purpose, a thermal model of the cabin is developed and a nonlinear regression procedure is applied to retrieve the overall heat exchange coefficient from the performed transient tests.

Low Capacity Diffusion Absorption Refrigeration …

1507

2 Experimental Setup 2.1 Description of the Cycle We refer in the following description to the schematic representation of the DAR in Fig. 1. The machine is energized when the electrical resistance begins to heat the ammonia-rich aqueous solution (1). The thermal energy, denoted Q˙ G , is supplied in the lower part of the combined generator/bubble pump ensemble. When enough heat is sullied, the liquid solution inside the vertical tube dealing as heat pump starts boiling. The generated vapor is composed of ammonia, the more volatile compound, and a small quantity of steam. The vapor forms first small bubbles that during their ascendant movement coalesce to form larger bubbles occupying the entire section of the tube. These bubbles rise up pushing the liquid above and carrying in their tail some liquid to the upper part of the heat pump (2). The pumped liquid is discharged by gravity into the annular space of the coaxial tube of the generator which is in direct contact with the electrical resistance. This heated and boiling solution generates an additional amount of refrigerant vapor. The ammonia-poor solution (13) exiting the generator is gravity-fed to the absorber via a solution exchanger. The vapor, on the other side, mounts to the condenser after purification in the rectifier where most of the accompanying steam is condensed. The condensed water returns to the annulus of the coaxial tube of the generator to rejoin the ammonia-poor solution moving to the absorber (14). The purified ammonia vapor liquefies in the air-cooled condenser by rejecting the thermal power Q˙ C D . The exiting liquid (5) is subcooled in the gas heat-exchanger (GHX) before accessing the evaporator at (7). GHX and evaporator form an assembly of two coaxial tubes: the low-density hydrogen-rich gas (12) leaving the absorber flows into the central tube upwards and the denser gaseous mixture of cold hydrogen and refrigerant vapor leaving the evaporator at (9), downwards in the annular space. The density difference between the two branches of the gaseous loop is at the origin of the circulation of the gases between absorber and evaporator. The small diameter tube carrying the liquid refrigerant from the condenser (5) to the evaporator entrance (7) is placed in direct contact with the outer tube of GHX. At the evaporator entrance the partial pressure of incoming liquid ammonia is suddenly so reduced that it starts evaporating at low temperature. The evaporation continues during its journey through the evaporator and is completed in the GHX. The energy Q˙ E V required for the evaporation of the refrigerant is supplied by the content of the refrigerator cabin where the evaporator is placed, which in its turn cools. The gaseous mixture entering the absorber (10) is thus ammonia-rich. Inside the absorber, the ammonia vapor in the gas mixture is absorbed by the lean solution (14) coming back from the generator. The liberated absorption heat is rejected to environment at the rate of Q˙ AB . The remaining gas, hydrogen-rich and ammoniafree (12) becomes lighter and rises towards the evaporator, and the ammonia–rich liquid solution from (15) starts moving back to the bubble pump through the solution heat exchanger. And the cycle starts again.

1508

I. Saâfi et al.

Fig. 1 Schematics of the DAR refrigerator

2.2 Measuring Devices To follow the operation of the refrigerator, we began by equipping it with adequate measuring devices as shown in Fig. 2. Eighteen type K thermocouples were fixed on the outer surfaces of the various elements of the refrigerator and connected to a data acquisition system. Fig. 2 Experimental set-up

Low Capacity Diffusion Absorption Refrigeration …

1509

3 Tests and Results We began our tests by determining the minimum input power required to start the machine and ensure the production of cold in the cabin. Increasing gradually the supplied power with each test and monitoring the evolution of the cabin temperature as well as the temperatures of various locations in the machine, it was found that a minimum of 22 W heating power is necessary to activate the DAR. For lower energy inputs (e.g. 20 W) no refrigerant vapor is generated in the boiler and hence, no cold can be produced, because the solution inside the generator is not hot enough to start boiling (Figs. 3 and 4). Temperatures of 205 °C and higher are required to initiate the boiling. For low heating power in the range 22–35 W, large amplitudes oscillations of the temperatures (up to 40 °C), particularly in generator (Fig. 3) and evaporator (Fig. 4) are observed. It was also noticed that by increasing the heating power, the frequency of the temperature oscillations is augmented and their amplitude reduced. These oscillations disappear completely for energy input of 43W and larger (Fig. 5). Fig. 3 Evolution of evaporator temperature for increasing heat supply: 20, 22, 24 and 30 W

Fig. 4 Evolution of generator temperature for increasing heat supply: 20, 22, 24 and 30 W

1510

I. Saâfi et al.

4 Thermal Characterization of the Machine To evaluate the DAR performance, i.e. the rate of cold production in evaporator Q˙ E V , the thermal exchange characteristics of the refrigerator cabin has to be known. In fact, in stationary mode operation, the cold production in evaporator compensates exactly the heat leakage Q˙ ext from outside, Q˙ ext = Q˙ E V = (U A)ext (T AM B − TC AB ) TC AB and T AM B denote the mean temperature inside the cabin and that of ambient air, respectively. (U A)ext , the product of overall heat transfer coefficient and exchange surface area, characterizes the thermal exchange between the DAR cabin and its environment. The knowledge of (U A)ext is thus necessary for the evaluation of the cooling capacity Q˙ E V and the coefficient of performance of the refrigerator, C O P COP =

Q˙ E V Q˙ G

We propose in this paper to deduce the coefficient (U A)ext from experiments performed in unsteady-state operation mode of the DAR. To this purpose twenty three tests in dynamic mode under various operating conditions were performed: 16 tests were run with empty cabin and the rest with the cabin loaded with different quantities of water. A simplified model of the thermal exchange between cabin, ambient air and evaporator is established. Similar to (U A)ext , an overall heat transfer coefficient (U A)int is defined for the heat exchange rate Q˙ int inside the cabin, between cabin content and the cold source, the evaporator. Both heat transfer coefficients are to be inferred from the measurements in dynamic mode operation of the DAR. The energy balance of the cabin writes dUcab = Q˙ ext − Q˙ int dt Fig. 5 Evolution of generator and evaporator temperatures for heat supply Q˙ G ≥ 43 W

(1)

Low Capacity Diffusion Absorption Refrigeration …

1511

where Ucab is the internal energy of the cabin and Q˙ ext = (U A)ext (T AM B − TC AB )

(2)

Q˙ int = (U A)int (TC AB − TE V )

(3)

TE V is the mean temperature of the evaporator. In steady-state operation, we have obviously Q˙ ext = Q˙ int = Q˙ E V Considering Eqs. (2) and (3), the energy balance becomes dUC AB = (U A)ext (T AM B − TC AB ) − (U A)int (TC AB − TE V ) dt

(4)

In the case of empty cabin (no water bottles inside), we can write dUC AB dTC AB = C p,C AB dt dt

(5)

with C p,C AB denoting the heat capacity of the cabin (hardware + inside air). When a mass m W of water is added, the internal energy of system is increased, and the lhs of Eq. (4) is correspondently modified  dTW dUC AB dUw dUC AB  + = + mC p W dt dt dt dt

(6)

  where mC p W is the heat capacity of the loaded water and TW , its temperature. Setting   mC p W (U A)int (U A)ext ;β = ;γ = α= C p,C AB C p,C AB C p,C AB the energy balance equation becomes dTC AB dTW = α(T AM B − TC AB ) − β(TC AB − TE V ) − γ dt dt

(7)

And in the case of the empty cabin (m W = 0), dTC AB = α(T AM B − TC AB ) − β(TC AB − TE V ) dt

(8)

1512

I. Saâfi et al.

Basing on unsteady-state measurements during the start up of refrigerator of the temperatures T AM B , TC AB and TE V , and TW in the case of water loaded cabin, the model parameters α and β in Eq. (8) or α, β and γ in model (7) are deduced by applying a nonlinear regression procedure. The calculations are performed using the algebra software Mathematica©. Figures 6, 7 and 8 illustrate the applied procedure in the case of empty refrigerator cabin. Fig. 6 depicts an example of measured temperatures of ambiant air, cabin and evaporator. To avoid the problems associated with differentation of numerical data series, because of the derivates dTdtC AB , dTdtW in the model Eqs. (7) and (8), the measured temperatures TC AB and TW are fitted to the empirical model T (t) = T0 (1 + Atexp[−Bt] + Cexp[−Dt] + Eexp[−Ft])

(9)

where T0 , A, B, C, D, E and F are smoothing parameters. As Fig. 7 shows, this model reproduces faithfully the experimental data. Finally, as means of validation of the thermal model Fig. 8 compares the derivative dTdtC AB ex p deduced from Eq. (9)   and dTdtC AB cal obtained from Eq. (8) with the experimental data of TC AB and TE V and the regressed values of α and β. As can be noted, the experimental findings are well reproduced by the model. Table 1 gives the obtained values of the parameters α, β and γ as well as their average values for all 23 tests. With the knowledge of the amount of water in the cabin, the mean value of γ allows to determine the heat capacity of the cabin C p,C AB . Once this parameter fixed, (U A)ext and (U A)int are respectively deduced from the average values of α and β. The obtained final results are C p,C AB = 1.91 kJ/K (U A)ext = 0.53 W/K

Fig. 6 Typical experimental temperature measurements (empty cabin) in dynamic mode for the determination of α and β

Low Capacity Diffusion Absorption Refrigeration …

1513

Fig. 7 Comparison of measured and calculated (Eq. 9) cabin temperature

Fig. 8 Thermal model validation: Comparison of measured and  calculated  C AB derivative dTdt

5 Dar Performance in Steady-State Mode Now, refrigerator tests are performed in stationary mode and for each heating power input, the cooling capacity evaluated using (U A)ext . The performances of the investigated refrigerator are graphically depicted in Figs. 9 and 10. As previously noted, a minimum heat supply is required to start production of cold by the refrigerator. To ensure sustainable operation however, a larger activation power, in the order of 36 W, is needed, producing a cooling capacity of 5 W. Beyond 43 W, the cooling capacity Q˙ E V stabilizes and the C O P begins to decrease. As the heat supplied to the generator is progressively increased, the flow rates of pumped solution and generated vapor in boiler increase too. Raising the flow rate of the refrigerant vapor in boiler means introducing more liquid ammonia in the evaporator and hence, larger cooling capacity. Further, an augmented flow rate of the pumped solution allows absorbing a larger amount of refrigerant vapor in the absorber, corresponding to an enhancement of the refrigerator performance. This explains the observed ascending trend of C O P and Q˙ E V in Figs. 9 and 10. Increasing the heat supply to generator however gives rise to an opposite effect on performance.

1514 Table 1 Model parameters α, β and γ (*: repeated test)

Fig. 9 Experimental C O P vs. supplied heat to generator (steady state operation)

I. Saâfi et al. Test #

α (h−1 )

β (h−1 )

1*

1.0586

0.4605

2

1.0586

0.4605

3*

0.9456

0.4428

4

0.9099

0.4027

5

0.9456

0.4428

6

0.9099

0.4027

7*

1.0667

0.4862

8

1.0666

0.4862

9

0.9166

0.3555

10

0.8865

0.3992

11*

0.9838

0.4468

12

0.9838

0.4468

13*

0.9026

0.3498

14

1.0579

0.4103

15

1.0579

0.4103

16

1.0579

0.4103

17

0.9549

0.4023

0.7890

18

0.9033

0.3917

0.7889

19*

0.9169

0.3847

0.9812

20

0.9169

0.3847

0.9812

21

1.1142

0.4963

0.9560

22*

1.2240

0.5685

0.9556

23

1.2234

0.5685

0.9556

Mean values

1.0027

0.4355

0.9154

γ (−)

Low Capacity Diffusion Absorption Refrigeration …

1515

Fig. 10 Cooling capacity vs. supplied heat to generator (steady state operation)

In fact, due to the raising temperature in the boiler, more vapor is produced but containing more steam. The purification of ammonia by condensation of the excess water vapor in the rectifier implies wasting more of the supplied energy in the boiler without producing more refrigerant vapor as Fig. 10 shows. Even if more refrigerant vapor is generated, the low value of the overall heat transfer coefficient (U A)int limits the heat supply to the evaporator and hence, the evaporation of more liquid refrigerant. The consequence is the declining refrigerator performance observed in Fig. 9 and the stagnation of the cooling capacity for Q˙ G larger than 43 W. Additional heating at the generator beyond the conditions for maximum C O P are not necessary and should be avoided.

6 Conclusion In this paper, experimental investigations of a low-power commercial refrigerator with a capacity of 25 L and powered by an electrical energy are presented. It is observed that this hermetic, uniform pressure refrigerator exhibits a stable oscillatory behavior for low energy input between 22–37 W. The frequency of the temperature oscillations increases with increasing heating power and the amplitudes reduce. For higher power input, the oscillations completely disappear. To evaluate the performance of the refrigerator transient as well steady-state tests are performed. A simple thermal model is established in order to deduce the thermal exchange characteristics of the machine cabin from the unsteady-state experiments. In particular, the overall heat transfer coefficient between the cabin and the ambient is determined. It was found that. (U A)ext = 0.53 W/K This parameter allows the evaluation of the cooling capacity Q˙ E V of the refrigerator and the coefficient of performance C O P in stable steady-state operation mode.

1516

I. Saâfi et al.

Following optimal performances for ambient air temperatures of 23–25 °C are found Q˙ E V = 7.5W C O P = 0.15

References 1. Von Platen BC, Munters CG (1928) Refrigerator, US Patent 1, 685–764 2. Schmid F, Bierling B, Spindler K (2018) Development of a solar-driven diffusion absorption chiller. Sol Energy 177:483–493 3. Mansouri R, Boukholdaa I, Bourouis M, Bellagi A (2015) Modelling and testing the performance of a commercial ammonia/water absorption chiller using Aspen-Plus platform. Energy 93:2374–2383 4. Pérez-García V, Rodríguez-Muñoz JL, Ramírez-Minguela JJ (2019) Theoretical modeling and experimental validation of a small capacity diffusion-absorption refrigerator. Int J Refrig 104:302–310 5. Chaves FD, Moreira MFS, Cortez MFB (2019) Experimental study and modeling within validation of a diffusion absorption refrigerator. Int J Refrig 101:136–147 6. Ben Jemaa R, Mansouri R., Bellagi A (2017) Experimental characterization and performance study of an ammonia–water–hydrogen refrigerator. Int J Hydrogen Energy 42:8594–8601 7. Rattner S, Garimella S (2016) Low-source-temperature diffusion absorption refrigeration. Experiments and Part II: Model assessment. Int J Refrig 65:312–329 8. Benhmidene A, Hidouri K, Chaouachi B, Gabsi S, Bourouis M (2016) Experimental investigation on the flow behaviour in a bubble pump of diffusion absorption refrigeration systems. Case Stud Thermal Eng 8:1–9 9. Chen J, Kim KJ, Herold KE (1996) Performance enhancement of a diffusion–absorption refrigerator. Int J Refrig 19:208–218 10. Taieb A, Mejbri K, Bellagi A (2016) Theoretical analysis of a diffusion-absorption refrigerator. Int J Hydrogen Energy 41:14293–14301 11. Taieb A, Mejbri K, Bellagi A (2016) Detailed thermodynamic analysis of a diffusion-absorption refrigeration cycle. Energy 115:418–434 12. Freeman J, Najjaran A, Edwards R, Reid M, Hall R, Ramos A, Markides CN (2017) Testing and simulation of a solar diffusion-absorption refrigeration system for low-cost solar cooling in India. ISES Solar World Congress

The Synchronized Electrical Charge Extraction Regulator for Harvesting Energy Using Piezoelectric Materials Youssef El Hmamsy, Chouaib Ennawaoui, Ikrame Najihi, and Abdelowahed Hajjaji

Abstract The aim of this paper is studied the Elements that affect the circuit SECE for piezoelectric energy harvesting and the influences on piezoelectric energy harvesting performance. In addition, the energy harvesting system based on piezoelectric energy plays an important role in the conversion of vibration energy from the environment into electrical energy, which can be used by low-power electronic devices. Concerning the interface circuit, the SECER (Synchronized Electrical Charge Extraction Regulator) circuit is usually required to rectify the alternating current (AC) signal into a direct current (DC) signal. In this paper, the properties of the SECER circuit and the influences on the energy recovery performance are analyzed and presented by power visualization simulation in the case of low load and high source frequency, as well as in the case of high load and low frequency. and whose characteristics can significantly influence the energy harvesting. It can be seen that the harvesting energy has a close relationship with the characteristics of the SECER circuit components. This study discloses the SECE influences on piezoelectric energy harvesting performance. In addition, the results show that the nonlinear SECER technique is 20% more efficient than the standard circuit, in terms of maximum power and bandwidth, for generators characterized by a moderate electromechanical coupling coefficient. Keywords Circuit SECER · Piezoelectric materials · Energy harvesting · LT1764 circuit

1 Introduction Recent advances in microelectronics and ambient energy harvesting now make it possible to envisage the design of completely autonomous electronic systems [1–5]. This possibility, combined with a strong demand for autonomous sensors from the industrial and biomedical sectors [6–8]. Y. El Hmamsy (B) · C. Ennawaoui · I. Najihi · A. Hajjaji Laboratory of Engineering Sciences for Energy (LabSIPE), National School of Applied Sciences EL Jadida, Chouaib Doukkali University, El Jadida, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_138

1517

1518

Y. El Hmamsy et al.

Fig. 1 SECE circuit

Non-linear energy extraction techniques have been developed to simply optimize the energy transfer between a piezoelectric generator and the load to be powered [9–13]. It is then a question of valorizing the extracted energy by associating a circuit allowing the shaping and storage of the mechanical energy converted into electrical energy [14–18]. The common point between these approaches is a nonlinear treatment of the piezoelectric voltage carried out synchronously with the vibration [19–21]. As well as approaches proposed by other research teams [21– 23]. SSHI (Synchronized Switch Harvesting on Inductor), SMFE (Synchronized Magnetic Flux Extraction), OSECE (Optimized Synchronized Electrical Charge Extraction) and FTSECE (Frequency Tuning Synchronized Electrical Charge Extraction). The SECE circuit, shown in Fig. 1, extracts all of the electrical charges generated at each voltage end at the terminals of the piezoelectric element [11]. It improves the power generated in the case of weakly coupled structures [17]. The SECE technique is a proposal to improve the behavior of the nonlinear energy extraction circuits in the case of highly coupled structures. In this paper, we will focus on the simulation of the energy harvesting by the SECER technique based on an integrated circuit LT1764, and the comparison with a simple rectifier bridge. It is important to specify that the performance for an optimal load. The simulation of the simple rectifier could thus give much poorer results than the SECER performance because of the high dependence of the energy harvesting on the electrical load, which implies the necessary use of a SECER circuit is much more tolerant to load variations, which makes these practical performances near the theoretical simulations. This paper is organized as follows. Section 2 introduces the theoretical model of the technique SECE. Section 3 provides a circuit SECER study. Then, Sect. 3 piezoelectric modeling and simulations the circuit SECE. Finally, conclusions from this study are presented in discussions.

The Synchronized Electrical Charge Extraction Regulator …

1519

2 The SECE Technique The energy extracted per cycle in the SECE technique corresponds to the electrostatic energy available on the piezoelectric element, which equals (01), with VM being the voltage at the instant of switching (Fig. 1). Due to the losses in the SECE converter, the energy recovered by switching is given by (02), with γC the efficiency of the converter (considered constant) [11]. 

Welectr ostatique

 max

=

1 C0 VM2 2

1 (W S EC E )max = γC C0 VM2 2

(1) (2)

The calculation of the voltage VM is done considering that the voltage of the piezoelectric element is reduced to zero when the displacement is minimum or maximum. The piezoelectric insert then being in open circuit until the next voltage extremum, VM is therefore expressed according to relation (3). The expression of the energy recovered by switching is then given by (4), and we therefore deduce the power recovered by the SECE, the value of which is given by relation (5). These two quantities are independent of the rectified voltage and the load, and 4γC times greater than the maximum energy or power recovered with the standard technique. VM = 2

α uM C0

(3)

W S EC E = 2γC

α2 2 u C0 M

(4)

P S EC E = 4f0 γC

α2 2 u C0 M

(5)

3 Circuit SECER (Synchronized Electrical Charge Extraction Regulator) 3.1 Piezoelectric Modeling We consider the piezoelectric as an alternating current generator whose amplitude is proportional to the strain rate. The piezoelectric has a significant dielectric constancy that we take into account by the parallelization of a CPZ capacitor. The model is therefore the following.

1520

Y. El Hmamsy et al.

Q = α piezo u I = α piezo

dU = α piezo V dt

(6) (7)

With Q the displaced charge, I the generated current, u the displacement of the piezoelectric membrane, V the speed of the membrane and α electromechanical constant [24]. We consider the piezoelectric as an alternating current generator whose amplitude is proportional to the strain rate. The piezoelectric has a significant dielectric constancy that we take into account by the parallelization of a CPZ capacitor. The model is therefore the following.

3.2 Description of LT1764 Circuit The LT1764 is a low dropout regulator optimized for fast transient response. The device is capable of supplying 3 A of output current with a dropout voltage of 340 mV. Operating quiescent current is 1 mA, dropping to P f cmax , FC will deliver less power and UC more and vice versa. If the SOC is around the desired level, FC supplies the reference power signal value as well as the UC. However, if SOC is less than the desired value (here 0.2), the FC delivers more power than its reference, and and as a result the UC will deliver less power that what is expected. Similarly, if SOC is higher than the desired value (here 0.75), then the FC provides less power.

2.3 Feed Forward Neural Network The power demand of the vehicle is calculated based on four driving cycles. First ones are the Atremis Driving cycles which are based on a statistical study done in Europe within the so called Artemis project. ArtRoad cycle that simulates driving on a rural road and ArtUrban that simulates driving on a rural road were used. The Highway Fuel Economy Driving Schedule (HWFET) was also used. This cycle represents highway driving conditions under 100 mph. Finally, The EPA Urban Dynamometer Driving Schedule (UDDS) which represents city driving conditions. It is used for light duty vehicle testing. This is a certified driving cycle for verifying emissions after a cold start.

Online Energy Management Strategy …

1589

Based on the power demand profiles required for these four driving cycles, the power needed from the FC and the UC are generated by the offline EMS. 70% of these data are used as datasets for the neural network training. 30% is used for model validation. The neural network has three inputs, the first is the load power demand, Second is a binary coefficient to indicate whether it is in motor or generator mode. Finally, the SOC of the UC. As neural networks work best with features that are at a relatively similar scale and close to the normal distribution, and in order to reduce the training time of the neural network. The power features (input power, FC and UC power Targets) have been normalized by subtracting the mean and then scaling to unit variance, which means dividing all values by the standard deviation: x=

x − mean stand deviation

(6)

The outputs of the NN will then be rescaled by multiplying with the same standard deviation and adding the same average used during training. In this article, a multiple layer neural network was applied. This network is used as a general function approximator. It can approach any function with a finite number of discontinuities arbitrarily well, given a sufficient number of neurons in the hidden layer. Its simple structure makes it a good solution for an online EMS, since it can be easily implemented on hardware. For this application, The NN has two hidden layers of 40 and 20 neurons respectively. Hyperbolic tangent sigmoid transfer function (tansig) is used to allow the network to learn nonlinear relationships between input and output vectors. These two layers are then followed by an output layer of linear neurons, purelin transfer function is employed for nonlinear regression. Weights and bias were updated using BP (back-propagation) which is the most powerful method to minimize the performance function [20]. In this paper, Trainlm network training function based on Levenberg-Marquardt optimization is used. It is the fastest backpropagation algorithm in Matlab toolbox. The NN performance was evaluated using the Mean square error. In the next section, the results of the wavelet decomposition coupled with the limit check algorithm are presented. Then, the results of the training and validation of the neural network are provided.

3 Simulation and Results The EMS based on wavelet transform coupled with the check limit algorithm was tested EPA Urban Dynamometer Driving Schedule (UDDS) presented in Fig. 1. The vehicle on which this study was conducted is a Citroen Berlingo. As shown in Fig. 1. this vehicle needs an energy system of about 40 kW to propel it. Considering the maximum power of the fuel cell studied (1.2 kW), we have reduced the scale of the

1590

N. Rifai et al.

Fig. 1 UDDS drive cycle (blue) and power needed for traction of the Citroen Berlingo vehcile

Fig. 2 First plot: Motor Power (red) FC power (black) UC power (Bleu), Second Plot (SOC of UC)

power profile required to 12.5 of the real scale. The offline EMS results are presented in Fig. 2. As can be observed, based on the EMS offline, the power demand is covered, in addition the power delivered by the FC is slow and of low frequency, on the other hand it does not exceed its limits which in this case are 200 W at the lower bound and

Online Energy Management Strategy …

1591

12 kW at the upper bound. Then, for UC it can be clearly noticed that its dynamic is much faster, which makes it possible to efficiently respond to a fast transition of power demand. And finally, the second plot in Fig. 2 represents the SOC of the UC. This one is as desired between 0.2 and 0.75 which proves the efficiency of the EMS in controlling the state of charge of the UC. Based on the results of the four driving cycles ArtUrban, ArtRoad, HWFET and UDDS. 70% of the data is standardized and used for NN training. Figure 3 presents the outputs of NN during training. The comparison between the output of the neural network and the target powers of the FC and UC, which are the outputs of the offline EMS, led to a root mean square error (RMSE) of 0.040. 30% of the data is used for NN validation. Three inputs are applied. The Power demanded by the load which is normalized, SOC of the UC and finally alpha the binary coefficient. At the output of the NN, the powers are rescaled. The result is

Fig. 3. Training Neural network

Fig. 4 Test and validation of the neural network

1592

N. Rifai et al.

shown in Fig. 4. First plot, In blue, the FC power Target output of the Offline EMS and in red, the power proposed by the NN. In the second plot, in blue the Target power UC and in black the power proposed by the NN.As can be noticed the feed forward NN employed is capable of reproducing the work of the wavelet decomposition algorithms with the verification algorithm. The error between the two (Targets and NN outputs) is small and does not influence the global rules of the EMS.

4 Conclusion In this article, we replace a management strategy based on wavelet transform by a feed forward neural network. The mean square error between the results of EMS based on WT and the neural network outputs is of 0.0720 which proves the efficiency of the neural network to predict the power of both energy sources. The neural network used in this paper has a simple architecture which make easily implementable on hardware and suitable for energy management applications in vehicles. The next steps would be to simulate the complete energy storage system, and validate it experimentally.

References 1. Pollet BG, Staffell I, Shang JL (2012) Current status of hybrid, battery and fuel cell electric vehicles: from electrochemistry to market prospects. Electrochimica Acta 84:235–249 2. Djerioui A et al (2019) Energy management strategy of Supercapacitor/Fuel Cell energy storage devices for vehicle applications. Int J Hydrogen Energy 44(41):23416–23428 3. Gao D, Jin Z, Lu Q (2008) Energy management strategy based on fuzzy logic for a fuel cell hybrid bus. J. Power Sources 185(1):311–317 4. Rodatz P, Paganelli G, Sciarretta A, Guzzella L (2005) Optimal power management of an experimental fuel cell/supercapacitor- powered hybrid vehicle. Control Eng. Pract 5. Zhou D, Al-Durra A, Gao F, Ravey A, Matraji I, Godoy Simões M (2017) Neural network. J Power Sources 366, 278–291 6. Zhang R, Tao J, Zhou H (2019) Fuzzy optimal energy management for fuel cell and supercapacitor systems using neural network based driving pattern recognition. IEEE Trans Fuzzy Syst 27(1):45–57 7. Thounthong P, Pierfederici S, Martin JP, Hinaje M, Davat B (2010) Modeling and control of fuel cell/supercapacitor hybrid source based on differential flatness control. IEEE Trans Veh Technol 59(6):2700–2710 8. Oukkacha I, Mamadou BC, Dakyo B (2018) Energy management in electric vehicle based on frequency sharing approach, using fuel cells, lithium batteries and Supercapacitors. France, Paris 9. Masih-Tehrani M, Ha’Iri Yazdi MR, Esfahanian V, Dahmardeh M, Nehzati H (2019) Waveletbased power management for hybrid energy storage system. J Mod Power Syst Clean Energy 7(4):779–790 10. Rifai N, Sabor J, Alaoui C (2021) Energy management strategy of a fuel-cell electric vehicle based on wavelet transform. In: Lecture Notes in Networks and Systems, vol 144, Springer, pp 220–235

Online Energy Management Strategy …

1593

11. MacKie DM, Jahnke JP, Benyamin MS, Sumner JJ (2016) Simple, fast, and accurate methodology for quantitative analysis using Fourier transform infrared spectroscopy, with bio-hybrid fuel cell examples. MethodsX 3:128–138 12. Chen Z, Mi CC, Xu J, Gong X, You C (2014) Energy management for a power-split plug-in hybrid electric vehicle based on dynamic programming and neural networks. IEEE Trans Veh Technol 63(4):1567–1580 13. Ansarey M, Shariat Panahi M, Ziarati H, Mahjoob M (2014) Optimal energy management in a dual-storage fuel-cell hybrid vehicle using multi-dimensional dynamic programming. J. Power Sources 250:359–371 14. Lin WS, Zheng CH (2011) Energy management of a fuel cell/ultracapacitor hybrid power system using an adaptive optimal-control method. J. Power Sources 196(6):3280–3289 15. Hemi H, Ghouili J, Cheriti A (2014) An optimal control solved by Pontryagin’s minimum principle approach for a fuel cell/supercapacitor vehicle. In: Proceedings - 2014 Electrical Power and Energy Conference, EPEC 2014, pp 87–92 16. Ettihir K, Boulon L, Agbossou K (2016) Optimization-based energy management strategy for a fuel cell/battery hybrid power system. Appl. Energy 163:142–153 17. Thounthong P, Chunkag V, Sethakul P, Davat B, Hinaje M (2009) Comparative study of fuel-cell vehicle hybridization with battery or supercapacitor storage device. IEEE Trans Veh Technol 58(8):3892–3904 18. Ibrahim M, Jemei S, Wimmer G, Hissel D (2016) Nonlinear autoregressive neural network in an energy management strategy for battery/ultra-capacitor hybrid electrical vehicles. Electr Power Syst Res 136:262–269 19. Zhang Q, Wang L, Li G, Liu Y (2020) A real-time energy management control strategy for battery and supercapacitor hybrid energy storage systems of pure electric vehicles. J. Energy Storage 31 20. An N, Zhao W, Wang J, Shang D, Zhao E (2013) Using multi-output feedforward neural network with empirical mode decomposition based signal filtering for electricity demand forecasting. Energy 49(1):279–288

The Potential Outcomes of Artificial Intelligence Applied to the Powered Two-Wheel Vehicle: Analytical Review F. Jalti, B. Hajji, and A. Mbarki

Abstract Modern modes of transport are experiencing notable progress in terms of the inclusion of driver assistance systems, which the distribution is however unfair in relation to the criticality of each mode of transport. Indeed, two-wheeled vehicles benefit the least from technological progress despite their vulnerability. With the aim of defining an inventory for our research work, this paper describes the potential of AI (Artificial Intelligence) in driver assistance systems. Through a bibliographic and technological analysis, we examined the state of the art around the PTW (Powered Two-Wheeled) to make the link between the potentialities and the key indicators related to this mode of transport. This study was carried out on numerous articles published on international platforms and journals, distributed between 1979 to 2020, of which 12% include the use of artificial intelligence. The study found that by applying ITS (Intelligent Transport Systems) to motorcycles, road accidents can be reduced and mobility would be more efficient. In addition, this study also suggested a new use of technology by involving AI to improve the management of problems with loss of control, speed, wearing of helmets, visibility and alertness. Keywords Intelligent transport systems (ITS) · Artificial intelligence (AI) · Powered two-wheeled (PTW). Advanced rider assistance systems for powered two-wheeler (ARAS-PTW) · Machine/Deep learning

1 Introduction In recent years, the number of motorized two-wheeler accidents has increased significantly, several causes are singled out for serious motorcycle accidents: Speeding F. Jalti (B) · B. Hajji Laboratory of Renewable Energy, Embedded System and Information Processing, National School of Applied Sciences, Mohammed First University, 60000 Oujda, Morocco e-mail: [email protected] A. Mbarki National School of Applied Sciences, Mohammed First University BP665, 60000 Oujda, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_145

1595

1596

F. Jalti et al.

or unsuitable for the conditions, Loss of control, Wrong turn/maneuver, Wrong estimation of the trajectory or speed of another road user, Negligent behavior or reckless. The first works which had tried to draw up a development framework began in 2009 with the SAFERIDER project [1], then came the first paper which characterizes the Advanced Rider Assistance Systems for Powered Two-Wheeler “ARAS-PTW” [2]. From this point on, research has diversified. Notably in terms of dynamics and stability, if we want to define the operating rules according to a complete mathematical model [3] we must be able to measure the huge number of parameters, so until then the best solution was the use of estimation and observation [4, 5], these conventional methods have shown their limit given the low development which is mainly due to the level of uncertainty, the complexity and the inability of these methods to predict the danger. In this paper, the application of artificial intelligence (AI) has been seen closely. By comparing the capacities that machine learning algorithms offer today with the weak aspects of PTWs, we established an inventory allowing to build a basis for our research work. Indeed, several interesting projects have arisen and in particular by exploiting object recognition by AI: Detection of sufficient space for a safe maneuver [6], Obstacle detection [7], prediction and pattern detection [8]. We observed that the trend has been shifted from the physical to data acquisition performance. Thus, the organization of the paper will be as follows: Sect. 2 presents an opportunities and weaknesses study, Sect. 3 details the state of the art around PTW linked with AI, Sect. 4 opens the horizon on the perspectives by discussion of results and finally Sect. 5 concludes on the results and sets out the research perspectives.

2 Opportunities and Weaknesses Inventory of PTW Vehicle 2.1 Accident Causality Analysis According to the WHO report in 2017 [9]. Although PTW vehicles are important for the mobility of goods and people, they also lead to an increased risk of traffic accidents. The significant number of deaths and injuries associated with this mode of transport can be explained by several factors, including the proliferation of these vehicles and the vulnerability of their users. In Fig. 1, by consulting the statistics relating to fatalities linked to each mode of transport, we can observe, worldwide, the percentage of PTW users having died in a road accident is the second highest after cars [9]. With around 516 million PTWs vehicles registered worldwide in 2013, accounting for 29% of all registered vehicles, the actual number in circulation will only be greater as a significant proportion of vehicles go unrecorded. Adding to this, according to WHO’s Global status report on road safety 2015, that 88% of all registered PTWs vehicles globally in 2013 were in low- and middle-income countries.

The Potential Outcomes of Artificial Intelligence ...

1597

Fig. 1 Distribution of road fatalities according to the mode of transport involved

By linking the factors, multiple studies have been conducted to reveal causalities and correlations in order to identify the most appropriate remedies and actions [10]. Our analysis showed that since the beginning of the 2000s, accident studies have tended to increase. From the batch of papers, we reviewed, 26 scientific articles (representing 22%) have dealt with PTW accidentology. Although, the scientific research has differed on how accidentological aspects have been treated, the common factor remains human error in the estimation of the danger.

2.2 PTW Ecosystem Potentiality Analysis With the increasing popularity of this transport mode and increased casualty levels, new national attention is currently being given to this area. Indeed, it is no coincidence that the number of users is increasing (Fig. 2), but it is rather due to the growth of emerging countries, where we can see the incentive to this mode of transport given its accessibility with a price of purchase and cost of ownership quite low, and also the flexibility to overcome traffic jams. Adding to this that despite the pollution of two motorized wheels being pointed out, PTWs remain a low-polluting alternative if we associate the track occupancy and consumption parameters (close to 1l/100 Km for some models) [11]. In terms of safety, the technological advance of the driving assistance systems initially developed for cars, offers an opportunity for PTWs if we succeed in popularizing these systems at the level of the motorcycle industry. At the same time, the application of artificial intelligence in this field is very promising.

1598

F. Jalti et al.

Fig. 2 The world according to PTW ownership. From www.worldmapper.org

3 State of Art Analysis Given the weaknesses of the PTW ecosystem, the challenges are faced on several levels (public policies, social, economic, R&D, etc.). In terms of scientific research, more than 80% of the batch of papers we reviewed were conducted after 2010, reflecting the growing interest in the subject.

3.1 Intelligent System Characterization Before reviewing the state of the art related to the use of AI in PTWs, it’s necessary to characterize the system made up of the {Driver, Vehicle, Environment}. The rider continuously participates in the acquisition of information relating to the environment as well as his role of Navigation and guidance through which he interacts with road traffic. As for the machine, it mainly participates in the action. In such a system we can assign three roles (Navigation, guidance and stabilization) and we can designate 3 activity levels (Perception, Decision, Action). So, one way to reflect “the level of intelligence” (or the level of automation) of the machine is to position the system on the chart of Fig. 3.

3.2 Scientific Research Trend Around PTW The work around PTW Vehicles was examined, through a classification according to field and theme as well as the focus provided by the research concerned. Concerning the themes, we observe in Fig. 4 that more than 80% of the articles focus on (in order of importance): ITS (35%), driving behavior (19%), Accidentology/safety (16%) and finally modeling (15%).

The Potential Outcomes of Artificial Intelligence ...

1599

Fig. 3 Tasks, Activities and Automation when driving (Godthelp, 1992)

By analyzing the statistics emerging from our bibliographic inventory, we can confirm the particularity of PTWs given that 70% of the work has affected the driver. Indeed through accident studies, driving behavior studies or studies on intelligent systems we address mainly the rider scope. By examining these documents, we noted that the studies, the models, the techniques or the perspectives drawn up tend to reinforce the automation of the system which today is not at the same level as that of the four wheels. The difficulty in achieving the same level of autonomy for automotive systems is due to several factors, the main ones being: The complexity of the dynamics of PTWs, the driver’s direct contact with the environment, the weakness of the development framework in software and hardware term, the cost of development which does not allow generalization on all ranges of the market. However, the popularization of ADAS systems has already crossed borders to reach PTWs, also several AI applications have started to be used in this sector.

3.3 The Application of AI in Relation to ITS Field The field of intelligent transport systems (ITS) brings together all the subjects that are interested in driving assistance, optimizing the use of transport infrastructures, improving safety and security as well as the use of artificial intelligence. Concerning machine learning (ML) techniques, they have been used with success in a wide variety of fields ranging from computer vision tasks, industry, autonomous vehicles, etc. [29]. We now recognize two branches in the field of artificial intelligence which depend on the type of learning, supervised and unsupervised learning [30]. In supervised learning, models are designed to learn based on the labeled training data. These trained models are used to make predictions about invisible data. The idea is to design models capable of learning the patterns, behaviors and characteristics of the data seen and to apply this knowledge while making decisions in the invisible data. In unsupervised learning, models are designed to identify relationships and

F. Jalti et al. QTY of PAPERS

1600 60 50 40 30 20 10 0

DOMAINE

Fig. 4 Scope Distribution of the papers around PTWs

Fig. 5 Trend of papers interested on ITS and AI for PTW - Qty of papers per year

correlations within unlabeled data. Depending on the level of precision desired, various ML algorithms are available to perform prediction tasks. we can roughly consider such an algorithm a learning function f which maps the input variables X (x1, x2…, xn) to output variables Y (y1, y2…, yn). Y = f (X)

(1)

Since the shape of the function is usually unknown, the task is to evaluate different ML algorithms to determine which one is best to approximate an underlying function. The learning model differs depending on whether it learns from a fixed set of parameters, regardless of the amount of data fed, so we will call it parametric ML. Or if it doesn’t make strong assumptions about the shape of the mapping function, in this case it is a nonparametric ML model. Parametric models are simple, quick to learn, and work well even if the data is not perfect. However, they tend to be unsuitable and heavily constrained to a specific shape. On the other hand, nonparametric techniques can adapt to many functional forms and can lead to more efficient prediction models. They require a large amount of data to estimate the underlying mapping function, take longer to practice, and can lead to overfitting. Besides, the trend on the importance given by scientific research to Artificial intelligence in the riding assistance for PTWs is at least recent as can be seen in graph of Fig. 5. In fact, before 2000 we have almost no project interested in the ITS related to PTWs. But since 2010 we have witnessed the increase in works that have focused on intelligent systems applied on PTWs. Also, we had to wait until 2015

The Potential Outcomes of Artificial Intelligence ...

