Communications, Signal Processing, and Systems: Proceedings of the 10th International Conference on Communications, Signal Processing, and Systems, Vol.1 (Lecture Notes in Electrical Engineering, 878) 9811903891, 9789811903892

Citation preview

Lecture Notes in Electrical Engineering 878

Qilian Liang · Wei Wang · Xin Liu · Zhenyu Na · Baoju Zhang   Editors

Communications, Signal Processing, and Systems Proceedings of the 10th International Conference on Communications, Signal Processing, and Systems, Vol.1

Lecture Notes in Electrical Engineering Volume 878

Series Editors Leopoldo Angrisani, Department of Electrical and Information Technologies Engineering, University of Napoli Federico II, Naples, Italy Marco Arteaga, Departament de Control y Robótica, Universidad Nacional Autónoma de México, Coyoacán, Mexico Bijaya Ketan Panigrahi, Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, Delhi, India Samarjit Chakraborty, Fakultät für Elektrotechnik und Informationstechnik, TU München, Munich, Germany Jiming Chen, Zhejiang University, Hangzhou, Zhejiang, China Shanben Chen, Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, China Tan Kay Chen, Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore Rüdiger Dillmann, Humanoids and Intelligent Systems Laboratory, Karlsruhe Institute for Technology, Karlsruhe, Germany Haibin Duan, Beijing University of Aeronautics and Astronautics, Beijing, China Gianluigi Ferrari, Università di Parma, Parma, Italy Manuel Ferre, Centre for Automation and Robotics CAR (UPM-CSIC), Universidad Politécnica de Madrid, Madrid, Spain Sandra Hirche, Department of Electrical Engineering and Information Science, Technische Universität München, Munich, Germany Faryar Jabbari, Department of Mechanical and Aerospace Engineering, University of California, Irvine, CA, USA Limin Jia, State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China Janusz Kacprzyk, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland Alaa Khamis, German University in Egypt El Tagamoa El Khames, New Cairo City, Egypt Torsten Kroeger, Stanford University, Stanford, CA, USA Yong Li, Hunan University, Changsha, Hunan, China Qilian Liang, Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USA Ferran Martín, Departament d’Enginyeria Electrònica, Universitat Autònoma de Barcelona, Bellaterra, Barcelona, Spain Tan Cher Ming, College of Engineering, Nanyang Technological University, Singapore, Singapore Wolfgang Minker, Institute of Information Technology, University of Ulm, Ulm, Germany Pradeep Misra, Department of Electrical Engineering, Wright State University, Dayton, OH, USA Sebastian Möller, Quality and Usability Laboratory, TU Berlin, Berlin, Germany Subhas Mukhopadhyay, School of Engineering & Advanced Technology, Massey University, Palmerston North, Manawatu-Wanganui, New Zealand Cun-Zheng Ning, Electrical Engineering, Arizona State University, Tempe, AZ, USA Toyoaki Nishida, Graduate School of Informatics, Kyoto University, Kyoto, Japan Federica Pascucci, Dipartimento di Ingegneria, Università degli Studi “Roma Tre”, Rome, Italy Yong Qin, State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China Gan Woon Seng, School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore, Singapore Joachim Speidel, Institute of Telecommunications, Universität Stuttgart, Stuttgart, Germany Germano Veiga, Campus da FEUP, INESC Porto, Porto, Portugal Haitao Wu, Academy of Opto-electronics, Chinese Academy of Sciences, Beijing, China Walter Zamboni, DIEM - Università degli studi di Salerno, Fisciano, Salerno, Italy Junjie James Zhang, Charlotte, NC, USA

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Qilian Liang Wei Wang Xin Liu Zhenyu Na Baoju Zhang •

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Editors

Communications, Signal Processing, and Systems Proceedings of the 10th International Conference on Communications, Signal Processing, and Systems, Vol. 1

123

Editors Qilian Liang Department of Electrical Engineering University of Texas at Arlington Arlington, TX, USA

Wei Wang Tianjin Normal University Tianjin, China

Xin Liu Dalian University of Technology Dalian, China

Zhenyu Na School of Information Science and Technology Dalian Maritime University Dalian, China

Baoju Zhang College of Electronic and Communication Engineering Tianjin Normal University Tianjin, China

ISSN 1876-1100 ISSN 1876-1119 (electronic) Lecture Notes in Electrical Engineering ISBN 978-981-19-0389-2 ISBN 978-981-19-0390-8 (eBook) https://doi.org/10.1007/978-981-19-0390-8 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 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 Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Contents

Data Fusion and Error Compensation Method for GPS/Star Sensor Integrated Navigation Using GPS Attitude Estimation . . . . . . . . . . . . . . Li Zhang, Chenglong He, Bo Wang, and Ziang Chen Simulation Design of Fourth-Order Bandpass Filter Based on Substrate Integrated Waveguide . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hai Wang, Peng Sun, Guiling Sun, Zhihong Wang, Ming He, Yi Zhang, and Ying Zhang Algorithm for Placement of Wireless Network Devices for Wide Areas with Variable Soil Moisture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Alexander Denisov and Ekaterina Cherskikh Optimization Design of a Discharge Control Circuit for Communication System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Qian Bi, Dongsheng Zhang, Yuanyuan Wang, Wenlong Wang, and Xiaojing Meng Online Multi-object Tracking Based on Deep Learning . . . . . . . . . . . . . Zheming Sun, Chunjuan Bo, and Dong Wang Power Grid Industrial Control System Traffic Classification Based on Two-Dimensional Convolutional Neural Network . . . . . . . . . . . . . . . Gang Yue, Zhuo sun, Jianwei Tian, Hongyu Zhu, and Bo Zhang Influence of Structure Information for Cross-domain Person Re-identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nuoran Wang, Cuiping Zhang, Shijie Lv, Yiming Luo, Xin Ding, Shuang Liu, and Zhong Zhang Course Quality Evaluation Based on Deep Neural Network . . . . . . . . . . Moxuan Xu, Nuoran Wang, Shaoyan Gong, Haijia Zhang, Zhong Zhang, and Shuang Liu

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Design of Remote Indoor Environment Monitoring System Based on STM32 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhifeng Jia, Peidong Zhuang, and Zhenzhen Huang Design of a Multi-channel Spectrum Perception Performance Verification Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bo Zhou, Tong-Qiao Xu, Zhao-Yang Li, Hong-Wei Cai, Chun-Guang Shi, and Zhen Chen

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SDN, Big Data and AI Technologies Design and Implementation of the Network of Intelligent CloudCampus . . . . . . . . . . . . . . . . . . . . . . Tong-qiao Xu, Bo Zhou, Chen-Zhi Xu, and Zhao-Yang Li

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Sum Rate Maximization for 6G IoT Resource Allocation: A Matching Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Peng Qin, Yiying Zhu, Xiongwen Zhao, and Jiayan Liu

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Application Research on an Automated Batch Network Reinforcement Method Based on SSH Protocol . . . . . . . . . . . . . . . . . . . 101 Zhao-yang Li, Bo Zhou, Wei-Gui Zhou, Tong-Qiao Xu, and Xiao-Feng Zhang Research on UAV Human Tracking Method Based on Vision . . . . . . . . 110 Sun Xiaodong, Wang Zhiqiang, Zhang Yao, and Wang Yijia Cloud Type Classification Using Multi-modal Information Based on Multi-task Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 Yaxiu Zhang, Jiazu Xie, Di He, Qing Dong, Jiafeng Zhang, Zhong Zhang, and Shuang Liu Research on Artificial Meteor Trail Emergency Communication System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126 Liu Jun-tao, Zhou Jian, Yang Kun, and Zhang Fan Programming to Identify Exfoliated 2D Nanosheets Using Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136 Zhibo Li, Kexin He, Xiao Li, Yinghui Zhang, and Cheng Wang A MCU-Based Wireless Motion Sensor Setup . . . . . . . . . . . . . . . . . . . . 142 Zhibo Li, Yinghui Zhang, Kexin He, Xiao Li, and Cheng Wang A Control System of Waveform Generation Based on LabVIEW . . . . . 150 Mingfeng Zhang, Jingjing Wu, Jinfan Ran, Ruiqing Xing, and Cheng Wang CDMA-Based High Precision Time Display System . . . . . . . . . . . . . . . . 157 Mingfeng Zhang, Xiao Li, Yinghui Zhang, Kexin He, and Cheng Wang Research on Path Planning and Tracking of Automatic Parking . . . . . . 163 Menghan Dong and Xin Yin

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The Design of a Household Intelligent Medicine Box Based on the Internet of Things . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176 Long Zhang and Peidong Zhuang Investigation on Coherent Receiving Technology in the Repeater-Less Transmission Systems . . . . . . . . . . . . . . . . . . . . . . 184 Yichao Zhang, Yupeng Li, Liang Han, Xiaoming Ding, and Xiaocheng Wang Research on Vehicle Parking Aid System Based on Parking Image Enhancement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192 Danxian Ye, Xin Yin, and Menghan Dong GPS/Star Sensor Attitude Accuracy Evaluation Method Based on Hybrid v2 Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201 Ziang Chen, Chenglong He, Bo Wang, and Li Zhang Robust Widely Linear Beamforming Based on Worst Case Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209 Jiashuo Yang, Lulu Zhao, Xinglong Jiang, Guang Liang, and Jinpei Yu Research on the Intelligent Guidance System of Empty Parking Spaces Based on Amap API and Edge Detection . . . . . . . . . . . . . . . . . . 217 Danxian Ye, Xin Yin, and Menghan Dong Data Augmentation for Communication Emitter Identification Using Generative Adversarial Nets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227 Jianwei Tian, Xu Zhang, Hongyu Zhu, Gang Yue, and Zhuo Sun Antenna Array Pattern Synthesis Based on a Hybrid Particle Swarm Optimization and Genetic Algorithm . . . . . . . . . . . . . . . . . . . . . 236 Hongming Hu, Lulu Zhao, Peng Gao, Guang Liang, and Huawang Li Study on Digital High Resolution APD Measurement Receiver . . . . . . . 244 Hao Gao, Lin Cao, Enxiao Liu, and Lei Yang Wireless Sensor Network Coverage Optimization Based on Sparrow Search Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251 Zehua Wang, Shubin Wang, and Haifeng Tang An Improved Stereo Matching Algorithm Based on AnyNet . . . . . . . . . 259 Haifeng Tang, Shubin Wang, and Zehua Wang Research on Anti-noise Activation Function Based on LIF Biological Neuron Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 268 Fengxia Li, Shubin Wang, and Yajing Kang Non-parameter Nonlinear Detectors for Multi-band Milimeter-Wave Radio-Over-Fiber Mobile Fronthaul . . . . . . . . . . . . . . . . . . . . . . . . . . . . 276 Yue Cui, Xiaofan Xu, Hailong Kang, Niwei Wang, and Yingyuan Gao

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CNN-Based Automatic Modulation Classification over Rician Fading Channel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285 Zikai Wang and Qilian Liang Schedio-Pro: An Interactive Virtual Reality Game for Protein Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293 Tianshu Xu, Venkata V. B. Yallapragada, Mark Tangney, and Sabin Tabirca A Method of Red-Attack Pine Trees Accurate Recognition Using Multi-source Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 301 Jinliang Xia, Jincang Liu, Qin Li, and Dixiong Lu Imbalanced Data Classification of Pathological Speech Using PCA, SMOTE, and Expectation Maximization . . . . . . . . . . . . . . . . . . . . . . . . 309 Camille Dingam, Xueying Zhang, Shufei Duan, Haifeng Li, and Xiaoyu Chen Survey of UAV Path Planning Based on Swarm Intelligence Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 318 Zhongwang Zhang, Sheng Liu, Jianqi Zhou, Yongtao Yin, Hanbo Jia, and Lin Ma Design of Portable ECG Monitoring System Based on STM32 Single Chip Microcomputer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 327 Jiawei Jin and Wei Wang A Pilot Based Phase Compensation Associative Algorithm in CO-OFDM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 334 Lei Li, Yupeng Li, Liang Han, Xiaoming Ding, and Xiaocheng Wang Research on Semantic Object Measurement Algorithm Based on Object Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 342 Wanqing Wu, Lin Ma, Bin Wang, and Zhongwang Zhang Tianlian Satellite Assisted the Emergency Remotely Sensed Data Achieves Download Quickly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 351 Gaosai Liu, Huawang Li, Xinglong Jiang, Guang Liang, and Long Wang Image Filtering and Edge Detection System Based on FPGA . . . . . . . . . 359 Sun Xiaodong, Wang Zhiqiang, and Jing Wei Attention-Based Video Disentangling and Matching Network for Zero-Shot Action Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 368 Yong Su, Shuang Zhu, Meng Xing, Hengpeng Xu, and Zhengtao Li Intelligent Waveform Optimization for Target Tracking in Radar Sensor Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 376 Zihan Luo, Jing Liang, and Zekai Xu

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Analysis of Phase Noise of Phase-Locked Loops Under Different Frequency Discrimination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 384 Xiaocheng Wang, Xiaoming Ding, and Yupeng Li Sense-Through-Foliage Target Detection Based on Stacked Autoencoder and UWB Radar Sensor Networks . . . . . . . . . . . . . . . . . . 390 Chengchen Mao and Qilian Liang Channel Estimation and Refinement via Equalization in FBMC Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 398 Baobing Wang, Jinpeng Xiao, Tian Xiang, Qian Wang, Dejin Kong, and Jianzhong Guo Study on the Impact of Virtual AtoN Setting on AIS in Harbour Areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 407 Li Weiyun, Liu Chang, Li Jinhao, and Ji Xiaoyan DMRS-based Channel Estimation Methods for Two-Port OFDM Systems in 5G Uplink . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 416 Jun Sun, Tian Xiang, Xiaomin Mu, Jinpeng Xiao, Baobing Wang, Qian Wang, Dejin Kong, and Jianzhong Guo Research on Epidemic Data Sharing Model Based on Cross-Chain Mechanism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 424 Liu Chang, Mei Yu, Zhang Xu, Ling Yuan, and Li Bo An Efficient Precoding Design for Massive MIMO Systems Under Channel Impairments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 431 Huayu Wang, Jing Lv, Yuxing Zhang, Qin Ren, and Yinghui Zhang Radar Adaptive Interference Suppression Algorithm Based on MVDR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 439 Yumeng Zhang, Jinliang Dong, Gan Wang, Lei Bian, and Huifang Dong Research on Overall Design of Mobile Phased Array Radar for Field Vehicle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 447 Jinliang Dong, Yumeng Zhang, Gan Wang, Lei Bian, and Huifang Dong Design of Rate Compatible Spatially Coupled LDPC Codes . . . . . . . . . 457 Liu Yang, Cheng Shuangyi, Wang Bin, and He Xing Sensor Path Planning for Target Tracking . . . . . . . . . . . . . . . . . . . . . . 465 XiaFei Huang and Jing Liang Noncontact Heartbeat Extraction Using UWB Impulse Radar . . . . . . . . 472 Fangfei Jing, Xiaoqing Tian, and Jing Liang Correlation Between Hail Diameter and VILD in Bayannur . . . . . . . . . 480 Yunong Xu, Debin Su, Ning Yang, Xiaoguang Sun, and Haijiang Wang

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A Method for Damage Assessment of Orbital Targets Based on Radar Detection Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 488 Zhengtao Zhang, Qiang Huang, and Jianguo Yu Speech Emotion Recognition Based on Henan Dialect . . . . . . . . . . . . . . 498 Zichen Cheng, Yan Li, Mengfei Jiu, and Jiangwei Ge Design and Simulation of an Anti-collapse CMUT Structure . . . . . . . . . 506 Jiajun Liu, Kaixin Li, Benxian Peng, and Fengqi Yu An Efficient Learning Automaton Scheme for Massive-Action Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 516 Hongyu Zhu, Jianwei Tian, Chong Di, Zheng Tian, Yizhen Sun, Qiyao Li, and Shenghong Li A Descriptor System Approach to Fault Estimation and Compensation for T-S Fuzzy Discrete-Time Systems . . . . . . . . . . . . 525 Yu Chen, Xiaodan Zhu, and Qian Sun Intelligent Water and Fertilizer System Based on NB-IoT . . . . . . . . . . . 533 Zhifeng Jia and Peidong Zhuang Deep Reinforcement Learning for Multi-user Resource Allocation of Wireless Body Area Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 541 Xiuzhi Xu, Jiasong Mu, and Tiantian Zhang Financial Risk Prediction Based on Stochastic Block and Cox Proportional Hazards Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 548 Xiaokun Sun, Jieru Yang, Junya Yao, Qian Sun, Yong Su, Hengpeng Xu, and Jun Wang Learning Residents’ Consumption Structures Based on the ELES Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 557 Zhenkuan Jiang, Zixiao Wang, Xuan Liu, Kun Liu, Yong Su, Hengpeng Xu, and Jun Wang Observer-Based Fault Estimation for Discrete T-S Fuzzy Systems . . . . . 566 Yu Chen, Xiaodan Zhu, Weifang Yan, and Dandan Han A Simple Design of Microstrip Patch Antenna for WLAN Application Using 5.4 GHz Band . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 574 Prince Mahmud Ridoy, Khan Md. Elme, Rezauddin Shihab, Pranta Saha, Md. Jawad-Al-Mursalin, and Nowshin Alam Estimation of Chlorophyll Concentration for Environment Monitoring in Scottish Marine Water . . . . . . . . . . . . . . . . . . . . . . . . . . 582 Yijun Yan, Yixin Zhang, Jinchang Ren, Madjid Hadjal, David Mckee, Fu-jen Kao, and Tariq Durrani

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Study on Tunable Frequency Selective Surface Technique . . . . . . . . . . . 588 Yixiu Hao, Tianyu Liu, Yitong Xie, Bo Zhang, Hui Guo, Menglin Li, Rui Li, and Chen Liang Miniaturized Tunable Frequency Selective Surface Design . . . . . . . . . . 595 Tianyu Liu, Yixiu Hao, Yitong Xie, Junjie Kang, and Bo Zhang Cooperative Routing Algorithm Based on Data Priority . . . . . . . . . . . . 602 Tiantian Zhang, Jiasong Mu, and Xiuzhi Xu Comparison of Walking Structure of High Voltage Line Deicing Robot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 610 Jin Li, Li Wang, and Hanfang Zhang Study of FlexRay Bus Test Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 620 Ruina Zhao, Linghui Zhang, Shao Li, Ming Gao, and Weizheng An Three-Dimensional Measurement Method for Tube’s Endpoints Based on Multi-view Stereovision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 628 Xiaoyu Hou, Mingzhou Li, Qi Lv, Jiang Zheng, and Jian Wang Deep Image Classification Model Based on Dual-Branch . . . . . . . . . . . . 636 Haoyu Chen, Qi Lv, Wei Zhou, Jiang Zheng, and Jian Wang Surface Defect Detection System of Condenser Tube Based on Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 644 Siyu Chen, Qi Lv, Yuzhe Zhang, Jiang Zheng, and Jian Wang Optimization Technology of Concurrent Routing Transmission in FANET . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 651 Guang Yang, Lin Ma, Liang Ye, and Danyang Qin Marker Codes Using the Data Punching in the Presence of Insertions/Deletions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 660 Yiwei Lu, Yuan Liu, and Xiaonan Zhao The Time Series Data Classification Method Based on Deep Spatial Transformer Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 667 Demeng Hu, Bo Hu, Zhao Li, and Fangmin Xv Development of Copper Indium Gallium Selenide (CIGS) /Perovskite Laminated Thin Film Solar Cell System and Prospect for Its Typical Application . . . . . . . . . . . . . . . . . . . . . . . . 672 Yanli Sun, Changbo Lu, Xudong Wang, and Xudong Xiao Subsequent Work of the Anomaly Detection and Fault Determination Engineering Framework: An IMA Core Processing Oriented Safety Assessment Approach Based on the Dynamic Fault Tree Analysis . . . . . 680 Yang Hong, Lisong Wang, Jiexiang Kang, Hui Wang, Zhongjie Gao, Rui Zhang, and Qin Zhang

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A Method for Improving Accuracy of DeepLabv3+ Semantic Segmentation Model Based on Wavelet Transform . . . . . . . . . . . . . . . . 688 Xin Yin and Xiaoyang Xu Design of D2D Wireless Communication System Based on ACO-AIA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 694 Jiatong Li, Zhibo Li, Xuanying Li, and Cheng Wang Research on Power Conversion Efficiency of Laser Wireless Power Transmission System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 703 Wanli Xu, Weigui Zhou, Wei Zhang, Shizhan Li, and Changfu Wang Synchronization of Complex-Valued SICNNs with Distributed Delays via a Module-phase-type Controller . . . . . . . . . . . . . . . . . . . . . . 711 Qiuyuan Chen, Honghua Bin, Zhenkun Huang, and Chao Chen Evaluation System and Data Analysis Method of Low-Temperature Cold Start for Hydraulic Winch Oil . . . . . . . . . . . . . . . . . . . . . . . . . . . . 719 Xudong Wang, Hua Li, Junbiao Hu, Shizhan Li, Weihua Zhang, and Yao Nie Primary Battery Electrical Performance Data Collection and Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 727 Chunhua Xiong, Lei Xu, Weishan Wang, Hui Sun, Weigui Zhou, Bingchu Wang, and Wanli Xu Volt-Ampere Characteristic Acquisition and Analysis of Thin Film Solar Cells Under Different Incident Angles . . . . . . . . . . . . . . . . . . . . . . 735 Hua Li, Yanli Sun, Junbiao Hu, Hui Sun, Changbo Lu, Bingchu Wang, and Xudong Wang Metal-Air Battery System Design and Electrical Performance Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 743 Lei Xu, Weigui Zhou, Xuhui Wang, Hui Sun, Yaohui Wang, Bingchu Wang, and Wanli Xu Review of Bias Point Stabilization Methods for MZ Modulator . . . . . . . 751 Mingzhu Zhang, Yupeng Li, Shuangxi Sun, Liang Han, Xiaoming Ding, and Xiaocheng Wang Research on Wireless Charging Queues Optimization Method of UAV Swarm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 758 Changfu Wang, Yaohui Wang, Bin Zhang, Mengyi Wang, and Xudong Wang Throughput Analysis of Slotted Aloha with Retransmission Limit in Fading Channels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 766 Peng Li, Yitong Li, Wen Zhan, Pei Liu, Yue Zhang, Dejin Kong, and Kehao Wang

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Optimization of NB-IoT Uplink Resource Allocation via Double Deep Q-Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 775 Han Zhong, Runzhou Zhang, Fan Jin, and Lei Ning Study on Application of Wireless Sensor Network for Spacecraft . . . . . 782 Yukun Chen, Guiming Zeng, Jinlong Li, Jiankang Wang, and Feng Qiu Preliminary Study on the Mosaic Algorithm of Weather Radar Network in Sichuan Province . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 791 Wei Zhang, Jie Yang, Yi Huang, Min Sun, and Haijiang Wang Research on Low Temperature Discharge Characteristics of Ternary Lithium Batteries for Unmanned Systems . . . . . . . . . . . . . . 798 Weihua Zhang, Yaohui Wang, Weishan Wang, Lijie Zhou, and Wanli Xu Design and Analysis of 1.5 GHz LNA for L-Band . . . . . . . . . . . . . . . . . 805 Hai Wang, Peng Sun, Guiling Sun, Ying Zhang, Xiaomei Jiang, and Zhihong Wang Transmission Optimization for Mobile Scenarios in 5G NB-IoT Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 812 Tongyi Zheng, Qingsong Ye, Runzhou Zhang, Fan Jin, and Lei Ning Fall Recognition Based on Human Skeleton in Video . . . . . . . . . . . . . . . 819 HongWei Liu and Jiasong Mu Automatic Localization of Multi-type Barcodes in High-Resolution Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 826 Yuanbiao Xiao, Jinwang Yi, and Guanhao Qiao Channel Estimation Based on Matrix Completion for mmWave Massive MIMO System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 834 Zhuangzhuang Guo and Weixia Zou Resource Allocation for Full-Duplex V2V Communications Underlaying Cellular Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 842 Fang Qu, Fei Wu, and Liang Han Research on Multi-modal Sensor Data Acquisition and Processing System for USV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 850 Lin Cao, Hao Gao, Puyu Yao, Junhe Wan, Hui Li, Jian Yuan, and Hailin Liu An Improved Filtering Method of Superfluous Relationship in Domain Model Based on Random Forest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 857 Mengyuan Yu, Lisong Wang, Buzhan Cao, and Yang Hong Hidden Markov Model and Its Application in Human Activity Recognition and Fall Detection: A Review . . . . . . . . . . . . . . . . . . . . . . . 863 Tingting Xue and Hui Liu

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Spectral–Spatial Feature Completely Separated Extraction with Tensor CNN for Multispectral Image Compression . . . . . . . . . . . . . . . . 870 Tongbo Cao, Ning Zhang, Shunmin Zhao, Kedi Hu, and Kang Wang Deep Inter-spectrum Prediction Network for Multispectral Image Compression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 876 Yuxin Meng, Kedi Hu, Tongbo Cao, and Mengyue Chen Spectral-Spatial Hyperspectral Unmixing Method Based on the Convolutional Autoencoders . . . . . . . . . . . . . . . . . . . . . . . . . . . . 881 Fanqiang Kong, Mengyue Chen, Tongbo Cao, and Yuxin Meng Multispectral Image Compression Algorithm Based on Silced Convolutional LSTM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 887 Kang Wang, Ning Zhang, Kedi Hu, and Tongbo Cao Automatic Generation Method of Temporal Fault Tree Based on AltaRica3.0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 892 Qin Zhang, Lisong Wang, and Jun Hu A Simulation Framework for VRM Requirement Model . . . . . . . . . . . . 901 WanLi Zhan, Jun Hu, LiSong Wang, and JiaRun Lv A Coupling Analysis Method Based on VRM Model . . . . . . . . . . . . . . . 910 Zhipeng Qiu, Lisong Wang, and Jun Hu A Framework for System Safety Modeling and Simulation Based on AltaRica 3.0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 919 Jun Hu, Jian Qi, Lisong Wang, Yanhong Dong, Qingfan Gu, and Hao Rong A Summary of 5G WiFi Security Issues . . . . . . . . . . . . . . . . . . . . . . . . . 928 Cui Zhuohan, Li Zheng, Liu Enfei, and Ai Xiaoxi An WiFi-Based Human Activity Recognition System Under Multi-source Interference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 937 Jiapeng Li, Ting Jiang, Jiacheng Yu, Xue Ding, Yi Zhong, and Yang Liu Target Detection and Recognition System Based on PYNQ Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 945 Youzhong Wang, Yanping Zhu, Zhiyuan Qi, and Jinli Chen Secure Transmission for Intelligent Reflecting Surface Assisted MISO Systems: Achievable Rate and Optimal Time Allocation . . . . . . . 953 Xiaoming Liu, Xiaokai Liu, Jianwei Tian, Bin Li, and Chenglin Zhao Formal Verification for VRM Requirement Models . . . . . . . . . . . . . . . . 961 Yang Zhang, Jun Hu, Lisong Wang, Qingfan Gu, and Hao Rong A Model-Based System Safety Analysis Tool and Case Study . . . . . . . . 970 Yanhong Dong, Jun Hu, Jian Qi, Qingfan Gu, and Hao Rong

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Joint CSI Acquisition Based on Deep Learning for FDD Massive MIMO Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 980 Mengxin Li, Jing He, and Yuan Cheng 3D Terrain Mapping and Object Detection Using LiDAR . . . . . . . . . . . 988 S. Bharath, S. Vinay, and S. Srividhya Fast Beam Tracking for Base Station and UAV Communications . . . . . 996 Yiwen Tao, Tao Feng, Bin Li, and Chenglin Zhao Superpixel Based Sea Ice Segmentation with High-Resolution Optical Images: Analysis and Evaluation . . . . . . . . . . . . . . . . . . . . . . . . 1004 Siyuan Chen, Yijun Yan, Jinchang Ren, Byongjun Hwang, Stephen Marshall, and Tariq Durrani A U-Net Based Multi-scale Feature Extraction for Liver Tumour Segmentation in CT Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1013 Ming Gong, John Soraghan, Gaetano Di Caterina, and Derek Grose Design of Test Platform for Fuel Cell Power Supply of Small UAV . . . . 1021 Changfu Wang, Yaohui Wang, Yanli Sun, Guang Hu, and Youjie Zhou Design and Implementation of UAV Based on Fuel Cell Power Supply . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1027 Youjie Zhou, Hua Li, Chun Wu, Mengyi Wang, and Changfu Wang Design and Trial Manufacture of Methanol Reforming Hydrogen Generator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1033 Changbo Lu, Lei Xu, Yan Qin, Guang Hu, Weigui Zhou, and Youjie Zhou Analysis of the Development Status of Micro Gas Turbine Generation Technology at Home and Abroad . . . . . . . . . . . . . . . . . . . . 1040 Lianling Ren, Liang Wen, Huakui Han, Ruiguo Zhu, Yongcheng Huang, and Youjie Zhou Biased-Infinity Laplacian Applied to Depth Completion Using a Balanced Anisotropic Metric . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1048 Vanel Lazcano, Felipe Calderero, and Coloma Ballester A Study on the Effects of Temperature Changes on the Rheological Properties of Lithium Lubricating Grease and Data Processing . . . . . . . 1056 Weigui Zhou, Long Huang, Changfu Wang, Weihua Zhang, Jinmao Chen, Xudong Wang, Youjie Zhou, and Jing Wang On-Chip Generation of PAM-4 Signals Based on a 3
3 MMI Architecture for Optical Interconnect and Computing Systems . . . . . . . 1064 Duy Tien Le and Trung Thanh Le

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All-Optical XNOR and XOR Logic Gates Based on Ultra-Compact Multimode Interference Couplers Using Silicon Hybrid Plasmonic Waveguides . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1072 Thi Hong Loan Nguyen, Duy Tien Le, Anh Tuan Nguyen, Le Minh Duong, and Trung Thanh Le Retrieval of Small and Medium-Scale Wind Field Based on Doppler Radar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1080 Liwei Zhang, Xu Wang, Caili Wang, and Haijiang Wang Energy-Efficiency of Full-Duplex-Enabled D2D Communications Underlaying Cellular Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1088 Ning Liang, Liang Han, and Yupeng Li Air Channel Measurement in Free-Space Optical Communications . . . . 1096 Xiaorui Yuan, Ruotong Zou, Ming Li, Jiawei Han, and Xiaocheng Wang Design and Realization of Dual-Axis Follower . . . . . . . . . . . . . . . . . . . . 1103 Zhenzhen Huang and Peidong Zhuang A Survey of Compatibility and Cooperative Construction Mode Between Public 5G Network and Smart Grid . . . . . . . . . . . . . . . . . . . . 1111 Wang Yudong, Liu Lirong, Xin Peizhe, and Li Yang Energy Efficiency Analysis for Sub-6G and mmWave Massive MIMO Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1121 Hao Gao, Xiaoting Xu, and Danpu Liu Advanced Nonintrusive Load Monitoring System and Method for Edge Intelligence of Electric Internet of Things . . . . . . . . . . . . . . . . 1131 Xiande Bu, Shidong Liu, Chuan Liu, Wenjing Li, and Liuwang Wang Design and On-Orbit Verification of Satellite-Borne AIS . . . . . . . . . . . . 1140 Chi Zhang, Tao Wang, Shuai Liu, Yawen Cai, Qingsong Li, and Xutao Hou The Design and Application of Space to Space Communication System in Space Station Missions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1147 Lu Zhang, Yuan Wen, Kai Ding, and Cai Huang Analysis and Simulation of Potato Combine Harvesting Machine . . . . . 1154 Tizhi Cao, Yanwei Wang, and Jiaping Chen Research on Pesticide Fog Droplet Drift Detection Applied in UAV . . . 1162 Jiaping Chen, Yanwei Wang, and Tizhi Cao Simulation of OFDM Index Modulation in VDES System . . . . . . . . . . . 1168 Zou Xu, Jie Yang, Dong Wang, and Jiaqi Zhen

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Color Image Enhancement Algorithm on the Basis of Wavelet Transform and Retinex Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1176 Xinzhe Xia, Jie Yang, Jinpeng Li, and Jiaqi Zhen A Polarization Identification Method of Full Polarization Phased Array Radar and Active Decoy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1184 Yang Zhou and Zhian Deng Effects of Fuselage Scattering and Posture on UAV Channel . . . . . . . . . 1192 Boyu Hua, Qiuming Zhu, Cheng-Xiang Wang, Haoran Ni, Kai Mao, Tongtong Zhou, Weizhi Zhong, and Hengtai Chang A UAV-Based Measurement Method for Three-Dimensional Antenna Radiation Pattern . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1201 Tianxu Lan, Hongtao Duan, Qiuming Zhu, Qihui Wu, Yi Zhao, Jie Li, Xiaofu Du, and Zhipeng Lin Height-Dependent LoS Probability Model for A2G MmWave Communications Under Built-Up Scenarios . . . . . . . . . . . . . . . . . . . . . . 1209 Minghui Pang, Qiuming Zhu, Fei Bai, Zhuo Li, Hanpeng Li, Kai Mao, and Yue Tian Simulation Analysis of Multi-region Receiver Based on Orthogonal Transmitter Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1218 Xiu Zhang, Yang Li, and Xin Zhang An Improved Frequency Measurement Algorithm Based on Rife and Its FPGA Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1225 Xin Hao, Heng Sun, Lei Guo, and Chao Wang Trajectory Optimization of Movable Platform with Time Difference Based on Particle Swarm Optimization . . . . . . . . . . . . . . . . . . . . . . . . . 1232 Heng Sun, Xin Hao, Hu Li, Chao Wang, and Guo Lei Machine Learning Enabled Sense-Through-Foliage Target Detection Using UWB Radar Sensor Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1239 Dheeral Bhole and Qilian Liang Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1247

Data Fusion and Error Compensation Method for GPS/Star Sensor Integrated Navigation Using GPS Attitude Estimation Li Zhang1,2 , Chenglong He1 , Bo Wang2(B) , and Ziang Chen2 1 National Key Laboratory of Satellite Navigation System and Equipment Technology,

Shijiazhuang 050081, China 2 Nanjing University of Aeronautics and Astronautics, College of Astronautics, Nanjing

210016, China [email protected]

Abstract. At present, the fusion of position and attitude information and accurate real-time target tracking are problems to be solved in measurement and service of geographic information. Aiming at the errors of GPS positioning and star sensor attitude determination, this paper proposes a GPS/star sensor integrated navigation data fusion and error compensation method (GAKF), in which GPS attitude estimation is used in Kalman filter. The attitude of GPS is calculated and fused with the star sensor data, and the integrated navigation error compensation is performed through Kalman filtering to correct the position and attitude errors. Experiments show that this method can effectively reduce position and attitude errors. Compared with traditional integrated navigation methods, it can avoid the cumulative error of the inertial navigation attitude measurement, thus improve the monitoring and service capabilities of GPS and star sensor integrated navigation positioning and attitude determination. Keywords: GPS · Star sensor · Integrated navigation · Kalman filter · Data fusion · Error compensation

1 Introduction With the rapid development of navigation technology, many different navigation technologies have been applied to various fields such as ocean, land and aviation. At present, the satellite navigation technology system based on GPS and Beidou has formed a comprehensive and dynamic global three-dimensional navigation and positioning service, which can quickly obtain the precise position of ground, ocean, and air targets and can provide strong technical support for precision guided weapons, space reconnaissance and modern geographic information services [1]. Generally, positioning and attitude determination are regarded as two independent processes, and their correlation is often ignored in the integrated navigation process [2–4]. Due to the limitations of a single navigation system, two or more navigation technologies can be combined together to provide navigation information to form high-precision, high-stability, and multi-functional integrated navigation system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 1–8, 2022. https://doi.org/10.1007/978-981-19-0390-8_1

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At present, the most widely used integrated navigation is the combination of satellite navigation system and inertial navigation system [5–7]. However, there are two problems with this approach. Firstly, the measurement error of the inertial equipment will accumulate over time. Although it can be corrected by technical means, the corrected speed cannot match the flying speed of the target. As a result, the longer flying distance or the longer positioning service time, the lower the accuracy of the instantaneous attitude calculation, so long-distance or long-time navigation will be severely restricted. Secondly, the inertial measurement equipment is a high-loss equipment, of which the manufacturing and maintenance cost is quite high. The higher the measurement accuracy, the higher the cost, and the lower cost performance for large-scale operations [8]. Research on multi-level and multi-platform positioning and attitude determination new models, new methods and new technologies is of great significance for enhancing the real-time measurement and control capabilities of aerial targets and reducing the cost of manufacturing and maintenance. As a high-precision attitude measurement instrument, the star sensor is more and more widely used in various space vehicles. It has significant advantages such as small size, low power consumption and high accuracy. In addition, the measurement errors do not accumulate over time [9]. This paper proposes a GPS/Star sensor integrated navigation data fusion and error compensation method (GAKF). Aiming at the errors of GPS positioning and star sensor attitude determination, the errors in the two types of processes are comprehensively processed to improve long-distance or long-term target navigation and tracking capabilities. Experiments show that the method can effectively reduce position and attitude errors, whose accuracy is equivalent to the accuracy of the mainstream integrated navigation algorithm, and can avoid the cumulative error of the inertial navigation attitude measurement, thus can improve the monitoring and service capabilities of GPS and star sensor integrated navigation positioning and attitude determination.

2 Methodology 2.1 GPS Attitude Estimation GPS attitude estimation methods include direct method, vector observation method, least square method, etc. [10–12]. The direct method needs to use the carrier coordinate system and the local horizontal coordinate system when calculating the attitude of the carrier. The calculation of the attitude angle by the direct method only requires two uncorrelated baseline vectors of the current epoch, so the calculation is relatively simple. The navigation parameters of the carrier are relative to a certain reference coordinate system, which is the basis for carrier navigation positioning and attitude measurement. The carrier coordinate system is used in GPS attitude measurement, defined by the user according to the carrier platform. Usually, its origin is taken as the carrier’s centre of mass. The X axis is along the direction of the carrier movement, the Y axis is perpendicular to the X axis and points to the right side of the carrier, and the Z axis is perpendicular to the carrier and points upwards. The carrier coordinate system is determined by the position of the GPS antenna on the carrier, as shown in Fig. 1.

Data Fusion and Error Compensation Method for GPS/Star Sensor

3

Z

(0,0,0)

Antenna1

O

α

(0,l12,0) l12 Antenna2

l13

Y l23

Antenna3

X

(l13sinα,l13sinα,0)

Fig. 1. Schematic diagram of carrier coordinate system

Using the coordinate conversion matrix, the vector in the reference coordinate system can be converted into carrier coordinates: ⎡ ⎤ ⎡ ⎤ xb xl ⎣ yb ⎦ = R2 (ψ)R1 (θ )R3 (φ)⎣ yl ⎦ (1) zb zl where bl = (x l , yl , zl )T represents the vector of the baseline in the local horizontal coordinate system, and bb = (x b , yb , zb )T represents the vector of the baseline in the carrier coordinate system. The values of the attitude angles ϕ, θ and ψ can be calculated by a certain algorithm. If the GPS antenna carrier coordinate system is set as shown in Fig. 1, and the coordinates of antenna 2 and antenna 3 in the local horizontal coordinate system are ul2 = (x l2 , yl2 , zl2 )T and ul3 = (x l3 , yl3 , zl3 )T , the formula (2) can be obtained to calculate the values of the three attitude angles: ⎤

⎡

⎤⎡ ⎤⎡ ⎤ 1 0 0 cos φ sin φ 0 xl2 ⎣ l12 ⎦ = ⎣ 0 cos θ sin θ ⎦⎣ − sin φ cos φ 0 ⎦⎣ yl2 ⎦ 0 zl2 0 − sin θ cos θ 0 − sin θ 1 ⎡

0

(2)

2.2 Integrated Navigation Method When performing error compensation, Kalman filter should have the same output components. The output of GPS system is position, velocity and the calculated attitude angle data, and the output of the star sensor is attitude angle data. Compared with the integrated navigation with the inertial navigation system, the errors eliminated are the constant drift of the gyro and the zero bias of the accelerometer, and the possible error is the low-frequency error of the star sensor [13]. The attitude kinematics equation is used to construct the relationship between attitude angle, angular velocity and angular acceleration, thus the system state equation is constructed. The difference between the attitude angle components output by the GPS receiver and the star sensor is used in the observation equation.

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Establish the state equation as: x(k + 1) = Fx(k) + v(k)

(3)

where F is the state transition matrix, x is the state vector matrix, and v is the system noise. Establish the observation equation as: z(k) = Hx(k) + w(k)

(4)

where z is the observation vector matrix, H is the observation matrix, and w is the observation noise. The state estimation equation is:   x˜ (k) = x˜ (k|k − 1) + K(k) z(k) − H x˜ (k|k − 1) (5) The filter gain equation is:  −1 K(k) = P(k|k − 1)H T HP(k|k − 1)H T + W

(6)

The error covariance matrix is: P(k) = [I − K(k)H ]P(k|k − 1)[I − K(k)H ]T + K(k)WK(k)T

(7)

  ˙ , ¨ , , ˙ , ¨ , , ˙  ¨ T as the attitude angle, angular Set state vector x = , , velocity, and angular acceleration output by the star sensor, set the observation vector   ˙ φ, ¨ θ, θ˙ , θ¨ , ψ, ψ, ˙ ψ¨ T as the attitude angle, angular velocity, and angular z = φ, φ, acceleration output by GPS. In general, if the three attitude angles are independent of each other, the Kalman filter can be decomposed into three independent Euler corner filters. A single attitude angle state vector (such as roll) can be simplified as:   ˙  ¨ T x = , , (8) The state equation can be written as: ⎡ ⎤ ⎡ ⎤⎡ ⎤ ˙  010  ⎣ ¨ ⎦ = ⎣ 0 0 1 ⎦⎣  ˙⎦ ... ¨ 000  

(9)

Since Euler angles can be directly measured, the measurement sensitivity matrix can be simplified as: H = [1, 0, 0, ]T

(10)

Since only a single attitude angle is estimated, the measurement noise covariance matrix is a scalar. In addition, the process noise covariance matrix can be obtained by the following formula: ⎡ 2 ⎤

t σ (t − t0 ) 0 0 Q= GQG T T = ⎣ (11) 0 0 0⎦ t0 0 00

Data Fusion and Error Compensation Method for GPS/Star Sensor

5

The initial value of the system error covariance matrix can be calculated by the following formula: ⎡ 4 3 2 ⎤ 1 + t + t4 + σ 2 t + t2 t2 3 ⎥ ⎢ (12) P0 = lT + Q = ⎣ t + t2 1 + t2 t ⎦ t2 t 1 2 According to the given initial value, Kalman filter integrated navigation process can be performed.

3 Experiment and Analysis 3.1 Attitude Determination We select the data of the Ziyuan-3 satellite from 4:00:00 to 4:20:00 on January 21, 2019, including GPS positioning data, star catalogs, APS star sensors, star map data, etc. The mobile least squares algorithm (MLS) is used to determine the attitude of the star sensor. Taking the first frame as an example, for the star map data, extract the pixel coordinates of the star point centroid in the star distribution area. The centroid location results of the first frame of star map are shown in Table 1. Table 1. The centroid coordinates of the star points of the first frame of star map Number

X/pixel

Y/pixel

1

488.0

942.5

2

21.5

397.5

3

1009.5

516.0

4

464.5

21.5

5

352.5

446.0

6

354.0

803.0

According to the imaging collinear condition equation of the star sensor, the stars observed in the field of view of the star sensor are matched with the stars in the navigating star catalog, so as to realize the one-to-one mapping between the stars in the star chart and the stars in the navigating star catalog. The first frame of star map recognition results are shown in Table 2. For the result of star map recognition, the mobile least squares algorithm (MLS) is used to determine the attitude of the star sensor. The Euler angle calculated based on the first frame of the star map and the Euler angle estimated by GPS are shown in Table 3.

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L. Zhang et al. Table 2. Recognition results of the first frame of star map.

Star number

X/pixel

72652

488.0

Y/pixel

Right ascension

Declination

942.5

5.94688421

0.65978110

53511

21.5

397.5

6.27035961

0.74452507

91019

1009.5

516.0

6.05241613

0.44450511

73743

464.5

21.5

0.02885458

0.50651879

214615

352.5

446.0

6.16524963

0.61003495

72883

354.0

803.0

6.01125516

0.68607075

Table 3. Attitude determination results of the first frame of the star map. ϕ/°

θ /°

ψ/°

Star sensor

65.45612427

57.71668467

89.27093250

GPS

65.45749128

57.71553486

89.26481141

3.2 Error Compensation Results Set the sampling time to 10 s. Set the mean square error of the GPS measurement noise to 1m, and the mean square error of the star sensor measurement noise to 20 . Figure 2 shows the real trajectory and Fig. 3 shows the output filtered trajectory. Figure 4 shows the output filter results, in which Fig. 4 (a) shows the position error and Fig. 4 (b) shows the attitude angle error. Max and RMS of the estimated deviation of each parameter are compared in Table 4. It can be seen from Fig. 3 and Fig. 4 that as time changes, the position and attitude error corrected by Kalman filter tends to converge smoothly, the estimation deviation curve of each parameter is stable, but the errors have a certain increase compared with the simulation data scene. Among the three attitude angles, the errors of yaw angle and roll angle have increased obviously while the error of pitch angle remains below 0.02°. This is due to the fact that the carrier speed and attitude angle are not changed much in the actual scene. It results in an increase in the attitude angle error estimated by the GPS output, and finally cause an increase in the error of the filtered result. But the accuracy and stability of this method are still considerable, which can avoid the inherent defects of inertial system errors accumulated over time. The verification results show that the GPS/star sensor integrated navigation GAKF method proposed in this paper can meet the requirements of correcting positioning and attitude errors.

Data Fusion and Error Compensation Method for GPS/Star Sensor

Fig. 2. Original trajectory.

7

Fig. 3. Filter trajectory.

Fig. 4. Output filter results. (a) Position error. (b) Attitude angle error. Table 4. Max and RMS of the estimated deviation. x/m

y/m

z/m

ϕ/°

θ/°

ψ/°

Max

1.3391

1.4567

1.6165

0.0589

0.0344

0.0521

RMS

0.6568

0.6320

0.6811

0.0521

0.0187

0.0356

4 Conclusions This paper proposes a GPS/star sensor integrated navigation data fusion and error compensation method (GAKF), in which GPS attitude estimation is used in Kalman filter. Aiming at the errors of GPS positioning and star sensor attitude determination, the errors in the two types of processes are comprehensively processed. First, the attitude of GPS is solved based on the position and velocity data of GPS and fused with the star sensor data. Then the system state equation is constructed based on the position, velocity and attitude data output by the GPS and the attitude data output by the star sensor, and the Kalman filter is used for data fusion and error compensation. Experiments show that the method has certain feasibility and superiority. It can avoid the cumulative error of the inertial navigation attitude measurement, and inhibit errors of GPS and star sensors,

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thus can achieve the requirements for correcting position and attitude errors, improve long-distance or long-term target navigation and tracking capabilities and improve the monitoring and service capabilities of GPS and star sensor integrated navigation. Acknowledgements. This work was supported in part by the Open Research Fund of National Key Laboratory of Satellite Navigation System and Equipment Technology under grant NO. GEPNT-2017KF-08.

References 1. Li, J.: Research on the key technology of APS star sensor. Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences (2003) 2. Eling, C., Klingbeil, L., Kuhlmann, H.: Real-time single-frequency GPS/MEMS-IMU attitude determination of lightweight UAVs. Sensors 15(10), 26212–26235 (2015) 3. Falco, G., Pini, M., Marucco, G.: Loose and tight GNSS/INS integrations: comparison of performance assessed in real urban scenarios. Sensors 17(2), 255 (2017) 4. Fan, P., Li, W., Cui, X., Lu, M.: Precise and robust RTK-GNSS positioning in urban environments with dual-antenna configuration. Sensors 19(16), 3586 (2019) 5. Zhang, P.: Research on On-line Calibration and Integrated Navigation Technology of Marine Star Sensor/Inertial Navigation System. Harbin Engineering University (2016) 6. Cao, J.: Research on Adaptive Filtering Algorithm of SINS/GPS Integrated Navigation. East China Normal University (2018) 7. Xu, G., Wang, Z., Wang, Z.: GPS/INS position integrated navigation based on adaptive Kalman filter. Electron. Design Eng. 21(1), 100–103 (2017) 8. Zhang, Z., Qian, S., Zhang, L., Li, H.: Federated nonlinear filtering for attitude determination system with star sensor and GNSS sensor. J. Geodesy Geoinformation Sci. 40, 1001–1595 (2011) 9. Liu, Y., Chen, Y.: Star sensor measurement model and its application in satellite attitude determination system. J. Astronaut. 3(2), 162–167 (2003) 10. Wang, J.: Research on GPS Positioning and Attitude Determination System. Nanjing University of Science and Technology (2006) 11. Zhou, W.: Research on GPS-based Attitude Measurement System. Beijing Jiaotong University (2009) 12. Cohen, C.E.: Attitude Determination Using GPS. PhD Dissertation. Department of Aeronautics and Astronautics, Stanford University (1992) 13. Mao, X., Du, X., Fang, H.: Precise attitude determination strategy for spacecraft based on information fusion of attitude sensors: Gyros/GPS/Star-sensor. Int. J. Aeronaut. Space Sci. 14(1), 91–98 (2013)

Simulation Design of Fourth-Order Bandpass Filter Based on Substrate Integrated Waveguide Hai Wang1 , Peng Sun2 , Guiling Sun1,3 , Zhihong Wang1 , Ming He3 , Yi Zhang4 , and Ying Zhang1,5(B) 1 Teaching Center for Experimental Electronic Information, College of Electronic Information

and Optical Engineering, Nankai University, Tianjin 300350, China [email protected] 2 Tianjin Key Laboratory of Optical Thin Film, Tianjin Jinhang Technical Physics Institute, Tianjin 300308, China 3 Department of Electronic Information Science and Technology, College of Electronic Information and Optical Engineering, Nankai University, Tianjin 300350, China 4 Institute of Photoelectronic Thin Film Devices and Technology and Tianjin Key Laboratory of Thin Film Devices and Technology, Nankai University, Tianjin 300350, China 5 Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology, Tianjin 300071, China

Abstract. The transition structure between substrate integrated waveguide (SIW) and microstrip line is studied in this paper, as well as the effect of slotted length on external quality factor and resonant frequency. The width of the coupling window is adjusted by altering the arrangement spacing of metal through-hole arrays, thus the magnetic field coupling between two coplanar resonators is realized. Resorted to the electromagnetic simulation software, a fourth-order filter with transition structure is designed based on SIW. The center frequency is 10.03 GHz and relative bandwidth is approximately 5.05%. In addition, the return loss is better than −16 dB and the passband ripple is 0.1 dB. Keywords: Substrate integrated waveguide (SIW) · Microstrip line · Resonant frequency · Bandpass filter

1 Introduction Wireless communication technology has been extensively applied in military, aerospace, mobile communication, Internet of things and other fields over recent years [1–3]. With the continuous development of modern communication system, the communication equipment components possess the property of miniaturization, low cost and high reliability [4–6]. The application field will be further extended and the industry technology will develop in the direction of higher frequency band, higher transmission rate, wider coverage and stronger compatibility in the era of 5G [7–9]. As the burgeoning on-chip integration solution, substrate integrated waveguide (SIW), which consists of a dielectric filling layer and metallized vias connecting the © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 9–17, 2022. https://doi.org/10.1007/978-981-19-0390-8_2

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bottom and top, provides a high-quality platform for filter design and implementation [10, 11]. The device with SIW structure can be easily integrated with other planar circuits due to the numerical consistency of metallized via’s length and dielectric filling layer’s thickness [12, 13]. As the result, the size of system is shrunk. SIW resonator can meet the application requirements of high-performance microwave filters because of the benefits of simple fabrication, high Q value, high power capacity, low insertion loss and planar integration [14–17].

2 Transition Structure of SIW and Microstrip Line Substrate integrated waveguide should be interconnected with planar circuits for practical use, so it is necessary to design a suitable interconnection structure from SIW to planar circuit, i.e. transition structure [18, 19]. The electric field distribution of microstrip line and SIW is basically the same, shown in Fig. 1 and Fig. 2, which means the microstrip line is suitable for interconnection with SIW.

Fig. 1. Electric field distribution of Quasi-TEM mode in microstrip line

Fig. 2. Electric field distribution of TE10 mode in SIW

Coplanar transition structure is one of the transition structures between SIW and microstrip line, which mainly includes direct transition, gradual transition and slotted coupling. For transverse electromagnetic wave (TEM) propagating in transmission line consisting of two or more conductors, the definitions of voltage, current and characteristic impedance are unique. As a contrast, for the transverse electric wave (TE) and transverse magnetic wave (TM) supported by a waveguide consisting of only one conductor, the definitions vary considerably. There are different ways, therefore, to represent the characteristic impedance of substrate integrated waveguide. Direct transition is the simplest structure, but sometimes the designed SIW may not match the fifty-ohm microstrip line, which easily leads to a lot of reflection. In the gradual transition, the impedance matching effect is better and the bandwidth is wider. However, the circuit is difficult to be miniaturized because the length of the transition section is usually larger than a quarter of the wavelength. Slotted coupling can adjust the coupling strength conveniently.

Simulation Design of Fourth-Order Bandpass Filter

11

3 Research and Design of SIW Filter In general, resonant frequency and coupling intensity are respectively determined by the size and the coupling structure of cavity resonator. The resonant frequency of the substrate integrated waveguide, however, can also be changed by coupling structure, which is equivalent to adjusting the equivalent size of the cavity. Similarly, the coupling intensity is influenced by these two aspects. How to efficiently fix the proper size of the cavity and the coupling structure is the vital link in the design and optimize process. The dielectric substrate with relative permittivity of 17.8 and thickness of 0.508 mm is adopted in simulation structures in this chapter. 3.1 Input and Output Coupling Design The gradual transition structure can meet the matching requirements of SIW and microstrip line interconnection. The slotted coupling (i.e. the transition of coplanar waveguide) can be applied to adjust the coupling intensity conveniently. Therefore, the combination of gradual transition and slotted coupling is utilized as the feeding structure of SIW resonator. In order to consider the influence of slotted length (Lslot ) on external quality factor (Qe ), which reflects the coupling strength, the slotted coupling model with single cavity is designed as shown in Fig. 3. The relevant parameters are set out in Table 1. The simulation result of Fig. 4 indicates the external quality factor, obtained by reflection group delay, decreases while slotted length increases. When the slotted length is greater than 0.8mm, the value of resonant frequency f0 rises up significantly with the increase of the slotted length, shown in Fig. 5. It means that the longer the slot is, the smaller the equivalent size of the cavity is.

d Lt W50

s

Lslot W

Wt Wslot

L

Fig. 3. The slotted coupling model with single cavity

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H. Wang et al. Table 1. The parameters in Fig. 3 (Unit: mm)

W

L

W50

Wt

Lt

Wslot

d

s

5

5.05

0.24642

0.8

1.5

0.2

0.3

0.5

Fig. 4. Simulation result of Lslot and Qe

Fig. 5. Simulation result of Lslot and f0

3.2 Interstage Coupling Design It is difficult to realize the coupling between two coplanar resonators by electric field coupling. Instead, magnetic coupling is an excellent solution. The coupling window with a certain width is left on the equivalent metal wall composed of metal throughholes between the two cavities, which can realize the magnetic field coupling between stages. The width of coupling window (Wwin ), determined by the arrangement spacing of metal through-hole arrays, has a major influence on the coupling intensity. Two resonant frequencies, f1 and f2 , can be obtained in weak coupling condition which is a no-slotted model. The value of coupling coefficient M12 can be determined by formula 1. The coupling model with second-order resonant cavity shown in Fig. 6 is established. The parameters are the same as the previous model but none of slot. The simulation results in Fig. 7 shows that the variation tendency of coupling coefficient and the width of coupling window is consistent. According to Fig. 8, the lower resonant

Fig. 6. The coupling model with second-order resonant cavity

Simulation Design of Fourth-Order Bandpass Filter

13

frequency shifts to the left with the increase of the width of coupling window, while the higher resonant frequency remains nearly constant. M12 = ±

f21 − f22 f21 + f22

(1)

Fig. 7. Simulation result of Wwin and M12

Wwin=2.2mm

Wwin=2.0mm

Wwin=1.8mm

Fig. 8. The influence of the width of coupling window on two resonance frequencies

3.3 Fourth-Order Filter Design On the basis of previous analysis, a fourth-order filter, which is symmetrical in structure, is shown in Fig. 9. The initial design of filter is not connected to 50  microstrip line.

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The four cavities, all equal in length and width, are coupled with each other through an inductive coupling window on the cavity wall composed of metal through holes. The relevant parameters and simulation results are illustrated in Table 2 and Fig. 10, respectively. The center frequency of this fourth-order filter is 9.94 GHz and the relative bandwidth is approximately 6.2%. Besides, the return loss is better than −20 dB and the passband ripple is 0.05 dB.

d

d1

s

Wslot Wt

Wwin1

Wwin2

Wwin1

W

Lslot L

Fig. 9. Initial model of the fourth-order filter

Table 2. The parameters in Fig. 9 (Unit: mm) W

L

Wt

Wslot

Lslot

Wwin1

Wwin2

d

s

d1

5

5

0.8

0.2

1.2

2.114

1.94

0.3

0.5

0.26

S21 S11

Fig. 10. Simulation result of the fourth-order filter

In practice, the transition structure is an indispensable part when the four-cavitystructure is connected with 50  microstrip line, shown in Fig. 11. The coupling parameters, meanwhile, should be adjusted to the appropriate value mentioned in Table 3. Reduce the input/output slotted length and the width of coupling window while the size

Simulation Design of Fourth-Order Bandpass Filter

15

of four cavities remain constant. It can be seen from the simulation result in Fig. 12 that the center frequency is 10.03 GHz and the relative bandwidth is approximately 5.05%. Furthermore, the return loss is better than −16 dB and the passband ripple is 0.1 dB.

W50

s

d1

Wwin1

Wwin2

d

Lt Wslot

Wt

W

Wwin1

Lslot

L

Fig. 11. The fourth-order filter model with transition structure

Table 3. The parameters in Fig. 11 (Unit: mm) W

L

W50

Lt

Wt

Wslot

Lslot

Wwin1

Wwin2

d

s

d1

5

5

0.24642

1.5

0.8

0.2

1.1

1.94

1.785

0.3

0.5

0.26

S21 S11

Fig. 12. Simulation result of the fourth-order filter model with transition structure

4 Conclusion The theoretical basis and design method of microwave filters based on the substrate integrated waveguide are analyzed in this paper. A fourth-order bandpass filter with transition structure and excellent characteristics is ultimately designed with the help of electromagnetic simulation software. The selectivity of the designed filter can be improved by introducing cross coupling and defected ground structure. In addition,

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this research can be continued to explore the miniaturization of the filter, including the selection of substrate materials, the use of nonholonomic mode transmission and multilayer structure. Acknowledgements. This work was supported by the 2021 Self-made Experimental Teaching Instrument and Equipment Project Fund of Nankai University (21NKZZYQ01); the 1st batch of industry university cooperative education projects in 2021 (202101186002 and 202101186014); the 2nd batch of industry university cooperative education projects in 2021 (202102296002); Tianjin Science and Technology Project (20YDTPJC00760); the 2020 Rolling Cultivation Project Fund of Self-made Experimental Teaching Instrument and Equipment of Nankai University; the 2020 Undergraduate Education Reform Project Fund (NKJG2020326); Transformation and Extension of Agricultural Scientific and Technological Achievements in Tianjin (201901090) and Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology.

References 1. Mekonnen, A.K., Tangdiongga, E., Koonen, A.M.J.: High-capacity dynamic indoor alloptical-wireless communication system backed up with millimeter-wave radio techniques. J. Lightwave Technol. 36(19), 4460–4467 (2018) 2. Bailey, W.H., Cotts, B.R.T., Dopart, P.J.: Wireless 5G radiofrequency technology – an overview of small cell exposures, standards and science. IEEE Access 8, 140792–140797 (2020) 3. Ogbebor, J.O., Imoize, A.L., Atayero, A.A.: Energy efficient design techniques in nextgeneration wireless communication networks: emerging trends and future directions. Wirel. Commun. Mob. Comput. 14, 1 (2020) 4. Bartolomeu, P., et al.: Supporting deterministic wireless communications in industrial IoT. IEEE Trans. Industr. Inf. 14(9), 4045–4054 (2018) 5. Zhao, F., Wei, L.N., Chen, H.B.: Optimal time allocation for wireless information and power transfer in wireless powered communication systems. IEEE Trans. Veh. Technol. 65(3), 1830– 1835 (2016) 6. Wang, H., et al.: Optimal design of an S-band low noise amplifier. In: 2019 Communications, Signal Processing, and Systems, vol. 571, pp. 439-447 (2020) 7. Myint, S.H., Yu, K.P., Sato, T.: Modeling and analysis of error process in 5G wireless communication using two-state markov chain. IEEE Access 7, 26391–26401 (2019) 8. Jeon, J., et al.: Design considerations for terahertz wireless communication systems. In: 2020 IEEE Wireless Communications and Networking Conference Workshops (WCNCW) (2020) 9. Wang, H.C., et al.: Localized mobility management for 5G ultra dense network. IEEE Trans. Veh. Technol. 66(9), 8535–8552 (2017) 10. Bensalem, B., Aberle, J.T.: Comparison of wideband interconnecting technologies for multiGHz novel memory architecture. IEEE Trans. Components Pkg. Manuf. Technol. 9(4), 719– 727 (2018) 11. Sirci, S., et al.: Advanced filtering solutions in coaxial SIW technology based on singlets, cascaded singlets and doublets. IEEE Access 7, 29901–29915 (2019) 12. Dhwaj, K., et al.: Low-profile diplexing filter/antenna based on common radiating cavity with quasi-elliptic response. IEEE Antennas Wirel. Propag. Lett. 17(10), 1783–1787 (2018) 13. Hussein, O.I., et al.: Substrate integrated waveguide bandpass filtering with fourier-varying via-hole walling. IEEE Access 8, 139706–139714 (2020)

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14. Esmaeili, M., Bornemann, J.: Novel tunable bandstop resonators in SIW technology and their application to a dual-bandstop filter with one tunable stopband. IEEE Microwave Wirel. Compon. Lett. 27(1), 40–42 (2017) 15. Sun, L., et al.: Compact-balanced BPF and filtering crossover with intrinsic common-mode suppression using single-layered SIW cavity. IEEE Microwave Wirel. Compon. Lett. 30(2), 144–147 (2020) 16. Holloway, J.W., et al.: 220-to-330-GHz manifold triplexer with wide stopband utilizing ridged substrate integrated waveguides. IEEE Trans. Microwave Theory Tech. 68(8), 3428–3438 (2020) 17. Azad, A.R., Mohan, A.: Substrate integrated waveguide dual-band and wide-stopband bandpass filters. Microwave Wirel. Compon. Lett. IEEE 28(8), 660–662 (2018) 18. Khan, A.A., Mandal, M.K.: A compact broadband direct coaxial line to SIW transition. IEEE Microwave Wirel. Compon. Lett. 26(11), 894–896 (2016) 19. Hassan, E., et al.: Multilayer topology optimization of wideband SIW-to-waveguide transitions. IEEE Trans. Microwave Theory Tech. 68(4), 1326–1339 (2020)

Algorithm for Placement of Wireless Network Devices for Wide Areas with Variable Soil Moisture Alexander Denisov

and Ekaterina Cherskikh(B)

Laboratory of Autonomous Robotic Systems, St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS), St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences, 39, 14th Line, 199178 St. Petersburg, Russia [email protected]

Abstract. When configuring a wide-area wireless network and connecting distributed autonomous devices to it, it is necessary to consider several factors that impair the quality of information interaction, such as radio signal attenuation, interference, transfer rate and others, which can cause emergence of areas without communication. Increase in number of heterogeneous devices with different properties and end use, interacting over a wireless network, causes the problem of providing a speed-satisfactory method for configuring a wireless communication network among heterogeneous distributed devices. To solve this problem, algorithms for repeater positioning have been developed, which provide layout of a wireless data transmission network, allowing to increase the speed of reconfiguration with a minimum number of active repeaters in a given service area with the presence of such obstacles as areas with high soil moisture. The proposed algorithm includes two ways to solve the problem of providing communication between two devices based on the points of greatest data transmission loss and bypassing areas with high soil moisture. Keywords: Wireless network · Reconfiguration · Radio Communication

1 Introduction Active digitalization and automation is an important process that is relevant today. It ensures the performance optimization of the systems in the agricultural sector. For the comprehensive and correct operation of such systems, the devices must be networked and have communication channels between each other [1, 2]. In urban environments, to ensure communication between digital and automated devices, they rely on telecom operators that provide their radio towers. But the provision of communication in extended agricultural areas is not profitable for telecom operators due to the small number of users and remoteness from cities, where the number of network subscribers is much higher. Thus, the task of ensuring radio communication of stationary and mobile devices in vast agricultural areas becomes relevant. This task is not trivial and assumes many conditions to be met that complicate the development of a viable solution method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 18–25, 2022. https://doi.org/10.1007/978-981-19-0390-8_3

Algorithm for Placement of Wireless Network Devices for Wide Area

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The principal prerequisites are the low configuration and maintenance-related costs for wireless network operation to ensure data transmission, communication with randomly moving mobile objects and communication over extended territories. The main solutions proposed for this task are based on the analysis of the terrain, the location of buildingsbarriers and landscaping of the territory. But the influence on the propagation of the radio signal is exerted, among other things, by the properties of the investigated territory, namely, soil moisture, due to which the loss of data transmission increases when radio waves are reflected [3–7]. Basically, to solve the problem of communication establishment over large areas, geometric methods are used, which consist in connecting to the gateway (the central part of the network) one device of the agricultural system, depending on the location and distance from the gateway using repeaters, the distance between which is calculated based on the required speed of data transmission [8–11]. In particular, they consider methods for repeater positioning to ensure P2P communication in areas with difficult terrain [12–15] using the general equation for calculating the radius of the Fresnel zone at any point between end nodes. Also, model-based methods are used, which take into account the total losses for signal propagation in conditions of difficult terrain, wooded areas and uneven buildings, the number of segments into which the propagation path is divided depending on the type of terrain segment (forest, urban development, irregular terrain, distance from the transmitting tower, propagation losses in free space, additional propagation losses within a segment, defined as the sum of losses in the environment (forest, urban development)) and losses due to uneven terrain [16, 17]. Networks, being established using these repeater location search methods are not always sufficient to maintain the required data transfer rate between devices. The reason for this is the variability of soil moisture in the study area and its influence on the propagation of the radio signal. Therefore, an algorithm for calculating the coordinates of the location of repeaters for connecting stationary and mobile devices to a common network in areas with variable soil moisture is presented.

2 Algorithm for Determining the Coordinates of the Repeater Locations The terrain features greatly influence the soil quality, including its moisture level, as it is one of the factors of soil formation [18]. The influence of terrain on soil moisture is inferred by the following parameters: steepness, exposure, horizontal curvature [19]. The effect of steepness can be described as follows: the steeper the slope, the less precipitation falls per unit area of the slope and the larger the evaporation area is, the more precipitation flows down. Exposure affects soil moisture by determining the magnitude of insolation and evapotranspiration. With negative values of horizontal curvature, soil moisture increases, with positive values, it decreases. The physical mechanisms of the distribution and redistribution of moisture in the landscape are influenced by the steepness of the slope, the exposure of the slope, horizontal curvature, vertical curvature, average curvature, drainage area. The dependence of soil moisture on height in flat areas actually reflects the influence of the catchment area on the spatial distribution of moisture in the soil. Although the height itself does

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not affect any physical mechanism of lateral movement of moisture in the soil under the influence of gravity, it is implicitly considered when calculating the catchment area [19]. Local morphometric characteristics are quantities that describe the local geometry of a surface, the determination (calculation) of which is possible when analyzing a small neighborhood of a given point of the surface [19]. Local morphometric characteristics include slope steepness (G), slope exposure (A), albedo (R), twelve parameters included in the complete curvature system: horizontal curvature (k h ), vertical curvature (k v ), minimum curvature (k min ), maximum curvature (k max ), mean curvature (H), Gaussian curvature (K), difference curvature (E), accumulation curvature (K a ), non-sphericity (M), annular curvature (K r ), excess vertical curvature (k ve ), excess horizontal curvature (k he ) and some others. The main characteristics for calculating soil moisture are G, A, R and H. For a surface area that is uniquely described by a smooth function z = f (x, y), where z is the height, x and y are Cartesian coordinates, we introduce the following auxiliary variables, which are functions of the partial derivatives of the relief height (formula 1). r=

∂z ∂z ∂ 2z ∂ 2z ,p = ,q = ,s = 2 ∂x ∂x∂y ∂x ∂y

(1)

The formulas for calculating the main local morphometric characteristics are presented below (2–5):  G = arctg p2 + q2 , (2)   q A = arctg , p 1 − p cos θ/ tan ψ − q sin θ/ tan ψ  , 1 + p2 + q2 1 + (cos θ/ tan ψ)2 + (sin θ/ tan ψ)2     1 + q2 r − 2pqs + 1 + p2 t 1 1  H = (kmin + kmax ) = (kh + kv ) = − , 3 2 2 2 1 + p2 + q2 R= 

(3) (4)

(5)

Thus, for the selected area, you can make a soil moisture map (table of coefficients). Based on the obtained table, it is possible to calculate the transmission loss (attenuation) of the radio signal (Eq. 6) [20]. L = h(λ + 5 lg(f ) + 5 lg(r))

(6)

where h is the value from the coefficient table, λ is the wavelength, f is the frequency, r is the distance from the transmitter to the receiver. Based on these equations, an algorithm was developed for calculating the coordinates of repeater locations (Fig. 1).

Algorithm for Placement of Wireless Network Devices for Wide Area

21

Begin Devices coordinates, robot movement trajectory, baud rate, soil moisture table Calculating distance sufficient to ensure required baud rate Adding the coordinates to the array of stationary device coordinates

Drawing up a table of coefficients (soil moisture table)

Finding the closest to gateway stationary device without a direct network connection (on a plane)

Splitting the robot trajectory into segments of size obtained at step 2 Calculating coordinates of the end points of trajectory segments

Yes

Finding the minimum distance to devices connected to the network

Is there a high value in table of coefficients between repeaters locationt?

No

Calculating coordinates of repeater at “highest value point” (in table of coefficients) between devices

Is there a high value in table of coefficients between repeaters locationt?

Yes

No Calculating the shortest path between devices by Dijkstra's algorithm

Calculating the shortest path between devices by Dijkstra's algorithm

Calculating coordinates of repeaters based on the table (H)

Calculating coordinates of repeaters based on the table (D)

Calculating coordinates of repeaters for connecting devices geometrically (G)

Choosing option (G, D, H) with least number of repeaters Are all devises connected to network?

No

Yes End

Fig. 1. Algorithm for determining the coordinates of repeater location for areas with variable soil moisture.

This algorithm calculates the connection of devices to the network by using repeaters in three possible ways: 1. Geometric - based on direct connection of devices. 2. Based on the points of greatest data transmission loss.

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3. Using the path of least resistance based on the coefficient tables and Dijkstra’s algorithm. It can be seen from the Fig. 1 that the first is the collection of data on soil moisture in the selected area and the compilation of tables of data transmission loss factors. Next comes the calculation of the location of the repeaters for mobile devices. At this stage, the algorithm is suitable only for mobile devices with known motion paths, which allows calculating the coordinates of the location of the repeaters in a geometric way. Next comes the calculation of the coordinates of the repeaters for connecting to a network of stationary devices. As described earlier, the calculation is performed in three different ways. The method of calculation based on the maximum value of soil moisture between the connected devices enables selection of the location points for the repeaters in places with the highest soil moisture to minimize data transmission losses through these areas (Fig. 2). This solution is more suitable for areas, where soil humidity is high, e.g., for swampy areas. The next method considers the approach of bypassing areas that negatively affect the propagation of a radio signal by a communicating agent using Dijkstra’s algorithm and coefficient tables. At the end we provide a comparison of repeater quantities required to connect the devices, and the option with the least number of devices involved is selected.

Fig. 2. Influence of an area with high soil moisture on the propagation of a radio signal.

Figure 2 shows the data transmission losses when the radio signal is reflected from the selected area of the territory. To connect devices 1 and 2, which are at a distance too large to maintain communication, into the network, it is proposed, according to the algorithm, to install an auxiliary repeater 3 at the point with the most significant data transmission losses. Thus, by adding one repeater, it is possible to ensure communication between devices and reduce radio transmission losses. Though during the operation of the algorithm, a solution with bypassing from the side of sections with high data transmission losses using Dijkstra’s algorithm is also considered. This version of the algorithm offers coordinates for the location of repeaters in areas with less loss of data transmission. The values of the coefficient table are used to build a graph that connects the two devices of the system with the minimum weight. The

Algorithm for Placement of Wireless Network Devices for Wide Area

23

weights of the graph correspond to the radio transmission losses, and the soil moisture values of the selected area affecting it. Further, depending on the selected radio modules, the coordinates of the location of the repeaters are calculated along the edges of the constructed graph. In the proposed solution to find a solution for providing communication between devices using the least number of repeaters, both methods are combined. Thus, the output is a combined solution (since the algorithm in Fig. 1 uses both methods to calculate the position of each of the repeaters and in the subsequent iteration uses the setting with the least number of repeaters), which contains both the coordinates of the repeaters to bypass the obstacle from above, and for bypassing from the outside, to reduce the required number of repeaters. For the experimental purposes, a test site was used, whose parameters of data transmission loss are shown in Fig. 3. Devices 1, 2 that need to be connected to the network are located on opposite sides of the selected site.

Fig. 3. The result of the algorithm for calculating the location of repeaters for connecting devices.

As can be seen from Fig. 3, the data transmission loss map indicates two possible ways to connect the devices 1 and 2. Device 3 facilitates a solution based on the points with the greatest data transmission loss. Devices 4 and 5 represent a solution by a bypass method. When using the first method, one repeater is required to connect the devices, and when bypassing from the side for this section, two repeaters are required. For a given operational area and the location of devices, the most suitable method for repeater positioning is based on the points of greatest data transmission loss.

3 Conclusion This paper considers the problem of configuring a wireless communication network among devices in vast agricultural areas. An algorithm for calculating the coordinates of the location of repeaters for connecting stationary and mobile devices to a common network in areas with high soil moisture is proposed. The proposed algorithm is combined

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for two ways to solve the problem of providing communication between two devices: based on the points of greatest data transmission loss and bypassing areas with high soil moisture. Further research will focus on improving the proposed algorithm for workflow automation and data exchange among robotic devices and distributed sensor networks. Ensuring of additional noise immunity of radio modules is also among the key objectives of the research, as well as efforts on elimination of collisions when retransmitting data packets.

References 1. Anikanov, G.A., Konovalchik, P.M., Morgunov, V.M., Ovcharov, V.A.: The multi-controlled media access to wireless data networks. SPIIRAS Proc. 1, 246–268 (2015). https://doi.org/ 10.15622/sp.38.14 2. Batenkov, K.A., Batenkov, A.A.: Analysis and synthesis of communication network structures according to the determined stability indicators. SPIIRAS Proc. 3, 128–159 (2018). https:// doi.org/10.15622/sp.58.6 3. Golubkov, G.V., Manzhelii, M.I., Berlin, A.A., Lushnikov, A.A., Eppelbaum, L.V.: Influence of the interaction of microwave radiation with the atmosphere on the passive location of the earth’s surface. Problems and solutions Chem. Phys. 37(6), 33–58 (2018). (In Russ) https:// doi.org/10.1134/S0207401X18070063 4. Liedmann, F., Holewa, C., Wietfeld, C.: The radio field as a sensok – a segmentation based soil moisture sensing approach. In: IEEE Sensors Applications Symposium (SAS), pp. 1–6. IEEE (2018). https://doi.org/10.1109/SAS.2018.8336755 5. Mironov, V.L., Mihaylov, M.I., Sorokin, A.V., Muzalevskiy, K.V., Fomin, S.V.: Method of measure of microwave radiation at the forest canopy using glonass- and gps-signals. Siberian J. Sci. Technol. 5(51), (2013). (In Russ) 6. Garrison, J.L., Nold, B., Lin, Y.C., Pignotti, G., Piepmeier, J.R., Vega, M., Fritts, M., DuToit, C. Knuble, J.: Recent results on soil moisture remote sensing using P-band signals of opportunity. In: 2017 International Conference on Electromagnetics in Advanced Applications (ICEAA), pp. 1604–1607. IEEE, (2017). https://doi.org/10.1109/ICEAA.2017.8065595 7. Salam, A.: An underground radio wave propagation prediction model for digital agriculture. Information 10(4), 144 (2019). https://doi.org/10.3390/info10040147 8. Vatamaniuk, I.V., Iakovlev, R.N.: Generalized theoretical models of cyberphysical systems. Proc. Southwest State Univ. 23(6), 161–175 (2019). (In Russ.). https://doi.org/10.21869/22231560-2019-23-6-161-175 9. Iakovlev, R., Saveliev, A.: Approach to implementation of local navigation of mobile robotic systems in agriculture with the aid of radio modules. Telfor Journal 12(2), 92–97 (2020). https://doi.org/10.5937/telfor2002092I 10. Denisov, A.V.: Development of a recommender system for parameter calculation in wireless network of sensor devices. Modeling Optim. Info. Technol. 7(4) (2019). (In Russ.) 11. Denisov, A., Sivchenko, O.: Modeling wireless information exchange between sensors and robotic devices. In: Ronzhin, A., Shishlakov, V. (eds.) Proceedings of 15th International Conference on Electromechanics and Robotics “Zavalishin’s Readings.” SIST, vol. 187, pp. 317–327. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-5580-0_26 12. Denisov, A., Iakovlev, R., Lebedev, I.: Mathematical and algorithmic model for local navigation of mobile platform and UAV using radio beacons. In: Ronzhin, A., Rigoll, G., Meshcheryakov, R. (eds.) ICR 2019. LNCS (LNAI), vol. 11659, pp. 53–62. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-26118-4_6

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13. Denisov, A., Shabanova, A., Sivchenko, O.: Data Exchange Method for Wireless UAV-Aided Communication in Sensor Systems and Robotic Devices. In: Ronzhin, A., Rigoll, G., Meshcheryakov, R. (eds.) ICR 2020. LNCS (LNAI), vol. 12336, pp. 45–54. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-60337-3_5 14. Ortega, A.J.: Finding repeater placement for P2P wireless links with NLOS in extremely mountainous regions. In: XXXVII Brazilian Symposium on Telecommunications and Signal Processing (2019) 15. Leushin, D.A.: Possibility of building a radio communication network in a high-mountainous area of localities. Innov. Sci. 5, 38–40 (2020). (In Russ.) 16. Malevich, E.S., Mikhailov, M.S., Volkova, A.A., Permyakov, V.A.: The research of radio wave propagation mechanisms in the forest vegetation with the complex landscape. In: Antennas Design and Measurement International Conference (ADMInC), pp. 87–89. IEEE, St. Petersburg, Russia (2019). https://doi.org/10.1109/ADMInC47948.2019.8969223 17. Zheng, X., Wang, H., Wen, H., Guo, J.: Propagation characteristics of electromagnetic waves in mine roadways. J. Appl. Geophys. 173, 103922 (2020) 18. Fridland, V.M.: Soil cover structure. Mysl, Moscow (1972) 19. Florinskii, I.V.: Theory and applications of mathematical and cartographic modeling of relief, Institute of Mathematical Problems of Biology, PhD thesis (2010) 20. Pishchin, O.N., Kalambatskaia, O.V.: Characteristics of uhf waves distribution in land and water surface tropospheric waveguide. Vestnik of astrakhan state technical university. Series: management, computer science and informatics. Rubrics: telecommunication systems and network technologies, 2019(4) (2019). https://doi.org/10.24143/2072-9502-2019-4-115-121

Optimization Design of a Discharge Control Circuit for Communication System Qian Bi(B) , Dongsheng Zhang, Yuanyuan Wang, Wenlong Wang, and Xiaojing Meng Northwest Institute of Mechanical and Electrical Engineering, Xianyang 712099, Shaanxi, China [email protected]

Abstract. The communication circuit system was affected by high temperature environment when the discharge circuit was working. The heating of the discharge resistor aggravates the temperature of the whole discharge circuit. This phenomenon will lead to the problem of the discharge function of the system. In order to solve the problem, a monitoring and optimization of a discharge control circuit based on communication system was designed. This paper analyzed the principle of the discharge circuit and proposed a method of increasing the conduction current to realize the optimal discharge control of discharge resistance. This method effectively avoided the trouble of discharge resistance with heating and smoking caused by temperature environment and improved the reliability and stability of communication circuit system. Keywords: Communication system · Discharge control circuit · Optimization design

1 Introduction Communication technology has been well developed with the rapid development of science and technology in modern society and plays an important role in people’s life. At the same time, this influence is expanding. As a new industry, it has great significance to our country with the development of communication engineering. In the current information age, people have got the demand of information through communication engineering, and communication engineering has brought convenience to people’s life, and people’s work efficiency is greatly improved. Communication circuit system is the core of communication technology reserch, it can realize the effective connection of communication information between user and terminal equipment [1, 2]. Discharge circuit is widely used in communication circuit system, and it was used to provide control power and dynamic power to the system, and complete the orderly power of the system and the pumping voltage discharge on DC generator. Discharge control circuit was the core component of the communication system. When communication circuit system worked at a high temperature, the temperature of communication circuit system rises sharply with the state of discharge. When it worked more than an hour, the temperature of the communication circuit system can reached 60 °C, and the system goes wrong with the failure of discharge function. This problem affects the stability of the system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 26–32, 2022. https://doi.org/10.1007/978-981-19-0390-8_4

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Therefore, how to solve this problem is a difficult thing faced by the stable operation of the system [3].

2 Schematic of Discharge Circuit The discharge function of communication circuit system consists of two parts. Discharge control circuit and discharge main circuit. The discharge control circuit mainly detects the bus voltage, and generate IGBT power tube signal of switch main circuit after setting the discharge point and stagnation point. The main discharge circuit includes IGBT power tube and discharge resistor, and it was mainly used to discharge the pumping energy of bus voltage. The schematic diagram is shown in Fig. 1.

Fig. 1. Schematic diagram of discharge circuit

From the Fig. 1, it was input to the relief control module U2 after the bus voltage (BUS + —BUS-) is divided. Compare with the discharge point and stagnation point, it output discharge control signal. When it is detected that the bus voltage is grater than the set discharge point voltage, from high level discharge signal of 1-pin of module U2 to front end of resistor R30, after optocoupler U5 isolation, then through the operational amplifier power amplification by U6, and output high level signal to turn on the power transistor of c2 → e2, and pumping energy on bus voltage is releasing through the circuit” BUS + → Discharge Resistor → BUS-”, the bus voltage decreases after energy release. When the bus voltage is detected to be less than the set stagnation point voltage, the resistance R30 front end signal becomes low level voltage, and the output signal of operational amplifier is also low level voltage, it can turn off the power tube IGBT [4]. The whole circuit loop is break off with “BUS + → Discharge Resistor → BUS-”, the discharge processing stops.

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3 Analysis of Discharge Problem The failure of discharge function occurs after the communication circuit system worked continuously for a long time. At this time, the temperature of the communication circuit system increases due to the heating of the discharge resistance during the discharge process. Consequently, under the condition that the discharge control circuit works normally, and discharge control module outputs the discharge open signal, heating model U2, optocoupler U5, operational amplifier U6 with heat gun and measuring the temperature [5]. When the temperature of the communication circuit system was rises to 61.5 °C, and from the oscilloscope, the output curve was different from that normal atmospheric temperature. Consequently, the relief control module U2 and operational amplifier U6 are no relationship with the problem of discharge function. Heating the optocoupler U5, and measuring the temperature of the optocoupler. It can seen that the discharge indicator light goes out gradually with heating the optocoupler to 61.5 °C, and stop heating, the indicator came on again. Under these circumstances, observed the output signal of the optocoupler with an oscilloscope. The waveform is shown in the Fig. 2.

Fig. 2. Output curve of optocoupler

Fig. 3. Output curve of operational amplifier

During the whole heating process of the optocoupler, the input signal is always at a high level voltage with 3.6 V, and the output signal increases gradually from low level 0.4 V to high level 2 V with the increase of temperature. Stop heating with optocoupler, and with the decrease of temperature, the output of optocoupler gradually changes from high level 2 V to low level 0.4 V with the increase of temperature. This moment, the optocoupler U5 outputs the signal to the next operational amplifier U6,and the U6 input and output voltage waveform with Fig.3. With the increasing temperature, the input voltage of operational amplifier U6 also changes. And the input voltage of U6 from low level 0.4 V changes to high level 2 V. The output voltage of operation changes from high level with 18 V to low level with 0 V after a high frequency oscillation transition period, then the temperature decreases, the output voltage of operation changes from low level with 0 V to high level with 18 V after a high frequency oscillation transition period, and the output signal of the operational amplifier is output to the grid of the power transistor. Consequently, the reason of abnormal operation of optocoupler under high temperature is the failure of IGBT and discharge resistance.

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More analytic discussion of the test and product data of optocoupler. (The model of optocoupler is 6N137). Replace the failed optocoupler with a new one [6]. With the discharge circuit working, heating the optocoupler to 60 °C, we find that the discharge circuit working well. But with the increasing temperature, there is still be anomalies. Therefore, it can draw a conclusion that optocoupler is unreliable at high temperature. According to the product manual of 6N137, the conduction current of the front-end diode of the optocoupler increases with the increase of temperature.

Fig. 4. Minimum on current of front end diode

Fig. 5. Output waveform of operational amplifier

For the discharge control circuit, the resistor of R15 and R30 is used to control the conduction current of the front-end diode of the optocoupler U5. According to resistance R15, R30, voltage of the front-end of the optocoupler, it can be calculated that conduction current value of the optocoupler U5 is 2 mA. According to the minimum on current of front end diode of Fig. 4. Below 60 degrees, when the conduction current is 2 mA, the optocouple in the circuit can be turned on. But when the temperature is higher than 60 °C, the minimum conduction current of the optocoupler is more than 2 mA. It makes the diode can not conduct reliably. At high temperature, when the optocoupler is unreliable, it can output a high frequency oscillation signal, as the curve2 of Fig. 3. The driving voltage of IGBT from high voltage level to low voltage level and from low voltage level to high voltage level, and the enlarged waveform is shown in Fig. 5. When the output signal frequency of the operational amplifier exceeds the maximum operating frequency of the power transistor, and it can damaged the G and E of IGBT [7, 8]. This damage can leads to a short circuit problem to burn out discharge resistance. By measuring the gate resistance of the damaged power transistor IGBT, the resistance value is less than 1  (The normal resistance is greater than 1 M). Consequently, the failure of the discharge resistor and IGBT is caused by the unreliable conduction of the optocoupler U6 at high temperature. And the unreliable conduction of the optocoupler U5 at high temperature is caused by the insufficient conduction current margin. Even if the current margin can ensure the normal operation of the normal operation of the

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Fig. 6. Welded position diagram of test wires

Fig. 7. Output curve of optocoupler

normal temperature discharge circuit, but it is easy to cause unreliable conduction of optocoupler due to insufficient conduction current at a high temperature. The type of IGBT in the main discharge circuit is CM100DY-12H, its specified current is 100 A, withstand voltage is 600 V. Since the discharge point of the discharge circuit is 70 V, and stagnation point is 62 V. By calculation, the discharge current of the main discharge circuit is 35 A. It can be seen from the value that withstand of IGBT which meets the specification well. Therefore, the main reason for the failure of the discharge function of the power supply box is that the resistance of the front end of the optocoupler is too large, and make the current margin of optocoupler is insufficient, this makes the temperature of the system increases. Form the product manual of 6N137, components cannot conduct reliably under high temperature, then damaged the gate and source stage of the power transistor IGBT, and it’s also causes the discharge resistance to burning-out.

4 Experimental Verification of Discharge Circuit Optimization 4.1 Conduction reliability Test of Optocoupler According to the above analysis on the failure of relief function, it can be seen that, if reduced the current limiting resistance of the front end of the optocoupler from 1 K to 0 , and the conduction current can be increased from 2 mA to 4 mA. At this time, the conduction current reaches the maximum threshold current. This method can solve the problem of unreliable conduction in high temperature. Next, the verification test of the optimized discharge circuit is carried out. As shown in the Fig. 6. The four test wires are respectively welded to the corresponding positions in the circuit, and extend the four test lines out of the test chamber. First, verify the box discharge function under normal temperature. Observe the waveform of oscilloscope. It can be seen that, the output waveform of oscilloscope is perfectly. Next, raise the chamber temperature to 60 °C, and then observe the waveform of oscilloscope. The waveform of oscilloscope discharge function is shown in the Fig. 7.

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It can be seen from the above test results that when the test chamber temperature is 60 °C, the output curve of U2 is consistent with the output curve of U5. Therefore, this test meets the discharge function requirements of communication circuit system. The optimized circuit meets the system performance requirements. 4.2 Continuous Working Test of Discharge Control Circuit Connect the components of the system. Power up the system, and after the system working continuously for one more an hour, and observe the phenomenon of the discharge indicator light at the power supply box. If the light continues to flash, it indicates that the discharge function of power supply box is normal. The test results are shown in Table 1. Table 1. Continuous working test of discharge control circuit Items Cable core voltage 1

No ***023

***024

***025

***026

***027

***028

56.35 V

55.67 V

55.83 V

56.26 V

56.38 V

55.72 V

Cable core voltage 2

55.91 V

55.58 V

56.35 V

56.42 V

56.58 V

56.15 V

Over voltage protection

Normal

Normal

Normal

Normal

Normal

Normal

Discharge function indicator

Normal

Normal

Normal

Normal

Normal

Normal

It can be seen from the above test data that the optimized discharge control circuit fully meets the performance index of communication circuit system. Furthermore, the discharge function of the communication circuit system completely normal.

5 Conclusion This paper analyzed the problem of discharge control circuit at a high temperature, and concluded that the unreliable optocoupler leads to the failure of the discharge function. Proposed an optimal design of discharge circuit, and verified the optimized circuit. The results show that the threshold current of the optocoupler can be increased by reducing the discharge resistance. This optimization method not only meets the functional requirements of the communication system, but also improves the reliability of the system.

References 1. Carr, J.: Microwave & Wireless Communications Technology, Newnes (1996) 2. Leven, A.: Telecommunication Circuits and Technology, Elsevier, 2000 3. Tuinenga, P.W.: SPICE: A Guide to Circuit Simulation and Analysis Using PSPICE. PrenticeHall, Englewood Cliffs, NJ (1988)

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4. The Simulation Standard Designing a High-Voltage IGBT Structure with TCAD April, May, June (2010) 5. Dubovenko, K.V., Kurashko, Yu. 1.: The design, fabrication and testing of a closing switch for compact electrical discharge industrial equipment, In: 11th IEEE Pulsed Power Conference, Baltimore, Maryland, pp. 868–874 (1997) 6. Kerkhoff, H. G.: Theory, design, and applications of digital charge-coupled devices PhD thesis, Twente University of Technology, Enschede, The Netherlands, Apr. (1984) 7. Hefner. Allen R.: Analytical modeling of device-circuit interactions for the power insulated gate bipolar transistor (IGBT). IEEE Trans. on Ind. Appl. 26(6), 995–1005 (1990) 8. Sheng, K., Williams, B.W., Finney, S.J.: A review of IGBT models. IEEE Trans. on Power Electronics 15(6), 1250–1266 (2000)

Online Multi-object Tracking Based on Deep Learning Zheming Sun1 , Chunjuan Bo2(B) , and Dong Wang1 1

2

School of Information and Communication Engineering, Dalian University of Technology, Dalian, China School of Information and Communication Engineering, Dalian Minzu University, Dalian, China [email protected]

Abstract. Multi-object tracking task aims to identify and track all targets in the video. It has important applications in intelligent monitoring and other fields. Two problems can affect the accuracy of the multi-object tracking task. First, occlusion between targets will lead to interruption of tracking trajectory and switch of tracking target. Second, quality of the object detection results will directly affect the tracking accuracy. In this paper, we adopt a single-object tracking algorithm based on deep learning is introduced to solve the first problem and develop a discriminant network scoring the accuracy of detection and prediction bounding boxes to solve the second problem. The experimental results show that the proposed tracker performs better than other competing methods. Keywords: Multi-object tracking network

1

· Deep learning · Discriminant

Introduction

Multi-object tracking aims to identify the target that appears in each frame of a video sequence and the same target in a continuous sequence of images. The tracking target can be pedestrians and cars. Multi-object tracking task for pedestrians is the main direction because of its practicality. Multi-object tracking task is the fundamental principle of action recognition, behavior analysis, and other fields and plays an important role in public safety and human-computer interaction. Multi-object tracking tasks can be divided into detection-based (DBT) [1,2] and detection-free (DFT) [3–5] tracking from the perspective of target initialization. DBT refers to the tracking based on the results of detection. First, we use the detector to determine the target in the video, followed by identity matching based on the detection results. The accuracy of the detector will remarkably affect the accuracy of multi-object tracking tasks in this mode. DFT refers to the manual labeling of the target in the first frame and tracking targets in subsequent frames. DBT is used more than DFT because the latter fails to solve the problem of a new target or disappearing target in subsequent frames. c The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022  Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 33–40, 2022. https://doi.org/10.1007/978-981-19-0390-8_5

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Fig. 1. Results of our algorithm on the MOT16 dataset.

The DeepSORT algorithm [13] is improved on the basis of the cost matrix in the Hungarian algorithm and introduces an additional cascade matching before iou matching and utilizes the appearance feature and Mahalanobis distance. Appearance features are extracted from the Re-ID network. The Mahalanobis distance is used as a constraint in the motion information because Euclidean distance ignores the calculation of the spatial domain distribution. DeepSORT first uses the Kalman Filter to predict the trajectory of the target and then uses the Hungarian algorithm to match predicted trajectories with detections in the current frame and finally updates trajectories with the Kalman Filter. Object detection algorithm based on the deep convolutional neural network (CNN) also appears after the deep CNN obtains excellent results on the classification problem. R-CNN series object detection algorithm [6–8] aims to generate and classify bounding boxes of targets. The algorithm classifies the background and foreground in the first stage by determining the anchor box of the target in the picture. The algorithm classifies targets of the rectangular anchor box in the second stage. Faster R-CNN [8] uses neural networks to generate bounding boxes of targets rather than generate candidate boxes with artificial rules to achieve end-to-end training and increase the algorithm speed. Appearance models, including both visual features of the target and measure of similarity between targets, such as corner points [9], color histograms [10], optical flow [11], gradient features [12], are often used in multi-object tracking tasks. Each feature has its advantages and disadvantages and is sometimes ineffective. The simple color histogram can easily calculate similarity. However, it only obtains statistics but loses pixel location information. Corner points are effective for transformation within the plane but ineffective when it comes to occlusion and out-of-plane. Gradient features positively affect light changes but fail to solve deformation and occlusion. A deep feature extraction network is added in this study to replace manually designed and general visual features. In this paper, an online multi-object tracking algorithm, including singleobject tracker, discriminant network, and deep feature extraction network, is designed. First, a single-object tracker is adopted to predict the target trajectory and deal with occlusion and background complexity. Second, a discriminant network is designed and trained to choose the more accurate candidate box generated by the detector and tracker.

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Fig. 2. Flow chart of our algorithm.

2

Proposed Method

First, the target position is determined with a detector. Second, we extract deep features of each target and use the tracker to predict the target trajectory. Finally, we use the discriminant network to score bounding boxes given by the tracker and detector and then match the candidate box with the target trajectory. The flow chart of our algorithm is shown in Fig. 2. The following sections are described separately. 2.1

Detection and Feature Extraction Based on Deep Learning

Multi-object tracking is based on visual detection. We start with detecting all targets that appear in each frame before subsequent processing. Faster R-CNN is used as the target detector in this study. Limitations of using only the motion information to track the target in the multi-object tracking task are as follows. The ineffective motion information processing for addressing variable motion, change of movement direction, and occlusion leads to low tracking accuracy. To this end, adding the appearance information of the target can improve the identification and matching of the target and this facilitate the tracking of the target. To be specific, we adopt ResNet-50 as the backbone to extract deep visual features. The last fully connected layer is removed from the ResNet-50 network structure to obtain a final output of a 2048-dimensional vector, which highly refines the appearance information for matching between pedestrian targets. We use the Market-1501 dataset to train the ResNet-50 network and obtain the deep feature extraction network. Market-1501 includes 1501 pedestrians taken by six cameras and 32668 pedestrian bounding boxes, which is very commonly used for person re-identification.

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Single-Object Tracker

The single-object tracking algorithm has progressed considerably in recent years. The single-object tracker has a remarkable effect in dealing with target deformation and occlusion. A single-object tracking algorithm is introduced in this study to solve the ID switch problem caused by the occlusion between targets in the multi-object tracking task and predict the pedestrian trajectory. Traditional tracking algorithms, such as Kalman filter and particle filter, can successfully solve the tracking problem in simple scenarios. However, such algorithms perform poorly in complex scenarios, such as multi-object tracking. A single-object tracking algorithm based on deep learning, such as SiamMask, can successfully track objects in complex scenes. The SiamMask algorithm [15] can perform both video target tracking and video target segmentation tasks simultaneously. The algorithm achieves target segmentation by adding a mask branch to the full convolutional Siamese neural network for target tracking, and enhances the loss to optimize the network. Once trained, the SiamMask algorithm can achieve real-time category-independent target tracking and segmentation. This model is simple, versatile, fast, and outperforms other tracking methods. The SiamMask algorithm is used to predict the position of the target in the new frame and obtain the trajectory prediction results of each target in the current frame. At the same time, the detection algorithm is used in this study to detect the target in the current frame to obtain the detected bounding box. We then utilize the matching algorithm between bounding boxes obtained by the tracker and the detector. The matching results are used for updating the target trajectory. 2.3

Discriminator

The detector can find the new target in the scene in a timely manner in the multi-object tracking task. However, the detection quality may be poor in case of cluttered background, occlusion, and interaction between targets. The singleobject tracking algorithm is suitable for these problems. Selecting the more accurate finding between the detection and tracking results will affect the accuracy of the task. To this end, a discriminant model is proposed to score the confidence of detection and tracking boxes. The result with a higher score is used to update the target trajectory. The structure of the discriminant network is shown in Fig. 3. The network consists of eight convolutional layers and two fully connected layers. The input size of the pedestrian picture is unified to 200 × 100. Convolutional layers aim to extract general visual features of pedestrians and are connected to fully connected layers to output the score. We collect the pedestrian bounding box as the training set and use the iou value between the bounding box and groundtruth as the score of the bounding box. The large iou value between the bounding box and groundtruth indicates a high score. The discriminator can score the input pedestrian bounding box after training. The bounding

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Fig. 3. Structure of the discriminant network that contains eight convolutional and two fully connected layers. The input size is 200 × 100 and the output is a score.

box is considered satisfactory if its score is high. Figure 4 shows the process of collecting the training set.

3 3.1

Experiment Dataset

MOT16, a dataset for pedestrian tracking that was proposed in 2016, is used to measure detection and tracking methods for multi-object tracking [14]. Evaluation indicators of the MOT16 dataset include MOTA, MOTP, ID switch (IDS), false positive (FP), and false negative (FN). MOTA evaluates all object matching errors during the tracking process, which contains false negative, false positive, and ID switch. This indicator measures the performance of matching targets and maintaining the trajectory during tracking. MOTA values are less than 1 but can become negative when the number of errors in the tracking is larger than the total number of groundtruths. MOTA can be expressed as follows:   (F N + F P + IDS) GT . (1) M OT A = 1 − MOTP is used to quantify the positioning accuracy of the detector and contains nearly zero unrelated information to the actual performance of the tracker.

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Fig. 4. Process of collecting training samples. The green bounding box is the groundtruth. The gray bounding box is generated by the detector and the tracker, with the iou of the two bounding boxes as the score. Table 1. Comparison of our tracker with other algorithms on the MOT16 dataset. Method

MOTA MOTP

EAMTT [16] 38.8

75.1

TBD [17]

33.7

76.5

JPDA [18]

26.2

76.3

Ours

44.4

76.8

The MOT16 dataset uses the overlap rate of the bounding box as the measurement.    M OT P = di,t ct , (2) i,t

t

where d is the average measurement distance between the detection target i and the groundtruth assigned to it and c is the number of successful matches in frame t. 3.2

Results

We test our algorithm on the MOT16 dataset. Figure 1 shows a portion of the results of our algorithm. Table 1 presents the comparison between our algorithm and other approaches. The satisfactory performance of the algorithm is indicated by the high values of performance indicators MOTA and MOTP. A comparison experiment (in Table 2) is conducted to explore the effects of using a single-object tracker to predict the target location and the accuracy of the discriminator. The first line in Table 2 is our baseline, which uses the faster R-CNN detector to detect the target. This baseline extracts deep appearance features of the detected target and uses the Kalman filter to predict the possible position of the

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Table 2. Ablation study of our tracker. Method

MOTA MOTP

Baseline

35.3

76.6

Baseline+SOT

44.0

77.0

Baseline+SOT+DIS 44.4

76.8

target in the next frame. We then match predicted and detected positions using the Hungarian algorithm and update the trajectory. The second line in Table 2 is used to replace the Kalman filter with the singleobject tracker, SiamMask, on the basis of the baseline algorithm. Using a deeplearning-based single-object tracker is suitable for predicting the target position because the Kalman filter performs poorly under complex motion conditions. We then match the tracking and detection results and update trajectories using the detection bounding box. The results improved significantly. The third line in Table 2 is used to add the single-object tracker SiamMask and the discriminator to the baseline. We obtain the target detection and target prediction results of the current frame. We then use the discriminator to score the two kinds of bounding boxes and the one with the higher score is used to update the target trajectory. The total accuracy of MOTA improves after adding the discriminator.

4

Conclusion

This paper presents a novel algorithm for multi-object tracking. A single-object tracker is added to solve the multi-object tracking problem, predict the target trajectory, and reduce the impact of target occlusion in tracking. In addition, a deep discriminant network is proposed to improve the accuracy by selecting the candidate box with higher confidence between tracking and detecting results to update the trajectory. The experimental results show that our tracker achieves performs better than other competing methods. Acknowledgement. This work was supported in part by the National Natural Science Foundation of China under Grant 61806037, in part by the Natural Science Foundation of Liaoning Province under Grant 2019-MS067, and in part by the Youth Technology Star Project of Dalian City under Grant 2018RQ57.

References 1. Bose, B., Wang, X., Grimson, E.: Multi-class object tracking algorithm that handles fragmentation and grouping. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007) 2. Song, B., Jeng, T.Y., Staudt, E., Roy-Chowdhury, A.K.: A stochastic graph evolution framework for robust multi-target tracking, European Conference on Computer Vision, pp. 605–619 (2010)

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3. Hu, W., Li, X., Luo, W., Zhang, X., Maybank, S., Zhang, Z.: Single and multiple object tracking using log-euclidean riemannian subspace and block-division appearance model. IEEE Trans. Pattern Anal. Mach. Intell. 34(12), 2420–2440 (2012) 4. Zhang, L., van der Maaten, L.: Structure preserving object tracking. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1838–1845 (2013) 5. Yang, M., Yu, T., Wu, Y.: Game-theoretic multiple target tracking. In: IEEE International Conference on Computer Vision, pp. 1–8 (2007) 6. Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014) 7. Girshick, R.: Fast R-CNN. In: IEEE International Conference on Computer Vision, pp. 1440–1448 (2015) 8. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015) 9. Brostow, G., Cipolla, R.: Unsupervised bayesian detection of independent motion in crowds. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 594–601 (2006) 10. Okuma, K., Taleghani, A., De Freitas, N., Little, J.J., Lowe, D.G.: A boosted particle filter: Multitarget detection and tracking. In: European Conference on Computer Vision, pp. 28–39 (2004) 11. Ali, S., Shah, M.: Floor fields for tracking in high density crowd scenes. In: European Conference on Computer Vision, pp. 1–14 (2008) 12. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 886–893 (2005) 13. Wojke, N., Bewley, A., Paulus, D.: Simple online and realtime tracking with a deep association metric. In: IEEE International Conference on Image Processing, pp. 3645–3649 (2017) 14. Milan, A., Leal-Taix´e, L., Reid, I., Roth, S., Schindler, K.: MOT16: a benchmark for multi-object tracking, arXiv preprint, arXiv:1603.00831 (2016) 15. Wang, Q., Zhang, L., Bertinetto, L., Hu, W., Torr, P.: Fast online object tracking and segmentation: a unifying approach. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1328–1338 (2019) 16. Sanchez-Matilla, R., Poiesi, F., Cavallaro, A.: Multi-target tracking with strong and weak detections. In: European Conference on Computer Vision Workshops, pp. 84–99 (2016) 17. Geiger, A., Lauer, M., Wojek, C., Stiller, C., Urtasun, R.: 3D traffic scene understanding from movable platforms. IEEE Trans. Pattern Anal. Mach. Intell. 36(5), 1012–1025 (2013) 18. Rezatofighi, H., Milan, A., Zhang, Z., Shi, Q., Dick, A., Reid, I.: Joint probabilistic data association revisited. In: IEEE International Conference on Computer Vision, pp. 3047–3055 (2015)

Power Grid Industrial Control System Traffic Classification Based on Two-Dimensional Convolutional Neural Network Gang Yue1(B) , Zhuo sun2 , Jianwei Tian2 , Hongyu Zhu2 , and Bo Zhang3 1

Beijing University of Posts and Telecommunications, 100876 Beijing, China [email protected] 2 State Grid Hunan Electric Power Company, 410007 Hunan, China [email protected], {tianjw,zhuhy}@sgcc.com.cn 3 Global Energy Internet Research Institute, 102209 Beijing, China [email protected]

Abstract. The power grid as the national manufacture steps toward combination with advanced information technology. The industrial control system of the power grid is exposed to the wide-opening internet with the industrial internet rapidly developing. Traffic classification is the significant step for security situation awareness platform which supervises the ICS operating status and suffers from the defect of low classification accuracy by conventional port-based or DPI methods. Therefore, the two-dimensional CNN model for the power grid industrial control traffic classification is proposed in this paper, which extracts the raw data’s features to train a two-dimensional CNN to fit the distribution of the features. The experiment’s result shows that this model can recognize and classify the ICS traffic accurately with an accuracy of 94%. Through the cross-validation, the result shows that this model also has outstanding generalization ability with an accuracy of 93%. Keywords: Industrial communication network · Power grid Industrial control system · CNN · Traffic classification

1

·

Introduction

Industrial control system (ICS) as the “neutral center” of industry controls the information exchanging. With the industrial internet developing, the physicallyisolated ICS in the past will be exposed to the wide-opening internet and vulnerable to be attacked. The fragile ICS which once is broken down will result in the terrible catastrophe to the nation [1]. The security situation awareness platform which supervises the industrial security level by analyzing the devices status, G. Yue—Supported by 2020 Industry internet innovation and development project Smart energy internet security situation awareness platform project. c The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022  Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 41–48, 2022. https://doi.org/10.1007/978-981-19-0390-8_6

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protocol interaction criterion and users’ behavior is proposed nowadays. The traffic classification of ICS is basic for the platform to aware of the tendency of the ICS and could provide the meta data to the awareness model. However, the conventional traffic classification methods is not adaptive to the Ethernet-based ICS network which adds some commercial network intrinsic features properly. Therefore, we propose the two-dimensional CNN to extract the features of data, mine the data interactive regulation and classify the traffic in the paper. The experiment shows that the two-dimensional CNN model can classify the power grid ICS traffic with the accuracy of 94% and has outstanding generalization capability. The rest of this paper is structured as follows. In Sect. 2, we briefly present the state-of-the-art model in network traffic classification. In Sect. 3, we introduce the power grid ICS network feature and the two-dimensional CNN structure. In Sect. 4, we utilize the data collected from an actual power grid ICS network to verify the model’s traffic classification accuracy and generalization capability. Finally, in Sect. 5, we present our conclusion and future work.

2

Relative Works

The traffic classification compares the meta data of packets with the traffic rules to recognize the traffic type [2]. The various ML-based methods are proposed in [3–5] to improve classification efficiency, such as SVM of the supervised learning model and k-means of the unsupervised learning model. But the methods above mentioned which are lack of analyzing and mining the relationship of data just rely on only a few data dimensions and have poor generalization ability. As the development of the data mining, some scholars use the deep learning and neutral network to extract the features of data and generalize the network traffic regular pattern more efficiently [6–9]. Although these methods start trying to analyze the network packet interaction features, they are still lack of mining deep relationship of interaction which results in the restricted range of application. The traffic classification through analyzing the message and protocol’s traits from L2 to L4 is as the base of network maintenance, network optimization and intrusion detection. The network packet which indicates the source, the destination, the content and the direction of transmitting is the binary sequence generated by the different components [10]. Compared with the traditional traffic classification, such as the method based on the destination port, deep packet inspection (DPI) and behavior features, the deep learning is rather overwhelming. At early stage of the Internet, the packet’s destination port is used to distinguish the different type traffic due to the small scale of Internet and one-to-one correspondence between traffic and ports [3]. As the Internet scale expanding, P2P protocol using dynamic port breaks the stable correspondence and the accuracy of classification plunges down apparently [4]. The DPI which matches payload features with the key strings of traffic is proposed [5]. But there exits the shortage in dealing with the private protocol and it might exhaust the computing resource. Thus, for improving the accuracy, the scholars utilize the packet

Two-Dimensional Convolutional Neural Network for Traffic Indentification

43

interactive behavior features to analyze the traffic and achieves the goals [6]. However, this method needs much more hardware resource to storage and cumbers the traffic process severely, as a result it could not be applied in the online and real-time situation. In recently, the deep learning which could automatically construct the connection between input and output is widely adopted to solve the classification problems and is proved to be more the efficient. The industrial internet connects the tradition ICS to the internet and orients the industry more automatic and intelligent to reduce costs and increase efficiency. It owns the features of both the ICS network and the commercial internet. As the ICS has the stable and unique topology, the total traffic types in ICS network are limited, periodical and predictable which is different from the commercial network [7]. In this paper, the two-dimensional CNN which is based on the industrial control system traffic rules is proposed to mine the relationship of interactive packets and could classify the power grid ICS traffic to support the management of the power grid.

3 3.1

Basic Theory The Features of Power Grid ICS

The industrial control system which utilizes the modbus or ethernet to control the concordant interactions of devices, sensors and actuator automatically is similar to the commercial internet. So the ICS network and commercial network have some common attributes. However compared with the traditional IT system, the ICS consists of specific hardware and software in different industry and has unique specificities [11]. As for the power grid system, the communication network leads the ICS more intelligent and supports to exchange more traffic’s data. Millions of terminals could be accessed to the network and the total data amounts boom up rapidly. Both the basic equipment’s data and the various type of data such as user data, AMI data, and managerment data are transmitted into the communication network which makes the power grid more complicated [12]. The power grid communication network is the core network for the ICS and consists of main-station, automatic terminals and the network equipments. It provides the channel for data exchanging and coordinates the power system to operate safely and effectively. The power distribution communication network as the node of the power grid backbone network has the features of complex and widely distributed equipment, high requirements of traffic continuity and real-time, and the private network transmission protocols. So traffic classification of communication network is benefit for manager to supervise and control the power grid. 3.2

The Traffic and Protocol of Power Grid ICS

The traffic of the power grid ICS network have their own inherent properties, which are that the upload traffic have obvious periodicity, the polling traffic have

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strong self-similarity, and fault-based protection traffic have the burst attributions. The ICS network has two main sectors which are divided into six main types of traffic. In each traffic, the remote terminal unit (RTU) interacts with the main-station through transmitting the sensor’s data, receiving the control signal and executing the command. The power grid protocol is constituted by the international Electrotechnical Commission (IEC) to regulate the processing of power generation, power switching and power transmission. The protocol as the basic standard guides the different components in the power grid how to share the information and interactive with each other. The power industry field consists of three main criterions: IEC60870, IEC61850 and IEC62351. The IEC60870 provides a framework to different factories and solves the equipments integration problems. The IEC61850 is widely used in smart grid and guides the equipment to operate automatically and standardly. For the lack of security systematic, IEC62351 is proposed to prevent the electrical system intrusion by communication protocol. The particular protocols MMS, GOOSE and SMV of IEC61850 used in the power grid system are transmitted over the TCP/IP transmitting protocol. As for the data collected from an actual power grid ICS network, it consists of limited IP address and limited traffic types as shows in Table 1. Table 1. The statistic of the ICS traffic data Source ip Source port Destination ip Destination port Periodic Equipment type 10.*.*.169 50456

10.*.*.14

8000

yes

PMU

10.*.*.14

10.*.*.169

8001

yes

to PMU RTU to NE

59809

10.*.*.162 2404

*.*.*.1

10.*.*.1

25360

yes

2404

*.*.*.1

35630

yes

59251

10.*.*.200

8800

yes

RTU to SM

0

NE to NE

*.*.*.4

0

yes

10.*.*.165 60123

10.*.*.254

123

no

10.*.*.1

10.*.*.34

0

yes

0

NE to NM

*.*.*.1 0 10.*.*.162/163 0 no NE to RTU Note: the internet IP is masked two sectors, the open IP is masked three sectors. PMU stands for power manager unit, RTU stands for remote terminal unit, stands for network equipment, SM stands for security management equipment, stands for network management equipment

3.3

The Structure of Two-Dimensional CNN

CNN has the capability of local receptive fields. The convolution kernel can learn the correlation from the input data, and the global sharing method can be used to reduce network learning parameters. So in this paper we construct a twodimensional CNN to learn the traffic integral features because of that the CNN could extract the data features by the convolution kernel of any dimension. The two-dimensional CNN has two inputs: one is common features for the 1-D CNN

Two-Dimensional Convolutional Neural Network for Traffic Indentification

45

and another is interactive features for the 2-D CNN. The two-dimensional CNN outputs are concatenated as input to the next deep neutral network (DNN) and the output of DNN is the traffic type. The structure of model is showed in Fig. 1.

Fig. 1. The Structure of two-dimensional CNN

The raw input features are generated by an open software CICFlowmeter which extracts raw packet features grouped by the source IP, destination IP, source port, destination port and protocol periodically and outputs 80 features in which the 77 features as the input to the CNN. The 77 features is divided in two parts: the common feature and the interactive feature. The common feature stands for the common statistic of data and consists of 33 features. Meanwhile, the interactive feature stands for the forward and backward statistic and has 22 features respectively as the Table 2 shows below: Table 2. The features of data extracted by the CICFlowmeter Common feature

Interactive feature Forward Backward

Total packet, packet size, Packet number, packet size, IAT, content flag, Subflow, window size arrival time, content flag packet header, packets/s average size, average length

The combination of common features and interactive features could describe the traffic interactive behavior completely. The forward features are corresponding to backward features, for instance the PSH packets count is equal to the ACK packets count. So the input of 2-D CNN is composed of corresponding features matrix of size (2*22) and in the matrix the same attribute feature values are clustered for the convolution kernel to extract relationship conveniently. Before training the two-dimensional CNN, we should transform the input features to unified format by steps below: Labelling the data: due to the power grid network has limited equipments between which the interactive traffic are limited, the traffic are labeled based on equipment type into 6 classes with one-hot coding shown in the Table 2.

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Cleaning the data: some features extracted by CICFlowmeter tool is calculated by the meta feature, so some of them may result in not a number or infinite number and the illegal feature interfere in training the model. Cleaning the data process replaces the illegal number with the feature mean value. Normalization: every feature has different scales and ranges widely which is disadvantageous to train the model, so normalization of different scale value could unify the metric using normalization Eq. (1) below: vN =

v − vmin vmax − vmin

(1)

vmin ,vmax ,v,vN represent the minimum, maximum, current value, normalization value of character respectively.

4

Experiment and Result

In this section, we will use the input features to train the two-dimensional CNN and verify the traffic classification accuracy. 4.1

Training Process and the Result

The software environment for the two-dimensional CNN relies on python and tensorflow. And the model is trained on the computer which has 8G RAM and Intel Core i5-9400F CPU with the windows system, The data are divided in 70% for training model, 20% for validating model and 10% for testing model. The parameters of the model are illustrated in Table 3 below: Table 3. The parameters of the two-dimensional CNN Model

Parameter

2-D CNN

Layer 1: kernel count: 32, kernel size(2,5), activate: relu Layer 2: kernel count: 32, kernel size: (1,2), activate: relu Maxpool-size: (1,2)

1-D CNN

Layer 1: kernel count: 32, kernel size(1,5), activate:relu Layer 2: kernel count: 32, kernel size: (1,3), activate: relu Maxpool-size: (1,2)

DNN

Layer 1: dense nodes: 256, activate: relu, dropout: 0.5 layer 2: dense nodes: 6, activate: softmax

Optimizer and loss optimizer: sgd, learn rate: 0.0001 loss: category crossentropy Others

batch size: 16, epoch: 20

The CICFlowmeter periodic time is 60s and the model accuracy of the training data and validating data is 94% and 93% as the Fig. 2(a) showed, so the model can learn and fit the distribution of the data.

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Fig. 2. (a) the accuracy and loss of training and validating process; (b) the confusion matrix of result with the 90s dataset

4.2

The Generalization Capability of Model

As for the generalization capability of model, the cross-validation method is adopted in this paper. We use the tool CICFlowmeter to generate the datasets with different periodic time and validate the classification accuracy of the trained model. The confusion matrix in Fig. 2(b) shows that the model could predict the 90s dataset with the accuracy of 93%. Moreover we use 30 s, 60 s, 90 s datasets to train the model respectively and validate the generalization capability by other sets. The results are listed in Table 4: Table 4. Cross-validate result of datasets with periodic of 30 s, 60 s, 90 s Training dataset Training accuracy Validation accuracy 30 s 60 s 90 s 30 s

98.1%

98.3% 85.9% 86.7%

60 s

94.9%

84.1% 96.3% 93.2%

90 s

93.1%

83.2% 95.2% 94.1%

In fact, owning to different type traffic amounts are unbalanced in the 30 s dataset, the model is overfitting to the dataset and the validation accuracy is just 85% by cross-validation. Whereas the model which is trained by 60 s or 90 s dataset could match the data’s distribution mutually, but the accuracy of the 30 s dataset is declined. As a result, the 60 s dataset is perfectly fit to the model and has the outstanding generalization capability.

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5

Conclusion

The two-dimensional CNN proposed to learn the relationship of the traffic in the paper could classify the traffic type, and the result shows that the model can fit the distribution of traffic interactive features accurately and be deployed in other node of the power grid ICS without re-training the model. The traffic classification as the most important component in security situation awareness platform could improve the robustness of the platform. In the future, our next goal is to predict the traffic features relying on the classification result joint with the timestamps.

References 1. Lu, G., Feng, D.: Industrial control system network security situation awareness modeling and algorithm implementation. Control Theory Appl. 33(8), 1054–1060 (2016). https://doi.org/10.7641/CTA.2016.50767 2. Ratner, A.S., Kelly, P.: Anomalies in network traffic. In: IEEE International Conference on Intelligence & Security Informatics. IEEE (2013) 3. Camacho, J., Macia-Fe Rnandez, G., Diaz-Verdejo, J., et al.: Tackling the Big Data 4 vs for anomaly detection. In: IEEE INFOCOM 2014 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). IEEE (2014) 4. Xu, W., et al.: Detecting large-scale system problems by mining console logs. In: Proceedings of the 27th International Conference on Machine Learning (ICML 2010), 21–24 June, 2010, Haifa, Israel (2010) 5. Therdphapiyanak, J., Piromsopa, K.: Applying Hadoop for log analysis toward distributed IDS. ACM, pp. 1–6 (2013) 6. Gupta, G.P., Kulariya, M.: A framework for fast and efficient cyber security network intrusion detection using apache spark. Procedia Comput. Sci. 93, 824–831 (2016) 7. Pajouh, H.H., Dastghaibyfard, G.H., Hashemi, S.: Two-tier network anomaly detection model: a machine learning approach. J. Intell. Inf. Syst. 48(1), 1–14 (2015) 8. Deshmukh, D.H., Ghorpade, T., Padiya, P.: Intrusion detection system by improved preprocessing methods and Na¨ıve Bayes classifier using NSL-KDD 99 Dataset. IEEE (2014) 9. Gao, N., Gao, L., Yi-Yue, H.E., et al.: A Lightweight Intrusion Detection Model Based on Autoencoder Network with Feature Reduction. Acta Electronica Sinica (2017) 10. Lin, L., Shang, W., Yao, J., et al.: Overview of one-class support vector machine in intrusion detection of industrial control system. Application Research of Computers (2016) 11. Ding-Hua, Z.: Dataflow feature analysis for industrial networks communication security. J. Northwestern Polytechnical Univ. 38(1), 199–208 (2020). https://doi. org/10.3969/j.issn.1000-2758.2020.01.025 12. Tang, Z.G., Huan-Zhou, L.I., Zhang, J.: Heuristic anomaly detection model of industrial control system based on combined neural network. J. Sichuan Univ. (Natural Science Edition) (2017)

Influence of Structure Information for Cross-domain Person Re-identification Nuoran Wang1,2 , Cuiping Zhang1,2(B) , Shijie Lv1,2 , Yiming Luo1,2 , Xin Ding1,2 , Shuang Liu1,2 , and Zhong Zhang1,2(B) 1

2

Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, Tianjin Normal University, Tianjin, China [email protected], [email protected] College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

Abstract. Cross-domain person re-identification (re-ID) is a challenging task in computer vision research. We intent to learn a discriminative model when given labeled source and unlabeled target datasets, in order to obtain good results on the target test datasets. In this paper, we add structure information for pedestrian images. To this end, we evaluate the influence of structure information for cross-domain re-ID in the deep networks and a series of experiments with different settings on Market1501 and DukeMTMC-reID are conducted. The influence of structure information on the effectiveness in the optimized deep models is shown in the experimental results.

Keywords: Cross-domain person re-identification information · Convolutional neural network

1

· Structure

Introduction

Person re-identification (re-ID) [1,2] aims to find the same pedestrian under different cameras, and it is widely applied in multi-camera body analysis [3], crossview action recognition [4,5], multi-object tracking [6] and many other fields. Previously, some work that used the same dataset for supervised learning has achieved impressive achievement [7] with convolutional neural network (CNN). But more re-ID tasks occur in unlabeled datasets. Therefore, researcheres are paying more attention to cross-domain re-ID, that is, given labeled source datasets and unlabeled target datasets, we aim to learn a discriminative model to obtain better results on the target test datasets. Many methods in the traditional cross-domain re-ID suppose that The target datasets have the same identity as the source datasets [8–10]. In fact, crossdomain re-ID is an open set problem [11], so these methods cannot be applied. Therefore, a more optimized method is needed to solve this problem. Some work concerns with source-target translation models [12,13] or using property and c The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022  Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 49–55, 2022. https://doi.org/10.1007/978-981-19-0390-8_7

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identity labels to learn a transferable model [14]. Considering domain connectedness and camera invariance, HHL is proposed [15]. This work utilizes StarGAN [16] network to transfer image styles in order to achieve camera invariance. Then, some researcheres make improvements on the basis of HHL and propose more complete methods. For example, an exemplar memory [17] is proposed to temporarily store the features to make the network more robust, and further propose exemplar invariance and neighborhood invariance. Ge et al. [18] also propose a network which can generate pseudo labels online for supervised learning to make the network more discriminative, and these methods have obtained promising results. In this work, we study influence of structure information for cross-domain Re-ID, and our work is summarized in two aspects: • We adopt the framework proposed by HHL as the baseline. The network has two properties. The one is camera invariance, and the other is domain connectedness. • We divide the features output by the baseline into N blocks to learn part-level features, thereby adding structure information for pedestrian image. The rest of this paper is arranged as follows. Our proposed network and loss functions are introduced in Sect. 2. The experimental results and settings are detailed in Sect. 3. We make a summary in Sect. 4.

2

Proposed Method

Preliminary. Under the condition of cross-domain re-ID, we are provided with a labeled source datasets {Xs , Ys }, where each image xs has a corresponding identity ys . There are a total of Ns pedestrian images and Ms identity labels in the source domain, so ys ∈ {1, 2, ..., Ms }. In addition, unlabeled target datasets {Xt } are provided, there are a total of Nt image. But it is unavailable to use the label of the target datasets. We aim to learn a discriminative model for target test set using the labeled source datasets and unlabeled target datasets. Before training, we employ StarGAN to perform image style transformation for images in the target datasets. Specifically, if there are a total of C cameras in the target datasets, and for each image xt , we employ the learned model to generate C fake images xt∗ ,1 , xt∗ ,2 ,..., xt∗ ,C remaining the original identity. 2.1

Framework

The structure of our proposed network is shown in Fig. 1. The network has two branches. One branch is the traditional image classification task, which utilizes cross-entropy loss, and only source domain images are used. The other branch utilizes source and target domain images and the triplet loss is applied. We employ Resnet50 [19] as the backbone, where the layers before the GAP layer are retained, and remove the GAP layer and subsequent layers. As the input

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51

Fig. 1. The structure of the proposed network.

image passes through the backbone, a three-dimensional tensor T is obtained. Then by setting the range of pooling layer, N vectors g i (i = 1, 2, ..., N ) are formed. Afterwards, each vector g i (i = 1, 2, ..., N ) passes through FC-1024 respectively, and then for the branch trained by the source dataset, FC-#ID is applied. Finally, the cross-entropy loss is followed to each local feature. For the branch optimized by the source and target datasets, it is composed by “FC-128” and the triplet loss. During training, N cross-entropy losses and N triplet losses are summed to form the final loss. During testing, the final descriptor G is formed by concatenating N vectors g i (i = 1, 2, ..., N ). 2.2

Loss Functions

In the proposed network, there are two kinds of loss functions. The cross-entropy loss is defined as, ns  N 1  (1) Lcross = − log pi,n (y) ns i=1 n=1 where ns represents the number of input source images, and pi,n (y) represents the probability that nth (n = 1, 2, ..., N ) block of pedestrian image belongs to the correct identity y. For the improved triplet loss, it is formulated as, LCD =

N  n=1

n∗

t s t LT ({xis,n }ni=1 ∪ {xit,n }ni=1 ∪ {xit∗ ,n }i=1 )

(2)

where LT represents the traditional triplet loss, ns , nt , nt∗ indicate the labeled source images, the unlabeled target real images, and the fake images corresponding to the target real images, respectively. Camera invariance and domain connectedness can be achieved by optimizing this loss function. For camera invariance, we treat xt,j and its corresponding fake image as the same class (where

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Fig. 2. Some pedestrian images sampled from Market-1501.

j is the number of cameras in the target domain, j ∈ {1, 2, ..., C}), and images from the target domain are treated as different classes from xt,j . For domain connectedness, an anchor image is given by the source, and it forms a positive pair with an image that have the same identity as the anchor in the source, while images from the target are negative samples of the anchor. Finally, the overall loss is formulated as, L = Lcross + βLCD

(3)

where β is the weight parameter.

3 3.1

Experiments Datasets

We evaluate the proposed network on Market-1501 [20] and DukeMTMCreID [21]. The Market-1501 database includes 6 cameras with a total of 1501 identities and 32,668 images. The training set has 751 identities and contains 12,936 images. The test set has 750 identities, containing 19,732 images. Figure 2 shows some pedestrian images sampled from Market-1501. The DukeMTMCreID database includes 8 cameras with 1,404 identities and 36,411 images. There are 16522 images in the training set, including 702 identities. The test set has 702 identities, containing 19,732 images. 3.2

Experiment Settings

To train our proposed network, We adopt the training strategy in [15], where images are resized to 256 × 128. The learning rate is initialized to 0.1 in the first 40 epochs, and set to 0.01 in the following 20 epochs. The dropout probability is set to 0.5. We set the batchsize of source images for the cross-entropy loss to 128 and 64 for the triplet loss, and the SGD optimizer is utilized in the training stage. As for some important parameters, we set the batchsize of real target images to 16 and β to 0.5.

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Table 1. Effectiveness of structure information. N Duke to market-1501 Market-1501 to duke mAP R-1 R-5 R-10 R-20 mAP R-1 R-5 R-10 R-20 0 30.7 63.5 79.3 85.2 89.6 26.5 47.8 62.3 67.4 72.0

3.3

2 28.6

58.6 77.1 83.7

88.8

24.5

44.4 60.7 66.5

71.9

4 27.3

58.2 77.4 83.6

88.7

22.4

43.6 59.0 65.1

69.4

8 29.6

59.8 77.7 84.0

88.9

21.9

43.1 57.7 64.6

69.6

Evaluation

Two domain adaptation tasks are applied to evaluate our proposed method, i.e., Duke to Market-1501 and Market-1501 to Duke. Effectiveness of Structure Information. The experimental results are shown in Table 1. First, we train the network without partitioning (N = 0). At this time, the network structure is the same as [15], where Duke to Market-1501 means that the Duke database is used as the source, and the Market-1501 database is used as the target. That is, we train the network on Duke and Market-1501, and then test the network on the Market-1501 test set. Then we set N to 2, 4, 8 respectively. It can be observed that no matter how many features are divided, the performance of the network has different degrees of decline. Specifically, the rank-1 accuracy of N = 4 is decreased from 63.5% to 58.2% when testing on Market-1501. It is worth noting that when N = 8 and Duke is used as the source, the network performance is not seriously affected. But when using Market-1501 as the source with the same setting, mAP drops by 4.6%. In addition, we notice that when testing on Duke, as N increases, the discrimination of the network gradually decreases.

4

Conclusion

In this work, we proposed to add structure information for pedestrian image for cross-domain person re-identification, and conduct a series of experiments on the proposed method. We have shown that adding structure information will reduce the performance of the deep model to some extent. We consider that the reason for this result is that simply dividing the features will input some unimportant parts to the triplet loss, which may generate some noise when training the network. Therefore, our future work is to study a more effective method to make the deep model more discriminative. Acknowledgements. This work was supported by National Training Program of Innovation and Entrepreneurship for Undergraduates under Grant No. 202010065021, Natural Science Foundation of Tianjin under Grant No. 20JCZDJC00180 and No. 19JCZDJC31500, the Open Projects Program of National Laboratory of Pattern Recognition under Grant No. 202000002, and the Tianjin Higher Education Creative Team Funds Program.

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References 1. Zheng, L., Yang, Y.: AG Hauptmann, Person re-identification: Past, present and future, arXiv preprint arXiv:1610.02984 (2016) 2. Li, W., Zhu, X., Gong, S.: Re-ranking person re-identification with k-reciprocal encoding. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2285–2294 (2018) 3. Loy, C., Xiang, T., Gong, S.: Multi-camera activity correlation analysis. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1988–1995 (2009) 4. Zhang, Z., Wang, C., Xiao, B., Zhou, W., Liu, S.: Action recognition using contextconstrained linear coding. IEEE Signal Process. Lett. 19(7), 439–442 (2012) 5. Zhang, Z., Wang, C., Xiao, B., Zhou, W., Liu, S., Shi, C.: Cross-view action recognition via a continuous virtual path. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2690–2697 (2013) 6. Milan, A., Leal-Taix, L., Reid, I., Roth, S., Schindler, K.: MOT16: A benchmark for multi-object tracking, arXiv preprint arXiv:1603.00831 (2016) 7. Zhang, X., et al.: Alignedreid: Surpassing human-level performance in person reidentification, arXiv preprint arXiv:1711.08184 (2017) 8. Chen, Y., Li, W., Sakaridis, C., Dai, D., Van Gool, L.: Domain adaptive faster R-CNN for object detection in the wild. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3339–3348 (2018) 9. Chen, Y., Li, W., Van Gool, L.: Road: reality oriented adaptation for semantic segmentation of urban scenes. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 7892–7901 (2018) 10. Motiian, S., Piccirilli, M., Adjeroh, D.A., Doretto, G.: Unified deep supervised domain adaptation and generalization. In: IEEE International Conference on Computer Vision, pp. 5715–5725 (2017) 11. Busto, P.P., Gall, J.: Open set domain adaptation. In: IEEE International Conference on Computer Vision, pp. 1528–1535 (2017) 12. Wei, L., Zhang, S., Gao, W., Tian, Q.: Person transfer gan to bridge domain gap for person re-identification. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 79–88 (2018) 13. Deng, W., Zheng, L., Ye, Q., Kang, G., Yang, Y., Jiao, J.: Image-image domain adaptation with preserved self-similarity and domain-dissimilarity for person reidentification. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 994–1003 (2018) 14. Wang, J., Zhu, X., Gong, S., Li, W.: Transferable joint attribute-identity deep learning for unsupervised person re-identification. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2275–2284 (2018) 15. Zhong, Z., Zheng, L., Li, S., Yang, Y.: Generalizing a person retrieval model heteroand homogeneously. In: European Conference on Computer Vision, pp. 1528–1535 (2018) 16. Choi, Y., Choi, M., Kim, M., Ha, J., Kim, S., Choo, J.: Stargan: unified generative adversarial networks for multi-domain image-to-image translation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 8789–8797 (2018) 17. Zhong, Z., Zheng, L., Luo, Z., Li, S., Yang, Y.: Invariance matters: Exemplar memory for domain adaptive person re-identification. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 598–607 (2019) 18. Ge, Y., Chen, D., Li, H.: Mutual mean-teaching: Pseudo label refinery for unsupervised domain adaptation on person re-identification, arXiv preprint arXiv:2001.01526 (2020)

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19. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) 20. Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., Tian, Q.: Scalable person re-identification: a benchmark. In: IEEE International Conference on Computer Vision, pp. 1116–1124 (2015) 21. Ristani, E., Solera, F., Zou, R., Cucchiara, R., Tomasi, C.: Performance measures and a data set for multi-target, multi-camera tracking. In: European Conference on Computer Vision, pp. 17–35 (2016)

Course Quality Evaluation Based on Deep Neural Network Moxuan Xu1 , Nuoran Wang2,3(B) , Shaoyan Gong4 , Haijia Zhang2,3 , Zhong Zhang2,3 , and Shuang Liu2,3 1 Teaching Affairs Office, Tianjin Normal University, Tianjin, China Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, Tianjin Normal University, Tianjin, China [email protected], [email protected], [email protected] College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China 4 Global Energy Interconnection Research Institute Co., Ltd., Beijing, China 2

3

Abstract. In this paper, we propose a deep model of course quality evaluation using deep neural network, which is simple and efficient to accurately evaluate the course quality. Specifically, we utilize multi-layer fully-connected (FC) layers with different activation functions to model the network. We perform a series of simulation experiments to evaluate the effect of FC layer number and activation functions. Simulation experiment results verify that the proposed model has excellent performance.

Keywords: Course quality evaluation learning

1

· Deep neural network · Deep

Introduction

Course quality evaluation [1–4] aims at reasonably estimating the teaching effect of a teacher or a course, which is helpful for teachers to carry out teaching reform and schools to carry out overall teaching planning. Hence, it is of great significance to accurately evaluate the teaching quality in colleges and universities. Early in the study of course quality evaluation, many researchers [5–7] mainly depends on manual way to evaluate the course quality, for example, the questionnaire, which may be adoptable for small-scale course quality evaluation issues but wastes too much time to summary results and analyze datas, and it is prone to causing inaccurate evaluation due to human error. Furthermore, manual way is extremely impractical for large-scale course quality evaluation owing to its obvious limitations aforementioned. Hence, manual way can not meet the requirements of modern teaching quality management. Recently, deep neural network [8–10] has made great strides with wide application in many fields, such as computer vision [11–13], natural language processing [14,15] and so on. Researchers resort to deep neural network to address issues and tasks, and improve the performance in their communities. Some methods c The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022  Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 56–62, 2022. https://doi.org/10.1007/978-981-19-0390-8_8

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inject deep neural network into course quality evaluation in order to accurately evaluate the course quality. Among them, Liu and Yin [16] improve traditional PSO algorithm using adaptive acceleration factors and inertia weights to speed up model convergence and enhance accuracy in multimedia course-aware evaluation. In [17], Jiang and Wang combine AHP with PSO-BP to address the limitation of slow convergence and obtain the global optimal solution in online teaching quality evaluation. However, these methods usually integrate some single technologies into one network and manually set weight for them, which increases model complexity and weakens flexibility. In this paper, we propose to construct a deep model of course quality evaluation using deep neural network, which is simple and efficient to accurately evaluate the course quality. Specifically, we utilize multi-layer fully-connected layers with different activation functions to model the network. Complex nonlinear transformation can be learned through activation functions, which could enhance model generalization ability. Simulation experiment results verify the proposed method has excellent performance. The structures of paper are organized as follows. Section 2 is the framework of the proposed method and activation functions including Sigmoid and ReLU. Section 3 shows experiment settings, simulation results and analyzes the effect of FC layer number and activation functions. We draw conclusion in Sect. 4.

2

Approach

In this section, the framework of the proposed method is firstly introduced. Afterwards, we detail two types of activation functions including Sigmoid activation function and ReLU activation function. 2.1

Framework

The proposed model of course quality evaluation is composed of multiple FC layers followed by activation functions. The model framework is shown in Fig. 1. Given a set of sample data X = {x1 , · · · , xn }, each sample data xi ∈ R1×40 and n denotes sample data number. Concretely, each sample data xi is first fed into the FC1 layer, and then activated by the Sigmoid or ReLU activation function. Afterwards, the output of FC1 is feature fi1 ∈ R1×256 . Similarly, fi1 is processed by the following layers with activation functions. Finally, we could obtain final features fi3 ∈ R1×32 . Afterwards, we feed fi3 into the classifier which consists of one FC layer and softmax function. We compute the loss of the proposed model using the cross-entropy loss function: K  (1) pk (fi3 ) log(qk (fi3 )) Loss = − k=1

where K denotes the course quality grades number (such as excellent, good, and so on), qk (fi3 ) denotes the estimation value computed by softmax function, and

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Fig. 1. The framework of the proposed course quality evaluation model.

pk (fi3 ) denotes ground truth course quality grade of fi3 , which is computed by the function:  1 if fi3 belongs to the k-th grade pk (fi3 ) = (2) 0 otherwise

2.2

Activation Function

In this subsection, we briefly introduce two types of activation functions including Sigmoid and ReLU. The Sigmoid function is a logistic function, which converts any value to (0, 1). It is formulated as: 1 (3) Sigmoid(z) = 1 + e−z The ReLU function is widely applied in deep learning to enhance the nonlinear representation of the model. The function is expressed as: ReLU (z) = max(0, z)

(4)

Compared with the Sigmoid activation function, the computational complexity of the ReLU activation function is lower because there is no need for exponential operation.

3

Experiments

In this section, experiment settings are first introduced. Afterwards, we detail sample data for training and testing the proposed model. Finally, the experimental results are shown and analyzed to evaluate the effect of FC layers number and activation functions. 3.1

Experiment Settings

The environment parameters of the simulation experiments are shown in Table 1.