1601

to notice a remarkable shift towards AI with 14 articles out of 17 that address the concept of AI. In our analysis, on the 57 articles concerning ITS, we classified our batch according to the affected area in 29 groupings. Where it was found that the percentage occupied by artificial intelligence represents 28% distributed according to the table below (Table 1):

3.4 Technology Advancement Review The technological advance in terms of intelligent systems that equip PTWs is naturally led by the manufacturing giants. Since the associated intellectual property is required, we will cite the main areas of development that the sector knows: Table 1 AI and Two-Wheeled vehicles - inventory of papers Title

Year

Scope

Automatic Braking System for Two-Wheeler with Object Detection and Depth Perception [14]

2020

Autonomous braking

Learning a Curve Guardian for Motorcycles [16]

2019

Curve characterization

Estimation of Mental Workload during Motorcycle Operation [18]

2015

Driver behavior

Machine Learning Algorithm in Two wheelers fuel Prediction [17]

2019

Fuel consumption

Helmet Use Detection of Tracked Motorcycles Using CNN-Based Multi-Task Learning [15]

2019

Helmet use

Automated Vehicle Classification with Image Processing and Computational Intelligence [19]

2017

Image recognition

Detecting motorcycle helmet use with deep learning [20]

2020

Image recognition

Performance evaluation of HMM and neural network in motorbike fault detection system [12]

2011

Maintenance

Applying knowledge structure to the usable fault diagnosis assistance system: A case study of motorcycle maintenance in Taiwan [21]

2005

Maintenance

Road Targets Recognition Based on Deep Learning and Micro-Doppler Features [7]

2018

Obstacle detection

Powered Two-Wheeler Riding Pattern Recognition Using a Machine-Learning Framework [8]

2015

Pattern recognition

Riding patterns recognition for Powered two-wheelers users’ behaviors analysis

2013

Pattern recognition

Automated Traffic Monitoring Using Image Vision [13]

2018

Traffic regulation

DeepWiTraffic_Low Cost WiFi-Based Traffic Monitoring System Using Deep Learning [22]

2019

Traffic regulation

1602

F. Jalti et al.

a) Instrument Development and Safety: technological development has allowed motorized two-wheelers to evolve in terms of performance and safety, namely: – Electronic injection: equipping a good part of the fleet, it allows consumption of less than one liter per 100 km for certain models and better acceleration performance [23] – Automatic or double-clutch transmissions: If the continuously variable model (CVT), is still widely used, some manufacturers have developed much more complex systems, these allow a shift automated reports managed by electronics [24]. – Ride-by-Wire (TCS, driving modes, anti-wheeling): The electronic accelerator (Ride-by-Wire) constitutes one of the main advances of recent years. Appeared around 2000s, this system replaces traditional mechanical cables with an electronic interface between the handle and the motor. It’s managed by an Electronic Control Unit (ECU) [25]. – Braking: In the EU, the obligation of ABS for cylinder capacity >125 cm3 is legislated (European regulation COM 2010/542). However, this is not the case for smaller displacements and is not necessarily applicable outside Europe. – Helmets: After the initial development of the modular helmet in 1980, it is in terms of comfort that the evolution has been felt the most. Today, expectations are raised at the level of the arrival of head-up display technologies which will allow speed and navigation information to be found directly on its visor. – Protective equipment: Anti-torsion and anti-reversal clothes had benefit of the advances of materials. e.g.: the airbag vest but accessible only for high-end vehicles [26]. b) ARAS and AI Applications: The development of ARAS has always been a real challenge for actors in the field. Given the complexity of the dynamics of this type of vehicle, the cost and integration constraints. In other hand, the applications AI technics are mainly seen at the level of image processing, this is partly explained by the opportunity offered by Databases to constitute a repository for the machine. In this context we have listed the following scopes: – Connectivity: today is manifested by the connection that certain systems offer with smart phones. These systems are accessible for medium ranges of vehicles; besides, projects regarding inter-vehicle communication are in progress to ensure communication between vehicles to signal the approach of a vehicle, using wifi or 5G waves [22]. – Stability control: developed primarily for high-end vehicles, the MSC is practically the best-known system in this field. However, a compressed gas propulsion system is being developed to overcome the fall in a maximum tilt situation [27]. – Blind spot warning: still for a relatively small proportion of the motorcycle fleet, retro-vision blind spot assistance is available today. – The electric motorcycle: several manufacturers have embarked on this path with technological variability, however what characterizes the electric motorcycle is its low noise level, which contributes to the sound comfort of the rider

The Potential Outcomes of Artificial Intelligence ...

1603

– Co-pilot: This project under development consists of driving assistance through dialogue with the machine. – Motobot: Promising example in the attainment of a level of skills by the machine equal to that of the man. Base on AI this machine succeeded the imitation, by a rate superior to 70%, of the Number 1 of pilots in 2018 [28]

4 Discussion The analysis of the state of the art around the PTW aims to constitute an image of the current situation of the ecosystem as a whole and in relation to the potential of AI. The accident side for two-wheeled vehicles was addressed to identify areas of weakness for this mode of transport. After the picture is completed by the synthesis of the various intelligent transport systems, the objective is to gauge the level of technological advance experienced by this transport sector. Our system being a triplet {Driver, Vehicle, Environment}, the driver influences the dynamics of the motorcycle by his actions (torque, roll and angle of inclination), the interaction with the infrastructure is manifested by the tire/road contact. However, the driver’s interaction with the environment is amplified [2], hence the need to improve the level of perception of the system by acting on the reliability and the quality of the information captured (which until today is mainly captured by the driver). Nowadays, ITS for PTWs are only accessible for a small percentage of the fleet, even for an instrument like ABS which must be fitted to practically all cars manufactured, the latter is not necessarily accessible for most motorcycles put on the market. By examining topics addressing AI, although we can require an investment in graphics processing, but the use of image processing associated with ML made it possible to achieve a fairly high performance (latency 1).

3.2 Design of a Tri-State Inverter Many logic gates require a tri-state output: high, low, and high impedance. The high impedance state is also called the high Z state and is used to connect many gates outputs to a single line, such as a data bus or address line. A potential conflict would exist if more than one door output tried to control the bus line simultaneously. A controllable high impedance state circuit solves this problem [9]. Figure 5.a shows the schematic of a tri-state inverter proposed in our architecture. When the level of the EN input is high (EN = 1), the component behaves like a typical inverter, its output always gives a level complementary to that applied to the “IN” input. On the other hand, when the level of the EN input is low (EN = 0), the “out” output goes to the state called “high impedance”. Figure 5.b shows the simulation obtained for the three-state inverter such that this result appears that the output signal “out” (the third waveform from top) is the inverse of the input signal “IN” (the second waveform from top). When the transmission gate ‘EN = 0’ (the first waveform from top) the output gives a state called high impedance.

3.3 D-Latch Design A D-Latch primarily generates a delay to the input data. It is based on two tri-state inverters. When CLK is high, the first tri-state inverter is active and the second is blocked so that our output Q takes the value of the input D, which is said to be transparent. If the clock CLK is low the first tri-state changeover switch is blocked

Low-Power High-Speed On-Chip 5 to 1 Serializer in 180 nm Technology

1635

C1 D1

D flip-flop

C0 C2 D2

D flip-flop

C0 C3 D3

out D flip-flop

C0 C4 D4

D flip-flop

C0 C0 D0

D flip-flop

D-Latch

C0

D flip-flop

C0

D flip-flop

D flip-flop

D flip-flop C4

C3

D flip-flop C2

C1

CLK

Fig. 4 A 5:1 serializer proposed circuit

and the second one is active then the output Q, in this case, takes the previous state. Figure 6.a shows the cadence virtuoso implementation of a D-Latch based on two tri-state [10]. Figure 6.b verifies the logical behavior of D-latch with 0.18 µm technology. We notice that if the clock “CLK” (the first waveform from top) takes the value “1” the output “OUT” (the third waveform from top) takes the value of input “D” (the second waveform from top) and if the clock is low “0” the output is memorizing the chairman value.

1636

A. Menssouri et al.

VDD

IN

OUT EN

(a)

(b) Fig. 5 A schematic (a) and simulation (b) of tri-state inverter

3.4 D Flip-Flop Design D flip-flops are also called “delay flip-flops” or “data flip-flops”. They are used to store 1-bit binary data. D flip-flops are one of the most commonly used flip-flops as essential memory and zero-order elements.

Low-Power High-Speed On-Chip 5 to 1 Serializer in 180 nm Technology

1637

CLK OUT

D CLK D-Latch (a) b

(b) Fig. 6 A schematic (a) and simulation (b) of a D-Latch based on two tri-state inverters

Our D flip-flop used are designed by connecting two series D-latches. When the clock signal is high (rising edge) and if the D input is high, then the output will be a delayed copy of the input signal, and it will also be high, and if the D input is low, then the output will become small. If we do not apply any clock input to the D flip-flop or during the falling edge of the clock signal, there will be no change in the output. It will keep its previous value at output Q. The output Q, therefore, follows the input D in the presence of a clock signal [11]. Figure 7.a shows a schematic of the D flip-flop that responds to the clock on its rising edge.

1638

A. Menssouri et al.

CLK

D

D-Latch

D-Latch

OUT

D flip-flop (a)

(b) Fig. 7 A schematic (a) and simulation (b) of a D flip-flop based on two D-latches

Figure 7.b shows the analog simulation waveform of flip-flop D. This analog simulation shows the output voltage “OUT” (the third waveform from top), the input voltage “D” (the second waveform from top) and clock voltage “CLK” (the first waveform from top) as a function of time. If the clock voltage on the rising edge, the output voltage copies the value of the input voltage otherwise the output voltage stores the previous value.

3.5 Operation of the Proposed Serializer Our proposed serializer shown in Fig. 8 was designed under Cadence virtuoso. It consists of ten D flip-flop, five parallel tri-state inverters and a D-latch. The serialization is done by selection signals generated by a register with ST and RST to rest

Low-Power High-Speed On-Chip 5 to 1 Serializer in 180 nm Technology

1639

Fig. 8 The proposed serializer implemented under cadence

circuit, one serial input and parallel circularly shifted outputs. Thus, the first output of this register is used as the clock signal of the D flip-flop which shift the parallel data to the parallel tri-state inverters with this high-speed clock signal. In turn, the parallel tri-state inverters take these shifted data for serialization. The addition of a D-latch is essential because the data obtained at the output of the D flip-flop is shifted by small delays which results in the crosstalk of the data. However, the D-latch allows generating a signal with a large delay to overcome the crosstalk problem. Figure 9 shows simulation result of the serializer block (Tables 1 and 2).

Fig. 9 Output of the proposed 5:1 serializer

1640 Table 1 Design specifications for proposed 5:1 serializer

Table 2 Performances comparison between a previous and this work

A. Menssouri et al. Parameter

Specification

Unit

Supply voltage

1.8

V

Data bus width

5

Bit

Clock frequency

160

MHz

Performance

[6]

[5]

This work

CMOS technology

65 nm

180 nm

180 nm

Supply voltage (V)

1.2

1.8

1.8

Type of serializer

4:1

8:1

5:1

Power consumption (mW)

80

1216

2.02

4 Conclusion This work presents a 5:1 serializer implemented in CMOS 180 nm technology is designed, created, and simulated using Cadence Virtuoso, in this article we discussed the reasons why we switched to use serial buses instead of parallel buses. The result obtained can confirm that it worked well, also the comparison of our serializer power consumption, which almost 2.02 mw with the previous work shows the efficiency of this serializer.

References 1. Nivedita J, Radheshyam G (2015) Design of a new serializer and deserializer architecture for on-chip SerDes transceivers. Sci Res 06(03):81 2. Jerry Y (2015) System-level design, simulation and measurement for high-speed data links, pp 1–3 3. Abdallah A, Mohamed A (2016) High speed serial link design (SERDES) introduction, architecture, and application. academia.edu 4. Mohammed H, Maher A, Fawnizu A, Israel Y (2012) Design and FPGA implementation of PLL-based quarter-rate clock and data recovery circuit. In: 4th international conference on intelligent and advanced systems (ICIAS2012) 5. Vinod K, Shalini C, Monika G (2016) Design of high speed serializer for interchip data communication with phase frequency detector. Int J Eng Res Appl 6(5):30–40 6. Huang K, Deng L, Ziqiang W, Xuqiang Z, Fule L, Chun Z, Zhihua W (2015) A 190mW 40Gbps SerDes transmitter and receiver chipset in 65nm CMOS technology. IEEE 7. Tongsung K (2016) High-speed low-power transmitter using tree-type serializer in 28-nm CMOS 8. Arunthathi G, Umamaheswari K, Vijeyakumar K (2018) Design of CMOS serialiser. J Netw Commun Emerg Technol (JNCET) 8(6) 9. Jaume S, Charles F (2004) CMOS electronics: how it works, how it fails. IEEE Solid-State Circuit Society Newsletter, Canada

Low-Power High-Speed On-Chip 5 to 1 Serializer in 180 nm Technology

1641

10. Kiran K, Fazal N (2017) Design of low voltage D-flip flop using MOS current mode logic (MCML) For high frequency applications with EDA tool. IOSR J VLSI Signal Process (IOSRJVSP) 7(4):9–14 11. Casper L, Sorin C, Stamatis V (2004) Single electron encoded latches and flip-flops. IEEE Trans Nanotechnol 3(2):237–248

Computing and Analyzing Through Silicon Via (TSV) Noise Coupling in 3D-IC Structures Khaoula Ait Belaid , Hassan Belahrach , Hassan Ayad, and Fatima Ez-zaki

Abstract In three-dimensional structures based on Through Silicon Via (TSV), an important design consideration is coupling noise between TSVs in addition to TSV and Active Circuits. Indeed, it is very important to estimate the mathematical form in the design of reliable 3D-structures. This paper proposes a technique to compute the TSV-TSV noise coupling in addition to the TSV-active circuit noise coupling. First both models are presented, then, the used Finite Difference Time Domain (FDTD) method is explained in detail for TSV-TSV noise, TSV-Active circuit noise, and TSVTSV noise with interconnect line. To validate the proposed mathematical models, Matlab and Pspice simulation tools are used. The results given by the FDTD method and Pspice tool are presented. These results show that the calculation of the FDTD method and Pspice are in a good agreement. Keywords 3D-ICs · TSV-TSV noise coupling · TSV-active circuit noise coupling · Interconnect line · FDTD

1 Introduction In the current and next decade, a major trend of microelectronics is to grow the integration density by utilizing three-dimensional (3-D) integration based on packages. Presently, 3D structures are achieved by stacking several dies of integrated circuits (IC), all these dies communicate with other sub-modules via vertical 3-D interconnections. Hence, 3D interconnection is a good solution that helps 3D integration to achieve the desired performance [1]. Higher power consumption and increased delay caused by the large resistance and capacitance of the wires are the main problems

K. Ait Belaid (B) · H. Belahrach · H. Ayad · F. Ez-zaki Department of Applied Physics, Electrical Systems, Energetic Efficiency and Telecommunications Laboratory, Cady Ayyad University, Marrakesh, Morocco H. Belahrach Electrical Engineering Department, Royal School of Aeronautics (ERA), Marrakesh, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_149

1643

1644

K. Ait Belaid et al.

that prevent the improvement of the integrated circuits quality [2, 3]. Indeed, 3Dintegration based on TSVs is a good solution for great integration permitting higher system speed and lower power consumption [4]. 3-D integrated architecture based on TSVs is an almost new technique and widely used. But these architectures have some difficulties in their modeling and analysis. Therefore, a precise model of TSV is crucial to improve 3D design performance. Several papers have proposed models of TSV interconnections embedded in a substrate. In [5, 6], authors proposed a technique based on RF characterizations and simulations, which conduct to an analytical model based on frequency and comprising MOS effect of high aspect ratio of TSV. Others in [7] gave an equivalent lumped element model for different multi-TSV structures and introduce closed form equations for resistive, inductive and capacitive coupling structures. The closed form equations depend of physical dimensions and material properties. An efficient method was detailed in [8] to model TSV interconnections. This procedure occurs an equivalent network parameter which covers the combined effect of silicon substrate, insulator, and conductors. The method uses Maxwell’s equation in integral form. Another equivalent circuit model of the chipto-chip vertical via, used in high frequency, and based on physical configuration of the structure was presented in [9, 10]. The parameters of the model were extracted and analyzed using S-parameters measurement plying a vector network analyzer up to 20 GHz range. One of the problems found in the modeling of 3-D technology is the modeling of the substrate in high frequencies. Because of substrate distribution nature, it cannot be converted into a dense analytical model. In general, the substrate coupling models could be extracted from a full 3-D numerical simulation, based on an appropriate discretization such as Maxwell’ equations [11]. In [12, 13], the box integration could be used to derive a distributed RC network of the substrate. In this technique, an equivalent circuit representation of the substrate is extracted using a 3-D rectangular RC mesh. In [14, 15], to solve the obtained Maxwell’s equations the Boundary Element Method is applied as an alternate approach. In relation to the topic, authors in [16–18] proposed a TSV-TSV, and TSV-Active circuit noise coupling models. Both of these noise coupling paths models were extracted and analyzed using a 3-D transmission line matrix approach. A model for coupling noise that takes into account substrate contacts between TSV in bulk CMOS technologies is proposed in [19]. The suggested model is dense but has reasonable accuracy for the compact substrate contacts. To study 3-D integrated circuit structures, based on TSVs, a computation of noise coupling is required [20–23]. Hence, this work consists to compute this noise in time-domain. First, both models of TSVTSV noise coupling and TSV-active circuit noise coupling are presented. Then, a method to calculate these coupling noises in the time-domain is given. The technique is the FDTD-1D. The rest of this paper is organized as follows. The TSV-TSV and TSV-active circuit noise coupling models are presented in Sect. 2. Then, the computing technique is explained in the same section. The results and simulations are presented and examined in Sect. 3. The conclusions are drawn in the fourth section.

Computing and Analyzing Through Silicon Via (TSV) Noise Coupling …

1645

2 Modeling and Computing TSV-TSV and TSV-Active Circuit Coupling Noise in the Time-Domain Using FDTD In general, the TSV is surrounded by an insulation layer. Although, this insulation layer cannot avoid the appearance of coupling noise in high frequencies. Signal TSV might be coupled to the silicon substrate, due to the presence of capacitance among TSV metal. The noise in 3-D ICs structures can be coupled through different paths: TSV to substrate, and TSV to another TSV. Both coupling paths are presented in this second part of our work.

2.1 Noise Coupling Between Two TSVs The 3-D ICs contain several TSVs; therefore, TSV-TSV coupling noise is a notable problem. To compute the TSV-TSV coupling noise, a basic structure was used. This structure contains two signal TSVs and two ground TSVs, that are linked by a ground line, as depicted in Fig. 1 [16]. The TSV-TSV coupling noise might be presented as an RLGC equivalent circuit as explained in [17]. Indeed, the structure in Fig. 1 can be presented by the lumped circuit given in Fig. 2. In this figure, the TSVs are represented by Ctsv-total, and the substrate among the TSVs is presented by a capacitance and resistance, except that between two ground TSVs. The ground TSVs are linked using a metal line. The lumped circuit model in Fig. 2 could be further reduced to the simplified equivalent circuit model in Fig. 3. This simplified circuit contains only three essential elements: substrate capacitance, substrate resistance, and the equivalent TSV capacitance. This equivalent circuit was studied with several terminations in Sect. 3.

Fig. 1 Coupling noise between two TSVs basic structure

1646

K. Ait Belaid et al.

Fig. 2 The equivalent lumped circuit model for coupling noise between two TSVs

Fig. 3 The reduced equivalent lumped circuit model for coupling noise between two TSVs

2.2 TSV-Active Circuit Coupling Noise In 3D-ICs, different silicon chips; with several MOS; are stacked using TSVs. Hence, the coupling noise among the active circuit and the TSV must be considered and modeled. Some difficulties appear, when modeling the TSV-active circuit noise coupling. An active circuit is composed of many CMOS devices and interconnections. Each CMOS device is divided into substrate contacts well contacts, n-well, deep n-well and gate metal. In this case, coupling between the substrate contact and TSV is significant, due to the direct connection via the bulk silicon substrate. Indeed, a substrate contact can be thought as an active circuit. Therefore, coupling noise among active circuit and the TSV might be modeled as the coupling among the contact and the TSV. TSV-active circuit coupling noise structure is depicted in Fig. 4. In similar manner with the coupling noise between two TSVs, the basic structure in Fig. 4 can be modeled by a lumped circuit model given by Fig. 5, then, reduced to the simplified circuit in Fig. 6, which consists of three elements: the substrate capacitance, the substrate resistance, and the total equivalent TSV capacitance [17].

Computing and Analyzing Through Silicon Via (TSV) Noise Coupling …

1647

Fig. 4 TSV-Active circuit coupling noise basic structure

Fig. 5 The equivalent lumped circuit model for TSV-Active circuit coupling noise

Fig. 6 The reduced equivalent lumped circuit model for TSV-Active circuit coupling noise

2.3 TSV Noise Coupling Using FDTD In general, TSVs are terminated by the Input/output drivers that must be considered. Consequently, the circuit in Fig. 3 becomes as illustrated in Fig. 7. The technique used to compute the TSV coupling noise is the FDTD method. By observing Fig. 7 Eqs. (1)–(9) could be written.

1648

K. Ait Belaid et al.

Fig. 7 The equivalent lumped circuit model for coupling noise between two TSVs with I/O drivers

i(t) = i 1 (t) + i 2 (t)

(1)

i 2 (t) = i 21 (t) + i 22 (t) = i 3 (t) + i 4 (t)

(2)

d V1 (t) dt

(3)

V1 (t) R3

(4)

d V2 (t) dt

(5)

V (t) R2

(6)

i 3 (t) = C4 i 4 (t) =

i 1 (t) = C2 i 21 (t) =

V in(t) = R1 i(t) + V2 (t)

(7)

V2 (t) = 2Vc1 (t) + V (t) + V1 (t)

(8)

1 V (t) = C3

 i 22 (t)dt

(9)

Using Eqs. (1)–(9) and replacing each other, then, applying the FDTD-1D method, Eqs. (10)–(12) are found. 1 C2 Vin (n) = Rt (V2 (n) − V2 (n − 1)) R1 C 4 + t (V1 (n) − V1 (n − 1)) + RR13 V1 (n) + V2 (n)

(10)

V2 (n) = V2 (n − 1) + AV1 (n) − BV1 (n − 1) + V (n) − V (n − 1)

(11)

Computing and Analyzing Through Silicon Via (TSV) Noise Coupling …



V (n − 1) + DV1 (n) V (n) = C − DV1 (n − 1) + E V 1 (n)

1649

 (12)

Replacing Eqs. (12) and (10) in Eq. (11), the TSV-TSV noise coupling V1 is obtained as Eq. (13). 

− Vin (n) + aV2 (n − 1) V1 (n) = −H + bV1 (n − 1) + cV (n − 1)

 (13)

Where: t is the sampling step which must be chosen carefully, A, B, C, D, E, H, a, b, and c depend on the circuit parameters. In the similar manner, the TSV-active circuit noise coupling in Fig. 6 was calculated. In this work, other circuit was studied. This circuit consists to add an interconnect line next to signal TSV2. The interconnect line is represented by its characteristic impedance Rc, and propagation delay τ. The equivalent lumped circuit of the novel structure is depicted in Fig. 8, where: E i (t) = V1 (t − τ ) + Rc i 4 (0, t − τ )

(14)

Er (t) = Vc4 (t − τ ) − Rc i 4 (l, t − τ )

(15)

Using Eqs. (1)–(9) in addition to (14)–(17) and applying the FDTD-1D method the noise Vc4 (n) is found as Eq. (18). Vc4 (t) = −Rc i 4 (l, t) + E i (t)

(16)

D B C Vin (n) + Vc1 (n − 1) + Vc4 (n − m) A A A E F G + Vc4 (n − m − 1) + Vc2 (n − 1) − V (n − 1) A A A

(17)

V1 (n) =

Fig. 8 The equivalent lumped circuit model for TSV-TSV coupling noise with interconnect line

1650

K. Ait Belaid et al.

Vc4 (n) = −H Vc4 (n − 1) + I V (n − m) − J V (n − m − 1) + K V1 (n − 1) (18) With: l is the length line, m represents the numerical delay, A, B, C, D, E, F, G, H, I, J and K according to the circuit parameters.

3 Results and Discussions In this section, the proposed method (FDTD) is verified using Matlab and Pspice simulation tools. The objective is to realize the circuits of the TSV coupling on Pspice and to program the equations found by the FDTD on Matlab. The proposed FDTD method is based on discretization, indeed a good choice of the discretization step is necessary. The circuit’s parameters used during the simulations are presented by Tables 1 and 2. A trapezoidal signal switching source from 0 to 1.8 V with rising/falling time of 50 ps at frequency 1 GHz was used. The simulated waveforms of each studied circuit are depicted in Fig. 9, Fig. 10 and Fig. 11. Based on the results, one can see that the proposed method is in good agreement with Pspice. Hence, the proposed method is valid, and it can be used for the studied technology. Observing Fig. 9 and Fig. 10 respectively, the peak-to-peak coupling noise in the first case is about 0.15 V. For the second case, the peak-to-peak coupling noise is about 0.045 V. These results show that the noise coupling of both paths, the coupling noise between two TSVs and TSV-Active circuit coupling noise, are significant, must be considered, and reduced. Observing Fig. 11, the peak-to-peak coupling noise is about 0.15 V. The shapes in addition to the peak-to-peak coupling noise are similar to those presented in case 1. The difference is in the disturbances added by the interconnect line. The results show Table 1 Lumped circuit elements of coupling noise between two TSVs

Table 2 Lumped circuit elements of coupling noise between and an active

Component

Value

Ctsv-equi = C1

201.3 fF

Rsub-equi = R2

928.5 

Csub-equiv = C3

11.2 fF

R3

50 

C2

10 fF

C4

10 fF

l

1 mm

Component

Value

Ctsv-equiv

817.5 fF

Rsub-equiv

879.5 

Csub-equiv

12 fF

Computing and Analyzing Through Silicon Via (TSV) Noise Coupling … Fig. 9 TSV-TSV coupling noise

1651

0.1

Noise Coupling (V)

FDTD Pspice 0.05

0

-0.05

-0.1

0

0.2

0.4

0.6

0.8

1 -9

Time (s)

x 10

0.03

Fig. 10 TSV-Active circuit coupling noise

FDTD Pspice

Noise coupling (V)

0.02

0.01

0

-0.01

-0.02

0

0.2

0.4

0.6

Fig. 11 TSV-TSV noise coupling with interconnect line

1

0.8

-9

Time (s)

x 10

0.15 FDTD Pspice

Noise coupling (V)

0.1 0.05 0 -0.05 -0.1 -0.15 -0.2

0

0.2

0.4

0.6

Time (s)

0.8

1 -9

x 10

1652

K. Ait Belaid et al.

that the interconnect line influences the noise coupling by adding some disturbances as depicted in Fig. 11. The interconnect line can also add reflections that can threaten the normal functioning of the circuits. The method proposed in this work shows good results similar to the one proposed and detailed in [20]. This is why it can be adopted for the modeling of TSV couplings in the time domain.

4 Conclusions A lot of papers in the literature treat TSV noise in the frequency domain, while, noise coupling modeling is more important in the time domain. Modeling in the time domain is more significant and helps to understand the effects of the noise. Hence, for a good design and construction of 3-D structures based on TSVs, modeling methods in the time domain must be proposed. In this paper, a technique to model the timedomain coupling noise in 3D-IC structures is explained in detail. The proposed method, FDTD-1D, was verified and validated using Matlab and Pspice tools. Then, a time domain analysis was done for all studied circuits. Based on the found results, it could be seen that the noise in all studied cases exceeds 40 mV, which means that TSV-TSV coupling noise and TSV-Active circuit, are significant and must be reduced, and that the interconnect lines influence the shape of waveforms simulated.

References 1. Han K (2009) Electromagnetic modeling of interconnection in three-dimensional integration. School of Electrical and Computer Engineering Georgia Institute of Technology 2. Erasekera R, Grange M, Pamunuwa D, Tenhunen H, Zheng L (2009) Compact modeling of through-silicon vias (TSVs) in three dimensional (3-D) integrated circuit. In: Proceedings of IEEE international conference on 3D system integration (3D IC), San Francico, USA 3. Kim D, Mukhopadhyay S, Lim S (2009) TSV aware interconnect length and power prediction for 3D stacked ICs. In: IEEE international interconnect technology conference, pp 26–28 4. Salah K (2015) A TSV to TSV, A TSV to metal interconnects, and A TSV to active device coupling capacitance: analysis and recommendation. In: 10th international conference on design & technology systems in nanoscale era, pp 1–2 5. Cadix L, Fuchs C, Rousseau M, Leduc P, Chaabouni H, Thuaire A, Sillon N (2010) Integration and frequency dependent parametric modeling of through silicon via involved in high density 3D chip stacking. ECS Trans 33(12):1–21 6. Bandyopadhyay T, Han KJ, Chung D, Chatterjee R, Swaminathan M, Tummala R (2011) Rigorous electrical modeling of through silicon vias (TSVs) with MOS capacitance effects. IEEE Trans Compon Packag Manuf Technol 1(6):893–903 7. Salah K, Ragai H, Ismail Y, El Rouby A (2011) Equivalent lumped element models for various n-port through silicon vias networks. In: 16th Asia and South Pacific design automation conference. IEEE, pp 176–181 8. Han KJ, Swaminathan M, Bandyopadhyay T (2010) Electromagnetic modeling of throughsilicon via (TSV) interconnections using cylindrical model basis functions. IEEE Trans Adv Packag 33(4):804–817

Computing and Analyzing Through Silicon Via (TSV) Noise Coupling …

1653

9. Ryu C, Chung D, Lee J, Lee K, Oh T, Kim J (2005) High frequency electrical circuit model of chip-to-chip vertical via interconnection for 3-D chip stacking package. In: 14th topical meeting on electrical performance of electronic packaging. IEEE, pp 151–154 10. Qu C, Ding R, Zhu Z (2017) High-frequency electrical modeling and characterization of differential TSVs for 3-D integration applications. IEEE Microw Wirel Compon Lett 27(8):721–723 11. Afzali-Kusha A, Nagata M, Verghese NK, Allstot DJ (2006) Substrate noise coupling in SoC design: modeling, avoidance, and validation. Proc IEEE 94(12):2109–2138 12. Kerns KJ, Wemple IL, Yang AT (1995) Stable and efficient reduction of substrate model networks using congruence transforms. In: Proceedings of international conference on computer aided design. IEEE, pp 207–214 13. Verghese N, Allstot DJ, Masui S (1993) Rapid simulation of substrate coupling effects in mixed-mode ICs. In: Proceedings of custom integrated circuits conference. IEEE, p 18.3.1– 18.3.4 14. Sedes T (1993) Substrate resistance extraction for physics-based layout verification. In: Proceeding of workshop circuits systems signal. IEEE/PRORISC, pp 101–106 15. Costa JP, Chou M, Silveria LM (1999) Efficient techniques for accurate modeling and simulation of substrate coupling in mixed-signal IC’s. IEEE Trans Comput-Aided Des Integr Circuits Syst 18(5):597–607 16. Lee M, Pa JS, Kim J (2014) Electrical design of through silicon via. Springer, Heidelberg 17. Cho J (2011) Modeling and analysis of TSV noise coupling and suppressing using a guard ring. IEEE Trans Compon Packag Manuf Technol 1:220–233 18. Lim J (2018) Modeling and analysis of TSV noise coupling effects on RFLC-VCO and shielding structures in 3D-IC. IEEE Trans Electromagn Compat 60:1–9 19. Watanabe M, Karel R, Niioka N, Kobayashi T, Fukase MA, Imai M, Kurokawa A (2014) Effect of substrate contacts on reducing crosstalk noise between TSVs. In: IEEE Asia Pacific conference on circuits and systems. IEEE, pp 763–766 20. Ait Belaid K, Belahrach H, Ayad H (2019) Numerical Laplace inversion method for throughsilicon via (TSV) noise coupling in 3D-IC design. Electronics 8(9):1010 21. Xu C, Kourkoulos V, Suaya R, Banerjee K (2011) A fully analytical model for the series impedance of TSV with consideration of substrate effects and coupling with horizontal interconnects. IEEE Trans Electron Devices 58:3529–3540 22. Attarzadeh H, Lim SK, Ytterdal T (2016) Design and analysis of a stochastic flash analogto-digital converter in 3D-IC technology for integration with ultrasound transducer array. Microelectron J 48:39–49 23. Beanato G, Gharibdoust K, Cevrero A, De Micheli G, Leblebici Y (2016) Design and analysis of jitter-aware low-power and high-speed TSV link for 3D-ICs. Microelectron J 48:50–59

Design of a CMOS Bandgap Reference Voltage Using the OP AMP in 180 nm Process Ahmed Rahali , Karim El Khadiri , Zakia Lakhliai, Hassan Qjidaa , and Ahmed Tahiri

Abstract This article proposes the implementation and design of a first-order CMOS Bandgap reference using an operational amplifier with negative feedback to improve the power supply rejection ratio (PSRR) and reduce the temperature coefficient (TC). The circuit is designed in 180 nm CMOS process technology and provides a reference output voltage of 1.2 V over an extended temperature range from −40 °C to 120 °C with a measured temperature coefficient of 54 ppm/°C. The BGR chip uses a 1.8 V supply. Keywords Bandgap reference (BGR) · Complementary to absolute temperature (CTAT) · Proportional to absolute temperature (PTAT) · Temperature coefficient (TC) · PSRR

1 Introduction An essential part in the design of most analog and digital electronic systems is to produce reliable and accurate voltage and current references. A reference in a circuit provides a stable point which is employed by other subcircuits to achieve predictable and repeatable results. It is important that this reference point should be unchanged significantly under different operating conditions. Analog-to-digital converter, AC-DC or DC-DC converter, and linear regulators are some examples of circuit applications where voltage reference are intrinsically required. The Bandgap is an advantageous solution for generating a stable and precise voltage reference regardless of temperature variations, power supply, and technology fluctuations. this circuit operates by combining two voltages having a temperature dependence of opposite signs Fig. 1. The first is the base-emitter voltage VB E of a A. Rahali (B) · K. El Khadiri · Z. Lakhliai · A. Tahiri Laboratory of Computer Science and Interdisciplinary Physics, ENS, Sidi Mohamed Ben Abdellah University, Fez, Morocco e-mail: [email protected] H. Qjidaa Faculty of Sciences Dhar el Mahraz, Sidi Mohamed Ben Abdellah University, Fez, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_150

1655

1656

A. Rahali et al.

Fig. 1 The principle of BGR

bipolar junction transistor (BJT), this voltage is a decreasing function in temperature; it is qualified as CTAT (Complementary to Absolute Temperature). The second voltage is an increasing function, called PTAT (Proportional to Absolut Temperature), and extracted from the difference between base-emitter voltages of two bipolar transistors traversed by different current densities [1, 2]. The reference voltage is then the sum of the PTAT and CTAT voltage with a factor K. This article presents a CMOS BGR circuit using an operational amplifier with negative feedback. the document is organized as follows: the principle of bandgap reference generated by a conventional architecture is presented in Sect. 2. Section 3 illustrates the proposed methodology and structure using OP-AMP. the results of the simulations are shown and discussed in Sect. 4. Finally, Sect. 5 is devoted to the conclusion of the overall document.

2 Conventional Bandgap Voltage Reference Structure Due to its temperature-dependence characteristic, a diode-connected bipolar transistor (BJT) is used as a basic device in most voltage reference architectures. Fig. 2 [3] shows a conventional first-order bandgap reference using a current mirror. When the BJTs are biased in the forward active region, the base-emitter voltage VB E can be developed as [4]  (η−χ )V       V −V (T )+(η−χ )VTr   Tr T− T ln TT − T + Tr V B E = VG0 + (η − χ )VTr − G0 B E r Tr

Tr

r

(1) Where VG0 is the bandgap voltage, η is a process-dependent constant, χ refers to the temperature dependence of the current forced through the collector, VTr is the

Design of a CMOS Bandgap Reference Voltage Using the OP AMP ...

1657

Fig. 2 Conventional first-order Bandgap reference

  thermal voltage at the reference temperature Tr , VG0 − VB E (Tr ) + (η − χ )VTr TTr is a linear first- order term in temperature. To have a voltage reference, it is necessary to cancel or reduce the temperature dependence of this term by using VT with a judiciously chosen multiplicative coefficient. The voltage Vr e f is written as: Vr e f = VB E + K VT

(2)

3 Study of the Proposed Voltage Reference The voltage reference circuit designed is composed of two bipolar transistors connected in diodes as illustrated in Fig. 3. The transistors MP2, MP3, and MP4 form the startup circuit, which plays the role of avoiding the undesirable zero current state when the power is applied. This block supplies or absorbs a low intensity current to ensure the normal operating state with non-zero current. Resistors R1 and R2 are chosen equal to have I1 = I2 . The operational amplifier in the BGR circuit is a typical two-stage operational amplifier as illustrated in Fig. 4, and imposes voltages V A and VB to be equal. Therefore, the current through resistor R0 can be expressed by: I1 =

VB E0 − VB E1 VT = ln N RO RO

(3)

1658

A. Rahali et al.

Finally, the voltage reference Vr e f is written: Vr e f



R1 VT ln N = VB E1 + 1 + R0

Fig. 3 Implementation of proposed Bandgap circuit

Fig. 4 Implementation Of two-stages operational amplifier

(4)

Design of a CMOS Bandgap Reference Voltage Using the OP AMP ...

1659

4 Simulation Results 4.1 Variation of Reference Voltage with Respect to Temperature Figure 5 illustrates the variation of output reference voltage as a function of temperature over the range from −40 °C to 120 °C. This variation shows a maximum voltage of 1.2073338 V at −40 °C, and a minimum of 1.2015818 V at 65 °C, which gives a temperature coefficient of 54 ppm/°C. Figure 6 shows the simulation results of the reference voltage given at different process corners FF, FS, SF, and SS which respectively represent fast-fast, fast-slow, slow-fast, and slow-slow over the same temperature range.

Fig. 5 Reference voltage Vref as function of temperature

Fig. 6 Reference voltage Vref at different process corners

1660

A. Rahali et al.

4.2 Variation of Reference Voltage Depending on Power Supply The reference voltage as function on power supply variation is represented in Fig. 7. It represents an almost constant reference with a maximum variation of 9.8 mV compared to the nominal voltage at 1.8 V. The PSRR simulated in Fig. 8 indicated a value of −35.6 dB for frequencies below 100 kHz. Figure 9 shows the parametric simulation of Vref versus VDD for different temperature values from −40 °C to 120 °C. Figure 10 illustrated the transient response of bandgap and indicated that the time taken to achieves the value 1.14 V (95% of Vr e f ) is 2.6 us, which proves the BGR responds quickly.

Fig. 7 Reference voltage Vref versus power supply VDD

Fig. 8 PSRR of Bandgap

Design of a CMOS Bandgap Reference Voltage Using the OP AMP ...

1661

Fig. 9 Parametric simulation of Vref versus VDD from −40 °C to 120 °C

Fig. 10 Transient response of Bandgap

Table 1 Comparison with other published results Parameter

This work

[3]

[5]

[6]

Supply voltage (V)

1.8

1.8

3.3

1.2

Reference voltage (V)

1.2

1.12

1.124

0.82

Temperature range (°C)

−40–120

−40–125

−25–125

−40–100

TC (ppm/°C)

54

152

65

250

PSRR (dB)

−35.6

–

−49.2@ 1 kHz

−47@ 40 kHz

CMOS Process (μm)

0.18

0.18

0.35

0.18

5 Conclusion A first-order CMOS bandgap reference circuit using an operational amplifier is presented in this article, to generate a stable output voltage reference of 1.2 V with a calculated temperature coefficient of 54 ppm/°C over a temperature range varying of −40 °C to 120 °C. The circuit exhibits a PSRR of −35 dB at 100 kHz and uses a 1.8 V supply.

1662

A. Rahali et al.

References 1. Jacob Baker R (2010) CMOS Circuit Design, Layout, and Simulation. Third Edition. WileyIEEE Press 2. Razavi B (2001) Design of Analog CMOS Integrated Circuits. McGraw-Hill Higher Education, international Edition 3. Akshaya R, Yellampali Siva S (2017) Design of an improved Bandgap Reference in 180 nm CMOS process technology. In 2nd IEEE International Conference on Recent Trends in Electronics Information & Communication Technology (RTEICT), pp 521–524. India 4. Rincon-Mora GA (2002) voltage reference from diodes to precision high-order Bandgap circuits. Wiley-IEEE Press 5. Zhao Y, Yue S, Bian Q (2008) A novel low temperature coefficient Band-gap reference without resistors. In International Multiconference of Engineers and Computer Scientists 2008, IMECS, vol II, Hong Kong 6. Shrivastava A, Kaur A, Sarkar M (2017) A 1.2 V, 33 ppm/°C, 40 nW, regeneration based BGR circuit for nanowatt CMOS. In International SoC Design Conference (ISOCC), pp 111–112, South Korea

Mechatronic, Robotic and Control System

Analytical Study of the CMOS Active Inductor Imane Halkhams, Wafae El Hamdani, Said Mazer, Moulhime El Bekkali, and Mohammed Fattah

Abstract Active inductors, widely used in filtering and amplification functions, and based on frequency filtering selection, are the subject of several investigations. Through this paper, we propose an analytical study of the active inductor to understand its frequency functioning. To do this, a small transistor signal study is carried out first in order to extract the intrinsic elements that will be used to study the active inductor, by calculating H and Y parameters. Keywords Active inductor · CMOS inductor · Small signal analysis · H parameters · Y parameters

1 Introduction Frequency filtering is a key element in electronic circuits, which explains the number of studies carried out on filtering operations [1, 2]. In the literature, different topologies of active inductors have been presented. Widely used in modern telecommunication systems such as the mobile phone network, wireless and satellites. Their manufacture has undergone a vertiginous evolution. Indeed, integrable filters are produced in tens of millions each year and are present in all daily telecommunication equipment (smartphones, TVs, ADSL, etc.). Active inductor-based filters reported in the literature [3, 4], have many advantages such as frequency tuning ease, high-quality factors, and on-chip integration, however, the transistor’s parasitic effects impact the linearity and stability of systems which I. Halkhams (B) SED, Research, Systems and Sustainable Environments Laboratory, The Private University of Fez, Fez, Morocco e-mail: [email protected] W. El Hamdani · S. Mazer · M. El Bekkali IASSE Laboratory, Sidi Mohamed Ben Abdellah University, Fez, Morocco M. Fattah IMAGE, Moulay Ismail University, Meknes, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_151

1665

1666

I. Halkhams et al.

calls for accurate noise and linearization studies. In this context, we propose a study of the intrinsic parameters of the CMOS transistor to optimize them and to provide adequate power to the active inductor circuit. We also propose an analytical study of the latter to highlight its role in the frequency filter chain. We propose a study of the active inductor described in [5], to facilitate the design of bandpass filters and LNAs. This article starts with the extraction of the intrinsic parameters of the transistor via the h and y matrices. In the third section, we present the analytical study of the active inductor, using the parameters calculated in Sect. 1.

2 Small Signal Study and Extraction of the Intrinsic Parameters of the Transistor The need for a high inductance value at the resonance frequency requires that the transconductances used in the active inductor design be properly configured. To do this, a small signal study from the transistor is fundamental. The term small signal is dedicated to signals small enough that the characteristics of the components vary slightly and can remain in a linear approximation. We used a CMOS transistor whose five-element equivalent model is given in Fig. 1. In this model, the components electrically characterize the transistor’s frequency behaviour. The channel is described by its gds conductance. The gm transconductance of the transistor determines the gains. The gate-channel interaction is mainly capacitive because of the existence of the MOS conjunction.

2.1 H Parameters H parameters are hybrid parameters that connect the input voltage VGS to the input current IG and the output voltage VDS to the output current ID. The equations according to the H matrix are written:

Fig. 1 Five elements transistor small signal model

Analytical Study of the CMOS Active Inductor



1667

V gs = H 11.I G + H 12.V ds I D = H 21.I G + H 22.V ds

(1)

Parameter H11 represents an impedance in Ω, parameters H12 and H21 are without unit, where H21 represents the current gain and parameter H22 is an admittance in Siemens (S). By short-circuiting the output of the transistor and injecting a voltage at its input, IG can be measured:  H 11 = VI Ggs (2) 1 H 11 = j.ω.(Cgs+Cgd) Parameter H12 represents the coupling of the output to the input. To calculate it, this time we inject a voltage at the output and we measure the voltage at the input of the circuit. H12 =

V gs =0 V ds

(3)

Parameter H21 is the current gain of the transistor. To calculate it we inject a voltage at the input while short-circuiting the output voltage, then we calculate the currents IG and ID.  H 21 = II GD (4) gm H 21 = j.ω.(Cgs+Cgd) At the transition frequency, the current gain of the transistor H21 is equal to the unit. |H21| = 1

(5)

2.2 Y Parameters To know the functionality of a quadruple, one method consists of studying its voltagecurrent transformation Y (matrix admittance), defined by the Eq. (6): ⎧ ⎪ ⎪ Y 11 = ⎨ Y 12 = ⎪ Y 21 = ⎪ ⎩ Y 22 =

I1 |V 2 V1 I1 |V 1 V2 I2 |V 2 V1 I2 |V 1 V2

=0 =0 =0 =0

(6)

1668

I. Halkhams et al.

To do this, we proceed by making each time short circuits for the admittance matrix. However, the realization of short circuits becomes almost impossible when we exceed 100 MHz, because of the parasitic capacitors and inductors, hence the interest to use the S matrix, which is very effective for high-frequency implementations and which is measurable by loading the input and the output with a load of 50 Ω. By studying the small-signal circuit of the transistor, the latter’s equations using the Y representations are given by: 

I G = Y 11.V gs + Y 12.V ds I D = Y 21.V gs + Y 22.V ds

(7)

When the voltage VDS is zero, Y11 is expressed: Y 11 =

IG V gs

Y 11 = j.ω.(Cgs + Cgd)

(8) (9)

When the VGS voltage is zero, Y12 is expressed: Y 12 =

IG V ds

Y 12 = − j.ω.Cgd

(10) (11)

When the Vds voltage is zero, Y21 is expressed: Y 21 =

ID V ds

Y 21 = gm − j.ω.Cgd

(12) (13)

When the VGS voltage is zero, Y22 is expressed: Y 22 =

ID V ds

Y 22 = gds + j.ω.(Cds + Cgd)

(14) (15)

Finally, from (9, 11, 13 et 15) we can extract the intrinsic parameters of the transistor:

Analytical Study of the CMOS Active Inductor

1669

(16)

Transconductance gm represents the slope of the ID characteristics as a function of Vgs for a certain channel length. gm =

∂Id(Vgs, Vds) ∂Vgs

(17)

The output conductance gds represents the resistance of the channel. The greater the width of the area under the gate, the smaller the conductance. The space load zone is an area in the vicinity of the metal-semiconductor interface. It is less rich in electrons than in the volume of the semiconductor. gds =

∂ I d(V gs, V ds) ∂ V ds

(18)

The Cgd capacitor is related to the variation of the load stored under the gate according to the gate-drain voltage when the gate-source voltage is fixed: Cgd =

∂ Qg(Vgs, Vgd) ∂Vgd

(19)

The Cgs capacitor is linked to the variation of the load stored under the gate according to the source-gate voltage when the gate-drain voltage is fixed. Cgs =

∂ Qg(V gs, V gd) ∂ V gs

(20)

The Cds capacitor takes into account the electrostatic coupling between the heavily doped regions below the source and drain contacts. The transition frequency is a characteristic of the transistor’s operating speed. Because the higher the frequency, the more the transistor can achieve a high transconductance for a low capacity. The transition frequency can be calculated from the current gain of the transistor. fT ≈

gm (Cgs + Cgd)

(21)

The calculated parameters will be used in the study of the active inductor in the following paragraph.

1670

I. Halkhams et al.

3 Analytical Study of the Active Inductor 3.1 Gyrator Circuit The gyrator circuit was invented by Bernard D. H. Tellegen (1900–1990) [6]. Its symbol is given in Fig. 2. It was first designed for low-frequency applications where transconductance operational amplifiers were used to simulate active inductors connected to the mass or floating using external integrated capacitors. The gyrator considered a 2-port component, when closed to an impedance (Fig. 2), is described by the following impedance matrix: 

   V1 I1 Z11 Z 12 I1 = [Z] = I2 Z 21 Z 22 I 2 V2

(22)

Thus, it transforms the impedance according to the following relation: 

  V1 0 −Rg I1 = Rg 0 I2 V2

(23)

Where Rg is the gyration resistance. Rewriting this equation yields: 

V1 = − Rg.I2 V2 = Rg.I1

(24)

V1.I1 + V2.I2 = 0

(25)

Thus:

This equation states that there is no energy generated, stored, or dissipated since the input power is equal to the output power.

Fig. 2 Symbol of a gyrator closed on an impedance

Analytical Study of the CMOS Active Inductor

1671

When the impedance is capacitive, the input impedance is as follows: 

2

Z e = Rg Zc Z e = Rg2 .C p

(26)

This equation means that when the ideal gyrator is closed on a capacitor, it simulates the effect of an inductive impedance of value: L = Rg2 .C

(27)

Equation (27) illustrates the possibility of using ideal gyrators in combination with capacitors to simulate inductors, hence the designation of the active inductor.

3.2 Active Inductor Circuit The active inductor consists of a common source transistor, in feedback with another transistor mounted in common gate. The two transistors form a gyrator that transforms the internal Cgs1 capacitor (between gate and source) of the first transistor into an inductor. The small equivalent signal model of the inductor used in [7] is given in Fig. 3. The Yin input admittance calculated from Fig. 3(a) is given in (28): gm1.gm2 + gm2.gds1 + gds1.gds2 + R −1 (gm1  + gds1 + gds2) +p. Cgs2(gm1 + gds1 + gds2) + Cgs1 gds1 + R −1 +p2(Cgs1.Cgs2) Yin = gds1 + R −1 + p.Cgs2

(28)

Using the small-signal model of the active inductor and taking into account the parasitic capacities, input admittance calculated from Fig. 3(b) is given in (29). gm1.gm2 + gm2.gds1 + gds1.gds2 + R −1 (gm1 + gds1 + gds2) +p.[C2(gm1 + + C1(gds1 + R −1 )  gds1 + gds2) +Cgd2 gds2 − R −1 + gm2 + gds1 +Cgs2(gm1 + gds1 + gds2)] +p2[C1.C2 + C1.Cgd2 + C1.Cgs2 + Cgd2.C2 + Cgd2.Cgs2] Yin = gds1 + R −1 + p.Cgs2

(29)

With C1 = Cgs1 + Cgb1; C2 = Cds1 + Cbd1; C3 = Cgs2 + Cgb2; C4 = Cds2 + Cbd2. The two admittances (28) and (29) can be simplified and give the expression (30):

1672

I. Halkhams et al.

(a)

(b) Fig. 3 Active inductor small signal equivalent model (a) simplified (b) with capacitors

Yin = gm1 + j.w.Cgs1 +

1 gds1+R −1 gm1.gm2

Cgs2 + j.w. gm1.gm2

(30)

The equation is a second-order transfer function whose terms constitute a parallel RLC resonator (31), containing an inductor with a serial resistor, in parallel with a capacitor and a resistor. Yin =

1 1 + Cp + R Rs + L

(31)

Analytical Study of the CMOS Active Inductor

1673

By investigating Eq. (30) and by identifying between (30) and (31), four terms can be deduced (32) [6]: ⎧ Cgs2 L = gm1.gm2 ⎪ ⎪ ⎪ −1 ⎨ Rs = gds1+R gm1.gm2 1 ⎪ Rp = gm1 ⎪ ⎪ ⎩ C p = Cgs1

(32)

This proves that the active inductor is equivalent to a resonator, which can be used for band-pass filtering applications as well as for oscillator design. The resonator circuit may be considered as an inductor if the operating frequency is less than the resonance frequency. The two parallel and serial resistors are the sources of resonator circuit losses. To remedy this, a resistor loss compensation circuit shall be added to the active inductor circuit.

4 Conclusion A CMOS active inductor analytical study was presented in this paper to understand its functioning. H and Y parameters were used to extract the transistor’s intrinsic elements. Active transistor-based inductances only and using internal capacity have been a giant step towards systems integration and in the implementation of preselection filters, bandpass filters, LNA, and oscillators. All these implementations exploit the inductive effect and the selectivity around the resonance frequency. This analytical study demonstrates the functioning of active inductors using CMOS transistors. The simplicity of their configuration makes them indispensable in the implementation of bandpass filters that exploit resonance to select a frequency band [8]. However, the selection of a restricted frequency band or even a single frequency requires a very narrow bandwidth, characterized by a very high-quality factor, which leads to selectivity and loss study to make bandwidth as narrow as possible. This study may be used in the design of bandpass filters and LNAs [9–11].

References 1. Erokhin VV et al (2019) Active tunable bandpass filter with voltage gain control in SiGe BiCMOS 130 nm. In International Siberian Conference on Control and Communications (SIBCON) 2. Pantoli L, et al (2017) Design considerations and effects of class-AB polarization in active filters realized by means of active inductors. In: 47th European Microwave Conference (EuMC), pp 37–40

1674

I. Halkhams et al.

3. Wu Y, Ismail M (2003) RF bandpass filter design based on CMOS active inductors. IEEE Trans Circuits Syst 50(12):942–949 4. Leoni A, et al (2017) Bandpass filter design with active inductor by means of wave digital approach. In 13th International Conference on Advanced Technologies, Systems and Services in Telecommunications (TELSIKS), pp 339–342 5. Halkhams I et al (2017) A Selective active filter for the 5G in the mm-wave band in pHEMT technology. Contemporary Eng Sci 10(4):193–202 6. Tellegen BDH (2014) The gyrator, a new electric network element. Philips Res. Rep. 3: 81–101. April 1948, archived from the original on 2014–04–23. Retrieved 20 Mar 2010 7. Halkhams I, et al (2017) Design of a dual-band bandpass filter using active inductor principle. Int J Commun Antenna Propagation (IRECAP) 7(4) ISSN 2039–5086 8. Ren S, Benedik C (2013) RF CMOS active inductor band pass filter with post fabrication calibration. AEU - Int J Electr Commun 67(12):1058–1067 9. Nawaz AA, et al (2019) A Ka/V band-switchable LNA with 2.8/3.4 dB noise figure. IEEE Microw Wirel Compon Lett 29(10) 10. Lee W, Hong S (2020) 28 GHz RF front-end structure using CG LNA as a Switch. IEEE Microw Wirel Compon Lett 30(1) 11. Mudavath M, et al (2020) Design and analysis of CMOS RF receiver front-end of LNA for wireless applications. Microprocess Microsyst 75

SM Versus PI Tunning for PWM Rectifier for SEIG in Wind Energy Application Ouafia Fadi , Soufiane Gaizen, and Ahmed Abbou

Abstract This work presents the sliding mode control applied on the self-excited induction generator, typically used in electrical energy production and particularly in wind turbines. The machine is associated with an active load through a bank of capacitors and operated as a generator. Its stator is associated with the AC/ DC converter. The SM control is presented by a cascade structure that includes tow loops: The internal loop controls the machine currents “id and iq” and the external loop controls the Vdc voltage. The whole model (SEIG/PWM rectifier/load) is simulated using the Matlab/Simulink software, then the results of this simulation validated the SM superiority over the typical PI regulator. The proposed SM method hold various advantages: it is recommended to control the systems that contain multyvariables, for both linear and non-linear systems, it provides great transient and steady performances, it’s robust against external disturbances, robust against uncertainties/parameters variations, and offers lower THD harmonics versus the typical PI. Keywords SEIG · PWM rectifier · SM strategy · PI controller · THD harmonics

1 Introduction The wind generation system has gained recently a great importance as a sustainable and secure source of renewable energy. A three-phase asynchronous machine could be operated as an asynchronous generator, either connected to the electrical network or operated in isolated power generation mode with a three-phase capacitance coupled to the stator windings [1]. This type of generator operated in excitation autonomous mode, called self-excited induction generator is the most useful generator for remote windy aeras because it doesn’t require an external power supplies to produce the excitation magnetic field, the excitation is delivered by supplemental stator terminal excitation capacitor bank. These references [2–4] have presented several algorithms O. Fadi (B) · S. Gaizen · A. Abbou Mohammadia School of Engineers, Mohammed 5 University, P.B765 Rabat, Morocco e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_152

1675

1676

O. Fadi et al.

and approaches to determine the minimum value of the excitation capacitor. SEIG has a several advantages such as simple and robust construction, minimal maintenance, absence of DC power supply for excitation and inherent overload protection [1]. However, the principal drawback of such generators is the voltage control that can be highly complex depending on the desired performance, this complexity is more precisely located at the power converter that is unable to produce a cleaned and continued DC voltage waveform, this issue is mainly caused by the analytical nonlinear model of the machine that hold numerous variables and that is very responsive to external disturbances (wind speed and load changes) [1]. The control of these power converters to maintain the output dc voltage regulated to a desired value and to generate the stator currents with a lowest possible harmonic distortion has been widely studied by engineers and researchers. The Conventional techniques of PI control, cover a wide range in industrial applications. These are linear control techniques and have the advantage of simplicity of implementation and ease of synthesis. Over time, the PI control applications will be inefficient, especially if the processes to be controlled have complex and non-linear structures [4]. Likewise, the PI tunning is used in a cascaded configuration [5]. In such a configuration, the internal loop is often accomplished by current controller, as well as the controller in the external loop regulates dc-link output voltage. There are typically two classical families of these control configurations [6, 7]: the VOC (Voltage oriented control) similarly as in VFOC (Virtual Flux oriented control) of induction generator, they are based on the coordinate transformation between stationary α-β and synchronous rotating d-q reference system. Both strategies guarantee rapid transient response and high static results via the internal current control loop. however, these configurations exhibit some disadvantages such as [6]: A coupled control between the active and reactive components of the current; A coordinate transformation and PI controllers are required. Another proposed method based on instantaneous direct active and reactive power control loops, called Direct Power Control (DPC) is developed in [8]. This method constitutes a viable alternative to the typical vector control methods (VOC-FVOC). However, the DPC has several disadvantages [9]: Variable switching frequency, High values of the line inductance and sampling frequency are needed to obtain an acceptable current waveform, also power and voltage estimation should be avoided at the moment of switching (it yields high errors). All these techniques mentioned above are employed when the controlled part of the machine is minimally disturbed, they may be sufficient if the requirements on the accuracy and performance of the system are not too stringent. However, in the opposite case and particularly when the controlled part is subjected to strong nonlinearities and to temporal variables, it is necessary to design control algorithms ensuring the robustness of the generator with respect to the uncertainties on the external parameters and their variations [10]. The sliding mode tunning is a robust control method [10] that has indisputable advantages for the poorly identified system or with variable parameters. This paper is divided into 4 important sections: in Sect. 1, the modeling of the asynchronous generator, powered by a tow-level inverter/rectifier system is presented. In Sect. 2, the basic principle of sliding mode is treated and detailed by mathematical equations. In

SM Versus PI Tunning for PWM Rectifier for SEIG ...

1677

Sect. 3, the internal and external regulation loops used the conventional PI controllers are replaced by the regulators based on sliding mode, the simulation results will be exposed to observe the validity of the PI compared with SM regulators. finally, in Sect. 4, a recap of the main points of this study are discussed.

2 System Units Modeling 2.1 D-Q Axes SEIG Modeling The system constitution based on a squirrel cage induction generator, prime mover, excitation capacitance and a PWM rectifier connected to an active load [8].   disq 1 2 = isd + Lm Rr iqr − wr Lm Lr idr + Lr Vsq −Ls Rs isq − wr Lm 2 dt Ls Lr − Lm

(1)

  1 disd 2 = −Lr Rs isd − wr Lm isq + Lm Rr idr + wr Lm Lr iqr + Lr Vsd 2 dt Ls Lr − Lm

(2)

  dirq 1 = Lm Rs isq + wr L m L s isd − Ls Rs iqr + wr Ls Lr idr + Lm Vsq 2 dt Ls Lr − Lm

(3)

  dird 1 Lm Rs isd − wr L m L s isq − Ls Rr idr − wr Ls Lr iqr + Lm Vsd = 2 dt Ls Lr − Lm

(4)

The excitation system of the SEIG presented by [8]: dVsd 1 = (isd − id ) dt C

(5)

 dVsq 1 = isq − iq dt C

(6)

2.2 D-Q PWM Rectifier Modeling The system dynamic of the three-phase two-level rectifier can be expressed using the park transformation, in dq synchronous reference frame as [8]: L

diq = Vqs − Riq − Lwid − Vrq dt

(7)

1678

O. Fadi et al.

L Cbus

did = Vds − Rid + Lwiq − Vrd dt

 Vbus  dVbus = ibus − il = Fd id + Fq iq − dt RL

(8) (9)

3 Sliding Mode Tunning Theory The SM system, is a non-linear control technique, it is characterized by the discontinuity of the control at the passages by a switching surface called the sliding surface. The basic idea of this control consists of bring the system to a stable switching hyperplane (sliding surface) and, to converge the system on this sliding area towards the desired equilibrium point [10].

3.1 Choice of Sliding Surface Consider the following state model [10]:   ˙ = [A][X] + [B][U] X

(10)

Where [X] Rn is the state vector, and [U] Rm is the control vector, with n > m. Generally, the choice of the sliding surfaces number is equal to the dimension of the control vector (U) [11]. In order to ensure the convergence of a state variable X towards its reference value X *, several works propose the following general form: S(x) = (

r−1 d + γ) ∗ e(x) dt

(11)

γ : Positive gain; e(X) = X - X*: The error of the variable to be regulated; r: Relative degree, it is the smallest positive integer representing the number of times of derivation to obtain the control (d S)/dt=0 ensuring the controllability; S(x): is an autonomous linear differential equation whose response e (x) tends to zero for a correct choice of gain γ and this is the main objective of this control.

3.2 Convergence Condition of the SM The condition of the convergence is the criteria which allow the dynamic of the system converges towards the sliding surface and remains constant against the disturbances

SM Versus PI Tunning for PWM Rectifier for SEIG ...

1679

[10]. For this reason, A candidate Lyapunov function V (x) > 0 is chosen for the system state variables and a control law is determined to make this function decreases V(x) < 0. The appropriate Lyapunov function for the system is expressed as follow [10]: V(x) =

1 S(x)S(x)2 2

(12)

By deriving this one, we obtain: ˙ = S(x)S(x)˙ < 0 V(x)

(13)

The Lyapunov candidate function decreases if (13) is negative.

3.3 Control Law Determination The law control is necessary to attract the trajectory of the desired state towards the surface and then towards its equilibrium point while ensuring the existence conditions of the SM [10]. The law control structure has two parts, the first concerns the exact linearization Ueq and the second Un is a stabilizer part, this one is used to eliminate the model uncertainties and to dismiss the external disturbances [10]. U(t) = Ueq + Un

(14)

The two parts expressions of the control U is determined by the set of equations: ∂S ∂x dS = . S˙ = dt ∂x ∂t

(15)

By replacing (10) and (13) in (14), we find:  ∂S ∂S  S˙ = [A][X] + [B]Ueq + [B]Un ∂x ∂x The equivalent control is deduced by considering that:

d S(t) dt

(16) = 0:

−1   ∂S ∂S ∗ [B] ∗ [A] ∗ [X] ∗ Ueq = − ∂x ∂x

(17)

Replacing the equivalent control by its expression in (15), we obtain the new expression of the derivative surface [10]: ˙ = ∂S ∗ [B] < 0 S(x) ∂x

(18)

1680

O. Fadi et al.

In order to satisfy the condition, the sign of Un must be opposite of S(x) = ∂S ∗[B]. ∂x The simplest form that the discrete control can take, is the ‘sign’ function presented as: Un = Kx ∗ sing(S(x))

(19)

However, this function generates on the sliding surface, a phenomenon called chattering which is generally undesirable because it adds high frequency components to the control spectrum. The chattering issue can be decreased by replacing the “sign” function with an adequate saturation function which filters out the great frequencies [10]. ⎧ ⎪ if S < −η ⎨ −1  S Sat(S) = η if |S| < −η ⎪ ⎩ 1 if S > −η η: A small and positive parameter, defines the bandwidth of ‘sat’ function.

4 SM Application on the SEIG In this study, the SM technique is applied to the asynchronous generator and the control expressions are established based on the following layout model: Figure 1 shows the cascade configuration of sliding mode regulator employed to the SEIG. The external loop output variable presents the current references of the internal loops [10]. The references output are the direct and quadrature control voltages to be applied (Vrd ,Vrq ) to the PWM rectifier. The cascade configuration imposes a choice of three aeras along the two axes (d,q). The internal loops regulate the currents “id and iq” and the external loop regulates the Vdc voltage.

Fig. 1 Outline schematic of the SM controller

SM Versus PI Tunning for PWM Rectifier for SEIG ...

1681

4.1 Direct Current Regulator Id: The sliding area related to this regulator is defined as follow [11]: S(id ) = id∗ − id

(20)

di∗ did dS(id ) = d − dt dt dt

(21)

The sliding aera derivative is:

Substituting the Eq. (8) in Eq. (21) we deduced:   di∗ 1 dS(id ) R = d − Vds − i d + wi q dt dt L L

(22)

The control low Vr∗d is defined as follow: ∗ n = Vrd + Vrd Vrd eq

(23)

The equivalent component of the voltage is: eq

Vrd = Vds − Ri d + wLi q − L

di ∗d dt

(24)

The discontinuous component of the voltage is given by: ∗ Vrd = −K d ∗ sat(Sd )

(25)

4.2 Quadrature Current Regulator Iq: By following the same approach detailed above, the equivalent component of the voltage Vrq∗ is given by: eq = Vqs − Riq − wLid − L Vrq

di∗q dt

(26)

The discontinuous component of the voltage is: ∗ Vrq = −K q ∗ sat(Sq )

(27)

1682

O. Fadi et al.

4.3 DC-Bus Voltage Regulator Vbus : The sliding area related to regulator Vbus is expressed as follow [11]: S(Vbus ) = Vbus ∗ −Vbus dV∗bus dVbus dV∗bus dS(Vbus ) = − = − dt dt dt dt



(28)

ibus Vbus − Cbus Cbus ∗ RL

 (29)

∗ The control low i bus is defined as follow: ∗ n = ibus + ibus ibus eq

(30)

With, the discontinuous and equivalent components are as follow: n = Kbus ∗ sing(S(Vbus ) ibus

eq

ibus =

(31)

Vbus RL

(32)

By neglecting the inverter/rectifier losses and assuming that i d = 0, we obtain: Pbus = Vbus *ibus = Vq *iq ⇒ i∗q =

Vbus ∗ *i Vq bus

(33)

Therefore, the control low of dc-bus is expressed as follow: iq∗

=

eq ibus

+

n ibus

   Vbus  Vbus = ∗ Kbus ∗ sing(S(Vbus + ) Vq RL

(34)

5 Simulation Results and Interpretations Figure 2 presents the dc bus voltage across the rectifier terminal; the regulation of this bus is examined by both SM and PI controllers. In spite of the presence of load step changing and, as illustrate in the figure below, the SM performances of the DC voltage is better than PI performances, in termers of, the robustness against the load disturbances, also, the dynamic property is better: there is no overshoot and the SM response time is smaller than the classical PI time response. Figure 3 demonstrates the performances of active and reactive powers in the presence of load variation. During the steady regime, the P power is maintained constant, and the Q power is maintained at 0 KVA which lead to obtain a good

SM Versus PI Tunning for PWM Rectifier for SEIG ...

Fig. 2 DC voltage in the terminal of the PWM rectifier

Fig. 3 P power, Q power and PF of the SEIG

1683

1684

O. Fadi et al.

power factor (PF = 1), furthermore, The SM ensures a good performance than the conventional PI regulator (smaller overshoot, smaller response time and constant steady state). Figure 4 shows the steady state for the SEIG currents, using both SM and PI control strategies. As can be observed, the SEIG currents, in SM regulator technique, are close to sine wave better more than the PI regulator technique. Furthermore, the Fig. 5 shows that the SM exhibits the best power quality (THD = 1.96%) and, the PI leads to a dispersed harmonic spectrum with a higher THD of around 6.24%.

Fig. 4 PI versus SM approaches for SEIG currents

Fig. 5 THD of SEIG currents (PI versus SM)

SM Versus PI Tunning for PWM Rectifier for SEIG ...

1685

6 Conclusion This work proposes a nonlinear control of the self-excited induction generator using SM approach. In the suggested control technique, stable and independent controls of the direct and quadrature currents are achieved. This method can be easily used in the isolated wind power systems. Furthermore, the simulation results demonstrate that the dc voltage obtained with the SM tunning is faster compared to the classical PI. Furthermore, the SM controller brings remarkable improvements compared to the classic PI regulator since the SM regulators offer good static and dynamic performances (stability, rapidity and precision), that is to say a shorter response time without overshoot, also they grant better tracking and rejection of load disturbances, and reduce THD of SEIG currents.

References 1. Kalaivani C, Rajambal K (2020) Modeling of an efficient high power wind energy conversion system using self-excited multi-phase machines. Microprocess Microsyst 74:103020. https:// doi.org/10.1016/j.micpro.2020.103020 2. Ouafia F, Ahmed A (2018) Elaboration of the minimum capacitor for an isolated self excited induction generator driven by a wind turbine. In Conference: International Conference of Computer Science and Renewable Energies, Ouarzazat 3. Chaturvedi Y, Kumar S, Gupta V (2020) Capacitance requirement for rated current and rated voltage operation of SEIG using whale optimization algorithm. Procedia Comput Sci 167:2581– 2589. https://doi.org/10.1016/j.procs.2020.03.315 4. Silva EO, Vanço WE, Guimarães GC (2020) Capacitor bank sizing for squirrel cage induction generators operating in distributed systems. IEEE 8:27507–27515 5. Wang F, Zhang Z, Mei X, Rodríguez J, Kennel R (2018) Advanced control strategies of induction machine: Field oriented control, direct torque control and model predictive control. Energies 11(1):120 6. Toufik A, Taibi D, Oualid A (2018) Voltage oriented control of self-excited induction generator for wind energy system with MPPT. In AIP Conference Proceeding 7. Abdelrahem M, Hackl C, Kennel R, Rodriguez J (2019) Sensorless predictive speed control of permanent-magnet synchronous generators in wind turbine applications. In PCIM Europe, International Exhibition and Conference for Power Electronics, Intelligent Motion, Renewable Energy and Energy Management (1–8) VDE 8. Ouafia F, Ahmed A (2020) A direct power control of the PWM rectifier for SEIG feeding resistive load in wind energy systems. In 5th International Conference on Renewable Energies for Developing Countries, pp 1–6. IEEE 9. Mohan H, Pathak MK, Dwivedi SK (2019) Direct power control of induction motor drives. In IEEE 13th International Conference on Compatibility, Power Electronics and Power Engineering pp 1–5 10. Boudries ZOUBIR, Aberbour A, Idjdarene KASSA (2016) Study on sliding mode virtual flux-oriented control for three-phase PWM rectifiers. Rev Roum Sci Techn–Électrotechn Énerg 61(2):153–158 11. Xiong L, Li P, Wang J (2020) High-order sliding mode control of DFIG under unbalanced grid voltage conditions. Int J Electr Power Energy Syst 117:105608

Improved DTC of the PID Controller by Using Genetic Algorithm of a Doubly Fed Induction Motor Said Mahfoud, Aziz Derouich, Najib El Ouanjli, Mohammed Taoussi, and Mohammed El Mahfoud

Abstract This paper presents a new strategy for the Direct Torque Control (DTC) of the Doubly Fed Induction Motor (DFIM) powered by two voltage inverters. The speed regulation of this control is done by a PID (proportional integral derivative) controller optimized by a Genetic Algorithm (GA). The classic DTC has several advantages such as (dynamic, robustness, ease of implementation, high performance…). However, it has ripples of the electromagnetic torque and variable switching frequencies that generate vibrations of the machine causing rapid aging. To overcome the disadvantages of classic DTC control, the new GA-DTC control strategy based on the PID controller optimized by the genetic algorithm, have the tendency to operate the system throughout its operating range. The entire system is implemented and validated on the Matlab/Simulink environment to improve machine performance and behavior. Keywords DFIM · DTC · GA-DTC · PID · GA

1 Introduction During the mid-80s, the control technique of the induction motor, known as Direct Torque Control or DTC, has appeared to compete with traditional techniques. This strategy has been introduced by TAKAHASHI [1] and DEPENBROCK [2]. Its principle for a DFIM is based on a direct identification of the control pulses applied to the voltage inverters switches, in order to preserve the stator and rotor flux and electromagnetic torque within two predefined hysteresis bands. Such an application of this technique makes it possible to ensure decoupling between the flux and the torque S. Mahfoud (B) · A. Derouich · N. El Ouanjli · M. Taoussi Industrial Technologies and Services Laboratory, Higher School of Technology, Sidi Mohamed Ben Abdellah University, Fez, Morocco e-mail: [email protected] M. E. Mahfoud Laboratory of Systems Integration and Advanced, Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fez, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_153

1687

1688

S. Mahfoud et al.

control without using the Pulse Width Modulation (PWM) or coordinate transformation. The important features of DTC are its fast dynamic torque response, robustness, ease of implementation, and high performance [3, 4]. However, this strategy has two important disadvantages: First of all, the frequency of switching is highly variable. Second of all, the torque and flux ripples remains poorly handled in the entire operating range. It is worthwhile to note that the torque ripples are making additional noises and vibrations. Therefore, they are causing fatigue in the machine shaft. In order to reduce the impact of these phenomena on machine life, it is estimated that intelligent techniques can provide improvements. Currently, many researchers have proposed solutions to improve the performance of classic DTC control, which are based on artificial intelligence. These approaches are very robust to measure noise and parametric variations. In [5–7], the authors have proposed many techniques to enhance the efficiency of direct torque control using intelligent techniques. They are called Direct Torque Fuzzy Control (DTFC), Direct Torque Neural Control (DTNC), and Direct Torque Neural-Fuzzy Control (DTNFC). They use fuzzy logic controllers or neural networks or an Adaptive Neuro-Fuzzy Inference System (ANFIS) combining artificial neural networks and fuzzy logic, which replace truth tables and hysteresis comparators, to generate the voltage vector in order to direct flux and torque to their references. In [9] the authors have proposed a new DTC strategy of the DFIM, The PI speed controller is optimized by using the genetic algorithm of DTC control at the stator, in order to minimize torque ripples. But this method does not allow us to take advantage of the double speed band of DFIM [10]. In order to solve this problem, this work is articulated on the application of the DTC control on both sides of the DFIM and the optimization of PID controller parameters based on genetic algorithm to resolve the precedent problems. In this article, the contributions are focused on the following objectives: • Minimization of the flux and torque ripples • Reduction of response time and speed rejection time • Minimization of the speed and torque overshoot This article is organized according to the following form: Sect. 2 Presents the mathematical model of the DFIM, Sect. 3 Describe the operation principal and mathematical model of the DTC, Sect. 4 is focused to study the genetic algorithm, Sect. 5 is articulated for interpreted the results of simulation and finally the conclusion of control results and suggestion of future works.

2 Doubly Fed Induction Motor Mathematical Model The mathematical model of doubly fed induction motor appropriate for studying the behavior of direct torque control is the model of two-phase represented by the coordinates (α, β), it is represented by the following equations [11]:

Improved DTC of the PID Controller by Using Genetic Algorithm ...

1689

• Electrical equations: ⎧ dψsα ⎪ ⎪ ⎪ vsα = Rs .i sα + ⎪ dt ⎪ ⎪ ⎪ ⎪ dψ ⎪ sβ ⎪ ⎨ vsβ = Rs .i sβ + dt dψr α ⎪ ⎪ ⎪ vr α = Rr .ir α + + ωm .ψrβ ⎪ ⎪ dt ⎪ ⎪ ⎪ ⎪ dψrβ ⎪ ⎩ vrβ = Rr .irβ + − ωm .ψr α dt

(1)

⎧ ψ = L s i sα + L m .ir α ⎪ ⎪ ⎨ sα ψsβ = L s i sβ + L m .irβ ψ = L r ir α + L m .i sα ⎪ ⎪ ⎩ rα ψrβ = L r irβ + L m .i sβ

(2)

⎧ ⎨ Tem = p.(ψsα i sβ − ψsβ i sα ) ⎩ J. d + f. = Tem − Tr dt

(3)

• Magnetic equations:

• Mechanical equations:

3 Direct Torque Control Strategy The DTC is less sensible in front of parametric variations of the motor and it allows us to obtain precise and fast torque response. The objective of this strategy is to control directly the machine torque and flux. In this context, a hysteresis comparator is used to compare the estimated value and the reference one, and then the state of the inverters is controlled by a predetermined selection table. Figure 1 illustrates the structure of the DTC control strategy applied to the doubly fed induction motor.

3.1 Control of the Vectors of Stator and Rotor Flux In the fixed (α, β) plane associated with the stator and rotor. The rotor and stator flux are estimated according to the equations below [11]. 

t ψ s (t) = 0 (V s + Rs .I s ).dt t ψ r (t) = 0 (V r + Rr .I r ).dt

(4)

1690

S. Mahfoud et al.

Fig. 1 Structure of the direct torque control strategy optimized by the Genetic Algorithm

The flux are defined by their modules and their positions, which are given by the following relationship: 

ψˆ = ψˆ α2 +ψˆ β2 ψˆ

θ = ar ctg( ψˆ β )

(5)

α

By using the flux in the reference (α, β), the electromagnetic torque can be calculated using a mathematical equation. Tˆem = p.(ψˆ sα .i sβ − ψˆ sβ .i sα )

(6)

Improved DTC of the PID Controller by Using Genetic Algorithm ...

1691

Fig. 2 Flux trajectory

Fig. 3 Hysteresis comparators (a) of the three-levels torque (b) of the two-levels flux

3.2 Hysteresis Comparators Two hysteresis comparators with two-levels, which are easy to use. Their roles are to maintain the end of each vector of the flux in a circular crown as shown in Fig. 2, and a three-levels hysteresis comparator Fig. (3, a), allows controlling the electromagnetic torque developed by the motor in both directions of rotation, they are generating either a positive torque or a negative one.

3.3 Elaboration of the Switching Table Depending on the sector and the evolution of the torque and flux, you should select the voltage vector to be applied in order to respect the references of the torque and flux. The truth table for selecting the appropriate vector is shown in Table 1.

1692

S. Mahfoud et al.

Table 1 Inverter sequences Sector 

Ccpl

S1

S2

S3

S4

S5

S6

1

1

ν2

ν3

ν4

ν5

ν6

ν1

0

ν7

ν0

ν7

ν0

ν7

ν0

−1

ν6

ν1

ν2

ν3

ν4

ν5

1

ν3

ν4

ν5

ν6

ν1

ν2

0

ν0

ν7

ν0

ν7

ν0

ν7

−1

ν5

ν6

ν1

ν2

ν3

ν4

0

Fig. 4 Structure of the speed PID controller optimization by GA

4 Optimization of the Speed Controller by the Genetic Algorithm Genetic algorithm is a research strategy to provide the best solution for non-linear systems. Classic DTC controller and machine are affected by variable parameters, which makes the system non-linear, for this situation, a genetic algorithm generates the optimal values for KP , KI and KD of the PID controller at each occurrence of a change, in the system in order to master the situation. Figure 4 illustrates the reduced structure of the system. The genetic algorithm belongs to the category of evolutionary algorithm, which uses techniques based on evolutionary biology, such as selection, crossover, and mutation [12], The sequences of operations involved in GA are described in Fig. 5, which presents a flowchart that respects the evolutionary rules of a genetic algorithm.

5 Simulation and Interpretation The implementation of the GA-DTC control and the DFIM is realized in the Matlab/Simulink platform. The configuration of the global system is done according to the parameters Tables 5, 6 and 7. To find out the behavior of the system, the latter is subjected to variable references of speed and torque to put it in situations similar to reality. The results of the simulation for both controls (GA-DTC and classic DTC) were tested using a 1.5 kW machine.

Improved DTC of the PID Controller by Using Genetic Algorithm ...

1693

Fig. 5 Flowchart of the speed PID controller optimization by GA

Figures (6, a), (6, b), (6, c) and (6, d), respectively show the variation of the speed, and the variation of the electromagnetic torque and the stator and rotor flux, which are obtained without load as a condition of starting and at nominal load from 0.5 s as normal operating condition. Figures (6, a), and (6, b) illustrate, the speed and torque characteristics of a doubly fed induction motor controlled by the GA-DTC, which quickly reach their references without overshoot compared with a classic DTC during no-load starting and during nominal load starting. For the speed characteristic the response time is minimized by a rate greater than 37% (48.5 ms for the classic DTC and 30.4 ms for the proposed GA-DTC), and the overshoot is reduced by approximately 100% (9 rad/s for classic DTC and 0 rad/s for proposed GA-DTC control), and the rejection time is reduced by 16.73% (23.3 rad/s for the classic DTC and 19.4 rad/s for GA-DTC control). For the characteristic of the electromagnetic torque, the ripples are reduced by more than 22% (4,264 Nm for classic DTC and 3,297 Nm for the proposed GA-DTC).

1694

S. Mahfoud et al.

Fig. 6 System responses (a) Speed (b) Torque (c) Stator flux (d) Rotor flux

For the stator and rotor flux characteristics, the ripples are reduced to 36.05% and 23.74% respectively (0.1079 Wb and 0.069 Wb for the classic DTC and 0.0514 Wb and 0.0392 Wb for the proposed GA-DTC).

Improved DTC of the PID Controller by Using Genetic Algorithm ...

1695

Table 2 Performance measures DTC classic and GA-DTC Characteristics

Optimized DTC by GA

Classic DTC

Improvement (%)

Response time (ms)

30.4

48.5

37.32

Overshoot (rad/s)

0

9

100

Rejection time (ms)

19.4

23.3

16.73

Response time (ms)

15.4

16.1

4.35

Overshoot (rad/s)

4.27

5.49

22.22

Ripples (Wb)

3.297

4.264

22.68

ψs

Ripples (Wb)

0.069

0.1079

36.05

ψr

Ripples (Wb)

0.0392

0.0514

23.74



T

The results presented in Table 2 showed that the optimization of the DTC control PID controller by the genetic algorithm improved the classic DTC control performances (dynamics, robustness…).

6 Conclusion In this article, the optimization of the PID controller of the classic DTC control is achieved by a genetic algorithm, which has been applied to a doubly fed induction motor powered through two inverters. The modeling and details of the system are introduced and implemented in the Matlab/Simulink platform. The results of the simulation improve the performance of the doubly fed induction motor, such as minimizing the response time of 37.32% and the rejection time is improved by 16.73%, and the torque ripples which are minimized by up to 22.68% . The proposed control has increased the performance of the classic DTC control at the transient and dynamic regime. In order to make a progress of the research the next work will be on the implementation of the proposed control on the dSPACE DS1104 board to test the validity of this technique.

Appendix (See Tables 3, 4, 5, 6 and 7).