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Table 1. Experiment settings. Environment Type Parameter description Parameter value

3.2

Hardware

CPU GPU

Intel Xeon, 24 GB RTX 2080Ti, 11 GB

Software

platform operation system

PyTorch Linux

Sample Data

In the training phase, we utilize 5,000 sample data to optimize the proposed course quality evaluation model. We divide these sample data into four grades according to the manual scores. Table 2 shows the statics information of sample data. Table 2. The statistical information of sample data. Grades

Number

Excellent 1200

3.3

Good

1300

Weak

1270

Poor

1230

Experiment Results

We adopt the correct rate of course quality evaluation as the result evaluation index. We randomly select 1,000 sample data in Table 2 to form the test set, and the others are used as the training set. Experimental results are shown in Fig. 2 where we can see that the proposed model achieves outstanding performance. The Effect of FC Layer Number. We stack different numbers of FC layer to evaluate the effect. Figure 3 shows the experimental results and we can see that the proposed method obtains highest accuracy when using three FC layers. The Effect of Activation Function. We test different activation functions to evaluate the influence. As shown in Fig. 4 we can see that the ReLU activation is beneficial to improving the accuracy of the proposed model.

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Fig. 2. Experimental results.

Fig. 3. The effect of FC layer number.

Fig. 4. The effect of activation function.

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61

Conclusion

In this paper, we have proposed the deep model of course quality evaluation. The proposed model is simple but efficient to accurately evaluate the course quality. We perform a series of simulation experiments to verify the effect of FC layer number and activation functions. Experimental results on sample data have proved that the effectiveness of the proposed deep model of course quality evaluation. Acknowledgements. This work was supported by College Student Innovation and Entrepreneurship Training Program under Grant No. 202010065021, Tianjin Normal University Teaching Reform Program under Grant No. JGZD01220014, Natural Science Foundation of Tianjin under Grant No. 20JCZDJC00180 and No. 19JCZDJC31500, and the Tianjin Higher Education Creative Team Funds Program.

References 1. Moss, J., Hendry, G.: Use of electronic surveys in course evaluation. Br. J. Educ. Technol. 33(5), 583–592 (2002) 2. Hoffman, K.M.: Online course evaluation and reporting in higher education. New Dir. Teach. Learn. 2003(96), 25–29 (2003) 3. Kogan, J.R., Shea, J.A.: Course evaluation in medical education. Teach. Teach. Educ. 23(3), 251–264 (2007) 4. Edstrom, K.: Doing course evaluation as if learning matters most. High. Educ. Res. Dev. 27(2), 95–106 (2008) 5. Wagner, Z.M.: Using student journals for course evaluation. Assess. Eval. Higher Educ. 24(3), 261–272 (1999) 6. Donovan, J., Mader, C., Shinsky, J.: Online vs. traditional course evaluation formats: student perceptions. J. Interact. Online Learn. 6(3), 158–180 (2007) 7. Kember, D., Leung, D.Y.: Establishing the validity and reliability of course evaluation questionnaires. Assess. Eval. Higher Educ. 33(4), 341–353 (2008) 8. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition, arXiv preprint arXiv:1409.1556 (2014) 9. Zhang, Z., Wang, C., Xiao, B., Zhou, W., Liu, S., Shi, C.: Cross-view action recognition via a continuous virtual path. In: IEEE Conference on Computer Vision and Pattern Recognition, Portland, Oregon, USA, pp. 2690–2697 (2013) 10. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, Nevada, USA, pp. 770–778 (2016) 11. Liu, S., Li, M., Zhang, Z., Xiao, B., Cao, X.: Multimodal ground-based cloud classification using joint fusion convolutional neural network. Remote Sens. 10(6), 822 (2018) 12. Zhang, Z., Huang, M., Liu, S., Xiao, B., Durrani, T.S.: Fuzzy multilayer clustering and fuzzy label regularization for unsupervised person reidentification. IEEE Trans. Fuzzy Syst. 28(7), 1356–1368 (2019) 13. Zhao, Z.Q., Zheng, P., Xu, S.T., Wu, X.: Object detection with deep learning: a review. IEEE Trans. Neural Netw. Learn. Syst. 30(11), 3212–3232 (2019)

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14. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding, arXiv preprint arXiv:1810.04805 (2018) 15. Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R., Le, Q.V.: Xlnet: generalized autoregressive pretraining for language understanding, arXiv preprint arXiv:1906.08237 (2019) 16. Liu, T., Yin, S.: An improved particle swarm optimization algorithm used for BP neural network and multimedia course-ware evaluation. Multimedia Tools Appl. 76(9), 11961–11974 (2016). https://doi.org/10.1007/s11042-016-3776-5 17. Jiang, L., Wang, X.: Optimization of online teaching quality evaluation model based on hierarchical PSO-BP neural network. Complexity (2020)

Design of Remote Indoor Environment Monitoring System Based on STM32 Zhifeng Jia, Peidong Zhuang(B) , and Zhenzhen Huang College of Electronics Engineering, Heilongjiang University, Harbin 150080, Heilongjiang, People’s Republic of China [email protected]

Abstract. With the development and progress of society, air quality issues have a greater impact on mankind. However, the number of indoor environment monitoring instruments currently on the market is small and the price is high, and among them, the remote monitoring function is almost missing. This makes it difficult for people to obtain the air pollution index conveniently and in real-time. Faced with such a situation, a remote indoor environment monitoring system was designed to promote the realization of the dream of the interconnection of everything in contemporary society. Compared with mainstream air monitors on the market, this remote indoor environment monitoring system not only has an LCD, but the biggest highlight is the realization of WIFI wireless transmission. People can remotely control the instrument through mobile phone software to realize monitoring and feedback the monitoring results immediately. The design can also realize interaction with the cloud, upload height information to the cloud for data analysis and storage. Keywords: STM32F103ZET6 · ESP8266Wifi module · MQ138 sensor · DHT11 temperature and humidity sensor · GP2Y1010AU dust sensor

1 Overall Design of Remote Indoor Environment Monitoring System With the improvement of social living standards and quality of life, people are paying more and more attention to physical changes. Especially for infants and young children and other people who are sensitive to air quality, air quality will have a more significant impact on them. It is very important to understand the air quality of the living environment, because only in this way can we take precautions. This design is not only limited to monitoring the home environment but can also be appropriately applied to monitor the industrial production environment. This paper designs a remote indoor environment monitoring system based on anstm32 single-chip microcomputer. This design is small in size, light in weight, and the monitoring results are accurate and intuitive. Compared with other instruments of the same type, it has the advantages of wireless transmission control, non-contact, precision fidelity, etc., and also has the function of uploading data to the cloud, which is not available in other traditional testing instruments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 63–70, 2022. https://doi.org/10.1007/978-981-19-0390-8_9

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The environmental temperature and humidity are measured by the DHT11 temperature and humidity sensor, and the PM2.5 and formaldehyde content are measured by the GP2Y1010AU dust sensor and the MQ138 sensor respectively. The measurement results are displayed in real-time on the LCD screen, and data upload and recording are realized through the ESP8266WIFI module. This topic is dedicated to the realization of smart home life and the realization of the dream of the interconnection of everything in contemporary society.

2 Sensors Introduction 2.1 MQ138 Formaldehyde Sensor The gas-sensitive material used in the MQ138 formaldehyde sensor is tin dioxide (SnO2) with low conductivity in clean air. When there is organic vapor in the environment where the sensor is located, the conductivity of the sensor increases with the increase of the concentration of organic vapor in the air. Using a simple circuit, the change in conductivity can be converted into an output signal corresponding to the gas concentration. The MQ138 formaldehyde sensor has a high sensitivity to toluene, acetone, ethanol, and formaldehyde. It is also ideal for monitoring hydrogen and other organic vapors. This sensor can detect a variety of organic vapors and is a low-cost sensor suitable for a variety of applications. Here, the arithmetic average filtering algorithm is used to improve the reliability and authenticity of the data. The sensor quickly obtains five samples, removes the maximum and minimum values, and uses the average of the remaining three samples as the detection result during this period. The specific test data is shown in Table 1. Table 1. Formaldehyde content test Number

a[0]

a[1]

a[2]

a[3]

a[4]

A

1

0.05

0.05

0.05

0.05

0.05

0.05

2

0.05

0.05

0.05

0.05

0.05

0.05

3

0.05

0.05

0.05

0.05

0.05

0.05

4

0.06

0.05

0.05

0.05

0.05

0.05

5

0.85

0.87

0.50

0.40

0.51

0.62

6

0.34

0.24

0.20

0.16

0.14

0.20

7

0.06

0.06

0.05

0.05

0.05

0.05

8

0.05

0.05

0.05

0.05

0.05

0.05

9

0.05

0.05

0.05

0.05

0.05

0.05

10

0.05

0.05

0.06

0.05

0.05

0.05

a—array. A—arithmetic average filter, mg/m3 .

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Conversion of concentration unit ppm and mg/m3: Mass concentration mg/m3 = Mgasmolecular weight/22.4*ppm number*[273/(273 + T gas temperature)]*(Ba pressure/101325). M is the molecular weight of the gas, ppm is the measured volume concentration value, T is the temperature, and Ba is the pressure. The molecular weight of formaldehyde HCHO gas is 30.0260. 2.2 DHT11 Temperature and Humidity Sensor The humidity measurement range of DHT11 temperature and humidity sensor is 5%– 95%RH, the measurement accuracy is ±5%RH, the temperature measurement range is -20.0–+60.0 °C, and the measurement accuracy is 0.2 °C. When heating in winter, the indoor humidity is not less than 15% RH, and the humidity above 90% RH feels similar to a sauna room. It seems that the DHT11 temperature and humidity sensor can meet the temperature and humidity measurement range and accuracy of the design, and It is simple to connect with the chip and has a low cost. Besides, it can correct the digital signal by itself to get closer to the real data. And its power consumption is not high. 2.3 GP2Y1010AU Dust Sensor The GP2Y1010AU dust sensor is compact, easy to install and maintain, and is usually used to detect indoor smoke and dust concentrations. Due to the built-in airflow generator, it can self-sample the outside air and quickly detect the absolute number of examples per unit volume. The GP2Y1010AU dust sensor has many advantages, including durability, high sensitivity, high accuracy, and good stability. Its detection uses laser method and particle counting method, which can detect small solid particles of 0.8 μm. Besides, when the GP2Y1010AU sensor works normally, the current is only 11 mA, and the voltage amplitude of the output analog signal is proportional to the dust concentration [1]. The principle uses the light scattering method. There is a hole in the center of the sensor to allow the air to flow freely, and the LED light is emitted directionally. The dust content is judged by detecting the light refracted by the dust in the air. According to the principle of Mie scattering, the mass concentration of suspended particles satisfies the relationship between light intensity after light scattering:   4π 3 r 2 ρ II0 (1) Mv =  )]nr (Di ) 3λ2 vS i [I1 (α,m,θ )+I2D(α,m,θ Di 3 i

Where: r is the distance from the particle to the light intensity detector;ρ is the density of dust particles; I0 is the incident light intensity; λ is the wavelength of the incident light; the detected gas flow rate; S is the cross-sectional area of the gas flow tube; I1 and I2 are the vertical and horizontal components of the scattered light intensity, which are related to the particle size α, the particle refractive index m and the scattering angle θ; nr (Di ) is a function of suspended particle A;D is the diameter of the particle.

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Under a specific structure, the wavelength of human light is known, an appropriate scattering angle is selected, and the mass concentration of suspended particles can be calculated by measuring the intensity of the scattered light. According to the basic theory of Mie scattering, the vertical and horizontal components of the scattered light intensity:  2  λ (2) I= (I1 + I2 )I0 8π 2 r 2 I1 (α, m, θ ) = |S1 |2

(3)

I2 (α, m, θ ) = |S2 |2

(4)

Scattering amplitude function expression S1 and S21 : S1 =

∞  2i + 1 [aπi (cos θ) + bi τi (cos θ )] i(i + 1)

(5)

∞  2i + 1 [aτi (cos θ ) + bi πi (cos θ )] i(i + 1)

(6)

i=1

S2 =

i=1

Where ai and bi are the Mie scattering coefficients, and the expression is: 

ai =



ψi (x)ψi (mx) − mψi (mx)ψi (x) 



ξ (x)ψi (mx) − mψi (mx)ξi (x) 

bi =

(7)



mψi (x)ψi (mx) − ψi (mx)ψi (x) 



mξ (x)ψi (mx) − ψi (mx)ξi (x)

(8)

πi and τi are scattering angle functions, and the expression is: (1)

πi (cos θ ) =

Pi (cos θ ) sin θ

τi (cos θ ) =

dPi (cos θ ) dθ

(9)

(1)

(10)

Where x = πα/λ, αi and bi areMie scattering coefficients, they are J(i+1/2) (z) (half-integer order Bessel function of the first kind) and H(i+1/2) (z) (Hank function of the second kind of half-integer order); πn (cosθ) and τn (cosθ) are Legendre polynomials related only to the scattering angle θ [2].

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From formulas (2) to (10), it can be seen that the scattered light intensity distribution of suspended particles is related to the human light wavelength λ, particle size α, and particle refractive index m. The GP2Y1010AU dust sensor can sense the dust content in the air and convert it into an electrical signal output proportional to it. Since the core component STM32F103ZET6 used in this design has its own ADC, there is no need for an external A/D conversion chip.

3 Remote Indoor Environment MonitorDesign The core component of the remote indoor environment monitoring system is STM32F103ZET6. The hard circuit includes a power module, reset circuit module, liquid crystal circuit module, crystal oscillator module, JATG download simulation module, ESP8266WIFI base module, independent button module, BOOT module, LED indicator module, DHT11 temperature and humidity sensor base module, GP2Y1010AU dust sensor base module and MQ138 formaldehyde sensor base module. 3.1 Overall Hardware Circuit Schematic Diagram All hardware circuit design drawings use Altium Designer15 (Fig. 1 and Fig. 2).

Fig. 1. Single-chip minimum system

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Fig. 2. Sensor and WIFI module circuit diagram

3.2 Sensor and WIFI Module 3.3 WIFI Data Transmission Module The framework of this part is composed of a WIFI module, Alibaba Cloud Internet of Things platform, and mobile APP client. Alibaba Cloud acts as a bridge to transmit data between the WIFI module and the mobile APP. This is one-to-one data transmission, so first, connect both the WIFI module and the mobile APP with Alibaba Cloud. Connect the ESP8266 WIFI module to the MCU and power it on. Then, the mobile phone searches for the WIFI of the module and establishes a connection. Set the name and password of the WIFI connected to the wireless router and the account password required to log in to the device. The scheme of this part is shown in Fig. 3 [3].

Fig. 3. IoT platform and application layer solution

The APP also needs to connect to the Alibaba Cloud IoT platform server via TCP, and then log in to the equipment of this project [4]. The mobile terminal is shown in Fig. 4.

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Fig. 4. Mobile terminal interface

4 Results Display 4.1 System Function Design Drawing

Fig. 5. System design block diagram

4.2 Test Outcome Tested twice in different rooms to get the test results are shown in Table 2 (B2 represents the second test in room B (Fig. 5)).

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A

B

C

D

A1

19

34

0.02

21

A2

19

33

0.02

21

B1

20

37

0.02

33

B2

20

37

0.02

32

C1

19

43

0.01

24

C2

19

43

0.01

24

D1

22

33

0.03

77

D2

22

33

0.03

75

E1

19

35

0.01

30

E2

19

35

0.01

29

A— temperature, °C.B— humidity, %RH. C— CH2O, mg/m3 . D— PM2.5, μg/m3 .

5 Conclusion From the test results, the monitoring results in multiple same rooms maintain good stability, and the different test results are controlled within a minimum unit. The main functions meet the requirements, and the overall design functions are complete and stable. In the future, combining some household products, such as humidifiers, air conditioners, air purifiers, etc., can improve intelligence.

References 1. Hu, J., Tian, X., Lai, J.: Design of air quality detection and alarm system based on MCU. Electronic Test 2020(19), 34–36 (2020) 2. Zhang, W., Yuan, L., Du, S.: Analysis of characteristics of Mie scattering. Opt. Technique 36(6), 936–939 (2010) 3. Shao, T., Wei, Q., Xiao, S.: Design of temperature and humidity monitoring system based on cloud platform. Elec. Design Eng. 28(06), 92–96 (2020) 4. Gong, Y., Fu, J., Wang, X.: Application of MQTT protocol in internet of things. Comp. Telecommun. 2017(11), 89–91+94 (2017)

Design of a Multi-channel Spectrum Perception Performance Verification Model Bo Zhou(B) , Tong-Qiao Xu, Zhao-Yang Li, Hong-Wei Cai, Chun-Guang Shi, and Zhen Chen Xichang Satellite Launch Center of China, Xichang 615000, Sichuan, China [email protected]

Abstract. With the development of wireless communication technology, the shortage of spectrum resources has become a serious problem in the field of wireless communication. Spectrum sensing technology is one of the main ways to solve the above problems because it can detect whether the channel is occupied to find the available idle frequency band. In this paper, a multi-channel broadband spectrum parallel sensing method is proposed, and the performance of the model sensing method in spectrum sensing is verified by experiments. Keywords: Wireless communication · Spectrum sensing · Detection probability

1 Introduction In recent years, the continuous development of wireless communication technology and the rapid growth of broadband wireless services make the demand for wireless spectrum increasing. Cognitive radio has become the focus of the next generation wireless network because it can solve the problem of lack of wireless spectrum resources and low spectrum utilization. In cognitive radio system, on the one hand, cognitive system needs to adopt physical layer transmission scheme with strong adaptability, good flexibility and high spectrum utilization; on the other hand, cognitive users need to reliably perceive wider spectrum resources at low SNR and detect the spectrum usage of users in order to solve the cognitive problem of wide wireless spectrum [1]. In this paper, a multi-channel broadband spectrum parallel sensing method is proposed, and the performance of the model sensing method in spectrum sensing is verified by experiments.

2 Spectrum Sensing Technology In the dynamic spectrum access mode, the hierarchical access model is more compatible with the current spectrum allocation mode and is widely used. The hierarchical access model divides users into primary users (PU) and secondary users (SU) [2]. The main user is the authorized user on the specific frequency band, and the secondary user refers to the unauthorized user. As an important part of cognitive radio system, spectrum sensing technology needs to find as many available spectrum resources as possible without © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 71–77, 2022. https://doi.org/10.1007/978-981-19-0390-8_10

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interference PU communication. SU the sensed signal is often composed of multiple channels with different bandwidths, the spectrum access points may also be dispersed in the whole frequency band, which requires the cognitive radio system receiver to have the ability to extract multiple channels at the same time. Generally speaking, after the broadband spectrum is divided into multiple narrowband channels, each channel is sensed one by one. Too long detection period will reduce the time of data transmission, increase the probability of transmission failure and energy consumption. therefore, we quantify the sensing performance with the following spectrum sensing performance metrics and verify the effectiveness of the performance through an energy detection algorithm. 2.1 Spectrum Sensing Performance In cognitive radio technology, spectrum sensing performance includes the following aspects. (1) Perceived efficiency. Excessive detection period will reduce the time of data transmission, thus increasing the probability of transmission failure and energy consumption. At the same time, spectrum sensing in a large spectrum range will require higher hardware. Therefore, how to realize efficient multi-channel spectrum sensing on the basis of reducing hardware cost is worth studying. (2) Perceptual sensitivity. Spectrum sensing needs to be able to determine whether the channel is available at a lower SNR. The IEEE 802.22 working group, for example, noted that the sensitivity of the sensing algorithm needed to meet –116 dBm [3]. (3) Perceiving time. The longer the sensing time, the greater the detection accuracy. However, in order to make the cognitive radio use the idle frequency band effectively, the sensing technology must detect the idle channel within a certain time limit to ensure the efficient transmission of data. 2.2 Spectrum Sensing Performance Indicators The most common detection method of spectrum sensing technology is to use binary hypothesis to judge whether each channel is idle. Common measures of perceptual performance include false alarm probability, detection probability, offset coefficient and receiver operating characteristics. (1) False alarm probability and detection probability. For a binary hypothesis testing problem, there are two kinds of error probability, namely false alarm probability (Pf ) and missed detection probability (Pmd ). Where, the false alarm probability (Pf ) represents the probability of H1 (channel occupied) when H0 (channel idle) appears, and the missed detection probability(Pmd ) represents the probability of H0 (channel idle) when H1 (channel occupied) appears. Pf = P(H1 |H0 )

(1)

Pmd = P(H1 |H0 )

(2)

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When H1 (channel occupied) appears, the detection probability (Pd ) determined as H1 (channel occupied) is expressed as: Pd = P(H1 |H1 ) = 1 − Pmd

(3)

(2) Offset coefficients. The offset coefficient is defined as: d2 =

[E(Y|H1 ) − E(Y|H0 )]2 (μ1 − μ0 )2 = Var (Y|H0 ) σ2

(4)

Where Y is the statistics of the observed data, and μ1 and μ0 are the mean values of the observed statistics when the channel is occupied and idle, respectively. When μ0 = 0, d2 = μ21 /σ2 is the signal-to-noise ratio. According to the central limit theorem, when the sampling points are large enough, the Y obeys the Gao Si distribution, and the false alarm probability and the detection probability are respectively:   λ − μ0 (5) Pf = Q σ        λ − μ1 μ1 − μ0 = Q Q−1 (Pf ) − = Q Q−1 (Pf ) − d2 Pd = Q (6) σ σ It can be seen that when the false alarm probability (Pf ) is fixed, the detection probability (Pd ) changes monotonously with the migration coefficient d2 . (3) Receiver operating characteristics. Another index to evaluate the performance of sensing algorithm is the relationship curve between detection probability and false alarm probability, that is, receiver operating characteristics (Receiver Operating Characteristic, ROC) [4]. each point on the curve can be obtained by adjusting the threshold parameters. Figure 1(a) shows the ROC. under different migration coefficients From the diagram, it can be seen that both the R0C and offset coefficients can better reflect the performance of spectrum sensing.

Fig. 1. Relationship between detection probability and false alarm probability

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In cognitive radio systems, in order to reduce the interference to PU while obtaining larger access opportunities, the detection probability is usually larger (such as Pd > 0.9) and the false alarm probability is smaller (such as Pf < 0.1). In order to more clearly reflect the relationship between Pd and Pf , the logarithmic relationship curve of Pmd = 1 − Pd and Pf as shown in Fig. 1 (b) can be used to describe the working characteristics of the receiver.

3 Spectrum Perception Model Suppose PU communication system occupies K channels for data transmission, the bandwidth of these K channels can be unequal, and the occupation of each channel is independent of each other. Spectrum access opportunity refers to the unused spectrum resources in a specific time, space and frequency domain. multi-channel spectrum sensing is to study how to accurately and efficiently sense multiple channels at the same time to improve SU spectrum access opportunities [5]. Let Sk (n) be the Pu signal of the kth channel to be detected, Ck be the gain of the kth channel, Xk (n) be the signal received by the Su receiver, and Vk (n) be the additive white Gaussian noise (which can also be other types of noise sources). Then, H0 and H1 are the assumptions when the kth channel is idle and occupied respectively, which can be expressed as: H0 : Xk (n) = Vk (n)

(7)

H1 : Xk (n) = Ck Sk (n) + Vk (n)

(8)

The N-1, N is the number of samples collected in the signal detection cycle to ensure that the detection is completed in a limited signal sample. The performance of spectrum sensing algorithm is described by the following two probabilities, namely detection probability(Pd ) and false alarm probability(Pf ). On the one hand, in order to minimize the interference of the SU to the PU, the smaller the missed detection probability is, on the other hand, in order to obtain more access opportunities, the probability of missing detection of the idle channel must be minimized, that is, the smaller the false alarm probability is, the better.

4 Simulation Experiment Verification 4.1 Spectrum Estimation Methodology Here, the most commonly used energy detection method in spectrum sensing is used to check the spectrum perception of multi-channel, so the detection statistical parameters of the k channel are defined as:Tk Tk =

N−1 N=0

|Xk (n)|2

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In each channel, two assumptions (7) and (8) are obtained by comparing the detection system parameters with Tk a specific threshold λk : H0 < λk Tk > H1

(10)

(k)

Therefore, the theoretical expressions of detection probability (Pd ) and false alarm (k) probability (Pf ) can be obtained by using Tk . Assuming that Vk (n) is a Gaussian stochastic process with zero mean variance σv2 = 1 and that Pu signal changes more slowly than noise, Pu signal can be considered as quasi-stationary, that is to say, Pu signal is a Gaussian process with non-zero mean. Then, the false alarm probability (k) (Pf ) can be expressed by the central chi square distribution with 2n degrees of free    N, λ2k (k) Pf = (11) (N) In the formula, where (·, ·) and (·) are incomplete gamma function and complete gamma function respectively [6]. (k) The detection probability (Pd ) of the k channel can be expressed by a non-central chi-square distribution of 2 N degrees of freedom:  

(k) 2 2 2 (12) Pd = QN 2σs /σs σv , λk In the formula, where QN [·, ·] is the generalized Marcum q-function and σs2 is the variance of signal Sk (n) [7]. Threshold is a trade-off factor between false alarm probability and missed detection probability. low threshold will lead to higher false alarm probability and lower missed detection probability. At the same time, perceptual time needs to be weighed between perceptual accuracy and perceptual efficiency. With the increase of sensing time, the detection accuracy will be improved, but the time used for data transmission will be reduced. Therefore, how to coordinate the relationship between the above parameters is the key to improve the spectrum sensing performance of multi-channel. 4.2 Simulation Experiment Through the simulation experiment, the detection performance under multi-channel is analyzed by energy detection method. 6 MH per channel bandwidthZThe design parameters of the C MFB prototype filter are as follows, the stopband loss As = 75 dB, v = 0.9, K = 128, and the performance perception checking calculation is carried out using the formula of 4.1.

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(1) False alarm probability and detection probability. Figure 2 describes the relationship curve between detection probability and false alarm probability of multiple channels at N = 1000. It can be seen from the diagram that the detection performance increases with the increase of signal-to-noise ratio.

Fig. 2. N = The relation curve between detection probability and false alarm probability of multi-channel at 1000

(2) Perceived time. Figure 3 gives the curves of the detection probability of multi-channel with perceived time when the false alarm probability is 0.1. We can see from the diagram that the detection probability of signal-to-noise ratio (SNR) of –15 dB and signal-to-noise ratio (SNR) of 20 dB ms、 with the increase of perception time, the detection performance is obviously improved. that is, the longer the sensing time, the more sampling points, the more accurate the estimation of the statistical characteristics of noise and signals, and the better the detection performance.

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Fig. 3. Curve of multi-channel detection probability with perception time when false alarm probability is 0.1

5 Concluding Remarks In this paper, the spectrum estimation and perception are proposed according to the characteristics of multi-channel spectrum perception, and the theoretical expressions of detection probability and false alarm probability under multi-channel are derived. The simulation results show that the detection performance increases with the increase of signal-to-noise ratio, and the longer the detection time, the better the detection performance. The proposed spectrum sensing method can be used as a new strategy to detect the available spectrum resources quickly and accurately in a large range of frequency bands.

References 1. Guo Wen Xiang, F.: A review of cognitive radio spectrum sensing technology. Communications Tech. (02), 51–51 (2018) 2. Zhang Jun Qiang, F.: Spectrum sensing technology of cognitive radio, Modern Info. Tech. (05), 02–03 (2018) 3. Zhao Nan, F.: Research on the multi-channel dynamic spectrum management method in cognitive radio, 2nd edn. China water resources and Hydropower Press, China (2019) 4. Qiao Yu, F.: Energy optimized spectrum sensing scheme in cognitive radio sensor networks, Computer Eng. Design (05), 40–41 (2019) 5. Mukhtaruzzaman Mohammad, F.: Atiquzzaman mohammed clustering in vehicular ad hoc network: algorithms and challenges, Computers and Electr. Eng. (08), 87–88 (2020) 6. Yao Kan, F.: Prediction based spectrum sensing scheme in cognitive radio networks, Computer Eng. Design (10), 41–42 (2020) 7. Hossain Mohammad Asif, F.: Spectrum sensing challenges & their solutions in cognitive radio based vehicular networks Int. J. Commun. Syst. (02), 23–25 (2021)

SDN, Big Data and AI Technologies Design and Implementation of the Network of Intelligent CloudCampus Tong-qiao Xu1(B) , Bo Zhou1 , Chen-Zhi Xu2 , and Zhao-Yang Li1 1 XiChang Satellite Launch Center of China, XiChang 615000, Sichuan, China

[email protected] 2 The 29th Research Institute of China Electronics Technology Group, ChengDu 610000,

Sichuan, China

Abstract. The network structure of traditional park network is complex, the service access is limited, and the maintenance workload is large. Enterprises and organizations have higher and higher requirements for the park network. With the impact of the expanding business demand on the existing park network, especially the problems of boundary disappearance, access control and network security brought by the introduction of wireless network. Under the existing conditions, how to quickly transform the park network by using SDN, big data and AI technology, improve the performance of the park network, and meet the increasing requirements of enterprises and organizations for the park network, which is an urgent problem to be solved. Keyword: SDN · Big data · AI · Intelligent CloudCampus · Network design · Implementation

1 Introduction The traditional park network adopts three-layer structure: access, convergence and core. The network structure is complex, low efficiency, high failure rate and large maintenance workload. With the introduction of wireless networks and the emergence of technologies such as SDN, NFV, cloud computing, big data, cloud computing and AI, enterprises and organizations have put forward high requirements for campus networks [1]. High bandwidth, low latency, strong security, high reliability and on-demand access have become the focus of attention of enterprises and organizations. How to combine advanced and mature technology and management methods to meet the growing business needs of enterprises and organizations, fully improve the network performance of the park, high starting point, high standards to meet the business requirements of enterprises and organizations, become the key to the design and implementation of the park network.

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 78–85, 2022. https://doi.org/10.1007/978-981-19-0390-8_11

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2 Problems to Be Solved by Intelligent CloudCampus Network The Intelligent CloudCampus Network needs to solve the problems of complex physical network construction (access layer, convergence layer, core layer three-layer structure), inconvenient access service, low intelligence of operation and maintenance management, and difficult security protection. The network structure is flattened by simplifying the network structure from three-tier structure to two-tier structure (access layer, core layer), introducing wireless network to realize random access of service, introducing SDN, big data and AI technology to realize intelligent operation and maintenance of campus network, introducing big data and AI technology to realize the transition of security defense from passive to active defense. 2.1 The Network of Intelligent CloudCampus Solves the Problem of Network Structure The traditional campus network logic structure is not reasonable, the network adopts access, convergence, core three-layer structure, the routing interconnection between switches, switch access and so on need to consume a lot of addresses, the address planning is more complex, easy to appear path non-optimal situation; because the network structure is more complex, fault location diagnosis efficiency is not high, the management of the network is more difficult. 2.2 The Network of Intelligent CloudCampus Solves the Problem of Business Access The traditional park network is mainly wired network, and the service access is fixed. With the development of wireless network technology and the demand of enterprises and organizations for mobile services, Park network has gradually developed into wired network and wireless network as the core network of the park. Wireless mobile technology is also developing rapidly, wireless mobile 5 G technology is gradually mature and large-scale deployment of applications, wireless mobile Wi-Fi standards continue to evolve, the empty port rate is also rapidly improving, has been raised from the first 2 Mbit/s to nearly 10 Gbit/s. The campus network composed of wired and wireless can meet the needs of enterprises and organizations. 2.3 The Network of Intelligent CloudCampus Solves Intelligent Operation and Maintenance Problem The traditional operation and maintenance management system of the park network has the problems of imperfect fault warning mechanism, passive fault response, slow fault recovery, difficult perception of business experience and low degree of intelligent management. At present, large-scale park network equipment composition is complex, quantity and variety, operation and maintenance management is difficult. All kinds of network equipment, server, storage equipment, business system and so on make it difficult for the operation and maintenance personnel to deal with it calmly, which often

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leads to the business interruption caused by various reasons, which seriously affects the normal operation of the park network. Some of these problems are due to the lack of intelligent management platforms such as event monitoring and diagnosis tools, the lack of efficient technical tools, the difficulty of active and rapid handling of fault events, and the lack of timely understanding of the running trend of network hardware equipment. 2.4 The Network of Intelligent CloudCampus Solves the Problem of Network Security and Active Defense The traditional park network has the risk point which is easy to be used, through the single point intrusion can threaten to infiltrate the entire network system, then carries on the organized, the scale network attack. The first is the static solidification of defense capability. Once the existing security protection equipment is installed and deployed, it is still. The update of algorithm library, virus library, rule library and vulnerability library is difficult to catch up with the attacker, and once it is found out, its defense efficiency is greatly reduced. second is the lack of active defense ability, most of the technical means in the existing security protection equipment are passive coping methods, it is difficult to cope with more and more complex network attacks. APT attacks with long latency, multi-point intrusion and complex technology are more difficult to resist. Compared with the traditional defense mode, big data security co-defense can mine the network behavior data deeply, threaten and close-loop disposal in time, and help enterprises and organizations to improve the intelligence and automation of security analysis and operation and maintenance, so that the key infrastructure of enterprises and organizations is stable and business sustainable.

3 Design and Implementation of Network of Intelligent CloudCampus The network mainly includes three parts: physical network, operation and maintenance management and security protection. As a result of its low deployment cost, flat, simple and efficient, less and more reliable fault points and more in line with the SDN development trend, the physical network architecture is the foundation of the campus network. The intelligent operation and maintenance management platform is mainly based on SDN controller, Hadoop big data platform, expert database knowledge base rule base and AI algorithm. The intelligent operation and maintenance platform is usually composed of access network equipment module, core network equipment module, transmission network equipment module, multi-layer planning tool, NetMatrix, IP PCE (path computing unit communication protocol to provide path computing service), optical layer PCE, uTraffic network management system, big data platform and AI to realize topology presentation, network planning and network simulation, business distribution, policy management and automation, As well as policy-based traffic optimization, configuration, alarm, provide network inventory information, and perform network infrastructure configuration, fault warning, fault handling and intelligent operation and maintenance [2].

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The security protection of Intelligent CloudCampus Network is based on big data security co-defense. Big data security co-defense collects a large amount of securityrelated source data information from each network element and relies on big data analysis platform for comprehensive analysis. Then the security threat can be accurately identified, and then the network controller can be used to deal with the security. 3.1 Physical Network Design of Intelligent CloudCampus Network The physical network structure of Intelligent CloudCampus Network is shown in the figure (Fig. 1). 3.1.1 Design of Physical Intelligent CloudCampus Network (1) Access Network: ONU-OLT, Switching Unit Tree wired access network, access bandwidth up to 100 M to 10 G. Wireless access network composed of Wi-Fi, 5G, access bandwidth up to 100 M to 10 G. (2) Core network: network core data exchange network consisting of switches and routers with bandwidth of up to 10 G to 100 G.

Fig. 1. Physical network structure of smart garden

(3) Transmission network: Intercity or wide area transmission network is composed of OTN, optical fiber direct connection, wavelength division multiplexing, etc. The transmission rate depends on the demand. (4) Cloud computing center: build cloud computing, cloud storage center, solve the problem of horizontal traffic traversing and convergence ratio, improve the information level of intelligent cloudcampus network.

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3.1.2 Design of Virtual Network Architecture of Intelligent CloudCampus Network (1) The smart campus network needs to decouple the business from the network, and realize the flexible and rapid deployment of one network without changing the basic network. (2) Virtual Architecture of Intelligent CloudCampus Network: the virtual network architecture of Intelligent CloudCampus Network is divided into two layers: underlay network and Overlay network. Underlay network is a bearer network, which consists of various kinds of physical devices, such as access switches, convergent switches, core switches, routers and firewalls. Overlay network is completely decoupled from the Underlay network, and a fully interconnected Fabric logic topology is constructed on the physical topology by VXLAN technology. Fabric logic topology, for users IP, VLAN, access points and other resources unified pool processing, on demand distribution to VN use, based on this logical topology, users can create multiple VN, according to service requirements to achieve a network of multi-use, service isolation, rapid deployment of services. (3) Multi-Virtualization Features of Intelligent CloudCampus Network: intelligent CloudCampus Network through virtualization to achieve a network of multipurpose, integration of server resources, rapid distribution of business. 3.2 Design of Intelligent Operation and Maintenance Architecture of Intelligent CloudCampus Network 3.2.1 Intelligent Operation and Maintenance Architecture for Intelligent CloudCampus Network The intelligent operation and maintenance system based on SDN is composed of GPON access network, switch access network, wireless access network, intelligent operation and maintenance part of IP core network, and intelligent operation and maintenance part of transmission network based on SDN-T [3] (Fig. 2).

Fig. 2. Architecture diagram of intelligent operation and maintenance system

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To deploy U2000 or other NMS network management systems, NetMatrix, SNC-T and SNC. In a network management centreNetMatrix is the information center of the architecture, which is responsible for cooperating with the controller and interfacing with the network management and Traffic at the same time, realizing the network configuration, collecting traffic information and sending the planning results. It is a business processing platform with application-oriented northward interface and a platform for business deployment and policy management. SNC-T optical layer controller: realize centralized topology management, path calculation and policy management, including optical layer algorithm module, based on optical layer topology and resources, configuration, calculate the optical layer path of UNI service. SNC IP layer controller: centralized topology management, path computing and policy management, including IP layer routing module, in addition to IP routing, but also support virtual network topology (VNT) routing and routing policy execution. 3.2.2 Intelligent Operation and Maintenance Features of Intelligent CloudCampus Network The intelligent operation and maintenance of smart campus network has the functions of topology presentation, network planning and network simulation, business distribution, policy management and automation; policy-based traffic optimization, configuration, alarm, network inventory information, and network basic configuration commands; fault identification and active prediction, fault location and root cause analysis based on big data and AI; real-time health monitoring based on KPI, personality fault analysis, audio and video service quality analysis, group analysis fault analysis, etc. 3.3 Design of Intelligent CloudCampus Network Based on Big Data Security Co-defense Architecture 3.3.1 Intelligent CloudCampus Network Based on Big Data Security Co-defense Architecture Big data security co-defense through the establishment of big data and AI based security analyser, the network infrastructure devices into security detection sensors, as sensors, switches, routers, firewalls and other network devices to provide the analyzer with traffic and NetFlow, Metadsta, logs, files and other information, at the same time based on network topology and threat scenarios to make “script”, to study the intent and propagation path of hacker intrusion, so as to build the Wei model and rules, and rely on big data analysis tools to complete the multi-dimensional threat comprehensive analysis of massive information [4]. Help administrators converge massive original event logs, automatically and accurately detect threats, and even predict threats. The big data security co-defense relies on all-network multi-scene multi-scene multi-dimensional comprehensive analysis of horizontal type, harpoon fishing, Web penetration, hacker remote C&C, account anomalies, internal traffic anomalies, data theft and other sub-scenes to conduct a comprehensive analysis, comprehensive calculation of the threat type, level, credibility of each person’s attack behavior, to understand the hacker’s attack intention, and then to match the attack behavior pattern output by the expert system, according to the hacker’s intrusion behavior dynamic adjustment model, identify and predict the attack type and

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Fig. 3. Map of safety protection architecture of smart garden

credibility, the probability of the occurrence of a single point of the original event converges to 1/10000, In order to reduce the administrator’s heavy log analysis work and shorten the traceability time, thus reducing the professional security analysis of human access (Fig. 3). 3.3.2 Intelligent CloudCampus Network Based on Big Data Security Co-defense Features Based on big data security co-defense, Intelligent CloudCampus Network has the characteristics of accurate identification of network threat, rapid disposal of cooperative network threat, automatic operation and maintenance of service network strategy and deep machine learning to help detect security threat.

4 Concluding Remarks In the face of the complexity of the park network and the diversification of business, in order to better ensure the normal operation of the network and improve the network performance, through the design of the smart garden network, the construction scheme of the park network suitable for large and medium-sized enterprises and organizations is selected.

References 1. Shen Guo Ning, F., Yu Bing, S.: Campus network architecture and technology, 2nd edn. China industry and Information Publishing Group, China (2019) 2. Wan Fen Zhang, F., Yu Lei, S.: The carrying network of 5g Dynasty, 2nd edn. People’s Posts and Telecommunications Association, China (2019)

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3. Wang Run Hua, F.: Research on distributed log analysis system based on Hadoop cluster, Sci. Tech. Info. (15), 108–109 (2007) 4. Sanqing Hu, F., Ye Ouyang, S.: A study of LTE network performance based on data analytics and statistical modeling. In: Wireless and Optical Communication Conference (WOCC), 2014 23rd, pp. 1–2 (2014)

Sum Rate Maximization for 6G IoT Resource Allocation: A Matching Approach Peng Qin(B) , Yiying Zhu, Xiongwen Zhao, and Jiayan Liu State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China [email protected]

Abstract. The sixth generation (6G) communication creates a fully connected world of terrestrial wireless and satellite communications, which achieves global seamless coverage. To obtain the tripartite balance of spectrum efficiency, system throughput and complexity at the receiving end, clustered-NOMA (C-NOMA) is recommended. With Low Altitude Platform Station like the UAV for strengthening communication in certain areas, and High Altitude Platform Station (HAPS) for ensuring a wide coverage, we put forward the C-NOMA-enabled 6G IoT system with one HAPS and multiple UAVs, and investigate the resource optimization problem of subchannel allocation and power control to maximize the overall system uplink achievable sum rate. Due to the strong coupling between aforementioned factors, it is a mixed integer nonlinear programming (MINLP) problem. Therefore, we model the problem as a many-to-one matching game to solve in a distributed manner, where UAVs and subchannels acting as two sets of players. Both theoretical analysis and simulations show that the proposed approach outperforms previous solutions in terms of system uplink sum rate with relatively low complexity. Keywords: Sum rate maximization · 6G IoT · Matching theory · Resource allocation

1 Introduction Although requirements of Internet of Things (IoT) applications in hot spots can be fulfilled with dense coverage of the fifth generation (5G) terrestrial network, there still exist urgent desires of high-efficiency and low-cost wireless coverage approaches in remote areas [1, 2]. One of the sixth generation (6G) communication’s visions is that, based on the existing 5G terrestrial network, the space-air-ground integrated network incorporating diverse satellites, aerial platforms applying artificial intelligence and big data technology will be established to satisfy the demands on Internet of Everything (IoE) at any corner around the world [3–5]. As the most representative Low Altitude Platform Station, unmanned aerial vehicle (UAV) has distinct characteristics from satellites and terrestrial networks [6, 7]. It has © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 86–100, 2022. https://doi.org/10.1007/978-981-19-0390-8_12

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lower path loss and can be deployed in hovering mode, which is more preferable for wireless transmissions in IoT scenarios [8]. Besides, UAV can obtain better line of sight (LoS) links because of its aerial superiority. However, since the limited coverage range, UAV cannot handle scenarios that demand wide coverage. In the meantime, the High Altitude Platform Station (HAPS) is becoming of great value, as it ensures wide coverage. Moreover, compared to other communication systems covering wide area they are characterized by low cost, latency sensitive, fast construction and large capacity [9, 10]. Therefore, we introduce the system model with one HAPS serving as an macro aerial base station to ensure wide coverage and several UAVsserving as small aerial base stations to strengthen the communication in areas like hot spots, remote areas, rural areas, etc. Concurrently, both HAPS and UAV communication attract lots of attention and there is tremendous research on them with OMA. However, considering the exponential growth of connections in future 6G network, it is essential to further improve spectrum efficiency and system throughput. Non-orthogonal multiple access (NOMA) allows multiple users to jointly access a specified resource block, which can perfectly satisfy above requirements [11–13]. Some related works exploited the benefits of NOMA over OMA [14]. In [15], the authors jointly investigated the energy-minimization task offloading and resource allocation considering task offloading decision and subchannel allocation for mobile edge computing. It considers a system with one macro base station (MBS) and multiple small base stations (SBS). However, due to the limit of altitude, both MBS and SBS have obvious disadvantages on LoS links compared to aerial base stations. Besides, NOMA performs successive interference cancellation (SIC) to recover information at the receiving end. Thus, with the increase of terminals’ number, the complexity brought by SIC grows linearly leading to increasing in processing latency. The work in [16] studied the trajectory planning and subslot allocation in UAV-supported clustered-NOMA for IoT, where there was only one single UAV taken into account. To improve spectrum efficiency, paper [17] let the terminals of HAPS be partitioned into several clusters, where C-NOMA was used for intra-cluster terminals, while OMA was used between clusters. In this paper, we put forward the C-NOMA-enabled 6G IoT model with one HAPS serving as macro aerial base station and multiple UAVs serving as small aerial base stations to cover numerous terminals, and investigate the joint subchannel allocation and power control optimization problem. However, due to the strong coupling between subchannel allocation and power control, it is a mixed integer nonlinear programming (MINLP) problem, which is hard to solve. Thus, we model the problem as a manyto-one matching game with peer effects, where UAVs and subchannels acting as two sets of players. Simulations show that our proposed solution not only can reach wide coverage, but also obtain improved spectrum efficiency and terminal complexity. The main contributions of this work are summarized as follows. • We consider a resource allocation model for C-NOMA-enabled 6G IoT with one HAPS and multiple UAVs serving as aerial base stations, where terminals of HAPS are clustered and NOMA is employed for both clustered terminals and UAVs’ terminals. • Since it is a MINLP problem, we search for the optimal resource allocation result by modeling it as a many-to-one matching game with peer effects to obtain a suboptimal solution, where UAVs and subchannels acting as two sets of players.

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• We conduct extensive simulations to evaluate the performance of the proposed approach by comparing it with many benchmark approaches including OMA Approach, NOMA Approach, etc. Simulation results show that the proposed approach outperforms previous solutions in term of uplink sum rate with relatively low complexity. The rest of this paper is organized as follows. We give the system model in Sect. 2. Problem formulation is described in Sect. 3. Sect. 4 elaborates the solution to the problem. In Sect. 5, simulation results and analysis are presented. The conclusions are followed in Section 6.

2 System Model 2.1 System Model

Fig. 1. System model: C-NOMA-enabled 6G IoT

As shown in Fig. 1, we consider a C-NOMA-enabled 6G IoT system with one HAPS and M UAVs. Denote M = {1, 2, . . . , M } as the set of all UAVs, where M ∈ N + is

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UAVs’ number. We denote m = 0 as the index of the HAPS. As such Ms = {0} ∪ M is the set of all HAPS and UAVs. The HAPS serves NKc terminals, which are divided into  i K clusters and cluster k consists of Ukk terminals, where K, Ukk ∈ N + , NKc = K i=1 Ui .   k k k Denote K = {1, 2, . . . , K} as the set of clusters and Uk = U1 , U2 , . . . , Uk  as the set of the k-th cluster’s terminals, while the set of HAPS’ terminals is NK = {{U1 }, . . . , {UK }}. UAV m serves Umm terminals, which is denoted by Um = U1m , U2m , . . . , Umm , Umm ∈ M U as the terminals’ number of all UAVs, N U = i N + . Denote NM i=1 Ui , while the set of M terminals of UAVs is NM = {{U1 }, . . . , {UM }}. As such N = NM ∪ NK is the set of all terminals, where N is terminals’ total number. Denote Unm as terminal n of UAV m and 0 as terminal n of cluster k. Consider a three-dimensional coordinate system, where Uk,n H and H0 represent the flight altitude of each UAV and HAPS, respectively. Denote H = {H0 , H } as the set of flight altitudes. Moreover, the horizontal coordinate of Unm T  T  0 = x0 , y0 is ρnm = xnm , ynm , while ρk,n represents the horizontal coordinate k,n k,n   T 0 . Analogously, ρ = x , y is the horizontal coordinate of HAPS, while of Uk,n 0 0 0 T  ρm = xm , ym represents the horizontal coordinate of the m-th UAV. Since terminal clustering scheme of the HAPS is beyond the research scope of this work, it is omitted and we assume that the terminal clustering result is obtained beforehand. We denote akm ∈ {0, 1} as the subchannel allocation indicator for the m-th UAV and the k-th subchannel, i.e., if UAV m occupies subchannel k, akm = 1; otherwise, akm = 0. akm

=

1, UAV m occupies subchannel k 0

Moreover, multiple UAVs are allowed to reuse the same subchannel to further improve spectrum efficiency. For restricting the inter-cell interference, the maximum number of UAVs occupying subchannel k is limited to amax . The intra-cell interference can be mitigated by applying the SIC technology for both UAVs and the HAPS. 2.2 Communication Model In this subsection, we provide the communication models by considering two kinds of interference, i.e., inter-cell interference Iinter and intra-cell interference Iintra . Assume that each HAPS, UAV and terminal are mounted with one single antenna. We denote the channel power gain between terminal Unm and the UAV m as m = gk,n

Gm Gn E0 2

dnm

, ∀m, n

(1)

where, Gm and Gn denote the directional antenna gains of the UAV and terminal, respectively. E0 represents the channel gain at the reference distance d0 = 1m. dnm stands for the distance between the terminal Unm and the UAV m, represented by

 2 dnm = H 2 + ρm − ρnm  , ∀m, n (2)

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as 0 = gk,n

G0 Gn E0 dn0

2

, ∀k, n

(3)

where, G0 and Gn denote the directional antenna gains of the HAPS and terminal, respectively. E0 represents the channel power gain at the reference distance d0 = 1m.dn0 0 and the HAPS, represented by stands for the distance between the terminal Uk,n dn0

=

2   0  , ∀n H02 + ρ0 − ρk,n 

(4)

Terminals served by UAVs transmit data to the associated UAV by applying NOMA, while terminals of HAPS transmit data to it using C-NOMA. SIC technology can be applied at UAVs and the HAPS to decode signals from terminals sharing the same subchannel. For SIC in uplink scenario, the HAPS or UAV first decodes signal from terminal with higher channel gain by treating signals of others as interference, then removes it from the interference terms of other terminals. Without loss of generality, for terminals served by UAV m on subchannel k, we assume their channel gains follow the m > gm . . . > gm . order as gk,1 k,2 k,n 1) Communication Model for HAPS The signal-to-interference-plus-noise-ratio (SINR) of the n-th decoded terminal at the receiver HAPS in cluster k is expressed as 0 = γk,n

0 go Pk,n k,n 0 0 In,intra + Ik,inter + N0

(5)

0 stands for the transmit power of terminal n in cluster k, I 0 where, Pk,n n,intra = k  U k 0 g 0 is the intra-cell interference from other terminals in the same Pk,i ak0 i=n+1 k,i Umm m 0  0 cluster, Ik,inter = m∈M (akm i=1 Pk,i gk,i ) is the inter-cell interference from term represents the transmit minals of UAVs occupying the same subchannel k, and Pk,n power of terminal n served by UAV m. Moreover, the noise power is denoted as N0 . 2) Communication Model for HAPS The SINR of the n-th decoded terminal Unm at the receiver UAV m on subchannel k is expressed as m = γk,n

m where, In,intra = akm

m gm Pk,n k,n m m In,intra + Ik,inter + N0

(7)

Umm

m m i=n+1 Pk,i gk,i is the intra-cell interference from terminals Ukk 0 m Utt t t  m = ak0 i=1 Pk,i gk,i + t∈M\{m} (akt i=1 Pk,i gk,i ) is the Ik,inter

of the same UAV, inter-cell interference from terminals occupying the same subchannel associated with other clusters and UAVs.

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Thus, the uplink achievable rate of terminal Unm occupying subchannel k is denoted as

 m Rm (8) k,n = Blog2 1 + γk,n

3 Problem Formulation In this section, we formulate an optimization problem to maximize the uplink achievable sum rate of all terminals by jointly considering subchannel allocation and power control. The uplink achievable sum rate of system can be written as Um

Rsum =

M  m 

Uk

Rm k,n

+

m=1 n=1

K  k 

R0k,n

(9)

k=1 n=1

Therefore, the formulated problem in this paper is expressed as max Rsum A, P

(10a)

s.t. Rsum ≥ RT

(10b)

P0 :



akm ≤ 1

(10c)

akm ≤ amax

(10d)

k∈K



m∈M

akm ∈ {0, 1}

(10e)

m 0 ≤ Pk,n ≤ Pmax

(10f)

0 0 ≤ Pk,n ≤ Pmax

(10g)

    m , m ∈ M, k ∈ K, n ∈ N where A = akm , m ∈ M, k ∈ K , Pm = Pk,n m , P0 =   m , m ∈ {0}, k ∈ K, n ∈ N , P = P ∪ P , and R represents the threshold of Pk,n m 0 T k uplink achievable sum rate. Constraints (10b) guarantees the system QoS. Constraints (10c) and (10d) ensure that each UAV is allowed to reuse only one subchannel while each subchannel can be occupied by at most amax UAVs. Constraint (10e) imply that the subchannel allocation decision is a binary variable. (10f) and (10g) are the power constraints for all terminals.

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4 Resource Allocation Solution We assume that the HAPS’ and UAVs’ 3D-locations are given, where all UAVs work in hovering mode, and the terminal associations do not change during the transmission task. Note that P0 is a mixed combinatorial non-convex problem involving binary and continuous variables, and the objective function is non-convex. Furthermore, the spectrum allocation and power control are coupled together to affect the uplink sum rate of system. Therefore, we decouple it into two sub-problems, i.e., a subchannel allocation problem and a power control problem to find the suboptimal solution with low complexity. 1) Subchannel Allocation To obtain the optimal solution of P0, we need to find out all possible resource allocations by the exhaustive method, which is intractable with the number of users and subchannels increasing. To develop a low-complexity algorithm, we model the problem as a many-to-one matching game with peer effects with UAVs and subchannels acting as two sets of players. The joint optimization problem can be solved when UAVs and subchannels are mutually matched with purpose of maximizing the uplink achievable sum rate of all terminals. Base on concepts of matching game theory, we have the following definitions. Definition 1. A many-to-one matching μ is a function from the set of UAVs M into the set of subsets of subchannels K such that. 1) |μ(m)| = 1, ∀m ∈ M; 2) |μ(k)| ≤ amax , ∀k ∈ K. 3) μ(m) = k, if and only if m ∈ μ(k). where, |μ(∗)| denotes the cardinality of matching result. μ(∗) = {0} represents the player is unmatched. Condition 1) states that each UAV is only matched with one subchannel. Condition 2) indicates each subchannel can be matched with at most amax UAVs. Condition 3) implies if UAV m is matched with subchannel k, then subchannel k is also matched with UAV m. Under this model, each UAV simply concerns about the uplink achievable rate of its own terminals, which leads to a utility function for UAV m on subchannel k of Um

Um (k) =

m 

Rm k,n

(11)

n=1

For each subchannel k, it concerns about the uplink achievable rate of terminals occupying subchannel k, which include both terminals of cluster k and terminals of UAVs being matched with subchannel k. Therefore, the utility function of subchannel k under matching μ can be defined as Uk

Uk (μ(k)) =

k 

n=1

Um

R0k,n +

m  

Rm k,n

(12)

m∈μ(k) n=1

To describe the concept of preference for any player over players of the other side, we introduce a preference relation for both UAVs and subchannels. For UAV

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m, we denote m as the preference relation over the set of subchannels. Therefore, for any two subchannels k1 , k2 ∈ K, k1 = k2 , two matchings μ1 , μ2 ∈ {μ}, k1 = μ1 (m), k2 = μ2 (m): (k1 , μ1 ) m (k2 , μ2 ) ⇔ Um (k1 ) > Um (k2 )

(13)

which indicates UAV m prefers subchannel k1 to subchannel k2 if its terminals have a higher uplink achievable rate on subchannel k1 than that on subchannel k2 . Similarly, for subchannel k, we denote k as the preference relation over the set of UAVs. Therefore, for any two subsets of UAVs B1 , B2 ∈ M, B1 = B2 , two matchings μ1 , μ2 ∈ {μ}, B1 = μ1 (k), B2 = μ2 (k): (B1 , μ1 ) k (B2 , μ2 ) ⇔ Uk (B1 ) > Uk (B2 )

(14)

which implies subchannel k prefers the subset of UAVs B1 to B2 if all the terminals served by UAVs in B1 have a higher uplink achievable rate than that served by UAVs in B2 . For each UAV, it can only occupy one subchannel, while for each subchannel, it can match with at most amax UAVs. Therefore, the resource allocation problem is a many-to-one matching game [18]. Besides, the utility of each UAV is influenced not only by the subchannel it is matched with, but also by other UAVs that are using the same subchannel due to the inter-cell interference. Consequently, this resource allocation problem is a matching game with peer effects [19]. Motivated by the manyto-one college admission problem, we propose the definition of swap matching into our matching model. / Definition 2. Given a matching μ with m1 ∈ μ(k1 ), m2 ∈ μ(k2 ) and m2 ∈ / μ(k2 ), a swap matching is defined as μm1 ,m2 = μ\{(m1 , k1 ), (m2 , k2 )}∪ μ(k1 ), m1 ∈ {(m1 , k2 ), (m2 , k1 )}, where m1 ∈ μm1 ,m2 (k2 ), m2 ∈ μm1 ,m2 (k1 ) and m1 ∈ / / μm1 ,m2 (k2 ). μm1 ,m2 (k1 ), m2 ∈ Note that two UAVs exchange their matched subchannels while keeping other UAVs’ matchings unchanged in a swap matching. Based on this concept, we introduce the blocking pair. Definition 3. A pair of UAVs (m1 , m2 ) is called a blocking pair for an existing matching μ with m1 ∈ μ(k1 ), m2 ∈ μ(k2 ), if for a swap matching μm1 ,m2 , the utilities of all the involved players do not decrease, and at least one of the players’ utilities increase. To elaborate, for a blocking pair (m1 , m2 ), each UAV has the desire to get matched with the other’s current partner as a pair under μm1 ,m2 , instead of staying with the current matched partners under μ. A matching μ is said to be stable if there exists no blocking pair for μ.

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According to above definitions, we propose a matching-based resource allocation algorithm for UAVs to find a stable matching result, which is described in Algorithm 1. Firstly, we give an initial matching μ0 , in which UAVs and subchannels are randomly matched with each other. Next, in the swap matching phase, we randomly choose two different UAVs and exchange their subchannels, while the other matchings remain unchanged. Then we calculate the utilities. If these two UAVs form a blocking pair, the swap operation is performed. The swap matching process continues until there is no blocking pair. Finally, a two-sided stable matching is achieved. Theorem 1. The uplink achievable sum rate of all terminals increases after each swap operation, and Algorithm 1 can converge in finite number of swap operations. Proof: Assume a swap operation between UAVs m1 and m2 with m1 ∈ μ(k1 ), m2 ∈ μ(k2 ), which makes the matching changes from μ to μm1 ,m2 . Combining Definition 3, after the swap operation, we have the following inequalities:

 Utotal μm1 ,m2 > Utotal (μ) (15)

 Rtotal μm1 ,m2 > Rtotal (μ)

(16)

Thus, we draw the conclusion that the uplink sum rate increases after each swap operation. Moreover, it is obvious that the number of terminals and UAVs are both limited, thus the number of swap operations is finite, which guarantees Algorithm 1 is to converge after a limited number of iterations.  2) Power Control Given the subchannel allocation, the power control problem for terminals on the same subchannel can be solved. Accordingly, the optimization problem for terminals on subchannel k can be expressed by Um

P1 :

Uk

m k    max Rm + R0k,n Rsum = k,n P

(17a)

Rsum ≥ RT

(17b)

m∈μ(k) n=1

s.t.

n=1

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m 0 ≤ Pk,n ≤ Pmax

(17c)

0 0 ≤ Pk,n ≤ Pmax

(17d)

where, P1 is a non-convex optimization problem. To find the local optimum solution based on successive convex approximation (SCA) approach by tightening the constraints of P1, we can transform the problem into a convex optimization problem [20]. We introduce the following inequality to tighten (17b) qlog2 x + p ≤ log2 (1 + x)

(18a)

where q and p are denoted as 

x˜ q = 1+˜ x p = log2 (1 + x˜ ) −

x˜ 1+˜x log2 x˜

(18b)

The bound is tight with x = x˜ when q and p are chosen as above. Applying (18) to P1 results in the relaxation: Um

m   max m m m B(qk,n log2 γk,n + pk,n ) Rsum  = P

m∈μ(k) n=1

(19a)

Ukk

+



0 0 0 B(qk,n log2 γk,n + pk,n )

n=1

Rsum  > RT

(19b)

m 0 ≤ Pk,n ≤ Pmax

(19c)

0 0 ≤ Pk,n ≤ Pmax

(19d)

s.t.

m , pm , q0 and p0 are fixed. Since the objective and constraint (19b) are where all qk,n k,n k,n k,n m and γ 0 , problem (19) is still nonconvex. By applying the transnot concave in γk,n k,n P 0 = logP 0 , problem (19) can be reformulated as formation  P m = logP m and  k,n

k,n

k,n

k,n

the following approximation problem: Um

m   max  m m + pm ) γk,n = B(qk,n log2 R k,n P sum

m∈μ(k) n=1

Ukk

+



(20a)

0 0 + p0 ) γk,n B(qk,n log2 k,n

n=1

s.t.

Rsum > RT

(20b)

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P. Qin et al. m 0 ≤ Pk,n ≤ Pmax

(20c)

0 0 ≤ Pk,n ≤ Pmax

(20d)

 m 0 Pk,n m and  0 are functions of e where  γk,n γk,n and ePk,n , denoted as (21a) and (21b).

m   Pm m Pm m m m = log e γk,n log2 2 k,n gk,n − log2 In,intra + Ik,inter + N0 = log2 e k,n gk,n Um

m 

−log2 (

m  akm ePk,i

i=n+1

Um



+

U k

k   0 Gm Gi E0 Gm Gi E0 0 + a ePk,i   2  k 2  0  Hm2 + ρm − ρim  i=1 Hm2 + ρm − ρk,i 

(akm

m 

i=1

m∈μ(k)

0 = γk,n log2

=

m 

ePk,i

Hm2

Gm Gi E0  2 ) + N0 ) + ρm − ρ m  i

 0 0 log2 ePk,n gk,n

0 o log2 Pk,n gk,n

⎛

  0 0 − log2 In,in + Ik,cr + N0

Uk

k 0 ⎜  0 P − log2 ⎝ ak e k,i

i=n+1

+



m∈μ(k)

Um

akm

m 

i=1

m 

ePk,i

G0 Gi E0 2   0  H02 + ρm − ρk,i  ⎞

(21a)

(21b)

G0 Gi E0 ⎟ 2 + N0 ⎠    2 0 H0 + ρm − ρk,i 

Note that, problem (20) is a standard concave maximization problem in the new P 0 , since constraint (20b) is sum of convex exponentials, which variables  P m and  k,n

k,n

is convex. Furthermore, the objective of (20) is a log-sum-exp function which is convex as well. Thus, problem (20) is a standard convex optimization problem. We propose Algorithm 2 to solve problem (20) iteratively. According to [20], this algorithm can converge to a local optimal solution that satisfies the KarushKuhn-Tucker (KKT) conditions of problem P1 in finite iterations.

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5 Simulations and Analysis In this section, we investigate the performance of the proposed solution by numberus simulations. In our simulation, we consider a various number of IoT terminals which are randomly distributed within a geographical area of size 1 km × 1 km, the HAPS is located in the center, each UAV is located in the geometric center of the associated terminals. Besides, each cluster contains 2 terminals and each UAV serves 2 terminals. The simulation result of the experiment is achieved from 500 random channel instances on average. The compared benchmark approaches are shown as follows: • OMA Approach: Terminals are equipped with OMA technique. • NOMA Approach: Terminals are equipped with NOMA technique but there is no UAV reusing subchannels of HAPS, i.e., no matching strategy is applied. • Random Matching & Optimize Power: Subchannels and UAVs are randomly matched, while all terminals adopt their optimal power. • Optimize Matching & Random Power: Contrary to Random Matching& Optimize Power, the matching between subchannels and UAVs are optimized, while the power of terminals is randomly set.

Fig. 2. CDF of number of swap operations (K Fig. 3. Uplink achievable sum rate vs. = 10) number of subchannels

Figure 2 shows the cumulative distribution function (CDF) of the number of swap operations for different numbers of UAVs with K = 10, where the maximum number of UAVs on each subchannel is amax = 4. It can be seen that the number of swap operations has a positive relation with the number of UAVs. Specifically, the proposed approach converges within at most 8 iterations when there are M = 30 UAVs. It also indicates that the complexity of the proposed algorithm is relatively low.

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In Fig. 3, we compare the uplink achievable sum rate in various circumstances, including OMA Approach, NOMA Approach and our proposed approach. It is obvious that the proposed solution outperforms all other approaches. Moreover, with the increase of amax , the uplink achievable sum rate also increases. The reason lies behind is that the larger amax is, the more terminals will be served reusing the same subchannel.

Fig. 4. Uplink achievable sum rate vs. number of UAVs: comparison of benchmark approaches

Fig. 5. Performance of the proposed approach with different number of subchannels

Figure 4 shows the uplink achievable sum rate versus the number of UAVs with dynamic number of subchannels from K = 5 to K = 8 corresponded with number of UAVs from M = 10 to M = 16. The threshold of UAV number amax = 3. Specifically, the benchmark approaches considered in this circumstance include Random Matching & Optimize Power Approach and Optimize Matching & Random Power Approach. It can be seen that the proposed approach always has the best performance in terms of system sum rate. Figure 5 plots the uplink achievable sum rate of our proposed approach with different number of subchannels from K = 5 to K = 8 versus the various number of UAVs from M = 10 to M = 30. Besides, the threshold of UAV number occupying each subchannel is set as amax = 3. It can be seen that with the increasing of UAVs’ number and subchannels, the system uplink achievable sum rate increases due to better choices for both subchannels and UAVs. But then it remains stable regardless of the number of subchannels or UAVs. This is because when the spectrum is fully reused, the resource utilization becomes saturated.

6 Conclusions In this paper, we formulated the resource allocation problem for C-NOMA-enabled 6G IoT, where HAPS employed C-NOMA to serve its associated clustered terminals, while UAV employed NOMA technology to serve terminals by reusing subchannels. To maximize the uplink achievable sum rate, we investigated this problem by leveraging the spectrum allocation and power control systemically. Since it is a MINLP problem, we optimized it by solving the many-to-one matching game coupled with power control. Both theoretical analysis and simulations show that the uplink achievable sum rate of our

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solution outperforms previous benchmark approaches with relatively low complexity. We can see that the matching game theory has pointed out a new way for resource allocation in 6G wireless communication. In the future, we will take the 3D-locations into consideration and optimize UAVs’ horizontal coordinate and hovering altitude jointly to better improve system efficiency.

References 1. Letaief, K.B., Chen, W., Shi, Y., Zhang, J., Zhang, Y.A.: The Roadmap to 6G: AI Empowered Wireless Networks. IEEE Commun. Mag. 57(8), 84–90 (2019) 2. Huang, X., Zhang, J.A., Liu, R.P., Guo, Y.J., Hanzo, L.: Airplane-aided integrated networking for 6G wireless: will It work? IEEE Veh. Technol. Mag. 14(3), 84–91 (2019) 3. Yang, P., Xiao, Y., Xiao, M., Li, S.: 6G wireless communications: vision and potential techniques. IEEE Netw. 33(4), 70–75 (2019) 4. Saad, W., Bennis, M., Chen, M.: A vision of 6G wireless systems: applications, trends, technologies, and open research problems. IEEE Netw. 34(3), 134–142 (2020) 5. Giordani, M., Polese, M., Mezzavilla, M., et al.: Towards 6G Networks: Use Cases and Technologies (2019) 6. Motlagh, N.H., Bagaa, M., Taleb, T.: UAV-Based IoT platform: a crowd surveillance use case. IEEE Commun. Mag. 55(2), 128–134 (2017) 7. Zhang, X., Cheng, W., Zhang, H.: Heterogeneous statistical QoS provisioning over airborne mobile wireless networks. IEEE J. Sel. Areas Commun. 36(9), 2139–2152 (2018) 8. Vondra, M., Ozger, M., Schupke, D., Cavdar, C.: Integration of satellite and aerial communications for heterogeneous flying vehicles. IEEE Net. 32(5), 62–69 (2018) 9. Zhou, D., Gao, S., Liu, R., Gao, F., Guizani, M.: Overview of development and regulatory aspects of high altitude platform system. Intell. Converg. Netw. 1(1), 58–78 (2020) 10. Gharbi, S., Zangar, N., Aitsaadi, N.: Overview: high altitude platform network for disaster and crises application. In: 2019 International Conference on Information and Communication Technologies for Disaster Management (ICT-DM), Paris, France, pp. 1–2 (2019) 11. Islam, S.M.R., Avazov, N., Dobre, O.A., Kwak, K.: Power-domain non-orthogonal multiple access (NOMA) in 5G systems: potentials and challenges. In: IEEE Communications Surveys & Tutorials, vol. 19, no. 2, pp. 721–742, Second quarter 2017 12. Liu, Y., Qin, Z., Elkashlan, M., Nallanathan, A., McCann, J.A.: Non-orthogonal multiple access in large-scale heterogeneous networks. IEEE J. Sel. Areas Commun. 35(12), 2667– 2680 (2017) 13. Zhao, J., Liu, Y., Chai, K.K., Nallanathan, A., Chen, Y., Han, Z.: Spectrum allocation and power control for non-orthogonal multiple access in hetNets. IEEE Trans. Wireless Commun. 16(9), 5825–5837 (2017) 14. Islam, S.M.R., Avazov, N., Dobre, O.A., et al.: Power-domain non-orthogonal multiple access (NOMA) in 5G systems: potentials and challenges. IEEE Commun. Surv. Tutorials 19(2), 721–742 (2016) 15. Wu, W., Zhou, F., Hu, R.Q., Wang, B.: Energy-efficient resource allocation for secure NOMAenabled mobile edge computing networks. IEEE Trans. Commun. 68(1), 493–505 (2020) 16. Na, Z., Liu, Y., Shi, J., Liu, C., Gao, Z.: UAV-supported clustered NOMA for 6G-enabled Internet of Things: trajectory planning and resource allocation. IEEE Internet of Things J. Early Access 8(20), 15041–15048 (2020) 17. Na, Z., Liu, Y., Wang, J., et al.: Clustered-NOMA based resource allocation in wireless powered communication networks. Mob. Netw. Appl. (5) (2020) 18. Lovasz, L., Plummer, M.D.: Matching Theory. North Holland, Press (1986)

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19. Bodine-Baron, E., Lee, C., Chong, A., et al.: Peer effects and stability in matching markets. Lect. Notes Comput. Sci. 6982, 117–129 (2011) 20. Papandriopoulos, J., Evans, J.S.: Low-complexity distributed algorithms for spectrum balancing in multi-User DSL networks. In: 2006 IEEE International Conference on Communications, Istanbul, pp. 3270–3275 (2006)

Application Research on an Automated Batch Network Reinforcement Method Based on SSH Protocol Zhao-yang Li(B) , Bo Zhou, Wei-Gui Zhou, Tong-Qiao Xu, and Xiao-Feng Zhang XiChang Satellite Launch Center of China, XiChang 615000, Sichuan, China [email protected]

Abstract. Based on an in-depth analysis of the difficulties faced by large-scale network security reinforcement activities in the implementation and deployment process, this paper proposes an automated batch network security reinforcement method based on the SSH protocol. This method uses multiple open source tools such as paramiko, netmiko, TextFSM and pandas to design an automated batch network security reinforcement software architecture that integrates multiple functions such as device scanning, parameter analysis, remote execution, template filtering, and logging.At the same time,this article also describes the programming implementation process of several key modules of the method in the python ecosystem. Practical application shows that the automated batch network reinforcement method based on SSH protocol proposed in this paper is reliable, operability, and has good application value and promotion prospects. Keywords: Network reinforcement · SSH · Netmiko · Paramiko · Pandas

1 Introduction Although network reinforcement plays an increasingly prominent role in network operation and maintenance practices, deploying a series of network security reinforcement strategies in large-scale enterprise production networks often faces many difficulties. For example, network security reinforcement strategies overlap and lack hierarchical division; the accuracy of manual reinforcement is difficult to guarantee; the low degree of normalization of IT resource operation and maintenance makes it difficult to implement batch operations. Therefore, this paper proposes an automated batch network reinforcement method based on the SSH protocol to achieve large-scale collaborative deployment for different levels of network security reinforcement strategies, and solve the problem of low automation and low efficiency of reinforcement activities in enterprise production networks.