1696 Table 3 Nomenclature

S. Mahfoud et al. Parameters

Description

Vsα , Vsβ , Vrα and Vrβ

Stator and rotor voltages in (α, β) plan

Udcs and Udcr

Stator and rotor directs voltages

Isα , Isβ , Irα and Irβ

Stator and rotor currents in (α, β) plan

 sα ,  sβ ,  rα and  rβ Stator and rotor flux in (α, β) plan

Table 4 Abbreviations

Table 5 DFIM parameters

Rs , Rr

Stator and rotor resistors

Ls , Lr

Stator and rotor inductors

Lm

Mutual Inductance

P

Number of pairs of poles

ωr

Rotor angular speed

ωs

Stator angular speed



Rotation speed

Tem

Electromagnetic torque

Tr

Resistant torque

f

Viscous friction coefficient

Abbreviation

Wording

DFIM

Doubly Fed Induction Motor

DTC

Direct Torque Control

GA

Genetic Algorithm

GA-DTC

Genetic Algorithm-Direct Torque Control

PID

Proportional Integrator Derivator

DTFC

Direct Torque Fuzzy Control

DTNC

Direct Torque Neural Control

DTNFC

Direct Torque Neural-Fuzzy Control

ANFIS

Adaptive Neuro-Fuzzy Inference System

Symbols

Values (unit)

Pn

1.5 Kw

Vs

230 V

Vr

130 V

P

2

f

50 Hz

Rs

1.75  (continued)

Improved DTC of the PID Controller by Using Genetic Algorithm ... Table 5 (continued)

Table 6 Parameters of the PID of GA-DTC and PID of Classic DTC

Table 7 Parameters of the genetic algorithm

1697

Symbols

Values (unit)

Rr

1.68 

Ls

0.295 H

Lr

0.104 H

Lm

0.165 H

f

0.0027 kg.m2 /s

J

0.001 kg.m2

Parameters/Control

GA-DTC

Classic DTC

KP

96.4889

50

KI

0.1576

4

KD

0.9706

0

Description

Type/Value

Population size

20

Maximum iteration

50

Selection

Uniform

Crossover

Roulette wheel selection

Mutation

Uniform

References 1. Takahashi I, Ohmori Y (1989) High-performance direct torque control of an induction motor. IEEE Trans Ind Appl 25:257–264 2. Depenbrock M (1988) Direct Self Control of Inverter-Fed Induction Machines. IEEE Trans Power Electron PE-3(4):420–429 3. Taoussi M, Karim M, Bossoufi B, Hammoumi D, Lagrioui A (2015) Speed backstepping control of the double-fed induction machine DRIVE. J Theor Appl Inf Technol 74(02):189–199 4. Mahfoud S, Derouich A, El Ouanjli N, Mohammed T, Hanafi A (2021) Field oriented control of doubly fed induction motor using speed sliding mode controller. In: E3S web of conferences, vol 229. EDP Sciences, pp 01061 5. Abbou A, Mahmoudi H (2009) Performance of a sensorless speed control for induction motor using DTFC strategy and intelligent techniques. J Electr Syst 5(3):64–81 6. Ouanjli NE, Derouich A, Ghzizal AE, Chebabhi A, Taoussi M, Bossoufi B (2018) Direct torque control strategy based on fuzzy logic controller for a doubly fed induction motor. In: IOP conference series: earth and environmental science 7. Cirrincione G, Cirrincione M, Chuan L, Pucci M (2016) Direct torque control of induction motors by use of the GMR . In: Neural networks, proceedings of the international joint conference, 20–24 July 2003, vol 3, pp 2106–2111 8. Jang J-SR, Sun C-T (1995) Neuro-fuzzy modelling and control. Proc IEEE 83:378–406 9. Piotr F (2001) Neuro-fuzzy control of inverter-fed induction motor drives. Thesis. Lublin University of Technology, Poland

1698

S. Mahfoud et al.

10. Zemmit A, Messalti S, Harrag A (2017) A new improved DTC of doubly fed induction machine using GA-based PI controller. Ain Shams Eng J 9:1877–1885 11. El Ouanjli N, Derouich A, El Ghzizal A et al (2019) Modern improvement techniques of direct torque control for induction motor drives - a review. Prot Control Mod Power Syst 4:11. https:// doi.org/10.1186/s41601-019-0125-5 12. Goldberg DE (1989) Sizing populations for serial and parallel genetic algorithms. In: Proceeding of the third international conference on genetic algorithms, CL, USA

State Feedback Control of DC-DC Converter Using LQR Integral Controller and Kalman Filter Observer Djamel Taibi , Toufik Amieur , Mohcene Bechouat , Sami Kahla , and Moussa Sedraoui

Abstract In this paper, the linear state feedback control using LQR controller for a DC/DC converter in the case of negative voltages topology is presented in order to achieve a particular desired behavior. To guarantee a zero steady-state error, we introduce an integral action, which will work out this problem by assuring that the steady-state error will end up to zero. For filtering and state estimation with a low cost and less complexity a state observer is obtained based a Kalman Filter observer. Detailed simulation study is presented to demonstrating the robustness and effectiveness of the proposed control scheme. Keywords Linear quadratic regulator · DC/DC Buck-Boost Converter · Kalman filter · Static error

1 Introduction Power supply technology enables technologies that allow us to operate circuits and electronic systems. All digital and analog circuits require a power source to operate. Many of these circuits require multiple DC supply voltages. DC power supplies D. Taibi (B) Département de Génie Électrique, Université de Kasdi Merbah, Ouargla, Algeria T. Amieur Département de Génie Électrique, Université de Larbi Tebessi, Tebessa, Algeria e-mail: [email protected] T. Amieur · M. Bechouat · M. Sedraoui Laboratoires des Télécommunications LT, Université 8 mai 1945, Guelma, Algeria e-mail: [email protected] M. Bechouat Faculté des Sciences et Technologie, Université de Ghardaïa, Noumirat BP 455, Route Ouargla Ghardaia, 47000 Ghardaia, Algeria S. Kahla Centre de Recherche en Technologies Industrielles CRTI, 16014 Cheraga, Alger, Algeria © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_154

1699

1700

D. Taibi et al.

are widely used in telecommunications, household appliances, defense, and medical electronics [1]. The DC voltage is generally obtained from a battery or by converting AC current into DC current using a transformer, rectifier, and filter [1, 2]. In some power supplies, a negative output voltage are required. There are many different methods for generating a negative voltage in the output from a positive voltage in the input. A very simple and inexpensive method for obtaining a negative voltage is the Buck-Boost converter. The main advantage of this converter is the simplicity of design. The topology requires very few components, which reduces the cost and complexity of the development. In [3] a proportional-integral voltage regulator (PI) is extended by a sensorless predictive control of Buck-Boost converter using a self-correction differential current observer. [4] Presents three adaptive optimal PI controllers that can be used for switching power converters with the unknown load resistance. Robust control used for the control of DC-DC converters in [5, 6]. Presents LQR control of power converters. Similar to this paper, [7] introduces an integral action for DC/DC Boost converter. In [8] a multi-loop based PI control and an LQG method are used to control a DC/DC Buck converter. The nonlinear control is used in [9] for Boost converter. This paper describes the LQR controller for a Buck-Boost with a negative output voltage. Finally, the Kalman filter is a special type of observer that enables optimal filtering of the different types of noise in the measurement and the system if the covariance of these noises is known [10]. In Sect. 2, the Buck-Boost converter model is used directly for simulation purpose using the LQR controller. The LQR is detailed in Sect. 3. The Kalman filter observer is examined in Sect. 4. The results of the numerical simulation are presented in Sect. 5. Finally, conclusion are presented in Sect. 6.

2 The Buck-Boost Model The Buck-Boost converter with negative output voltages topology is as shown in Fig. 1. Mosfet Q is ON (Fig. 2), the charging current in the capacitor C is partially discharged. During the second interval in which the Mosfet Q is switched off (Fig. 3), the polarity of the voltage on the inductor is reversed and the diode is conductive [11, 12]. Fig. 1 Buck-boost converter

State Feedback Control of DC-DC Converter ...

1701

Fig. 2 Switched ON of the Buck-Boost converter

Fig. 3 Switched OFF of Buck-Boost converter

2.1 Model of Switched Buck-Boost Converter By applying Kirchhoff’s laws in the two previous circuits, we get the following dynamics: In the switching ON, the following dynamic is obtained [12]: ⎧ di ⎪ =E ⎨L dt ⎪ ⎩ C dv = − v dt R

(1)

In the switching OFF, the following dynamic is obtained [12]: ⎧ di ⎪ =v ⎨L dt ⎪ ⎩ C dv = −i − v dt R

(2)

From the two Eqs. (1) and (2), we can obtain a single unified model, which is u ∈ {0, 1}.The dynamics of the converter are therefore as follows [13]: 

L di = (1 − u)v + u E dt = −(1 − u)i − Rv C dv dt

(3)

1702

D. Taibi et al.

3 LQR Control 3.1 LQR Controller The Eq. (4), describe the state space model of a system: [14] 

x(t) ˙ = Ax(t) + Bu(t) y(t) = C x(t) + Du(t)

(4)

The vector of optimal control is as follows: u(t) = −K x(t)

(5)

To define the optimal control inputs and optimize the state variables, the following cost function J must be minimized [15]:  J=

∞



x T Qx + u T Ru dt

(6)

0

Q and R two positive definite matrix, Q and R are chosen, the LQR control problem reduces to finding K that minimizes (6). Solution P of Riccati equation [15]: P A + A P + Q − P B R −1 B  P = 0

(7)

The gain matrix of the optimal control vector can be calculated using the following equation [15]: K = R −1 B  P

(8)

Therefore, optimal control equation becomes [16]: u(t) = −K x(t) = −R −1 B T P x(t)

(9)

3.2 LQR Control with Integral Action The schematic diagram LQR with integral is shown in Fig. 4 [10, 16]. The LQR with integral action require the following extended matrix configuration: Aˆ =









A 0 B , Bˆ = , Cˆ = 0 0 , Kˆ = k −ki −C 0 0

State Feedback Control of DC-DC Converter ...

1703

Fig. 4 Schematic of LQR with integral action

With k: State-feedback vector gain. ki : Integral action. The cost function is known by the following relationship [15]:  J=

∞

 T e (t)Qe(t) + u T (t)Ru(t) dt

0

The vector e(t) is defined by:

x(t) ˆ − x(∞) e(t) = ξ (t) − ξ (∞)

 (10)

Where ξ (t) represents the integral action. The gain K in (12) is obtained by solving the: Aˆ T P + P Aˆ − P Bˆ R −1 Bˆ T P + Q = 0

(11)

Kˆ = R −1 Bˆ T P

(12)

4 Kalmen Filter 4.1 Kalman Observer We consider that only the output voltage is sensed. Then, to estimate the inductor current and reduce the noisy environment effects, a Kalman filter is introduced [10]: 

ˆ˙ = A x(t) x(t) ˆ + Bu(t) + ω(t) yˆ (t) = C x(t) ˆ + ν(t)

(13)

1704

D. Taibi et al.

where ω(t) is a process noises, and ν(t) is the voltage sensor noise. The state equations of the Kalman filter can be carried out as follows [17]: xˆ˙ = (A − K K C)xˆ + Bu + K K y

(14)

Where K is the Kalman gain matrix. The Kalman filter is usually designed for linear time-varying systems is as follows [17]: ˆ˙ ˆ˙ d xˆ˙ = A(t)x(t)dt + B(t)u(t)dt + K K (t)(y(t)dt − C(t)x(t)dt)

(15)

K K (t) = PK (t)C T (t)R −1 K (t)

(16)

d PK (t) = A(t)PK (t) + PK (t)A T (t) − PK (t)C(t)T R −1 K (t)C(t)PK (t) + Q K (t) dt (17) x(t ˆ 0 ) = x0 , P(t0 ) = P0

5 Simulation Results In the MATLAB/SIMULINK using SimPowerSystems toolbox. In simulation, the Kalman filter observer algorithm was implemented using the continuous-time Kalman filter block. The buck-boost converter with negative output voltages topology was simulated and switched by the PWM technique. The parameters used during the analysis of simulation results are shown in Table 1. Table 1 The Buck-Boost parameters

E

Input voltage

20 [V]

L

Inductance

15.91 [mH]

C

Capacitance

470 [µF]

R

Loading resistance

20 []

Vref

The reference output voltage

−40 [V]

Iref

The reference inductor current

6 [A]

Uref

Initial duty cycle

66.67%

VD

Diode voltage drop

0.7 [V]

RD

Diode on-resistance

0.05 []

RSW

Mosfet on-resistance

0.1 []

FSW

Commutation frequency

20 [kHz]

State Feedback Control of DC-DC Converter ...

1705

Fig. 5 Open and closed loop control

Open-Loop Closed-

Open-Loop Closed-

Fig. 6 Simulation results of open and closed-loop with LQR controller

5.1 Analysis for Open and Closed-Loop Control In this case, the simulation of the buck boost converter is carried out for two cases for the control behavior of the converter in the Open and Closed-Loop control using LQR. The SimPowerSystems model is shown in Fig. 5. According to results of the simulation, the waveform of output voltage and the inductance current have the same waveform characteristic for the open and closed loop control (Fig. 6). The value of the output voltage in the closed-loop is approximately −40 V and about −37 V in the open-loop. Then the average value of the inductance current is about 6 A and 5.6 A in closed and open-loop respectively. A comparison between the simulation results shows that the control with closed-loop using LQR has better dynamics response compared with open-loop as well as in the maximum peak overshoot/ undershoots, settling time.

1706

D. Taibi et al.

Fig. 7 SimPowerSystems model of buck-boost converter with LQR and Kalman filter observer

Estimated Measured

Fig. 8 Simulation results for estimated and measured output voltage

Estimated Measured

Fig. 9 Simulation results for estimated and measured inductor current

State Feedback Control of DC-DC Converter ...

1707

5.2 Analysis for Buck-Boost Converter with LQR and Kalman Filter Obsever This section presents a simulation of the dynamic performance of an LQR controller with a Kalman filter observer. The algorithm of the observer was implemented using the Continuous-Time Kalman filter block. The Simulink model with LQR and Kalman filter observer is shown in Fig. 7. In the Kalman filter observer, the matrices QK and RK are difficult to know exactly, since the noises w and v are not known. The only possible method is to adjust the QK and RK values using simulations. In this simulation, we use the values as follows:

QK =

109 0 0 104



R K = 103

The estimated and measured output voltage and inductance current at an output voltage reference of −40 V are shown in Fig. 8 and Fig. 9. In both static and dynamic cases, it can be seen that the signals estimated by the KF observer track the real signals very well.

5.3 Effect of a Measurement Noise In many applications, the measurement noise caused by the hardware or the environment has a significant impact on the system. The estimation accuracy of KF is tested in this paper under noisy output voltage measurement. Figure 10 shows the noise injected at the output voltage in the range of [0–2 A]. The noise is zero mean, white Gaussian. The aim of the voltage injection is to observe the low pass filter characteristics of KF. Figure 11 shows the output voltage, the inductor current shown in Fig. 12. The accuracy of the state estimate can be increased by increasing the covariance of

Fig. 10 Injected noise to output voltage

1708

D. Taibi et al.

Noisey Estimated Measured

Fig. 11 Measured and estimated, output voltage with measured noisy

Estimated Measured

Fig. 12 Measured and estimated inductor current

the measurement noise under noisy conditions, which makes the system model will have more importance.

6 Conclusion This paper has demonstrated the process of applying the state feedback control of a buck-boost converter using LQR with integral action controller. The clear advantages of state feedback, that has a positive effect on response settling time, reducing the undesirable peak overshoots and serve having a less oscillated performance, referring that this approach doesn’t provide a zero static error, this latter has been solved by adding an integral action to state feedback control. The simulation results found that adding a Kalman filter reduces the cost and impact of the sensor on LQR tracking performance.

State Feedback Control of DC-DC Converter ...

1709

References 1. Kazimierczuk MK (2015) Pulse-width modulated DC-DC power converters. Wiley, Hoboken 2. Boyette A (2006) Contrôle-commande d’un générateur asynchrone à double alimentation avec système de stockage pour la production éolienne. Thèse de doctorat. Université Henri PoincaréNancy 1 3. Zhang Q, Min R, Tong Q, Zou X, Liu Z, Shen A (2014) Sensorless predictive current controlled DC–DC converter with a self-correction differential current observer. IEEE Trans Industr Electron 61(12):6747–6757 4. Michel L (2012) Pilotage optimal des IGBT et commande sans-modèle des convertisseurs de puissance. Thèse de doctorat. Université du Québec à Trois-Rivières 5. Zhuo S, Gaillard A, Xu L, Bai H, Paire D, Gao F (2020) Enhanced robust control of a DC–DC converter for fuel cell application based on high-order extended state observer. IEEE Trans Transp Electrif 6(1):278–287 6. Agrawal N, Samanta S, Ghosh S (2020) Modified LQR technique for fuel-cell-integrated boost converter. IEEE Trans Ind Electron 68(7):5887–5896 7. Battiston A (2014) Modélisation, commande, stabilité et mise en oeuvre des onduleurs à source impédante: application aux systèmes embarqués. Thèse de doctorat. Université de Lorraine 8. Mojallizadeh MR, Badamchizadeh MA (2018) Hybrid control of single-inductor multipleoutput converters. IEEE Trans Ind Electron 66(1):451–458 9. Gateau G, Maussion P, Meynard T (1997) De la modélisation a la commande non linéaire des convertisseurs multicellulaires série Application à la fonction hacheur. J Phys III 7(6):1277– 1305 10. Taibi D, Titaouine A, Benchabane F, Bennis O (2015) Stability analysis of the extended Kalman filter for permanent magnet synchronous motor. J Appl Eng Sci Technol 1(2):51–60 11. Fang S, Wang X (2020) Modeling and analysis method of fractional order buck boost converter. Int J Circuit Theory Appl 48(9):1493–1510 12. Ahmed NA (2005) Modeling and simulation of AC–DC buck-boost converter fed DC motor with uniform PWM technique. Electr Power Syst Res 73(3):363–372 13. Hernandez L (2017) Etude et réalisation d’un convertisseur AC/DC Buck Boost réversible à haut rendement pour alimentation de secours. Thèse de doctorat. Université de toulouse 14. Park M, Kang Y (2021) Experimental verification of a drift controller for autonomous vehicle tracking: a circular trajectory using LQR method. Int J Control Autom Syst 19(1):404–416 15. Abbas NH, Algamluoli AF (2020) Designing an integral LQR controller for DC-DC Xconverter based on enhanced shuffled frog-leaping optimization algorithm. J Electr Syst 16(2):152–170 16. Dabin V (2018) Commande d’un quadricoptère par rejet actif de perturbations. Thèse de doctorat. École Polytechnique de Montréal 17. Bai W, Xue W, Huang Y, Fang H (2018) On extended state based Kalman filter design for a class of nonlinear time-varying uncertain systems. Sci China Inf Sci 61(4):1–16

Backstepping and Indirect Vector Control for Rotor Side Converter of Doubly Fed-Induction Generator with Maximum Power Point Tracking Elmostafa Chetouani, Youssef Errami, Abdellatif Obbadi, and Smail Sahnoun Abstract This manuscript proposes a comparative analysis between the Backstepping and Indirect Field-Oriented Control (IFOC) based on conventional ProportionalIntegral regulator strategies of a wind energy conversion system based on a doublyfed induction generator. The stator of the generator is directly coupled to the electrical network. However, the rotor is branched to the grid through two pulse width modulation converters. This article aims to control the active and reactive power independently by the two proposed methods. Furthermore, the maximum power tracking with speed regulation is established and analyzed. The simulation results, taking into consideration the wind speed variation of the wind energy chain using doubly-fed induction of 5 MW, are realized by the Matlab/Simulink environment. Keywords Backstepping (BS) · Indirect Field-Oriented Control (IFOC) · Maximum Power Tracking (MPT) · Proportional-Integral controller (PI)

1 Introduction In the world as it is today and without electricity, daily life would be difficult to imagine. It is, therefore, necessary to produce electric energy efficiently and continuously. Following the terrible damage caused by the production of electricity on the environment employing fossils combustible such as coal, oil, and natural gas. Renewable energy-based on inexhaustible energy sources remains the best solution to replace the existing energy sources [1]. Recently, wind energy, which is considered clean and sustainable energy, has known a great development and exploitation, thanks to the strong growth in the research domain. The wind energy system has several configurations. Figure 1 represents the studied system configuration, which is widely used and dedicated to wind energy applications. The wind turbine shaft is connected to the doubly fed-induction generator through the gearbox to conform the slow-speed to the high one. DFIG stator is directly coupled to the electrical network. E. Chetouani (B) · Y. Errami · A. Obbadi · S. Sahnoun Laboratory: Electronics, Instrumentation and Energy, Faculty of Science, University Chouaib Doukkali, Eljadida, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_155

1711

1712

E. Chetouani et al.

Fig. 1 Whole chain of wind energy conversion system

However, the rotor is branched to the power grid via bidirectional converters. Using the generator integrated with a wind energy system has many advantages such as the ability to use a partial sized converter in the rotor to control the power, minimizing power losses and cost, making fewer efforts on mechanical parts, noise reduction, the control of active and reactive power, and a controllable power factor [2]. The generator is a non-linear system and has complex and coupling equations. The vector control based on the stator or rotor field orientation combined with the Park transformation makes the control of the generator a simple and easier task. Besides, this vector control permits the command of the DFIG as a separately excited DC machine, which makes it very popular in the industry. The direct and indirect methods are two variants of the vector control. These types have been compared by Bouderbala et al. [3]. The authors have shown that indirect control presents a satisfactory performance than direct vector controls. The vector command based on the classical PI controller has demonstrated performance limitations, especially when the parameters of the generator change. Besides, the non-linear Backstepping approach, based on the stability of the Lyapunov function, has been proposed by several researchers to improve the performance of the system. S. Mensou et al. [4] have established a Backstepping strategy to control the whole wind energy conversion system. The produced mechanical power from wind energy depends on the characteristics of each turbine, and eventually the wind variable speed. Consequently, tracking the maximal power generated is required when the profile airspeed changes. To perform the MPT, several techniques have been developed, and they can be divided into two categories [5, 6]. The first one has required the characteristic aerodynamic curve of wind speed information, and the second one does not need any information about wind speed to generate excellent speed reference. The objective of this paper is to establish a comparative study between the Backstepping and the Open Loop Indirect Field

Backstepping and Indirect Vector Control for Rotor Side Converter ...

1713

Oriented strategy for piloting the RSC converter of the induction generator. Also, the maximum power following with speed control is established to generate the power reference and the optimal rotation speed driving the generator. The article is organized in this way: Sect. 2 introduces the modeling of the WECS. Section 3 discusses the MPT with a speed control strategy computed by the classical PI controller. Section 4 proposes the control of the stator powers (Active and Reactive) by using Backstepping and indirect vector control strategies. Section 5 examines and compares the simulation results. Finally, Sect. 6 highlights the conclusion.

2 Modeling of the Wind Power System 2.1 Model of the Turbine The turbine mechanical power is expressed as [7, 8]: PTu =

1 · Cp (λ, β) · ρ.π · R2 · V3W 2

(1)

The mechanical torque TTu is written as below: TTu =

PTu Tu

(2)

Where, ρ is the air density (kg/m3 ), R is the blade ray (m), Tu is the angular velocity of the turbine, and CP represents the performance factor of the turbine. CP can be formulated as [8]:  CP (β, λ) =[0.5 − 0.0167.(β − 2)].sin − 0.00184.(λ − 3).(β − 2)

π(λ + 0.1) 18.5 − 0.3.(β − 2)



(3)

This coefficient is calculated by the tip speed λ and the angle β of the blade pitch. The latter is fixed to β = 2◦ for having CPmax . Equation (4) defines the expression of λ [7]: λ=

R.Tu V

(4)

1714

E. Chetouani et al.

2.2 Model of the Gearbox Equation (5) presents the mechanical equation of the system, taking into consideration that the overall mechanical dynamics are brought back to the turbine shaft, [5–7]: Jtot .

dmec + f.mec = Tg − Tem dt

(5)

Where Tg =

TTu GB

and

GB =

mec Tu

(6)

Where Jtot is the overall inertia of wind energy system, TTu is the turbine torque, TTem is the electro-magnetic torque of the generator, f is the overall viscous coefficient of friction, mec is the rotational speed at the rotor shaft of the gearbox (rad/s) and GB is the gearbox multiplier.

2.3 Model of The Doubly Fed-Induction Generator The Park transformation allows simplifying the general electrical model, which is determined by the following equations. Stator and rotor voltages equations are [2, 8]: ⎧ ⎪ V = Rs isd + dϕdtsd − ωs ϕsq ⎪ ⎪ sd ⎨ dϕ Vsq = Rs isq + dtsq + ωs ϕsd (7) dϕrd ⎪ ⎪ Vrd = Rr ird + dt − ωr ϕrq ⎪ ⎩ V = R i + dϕrq + ω ϕ rq r rq r rd dt Where, Vr and Vs are the voltages; is and ir are the currents; ϕs and ϕr are the flux; Rs and Rr are the resistances; ωs and ωr are the angular frequencies; Ls and Lr are the inductances; M is the mutual inductance. The r and s denote the rotor and stator, respectively. The electromagnetic torque can be expressed as follows [8]: Tem

M = −p. (irq ϕsd − ird ϕsq Ls

 (8)

Where p is the number of generator pole pairs. The stator and rotor powers are expressed as follows [2, 8]: Ps = Vsd .isd + Vsq .isq

(9)

Backstepping and Indirect Vector Control for Rotor Side Converter ...

Qs = Vsd .isd − Vsd .isq

1715

(10)

3 Maximum Power Point Tracking During the normal functioning of the wind turbine, the maximum power control method is developed to exploit the energy available in the wind as much as possible. The MPT method with mechanical speed control is established. This technique consists of maintaining the generator speed at its reference, which is maximized when the Cp is optimal. The electro-magnetic torque developed by DFIG is equal to its reference value imposed by the control defined as: Tem = Tem−opt

(11)

The optimal electro-magnetic torque Tem-opt for obtaining a rotation speed equal to the optimal speed is given as follows: Tem−opt

 1 = Kpmppt + Kimppt . .[mec−opt − mec ] S

(12)

Where, KPmppt and Kimppt are the PI controller gains. The optimal speed (mec-opt ) is: mec−opt = GB .Tu−opt ; With Tu−opt =

Vw .λopt R

(13)

Determination of the PI Gains for MPPT. The PI controller parameters are determined by the pole compensation method. The time constant of the system (Tsys ) is: Tsys =

Jtot f

(14)

The gains of the controller are expressed as: Kimppt =

Tsys −K imppt .Jtot 1 And Kpmppt = ; with τ = τ.f f 1000

(15)

1716

E. Chetouani et al.

4 Rotor Side Converter (RSC) Control Strategies 4.1 Powers Control by Using Indirect Field-Oriented Method To command independently the powers of generator and to deal with the coupling problem of the system, the indirect vector control is applied. In this paper, the powers are controlled in the open loop and the rotor currents are controller in the closedloop. Stator flux is considered constant and is oriented according to d-axis. The stator resistance is neglected and the stator voltage equation can be simplified as [2, 8]: ϕsd = ϕs , ϕsq = 0; Vsd = 0 Vsq = Vs = ωs .ϕs

(16)

Equations of rotor voltages are [2, 8]:      M2 M2 .s ird − gωs Lr − irq Vrd = Rr + Lr − Ls Ls      M2 M2 Vs M .s irq + gωs Lr − ird + g Vrq = Rr + Lr − Ls Ls Ls

(17)

(18)

From Eqs. (17)–(18), the rotor currents expressions are deduced as follows:      M2 M2 irq / Rr + Lr − .s ird = Vrd + gωs Lr − Ls Ls      M2 Vs M M2 irq = [V rq − gωs Lr − ird − g .s ]/ Rr + Lr − Ls Ls Ls 

(19)

(20)

The current references can be expressed as follows [2, 8]: Ls .P∗ M.Vs s

(21)

Ls Vs2 .(Qs∗ − ) M.Vs Ws Ls

(22)

∗ irq =−

∗ ird =−

The voltage references are expressed easily from Eqs. (17)–(18) as follows: 

1 Vs M ∗ ∗ Vrq = irq − irq .[Kp−rsc1 + Ki−rsc1 . ] + erd + g S Ls

(23)

∗ 1 ∗ Vrd = ird − ird .[Kp−rsc2 + Ki−rsc2 . ] + erq S

(24)

Backstepping and Indirect Vector Control for Rotor Side Converter ...

1717

Where:     M2 M2 erd = gωs Lr − .ird And erq = gωs Lr − .irq Ls Ls

(25)

Determination of the Gains of PI Controller. To set the PI parameters (Kprsc ,Kirsc ), the pole compensation method is utilized. The time constant of the system is: Ts = (Lr −

M2 )/Rr Ls

(26)

The equations of PI parameters (Kprsc ,Kirsc ) are given as follows: Kprsc =

Kprsc .Rr 1 M2 Ts With Trsc = .(Lr − ) And Kirsc = 2 M Trsc Ls 100 (Lr − Ls )

(27)

4.2 Powers Control by Backstepping Technique (BS) The RSC is designed by Backstepping procedure to pilot the powers. For this task, virtual control should be designed based on the rotor currents. The system stability and performance are attained by utilizing a Lyapunov theorem, which is exploited to establish the control of the system. Considering, the Eqs. (19)–(20), the derivation of the rotor currents equations can be deduced as follows: 1 dird  = dt Lr . 1 − dirq 1  = dt Lr . 1 −

M2 Lr .Ls

M2 Lr .Ls

  M2  (Vrd − Rr ird − g.ωs Lr 1 − .irq ) Lr .Ls

  M2 M.Vs .g  (Vrq − Rr irq − g.ωs Lr 1 − .irq − ) Lr .Ls Ls

(28)

(29)

Calculating the errors between desired rotor currents and the actual one to stabilize it by Backstepping in the first step as: ∗ ∗ − irq And εrsc2 = ird − ird εrsc1 = irq

(30)

The derivative of the errors is given by: ∗ dirq dirq dεrsc1 dεrsc2 di∗ dird = − And = rd − dt dt dt dt dt dt

(31)

1718

E. Chetouani et al.

So, by replacing the time derivative of currents in Eq. (31), we obtain: ∗ dirq dεrsc1 1  = − dt dt Lr . 1 −

M2 Lr .Ls

  M2 M.Vs .g  (Vrq − Rr .irq − g.ωs Lr 1 − .irq − ) Lr .Ls Ls (32)

dεrsc2 di∗ 1  = rd − dt dt Lr . 1 −

M2 Lr .Ls

  M2  (Vrd − Rr .ird + g.ωs Lr 1 − .irq ) Lr .Ls

(33)

The Lyapunov candidate function is defined, in a second step, as follows [4, 9]: ε2 ε2rsc1 + rsc2 2 2

Lf−rsc =

(34)

The derivation of Lyapunov function is expressed as: L˙ f−rsc = εrsc1 .˙εrsc1 + εrsc2 .˙εrsc2

(35)

So, replacing the errors derivation in Eq. (35), the Lyapunov derivative rewrites as: ⎡ ⎤   di∗ M2 M.Vs .g ⎦ 1 ˙Lf−rsc =εrsc1 .⎣ rq −   (Vrq − Rr irq − g.ωs Lr 1 − .irq − ) 2 dt Lr .Ls Ls Lr . 1 − LM.L r s ⎡ ⎤   ∗ 2 di M 1 ⎢ ⎥  (Vrd − R ird + g.ωs Lr 1 −  + εrsc2 .⎣ rd − (36) .irq )⎦ 2 dt Lr .Ls L . 1− M r

Lr .Ls

r

To ensure the system stability, according to Lyapunov function, the derivation of Lf−rsc must be negative and is given as follows [7, 9]: L˙ f−rsc = −K1 .ε2rsc1 − K2 .ε2rsc2

(37)

Where, K1 and K2 are positive constants. By equalizing the Eq. (36) and the Eq. (37), the virtual commands Vrd and Vrq are deduced directly as follows: ⎡ ⎢ ⎢ ⎢ ⎢ ∗ =L . 1− Vrq .⎢ r Lr .Ls ⎢ ⎢ ⎣ 

M2

  ⎤ 2 g.ωs Lr 1 − LM.L r s  irq +  .ird ⎥   + ⎥ 2 2 dt ⎥ Lr . 1 − LM.L Lr . 1 − LM.L ⎥ r s r s ⎥ M.Vs .g ⎥ ⎥ Ls ⎦   + .ε + K 1 rsc1 2 Lr . 1 − LM.L

∗ dirq

Rr

r

s

(38)

Backstepping and Indirect Vector Control for Rotor Side Converter ...

1719

  ⎤ 2 ⎡ ∗ 2 g.ωs Lr 1 − LM.L di R M r s r ∗ =L . 1−  ird −  .irq + K2 .εrsc2 ⎦   Vrd .⎣ rd + r 2 2 Lr .Ls dt Lr . 1 − LM.L Lr . 1 − LM.L r s r s 

(39)

5 Simulation Results and Discussion With help of the before-mentioned mathematical models, the wind chain is modeled and simulated employing the parameters presented in Tables 1 and 2 under Matlab/Simulink software. The wind profile used in this work is presented in Fig. 2. However, Fig. 3 illustrates the produced mechanical power, which is pursuing the evolution of the wind speed. From 0.5 (s) to 0.8 (s) the power is equal to 5 MW, which corresponds to the rated value of the wind velocity that is 12.5 (m/s). However, Fig. 4 shows the mechanical speed computed by the MPT strategy based on PI controller. This power will be utilized as a reference of power for driving the system. Figures 5 and 6 show the efficiency of the MPT strategy for making constant the tip speed ratio at its best value, which is λopt = 9.19 and the coefficient of power is preserved at its maximum (Cpmax = 0.5). So, the maximal power is achieved. As you can see in Fig. 7, the stator active power is computed by the Backstepping (BS), in which the positive constants are chosen by trial and error method as follows: K1 = K2 = 9000, to satisfy convergence condition, and the Indirect Field-Oriented control based on the

Fig. 2 Wind speed profile (m/s)

Fig. 3 Extracted mechanical power by MPT strategy

1720

Fig. 4 Mechanical rotation speed of the DFIG

Fig. 5 Tip Speed Ratio (TSR)

Fig. 6 Coefficient of power (Cp)

Fig. 7 Stator active power (W)

E. Chetouani et al.

Backstepping and Indirect Vector Control for Rotor Side Converter ...

1721

PI controller parameters. It is notable, in Fig. 8, that the time response is ameliorated from 10 (ms) to 1.4 (ms). Besides, the static error is improved. The desired reactive power is zero (Qs * = 0) to ensure a power factor equal to 1, as shown in Fig. 9. It is observable that the response time is also improved, and the stability is guaranteed when especially wind changes. Figure 10 shows the unit power factor, which is approved by the proposed method. The robustness test is established by varying the stator leakage inductance of 100% (Ls = 2.Ls), as shown in Fig. 11. However, Fig. 12 shows a zoom of stator active power when the turbine operates at its rated power. As you can see, the stability is guaranteed by the Backstepping and is lost by using the PI controller. So, the Backstepping control gives better performance than the vector control.

Fig. 8 Stator active power (W) -Zoom

Fig. 9 Stator reactive power (VAR)

Fig. 10 Unit power factor (UPF)

1722

E. Chetouani et al.

Fig. 11 Stator active power- robustness test (Ls = 2.Ls)

Fig. 12 Stator active power- robustness test (Ls = 2.Ls) - zoom

6 Conclusion In this paper, the wind energy conversion system is modeled and simulated based on variable wind speed. The Maximum Power Tracking with speed regulation is established. Then, the control of the rotor side converter is designed by employing the IFOC based on the classical PI controller, in which the gains are calculated by the pole compensation method, and the Backstepping based on the Lyapunov stability which the positive constants are chosen. The results of the two strategies are compared and analyzed. It is observed that the Backstepping brings satisfactory and attractive results than the indirect vector control under wind speed variation. The robustness test shows that the proposed method ensures good stability of the system when the inductance of the DFIG changes. The Backstepping technique improves the stability, the time response, and ameliorates the static error than indirect vector control based on the classical PI controller.

Appendix (See Tables 1 and 2)

Backstepping and Indirect Vector Control for Rotor Side Converter ... Table 1 Proportional and Integral (PI) gains for IFOC and MPPT control

Table 2 Set of parameters used in the simulation

1723

Kpmppt

Kimppt

Kprsc1, 2

Kirsc1, 2

−7.2e + 6

51.87

0.1446

0.2376

Turbine

DFIG

Ray of blade

R

51,583 m

Coefficient of multiplier

GB

47,23

Total moment of inertia

Jtot

1000 kg.m2

DFIG rated power

Ps

5 MW

Stator inductance

Ls

1,2721 mH

Rotor resistance

Rr

1,446 m

Rotor inductance

Lr

1,1194 mH

Mutual inductance

M

0,55182 mH

References 1. Noussi K, Abouloifa A, Katir H, Lachkar I (2019) Modeling and control of a wind turbine based on a doubly fed induction generator. In: 2019 4th world conference on complex systems (WCCS), Ouarzazate, Morocco, pp 1–5. https://doi.org/10.1109/ICoCS.2019.8930738 2. Lamnadi M, Trihi M, Bossoufi B, Boulezhar A (2016) Modeling and control of a doublyfed induction generator for wind turbine- generator systems. Int J Power Electron Drive Syst (IJPEDS). 7(3):973–985. https://doi.org/10.11591/ijpeds.v7.i3.pp982-994 3. Bouderbala M, Bossoufi B, Lagrioui A, Taoussi M, Aroussi H, Ihedrane Y (2018) Direct and indirect vector control of a doubly fed induction generator based in a wind energy conversion system. Int J Electr Comput Eng (IJECE). 9(3):1531–1540. https://doi.org/10.11591/ijece.v9i3. pp1531-1540 4. Mensou S, Essadki A, Minka I, Nasser T, Bououlid Idrissi B (2018) Backstepping controller for a variable wind speed energy conversion system based on a DFIG. Int J Electr Comput Eng 12(9) 5. Omar Baba A, Liu G, Chen X (2020) Classification and evaluation review of maximum power point tracking methods. Sustainable Futures 2:100020. https://doi.org/10.1016/j.sftr.2020. 100020 6. Errami Y, Ouassaid M, Cherkaoui M, Maaroufi M (2015) Maximum power point tracking control based on a nonlinear backstepping approach for a permanent magnet synchronous generator wind energy conversion system connected to a utility grid. Energy Technol 3:743–757. https://doi. org/10.1002/ente.201500026 7. Errami Y, Obbadi A, Sahnoun S, Benhmida M, Ouassaid M, Maaroufi M (2016) Design of a nonlinear backstepping control strategy of grid interconnected wind power system based PMSG. In: AIP conference proceedings, vol 1758, p 030053. https://doi.org/10.1063/1.4959449 8. Bouderbala M, Bossoufi B, Lagrioui A, Taoussi M, Alami Aroussi H, Ihedrane Y (2018) Direct and indirect vector control of a doubly-fed induction generator based in a wind energy conversion system. Int J Electr Comput Eng (IJECE) 9(3):1531–1540. https://doi.org/10.11591/ijece.v9i3. pp1531-1540 9. Nadour M, Essadki A, Nasser T (2017) Comparative analysis between PI & backstepping control strategies of DFIG driven by wind turbine. Int J Renew Energy Res 7(3):1307–1316

Integral Backstepping Power Control of DFIG Based Nonlinear Modeling Using Voltage Oriented Control Mourad Loucif, Abdelkader Mechernene, and Badre Bossoufi

Abstract In this paper, the control of the wind system, the mechanical part consisting of the wind turbine with maximum power point tracking control, and the electrical part using a double fed induction generator is studied, taking into consideration the full nonlinear model of the DFIG with voltage oriented control. The control strategy is applied to the rotor side converter with an integral backstepping controller of the active and reactive stator powers operating at sub-synchronous speed. The simulation results show a good capability to monitor the approach proposed at different variable wind speeds. Keywords Wind turbine · MPPT · DFIG · Voltage oriented control · Integral backstepping

1 Introduction The wind has many advantages and has been used by man for many thousands of years for both sailing and milling. Today, it is one of the main uses of wind energy, because of its free availability, inexhaustible and its clean energy [1, 2]. DFIG is broadly applied in the Wind Energy Conversion System (WECS) due to its mature technology and high power production efficiency. With the advantages of long distance and large transmission capacity, series compensated transmission line is often used to transfer large capacity wind energy to remote load center. Backstepping control uses the Lyapunov function to ensure convergence of the error of position tracking from all available initial conditions [3, 4]. An integral added is integrated in the reverse design. The error integral is added to the first function of stabilization in the backstepping approach design to remove the steady state error and increase the rejection of disturbances [5]. M. Loucif (B) · A. Mechernene Laboratoire d’Automatique de Tlemcen (LAT), Université de Tlemcen, Chetouane, Algeria B. Bossoufi Laboratory of Engineering Modeling and Systems Analysis, SMBA University, Fez, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_156

1725

1726

M. Loucif et al.

The structure of the paper is as follows. Section 1 discusses the modeling and control of the wind turbine to get the maximum power point tracking with linear controller PI. Section 2 describes a complete model of the double fed induction generator within the framework of synchronous rotation dq and a new modeling approach that uses active and reactive power as states. We adopt the voltage oriented approach for DFIG to propose a simplified active and reactive stator power control. The integral backstepping design is discussed in Sect. 3. Section 4 and 5 provide the numerical results and concluding remarks, respectively.

2 MPPT Control of Wind Turbine The power and the torque delivered by the wind turbine are defined by [6, 7]: ⎧ 1 ⎪ 2 3 ⎪ ⎨ Paer = C p (λ, β).ρ.π.R .V 2 1 ρ ⎪ ⎪ ⎩ Taer = C p (λ, β). .π.R 2 .V 3 2 tur b

(1)

With ρ is the air density, V is the wind speed; R is the rotor radius. The expression of power coefficient Cp(λ, β) who is a function of pitch angle β and tip speed ratio λ, is written as shown below [8]:  C p = 0, 5176 − 116.(β − 0, 4). sin

π.(λ + 0.1) 14.8 − 0.3.(β − 2)

 − 5.(λ − 21).(β − 0, 4) (2)

λ=

tur b .R V

(3)

The expression of the gearbox G is given by: G=

Ttur b  = Tg tur b

(4)

The mechanical equation is given by [18]: J.

d = Tg − Tem − B. dt

(5)

The power coefficient expression (2) has been shown to have a unique maximum, corresponding to the optimal choice of the tip speed ratio λopt = 9,15 and the power coefficient take a maximum value of C pmax = 0,5 for a pitch angle is fixed β = 2° [9].

Integral Backstepping Power Control of DFIG Based Nonlinear Modeling ...