2 Technical Approach Network security reinforcement includes boundary reinforcement, intranet reinforcement and service reinforcement. Network boundary reinforcement focuses on security © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 101–109, 2022. https://doi.org/10.1007/978-981-19-0390-8_13

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policy designation around router access control lists and firewall security configuration. The internal reinforcement of the network focuses on computer terminal security and internal network switch security to carry out corresponding reinforcement activities. Service reinforcement aims to prevent external damage to the internal network information system through methods such as content grading, information encryption, and vulnerability repair. In order to realize the large-scale collaborative deployment of different levels of network security reinforcement strategies, this article uses an open source tool based on the SSHv2 protocol to realize remote management and control of border routers, border firewalls, intranet switches and servers. Through paramiko [6] or netmiko [7] library, the scripted various network reinforcement strategies are transmitted to the corresponding remote device for execution, so as to realize the automated operation of batch network reinforcement activities [2].

3 Structure Design The automated batch network reinforcement method based on the SSHv2 protocol consists of an input layer, an information processing layer and an output layer, as shown in Fig. 1. The input layer includes GUI module, configuration module and input module. Among them, the GUI user interface based on PyQt5 is the main framework for loading other modules; the configuration module provides various information required for SSH network connection; the input module provides parameters for the dynamic generation of security reinforcement scripts.

Fig. 1. The diagram of structural design of automated batch network reinforcement method

The information processing layer composed of thread management module and key function module is the core of the whole structure design. Among them, the thread

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management module is the commander and scheduler of the design, completes the ondemand scheduling of various key functions, and makes the automated batch network reinforcement process run in a multi-threaded manner. The realization of key functions includes four steps: core library call, template library call, script library call and log module call. The core library is mainly implemented based on the paramiko for reliable execution of remote reinforcement scripts. The template library is mainly implemented based on the TextFSM, which is used to filter the execution results of the reinforcement script [4]. The script library depends on the type of remote device. The log library is used to track and record the execution process of various network security reinforcement activities in different levels and categories. The output layer mainly completes the safe modification of the device configuration file and outputs various log information files.

4 Implementation Process 4.1 Normalization The normalization of various network resources is the prerequisite and basis for automated batch network reinforcement activities. This paper divides the network normalization types into six types: network resource normalization, remote control protocol normalization, equipment operation normalization, information output normalization, script writing normalization, and template writing normalization. Taking the normalization of the remote control protocol as an example, it is required to change the remote control protocol from telnet to SSH, and select the appropriate host key mechanism and SSH user authentication mechanism. In the specific implementation process, open source tools can be used to realize automated batch operations of large-scale network devices. 4.2 Parsing Module The main function of the parsing module is to provide various parameters for the dynamic generation of security reinforcement scripts, the main work objects are various exchanged and shared information, and the main dependent libraries are pandas, python-docx, and xml.etree.ElementTree. This article assumes that it is necessary to get a set of IP address, MAC address and switch port information from an Excel file. For the above scenarios, a pandas-based parameter parsing module can be built [10]. The parsing process includes five steps, as shown in Fig. 2. The first step is to load the original Excel data file through pandas; the second step is to verify the normalization of the data structure; the third step is to clean the data and parameter extraction through the pandas. dataframe tool; the fourth step is to filter invalid parameters; the last step is the dynamic generation of network security reinforcement scripts [11, 12].

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Fig. 2. Parsing module based on pandas

4.3 Core Libraries The four core libraries shown in Fig. 1, pingTools, ExecScriptNet, ExecScriptPara, and loginCheck, are the basis for realizing network reinforcement key functions. In the python3 ecosystem, ExecScriptNet, loginCheck and ExecScriptPara are generally implemented based on paramiko or netmiko, and the implementation of the pingTools library is mainly shown in Table 1. This article focuses on the implementation of pingTools and ExecScriptNet. PingTools Library. Table 1 lists five methods to implement connectivity testing based on python3. They are: 1) Execute the ping command with the call function of the subprocess module. 2) Execute the ping command with the system function of the os module. 3) Realize the ping test with the open source library pythonping [1]. Table 1. Performance comparison between different implementations of ping by python Type of Ping/number of hosts

1 × 255

5 × 255

10 × 255

20 × 255

subprocess.call(‘ping –n 5 host’)

50 min

390 min

750 min

1500 min

os.system(‘ping –n 5 host’)

55 min

380 min

780 min

1560 min

pythonping(single-threading)

65 min

390 min

760 min

1580 min

Pythonping(multithreading)

20 min

210 min

420 min

900 min

Scapy(multiprocessing)

8 min

55 min

90 min

190 min

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4) The pythonping tool based on python multithreading. 5) The ping tool is realized based on the scapy library in a multi-process way [9]. If the function of the above five methods to achieve ping test is marked as ping_method_x(), the typical application of pingTools in device scanning and detection is shown in the pseudo code of Table 2. In the pseudocode, octet3 represents the third octet of the IP address, octet4 represents the fourth octet of the IP address, and the values of octet3 and octet4 are listed in the Number line of Table 1. For example, when the value of octet3 is (0:10) and the value of octet4 is (1:255), the time consumed by the above five ping tests is approximately 750 min, 780 min, 760 min, 420 min, and 90 min, respectively. Table 2. A typical application example of pythonTools in pseudo-code def scan_switch(self): octet3 = range(0, 10) octet4 = range(1, 255) for ip3 in octet3: for ip4 in octet4: ip_address = "10.0." + str(ip3) + "." + str(ip4) ping_ret = ping_method_x(target=ip_address, count=5) if 'Reply' in str(ping_ret): print(ip_address + " is reachable!") else: print(ip_address + " is not reachable!")

Taken together, in large-scale network reinforcement activities, the first three methods are more suitable for simple device address filtering, while the latter two methods are suitable for normalized processing of information. ExecScriptNet Library. ExecScriptNet is a python class implemented based on netmiko. See Table 3 pseudo code for its implementation details, which mainly include 7 methods [8]. They are: 1) 2) 3) 4) 5) 6) 7)

The construction method accepts the IP address of a network device. The login_device() method is used to log in to the remote host. The logout_device() method is used to save settings and release resources. The exec_single_command() method is used to execute a single command. The exec_multi_command() method is used to execute multiple commands. The clean_annotation() is used to clean the annotations in scripts. The output_handler() is used to normalize the output results.

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Z. Li et al. Table 3. Implementation of ExecScriptNet class in pseudo code class ExecScriptNet(object): def __init__(self,m_sw_ip): self.sw_ip=m_sw_ip self.ssh_conn=None def login_switch(self): cp = LoginParaReader() sw_huawei = { 'device_type': 'cisco','ip': self.sw_ip, 'username': cp.get_username(),"port": cp.get_ssh_port(), 'password': cp.get_password() } try: self.ssh_conn = ConnectHandler(**sw_huawei) logging.warning("%s ,SSH connection is ok!\n" % self.sw_ip) except NetmikoTimeoutException as te: print(te,'Connection timed out for {0}.\n'.format(self.sw_ip)) except NetmikoAuthenticationException as ne: print(ne,'Authentication Error for {0}.\n'.format(self.sw_ip)) return

self.ssh_conn

def logout_switch(self): if self.ssh_conn != None: self.ssh_conn.save_config() self.ssh_conn.disconnect() def exec_single_command(self,single_script): single_script=self.__clean_annotation(single_script) ret=self.ssh_conn.send_command(single_script) return self.__output_handler(ret) def exec_multi_commands(self,multi_script): multi_script = self.__clean_annotation(multi_script) ret=self.ssh_conn.send_config_set(multi_script) return self.__output_handler(ret) def clean_annotation(self, cmd): pass def output_handler(self, output): pass

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4.4 General Key Function The general key function focuses on the on-demand calling of the core library, template library and script library. The implementation process of each key function includes eight steps, as shown in Fig. 3.

Fig. 3. The flow chart of general key function

First, accept the parsing parameters passed by the thread management module. Second, load the template library to realize the normalized processing of irregular information. Third, load the script library to cope with the different network reinforcement tasks. Fourth, load the core library and return the SSH connection object. Fifth, log in to the remote terminal device. Sixth, automate execution of specific scripts on remote devices and return the results. Seventh, exit the remote device. Eighth, record log information. 4.5 Other Modules The important modules in the method designed in this paper also include thread management module, logging module, template library and script library. The thread management module is implemented based on PyQt5.Qthread [3]. The logging module is implemented based on the python open source tool loguru. The template library is mainly implemented based on TextFSM and ntc-templates [5]. The writing of the script library differs depending on the device type. Due to space limitations, this article will not give specific pseudo-codes for the specific implementation details of each module.

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5 Application

Fig. 4. Automated batch network reinforcement software written in PyQt5

Automated batch network reinforcement software based on python3 consists of six modules: system setting, normalized operation, network reinforcement, common command execution, progress display and log query.The software can be used for batch automated modification of remote control protocol for network devices, batch automated binding of user information, batch automated closing of idle ports, batch automated configuration of ACL policies, batch automated modification of firewall blocking policies and batch automated shutdown of invalid service, etc., as shown in Fig. 4.

6 Conclusions With the increasingly severe network security situation and the increasing complexity of the network scale and structure, large-scale network security reinforcement activities in the corporate intranet are facing more and more difficulties. Therefore, this paper proposes an automated batch network reinforcement method based on the SSH protocol. Based on this method, a software implementation architecture for integrating multiple functions such as device scanning, parameter analysis, remote execution, template filtering and log recording is designed and implemented by programming. Practical application shows that the automated batch network reinforcement method based on SSH protocol proposed in this paper is safe, reliable, operability, and has good application value and promotion prospects.

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References 1. Maggio, A.: pythonping Documentation. (2020-11-10)(2021-01-01) https://github.com/ale ssandromaggio/pythonping 2. Aly, B.: Hands-On Enterprise Automation with Python. Packt Publishing, Birmingham UK. 2018–06:[250–261] 3. Meier, B.: Python GUI Programming Cookbook: Develop functional and responsive user interfaces with tkinter and PyQt5. 3rd Edition. Packt Publishing, Birmingham UK 201910:[361-378] 4. Google. TextFSM Documentation. (2019-02-10) (2020-01-01) https://github.com/google/tex tfsm 5. Edelman, J.: ntc-templates Documentation.(2019-05-10) (2021-01-01) https://github.com/net worktocode/ntc-templates 6. Forcier, J.: Paramiko’s API documentation. (2020-09-12) (2021-01-01) http://docs.paramiko. org/en/stable/ 7. ktbyers.Netmiko API-Documentation. (2020-09-10) (2021-01-01) https://ktbyers.github.io/ netmiko/#api-documentation 8. ktbyers. Netmiko Tutorials/Examples/Getting Started. (2020-11-01) (2021-01-01) https://git hub.com/ktbyers/netmiko 9. Biondi, P.: scapy GENERAL DOCUMENTATION.(2020-10-01) (2021-01-01) https://scapy. readthedocs.io/en/latest/installation.html 10. Molin, S.: Hands-On Data Analysis with Pandas. Packt Publishing, Birmingham UK, 201907:[301-31] 11. Wes McKinney and the Pandas Development Team. pandas: powerful Python data analysis toolkit Release 1.2.0. (2020-11-26)(2021-01-01). https://pandas.pydata.org/docs/ 12. Wes McKinney. Python for data analysis: data wrangling with Pandas, NumPy, and IPython. 2nd Edn. Farnham, UK: O’Reilly Media, 2012-11:[231-238]

Research on UAV Human Tracking Method Based on Vision Sun Xiaodong(B) , Wang Zhiqiang, Zhang Yao, and Wang Yijia School of Intelligence and Electronic Engineering, Dalian Neusoft University of Information, Dalian 116023, China [email protected]

Abstract. Based on the Adaboost method and the MHT multi-hypothesis tracking algorithm, the DMW dynamic minimum window algorithm is proposed to detect and track the human body image. It optimizes the image processing speed, and achieves the effect of smooth operation on the mobile processor. Combining this algorithm with UAV target tracking application scenarios, a complete UAV visual tracking system design scheme is proposed. In the system implementation, the third-generation Raspberry Pi’s quad-core A53 processor is used. According to the work flow of image acquisition, target detection and tracking, and drone flight control, the automatic tracking flight function is successfully realized. The effect of algorithm application is evaluated qualitatively and quantitatively. Keywords: UAV · Adaboost method · MHT tracking algorithm · DMW algorithm · Human body tracking

1 Introduction In recent years, small four-axis UAVs have been used in various application. One of the important applications is using UAVs for long-term aerial photography of target human tracking. The popularity of artificial intelligence technology has promoted unprecedented rapid development of image recognition technology. Applying image recognition technology to UAVs, using the UAV’s vision system to detect and track the human, then lock and follow the human automatically is of important meanings in practical applications. At present, the main research methods on the UAV dynamic target tracking and positioning can be divided into two groups. One is tracking based on airborne sensors and GPS positioning. But the tracked target needs to wear a tracker, and the positioning accuracy deviation is large. Another one is tracking based on the visual recognition. The biggest challenge is that the dynamic and complex background causes great interference to the target detection, which makes many static background based tracking algorithms such as frame difference method and background difference method is not applicable [1]. Another issue is that image processing algorithms require high processor performance, which makes it difficult to process in real time on the airborne processor. Most of the existing schemes return the UAV image to the local workstation for processing, and then © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 110–118, 2022. https://doi.org/10.1007/978-981-19-0390-8_14

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feedback the image detection and tracking results to the UAV for flight control, thereby increasing the system transmission delay and complexity [2]. In this paper, the human upper body image detection technology is combined with the drone, and a dynamic minimum window algorithm (Dynamic Minimized Window, DMW) is proposed, so that the efficiency of the target detection algorithm is optimized significantly and using the multiple hypothesis tracking algorithm (Multiple Hypothesis Tracking, MHT) to track the human body dynamically. With the third-generation Raspberry Pi’s quad-core A53 processor as the airborne processor, a set of UAV target automatic tracking flight system is successfully designed and implemented.

2 Visual Tracking Algorithm Analysis There are generally four types of visual tracking algorithms: region-based tracking, feature-based tracking, deformation template-based tracking, and model-based tracking [3]. Based on the feature tracking algorithm. This paper integrated uses feature-based tracking algorithm and improves the performance, selects the more complex and changeable human body as the tracking target, researches on the practicality and usability of the system. In terms of processing steps, the entire tracking algorithm can be split into two parts: target detection and target tracking. In order to speed up target detection, a moving target detection efficiency optimization algorithm is designed. 2.1 Human Object Detection Algorithm There are several algorithms for moving object detection: adjacent frame difference method, optical flow method, background subtraction method, statistical learning method, and hybrid method [4]. In the UAV application scenario, the camera is in motion, so the adjacent frame difference method and background subtraction method which are more sensitive to the background cannot be applied. As the optical flow method needs to perform calculations on all pixels in all frames, the amount of calculation is too large, it is not suitable for drones which has the limited calculate ability. Therefore, in this paper, a Adaboost method based on statistical learning methods which is more effective is chosen to realize the detection of human upper body features. There are two reasons for choosing the human upper body as the detection target. One is that compared to the head, the tracking target is larger, and it is not easily interfered by hats and other accessories. The second is that it is not necessary to maintain a vertical angle to collect images of the human upper body, which is more in line with aerial photography requirements. The principle of the Adaboost method proposed by P. Viola et al. [5] has three main steps. The first step is the image description method of “Integral Image”. The second step is to use the Adaboost algorithm to extract feature values from a large number of sample data and learn the classifier. The third step is to further build a more complex hierarchical classifier to quickly discard the background area in order to save time to process the target area. The last child window that meets the requirements is the window where the object is located. This method is effective in human detection [6, 7]. This paper uses this method to detect the human upper body on the Raspberry Pi 3B + platform (quad-core A53 64-bit @1.4 GHz), the continuous detection speed of 1280x720 resolution images is about 4 frames per second.

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2.2 Single Moving Object Detection Efficiency Optimization Algorithm The object detection rate in Sect. 2.1 does not meet the object tracking requirements in a UAV processor as its limited resources obviously. In order to improve the detection speed of the target human body, this paper proposes a dynamic minimum detection window method (DMW) to optimize the single object detection algorithm. This method is based on the premise that the tracking object moves slowly and the image frame rate is higher. Generally, it is more effective when the object moving speed is below 10 m/s, and the video frame rate is above 10 frames/s.

Fig. 1. DMW method schematic

The computation of the object detection algorithm is proportional to the image pixel size. The working principle of the DMW method is to reduce the scope of object detection as much as possible, thereby increasing the processing speed. The specific method is to predict the maximum possible range of motion of the object in the next frame according to its size and motion speed, which is the smallest image including the object. Then the image which is clipped according to the predicted object range is used as input to the object detection algorithm. In this way, invalid images can be clipped before the images are input to the object detection module, and non-tracking target images can be clipped at the same time. This improves the efficiency and accuracy of target detection greatly. Figure 1 shows the working principle of the DMW method. Among them, the size and position of image window of the next frame is predicted according to the object speed V, frame rate R and the current object position (X , Y ) and size (W × H ). The calculation equation is shown in formula (1) to formula (4). α and β represent the actual width and height of the object which are generally set as a constant. V can be further accurately calculated using the maximum movement speed of the object or predicted speed of the object at the next moment. W = W +

2WV Rα

(1)

H = H +

2HV Rβ

(2)

X = X −

WV Rα

(3)

Y = Y −

HV Rβ

(4)

The multiple calculation formula for the image area reduction after clipping is shown in Eq. 5. Where the original size of the image is (W  × H  ), (1 + 2V/R/α) (1 + 2V/R/β)

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is regarded as constant. Therefore, the image reduction factor is proportional to the original image area and the object frame area, that is, proportional to the square of the distance between the object and the camera. So that, the longer the shooting distance is, the higher the efficiency is. T=

W  H    WH 1 + 2V Rα 1 +

2V Rβ



(5)

After detecting the object position in the clipped small window image, it is necessary to restore the coordinates of the object to the original image. In the case of multi-object tracking, using multi-thread programming based on single-object algorithm to extend. 2.3 MHT Human Tracking Algorithm The MHT multi-hypothesis tracking algorithm is a very effective algorithm for tracking moving objects. This method is proposed by Reid as early as 1979, and is gradually optimized for engineering applications [8]. Two improved m2 optimal MHT techniques were proposed to optimize calculation amount.MHT establishes multiple candidate hypotheses, and performs delayed association judgments through subsequent data detection. The flow implementation is shown in Fig. 2.

Fig. 2. MHT flow implementation

The new observations adopt the object detection algorithm in the previous section, which can detect the characteristics of the human upper body from the complex background and obtain the contour position of the upper body. Compared with the head recognition tracking algorithm, the upper body has a large target and does not depend on the angle and cannot be occluded easily, so the object detection is more accurate. Since people move slowly, this paper uses the nearest neighbor method(Nearest Neighbor, NN) in data association processing [9]. The idea of this method is to set the correlation gate to limit the number of potential decisions. When there are multiple data points within the correlation gate, the measured value which is closest to the predicted value will be correlated. Considering the running speed of a person is generally below 5 m/s, the moving distance of the interval of each frame of image is converted into 33 cm, which is exactly the same as the width of the human body. Therefore, the size of the correlation gate is set to a human body width, and multiple data in the correlation gate is correlated choosing the minimum ratio of distance squared to the human body width according to formula (6).   X 2 + Y 2 (6) Min W2

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Further, in this paper, the Kalman filter algorithm is used to smooth the trajectory data to eliminate the jitter of the pedestrian trajectory which can solve the problem of Gaussian noise when obtaining the position and size of the human body [10]. The Kalman algorithm mainly estimates the predicted value of the current state according to the optimal probability based on the predicted value of the previous state and the measured value of the current state. The formula for calculating the optimal predicted value in the current state is shown in Eq. (7), where X(k|k) is the optimal estimated value in the k state, Z(k) is the measured value in the k state, and H is the system parameter, Kg(k) is Kalman gain. Kg(k) should be judged by the covariance originally, and the calculation formula is shown in formula (8). In order to simplified calculation, the ratio of the square of the measured value displacement to the square of human body width in the k-1 state is used for calculation. The measured system parameter H’ takes 0.5 as the optimal value, as shown in Eq. (9). X (k|k) = X (k|k − 1) + Kg(k)(Z(k) − HX (k|k − 1)) P(k|k − 1)H  HP(k|k − 1)H  + R   H  X (k)2 + Y (k)2

Kg(k) = Kg(k) =

W (k − 1)2

(7) (8)

(9)

2.4 Tracking Height and Distance Control Algorithm According to the requirements of human upper body recognition algorithm and aerial photography angle, the best shooting angle is to keep the camera at 45° diagonally above the human body. The tracking position of the drone depends on two elements: the height of the drone and the shooting distance of the camera. The height of the drone can be directly obtained from the ultrasonic distance sensor, while the shooting distance needs to be calculated according to the image size of the tracking human body. The calculation formula is as Eq. 10 shows. d =f ×

H p

(10)

 h=

d2 2

(11)

d is the actual distance between the drone and the object, f is the focal length coefficient, H is the pixel height of each frame, and p is the pixel of the human upper body image. According to the Pythagorean theorem, the formula for UAV height h is shown in Eq. 11 f needs to be tested according to the selected camera focal length. In this paper, a 6 mm focal length, 720P resolution camera is used, and f is tested as 0.83. In order to maintain 5 m tracking distance, the human body detecting pixel should be about 120 pixels, and the drone height should be controlled at about 3.5 m.

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3 System Design In this paper, the UAV of DJI N3 is used as the experimental platform and an object tracking subsystem is added to the structure. The subsystem includes a three-axis pan tilt, data processing unit, ultrasonic sensor, and camera. The N3 flight control main board is the core module. Comprehensive utilization of IMU, barometer, GNSS and compass modules can achieve precise attitude control and high-precision positioning functions of the aircraft. Raspberry Pi 3B+ is the core module of the object tracking subsystem. It connects to the camera, three-axis pan tilt, buzzer, and ultrasonic, and connects to the N3 flight control main board through the UART serial port to achieve flight control. The object tracking subsystem realizes the image tracking algorithm and the control of following height and distance. The software is designed based on ARM Linux architecture and is divided into six modules: image acquisition, human upper body detection, object tracking, flight control, communication control and safety obstacle avoidance. Each module adopts multi thread independent task, and the interface between modules is realized by thread communication and production-consumer model, which optimizes system parallel processing and improves execution efficiency greatly. The upper body detection and tracking is the core function of the entire software. After acquiring a frame of image, the software decides whether to enter the DMW clipping algorithm or not according to the detection of the previous frame, then send the processed image to the upper body detection algorithm for object detection. The tracking data is updated according to the detection results through the MHT tracking algorithm. If the tracking is lost, the software starts the target scanning program, otherwise controls the drone to fly following the object.

4 System Testing First UAV object tracking is tested to evaluate the tracking system qualitatively. After starting tracking, the drone can move with the movement of the person, adjust the object within the drone’s image range, and maintain an appropriate distance from the object. When there is no interfering people around the tracking target, or the tracking target keeps a distance of more than 1 m from the surrounding people, and moves at a low speed, the tracking effect is better, and the detection algorithm has a higher tolerance for background complexity, which can reach the system function goal. However, when the distance between the tracking object and the surrounding people is less than 1 m or the object moves fast, the probability of tracking failure will increase significantly. Then the target tracking algorithm is tested. Using a third-party human body detection library, its detection success rate is about 85%, and the effect is relatively stable. This article focuses on the effects of DMW algorithm and tracking algorithm. It is found that the frame rate of the recognition image per second has a great influence on the human tracking effect. Comparing tests of DMW algorithm and common full-frame recognition algorithm at different shooting distances are done under human moving at

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speed of 1 m/s. In Fig. 3. it can be seen that the DMW algorithm is very sensitive to the shooting distance, the farther the distance is, the higher the recognition speed is, but the distance has little effect on the ordinary algorithm. In addition, the efficiency of the DMW algorithm is almost unchanged under different numbers of people, but the effect is relatively large to the ordinary algorithm. Figure 4 shows the statistics of the tracking success rate. The DMW algorithm has the best success rate in the distance range of 5 ~ 10 m. Within 3 m, although the human body detection rate is higher, the tracking algorithm is delayed due to the lower recognition frame rate and the speed of adjusting the camera posture cannot keep up with the moving speed of the human body, which will result in the human body detach from the image and losing tracking eventually. When the shooting distance exceeds 10 m, the target human body pixels become lower, and the success rate of human body detection begins to decrease, resulting in a decrease in the tracking rate gradually.

Fig. 3. Relationship between shooting distance and frame recognition rate

Fig. 4. Relationship between shooting distance and detection/tracking rate

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Fig. 5. Relationship between target moving speed and tracking success rate

It can also be seen that the DMW algorithm plays an obvious role in improving performance. If there is no DMW algorithm, the entire image will be scanned regardless of the size of the target object, and the recognition frame rate will always be low, which cannot be applied in practical application. The relationship between human body moving speed and tracking success rate are tested under the recognition distance is 2 m, 5 m, and 10 m. The results are shown in Fig. 5. As the speed increases, the tracking success rate tends to decrease rapidly, and the longer the distance is, the better the tracking effect is. When the distance is far, as the recognition frame rate is larger, the tracking continuity will be better, the image vision becomes larger, and the jitter caused by drone flight control is more adaptable, so the adaptability to speed is better. The results are consistent with the theoretical analysis.

5 Conclusion This paper proposes a more effective design for the application of UAV target tracking comprehensively using current popular image processing algorithms. The efficiency of the algorithm is improved obviously. The originally complex image processing algorithm can be applied to the embedded platform, and implemented and verified on the UAV successfully, which achieves certain practical value. However, the current research in this paper only achieves the tracking of the human body, which cannot be extended to the tracking of any object. Further research and investigation should be focus on the effective tracking under severe conditions such as multiple object interference and overlap.

References 1. Lin, F., et al.: Development of a vision-based ground target detection and tracking system for a small unmanned helicopter. Science in China Series F (2009) 2. Campbell, M., Wheeler, M.: A vision based geolocation tracking system for UAV’s. Aiaa Guidance, Navigation, & Control Conference & Exhibit (2006) 3. Ge, Y., Hong, L.: Survey of Visual Tracking Algorithms. CAAI Trans. Intell. Syst. 5(2), 95–105 (2010) 4. Ying, W., Yi, H., Hanqing, L.: The Methods for Moving Object Detection. Computer Simulation 23(10), 221–226 (2006)

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5. Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. IEEE Computer Society Conference on Computer Vision & Pattern Recognition (2003) 6. Okuma, K.: A boosted particle filter: multitarget detection and tracking. Proc. ECCV, 2004 (2004) 7. Opelt, A., et al.: Weak Hypotheses and Boosting for Generic Object Detection and Recognition. Eccv 3022(10), 71–84 (2004) 8. Dongliang, P., Yingjie, S.: Two Improved m-best Multiple Hypothesis Tracking Algorithms. FIRE Control & Comd. Control 36(5), 8–12 (2011) 9. Rong Li, X., Bar-Shaalom, Y.: Tracking in clutter with nearest neighbor filters: analysis and performance. IEEE Trans. Aerosp. Electron. Syst. 32(3), 995–1010 (1996) 10. Forsyth, D.A., Ponce, J.: Computer vision: a modern approach. Pearson Education, 534–549 (2002)

Cloud Type Classification Using Multi-modal Information Based on Multi-task Learning Yaxiu Zhang1,2 , Jiazu Xie1,2(B) , Di He1,2 , Qing Dong1,2 , Jiafeng Zhang1,2 , Zhong Zhang1,2 , and Shuang Liu1,2(B) 1

Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, Tianjin Normal University, Tianjin, China [email protected], [email protected] 2 College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

Abstract. Cloud classification is an important and challenging task in cloud observation technology. For better classification, we present a method based on multi-task learning using multi-modal information. We utilize different loss functions to conduct multi-task learning. We implement a series of experiments on multi-modal ground-based cloud datasets for different tasks. Experimental results show that multi-task learning is effective for cloud image classification using multi-modal information, and it can improve the results of cloud image classification.

Keywords: Cloud type classification Multi-task learning

1

· Multi-modal information ·

Introduction

Cloud is an important meteorological phenomenon, and it affects the energy balance locally and globally by the interaction of solar and terrestrial radiation. There are some characteristics in the process of cloud formation and evolution, such as cloud water vapor, degree of stability, cloud height, cloud thickness and so on. These characteristics are all key features for predicting weather. Therefore, accurate cloud observation technology is of great significance in estimating precipitation, forecasting weather conditions, air traffic control etc. Cloud classification using cloud visual information (cloud images) is an important and challenging task in cloud observation technology. At present, the research on cloud image classification is mainly carried out on two kinds of data samples: satellite cloud images and ground-based cloud images. The latter can not only describe low cloud and regional cloud, but also reflect the texture information of cloud. Hence, cloud images classification method based on ground-based cloud has a strong application demand. c The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022  Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 119–125, 2022. https://doi.org/10.1007/978-981-19-0390-8_15

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Many works have been done in this field. Liu et al. [1] extracted structural features such as cloud blocks, cloud mean and edge sharpness from edge images and segmentation images to realize the classification of ground-based clouds. Xiao et al. [2] take the fused features as the visual features of the clouds. Fusion features include: texture, structure, and color. Afterwards, because local binary patterns (LBP) [4] has the advantages of gray level invariance and rotational invariance, LBP is widely used in ground-based cloud classification. Oikonomou et al. [3] used improvement of LBP to extract local and global features of groundbased cloud images. All the above methods are based on the manual features, and they do not work well for different distributed databases. At the present stage, researchers employ convolutional neural network to automatically classify cloud images. Automatic acquisition of high-level features from raw data is the most prominent advantage of convolutional neural network [5–7]. Therefore, valid information can be captured to a large extent. For example, Zhang et al. [8] proposed a significant dual activation clustering algorithm. The algorithm extracts the salient vectors from the shallow convolutional layer and the corresponding weights from the high convolutional layer. However, most existing methods ignore the fact that cloud formation and evolution are also related to multi-modal information. The mullti-modal information include temperature, humidity, air pressure, instantaneous wind speed, maximum wind speed, and average wind speed. The accuracy of ground cloud classification can be greatly improved by making full use of multi-modal information. In this paper, we use multi-modal information to study the impact of different tasks on cloud classification: • Verification task using triplet loss function. • Classification task using cross-entropy loss function. • Multi-task learning is realized by combining cross-entropy loss function and triplet loss function. The paper is organized as follows: In Sect. 2, we describe the principles of the approach. The implementation details are provided in Sect. 3. We conclude in Sect. 4.

2

Approach

In this section, we first introduce the network we used. Secondly, we introduce the implementation of multi-task learning and the loss functions used in this paper. 2.1

Framework

The multi-modal fusion network combines visual sub-network and multi-modal sub-network. The framework is shown in Fig. 1. We treat resnet-50 [9] as the main part of the visual sub-network. The visual sub-network is used to extract

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the deep visual features from cloud images. Because of the deep composition of resnet-50, the deep visual features contain complex nonlinear visual information. We treat the output of the averaging pool as a deep visual feature. The deep visual feature is a vector with a dimension of 2048. The multi-modal sub-network is constructed with multi-layer perceptron (MLP). The number of neurons in six FC layers of MLP is 64, 128, 256, 512, 1024 and 2048 respectively. The multi-modal feature dimension of the multi-modal sub-network corresponds to the feature dimension of deep vision. Finally, the deep multi-modal feature and deep vision feature are fused in series. The fused features are fed into the loss function through the fully connected layer.

Fig. 1. The framework of the multi-modal fusion network.

2.2

Loss Function

The loss function can reflect the difference between the predicted results and the actual data. So it measures the quality of the model’s predictions. The appropriate loss function is helpful to the subsequent model optimization. Here is the loss function used in this article: Cross-Entropy Loss. The cross-entropy loss function is a loss function often used in classification problems. It evaluates the difference between the probability distribution given by current training and the true distribution. The ultimate goal of optimization is to make the trained probability distribution as close as possible to the real probability distribution. The formula is as follows: Lcross = −

I  i=1

qi log yi

(1)

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where qj is the probability of the actual probability distribution. When i is the true label, qi is equal to 1, otherwise to zero. Triplet Loss. The triplet loss is first proposed in the literature [10]. It could learn better embedding. Similar images are similar in embedded space, and different images are far apart in the embedding space. The input of triplet loss is < a, p, n >, where a is the chosen anchor, p is a sample of the same class as a and n is a different kind of sample from a. The final optimization goal is: close the distance between a and p and pull the distance between a and n. The metric function for measuring the distance of the embedding space is D(x, y) : RD × RD → R. The formula is as follows:  [m + Da,p − Da,n ]+. Ltri = (2) a,p,n,ya =yp =yn

This formula ensures that the distance from a sample of the same class to the anchor is at least m closer than that from a sample of a different class to the anchor in the mapping space. Such an approach allows all mapping points of the same class to eventually form a cluster without having to fold to a point. They just need to get closer to each other. 2.3

Multi-task Learning

We use the multi-loss function to conduct multi-task learning. The multi-loss function is combined cross-entropy loss function with triplet loss function. The formula is as follows: L = Lcross + αLtri

(3)

where α is the weight parameter.

3

Experiments

In this section, we evaluate the effects of three loss functions on classification of multi-modal ground cloud images. Firstly, we introduce the dataset used in the experiment. Next, we describe detailedly the parameters of the experiment. The results and analysis of our experiment are presented in the end. 3.1

Dataset

The dataset we used is the multi-modal ground-based cloud images dataset (MGCD). The size of each image is 1536 × 1536. A cloud image and its corresponding multi-modal information constitute a multi-modal ground-based cloud sample. The samples are shown in Fig. 2. The MGCD includes seven categories: cumulus, altocumulus, cirrus, clearsky, stratocumulus, cumulonimbus and miexed.

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Fig. 2. Ground-based cloud samples.

3.2

Experiment Settings

The ground-based cloud images are normalized to 256 × 256. In addition, since our goal is to test the effect of different learning tasks on classification rather than to achieve optimal performance, we do not extend the data in our experiment. To improve the compatibility, the values of these multi-modal information are all mapped to the range of [0–255]. Then, the mapped value is subtracted from the corresponding mean value. The initialization parameters for multi-modal fusion network include weight and bias. Weights are initialized to normal distribution and biases are initialized to zero. We employ Adam [12] to optimize the multi-modal fusion network. We set Epoch to 50. The size of each batch is 32. Each batch contains four categories of ground cloud images and each categorie contains eight ground cloud images. Set the learning rate to 3 × 10−4 . And according to the literature [11], we set m to 0.3. 3.3

Analysis

Results. In order to compare the ground-based cloud classification method used multi-task learning with other methods, we used three loss function separately. The network model, optimization method and parameters of the three experiments are consistent. The classification accuracy is shown in Table 1. Table 1. The accuracy of different learning task classification methods. Method

Accuracy(%)

L cross (Epoch = 50) 83.12 L tri (Epoch = 50)

32.56

L (Epoch = 50)

86.60

L (Epoch = 200)

89.43

We apply the accuracy of the test results to evaluate the classification effect of different task learning. From Table 1, we can draw the following conclusion. Firstly, compared with other task learning mentioned above, the best classification accuracy is obtained by multi-task learning. Therefore, the validity of

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this method is verified. Secondly, we found that the cross-entropy loss function achieve final accuracy in the first few iterations. In subsequent iterations, they keep this accuracy up and down in the experiment. But it is worth noting that as the number of iterations increased to 200, the classification accuracy increased to 89.43% in the experiment of multi-tasking learning. Parameter. The multi-task learning method presented in this paper involves an important parameter that appears in Eq. 3. This parameter is used to adjust the ratio between the cross-entropy loss function and triple loss function. The accuracy of cloud classification for multi-task learning can be optimized with appropriate settings. Figure 3 shows the classification accuracy when the α is set to different values. And the classification results are best when α is set to 0.05.

Fig. 3. Accuracy of different values of α

4

Conclusion

We evaluate the cloud type classification using multi-modal information based on multi-task learning in this paper. We compare the efficiency of multi-task learning with other task learning in the classification of multi-modal information ground-based cloud and prove that multi-task learning is effective for cloud type classification which use multi-modal information. In addition, we have done a lot of experiments based on multi-task learning. Acknowledgement. This work was supported by College Student Research and Career-creation Program of Tianjin City for Undergraduates under Grant No. 2020 10065088, Natural Science Foundation of Tianjin under Grant No. 20JCZDJC00180 and No. 19JCZDJC31500, the Open Projects Program of National Laboratory of Pattern Recognition under Grant No. 202000002, and the Tianjin Higher Education Creative Team Funds Program.

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References 1. Singh, M., Glennen, M.: Automated ground-based cloud recognition. Patt. Anal. Appl. 8(3), 258–271 (2005) 2. Xiao, Y., Cao, Z., Zhuo, W., Ye, L., Zhu, L.: A multiview visual feature extraction mechanism for ground-based cloud image categorization. J. Atmos. Oceanic Technol. 33(4), 789–801 (2016) 3. Oikonomou, S., Kazantzidis, A., Economou, G., Fotopoulos, S.: A local binary pattern classification approach for cloud types derived from all-sky imagers. Int. J. Remote Sens. 49(7), 2667–2682 (2019) 4. Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Patt. Anal. Mach. Intell. 24(7), 971–987 (2002) 5. Zhang, Z., Wang, C., Xiao, B., Zhou, W., Liu, S., Shi, C.: Cross-view action recognition via a continuous virtual path. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2690–2697 (2013) 6. Shuang, L., Mei, L., Zhong, Z., Baihua, X., Xiaozhong, C.: Multimodal groundbased cloud classification using joint fusion convolutional neural network. Remote Sens. 10(6), 822 (2018) 7. Shuang, L., Linbo, Z., Zhong, Z., Chunheng, W., Baihua, X.: Automatic cloud detection for all-sky images using superpixel segmentation. IEEE Geosci. Remote Sens. Lett. 12(2), 354–358 (2014) 8. Zhang, Z., Li, D., Liu, S.: Salient dual activations aggregation for ground-based cloud classification in weather station networks. IEEE Access 6, 59173–59181 (2018) 9. Theckedath, D., Sedamkar, R.R.: Detecting affect states using VGG16, ResNet50 and SE-ResNet50. SN Comput. Sci. 1(2), 1–7 (2020) 10. Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 815–823 (2015) 11. Hermans, A., Beyer, L., Leibe, B.: In defense of the triplet loss for person reidentification. arXiv preprint arXiv:1703.07737 (2017) 12. Da, K.: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2015)

Research on Artificial Meteor Trail Emergency Communication System Liu Jun-tao1(B) , Zhou Jian2 , Yang Kun1 , and Zhang Fan1 1 Beijing Institute of Astronautical Systems Engineering, Beijing 100076, China

[email protected] 2 China Academy of Launch Vehicle Technology, Beijing 100076, China

Abstract. The artificial meteor trail emergency communication system is an emergency communication system that releases specific chemical substances into the ionosphere from satellites or carrier rocket, which can generate artificial plasma clouds. The reflection and scattering effects of plasma clouds on radio waves can be used to achieve reliable Trans-horizon communication. This paper studies on the emergency communication system with artificial meteor trails, focusing on system composition and functions, payload design, telemetry system, ground communication equipment, and simulates the effect of releasing metallic barium in the ionosphere. The results show that the system is reasonable and feasible, and can form a reliable emergency communication channel. Keywords: Artificial meteor trails · Emergency communication · Plasma · Barium cloud

1 Introduction Meteor burst communication is a kind of communication which utilizes the phenomena of reflecting radio wave made by meteor trails [1]. However, as a completely uncontrollable natural phenomena, the communication time, duration time, the amount of information transmitted and antenna alignment direction are all uncontrollable [2]. We can only figure out key parameters such as throughput and waiting time within a certain time relying on statistical regularities. In the 1960s, Human beings first discovered the disturbance of ionospheric electron density during rocket launch. Since then, a large number of experiments on the active release of chemical substances to disturb the ionosphere have been carried out, and it has been found that by releasing alkali metals or alkaline earth metals in the ionosphere, such as Ba, Cs, Li, Na, Ca, etc., high-density plasma can be formed in the ionosphere [2–5]. In the space plasma active experiment, barium (Ba) atoms are released into space from satellites or rockets, resulting in the local environment modification ranging from tens to thousands of kilometers. In April 2013, the first barium release experiment of China was successfully implemented by Center for Space Science and Applied Research, Chinese Academy of Sciences, in Hainan Province [6]. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 126–135, 2022. https://doi.org/10.1007/978-981-19-0390-8_16

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In the space range of 160km ~ 200km, the artificial meteor trail payload is separated from the carrier, and an artificial meteor trail ion cloud is generated in the predetermined airspace to form an artificial meteor trail channel. The ground communication equipment is aligned with the predetermined airspace. The handshake signal sent by the master station of the meteor trail communication equipment is received by the slave station after being reflected by the artificial ion cloud and the communication link is established.

2 System Composition and Function The artificial meteor trail emergency communication system is mainly composed of carrier rocket, artificial meteor trail payload, ground meteor trail communication equipment and telemetry equipment. The main task of the carrier rocket is to load the payload of the artificial meteor trail generator and transport it to a space of 160km ~ 200km. The payload starts to work in this effective action space to complete the material release and the generation of the ionization cloud of the artificial meteor trail, so as to meet the communication requirements of the artificial meteor trail. The artificial meteor trail payload is designed to provide a stable, continuous, and effective meteor trail communication channel in the space by controlling the ignition to release the active material, produce ionization effect, and bring the electron concentration up to a certain level. When the payload is separated from the rocket, the electronic controller receives the separation signal, which is used as the delay start signal of the electronic controller, and the electronic controller starts to delay counting. The delay amount is determined according to the time required for the payload to predict the trajectory from separation to the predetermined height. After the counter delay is completed, the artificial meteor trail payload reaches the predetermined working height, and the electronic controller outputs an electronic ignition signal to the pressure vessel generator. The electronic ignition circuit in the pressure vessel generator ignites the internal combustion aid after receiving the electronic ignition signal. The combustion aid generates high heat and vaporizes the alkali metal in the pressure vessel. When the pressure of the pressure vessel reaches a certain level, it breaks through the weakening groove spout of the pressure vessel, vaporized alkali metal is injected into the space to produce an ionization effect under the action of the space environment. When the electron concentration reaches a certain level and the ionized cloud reaches a certain size, a residual communication channel is formed.

3 Payload The main function of the artificial meteor trail payload is to release effective materials in the effective airspace, produce ionization effects, and make the electron concentration reach a certain level, so as to provide a stable, long-term and effective meteor trail communication channel. The artificial meteor trail payload is mainly composed of pressure vessel generator, electronic controller, battery, power supply and distribution system and interconnected cables.

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3.1 Pressure Vessel Generator The pressure vessel generator is a pressure vessel that stores alkali metal substances, and needs to have the ability to withstand the pressure and high temperature in the process of filling and spraying alkali metal substances. The electron concentration and diffusion of ionized clouds are related to the total amount of alkali metal, the gas jet speed, and the speed of the launch vehicle during jetting. The pressure vessel generator is composed of a pressure shell, heat-resistant layers, combustion aid, and an igniter. The pressure shell is made of alloy steel to ensure sufficient strength and heat resistance; the heat-resistant layers can ensure that the pressure shell is not burnt, and reduce the impact of the pressure wave on the shell. The weakening groove of the pressure vessel generator used for ejection should meet the requirements of the thermal environment and vibration environment on the carrier rocket, and should not rupture in advance and release substances that affect the flight of the launch vehicle. 3.2 Electronic Controller The electronic controller is the control unit of the release device, which is composed of a parameter acquisition circuit, a delay circuit, an ignition circuit, and an interface circuit. The controller is connected to the pressure vessel generator through a cable to realize the functions of the system. Its main functions include: 1. Upload the delay parameters before launching through interface between the launch vehicle and the ground; 2. Detect the separation signal and start delay counting; 3. After the delay is over, send an ignition signal to the pressure vessel generator; 4. Send the secondary power supply voltage, delay start signal, delay counter, and ignition signals to the telemetry system (including the black box).

Fig. 1. Schematic diagram of electronic controller

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3.3 Power Supply and Distribution System Batteries and power supply and distribution systems include batteries, distributors, etc. It is used to realize the ground power supply and the control of the power transfer before the launch, the battery power supply and the electronic controller, the black box during the flight.

4 Telemetry System Telemetry system includes rocket and ground telemetry equipment, mainly used for the acquisition, transmission and monitoring of the relevant parameters and status of the artificial meteor trail payload on the rocket. A black box is installed on the rocket, in which the relevant data is stored for post-analysis. 4.1 Telemetry Parameters In addition to collecting rocket control system data, it is also necessary to collect payload working process data. The electronic controller outputs the secondary power supply voltage, delayed start signal (separation signal), timing signal, ignition command signal and ignition action signal to the telemetry system. 4.2 Black Box The black box mainly has the following functions: 1. Receive electronic controller, power supply and distribution system parameter acquisition signals (including battery voltage, distributor output voltage, electronic controller secondary power supply voltage, delay start signal (separation signal), delay counter value, ignition command signal, Ignition action signal, etc.), Realize parameter digital conversion, encoding, parallel-serial conversion, framing, etc., and store the data after the ground equipment starts the storage control. 2. With the function of reading data afterwards, the stored data can be read out through a suitable interface for data interpretation.

5 Ground Communication Equipment The natural meteor trail communication equipment is appropriately modified for artificial meteor trail communication. Because the height and direction of the artificial meteor trail are different from the natural meteor trail, it is necessary to improve the artificial meteor trail communication station and related antenna feeding equipment. Meteor trail equipment includes meteor trail master station and meteor trail slave station. Generally, there is one master station and multiple slave stations. The ground meteor trail communication equipment is mainly artificial meteor trail communication stations, controllers, management computers and related antenna feeding equipment, which realize the transmission and reception of meteor trail information [7, 8].

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Ground communication system includes sensitive automatic monitoring and control equipment, information storage devices. When there is no artificial meteor trail in the area where the antenna beams of the transmitter and receiver intersect, the entire system is in a state of waiting for transmission. At this time, the information to be sent (nonconversational information) is stored in the sender’s memory. According to the predesigned position and direction, the transmitter continuously sends a probe signal to the receiver. When the trail of an artificial meteor appears, the probe signal is transmitted to the opponent. The receiving end recognizes the signal, and if it confirms that it was sent by its communication partner, it sends an answer signal. After the sender receives the answer signal, the entire communication system quickly switches from the standby state to the burst state. The automatic control device quickly activates the transmitter, and the transmitter quickly transmits the information stored in advance to the trail of the artificial meteor. At the same time, the receiver, which is usually in the waiting state, automatically receives the information sent from the other side.

6 Plasma Cloud Cluster Effect Analysis The ionization effect of vaporized alkali metal under the action of space environment is closely related to the amount of metal release, release height, initial release velocity, jet velocity, etc. At the same time, the plasma cloud will diffuse and evolve in the air, the diffusion range will become larger, and the concentration will continue to decrease. The morphological evolution and concentration change process of the plasma cloud in the air have an important influence on the meteor trail channel, including communication frequency, communication time and communication effect. The alkali metal barium is selected to simulate the effect of plasma cloud communication. 6.1 Communication Frequency and Ion Concentration The electromagnetic wave frequency of artificial meteor trail communication is related to the density of free electrons. The plasma angular frequency is defined as:  Ne e2 (1) ωp = ε0 me In the above formula, Ne is the free electron density in the plasma; e and me are the electric quantity and mass of a single electron, respectively. ε0 is the dielectric constant in vacuum. The plasma angular frequency (also called the plasma cut-off frequency) ωp depends on the free electron density Ne in the plasma. The movement of particles in the plasma causes collisions, so electromagnetic waves are absorbed and attenuated by the plasma while propagate in the plasma. When the angular frequency of the incident wave is ω > ωp , the electromagnetic wave can propagate in the plasma; when ω < ωp , the electromagnetic wave is completely reflected at the interface and cannot enter the plasma. In order for the artificial plasma to fully reflect

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a given electromagnetic wave, the electron concentration in the plasma must meet the following conditions: Ne >

ε0 me · ωp 2 e2

(2)

6.2 Simulation Model After the barium atoms are released, they move relative to the background particles at a certain initial speed, and gradually slow down due to the collision effect. In addition, the neutral barium cloud will continue to expand to the surrounding at the speed of the thermal speed, and the density will continue to decrease due to photoionization and oxidation. The evolution in space can be described by a diffusion equation [5]. ∂nBa (r, t) = D∇ 2 nBa (r, t) − L ∂t

(3)

In the above formula, nBa is the particle number density of barium atoms, L is the loss rate of particle mass due to photoionization and oxidation, that is, L = vph,Ba nBa + kT kc nO2 nBa , where kc = 0.9 × 1016 m3 · s−1 is the oxidation reaction constant; D = mv D is the diffusion coefficient, where vD is the collision frequency related to diffusion. For heat release, the distribution of barium cloud in space at the initial moment is approximately Gaussian [9]: nBa (r)|t=0 =

N0 exp(−r 2 /r02 ) 3/2 π r03

(4)

In the above formula, N0 is the number of barium atoms at the initial moment, and r0 is the characteristic radius of the barium cloud at the initial moment. From this, the spatial distribution function of the barium cloud can be obtained as: nBa (r, t) =

N0 π 3/2 (r 20

+ 4DT )

3/2

exp[−

(r − rc ) − vph,Ba t − kc nO2 t] r02 + 4DT

(5)

In the above formula, rc is the coordinate of the barium cloud center relative to the reference origin. The magnetic fluid equations play an important role in the study of various space physics problems, but the usual magnetic fluid equations are single-component. After barium is released, it is a process of interaction between partially ionized plasma and background magnetized plasma. The height of the barium cloud release is approximately in the F layer of the ionosphere. The main component of the background ions is O+ , and ionization, recombination effects and oxidation reactions need to be considered [5]. According to the space plasma environment of the ionosphere and the self-diffusion model of the neutral barium cloud, the Ba+ and O+ two-component magnetic fluid model released by the ionospheric barium cloud can be established [10]. ∂ρBa+ + ∇ · (ρBa + u) = SBa+ ∂t

(6)

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∂(ρu) ∂t

∂ρO+ (7) + ∇ · (ρO + u) = 0 ∂t   B2 1 + ∇ · ρuu + pI + 2μ I − BB = (ρBa+ vBa+ ,Ba + ρO+ vO+ ,Ba )(uBa − u)+ μ0 0 (ρBa+ vBa+ ,O + ρO+ vO+ ,O )(uO − u) + SBa+ + uBa



∂B + ∇ · (uB − Bu) = 0  ∂t  2

(8)



(9)

1 γ 1 1 2 1 2 B +∇ · u− ρu + p+ ρu + p+ (B · u)B 2 γ −1 2μ0 2 γ −1 μ0 μ0   vBa+ ,Ba vBa+ ,O = ρBa+ mBa (uBa − u)2 + mO (uO − u)2 mBa+ + mBa mBa+ + mO   vO+ ,Ba vO+ ,O mBa (uBa − u)2 + mO (uO − u)2 +ρO+ mO+ + mBa mO+ + mO

+ρBa+ vBa+ ,Ba u · (uBa − u) + vBa+ ,O u · (uO − u)

1 kTBa SBa+ 2 + + nBa vph,Ba ) ( +ρO+ vO+ ,Ba u · (uBa − u) + vO+ ,O u · (uO − u) + SBa+ uBa 2 γ − 1 mBa+

∂ ∂t

B2

(10) In the above formula, ρBa+ , ρBa and ρO+ , ρO respectively represent the mass density of each component, mBa+ , mBa and mO+ , mO is the mass of the corresponding atom, SBa+ = mBa nBa vph,Ba is the photoionization source of Ba+ , where vph,Ba = 0.0357s−1 [11], TBa and uBa are the temperature and velocity of neutral barium, uO is the velocity of neutral oxygen atom, vO+ ,O and vBa+ ,Ba is the resonant collision frequency of the same element, vBa+ ,O and vO+ ,Ba are the collision frequency of different elements, and γ is the polarizability of the atom. 6.3 Barium Cloud Simulation Consider the initial velocity of the barium cloud V0 = 2.5 km/s in the X-axis direction, the background magnetic field B0 = 35000 nT in the Y-axis direction, and the line of

Fig. 2. Simulation coordinate system diagram

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sight direction in the Z-axis direction. As shown in the figure below, the barium cloud is initially at the origin of the coordinates and the calculation domain is X = Y = Z = [−25, 25] km, the radial velocity and the thermal velocity are determined by the local temperature for simulation. Considering that 8 kg of barium is released at a height of 160 km and 180 km respectively, the corresponding parameters obtained according to International Reference Ionosphere (IRI-2007) are shown in the following table. Table 1. Parameters used in the simulation Plasma thermal velocity (km/s)

O density (cm−3 )

O Temp. (K)

O2 density (cm−3 )

0.3 × 105 813

0.313

1.28 × 1010

775

8.13 × 108

0.341 × 105

0.333

7.5 × 109

858

2.8 × 108

Height (km)

Plasma density (cm−3 )

160 180

Plasma Temp. (K)

918

Since the total amount of barium cloud released at the two heights is the same, and the photoionization rate that determines the barium ion number density is also the same, the simulation results of the two situations will not be much different. The difference in the number density of barium ions mainly comes from the difference in the thermal velocity and the number density of the background component, but the differences caused by these factors are very small. Figure 3 and 4 show the simulation results of 160 km and 180 km altitude. Considering that the radio frequency used for detection is 35MHz, according to the relationship between communication frequency and ion concentration, the corresponding plasma number density is 1.523 × 107 cm−3 . The results of 1 s, 3 s, and 5 s after release are given. It can be seen that as the heat velocity increases and the number density of the background component decreases, the diffusion speed of the barium cloud will increase, resulting in a faster decrease in the number density of barium ions.

Fig. 3. Simulation results of 160 km altitude

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Fig. 4. Simulation results of 180 km altitude

Through the numerical simulation of the two-component (Ba+ ,O+ ) MHD model, we obtain that for the release of 8 kg of barium at the heights of 160 km and 180 km, considering the initial speed of 2.5 km/s and the radio frequency used for ground observations of 35 MHz, We can observe the barium cloud in the first 5 s after the release, and the largest impact scale in space is about 4 km × 8 km.

7 Conclusion Through the simulation of the effect of metal barium released by the ionosphere, it can be seen that the barium cloud can form a reliable emergency communication channel in a short time, and the principle of the artificial meteor trail emergency communication system is feasible. However, the impact of artificial meteor trails on electromagnetic wave communication in terms of the height, shape and diffusivity of ion clouds, and how to ensure that it meets emergency communication requires needs further simulation and analysis.

References 1. Zhang, G.X.: Meteor burst communication. J. Military Commun. Technol. (2004) 2. Juntao, L., Xijun, Y., Li, J.: Research on artificial meteor trail emergency communication. In: Cospar Scientific Assembly Held August, in Moscow, Russia, Abstract C. 40th COSPAR Scientific Assembly. Held 2–10 August 2014, in Moscow, Russia, Abstract C0.2–58–14 (2014) 3. Haerendel, G., Foppl, H., Melzner, F., et al.: The AMPTE artificial comet experiments. Nature 320(6064), 700–703 (1986) 4. Huba, J.D., Bernhardt, P.A., Lyon, J.G.: Preliminary study of the CRRES magnetospheric barium releases. J. Geophys. Res. Space Phys. 97 (2012) 5. Li, L., Xu, R.L.: Neutral barium cloud evolution at different altitudes. Chin. Phys. Lett. 19(008), 1214 (2002) 6. Wang, J.D., Li, L., Tao, R., et al.: Research on active plasma release experiment in ionosphere. Chin. J. Space Sci. (2014). (In Chinese) 7. Chang, S., Schilling, D.L.: Efficient communications using the meteor-burst channel. IEEE Trans. Commun. 40(1), 119–128 (1992) 8. Schanker, J.Z., Hollands, D.H.: Remote data acquisition and communication system. US (1995) 9. Mitchell, H.G., Fedder, J.A., Huba, J.D., et al.: Transverse motion of high-speed barium clouds in the ionosphere. J. Geophys. Res.: Space Phys. 90(A11) (1985)

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10. Xie, L.H.: Two-species MHD simulation and observation of the barium release in the ionosphere. University of Chinese Academy of Sciences. (In Chinese) 11. Bernhardt, P.A., Huba, J.D., Pongratz, M.B., et al.: Plasma irregularities caused by cycloid bunching of the CRRES G-2 barium release. J. Geophys. Res. Space Phys. 98(A2), 1613–1627 (1993)

Programming to Identify Exfoliated 2D Nanosheets Using Machine Learning Zhibo Li1,2 , Kexin He1,2 , Xiao Li1,2 , Yinghui Zhang1,2 , and Cheng Wang1,3(B) 1 Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, Tianjin

Normal University, Tianjin 300387, China [email protected] 2 Department of Communication Engineering, College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin 300387, China 3 Department of Electronic and Communication Engineering, College of Artificial Intelligence, Tianjin Normal University, Tianjin 300387, China

Abstract. Programming through machine learning methods has been gradually replacing many repetitive and tedious works. In the mission of identifying of mechanically exfoliated 2D materials using microscope, which is a laborious work inappropriate to be manually executed, the machine learning methods perform attractive potentials in the rapid and accurate targeting of available productions. Based on the algorithm of image segmentation and target positioning, this paper discusses the feasibility of program-controlled searching of 2D nanosheets from miscellaneous cracks. By intruding visual GUI displaying, the program-enabled automatic recognition may emancipate humans from the hard work. Keywords: Machine learning · Image segmentation · Visual GUI · 2D material · Automatic targeting

1 Introduction Machine learning are capable of helping humans to extract the characteristics of objects and find unknown nonlinear relationships among things, which can be achieved only by providing them with a large amount of data and establishing a reasonable model. Although the time cost is high, it is of great significance for scientific research. The advantages of machine learning have been demonstrated in a variety of research, such as biology, medicine, physics, communications. Of all the fields, it stands out in image analysis. Due to the low contrast and frequent overlap of the exfoliated graphene in light microscope images, it is not easy to identify and detect in traditional target detection networks. Generally speaking, there are four types of machine learning: supervised learning, which is usually manually annotated. It can learn to map input data to a known target, which includes [1] target detection and [2] image segmentation. Unsupervised learning, which is used for data compression and denoising through dimensionality

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 136–141, 2022. https://doi.org/10.1007/978-981-19-0390-8_17

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reduction and clustering. Self-supervised learning, without manual labeling. Label built from input data. Reinforcement learning has wide applications in autonomous driving, robotics, resource management, and education. At present, the deep learning network based on Keras and Tensor Flow has been relatively perfect. This paper is based on the image segmentation algorithm to analyze. A good visual interface will make all kinds of work easier. Since deep learning networks are mostly built in Python, Python itself has a number of GUI libraries. This paper builds the interface based on Tkinter. Since Tkinter has the longest history and uses the standard interface of TK GUI toolset in Python, it has been included in the standard Python Windows installation, and the famous Idle is realized by using Tkinter. The GUI created with it is highly operational and practical.

2 Algorithm Image segmentation is actually to try to find a corresponding relationship between the object and its pixel contour. The first time to combine ordinary image segmentation with deep learning is the full convolutional network FCN. FCN network uses sampling and deconvolution to get the original image size, and then does pixel-level classification. In other words, it turns the segmentation problem into the classification problem, which is the strength of deep learning. The advantage of FCN is end-to-end segmentation, but the disadvantage is that the segmentation results are not good enough on details. As for the low contrast of the image under graphene optical microscope, [3] the target convolution of the image can be calculated first. The calculation formula is as follows: X =

j i     kXa,b + A

(1)

a=0 b=0

In Eq. (1), X represents the representation quantity of image labeling characteristics, i and j represent the width and length of image respectively, K represents all the feature maps connected by the convolution kernel of the static image, A represents the cross variable between the image feature graph and the convolution kernel. Xa,b represents each pixel of the image. Each one should correspond to a certain feature in the image, so the process of convolution serves for the deep learning afterwards. Meanwhile, images processed by convolution can reduce the number of training times and reduce the risk of overfitting. U-Net shows in Fig. 1. U-Net is one of the early algorithms that use full convolutional networks for semantic segmentation. [4] The use of a symmetric U-shaped structure with compression and expansion paths in this model was very innovative at the time, and to some extent influenced the design of several subsequent segmented networks, whose name is derived from its U-shaped shape.U-Net is often used to solve the segmentation problem between closely adjacent objects, and is widely used in the segmentation and recognition of medical images.

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Fig. 1. The network architecture of U-Net.

[5, 6] Mask RCNN is based on FAST RCNN and adds a branch for splitting tasks. Compared with Fast RCNN’s speed in real-time traffic detection and control, Mask RCNN pays the price of some speed in exchange for improved accuracy, which is very suitable for the segmentation of static images. It uses a full convolutional network to predict classes, boxes, and masks faster than FCIS. However, it is not effective for the segmentation of overlapping objects, which will lead to a decrease in its accuracy in the segmentation of graphene. Before YOLO, classical target detection algorithms, such as RCNN, Fast-RCNN, Faster RCNN, etc. The idea is to first extract ROI (Region of Interest), that is, regions with possible objects, based on target clustering. Try to ensure that there is only one object in each area, and then identify the type of object in each area. These two steps are realized by two neural networks, one is ROI extraction, the other is target recognition. [7] The emergence of YOLO solves the problems of the above classical algorithm, such as complexity, many layers, slow computation speed and low processing frequency, which are difficult to achieve real-time processing. [8] The PP-YOLO network shows in Fig. 2, developed by the Baidu team in August 2020, is an optimization of the YOLO3-based network, enabling it to achieve 45.2%mAP and 72.9FPS. PP-YOLO has not done more experiments on backbone, data enlargement and NAS, so it has strong scalability and is easy to study a variety of specific problems. The PP-YOLO model uses EMA technique to increase the robustness of the model. The formula is as follows: WEMA = λWEMA + (1 + λ)

(2)

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Fig. 2. The network architecture of YOLOv3 and inject points for PP-YOLO.

In Eq. (2), λ = 0.99998. PP-YOLO introduces IOU loss by adding an additional branch, so that L1 loss is retained. In terms of feature extraction, it used residual network to replace Darknet53 to extract image features and introduced DCN to ensure the accuracy of the model. At the same time, SPP is introduced to increase the network receptive field, and only a few additional parameters are introduced.

3 Results [9] Model ensembling refers to the pooling of predictions from a range of different models to produce better predictions. It can be said that for specific object recognition, classification does not have an accurate model corresponding to it. For the recognition of graphene, the learning of Mask RCNN in image recognition and the learning results of YOLO network can be combined to determine the prediction results of classification. [10] By giving more weight to good classifiers and less weight to poor classifiers. The nelder-mead method is used to find a good set of weights. A clear and concise visual interface connects the trained model to the input and presents the results directly to the user. The interaction with the program can be completed with a simple button and the corresponding response event. Some of the code is shown in Fig. 3. The interface can realize the input, storage and image size adjustment of the picture to be detected. When a mature model as a background, you can achieve the contrast between the input picture and the output picture. Effects are as follows in Fig. 4.

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Fig. 3. Critical codes for displaying GUI.

Fig. 4. The visual interface effect.

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4 Conclusion Due to the low contrast of the images under the light microscope, the randomness and overlapping effect of graphene drop, and the difficulty of obtaining diverse samples, the recognition problem of peeling graphene images by deep learning target detection is very challenging. For problems with multiple factors, transfer learning is a feasible method. The model integration method can improve the generality of the model. The GUI can also be further deepened by adding more visual tables and result data, which can make it more intuitive and improve the level of interaction with users. Acknowledgements. This work was partially funded by the National Nature Science Foundation of China (61901300), and made use of the Funding Program of Tianjin Higher Education Creative Team.

References 1. Bochkovskiy, A., Wang, C.-Y., Liao, H.-Y.M.: Yolov4: optimal speed and accuracy of object detection, CoRR, vol. abs/2004.10934 (2020) 2. Bulan, O., Kozitsky, V., Ramesh, P., Shreve, M.: Segmentation-and annotation-free license plate recognition with deep localization and failure identification, IEEE Trans. Intell. Transp. Syst. 18(9), 2351–2363 (2017) 3. Zhang, Z., Zhang, G.: Research on target detection method in static image based on deep learning, In: 1st International Conference on Intelligence Education and Artificial Intelligence. Beijing, December 23–24, 2307–0692 (2018) 4. Ronneberger, O., Fischer, P., Brox, T.: U-Net: Convolutional Networks for Biomedical Image Segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-31924574-4_28 5. Ren, S., He, K., Girshick, R.B., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2017) 6. Hek, M., Gkioxari, G., Dollar, P.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV). Venice, Italy, 2980–2988 (2017) 7. Hsu, C.C., Chen, C.Y.: Applications of improved grey prediction model for power demand forecasting. Energy Convers. Manag. 44(14), 22241 – 2249 (2003) 8. Long, X., et al.: PP-YOLO: An Effective and Implementation of Object Detector, arXiv:2007. 12099v3 [cs.CV] 3 Aug (2020) 9. Dang, T., Nguyen, T.T., McCall, J., Elyan, E., Moreno-Garcia, C.F.: Two layer ensemble of deep learning models for medical image segmentation. arXiv:2104.04809 [cs.CV] 10 Apr 2021 10. Iscen, A., Araujo, A., Gong, B., Schmid, C.: Class-Balanced Distillation for Long-Tailed Visual Recognition. arXiv:2104.05279v1 [cs.CV] 12 Apr 2021

A MCU-Based Wireless Motion Sensor Setup Zhibo Li1,2 , Yinghui Zhang1,2 , Kexin He1,2 , Xiao Li1,2 , and Cheng Wang1,3(B) 1 Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, Tianjin

Normal University, Tianjin 300387, China [email protected] 2 Department of Communication Engineering, College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin 300387, China 3 Department of Electronic and Communication Engineering, College of Artificial Intelligence, Tianjin Normal University, Tianjin 300387, China

Abstract. Heart disease has been present younger trend due to the surge of life and work pressure. Therefore more and more people begin to pay close attention to their heart condition, temperature, and motion information, in order to monitor their health. In medicine, ADS129R is used to collect, amplify and filter ECG signals, and LMT70 is used to measure body surface temperature. Based on STM32, 51 MCU, this paper displays the temperature information, the number of steps, the distance information, the ECG waveform and the heart rate processed and calculated by the algorithm on the LCD screen in real time. This paper establishes a complete set of wireless motion sensors, and uploads the data to the server through WIFI. Keywords: STM32 · 51 MCU · WIFI

1 Introduction The heart is an important organ in the human circulation system and the motive force of human blood circulation. Cardiac self-regulation cardiomyocytes can generate rhythmic action potential through pacemaker current, causing the heart to contract in accordance with a certain rhythm. [1–3] The human body is a special conductor of capacitors. The bioelectric current generated when the heart contracts will cause the potential difference on the surface of the human body to produce the corresponding change, which is the ECG signal. Electrocardiogram (ECG) is an analog signal waveform drawn corresponding to the ECG signal. ECG reflects the changes of central potential in the process of cardiac excitation generation, conduction and recovery. For a normal heartbeat, the cycle consists of P waves, QRS sets, T waves and U waves. Heart rate measurements are made by counting the number of ECG waveforms over time. Therefore, the quality of ECG waveform filtering and the threshold value set by the heart rate algorithm are the two main factors to determine the accuracy of heart rate calculation. [4, 5] In the post-epidemic world, it is a matter of daily life for people to check their temperature. Temperature measurement is generally divided into contact measurement and non-contact temperature measurement. This set of device © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 142–149, 2022. https://doi.org/10.1007/978-981-19-0390-8_18

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adopts contact temperature measurement, through the change of thermistor resistance value, to achieve the measurement of body temperature. [6] The step measuring device is the most common one in the motion module. It realizes the step counting function by balancing the module to obtain the three-dimensional coordinate change amplitude of x, y and z of the object in real time and setting the threshold. And the simple principle of distance measurement is to set the adult normal step distance as a constant to achieve. The real-time data will be stored and uploaded to the server, which will facilitate the analysis of the motion data later. [7] Comprehensive consideration of the above requirements, the device selected ADS129R chip, which uses advanced DDS technology, internal integration of high speed, strong functionality, simple operation, stable performance, cheap price. The LMT70 chip is selected, which is a precision analog temperature sensor with low power supply requirements, simple pins and a wide temperature measurement range. It is a medical grade sensor and has the advantages of accurate measurement results, small size and low power supply requirements. The Esp8266 chip is selected, which is a serial port to wireless module chip with internal firmware. It is easy for users to operate, and there is no need to write timing signals. Moreover, this chip uses 3.3 V DC power supply, with small volume, low power consumption, transparent transmission, no serious packet loss and low price. It is a cost-effective chip. The STM32F103V microcontroller is selected. The STM32 series is specially designed for embedded applications with high performance, low cost and low power consumption. STM32F103V MCU has built-in high-speed memory, abundant enhanced I/O ports and peripherals. The MCU program is modular, the interface is relatively simple, and the working speed is fast.