1727

To get the maximum power, we use the MPPT control with wind speed measurement developed in paper [6]. For the optimal tip speed ratio λopt , (3) and (4) provide the optimal rotational speed generator as ∗ =

λopt .V R

(6)

3 Voltage Oriented Control of DFIG The mathematical model of DFIG has been obtained after the according to the voltage equations and equations of flux, expressed in a dq reference frame rotating at synchronous speed, the differential equations of the stator and rotor circuits of the generator with stator and rotor current as state variables can be obtained by [10, 13]. ⎧ di sd ⎪ ⎪ ⎪ ⎪ dt ⎪ ⎪ ⎪ ⎪ di ⎪ sq ⎪ ⎨ dt ⎪ ⎪ dir d ⎪ ⎪ ⎪ dt ⎪ ⎪ ⎪ ⎪ di ⎪ ⎩ rq dt

= −a1 .i sd + (a.ω + ωs ).i sq + a3 .ir d + a5 .ω.irq + b1 .Vsd − b3 .Vr d = −(a.ω + ωs ).i sd − a1 .i sq − a5 .ω.ir d + a3 .irq + b1 .Vsq − b3 .Vrq  ω = a4 .i sd − a5 .ω.i sq − a2 .ir d + ωs − .irq − b3 .Vsd + b2 .Vr d σ  ω .ir d − a2 .irq − b3 .Vsq + b2 .Vrq = a6 .ω.i sd + a4 .i sq − ωs − σ

(7)

M2

Rs Rr .Msr Rs .Msr Rr , a1 = σ.L , a2 = σ.L , a3 = σ.L , a4 = σ.L , Where, σ = 1 − L s .Lsr r , a = 1−σ σ s r s .L r s .L r Msr Msr Msr 1 1 a5 = σ.L s , a6 = σ.L r , b1 = σ.L s , b2 = σ.L r , b3 = σ.L s .L r . Stator and rotor resistances (Rs and Rr ). ω = p. electrical speed and p is the number of poles pair. Stator and rotor inductances (L s L r ) and the magnetizing inductance (M sr ). The direct and quadrature stator and rotor currents and voltage are given as follow (isd , isq , ird , irq and V sd , V sq , V rd , V rq ). The electromagnetic torque can be described by fundamental torque Eq. (8):

⎧  Msr

⎪ ⎪ ϕsq .ir d − ϕsd .irq ⎨ Te = p Ls ⎪ 1 d ⎪ ⎩ = (Te − Tl − f.) dt J

(8)

The active and reactive powers of the stator are given as described below [18]:

Ps = Vsd .i sd + Vsq .i sq Q s = Vsq .i sd − Vsd .i sq

(9)

1728

M. Loucif et al.

Derivatives active and reactive powers of the stator: ⎧ di sq di sd ⎪ + Vsq . ⎨ P˙s = Vsd . dt dt di di ⎪ sq sd ⎩ Q˙ = V . − Vsd . s sq dt dt

(10)

Substituting (7) in (10), the derivatives stator powers can then be expressed as: ⎧  



P˙s = −a1 .Ps − (ωs + a.ω)Q s + a5 .ω.Vsd + a3 .Vsq .irq + b1 . Vsd2 + Vsq2 ⎪ ⎪ ⎪ ⎪ ⎨ + a .V − a .ω.V i − b V .V + V .V  3 sd 5 sq r d 3 sd r d sq rq



 ˙ ⎪ Q = (ω + a.ω)P − a .Q + b .V − Vsq .Vr d + a5 .ω.Vsd + a3 .Vsq ir d V ⎪ s s s 1 s 3 sd rq ⎪ ⎪  ⎩

+ a5 .ω.Vsq − a3 .Vsd irq (11) The idea of the proposed control is a voltage oriented control (VOC), in which the control reference is chosen to align with the q-axis voltage grid component [11, 12]: Vsd = 0, Vsq = Vs (cst)

(12)

The expressions of the derivatives stator powers (10) are easily simplified as:

P˙s = −a1 .Ps − (ωs + a.ω)Q s + a3 .Vs .irq + b1 .Vs2 − a5 .ω.Vs .ir d − b3 .Vs .Vrq Q˙ s = (ωs + a.ω)Ps − a1 .Q s − b3 .Vs .Vr d + a3 .Vs .ir d + a5 .ω.Vs .irq (13)

4 Integral Backstepping Control of Rotor Side Converter Backstepping is a nonlinear control scheme which is based on Lyapunov’s stability theorem. The technique may be applied to synthesize a command function in order to force a system to follow a desired trajectory [16, 17]. The control law is designed by iteratively selecting various state variables appropriate as virtual inputs for the smaller subsystems of the overall system. Lyapunov functions are then designed for each stable virtual controller. As a result, the final real control law guarantees the asymptotic balance of the total control system [14]. The additional integrator improves the robustness of the system against model uncertainties and outside disturbances. A more detailed discussion of the backstepping control approach can be seen in [3, 15]. The active and reactive power tracking errors e1 and e2 are defined respectively by:

Integral Backstepping Power Control of DFIG Based Nonlinear Modeling ...



e1 = Psr e f − Ps

(14)

e2 = Q rs e f − Q s ref

1729

ref

Where Ps and Q s are the expected active and reactive powers benchmarks. The derivative of the equation error variables are derived as:

e˙1 = P˙sr e f − P˙s e˙2 = Q˙ rs e f − Q˙ s

(15)

⎧ ⎨ e˙ = P˙ r e f + a .Ps + (ωs + a.ω)Q s − b .V 2 − a .Vs .irq + b Vs .Vrq + a .ω.Vs .i 1 1 1 s 3 3 rd s 5 ⎩ e˙ = Q˙ r e f − (ω + a.ω)P + a .Q − a .V .i + b .V .V − a .ω.V .i s s s rq 2 1 s 3 s rd s 5 s rd 5

(16) Let us consider: ⎧ ⎨ e˙01 = e1

t , ⎩ e01 = λ e1 (τ )dτ

⎧ ⎨ e˙02 = e2

t ⎩ e02 = λ e2 (τ )dτ

0

(17)

0

Select a Lyapunov function candidate: ⎧ 1 ⎪ ⎨ V (e1 ) = .e12 + 2 ⎪ ⎩ V (e ) = 1 .e2 + 2 2 2

1 2 .e 2 01 1 2 .e 2 02

(18)

The derivative of Lyapunov function are variables gives:

V˙ (e1 ) = e1 .e˙1 + λe01 .e1 V˙ (e2 ) = e2 .e˙2 + λe02 .e2



V (e1 ) = e1 .(e˙1 + λe01 ) V˙ (e2 ) = e2 .(e˙2 + λe02 )

(19)

⎧  re f ⎪ ⎨ V˙ (e1 ) = e1 . P˙s + a1 .Ps + (ωs + a.ω)Q s − b1 .Vs2 + b3 .Vs .Vrq + a5 .ω.Vs .ird − a3 .Vs .irq + λe01  f ⎪ ⎩ V˙ (e2 ) = e2 . Q˙ re − (ωs + a.ω)Ps + a1 .Q s + b3 .Vs .Vrd − a5 .Vs .ird − a5 .ω.Vs irq + λe02 s

(20) The control algorithm proposed which is based on integral backstepping has been introduced: ⎧ 1  re f 2 ⎪ ⎪ ⎨ Vrq = b .V − P˙s − a1 .Ps − (ωs + a.ω)Q s + b1 .Vs − a5 .ω.Vs .ird + a3 .Vs .irq − k1 .e1 − λ.e01 3 s ⎪ 1  ˙ re f ⎪ ⎩ Vrd = − Q s + (ωs + a.ω)Ps − a1 .Q s + a5 .Vs .ird + a5 .ω.Vs .irq − k2 .e2 − λ.e02 b3 .Vs

(21)

1730

M. Loucif et al.

Fig. 1 Diagramme of simulation integral backstepping associate with mechanical part

With λ, k1 and k2 are positives constants.

V˙ (e1 ) = −k1 .e12 V˙ (e2 ) = −k2 .e22

(22)

The diagram of simulation nonlinear integral backstepping powers control of DFIG associate with MPPT control of wind turbine is shown in the Fig. 1.

5 Interpretation and Results The purpose of validating the comportment of the association of the mechanical part with wind turbine, to extract the MPPT we use linear PI controller and electrical part with DFIG, nonlinear integral backstepping controller is being applied to control the active and reactive stator power. The wind turbine and DFIG parameters are provided in the appendix. The wind speed profile will be simulated in deterministic representation by a sum of many harmonics is defined by the relation: V = 7 + 0, 2 sin(0, 1047t) + 2 sin(0, 2665t) + sin(1, 293t) + 0, 2 sin(3, 6645t) (23) Figure 2 show the wind speed model used in this validation simulation chosen for an average speed of 7 m/s, operate into sub-synchronous mode. It is observed from Fig. 3, that the controller proportional and integral for maximum power point closely tracks the generator speed as well the profile of the wind proposed in steady state, and a slight overshoot in transient state. The operation is always sub-synchronous mode; the slip is always positive in all profile of wind

Integral Backstepping Power Control of DFIG Based Nonlinear Modeling ...

1731

speed propose. Figure 4 the active power of the stator has a negative sign, signifying that the generator produces energy and transmits it to the grid, are correctly follows almost perfectly its reference generated by the turbine. In order to maintain a unitary power factor on the grid side, the stator reactive power reference value will be fixed at zero in order to ensure that the quality of the power delivered to the grid is optimized, the power produced will track almost their reference after the end of its transient regime. We can observe that the integral backstepping controller ensures a exactly decoupling between them. Figure 5 the stator currents in the three phases network generated by DFIG are sinusoidal shape and which has a excellent power quality for the electrical grid system of frequency 50 Hz, it is very clear in the zoom of stator currents. The rotor currents in the three phases are also sinusoidal, present a good tracking and the frequency of the current varies with the slip shown in Fig. 6. The electromagnetic torque variation gives the identical profile of the stator active power.

Wind speed (m/s)

9 7 5 3

0 0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

Time (s)

Fig. 2 Variable wind speed profile

0.5 0.4

120 100

slip

Generator speed (rad/s)

170 157 140

0.3 0.2

50

0 0

W W 0.5

1

1.5

2

2.5 3 3.5 Time (s)

4

4.5

0.1

*

5

Fig. 3 Generator speed with MPPT and the slip

0

0.5

1

1.5

2

2.5

3

Time (s)

3.5

4

4.5

5

1732

M. Loucif et al.

Ps

2000

Ps

0 -2000 -4000

1

Reactive power (Var)

Active power (W)

4

*

4000

x 10

*

Qs Q

s

0.5

0

-0.5

-6000 -8000 0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

-1 0

5

0.5

1

1.5

3

2.5

2

Time (s)

3.5

5

4.5

4

Time (s)

Fig. 4 Generator active and reactive powers 10

Stator current zoom (A)

Stator current (A)

20

10

0

-10

-20 0

0.5

1

1.5

2

2.5

3.5

3

4.5

4

5

0

-5

-10 3

5

3.02

3.04

Time (s)

3.06

3.08

3.1

3.12

3

Time (s)

Fig. 5 Stator currents with zoom 20

Torque Tem (Nm)

Rotor current (A)

40

20

0

-20

-40 0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

Time (s)

Fig. 6 Rotor currents and electromagnetic torque

0

-20

-40

-60 0

0.5

1

1.5

2

2.5

3

Time (s)

3.5

4

4.5

5

Integral Backstepping Power Control of DFIG Based Nonlinear Modeling ...

1733

6 Conclusion In this paper, a modeling, control and simulation of the mechanical part of a wind turbine using a PI controller to extract the MPPT for the variable speed operating in region two is presented. It also presents the stator active and reactive powers control of a double-fed induction generator by considering the complete non-linear model in the dq reference frame, using a stator voltage oriented control to simplify the derivatives of the stator powers. Backstepping controller with integral action applied to the rotor side converter. The slip is always positive; DFIG operates in sub synchronous speed mode. The results of simulation are presented and discussed confirming its feasibility of the proposed control scheme had a good follow speed and stator powers response, regardless of variation wind speed profile.

Appendix (See Tables 1 and 2). Table 1 Wind parameters

Table 2 DFIG parameters

Parameters

Value

Unit

Nominal power Pn

10

Kw

Air density ρ

1,22

Kg/m3

Rotor radius R

3

m

Turbine inertia Jt

0,02

Kg.m2

Turbine friction coefficient Bt

0,0016

Nm/rad/s

Number of blade

3

–

Gearbox ratio G

5,4

–

Parameters

Value

Unit

Nominal power Pn

7, 5

Kw

Nominal speed Nn

1440

Rpm

Number of poles pair p

2

–

Stator resistance Rs

0.455



Rotor resistance Rr

0.62



Stator inductance Ls

0,084

mH

Rotor inductance Lr

0,081

mH

Stator-rotor mutual inductance Msr

2,691.105

mH

Moment of inertia J

0,3125

N.m.s2

Coefficient of friction B

6.73e−3

kg.m/s

1734

M. Loucif et al.

References 1. Abolhassani MT, Enjeti P, Toliyat M (2008) Integrated doubly fed electric alternator/active filter (IDEA), a viable power quality solution for wind energy conversion systems. IEEE Trans Energy Convers 23(2):642–650 2. Loucif M, Boumediene A, Mechernene A (2014) Robust nonlinear combined backstepping sliding mode control of DFIG under wind speed variation. In: The second international conference on electrical engineering and control applications 3. Krstic M, Kanellakopoulos I, Kokotovic PV (1995) Nonlinear and adaptive control design. Wiley, New York 4. Krstic M, Kanellakopoulos I, Kokotovic PV (1994) Nonlinear design of adaptive controllers for linear systems. IEEE Trans Autom Control 39:738–752 5. Yaolong T, Jie C, Hualin T, Jun H (2000) Integral backstepping control and experimental implementation for motion system. In: Proceedings of the IEEE international conference control applications 6. Loucif M, Boumediene A, Mechernene A (2014) Maximum power point tracking based on backstepping control of wind turbine. J Electrotehnica Electronica Automatica (EEA). 62(3):103–109 7. Ayrir W, Ourahou M, Haddi A (2018) DFIG stator reactive and active power control based fuzzy logic. Int J Circ Syst Signal Process 12:262–267 8. El Aimani S (2004) Modélisation de différentes technologies d’éoliennes intégrées dans un réseau de moyenne tension. Doctorat thesis, Lille, France 9. Loucif M, Boumediene A, Mechernene A (2015) Nonlinear sliding mode power control of DFIG under wind speed variation and grid connexion. J Electrotehnica Electronica Automatica (EEA) 63(3):103–110 10. Lihui Y, Xikui M, Dong D (2009) Hopf bifurcation in doubly fed induction generator under vector control. Chaos Solitons Fractals 41:2741–2749 11. Bourdoulis M, Alexandridis A (2013) A new controller design and analysis of DFIG wind turbine systems for mpp operation. In: EuroCon. Zagreb, Croatia 12. Bourdoulis M, Alexandridis A (2013) Nonlinear stability analysis of DFIG wind generators in voltage oriented control operation. In: European Control Conference (ECC), Zürich, Switzerland 13. Loucif M (2016) Synthèse de lois de commande non-linéaires pour le contrôle d’une machine asynchrone à double alimentation dédiée à un système aérogénérateur. Doctorat thesis, Tlemcen, Algerie 14. Laoufi A, Hazzab A, Bousserhane IK, Rahli M (2006) Direct field oriented control using backstepping technique for induction motor speed control. Int J Appl Eng Res 1(1):37–50 15. Haruna A, Mohamed Z, Basri MAM, Ramli L, Alhassan A (2017) 2-step integral backstepping control of the two-rotor aero-dynamical system (TRAS). J Fund Appl Sci 9:395–407 16. El Mourabit Y, Derouich A, El Ghzizal A, El Ouanjli N, Zamzoum O (2020) Nonlinear backstepping control for PMSG wind turbine used on the real wind profile of the Dakhla-Morocco city. Int Trans Electr Energ Syst 30:e12297 17. Loucif M, Boumediene A, Mechernene A (2013) Backstepping control of double fed induction generator driven by wind turbine. In: 3rd international conference on systems and control 18. Loucif M, Boumediene A, Mechernene A (2015) Modeling and direct power control for a DFIG under wind speed variation. In: 3rd international conference on control, engineering and information technology

Smart Monitoring PID of Solenoid Response Based on Fiber Squeezer Said Amrane, Abdallah Zahidi, Nawfel Azami, Mostafa Abouricha, Naoual Nasser, and Mohamed Errai

Abstract Connecting of integrated fiber systems depends on the polarization of the light at the output of polarization controllers based on solenoid, as squeezer fiber is suitable for coherent fiber optic systems because of its low insertion loss as well as low power penalty. However, the stability in open loop of the solenoid caused by their magnetic circuit affects the performance of these polarization controllers and limits their applications. To improve stability performance, first; a feedback PID corrector is proposed to correct the dynamic response. Then, the authors propose to study the effect of electrical parameters variations of the PID gains of solenoid. Finally, the artificial neural network is using to predict the PID gains that will correct the solenoid dynamic response. The simulation results show the need to adjust the PID corrector in the event of system parameter changes to maintain the optimum dynamic response. Keywords Solenoid · Fiber squeezer · PID corrector · ANN

S. Amrane (B) · M. Errai Polydisciplinary Faculty of Taroudant, University Ibn Zohr, Agadir, Morocco e-mail: [email protected] M. Errai e-mail: [email protected] A. Zahidi · N. Azami National Institute of Posts and Telecommunications, Rabat, Morocco e-mail: [email protected] M. Abouricha Faculty of Science, University Ibn Zohr, Agadir, Morocco e-mail: [email protected] N. Nasser Faculty of Sciences and Techniques, University Hassan 1st, Settat, Morocco e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_157

1735

1736

S. Amrane et al.

1 Introduction Solenoids are used in several technical applications such as robotics [1], valves [2, 3], positioners [4], anti-braking systems [5]. They are also used in polarization controllers to adjust the light power at the output of an optical fiber by applying mechanical stress to it [6]. This type of actuator is characterized by a stable amplitude of movement, which does not extend over its entire range [7]. Several studies are devoted to extending the stability interval of this type of devices such as the nonlinear magnetic field mapping [8], adaptive control [9], dual solenoid configuration [10], sliding mode control [11], pulse with modulation control [12] and PID corrector that is easy to implement [13]. However, it is useful to readjust the PID corrector in case of the physical parameters variation of the solenoid, which affects the performance of its response. This variation is considered to be a major problem for this type of actuator [14–18]. The effect of fluctuations in mechanical parameters is studied in [19]. To control the solenoid response intelligently, we propose to use an optical fiber as a force sensor between the frame and the plunger of the solenoid. This force produces a birefringence on the fiber, which changes the polarization state of light through the fiber [6]. The change in the polarization state of the light results in the variation of the light power measured by a photodiode. First, we propose to correct the light power dynamic response at the output of the fiber by adjusting the gains of the PID corrector. In the second step, we studied the effect of electrical parameters variation of the solenoid the gains of the PID corrector. The electrical parameters including resistance and inductance of solenoid. Finally, the ANN is used to predict the values of the gains of the PID corrector to allow maintaining the stability of the system.

2 The Methodology of the Study 2.1 Structure of Solenoid with Fiber Squeezer The solenoid is the electromagnetic actuator that exerts pressure on the fiber. Its structure is shown in Fig. 1

2.2 Solenoid Mathematical Modeling The mathematical model of the solenoid is described in detail in [20]. The mechanical part of the system can be modeled by Eq. 1:

Smart Monitoring PID of Solenoid Response Based on Fiber Squeezer

1737

Fig. 1 Scheme of the solenoid with fiber squeezer

  d2 x 1 dx Fem − k(x − x0 ) − B = m dt dt 2

(1)

Where x is the displacement of the armature in (m), m is the mass of the armature in (Kg), Fem is the force produced by the magnetic field in (N), K is the stiffness of the spring in (N/m), x0 is the initial air gap between the armature and the backside of the frame in (m), and B is the system damping coefficient in (N.s/m). The electrical part is modeled by Eq. 2:   1 dL(x) dx di = u − Ri − i dt L(x) dx dt

(2)

Where i(t) is the coil current in (A), L is the coil inductance in (H) that depends on the air gap [7], and R is the coil resistance in (). Equation 3 and 4 models the optical part. δ = 6.10−5 Po =

F λd

Pi (1 + cos δ) 2

(3) (4)

Where δ in (rad) is the magnitude of the phase difference between the polarized light along the squeezing axis and it is orthogonal [21], F is squeezing force in (N), λ is the light wavelength used in (m), d is the fiber diameter in (m), Po is the output light intensity, and Pi is t the input light intensity in (W).

1738

S. Amrane et al.

Fig. 2 Simulink model of the solenoid with fiber squeezer

2.3 System Modeling in Simulink Environment Taking into account Eqs. 1 and 2 that respectively models the mechanical block and the electrical block; Eqs. 3 and 4 that models the optical block, the model Simulink is generated to simulate the solenoid as fiber squeezer (Fig. 2).

3 Simulation and Result 3.1 The Proposed Method of Simulation First, we use the Simulink model to generate the open-loop step response of the simulated system. After identification from the step response, the system is looped and corrected using a PID controller. Finally, we study the effect of fluctuation of coil resistance and coil inductance on PID gains (Kp, Ki and Kd). The parameter variation ranges are the resistance R for 5 to 50  and inductance L for 10 to 35 mH. The identification of the system is done using the “tfest” function, of the Matlab/Simulink environment, to estimate the system transfer function [22]. This transfer function Tf has the coefficients (num, den2, den3), and can be expressed by the following form: Tf =

s2

num + den2 s + den3

(5)

Smart Monitoring PID of Solenoid Response Based on Fiber Squeezer

1739

After estimating the transfer function of the system, the gains of the PID corrector are determined using the “pidtune” function [23].

3.2 Open-Loop System Step Response of Solenoid with Fiber Squeezer The Simulink model is running using the values of the solenoid with fiber squeezer represented in the following Table 1: Figure 3 represents the curve of variation of the light power at the output of the fiber as a function of time when a voltage step is applied to the input of the open-loop system (open-loop step response). The curve shows a stable response with damped oscillations. The overshoot is relatively large (around 80%). Table 1 The values of solenoid parameters with fiber squeezer R ()

K (N/m)

B (Ns/m)

M (g)

L (mH)

Pi (mW)

10

2000

2

200

20

1

Fig. 3 Open-loop step response of solenoid with fiber squeezer

1740

S. Amrane et al.

Table 2 The value of the PID gains used to correct the system response Kp

Ki

Kd

2.9

757.5

39193

Fig. 4 Close-loop system step response of solenoid with fiber squeezer

3.3 Close-Loop System Step Response of Solenoid with Fiber Squeezer Using the pidtune function, the values of the PID gains obtained are shown in the following Table 2: Figure 4 represents the curve of variation of the light power at the output of the fiber as a function of time when a voltage step is applied to the input of the closed-loop system (close-loop step response). The curve shows a stable response. The overshoot improved has decreased significantly (from 80% to 13%.)

3.4 Influence of Coil Electrical Resistance and Coil Inductance on PID Gains The effect of the coil resistance and inductance variation the PID gains is shown in the following maps:

Smart Monitoring PID of Solenoid Response Based on Fiber Squeezer

Fig. 5 Map of Kp = f(R, L)

Fig. 6 Map of Ki = f(R, L)

1741

1742

S. Amrane et al.

Fig. 7 Map of Kd = f(R, L)

3.5 The Monitoring Process of PID Gains The results of the simulation are stored in a database. Each record consists of: – Physical parameters of solenoid: the mass m, the resistance of coil R, inductance L, spring stiffness K, and the damping coefficient B. – The coefficients of the transfer function: num, den2, and den3. – The PID gains: Kp, Ki, and Kd. The generated database is used to train the artificial neural network. The coefficients of the transfer function represent the inputs of the neural network model while the gains of the PID corrector represent these outputs. To predict the gains of the PID corrector, we estimate the transfer function coefficients of the open-loop system by using the tfest function; the parameters of this function (num, den2, and den3) are introduced into the ANN to generate the PID gains.

4 Interpretation of Results Figures 5, 6, and 7 represent the maps of variations of the PID corrector gains as a function of the resistance R of the coil and the inductance L of the solenoid. These figures show that the three gains have the same monotony when R and L vary. These

Smart Monitoring PID of Solenoid Response Based on Fiber Squeezer

1743

gains increase with R and decrease with L. We also notice that varying the coil resistance and the coil inductance affects the solenoid response and therefore the intensity of the output power. Consequently, it is necessary to readjust the PID gains in the case of the resistance and inductance variation in order to maintain the response with sufficient stability. Using the ANN is useful to predict the gains of the PID corrector in the event of any fluctuations in the coil resistance or the coil inductance of the solenoid. However, it is still necessary to study and improve the performances of the ANN and its training to minimize the error on the prediction of the PID gains.

5 Conclusion The force applied on the optical fiber by the armature of the solenoid varies the light power at its output. Mathematical modeling makes it possible to describe the dynamic response of this system. PID controllers provide powerful control tools for the transient response. The overshoot is greatly reduced from 80% to 13%. This study also highlighted the influence of the variations of the coil resistance and coil inductance on the solenoid response with fiber squeezer. Hence, is necessary to readjust the PID gains in the case of the coil resistance and inductance variations. Therefore, to keep the solenoid dynamic response in an optimal state, it suffices to analyze its response to determine the changes that the solenoid parameters have undergone in order to readjust the PID gains again. The use of an ANN can be used for monitoring the PID gains to keep the solenoid dynamic response with sufficient stability.

References 1. Takai A, et al (2013) Prototyping the flexible solenoid-coil artificial muscle, for exoskeletal robots. In: ICCAS 2013, pp 1046–1051 2. Kajima T, Kawamura Y (1995) Development of a high-speed solenoid valve: investigation of solenoids. IEEE Trans Ind Electron 42(1):1–8. https://doi.org/10.1109/41.345838 3. Mutschler K et al (2014) Multi physics network simulation of a solenoid dispensing valve. Mechatronics 24(3):209–221 4. Chen M-Y et al (2010) A new design of a submicropositioner utilizing electromagnetic actuators and flexure mechanism. IEEE Trans Ind Electron 57(1):96–106 5. Chu L, et al (2007) Study on the dynamic characteristics of pneumatic ABS solenoid valve for commercial vehicle. In: 2007 IEEE vehicle power and propulsion conference, Arlington, TX, USA, pp 641–644 6. Rumbaugh SH et al (1990) Polarization control for coherent fiber-optic systems using nematic liquid crystals. J. Lightwave Technol 8(3):459–465 7. Li C et al (2016) Enhanced-performance control of an electromagnetic solenoid system using a digital controller. IEEE Trans Contr Syst Technol 24(5):1805–1811

1744

S. Amrane et al.

8. Lim KW, et al (1994) Proportional control of a solenoid actuator. In: Proceedings of IECON 1994 - 20th annual conference of IEEE industrial electronics, Bologna, Italy, vol 3, pp 2045– 2050 9. Nataraj C, Lee D (2010) Model-based adaptive control for a solenoid-valve system. In: Volume 8: dynamic systems and control, parts A and B, Vancouver, British Columbia, Canada, pp 1009–1015 10. Yu L, Chang TN (2010) Zero vibration on–off position control of dual solenoid actuator. IEEE Trans Ind Electron 57(7):2519–2526 11. Nguyen T et al (2007) Accurate sliding-mode control of pneumatic systems using low-cost solenoid valves. IEEE/ASME Trans Mechatron 12(2):216–219 12. Taghizadeh M et al (2009) Modeling and identification of a solenoid valve for PWM control applications. Compt Rendus Mécan 337(3):131–140 13. Lunge SP, Kurode SR (2013) Proportional actuator from on off solenoid valve using sliding modes. In: Proceedings of the 1st international and 16th national conference on machines and mechanisms, IIT Roorkee, India 14. Steiner G et al (2004) Managing uncertainties in electromagnetic design problems with robust optimization. IEEE Trans Magn 40(2):1094–1099 15. Kabib M et al (2016) Modelling and simulation analysis of solenoid valve for spring constant influence to dynamic response. JEAS 11:2790 16. Dülk I, Kovácsházy T (2014) Resistance estimation in solenoid actuators by considering different resistances in the PWM paths. Period Polytech Elec Eng Comp Sci 58(3):109–120 17. Zahidi A, Said A, Azami N, Nasser N (2020) Effect of fiber and solenoid variation parameters on the elements of a corrector PID for electromagnetic fiber squeezer based polarization controller. IJECE 10(3):2441 18. Abedallah Z, Said A, Nawfel A, Naoual N (2019) Study and simulation of fiber press control for solenoid parameters monitoring for Smart preventive maintenance. In: 2019 1st ICSSD, Rabat, Morocco, pp 1–6 19. Zahidi A, Amrane S, Azami N, Nasser N (2020) Self-tuning PID of the solenoid response based on fiber squeezer. In: 13th SITA, Rabat, Morocco, pp 1–6 20. Ikeda K, et al (2003) Endless tracking polarization controller 21. Smith AM (1980) Single-mode fibre pressure sensitivity. Electron. Lett. 16(20):773 22. Najdek K (2019) Identification of dual-active-bridge converter transfer function. Electrotech Rev 1(3):153–156 23. Bansal H (2009) Tuning of PID controllers using simulink. Int J Math Model Simul Appl 2(3):337–344

Fuzzy Logic Based MPPT Control of an Isolated Hybrid PV-Wind-Battery Energy System Hadjer Abadlia and Riad Toufouti

Abstract Recently the generations by the traditional sources are insufficient to meet the increasing power demands due to continuous depletion of fossil energy sources, therefore distributed generators (DGs) based renewable energy sources (RES) to system is necessary to overcome these issues. In this study we present a stand-alone hybrid power generation system composed by a photovoltaic and wind power generation sources with energy system storage to achieve high performance while supplying unbalance AC load, PV system and Win turbine is operated with a DC/DC boost converter to extract the Maximum Power Point Tracking (MPPT) using the fuzzy logic technique. The total power generation supply the load via an inverter controlled by pulse width modulation technique. A Simulink model of the proposed hybrid system with the MPPT controlled Boost converter of the PV generator and Voltage regulated voltage source inverter for standalone system is simulated in MATLAB. Keywords Photovoltaic generator · Wind turbine · Permanent magnet synchronous generator (PMSG) · Maximum power point tracking · Fuzzy logic controller (FLC) · Hybrid renewable energy system (HRES)

1 Introduction Decentralized electricity generation is currently undergoing a very significant development, much of the energy design is introduced by fossil energy sources. But these conventional energies drain quickly and intensify greenhouse gas emissions results the air pollution [1]. Faced with the consequences it is important to take into account the development of alternative energy which is known as renewable energy sources. Several sources of renewable energy are increasingly used and developed to be the future for power systems like photovoltaic (PV), wind, geothermal, biomass, hydro power are most commonly utilized to meet the needs growing energy [2]. H. Abadlia (B) · R. Toufouti Department of Electrical Engineering, Laboratory of Electrical and Renewable Energies, LEER, Souk Ahras University, Souk Ahras, Algeria © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_158

1745

1746

H. Abadlia and R. Toufouti

They give promising solutions in building sustainable and ecofriendly electric power [2]. Moreover, they decrease pollution in limiting the emission of carbon dioxide coming from the conventional methods for energy generation based on fossil fuels. Wind and photovoltaic power generation systems can be used for power generation as isolated system, but their benefits are considerably improved when they are integrated into electrical grid. But the intermittent nature of these two sources according the time of day, season and year, the random nature of these sources has led researchers to turn to the combination of them to form the hybrid energy system (HES) which is very interest by their reliability, their incomparable flexibility and their attractive cost [3, 4]. HES are a good solution for alimenting rural areas located in isolated regions and rural remote areas, where the extension of the conventional power network is more costly and online losses become significant. But the main problem of is that (RES) must work in a complementary way to manage the response for demand of power in any moment. For improvement of the complexity of the RES involved in a hybrid power system (HPS), several researches have been devoted by several researchers in worldwide. In order to manage the energy consumption without any problems Many studies discussed control and energy management sources of HES presented in [5, 8, 9]. Various papers has been presented for the use sizing and optimization of hybrid renewable systems [4, 6, 7]. For optimization HRES using intelligent techniques, many advanced optimization techniques have been presented for the optimal design of hybrid RES especially: Particle Swarm Optimization (PSO), Genetic algorithm (GA) [12, 16, 23]. To extract the maximum power from RES, many MPPT algorithms are Developed in literature such as Perturb and Observe technique (P&O), [24], incr-conductance [25], fuzzy logic [23], sliding mode [26], also the combination of fractional order control and incremental conductance [27]. These control strategies differ among them in: simplicity, reject disturbances, sensors required who needed the exact parameterization, cost and efficiency [28]. In this paper has the objective of modeling and control, inside the Matlab/Simulink environment of isolated hybrid PV/Wind/Battery, distributed power system under changing weather conditions to supply a variable AC load and the MPPT controller is implemented using the fuzzy logic technique to obtained the maximum power from the PV panels and Wind turbine to ensure the availability of power generation. The robustness of the proposed control strategy is verified by numerical simulation.

2 Description of the Proposed HES The proposed hybrid renewables energy resources combine PV and wind energy sources as fellows in Fig. 1: wind generator which is used with a diode rectifier and DC/DC converter and a PV generator includes PV panels connected to high boost converter to obtain the

Fuzzy Logic Based MPPT Control ...

1747

Fig. 1 Configuration of proposed hybrid system

(MPPT) using fuzzy logic controller. The total energy produced from the wind and PV sources is used to supply the AC loads through three phase inverter controlled by pulse width modulation, the output voltage of DC/AC converter is filtered by LC filter to given pure sine wave in the load. The IGBT invertetr is controlled by sinus triangular modulation (SPWM).

2.1 Modeling of the HRES The conception of the hybrid system is mainly dependent on the performance of each source of energy, which must be first modeled individually and then combined to evaluate the performance of the system. • Modeling of PV generator. Solar energy is the most encouraging source of energy among renewable energy, it is an attractive option due to zero CO2 emission, noise less in operation and with lower maintenance cost [10]. The PV solar energy is the direct transformation of light into electric power through solar cells which is the essential element building the PV panel, PV cell is Generally a semiconductor device composed of PN Junction almost identical to a diode [11], these cells are connected in series to form a PV module, a number of PV modules are connected in series to generate higher voltage which formed a string, and a number of strings are connected in parallel to given the higher current and formed a PV array for obtaining the required power [12].

1748

H. Abadlia and R. Toufouti

Fig. 2 PV model of single diode

Like all the physical systems, the PV cell model is done with different levels of exactitude depending on the users intended; this operation is realized through an equivalent circuit with précising parameters and variables, that is why the field of PV cell equivalent circuit model always knows advancement [13]. The one diode model is the most used models [10, 13–15], also the model of three diode is developed [16, 17]. We present in this paper the model of one diode due to their Simplicity [10, 18, 19], and the least number of parameters [20],this classic model consists of a current source antiparallel with a parallel and series resistor connected in the terminal of the diode the equivalent circuit is shown in Fig. 2 [21, 22]. Using a loop the voltage and node current laws, the generated current in the PV generator is given: I = I ph

     V + I Rs V + I Rs −1 − − I0 ex p q nK T Rsh

(1)

Where: Iph : The current generated by PV panel, Io: reverse saturation diode current off the grid solar, n: the diode ideality constant. Rs and Rsh series and shunt resistance, K: Boltzmann’s constant q: the electron charge. • Wind generator. Wnd turbine convert’s the mechanical energy in the wind into electrical energy, it’s composed of turbine coupled to a generator wich transforms the mechanical power into electrical power [29]. The generator can be a (PMSG) or induction generator, Which are the most common generators that are used in wind turbines [30]. Power produced by wind turbine is given as fellow: [31–33] Pw =

1 C p (λ, β)ρ AV 3 2

(2)

Where: ρ: air density (kg/m), Cp : power coefficient, A = π × R2 : intercepting area of the rotor blades (m), V: average wind speed (m/s), λ: tip speed ratio, β: the blade pitch angle (in degrees).

Fuzzy Logic Based MPPT Control ...

1749

The power coefficient Cp of the turbine (also known as performance coefficient), the power coefficient presents a fraction of power in the wind captured by the turbine and has a theorical maximum of 0.59 [29, 31]. The tip speed ratio (TSR) for turbine is given as fellow, [33] λ=

Rω V

(3)

Where: R: radius of turbine (m), ω: angular speed (rad/s). • Battery storage and controller by bidirectional converter. The model of battery used in this study is been introduced in this section. The mathematical equations based on the actual SOC of the battery as follows [4, 7, 36]: Vbt = E batt − R I

(4)

During charge Q Q It − K I ∗ + A.exp(−bit) Q − it Q − it

(5)

Q Q It − K I ∗ + A.exp(−bit) Q − it It + 0.1Q

(6)

E batt = E 0 − K During discharge E batt = E 0 − K

For charging and discharging we used a DC/DC bidirectional buck/boost converter. • MPPT technique. Changing of environment conditions with the increasing demands of PV system cause to modification voltage, current and also the MPPT of PV panels this needs optimizing the power generation to minimize the number of PV panels needed for required load demand and increase the efficiency of the system with reducing the total cost, It’s provided by the MPPT algorithm [23]. In this work Fuzzy logic controller is used for the control of tracking the maximum power of the Photovoltaic generator (PVG), because of their advantages such as robustness, simplicity to design, it require the thorough knowledge about the system and doesn’t needs the model of the system. The proposed FLC, shown in Fig. 3 [34, 35]. In the simulation and for extract the maximum power from PV panels and Wind turbine, A Boost Converter is widely used [36]. The inputs variables of FLC are the error E and the change of error CE at sampled times k defined by E(k) =

Pph (k) − Pph (k − 1) V ph (k) − V ph (k − 1)

(7)

1750

H. Abadlia and R. Toufouti

Fig. 3 Diagram of a fuzzy logic controller

Table 1. Fuzzy rules table

C E(k) = E(k) − E(k − 1)

(8)

Where: Pph (k) power of the PVG at sampled times k. E(k) obvious if the load operation point is situated on the left or on the right of the MPPT on the PV characteristic, and CE(k) give the change direction of this point. Using Madani’s method the fuzzy inference are given in (Table 1), and the defuzzification uses the centre of gravity to compute the duty cycle D which is the output of this FLC: [35] n D=





j=1 μ D j − D j   n j=1 μ D j

(9)

The inputs and E(k), Ce(k) and the output D for the tracking of the maximum power point are simulated in matlab and illustrated in Fig. 4.

Fuzzy Logic Based MPPT Control ...

1751

2.2 Simulation Results The proposed hybrid system has been modeled and simulated in this section, The PV panels are modeled for 5.5 KW generated power, 6 panels are connected in series to form string and 3 strings are connected in parallel, in total 18 panels are used for building the solar PV system which based on the fuzzy logic MPPT algorithm. A 4 KW PMSG is driven by wind turbine, the system supplies an AC load of 8 KW maximum considered as 6 KW principal load and additional load of 2 KW. The simulation model permit to study different parameters of the system like voltage, current, power also confirm the efficiency of control strategy used. With a different metrological conditions, the simulation tests of the HRES are implemented and interpreted. The air temperature and solar radiation have been used as the input parameters of the PV generator. The results are obtained to evaluate the control performance to confirm the efficiency of FLC techniques.

(a) Error e

(b) Change of error ce

Fig. 4 I/O membership functions for DC/DC boost converter

Fig. 5 a. Irradiances variation. b. Solar power

(c) Duty ratio

1752

H. Abadlia and R. Toufouti

Figure 5 presents the variation of power generated from the PV system in different values of solar irradiance (1000,400,800 w/m2 ) respectively, The output PV power takes the same shape of the solar irradiance due to the direct proportional relationship between them, the MPPT controller give maximum power output at low values of solar irradiances. The wind turbine generated power is depended of the wind speed variations the output wind power depict in Fig. 6. While the wind speed is over 11 m/s, the output power is limited to 3.5 kW. When the wind speed is less than the wind speed (6 m/s), the output power is 1 kW. Figure 7(a-b) the DC voltage and and load powers, we can see the Vdc voltage I the input inverter is not affected by the power generated by the renewable sources it’s maintained stable through MPPT controller. The results simulation shown in Fig. 8 demonstrate that the power of hybrid system is almost equal to the total power generated by the PV and wind generators that’s obvious the efficiency of the fuzzy logic technique for the MPPT of PV generator. When PHRES < Pload, the sum of power generated by the wind and the PV is insufficient to supply the load demand. For this reason the battery start supplying

Fig 6 a. Wind speed variation. b. Wind power

Fig. 7 a. DC bus voltage VDC b. Active and reactive load powers

Fuzzy Logic Based MPPT Control ...

1753

Fig. 8 Output power for PV generator, Wind generator and hybrid system

load, the state of charge start decreasing and also during discharging current become positive. When PHRES > Pload, there is excess power obtainable for generation. The excess power of HRES is used for battery charging. From these simulation results we conclude that the HRES based Fuzzy logic MPPT controller has a good performances in the presence of irradiance and wind speed variations, is good solution for alimenting rural areas located in isolated site or hard-to-access locations and do not allow connection to an power network since the investment cost.

3 Conclusion In this paper, an AC-linked stand-alone Photovoltaic-Wind-bettery hybrid system coupled to an alternative AC load is proposed. First, in the work of this paper we have present a modeling of different power sources that consist of PV- Wind-battery storage and the MPPT controller to extract the maximum. Secondly, with the metrological conditions (radiation and wind speed), we have applied numerical simulation to validate the performances of a standalone hybrid system by supporting of a charging and discharging of Battery storage with terminal DC bus. The output PV and wind energy are modeled and a MPPT controller based of FLC technique, the simulation result depicts than the combination of the two sources assures the reliabilty of the system, better than systems used only one source, with a complementary way to manage the response for demand of power in any moment.

1754

H. Abadlia and R. Toufouti

References 1. Lopes LAC, Lienhardt AM (2003) A simplified nonlinear power source for simulating PV panels. In: IEEE 34th annual conference on power electronics specialist, PESC 2003, vol 4. IEEE, pp 1729–1734 2. Javed MS, Ma T, Jurasz J, Amin M (2020) Y: Solar and wind power generation systems with pumped hydro storage: review and future perspectives. Renew Energy 148:176–192 3. Bagen, Billinton R (2005) Evaluation of different operating strategies in small stand-alone power systems. IEEE Trans. Energy Convers 20(3):654–660 4. Ogunjuyigbe ASO, Ayodele TR, Akinola OA (2016) Optimal allocation and sizing of PV/Wind/Splitdiesel/Battery hybrid energy system for minimizing life cycle cost, carbon emission and dump energy of remoteresidential building. Appl Energy 171:153–171. https://doi. org/10.1016/j.apenergy.2016.03.051 5. Bakir H, Kulaksiz A (2020) A: Modelling and voltage control of the solar-wind hybrid microgrid with optimized STATCOM using GA and BFA. Eng Sci Technol Int J 23(3):576–584 6. Al Ghussain L, Taylan O (2019) Sizing methodology of a PV/wind hybrid system: case study in cyprus. Environ Progr Sustain Energy 38(3):e13052 7. Diab AAZ, Sultan HM, Mohamed IS, Kuznetsov ON, Do TD (2019) Application of different optimization algorithms for optimal sizing of PV/wind/diesel/battery storage stand-alone hybrid microgrid. IEEE Access 7:119223–119245 8. Boussetta M, Motahhir S, El Bachtiri R, Allouhi A, Khanfara M, Chaibi Y (2019) Design and embedded implementation of a power management controller for wind-PV-diesel microgrid system. Int J Photoenergy. https://doi.org/10.1155/2019/8974370 9. Rezkallah M, Chandra A, Saad M, Tremblay M, Singh B, Singh S, Ibrahim H (2018) Composite control strategy for a PV-wind-diesel based off-grid power generation system supplying unbalanced non-linear loads. In 2018 IEEE Industry applications society annual meeting (IAS). IEEE, pp 1–6 10. Xiong G, Zhang J, Shi D, He Y (2018) Parameter extraction of solar photovoltaic models using an improved whale optimization algorithm. Energy Convers Manage 174:388–405 11. Sakib S, Siddique MAB (2019) Modeling and simulation of solar photovoltaic cell for the generation of electricity in UAE. In: 5th international conference on advances in electrical engineering (ICAEE). IEEE, pp 66–71 12. Boukenoui R, Salhi H, Bradai R, Mellit A (2016) A new intelligent MPPT method for standalone photovoltaic systems operating under fast transient variations of shading patterns. Sol Energy 124:124–142 13. Chaibi Y, Allouhi A, Malvoni M, Salhi M, Saadani R (2019) Solar irradiance and temperature influence on the photovoltaic cell equivalent-circuit models. Sol Energy 188:1102–1110 14. Ayang A, Wamkeue R, Ouhrouche M, Djongyang N, Salomé NE, Pombe JK, Ekemb G (2019) Maximum likelihood parameters estimation of single-diode model of photovoltaic generator. Renew Energy 130:111–121 15. Yahya-Khotbehsara A, Shahhoseini A (2018) A fast modeling of the double-diode model for PV modules using combined analytical and numerical approach. Sol Energy 162:403–409 16. Khanna V, Das BK, Bisht D, Singh PK (2015) A three diode model for industrial solar cells and estimation of solar cell parameters using PSO algorithm. Renew Energy 78:105–113 17. Allam D, Yousri DA, Eteiba MB (2016) Parameters extraction of the three diode model for the multi-crystalline solar cell/module using Moth-Flame Optimization Algorithm. Energy Convers Manage 123:535–548 18. Peter AG, Saha AK (2019) Electrical Characteristics improvement of photovoltaic modules using two-diode model and its application under mismatch conditions. In: Southern African Universities Power Engineering Conference/Robotics and Mechatronics/Pattern Recognition Association of South Africa (SAUPEC/RobMech/PRASA). IEEE, pp 328–333 19. Bader S, Ma X, Oelmann B (2019) One-diode photovoltaic model parameters at indoor illumination levels–a comparison. Sol Energy 180:707–716

Fuzzy Logic Based MPPT Control ...

1755

20. Gnetchejo PJ, Essiane SN, Ele P, Wamkeue R, Wapet DM, Ngoffe SP (2019) Important notes on parameter estimation of solar photovoltaic cell. Energy Convers Manage 197:111870 21. Praiselin WJ, Edward JB (2017) Improvement of power quality with integration of solar PV and battery storage system based micro grid operation. In: Innovations in power and advanced computing technologies (i-PACT). IEEE, pp 1–5 22. Ayodele TR, Ogunjuyigbe ASO, Ekoh EE (2016) Evaluation of numerical algorithms used in extracting the parameters of a single-diode photovoltaic model. Sustain Energy Technol Assess 13:51–59 23. Yilmaz U, Kircay A, Borekci S (2018) PV system fuzzy logic MPPT method and PI control as a charge controller. Renew Sustain Energy Rev 81:994–1001 24. John R, Mohammed SS, Zachariah R (2017) Variable step size Perturb and observe MPPT algorithm for standalone solar photovoltaic system. In: 2017 IEEE international conference on intelligent techniques in control, optimization and signal processing, pp 1–6 25. Selvan DS (2013) Modeling and simulation of incremental conductance MPPT algorithm for photovoltaic applications. Int J Sci Eng Technol 2(7):681–685 26. Kchaou A, Naamane A, Koubaa Y, M’sirdi N (2017) Second order sliding mode-based MPPT control for photovoltaic applications. Sol Energy 155:758–769 27. Al-Dhaifallah M, Nassef AM, Rezk H, Nisar KS (2018) Optimal parameter design of fractional order control based INC-MPPT for PV system. Sol Energy 159:650–664 28. Faranda R, Leva S (2008) Energy comparison of MPPT techniques for PV Systems. WSEAS Trans Power Syst 3(6):446–455 29. Chow JH, Sanchez-Gasca JJ (2020) Power System Modeling, Computation, and Control. Wiley, Hoboken 30. Arturo Soriano L, Yu W, Rubio JDJ (2013) Modeling and control of wind turbine. Math Probl Eng. https://doi.org/10.1155/2013/982597. 31. Patel MR (2005) Wind and solar power systems: design, analysis, and operation. CRC Press 32. Salehi J, Badr AA (2019) Dynamic modeling and optimal control of a wind turbine with doubly fed induction generator using imperialist competitive and artificial bee colony algorithms. J Circ Syst Comput 28(04):1950070 33. Natsheh EM, Albarbar A, Yazdani J (2011) Modeling and control for smart grid integration of solar/wind energy conversion system. In: 2011 2nd IEEE PES International conference and exhibition on innovative smart grid technologies, pp 1–8 34. Takun P, Kaitwanidvilai S, Jettanasen C (2012) Maximum power point tracking using fuzzy logic control for photovoltaic systems. In: World congress on engineering 2012, 4–6 July 2012, London, UK, vol 2189. International Association of Engineers, pp 986–990 35. Cheikh MA, Larbes C, Kebir GT, Zerguerras A (2007) Maximum power point tracking using a fuzzy logic control scheme. Rev Energies Renouvelables 10(3):387–395 36. Feddaoui O, Toufouti R, Jamel L, Meziane S (2020) Fuzzy logic control of hybrid systems including renewable energy in microgrids. Int J Electr Comput Eng (IJECE), 10(6). (2088-8708)

Implementation of a Digital Control for PV Power Systems Badreddine Lahfaoui

Abstract Our laboratory LGEM is equipped with a Monocrystalline solar module. We want to control the PV system Monocrystalline solar module 30W - ETM53930. To this end, we have implemented a digital control in the DSPACE 1104 card. Our experiments are carried out at the Oujda Higher School of Technology (ESTO) at the LGEM laboratory with an ambient temperature of the room around 16 °C. Several tests are performed in order to validate our proposed controller. We mention that our experimental work is done by implementing a digital control for a PV system studied in the absence of disturbance: tests with a fixed radiation and a fixed charge. Keywords System identification and control · Power electronics · Renewable energies · Wind turbine · PV systems · Hybrid systems · Control card

1 Introduction Solar panels are the main sources of any photovoltaic system. They are connected in parallel or in series to remove their operating voltage or current. The energy supplied by the system can be used to charge batteries which will provide electricity when needed. It can also be used by directly connecting the modules to the load without batteries (i.e. for a solar pump, water is used as storage), or by plugging them into an electrical network. It is also possible to combine the output of the PV field with other energy sources such as a generator or a wind turbine which will serve as back-up, if there is not enough sunlight. Photovoltaic systems can be either (Autonomous system without battery, Autonomous system with battery, Hybrid PV/generator system) the stand-alone systems, not connected to a power grid. Or the other three types (PV system on

B. Lahfaoui (B) Mohammed First University Oujda, EST Oujda, LGEM, 60000, Oujda, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_159

1757

1758

B. Lahfaoui

diesel grid, PV system on decentralized grid, Centralized PV system) the PV systems connected differently to the electricity grid [1, 2]. To control the power of the PV system to the maximum, it is essential to operate the photovoltaic installations near this point (voltage VPVpmax and current IPVpmax) that is to say to have this power even if there are climatic or physical variations. Several analog and digital controls [3–8] are developed to solve this problem. The objective of this paper is to present all the practice works of a PV system connected to the Boost converter using a digital control. For this reason electrical circuit within the laboratory were implemented in order to test this numerical control. This paper is organized as follows: we start first in Sect. 2 by presenting the technical specifications of the photovoltaic source (manufacturer characterization and also experimental characterization performed in the laboratory). In Sect. 3 a detailed description of the system architecture is made by presenting the numerical control algorithm as well as the specifications of the Boost converter used during the experiments. The Sect. 4 shows the experimental results obtained, and at the end we finish with a conclusion.

2 The Specifications of the Photovoltaic System 2.1 The Solar Module - ET-M53930 This subset converts solar energy into electrical energy. It is made up of the following elements: – – – –

A 30 W “Monocrystalline” type photovoltaic solar module. An aluminum structure tilting from 15 to 75°. 3 safety connectors (4mm) (+, −, PE). A non-return diode located in the module connection box.

The ETM53930 solar panel consists of 39 monocrystalline silicon cells connected in series. Table 1 presents the electrical specifications, of this module, which are obtained under standard test conditions (STC): – Solar irradiation of 1000 W/m2 . – 1.5 air masses. – And temperature of 25° C. We also present the electrical performance of this solar module illustrated by me in the labs which represents on the one hand the electrical power as a function of voltage and on the other hand the electric current as a function of voltage.

Implementation of a Digital Control for PV Power Systems Table 1. PV system characteristics

Parameters

1759 Values

Weight

3.5 kg

Dimensions

633 × 427 × 35 mm

Maximum power voltage (Vmp)

19.4V

Maximum power current (Imp)

1.55A

Open circuit voltage (Voc)

23.8V

Short circuit current (Isc)

1.74A

Temp.coeff. of Isc (Tk Isc)

0.06%/°C

Temp.coeff. of Voc (Tk Voc)

−0.397%/°C

Temp.coeff. of Pmax (Tk Pmax)

−0.549%/°C

Normal operating cell temperature

44.4+− 2 °C

2.2 Experimental Characterization The electrical specifications of our PV system are determined by making circuits in the laboratory using an artificial source of solar energy that generates a desired solar irradiation to attack the photovoltaic system. For this, several measurements sampling works are taken in our research laboratory by linking our panel to electronic converter boost. The voltage and the current at the terminals of the solar panel (Vpv , Ipv ) are measured at each solar irradiance while varying the value of the resistive load. The Fig. 1 illustrates all these data taken.

Fig. 1 The experimental electrical characteristics of the PV system

1760

B. Lahfaoui

Fig. 2 The equipment used in the test.

3 Numerical Control of the Solar System 3.1 The Numerical Experimental Control PV System Wanting to exploit the optimum power of the solar module ET-M53930.For this we have implemented the numerical control algorithm in the DSPACE 1104 card. Figure 2 represents the experimental hardware formed by: – – – – – – – –

PV source 30W monocrystalline solar module - ET-M53930 Artificial solar source (W/m2 ) One Boost Load Current sensor Voltage sensor DSPACE 1104 card Computer with ControlDesk software

3.2 The Numerical Method Implemented in the Control Card The Perturb and Observe Numerical control has been implemented on a DSPACE 1104 card to perform power regulation.

Implementation of a Digital Control for PV Power Systems

1761

Fig. 3 Digital control implemented in DSPACE 1104.

The algorithm of this method as illustrated in the Fig. 3 begins with the measurement of the two parameters current and voltage Vpv (k), Ipv (k) to calculate the instantaneous power value at the instant k and the previous power at the instant k-1. Also the algorithm calculates the difference in voltages Vpv = Vpv (k) − Vpv (k−1). Then the command checks the sign of the value of Ppv and following the sign verification of the value of Vpv the algorithm increments or decrements the duty cycle Dpv with a value of Dpv . This algorithm is experimentally translated into a programming language using the Simulink tool from MATLAB software and is then sent to the DSPACE control card handled via the Control Desk software.

1762 Table 2 Components of the Step-Up converter

Table 3 Measuring equipment

B. Lahfaoui Elements

Value of component

Inductances

L’1 = 100 Uh

Internal resistance

r’1 = 0.2 

Input capacities

C1 = 1000 uF/250V

Output capacities

C2 = 1000 uF/250V

R(Load)

500 

Switch

IRFp450

Diode

BYW98-200

Equipment

Parameters

Current sensor

10A to 1V and 100A to 1V

Voltage sensor

ST1000

3.3 Specification of Step-Up Converter So as to obtain experimental results, we had to go through the realization phase, of which we carried out Step-Up converter for our solar panel system with the values and parameters calculated mentioned in Table 2. The Table 3 shows the set of equipment used during the test.

4 Experimental Results of Numerical Control System We present above the experimental results of the various study tests in the absence of disturbance in Figs. 4 which include the power at the terminals of the solar panel Ppv as function as the value of the duty cycle Dpv . The optimum power is reached for all of these tests through the use and implementation of the MPPT control in the DSPACE 1104 card. – The maximum power at the out of the solar panel oscillates approximately around 18W. – The optimal duty cycle oscillates approximately around 0.7. The experimental results illustrated in Fig. 4 are captured by Controdesk software over a time interval of 500 s and a solar irradiance equal to approximately 1000W/m.

Implementation of a Digital Control for PV Power Systems

Fig. 4 Tracking the maximum power using DSPACE 1104 Card (In Controldesk).

1763

1764

B. Lahfaoui

5 Conclusions The maximum optimization of renewable energy sources has become essential for socio-economic and environmental reasons. For this, various research works are initiated on several sources such as photovoltaic sources, wind sources, and other sources. Our research work described in this paper presents the optimization of a photovoltaic system driven by a numerical control to satisfy a resistive load under solar irradiance and constant temperature. The experience was carried out in the laboratory, LGEM, using several control measuring equipment and artificial sources. A validation of the experimental results shown in this paper proves the success of our proposed architecture.

References 1. Labouret A, Villoz M (2009) Energie solaire photovoltaïque. DUNOD, Paris 2. Royer J, Djiako T, Schiller E (1998) BocarSadaSy “le pompage photovoltaïque” (1998) 3. Lahfaoui B, Zouggar S, Elhafyani ML, Rabhi AH (2019) Implementation of a real-time MPPT of hybrid renewable energy system composed of wind turbine and solar PV cells. In: Hajji B, Tina G, Ghoumid K, Rabhi A, Mellit A (eds.) proceedings of the 1st international conference on electronic engineering and renewable energy. ICEERE 2018. Lecture Notes in Electrical Engineering, vol 519. Springer, Singapore. https://doi.org/10.1007/978-981-13-1405-6_58 4. Lahfaoui B, Mohammed SZB, Elhafyani ML (2017) Real time study of P&O MPPT control for small wind PMSG turbine systems using arduino microcontroller. Energy Procedia 111:1000– 1009 5. Lahfaoui B, Zouggar S, Elhafyani ML, Seddik M (2015) Experimental study of P&O MPPT control for wind PMSG turbine. In: IEEE international, renewable and sustainable energy conference (IRSEC 2015), Marrakech, Morocco, 10–13 Dec, pp 1–6 6. Lahfaoui B, Zouggar S, Elhafyani ML, Benslimane A (2015) Modeling validation and MPPT technique of small wind PMSG turbines using DSPACE hardware. In: IEEE international, renewable and sustainable energy conference (IRSEC 2015), Marrakech, Morocco, 10–13 Dec, pp 1–6 7. Lahfaoui B, Zouggar S, Elhafyani ML, Seddik M (2016) An Experimental Study of P&O MPPT Control for Photovoltaic Systems. Int J Power Electron Drive Syst (IJPEDS) 7:954 8. Lahfaoui B, Zouggar S, Elhafyani ML, Kadda FZ (2014) Experimental modeling and control of a small wind PMSG turbine. In: IEEE international, renewable and sustainable energy conference (IRSEC 2014), Ouarzazat, Morocco, pp 802–807

Towards a Digital Modeling of the Optimal Mechanical Properties of a Green Eco Composite Based on Renewable Resources Aziz Moumen, Abdelghani Lakhdar, Mustapha Jammoukh, and Khalifa Mansouri

Abstract In order to respond to the industrialists, environmental and sustainable development needs, researchers are currently studying a new category of materials called eco-composites based on natural biofillers. The goal is to use animal and vegetable fibers to load polymers. Several sectors integrate these eco-composites to manufacture their products by taking advantage of the important mechanical properties obtained by this bio-loading compared to the polymers in pure state and also to attract consumers to the bio-sourced products. In this research, the optimal properties of Nylon 66 bio filled by Argan Nut Shell (ANS) Particles with the Mori Tanaka and the finite element methods were studied. The mechanical properties of the eco composites studied are optimal at 20 Wt% of particles and by increasing their aspect ratios. The rigidity decreases slightly by moving away from 0°. The use of these types of natural bio fillers can replace the use of conventional fillers and move towards eco composites that respect the environment with low cost and density. Keywords Eco composite · Sustainable development · Environment · Argan Nut Shell · Nylon 66 · Digital modeling

1 Introduction The use of classical loads as reinforcement of polyamides is more and more inadvisable thanks to their environmental, economic and sustainable development drawbacks [1, 2]. Scientific research is more and more interested in a new category of materials called “eco composites” or “bio composites” meeting the requirements of sustainable development, manufacturers and environmental constraints. They are based on natural bio fillers also offering a lower cost and density with a high biodegradability. In this work, the Argan Nut Shell (ANS) particles will be used in order to give them the good value in the socioeconomic and environmental context. The Nylon PA66 A. Moumen (B) · A. Lakhdar · M. Jammoukh · K. Mansouri SSDIA Laboratory, Hassan II University of Casablanca, ENSET Mohammedia, Casablanca, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_160

1765

1766

A. Moumen et al.

Table 1 Characteristics of ANS

ANS

Values

Content

20 Wt%

Orientation

0,45 and 90°

Aspect ratios

0.5,2 and 20

used from the family of thermoplastics has a light weight and good thermomechanical properties [3–5]. Several Nylons can be quoted: 6,11,12,46 and 66 etc. [6–11]. They are more used in transportation, automotive, textile and packaging industries. The PA66 is one of the most used Nylons. Its synthesis is made by the hexamethylene diamine and adipic poly condensation. Filled Nylon has better characteristics in terms of strength and stiffness. We will study in this work the optimal properties of the Nylon PA66 bio filled by ANS Particles with the Mori Tanaka and finite element methods by varying the content, orientation and aspect ratios of particles.

2 Materials and Methods 2.1 Materials The ANS particles are rich in cellulose, hemi cellulose, lignin and Ashes by (25.7%, 34.3%, 34.5% and 5.4% respectively) as determined by Essabir et al. [2]. The Table 1 presents the properties of the particles used. The Nylon 66 used is from the family of thermoplastics having a light weight and good thermo-mechanical properties [5, 12, 13].

2.2 Methods 2.2.1

Mori-Tanaka Method

We will use this model to predict the mechanical behavior of Nylon 66 bio filled by ANS particles. It is advantageous compared to other homogenization models such as Voigt, Tsai Pagano and Reuss etc. [14–16] it gives more efficiency and reliability of predicted behaviors. The homogenization parameters are cited in the Table 2. In order to have better convergence, we are going to use a minimal, maximal and final times respectively of 0.01, 0.1 and 1 s. The values chosen in Table 2 are the result of several numerical simulations leading to good convergence and reliability of the results.

Towards a Digital Modeling of the Optimal Mechanical Properties …

1767

Table 2 Homogenization, loading and orientation control Parameters

Values

Stopping criterion

1E-006

Maximal stage of error

1E-005

Iterations prior to control

4

Highest iterations

20

Increments for angle

12

Trace-tolerance

0.1

Fig. 1 Geometry of Nylon PA66 bio filled by ANS particles a Cylinder particles, b Ellipsoid particles, c Sphero cylinder particles

2.3 Finite Element Method A. Geometry of the eco composite The PA66 bio filled by ANS particles is numerically presented as a RVE in order to perform the finite element modeling which is powerful in predicting the complex non linear behaviors [17–21]. The Fig. 1 presents the eco composite geometry. B. Mechanical loading We are going to use the periodic boundary conditions that are periodic to all the faces of the volume element. This is enforced through a large set of equations relating the degrees of freedom of the nodes lying on one face with those of the corresponding nodes and lying on the opposite face, on a 2-by-2 basis. Node duplication is used to prevent issues coming from non periodic meshes. Figure 2 illustrates the definition of periodic boundary conditions in the case of a mechanical loading with a macroscopic uniaxial peak strain 11 . Periodic boundary conditions usually lead to the best predictions when compared to the Dirichlet and Mixed boundary condition type. It also shows a faster convergence rate as the size of the volume element increases, but to the expense of increased CPU time and memory requirements to solve the finite element problem. This decrease of

1768

A. Moumen et al.

Fig. 2 Periodic boundary conditions

Fig. 3 Uniaxial_1 loading of the eco composite

performances is due to the potentially large set of constraint equations that has to be imposed. A macroscopic uniaxial strain state in the 1-direction is imposed (macroscopic uniaxial stress state) as presented in the Fig. 3. C. Incrementation and Mesh We are going to use a max of 20 increments with initial, minimum, maximum and final times respectively of 0.1, 0.01, 0.1 and 1. The mesh used is the voxel nonconforming, having several advantages in terms of convergence, flexibility of use and the best accuracy [20].

Towards a Digital Modeling of the Optimal Mechanical Properties …

1769

3 Results and Discussion 3.1 Eco Composite Mesh Figure 4 presents the mesh performed by the finite element method.

3.2 Density The density of ANS particles is around 1.29 g/cm3 which is lower compared to mineral or inorganic fillers such as 2.6g/cm3 clay [2] and talc 2.8 g/cm3 [2] showing a lightness of our bio load which can benefit the polyamides studied. The Table 3 and the Fig. 5 show the density of the eco-composite based on ANS particles compared to other composites loaded with mineral and inorganic particles (clay and talc).

Fig. 4 Mesh of Nylon PA66 bio filled by ANS particles a Cylinder particles, b Ellipsoid particles, c Sphero cylinder particles

Table 3 Density of different loads and bio composites using Mori-Tanaka method

Density

Values

ANS

1.29

Clay

2.6

Talc

2.8

Nylon 66

1.14

Nylon 66- ANS

1.1671

Nylon 66-Clay

1.2842

Nylon 66-Talc

1.2934

1770

A. Moumen et al.

Fig. 5 Density of different bio composites using Mori-Tanaka method (M-T)

3.3 Effect of the Orientation 3.3.1

Random Orientation

In order to study the effect of ANS particles orientation on the behavior of the polymer studied, we modeled different orientations ranging from 0° to 90°.The orientation tensor is defined by: ⎡

⎤ a11 a12 a13 ai j = ⎣ a12 a22 a23 ⎦ a13 a23 a33

(1)

The trace of the orientation tensor must be equal to1. a11 + a22 + a33 = 1

(2)

In order to obtain a trace equal to 1, we define a trace tolerance of 0.1. If the result is far from [1-tol; 1 + tol], the numerical modeling corrects the orientation tensor by this way (Table 4): ⎡ a ⎤ 11 a11 a12 a13 trace(a)  ⎢ ai j = ⎣ a12 a22 a23 ⎦is corrected to a = ⎣ a12 a13 a23 a33 a13 ⎡

a12 a22 trace(a)

a23

⎤ a13 ⎥ a23 ⎦

(3)

a33 trace(a)

The reference orientation tensor for the three morphologies is as follows: ⎡

⎤ 0.333 0 0 ai j = ⎣ 0 0.333 0 ⎦ 0 0 0.333

(4)

Towards a Digital Modeling of the Optimal Mechanical Properties … Table 4 Global error on orientation tensor

1771

ANS

Values

Cylindrical

0.0779

Ellipsoid

0.0709

Sphero cylindrical

0.0472

⎡

⎤ 0.3016 −0.0157 −0.0218 ai j (cylindrical) = ⎣ 0 0.3919 −0.0138 ⎦ 0 0 0.3065 ⎡ ⎤ 0.3016 −0.0113 −0.0477 ai j (ellipsoid) = ⎣ 0 0.3263 0.00797 ⎦ 0 0 0.3721 ⎡ ⎤ 0.3472 0.00351 0.00244 ai j (sphero cyl) = ⎣ 0 0.3517 0.02548 ⎦ 0 0 0.3012

(5)

(6)

(7)

3.4 Fixed Orientation If we want to express the aligned Representative Volume element, it’s better to choose the fixed orientation tensor to save computing time. We define the angles θ and φ. The first one is closed to 90° and the second one varies from 0° to 180° depending on the orientation in the plane (1–2). The following figures obtained by the finite element method present the elastic behaviors of composites for 0°, 45 and 90° orientations (Fig. 6). The analysis of the curves in the figure below shows a slight stiffness in terms of Young modulus and the stress at break for cylindrical, sphero cylindrical and ellipsoid particles at 0°. This rigidity decreases slightly away from 0°.The less rigid behavior is the same for cylindrical and sphero-cylindrical particles at 45° and 90° whereas for ellipsoid particles, the behavior at 45° is slightly higher than 90°. In order to validate the results obtained for the orientation effect, a comparison between the results of theMori-Tanakaand finite element methods is carried out as presented by the following figures: The analysis of the curves of the Figs. 7, 8 and 9 obtained numerically shows that the results obtained by the finite element method are similar to those obtained by the Mori-Tanaka model. The rigidity decreases slightly by moving away from 0° in terms of Young modulus and the stress at break for cylindrical, sphero-cylindrical and ellipsoid particles. Compared to the orientation, it is slightly advised to use cylindrical and sphero cylindrical morphologies especially when using particles in orientations close to 90°.

1772

A. Moumen et al.

Fig. 6 Particle orientation effects for the three morphologies using the finite element method

Fig. 7 Elastic curves for cylindrical and sphero cylindrical particles at 0° by the FE method and M-T model

3.5 Effect of Aspect Ratio In order to study the effect of ANS particles aspect ratio on the behavior of the polymer studied, we modeled several aspect ratios (0.5, 2 and 20). The Figs. 10 and 11 present respectively the tensile curves obtained by theMori-Tanaka and finite element methods for these different aspect ratios.

Towards a Digital Modeling of the Optimal Mechanical Properties …

1773

Fig. 8 Elastic curves for cylindrical and sphero cylindrical particles at 45° and 90° by the FE method and M-T model

Fig. 9 Elastic curves for ellipsoid particles at 0°, 450 and 90° by the FE method and M-T model

The results illustrated in Figs. 10 and 11 shows that the Mori-Tanaka and finite element methods give the same results for aspect ratios of 0.5,2 and 20.The two numerical methods clearly show that the rigidity of the bio composite improves by increasing the aspect ratio of Argan Nut Shell. At an aspect ratio of 20, the rigidity is much better. The stress at break is greater with these high aspect ratios. These results are valid for all the morphologies studied (cylinder, sphero cylinder and ellipsoid).

1774

A. Moumen et al.

Fig. 10 Tensile curves obtained by the finite element method

Fig. 11 Tensile curves obtained by the Mori-Tanaka model

4 Conclusion In the present work, the importance of bio loading was studied in order to have green eco composites meeting the expectations of researchers in terms of respecting the environment, regulations and sustainable development requirements.

Towards a Digital Modeling of the Optimal Mechanical Properties …

1775

The Mori Tanaka and finite element methods were used to characterize the mechanical behavior of Nylon 66 bio filled by ANS particles. These two digital methods have proved a great ability to predict the behavior of the eco composite studied. They are reliable, simple in use and have more speed in computing. At 20 Wt% of particles and by increasing their aspect ratios, the mechanical properties of the eco composites studied are optimum. By moving away from 0°, the rigidity slightly reduces. The use of these bio fillers will replace the use of traditional fillers and move towards environment friendly eco composites. Future Scope In this study, we determined the mechanical behavior of Nylon PA66 bio loaded by ANS particles. It will be interesting to study the thermal behavior and apply other tests on this eco composite such as fatigue and aging tests. Other bio loads of animal or plant origin can be studied for the reinforcement of this Nylon using experimental tests and digital modeling.

References 1. Russo P, Simeoli G, Vitiello L, Filippone G (2019) Bio-polyamide 11 hybrid composites reinforced with basalt/flax interwoven fibers: a tough green composite for semi-structural applications. Fibers 7:41 2. Essabir H (2015) Bio-composites à base de coque de noix d’arganier: Mise en œuvre, caractérisation et modélisation du comportement mécanique, p 184 3. Rizkalla S, Dawood M, Schnerch D (2008) Development of a carbon fiber reinforced polymer system for strengthening steel structures. Compos Part A Appl Sci Manuf 39:388–397 4. Choi EY, Kim MH, Kim CK (2019) Fabrication of carbon fiber grafted with acyl chloride functionalized multi-walled carbon nanotubes for mechanical reinforcement of nylon 6,6. Compos Sci Technol 178:33–40. https://doi.org/10.1016/j.compscitech.2019.05.012 5. Lyu WY, Chen XM, Li YB, Cao S, Han YM (2019) Thermal stability and heat release effect of flame retarded PA66 prepared by end-pieces capping technology. Compos Part B Eng 167:34– 43. https://doi.org/10.1016/j.compositesb.2018.12.016 6. Tang Q, Wang K, Ren X, Zhang Q, Lei W, Jiang T, Shi D (2020) Preparation of porous antibacterial polyamide 6 (PA6) membrane with zinc oxide (ZnO) nanoparticles selectively localized at the pore walls via reactive extrusion. Sci Total Environ 715:137018 7. Nagasawa N, Tago T, Kudo H, Taguchi M (2017) Radiation-induced crosslinking of polyamide11 in the presence of triallylisocyanurate. Polym Degrad Stab 136:98–102. https:// doi.org/10.1016/j.polymdegradstab.2016.12.014 8. Li M, Chen AN, Lin X, Wu JM, Chen S, Cheng LJ, Chen Y, Wen SF, Li CH, Shi YS (2019) Lightweight mullite ceramics with controlled porosity and enhanced properties prepared by SLS using mechanical mixed FAHSs/polyamide12 composites. Ceram Int 45:20803–20809. https://doi.org/10.1016/j.ceramint.2019.07.067 9. Umesh GL, Prasad NJK, Rudresh BM, Devegowda M (2020) Influence of nano graphene on mechanical behavior of PA66/PA6 blend based hybrid nano composites: Effect of micro fillers. Mater Today Proc 20:228–235 10. Lee CS, Kim HJ, Amanov A, Choo JH, Kim YK, Cho IS (2019) Investigation on very high cycle fatigue of PA66-GF30 GFRP based on fiber orientation. Compos Sci Technol 180:94–100. https://doi.org/10.1016/j.compscitech.2019.05.021 11. Spina R, Cavalcante B (2020) Thermal analysis of PA66 grinding. Procedia Manuf 47:910–914

1776

A. Moumen et al.

12. Leconte N, Bourel B, Lauro F, Badulescu C, Markiewicz E (2020) Strength and failure of an aluminum/PA66 self-piercing riveted assembly at low and moderate loading rates: experiments and modeling. Int J Impact Eng 142:103587 13. Moumen A, Lakhdar A, Mansouri K (2020) Numerical study of the mechanical behavior of polyamide 66 reinforced by argan nut shell particles with the finite element method and the mori-tanaka model. Int J Adv Trends Comput Sci Eng 9:7723–7730. https://doi.org/10.30534/ ijatcse/2020/115952020 14. Moumen A, Jammoukh M, Zahiri L, Mansouri K (2020) Study of the optimal micromechanical behavior of a polymer reinforced by snail shell particles using the mori-tanaka numerical model. In: 2020 IEEE international conference of Moroccan geomatics (Morgeo). IEEE, pp 1–6 15. Lakhdar A, Moumen A, Zahiri L, Jammoukh M, Mansouri K (2020) Experimental and numerical study of the mechanical behavior of bio-loaded PVC subjected to aging. Adv Sci Technol Eng Syst J 5:607–612 16. Chihaoui B, Serra-Parareda F, Tarrés Q, Espinach FX, Boufi S, Delgado-Aguilar M (2020) Effect of the fiber treatment on the stiffness of date palm fiber reinforced PP composites: macro and micromechanical evaluation of the young’s modulus. Polymers (Basel) 12:1–21. https://doi.org/10.3390/POLYM12081693 17. Moumen A, Jammoukh M, Zahiri L, Mansouri K (2020) Numerical modeling of the thermo mechanical behavior of a polymer reinforced by horn fibers. Int J Adv Trends Comput Sci Eng 9:6541–6548. https://doi.org/10.30534/ijatcse/2020/342942020 18. Lakhdar A, Jammoukh M, Zahiri L, Mansouri K, Moumen A, Salhi B (2020) Numerical and experimental study of the behavior of PVC material subjected to aging. In: 2020 1st international conference on innovative research in applied science, engineering and technology (IRASET). IEEE, pp 1–6 19. Yu G, Shi B, Shen Y, Gu J (2020) A novel finite element method for predicting the tensile properties of 3D braided composites. Mater Res Exp. 6:125626 20. Rao MVP, Raj D, Harursampath D, Renji K (2020) Estimation of material properties of metal matrix composites using finite element method in the presence of micromechanics damages. Mater Today Proc 21:1135–1143 21. Sokolov A, Schetinin V, Kozlov M (2020) Surface finite element for imperfect interface modeling in elastic properties homogenization. Key Eng Mater 833:101–106

Experimental Evaluation of MEMS Accelerometers Integrated into Smartphones: A Case Study of Bearing Condition Monitoring Abdelbaset Ait Ben Ahmed , Abdelhamid Touache, Abdelhadi El Hakimi, and Abderrahim Chamat Abstract Over the last decade, smartphones have become an essential part of numerous applications in daily life activities. Contrary to professional equipment, smartphones are available to everyone and are not expensive, which is why it is a valuable opportunity to exploit smartphone capabilities for professional applications. This paper presents an experimental evaluation of integrated MEMS accelerometers into smartphones to assess their performance for vibration monitoring, with the expectation to use them for educational and modest industrial purposes. This study presents an evaluation of two different devices having different specifications, which evaluates the extracted features of the vibration data collected from both devices. A shaking table test was carried out to compare the two MEMS accelerometers results with professional equipment. Finally, a case study was conducted to monitor the condition of two bearings running at low rotational speed (600 rpm) using the vibration data acquired through both smartphones. Experimental results indicate that smartphones can be a useful instrument for monitoring the condition of bearings. Keywords Vibration measurement · Experimental testing · MEMS accelerometers · Condition monitoring · Smartphone’s sensors

A. Ait Ben Ahmed (B) · A. Touache · A. El Hakimi Laboratory of Mechanical Engineering, University of Sidi Mohamed Ben Abdellah, Fez, Morocco A. Touache e-mail: [email protected] A. El Hakimi e-mail: [email protected] A. Chamat Laboratory of Industrial Techniques, University of Sidi Mohamed Ben Abdellah, Fez, Morocco e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_161

1777

1778

A. Ait Ben Ahmed et al.

1 Introduction Vibration analysis is very important to identify and to monitor the health of mechanical systems [1], but many people consider that the cost of vibration analysis equipment is very expensive and not at hand for all. Contrary to professional equipment (Pro. Equip.), the smartphones are characterized by low cost compared with professional equipment, and by their several functionalities. So, it’s beneficial to look for replacing professional equipment with smartphones to use them for educational purposes such as mechanical vibration practical works in the university curriculum, or modest industrial purposes such as monitoring the rotating machinery operating under low speed, and correcting the unbalance defect in rotors [2, 3]. Each day, smartphones have more and more features to add to existing devices. The vast majority of smartphones contain a wide variety of sensors, it has become a favored tool in the world and even an important part of daily life activities [4]. The sensor system of smartphones contains motion sensors such as accelerometers and gyroscopes, magnetic field measurements, light detection, and proximity detection. Besides, sound can be detected using the microphone when the images are captured using the camera. Sensors have been used in smartphones since their introduction, the new smartphones are powerful devices that are always on hand and have access to a wireless connection at anytime and anywhere [5]. Nowadays, most smartphones are programmable and equipped with autonomous, inexpensive, small, and low energy sensors, this gives smartphones more powerful benefits. This paper aims to presents an experimental evaluation of the accelerometer integrated into the smartphones, to assess its performance in vibration analysis, and compare them with professional equipment. This study shows the evaluation of two accelerometers with different specifications and compares their performance with professional equipment, the comparison will be based on the time and the frequency features extracted from the vibration data recorded by each accelerometer.

2 Smartphone’s Data Acquisition 2.1 Smartphone’s Accelerometer Smartphone’s accelerometer measures the acceleration force applied to the device, which includes the gravitational force. In general, the sensor structure uses a standard 3-axis coordinate system (X, Y, and Z) that can be represented in the Cartesian coordinates [6], and the accelerometer provides a measurement for each of the X, Y, and Z directions, as shown in Fig. 1. In this study, two different smartphones were evaluated, the first labeled old smartphone and the second labeled new smartphone. The specifications of each of them are listed in Table 1.

Experimental Evaluation of MEMS Accelerometers …

1779

Fig. 1 The coordinate system of smartphone sensors

Table 1 Specifications of the smartphone’s accelerometers used in this study

Specifications

New Smartphone (New Sp.)

Old Smartphone (Old Sp.)

Name

MTK Inc

BOSCH BMC150

Resolution

0.0012 m/s2

0.0095 m/s2

Range

±39 m/s2 (±4 g)

±159 m/s2 (±16 g)

Sampling frequency

Up to 100 Hz

Up to 100 Hz

2.2 Data Pre-processing Pre-processing the accelerometer data is very important to ensure the accuracy and the quality of the measured data. To measure the real movement of the device it is recommended to remove the gravity component of the acceleration measurements. This can be achieved by applying a high-pass filter to eliminate the effect of gravity from three directions [7]. For noise filtering, a Butterworth Infinite Impulse Response (IIR) bandpass filter has been applied to remove noise from the vibration signal, with appropriate cut-off frequencies according to the range of phenomena to be observed.

3 Evaluation of Smartphone’s Accelerometer 3.1 Shake Table Test In this section, we will present the shake table to evaluate the smartphone’s accelerometer and compare its results with those of professional equipment.

1780

A. Ait Ben Ahmed et al.

Figure 2 illustrates the experimental setup which contains the rotor system that produces a uniform vibration (≈16 Hz) using an unbalanced mass. The vibration of the rotor causes the vibration of the table. The smartphone and professional equipment capture the vibrations of the table along the Z-axis direction of the smartphone. Figure 3 shows the vibration signal obtained using the smartphone and using the professional equipment during the evaluation test in the Z-axis direction.

Fig. 2 Experimental setup of the evaluation test

Smartphone Pro. Equi.

1 0.8

Acceleration (m/s²)

0.6 0.4 0.2 0 -0.2 -0.4 -0.6 -0.8 -1

0

0.05

0.1

0.15

0.2

0.25

0.3

Time (s)

Fig. 3 Smartphone vs. professional equipment vibration signal during the evaluation test

Experimental Evaluation of MEMS Accelerometers … Table 2 The sampling rate of the smartphone’s accelerometer for each mode

Table 3 Time-domain statistical features of vibration data (Fastest mode, Z-axis direction)

1781

Mode

Sampling rate

Fastest

≈100 Hz

Game

≈50 Hz

Normal

≈2.5 Hz

User interface (UI)

≈1.5 Hz

Features

New Sp.

Old Sp.

Pro. Equip.

Mean

−0.0004

0.0027

−0.0258

RMS

0.5585

0.5437

0.4937

Kurtosis

1.5260

1.6093

1.5134

Skewness

0.0024

−0.0082

0.0167

Variance

0.3125

0.2962

0.2433

Crest factor

1.6090

1.8119

1.4382

3.2 Sampling Frequency Concerning the sampling frequency, the smartphone’s accelerometer was sampled regularly due to the implementation of the sampling mechanism by the Android structure. The Android API provides four different sampling rates for its accelerometer sensor: The Fastest, Game, Normal, and User Interfaces (UI) [8], listed from the fastest to the slowest.

3.3 Features Extraction Statistical features of the time-domain, such as mean, root mean square (RMS), Crest factor, and variance were generally used to identify distinctions between the vibration signals. More advanced statistical features, such as skewness, and kurtosis are also applied as features [9, 10], all these features are used in this study to evaluate the smartphone vibration data. Also, the frequency spectrum of the vibration data generated by the Fast Fourier transform (FFT) algorithm is exploited for extracting and evaluating other features of the vibration data [11].

4 Results and Discussion Tables 3 and 4 show the time-domain features of the vibration signal recorded by the old and new smartphone compared with the professional equipment results. The

1782 Table 4 Time-domain statistical features of vibration data (Game mode, Z-axis direction)

A. Ait Ben Ahmed et al. Features

New Sp.

Old Sp.

Pro. Equip.

Mean

0.0007

−0.0003

−0.0258

RMS

0.5765

0.5144

0.4937

Kurtosis

1.6153

1.7024

1.5134

−0.0551

−0.1139

0.0167

Variance

0.3330

0.2649

0.2433

Crest factor

1.7442

1.8042

1.4382

Skewness

measurement concerned just the two first modes (Fastest and Game, see Table 2) because the sampling rate of each mode is higher than the frequency of vibration phenomena (Shake table frequency ≈16 Hz). As shown in Tables 3 and 4, the statistical features of the vibration data, RMS, Kurtosis, Variance, and Crest factor are more stable for both devices and in both sampling frequency modes, compared to professional equipment results. And concerning the frequency-domain, the new smartphone has a clear spectrum than the spectrum obtained by the old smartphone, which is very noisy. So, this difference was produced owing to the resolution and the accuracy of the two sensors. The resolution and the sampling rate of the smartphone’s accelerometer play an important role in the accuracy of the measurement and the amount of noise in the vibration signal. Also, the type of sensor mounting on the structure affect the obtained results, it’s recommended to be adhesive mounting. Figure 4 shows the frequency-domain of the vibration signal recorded by the old and new smartphone for the Fastest and Game modes in the Z-axis direction. Based on Fig. 4, the new smartphone has a clearer spectrum than the spectrum obtained by the old smartphone which is very noisy. It can therefore be said that the resolution is responsible for this difference. Besides, the sampling frequency plays an important role in the spectrum clarity, so that the spectrum of the Fastest mode (100 Hz) is better than the spectrum of the Game mode (50 Hz). Generally, the smartphone presents acceptable results, but the low sampling rate of the smartphone’s accelerometer (Max 100 Hz) still the main disadvantage which limits the interval of using smartphones in the professional areas. So, in this case, it is recommended to evaluate the vibration phenomena in which the main frequency is located below half of the Smartphone sampling rate (i.e. 50 Hz).