2 Algorithm ECG signal is very weak and vulnerable to interference from the outside world, human body and circuit, such as baseline drift, power frequency interference, EMG noise and so on. The mixed interference signal will lead to signal distortion, and even completely submerge the useless signal, which makes it difficult to extract the characteristic value of the useful signal, so the corresponding filtering denoising processing is necessary. Generally, there are two kinds of de-noising methods: hardware de-noising and software filtering. By building the corresponding circuit to achieve the hardware noise reduction of filtering function, this hardware filter circuit is not only difficult to build and debug, but also increases the cost, volume and power consumption, so here we choose the digital processing method to process the ECG signal. [8] Considering filter selectivity and system stability, FIR filter with strict linear phase and non recursive structure is selected. System function of FIR filter as follows: H (z) =

N −1  i=0

h(i) zi

(1)

In Eq. (1). [9] In the filtering part, a 1 Hz DC filter is designed to filter the DC component. System function of DC filter as follows: H (z) =

Y (z) 1 − z −1 = X (z) 1 − az −1

(2)

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In Eq. (2) After the ECG signal is filtered, the heart rate is calculated while the waveform is displayed, and the heart rate is displayed by threshold analysis method. The entire process is shown in Fig. 1.

Fig. 1. Flow chart of ECG signal filtering and heart rate calculation.

ECG signal acquisition ECG filtering waveform display based on MATLAB is shown in Fig. 2.

Fig. 2. Simulation results of MATLAB central electrical filtering waveform.

Fig. 3. LMT70 output characteristic curve.

The LMT70 is an analog temperature sensor that is buffered by an amplifier and has a push-pull analog outlet for increased load capacity. The output characteristic curve

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is shown in Fig. 3, which appears to be linear. In fact, within its temperature range, a third-order temperature calculation formula is adopted: 3 2 + bVTAO + cVTAO + d TM = aVTAO

(3)

In Eq. (3). VTAO represents the temperature that was collected, a, b, c, d values are shown in Table 1. Table 1. Third-order temperature formula parameter table. Best fit for –55 °C to 150 °C

Best fit for –10 °C to 110 °C

A

– 1.064200E–09

– 1.809628E–09

B

– 5.759725E–06

– 3.325395E–06

C

– 1.789883E–01

– 1.814103E–01

D

2.048570E+02

2.055894E+02

The step - counting module collects step - counting information based on acceleration sensor ADXL345. The acceleration in the directions of the three orthogonal components X, Y and Z is perceived by the 3-Axis sensing device. After the A/D conversion and digital filtering, the output is interrupted to complete the measurement of the acceleration and apply it to the walking features. Peak detection and a dynamic threshold decision algorithm for acceleration on all three axes are crucial for detecting unit walking or running cycles. [10] WiFi module chip ESP8266 first enters “ + + + “ instruction to exit through mode, and then generates a new IP address through “AT + CWMDDE” instruction. Then connect to the hot spot of the computer, assign a new address through “AT + CIPSTART”, and then connect to the server to realize the transmission of server data. The overall circuit diagram of the wireless sensor node system obtained by integrating all modules is shown in Fig. 4.

Fig. 4. System general flow chart.

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3 Results The connection status of each module is shown in the Fig. 5.

Fig. 5. Wireless motion sensor hardware connection.

Sensor test method is as follows: the battery provides a DC voltage regulator. The program is imported into the STM32 core board, and the ADS1292 ERA and ADS1292 ELA are used to receive the human ECG signals. The waveforms presented are compared with theoretical data, and then the parameters of the program are analyzed and adjusted until they reach the standard. On the one hand, the dynamic waveform is displayed. On the other hand, the ECG signal is counted and analyzed to obtain the user’s heart rate information. The temperature of the body surface is measured and recorded by the LMT70 chip connected on the single chip microcomputer, and the parameters are adjusted according to the LCD screen to improve the sampling rate and reduce the error. The motion information of the user is detected by the accelerometer chip, and the data is transmitted to the STM32 board through the 51 microcontroller. Finally, all the data is transmitted to the server through the WIFI module for real-time display. The hardware test effect drawing is shown in the Fig. 6 and Fig. 7. The test data results are shown in Tables 2, 3, 4. In order to test the accuracy of the device in measuring heart rate, we additively prepared sports bracelets that can more accurately measure human heart rate for comparison (the data of the bracelets are taken as theoretical values here).

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Fig. 6. Data presentation of hardware results.

Fig. 7. Server-side results display. Table 2. Heart rate measurement data sheet. Analog meter

Analog meter

Analog meter

Human body

Human body

Human body

Theoretical 60 heart rate(bpm)

100

120

68

73

75

Actual value (bpm)

100

120

66

74

72

60

147

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Number of times

1

2

3

4

5

6

Actual temperature(°C)

34.2

35.0

34.6

34.6

34.8

35.2

Measuring temperature(°C)

34.401

34.210

34.302

34.901

35.023

35.410

Table 4. Movement measurement data sheet Number of times

Steps

Distance(m)

Actual steps

Actual distance(m)

1

9

5

9

5

2

20

11

19

10

3

30

16.5

27

15

4 Conclusion When we measured the heart rate, the accuracy rate of the analog signal input was close to 100% In the debugging process. If the heart rate was measured by human body, there was a certain error in the experimental results due to the interference of polarization voltage in human body, but the error range was all within 5%, which met the requirements. In addition, the collected and recorded body surface temperature and motion information accurately reflect the actual situation, and the node sensor data is also displayed normally at the server end, basically achieving the expected effect. Problems in the test included unstable temperature collection, delay of uploading data to the server of about 2 s, and poor real-time display. In the future, efforts should be made to adjust sampling frequency and reduce transmission delay, and to reduce the influence of hardware errors through software programming. Acknowledgements. This work was partially funded by the National Nature Science Foundation of China (61901300), and made use of the Funding Program of Tianjin Higher Education Creative Team.

References 1. Khalil, M., Duchene, J.: Uterine EMG analysis: a dynamic approach for change detection and classification. IEEE Trans. Biomed. Eng. 47(6), 748–756 (2000) 2. Khezri, M., Jahed, M.: Real-time intelligent pattern recognition algorithm for surface EMG signals. Biomed. Eng. Online 6, 45 (2007) 3. Kohler, B.U., Henning, C., Orglmeister, R.: The principles of software QRS detection. IEEE Eng. Med. Biol. Mag. 21(1), 42–58 (2002) 4. Cui, G., Cao, X., Wang, Y.: Digital signal processing in bioinformatics. Science Technology and Engineering 5(20), 1494–1502 (2005)

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5. Tropp, J., Laska, J.N., Duarte, M.F.: Beyond Nyquist: efficient sampling of sparse bandlimited signals. Inf. Theory, IEEE Transactions 56(1), 520–544 (2010) 6. Li, Y., Liu, Y., Yuan, T.: Multi-level walking pattern classification algorithm based on acceleration signal. Electronic journals, 2009(37) 7. Zhao, L., Qu, S., Zhang, W.: Design of multi-channel data collector for highway tunnel lighting based on STM32 and Modbus protocol. Optik 213 (2020) 8. Niu, X.: FIR digital filter implementation method. Communication world 14, 289 (2017) 9. Tan, H.: Programming Design of C Language (Second Edition). Tsinghua University Press, Beijing (1999) 10. Xu, Y., Shi, Z., Xia, J.: Design of intelligent air conditioning control system based on ESP8266. Information and Computers (The theory of version) 30(9), 82–83 (2018)

A Control System of Waveform Generation Based on LabVIEW Mingfeng Zhang1,2 , Jingjing Wu1,2 , Jinfan Ran1,2 , Ruiqing Xing1,3(B) , and Cheng Wang1,3(B) 1 Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, Tianjin

Normal University, Tianjin 300387, China [email protected], [email protected] 2 Department of Communication Engineering, College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin 300387, China 3 Department of Intelligence Science and Technology, College of Artificial Intelligence, Tianjin Normal University, Tianjin 300387, China

Abstract. This study presents a computer control system for the waveform generation based on LabVIEW programming. Users can easily operate computer panel to control the waveform output of standard and arbitrary waveforms by the means of serial communication. When it comes to standard waveform output, it can change and save the parameters of waveform at any time for readily d waveform update. By using the program-controlled method, the works in electrical tests can be greatly reduced. The operating experience of users can thus be simplified via the visualized human-computer interfaces. Keywords: Waveform generator · NI-VISA · LabVIEW · Serial communication

1 Introduction LabVIEW is a programming environment developed by the U.S. National Instrument (NI) using a graphical language and the methods of flow control. Appearing as block diagram, the program is comprehensible and user-friendly. Up to now, the most of existing instruments support LabVIEW programming, for which LabVIEW is widely applied in the field of industrial control. Waveform generator is a kind of instrument which can provide signal of different frequency, waveform or amplitude. It is widely used in laboratory as excitation source to test telecommunication systems or electrical components. Most waveform generators support standard waveform output such as sine function, as well as arbitrary waveform output. As for standard output waveform, it is easy to set the waveform parameters. But it is difficult to save the outputted waveform for which the former waveform is difficult to reappear. Worse still, because of the instrument panel is simple, the function of arbitrary waveform is complex to write [1]. In order to improve the experience of using waveform generators, the predecessors put forward the signal waveform generator control system based on LabVIEW, gathering © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 150–156, 2022. https://doi.org/10.1007/978-981-19-0390-8_19

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the signal producing function of signal generators and powerful operation ability of computers. However, most of the system is only to expand the control panel of waveform generator to the computer interface with no more function added. Through research the existing signal waveform generator control system based on LabVIEW, we improved the system, enabling the computer to save the parameters of outputted waveform without data coverage. It greatly reduced the work of data recording.

2 Results and Discussion In this study, we take Windows 10 computer system and Agilent 33511B waveform generator for example. To realize the computer control of the waveform generator which involved in the serial communication between the two machines, we must install NIVISA driver. NI-VISA driver is an advanced application programming interface developed by NI company to help communicate with instruments bus. VISA-BUS-I/O software, which can be used to the USB, GPIB, serial interface for configuration, programming and debug, is an integrated software package, without limited by the platform and the environment. Together with powerful control function and resource management function, users can rapidly program and develop on the promise of not understanding the underlying theory [2]. In order to make the computer communicate with the Agilent 33511B type waveform generator in the method of serial communication, we still need to install the instrument corresponding instrument drivers and function library. The LabVIEW developers have packed the basic functions of serial communication into sub VIs that users only need to cascade them to realize the function of waveform generator reappear on the computer. With easy improvement, additional function can be added to the system. What’s more, a tool of MS Office Report Generation is needed to automatically save the parameters of waveform outputted to the local documents without coverage every time. 2.1 Sub VI The computer’s control of waveform generator requires three core functions, which is VISA open, VISA write and VISA close [3]. These three functions are packed in four sub VI, they are Initialize VI, Configure Waveform VI, Enable Channel Output VI and Close VI. The core function of Initialize VI is VISA open function whose function is to set the communication mode and parameters between the upper machine and the lower machine and to define the default settings and reset the settings as well. It contains 4 external input parameters which are VISA resource name, Error input, ID query and Reset. The computer can automatically identify the VISA sources of instruments connected and generate VISA resource name selection list. What is users need to do is to select the VISA resource name corresponding to the instrument from the list. The VISA resource name can be sent to the next sub VI due to cascading. Error input can be regarded as a global variable, whose default value is 0. When error or exception occurs, it turn to non-zero, and the program report errors and error type to system. ID query and Reset for Boolean type variable, is controlled by the front panel. When True, it allows the

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computer query device address, otherwise query is forbidden. Reset is False for default. When it is set to True, the instrument parameters can be reset. 2.2 Configure Waveform Sub VI Configure Waveform sub VI is mainly responsible for generating the waveform parameter setting. There are 10 external input parameters. In addition to VISA resource name and Error input/output, the other 8 parameters and the value types are as shown in Table 1. By selecting from list and typing, the waveform parameter can be written into the VISA write register. Table 1. The external input parameters and the value type. Variable name

Value type

Variable name

Value type

Channel

List(select)

DC offset

Numeric

Waveform function

List

Amplitude

Numeric

Angle unit

List

Amplitude unit

List

Frequency

Numeric(key in)

Phase angle

Numeric

2.3 Enable Channel Output Sub VI and Close Sub VI Output sub VI is to receiving the signal channel selection from the superior sub VI and enable the channel output. Output controller is a Boolean value and when True, the channel open and waveform generator output corresponding waveform. Close sub VI is mainly composed of VISA close function which execute the ending of serial communication between computer and instrument. 2.4 Visualized Programming As shown in Fig. 2, cascading the 4 sub VIs above, we can realize the control of waveform generation signal output for once by computer. To make continuous control of signal output come true, we must add an while loop on the Configure Waveform sub VI. And an event structure is added inside the while loop. A controller named “refresh” whose type is Boolean is added to the event0. When the value of “refresh” is changed, the program in the event0 is triggered to scan and update the waveform parameters and refresh the outputted waveform. When the value of “refresh” remains unchanged, the status of program is waiting in the while loop and the outputted waveform keep still [4] (Fig. 1).

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Fig. 1. The main program is formed by initialize VI, configure waveform VI, enable channel output and close VI. The while loop is beside the configure waveform and enable channel output VI. The event structure is inside the while loop and outside the configure waveform VI.

In order to realize computer automatically save the waveform parameters without coverage, a MS Office Report Generation Tool is placed outside the while loop and the channel mode is indexing mode [5]. 2.5 Arbitrary Waveform Generation The principle of control of arbitrary waveform output by computer is different from the former. In the control of standard waveform output, the computer just needs to send waveform parameters to the waveform generator and the waveform is generated by the instrument actually [6]. But the computer needs to send the signal array of certain function to the waveform generator as well as the parameters in the case of arbitrary waveform output control. That is the data of arbitrary waveform need to be written by

Fig. 2. With the help of simulate signal tool, we don’t need to write the basic function by ourselves. We just need to set the parameters of basic function such as frequency, amplitude, noise type and so on.

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PC and sent to the instrument. So, there are two more sub VIs are used in that case, as shown in the Fig. 2, they are Create Arbitrary Function sub VI and Configure Arbitrary Function Phase. As Fig. 2 shows, with the help of Simulate Signal Tool, we don’t need to focus on the basic function writing but just need to set the function parameters such as frequency, sample rate, sample number and so on. The tool can help us generate the specific waveform array. And then we can generate new waveform array by numerical computing different basic functions. The final array is sent to Create Arbitrary Signal tool to form a waveform. 2.6 Experimental We tested our program with the Agilent 33511B waveform generator. The front panel of control of standard waveform output is as shown in Fig. 3. The function name can be elected from the list. The amplitude, frequency, DC Offset can be typed in. When the state of the button named “refresh” keep still, the output waveform keeps still too. After changing the parameters of the waveform and clicking the button, the waveform generator refreshes the waveform.

Fig. 3. When click the button named “Refresh”, the waveform generator updates the waveform output. And the parameters of waveform are saved to local document.

We use computer controlling waveform generator output a waveform whose frequency is 10 K, amplitude is 0.5 V, without DC Offset for experiment. The output waveform are as shown in Fig. 4. The result reached our expectation well. When the output waveform updated, the parameters of the waveform are saved to local excel file without covering the history data.

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Fig. 4. Computer control waveform generator output a sine waveform whose frequency is 10 k and amplitude is 1 V.

We then use a waveform for example to test the program, which is combined of two different frequency sine waveform. The front panel of control of arbitrary waveform is as shown in Fig. 5. The waveform 1 is of 10 k frequency and 1 V amplitude, and the waveform 2 is of 5 k frequency and 0.5 amplitude. The waveform 3 is to add the waveform 1 and waveform 2 and is the objective output waveform theoretically. The practical output by waveform generation is as shown in Fig. 5. The result of experiment satisfies the theoretic result perfectly.

Fig. 5. The output waveform is the combination of two sine function of different frequency and amplitude.

3 Conclusion This paper presents a computer control waveform generator output system which support standard waveform and arbitrary waveform. And for standard waveform output, the computer can automatically save the waveform parameters without covering the history data to local documents. It can reduce the work of regarding in the work of electrical

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test. Drawback still exists, that is, the writing of arbitrary waveform function must in the block panel because the number and type of base function that form the new function are uncertain. However, it still less complicated than write waveform function on the waveform generator panel. Acknowledgements. This work was partially funded by the National Nature Science Foundation of China (61901300), and made use of the Funding Program of Tianjin Higher Education Creative Team.

References 1. Zhang, A.-L., Cheng, Q.-H., Song, H.-Y., Pan, H.-G.: Optical arbitrary waveform generation based on an array of tunable apodized waveguide Bragg gratings. Optoelectron. Lett. 16(3), 195–199 (2020). https://doi.org/10.1007/s11801-020-9083-4 2. Sun, X., Li, Y., Zhang, Y., Hu, C., Chen, Z.: Study on the laser beam polarization based on LabVIEW. J. Russ. Laser Res. 42(2), 232–236 (2021) 3. Murali, A., Kakarla, H.K., Anitha Priyadarshini, G.M.: Improved design debugging architecture using low power serial communication protocols for signal processing applications. Int. J. Speech Technol. 24(2), 291–302 (2021). https://doi.org/10.1007/s10772-020-09784-x 4. Jaiswal, S., Ballal, M.S.: FDS-based PQ event detection and energy metering implementa-tion on FPGA-in-the-loop and NI-LabVIEW. IET Sci. Measurement Techcnol. 11(4), 453–463 (2017) 5. Girija, C., Sivakumar, M.N.: Determination of physiological parameters using biomedical monitoring system based on Labview FPGA. Res. J. Pharm. Technol. 11(9), 4021–4023 (2018) 6. Yuan, J.: An improved frequency-quadrupling triangular waveform genera-tion based on external modulation and polarization control. Results Phys. 22, 103885 (2021)

CDMA-Based High Precision Time Display System Mingfeng Zhang1,2 , Xiao Li1,2 , Yinghui Zhang1,2 , Kexin He1,2 , and Cheng Wang1,3(B) 1 Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, Tianjin

Normal University, Tianjin 300387, China [email protected] 2 Department of Communication Engineering, College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin 300387, China 3 Department of Intelligence Science and Technology, College of Artificial Intelligence, Tianjin Normal University, Tianjin 300387, China

Abstract. The high precision time display system of CDMA base station timing includes CDMA signal receiving circuit, power supply circuit, temperature and humidity acquisition circuit, power-down protection circuit, self-keeping circuit, serial port debugging circuit, LED display circuit and STM32 processor. The system communicates with the CDMA base station through the high-precision data receiver chip, receives the time information of the CDMA base station, synchronizes the electronic clock time, and displays the time with the large size LED digital tube, so as to achieve the purpose of displaying the high-precision time. This system has designed a time - keeping circuit, to ensure that the time display can still ensure the accuracy under the condition of loss of CDMA base station data information. The system aims to solve the problem of time synchronization and can easily deploy the device anywhere there is a CDMA signal. It solves the location, weather, the limitation on the GPS/BD receiving antenna installation. Keywords: CDMA · Synchronous clock · Self-punctuality circuit

1 Introduction With modern navigation, aerospace, communications, electric power, especially the development of the field of military, get a uniform standard time is more and more important. The accuracy of time and frequency reflects the comprehensive strength of a country. In the field of communication, the problem of clock frequency and phase synchronization of each node of the network has been basically solved, but the synchronization of time has not been well solved. The normal operation of various industries, such as communications and electric power, requires the support of stable time. Therefore, the stability and accuracy of timing equipment are very important [1]. At present, there are three main ways to time-granting: traditional timing, communication channel timing and satellite timing. With the development of modern science and © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 157–162, 2022. https://doi.org/10.1007/978-981-19-0390-8_20

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technology, traditional timing is almost not used now. Network Time Protocol (NTP) is mainly used for timing of communication channels. This method relies heavily on the Internet. In the area where the network signal coverage is incomplete or the network signal is unstable, this method cannot guarantee the accuracy of time. The satellite timing method is mainly to receive the signals sent by GPS satellite, so as to achieve accurate timing in the global scope. The satellite timing method can only receive the American GPS satellite network signal, and the satellite signal receiving module has strict requirements and high accuracy.

2 Schematic CDMA base station timing can obtain time information through CDMA network, which is convenient to deploy in the place with CDMA signal, especially suitable for deployment in the network communication room where it is not convenient to deploy outdoor GPS/BD antenna. In addition, because the CDMA antenna is convenient to be placed indoors, it can eliminate the damage of lightning weather to synchronous clock equipment. This timing method combines communication channel timing and satellite timing, which overcomes the problem of unstable signal of communication channel timing network. Compared with GPS/BD, it avoids the trouble of installing antenna, and there is no aging problem of antenna. It can be applied in buildings or placed inside the case, so that timing and system can be integrated [2]. CDMA base station equipment is generally composed of antenna feed subsystem, RF subsystem, base-band subsystem, power subsystem and so on. As shown in Fig. 1, Composition of CDMA base station system.

Fig. 1. Composition of CDMA base station system.

CDMA refers to code division multiple access. It is a spread spectrum multiple access digital communication technology, through a unique code sequence to establish channels, strong anti-interference ability, broadband transmission and anti-fading ability. CDMA is a multiplexing approach in which the multiplexing signal occupies only one

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channel and greatly improves bandwidth utilization. It is used in 800 MHz and 1.9 GHz UHF mobile phone systems. The principle of CDMA technology is to use high speed pseudo-random code whose bandwidth is much larger than the signal bandwidth to modulate, which will need to transmit information data with a certain signal bandwidth, that is, the bandwidth of the original data signal will be extended. It is then transmitted by carrier modulation. The receiver uses exactly the same pseudo-random code to correlate with the received bandwidth signal, which changes the wide-band signal into the narrow-band signal of the original information data to realize the information communication. CDMA communication is synchronous communication, Each CDMA base station is equipped with GPS/ Beidou satellite clock, which provides standard UTC time for each base station equipment, making the whole CDMA communication at the same standard time. The general telecom mobile phone is not specially calibrated time, because the CDMA signal inside the clock signal. As long as there is a base station, the CDMA clock will have a clock signal. As long as the phone can receive the telecommunication signal, CDMA clock can work normally [3]. Each CDMA clock inside are equipped with a CDMA receiver, used to receive CDMA clock signal. Through the CPU, it read CDMA clock inside the clock information, the accuracy in the sub-millisecond level. The output is carried out by another efficient clock encoding method. Generally, serial port TOD, 1PPS, NTP, SNTP and other ways are used for output. The client through these timing modes receives and can achieve accurate instruction in this way. The entire CDMA timing process is completed. There is GPS clock in CDMA base station to provide standard UTC time for each base station equipment. For a single satellite, the satellite transmits the pseudo code signal at time t0 , and the receiver receives the pseudo code signal at time tr . By modifying δt iA with the time delay, the launch time of satellite signal can be obtained: t0 = tr − δt iA

(1)

When the coordinates of station A (xiA ,yiA ,z iA ) are known, the pseudo-distance equation is: iA iA ρ iA = r iA + ct i − ct iA − δion − δtrop − εiA

Thus, the distance from the satellite i and the point A is given by:  r iA = (X i − xiA )2 + (Y i − yiA )2 + (Z i − z iA )2

(2)

(3)

In the above equation, ρ iA is the observed value of pseudo distance from station A to satellite i. r iA is the distance from A to satellite i. c is the speed of light. t i for satellite iA is the ionospheric clock difference. t iA for station A receiver clock difference. δion iA iA error. δtrop is troposphere error. ε is other related errors. Therefore, the time difference between station A and the navigation system can be obtained as: t iA =

iA − δ iA − ε iA ρ iA − r iA + t i − δion trop

c

(4)

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GPS and Beidou time is an important source of CDMA base station time information. From the above formula, we can get the time difference between the local clock and the system time. The embedded chip in the system communicates with the CDMA base station network, receives the time information of the CDMA base station, and then synchronizes the local time to the system time directly or indirectly [4].

3 Hardware The high precision time display system based on CDMA base station timing comprises a CDMA signal receiving circuit, a power circuit, a temperature and humidity acquisition circuit, a power-down protection circuit, a self-keeping circuit, a serial port debugging circuit, a LED display circuit, and a STM32 processor. The STM32 processor is based on the STM32F103RCT6 chip. Among them, the CDMA signal receiving circuit is a CDMA timing data receiving module, in which the Rx pin and Tx pin of the function chip are directly connected with the processor STM32F103RCT6. The power supply circuit adopts a voltage regulator circuit, it is directly connected with the processor STM32F103RCT6 module. The temperature and humidity acquisition circuit respectively uses a temperature sensor and a humidity sensor, and the data line (SDA) and control line (SCL) port of the sensor is directly connected with the processor STM32F103RCT6 module.

Fig. 2. Hardware connection diagram based on CDMA base station timing.

The power down protection circuit adopts the EEPROM circuit composed of a function chip, and the data line (SDA) and control line (SCL) port of the chip is directly connected with the processor STM32F103RCT6 module. The self-keeping circuit is composed of a function chip and an RC oscillation circuit, and the data line (SDA) and control line (SCL) ports of the chip are directly connected with the processor STM32F103RCT6 module. The serial port debugging circuit is composed of a function chip and a special USB-Mini seat. The reset port and the start port of the circuit composed of the chip are directly connected with the processor STM32F103RCT6 module. The LED display circuit adopts the dynamic display circuit of driving digital tube composed of logic control chip, which is directly connected with the processor STM32F103RCT6 module. As shown in Fig. 2, the hardware circuit connection diagram of CDMA base station system.

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4 Software After the power is turned on, the CDMA signal acquisition module starts to run and continuously detects the 1PPS signals generated by the two chips. If the 1PPS signals of the chip pass the detection, it will be judged that the acquisition chip modules are all working normally. If the signal cannot be detected, the corresponding module will automatically repeat the signal acquisition. According to the corresponding algorithm principle, the signal acquisition chip can convert the time information in the signal into the time of the current time zone, and then send the time data to the processor. Fig. 2 is the program framework of the signal acquisition module. After the processor starts, the Tx1 and Rx1 pins of the CDMA signal acquisition circuit collect the time data, and then send the time data to the local self-holding circuit. According to the time calculation algorithm, the local self-timing circuit guarantees the accuracy and automatically calculates the time. It compares the time with the time data sent by the processor to ensure the accuracy of the time. In the case of a loss of the CDMA time source, the processor takes the calculated time as the current time, which is almost indistinguishable from the actual time the satellite signal was sent. After the system is started, the processor stores the driver data of each pin in the power-down protection circuit for data protection. The circuit composed of temperature sensor and humidity sensor sends the acquired temperature and humidity parameter information to the processor through the data line (SDA) and control line (SCL) ports.

Fig. 3. CDMA timing high precision time display system program flow chart.

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The processor converts the parameter information into the current actual temperature and humidity value through digital - to - analog conversion. Finally, the obtained time information, temperature information and humidity information are sent to the large-size LED digital tube display. As shown in Fig. 3, CDMA timing high precision time display system program flow chart.

5 Conclusion The system solves the high precision requirement of satellite timing method. CDMA base station timing can obtain base station information through CDMA. GPS/BD is used as the system clock reference to obtain timing synchronization information, which has lower cost and more stable signal transmission. At the same time, it can ensure time synchronization and has the function of displaying temperature and humidity, power down protection and local self-punctuality. The system can be used in government, finance, mobile communications, schools, power, transportation, industry and national defense and other fields. For example, when banks conduct transaction management and big data analysis, CDMA base station timing can provide highly reliable precise time, effectively helping banks to carry out more comprehensive and refined transaction management, and ensure that the measurement and statistical records of the banking system in settlement are more real-time and efficient. The investment of the device in the school system forms the time synchronization effect of the system. At the same time, the electronic time display of each examination room in the test system can be consistent with the time of CDMA base station in the school standardized test system. Acknowledgements. This work was partially funded by the National Nature Science Foundation of China (61901300), and made use of the Funding Program of Tianjin Higher Education Creative Team.

References 1. You, C.W., Hong, D.S.: Multicarrier CDMA systems using time-domain and frequency-domain spreading codes. IEEE Commun. Trans. 51(1), 17–21 (2003) 2. Kameda, S., Taira, A., Miyake, Y., Suematsu, N., Takagi, T., Tsubouchi, K.: Evaluation of synchronized SS-CDMA for QZSS safety confirmation system. IEEE Trans. Veh. Technol. 99, 1–1 (2019) 3. Chris, R., Yang, L.: Background and recent advances in the locata terrestrial positioning and timing technology. Sensors 19(8), 1821 (2019) 4. Farzamnia, A., Yew, E.S., Moung, E.G.: A non-cooperative uplink power control for CDMA wireless communication system. Wirel. Pers. Commun. 114(2), 1249–1265 (2020)

Research on Path Planning and Tracking of Automatic Parking Menghan Dong and Xin Yin(B) Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, Tianjin Normal University, Tianjin 300387, China [email protected]

Abstract. With the development of automatic parking system, path planning and path tracking have been the focus of attention. For parallel parking and vertical parking, this paper mainly studies the implementation of the strategy of the parking path planning and tracking algorithm. The fitting method based on a polynomial function is used to plan the optimal parking path reasonably, and the pure pursuit algorithm is used to track the path after planning. The feasibility of path planning and the effectiveness of path tracking are verified by simulation experiments. This paper is also of great significance to the field of intelligent driving. Keywords: Automatic parking · Path planning · Path tracking · Intelligent driving

1 Introduction In recent years, with the rapid development of automobile industry, automobile manufacturers have invested a lot of manpower and material resources in the research and development of on-board systems, and the automatic assisted parking system emerges at the historic moment. Path planning and path tracking have always been the key and difficult problems in automatic parking system. The automatic parking system mainly relies on various sensors to obtain the surrounding environment and body attitude information, and plans a reasonable parking path automatically in the parking processor, reduces the possibility of the driver due to the operation error of the car cut accident, effectively improves the safety of driving and reduces the economic loss. At present, domestic and foreign scholars have done a lot of research work in the field of path planning and tracking of automatic parking technology [2]. In terms of path planning, the path constructed by using arcs and straight lines [6–8], has high accuracy requirements for the initial position coordinates of vehicles, while the polynomial function method can allow a certain error in the initial coordinates [12–14], and can also make the path smoother. In terms of path tracking, many tracking control algorithms has high accuracy [10], but the amount of calculation is large and it has its own limitations. Therefore, for the path tracking in the parking process, geometric algorithms can reduce the workload and ensure a small tracking error. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 163–175, 2022. https://doi.org/10.1007/978-981-19-0390-8_21

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This paper mainly studies the automatic parking path planning and path tracking, for the research of parallel parking and vertical parking in both cases [14], the fitting method based on a polynomial function of the optimal path planning of parking, the pure pursuit algorithm for path tracking, and using MATLAB software and Python compiler to simulation experiment on the two parts respectively to verify the feasibility of this research method.

2 Research on Parking Path Planning and Tracking 2.1 Kinematic Model of Vehicle In the parking process, the vehicle can be regarded as a rigid body due to its slow speed. During the parking process, the vehicle always moves on a plane without slip. When building a digital model of it, the body can be simplified into a rectangular model. In this paper, vehicle parameters of ordinary household models are selected for research [3]. The values, symbols and units of the parameters are shown in Table 1, and the diagram of the rectangular model is shown in Fig. 1. Table 1. Vehicle parameters of ordinary models Vehicle parameters

Symbol

Value

Unit

The length of the body

L

4.5

m

The width of the body

W

1.8

m

Wheel base

l

2.7

m

Front overhang

la

0.9

m

Rear overhang

lb

0.9

m

Tread

w

1.6

m

Min. turning radius

Rm

6

m

Fig. 1. Rectangular model of vehicle

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2.2 Research on Path Planning The classical method of parallel path planning is the two-section arc method, which has few parameters and simple design, but it may turn in place and cause great friction damage to the tire. Therefore, in this paper, polynomial fitting steps are added on the basis of the two-segment circular arc method. Assuming that there is a quintic polynomial function with unknown variables, such as Eq. (1), the unknown variable values in the formula are calculated according to the mathematical characteristics of the vehicle’s starting and ending positions, so as to determine the unique quintic polynomial expression. y = a5 x5 + a4 x4 + a3 x3 + a2 x2 + a1 x + a0

(1)

This method can optimize the curvature of the parking path, effectively make up for the deficiency of the two-section arc method, and make the parking process more stable and comfortable. The vertical path planning is similar to the parallel path planning, but due to its simple process, this paper adopts the quaternary polynomial function for fitting, as shown in Eq. (2). The path planning problems of parallel parking Spaces and vertical parking Spaces are respectively discussed below. y = b4 x4 + b3 x3 + b2 x2 + b1 x + b0

(2)

2.2.1 Parallel Parking Path Planning According to the guidelines for the construction of urban parking facilities issued by the Ministry of Housing and Urban-Rural Development [15], the minimum parking space size standard and minimum parking lane width of ordinary household small cars are shown in Table 2. Table 2. Minimum parking space and minimum width of parking lane for small cars Parking way

Parking length (m)

Parking width (m)

Parking lane width (m)

Parallel type

6.0

2.4

3.8

Vertical type

5.3

2.4

5.5

In the process of parallel parking, the distance between the obstacles on both sides of the parking space should be taken into consideration first. Since it is difficult to directly analyze the parking process, this paper adopts the method of reverse driving to analyze it, that is considering the process of the vehicle pulling out of the parking space. It is assumed that the outer side of the vehicle is level with the outer edge of the parking space, and the four corners of the rectangular model of the vehicle are set as A, B, C and D respectively. The minimum parking distance includes the space required for turning, the safe distance on both sides and the length of the vehicle body. As shown in Fig. 2, let the origin be the point P, the lateral to the right is the positive X-axis, and the longitudinal to the upward is the positive Y-axis. Let the middle point

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of the rear wheel be M 2 , and the length from point O to point M 2 is R. Since RC is approximately equal to the minimum turning radius Rm , according to the Pythagorean Theorem,   W 2 R2c = (l + la )2 + R + (3) 2 According to the data in Table 1, R = 3.9 m can be calculated. Assume that the safety distance n is 0.2 m, then the calculation formula of the minimum parking distance L n is,    W 2 + lb + 2n (4) Ln = R2c − R − 2 Put in the above data to calculate L n = 6.5 m. Therefore, parallel parking must first meet the requirement that the available parking space is greater than the minimum parking distance L n .

Fig. 2. Two-dimensional Cartesian coordinate system for parallel parking

In the coordinate system, the center point of the front wheel of the vehicle is set as the coordinate point of the vehicle, the initial position coordinate of the vehicle is set as M 0 (x 0 ,y0 ), the intersection point is set as M 1 (x 1 ,y1 ), and the terminal position coordinate is set as M 2 (x 2 ,y2 ). Put the values of the three coordinates into Eq. (1) respectively, y0 = a5 x05 + a4 x04 + a3 x03 + a2 x02 + a1 x0 + a0

(5)

y1 = a5 x15 + a4 x14 + a3 x13 + a2 x12 + a1 x1 + a0

(6)

y2 = a5 x25 + a4 x24 + a3 x23 + a2 x22 + a1 x2 + a0

(7)

In the process of parallel parking, it is considered that the vehicle is parallel to the road at the beginning and the end. Therefore, the first derivatives of points M0 and M2 are both 0, k  y0 = 5a5 x04 + 4a4 x03 + 3a3 x02 + 2a2 x0 + a1

(8)

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k  y2 = 5a5 x24 + 4a4 x23 + 3a3 x22 + 2a2 x2 + a1

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(9)

In order to ensure that the range of wheel Angle is as small as possible when the parking is about to be completed, the second derivative of M0 is also 0, k  y0 = 20a5 x03 + 12a4 x02 + 6a3 x0 + 2a2

(10)

According to the above 6 constraint equations, the six independent variable values of Eq. (1) can be obtained, and then the original equation can be brought back, so that the unique polynomial function can be determined. The coordinates of points M 0 , M 1 and M 2 are derived below. It can be seen from the figure that the coordinates of M 2 are first obtained. M2 (x2 ,y2 ) = [n + la ,LA /2]

(11)

So the coordinates of M 0 and M 1 can be deduced according to geometric knowledge, M1 (x1 ,y1 ) = [x2 + R sin θ ,y2 + R(1 − cos θ )]

(12)

M0 (x0 ,y0 ) = [x1 + R sin θ ,y1 + R(1 − cos θ )]

(13)

and the initial coordinates of the vehicle are,  x0 = n + la + 2R sin θ y0 = LA /2 + 2R(1 − cos θ )

(14)

2.2.2 Vertical Parking Path Planning The principle of vertical parking process is similar to the above, but the process is simpler without considering the shortest parking distance and other issues. The schematic diagram is shown in Fig. 3.

Fig. 3. Schematic diagram of vertical parking

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When the vehicle arrives at the parking space, it first drives a 1/4 arc from point N 0 to point N 1 in the figure, and straighten the front. At this time, the vehicle’s driving direction is parallel to the long side of the parking space, and then it drives straight to point N 2 , that is, the parking is completed. Similar to the parallel formula, a two-dimensional Cartesian coordinate system is established at point P, which is the origin of the coordinate system. Coordinates of the vehicle at different times are set as N 0 (x 0 ,y0 ), N 1 (x 1 ,y1 ) and N 2 (x 2 ,y2 ) respectively. The derivation process of path planning is as follows: First, put the coordinates of N0 , N1 and N2 into Eq. (2), y0 = b4 x04 + b3 x03 + b2 x02 + b1 x0 + b0

(15)

y1 = b4 x14 + b3 x13 + b2 x12 + b1 x1 + b0

(16)

y2 = b4 x24 + b3 x23 + b2 x22 + b1 x2 + b0

(17)

When the vehicle approaches the parking space, it is assumed that the direction of the head is parallel to the road, so the first derivative of the point N 0 is 0, k  y0 = 4b4 x03 + 3b3 x02 + 2b2 x0 + b1

(18)

When the parking is about to be completed, the head direction of the vehicle should be aligned, parallel to the long side of the parking space. Therefore, the first derivative of point N 2 is∞, k  y2 = 4b4 x23 + 3b3 x22 + 2b2 x2 + b1

(19)

Then, according to the above five constraint equations, the values of the five unknown variables in Eq. (2) can be solved, and the unique quartic polynomial function can be obtained to determine the parking path. So the coordinates of N 0 , N 1 and N 2 can be deduced, N2 (x2 ,y2 ) = [Lc /2,n + la ]

(20)

N1 (x1 ,y1 ) = [x2 ,y2 + l0 ]

(21)

N0 (x0 ,y0 ) = [x1 + R,y1 + R]

(22)

and the initial coordinates of the vehicle are,  x0 = Lc /2 + R y0 = n + la + l0 + R

(23)

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Fig. 4. Schematic diagram of path tracking algorithm

2.3 Research on Path Tracking As shown in Fig. 4, the G-spot is to track points, U point is the center point of the rear wheel, two attachment to lG , the Angle between lG and front and rear wheels is set as α. According to Ackermann steering principle and triangle knowledge can be obtained, the angle between OG and OU is 2α. The blue arc in the figure is the tracking path obtained by real-time adjustment of the vehicle. Therefore, the front wheel steering angle β should be deduced from the above conditions, and the steering should be carried out according to the β. According to trigonometry and the law of sines,   l (24) β = arctan RC  π RC sin(2α) = lG sin −α (25) 2 Equation (25) can be simplified as follows: RC =

lG 2 sin α

Substitute the derivation of Eq. (26) into Eq. (24),   2l sin α β = arctan lG

(26)

(27)

It is generally considered that lG is a linear function of vehicle speed v, and the front wheel steering Angle β is obtained,   2l sin α (28) β = arctan kv In the formula, k is the adjustment coefficient of the pure pursuit algorithm, called the forward-looking distance coefficient, which is generally 0.1.

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3 Simulation Experiment of Path Planning and Tracking 3.1 Simulation Experiment of Path Planning In the simulation experiment of parallel parking, as shown in Fig. 2, the vehicle completes parking from the M 2 position through the M 1 position to the M 0 position. The parameter table is summarized from the above data, as shown in Table 3. Table 3. Parameter table of parallel parking simulation experiment Meaning

Symbol

Value (m)

Meaning

Symbol

Value (m)

The starting coordinates

M 2 (x 2 , y2 )

(1.1, 1.2)

Parking Length

LA

6.5

The intersection point coordinate

M 1 (x 1 , y1 )

(3.86, 2.34)

Parking Width

LB

2.4

End point coordinates

M 0 (x 0 , y0 )

(6.62, 3.48)

Turning radius

R

3.9

Put these values into the constraint equation and calculate in MATLAB to get the fitted Eq. (1). Use MATLAB graph function to draw the function curve, as shown in Fig. 5. The red line is the parking path of the vehicle’s central axis, while the blue line is the parking space boundary, indicating that the whole parking path is relatively gentle.

Fig. 5. Simulation experiment of parallel parking paths

The derivative and curvature of the path are shown in Fig. 6 and Fig. 7. It can be seen that the first derivative of the path approaches 0 at both the starting point and the end point, and the minimum value of the curvature of the path is −0.32 and the maximum value is 0.30, with a small range of changes, which meets the requirements of the above planning.

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Fig. 6. First-order derivative of parallel parking paths

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Fig. 7. Curvature diagram of parallel parking path

In the simulation experiment of vertical parking, as shown in Fig. 3, the vehicle passes from the position N2 to the position N1 to the position N0 . Firstly, the data in the parameter table is summarized, as shown in Table 4. Table 4. Parameter table of vertical parking simulation experiment Meaning

Symbol

Value (m) Meaning

Symbol Value (m)

The starting coordinates

N 2 (x 2 , y2 )

(1.2, 1.1)

Parking width

LC

The intersection point N 1 (x 1 , y1 ) coordinate

(1.2, 3.8)

The distance between l 0 N1 and N0

2.7

Turning radius

3.9

End point coordinates N 0 (x 0 , y0 ) (5.1, 7.7)

R

2.4

Since the coordinate derivative of N 2 is infinite, the coordinate axis in Fig. 3 will be rotated 45° to the right in this paper, and the coordinates of N 2 , N 1 , and N 0 will change to N 2 (1.63, -0.07), N 1 (3.54,1.84), and N 0 (10.39, 0.5). Derivative of point N 2 turns to 1, and derivative of point N 0 turns to −1. The function image of Eq. (2) can be obtained through MATLAB, that is, the vertical parking path, as shown in Fig. 8. The derivative and curvature of the path are shown in Fig. 9. Figure (a) is the firstorder derivative, and Figure (b) is the curvature of the curve. Because of the axis rotation, the first derivative of the path approaches 1 and −1 at the starting and ending positions respectively, the minimum value of curvature is −0.385066139, and the maximum value is 0.016482145, which meets the requirements of the above planning. 3.2 Simulation Experiment of Path Tracking The polynomial function coefficients of parallel and vertical paths have been obtained by the simulation experiment of path planning. When the two functions are put into the path pursuit algorithm respectively, the tracking effect can be clearly seen. The parameter table required by the pure pursuit algorithm adopted in this paper in the simulation experiment is summarized, as shown in Table 5.

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Fig. 8. Vertical parking simulation experiment

(a) First-order derivative

(b) Curvature diagram

Fig. 9. Derivative and curvature diagram of vertical parking path

Table 5. Parameter table of pure pursuit simulation experiment Meaning

Symbol

Value(m)

Forward-looking distance coefficient

k

0.1

Forward-looking distance

L fc

0.5

Time interval

dt

0.1

Firstly, the status class of the vehicle and the corresponding update function are defined to simulate the status update of the vehicle. Then the pure pursuit algorithm function and path point finding function are designed, and the point-by-point tracking

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is carried out according to the planned path points and forward-looking distance. The experimental results are shown in Fig. 10 and Fig. 11. The left picture is the parallel parking path and the right one is the vertical parking path. The red dots in the figure represent the planned path points, and the blue line is the actual driving track of the vehicle.

Fig. 10. Simulation of parallel parking path tracking

Fig. 11. Simulation of vertical parking path tracking

In MATLAB, the actual path represented by the blue line and the planned path represented by the red dot are analyzed for errors, as shown in Fig. 8. The minimum error of parallel parking is −17 mm and the maximum error is 11 mm, while the minimum error of vertical parking is 66 mm and the maximum error is 45 mm, which is completely acceptable in the actual vehicle running, verifying the effectiveness of the pure pursuit algorithm (Fig. 12).

(a) Parallel parking

(b) Vertical parking

Fig. 12. Error between parking path and actual path

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4 Conclusion This paper analyzes the research background and significance of automatic parking system, mainly studying the path planning and path tracking. In the case of parallel parking and vertical parking, a polynomial function fitting method is used to plan the parking path of the vehicle. When the target parking is completed, the function derivative of the path is continuous and the curvature range is small, which can bring better driving experience to the user. Using the pure pursuit method to track the path, the error range is only centimeter level. So it has a good tracking effect, and the workload is small. The feasibility of path planning and the effectiveness of path tracking are verified through simulation experiments. Acknowledgements. This work was supported by Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, and funding of innovation and entrepreneurship training project for Tianjin university students (202010065086).

References 1. Ministry of Public Security of the People’s Republic of China: In 2020, there will be 33.28 million newly registered motor vehicles nationwide and 4.92 million new energy vehicles [EB/OL]. https://www.mps.gov.cn/n2254098/n4904352/c7647179/content.html. Accessed 7 Jan 2021 2. Zips, P., Böck, M., Kugi, A.: Optimisation based path planning for car parking in narrow environments. Robot. Auton. Syst. 79, 1–11 (2016) 3. Ye, L.: Research on Autonomous Parking Path Planning Method Based on Pseudo-spectral Method. University of Science and Technology of China (2016) 4. Hong, L., Wenjun, W., Keqiang, L.: Parallel parking path planning based on B-spline theory. China J. Highway and Transp. 29(09), 143–151 (2016) 5. Das, S., Yarlagadda, Y., Vora, P.B., Nair, S.R.P.: Trajectory planning and fuzzy control for perpendicular parking. In: IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI). IEEE, pp. 518–523 (2017) 6. Xu, L.: Research on Path Planning and Tracking Control of Automatic Parking Based on EPS. Hefei University of Technology (2017) 7. Wuwei, C., Yujie, F.: Wei Zhenya. Automot. Eng. 39(11), 1325–1332 (2017). (in Chinese) 8. Li, P.: Research on Path Planning and Tracking Algorithm of Automatic Parking System. Chongqing University of Technology (2018) 9. Zhao, L., Xu, L., Chen, W.W.: Path tracking for automatic parking based on active disturbance rejection control. China Mech. Eng. 28(8), 966–973 (2017) 10. Yin. G.: Research on Path Planning and Tracking of Vehicle Vertical Parking System. Chang‘an University (2019) 11. Liang. J.: Research on Vertical Automatic Parking System Based on Path Planning and Tracking Control. Chongqing University (2019) 12. Li, T.: Path Planning and Tracking of Automatic Parking System. Harbin Institute of Technology (2017

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13. Huang, J., Wei, D., Qin, L., Li. P., Du, J.: Research on path planning and tracking control method of automatic parking system. Automobile Technol. (08), 39–45 (2019) 14. Wei, D.: Research on Path Planning and Tracking Control Method of Automatic Parking System. Chongqing University of Technology (2020) 15. Ministry of Housing and Urban-Rural Development, PRC: Notice of the Ministry of Housing and Urban-Rural Development on the issuance of guidelines for the construction of urban parking facilities [EB/OL]. http://www.mohurd.gov.cn/wjfb/201509/t20150925_225 056.html. Accessed 20 Sep 2015

The Design of a Household Intelligent Medicine Box Based on the Internet of Things Long Zhang and Peidong Zhuang(B) College of Electronics Engineering, Heilongjiang University, Harbin 150080, Heilongjiang, People’s Republic of China [email protected]

Abstract. Ordinary household medicine boxes store various medicines for emergencies, but their function is limited to medicine storage. Most elderly people need to take medicines for a long time due to physical reasons, but they often miss or forget to take medicines due to memory decline, decreased vision, and inconvenience, which causes great harm to their health. Based on this, this work has designed a “household intelligent medicine box based on the Internet of Things”. The work has the functions of medication time monitoring, medication quantification, medication classification, medication reminder, etc. Based on the results of the SolidWorks mechanical structure modeling, the Arduino UNO controller is used to control the precise rotation of the MG945 steering gear to control the output of the medicine. The user presses the button and the device automatically delivers the medicine. The work provides health testing services such as heartbeat detection and uses the voice module to remind you to take medicine at the time set by the timer. Also, according to the function of the medicine box, a matching client APP has been developed. By using the HC-05 Bluetooth module for intelligent control of the medicine box, the user can open the APP with a mobile phone, input corresponding instructions to control and insert medicines information. The APP can judge whether the elderly take medicine on time and set the time, type, and quantity of medicine by checking the feedback data of the medicine box. APP records the remaining amount of medicine and the time of taking medicine for the elderly by connecting to the remote database. Keywords: Internet of things · Help old machinery · Intelligent medicine box · APP · Arduino UNO

1 Research Background and Significance According to the UN’s World Population Prospects: 2019 Revision, by 2050, one in six people in the world will be aged 65 or above (16%), up from 11 (9%) in 2019; By 2050, one in four people in Europe and North America will be 65 or older. In 2018, for the first time in history, there were more people aged 65 or older than there were people under five. Besides, the number of people aged 80 and over is expected to triple, from 143 million in 2019 to 426 million in 2050. As the elderly get older, their physical conditions have changed to some extent, including visual aging, decreased mobility, auditory aging, © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 176–183, 2022. https://doi.org/10.1007/978-981-19-0390-8_22

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and decreased language expression ability. Especially for the elderly who suffer from chronic diseases and need long-term medication, due to decreased vision and memory loss, long-term regular and quantitative medication activities have become a burden to their lives. Like children, they need to work and cannot remind their parents to take the medication in time. Prone to accidents. Based on the above social background, the team developed a household intelligent medicine box based on the Internet of Things. The medicine box cooperates with the developed APP and WeChat applet to realize the functions of setting the medicine delivery time, checking the remaining medicine amount, accurately delivering the medicine on time, and voice prompting to take the medicine.

2 Design Scheme 2.1 The Mechanical Structure The mechanical structure of the household intelligent medicine box based on the Internet of Things mainly includes the top cover body, the medicine box, and the base. A laser signal channel of a photoelectric distance sensor is provided on the left side of the top cover body; A height limiting plug with an infrared distance sensor is installed in the middle of the cover body and driven by a gear steering gear, which can limit the height of the drug. The bottom layer of the middle part of the cover body is provided with a propellant device and is driven by a gear steering gear; The right side of the cover body is designed with a funnel charging channel, which is provided with a clamshell; The middle part and the left side, the left side and the lower outlet is provided with a partition plate; The top surface of the cover body is provided with a removable cover plate (Fig. 1).

Fig. 1. Section view of mechanical structure: 1. Base 4. Electric engine room hatch covers 8. Push plug of cartridge 9. Places the motor in bilge 10. Seat bottom cover 11. Control center enclosure 12. Flip the lid 15. Control center top hatch cover 18. Cylindrical cartridge with rotating shaft 20. The main steering gear of the medicine box control system

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The bearing in the cartridge is driven by the connecting rod of the base steering gear; Cover body bottom shaft column limit damping; The cartridge has 16 vertical holes with a diameter of 18 mm in a circular array 50 mm from the center, theoretically accommodating 160–320 capsules of conventional size /900 tablets with a thickness of 4 mm and a diameter of 6–9 mm (Fig. 2).

Fig. 2. 3D model of household intelligent medicine box

The center of the base is provided with a drug box to drive the steering gear; The front side of the center of the base is provided with a receiving and releasing line pushing gear steering gear mechanism; The backspace of the base center is integrated with the medicine box control management and wireless network system; The back of the base is provided with a wiring channel to connect with the internal mechanical and electrical equipment of the top cover body. The overall picture of its 3D model is shown below: By 3D printing the model, the actual picture is shown below (Fig. 3):

Fig. 3. The physical picture of household intelligent medicine box

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2.2 Control System In this work, Arduino Uno development board is used as the main controller, with electronic components such as photoelectric switch, infrared switch, buzzer, steering gear, Bluetooth module, etc., to realize the control of the mechanical structure of the medicine box and complete the operation of drug delivery on time. The number of steering gear used is 6, including 4 for 360-degree steering gear and 2 for the 180-degree steering gear. The functions of each component in the control system are shown below. HC-05 The Bluetooth module: Be responsible for connecting the APP of Arduino Uno control board and WeChat applet. MG945 The steering gear: Responsible for driving the medicine storage barrel to rotate, the infrared switch up and down, and the discharge and take-up functions of the medicine discharge device. buzzer: Responsible for making a sound to remind users to take medicine。 Photoelectric switch: Responsible for testing manpower, that is, testing whether someone takes medicine。 Infrared switch: Responsible for checking the quantity of medicine in the medicine storage tube, and checking whether the quantity of medicine is remaining。 Buck regulator module: Responsible for reducing the voltage of the power supply to 5v, and then supplying power to the Arduino UNO and various electronic components. Finger detection heartbeat module: Responsible for detecting the user’s heartbeat and giving feedback. The physical picture of the Arduino UNO controller is shown below (Fig. 4):

Fig. 4. Physical map of some electronic components

The control logic of Arduino UNO for electronic components when dispensing medicine from the household intelligent medicine box based on the Internet of Things: When the human hand reaches under the medicine box outlet, the photoelectric switch detects the human hand and returns an electrical signal to the Arduino UNO. After

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Arduino UNO receives the electrical signal of the photoelectric switch, it drives the steering gear at the bottom of the medicine storage bin to rotate, rotate the medicine storage tube where the medicine needs to be discharged to a certain angle and stop. After that, the take-up and pay-off device will release the line and send the medicine to the top. If the infrared sensor detects the medicine at the top, it will return an electrical signal to the Arduino UNO controller. The introduction of medicines enables precise control of the number of medicines. The flow chart is shown below: 2.3 Software Part Two intelligent medicine box clients, APP and are used. The login and registration page are set. The cloud database is used to store the user’s user name and password. The APP uses fragment API and the WeChat applet uses TabBar to switch between different pages. Add drugs, view drug information, and connect to the Bluetooth drug delivery page to switch. When adding drugs, you need to record the name of the drug, the time of taking the drug, the number of drugs, etc., use the listview component to display the drug information in the medicine box and connect to the intelligent medicine box by using Bluetooth API, Click Dispense, and the drug can be dispensed according to regulations, and the drug information will be changed accordingly, and the dispensation record will be stored in the cloud database (Fig. 5).

Fig. 5. App interface diagram

2.4 Component Parameters and Performance The performance of 3D printing materials: The medicine box is light-cured by domestic resin 9400 (0.46 MPA melting point 46 °C), strong heat insulation capacity, high

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hardness, anti-beating, good internal sealing, and can be stored naturally for one month under dark, cool and dry conditions. The box theory can hold 160–320 regular size capsules/900 tablets with a thickness of 4 mm and a diameter of 6–9 mm. The type of medicine can be switched and the medicine can be discharged one by one. The internal power supply can last for 2–3 days. The parameters and performance of the electronic components used in this work are shown in the Table 1 below. Table 1. Component parameters and performance Component name

Rated voltage (V) Rated current (MA) Rated power (W)

Arduino UNO core board

5

300

3.6

Photoelectric sensor module

5

30

0.15

MG945 steering gear

4.8

30

0.15

Finger detection heartbeat module 5

30

0.15

Depressurization and stabilization 25 module

30

0.5

Voice play module

5

30

0.15

Led lamp

5

200

1

Buzzer

3.3

30

0.15

Infrared switch

5

30

0.15

hc-05 bluetooth module

3.3

30

0.15

Rechargeable battery

12

7000

84

3 Main Functions and Usage 3.1 Key Function The basic function of the intelligent medicine box is to release medicine one by one, which can customize the type of drugs, the number of drugs and the reminds time of drugs. With access to the Internet, users can remotely control the number of drugs consumed by patients through the intelligent medicine box APP, and can timely know the situation of patients’ consumption of drugs. Users can remotely access the medicine box to call and remind patients, and the medicine box is fed back to the guardian APP after a long period when the patients do not use drugs in time. Prevent accidents, solve the guardian can not take care of patients promptly, remind patients to take medicine on time, patients find medicine trouble, the elderly eye can not accurately eat prescribed drugs and other issues. The device also has reserved space, which can be followed by adding a voice reminder module, face recognition module, voice recognition module, touch screen heartbeat

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detection module, low-temperature refrigeration and drying module, remote control, and mobile extension functions, to strengthen the convenience of the use of the drug box. 3.2 Method of Application Step 1 Add the drug to the container; Step 2 Use APP or WeChat applet to add the drug information (name, number, etc.), and set the time of drug delivery; Step 3 The medicine box is prompted using a buzzer according to the user’s preset time. If you arrive at the present, the buzzer will sound to prompt the user to take medicine; Step 4 After the user reaches the drug box, the hand is placed at the drug outlet of the drug box, and the drug box is issued according to the drug delivery information set by the user in the APP; Step 5 After the medicine box is finished, enter the low power mode to prepare for the next time the user sets the medicine; The use flow chart of household intelligent drug boxes based on the Internet of Things is shown below (Fig. 6).

Fig. 6. Flow chart of drug box use

4 Innovations and Features Household intelligent medicine box based on Internet of Things is mainly composed of shell base, cylindrical shaft medicine box, and control center. This design greatly saves space. By setting up 16 cylindrical structures for drug storage, up to 16 kinds of drugs can be stored, the types of drugs stored include flakes and capsules. By setting up a pull-up drug delivery structure, combined with the control of the steering gear by the Arduino UNO controller, the precise drug delivery can be achieved, that is, the drug is delivered in grains. The sensors such as diffuse reflection infrared photoelectric switch have high sensitivity, and the detection distance can be set according to the actual situation, which is suitable for various scenarios. Besides, based on the mechanical structure of the medicine box and the single-chip microcomputer control system, the team developed a supporting Android APP to be compatible with a variety of mobile phone operating systems. Its innovations and features are as follows:

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• Design and Manufacture of Mechanical Structure by SolidWorks Modeling and 3D Printing Technology; • The APP client based on the Internet of Things and WeChat applet display the internal capacity and medication of the drug box in real-time, and realize the intelligent interaction between the user and the drug box; • The client controls the buzzer to ring, records the time of drug release, and detects the remaining dose function; • Drug box integrated medication monitoring, medication reminder, drug classification, drug quantification, health detection function; • Intelligent drug box provides on-time, on-volume, and accurate services to meet the requirements of drug users and avoid drug leakage, mistaking, and abuse; • The function is realized through the sensor and controller sold on the market, the total cost is low, suitable for promotion;

References 1. Gong, H., Huang, X.: Design of smart pill box with the function of alarm and SMS alert. J. Xihua Univ. Nat. Sci. Ed. 28(06), 92–96 (2015) 2. Yuqi, Z., et al.: Intelligent platelet morphometry. Trends Biotechnol. 39(10), 978–989 (2021) 3. Shaantam, C.: The autonomous pill dispenser: mechanizing the delivery of tablet medication. IEEE Int. Conf. Consum. Electron. 15(03), 47–51 (2016) 4. Hiba, Z., Khalil, K., Roy, A.Z.D., Josef, B., Ali, H.: Intelligent medicine box System. IEEE Int. Multi. Conf. Eng. Technol. 32(05), 73–77 (2018)

Investigation on Coherent Receiving Technology in the Repeater-Less Transmission Systems Yichao Zhang1,2 , Yupeng Li1,2(B) , Liang Han1,2 , Xiaoming Ding1,2 , and Xiaocheng Wang1,2 1 Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, Tianjin

Normal University, Tianjin 300387, China [email protected] 2 College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin 300387, China

Abstract. The application of PS-QPSK (Polarization Switched Quadrature Phase Shift Keying) modulation and coherent receiving technology in the repeater-less systems are studied. The VPI system structure is built and the system transmission rate of 24 Gbit/s is realized. The experimental results show that PM-QPSK (Polarization Multiplexing Quadrature Phase Shift Keying) compatible system can be used as a scheme to generate PS-QPSK at the cost of 25% transmission rate. The data recovery of PS-QPSK was completed after delayed data processing, phase detection, phase joint decision of two polarization states. Keywords: Polarization switching quadrature phase shift keying · Polarization multiplexing quadrature phase shift keying · Coherent demodulation algorithm · Phase modulation

1 Introduction The bandwidth of the signal has more and more requirements in recent years. The simple NRZ code type and direct detection technology can’t meet the needs of long-distance transmission, so new modulations, demodulation technology and detection technology are needed. Coherent optical communication system is considered to have the advantage of high sensitivity, which can extend the transmission distance of optical signals. With the development of coherent optical communication technology, the optical amplitude, phase, frequency and polarization state can be used to transmit information. Compared to the previous coherence detection, the present coherent detection technology is the digital signal processing technology detection system. When the optical signal is transmitted, various channel damage can affect the quality of the transmission signal, so it is important to study the effective transmission mode. In a transmission link with high nonlinear damage, PM-QPSK data may not arrive smoothly with a repeater-less system, while PS-QPSK data can be transmitted at a cost of 25% transmission rate [1, 2]. Although the spectral efficiency of PS-QPSK is not as good as that of PM-QPSK, PSQPSK can run at the same baud rate with the same transmitter hardware configuration, © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 184–191, 2022. https://doi.org/10.1007/978-981-19-0390-8_23

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and only a few adjustments are needed on the DSP on the side of the receiver to get the system that generates PS-QPSK. In this paper, a PS-QPSK system principle and coherent demodulation algorithm are studied, and the simulation experiment is completed with the help of VPI and MATLAB. At the cost of 25% transmission rate, the data recovery is realized.

2 Optical Signal Modulation and Optical Signal-to-Noise Ratio 2.1 Modulation Principle and Mapping Relation The light passes through a polarization splitter and is divided into two orthogonal polarized beams (marked as X, Y polarized beams), which are then modulated. In order to eliminate phase ambiguity, DQPSK is adopted to carry out differential encoding of the input data. After modulation, the synthesized optical signals of two polarization states are: √ 2 (1) EQPSK , x = jE ejβL × ejϕx (t) 2 x √ 2 EQPSK , y = (2) jE ejβL × ejϕy (t) 2 y β is the transmission constant and L is the modulation length. ϕx (t) and ϕy (t) match with the modulated phases of the x and y polarization states, respectively. The mapping relationship between bit sequence and optical modulation phase is given in Table 1. Table 1. Mapping relation between optical modulated phase and bit sequence xI ,yI

0

1

1

0

xQ ,yQ

0

0

1

1

Optical modulation phase

π 4

3π 4

5π 4

7π 4

2.2 Optical Signal-to-Noise Ratio Before analyzing PS-QPSK coherent demodulation algorithm, the definition of optical signal-to-noise ratio is introduced here. OSNR is defined as the ratio of the total signal power to the noise power in the 0.1 nm bandwidth, which can be represented as: OSNR =

Eb · Rb 2N0 Bref

(3)

Where Eb is the average energy per bit, Rb is the bit rate of the whole system, N0 is the noise power spectral density in the single polarization state, and Bref is the reference bandwidth. For 0.1 nm, the Bref reference bandwidth frequency corresponds to 12.5 GHz.

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In the design of actual optical transmission link, if we could determine desired bit error rate, then the theoretical minimum value of the required OSNR can be obtained by the formula corresponding to the QPSK system under the AWGN channel and the optimal signal receiving condition [3–5]:   Eb 1 BER = erfc (4) 2 N0

3 Modulation and Module 3.1 PS-QPSK Modulation and System Diagram Describes the constellation point vector with (Exr ,Exi ,Eyr ,Eyi ) and the constellation point set of PM-QPSK is CPM −QPSK = {(±1, ± 1, ± 1, ± 1)}

(5)

In [6], symbol set of PS-QPSK can be expressed as: CPS−QPSK = ±{(1,1,1,1),(1,1, − 1, − 1),(1, − 1,1, − 1),(1, − 1, − 1,1)}

(6)

After the spatial basis is rotated by 45°. So we can see all of the representations of CPS−QPSK can be taken from CPM −QPSK . The positive and negative signs of the symbol reflect the angle relationship in the constellation diagram. The phase difference of the orthogonal polarization state of PS-QPSK is 0 or π. A system block diagram of PS-QPSK generation compatible with PM-QPSK is given in Fig. 1.

Fig. 1. PS-QPSK system diagram

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3.2 DSP Module DSP processing mainly includes orthogonal unbalance compensation, fixed coefficient large dispersion coarse equalization, adaptive equalization depolarization multiplexing, frequency offset compensation, phase offset compensation, and finally in the data recovery module for decision decoding to restore the original sent data. The above processing, PM-QPSK system coherent demodulation algorithm has been described in detail in [7–11], this paper mainly analyzes PS-QPSK different from PM-QPSK algorithm. The main difference between PS-QPSK and PM-QPSK DSP is polarization demultiplexing. CMA algorithm can’t be used directly, we introduce three modules to process on the basis of PM-QPSK coherent demodulation algorithm in order to be compatible with PM-QPSK system, so as to ensure the use of CMA algorithm under the condition of constant mode. The module diagram of PS-QPSK system data processing is given in Fig. 2.

Fig. 2. DSP processing module of coherent demodulation

3.3 Coherent Demodulation Processing Module Delayed Time Processing. In the PM-QPSK system, four channels, Xi, Xq, Yi and Yq, constitute the original data sent, and there are 16 states. In PS-QPSK, three channels Xi, Xq and Yi constitute the original data sent, and there are eight states. The fourth channel data Yq is obtained from the data of the first three channels by XOR [12], as shown in Fig. 3. In order to remove the correlation between the orthogonal polarization states, a certain number of symbols can be introduced in the Y (or X) polarization state to delay the signal. Analysis shows that, after polarization de-multiplexing, the same number of symbols should be introduced to the X (or Y) polarization states for processing. Phase Detection. The phase can be set to θ = ϕ + k · 2π (K as an integer). At the end of the Viterbi algorithm with the fourth power of phase estimation, there is an operation divided by 4, so the phase estimation of the two polarization states can be expressed as: π · kx 2 π θ y = ϕy + · k y 2

θ x = ϕx +

(7) (8)

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Fig. 3. Valid data CSV file storage

In this case, the phase relation of the two polarization states is expressed as:     π π π θ = θx − θy = ϕx + · kx − ϕy + · ky = (ϕx − ϕy ) + (kx − ky ) 2 2 2

(9)

ϕx − ϕy = kπ, where k is an integer. In order to ensure that the phases of the two polarization states meet the constraint relationship, the quadratic processing can be carried out based on Viterbi algorithm to obtain the difference of phase ambiguity: θ =

π (kx − ky ) 2

(10)

Then θ  is added to the Y polarization state, and the phase is estimated as: θ  y = ϕy +

π · kx 2

(11)

Joint Decision. The two polarization states in PM-QPSK are independent of each other, which can be directly determined in two-dimensional space respectively. However, in PSQPSK, the two polarization states are related, and they cannot be determined separately. According to the constellation point set of CPS−QPSK , the corresponding reference phases 5π 7π are π4 , 3π 4 , 4 , 4 . One symbol is denoted by (exr ,exi ,eyr ,eyi ), and calculate the Euclidean distance corresponding to the reference phase:  (12) d = (exr − Exr )2 + (exi − Exi )2 + (eyr − Eyr )2 + (eyi − Eyi )2 According to the Euclidean distance of the minimum four-dimensional space, to determine and restore PS-QPSK signal.