5 Case Study: Bearing Condition Monitoring In this section, two bearings were examined to identify the degree of damage, the first was a healthy bearing and the second was defective with a localized fault in one ball of the bearing, as shown in Fig. 5. Bearing fault signals are considered as amplitude modulated signals. Where the faults excite the resonance frequencies of the bearing structure. For this reason, it is difficult to identify fault frequencies in the

Experimental Evaluation of MEMS Accelerometers … 0.9

0.9

(a)

0.8

0.7

0.6

0.6

0.5 0.4 0.3

(b)

0.8

0.7

Amplitude (m/s²)

Amplitude (m/s²)

1783

0.5 0.4 0.3 0.2

0.2

0.1

0.1

0

0 0

5

10

15

20

25

30

35

40

45

0

50

5

Frequency (Hz) 0.6

10

15

Frequency (Hz)

20

0.6

(c)

(d) 0.5

Amplitude (m/s²)

0.5

Amplitude (m/s²)

25

0.4

0.3

0.2

0.4

0.3

0.2

0.1

0.1

0

0 0

5

10

15

20

25

30

Frequency (Hz)

35

40

45

50

0

5

10

15

Frequency (Hz)

20

25

Fig. 4 Frequency-domain of vibration signal recorded by both smartphones, a New Sp. Fastest Mode, b New Sp. Game Mode, c Old Sp. Fastest Mode, (d). Old Sp. Game Mode

Fig. 5 The rotor system with the smartphone mounted tightly to the bearing

signal spectrum. Also, envelope analysis is not useful in our case due to the lower sampling frequency (Fs) of the smartphone accelerometer. Therefore, we confine to perform a statistical evaluation. The smartphone was mounted near the bearing to acquire vibration data in the three directions (X, Y, and Z axes). Time-domain features, selected in Sect. 4, will be extracted from the vibration data to examine the health of the two bearings where the rotor is operating at 600 rpm (10 Hz).

1784

A. Ait Ben Ahmed et al.

0.1

0.8

(a)

0.08

(b)

0.6

0.06

Acceleration (m/s 2)

Acceleration (m/s 2)

0.4 0.04 0.02 0 -0.02 -0.04

0.2 0 -0.2 -0.4

-0.06

-0.6

-0.08 -0.1

0

5

10

15

-0.8

0

Time (s)

5

10

15

Time (s)

Fig. 6 Time-domain of smartphone vibration data measured in the vertical direction (Y-axis), a Healthy bearing, b Faulty bearing

Table 5 Time-domain statistical features of vibration data (Old Sp., X-, Y-, and Z-axis) Features

Direction

Healthy bearing

RMS

X-axis

0.0479

0.0980

Y-axis

0.0120

0.0956

12.5%

Z-axis

0.020

0.0447

44.7%

X-axis

3.8118

5.5676

68.4%

Y-axis

3.1493

Z-axis

3.4848

5.6071

62.1%

X-axis

0.0023

0.0096

23.9%

Y-axis

0.0001

0.0091

1.09%

Z-axis

0.0004

0.002

20.0%

X-axis

3.5314

4.7288

74.6%

Y-axis

3.2845

7.7377

42.4%

Z-axis

2.6394

5.6562

46.6%

Kurtosis

Variance

Crest factor

Faulty bearing

15.299

Ratio (Healthy/Faulty) 48.8%

20.5%

Figure 6 shows the Time-domain of vibration data recorded by the smartphone measured in the vertical direction corresponding to the Y-axis coordinate of the smartphone. As indicated in Fig. 6, the amplitude of vibration of the healthy bearing is more attenuated compared to the vibration amplitude of the faulty bearing. Table 5 summarizes the results of the statistical evaluation of the vibration data for healthy and defective bearings, which are performed according to the directions of the smartphone’s coordinate system (X, Y, and Z axes). According to the ratio (healthy/defective), which represents the percentage of the amount of the statistical feature of the healthy bearing compared to the defective bearing, the RMS level, Kurtosis value, variance, and crest factor of the defective bearing were increased in the three directions (X, Y, Z axes). The Kurtosis value of a normal rolling element bearing is well recognized at 3. Where, for a defective bearing, the probability density function becomes more peak,

Experimental Evaluation of MEMS Accelerometers …

1785

and the dispersion has also increased around the reference mean value, and the signal becomes more impulsive. In sum, for efficient monitoring, it is recommended to make a schedule to systematically acquire vibration data at predefined points and to establish graphs of the evolution as a function of time for each feature in order to monitor the bearing degradation and to create a smartphone-based system for bearing condition monitoring.

6 Conclusion The evaluation of the two different smartphone’s accelerometers leads us to indicate that: • Time-domain statistical features show good stability for most of the features, these features can be used in condition monitoring. • The frequency-domain show that the new smartphone has a clean and clear spectrum than the old smartphone, these features can be exploited to extract the necessary information from the vibration signal, and identify the vibration phenomena. • The difference between the smartphone’s results and the results of the professional equipment can be justified by the low sampling frequency, and the poor resolution of the sensor, as well as by the mounting of the sensor to the structure, as in this study, the mounting is not adhesive, neither for both smartphones nor for the professional equipment. In summary, this study has evaluated the smartphone’s accelerometer performance in vibration analysis to use it for educational purposes to overcome the lack of measuring equipment and to help students to apply their theoretical knowledge in vibration analysis at home. Also, it can be used for modest industrial purposes as indicated in the case study presented in Sect. 5. Acknowledgment Experimental equipment was provided by the Mechanical Engineering Laboratory, Faculty of Science and Technology, University of Sidi Mohamed Ben Abdellah, Fez, Morocco.

References 1. Looney M (2014) An introduction to MEMS vibration monitoring. Analog Dialogue 48(06):1– 3 2. Ait Ben Ahmed A, Touache A, El Hakimi A, Chamat A (2019) The possibility of using a smartphone in a single plane rotor balancing. In: MATEC web of conferences. EDP Sciences, p 03001. https://doi.org/10.1051/matecconf/201928603001

1786

A. Ait Ben Ahmed et al.

3. Ait Ben Ahmed A, Touache A, El Hakimi A, Chamat A (2020) A new hybrid method for rigid and flexible rotor balancing without phase response measurements. Aust J Mech Eng. https:// doi.org/10.1080/14484846.2020.1842616 4. Feng M, Fukuda Y, Mizuta M, Ozer E (2015) Citizen sensors for SHM: Use of accelerometer data from smartphones. Sensors 15(2):2980–2998. https://doi.org/10.3390/s150202980 5. Cerutti S, Magenes G, Bonato P (2010) Special section on smart wearable devices for human health and protection. Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ TITB.2010.2048937 6. Google: Sensor—Android Developers. https://developer.android.com/guide/topics/sensors/ sensors_overview. Accessed 29 Nov 2020 7. Google: Sensor—Android Developers. https://developer.android.com/guide/topics/sensors/ sensors_motion. Accessed 11 Sept 2020 8. SensorDelay Enum (Android.Hardware)—Microsoft Docs. https://docs.microsoft.com/enus/dotnet/api/android.hardware.sensordelay?view=xamarin-android-sdk-9. Accessed 09 Dec 2020 9. Soong TT (2004) Fundamentals of probability and statistics for engineers. Wiley, Hoboken 10. Caesarendra W, Tjahjowidodo T (2017) A review of feature extraction methods in vibrationbased condition monitoring and its application for degradation trend estimation of low-speed slew bearing. Machines 5(4):21 11. Chu E, George A (1999) Inside the FFT black box: serial and parallel fast fourier transform algorithms. CRC Press, Boco Raton

MPPT Using Adaptive Genetic-Fuzzy Logic Control for Wind Power System Chakib Alaoui, Hajar Saikouk, and Anass Bakouri

Abstract This study proposes a fuzzy-based MPPT controller used to extract the maximum power point (MPPT) from the WECS using doubly fed induction generator (DFIG). The MPPT was accomplished by applying control to the generator speed and blade pitch angle. The parameters of the FLC are optimized by using a genetic algorithm (GA). Initial simulation results prove that the proposed MPPT algorithm possesses good dynamic and steady state performances under different operating conditions. Keywords Wind turbine · Doubly fed induction generator (DFIG) · Fuzzy Logic Controller (FLC) Genetic algorithm optimization

1 Introduction Several challenges are still confronting the large-scale adoption of WECS (wind energy conversion system). The most urgent challenge concerns the wind speed variations at the various spots over the blades covered area. Thus, making direct sensing of wind speed a challenging task [2, 9]. In addition, deploying mechanical sensors rises the overall costs of WECS, while reducing its reliability and roughness [2–4]. In order to meet these challenges, the author of [1] have proposed an estimation of the actual wind speed indirectly by using electric indicators, such as output electric power or generator shaft speed using a tachometer which are reasonably easy to measure according to [4]. The authors of [5–7] combined these results with a dynamic model of wind turbine. However, these dynamic equations are highly nonlinear and C. Alaoui (B) · H. Saikouk · A. Bakouri INSA EuroMediterannée, Euromed University, Fez, Morocco e-mail: [email protected] H. Saikouk e-mail: [email protected] A. Bakouri e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_162

1787

1788

C. Alaoui et al.

Grid

Fig. 1 Wind turbine, doubly fed induction generator and power electronic converters block diagram

having hard to find parameters. For that reason, the wind speed is usually estimated by using some kind system identification, data mining, state observer and/or some advanced fuzzy techniques [1, 3]. Modern wind turbine systems include embedded controllers to predict the best pitch angle for the wind turbine blades to operate under various wind speed scenarios. This will serve to adjust the pitch angle. The objective of the presented project is to implement a controller that will drive the pitch angle of the blades of the wind turbine system to the preset pitch angle in order to regulate the input wind power. Hence extracting the maximum allowable power without damaging the wind turbine nor the electric generator. In this project, we consider a wind power system comprising of a wind turbine, a doubly fed induction generator (DFIG) connected to a three-phase grid through appropriate AC-DC and DC-AC converters [8]. The proposed system is illustrated in Fig. 1. The wind turbine transforms the kinetic energy of the air particles, captured by the blades, into mechanical power that rotates the electric generator. The static converters transmit the electric power to the grid. This is accomplished by the drive train system, rotor/stator of the induction machine and an AC-DC converter coupled with a DCAC converter through a capacitor. The control system reads the output power and adjusts the pitch angle of the blade system of the wind turbine in order to regulate the output power.

MPPT Using Adaptive Genetic-Fuzzy Logic Control …

1789

2 System Modeling and Implementation 2.1 Wind Turbine The power accessible from the wind in a wind turbine system is expressed by (1). P = 0.5ρ Aυ 3

(1)

where ρ is air density (1.225 kg/m3 ), A is the area swept by the blades (m2 ), and v is wind speed (m/s). Figure 2 shows the wind power as a function of wind speed for an uncontrolled system. The power available in the air molecules cannot be completely transferred to a mechanical power in the shaft of the turbine; it is rather limited by the Betz threshold of 59%. This limit is expressed by the power coefficient C p of the wind turbine. It is a function of the blade pitch angle (β) and the blade tip speed ratio (λ). The mechanical power available at the shaft of the wind turbine becomes (2). P t = 0.5ρ Aυ 3 C p (β, λ)

(2)

where β is the blade pitch angle, C p is the power coefficient of the turbine, λ is the blade tip to speed ratio (TSR). C p is a nonlinear function of β and λ and given in (3) and (4).   −21 116 C p = 0.5176 − 0.4β − 5 e λi + 0.0068λ λi

(3)

0.035 1 1 − = λi λ + 0.08β 3β + 1

(4)

Fig. 2 Wind power as function of wind speed

1790

C. Alaoui et al.

Cp

β

λ

Fig. 3 The power coefficient Cp -TSR curve for different pitch angle values

Fig. 4 Output generator power vs Wind speed

Finally, blade tip to speed ratio-TSR is defined in (5), where ω is the blade angular velocity (rad/s) and R is the wind turbine’s blade radius (m). λ=

ω t R υ

(5)

Figure 3 illustrates the nonlinear relationship between the blade tip speed ratio (TSR) and the power coefficient (C p ), with the pitch angle (β).

2.2 Wind Turbine Pith Angle Regulator Blade pitch regulation means to adjust the angle of attack of the blades of a wind turbine rotor in order to control the electric power produced by the DFIG. The main objective of the blade pitch control system is to keep the rotor speed within

MPPT Using Adaptive Genetic-Fuzzy Logic Control …

1791

the acceptable operation boundaries, as shown in Fig. 4. Figure 5 illustrates the relationship of the mechanical shaft power, transmitted to the DFIG, to the wind speed and the blades pith angle. It can be seen from Fig. 5 that the maximum output is determinated when the pitch angle is zero and thence as the pitch angle increases, as the value of the output power decreases. Hereafter, it is necessary to control the pitch angle in order to attain the MPP of the wind turbine. By applying the TSR control, it is necessary to maintain the TSR to an optimum value at which extracted power is maximized by controlling the rotational speed of the DFIG [9–12]. This technique needs a precise measurement of the shaft rotational speed combined with an estimation of the wind speed in order to get the optimum TSR

Fig. 5 Output power vs. turbine rotational speed for various pith angle values

Fig. 6 Tip to speed ratio based MPPT for wind turbine block diagram

1792

C. Alaoui et al.

Fig. 7 Fuzzy-based MPPT block diagram for a wind power system

(λopt ) of the turbine. The system would then be able to extract the maximum attainable power as shown in Fig. 6. The optimum rotational speed is determined as ωopt = λopt V w /R.

2.3 Fuzzy Based MPPT Controller Several control approaches [13, 14] have been used that use the Fuzzy Logic Control (FLC) for WECS MPPT applications, as illustrated in Fig. 7. The foremost advantage of such controllers is that their respective parameters can be modified rapidly in response to sudden variations in the system dynamics. When the weather conditions changes, the performance of a FLC based MPPT is robust. However, their robustness depends heavily on the knowledge base (KB) and the experience of the operator in choosing the rule base (RB), the membership functions and tolerable levels of output errors. Additionally, the memory requisite poses additional limitations in FLC application. This is the main reason to include a proper optimization algorithm, such as genetic algorithm (GA), in order to optimize the fuzzy engine of the FLC, and hence the adaptive FLC.

2.4 Adaptive Fuzzy Controller The adaptive fuzzy controller system has many advantages when used for non-linear system. Some of these advantages are: • Automatically adapts it parameters to new values as the environmental settings are highly dynamic. • The parameters are altered to get the desired and fixed output values.

MPPT Using Adaptive Genetic-Fuzzy Logic Control …

1793

• Very recommended for non-linear systems, such as wind power systems. • The values of the controller K p , K i and K d are constantly tuned by a proposed adaptive fuzzy knowledge based controller (FKBC). In addition to the FKBC, two more block are added; (1) process monitor and (2) an adaptive mechanism. The process monitor continuously monitors the system and detects any variations in the process characteristics. It evaluates the performance of the controller in order to estimate new values for the controller parameters K p , K d and K i . Some of the parameters that are monitored are: the overshoot, rise-time, settling time, delay rate, frequency of oscillation, error, absolute error, weighted absolute error, gain, phase margin among others. The adaptive mechanism uses the information passed to it by the performance monitoring unit and updates the controller parameters in order to adapt it to the targeted results. The fuzzy parameters that are updated online are typically: • • • • •

Scaling factor Rules Shape of the membership function Number of cross points and ratios Number of term sets, and definition of rules.

Moreover, there are two types of adaptive FKBC, such as self-tuning controller, in which the membership function and/or the scaling factor are altered, as illustrated in Fig. 8 (most common) and self-organized controller, in which the rules are altered, as illustrated in Fig. 9 (rarely used). Scaling functions normalize the universes of discourse in which the fuzzy membership functions are described. In a conventional fuzzy system, the scaling functions are parameterized by a single scaling factor or a lower and/or upper limit for linear scaling and a contraction/dilatation factor for non-linear scaling. However, in an optimized fuzzy system, these parameters are adjusted in order to have the scaled universe of discourse better matches the underlying variable range. This is illustrated in Figs. 8. and 9.

3 Simulation Results and Discussion The system shown in Fig. 10 was implemented in Matlab/Simulink environment and simulated. The first block named ‘controlled system’ receives pulse-width-modulated signal (PWM) from the fuzzy controller in order to control the power transferred to the grid through the static converters AC-DC and DC-AC as was illustrated in Fig. 1. The process parameters, also referred to as the state parameters, are I g , V g , ωm and Pm , which are the current and the voltage at the terminal of the generator, the rotational speed and the mechanical power of the generator respectively. The performance of

1794

C. Alaoui et al.

Fig. 8 Altering the scaling function. Having the scaling function of 200, the fuzzy subset positive P = [0 0.5 0.7], however, with the scaling function of 400, P = [0 0.25 0.35]

Fig. 9 Adapting the fuzzy set definition. The fuzzy subset P = [0 0.5 0.7] becomes P = [0 0.19 0.4]

the wind power system, represented by the ‘controlled system’ block, is evaluated, and its output power is used as fitness value for the genetic algorithm (GA). The GA block produces the optimized parameters for the fuzzy controller block that, in combination with the state parameters, produces the PWM signal to the control the converters. Fuzzy modeling of type Takagi–Sugeno was selected in this study since accuracy was a preferred criterion for the MPPT system.

MPPT Using Adaptive Genetic-Fuzzy Logic Control …

1795

Fig. 10 System’s block diagram

The simulation was executed on a wind power system with specifications listed in Table 1. The proposed genetic-fuzzy control system for MPPT was done by Matlab/Simulink. The simulation results, shown in Fig. 11, compare the theoretical values of the generator power and the shaft speed, with the actual output power and rotational speed as obtained by the proposed method. The wind speed was set to change from 6 to 8 m/s at t = 2.5 s and from 8 to 10 m/s at t = 4.5 s during the Matlab simulation. The Simulation results, as illustrated in Fig. 11, reveal that the proposed method has good dynamic response, with steady state performances during the course of the MPPT process. Table 1 Wind power system: turbine and generator specifications

Symbol

Description

Numerical value

R

Turbine blade radius

1m

Cp(max)

Maximum power coefficient

0.475

λopt

Optimal TSR

8.12

Vw

Rated wind speed,

10 m/s

ωm

Rated rotor speed

1400 rpm

Ig

Rated current

23.3 A

P

Number of poles

8

Pg

Rated power

3 kW

1796

C. Alaoui et al.

Fig. 11 Theoretical vs. simulated output power de the wind power system

4 Conclusion In this project, a fuzzy logic controller (PLC) was proposed to wind turbine pitch control in order obtain the MPPT throughout the wind speed values. A genetic-fuzzy control-based for DFIG wind power system has been discussed and modeled. The efficacy of the projected MPPT strategy has been proved by initial Matlab/Simulink simulations.

References 1. Zhiqiang X, Qinghua H, Ehsani M (2012) Estimation of effective wind speed for fixed speed wind turbines based on frequency domain data fusion. Sustain Energy IEEE Trans 3(1):57e64 2. Tan K, Islam S (2004) Optimum control strategies in energy conversion of PMSG wind turbine system without mechanical sensors. Energy Convers IEEE Trans 19(2):392e9 3. Wei Q, Wei Z, Aller JM, Harley RG (2008) Wind speed estimation based sensorless output maximization control for a wind turbine driving a DFIG. Power Electron IEEE Trans 23(3):1156e69 4. Meyer DG, Srinivasan S, Semrau G (2013) Dynamic wind estimation based control for small wind turbines. Renew Energy 50:259e67 5. Kodama N, Matsuzaka T, Tuchiya K, Arinaga S (1999) Power variation control of a wind generator by using feed forward control. Renew Energy 16(1):847e50 6. Van der Hooft E, Van Engelen T. Estimated wind speed feed forward control for wind turbine operation optimisation. In: Conference estimated wind speed feed forward control for wind turbine operation optimisation 7. Boukhezzar B, Siguerdidjane H (2009) Nonlinear control with wind estimation of a DFIG variable speed wind turbine for power capture optimization. Energy Convers Manag 50(4):885e92 8. SimPowerSystems for Use with Simulink (2004) User’s Guide Version 4, MathWorks, Inc. Natick, MA

MPPT Using Adaptive Genetic-Fuzzy Logic Control …

1797

9. Buehring IK, Freris LL (1981) Control policies for wind-energy conversion systems. IEE Proc C Gener Transm Distrib 128:253 10. Abdullah MA, Yatim AHM, Tan CW, Saidur R (2012) A review of maximum power point tracking algorithms for wind energy systems. Renew Sustain Energy Rev 16(5):3220–3227 11. Knight AM, Peters GE (2005) Simple wind energy controller for an expanded operating range. IEEE Trans Energy Convers 20:459–466 12. De Kooning JD, Gevaert L, Vyver JVD, Vandoorn TL, Vandevelde L (2013) Online estimation of the power coefficient versus tip-speed ratio curve of wind turbines. In: 39th annual conference IEEE industrial electronics society, pp 1792–1797 13. Simoes MG, Bose BK, Spiegel RJ (1997) Fuzzy logic based intelligent control of a variable speed cage machine wind generation system. IEEE Trans Power Electron 12:87–95 14. Hilloowala RM, Sharaf AM (1996) A rule-based fuzzy logic controller for a PWM inverter in a stand-alone wind energy conversion scheme. IEEE Trans Ind Appl 32:57–65

Development of an Automated Measurement Platform for an Electrical Traction System Test Bench Amine El Houre, Driss Yousfi, Zakaria Bourzouk, and Mohammed Chaker

Abstract In this paper, a test bench for electrical traction systems is presented and discussed. A simulation model in Matlab/Simulink is proposed to bypass the limitations imposed by the mentioned test bench. The developed Simulink model allows the emulation of the electric vehicle performance for urban driving conditions using ECE 15 drive cycle. An automated measurement platform is made, with the aim of improving the test bench, by making it possible to reproduce the simulation results with fidelity and ease. Keywords Electric vehicle · Electrical drive · Test bench · Matlab/Simulink · DAQ LabJack · Graphical user interface · LabVIEW · Drive cycle

1 Introduction Today’s global society is very concerned with reducing the negative effects of road transportation on the environment due to toxic emissions and greenhouse gases. Therefore, these types of vehicle emissions are legally regulated in multiple countries across the world, in order to comply with the stricter regulations that are anticipated to be adopted in the future, vehicle manufacturers are obliged to invest in various fuel-saving technologies. This has resulted in an increased interest in the electrification of vehicles, primarily hybrid electric vehicles (HEVs) which can reduce fuel consumption compared to conventional vehicles, but also battery electric vehicles (BEVs) [1, 2]. The latter offer high driveline efficiency and do not emit exhaust gases, which is why they have been considered CO2 free in regulations to date [3, 4].

A. El Houre (B) · D. Yousfi · M. Chaker ESETI Laboratory, National School of Applied Sciences, Mohamed First University, Oujda, Morocco Z. Bourzouk ALTRAN, Casablanca, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_163

1799

1800

A. El Houre et al.

Fig. 1 Experimental test bench

Fig. 2 Test bench structure

Electric vehicles are expected to replace regular vehicles in the near future [5]. It is therefore very important to conduct research and train a new generation of engineers in this field. There are different methods for developing and studying electric vehicles, each with its own advantages and disadvantages. One of the best-known methods is the test bench based study. This approach allows researchers to evaluate the performance of electric propulsion and develop new control strategies in closed scientific research environments [6, 7]. In order to maximize the benefit of the test bench, automation of test measurement and data’s graphical representation is necessary since it reduces time and effort compared to manual testing, in addition to providing increased productivity and reliability and increased test predictability [8]. In this paper an experimental test bench of an electric traction motor is presented, then a Matlab/Simulink test bench simulation model is made and discussed in order to emulate the performance of the electric vehicle’s traction motor for on-road driving conditions. Afterwards, a platform for the automation of measurement and graphical display of data generated from the electric traction motor test bench is built.

2 Experimental Test Bench Figure 1 illustrates the experimental test bench concerned in this study, and Fig. 2 shows the structure of the experimental test bench. The test bench consists of a

Development of an Automated Measurement Platform …

1801

Fig. 3 Synoptic diagram of the drive cycle based test

10 kW Permanent-magnet (PM) motor, which is controlled efficiently using a vector controller, this drive motor is connected to a 13 kW PM machine, which acts as a dynamometer that feeds electrical power to a resistive load through a three phase rectifier bridge. The main purpose of this test bench is to evaluate the performance of the motor that will be integrated into a lightweight electric vehicle. One major advantage of this test bench is the possibility to estimate the torque applied on the drive motor’s shaft without the need for a load cell. The estimation of this resistant torque is achieved by knowing the current absorbed by the resistive load in addition to the torque constant of the dynamometer. By varying the resistive load, it is possible to control the resistive torque applied on the drive motor’s shaft, allowing us to emulate in a discrete manner certain driving conditions on road. The most recognized method of representing road driving conditions is by employing standard drive cycles [9–11]. To emulate the electric drive vehicle performance on these test cycles using a test bench, we would have to control the speed of the drive motor and the torque of the dynamometer continuously and precisely in order to follow the drive cycle and also to mimic the resistive forces acting on the actual vehicle [6], which is not possible with the current version of the test bench since the resistive load is varied manually meaning that the generated resistive torque will not match the requirements of the vehicle on the drive cycle correctly.

3 Simulation Model Figure 3 shows the synoptic diagram of the drive cycle based test that allows the emulation of the vehicle performance on city driving conditions, and Fig. 4 demonstrates the Simulink model created to reproduce the mentioned test. The simulation idea of the test bench is based on Fajri’s work [6] with certain modifications to suit

1802

A. El Houre et al.

Fig. 4 Simulation model of the drive cycle based test

our case. The motor and dynamometer model parameters used in the simulation were extracted experimentally in order to get closer to reality. The drive cycle model generates speed and acceleration references corresponding to the ECE 15 cycle that represents city driving conditions [12]. These references are sent to the vehicle’s dynamic model, which describes all the forces exerted on the vehicle, and calculates the speed and the torque references for the speed controlled motor and the torque controlled dynamometer, respectively. A mechanical shaft model is used to simulate the mechanical coupling of the two machines. The test bench model generates the torque and speed data of the motor; this data will be used to plot the torque/speed curve. Figure 5 illustrates the simulation results. Figure 5(a) shows that the dynamometer motor follows almost perfectly the torque reference, meaning that the torque regulation is working as intended. Figure 5(b) indicates that the motor torque opposes the dynamometer motor torque with the same amplitude. However, the torque peaks were caused by the sudden change of the resistive torque, which affects the speed profile as well. Figure 5(c) confirms the speed regulation of the motor since it’s almost the same as the reference. In order to verify the validity of the current simulation model results, another simulation method was used, which is in our case the Advanced Vehicle Simulator (ADVISOR) [13]. A vehicle with almost the same specifications as our vehicle is designed and simulated using ADVISOR. This simulated vehicle follows perfectly the speed profile of the ECE 15 drive cycle. Figure 6 indicates that the drive motor torque resulted from the simulation model and ADVISOR on the ECE 15 drive cycle are nearly similar most of the time. However, since the vehicle’s specifications for both simulations are not exactly the same because of the limitations imposed by ADVISOR and the fact that our test bench model doesn’t include regenerative braking as opposed to ADVISOR, the results of the two simulations were therefore slightly distorted, particularly with regard to the negative torques.

Development of an Automated Measurement Platform …

1803

Fig. 5 Simulation results for the drive cycle based test. a Dynamometer motor reference and generated torque. b Drive motor torque. c Drive motor reference and generated rotational speed

Fig. 6 Drive motor torque comparison between our Simulink model and ADVISOR

1804

A. El Houre et al.

Fig. 7 Simulation result of the drive motor torque/speed data points corresponding to the ECE 15 drive cycle

These results show that our simulation model emulates the electric-drive vehicle performance on ECE15 drive cycle correctly. After verifying our simulation results using ADVISOR, the torque/speed data points of the drive motor according to the ECE 15 cycle were plotted as shown in Fig. 7. This simulation model proved that the simulated drive motor based on the actual motor parameters is able to achieve the whole ECE 15 operating points accurately. So as to verify this result experimentally, the resistive load and the speed of the drive motor should be manipulated in a way to reproduce the same operating points as Fig. 7. Another way for doing so would be to reproduce the experimental drive motor acceleration performance and projecting it on the ECE 15 operating points, and if these points fall within the acceleration torque/speed envelope, then we could say that these ECE 15 operating points are achievable by the drive motor experimentally. In both cases, the automation of measurement and graphical display of data generated from the test bench, would be a priority since we want to be able to follow the performance of the test bench faithfully and with ease.

4 Automated Measurement Platform Automation of measurement and data’s graphical representation reduces time and effort compared to manual testing. It improves productivity, reliability, and increases test predictability. Figure 8 demonstrates the structure of the made automated measurement platform. During the testing of the drive motor, the measurement circuit acquires the measurand through the sensors. These sensors provide analog electrical signals, that will be conditioned, in order to be suited for use by the DAQ device, which is the LabJack U3. This device then processes these signals and sends them in a digital form through the USB data bus, in order to be displayed and monitored in the LabVIEW interface.

Development of an Automated Measurement Platform …

1805

Fig. 8 The structure of the automated measurement platform

4.1 Torque and Speed Measurement The main purpose of the measurement circuit is measuring the required electrical signals and conditioning them to be usable by the DAQ device. The most important quantities in our test bench are: current, voltage, speed, torque, power and efficiency. For the torque T, it’s estimated using the measured load current and dynamometer torque constant, as described by Eq. (1) which was validated experimentally. Concerning the speed N, it’s calculated based on the frequency of the hall effect signal of either the motor or the dynamometer [14, 15], and the pole number of one of the two machines, as determined by the relation (2). T = kt × I

(1)

Where kt is the torque constant in N.m/A of the dynamometer motor, and I is the measured load current. N=

120 × f p

(2)

f is the frequency of the hall effect signals, and p is the pole number of the machine. As for the power and efficiency, they are deducted from the voltages, currents, speed and torque. The calculations of the quantities stated above are implemented in the LabVIEW interface.

4.2 The LabVIEW Interface Figure 9 illustrates the user-friendly LabVIEW interface that was created.

1806

A. El Houre et al.

The control and configuration section of the interface Fig. 9, allows the user to control the execution of the program, to insert the necessary quantities to calculate the torque and the speed. It is possible, via this sheet, to save the test results in an Excel file and to display the elapsed time of the test and errors that might occur during its execution. Figure 9(b) indicates the XY representation of speed versus torque, source current vs torque, mechanical power versus torque, efficiency versus torque, and finally the efficiency map of the electrical drive. The dashboard section showed in Fig. 9(b) visualizes the important quantities extracted from the test bench in real-time.

Fig. 9 The LabVIEW interface. a The control and configuration section of the interface. b The XY data representation section. c The dashboard section

Development of an Automated Measurement Platform …

1807

5 Conclusion In this paper, an experimental test bench was introduced and discussed. This test bench presents a drawback in terms of the emulation of the electric vehicle performance for on-road driving conditions. To overcome this, a simulation model was proposed in the Matlab/Simulink environment and validated using ADVISOR. This simulation contains a model of the test bench based on real experimental parameters of the drive motor, dynamometer, and vehicle dynamics, which allows the reproduction of the drive cycle based test. The next objective would be reproducing the data operating points resulted from this simulation using the actual test bench to validate that the electrical drive is suitable for our application. In order to do so, an automated measurement platform was developed to monitor and to follow faithfully and effectively the performance of the test bench. Acknowledgement This work was funded by ALTRAN Maroc, a Global Engineering and R&D Services Company, and was supported by the Moroccan Research Institute for Solar Energy and New Energies (IRESEN).

References 1. de Santiago J, Bernhoff H, Ekergård B, Eriksson S, Ferhatovic S, Waters R, Leijon M (2012) Electrical motor drivelines in commercial all-electric vehicles: a review. IEEE Trans Veh Technol 61:475–484 2. Ehsani M, Gao Y, Miller JM (2007) Hybrid electric vehicles: architecture and motor drives. Proc IEEE 95:719–728 3. Regulation No 101 of the Economic Commission for Europe of the United Nations (UN/ECE) (2012/5). https://eur-lex.europa.eu/LexUriServ/LexUriS-erv.do?uri=OJ:L:2012: 138:0001:0077:en:PDF. Accessed 29 Oct 2020 4. Grunditz E (2014) BEV powertrain component sizing with respect to performance, energy consumption and driving patterns (2014) 5. Ehsani M, Gao Y, Gay S, Emadi A (2004) Modern electric, hybrid electric, and fuel cell vehicles: fundamentals, theory, and design (2004) 6. Fajri P, Prabhala VAK, Ferdowsi M (2016) Emulating on-road operating conditions for electricdrive propulsion systems. IEEE Trans Energy Convers 31:1–11 7. Marignetti F, D’Aguanno D, Volpe G (2017) Design and experiments of a test equipment for hybrid and electric vehicle drivetrains. In: 2017 twelfth international conference on ecological vehicles and renewable energies (EVER), pp 1–6 8. Washington C, Delgado S (2008) Improve design efficiency and test capabilities with HIL simulation. In: 2008 IEEE AUTOTESTCON, pp 593–594 9. Lazari P, Wang J, Chen L (2014) A computationally efficient design technique for electricvehicle traction machines. IEEE Trans Ind Appl 50:3203–3213 10. Günther S, Ulbrich S, Hofmann W (2014) Driving cycle-based design optimization of interior permanent magnet synchronous motor drives for electric vehicle application. Presented at the 2014 international symposium on power electronics, electrical drives, automation and motion, SPEEDAM June 1 2014 11. Watson H (1978) Vehicle driving patterns and measurement methods for energy and emissions assessment. Bureau of Transport Economics, Canberra

1808

A. El Houre et al.

12. A reference book of driving cycles for use in the measurement of road vehicle emissions: version 3. IHS (2009) 13. Wipke K, Cuddy M, Burch S (1999) ADVISOR 2.1: a user-friendly advanced powertrain simulation using a combined backward/forward approach. IEEE Trans Veh Technol 48:1751– 1761 14. Naveen V, Isha TB (2017) A low cost speed estimation technique for closed loop control of BLDC motor drive. In: 2017 international conference on circuit power and computing technologies (ICCPCT), pp 1–5 15. Dumitru D. Module 8: Speed Control - NXP Community, https://community.nxp.com/t5/NXPModel-Based-Design-Tools/Module-8-Speed-Control/m-p/727476. Accessed 29 Oct 2020

Simulation of a Guidance Law on a ROS-Based Nonholonomic Mobile Robot Nabil Rezoug

and Mokhtar Zerikat

Abstract In view of computational challenges that may span autonomous navigation of mobile robots, mainly due to its complex structure, a low cost, simple structure scheme is proposed and verified under Gazebo in a ROS-based nonholonomic mobile robot “Turtlebot Kobuki”. Relying on LQI control strategy, the algorithm ensures the task of steering the robot through a set of waypoints allowing high performance and accurate path tracking. Simulating a virtual robot under Gazebo, experimental results demonstrate the effectiveness of the proposed scheme. Keywords ROS · Gazebo world · Turtlebot Kobuki · Waypoint guidance · LQI control · Trajectory tracking

1 Introduction Wheeled mobile robots play a major role in both industrial and service robotics, particularly when autonomous motion capabilities are required, however developing a fully autonomous robot that can navigate in unknown environments is problematic due to difficulties that span dynamics modeling, on-board perception, trajectory generation, and optimal control, not to mention the heavy computational bill that may results from such methods. Although several studies denounce limitations that are imposed by the structural obstruction of the kinematic model, for instance designing feedback laws, little attention has been drawn to address these obstacles. This paper develops a waypoint trajectory tracking method that demonstrates the possibility to overcome the limitation imposed by the kinematic model. One of its most attractive features is computational simplicity, and the ability to control the accuracy of generated paths by changing the number of waypoints. in the last decade, Mobile robot path planning has drawn lot of interest from researchers in deep learning, however, as reported by [1] real-time processing require a large neural network with multiple parameters, which represent a computational burden, even for modern CPU, in [2] N. Rezoug (B) · M. Zerikat Ecole Nationale Polytechnique Maurice Audin, Oran, Algeria © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_164

1809

1810

N. Rezoug and M. Zerikat

they used fuzzy PID controller for autonomous motion of a differential drive robot, in order to solve the path and time optimization problem they developed in [3] a fourlayer Neural network as a controller, in [4] they used as an experimental platform a car like mobile robot where they focused on designing an effective neural network controller. By using linearized model predictive control (LMPC) in [5] they dealt with the system physical constraints, therefore, overcoming the limitations caused by nonholonomic constraints of mobile robot. In [6] time varying state feedback control law based on back-stepping technic was implemented on the Turtlebot to test the performance of the studied strategy in [7] they consider the problem of robust adaptive trajectory tracking control for a mobile robot dealing with system uncertainties. In order to achieve robust tracking two control strategies were used in [8] a proportional integral control and sliding mode control, in order to simplify the structure of the control scheme, a new kinematic control approach was used for position tracking. Path following is basically dealing with the problem of designing a guidance law that steers the robot to follow a desired path, based on the approach presented in [9], and with the sole purpose of avoiding any latency related problem that may occur from computational complexity, we aim in this paper to design guidance law, in order to simplify the structure of the control system. The reminder of this paper is organized as follows: in Sect. 2 a thorough description of the experimental test bed is presented, Sect. 3 presents the model of the robot and outline the problem that motivated this work, Sect. 4 describe the strategy used to control the robot, Sect. 5 shows the simulation results and comparison, Sect. 6 contains the conclusion and future work.

2 Experimental Testbed To assess the framework reliability, we used for practical experimentation a ROSenabled nonholonomic mobile robot, Turtlebot Kobuki 2. It is the new version of the Turtlebot robot series, powered by Willow Garage and ClearPath Robotics, more details are provided in [10]. As illustrated in Fig. 1, the robot is equipped with a Kinect sensor, a notebook and a Kobuki base. It is a suitable mobile platform for developing robot applications. The robot’s base has two identical no deformable rear wheels each rear wheel is powered by a motor and equipped with an encoder, the robot also contains proximity sensors and gyro meter for each axis [10]. To conduct our experiments, Robot Operating System (ROS) is chosen as the main computing and management unit of the wheeled robot Turtlebot Kobuki. Figure 2 is an overview of the software structure; it provides a simplified illustration on how different structure interact with each other. Robot Operating System (ROS) handles sensors and actuators data flow in the system. In the presence of a running ROS master in our platform, we initialize a MATLAB ROS subsystem in another computer with the IP address and port number of the ROS master, allowing real time monitoring of the robot position and velocities [11].

Simulation of a Guidance Law on a ROS-Based Nonholonomic Mobile …

1811

Fig. 1 Experimental platform Turtlebot Kobuki

Fig. 2 Experimental control scheme

3 Robot Model and Path Following Problem Formulation Typically, the guidance law allows an accurate path tracking in two-dimension frame. In this section, we briefly review the kinematic model of a differential drive mobile robot, and present the concept of waypoint guidance method which provides a rigorous formulation of the problem of steering the differential drive mobile robot along a desired path.

3.1 Kinematic Model The kinematic model for nonholonomic ground robot is given by:

1812

N. Rezoug and M. Zerikat

x˙r = Va cos(ψ), y˙r = Va sin(ψ), ψ˙ r = ω;

(1)

Where xr andyr denote the robot position in the fixed frame, and ψ it’s orientation. Va is the linear velocity, and ω is the angular velocity.

3.2 Waypoint Guidance Strategy Because of its ability to move in challenging environments, wheeled mobile robots present a great medium for bringing sensors or tools to specified locations, it is important to mention that the exact path leading to the target location has little interest as long as the robot reaches its destination within reasonable amount of time. In this section we propose a guidance law that drives the robot through reference locations. Waypoint guidance strategy drives the robot through each waypoint in a consecutive order by taking the shortest possible path from one waypoint to the next, it is assumed that the waypoint has been reached once the robot is within an acceptance radius enclosing the current waypoint, for simplifying purposes, we consider the following assumptions. • The robot’s path is built by joining a set of reference locations called waypoints. in this paper we consider a set of waypoints Pk , where k{1, . . . , n} • Basically we are steering the robot into each of the waypoints P1 , P2 , . . . , Pn in consecutive order. • Orientation of the robot around the z axis is defined relative to the North axis • The proximity distance is defined as an acceptance circle around the waypoint with radius R = 0.01 m. • Linear speed is set to 0.3 m/s. The desired path is defined by the straight-line from the precedent waypoint Pk (xk ; yk ) to the destination waypoint Pk+1 (xk+1 ; yk+1 ), where (xk ; yk ) and (xk+1 ; yk+1 ) are respectively the coordinates of the waypoints Pk and Pk+1 in the inertial frame [9], as shown in Fig. 3. d is the shortest distance from the position of the robot to the reference path, given by     d = −(xr − xk ) sin ψ p + (yr − yk )cos ψ p

(2)

where ψ p is the orientation of the path relative x axe direction, defined by ψ p = tan−1



yk+1 − yk xk+1 − xk

 (3)

Simulation of a Guidance Law on a ROS-Based Nonholonomic Mobile …

1813

Fig. 3 Waypoint following (N: North, E: East)

ψe is the orientation of the robot relative to the desired path ψe = ψ p − ψ

(4)

By differentiating (2) with respect to time and using (1), it follows that d˙ = −Va sin(ψe )

(5)

ψ˙ e = ω

(6)

The considered state vector x = [d, ψe ]T

4 Control System Design In the following we illustrate the functional structure of the control system design (Fig. 4). The control law is given by u(t) = −K x x(t) − K y ∫ y(t)dt

(7)

  With a gain matrix K 1×3 = K x , K y chosen so that the closed loop system is stable, any classical linear control technique can be applied to ensure convergence of the controlled robot toward the planned path. To this end, we first derive the linear model.