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4 Simulation Experiment 4.1 VPI Simulation Set the total data amount of simulation as 65536*4, the IQ unbalanced angle offset as 20, the distance as 100 km, the frequency offset as 500 MHz, the line width of the laser as 1 MHz, the CD coefficient as 16 e−6 s/m2 , and the PMD coefficient as 0.5 e−12 /31.62 s/m1/2 , respectively. The system transmission rate is set at 24 Gbps. The incident optical power was measured to be − 9.3 dBm. In order to facilitate the simulation experiment, the nonlinear parameters of the fiber are set to 0, and the optical fiber link is attenuated to 0. Finally, the constellation diagram of the polarization state of X was observed as Fig. 4.

Fig. 4. Constellations of X polarization state

4.2 DSP Algorithm Running Results To break the relevance between the information of two orthogonal polarization states, 10 symbol cycles are introduced in the Y polarization state. After the polarization de-multiplexing, 10 symbol periods are introduced correspondingly in the X polarization state. After data processing, the observed constellation image and the recovered constellation image received by the system in a polarization state are shown in Fig. 5.

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Fig. 5. The observed constellation image and the restored constellation image received by the system in a polarization state

The joint phase diagram of X and Y polarization states before and after phase compensation is as follows:

Fig. 6. Joint phase diagram before and after fuzzy phase compensation

From Fig. 6, we know that the data recovery of PS-QPSK is completed under the condition of delayed data processing, phase fuzzy detection, and joint decision of two polarization states.

5 Conclusion In this paper, PS-QPSK modulation technology and coherent demodulation algorithm were analyzed, and PS-QPSK system implementation scheme compatible with PMQPSK system was designed, and system simulation experiments were carried out on the VPI platform. After the coherent demodulation algorithm of MATLAB was run, the PS-QPSK optical transmission system with a data rate of 24 Gbps can transmit effectively at 100 km. The simulation experimental research in this paper has reference value and practical application significance for coherent receiving technology in repeaterless system.

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Acknowledgment. This work was supported in part by the Natural Science Foundation of China under Grant 61901301, 62001328, 62001327 and in part by the Natural Science Foundation of Tianjin under Grant 18JCQNJC70900, 18JCZDJC31900 and in part by Doctor Fund of Tianjin Normal University under Grant 52XB2005.

References 1. Sjödin, M., et al.: Transmission of PM-QPSK and PS-QPSK with different fiber span lengths. Opt. Express 20(7), 7544–7554 (2012) 2. Fei, X., et al.: Comparative analysis of inter-channel nonlinear distortions from PM-QPSK and PS-QPSK interferers. Elsevier J. 372, 53–59 (2016) 3. Chapin, L. (ed.): Communication systems. ITIFIP, vol. 92. Springer, Boston, MA (2002). https://doi.org/10.1007/978-0-387-35600-6 4. Proakis, J.G.: Digital Communications, 4th edn. McGraw-Hill, New York (2001) 5. Sklar, B.: Digital Communications, 2nd edn. Prentice-Hall, Englewood Cliffs, NJ (2001) 6. Renaudier, J., et al.: Generation and detection of 28 Gbaud polarization switched-QPSK in WDM long-haul transmission systems. J. Lightwave Technol. 30(9), 1312–1318 (2012) 7. Kudo, R., et al.: Coherent optical single carrier transmission using overlap frequency domain equalization for long-haul optical systems. J. Lightwave Technol. 27(16), 3721–3728 (2009) 8. Seb, J.S.: Digital filters for coherent optical receivers. Optics Express 16(2), 804–817 (2008) 9. Fatadin, I., Savory, S.J., Ives, D.: Compensation of quadrature imbalance in an optical QPSK coherent receiver. IEEE Photon. Technol. Letters 20(20), 1733–1735 (2008) 10. Kuschnerov, M., et al.: DSP for coherent single-carrier receivers. J. Lightwave Technol. 27(16), 3614–3622 (2009) 11. Viterbi, A.J., Viterbi, A.M.: Nonlinear estimation of PSK-modulated carrier phase with application to burst digital transmission. IEEE Trans. Inform. Theory 29(4), 543–551 (1983) 12. Karlsson, M., Agrell, E.: Which is the most power-efficient modulation format in optical links? Opt. Express 17(13), 10814–10819 (2009)

Research on Vehicle Parking Aid System Based on Parking Image Enhancement Danxian Ye, Xin Yin(B) , and Menghan Dong Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, Tianjin Normal University, Tianjin 300387, China [email protected]

Abstract. At present, the visual parking assistance system in intelligent driving generally has the problems of unclear parking image quality and high hardware cost. In order to reduce the difficulty of parking and improve the ability to adapt to the environment, this paper proposes a vehicle assistance system based on parking image enhancement. Firstly, Retinex algorithm is used to balance the image illumination information and enhance the color saturation, so that it can adapt to more complex environmental conditions; secondly, Ackerman steering theorem is used to draw the dynamic parking aid line, and the coordinate transformation technology is used to output it to the vehicle screen. The adaptability and effectiveness of the developed system are verified by the relevant experimental research. Keywords: Image enhancement · Retinex algorithm · Parking system · Intelligent driving

1 Introduction At present, with the development of science and technology, the number of cars is increasing. The crowded parking spaces in urban space make parking more and more difficult, and novice drivers are often overwhelmed by this. Furthermore, failure to park quickly and accurately at once can also lead to traffic jams, increase vehicle emissions and cause air pollution and energy waste. With the rapid development of electronic industry, automotive electronics has also been developed rapidly [1]. Many advanced electronic driving assistance systems have been designed, such as reversing radar, tire pressure detection, 360° panoramic view, navigation, etc. [2], and gradually become the standard configuration of vehicles, among which the advanced parking assistance system is one of the most eye-catching new technologies, and it is also a hot issue of current research. In the field of Parking Aid [3], foreign traditional industrial powers are still in the forefront in the world, which makes it hard for other countries to catch up in both theoretical research and technical support. Many famous automobile companies are equipped with Parking Aid System on most models, such as Audi, Mercedes Benz, BMW and other top automobile brands. In China, domestic models are still limited to displaying the rear image of the car to the driver, and the information such as reversing route © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 192–200, 2022. https://doi.org/10.1007/978-981-19-0390-8_24

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planning still can not be better processed. Although most premium cars are equipped with the function of dynamic reversing auxiliary line, the camera itself needs to have the function of reversing trajectory, and it needs to be customized according to the model. Hardware is expensive and basically limited to premium models. It is a hot issue how to realize the Parking Aid System with low cost in the field of intelligent driving. The research on the Parking Aid System [4] which can predict the vehicle’s reverse trajectory according to the steering wheel angle and make the result clearly display on the display will be helpful for the driver to reverse the vehicle and greatly reduce the judgment errors caused by the lack of driving experience. Therefore, firstly, the image of the reversing video is enhanced. Then the reversing trajectory is predicted according to the steering angle of the front wheel. Thirdly, dynamic reversing estimation algorithm is developed, and the predicted reversing trajectory is displayed on the multimedia screen of the vehicle. The system can realize the vision of the backup assist system, which can not only clearly see the situation of the rear of the car, but also dynamically predict the reversing direction in real time, guide the driver to adjust the reversing route in time, and help the driver to finish parking safely and quickly. Effectively solve the driver reverse difficult problem. Realize the use of ordinary camera to complete the function of reversing trajectory, greatly save the hardware cost.

2 Overview of On-Board Parking Aid System Based on Parking Image Enhancement 2.1 Image Enhancement The existing image enhancement methods include the traditional spatial and frequency domain image enhancement algorithm [5], the image enhancement method based on artificial neural network, the image enhancement method based on fuzzy set, the image enhancement method based on wavelet transform, the image enhancement method based on human visual characteristics, etc. [6]. Histogram equalization is one of the most effective spatial image enhancement methods, but there are still some defects in histogram equalization, such as the reduction of gray level of transformed image, the disappearance of some image details, and image over enhancement and so on. The image method based on human visual characteristics improves the brightness and contrast of the image to a great extent, avoids the phenomenon of color distortion, makes the edge of the enhanced image clearer, enriches the detail information, and is more in line with human visual experience. At present, the image enhancement method based on human visual characteristics is based on Retinex algorithm. In order to intuitively observe the effect of different methods, this study compares the traditional image enhancement method with the image method based on human visual characteristics, and objectively evaluates the processing results of the algorithm. The original image of the experiment is from Baidu website, as shown in Fig. 1. It can be clearly seen that the image processed by Retinex algorithm is clearer and the color fidelity is better.

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(a) Original graph (b) Histogram equalization algorithm (c) Retinex algorithm

Fig. 1. The effect of different algorithms on image processing

Taking PSNR, MSE and entropy as evaluation indexes, the processing effects of different algorithms in Fig. 1 are objectively compared [7]. Table 1. Comparison of common image enhancement algorithms Evaluation comparison

Algorithm Original

Histogram equalization algorithm

Retinex algorithm

PSNR

21.4340

15.2110

17.4239

MSE

0

1.1782

0.3801

Information entropy

6.487076

7.955850

7.918640

It can be seen from Table 1 that the peak signal-to-noise ratio reflects the image distortion. The higher the peak signal-to-noise ratio, the less the image distortion. The mean square error method first calculates the mean square value of the pixel difference between the original image and the distorted image, and then determines the degree of distortion of the distorted image through the size of the mean square value. Entropy refers to the degree of confusion of the system. The entropy of a well focused image is greater than that of an image without clear focus. The greater the entropy of the measured information, the clearer the image. Through the comparison and comprehensive study, we can objectively see that Retinex algorithm has less distortion and more clarity. The Retinex [8] algorithm has the advantages of high contrast, high color fidelity and real-time. In the process of vehicle driving, due to the change of light intensity, the video sequence captured by the camera will appear the phenomenon of inconsistent light. For example, there is a big difference between the light in the daytime and driving at night. The light intensity in the daytime is generally higher than that in the evening. When driving at night, due to the influence of environmental factors, will make the car in a relatively large contrast of the field of view. Retinex algorithm can balance the

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light intensity well, reduce the adverse effects caused by weather or environment, and improve the adaptability and safety of driving. To sum up, Retinex algorithm is used to enhance the image of reversing image. 2.2 Calculation of Reversing Trajectory Ackerman’s basic principlem [9] means that in the process of driving (straight line and turning), the motion estimation of each wheel must conform to its natural motion trajectory, so as to ensure that the tire and the ground are always in pure rolling. That is to say, when the vehicle is turning, all wheels must circle around an instantaneous center point, as shown in Fig. 2.

Fig. 2. Schematic diagram of vehicle reversing trajectory

By substituting the vehicle parameters such as wheelbase a, wheelbase B, vehicle width D and the distance C from bumper to rear wheel into the formula, the coordinates of point O and the values of R1 and R2 can be obtained according to θ. O(x, y) = Bcotθ R1 = R2 =

 ((Bcotθ − D/2))2 + C 2 

((Bcotθ + D/2))2 + C 2

(1) (2) (3)

According to the value of turning radius R1 and turning radius R2, the ground track of vehicle reversing can be obtained.

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Using Ackerman steering principle, the predicted vehicle reversing trajectory line is based on the ground coordinates, while the reversing image seen by the driver on the vehicle screen is based on the imaging plane of the camera [10–12]. The actual track line of vehicle reversing needs to be projected onto the reversing camera. But because the calculation is based on the real coordinates, if it is directly projected to the display screen, the calculation result will not correspond to the actual image, which has a very bad impact on the auxiliary reversing. Therefore, it is necessary to convert the predicted trajectory coordinates under the ground coordinates into the camera imaging plane coordinates through coordinate transformation [13], and then overlay them on the reversing image. Assuming that the rear camera of the vehicle is installed in the middle of the rear of the vehicle and the viewing angle range of the camera is 2α, the focus plane perpendicular to the focal length of the camera is selected as the imaging plane, and the imaging is clearer on this plane. The coordinate transformation schematic diagram is shown in Fig. 3.

Fig. 3. Schematic diagram of ground scenery projected onto camera imaging surface

As shown in the Fig. 3 above, point a is the installation position of the camera at the rear of the vehicle, and the ground track coordinate system is oxy, where y direction is the direction directly behind the vehicle, and x direction is the direction on the right side of the vehicle. OXY camera imaging plane coordinate system, where the upper left corner of the imaging plane is set as the imaging plane coordinate origin O, AK is the focal length of the camera, K is the focal point, the angle between the center line of the camera and the vertical direction is θ, the imaging plane height OV = v, and the point t (0, y) in the figure is the ground point m (x, y) for the projection point of the point on the y-axis in the ground coordinate system oxy, let  tAo = β and the distance from the camera to the ground Ao = h, the conversion formula from the ground coordinate point m (x, y) to the imaging plane coordinate point M (X, Y) can be obtained as follows (Fig. 4): Y =

v ∗ tan(θ − β) 2tanα

(4)

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Fig. 4. System design flow chart

 v ∗ cosβ /(h ∗ cos(θ − β)) (5) 2tanα It can be seen from the above formula that the conversion of the ground Y-direction coordinates to the imaging plane y coordinates is only related to the camera parameters and the ground y coordinates, while the conversion of the ground X-direction coordinates to the imaging plane x coordinates is not only related to the ground x coordinates, but also related to the ground y coordinates. Combined with the actual situation, the farther away from the camera, the shorter the object displayed on the screen. Therefore, the above formula is in line with the actual situation. On the premise of fixed vehicle type, the parameters such as the visual angle range 2α, the height h from the camera to the ground, and the angle θ between the camera center line and the vertical direction are fixed. Therefore, as long as the above parameters are entered in advance, the transformed coordinates can be obtained, and then the transformed dynamic parking aid line is displayed on the image frame, and finally the image frame is converted back to the video format, Output to vehicle screen. X =x∗



3 Simulation Experiment Research This research is based on the Intel Core 9000 h processor, win10 ultimate operating system, 16 GB of memory environment to carry out the research of car parking assistant system based on parking image enhancement. The experimental flow chart is shown as follows: 3.1 Experimental Results of Reversing Image Improvement Using pycharm software, opencv is used to process video. Firstly, the reverse video captured by camera is processed into image frames. Figure 5 is a few frames of images

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randomly extracted by converting the collected back video stream into image frame after code processing.

Fig. 5. Original image captured by camera

In pycharm software, the image frame is enhanced by Retinex algorithm. The running results are shown in Fig. 6. It is obvious that Retinex algorithm can balance the light intensity well, and the image quality of reversing image becomes clear.

Fig. 6. Image frame processed by Retinex algorithm

3.2 Predicting the Results of Reversing Trajectory The processed pictures can be predicted by Ackerman steering principle. The camera visual angle range, camera distance from the ground height, the included angle θ between

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the camera center line and the vertical direction can be input in advance to obtain the transformed coordinates. Then the transformed dynamic backup assist line is displayed on the image frame, and finally the image frame is turned back to the video format, output to the on-board screen. The experimental results are shown in Fig. 7. When the vehicle is in reverse, the reverse image system will automatically turn on the camera at the rear of the vehicle, and display the rear status of the vehicle on the LCD screen. At this time, a border area will appear on the screen, and the line in the border area is the static track line of the backup image assist, while the Yellow curve is the dynamic auxiliary line, which will deflect according to the rotation of the steering wheel. Thus, the track of the wheel can be predicted intuitively, and the driver can be reminded to adjust the direction in time to further improve the parking safety.

Fig. 7. Prediction of reversing trajectory

4 Conclusion Aiming at the problems of unclear image quality and high hardware cost in the current visual parking aid system, in order to reduce the difficulty of parking and improve the adaptability to the environment. In this paper, the reversing image is improved to adapt to more complex environmental conditions; then the dynamic reversing auxiliary line is drawn by Ackerman steering theorem, and the coordinates of the camera are obtained by coordinate transformation technology, and then output to the vehicle screen, so as to realize the function of using ordinary camera head to complete the reversing trajectory, save the cost of hardware manufacturing and reduce the difficulty of parking. Finally, the adaptability and effectiveness of the developed system are verified by experiments. The research of this paper also has important application significance in military guidance and intelligent transportation.

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Acknowledgements. This work was supported by Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, and funding of innovation and entrepreneurship training project for Tianjin university students (202010065086).

References 1. Zhan, Z., Du, X., Jia, H.: Analysis and prospect of automotive electronics frontier technology. Light Vehicle Technol. 35(Z3), 3–5 (2011) 2. Xue, Y., Wang, X., Shi, J.: Research review of vehicle safety driving assistance system. Heilongjiang Traffic Sci. Technol. (2), 124–126 (2016) 3. Li, Z.: Design and Implementation of Parking Aid System Based on Android. Dalian University of Technology (2018) 4. Shi, D.: Reliability analysis of reversing image display system. Automotive Electrical Appliances (02), 44–45 (2017) 5. Celik, T.: Two-dimensional histogram equalization and contrast enhancement. Pattern Recognit. 45(10), 3810–3824 (2012). https://doi.org/10.1016/j.patcog.2012.03.019 6. Cai, L.M., Qian, J.S.: Night Color image enhancement using fuzzy set. In: The 2nd International Congress on Image and Signal Processing, Tianjin (2009) 7. Xiang, W.: Research on quality evaluation of digital Orthophoto Image. Technological Innovation Productivity 6, 48–50 (2015) 8. Li, X.: Image enhancement algorithm based on Retinex theory. Comput. Appl. Res. 22(2), 235–237 (2005) 9. Ran, G., Ma, L., Huang, C., Zhai, W.: Design of dynamic parking aid line for passenger cars. Auto Parts (11), 9–12 (2017) 10. Chen, H., Liu, Y., Li, C.: Research and design of multi view intelligent visual Parking Aid System. Modern Comput, (Professional Edition) (15), 51–57 (2014) 11. Tang, Z.: Design and Implementation of Intelligent Parking Guidance System. Southwest Jiaotong University (2017) 12. Li, S., Lei, Z., Xiao, Z., Wang, Y.: Research and Simulation of automatic parking trajectory based on fuzzy control. Comput. Technol. Develop. (2), 163–166 (2017) 13. Li, T.: Path Planning and Tracking of Automatic Parking System. Harbin Institute of Technology (2017)

GPS/Star Sensor Attitude Accuracy Evaluation Method Based on Hybrid χ 2 Detection Ziang Chen1,2 , Chenglong He1 , Bo Wang2(B) , and Li Zhang2 1 National Key Laboratory of Satellite Navigation System and Equipment Technology,

Shijiazhuang 050081, China 2 College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing

210016, China [email protected]

Abstract. GPS and star sensor integrated navigation is a new direction of joint positioning and attitude determination. Based on the integrated navigation method of Kalman filter, an attitude accuracy evaluation method of GPS/star sensor based on hybrid χ 2 detection is proposed in this paper. Aiming at the possible failure problem of the combined filter of GPS and star sensor in the actual scene, an attitude filtering accuracy evaluation method based on the hybrid detection of innovation χ 2 detection and state χ 2 detection is proposed. By constructing equivalent weight factors based on the hybrid χ 2 detection in Kalman filter, the attitude accuracy of GPS and star sensors can be evaluated, so as to realize error compensation in the case of failure and improve the anti-deviation performance of filtering. Experimental results show that the proposed method can effectively evaluate the accuracy and improve the anti-deviation performance, and has certain advantages compared with the standard Kalman filtering method in the case of failure. Keywords: GPS · Star sensor · χ 2 detection · Accuracy evaluation

1 Introduction GPS system has high positioning accuracy and stability, and has a good prospect in attitude measurement. Star sensor has the advantages of high attitude determination accuracy and no accumulated error. GPS/star sensor combination can complement each other’s advantages [1, 2], but there are still some problems when they are combined in navigation on the satellite. GPS system stability is better, and the GPS navigation precision is higher when the satellite signal is stronger. However, when the carrier operates in a complex environment, GPS signal will be vulnerable to the shade of the external environment and disturbance and multipath effect will be more serious. It will lead to a larger amount of GPS observation error and have great effect to the attitude measurement. GPS signal interference can cause measuring pose accuracy greatly reduced. The error will change the characteristics of Kalman filtering zero mean white noise for measurement innovation, which affects the optimal estimation performance of Kalman filtering and even causes filtering divergence [3]. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 201–208, 2022. https://doi.org/10.1007/978-981-19-0390-8_25

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The star sensor compares the acquired star map with the ephemeris and then calculates the attitude information of the carrier relative to the inertial system, which has the advantage of high attitude determination accuracy. However, the star sensor requires high environmental conditions at night, so it also needs to consider its possible failure situation. When the number of stars too little or the sky was briefly covered by clouds, star sensor cannot recognize star map, lose the function of pose determination and is no longer able to perform the normal navigation [4]. So the attitude data of GPS and star sensor required accuracy evaluation when we perform the integrated navigation. We should detect the failure state and repair by filtering, so as to promote the anti-deviation performance of integrated navigation filtering [5]. This paper proposes an attitude accuracy evaluation method based on hybrid χ 2 detection. First of all, the method respectively uses the innovation χ 2 detection to evaluate its attitude filtering accuracy of the abnormal situation for GPS and star sensor attitude measuring and then join the state χ 2 detection in integrated navigation. Then evaluate the results for two kinds of detection, construct equivalent weight factor in the Kalman filter and add the constructed equivalent observation covariance matrix to filter gain. By this way, the method can carry on the attitude accuracy evaluation to the GPS and the star sensor, so as to realize the error compensation of failure cases and improve anti-deviation performance of the filtering.

2 Methodology Aiming at the problem of observation fault detection, χ 2 detection algorithm is a common accuracy evaluation method including innovation χ 2 detection method and state χ 2 detection method. For the sudden increase of errors caused by state mutation, the innovation χ 2 detection method can accurately identify the noise characteristics, so as to carry out fault elimination or anti-deviation processing, but the slow increase of errors cannot be better identified [6]. The state χ 2 detection method detects the state obtained by the filter and the state obtained by the state transmitter without measurement update, so as to judge whether there is abnormal situation in the system and realize the identification of the slow increase of errors [7]. Complementing the advantages of the two detection methods is an important way to evaluate the attitude accuracy of GPS and star sensor and improve the performance of anti-deviation. In this chapter, an attitude accuracy evaluation method based on hybrid χ 2 detection is proposed. 2.1 The Innovation χ 2 Detection Method According to the attitude accuracy evaluation process of GPS/star sensor, the attitude accuracy of star sensor and GPS is firstly evaluated by using innovation χ 2 detection, and the measurement innovation after state estimation in filtering can be expressed as: v(k) = z(k) − H x˜ (k|k − 1)

(1)

In general, the measurement innovation is a white noise sequence and obey the normal distribution with the mean value of zero. However, when GPS or star sensors fail, the detection of measurement innovation statistics can reflect whether the observed value is

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abnormal. In abnormal circumstances, the mean value of measurement innovation is no longer zero, and the following assumptions can be listed:  H0 : v ∼ N (0,C) (2) H1 : v ∼ N (∇,C) Among them, the H 0 denotes that there is no abnormal situation in the system, H 1 denotes the abnormal situation in the output of GPS or star sensor, C is the covariance matrix of innovation measurement, C(k) = HP(k|k − 1)H T + V (k) and V is the covariance matrix of observed noise. Take the hypothesis testing statistic as: T (k) = v(k)T C(k)−1 v(k)

(3)

Wherein, T obeys the χ 2 distribution of degrees of freedom as t, and this assumption can be listed as:  H0 : T ∼ χ 2 (t,0) (4) H1 : T ∼ χ 2 (t,λ) The boundary condition to determine whether abnormal conditions occur is: TD ∼ χα2 (t)

(5)

Among them, α is the significance level, and its value range is usually set from 0.01 to 0.15, which can be set to 0.05 for attitude accuracy evaluation. If T (k) ≤ TD , it is considered that the observed value has no abnormal condition. Otherwise, it is considered that there is a failure condition. For the star sensor and GPS output, their hypothesis testing statistics respectively set as T 1 (k) and T 2 (k), and be detected by innovation χ 2 detection respectively. 2.2 State χ 2 Detection and Accuracy Evaluation Methods After the innovation χ 2 detection, the state χ 2 detection is added to the combination ˙ , ¨ ,, ˙ ,, ¨ ˙ ] ¨ T is set as of the two attitudes, and the state vector x = [,, , the attitude angle, angular velocity and angular acceleration output by the star sensor. Establish the equation of state: x(k + 1) = Fx(k) + w(k)

(6)

Wherein, F is the state transition matrix, x is the state vector matrix, and w is the system noise. ˙ ψ] ¨ T : attitude Angle, angular velocity Set observation vector z = [ϕ,ϕ, ˙ ϕ,θ ¨ ,θ˙ ,θ¨ ,ψ,ψ, and angular acceleration output by GPS. Establish the observation equation: z(k) = Hx(k) + v(k)

(7)

Wherein, z is the observation vector matrix, H is the observation matrix, and v is the observation noise.

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State χ 2 detection is based on the difference between the two state estimates x˜ 1 (k) and x˜ 2 (k). x˜ 1 (k) is obtained by the system observation value z(k) through Kalman filtering, and x˜ 2 (k) is obtained by the state recursive device using prior information. Therefore, x˜ 1 (k) is related to the measurement information and will be affected by abnormal conditions. On the contrary, x˜ 2 (k) will not be affected. It can be respectively expressed as: x˜ 1 (k) = x˜ 1 (k|k − 1) + K(k)[z(k) − H x˜ 1 (k|k − 1)]

(8)

x˜ 2 (k) = F x˜ 2 (k − 1)

(9)

The estimation accuracy can be detected effectively by the difference between x˜ 1 (k) and x˜ 2 (k). Estimation error e1 (k) and e2 (k) can be expressed as:  e1 (k) = x˜ 1 (k) − x(k) (10) e2 (k) = x˜ 2 (k) − x(k) The difference between the two state estimates can be expressed as: β(k) = e1 (k) − e2 (k) = x˜ 1 (k) − x˜ 2 (k)

(11)

β(k) is a zero-mean Gaussian random vector, and its variance can be expressed as: R(k) = E{β(k)β T (k)}

(12)

The hypothesis testing statistics can be expressed as: T (k) = β(k)T R(k)−1 β(k)

(13)

We also set the boundary condition TD. If T (k) ≤ TD , it is considered that the observed value has no abnormal condition. Otherwise, it is considered that there is failure condition. In the combined state χ 2 detection, the hypothesis testing statistic is defined as T 3 (k). According to the detection results of the hybrid χ 2 detection method, the attitude accuracy of GPS and star sensors can be evaluated by constructing the equivalent weight factor in Kalman filter and adding the equivalent observation covariance matrix into the filter gain, so as to realize the error compensation in the case of failure and improve the anti-deviation performance of filtering. The constructed equivalent weight factor is expressed as:    Ti,j (k) ≤ TD 1,   (14) ωi = TD   |Max{Ti,1 (k),Ti,2 (k),Ti,3 (k),}| , Ti,j (k) > TD Set ω = diag[ω1 ,ω2 , · · · ,ωn ], the equivalent covariance matrix of the observed values can be obtained as follows: V (k) = V (k)/ω

(15)

By adding the equivalent covariance matrix to the Kalman filter gain, the mutual anti-deviation fusion estimation of GPS and star sensor can be realized. When the

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detected value is abnormal, the weight of the abnormal component can be reduced by the weight factor and the equivalent covariance matrix, so as to enhance the anti-deviation performance of GPS/star sensor fusion estimation Kalman filter. The filter gain equation is: K(k) = P(k|k − 1)H T [HP(k|k − 1)H T + V (k)]−1

(16)

The state estimation equation is:   x˜ (k) = x˜ (k|k − 1) + K(k) z(k) − H x˜ (k|k − 1)

(17)

According to the gain K, the weights of state variables and observed variables can be adjusted dynamically. It can be seen from Eq. (16) that the K value can iterative updated by the given initial value P0 of the covariance matrix. As can be seen from the state estimation Eq. (17), when K is smaller, the weight of the state estimation will be larger, and when K is larger, the weight of the observed value will be larger.

3 Simulation Experiments and Analysis This section focuses on the anti-deviation performance of the GPS/star sensor accuracy evaluation method simulation analysis in failure case. Firstly, consider the GPS and star sensor without failure, set up the GPS output attitude measurement noise variance is 0.1°, star sensor output attitude measurement noise variance of 20 . Using the hybrid χ 2 detection KF attitude combination filtering method and the standard KF algorithm respectively, two methods of the output filtering result is shown in Fig. 1, and estimation measurement error of attitude angle maximum value (Max) and root mean square (RMS) are shown in Table 1 and Table 2. It can be seen that the two methods can obtain stable filtering level when the accuracies of GPS and star sensor attitude are high, and the estimation error were similar. The estimation error of the hybrid χ 2 detection KF attitude combination filtering method is slightly less than the standard KF method. The accuracy of the yaw Angle is 0.015°, the

(a)

(b)

Fig. 1. Filter estimation error results. (a) the hybrid χ 2 detection KF method. (b) the standard KF method.

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ϕ/°

θ /°

ψ/°

Max RMS

0.0527

0.0521

0.0510

0.0527

0.0139

0.0179

Table 2. Max and RMS of estimation error for standard KF method. Attitude angle

ϕ/°

θ /°

ψ/°

Max

0.0615

0.0578

0.0568

RMS

0.0155

0.0163

0.0199

accuracy of the pitching Angle is 0.014° and the accuracy of the rolling Angle of 0.018°. It can satisfy the attitude accuracy of integrated navigation. The anti-deviation performance in the presence of failure is analyzed below. First, the failure of star sensor is considered and the accuracy of star sensor is reduced to 60 . Using the hybrid χ 2 detection KF attitude combination filtering method and the standard KF algorithm respectively, two methods of the output filtering result is shown in Fig. 2, and estimation measurement error of attitude angle maximum value (Max) and root mean square (RMS) are shown in Table 3 and Table 4.

Table 3. Max and RMS of estimation error for the hybrid χ 2 detection KF method in the case of star sensor failure. Attitude angle

ϕ/°

θ /°

ψ/°

Max

0.0547

0.0635

0.0537

RMS

0.0160

0.0174

0.0199

Table 4. Max and RMS of estimation error for standard KF method in case of star sensor failure. Attitude angle

ϕ/°

θ /°

ψ/°

Max

0.0829

0.0710

0.0812

RMS

0.0192

0.0205

0.0246

Comparing the precision of the two methods, it can be seen when the accuracy of star sensor reduce, filtering estimation curve of the hybrid χ 2 detection KF method

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remains relatively stable, while the curve of the standard method of KF sometimes jump, occasionally appear larger error value, and the error exceeds the maximum 0.08°, the accuracy of the hybrid χ 2 detection KF method is significantly higher than the standard KF method, improved accuracy reached about 0.003°, the accuracy of yaw Angle reached 0.016°, the accuracy of pitching Angle reached 0.017°, the accuracy of rolling angle reached 0.020°. It is shown that the GPS/ star sensor attitude accuracy evaluation method with the hybrid χ 2 detection can effectively detect the attitude accuracy degradation caused by the failure of star sensor, so as to reduce the weight of the output component in the filtering, and improve the performance of filtering.

(a)

(b)

Fig. 2. Filter estimation error results in the case of star sensor failure. (a) the hybrid χ 2 detection KF method. (b) the standard KF method.

4 Conclusions Based on Kalman filter for integrated navigation, this paper puts forward a GPS/star sensors attitude accuracy evaluation method based on the hybrid χ 2 detection. Firstly, we analyzed the problems in the GPS/star sensors navigation. Then aiming at the possible failure problem of GPS/star sensor integrated navigation in actual scene, this paper proposes an attitude accuracy evaluation method based on the innovation χ 2 detection and state χ 2 detection. By constructing equivalent weight factor in the Kalman filter based on hybrid χ 2 detection and adding the constructed equivalent observation covariance matrix to filter gain, the method can carry on the attitude accuracy evaluation to the GPS and the star sensor, so as to realize the error compensation of failure cases and improve anti-deviation performance of the filtering. Finally, the accuracy evaluation and anti-deviation performance of the proposed method are analyzed through simulation experiments, which shows the superiority of the proposed method compared with the standard Kalman filtering method in the case of failure. Acknowledgements. This work was supported in part by the Open Research Fund of National Key Laboratory of Satellite Navigation System and Equipment Technology under grant No. GEPNT2017KF-08.

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Robust Widely Linear Beamforming Based on Worst Case Optimization Jiashuo Yang1,2,3(B) , Lulu Zhao1,2 , Xinglong Jiang1,2 , Guang Liang1,2,3 , and Jinpei Yu1,2,3 1

Innovation Academy for Microsatellites of CAS, Shanghai 201203, China [email protected] 2 Shanghai Engineering Center for Microsatellites, Shanghai 201203, China 3 University of Chinese Academy of Sciences, Beijing 100049, China

Abstract. The widely linear minimum variance distortionless response (WL-MVDR) beamformer has a better performance than the conventional MVDR beamformer when the received signals are potentially noncircular. While it is more sensitive to the mismatches encountered in practical. In this paper a WL-WCPO beamformer is proposed. It is based on the Worst-Case Performance Optimization (WCPO) method and exploits the noncircularity of received signals simultaneously. Simulation results are provided and illustrate that the proposed method is more robust against the DOA estimation errors and calibration errors compared with other WL beamformers.

Keywords: Robust adaptive beamforming Worst-case performance optimization

1

· Noncircular signal ·

Introduction

Adaptive beamforming plays an important role in array signal processing, and has been widely applied in various fields, like radar, sonar, wireless communications and so on. Conventional beamformers mainly consider signals to be second-order (SO) circular. Nevertheless, there are many SO noncircular signals used in the areas of radio communication or satellite communication, such as ASK, BPSK and UQPSK signals [1]. For noncircular signals, traditional approaches like minimum variance distortionless response (MVDR) beamformer become suboptimal, and the optimal complex filters are proved to be widely linear (WL) [2]. By exploiting the conjugate covariance relationships, WL beamformers can make full use of the noncircularity property of signals. The output signal-to-noise ratio (SINR) will be improved further, which breaks through the limitation of J. Yang—This work is supported by the Shanghai Rising-Star Program (No. 18QA1404000) and Scientific Research Plan Program of Shanghai Science and Technology Committee (No. 18DZ112001). c The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022  Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 209–216, 2022. https://doi.org/10.1007/978-981-19-0390-8_26

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the physical array aperture. The WL MVDR beamformer was firstly proposed in [3], but it remains suboptimal since it only exploits the SO noncircularity of interferences. In [4], the optimal WL MVDR beamformer was proposed, which takes the SO noncircularity of both desired signal and references into consideration. Although the optimal WL MVDR beamformer outperforms the traditional MVDR beamformer for noncircular signals in ideal condition, it is more sensitive to the errors usually encountered in practical applications. Besides the typical errors like DOA estimation errors and array calibration errors, the optimal WL MVDR beamformer is vulnerable to the estimation errors of the desired signal’s noncircularity coefficient additionally [5]. Therefore, a variety of robust WL beamformers have been proposed in recent years. In [6], the robust WL beamforming algorithm was presented based on the estimation of noncircularity coefficient, but it relies much on the precision of the desired signal’s steering vector. Wen extended the robust capon beamforming method to WL models [7]. And an algorithm combining the iterative adaptive method and interferenceplus-noise covariance matrix reconstruction was proposed in [8]. In this paper, a novel robust WL beamformer is developed. This approach is based on the Worst-Case Performance Optimization (WCPO) method and exploits the noncircularity of received signals simultaneously. It takes both DOA and noncircularity coefficient’s uncertainty into consideration, and has a good robustness against the array calibration error as well.

2

Signal Model

Consider a uniform linear array (ULA) with M omni-directional sensors that receives one desired signal and P interferences. The observation vector at the nth snapshot can be written as x(n) = a0 s0 (n) +

P 

Δ

ai si (n) + n(n) = a0 s0 (n)+v(n)

(1)

i=1

where s0 (n) and si (n), i = 1, 2, ..., P are the complex envelope of the desired signal and interferences, assumed to be zero-mean and potentially noncircular. Both of them are far-field narrowband signals and uncorrelated with each other. ai , i = 0, 1, ..., P are the steering vectors of these incident signals respectively. n(n) is the additive white Gaussian noise vector with covariance matrix σn2 I, where I is an identity matrix of order M . And v(n) represents the whole interference-plus-noise (IPN) vector. The second order statistics of x(n) are defined by P     2 + σi2 ai aH Rx = E x(n)xH (n) = σ02 a0 aH 0 i + σn I i=1 Δ

=

σ02 a0 aH 0

+ Rv

(2)

Robust Widely Linear Beamforming Based on Worst Case Optimization P     Cx = E x(n)xT (n) = γ0 σ02 a0 aT0 + γi σi2 ai aTi i=1 Δ

=

γ0 σ02 a0 aT0

211

(3)

+ Cv

where Rx and Cx are the covariance matrix and the conjugate covariance matrix 2 of x(n) . σi2 =< E[|si (n)| ] >, i = 0, 1, ..., P are the power of the desired signal and interferences. < · > denotes the time-averaing operation, with respect to the time index n, over the observation window, as the noncircular signals are nonstationary. γi is the second order noncircularity coefficient of the ith signal defined by   2  E si (n)   = |γi | ejϕi , 0 ≤ |γi | ≤ 1 γi = (4) 2 E |si (n)| |γi | and ϕi are the noncircularity rate and phase of si , when |γi | = 0, si is noncircular. In order to exploit the second order noncircularity of received signals, we define the extended observation vector by

x(n) Δ (n) =  (n) x a1 + s∗0 (n) a2 + v (5) = s0 (n) x∗ (n) T T  T Δ  Δ  1 = aT0 , 0TM , a 2 = 0TM , aH  (n) = vT (n), vH (n) . Then where a , and v 0 the output of the widely linear beamformer can be described as y(n) = w ˜ Hx ˜(n) = w1H x(n) + w2H x∗ (n)

(6)

T  is the weight vector of the WL beamformer. where w ˜ = w1T , w2T

3 3.1

Proposed Method Optimal Widely Linear MVDR Beamformer

For |γ0 | = 0, s∗0 (n) is correlated with s0 (n) and contains both a desired signal and an interference component. Let’s consider the Hilbert space fitted with the inner Δ product (u(n), v(n)) =< E[u(n)v ∗ (n)] >, s∗0 (n) can be orthogonal decomposed as (7) s∗0 (n) = γ0∗ s0 (n) + [σ02 (1 − |γ0 |2 )]1/2 s0 (n) where s0 (n) is the component orthogonal to s0 (n), thus < E[s0 (n)s0 (n)∗ ] >= 0 (n) can finally be written as and < E[|s0 (n)|2 ] >= 1. Using (7) in (5), x 2 ) + s (n)[σ02 (1 − |γ0 |2 )] (n) = s0 (n) ( x a1 +γ ∗ a   0  0 ˜ aγ

Δ

= s0 (n)˜ aγ + v ˜γ (n)

v ˜γ (n)

1/2

2 + v  (n) a 

(8)

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T  where ˜ aγ = aT0 , γ0∗ aH is the equivalent extended steering vector (ESV) of the 0 (n). The interferencedesired signal, and v ˜γ (n) is the global noise vector of x plus-noise covariance matrix can be expressed as

   Rv Cv ˜γ (n)˜ vγH (n) = (9) Rv˜γ = E v C∗v R∗v + Rγ where Rγ = σ02 (1 − |γ0 |2 )a∗0 aT0 . Then the output of the WL beamformer with ˜(n) = s0 (n)w ˜ H˜ aγ + w ˜ Hv ˜γ (n). weight vector w ˜ is given by y(n) = w ˜ Hx Similar to the well-known Capon’s MVDR beamformer, the optimal WL MVDR beamformer [4] is desired via solving min w ˜ H Rv˜γ w ˜ w ˜

s.t. w ˜ H˜ aγ = 1

(10)

then the optimal solution of (10) is −1  −1 w ˜ MVDR = ˜ aH aγ R−1 aγ γ Rv ˜γ ˜ v ˜γ ˜ the SINR at the output of a WL filter w ˜ is defined by  2 2 H ˜ ˜ aγ  Δ σ0 w SINR[w] ˜ = w ˜ H Rv˜γ w ˜

(11)

(12)

In practical applications, Rv˜γ and ˜ aγ are typically unavailable. Therefore, Rv˜γ is commonly replaced by the sampled extended covariance matrix Rx˜ =

N 1  x ˜(n)˜ xH (n) N n=1

(13)

where N is the number of snapshots. And ˜ aγ is replaced by the nominal extended aγ of desired signal with an estimated γˆ0 . As there are mismatches steering vector ˜ aγ and ˜ aγ , the beamformer may suffer form severe perbetween Rv˜γ and Rx˜ , ˜ formance degradation, especially with high SNR input. 3.2

Robust WL Beamformer Based the WCPO Method

In this section, a novel robust WL beamformer is proposed. We extend the WorstCase Performance Optimization (WCPO) method [9] to exploit the second-order noncircularity of received signals. The WCPO method assumes that the actual steering vector of the desired signal a0 belongs to an uncertainty set A and imposes a constraint that for all vectors in A the absolute value of the array response should not be smaller than one. Now with the extended model (8) for noncircular signals, we assume that the extended steering vector belongs to an uncertainty set A˜ as well, which contains the uncertainty of both DOA and noncircularity coefficient of the desired signal.

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Firstly, the uncertainty set A can be described as A = {a | a = p + e, e ≤ ε}

(14)

where p is the nominal steering vector of desired signal, e and ε are the error vector and the radius of the uncertainty set respectively. And we assume that the noncircular coefficient is confined within a set |γ − γˆ | ≤ εγ

(15)

where γ and γˆ are the actual and estimated noncircular coefficient for a respectively. The common estimator of γˆ is given by [6] γˆ = − where

aH Ea∗ aH a · H H a Da a (IM − σ ˆn2 R−1 x )a

(16)

Δ

∗ −1 D = (Rx − Cx R∗−1 x Cx )

(17)

Δ

E = −DCx R∗−1 x

(18)

σ ˆn2

can be estimated by the minimum eigenvalue of Rx˜ , and the estimated error of γˆ can be denoted as γΔ = γ − γˆ . Then, the extended error vector can be written as

a−p ˜ e=˜ a−p ˜= γ ∗ a∗ − γˆ ∗ p∗

(19) e = ∗ γ ∗ e∗ + γΔ (p + e)

Therefore, the upper bound of the extended error vector is calculated by ˜ e = ˜ eH ˜ e = e + ˆ γ e + γΔ p + γΔ e 2

2

2

2

2

≤ e + (ˆ γ e + γΔ p + γΔ e) √ 2 ≤ ε2 + (|ˆ γ | ε + εγ M + εγ ε)   

(20)

ε˜2

Thus, ˜ e ≤ ε˜, ε˜ is the smallest radius of the ESV’s uncertainty set. The constraint region can finally be described as A˜ = {˜ a|˜ a=p ˜+˜ e, ˜ e ≤ ε˜}

(21)

Then the WL-WCPO beamformer with the uncertainty set of extended steering vector can be expressed as min w ˜ H Rx˜ w ˜ w ˜   H a ∈ A˜ ˜ ˜ a ≥ 1, for all ˜ s.t. w

(22)

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which is a nonconvex problem. With the similar procedure in [9], it can be convert into a convex SOC program (23) and solved via the interior point method. ˜ H Rx˜ w ˜ min w w ˜

a − ε˜ w ˜ ≥1 s.t. w ˜ H˜  H  a =0 Im w ˜ ˜

4

(23)

Simulation Results

In the simulation, a uniform linear array (ULA) with M = 10 omnidirectional sensors spaced half a wavelength is considered. The desired signal comes from the direction θ0 = 5◦ and two interferences impinge from the directions θ1 = −30◦ , θ2 = 40◦ . Both of these three signals are BPSK signals, with the noncircularity rate 1 and the noncircularity phase 0, π/3, π/4, respectively. The interferenceto-noise ratio (INR) in each sensors is 25 dB and the number of snapshots is fixed at 800. For each scenario, 200 Monte-Carlo trials are performed. The proposed method is compared to the SMI beamformer, the WL-SMI beamformer with the estimated noncircularity coefficient, the WCPO beamformer [9], and the robust WL beamformer based on diagonal loading method [10]. The optimal SINR of standard and WL beamformers are also plotted as the performance benchmarks. We assume the parameter ε = 3 for [9], the diagonal loading factor ξ = 10ˆ σn2 for [10]. And for the proposed method, the parameter ε˜ is set to be 3.5. 4.1

Robustness Against DOA Errors

In this simulation, we consider the scenario where the input SNR varies form –10 dB to 30 dB with the steering direction error Δθ = 2◦ , and the performance curve is shown in Fig. 1. It can be observed that the optimal output SINR of WL beamformer is 3 dB higher than conventional beamformer in ideal condition. While when the DOA error occurs, the desired signal coming from the actual DOA is suppressed, the performances of SMI and WL-SMI beamformers degrade severely. Especially when the SNR increases, the SINR of WL-SMI even becomes worse than SMI. The WCPO and WL-WCPO method gain a significant improvement over the SMI and WL-SMI, while performance of the robust beamformer in [9] is improved limitedly. Furthermore, the WL-WCPO method outperforms the WCPO as it utilizes the second order noncircularity of received signals and extends the virtual aperture of the array. 4.2

Robustness Against Amplitude and Phase Perturbations

In this simulation, we examine the robustness of these beamformers to the amplitude and phase perturbations. It is supposed that the amplitude and the phase error of each sensor are uniformly distributed in [−0.2, 0.2] and [−2.5◦ , 2.5◦ ]

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respectively. There is no DOA mismatch and the SNR still varies form –10 dB to 30 dB. Figure 2 describes the performances of these beamformers. We can see that when the SNR is lower than 5 dB, all robust beamformers have excellent performance. As the SNR increases, the performance of WL-WCPO is effected less than the other beamformers and has a better robustness against amplitude and phase perturbations.

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Fig. 1. Output SINR versus SNR with the steering direction error Δθ = 2◦

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Fig. 2. Output SINR versus SNR with amplitude and phase perturbations

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Conclusion

In this paper, we propose a novel robust WL beamforming method. In this method, the Worst-Case Performance Optimization (WCPO) algorithm is extended to the WL beamformer and both DOA and noncircularity coefficient’s uncertainty are taken into consideration. Simulation results demonstrate the sensitivity of the WL-MVDR beamformer to those mismatches occurring in practical, and illustrate the performance improvement brought by the proposed method. Compared with existing WL beamformers, the proposed method can not only be more robust against the DOA errors but also have a good tolerance to the amplitude and phase perturbations.

References 1. Picinbono, B.: On circularity. IEEE Trans. Signal Process. 42(12), 3473–3482 (1994) 2. Chevalier, P.: Optimal array processing for non-stationary signals. In: IEEE International Conference on Acoustics. IEEE (1996) 3. Chevalier, P., Blin, A.: Widely linear MVDR beamformers for the reception of an unknown signal corrupted by noncircular interferences. IEEE Trans. Signal Process. 55(11), 5323–5336 (2007) 4. Chevalier, P., Delmas, J.P., Oukaci, A.: Optimal widely linear MVDR beamforming for noncircular signals. In: IEEE International Conference on Acoustics, Speech & Signal Processing. IEEE (2009) 5. Ye, Z., Xu, D., Cao, S., et al.: Review for widely linear beamforming technique. J. Data Acquisit. Process. (2014) 6. Xu, D., Gong, C., Cao, S., et al.: Robust widely linear beamforming based on spatial spectrum of noncircularity coefficient. Signal Process. 104(6), 167–173 (2014) 7. Wen, F., Wan, Q., et al.: Robust Capon beamforming exploiting the second-order noncircularity of signals. Signal Process. 102, 100–111 (2014) 8. Liu, J., Xie, W., Wan, Q., et al.: Robust widely linear beamforming via the techniques of iterative QCQP and shrinkage for steering vector estimation. IEEE Access 6, 17143–17152 (2018) 9. Vorobyov, S.A., Gershman, A.B., Luo, Z.Q.: Robust adaptive beamforming using worst-case performance optimization: a solution to the signal mismatch problem. IEEE Trans. Signal Process. 51(2), 313–324 (2003) 10. Xu, D., Huang, L., et al.: Widely linear MVDR beamformers for noncircular signals based on time-averaged second-order noncircularity coefficient estimation. IEEE Trans. Veh. Technol. 62, 3219–3227 (2013)

Research on the Intelligent Guidance System of Empty Parking Spaces Based on Amap API and Edge Detection Danxian Ye, Xin Yin(B) , and Menghan Dong Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, Tianjin Normal University, Tianjin 300387, China [email protected]

Abstract. At present, due to the improvement of people’s material living standards, the number of cars is growing rapidly. Urban road congestion and parking space tension, bring more and more safety problems. Intelligent parking has become a hot issue in assistant driving research. Therefore, this paper develops the open web interface of Amap to realize the function of automatically finding parking space and obtaining parking route. Then the edge detection operator is used to detect the parking space, and finally the Amap smart positioning is used to guide the driver to park. Users can query the real-time information of the use of parking space in the destination parking lot anytime and anywhere, and improve the success rate and accuracy of parking. It not only saves the parking time, but also improves the utilization rate of the parking lot. The real-time and effectiveness of the developed system are verified by the simulation experimental research. Keywords: Amap API · Canny detection · Parking space recognition · Intelligent parking

1 Introduction At present, with the increasing number of motor vehicles in China and the world, as well as the rapid development of urbanization in China, the contradiction between urban motor vehicle ownership and the number of parking spaces is gradually emerging [1]. The rapid growth of vehicle ownership has brought great pressure to the traffic environment, and exacerbated the problem of parking. How to display the free parking spaces around the driving area and solve the problem of low parking efficiency has become a hot issue in current research [2]. According to the statistics of strategy analytics, a famous foreign market analysis company, in 2015, the global market demand for parking aid systems reached 24 million sets, with an output value of more than US $1.1 billion. But consumer interest in assisted systems began to cool since 2015, and the immature assisted driving technology conflicts with the huge market demand. Therefore, how to improve the auxiliary function of parking system is of great significance to solve the problem of parking difficulties [3]. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 217–226, 2022. https://doi.org/10.1007/978-981-19-0390-8_27

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The results of a survey on the use of automatic parking system initiated by a domestic car brand are shown in Fig. 1: among the 1970 valid sample data, only 1% of the participants indicated that they had no problems in using the automatic parking system, and 76% of the participants indicated that they had made mistakes in using the automatic parking system. For example, the vehicle can not quickly identify a variety of types of parking spaces, parking time is too long and so on.

Fig. 1. Pie chart of parking system error voting results

Therefore, this paper proposes an intelligent parking guidance system based on Amap and edge detection. Edge detection operator is used to help drivers identify idle parking spaces [4–6], which improves the efficiency of parking. Open source Amap can guide the empty parking space and the driver’s GPS position information to realize the purpose of parking nearby [7]. At the same time, it also reduces the distance and time for the driver to find parking space on the road, alleviates the traffic pressure, and achieves the effect of energy saving and emission reduction.

2 Theoretical Research on the Intelligent Guidance System of Empty Parking Spaces Based on Amap API and Edge Detection 2.1 Amap Port High precision map is one of the core capabilities to achieve high-level automatic driving [8]. As one of the earliest mapping companies to set foot in the field of high-precision map, Amap has established a complete high-precision map production line as early as 2015. At present, Amap high-precision map covers more than 350000 km of expressways and urban expressways in China. As the first domestic travel service platform with daily life exceeding 100 million and monthly life exceeding 400 million, Amap has the most real-time and accurate dynamic traffic information in the industry, which also provides important decision-making basis for the planning and guidance of automatic driving

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assistance system [9]. This paper mainly uses the API and SDK provided by Amap, which can display maps and location tags on Android [10]. The second development of open web interface of Amap is carried out to realize the function of automatically finding parking space and obtaining parking route. 2.2 Edge Detection Algorithm Edge detection is a basic problem in image processing and computer vision. The purpose of edge detection is to identify the points with obvious brightness change in digital image. Different images have different gray levels [11]. Generally, there are obvious edges at the edges, which can be used to segment images. The common detection operators are Roberts, Sobel, Prewitt, Laplacian and canny. In order to intuitively observe the effect of different methods, this study compares different edge detection methods. The original picture of the experiment is from Baidu website, and the experimental results are shown in Fig. 2. The advantages and disadvantages of different algorithms are shown in Table 1.

Fig. 2. The processing results of different edge detection algorithms

To sum up, canny method is the most effective edge detection method in edge function. This method is not easy to be “filled” by noise, and it is easier to detect real weak edge. Canny operator [12] is a multi-level edge detection algorithm developed by Australian computer scientist John F. canny in 1986. Its goal is to find an optimal edge. The definition of the optimal edge is: 1. Good detection: the algorithm can mark the actual edge of the image as much as possible. 2. Good positioning: the marked edge should be as close as possible to the actual edge of the actual image. 3. Minimum response:

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Table 1. Comparison of advantages and disadvantages of different edge detection operators Operator

Comparison of advantages and disadvantages

Roberts

It is better to process the image with steep and low noise, but the result of edge extraction by Roberts operator is coarse, so the edge location is not very accurate

Sobel

Both Prewitt operator and Sobel operator are weighted average, but Sobel operator thinks that the influence of neighborhood pixels on current pixels is not equivalent, so pixels with different distances have different weights, and the influence on operator results is also different. Generally speaking, the farther the distance, the smaller the impact

Prewitt

The image processing effect is better when the gray gradients and noise are more. The principle of noise suppression is through the average of pixels, but the average of pixels is equivalent to the low-pass filtering of the image, so the edge location of Prewitt operator is not as good as Roberts operator

Laplacian It can locate the step edge points accurately, and is very sensitive to noise. It loses part of the direction information of the edge, resulting in some discontinuous edge detection Canny

It is not easy to be disturbed by noise and can detect real weak edge. The advantage is that two different thresholds are used to detect the strong edge and the weak edge respectively, and when the weak edge and the strong edge are connected, the weak edge is included in the output image

the edge of the image can only be marked once. This operator is the most perfect edge detection algorithm in theory, so Canny algorithm is used as the key technology of empty parking space detection [13, 14]. 2.3 Design of Empty Parking Space Intelligent Guidance System Based on Amap API and Edge Detection First, apply for the web side map JS API developer account by using Amap open platform, add the application in the console and apply for the key value. According to the open source code provided by Amap open platform, add Amap JS in the web code for secondary development. The driver starts the GPS positioning function to obtain the information of the parking lot near the current position, sets the anchor point at the corresponding position on the interface map, automatically obtains the empty parking space information of the parking lot when one of the anchor points is selected, and obtains the first driving path recommended in the Amap, which is displayed on the screen. The overall flow of system design is shown in Fig. 3.

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Fig. 3. System design flow chart

2.3.1 Parking Information Acquisition The main interface of map display system includes map display and adding start point, end point and passing point functions. The main interface implementation process is as follows: (1) register and obtain key: apply for Amap development key through Amap open platform website. (2) configuration list file: create a new project, configure the applied development key in the list file, and add location, location and location Navigation related permission configuration and location service configuration (3) Add SDK: download the SDK from Amap official website, including basic positioning, basic map, search function and driving navigation. 2.3.2 Design of Empty Parking Space Detection Algorithm The empty parking space detection steps are mainly divided into four steps, and the detection flow chart is shown in Fig. 4. (1) Area selection: manually select a parking lot according to the information obtained from Amap. Get parking video and capture pictures from the video. (2) Preprocessing: preprocessing the image frame of the parking lot, first converting the color image into gray image, and then using Canny operator to detect the edge of the image to locate the contour of the parking space. (3) Production of data sets: after the processing of the image, the edge is obvious. In the form of clusters, the parking data of each column is saved, and the coordinate points of each column matrix are saved. Then according to the ordinate spacing, the parking spaces are continuously divided into small pieces, each of which represents the location of a parking lot. The purpose of this step is to obtain the dictionary of parking space coordinates and make data sets. (4) In depth training: using the data set made in (3), the empty parking spaces are classified by VGG network to identify the remaining parking spaces in the parking

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Fig. 4. Flow chart of empty parking space detection

lot. Empty parking spaces are marked with mask, and the total number of parking spaces and remaining parking spaces are counted.

3 Simulation Experiment Research Under the environment of Intel Core 9000 h processor, win10 ultimate operating system and 16 GB memory, this study calls OpenCV to program through pycharm software, and carries out the simulation experiment research of empty parking space guidance system based on Amap API and edge detection [15]. 3.1 Parking Information Acquisition The map display system is shown in Fig. 5, and the parking information acquisition is shown in Fig. 6.

Fig. 5. Map display system

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Fig. 6. Parking information acquisition results

3.2 Empty Parking Space Detection Results (1) Image of the area where the parking lot is located. As shown in Fig. 7, 555 available parking spaces are detected in the parking lot.

Fig. 7. Parking area map

(2) Preprocess Fig. 7, Fig. 8 (a) is the image after gray processing, and the image after Canny edge detection is shown in Fig. 8 (b).

(a) Gray processing

(b) edge detection

Fig. 8. Preprocessing results of parking area map

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(3) If the specified row spacing is less than 10, it is divided into different columns, with a total of 12 clusters. The results are shown in Fig. 9 (a). Then, according to the spacing of ordinates, the parking spaces are continuously divided into small blocks, each of which represents the location of a parking lot. The result is shown in Fig. 9 (b).

(a) Partition into clusters

(b) partition into blocks

Fig. 9. Dataset production results

(4) After in-depth training, the real-time detection results of empty parking spaces are shown in Fig. 10.

Fig. 10. Real time detection results of empty parking spaces

3.3 Experimental Results of Intelligent Navigation If an empty parking space is detected, the vehicle owner can choose the desired location by himself and arrive at the destination by intelligent navigation of Amap. The experimental results are shown in Fig. 11.

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Fig. 11. Smart navigation results

4 Conclusion With the rapid development of domestic economy and the continuous increase of urban vehicle ownership, the lack of parking space has become an important social problem. In this paper, through the secondary development of Amap web open interface, the function of automatically finding parking space and obtaining parking route is realized. Then, the edge detection operator is used to identify and detect the parking space, and the intelligent positioning of Amap is used to guide the driver to reverse successfully, so as to improve the success rate and accuracy of parking. It solves the problem that it is difficult for the driver to enter the roadside or public parking lot to find the free parking space, realizes that the user can query the real-time information of the parking space usage at the destination anytime and anywhere, saves the parking time, and improves the utilization rate of the parking lot at the same time. Finally, through the simulation experiment, the real-time and effectiveness of the developed system are verified. The research of this paper also has important application significance in military guidance and intelligent transportation. Acknowledgements. This work was supported by Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, and funding of innovation and entrepreneurship training project for Tianjin university students (202010065086).

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6. Lu, G., Yang, L., Li, J.: Automatic detection and recognition of parking line in parking aid system. Electron. Technol. 4, 146–150 (2014) 7. Luo, Q.: Research on Short Term Prediction and Parking Guidance Information Service System of Roadside Free Parking Spaces. Jiangxi University of technology, Ganzhou (2016) 8. Hu, L., Deng, Z.: Research on real time parking information release system based on navigation map. Modern Inform. Technol. 3(17), 145–147 + 151 (2019) 9. Xu, H.: Shou de yun kai jian yue ming amap becomes the first car navigation in China. Friends of cars (22), 86-87 (2018) 10. Jia, S., Luo, W., Wang, Y.: Design of parking information query system based on Android. Comput. Dig. Eng. 45(8), 1682–1686 (2017) 11. Li, W., et al.: Vacant parking slot detection and tracking during driving and parking with a standalone around view monitor. Proc. Inst. Mech. Eng. 235(6), 1539–1551 (2021) 12. He, X., Zhang, R.: Application Research of image edge detection algorithm. Small Medium Sized Enterprise Manag. Technol (zhongxunjiao) (04), 180–181 (2021) 13. Chen, F.: Research on automatic parking technology based on machine vision. Univ. Electron. Sci. Technol. 25–27 (2016) 14. Liang, G.: Research and implementation of parking aid technology based on computer vision. Northeast Univ. 22–23 (2012) 15. Zheng, Z., Gu, S.: Tensorflow: A Practical Google Deep Learning Framework, pp. 10–13. Electronic Industry Press, Beijing (2017)

Data Augmentation for Communication Emitter Identification Using Generative Adversarial Nets Jianwei Tian1 , Xu Zhang2(B) , Hongyu Zhu1 , Gang Yue2 , and Zhuo Sun2 1

2

State Grid Hunan Electric Power Company, 410007 Hunan, China [email protected] Beijing University of Posts and Telecommunications, 100876 Beijing, China {Xu Zhang,zhuosun}@bupt.edu.cn

Abstract. Specific emitter identification has been widely applied in military reconnaissance, wireless security and other fields. With the help of deep learning technology, it has realized the automatic identification of emitter and achieved good performance. However, communication emitter identification is often affected by insufficient data and overfitting in practical application. To address the challenges, we propose a data augmentation method for communication emitter identification based on generative adversarial nets, which generates additional synthetic data to expand training set. With the result of original small signal set as our baseline, we will evaluate the result of emitter identification on the enlarged data set to validate performance of the proposed method. The result shows that training set augmentation increases the accuracy of emitter identification significantly. Keywords: Communication emitter identification adversarial net · Data augmentation

1

· Generative

Introduction

Specific emitter identification is to match the received signal with the communication emitter, which is realized by extracting the individual unique subtle features [1]. The technology has been successfully applied in many fields, including wireless security, military reconnaissance, fault detection and so on [2]. In the power system, we often need to identify the illegal wireless device access authentication. As the physical layer is the best choice to enhance security, RF fingerprint identification as one of the most promising methods of physical layer has received more and more attention. By extracting the RF fingerprint identification from the legitimate devices, the terminal white list database can be formed, and the wireless devices connected to the network can realize illegal identification. X. Zhang—Supported by 2020 Industry internet innovation and development project - Smart energy internet security situation awareness platform project. c The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022  Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 227–235, 2022. https://doi.org/10.1007/978-981-19-0390-8_28

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With the development of artificial intelligence, deep learning has been used to solve the problem of communication emitter identification. In paper [3], an optimized convolutional neural network is proposed to identify emitters, and the advantage of the model is verified by quantitative comparison with support vector machine and logistic regression. It can be noted that the input is raw IQ data, and it does not need higher-level decoding, feature engineering and other work. In paper [4], the author proposed a RF fingerprint identification algorithm based on recurrent neural network for automatic identification and classification of emitters. The emitter identification is often applied in non-cooperative scenarios, and the appearance of target emitter signal is uncertain, which makes the probability of capturing target signal limited. Meanwhile, the data obtained by long-term monitoring of sensing devices need to be labeled by a lot of manual and expert knowledge. The above reasons make the number of available effective labeled signal samples difficult to meet the need. If the deep learning model is directly used for training, the network may not be able to learn the essential feature of the signal from a small number of samples, resulting in the failure to achieve the ideal identification performance. Therefore, focusing on the real fact that our labeled data is limited, we propose an emitter signal data augmentation method based on generative adversarial nets. In order to train model to recognize emitters under small sample data, GAN is used to generate signals to expand the dataset. The method is making the generator to learn the distribution of original signals in the adversarial training, so as to increase the emitter identification accuracy. The rest of this paper is presented as follow. Section 2 offer a detailed introduction to the emitter signal data augmentation model we proposed. Section 3 analyzes the performance comparison between original dataset and expanded dataset. Finally, Sect. 4 makes a simple summary.

2 2.1

Emitter Signal Data Augmentation Model Generative Adversarial Nets

In recent years, generative adversarial nets (GAN) [5] have been widely concerned in the field of machine learning because of its learning potential for highdimensional and complex real data distribution. Specifically, it does not rely on any assumptions about distribution, and can generate real samples from latent space in a simple way. This powerful feature makes GAN widely used in image translation [6], object detection [7], video generation [8]and other fields.

Fig. 1. Basic framework of GAN

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Obviously, GAN is a generation model, which consists of two parts: generator and discriminator. The generator and discriminator act as adversaries to each other, and gradually achieve balance in adversarial training. The goal of generator is mapping from the latent space to a specified distribution close to the real data distribution, and the discriminator attempt to distinguish whether the input is from the generator or the real data. The basic idea behind adversarial learning is that when the generator tries to cheat the discriminator, the result of the discriminator will be fed back to the generator. Therefore, the generator will be optimized and learn similar distribution of real data. The adversarial training makes GAN have a significant advantage over other generation models. The basic framework of GAN is shown in Fig. 1. The latent variable z usually obeys Gaussian distribution. Mathematically, the function of optimization is a minimax problem: max min V (G, D) = max min Ex ∼ pdata [log(D(x))] + Ez ∼ pz [1 − log(D(G(z)))] G

D

G

D

(1)

where pdata and pz represents the distribution of real data and the latent space, respectively. G(z) is the signal generated by generator. D(x) is the result of discriminator to predict the real signal, and D(G(z)) is the result of discriminator to predict the fake signal. 2.2

Emitter Signal Data Augmentation Model

In this paper, we try to use convolutional neural network to implement generator and discriminator. This is because the emitter signal has more significant characteristics in frequency domain. Convolution operation for communication signals can be regarded as transforming the time domain to the frequency domain, which is more helpful for the generator to learn the distribution of the original signals. Generally, pooling layer is necessary in CNN, which can achieve translation invariance. In this model, we use the convolution operation with specified step size instead of pooling. The sampling process is no longer fixed, abandoning the values of some positions, but letting the network learn the way of down sampling or up sampling. Compared with images, the communication emitter signal is relatively simple and has low complexity. When training the discriminator, it is easy to train very well, and the loss function of the discriminator converges rapidly. The generator is always in the weak side and can’t form a real confrontation, which leads to the generated signals cannot cheat the discriminator. Therefore, through experimental verification and theoretical analysis, we consider to reduce the performance of the discriminator. The specific method is to reduce the number of layers of the convolution layer and the size of convolution kernel. At the same time, because the emitter signal carries noise, it cannot be well normalized to [–1,1]. We try

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to remove the activation function of the last layer of generator, and get better results from the experiment. In order to help the model converge better, the batch normalization layer is used in the generator and discriminator. The specific structure of emitter signal data augmentation model is shown in Fig. 2.

Fig. 2. Network structure of communication emitter signal data augmentation

It can be seen that the generator designed in the model has three convolution layers, while the discriminator has only two layers, and the pooling layer is replaced by convolution with step size. The convolution step size is 2, and the convolution kernel size is 3. 2.3

Training and Optimization

The experimental dataset is generated by the simulation software MATLAB, and the RF fingerprints include: I/Q imbalance, phase noise, carrier frequency and phase offset, and nonlinearity of power amplifier. In this paper, we simulate three classes of emitter, and assume that each class of emitter has only 200 real signals. We use cross entropy as the loss function, and Adam optimizer is selected for optimization. The learning rate is set to 0.0001 and the momentum is set to 0.5. The label is multiplied by a coefficient, which is equivalent to adding a certain amount of noise to the real distribution. All the work is based on TensorFlow. Based on the above parameter setting, we trained the signal augmentation model, and carried out 100 epochs iterations. Figure 3 shows trend of the loss of generator and discriminator with epoch. As can be seen from the plot, at the beginning of the training, the loss of the generator is rising, because the signal generated by the generator is not enough to cheat the discriminator. Then, with the increase of epoch, the generator gradually learns the distribution of the real data, and the loss begins to decline. However, the discriminator is opposite to the generator. Finally, as the loss oscillates in a small range, the network converges gradually. Once the model is trained successfully, we can use the generator to generate numerous fake signals to expand the training set. Figure 4 is the fake signal generated by our model.

Data Augmentation for Communication Emitter Identification

Fig. 3. Training loss trend of G and D

3

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Fig. 4. The signal generated by generator

Results and Analysis

In this section, we will analyze and verify the performance of the emitter signal data augmentation method in varies aspects. In the Sect. 2, the signal data augmentation model based on GAN is trained successfully. This section focuses on verifying that the generated signal and the original signal have similar RF fingerprint feature distribution and the generated signal can help improve the performance of communication emitter identification model. Firstly, we extract the traditional RF fingerprint features of the original signal and the generated signal, and analyze whether their distribution is similar. Then, in order to verify the effectiveness of the generated signal, we combine the signal data augmentation network with the emitter identification network to compare the identification accuracy of the enlarged data set and original data set. 3.1

Analysis of RF Fingerprints of Generated Signal Data

RJ feature and bispectrum are choosed to analyze the signal distribution similarity between original signal and generated signal. In the experiment, we randomly selects 50 original samples and uses the trained generator to generate 50 fake samples for each class of emitter. RJ feature reflect the envelope of the signal, which is a common analysis method of spurious characteristics. Figure 5 shows the distribution of RJ feature of original signals and generated signals. From the figure, it can be clearly seen that three clusters are formed in the feature space, and the original signals and generated signals of the same class converge in one cluster. This directly shows that the signal generated by generator we proposed in this paper has similar feature distribution with the original signal, and also shows that RJ feature can be used as fingerprint feature to distinguish specific emitter. Bispectrum belongs to high-order statistics, which can be regarded as the decomposition of signal skewness in frequency domain. The special product form retains the phase information of signal. Figure 6 shows the bispectrum of the

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Fig. 5. RJ distribution of original signals and generated signal

original signal and the generated signal respectively, which hava same label of emitter. We can see that the generated signal and the original signal have similar bispectrum. The color in the bispectrum represents the height of the amplitude. Obviously, the left and right images have the same shape distribution in the dark places, and there will be slight differences in some small part, which is caused by the influence of Gaussian white noise. However, this does not affect subsequent recognition of the emitter, because the usual way is use integral to transform bispectrum into one-dimensional vector, and then use the classifier to recognition. By the visual comparison, it can be proved that the signal generated by the model and the original signal have similar bispectral feature.

(a)

(b)

Fig. 6. Fig (a) shows the bispectrum of original signal; Fig (b) shows the bispectrum of generated signal.

3.2

Analysis of Emitter Identification in Enlarged Data Set

In order to verify the effect of the emitter signal data augmentation model proposed in this paper, the augmentation model and emitter identification model are combined for experiments. In order to verify the robustness of the method

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we proposed, experiments are conducted under various SNR conditions. Gaussian white noise is added to the signal. The SNR is 0 dB, 2 dB, 4 dB, 6 dB, 8 dB and 10 dB respectively. The number of generated signals for each class of emitter is 5000. The emitter identification model we used is proposed by our previous work [9]. Because it is only used to test the performance of signal augmentation model, we don’t describe it in detail. Our experimental results are shown in Fig. 7. It is obvious that the expanded dataset has better performance on the same verification set. When SNR = 10 dB, the accuracy can be improved by about 14%, which shows that the signals generated can improve the identification performance effectively and help identification model to extract more useful features. At the same time, it can be observed that when the SNR becomes low, the effect of improvement gradually decreases. This is because the distribution of the noise will largely submerge the signal itself when SNR is low, so that the signal augmentation model cannot learn the distribution of the real signal effectively, and the quality of the generated signals will be reduced. However, it can be seen that under any signal-to-noise ratio, the generated signal expansion has a positive effect, and the accuracy is improved by 5% at least, which shows that the signal data augmentation model proposed in this paper has strong robustness.

Fig. 7. Comparison of identification accuracy under different SNR

In order to see the improvement effect of the generated signals on the emitter identification model in more detail, Fig. 8 shows the identification confusion matrix in the case of original data set and enlarged data set. It can be seen that there is a great confusion between the second class and the third class, when training set is original data set. After the expansion, the number of misclassification samples is reduced significantly. The result shows that the signal data augmentation model we proposed is effective.

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(a)

(b)

Fig. 8. Fig (a) is the confusion matrix for baseline; Fig (b) is the confusion matrix for enlarged dataset.

4

Conclusion

In this paper, we propose a data augmentation method for communication emitter identification based on generative adversarial nets. The purpose is expanding data set to solve the problem that the accuracy of emitter identification decreases under small samples conditions. The experimental results show that the generated signals and the original signals have similar fingerprint feature distribution, and can greatly improve the accuracy of emitter identification. In the future, we will further study other generative models to augment emitter signal data for better performance.

References 1. Choe, H.C., Poole, C.E., Yu, A.M.: Novel identification of intercepted signals from unknown radio transmitters. In: Proceedings of SPIE - The International Society for Optical Engineering, vol. 2491, no. 1, pp. 504–517 (1995) 2. Xu, Q., Zheng, R., Saad, W.: Device fingerprinting in wireless networks: challenges and opportunities. IEEE Commun. Surv. Tutor. 8(1), 94–104 (2016) 3. Riyaz, S., Sankhe, K., Ioannidis, S.: Deep learning convolutional neural networks for radio identification. IEEE Commun. Mag. 56(9), 146–152 (2018) 4. Wu, Q., Feres, C., Kuzmenko, D.: Deep learning based RF fingerprinting for device identification and wireless security. Electron. Lett. 54(24), 1405–1407 (2018) 5. Goodfellow, I., Pouget-Abadie, J., Mirza, M.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, Canada, pp. 2672–2680 (2014) 6. Lata, K., Dave, M., Nishanth, K.N.: Image-to-image translation using generative adversarial network. In: 2019 3rd International conference on Electronics, Communication and Aerospace Technology (ICECA), India, pp. 186–189 (2019) 7. Ehsani, K., Mottaghi, R., Farhadi, A.: SeGAN: segmenting and generating the invisible. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, pp. 6144–6153 (2018)

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8. Walker, J., Marino, K., Gupta, A.: The pose knows: video forecasting by generating pose futures. In: 2017 IEEE International Conference on Computer Vision (ICCV), Venice, pp. 3352-3361 (2017) 9. Xu, Z., Sun, Z., Huang, S.: Feature extraction and classification of unknown types of communication emitter. In: Proceedings of the 2nd International Conference on Artificial Intelligence in China, pp. 93–101 (2020)

Antenna Array Pattern Synthesis Based on a Hybrid Particle Swarm Optimization and Genetic Algorithm Hongming Hu1,2,3(B) , Lulu Zhao1,2 , Peng Gao1,2 , Guang Liang1,2,3 , and Huawang Li1,2,3 1

Innovation Academy for Microsatellites of CAS, Shanghai 201203, China [email protected] 2 Shanghai Engineering Center for Microsatellites, Shanghai 201203, China 3 University of Chinese Academy of Sciences, Beijing 100049, China

Abstract. This paper introduces a hybrid optimization method, which combines particle swarm optimization and genetic algorithm for antenna array pattern synthesis. The algorithm first removes a quarter of the individuals with a lower fitness value, and copies the remaining individuals with a middle third of the fitness values to form a new population. Then particle swarm optimization and genetic algorithm are carried out, and particle swarm thinking is introduced into mutation operator. This algorithm can better solve the optimization problems of multidimensional and nonlinear problems. The excitation amplitude and phase of the array elements are optimized to achieve the desired low sidelobe level and deep zeros. Our empirical results show that, the proposed method outperforms existing methods according to convergency, efficiency and optimization.

Keywords: Antenna array pattern synthesis Particle swarm optimization

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Introduction

Antenna array plays an important role in both traditional antenna and recent smart antenna for mobile communication. The basic idea of antenna array is to select suitable array element incentive weight or optimize the spatial distribution of antennas. The traditional methods to solve this problem is based on Chebyshev synthesis, Taylor Synthesis, and Woodward synthesis. Taylor synthesis [1] and Chebyshev synthesis [2] are weighted sum methods only upon amplitude. Woodward Synthesis [3] considers the amplitude and direction simultaneously. H. Hu—This work is supported by the Shanghai Rising-Star Program (No. 18QA1404000) and Scientific Research Plan Program of Shanghai Science and Technology Committee (No. 18DZ112001). c The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022  Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 236–243, 2022. https://doi.org/10.1007/978-981-19-0390-8_29

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However, all of them cannot handle complex beam-forming (such as suppression of sidelobes in specific areas) well. Recently, different optimization algorithms have been applied to antenna array [4–6], e.g. Genetic Algorithm (GA) [7,8], Particle Swarm Optimization (PSO) [9]. However, both GA and PSO are suffering from converging to unexpected local minimal. But GA and PSO have different advantages in different aspects. The solutions produced by GA are both diverse and redundant, which results in inefficiency problem. The solutions produced by PSO are homogeneous and converged fast. As both methods are global search based on random search upon population, they can overcome the non-continuous issue. If we can combine GA and PSO properly, we are expected to enjoy the benefits from both side and resolve the challenges existing in antenna array. In this paper, we propose a hybrid method of GA and PSO named (MGAPSO) for antenna array synthesis. The proposed MGAPSO is applied to the pattern synthesis of the array antenna, optimized for the low sidelobes and deep zeros in the array antenna, and achieved good results.

2 2.1

Particle Swarm Optimization and Genetic Algorithm Particle Swarm Optimization

In the particle swarm algorithm, each individual is called a particle, imagined in a group consisting of m d-dimensional particles, let xi = (xi1 , xi2 , . . . , xid )T be the d-dimensional vector of the ith particle. By bringing Xi into a specific objective function to solve its fitness value, the quality of xi is judged according to the fitness value. Each particle has two variables, namely the flight speed vi and the optimal position pbi , vi = (vi1 , vi2 , . . . , vid )T , pbi = (pbi1 , pbi2 , . . . , pbid )T . Finally, the best position currently searched by the entire particle swarm is represented by the variable pg , pg = (pg1 , pg2 , . . . , pgd )T . The speed and position of the particles are updated according to the following formula: n+1 n vid = wvid +c1 r1 (pnbid − xnid ) + c2 r2 (pnbd − xnid )

(1)

n+1 xn+1 = xnid + vid id

(2)

During the trial: i = 1, 2, . . . , m; w is the weight coefficient, used to balance local search and global search; c1 , c2 are learning factors; n is the number of iterations; r1 , r2 are (0, 1) random, the number is used to maintain the diversity of the population; the speed of the particle cannot exceed the maximum speed VM ax . 2.2

Improved Genetic Algorithm

The commonly used encoding methods of genetic algorithm are binary encoding and real number encoding. This article uses real number encoding. The re-al number encoding method is suitable for representing numbers with a large value

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range, and the complexity of chromosome processing is greatly reduced, which improves the accuracy requirements of the algorithm. Each chromosome xi can be expressed as an n-dimensional vector, that is, xi = (xi1 , xi2 , ..., xin ); Selection operator: Determine the probability of each individual being selected. The greater the probability, the easier it is to be selected for subsequent crossover and mutation operations. Generally, it is based on the fitness of the individual, sorted by fitness, and then the selection probability is as the formula Assign as shown in (3) and (4). 1 q(r) = √ r p(r) =

q(r) sum(q(r))

(3) (4)

p(r) represents the probability of the rth individual being selected, r represents the ranking of the population individuals according to their fitness. Crossover operator: Choose two parents to be x1 and x2 respectively, generate a random number r between [0, 1], if r < pc (crossover probability), then perform interpolation. Assuming that the two individuals are x1 = (x11 , x12 , . . . x1n ), x2 = (x21 , x22 , . . . x2n ), the new individual after the crossover is: x1new = cx2 + (1 − c)x1

(5)

x2new = cx1 + (1 − c)x2

(6)

In the formula, c ∈ (0, 1) is a random number. Mutation operator: The particle swarm algorithm has fast convergence speed, simple calculation, and has global optimization characteristics similar to genetic algorithm. Introducing the evolutionary idea of particle swarm algorithm into the mutation operator can improve the algorithm speed. xt+1 = wxti + cr1 (ptgb1 − xti ) + cr2 (ptgb2 − xti ) i

(7)

Where w is the inertia weight, t is the current iteration number, c is a constant, r1 and r2 are random numbers between (0,1), xti = (xi1 , xi2 . . . , xin ) is the position of the ith particle of the tth generation, ptgb1 = (xgb1 , xgb2 , . . . , xgbn ) is the position of the global optimal solution under the current algebra, ptgb2 = (xgb1 , xgb2 , . . . , xgbn ) is the position of the global suboptimal solution under the current algebra.

3 3.1

MGAPSO Modified Hybrid Optimization Algorithm

The genetic algorithm has good population diversity and the particle swarm algorithm has a fast convergence speed. The combination of these algorithms can give full play to the advantages of the two algorithms and avoid the disadvantages of the algorithms.

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Therefore, this paper combines the two search algorithms to form a new optimization algorithm. First, the individuals are arranged in order of fitness value from large to small, and a quarter of individuals with lower fitness values are removed, which is in line with “survival of the fittest”. The principle of not destroying the diversity of the population and improving the convergence speed of the algorithm. Then copy the one-third of the remaining individuals with the fitness value in the middle to form a new population, keeping the original number of individuals unchanged. This operation allows the number of individuals with better fitness values in the population to increase rapidly, and then proceed Particle swarm optimization, and finally the improved genetic algorithm optimization, and the particle swarm idea is introduced into the mutation operator to mutate the individuals who have completed the crossover operation. 3.2

MGAPSO Algorithm Flow And Process Analysis

Through the analysis of the above optimization ideas, the specific flow chart and detailed optimization steps of the MGAPSO algorithm are given below. The flow chart is shown in Fig. 1. The algorithm steps are as follows:

Fig. 1. The flow chart of the MGAPSO algorithm

1) Initialize the population, the population number is P ; 2) Calculate the fitness of the population, and the individuals of the population are sorted according to their fitness;

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3) Judge whether the population reaches the termination condition, if the termination condition is met, output the optimal solution, if not, proceed to the fourth step; 4) Delete the 1/4 part of individuals with the worst fitness value; 5) Copy the 1/3 part of the remaining three equal parts of individuals with the fitness value in the middle and keep the other two individuals to the next generation to form a new population; 6) Use (1) and (2) to update the speed and position of the individual for the newly formed population; 7) Then use formulas (3), (4), (5), (6), (7) for selection, crossover, and mutation operations; 8) To get a new population, repeat steps 3 to 7.

4

Simulation and Conclusion

First consider a linear array composed of N array elements, the array factor is: AF (θ) =

N 

wi ej

2π λ xi

sin θ

(8)

i=1

Among them: wi is the element excitation; xi is the position of the antenna element relative to the origin; λ is the wavelength. For a uniform linear array, the array spacing is d, and the above formula can be written as: N −1  wn ej2πkdn sin θ (9) AF (θ) = n=0

Considering a uniform linear array composed of 16 antenna array elements, only low sidelobe control and zero notch are analyzed, so the phase of the initial current is set to zero (Side shot array pattern). The objective function is defined as follows: f = α | M SLL − SLV L| + β | N U LL M AX − N LV L|

(10)

MSLL is the highest side lobe level, SLVL is the design highest side lobe level, NULL MAX is the maximum value of the null level at the specified angle, NLVL is the design null level value, and α and β are the weights of each item. 4.1

Experiment 1 Testing the Effectiveness of the Algorithm

The Rastrigin function is a typical function used to test the effectiveness of an algorithm. When testing the algorithm, the population size of the algorithm is 50, the number of iterations is 100 generations, c1 = 1.6, c2 = 1.4, the crossover probability of the algorithm is 0.7, and the mutation probability is 0.3. The Rastrigin function is used as the objective function and compared with the results of GA and PSO. The result is shown in Fig. 2.

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Fig. 2. The effectiveness of MGAPSO-based algorithm

4.2

Experiment 2 Synthesize the Pattern with Low Sidelobes

In this paper, a linear array antenna with 10 array elements is designed, the distance between the array elements is half a wavelength, the index SLVL = –35 dB, and the main lobe is required to be aligned to the 0 degree direction. The phase of excitation current is 0 (Side shot array pattern), and the current assignment is optimized. Because this experiment only needs to lower the side lobe, so α = 1, β = 0. In this experiment, the number of particles is 50, and the number of particle iterations is 20. Figure 3 is the optimized antenna gain pattern. It can be seen from the figure that all the sidelobe levels are lower than −35 dB on average, and the maximum sidelobe level is –36.1 dB. Compared with the traditional Chebyshev antenna synthesis, the results obtained by this method have lower side lobes and better nulling. 4.3

Experiment 3 Synthesize the Pattern with Deep Zero Point

In this paper, a linear array antenna with 10 array elements is designed, the distance between the elements is half wavelength, the index SLVL = –25 dB, and it is required to generate a –75 dB null at 18◦ . After many experiments, α = 1, β = 0.4 , The number of particles is 50, and the number of iterations is 20. The comprehensive direction map is shown in Fig. 4. In Fig. 4, a 78 dB null is generated at a position of 18◦ , and the maximum sidelobe level is –25.6 dB, indicating that the improved algorithm in this paper has achieved good results.

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Fig. 3. The optimized antenna gain pattern

Fig. 4. The comprehensive direction map

5

Conclusion

Theoretical analysis and simulation results show that the algorithm balances the local and global search capabilities and has advantages in convergence, computing speed and optimization capabilities. The algorithm can better solve the optimization problems of multi-dimensional and nonlinear problems. In antenna synthesis, the excitation amplitude and phase of the array elements are optimized. Compared with the traditional Chebyshev antenna synthesis, the sidelobes and nulls of the antenna pattern can be better optimized.

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References 1. Balanis, C.: Antenna Theory: Analysis and Design. Wiley, New York (1982) 2. Safaai-Jazi, A.: A new formulation for the design of Chebyshev arrays. IEEE Trans. Ant. Prop. 42(3), 439–443 (1994) 3. Cid, J.M., Rodriguez, J.A., et al.: Synthesis of satellite footprints by perturbation of Woodward-Lawson solutions for planar array antennas. J. Electromagn. Waves Appl. 14(1), 3–10 (2000) 4. He, Z., Hua, Z., Hongmei, L., Beijia, L., Qun, W.: Array antenna pattern synthesis method based on intelligent algorithm. In: IEEE International Conference on Electronic Information and Communication Technology (ICEICT), vol. 2016, pp. 549–551 (2016) 5. Yigit, M.E., Gunel, T.: Pattern synthesis of linear antenna array via a new hybrid taguchi - genetic - particle swarm optimization algorithm. In: 2018 18th Mediterranean Microwave Symposium (MMS), pp. 17–21 (2018) 6. Changxing, Q., Yiming, B., Huihua, H., Yong, L.: A hybrid particle swarm optimization algorithm. In: 2017 3rd IEEE International Conference on Computer and Communications (ICCC), pp. 2187–2190 (2017) 7. Chen, L., Qiu, J., Chen, Y., et al.: Improved hybrid optimization algorithms based on genetic algorithm and particle swarm optimization. Comput. Eng. Design 38(02), 395–399 (2017) 8. Li, S., Xin, C., et al.: Pattern synthesis of antenna based on an adaptive genetic algorithm. Chin. J. Radio Sci. 29(01), 169–177 (2014) 9. Yang, X.: Hybrid particle swarm optimization algorithm in the comprehensive application of array antenna. Hang Zhou Dianzi University (2017)

Study on Digital High Resolution APD Measurement Receiver Hao Gao1,2 , Lin Cao2(B) , Enxiao Liu3 , and Lei Yang2 1 College of Geodesy and Geomatics, Shandong University of Science and Technology,

Qingdao 266590, China 2 Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong

Academy of Sciences), 37 Miaoling Road, Laoshan District, Qingdao 266061, China [email protected] 3 China Electronics Technology Group Corporation, The 41st Research Institute, Qingdao 266555, China

Abstract. With the acceleration of 5G commercial and new infrastructure construction process, Amplitude Probability Distribution (APD) measurement will be widely used in EMC testing of digital communication systems. The International Special Committee on Radio Interference (CISPR) has made requirements for APD measurement receivers in the new international standard for electromagnetic compatibility testing. By analyzing the mathematical model of APD, this paper completes the overall design of digital high-resolution APD receiver, and realizes the long-term statistical measurement of random interference with low probability. The research work in this paper lays a foundation for the development of high performance APD measuring receiver. Keywords: 5G · Amplitude probability distribution · Electromagnetic compatibility testing · High resolution APD receiver

1 Introduction At present, in the measurement of electromagnetic interference, the peak quasi-peak detection level is generally used to evaluate the interference, but with the rapid development of digital communication technology, it is found that the traditional detection measurement method is affected by the time constant of geophone charge and discharge, and can not be used to predict the impact of electromagnetic interference on the performance of digital communication system Especially in the aspect of predicting the influence of random disturbance of pulse nature on the performance of digital communication system, the traditional measurement method is more powerless. Based on this consideration, the international academic circles began to pay attention to the relationship between the statistical parameters of interference and the performance of digital communication systems. Therefore, APD statistical measurement method has attracted more and more attention in the International academic circle in recent years, and has been published by the International Special Committee on Radio Interference (CISPR), as a new method for measuring and evaluating radio Interference in EMC testing standards. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 244–250, 2022. https://doi.org/10.1007/978-981-19-0390-8_30

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2 Requirements of International Standard for APD Measurement Receiver The International Special Committee on Radio Interference has made a clear technical specification for the APD measurement receiver in the latest EMC test standard. 1) The dynamic range of amplitude should be greater than 60 dB; 2) Amplitude allowance (including threshold level setting error) should be greater than 2.7 dB; 3) Maximum measurement time of interference should be greater than or equal to 2 min. If the interval time is less than 1% of the total measurement time, intermittent measurement could be used; 4) The minimum measurable probability should be 10–7 ; 5) The APD measurement function should be able to preset at least two amplitude levels. The probability corresponding to all preset levels should be measured simultaneously and the preset level amplitude resolution should be at least 0.25 dB; 6) With a resolution bandwidth of 1 MHz, the sampling rate should be greater than or equal to 10 Msa/s.

3 Design of Digital High Resolution APD Measuring Receiver 3.1 Mathematical Model of APD APD is a parameter used to describe the statistical characteristics of radio interference and is defined as the probability of the time when the interference intensity exceeds a specified level. In fact, APD describes the statistical characteristics of the signal envelope output by radio disturbance after passing through the IF filter of the measuring receiver. It is defined as the probability of the time when the envelope amplitude exceeds a certain threshold level E.

Fig. 1. Intermediate frequency envelope waveform

As is shown in Fig. 1, within the total measurement time T, there are 3 periods (t 1 -t 2 , t 3 -t 4 , t 5 -t 6 ) where the amplitude of the envelope exceeds the level E, then the

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probability of the signal amplitude exceeding the level E could be obtained according to the following formula: P(A > E) =

(t2 − t1 ) + (t4 − t3 ) + (t6 − t5 ) T

(1)

In general, the amplitude probability for level E can be expressed by the following formula: N ti P(A > E) = i=1 (2) T Where, ti is the duration when the envelope level exceeds the threshold E, and N is the number of times the envelope exceeds the threshold within the measurement time T. For the digital IF signal, the digital envelope is obtained after digital carrier removal and low frequency filtering. P(A > Ei ) =

Ni L

(3)

Where L is the sampling points within the measurement time T, and N i is the sampling points whose amplitude is greater than the level E i . Equation (3) is the APD measurement model of the digital receiver. 3.2 General Design of the Digital Receiver APD describes the statistical characteristics of the signal envelope output by electromagnetic interference after passing through the IF filter of the measuring receiver. Therefore, it is necessary to extract the envelope of the digital IF signal first, and then carry out APD statistical calculation. The principle block diagram of the APD measurement receiver is as shown in Fig. 2. The received signal is firstly adjusted by the attenuator to make it at the optimal receiving level, and then the RF signal from 1 GHz to 18 GHz is reduced to 425 MHz IF through the multi-stage mixing and IF anti-aliasing filter. After flatness compensation, AD sampling with clock of 10 MHz is carried out to obtain digital IF signal. Then the digital decarrier is carried out to obtain the digital IQ data, and then the IQ data is processed in two ways. The conventional detection measurement was carried out in one way, and the envelope extraction was carried out in the other way. Finally, the APD measurement results were obtained and displayed by statistical calculation of the envelope signal.

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3.3 Fully Digital Level Comparison and Statistical Methods According to the requirements of EMC test standards for APD measurement receivers in Sect. 2, the traditional EMI test receivers cannot realize APD measurement. The difficulty mainly lies in that under the 10 MHz sampling clock, long time statistical calculation is needed for the sampling data greater than 2 min, namely 120 s. Meanwhile, the resolution of the amplitude level is required to be better than 0.25 dB and the dynamic range is greater than 60 dB. This calculation requires at least 1.2 G sampling points to be processed each time, and these sampling points are compared with 240 voltage levels. The computation of storage capacity and the complexity of comparison circuit are difficult for ordinary receivers to bear and realize. Therefore, the statistical calculation of APD is the most difficult part in the design of APD measurement receiver. According to the index requirements of 60 dB dynamic range and 0.25 dB level resolution, this paper adopts 14 bit quantized ADC with sampling clock of 10 MHz, so the level resolution can reach up to 0.0037 dB, which is far better than the standard requirements. According to the mathematical model of APD in Eq. (3), this paper adopts address mapping and multi-channel parallel integration to realize high-speed statistical calculation of high-resolution APD. This could reduce computation by a factor of 64, reducing the need for system hardware.

Fig. 3. Digital circuit realization of APD

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Figure 3 is the digital circuit implementation scheme of high resolution APD highspeed statistical calculation. The digitized signal is mapped and the address line is allocated through the decoder. For the signal with the quantization number of N, a register of 2N length is adopted. In this paper, 14bit quantization is adopted, and a register of 16 K length is needed. Each sampling clock updates the register data once, and performs + 1 operation on the register value of the corresponding address according to the address in which the value is used. This operation is carried out continuously throughout the statistical measurement process, so that seamless statistics without interruption can be achieved. Finally, APD(i) is integrated through the multi-channel parallel integration module to obtain the amplitude probability distribution. The integral calculation formula is shown in Eq. (4). 2N −1 RAM (n) P(A > i) = n=i 2N −1 n=0 RAM (n)

(4)

The numerator part represents the sum of the address register numbers greater than i, which means the number of sampling points is greater than i. The denominator part represents the number of all sampling points. For the APD calculation of wideband multi-frequency points, the same multi-channel registers need to be developed for statistical calculation, and the storage capacity and calculation amount increase linearly with the number of channels.

4 Simulation Tests For random interference signal, exponential modulated Gaussian pulse signal is used as signal model. The pulse repetition period is 50 kHz, the time length is 10 ms, the exponential power of 0.8 is reduced, the bandwidth of the Gaussian pulse is 10 MHz, and the time domain waveform of the signal is shown in Fig. 4. The amplitude of the interference signal changes with time, and the traditional detection measurement will lose the information of the interference signal.

Fig. 4. Time domain waveform of random interference signal

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Fig. 5. APD measurement results of white Gaussian noise

Figure 5 and Fig. 6 respectively show the APD measurement results of white Gaussian noise and random interference signals. By comparing with the APD measurement sample in CISPR16-1-1, it can be seen that the simulation results are consistent with the measurement results of actual interference signals, and the signal model and measurement model meet the requirements of the standard for receiver.

Fig. 6. APD measurement results of random interference signals

5 Conclusion In this paper, the definition and mathematical model of APD measurement are firstly analyzed in accordance with the specification of APD measurement receiver in the new international standard of EMC test by the International Special Committee on Radio Interference (CISPR). Then a digital high resolution APD receiver is designed by using fully digital level comparison and statistical method to realize long time statistical measurement of random interference with low probability. The simulation of random interference signal APD measurement is established by means of the key statistical calculation

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method of signal level. By comparing with the test examples of real signals in the standard, the design of the APD measurement receiver proposed in this paper is verified to be reasonable and feasible, and conforms to the standard specification. The results lay a foundation for the development and application of high performance APD measuring receiver in the future. Acknowledgments. This work was supported in part by the Youth Innovation and Technology Support Program in Colleges and Universities of Shandong Province under Grant 2019KJN009, in part by the Training Fund in Institute of Oceanographic Instrumentation of Shandong Academy of Sciences under Project HYPY202108, in part by the State Key Laboratory of Tropical Oceanography in South China Institute of Oceanology under Project LTO2017 and in part by the Open Research Fund of the State Key Laboratory of Estuarine and Coastal Research under Project SKLEC-KF202001.

References 1. Yang, F., Kan, R., Sha, F.: Research on statistical seasurement of radio noise and electromagnetic disturbance. J. Electron. Measurement Instrumentation 23(1), 22–26 (2009) 2. Jiang, H., et al.: APD based evaluation of electromagnetic immunity of HSR wireless communication. J. China Railw. Soc. 41(4), 96–101 (2019) 3. IEC CISPR 16-1-1-2015: Specification for Radio Disturbance and Immunity Measuring Apparatus and Methods-Part 1-1: Radio Disturbance and Immunity Measuring ApparatusMeasuring Apparatus (2015) 4. GB/T 6113.101-2016: Specification for Radio Disturbance and Immunity Measuring Apparatus and Methods-Part 1-1: Radio Disturbance and Immunity Measuring ApparatusMeasuring apparatus (2016) 5. Hu, R., et al.: Design and research of low-light wireless detection system based on APD. Optical Commun. Technol. 44(5), 48–52 (2020) 6. Mohammad, T.D., Seyed, M.S.S., Mohammad, A.K.: FSO channel estimation for OOK modulation with APD receiver over atmospheric turbulence and pointing errors. Optics Commun. 402, 577–584 (2017) 7. Zhou, H., Xie, W., Zhang, L., Bai, Y., Wei, W., Dong, Y.: Performance analysis of FSO coherent BPSK systems over Rician turbulence channel with pointing errors. Optics Express 27(19), 27062 (2019) 8. Yasushi, M., Kia, W.: Evaluation of impact on digital radio systems by measuring amplitude probability distribution of interfering noise. IEICE Trans. Commun. E98.B(7), 1143–1155 (2015)

Wireless Sensor Network Coverage Optimization Based on Sparrow Search Algorithm Zehua Wang, Shubin Wang(B) , and Haifeng Tang College of Electronic Information Engineering, Inner Mongolia University, Hohhot, China [email protected]

Abstract. To effectively solve the problem of insufficient node coverage in wireless sensor networks, a network coverage optimization method based on a hybrid strategy to improve the sparrow search algorithm named CF-SSA is proposed. Firstly, a logistic chaotic mapping is introduced to increase the population diversity and thus improve the search speed of the algorithm. Secondly, a hybrid strategy of Cauchy variance factor and firefly perturbation is introduced in the local search phase of the algorithm, which enables the algorithm to effectively jump out of the local optimum. Finally, the improved algorithm is applied to wireless sensor network coverage optimization. The simulation results show that CF-SSA improves wireless sensor network coverage and optimizes more uniform node distribution compared to other optimization algorithms. Keywords: Coverage optimization · Sparrow search algorithms · Logistic chaos · Cauchy variance · Firefly disturbance

1 Introduction A wireless sensor network (WSN) is a self-organizing network consisting of sensor nodes capable of sensing, processing, and wireless communication [1], are widely used in military, agriculture, and industry [2, 3], and in recent years, they are increasingly used in smart homes and smart cities [4]. Network area coverage is a key issue in WSN, and optimization of the coverage distribution of sensor nodes can improve the quality of service and energy balance of the network. Swarm intelligent computing, also known as population intelligent computing or swarm intelligent computing, refers to a class of intelligent algorithms with distributed intelligent behaviors inspired by the group behaviors of insects, herds of animals, flocks of birds, and schools of fish. In recent years, more and more scholars have applied swarm intelligence algorithms to wireless sensor network node deployment to improve network coverage. In reference [5], variance factors and inertia parameters were introduced to optimize the particle swarm algorithm and applied to the WSN coverage, improved network coverage rate. In reference [6], to be able to improve the network coverage, the Levy flight is combined with the Gray Wolf algorithm and applied to the WSN, although © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 251–258, 2022. https://doi.org/10.1007/978-981-19-0390-8_31

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the coverage and convergence speed is better than the basic grey wolf optimization algorithm, it does not have an advantage over other algorithms. In reference [7], the WSN coverage rate was optimized using a clustering method and an artificial bee colony algorithm, the algorithm tends to fall into local optimum and thus affects the network coverage. In reference [8], a heuristic-based genetic algorithm is proposed for network coverage optimization, which effectively improves the node coverage but still suffers from uneven node distribution. In 2020, a new swarm intelligence algorithm, the sparrow search algorithm (SSA), was proposed [9]. It has the advantage of strong search ability and convergence speed. In this paper, an improved algorithm CF-SSA based on logistic chaos mapping, Cauchy variance, and firefly perturbation is proposed and applied to WSN coverage optimization. The superiority of the proposed algorithm can be observed in the simulation results.

2 Related Works 2.1 WSN Coverage Model Discretize the monitoring area into A × B grid points. The coverage of the sensor node is a circular area centered on the node position. Its perceptual radius is Rn . Assuming that in the above region, the coordinate of the sensor node is (xn, yn), for a grid coordinate point p in the region, its coordinate is (xp, yp). The probability that p can be monitored by the sensor node is given in the following Eq. (1).  1, d (n, p) ≤ Rn Pr (n, p) = (1) 0, else  2  Among it, d (n, p) = (xn − yn)2 + yn− yp is the Euclidean distance from the node to point p. The probability of being covered within the range is recorded as 1, and the rest is recorded as 0. Therefore, the total number of grid points covered in  the detection area is Numcov = A×B P, the coverage formula of the wireless sensor network for the monitoring area is: CoverRatio =

Numcov A∗B

(2)

2.2 Sparrow Search Algorithm The Sparrow search algorithm is inspired by the behavior of the sparrow swarm. In the sparrow population, the sparrow population is divided into producer and follower. Producers search for food for the population. The follower follows the producer to get food. According to observations, sparrows can flexibly switch between producer and follower. Studies have also shown that some sparrows watch the behavior of other individuals in the population and compete with companions who are rich in food. Let there be N sparrow individuals in a D-dimensional solution space, and each iteration

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selects the M with the best fitness as producer and the remaining N-M as followers. The position of the sparrow population in the space can be defined as.   (3) Xi = xi1 , xi2 · · · xid , · · · xiD i = 1, 2, · · · N Among it, xid denotes the location of the sparrow. The producer position is updated as shown in Eq. (4).   t · exp −i , R < st xi,d 2 t+1 a·M xi,d = (4) t + Q, R ≥ st xi,d 2 Where t represents iteration. M represents the maximum iteration. a is the random number between (0, 1]. Q is a random number that follows a normal distribution. R2 ∈ [0, 1] indicates a warning value and st is the safety threshold. st usually takes a value of 0.8. When R2 < st, it shows that the sparrow population is safe and will search on a large scale. Conversely, the sparrows quickly move to other areas. For followers, they update the position according to Eq. (5).

t t  ⎧ ⎨ Q · exp xwd −xi,d , i > n/2 t+1 i2 (5) = xi,d ⎩ t t xpd + |xi,d − xpdt+1 | · A+ · L, else In Eq. (5), xwdt which represents the worst position of the current population, xpdt is the best position among producers. A is a 1 × d matrix and the elements are randomly assigned as 1 or −1. It indicates search direction. L is a matrix where all the elements are 1, and the size is 1 × d. The algorithm assumes that 10% to 20% of the sparrows can be aware of the danger. The sparrows were randomly selected and their position is updated according to Eq. (6). ⎧ t t t ⎨ xbd + β|x

i,dt − xbt d|, fi > fg t+1 (6) xi,d = t + k |xi,d −xwd | , f = f ⎩ xi,d i g (fi −fw )+ε In Eq. (6), fi is the current fitness value, fg is the best fitness value, fw is the worst fitness, and β is a Gaussian distributed random number with a mean of 0 and variance of 1. k is a random number between −1 and 1. ε is a constant that avoids the denominator is zero.

3 CF-SSA Optimization Algorithm 3.1 Logistic Chaos Initialization SSA suffers from insufficient global search capability, the main reason is that the search ability of the algorithm decreases in the late iterations due to insufficient population diversity. Chaotic mapping is random and irreducible within a certain range, therefore, this paper proposes to use logistic chaos mapping to initialize the population to increase the

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population richness. Logistic chaos mapping is a one-dimensional mapping. Applying it to population initialization, as shown in the following Eq. (7). xi = r ∗ xi−1 ∗ (1 − xi−1 )

(7)

Among it, r ∈ [0, 4] is the chaos parameter. The chaos parameter is taken as 4 to ensure a fully chaotic state. By initializing the population in this way, the diversity of the population can be guaranteed and the global search capability is enhanced compared to the SSA. 3.2 Cauchy Variation and Firefly Perturbation Strategy There are two broad ways to update the SSA position: moving to the current optimal position for updating and moving closer to the origin for updating. Using these two methods to update the positions of individual sparrows tends to cause the algorithm to fall into a local optimum. Therefore, in this paper, we use a hybrid strategy of Cauchy variance factor and firefly perturbation to help the algorithm jump out of the local optimum. The Cauchy variation originates from the Cauchy distribution, which is a common statistical distribution. The peak value of the Cauchy distribution at the origin is smaller than that of the Gaussian distribution, and the process of approaching the zero value is smoother. Therefore, the Cauchy variation has stronger disturbance characteristics than the Gauss variation. The Cauchy variation operation is performed on the optimal individual of the current population as in Eq. (8). t+1 xbt+1 i,d = xbi,d ∗ (1 + Cauchy(0, 1))

(8)

In the above equation, Cauchy(0, 1) is the Cauchy variant. To further improve the ability of the algorithm to jump out of the local optimum, the firefly perturbation strategy in the firefly algorithm is introduced. The specific perturbation to the individuals of the population is shown in Eq. (9). 1 t+1 t+1 t+1 xi,d = xi,d + β ∗ (xi,d − xbt+1 i,d ) + α ∗ (rand − 2 )

(9)

Among it, xbt+1 i,d is the global best individual, rand is a random number that follows −γ 2

a uniform distribution between 0 and 1, α ∈ [0, 1] is the step size factor. β = β0 ∗ e i,j is relative attractiveness. The value of β0 is 2, γij is the distance between individuals from each other and the optimal individual. 3.3 CF-SSA Coverage Optimization When applying CF-SSA to wireless sensor network coverage optimization, set the individual fitness function in SSA as Eq. (2), suppose the number of populations is N, each deployment scenario can be thought of as an individual. The goal of wireless sensor network coverage optimization is to find the individual that maximizes the fitness function value.

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Step1 Initialize the input parameters of the sparrow population and wireless sensor network coverage. Such as the number of populations M, upper and lower boundary range ub and lb, dimension d, number of sensor nodes N, sensing radius r, area range size, the ratio of discoverers and followers, the maximum number of iterations T, etc. Step2 Use Eq. (7) for sparrow population initialization. Use Eq. (2) as the objective function to calculate the population fitness and rank it. Find out the current best fitness value and the worst fitness value and their positions. Classify the population into discoverers and followers according to the good or bad fitness. Update the positions according to Eqs. (4) and (5), respectively. Randomly select the vigilantes and update the position according to Eq. (6). Step3 Perturbation of Cauchy variation was performed for the best individuals of the current population according to (8). Use Eq. (9) for firefly perturbation. The firefly perturbed individuals are compared with the unperturbed individuals, and if better, the original individuals are replaced and the positions are updated. Step4 Determines whether the maximum number of iterations is reached, if yes, the optimal position of the sensor node is output. Otherwise, return to Step2.

4 Simulation Analysis 4.1 Experimental Parameter Setting To measure the coverage optimization performance of the proposed algorithm, MATLAB software was used to implement numerical simulation. In this algorithm, 20 sensor nodes are randomly arranged in a 100 × 100 m network; All sensor nodes have a communication radius of 10 m; The population size was set to 30; the maximum number of iterations is 100. 4.2 Result of Simulation To fully verify the optimization performance of the CF-SSA algorithm on the coverage of WSN nodes, the basic SSA algorithm, the particle swarm optimization algorithm (PSO), and the gray wolf optimization algorithm (GWO) are selected and applied to wireless sensor network coverage for comparison. At the same time, the final result applies the average of 30 experiments to avoid contingency. The coverage curve is shown in Fig. 1. It can be seen from Fig. 1 that PSO optimization is the least effective, with a node coverage rate of only 55.02%, SSA and GWO have better performance and the initial search capability is stronger, much better than PSO. But as the number of iterations increases, they fall into the problem of local optimum and cannot find the global best position, and finally achieve coverage rate of 82.71% and 86.45%. The improved sparrow search algorithm, CF-SSA, has a better performance compared to the other three algorithms. The search ability is stronger due to the introduction of logistic chaos mapping, it can also jump out of local optimum through Cauchy variation factor and firefly disturbance. The final optimized coverage rate can reach 94.03%. Figure 2 shows the node distribution of the above algorithm. The results show that CF-SSA is used to optimize the node distribution more evenly than the other three algorithms, and there are fewer blank areas, which greatly improves the node clustering problem and has the best coverage effect.

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Fig. 1. Four algorithms coverage curve

(a) PSO optimization distribution

(c) GWO optimization distribution

(b) SSA optimization distribution

(e) CF-SSA optimization distribution

Fig. 2. Nodes distribution

In order to further verify the coverage performance of the algorithm, the experimental environment remains unchanged, and the different number of nodes are selected for the experiment. The results are shown in Fig. 3. It can be seen from Fig. 3 that the coverage

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rate of the four algorithms increases as the number of sensor nodes increases. At the same time, the coverage rate and uniformity of the CF-SSA algorithm are better than the other three algorithms. When the number of nodes is 40, the coverage rate can reach 98.77%.

Fig. 3. Coverage of different number of nodes

5 Conclusion For the problem of insufficient node coverage of wireless sensor networks, this paper proposes CF-SSA to achieve sensor node coverage optimization, introduce logistic chaos mapping, Cauchy variance factor, and firefly perturbation strategy into SSA to increase the richness of the population, improve the search ability, and avoid falling into the local optimum problem. Through simulation experiments, the proposed algorithm can effectively improve the coverage of wireless sensor networks. It can make the nodes more evenly distributed, with smaller coverage blind areas, and the final node coverage can reach 94.03%, which improves the overall network performance. Acknowledgment. This work was supported by the National Natural Science Foundation of China (61761034).

References 1. Xu, Y., et al.: Hybrid multi-objective evolutionary algorithms based on decomposition for wireless sensor network coverage optimization. Appl. Soft Comput. 68, 68–282 (2018) 2. Bahi, J., Elghazel, W., Guyeux, C., Hakem, M., Medjaher, K., Zerhouni, N.: Reliable diagnostics using wireless sensor networks. Comput. Ind. 104, 103–115 (2019) 3. Yue, Y.-G., He, P.: A comprehensive survey on the reliability of mobile wireless sensor networks: Taxonomy challenges and future directions. Inf. Fusion 44, 188–204 (2018)

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4. Naranjo, P.G., et al.: P-SEP: a prolong stable election routing algorithm for energy-limited heterogenous fog-supported wireless sensor networks. J. Supercomput. 73(2), 733–755 (2017) 5. Kong, H., Yu, B.: An improved method of WSN coverage based on enhanced PSO algorithm. In: 2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), Chongqing, China, pp. 1294–12972 (2019) 6. Wang, S., Yang, X., Wang, X., Qian, Z.: A Virtual Force Algorithm-Lévy-Embedded Grey Wolf Optimization Algorithm for Wireless Sensor Network Coverage Optimization. Sensors 19(12), 2735 (2019). https://doi.org/10.3390/s19122735 7. Johnny, E.K.: Clustering and coverage using artificial bee colony (ABC) optimization in heterogeneous WSN (HWSN). J. Adv. Res. Dyn. Control Syst. 12(3), 182–194 (2020) 8. Manju, S.C., Kumar, B.: Genetic algorithm-based meta-heuristic for target coverage problem. IET Wirel. Sens. Syst. 8(4), 170–175 (2018). https://doi.org/10.1049/iet-wss.2017.0067 9. Xue, J., Shen, B.: A novel swarm intelligence optimization approach: sparrow search algorithm. Syst. Sci. Control Eng. Open Access J. 8(1), 22–34 (2020)

An Improved Stereo Matching Algorithm Based on AnyNet Haifeng Tang, Shubin Wang(B) , and Zehua Wang College of Electronic Information Engineering, Inner Mongolia University, Hohhot, China [email protected]

Abstract. In many applications of depth estimation, accurate disparity map needs to be generated quickly. In order to obtain accurate disparity map, the current mainstream algorithm mostly adopts deep complex network architecture, which requires a large amount of computation and is difficult to be applied in real-time scenes. However, some real-time networks have low disparity accuracy, which also limits their application scenarios. Based on the above shortcomings, this paper improves AnyNet stereo matching algorithm and proposes a stereo matching algorithm with high real-time performance and high accuracy. First, a multiscale feature extraction module is designed to capture and fuse contextual feature information, and then an attention module is constructed to reduce the mismatch problem of ill-posed regions (repetitive, no/weak texture regions). The algorithm proposed in this paper can predict the disparity map in multiple stages during reasoning, weigh the amount of calculation and accuracy according to actual needs, and select the corresponding stage adaptively. Evaluated on the KITTI 2015 dataset, compared with the reference algorithm AnyNet, the final predicted disparity map error rate is reduced by 2.43%, and the running speed is only 1.24% slower. Keywords: Depth estimation · AnyNet stereo matching network · Attention module · Convolutional neural network

1 Introduction In many computer vision tasks (such as unmanned driving, robotic applications, and virtual reality, etc.), obtaining depth information of location is crucial. Binocular cameras provide a good solution for estimating depth. Compared with lidar, it is actually more economical and energy-efficient [1]. In the task of depth estimation, how to quickly and accurately obtain disparity information is a key issue that needs to be solved. Given a pair of corrected images, depth estimation can be converted into a stereo matching task, the purpose of which is to calculate the disparity value of the corresponding pixels in the two images. In the past few decades, excellent traditional stereo matching algorithms have emerged, such as semi-global matching SGM [2]. The traditional algorithm divides stereo matching into four steps: matching cost calculation, cost aggregation, disparity © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 259–267, 2022. https://doi.org/10.1007/978-981-19-0390-8_32

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calculation and disparity optimization. However, traditional algorithms generally have the problems of low matching accuracy and slow calculation speed, which greatly limits the application of stereo matching algorithms. In recent years, the research of stereo matching algorithms based on deep learning has made significant progress. Compared with traditional algorithms, it has obvious advantages in terms of speed and accuracy. Although the current stereo matching model has achieved high accuracy, it is difficult to apply in real-time scenarios. In order to solve the above problems, this paper improves AnyNet [3]. First, the multi-scale feature extraction module is designed to capture and integrate the contextual feature information, and then the attention module is constructed to conduct channel interaction and spatial interaction on the feature information of different scales, and finally the rapid prediction of the disparity map is achieved by gradually correcting the error. The network can predict in real time and has high accuracy, and can adaptively weigh tasks in different scenarios, select the depth map of the appropriate stage as the final output value, and ensure that the goal is achieved with the least calculation time.

2 Related Work 2.1 End-to-End Disparity Prediction Affected by the success of convolutional neural networks in stereo matching tasks, DispNet (Disparity Network) [4] designed an end-to-end stereo matching model. GCNet (Geometry and Context Network) [5] uses a 3D codec structure to aggregate global information. PSMNet (Pyramid Stereo Matching Network) [6] obtains the information of different receptive fields through the spatial pyramid pooling module. StereoNet (Stereo Network) [7] trains a real-time progressive stereo matching architecture. AnyNet (Anytime Stereo Network) uses the residual prediction method to greatly speed up the reasoning time of the network, so as to perform depth estimation anytime and anywhere. The work of this article is inspired by AnyNet. In order to realize real-time depth estimation, AnyNet uses the U-Net structure to extract feature maps of different scales, participates in the process of predicting the disparity map on multiple scales, and realizes the real-time reasoning process. The model in this article is related to AnyNet, because it also has multi-scale feature extraction and stepwise disparity prediction steps, but the difference is that it can better integrate contextual information and focus on ill-posed regions, thereby predicting a higher-precision disparity map. 2.2 Feature Extraction and Attention Mechanism Feature extraction and attention mechanisms have a greater impact on network performance. FPN (Feature Pyramid Networks) [8] uses both low-level spatial information and high-level semantic information. PANet (Path Aggregation Network) [9] introduces a bottom-up convolutional layer to fuse different levels of information. SENet (Squeezeand-Excitation Networks) [10] builds the interdependence between feature channels through Squeeze and Excitation. ECANet (Efficient Channel Attention Networks) [11] proposed a local cross-channel interaction strategy without dimensionality reduction to achieve performance improvement.

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The work of this paper is inspired by PANet and ECANet. PANet has designed a topdown and bottom-up approach, which fully integrates feature information at different levels. ECANet uses a channel attention mechanism that does not reduce dimensionality to achieve attention to feature information. On this basis, this paper designs a lighter feature extraction module, the fused information is more suitable for stereo matching, and the attention mechanism introduces spatial information to facilitate better disparity prediction.

3 Model Architecture The algorithm in this paper is composed of three main modules: multi-scale feature extraction module, attention module and disparity prediction module. The network structure is shown in Fig. 1. Specifically, the left and right images are input to the feature extraction module and the attention module, and three feature maps of different scales (1/16, 1/8, 1/4) are output. In the first stage, the disparity prediction is performed on the feature map of the lowest scale. This process only takes about ten milliseconds. In the second stage, only the disparity map of the first stage needs to be fine-tuned to correct the error, and then fused and added. For the disparity map of the second stage, the process is also very fast, and the process of the third stage is similar to the second stage. Disparity prediction module Multi-scale weight share Attention feature extraction module module

warp

Disparity Stage 1 46.1FPS Disparity prediction module

add

warp

Disparity prediction module

Disparity Stage 2 44.1FPS

add

Disparity Stage 3 40.3FPS

Fig. 1. Network structure

3.1 Multi-scale Feature Extraction Module Different from the AnyNet feature extraction method, in order to make better use of multiscale information, this research uses bottom-up and top-down feature pyramid structures to fully capture multi-scale information. In addition, considering the lightweight of the network and the characteristics of the stereo matching task, the convolution kernel, step size, and feature number of the convolutional layer of the bottom-up path are 3, 2, 32, the rest of the convolutional layer the step size is all 1, so as to retain more spatial structure information. The multi-scale feature extraction module is shown in Fig. 2.

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3 × 3 conv

1 × 1 conv

1/16 upsampling

add

1 × 1 conv

add

1 × 1 conv 1 × 1 conv

1/8 upsampling

1/4

add

Fig. 2. Multi-scale feature extraction module

3.2 Attention Module Considering that the model has the same information attention to each region, the matching effect of some ill-posed regions is not ideal. Therefore, an attention module is designed to extract key information in channels and spaces, so as to achieve the fusion of contextual information and the attention to ill-posed regions. This module will not add many parameters, but it can effectively enhance the feature representation, as shown in Fig. 3. Specifically, global average pooling and maximum pooling are performed without reducing the channel dimension. Then, 1 × 1 convolution is used to increase the information interaction, and the feature tensors with different weight distributions are normalized by sigmoid activation function, and multiplied with the input feature map. Finally, the spatial information and channel information are fused to optimize the features and obtain more accurate disparity information.

Sigmoid

ReLU

Adaptive Max Pooling

add mul C×H×W

ReLU

Sigmoid

Adaptive Average Pooling 1 × 1 Convolution

Fig. 3. Attention module

3.3 Disparity Prediction Module The disparity prediction module concatenate the input left and right feature maps to obtain the matching cost volume, then uses 3D convolution to aggregate the cost volume, and finally obtains the disparity map through disparity regression, the structure is shown in Fig. 4.

Left/Right Feature Maps

Cost Volume

3D conv

Disparity regression

Fig. 4. Disparity prediction module

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In this paper, the soft argmin method is used to regress the disparity map. This method can obtain a smooth disparity map and is completely differentiable, enabling end-to-end network training. The cost value is transformed into disparity probability distribution by softmax function. The predicted disparity d pred is obtained by weighting the probability of each disparity d. The specific formula is shown in (1): dpred =

D max 

d × σ (−cd )

(1)

d=0

Where Dmax is the maximum disparity value, σ(·) is the softmax operation, and cd is the cost value in disparity d. 3.4 Loss Function This paper uses the smooth L 1 loss function to train the proposed algorithm, and the loss function L is shown in Eq. (2): N 1  L= (dgt (p) − dpred (p)) N

(2)

i=1

in which

 (x) =

0.5x, if |x| < 1 |x| − 0.5, otherwise

Where N represents the total number of pixels, d gt (p) represents the ground truth disparity of pixel p, and d pred (p) represents the predicted disparity of pixel p.

4 Experimental Simulation 4.1 Datasets The algorithm proposed in this paper is first trained on the software synthetic dataset Sense Flow [4], and then evaluated on two real datasets KITTI 2012 [12] and KITTI 2015 [13]. Sense Flow contains 35454 training image pairs and 4370 test image pairs. KITTI 2012 contains 194 training image pairs and 195 test image pairs, and KITTI 2015 contains 200 training image pairs and 200 test image pairs. 4.2 Experiment Details In this paper, NVIDIA Tesla P100 GPU is used to train the proposed algorithm end-to-end under the PyTorch deep learning framework, and the training batch size is set to 6. Adam optimizer is used to optimize the model, in which the parameters β 1 = 0.9, β 2 = 0.999, the maximum disparity Dmax is set to 192, and the loss weights of the three stages are 0.25, 0.5 and 1 respectively. In order to improve the generalization ability of the model, a series of data enhancement techniques are used for the training data set, including brightness

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and contrast enhancement, Gaussian blur enhancement, random cropping and flipping. For Sense Flow, a total of 24 epochs of training, the learning rate of the first 12 epochs is 0.001, and then every 6 epochs, the learning rate is halved. For KITTI 2012/2015, the pretraining model of Sense Flow is used for optimization training, a total of 500 epochs are trained, the learning rate of the first 200 epochs is 0.0005, and the learning rate is halved every 100 epochs thereafter. 4.3 Compared with AnyNet Algorithm This paper selects a pair of images in the KITTI 2015 dataset, and conducts a comparative experiment with the AnyNet algorithm. The experimental results are shown in Fig. 5. It can be seen from that at each stage, the error rate of the algorithm in this paper is much lower than that of AnyNet, and the matching effect is better in repeated texture regions (such as roads), and in the edge regions of objects (such as vehicles, tree trunks and telephone poles), The contour can be restored more accurately.

Fig. 5. As a result of the evaluation, (a) left input image, (b)-(d) three-stage output disparity map, (e) ground truth LiDAR image, and err represents the three-pixel error.

For further research, this paper uses the pre-trained model to evaluate the algorithm on the KITTI 2015 data set, and divides the training image into the training set and the test set at a ratio of 8:2. The test results are shown in Table 1, where the evaluation index adopts the three-pixel error of all pixels (D1-all%). Compared with AnyNet, the algorithm in this paper has a considerable FPS at each stage, and the error rate is much lower. In addition, the error rate of this algorithm in Stage1 and Stage2 is lower than the

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error rate of AnyNet in Stage2 and Stage3. The error in Stage3 is reduced by 2.43%, while the running speed is only 1.24% slower. Table 1. Validation results on the KITTI 2012/2015 dataset Stage1

Stage2

Stage3

Method(dataset)

FPS

D1-all%

FPS

D1-all%

FPS

D1-all%

AnyNet(KITTI 2012)

51.3

14.15

46.3

9.54

40.8

6.87

Ours(KITTI 2012)

46.1

9.32

44.1

5.97

40.3

4.88

AnyNet(KITTI 2015)

51.3

12.80

46.3

9.27

40.8

6.51

Ours(KITTI 2015)

46.1

7.68

44.1

5.30

40.3

4.08

4.4 Compared with Other Algorithms Table 2 reports the comparison results of the algorithm in this paper and the advanced algorithms in recent years. The evaluation indicators use the three-pixel error of the foreground (D1-fg%), the background (D1-bg%) and all pixels (D1-all%). Compared with these algorithms, the algorithm in this paper is also competitive. In addition, while maintaining the same running time, D1-all% is 0.35% and 0.52% lower than MADNet and StereoNet, respectively. Table 2. Evaluation results on the KITTI 2015 dataset Method

D1-bg%

D1-fg%

D1-all%

Runtime(s)

GCNet [5]

2.21

6.16

2.87

0.90

SegStereo [14]

1.88

4.07

2.25

0.60

PSMNet [6]

1.86

4.62

2.32

0.41

DispNetC [4]

4.32

4.41

4.34

0.06

MADNet [15]

3.75

9.20

4.66

0.02

StereoNet [7]

4.30

7.45

4.83

0.02

Ours

3.78

6.97

4.31

0.02

5 Conclusion This paper proposes an improved real-time stereo matching algorithm based on AnyNet, which can adaptively select the most reasonable stage to output the disparity map, so as to meet the actual needs with the smallest amount of calculation. Evaluated on the

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KITTI 2015 dataset, compared to AnyNet, the algorithm has higher accuracy at each stage. In addition, the final predicted disparity map error is reduced by 2.43%, while the running speed is only 1.24% slower. Compared with the advanced algorithms in recent years, the algorithm in this paper is also competitive. Acknowledgment. This work was supported by the National Natural Science Foundation of China (61761034) and the Natural Science Foundation of Inner Mongolia (2020MS06022).

References 1. Luo, W., Schwing, A.G., Urtasun, R.: Efficient deep learning for stereo matching. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5695–5703 (2016) 2. Hirschmuller, H.: stereo processing by semiglobal matching and mutual information. IEEE Trans. Pattern Anal. Mach. Intell. 30(2), 328–341 (2008) 3. Wang, Y., et al.: Anytime stereo image depth estimation on mobile devices. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pp. 5893–5900 (2019) 4. Mayer, N., et al.: A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, pp. 4040–4048. IEEE, New York (2016) 5. Kendall, A., et al.: End-to-end learning of geometry and context for deep stereo regression. In: Proceedings of the IEEE Conference on Computer Vision (ICCV), Oct. 22–29, 2017, Venice, Italy, pp. 66–75. IEEE, New York (2017) 6. Chang, J., Chen, Y.: Pyramid stereo matching network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA, pp. 5410–5418. IEEE, New York (2018) 7. Khamis, S., Fanello, S., Rhemann, C., Kowdle, A., Valentin, J., Izadi, S.: StereoNet: guided hierarchical refinement for real-time edge-aware depth prediction. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11219, pp. 596–613. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01267-0_35 8. Lin, T., et al.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, pp. 936–944 (2017) 9. Liu, S., et al.: Path aggregation network for instance segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA, pp. 8759–8768 (2018) 10. Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA, pp. 7132–7141. IEEE, New York (2018) 11. Wang, Q., et al.: ECA-Net: efficient channel attention for deep convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11531–11539 (2020) 12. Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? The KITTI vision benchmark suite. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3354–3361. IEEE, Providence, RI, USA (2012) 13. Menze, M., Geiger, A.: Object scene flow for autonomous vehicles. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3061–3070. IEEE, Boston, MA, USA (2015)

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14. Yang, G., Zhao, H., Shi, J., Deng, Z., Jia, J.: SegStereo: exploiting semantic information for disparity estimation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 660–676. Springer, Cham (2018). https://doi.org/10.1007/9783-030-01234-2_39 15. Tonioni, A., et al.: Realtime self-adaptive deep stereo. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 195–204 (2019)

Research on Anti-noise Activation Function Based on LIF Biological Neuron Model Fengxia Li, Shubin Wang(B) , and Yajing Kang College of Electronic Information Engineering, Inner Mongolia University, Hohhot, China [email protected]

Abstract. In the environment of Internet of vehicles ranging, the image sensor works for a long time, and the brightness is not enough or uneven when shooting, which will produce noise, resulting in poor image quality. In this paper, based on the LIF (Leaky Integrate-And-Fire) biological neuron model with noise immunity, NST (Noisy Softplus and Tanh) activation function is proposed and applied to PSM-Net to effectively overcome the influence of noise on image quality. The NST function is used to fit the response mechanism of LIF model, and the unknown α are added to make the NST function curve change with the change of input noise, so it has anti-noise performance. On this basis, the non monotonic property is added to the negative half axis to improve the processing accuracy of the function in the deep network. Experiments show that NST function is stable in the presence of noise input, and the error value is 13.504 lower than ReLU function in the presence of large noise input with variance value of 0.2, which effectively improves the processing accuracy of binocular stereo disparity prediction network. Keywords: Internet of vehicles · Activation function · LIF biological neuron model · Noise immunity

1 Introduction With the increasing number of family vehicles, the following traffic congestion and traffic safety problems have gradually become the focus of social attention. Internet of vehicles (IoV) came into being. In the IoV, radar and vision sensor are the two main ranging sensors. Compared with radar, visual sensor can obtain environmental information and color scene information, with low hardware cost, high efficiency and simple system structure. Binocular stereo vision technology can solve the problem of insufficient stereo vision perception of drivers, accurately judge the position and speed of objects, and improve driving safety. For the IoV application scenarios, the image sensor working for a long time will lead to high temperature or insufficient brightness and uneven brightness, which will produce noise, thus affecting the processing accuracy of binocular stereo vision. The traditional binocular stereo ranging technology is roughly divided into four steps: image acquisition, stereo correction, stereo matching and 3D reconstruction, in which stereo matching is an important part [1]. Stereo matching consists of four parts: matching cost calculation, matching cost aggregation, disparity calculation and © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 268–275, 2022. https://doi.org/10.1007/978-981-19-0390-8_33

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disparity optimization [2], and complex algorithm leads to high hardware cost. The end-to-end binocular stereo disparity prediction network based on deep learning solves this problem. Mayer et al. Designed the Dispnet (Disparity Estimation Network). The network directly generates the final disparity prediction image through symmetrical convolution and deconvolution structure, which is about 1000 times faster than the traditional stereo matching link, but the disparity processing accuracy is not improved [3]. Kendall A et al. Proposed GC-Net (Geometry and Context Network). It introduced geometric characteristics by constructing the cost volume of left and right eye images, then used 3D convolution to obtain more context information in the images, which effectively improved the accuracy of parallax prediction [10]. In the disparity prediction network, ReLU function [5] is usually used as the activation function. It overcomes the problem of gradient dispersion of Sigmoid and Tanh functions, and has very good sparsity. However, rough sparsity makes neurons “necrosis”. Qian Liu et al. Proposed NSP (Noisy Softplus) activation function to make neural network have anti-noise performance, but with the deepening of the network, the error also increases [6]. Y Chen et al. Introduced parameters reflecting the randomness of each neuron to construct RSP (Rand Softplus) activation function with strong noise immunity, which is superior to other activation functions when the input has large noise, but it is not much different from the NSP function in essence [7]. All kinds of improvements to the network and function are only improvements to the parallax prediction network, which can not specifically solve the problem of image noise generated by sensors in the environment of IoV. In this paper, based on the LIF biological neuron model with anti-noise, NST (Noisy Softplus and Tanh) activation function is proposed and applied to PSM-Net to effectively overcome the influence of noise on the parallax processing accuracy in the IoV environment.

2 Relate Work 2.1 PSM-Net Parallax Prediction Network The end-to-end disparity prediction network based on deep learning omits these algorithms. The concept of end-to-end is to input the left and right images obtained by image sensor into convolution neural network, and then output the disparity map directly. The output parallax map is compared with the original disparity graph and then propagates the tuning network in reverse direction, so it can effectively improve the speed and accuracy of disparity prediction. PSM-Net (Pyramid Stereo Matching Network) [4] is an end-to-end binocular stereo matching parallax prediction network, which includes a pyramid pooling module for combining global environment information and an overlapping hourglass module for matching cost aggregation. In the process of stereo matching, a single pixel value can’t reflect the environmental information, and it is easy to mismatch in areas such as reflection and texture repetition. Therefore, we need to combine the full-text information of the whole image to match the corresponding points. The pyramid pooling module adopted by PSM-Net selects hierarchical global pooling which contains information of different scales and sub regions to compress features to four scales, so as to combine the global environmental information.

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PSM-Net uses a stacked hourglass 3D convolution module to deal with cost body. The hourglass module is to connect multiple hourglass shaped 3D convolutions in series, which is a process of changing the cost body from large to small, and then from small to large, so as to improve the utilization of global information. Experiments show that PSM-Net has the highest accuracy in KITTI data set. 2.2 LIF Neuron Model Artificial neural network simulates the response mechanism of human brain to process data. However, the activation function is only a simple simulation of the response mechanism of biological neurons, so the artificial neural network lacks biological characteristics. So far, the existing biological neuron models include Hodgkin-Huxley model [11], Izhikevich model [13], SRM model and LIF model [12], etc. LIF neuron model can well reflect the response mechanism of biological neurons and has simple calculation, so it is widely used. LIF neuron model follows the following membrane potential dynamics. τm ·

dV = Vrest − V + Rm · I (t) dt

(1)

The membrane potential V starts from the resting membrane potential Vrest and changes with the change of input current I(t), where t is the time, τm is the membrane time constant, and τm is the product of membrane resistance and membrane capacitance τm = Rm · Cm . When the input current is no longer stationary but noisy, the response function of LIF model is as follows.  Vth√−μτm σ τm √ π · exp(u2 )(1 + erf (u))du]−1 (2) λout = [tref − τm · Vrest√−μτm σ τm

Where Vth is the threshold of pulse release, tref is the refractory period, and λout is the discharge rate, and μ and σ 2 represent the mean and variance of the input noise current respectively, and erf(u) is the error function. With the increase of input current, the response curve changes from approximate ReLU function to approximate Softplus function. The property of this function curve changing with the change of noise value makes LIF model have biological anti-noise property.

3 NST Activation Function Activation function plays an important role in convolutional neural network, which brings nonlinear factors to neural network and enhances the expression ability of neural network. The construction of activation function needs to consider all aspects of characteristics. The first is the gradient problem of function. If the input value of function tends to positive or negative infinity and the output value is saturated, the gradient will approach zero and the gradient dispersion will occur, which will affect the convergence of the network. If the function fluctuates too fast, the gradient explosion will occur if the gradient is too high, which will affect the network prediction results.

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The second is the characteristic of zero mean value. If the function is non-zero mean value and only has one side function value, the sign of the weight value will be completely consistent when it is back-propagation, which makes it update in the same direction, causing “Z-type update” and affecting the convergence speed. One of the main characteristics of human brain neuron cell activation is the highly sparse representation of external information. For the image information that the visual nerve needs to process, the original image data is usually wrapped with highly dense features. Increasing the difference between the positive and negative half axis function values of convolution neural network activation function, uncovering the complex relationship between features and converting them into sparse features can enhance the original image robustness, and then improve the processing accuracy. The sparse activation function is often easy to calculate, which can reduce the burden of the network and accelerate the convergence of the network. But rigid sparsity, such as the ReLU function has no response to the negative half axis, resulting in neuron “necrosis”, which will lead to the unavailability of the neuron and affect the final training results. Finally, the non-monotonic characteristic. At present, most of the activation functions are monotone increasing functions. According to the combination of exhaustive learning and reinforcement learning, Swish function proves that the accuracy of nonmonotone function without upper bound but with lower bound is better than other functions including ReLU function in all kinds of data sets. The non-monotone characteristic can effectively improve the processing accuracy of neural network deep model. Therefore, on the premise of fitting the LIF response curve to obtain the biological noise immunity, we need to comprehensively consider the gradient characteristics, zero mean characteristics, sparsity and non-monotonicity of the activation function, so that the activation function can obtain higher accuracy while resisting noise. Based on LIF biological neuron model, the NST function is designed. The function is as follows. NST (x) = log(α + exp(x)) · tanh(α · x + α).

(3)

Where x is the input data, α is related to the noise level of the input data, which is brought by the input data. The LIF response curve is fitted by changing the value of α, and the value of α can be calculated by the following formula. αi =

1 i

2

(wij2 ) · xi

(4)

The non-linear characteristic of the negative half axis of NST function mainly comes from Tanh function. Tanh function itself has zero mean value characteristic, and it origin of the positive and negative half axis function curve is symmetrical. The existence of function value in the negative half axis of the function can avoid the neuron “necrosis” phenomenon of NST function. After multiplying with log function, the non-linear characteristic of the negative half axis of NST function is brought. The curve of log(α + exp(x)) function is smooth at the zero point, which makes the NST function fit the response curve of LIF neuron model. The gradient unsaturated characteristic of positive half axis function makes the derivative of NST function stable at the positive half axis, and it will not cause the phenomenon of gradient explosion or gradient dispersion when the neural network propagates backward.

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In back propagation, the derivatives of NST function are as follows. ex · tanh(α + α · x) ∂f = − α · log(a + ex ) · (tanh(α + α · x)2 − 1) ∂x (α + ex )

(5)

PSM-Net can work effectively when the picture quality is normal when using ReLU as the activation function, but when the picture contains a lot of noise, it can’t output high-precision parallax. Replacing the ReLU function in PSM-Net with NST activation function in this paper can effectively overcome the noise effect on image quality after the vehicle camera works continuously in the context of vehicle network, and can still output parallax diagram which can be operated later.

4 Experimental Analysis 4.1 Analysis of NST Activation Function Curve As shown in Fig. 1, NST functions have different curves under the four different α values of α = 0.2, α = 0.4, α = 0.45 and α = 0.8. When fitting LIF response curve, the function curve retains the sparse characteristics and the negative half axis increases the nonlinear characteristics. And the NST function curve changes with the α value in three-dimensional space, which is similar to the response coverage of LIF model after mapping to the plane.

Fig. 1. The function curve of NST function at different α values

4.2 Experimental Data Set The experimental data sets are KITTI2012 and KITTI2015 [8, 9]. KITTI2012 training set contains 194 pairs of left and right eye images, the number of training scenes is 194, the number of test images is 195 pairs, and the resolution is 1226∗370. KITTI2015 training set contains 200 pairs of left and right eye images, 200 training scenes and 200 pairs of test images, with a resolution of 1242∗375.

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4.3 Training Environment and Network Training This experiment takes Pytorch as the framework and uses Python language to program. The operating system is Ubuntu 16.04.4, the GPU model is NVIDIA Tesla P100 PCI 16GB, the CPU is Intel Xeon CPU E5-2640 v4 @ 2.40GHz, and the memory is 32G. In order to speed up the training speed, PSM-Net carries out the per-training first. After the network converges, the activation function training is replaced in turn. After 50 rounds of pre training, each activation function trained for 200 rounds, in which all images in KITTI2015 data set are used for training, and some image pairs in KITTI2012 data set are used for training. The learning rate of the first 165 rounds is set to 0.001, and the learning rate after 165 rounds is set to 0.0001. Batch_size is 3. 4.4 Experimental Result In this experiment, four activation functions are selected, among which three functions are ReLU function, NSP function and RSP function as the control. Five different noise values of σ 2 = 0, σ 2 = 0.04, σ 2 = 0.10, σ 2 = 0.16 and σ 2 = 0.2 are tested respectively. As shown in Fig. 2, a left view of KITTI2012 data set is compared after adding different noises.