1814

N. Rezoug and M. Zerikat

Fig. 4 Schematic diagram of the proposed controller

The model used for linear path is as follow: x˙ = f (x(t), u(t)) The input u is the angular speed ω, our objective is to design a control law u(t) that stabilizes the system and consequently steers the Turtlebot to the reference path.  T For the equilibrium point xeq = deq , 0 , u eq = 0, where deq is arbitrary, we obtain a linearized state space model of the system as follows x = A p x + bpu

(8)

Where  Ap =

 0 −Va 0 , bp = 0 0 −1

(9)

The regulated output y is the cross-track error d, i.e., the output vector is   c= 10

(10)

To provide better robustness against disturbances, the integral of the regulated output error, denoted e I , is considered for the linear system. Where e I = yg − y

(11)

yg denotes the reference output. Since the system output is the distance from the robot to the waypoint reference position, naturally we set yg = 0 in order for the robot heading direction to converge toward the target location. e I = 0 − cx

(12)

Simulation of a Guidance Law on a ROS-Based Nonholonomic Mobile …

1815

The augmented system is written as follows X˙ = AX + Bu 

x˙ e˙ I





A p 02×1 = −c 0



x eI



 b + u 0

(13)

Where A(3 × 3), B(3 × 1) and X 3×1 = [d, ψe , e I ]T In our paper we relied on Linear Quadratic Regulator, to provide a practical feedback gain in order to minimize the quadratic cost: +∞  J = ∫ X T (t)Q X (t) + Ru 2 (t) dt

(14)

0

Q(3 × 3) and R(1 × 1) are the state weighting matrix and the control weighting matrix, respectively. As shown in [12], using the state feedback control law (15) will result in the minimization of the cost function (14) u(t) = −K X (t) = − R −1 B T P X (t)

(15)

P(3 × 3) is the symmetric positive definite solution of the continuous algebraic Riccati equation. A T P + P A − P B R −1 B T P + Q = 0

(16)

5 Experimental Results To show the effectiveness and accurate tracking capabilities of the Turtlebot using the guidance law presented in this paper, we used two kind of reference trajectories generated in a 2D frame, square trajectory and circular trajectory. The simulations were carried out on the Turtlebot Kobuki under Gazebo with parameters as follows. Diameter: 351.5 mm; Height: 124.8 mm; Weight: 2.35 kg. The simulation was conducted under GAZEBO to assess the effectiveness of the trajectory tracking controller, results of the simulation are shown in Figs. 5 and 8. The environment GAZEBO was used for simulating a virtual “Turtlebot” seeking to track a squared and circular trajectory. Test results are discussed in the following.

1816

N. Rezoug and M. Zerikat

Fig. 5 Square trajectory diagram of the Turtlebot

The robot initially positioned at the origin (0, 0), is first required to reach the closest point on the desired trajectory and then follow the given waypoints. The square trajectory is located at (2, 2); (4, 2); (4, 4); (2, 4). Figure 5 shows that the robot tracks the given waypoints with a negligible error around the edges of the square trajectory, the error is mainly due to the imposed range tolerance, which basically define the exact reach the robot has to get within in order to move to the next waypoint. Next, we show the results as presented in [13], where the robot seems to give a satisfactory result under MATLAB environment, as shown in Fig. 6, however the simulation result under gazebo world does not follow the desired trajectory Fig. 7. The results output in [13] can be explained by the fact that under gazebo world various dynamic properties are taken into account, while under MATLAB simulation many of these dynamics are not considered. Which justifies the efficiency of the adopted control law in MATLAB simulation, and the poor performance under Gazebo world.

Simulation of a Guidance Law on a ROS-Based Nonholonomic Mobile …

1817

Fig. 6 Circular trajectory diagram of the Turtlebot under MATLAB as shown in [13]

Fig. 7 Circular trajectory diagram of the Turtlebot under Gazebo as shown in [13]

The circle is centered in (2, 2), we can see in Fig. 8 that the robot started tracking with an initial error, which is natural due to the large initial heading error, so basically there was a time of transition in which the tracking error of heading direction was stabilized.

1818

N. Rezoug and M. Zerikat

Fig. 8 Circular trajectory diagram of the Turtlebot

6 Conclusion In this paper, a waypoint guidance law was developed for differential-drive wheeled mobile robots. An accurate tracking is obtained from various reference trajectories, 3D simulation was developed in Gazebo world using MATLAB-Simulink for monitoring and data processing. In future work, we plan to test the proposed scheme in a real robot, adding more challenging tasks, for instance the difficulty to identify external dynamics, which will result in a limited dynamic compensation, hence the necessity for an additional control scheme to compensate the dynamic properties in order to have more accurate tracking performance. We will aim to achieve the goal of composite nonlinear control and guarantee that the output tracking error will ultimately converge to zero. In order to further reduce the oscillation amplitude and frequency, friction compensation schemes can be incorporated, an extra control level will focus on improving the robot’s behavior such as obstacle avoidance. To sum up, this paper lay the ground work for future projects with the sole purpose of improving the performance of ground robots.

Simulation of a Guidance Law on a ROS-Based Nonholonomic Mobile …

1819

References 1. Vanhoucke V, Senior A, Mao MZ (2011) Improving the speed of neural networks on CPUs. In: Deep learning and unsupervised feature learning, NIPS 2. Heikkinen J, Minav T, Stotckaia AD (2017) Self-tuning parameter fuzzy PID controller for autonomous differential drive mobile robot. In: 2017 XX IEEE international conference on soft computing and measurements (SCM), pp 382–385. https://doi.org/10.1109/SCM.2017. 7970592 3. Parhi DR, Singh MK (2009) Real-time navigational control of mobile robots using an artificial neural network. Proc Inst Mech Eng Part C: J Mech Eng Sci 223(7):1713–1725 4. Dewi T, Risma P, Oktarina Y, Roseno MT (2017) Neural network controller design for a mobile robot navigation; a case study. In: 2017 4th international conference on electrical engineering, computer science and informatics (EECSI) 5. Fetter Lages, W, Augusto Vasconcelos Alves J (2006) Real-time control of a mobile robot using linearized model predictive control. In: Proceedings Volumes, vol 39, no 16, pp 968–973. ISSN 1474-6670, ISBN 9783902661173 6. Besseghieur, K.L., Tr˛ebi´nski, R., Kaczmarek, W., Panasiuk, J.: Trajectory tracking control for a nonholonomic mobile robot under ROS. In: Journal of physics: conference series, volume 1016, 6th international conference on mechatronics and control engineering (ICMCE 2017) 7. Xin L, Wang Q, She J, Li Y (2016) Robust adaptive tracking control of wheeled mobile robot. Robot Auton Syst 78:36–48 ISSN 0921-8890 8. Becerra HM, Colunga J, Romero J (2018) Simultaneous convergence of position and orientation of wheeled mobile robots using trajectory planning and robust controllers. Int J Adv Rob Syst 15:172988141875457. https://doi.org/10.1177/1729881418754574 9. Souanef T (2019) Adaptive guidance and control of small unmanned aerial vehicles. https:// doi.org/10.13140/RG.2.2.21153.56166 10. Kobuki Documentation: relase 2.0, Dabit industries 11. Corke P (2015) Integrating ROS and MATLAB [ROS Topics]. IEEE Robot Autom Mag 22:18– 20 12. Zak, SH (2003) Systems and control. vol 198. Oxford University Press, Oxford 13. Bensaci C, Zenir Y, Pomorski D (2017) Control of mobile robot navigation under the virtual world matlab-gazebo. In: A special issue of the international conference on advanced Engineering in Petrochemical Industry (ICAEPI’17), Skikda Algeria, vol 2, no 4

Speed Sensorless Direct Torque Control of Doubly Fed Induction Motor Using Model Reference Adaptive System Mohammed El Mahfoud, Badre Bossoufi, Najib El Ouanjli, Said Mahfoud, Mourad Yessef, and Mohammed Taoussi

Abstract This study emphasis on a direct torque control (DTC) strategy with a speed estimator utilizing the Model Reference Adaptive System (MRAS) for a doubly fed induction machine (DFIM). The suggested adaptive reference model uses the values obtained as the disparity between two voltage-to-current ratios along the d and q axes for a hypothetical resistance quantity. The resulting sensorless formulation is totally independent of any expression of stator/rotor resistance. And thereby, DTC command brings numerous benefits over traditional control strategies like field-oriented control (FOC) due to its clear architecture, quick dynamic response and less system parameter dependency. The suggested control algorithm is simulated and evaluated in MATLAB/Simulink for a small speed range program. Keywords DTC · MRAS · DFIM · Speed sensorless

1 Introduction The DFIM is one of the most common high-power devices, particularly for the fields of wind and hydropower generation [1]. It is also used in systems of attraction, marine drives and electric and hybrid cars [2]. But the difficulty of these calculations and the combination of torque and flux cause many difficulty. In order to solve these issues, some researchers are focused on designing rigorous control strategies. Among the first methods, the field orientation control technique offers a decoupling of flux and torque. This covers a diverse range of modulation velocity, quick torque reactions and high efficiency for several variations in load [3]. Despite the appealing features of the FOC control, its execution involves several coordinate transformations and a reliable configuration of a variety of PI M. El Mahfoud (B) · B. Bossoufi · M. Yessef Laboratory of Engineering Modeling and Systems Analysis, SMBA University, Fez, Morocco e-mail: [email protected] N. El Ouanjli · S. Mahfoud · M. Taoussi Technologies and Industrial Services Laboratory, SMBA University, Fez, Morocco © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2_165

1821

1822

M. E. Mahfoud et al.

controllers, and requires an external pulse width modulator (PWM) [1]. To solve the problems of FOC control, TAKAHASHI implemented Direct Torque Control in the early 1980s [4]. In comparison to the FOC, the DTC approach focused on hysteresis comparators and turn tables provides many advantages such as easy execution, limited reliance on parameters, without any need for current controllers and several coordinate transformations [5]. While in most industrial uses, speed and/or position detection is needed for robust and significant command. As a consequence, DFIM speed regulation using the DTC uses a speed sensor for its application, this speed sensor [6]: • • • • •

Diminishes machine performance; Raises the cost of the control device and is challenging to install Diminished reliability; Extra area for assembly, The wire.

That is why speed estimation methods are an amazing option. Literature has proposed various methods such as Luenberger observer, Kalman filter, slipping mode control, Model Reference Adaptive Systems (MRAS) and artificial intelligence techniques [6, 7]. Of all the techniques, the MRAS Observer for Sensorless DFIM drives is well known and most desirable due to its flexibility, simple physical perception and tolerance to variations in system parameters [8]. The MRAS methodology therefore involves many forms of methods based on the parameter selected for the estimate. Among these methods, the R-MRAS estimator uses a resistance value corresponding to the voltage/current ratio that provides high independence and high efficiency [9]. The goal of this paper is to combine the benefits of DTC control with the efficiency of the MRAS approach for speed control and estimation. This paper is organised as follows: Sect. 2 introduces a DFIM modelling. Section 3 addresses traditional DTC approach based on switching tables and hysteresis controllers. Section 4 addresses speed and flux measurement requirements. Section 5 discusses and interprets simulation curves using MATLAB/SIMULINK environment. Finally, Sect. 6 summarises the conclusions and recommendations for future work.

2 Modeling of the DFIM The DFIM model for DTC is the two-phase system represented in (α, β) coordinates. The complexity of (a, b, c) representation of the motor is decreased by the Concordia transformation [5]. Figure 1 indicates the spatial structure of these various frames of reference. The angle θ r represents the position of the rotor in relation to the reference axis of the stator, while the angles θ and θ s show the relative positions of the direct axis respectively with respect to the rotor and stator axes.

Speed Sensorless Direct Torque Control …

1823

β

d

ω

(Quadrature axis)

q

(Direct axis)

ω

a

(Rotor)

θ

θ

α

θ

O

0

A (Stator)

Fig. 1 Spatial position of the different reference frames

With: θs = θr + θ

(1)

Hence the autopilot equation is expressed by: dθr dθ dθs = + ⇒ ωs = ωr + ω dt dt dt

(2)

The DFIM stator and rotor voltage can be represented in (α, β) reference frame [10]: ⎡

⎤ ⎡ vsα Rs 0 0 0 ⎢v ⎥ ⎢ 0 sβ ⎢ ⎥ ⎢ Rs 0 0 ⎢ ⎥=⎢ ⎣ vr α ⎦ ⎣ 0 0 Rr 0 0 0 0 Rr vrβ

⎤⎡

⎤ ⎤ ⎤ ⎡ ⎡ i sα ψsα 0 ⎥ ⎢i ⎥ ⎥ ⎥ ⎢ d ⎢ ⎥ ⎢ sβ ⎥ ⎥ ⎢ ψsβ ⎥ dθ ⎢ 0 .⎢ ⎥.⎢ ⎥ + .⎢ ⎥+ ⎥ ⎦ ⎣ ir α ⎦ dt ⎣ ψr α ⎦ dt ⎣ −ψrβ ⎦ ψrβ

irβ

(3)

ψr α

The expression of stator and rotor flux in diphase reference frame (α, β): ⎡

ψsα

⎤

⎡

Ls ⎢ψ ⎥ ⎢ sβ 0 ⎢ ⎥ ⎢ ⎢ ⎥= ⎣ ψr α ⎦ ⎣ M. cos θ −M. sin θ ψrβ

0 Ls M. sin θ M. cos θ

M. cos θ M. sin θ Lr 0

⎤ ⎡ i sα ⎤ −M. sin θ ⎢i ⎥ M. cos θ ⎥ sβ ⎥ ⎥.⎢ ⎢ ⎥ ⎦ 0 ⎣ ir α ⎦ Lr irβ

(4)

The electromagnetic torque can be determined from: Tem = p.(ψsα .i sβ − ψsβ .i sα ) With Tem = TL + J.

d + f. dt

(5) (6)

1824

M. E. Mahfoud et al.

3 Direct Torque Control Strategy Applied to DFIM Takahashi proposes the DTC technique for electric motors to address field-oriented control problems and particularly the sensitivity to variation in machine parameters. DTC has a lot of advantages, such as reduced dependence on outside disruptions and machine parameters, simple operation and a relatively quicker response of dynamic torque to other controls [5]. The power of flux and torque in the stationary structure (α, β) is decoupled. The switching table is used to choose a suitable voltage vector [10]. The choice of switching states is specifically linked to flux and torque variations. The electromagnetic torque and flux amplitudes in hysteresis regulator bands are thus reduced. These regulators control these two dimensions separately. Hysteresis controller inputs are flux and torque errors and their performance defines the necessary voltage vector to strike the inverter at each switching cycle. The inverter supplies eight voltage vectors, two of which are null and void. These vectors are chosen from a switching table that was calculated with the flux, torque and flux vector positions [11]. The distribution of these vectors is seen in Fig. 2 (α, β). The flux error εψ is introduced to a two-levels hysteresis comparator, which produces the binary output (Hψ = 0,1) describing the ideal flux progression. The torque error εTem is also inserted into a three-levels hysteresis controller, which produces the variable (HTem = −1, 0, 1) at its output, which corresponds to the path of the torque creation required. The switching table provided in Table 1 is drawn up by the variables HTem and Hψ division as well as the sector presenting details on flux vector locations. Fig. 2 Voltage vectors delivered by the inverters

Speed Sensorless Direct Torque Control …

1825

Table 1 Switching table

4 Design of Speed and Flux Estimators 4.1 Speed Estimation by Model Reference Adaptive System The MRAS Theory is the most popular method used to observe machine parameters and states using only the measurements of stator and rotor voltage and current [7]. The MRAS observer system consists of two systems with separate mechanisms for estimating the same state variable depending on the separate inputs [12]: • The first technique is focused on the equations in the stationary frame (α, β), being independent of the speed to be estimated; It’s the reference model. • the adaptive model is the second structure, which relies on the value to be estimated, is described in the (d,q) reference (Fig. 3). The values extracted from the models are compared and the variance is used to operate an adjustment process generating the value to be computed. Centered on this error, the adjustment mechanism generates the speed value calculated by the PI controllerIf the algorithm is constructed right, the steady state would go to 0. [6]. Figure 4 reflects. MRAS observer design. • Reference model: R∗ =

vαr vβr − i αr i βr

(7)

vqr vdr ∗ − ∗ i dr i qr

(8)

• Adaptive model: 

R=

1826

M. E. Mahfoud et al.

Reference Model Comparator α,β

R

Adaptive Model

d,q

PI

Integrator Fig. 3 Structure of the MRAS observer

4.2 Flux and Torque Estimation In DTC, depending on the errors in the control variables, the switching tables indicate the positions of the inverters switches to generate the three-phase voltages that attack the DFIM. The discrepancy between approximate variables and their references measures these errors. Therefore, flux and torque accuracy is also very critical to ensure satisfactory performance. Many variables must be defined, the stator and the rotor current calculated, while the rotor and stator voltages may depend on the states (Sa , Sb and Sc ). The electromagnetic torque, rotor flux and stator flux are estimated by following equations [5]: 





T em = p.(ψ sα .i sβ − ψ sβ .i sα ) ψˆ r = ψˆ s =



2 ψˆ r2α + ψˆ rβ



2 2 +ψ ˆ sβ ψˆ sα

(9) (10) (11)

The positions of the rotor and stator flux is calculated from: θr = ar ctg(

ψˆ rβ ) ψˆ r α

(12)

θs = ar ctg(

ψˆ sβ ) ψˆ sα

(13)

The block diagram for the calculation of flux and torque is illustrated in Fig. 4.

Speed Sensorless Direct Torque Control …

1827

Rotor flux estimation

Stator flux estimation

Torque estimation

T

Fig. 4 Block flux and torque calculation diagram

5 Simulation Results and Discussion Several numerical simulation series are implemented and realized on the MATLAB/Simulink to evaluate the DFIM performance, robustness and stability. The main characteristics of this simulation are: • • • •

DC bus voltages: E s = 300 V et E r = 100 V. Application of a nominal load (T L = 10 N.m) at t = 1.8 s. The sampling frequency f s = 10 kHz. The DFIM parameters are listed in Table 2.

The following figure show the simulation results for Step speed of 157 rad/s (equivalent 1500 rpm) (Fig. 5). Table 2 Parameters of the DFIM

1828

M. E. Mahfoud et al.

Fig. 5 Simulation results

According to these results, the rotational speed follows the reference value perfectly and quickly, It attains the reference value after tr = 0.28 s without overshooting and static errors at the start. On the other hand, the electromagnetic torque leads with very rapid dynamics the set reference. At runtime, the torque is maximum and maintained in constant state at nearly value 0. Also, Where a load torque application at t = 1.8 s, a speed change of 1.8 rad/s occurs the time to reject this disruption is quite quickly equivalent to 30 ms, which illustrates the effectiveness of this regulation technique against changes in load torque. But, the electromagnetic torque has ripples of the range of Tem = 2.7 N.m. Furthermore, the evolution of each flux represents a circular course with a constant radius identical to the reference.

Speed Sensorless Direct Torque Control …

1829

The stator and rotor current aspects are sinusoidal in shape and react well to the changes caused by the load torque. It can also be remembered that the currents frequencies are equivalent to the autopilot equation’s rotational speed. Finally, the positions flux are perfectly respected.

6 Conclusion This paper has presented a speed sensorless DTC scheme for DFIM powered by two voltage inverters. The goal is to boost traditional DTC efficiency. The hierarchical DFIM model and the DTC strategy theory were introduced. The control technique and the speed and flux calculation are comprehensive. After device controls have been checked with MATLAB/SIMULINK the major results are as below.: • The MRAS approach can be applied successfully in DFIM drives. • The fast response, torque dynamics and robustness of the classic DTC are maintained. The future studies will concentrate on experimental DTC-MRAS control validation utilising the dSPACE DS1104 platform.

References 1. Elmahfoud M, Bossoufi B, Taoussi M, El Ouanjli N, Derouich A (2020) Comparative study between backstepping adaptive and field oriented controls for doubly fed induction motor. Eur J Electr Eng 22(3):209–221 2. Taoussi M, Karim M, Bossoufi B, Hammoumi D, Lagrioui A, Derouich A (2016) Speed variable adaptive backstepping control of the doubly-fed induction machine drive. Int J Autom Control 10(1):12–33 3. Elmahfoud M, Bossoufi B, Taoussi M, El Ouanjli N, Derouich A (2019) Rotor field oriented control of doubly fed induction motor. In: 2019 international conference on optimization and applications, ICOA 2019 4. Abu-Rub H, Stando D, Kazmierkowski MP (2013) Simple speed sensorless DTC-SVM scheme for induction motor drives. Bull Polish Acad Sci: Tech Sci 61(2):301–307 5. El Ouanjli N, Motahhir S, Derouich A, El Ghzizal A, Chebabhi A, Taoussi M (2019) Improved DTC strategy of doubly fed induction motor using fuzzy logic controller. Energy Rep 5(February):271–279 6. Korzonek M, Tarchala G, Orlowska-Kowalska T (2019) A review on MRAS-type speed estimators for reliable and efficient induction motor drives. ISA Trans 93:1–13 7. Horch M, Boumediene A, Baghli L (2017) MRAS-based sensorless speed integral backstepping control for induction machine, using a flux backstepping observer. Int J Power Electron Drive Syst 8(4):1650–1662 8. El Daoudi S, Lazrak L, El Ouanjli N, Ait Lafkih M (2020) Improved DTC-SPWM strategy of induction motor by using five-level POD-PWM inverter and MRASSF estimator. Int J Dyn Control (0123456789) 9. Kumar R, Das S (2017) MRAS-based speed estimation of grid-connected doubly fed induction machine drive. IET Power Electron 10(7):726–737

1830

M. E. Mahfoud et al.

10. El Ouanjli N, Derouich A, El Ghzizal A, Motahhir S, Chebabhi A, El Mourabit Y, Taoussi M (2019) Modern improvement techniques of direct torque control for induction motor drives-a review. Prot Control Mod Power Syst 4(1) 11. Ammar A, Bourek A, Benakcha A (2017) Nonlinear SVM-DTC for induction motor drive using input-output feedback linearization and high order sliding mode control. ISA Trans 67:428–442 12. Giribabu D, Srivastava SP, Pathak MK (2019) Modified reference model for rotor flux-based MRAS speed observer using neural network controller. IETE J Res 65(1):80–95

Author Index

A A.Monem, O., 1433 Aarab, Abdellah, 123 Ababssi, Najib, 1223 Abadlia, Hadjer, 1745 Abbou, Ahmed, 155, 1387, 1675 Abd Elsadek, Ahmed, 1433 Abdelhadi, Elmoudden, 1245 Abdoun, Farah, 947 Abdoun, Otman, 585, 947, 967, 1073 Abenna, Said, 189 Abou El Hassan, Adil, 259 Aboubakr, Herouane, 1191 Abouchabaka, Jâafar, 1073 Abounada, Abdelouahed, 1213 Abouricha, Mostafa, 1735 Abouzahir, Mohamed, 399 Achchab, Sanaa, 991 Achouyab, El Hassan, 1467 Adithya, V., 731 Aditya, S., 707 Agoumi, Ali, 609 Ahmed, Taqi, 741 Aicha, Wahabi, 1245 Aissaoui, Karima, 663 Ait Belaid, Khaoula, 1643 Ait Ben Ahmed, Abdelbaset, 1777 Ait Lahcen, Ayoub, 527 Ait Nouh, Fatima, 1497 Aitelkadi, Kenza, 25 Ajaamoum, Mohamed, 1181 Ajallouda, Lahbib, 335 Ajjaj, Souad, 991, 1021 Akpoviroro, Eric Obar, 959 Alami Aroussi, Hala, 1445, 1455

Alaoui, Chakib, 1327, 1585, 1787 Alhazmi, Omar H., 355 Alhejaili, Anwar D., 355 Alidrissi, Youssef, 279 Amane, Meryem, 663 Ambrosetti, Damien, 13 Amgoune, Hafida, 457 Amieur, Toufik, 1477, 1699 Amine, Aouatif, 47, 1101 Amine, El Hachimi Mohamed, 741 Amrane, Said, 1735 Amraqui, Samir, 1205, 1489 Anand, Sampurn, 813 Annaki, Ihababdelbasset, 233 Aouragh, Si Lhoussain, 199 Arsalane, Nabil, 1039 Arsalane, Sara, 1039 Asaidi, Hakima, 543 Attoui, Issam, 1147, 1171 Ayad, Hassan, 1643 Azami, Nawfel, 1735 Azdoud, Y., 47 Azizi, Mostafa, 515 Azizi, Yassine, 515 Azzouazi, Mohamed, 781 B Badri, Abdelmajid, 749, 1365 Bahij, Mouad, 165 Bajit, Abderrahim, 189 Bakhouya, Mohamed, 279 Bakouri, Anass, 1787 Bakouri, Saber, 25 Balanuta, Daniel Ciprian, 1353 Balboul, Younes, 1081

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 S. Motahhir and B. Bossoufi (eds.), Digital Technologies and Applications, Lecture Notes in Networks and Systems 211, https://doi.org/10.1007/978-3-030-73882-2

1831

1832 Bannari, Rachid, 3 Barakat, M. R., 1409 Barara, Mohamed, 1409 Bawany, Narmeen Zakaria, 469 Bechouat, Mohcene, 1477, 1699 Bekkali, Moulhime El, 1081 Belahrach, Hassan, 1091, 1643 Belarhzal, Samya, 37 Belattar, Sara, 585 Belebrik, Mohamed, 25 Belkacem, Ghania, 1409 Bellagi, Ahmed, 1505 Bellouki, Mohamed, 543 Benabdellah, Mohammed, 291 Benazzi, Abdelhamid, 847 Benbrahim, Mohammed, 345, 759 Bendali, Wadie, 1305 Benhaim, Mickael, 1497 Benhlima, Laila, 1061 Bennouna, Fatima, 825 Benomar, Mohammed Lamine, 13 Bensegueni, Skander, 245 Benslimane, Mohamed, 871 Bentaleb, Asmae, 527 Berdai, Abdelmajid, 1421 Berrada, Mohammed, 99, 663 Berrajaa, Achraf, 77 Berrich, Jamal, 233 Berthoz, Alain, 233 Bossoufi, Badre, 387, 1255, 1279, 1291, 1445, 1455, 1725, 1821 Bouachrine, Brahim, 1181 Bouakkaz, Mohammed Salah, 1147, 1171 Bouchaala, Abdel Djabar, 1171 Boudebbouz, Omar, 1171 Bouderbala, Manale, 1445, 1455 Boudi, El Mostapha, 37 Bouju, Alain, 99 Boukadoum, Ahcene, 1171 Boulaiche, Mehdi, 1135 Bouraiou, Ahmed, 1147, 1171 Bourhaleb, Mohammed, 233 Bourzouk, Zakaria, 1799 Bousalem, Zakaria, 209, 221 Boussetta, Mohammed, 1305 Boutaghlaline, Anas, 1619 Boutasseta, Nadir, 1147, 1171 Bouzi, Mostafa, 1377 Bouzoubaa, Karim, 199 C Chaatouf, Dounia, 1205, 1489 Chabani, Zakariya, 505

Author Index Chadli, Sara, 311, 323 Chafi, Mohammed, 447 Chafiq, Tarik, 111 Chahid, Yassine, 291 Chaker, Mohammed, 1799 Chalh, Zakaria, 825 Chama, Jaride, 741 Chamat, Abderrahim, 1777 Chaouni, Samia Benabdellah, 907 Charroud, Mohammed, 1111 Chatri, Chakib, 165 Chebak, Ahmed, 1377 Cheddadi, Fatima, 301 Cheddadi, Hafsa, 301 Cheddadi, Youssef, 301 Chennoufi, Khalid, 1159 Cherkaoui, Mohamed, 165, 1549, 1561 Chetouani, Elmostafa, 1711 Chillali, Sara, 835 Chinta, Mukesh, 939 Chojaa, Hamid, 1235, 1279 Chrita, Hanae, 597 Chtaina, Noureddine, 25 D Dabou, Rachid, 1147 Dahmane, Kaoutar, 1181 Dahmani, Soufiane, 1003 Davis, Peter, 719 Dbaghi, Yahya, 1399 Deepak, Gerard, 145, 271, 555, 565, 707, 731, 791, 801, 813 Derouich, Aziz, 421, 1235, 1687 Descombes, Xavier, 13 Didi, Salah-Eddine, 1081 Didi, Zaidan, 365 Djekidel, R., 1529 Dusa, Alexandru, 1353 E Echchelh, Adil, 1341 Eid, Ahmad, 65 El Aamery, Salma, 1467 El Abkari, El Hassan, 377, 1051 El Abkari, Safae, 377, 1051 El Akkad, Nabil, 495, 871, 893 El Alami, Rachid, 433 El Alaoui, Mustapha, 433 El Allali, Naoufal, 543 El Asri, Hayat, 1061 El Azami, Ikram, 365 El Azzab, Driss, 1111 El Bakkali, Chakib, 1291

Author Index El Bekkali, Chakib, 1255 El Bekkali, Moulhime, 1665 El Bouzekri El Idrissi, Younès, 527 El Faddouli, Nour-eddine, 621 El Filali, Sanaa, 335 EL Garouani, Said, 177 El Gettafi, Mohammed, 1111 El Guabassi, Inssaf, 209, 221 El Habib Daho, Mostafa, 981 El Hajjami, Salma, 99 El Hakimi, Abdelhadi, 1777 El Hamdani, Wafae, 1665 El Hmamsy, Youssef, 1517 El Houre, Amine, 1799 El Houssaini, Mohammed-Alamine, 991, 1021 El Houssaini, Souad, 991, 1021 El idrissi, Rafika, 1387 El Idrissi El-Bouzaidi, Youssra, 967 El Kasmi Alaoui, My Seddiq, 781 El Khadiri, Karim, 433, 1607, 1619, 1631, 1655 El Mahfoud, Mohammed, 1687, 1821 EL Majdoubi, Omayma, 947 El Makoussi, Nadia, 1033 El Mehdi, Abdelmalek, 259 El Merrassi, Weam, 1213 El Mhamdi, Jamal, 377, 1051 El Ouanjli, Najib, 1687, 1821 El Ouardi, Abdelhafid, 411 El-Attar, A., 47 Elbakkali, Outhman, 1341 Elboukhari, Mohamed, 515 Elhaj, Nabil, 1409 ELkafazi, Ismail, 3 Elkamoun, Najib, 279 Elkhatir, Haimoudi, 643 Elkhatiri, Ali, 1549, 1561 El-Kishky, Hassan, 65 Elmouatamid, Abdellatif, 279 Elmoudden, Aicha, 1399 Elouardighi, Abdeljalil, 675, 695 Eloudrhiri Hassani, Abdelhadi, 749 Elsayed, Yasser, 1433 Emharraf, Mohamed, 311 Ennawaoui, Chouaib, 1517 Ennouni, Assia, 123 Errai, Mohamed, 1735 Errahimi, Fatima, 301 Errami, Youssef, 1711 Ershadi, Mahmoud, 719 Essaid, Mohamed, 847

1833 Essakhi, Hassan, 1399 Ettifouri, El Hassane, 77 Ez-Zaki, Fatima, 1091, 1643 F Fadi, Ouafia, 155, 1675 Fagroud, Fatima Zahra, 335 Faham, Hamza, 781 Fahmani, Lamyae, 859 Faquir, Sanaa, 57 Farhat, Sadik, 1399 Fariss, Mourad, 543 Faska, Zahra, 893 Fatima, Cheddadi, 1317 Fatima-Zohra, Hibbi, 643 Fattah, Mohammed, 1081, 1665 Ferfra, Mohammed, 1159 Ferro, Alexandre Castilla, 233 Ferroud, Chorouk, 675 G Gabli, Mohammed, 1003 Gaizen, Soufiane, 155, 1675 Garfaf, Jamila, 859 Garouani, Moncef, 597 Ghammaz, Abdelilah, 1091 Gouiouez, Mounir, 575 Gourma, Anass, 1421 Guerouate, Fatima, 685 Gurguiatu, Gelu, 1353 H Habib, Mubbra, 925 Hachimi, Hayat, 111 Haddouch, Khalid, 893 Hafian, Asmae, 345 Hain, Mustapha, 135, 1021 Hajjaji, Abdelowahed, 1517 Hajji, B., 1595 Halkhams, Imane, 1081, 1665 Hamdi, Hassan, 1497 Hamouche, Salima, 505 Hanafi, Ahmed, 421 Hashmi, Mehmood Ahmed Husnain, 925 Hassani, Moha M’Rabet, 1013 Himi, Mahjoub, 1111 Houam, Lotfi, 769 Houicher, S., 1529 Hussain, Saddam, 925 Hussien, Mokhtar, 1433

1834 I Ibnyaich, Saida, 1013 Ikram, Saber, 1317 Iqbal, Sana, 469 J Jabrane, Oussama, 1111 Jabrane, Younes, 919, 1033 Jadli, Aissam, 135 Jai Andaloussi, Said, 907 Jaize, Abderrahman, 135 Jalti, F., 1595 Jammoukh, Mustapha, 1765 Janan, Mourad Taha, 1191 Jarjar, Abdeltif, 847 Jebbor, Fatine, 1061 Jefferies, Marcus, 719 Jeghal, Adil, 177 Jennane, Rachid, 769 Jihani, Nassima, 759 Jilbab, Abdelilah, 377, 1051 K Kabbaj, Mohammed Nabil, 345, 759 Kahla, Sami, 1477, 1699 Kaissari, Soufiane, 1051 Kannouf, Nabil, 291 Kaoutar, Senhaji Rhazi, 1245 Karim, Mohammed, 421, 1255 Karima, El hammoumi, 1317 Karra, Rachid, 655 Karrouchi, Mohammed, 87, 1539 Kassmi, Kamal, 87, 1539 Kerrakchou, Imane, 311, 323 Khabba, Asma, 1013 Khaidar, Mohammed, 279 Khaldoun, Mehdi, 1497 Khalil, Sara, 1549 Khankhour, Hala, 1073 Kharbach, Amina, 323 Kharroubi, Jamal, 597 Khatir, Haimoudi El, 585 Khemaissia, Seddik, 769 Khouani, Amin, 981 Khrissi, Lahbib, 893 Koumina, Abdelaziz, 919, 1033 Kourchi, Mustapha, 1181 L Laababid, Younes, 1607 Laabidine, Nada Zine, 1291 Laaroussi, Houria, 685 Laayati, Oussama, 1377 Labbadi, Moussa, 165, 1549, 1561

Author Index Lagouir, Marouane, 1365 Lagriou, Ahmed, 1455 Lagrioui, Ahmed, 387, 1279 Lahfaoui, Badreddine, 1757 Lahmar, El Habib Ben, 335 Lakhdar, Abdelghani, 1765 Lakhliai, Zakia, 1655 Lakhssassi, Ahmed, 1607, 1631 Lakrit, Soufian, 165 Lamghari, Said, 1497 Lasfar, Abdelali, 655 Latif, Rachid, 411 Lazouni, Mohammed El Amine, 981 Lemmassi, Assiya, 421 Lhayani, Mouna, 1561 Livinti, Petru, 1353 Loubna, Atarsia, 1267 Loucif, Mourad, 1725 Loulijat, Azeddine, 1223 M M’dioud, Meriem, 3 Maaroufi, Mohammed Mouhcine, 609 Mabrouki, Mustapha, 1573 Machhour, Naoufal, 881 Maghfour, Mohcine, 695 Mahfoud, Said, 1687, 1821 Mahmoudi, Sidi Ahmed, 981 Majid, Muhammad Sakandar, 925 Majout, Btissam, 1255 Makhad, Mohamed, 1223 Malik, Saba, 925 Malki, Jamal, 99 Mamri, Ayoub, 399 Manaswini, S., 145 Mansouri, Khalifa, 1765 Mansouri, Merieme, 907 Marah, Rim, 209, 221 Matar, Zaki, 1433 Mazer, Said, 1081, 1665 Mazri, Tomader, 457, 633 Mazwa, Khawla, 633 Mbarki, A., 1595 Mebarkia, Meriem, 769 Mechernene, Abdelkader, 1725 Mediouni, Mohamed, 1399 Medromi, Hicham, 859 Mehdaoui, Youness, 433 Menssouri, Aicha, 1631 Meraoumia, Abdallah, 769 Mermri, El Bekkaye, 1003 Mermri, El Bekkaye, 1003 Messaoudi, Abdelhafid, 87, 1539 Mezrhab, Ahmed, 1205, 1489

Author Index Mohammed, Boussetta, 1317 Mohammed, Karim, 1291 Mojtahedi, Mohammad, 719 Mokhlis, Mohcine, 1387 Morchid, Abdennabi, 433 Mostafa, Harti, 99 Motahhir, Saad, 1147, 1171 Moumen, Aziz, 1765 Mounich, Kamilia, 447 Mourad, Youssef, 1305 Moussaoui, Hanae, 871 Moutaouakil, Abdelhakim, 919, 1033 Muhil Aditya, P., 707 N Nahid, Mohammed, 189 Najihi, Ikrame, 1517 Nasri, Ismail, 87, 1539 Nasri, M’barek, 881 Nasser, Naoual, 1735 Nassih, B., 47 Neçaibia, Ammar, 1147 Ngadi, M., 47 Nisar, Muhammad, 925 Nouaiti, Ayoub, 1421 Nouh, Saïd, 781 O Obbadi, Abdellatif, 1711 Otman, Abdoun, 643 Ouacha, Ali, 1121 Ouajji, Hassan, 1467 Oubella, Mhand, 1181 Oughdir, Lahcen, 835 Ouladsine, Radouane, 279 Oumidou, Naima, 1549, 1561 Outzourhit, Abdelkader, 1497 Q Qazdar, Aimad, 209, 221 Qjidaa, Hassan, 433, 1607, 1619, 1631, 1655 Qobbi, Younes, 847 R Rabbah, Nabila, 959 Rachid, El Bachtiri, 1317 Rahali, Ahmed, 1655 Rahmoune, Mohammed, 233 Raillani, Benyounes, 1205, 1489 Raji, Mohammed, 111 Ramzi, Mohamed, 1213 Ramzi, Mustapha, 399 Razouki, Hassan, 483 Reddak, Moussa, 1421

1835 Redjimi, Kenza, 1135 Redjimi, Mohammed, 1135 Reha, Abdelati, 919, 1033 Rezoug, Nabil, 1809 Riad, Toufouti, 1267 Rifai, Nada, 1585 Rifi, Mounir, 1039 Rithish, Harish, 791 Rohini, Valaparla, 939 S Saâfi, Ikram, 1505 Saber, Ikram, 301, 1305 Saber, Mohammed, 259, 311, 323 Sabir, Zakaria, 1101 Sabor, Jalal, 1585 Sabri, My Abdelouahed, 123 Saddik, Amine, 411 Saeed, Muhammad Tariq, 925 Sahel, Aicha, 749 Sahnoun, Smail, 1711 Said, Raed, 505 Saikouk, Hajar, 1327, 1787 Sajid, Muhammad, 925 Salhi, Mourad, 1205, 1489 Salima, Meziane, 1267 Santhanavijayan, A., 145, 271, 555, 565, 707, 731, 791, 801, 813 Satori, Khalid, 495 Sayouti, Yassine, 1365 Sbihi, Mohamed, 399, 685 Sebbaq, Hanane, 621 Sedraoui, Moussa, 1477, 1699 Senhaji, Saloua, 57 Serag, Saif, 1341 Serghini, Abdelhafid, 1003 Settouti, Nesma, 13 Shahzad, Shahbaz Ahmad, 925 Sihamman, Noura Ouled, 123 Smitha Chowdary, Ch., 939 Snoussi, Hajar, 87, 1539 Sobhana, M., 939 Stour, Laila, 609 Surya, Deepak, 555, 801 T Tahiri, Ahmed, 1619, 1631 Taibi, Djamel, 1607, 1477, 1699, 1655 Taieb, Ahmed, 1505 Taleb, Thami Ait, 1191 Taouni, Abderrahim, 1387 Taoussi, Mohammed, 1235, 1279, 1455, 1687, 1821 Tilioua, Narjiss, 825

1836 Timmi, Mohamed, 177 Tmiri, Amal, 991 Tnaji, Khalid, 199 Touache, Abdelhamid, 1777 Touairi, Souad, 1573 Toufouti, Riad, 1745 Touil, Hamza, 495 Toumi, Hicham, 335 Touzani, Halima Drissi, 57 U ul Huda, Anwaar, 925 V Varghese, Levin, 271 Venna, Deepa, 939 Vijaykumar, Chaya, 813 W Wadie, Bendali, 1317 Wahabi, Aicha, 447 Wakrim, Layla, 1013

Author Index X Xiao, Rudan, 13

Y Yahyaouy, Ali, 57, 177 Yessef, Mourad, 1235, 1279, 1821 Yousfi, Driss, 1181, 1799 Z Zahidi, Abdallah, 1735 Zahra, Syeda Anam, 925 Zamzoum, Othmane, 1235 Zaoui, Mohamed, 233 Zellou, Ahmed, 335 Zerikat, Mokhtar, 1809 Zerraf, Soufiane, 111 Ziane, Abderrezzaq, 1147 Ziani, El Mostafa, 1445 Zine-Dine, Khalid, 279 Zulfiqar, Ayesha, 469