Fig. 2. Comparison of the images without noise and with different noise variance values

The experimental results are shown in Table 1, and the results of each group are the average values after 5 tests. The performance of ReLU function is relatively best at σ 2 = 0.04, but with the increasing noise value, the Error Val of ReLU function is very large, which is not as good as the other three anti-noise functions. NSP function is stable and has better anti-noise performance than ReLU function, but its performance is not the best. When σ 2 = 0.16, the Error Val of RSP function is obviously lower than other functions, but horizontally, the performance of RSP function is not stable. The NST function in this paper is stable. When σ 2 = 0.2, the Error Val of NST function is 13.504 lower than that of ReLU function, 3.575 lower than that of NSP function, and 3.602 lower than that of RST function.

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σ 2 = 0.04

σ 2 = 0.10

σ 2 = 0.16

σ 2 = 0.2

ReLU

1.258

4.182

7.604

14.026

24.501

NSP

1.381

4.310

8.370

14.219

14.570

RSP

1.403

4.274

2.863

4.000

14.599

NST

1.167

4.182

5.958

10.079

10.997

When the noise of left and right eye images is σ 2 = 0.2, the network disparity prediction map trained by each function is shown in Fig. 3. It can be seen that the NST function performs well and has anti-noise performance.

ReLU function

NSP function

RSP function

NST function

Fig. 3. Network disparity prediction map of each function at σ 2 = 0.2

5 Conclusion In this paper, according to the problem of poor image quality caused by noise generated by image sensor in the environment of IoV, a NST function based on LIF biological neuron response model is proposed. This function not only fits LIF response model, but also adds non monotonically. Experiments show that NST function performs stably in the case of noise input, and performs best in the case of large noise input. It can effectively overcome the impact of noise on image quality in the IoV environment. Acknowledgment. This work was supported by the National Natural Science Foundation of China (61761034), the Natural Science Foundation of Inner Mongolia(2020MS06022) and the special fund for innovation and Entrepreneurship of Postgraduates of Inner Mongolia University (11200-121024).

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References 1. Shubo, Y., Gao, Y., Liu, Z., Zhang, G.: A calibration method for binocular stereo vision sensor with short-baseline based on 3D flexible control field. Opt. Lasers Eng. 124, 105817 (2020). https://doi.org/10.1016/j.optlaseng.2019.105817 2. Wei, Z., Zhao, K.: Structural parameters calibration for binocular stereo vision sensors using a double-sphere target. Sensors 16(7), 1074–1097 (2016) 3. Mayer, N., et al.: A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4040–4048 (2016) 4. Chang, J.R., Chen, Y.S.: Pyramid stereo matching network. In: Proc. of Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5410–5418 (2018) 5. Glorot, X., Bordes, A., Bengio, Y.: Deep sparse rectifier neural networks. In: Proceedings of the fourteenth international conference on artificial intelligence and statistics, pp. 315–323. MIT Press, Cambridge (2011) 6. Liu, Q., Furber, S.: Noisy softplus: a biology inspired activation function. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, M. (eds.) Neural Information Processing: 23rd International Conference, ICONIP 2016, Kyoto, Japan, October 16–21, 2016, Proceedings, Part IV, pp. 405–412. Springer International Publishing, Cham (2016). https://doi.org/10. 1007/978-3-319-46681-1_49 7. Chen, Y., et al.: Improving the Antinoise ability of DNNs via a bio-inspired noise adaptive activation function rand softplus. Neural. Comput. 31(6), 1215–1233 (2019) 8. Urtasun, R., Lenz, P., Geiger, A.: Are we ready for autonomous driving? The KITTI vision benchmark suite. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition(CVPR), Providence, RI USA, pp. 3354–3361 (2012) 9. Menze, M., Geiger, A.: Object scene flow for autonomous vehicles. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3061–3070 (2015) 10. Kendall, A., et al.: End-to-end learning of geometry and context for deep stereo regression. IEEE (2017) 11. Hodgkin, A.L., Huxley, A.F.: A quantitative description of membrane current and its application to conduction and excita- tion in nerve. J. Physiol. 117, 500–544 (1952) 12. Stein, R.B.: A Theoretical Analysis of Neuronal Variability. Biophys. J. 5(2), 173–194 (1965). https://doi.org/10.1016/S0006-3495(65)86709-1 13. Izhikevich, E.M.: Which model to use for cortical spiking neurons. IEEE Trans. Neural. Networks 15(5), 1063–1070 (2004)

Non-parameter Nonlinear Detectors for Multi-band Milimeter-Wave Radio-Over-Fiber Mobile Fronthaul Yue Cui(B) , Xiaofan Xu, Hailong Kang, Niwei Wang, and Yingyuan Gao China Academic of Electronics and Information Technology, No. 11 Shuangyuan Road, Shijingshan District, Beijing 100041, China [email protected]

Abstract. Three non-parameter nonlinear detectors are proposed in multi-band milimeter wave radio-over-fiber(mmw RoF) system to achieve the suppression of system damages. First, we introduce the theory of the three detectors. Without requiring the prior estimation of the system link, the detectors can learn and capture the link characteristics from only a few training data. The experimental investigations are conducted to verify the feasibility of the proposed methods and compare the suitability of different methods. Keywords: Milimeter wave(mmw) · Radio-over-fiber(Rof) · Nonlinear detector · Support vector machine

1 Introduction With the maturity of the 5th generation mobile communication system (5G) and the development of the 6th largest mobile communication system (6G), the communication network combining optical fiber communication technology and wireless communication technology has attracted unprecedented attention due to its many advantages [1, 2]. Radio-over-fiber(RoF) technology is considered to be the key technology in the next generation of optical fiber wireless converged access network due to its advantages of large bandwidth, low transmission loss, and flexible access methods [3, 4]. However, the traditional and widely used microwave bands with frequencies below 10 GHz will soon be occupied. The higher frequency bands, millimeter wave (mmw) bands, with the advantages of high bandwidth, high frequency, and short wavelength are considered the most promising use band in the first generation of RoF access network [5]. Therefore, the mmw-RoF system is one of the most eye-catching technologies in the next generation of optical fiber wireless converged access networks. In the mmw-RoF system, in order to meet the multiple service requirements of different users, a multi-band/service signal transmission scheme was proposed and adopted [4]. The system block diagram is shown in Fig. 1. The multi-service of BBU transmission can be sent to different users on the RRH side. In the multi-band mmw-RoF system, the signal will be affected by a variety of system damages, resulting in signal quality © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 276–284, 2022. https://doi.org/10.1007/978-981-19-0390-8_34

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degradation, which in turn degrades system performance. The main system damages include phase noise, fiber and optical modulator nonlinear noise, carrier frequency deviation (CFO), and I/Q imbalance in down-conversion and so on. In this paper three nonparameter nonlinear detectors are proposed in multi-band mmw-RoF system to achieve the suppression of system damages, each of which plays an important role in mitigating different impairments. The optimal nonlinear detector can be selected according to the different damages of the system.

Fig. 1. Multi-band mm-wave RoF based fiber-wireless transmission system

2 Principle of Three Detectors In the multi-band mmw-RoF system, the signal transmission link and the parameters in the entire system are dynamically changed, and the parameters of the various optoelectronic devices passing through may also be unknown. Therefore, we propose three detectors which are all parameter-free to suppress the different types of system damages to the signal, that is, all the three nonlinear detectors do not depend on the various parameters of the link, optoelectronic device or system, and have strong adaptability and flexibility. The following is a brief description of the principles of the three nonlinear detectors. 2.1 A Bit-Based Support Vector Machine Nonlinear Detector The bit-based SVM detector is based on the theory of binary SVM classifier. If the different constellation points are regarded as different clusters of data, it is inevitable to build multiclass classifiers by using many binary SVMs to separate them precisely. In this paper, the bit-based SVM scheme we propose only requires log2 M two-class SVM for MQAM signal. In our method, each signal’s category is labeled in binary format with each bit modeled by a conventional two-class SVM. Each SVM conducts binary classification with nonlinear boundary under a learned classification strategy for the received signal. There are two process in the whole classification procedure, namely, training process and testing process. The purpose of training process is to obtain the nonlinear hyperplane

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from few training data, then classification will be completed according to the previous hyperplane namely the sign of f(x) in the testing process:   C+ = sign f (x) > 0 ⇒ l+ = +1 ⇒  1 (1)   C− = sign f (x) < 0 ⇒ l− = −1 ⇒  0

(2)

Figure 2 illustrates the classification strategy for 16-QAM. It can be seen that all the constellations points of 16-QAM can be detected correctly by only 4 binary SVMs. With the help of the kernel function, nonlinear compression and distortion of constellations can be detected precisely because of the nonlinear hyperplane. For higher order modulation signals, the tolerance of nonlinearity is relatively lower than low-order modulation signals.

Fig. 2. Example of binary SVM and classification strategy for16-QAM

2.2 Maximum Likelihood(ML) Based Detector (Two-Dimensional Gaussion Distribution) We regard the real and imaginary parts of each data on the constellation diagram as the x and y components on a two-dimensional plane, and each set of constellation points corresponds to a different signal symbol. The decision process of the detector is divided into two parts, namely the training process and the detection process. In the training process, we use the training sequence to obtain the expected matrix (μx and μy) and variance matrix (σx and σy ) of the real part (x) and imaginary part (y) of the data respectively corresponding to each symbol; then use the formula 3 covariance matrix to get the correlation coefficient matrix of x and y corresponding to each symbol. In the detection process, according to the formula 4 two-dimensional Gaussian distribution probability density function and the expected matrix, the variance matrix and the correlation coefficient matrix corresponding to the different symbols obtained in the training process, the probability of the received data for each symbol is calculated, and then the maximum probability value is selected. The data is classified to the symbol

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corresponding to this maximum probability value, and the decision process is finally completed.   σx2 ρσx σy (3) cov(X , Y ) = ρσx σy σy2 

f (x, y) =

1  e 2π σx σy 1 − ρ 2

−

1 2(1−ρ 2 )

(x−μx )(y−μy ) (y−μy ) (x−μx )2 −2ρ + 2σx σy σx2 σy2

2



(4)

2.3 Semi-supervised k-means Detector As a clustering algorithm, the semi-supervised k-means algorithm can be used as a nonlinear detector of the signal constellation, which principle is as shown in Fig. 3. The decision process also consists of two parts, namely the training process and the detection process. We regard each constellation point in the constellation diagram as a data cluster in the clustering algorithm. In the training process, the center point of each constellation point is obtained through the training sequence, that is the center of each data cluster. In the detection process, the received data is first represented on the constellation plane, and then the Euclidean distance between the received data and each center point is calculated, the smallest Euclidean distance is selected, and the data is classified into the data cluster corresponding to the minimum Euclidean distance. Then we calculate the average value of each data cluster and update the center of each cluster. The next step is to compare the new center with the previous center, that is, calculate their root mean square error (RMSE), and determine whether the value of RMSE meets the condition, if it is greater than the given known value a, return to recalculate the center value, otherwise stop the

Fig. 3. Algorithm flow chart of semi-supervised k-means detector

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iterative process to get the center of each data cluster, and then make a decision according to the hard decision method, and finally get detection result. From the above introduction to the principles of the three detectors, it can be seen that these three types of nonlinear detectors do not require any link or system parameters at all, and only a small part of the training sequence is needed to realize the nonlinear decision of the signal, and it has the adaptability and flexibility of the signal modulation format.

3 Experiment Setup and Results We built a point-to-point multil-band mmw-RoF experimental system to verify the effectiveness and applicability of the three detectors. The experimental device is shown in Fig. 4, including BBU, RRH, and a user terminal (UE). In this experiment, we modulate different signals on different intermediate frequencies. Here we modulate two different 16QAM signals (16QAM1 and 16QAM2) on 300 MHz and 750 MHz respectively. Their power spectrum is shown in Fig. 4. V 1 is the average voltage value of the input 16QAM1 signal, V 2 is the average voltage value of the input 16QAM2 signal.

Fig. 4. Experimental setup of multi-band mm-wave RoF system

As we know, MZM has a nonlinear transfer function, which will cause the selfmodulation of the single-sideband signal and direct cross-modulation of the sideband in the loaded signal [4], which will generate nonlinear noise on the signal and affect the signal quality. V in is the voltage value before the signal enters the second MZM, and its value will directly determine the magnitude of the nonlinear damage caused by the MZM to the signal. In addition, the relationship between the power of the two sideband signals (V 1 /V 2 ) will also interfere with each other to a certain extent. Figure 5 shows the constellation diagram of the 16QAM1 signal under different values of V in and V 1 /V 2 . It can be seen from this figure, when V 1 /V 2 remains unchanged, the increase of V in will aggravate the nonlinear damage of MZM to the signal, and when V in remains unchanged, the smaller the ratio of V 1 /V 2 , the stronger the cross-modulation between the two sidebands, the worse the signal quality of 16QAM1.

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Fig. 5. 16-QAM1 signal constellations at different values of V 1 /V 2 and V in

Firstly, we tested the compensation effects of three nonlinear detectors on the nonlinear damage caused by the nonlinear response of MZM. The three graphs in Fig. 6 are the relationship graphs between the BER and V in of 16QAM1 when V 1 /V 2 is 1, 0.8 and 0.6. It can be seen from these three figures that, overall, the MLE detector has a stronger suppression effect on nonlinear noise than the other two detectors. The performance of the SVM detector is slightly stronger than the k-means detector. In the case of a large V in , that is, when the nonlinear noise is strong, the decision effect of k-means and SVM is almost the same, and the advantage of MLE is no longer obvious. In addition, the smaller the ratio of V 1 /V 2 , the more severe the cross-modulation between the two sidebands, therefore the worse the performance of the 16QAM1 signal. In our experimental system, IF modulation is used, that is, the baseband QAM signal is moved to the IF, and then modulated to the RF millimeter wave and sent to the UE through the antenna. Therefore, two down-conversions are required on the user side. Due to the limitation of the performance of analog electronic devices, the two I/Q channels of the quadrature down-conversion part are not necessarily orthogonal, and the amplitude gains of the two I/Q branches are not completely the same, so I/Q imbalance will be generated, which will cause nonlinear damage to the signal, as shown in Eq. 5 [6]. In order to verify the compensation effect of the three types of arbiters for I/Q imbalance, when V in is constant and V 1 /V 2 is equal to 1, the relationship between the I/Q imbalance and BER of the 16QAM1 signal is tested, as shown in Fig. 7(a), the abscissa is the amplitude and phase error caused by the I/Q imbalance. As can be seen in this figure, the effect of the k-means detector on I/Q imbalance is significantly better than the other two detectors, while the SVM and MLE judges have almost the same effect. u = cos( β2 ) − j ∗ α ∗ sin( β2 ); v = α ∗ cos( β2 ) + j ∗ sin( β2 ) output = input ∗ u + v ∗ imag(input)

(5)

In the mmw-RoF system, due to the influence of the laser linewidth or fiber dispersion, phase noise (PN) will be generated, which will affect the signal quality [7]. In this experiment, we tested the suppression effect of the three detectors on phase noise. Phase deflection is the most common type of phase noise. As shown in Fig. 7(b), when V in is constant and V 1 /V 2 is equal to 1, the abscissa is the angle of signal phase deflection, the

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(a)

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Fig. 6. BER as a function of the V in for 16-QAM1 at different values of V 1 /V 2

1E-5

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(b) Fig. 7. BER as a function of the I/Q imbalance and phase rotation for 16-QAM1

ordinate is the BER of the 16QAM1 signal. It can be seen that the performance of the k-means detector is better than the SVM and MLE detectors, and the performance of the SVM and MLE are almost the same.

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Finally, we tested the compensation effect of the three detectors on the carrier frequency offset (CFO). The main reasons for CFO are: the carrier frequency of the transmitter and the receiver are not exactly the same; the phase noise introduced by the nonlinear channel and the Doppler shift produced by high speed motion of users [8]. Figure 8 shows the relationship between the CFO and BER of the 16QAM1 signal. Here we define the CFO as the difference between the transmitter carrier frequency and the receiver carrier frequency. It can be seen from this figure that the performance of the SVM detector for suppressing CFO is significantly better than the other two detectors.

Fig. 8. BER as a function of CFO for 16-QAM1

4 Conclusion From the above experimental results, it can be seen that the three detectors have their own advantages in suppressing a variety of different system damages. Among them, the suppression effect of the MLE detector is better than the other two detectors for the nonlinear noise generated by the MZM nonlinear response. As for the I/Q imbalance and phase noise, the semi-supervised k-means detector has better performance than the other two detectors; in terms of suppressing CFO, the SVM detector has obvious advantages. In addition, the complexity of these three algorithms is related to the length of the training sequence. When the length of the training sequence is constant, the relationship between the complexity of the algorithm is: k-means > MLE > SVM. Therefore, we can judge the type of system damage in the mmw-RoF system according to the analysis of different damage levels and types of the signal at the receiving end. And then we combine the requirements for the complexity of the algorithm to select the optimal nonlinear detector without relying on any parameter of system link and photoelectric device, which can realize the compensation of signal system damage and the improvement of system performance. Acknowledgement. This work is supported by the Development Strategy Research of Land, Sea, Air and Space Integrated Information Infrastructure under Project 19119401.

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References 1. Andrews, J.G., et al.: What will 5G be? IEEE J. Sel. Areas Commun. 32(6), 1065–1082 (2014) 2. Pfeiffer, T.: Next generation mobile fronthaul architectures. In: Optical Fiber Communication Conference, Optical Society of America (2015) 3. Chang, G.K., Cheng, L.: Fiber-wireless fronthaul: The last frontier. In: OptoElectronics and Communications Conference (OECC) held jointly with 2016 International Conference on Photonics in Switching (PS), 2016 21st. IEEE (2016) 4. Wang, J., Liu, C., Zhu, M., Yi, A., Cheng, L., Chang, G.K.: Investigation of data-dependent channel cross-modulation in multiband radio-over-fiber systems. J. Lightwave Technol. 32(10), 1861–1871 (2014) 5. Cheng, L.: et al.: Millimeter-wave cell grouping for optimized coverage based on radio-overfiber and centralized processing. In: Optical Fiber Communication Conference. Optical Society of America (2016) 6. Wang, Z., Yin, H., Zhang, W., Wei, G.: Monobit digital receivers for QPSK: design, performance and impact of IQ imbalances. IEEE Trans. Commun. 61(8), 3292–3303 (2013). https:// doi.org/10.1109/TCOMM.2013.061913.120304 7. Ragheb, A.M., Fathallah, H.: Experimental investigation of the laser phase noise effect on next generation high order MQAM optical transmission. In: 2015 International Conference on Information and Communication Technology Research (ICTRC), pp.168–170 (2015) 8. Huang, D., Letaief, K.B.: An interference-cancellation scheme for carrier frequency offsets correction in OFDMA systems. IEEE Trans. Commun. 53(7), 1155–1165 (2005). https://doi. org/10.1109/TCOMM.2005.851558

CNN-Based Automatic Modulation Classification over Rician Fading Channel Zikai Wang1(B) and Qilian Liang2 1

2

Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX 76019, USA [email protected] The University of Texas at Arlington, 701 S. Nedderman Drive Arlington, Arlington, TX 76019, USA [email protected]

Abstract. Automatic modulation classification (AMC) is a fundamental process in wireless communication. In our work, we proposed a Convolutional neural network based Automatic Modulation Classification (AMC) over Rician fading channel. We constructed a system to simulate the received modulated samples through the fading channel where 6 commonly used modulation methods are considered. The scheme of proposed convolutional neural network classifier is illustrated, and the classification accuracy is demonstrated. The classification accuracy of different mod methods are shown, respectively. And accuracies among different K-factor and maximum Doppler shift are investigated. Finally, we demonstrated the bit error rate of the system assume a successive modulation detection.

Keywords: Automatic modulation classification network · Deep learning · Fading channel

1

· Convolution neural

Introduction

Automatic Modulation Classification (AMC) is an essential process in wireless communication, since the receiver will be able to identify the modulation format of the received signal blindly [2]. As the demand for high-speed wireless communication growing rapidly, not only the number of modulation methods but also the bit rate increases, making modulation recognition a harder job. As Convolutional Neural Network (CNN) becomes a more and more powerful tool in doing classification task, neural network driven Automatic Modulation Classification has become a subject of great interest. From the machine learning perspective, modulation classification can be framed as a multi-class decision-making problem, where the intrinsic radio characteristics are obtained for learning a trainable classification model [1]. In [4], the authors proposed an SCF pattern based AMC method that employs a deep learning technology. The architectures of ResNet, DenseNet, and CLD c The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022  Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 285–292, 2022. https://doi.org/10.1007/978-981-19-0390-8_35

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Fig. 1. The system digram

neural network for the modulation recognition task are studied in [5]. The authors of [6] convert complex signals into data formats with a grid-like topology and applied convolutional neural network to the constellation diagram and gray image. More approaches regarding convolutional neural network based modulation classification have been done, for example, [7–9], which have been organized and summarized in [1]. In this paper, we focused on the automatic modulation classification problem over Rician fading channel. We proposed a system to simulate the received modulated samples through Rician fading channel. The designed convolutional neural network is trained with the output of the fading channel to do the classification. 6 commonly used modulation methods are considered. The classification accuracy of the proposed convolutional neural network classifier is simulated. As illustrated in the simulation results, our proposed network is able to distinguish different modulation types. We also demonstrated the classification accuracies among different K-factor and maximum Doppler regarding to the Rician fading channel. The rest of the paper is organized as follows. In Sect. 2, we formulate the system model. In Sect. 3 the structure of the proposed convolutional neural network is illustrated. In Sect. 4, the classification result of the proposed convolutional neural network is shown the performance of the system is simulated. Finally, we conclude our work in Sect. 5.

2

System Model

Figure 1 shows the structure of the simulation system. The data source is generated and then encoded by a channel encoder. The samples are then up-sampling by 8 and been filtered by a root-raised cosine filter has a roll-off factor of 0.35 and spans four symbols. The filtered samples are now passing through a Rician fading channel and adding additive white Gaussian noise (AWGN), which we will illustrate briefly in next section. At the receiver side, automatic modulation classification is performed to identify the modulation methods that has been used. After channel decomposition, the samples are then been filtered by a root-raised cosine filter with same setup and then down-sampling before been demodulated. After demodulating and channel decoding, the source data is recovered and the bit error rate (BER) can be evaluated.

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Rician Fading Channel Model

Rician fading is a stochastic model that has been widely used to describe radio propagation anomaly caused by partial cancellation of a radio signal by itself the signal arrives at the receiver by several different paths [10]. K-factor is used to describe the ratio between the power of the line of sight (LOS) part and the power of the scatters in Rician fading. As K → ∞, the Rician fading actually turns into AWGN. And Rayleigh fading is recovered for K → −∞, as there is no dominant propagation along a LOS between the transmitter and receiver. By implementing the Jakes’s Model [11], we formulate the Rician fading channel as follow,   GQ 1 GI 1 ) (1) +√ 1 + −K/20 (  CRician = √ 10 2M 2(M + 1) 1 + 10−K/10 where GI and GQ are the real imaginary part of scatteres, which follow Rayleigh distribution: GI = 2

M 

cos βn cos 2πfn t +

√

2 cos α cos 2πfd t

(2)

n=1

GQ = i ∗ 2

M 

sin βn cos 2πfn t +

√

2 sin α cos 2πfd t

(3)

n=1

Here, fn is the Doppler shift on the n-th scatter subjects to the maximum Doppler shift fd . α is the model parameters which is usually set to zero, βn is chosen so that there is no cross-correlation between the GI and GQ [12]. The LOS part and the scattered part are normalized according to K so that the average channel gain of the fading channel is 1.

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Neural Network Architecture

The architecture of the proposed network is illustrated in Fig. 2. As is shown, the network contains 6 convolution layers and 1 fully connected layer. The complex vector of received samples is converted into a 1 × 1024 × 2 array and been entered into input layer and then filtered in convolution layer. Each convolution layer is followed by a batch normalization layer, where data is normalized for each channel independently before entered activation layer. As for activation layer, rectified linear unit (ReLU) [13] is implemented with activation function f (x) = max(0, x). A max pooling layer with pooling size of (1, 2) is performed after each activation layer except for the last, where an average pooling layer of (1,32) is implemented. The fully connected layer combines all the features learned by previous layers, and the output is normalized into a probability distribution consisting of K probabilities in the softmax layer with the softmax function ez(k) , k = 1, . . . , K σ (zk ) = K z(j) j=1 e

(4)

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Confusion Matrix for Test Data 7dB 20Hz 16QAM

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Fig. 2. The architecture of the network

Fig. 3. Confusion matrix of the testing data

Hence, the summation of the output equals 1. The output layer takes the values from the softmax layer and assigns each input to one of the 6 classes using the cross entropy function [14]: L(θ) = −

K 

wk tk log σk

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k=1

where K = 6 is the number of modulation classes, wk is the weight for class k, tk is the class indicator and σk represents the output of the softmax layer, i.e., the predicted probability sample is of class k. Table 1 shows the output dimension of the network. The total number of trainable parameters is 96,910.

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Experiment Results

The transmitted data source is generated with length according to the modulation type so the sample sizes after modulating remain same. 10,000 frames with 1024 samples per frame for each modulation methods. and then pass through the proposed Rician fading channel of different SNR setup and then feed into the convolutional neural network. The dataset is divided into training, validation and test with a ratio of 80%, 10% and 10%. After 12 epochs, the neural network converges at a validation accuracy of 94.57%. The test data is then classified by the network and the test accuracy is 94.42%. Figure 3 shows the confusion matrix of the neural network.

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Table 1. Network layout Layer (filter size)

Output dimensions

Input

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Conv1 (filters 16, 1 × 8 × 2) + ReLU

1 × 1024 × 16

Max-pooling1

1 × 512 × 16

Conv2 (filters 24, 1 × 8 × 16) + ReLU 1 × 512 × 24 1 × 256 × 24

Max-pooling2

Conv3 (filters 32, 1 × 8 × 24) + ReLU 1 × 256 × 32 1 × 128 × 32

Max-pooling3

Conv4 (filters 48, 1 × 8 × 32) + ReLU 1 × 128 × 48 1 × 64 × 48

Max-pooling4

Conv5 (filters 64, 1 × 8 × 48) + ReLU 1 × 64 × 64 1 × 32 × 64

Max-pooling5

Conv6 (filters 96, 1 × 8 × 64) + ReLU 1 × 32 × 96

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We then specified the classification accuracy by different modulation methods. 10000 frames are generated for each SNR values and then been classified by the trained network. Accuracy by different modulation methods at K=7

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As shown in Fig. 4, BPSK modulation remains a high accuracy regardless high or low SNR, this is straight forward since the average distance between

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symbols are largest in BPSK modulation. Almost only the BPSK samples could be classified correctly at 0 dB. Both 16-QAM and 64-QAM has poor accuracy at low SNR range and increases as the SNR increases. At high SNR, 64-QAM still contributes most to the classification error, which is as expected since the average distance between symbols is minimal. At 30 dB, most of the BPSK, QPSK, 8-PSK and 4-PAM samples can be correctly classified, 78.4% of the 16-QAM samples and 89.0% of the 64-QAM samples have been classified correctly and the confusion mainly happens between 16-QAM and 64-QAM samples. At 0 dB, most of the modulated samples are recognized as BPSK and QPSK, makes BPSK the only modulation method that have high accuracy at low SNR. Accuracy with different doppler shift

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Fig. 7. Accuracy for different training dataset

Accuracy versus SNR at different K factor

In this section, we investigated the classification performance of the neural network versus SNR through the fading channel with different values of K-factor. The test data sets with the K-factor of 2, 7 and 20 are classified by the trained neural network while the maximum Doppler shift of the fading channel set to 50 Hz. Figure 5 shows the classification performance versus SNR based on different value of K-factor. We can see that the neural network performs better for test data with higher K-factor. Which is as expect since the higher the K-factor, the stronger the power of the LOS component compares to the scatters. While SNR is 20 dB, the accuracy difference between the test data of K = 2 and K = 20 is 1.4%. However, the accuracy difference is not significant in both high SNR and low SNR range. Hence, we conclude that the neural network performs constant over fading channel among different K-factor.

CNN-Based AMC over Rician Fading Channel

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Accuracy versus SNR at different Maximum Doppler shift

We then investigated the classification performance of the neural network versus SNR at different values of maximum Doppler shift. The test data sets with the maximum Doppler shift of 4 Hz, 50 Hz, 80 Hz and 100 Hz are classified by the trained neural network while the K-factor of the fading channel is set to 7. As for a stationary transmitter with a carrier frequency of 906 MHz, 4 Hz represents the approximated Doppler shift of a receiver which moves at a speed of a walking pedestrian. And 80 Hz represents the approximated Doppler shift of a receiver which moves at a speed of a driving car. Figure 6 shows the classification accuracy versus SNR based on different value of maximum Doppler shift. Contrary to what expected, the neural network shows a higher accuracy when maximum Doppler shift is higher. One possible explanation is that the higher Doppler shift amplified certain features of different modulation methods and thus enhanced the capacity of discernment. Further research shall be done to investigate this issue. 4.4

Training neural network with different dataset

In this section, we tried to investigate the influence of different training dataset on the proposed neural network structure. Instead of training the neural network with mixed data of all different SNR values, we now train the neural network with data that are generated at a fixed SNR value. Dataset of SNR = 15 dB and SNR = 25 dB are generated, with the same K-factor of 7 and maximum Doppler shift of 50 Hz, while the size of the dataset is reduced. After training the neural network, we still perform the classification task with test dataset of SNR from 0 dB to 30 dB for both neural network and the results are compared. From Fig. 7 we can see, the neural network trained with high SNR dataset performs better at high SNR range, meanwhile the neural network trained with low SNR dataset performs better at low SNR range. And the neural network trained with mixed data of all different SNR values lies in between. That may suggest, adding an estimated SNR term as an extra input of the neural network could enhanced the classification accuracy.

5

Conclusion

In this chapter, we introduced a Convolutional neural network based Automatic Modulation Classification (AMC) methods over Rician Fading Channel. We constructed a system to simulate the received modulated samples through Rician fading channel. 6 commonly used modulation methods are considered. The scheme of proposed convolutional neural network classifier is illustrated, and the neural network is trained with the generated samples. The neural network achieves an accuracy of 94.42% over a Rician fading channel with K = 7 dB and maximum Doppler shift of 50 Hz. The classification accuracy of different mod methods is shown, respectively. And accuracies among different K-factor and

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maximum Doppler are investigated. Finally, we demonstrated the bit error rate of the system assume a successive modulation detection. As for future improvement, the simulation result in Sect. 4.4 suggests, taking SNR into consideration might enhanced the classification accuracy of the neural network. One possible way of doing that is adding an estimated SNR term as an extra input.

References 1. Pham, Q.V., et al.: Intelligent radio signal processing: a contemporary survey,’ arXiv, pp. 1–30 (2020) 2. Dobre, O.A., Abdi, A., Bar-Ness, Y., Su, W.: Survey of automatic modulation classification techniques: classical approaches and new trends. IET Commun. 1(2), 137–156 (2007) 3. Hameed, F., Dobre, O.A., Member, S., Popescu, D.C., Member, S.: On the likelihood-based approach to modulation classification. IEEE Trans. Wirel. Commun. 8(12), 5884–5892 (2009) 4. Mendis, G.J., Wei, J., Madanayake, A.: Deep learning-based automated modulation classification for cognitive radio. In: 2016 IEEE International Conference on Communication Systems, ICCS 2016 (2017) 5. Liu, X., Yang, D., El Gamal, A.: Deep neural network architectures for modulation classification. In: 2017 51st Asilomar Conference on Signals, Systems, and Computers, pp. 915–919 (2017) 6. Peng, S., et al.: Modulation classification based on signal constellation diagrams and deep learning. IEEE Trans. Neural Netw. Learn. Syst. 30(3), 718–727 (2019) 7. Wang, Y., Wang, J., Zhang, W., Yang, J., Gui, G.: Deep learning-based cooperative automatic modulation classification method for MIMO systems. IEEE Trans. Veh. Technol. 69(4), 4575–4579 (2020) 8. Luo, B., Peng, Q., Cosman, P.C., Milstein, L.B.: Robustness of deep modulation recognition under AWGN and rician fading. In: Conference Record - Asilomar Conference on Signals, Systems and Computers, vol. 2018-October, pp. 447–450 (2019) 9. Peng, S., Jiang, H., Wang, H., Alwageed, H., Yao, Y.D.: Modulation classification using convolutional neural network based deep learning model. In: 2017 26th Wireless and Optical Communication Conference, WOCC 2017, no. 1, (2017) 10. Goldsmith, A.: Wireless Communications. Cambridge University Press, Cambridge (2005) 11. Jakes, C.: Microwave mobile (1974) 12. Dent, P., Bottomley, G., Croft, T.: Jakes fading model revisited. Electron. Lett. 29(13), 1162 (1993) 13. Glorot, X., Bordes, A., Bengio, Y.: Deep sparse rectifier neural networks. In: Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics. JMLR Workshop and Conference Proceedings, pp. 315–323 (2011) 14. Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Boston (2006). https://doi.org/10.1007/978-1-4615-7566-5

Schedio-Pro: An Interactive Virtual Reality Game for Protein Design Tianshu Xu1(B) , Venkata V. B. Yallapragada2,3,4 , Mark Tangney2,4,5 , and Sabin Tabirca1 1 School of Computer Science and Information Technology, University College Cork, Cork,

Ireland [email protected] 2 CancerResearch@UCC, University College Cork, Cork, Ireland 3 Centre for Advanced Photonics and Process Analysis, Cork Institute of Technology, Cork, Ireland 4 SynBioCentre, University College Cork, Cork, Ireland 5 APC Microbiome Ireland, University College Cork, Cork, Ireland

Abstract. Protein design is a rapidly expanding field complemented with the latest surge of computational tools to predict the 3D structure and to create entirely novel proteins using de novo design. In its simplistic terms, the process of protein design involves combining basic structures such as Sheet, Helix, and Coils for various intended applications. Different permutations and combinations of these basic structures can result in a wide variety of proteins. Molecular visualisation and the ability to interact with the 3D structure are crucial for protein design and edutainment. Using virtual reality for protein design and interaction provides users with an enhanced immersive experience. To simulate a cutting-edge protein interaction experience within a Virtual Reality (VR) setting, an interactive protein design VR game Schedio-Pro has been developed. Schedio-Pro has unique features that make it an indispensable tool. Gamification: Basic protein molecules can visualise in the game as 3D models. They can be grabbed by players and thrown to another molecule. The simulation of the combination process can be achieved by replacing two basic proteins with the model of their combination. Manipulating the structure: This is achieved using 3D rigging. In other words, the combination model can be deformed properly by filling bones into it. By bending and twisting the bones with VR controllers, the protein can be folded to different shapes. Visualising structures from PDB (Protein Data Bank): Protein model can be packed as an Asset Bundle and loaded in the game at run time. This feature can benefit protein engineers to visualise all sorts of protein models on demand in a VR environment. Keywords: Virtual reality · Protein gaming · Immersive visualisation

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 293–300, 2022. https://doi.org/10.1007/978-981-19-0390-8_36

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1 Introduction 1.1 The Importance of VR (Virtual Reality) for Protein Design VR technology can bring the protein design to a new dimension. The designing process comprises countless protein components and complex algorithms. The VR application, however, makes this ambiguous workflow visible, touchable, and accessible. Microscopic protein molecules can be scaled up and projected to a virtual environment. VR technology is an alternative way of popularizing the process of protein design since it can transfer the process to an intuitive and immersive experience. Moreover, protein engineers with professional skills can also benefit from VR applications. Complicated computing algorithms would be complemented by interactive protein models. The old procedure would be simplified into interactive design by mere actions such as point & click or grab & throw. The trivial algorithm of prediction can be left to some online servers (like I-TASSER [1]). Furthermore, the well-predicted protein 3D (three dimensional) models can be visualised in a VR tool. Instead of viewing them on the PC end (Personal Computer) with some visualisation tools (such as UCSF Chimera [2]), observing protein models in a virtual world would render an immersive experience close to reality. 1.2 The Contribution of the Writer PDB File Browser. The most remarkable functionality in Schedio-Pro would be the PDB (Protein Data Bank) file browser since it enabled the user to open PDB files downloaded from online databases at runtime, and this feature is needed since it can benefit protein engineers to visualise PDB files, as shown in Fig. 1. This feature was implemented by a function in Unity (a game engine) called “AssetBundle”. It basically comprised two workflows, which are compressing and extracting. New PDB files downloaded or imported elsewhere could be packed up to asset bundles by a Unity editor. The VR APP developed by Unity could extract PDB data from those bundles and instantiate the data to protein 3D models.

Fig. 1. A complex protein model instantiated with the file browser feature.

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Grab and Throw Feature. This feature is needed since it’s intuitive and natural. Users could grab the protein models by their virtual hands (controlled by some pairs of Oculus Touch Controllers) and throw them elsewhere (by releasing the grip buttons on the relative controllers). A crosshair (reticle) would be attached to each protein model to show the status of the model. This status benefited the functionality of grabbing objects from a distance. Once the crosshair turned to blue from grey, it meant the relative model is pointed by a controller and ready to be grabbed, as shown in Fig. 2. This feature was achieved by the open-source Oculus development kits (called Oculus Integration and available in the Unity Asset Store).

Fig. 2. The crosshair turns blue indicates the model is ready to be grabbed.

After throwing a protein model, the combination feature was implemented by the writer by exploiting the Rigid Body and Collider functionalities in Unity. Once a valid collision was detected, the system would read the names of two bumping protein models with an algorithm in a C# script (a programming language which typically worked with Unity). The new model of the combination would be instantiated according to the concatenation of those two names. Artistic Contributions. The UI layout was designed by the author, as represented in Fig. 3. Instead of overlaying the UI elements on the screen, the VR UI panels (which contained UI elements such as labels, buttons, and sliders) were fixed on some positions in the virtual space. Moreover, a “LookAt” function was added to each of UI panel. This

Fig. 3. The output of the VR App.

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made sure that when the players wandering in the space, the UI panels would always be facing at the player.

2 Literature Review There are several molecular visualisation tools available today. Molecular Rift [3] is a VR tool for observing protein models with a PDB file browser feature. It used the gaming sensor MS Kinect v2 and Leap Motion technique to detect the motion of a user’s hands. With this feature, the user can interact with this VR app by some gestures other than handling controllers. Moreover, the file browser feature was implemented by utilising the data of protein coordinates and predefined protein model parts. It could calculate the coordinates of each protein parts from a PDB file and then assemble the protein with the predefined parts based on those coordinates. UCSF Chimera X [4], on the other hand, didn’t incorporate the gesture feature. However, it’s also a PDB file browser for observing protein 3D models. Its well-known PC version, USCF Chimera [2], contained many features to demonstrate different parts and aspects of a protein model.

3 Methodology 3.1 Analysis The Requirement of this App. Since this application would be a VR tool, the feeling of immersive and realism were two basic requirements. Furthermore, as an edutainment tool, the gameplay and interactive would also be critical. To be specific, the click and drag & drop features were unneglectable since the protein engineers might be looking for a way to show different protein models and combine them. Clicking would be an intuitive interaction. Once a button presented to us, the first thing off the top of our head would be tapping it to see what will happen. The same theory could be introduced to Schedio-Pro. If some buttons attached with a protein component’s icon on each of them were displayed in front of us, we would really be willing to click them. The intuitive method was required in this game to display different protein component models and trigger some events. Basic protein models were also needed. They could be presented with a large scale in the middle or grabbed on the players’ hands to be observed from different aspects. In that case, the protein models should be classified into two categories. One for displaying in the middle, the other for being grabbed. A mechanism which could combine two protein models and generate a combination protein model would also be important in Schedio-Pro. A system was also required to detect when would this combination event be triggered. For instance, when the two components got close enough to each other, or the two models were physically intersecting. No matter how this event was detected, the previous component models should be destroyed and a new combination model should be shown instead.

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Potential Methods to Implement the Requirements. The basic buttons can be pressed to trigger some events. A toggle button, on the other hand, could be treated as a basic button the first time it’s pressed. When it’s clicked the second time, the events would be triggered inversely. Unity would be the software to complete this game since it’s a cross-platform game engine [5]. C# was the default programming language for Unity to achieve some methods and functionalities. Many basic features and gameplay could be implemented by using Unity and C#. Oculus Integration was an open-source asset available in the Unity asset store. This tool could achieve many VR features, such as grab and distance grab. For instance, players could grab things with their virtual hands (controlled by Oculus touch controllers) from a distance by exploiting the distance grab gadget. 3.2 Implementation Schedio-Pro was designed for the Oculus platform, and the Oculus Rift is recommended for testing this game. The first level of the PC version (with drag & drop as its gameplay) was achieved in this VR version. Instead of drag & drop, grabbing and throwing became the leading interactive elements. Bend & twist, on the other hand, has been a testing feature in this game. Even though the protein parts could be manipulated and twisted by hands like a puppet, the accuracy was still an issue to be concerned. A PDB file browser feature was also tested. Now the PDB file could be converted and reorganised to a DAE file (a 3D format) and imported with the built game file. However, the procedure of converting PDB file to other 3D formats was long and trivial which will be improved in later stages. The UI elements in this VR game includes labels, toggles, and buttons. Buttons and toggles can be clicked by the trigger buttons on the Touch Controllers. A built-in prefab in OVR bundle achieves this feature, which has three children (LaserPointer, Sphere, and EventSystem). LaserPointer is a line object with a C# script attached. The Sphere is a ball for making the target point distinct. EventSystem is similar to Unity UI EventSystem. It helps the system to detect all the events (for instance, if any button on the Controller is pressed). A public variable (JoyPad Click Button) can be assigned with any button on a Controller. Since the trigger button is more significant than other buttons on a Controller, it would be an excellent idea to set it (the Primary Index Trigger) as the click button, as shown in Fig. 4(a). The grabbing functionality is achieved by attaching a DistanceGrabbable C# script to each protein model (Such as Sheet, Helix, fCoil, or rCoil). Moreover, a pair of crosshairs (with grey and blue colours) can also be attached to each model. Once a player’s virtual hand is pointing at a model, the crosshair on that model would switch from grey to blue (which means it’s active and grabbable). In that case, the grip button can be pressed by the user to grab that protein model, as shown in Fig. 4(b).

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Fig. 4. A graph to show the Oculus Controllers with (a) Trigger button and (b) Grip button.

4 Experiment 4.1 Description An experiment has been conducted in the VR lab. around twenty students attended gradually and tested the VR app. Oculus Rift and Oculus Rift S were two types of VR Head Mounted Displays (HMD) equipment that used to test the app. Students were wearing the headsets and put on Touch controllers on both hands, as shown in Fig. 5.

Fig. 5. The VR game is being tested with: (a) Oculus Rift headset, (b) Touch Controllers, and (c) the VR game.

Some issues were tested out during the experiment. Firstly, students weren’t able to click on buttons with the trigger button without the vocal instructions from the author. They didn’t know which button to click since they could see neither the controllers nor their hands. Secondly, some of them found it hard to grab and throw protein models since

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they hadn’t got used to the weight of the Touch controller. Throwing a virtual protein model in the app is different from throwing a stone in realistic, the grip button on the Touch controller should be held and released with good timing. Finally, it took a while for some of them who didn’t have some gaming experience to get used to move around and turn around with the joysticks on the controller. User feedback suggested a guided tutorial to introduce the user to the interface. An interactive tutorial stage will be added in the future versions. 4.2 Feedback However, some good feedback was also received. For instance, the protein models on displaying had a good looking since the shader graph technique were implemented. Moreover, the UI elements had a sense of consistency which helped the user to memorise the functionalities easily. Furthermore, the combination mechanism was intuitive and entertaining. Instead of exploiting complicated and realistic research procedures, throwing the protein to combine would be easier for understanding. A questionnaire was also used to collect the feedback, the result is shown below in Table 1. Table 1. The feedback questionnaire. Strongly disagree

Disagree

Neutral

Agree

Strongly agree

I found this game appealing

0

0

1

1

18

I thought the tutorial in this game was helpful

0

1

0

2

17

I thought this game was easy to play

1

5

4

2

8

I thought there was too much inconsistency in this game

19

0

1

0

0

I would like to play this game again

0

1

3

6

10

5 Conclusion 5.1 Personal Contribution The UI elements in the game scenes were all drawn by the author with Illustrator. In order to fulfil the sci-fi theme, the dark green and cyan colour are used as the theme colour in this game. All the UI buttons were paired with standard images and highlighted images. This feature made the interactive more responsive since the buttons would respond by swapping to highlighted images once the pointer is hovering on them.

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Secondly, the combination mechanism was implemented with an invisible plane attached with a plane collider. This was also designed and implemented by the author. Once a protein model collider with the plane, the name of it would be transferred to the system and concatenated with the displaying model’s name. The combination model would be instantiated with the concatenated name. Finally, the bend & twist feature was also designed by the author. That was utilising the similar trick as the grab feature. A cube with box collider was attached to a bone on the displaying model. The cube was also attached with a distanceGrabbable script. This enabled the player to grab the cube from distance. The rotation data would be transferred from the cube to the bone in order to bend the protein model. In that case, players could tweak their hands’ position to manipulate the protein like a puppet. 5.2 Future Work This app has the potential to be both an educational tool and also a protein visualisation tool. The interactive design and real-time molecular manipulation features such as the bend & twist and grab & throw feature will attract many potential players attention to popularise the protein design knowledge and educate the masses. Since the PDB file browser feature can be implemented with the asset bundle technique, this app may benefit the protein engineers to also observe pre-existing protein 3D models in a VR world easily.

References 1. Zhang, Y.: I-TASSER server for protein 3D structure prediction. BMC Bioinform. 9, 1 (2008). https://doi.org/10.1186/1471-2105-9-40 2. Pettersen, E.F., et al.: UCSF Chimera?A visualization system for exploratory research and analysis. J. Comput. Chem. 25(13), 1605–1612 (2004). https://doi.org/10.1002/jcc.20084 3. Norrby, M., Grebner, C., Eriksson, J., Boström, J.: Molecular rift: virtual reality for drug designers. J. Chem. Inf. Model. 55(11), 2475–2484 (2015). https://doi.org/10.1021/acs.jcim. 5b00544 4. Goddard, T.D., et al.: UCSF ChimeraX: meeting modern challenges in visualization and analysis: UCSF ChimeraX visualization system. Protein Sci. 27(1), 14–25 (2017). https://doi.org/ 10.1002/pro.3235 5. Goldstone, W.: Unity Game Development Essentials: Build Fully Functional, Professional 3D Games with Realistic Environments, Sound, Dynamic Effects and More. Packt Publ, Birmingham (2010)

A Method of Red-Attack Pine Trees Accurate Recognition Using Multi-source Data Jinliang Xia1 , Jincang Liu1,2(B) , Qin Li1 , and Dixiong Lu1 1 2

Surveying and Mapping Institute, Lands and Resource Department of Guangdong Province, Guangzhou 510500, China 28 North Wu Xian Qiao Street, Guangzhou 510500, Guangdong, China [email protected]

Abstract. In this paper, multi-spectral satellite images, Light Detection and Ranging (LiDAR) point clouds and thematic data are used for recognition and extraction of interference areas, and based on high resolution digital orthophoto map, use hue-saturation-value (HSV) or red-green-blue (RGB) color model to identify individual red-attack pine trees. Then compare the recognition results and accuracy between the different color models according to visual interpretation and field verification. Research shows that the recognition accuracy based on HSV color model is much higher than that based on RGB color model. At the same time, the collaborative utilization of multi-source data can greatly reduce the interference of dead grasslands, shadows, bare land, and roads, and reduce the error rate from 60% to 20%. This paper provides technical ideas and methods for accurate recognition of red-attack pine trees based on unmanned aerial vehicle (UAV) high resolution images.

Keywords: High resolution images color model · Red-attack pine trees

1

· High density point cloud · HSV

Introduction

Bursaphelenchus xylophilus is the most serious cause of forest resources loss in China. It has strong destructive power and has no effective control drugs. At present, the main method is timely detection of infected trees and cutting them down [1,2]. Therefore, the identification of infected trees is an important part of bursaphelenchus xylophilus treatment. According to the characteristic that the color of pine trees will change significantly after infection [3], many scholars have carried out researchs on monitoring of red-attack pine trees based on remote sensing images [4–17], and have achieved certain results, mainly including satellite remote sensing monitoring and UAV remote sensing monitoring, but they have certain room for improvement in the recognition rate and accuracy of red-attack pine trees. c The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022  Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 301–308, 2022. https://doi.org/10.1007/978-981-19-0390-8_37

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Based on multi-source remote sensing data such as UAV ultra-high resolution remote sensing image, high-density LiDAR point cloud, multi-spectral satel-lite image and thematic data, this paper uses HSV (hue - saturation - brightness) color transformation method to convert ultra-high resolution image from RGB color space to HSV color space, and constructs the HSV threshold extraction rule of red-attack pine tree. Then, by means of geometric feature extraction of pine trees canopy from multi-source remote sensing data and interference area filtering, the accurate recognition of red-attack pine trees is realized. Finally, the reliability of the results was verified by visual interpretation and field verification, and a technique solution and method for recognition of red-attack pine trees by ultra-high resolution images and high density LiDAR point clouds was summarized and proposed, which provided technical support for the accurate recognition and positioning of red-attack pine trees in Guangdong Province.

2

Generalization of Study Region

According to the general survey of pine wilt disease in the spring of 2020 in Guangdong Province, there are many infected sublots in Laolong Town, Longchuan County. Therefore, we combines image analysis and field survey to select a pine forest in the north of Bantang Village as the research area, with an area of 0.5 km2 . The annual average temperature of the region is 19 ◦ C–24 ◦ C, the terrain is mainly hilly, the vegetation coverage is relatively sparse, the tree species are mostly Pinus massoniana, fewer broadleaf trees, and there are more weeds and bare land. Pine trees are mainly located at the edge of the hillside, which can be divided into two categories: red-attack pine needles without falling off and red-attack pine needles have fallen off (old dead pine trees), and the death time is long.

3 3.1

Research Methods and Data Research Methods

In the recognition of red-attack pine trees, the non-pine region has a great influence on the accuracy of extraction. In this paper, Collaborative analysis of multisource remote sensing data are used to generate the mask of interference area to maximize removal of interference from non-pine areas. The specific methods are as follows. Firstly, determining the broad range of pine forest by using the forest resource survey data which contains sublot of pine trees. Then NDVI threshold method (NDVI >= 0.4) was used to calculate the normalized difference vegetation index (NDVI) of multispectral satellite images on the basis of the pine forest sublot, in order to preliminarily remove the interference objects such as houses, low vegetation, roads and bare land. Finally, process the LiDAR point cloud data by denoising, ground point recognition and vegetation classification. The vegetation higher than 2.0 m was segmented into single tree crowns, and the position (X, Y, Z), height, crown diameter, crown area and other information

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were extracted from it, so as to further refine the removal of interference ground objects on the basis of preliminary interference removal. Next, correct and stitch UAV photos to generate high resolution orthoimage. Aiming at the above interference removal areas, HSV color transformation is carried out on the orthoimage. The RGB color model and HSV color model were used to identify the red-attack pine trees, respectively. Then, compared the ex-traction effects of the two color models, and verified the recognition accuracy of the red-attack pine trees by visual interpretation and field investigation. 3.2

Research Data

According to the research method, we obtained high resolution orthoimage, high density LiDAR point cloud, multispectral satellite image, thematic data and verification data. See Table 1 for details of actual acquisition of multi-source data. Among them, in order to prevent the red-attack pine tree from being filtered out in advance due to the calculation of vegetation index, the time phase of the multispectral satellite image was roughly before the pine forest was infected. According to the relevant data of the forestry department and field investigation, most of the red-attack pine trees in the study area are old dead pine trees, and the dead time is about 1 year. Therefore, this research selects the WorldView-2 satel-lite image in February 2018.

4

Experimental Process and Analysis

4.1

Interference Region Removal

By calculating the normalized vegetation index of WorldView-2 satellite images, the results are shown in Fig. 1 (left). On this basis, the refined high vegetation canopy extracted by laser point cloud is shown in Fig. 1 (right), Table 1. Specifications of the key equipment Data type

Time phase

Explanation

High resolution orthophoto

January 2021

Overlap: heading 85%, lateral 65%, GSD 0.04 m

LiDAR point cloud

January 2021

Point cloud density approximately 150 p/m2

Multispectral satellite image February 2018 WorldView-2, GSD 0.5 m Thematic data

June 2018

Forest resource survey data, contains sublot of pine trees

Verification data

January 2021

34 ground survey samples, include 20 red-attack pine trees samples and 14 other samples

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Fig. 1. Vegetation index calculation (left) and high vegetation canopy extraction results (right)

4.2

Construction of Threshold Equation for Red-Attack Pine Tree Recognition

The samples were divided into six types: red-attack pine needles without falling off, red-attack pine needles have fallen off, bare soil and roads, dead grassland, shadows, and other ground objects (healthy pine trees, other green vegetation, etc.). Two-dimensional scatter plot was used to analyze the characteristics of redattack pine trees, other trees and a small amount of interference ground objects in HSV and RGB color space, and constructed the threshold linear equation of red-attack pine trees recognition. As shown in Fig. 2, the red-attack pine trees have obvious aggregation in H-V color space. And there is no significant difference between the red-attack pine needles without falling off and the red-attack pine needles have fallen off, which is easy to distinguish from other ground objects. However, different from the existing research results [15], the gathering area of red-attack pine trees in this study includes two partitions, which are composed of straight lines 1 2 3 4 (the first partition) and 5 6 7 (the second partition), respectively. The first partition: H 0.5, and the second partition H >= 330, V > 0.45. In the H-V col-or spatial scatter plot, the red-attack pine trees in regions a and d can be identified with high accuracy by HSV threshold method, while the red-attack pine trees in region b are difficult to distinguish from bare land and roads, the red-attack pine trees and dead grassland are difficult to distinguish in region c, and the red-attack pine trees in region e are difficult to distinguish from shadow and dead grassland. The accuracy of red-attack pine trees recognition in these regions can be improved by the method in this paper. The characteristics of red-attack pine trees in R-G color spatial scatter diagram are basically the same as the existing research results, it’s also easy to distinguish from other ground objects, but the correlation between R and G is strong, it does not show aggre-

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gation characteristics, and shows a banded distribution. The threshold linear equation based on HSV and RGB color model is shown in Table 2.

Fig. 2. H-V(left)and R-G(right) color space distribution map.

Table 2. Threshold equation of red-attack pine tree recognition based on HSV and RGB HSV

1 V=330 6 V= –0.002H+1.2

RGB

1 802. IV. The cross-chain node B calls the distributed private key control system in the execution contract in the cross-chain mechanism to lock the transaction data. Finally, the cross-chain mechanism collects the private key of the account D and sends it to the cross-chain node A, which unlocks and verifies the received data and confirms the completion of the transaction to the cross-chain node B. The cross-chain node B responds and requests the completion of the transaction from the cross-chain mechanism. Update status 2—>3. V. If it is a query transaction, the chain operation is not required; if it is other, the local smart contract needs to be called for the chain operation. Transaction complete. VI. The blockchain network platform updates and publishes some epidemic data on the sharing platform through the corresponding service interface.

3 Conclusion Nowadays, because of its decentralization, distributed storage, traceability and other characteristics, blockchain technology has produced a “blockchain + industry” situation. It will also usher in a situation where multiple blockchains coexist; cross-chain technology will also become a major trend in the development of blockchain. This article analyzes the shortcomings of the current epidemic prevention and control data sharing and the existing case of blockchain technology in the field of epidemic prevention and control, combined with the needs of epidemic prevention and control data sharing, and introduces cross-chain technology. A model with high execution efficiency, strong data sharing ability, scalability, and high security is proposed, which provides a new direction for epidemic data sharing and other big data sharing fields. Acknowledgement. This research was supported by the National Natural Science Foundation of China: Research on the Dynamic Assessment Model of Navigation Risk in Complex Waters Based on Random Sets (51679116).

References 1. Maohan, H.: Research on the model of epidemic prevention and control intelligence system based on blockchain technology. Information Sci. 1–8 (2021). http://kns.cnki.net/kcms/det ail/22.1264.G2.20200911.1534.030.html 2. Jianbiao, Z., Yanhui, L., Zhaoqian, Z.: Research on cross-chain technology architecture system based on blockchain. CSPS 2609–2617 (2019)

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3. Xiao, W., Shuyi, Z.: A data synchronization system based on blockchain cross-chain technology. Jiangsu Province: CN111797171A (2020) 4. Zhuoyan, X., Xuan, Z.: Overview of the development of cross-chain technology. Comput. Appl. Res. 38(02), 341–346 (2021) 5. Yi, D., et al. A blockchain multi-chain cross-chain system and its implementation mechanism. CN112287029A, Beijing (2021) 6. Dawei, L., Jia, Y., Xue, G., Najla, A.-N.: Research on multidomain authentication of IoT based on cross-chain technology. Secur. Commun. Networks 2020, 6679022:1-6679022:12 (2020) 7. Hang, L., Zhuoyi, Y.: Research on the application of data governance in the normalized prevention and control of the new crown epidemic. Decision Consulting (01), 43–46+51 (2021) 8. Wenming, S.: Research on the application of blockchain technology in the field of epidemic prevention and control. Res. Development Strategy Sci. Technol. Innovation 4(3), 39–44 (2020) 9. Yao, Z., Jinghong, T., Wei, S.: Internet of Things data sharing method and system based on cross-chain technology. CN110941850A, Jiangsu Province (2020) 10. Yihua, L., Kang, C.: New progress in blockchain consensus mechanism. Comput. Appl. Res. 37(S2), 6–11 (2020) 11. Xingtang, X.: Design and implementation of IoT data sharing system based on cross-chain technology. Beijing University of Posts and Telecommunications (2020)

An Efficient Precoding Design for Massive MIMO Systems Under Channel Impairments Huayu Wang, Jing Lv, Yuxing Zhang, Qin Ren, and Yinghui Zhang(B) College of Electronic Information Engineering, Inner Mongolia University, Hohhot, China [email protected] Abstract. Massive MIMO is widely adopted to improve system capacity in the 5G wireless communication system. However, the channel capacity and the performance of the precoding in massive MIMO systems heavily depend on the channel state information. We investigate the impact of channel estimation error, which has become one of the bottlenecks that restrict system performance. Using the results of this investigation, precoding based on channel estimation error prediction (PCEEP) is proposed. We employ batch descent with restarted annealing that improves the performance. Furthermore, two modes of 1-bit and unlimited digitalto-analog converters (DAC) are also established in this paper. Simulation results show that the proposed algorithm can significantly improve the performance of BER even in the mode of the 1-bit DAC, especially when the signal to interference plus noise ratio (SINR) is low.

Keywords: Massive MIMO Channel estimation error

1

· Gradient descent · ZF precoding ·

Introduction

With the explosion of data rate requirements for the Internet of Things and mobile devices, massive multiple input multiple output (MIMO) technology is widely regarded as the key technology for higher data rates [1]. Due to the increase in the number of antennas, the precoding can also significantly increase the capacity and spectral efficiency of the massive MIMO system. Moreover, because the interference between data streams or users can be eliminated by channel state information (CSI) at the transmitter, complex correlation processing is not necessary at the receiver, precoding can also reduce the complexity of This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 61761033 and Grant 62071257, supported by the Natural Science Foundation of Inner Mongolia Autonomous Region of China under Grant 2019MS06033, and supported by the High Education Research Project of Inner Mongolia Autonomous Region of China under Grant NJYT-20-A11. c The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022  Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 431–438, 2022. https://doi.org/10.1007/978-981-19-0390-8_53

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the receiver. Therefore, precoding has attracted many interesting in the field of wireless communication. The design of the optimal precoder usually requires accurate CSI at the transmitter [2]. Adopting conventional multi-user MIMO precoding to achieve spatial multiplexing gain requires immediate CSI at transmitters [3]. In [4], a massive MIMO system relies on CSI feedback to perform precoding and achieve performance gain in frequency division duplexing networks. Although the Massive MIMO system has the advantages of network coverage and cell throughput, the significant increase of radio frequency (RF) chain at the BS will lead to the significant increase of hardware complexity, circuit power consumption, and system cost. Massive MIMO systems require lower cost and more efficient algorithms and hardware components [5]. Therefore, this paper proposes precoding based on the channel estimation error prediction (PCEEP) algorithm, which optimizes the precoding vector by reducing the mean square error (MSE) between the matrix of the transmitter and the receiver, thereby reducing the bit error rate (BER) of the user and improve system performance. Although only the zero forcing (ZF) precoding algorithm is studied in this paper, the CEEP algorithm can be applied to any precoding.

2 2.1

System Model and Linear Quantized Precoding System Model

We consider the massive MIMO network, in which M antennas are equipped and K single antenna users. It is assumed that all RF hardware is ideally working, the analog-to-digital converter (ADC) at the user end is unlimited resolution, and the DAC sampling rate of the base station (BS) is equal to the ADC sampling rate of the user. The BS sends QPSK signals with a mean value of zero and independent of the downlink transmission power. The transmission data can be expressed as a M × 1 vector pk X, where pk ≥ 0 is the downlink transmission power of the BS to user k, X is the orthogonal component of the same ADC at the BS. The channel is composed of large-scale and small-scale Rayleigh fading. The channel matrix H between the M antenna and K users is a M × K vector, H ∼ CN (0, 1). Then, the received signal can be expressed as Y = P d H T · W X + N0 ,

(1)

where Y = [y1 , y2 , ..., yk ]H , yk is the target received signal of user k. Pd is the downlink transmission power, H T is the transposition of the uplink channel matrix H detected by the BS, W is the precoding vector, and N0 is the additive noise matrix subject to an independent random distribution CN (0, 1). 2.2

Linear Quantized Precoder

Adopting a DAC with the unlimited resolution, the BS can obtain the precoding vector through the precoding matrix W and the signal vector X, then the

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quantization of linear precoding can be expressed as r = Qu (W · X). In the 1-bit DAC scheme, the quantization function Qu (·) is defined as  P Qu (a) = 2M (sgn( {a}) + jsgn( {a})), (2) where a is the parameter to be quantified, { · } and { · } represent the real part and imaginary part. The quantized downlink transmission signal from the BS to the user is expressed as Y = Pd · H T · Qu (W X) + N0 .

(3)

Bussgang’s theorem [6] is exploited to perform a 1-bit quantization on the precoding vector and analyzes the effect of the quantization on the signal. Bussgang’s theorem can be used to obtain xQ = Qu (W X) = F · W X + q = F · x + q,

(4)

where the quantization distortion q and x are to each other, and  not related   2P H −1/2 [7]. The the diagonal matrix F is expressed as F = πM · diag W W second stage of processing of the signal vector is to obtain the quantized linear representation by Bussgang’s theorem, which can be written as RxQ x = E(xQ xH ) = E({F · x + q}xH ) = F Rxx ,

(5)

where Rxx =σ 2 W W H , The matrix F and Rxx cannot be directly operated [8]. Since x is a Gaussian distribution and W is a column full rank, it can be obtained  [9] as F W =

3 3.1

1 σ

2 H −1/2 W. π {diag(W W )}

Proposed Channel Estimation Error Prediction Algorithm Channel Estimation Error Prediction (EEP)

The channel matrix Hik = [hk1 , hk2 , ..., hkM ]H between BS i and the user k in the cell where the BS is located can be expressed as Hik = Rik Xik , where Xik = [xi1k , xi2k , ..., ximk ]H , ximk ∼ CN (0M , IM ). Rik is the channel fading matrix between BS i and user k given by Rik = Rmin d−α ik , and Rmin > 0 is the channel attenuation at dmin = 35m. ¯ is defined as When there is an error in the CSI, the feedback channel H ¯ = H + ξ H. ˜ H

(6)

¯ 1, h ¯ 2 , ..., h ¯ s ]H , where h ¯ s is the ¯ = [h BS can obtain channel information H ˜ s−th CSI previously obtained by the BS, and H is the CSI error matrix, ξ is the different weight of CSI error, which determines the size of CSI error. Then the estimated error prediction factor JRK obtained by   1 ¯H ¯T . H (7) JRK = M ×K

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In the actual transmission environment, the movement of the receiver will also cause bit errors in the transceiver signal with the doppler frequency is fdop = Vc·f . According to the Bessel function, the prediction factor Jrk can be expressed as Jrk =

∞  ¯z v  2

k −¯ z2 4 k!γ(v+k+1) , 

(8)

k=0

where v is a real constant, called Bessel constant, and γ(·) is the gamma function. f ¯z = 2π fdop , where fs is the QPSK transmission symbol length. According to s the prediction factors JRK and Jrk , the predicted channel HP for the next transmission by the BS can be obtained as ¯ HP = Jrk T · JRK −1 · H.

(9)

  ¯H ¯ T + HP −1 . where ¯T H Therefore, the prediction precoding matrix Wd = β · H  T β is the power control parameter, β = tr (H¯ H¯ TP+H −1 . ( ) P) The BS performs precoding and transmission according to the prediction channel, the minimum mean square error ε between the ideal reception matrix and the prediction transmission matrix can be written as     (10) ε = E  W − Wd 2 = E  β −1 (H · W S + N0 ) − S2 . When the prediction channel is known, the ideal precoding matrix between the ¯H ¯ T )−1 . BS and the user can be expressed as Wr = β · (H In this paper, the BGDRA algorithm is proposed to reduce the MSE between the prediction precoding matrix Wd and the ideal precoding matrix Wr , expressed as  T (11) ε = tra (Wd − V1 · Wr · V2 )(Wd − V1 · Wr · V2 ) ,  where V1 = 3.2

T

diag(Wd Wd ), V2 =

 −1 diag (|Wr 2 |T |Wr 2 |) |Wr 2 |T .

Batch Gradient Descent (BGD)

Due to the limited number of samples and good performance of the parallel operation in the massive MIMO system, batch gradient descent (BGD) algorithm is selected in this paper, in which all samples are used to update each parameter. The iteration formula of the BGD algorithm is defined as θk+1 = θk − ηb · ∇θ J(θ),

(12)

where ηb is the learning rate. In this paper, the hot restart method is adopted to replace the learning rate annealing mechanism to improve the BGD algorithm. Adjusting the learning rate ηr through the hot start scheme can significantly shorten the number of

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cycles. The time required is 2–4 times less than the learning rate annealing mechanism, which effectively reduces the computational complexity and can achieve comparable or better performance [10]. ηr = ηi1 +

1 2

(ηi1 + ηik ) (1 + cos (μπ)) ,

(13)

where ηi1 and ηik are the change interval of the learning rate during the i−th training, and μ is the frequency of restarting the learning rate. According to the principles of thermodynamics, when the temperature is T, the probability of a temperature drop with an energy difference of dE is P(dE ). When dE = εi+1 − εi > 0, a better solution is obtained after moving, then the move is accepted with probability 1 and the next iteration is entered. When dE = εi+1 − εi < 0, the temperature rises, indicating that the MSE increases after the movement. dE At this time, judge whether to move according to the probability P(dE ) = e rT , where T is the current temperature, that is, the number of cycles. r is a constant, which determines the cooling rate. The larger the value, the slower the cooling. Then, corresponding to (12), the improved BGD algorithm can be expressed as θk+1 = θk − ηr · ∇θ J(θ) + ΔP (dE ),

(14)

where ΔP (dE ) is the judgment of the moving distance difference dE in annealing. 3.3

ZF Precoding Based on Channel Estimation Error Prediction (ZF-PCEEP)

The precoding vector between BS j and user k can be written as ¯ k + HP )−1 Hk , ¯ kH H Wd = (H

(15)

H

where Hk = [hk,1 , hk,2 , ..., hk,M ] denotes the CSI between BS j and user k, ¯ k is the estimated channel matrix between BS j and user k, and HP is the H predicted channel deviation obtained by error estimation using EEP algorithm. After the prediction matrix is generated, the basic optimization model of the ˆ is established as precoding target optimization matrix W ˆ k − η · ∇θ J(W ˆ k ) + ΔP (dE ), ˆ k+1 = W (16) W where Wk+1 is the k+1 − th optimization result of BGDRA algorithm, the starting matrix W1 is set to Wd , the number of cycles is determined by k value, and ∇θ J( · ) function is the direction of gradient descent. In the cycle, the MSE εk+1 ˆ k+1 and the ideal precoding matrix between the k+1 − th optimization result W Wr is calculated, and the annealing is judged according to the energy difference dE between εk+1 and εk . After that, the BGDRA algorithm is used to reduce the MSE between the prediction precoding matrix Wd and the ideal precoding ˜ is finally obtained. matrix Wr , and the precoding target optimization matrix W If the BS uses a low bit DAC, the precoding matrix needs to be quantized by ˜ · X). Then the receiver signal Yjk of user k in cell j can be expressed r = Q(W as K  ˜ jk · X) + ˜ js · X) + N0 . Yjk = pjk Hjk Q(W pjk Hjk Q(W (17) s=1,s=k

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Fig. 1. Comparison of the BER performance between ZF precoding using CEEP algorithm and traditional ZF precoding.

3.4

Analysis of System

In the massive MIMO system, the decoding can be obtained as s¯r = ϑY = sr + ϑ (Ne + N0 ) ,

(18)

where Ne is the system error. Then, the output of the user can be expressed as ˆe + N0 ), where the distribution of N ˆe is the same as that of the sˆr = sr + ϑ(N ˆe obeys CN (0, E[|Ne |2 ]). The SINR γk true error Ne and can be expressed as N of user k can be obtained as  2  ˆ k xk ) pk HkT Qu (W , (19) γk =  2   K T Q (W ˆ H x ) + N p  i k u k i 0 i=1,i=k 2  K  ˆ k xi ) is the interference between multiple users. where i=1,i=k pi HkT Qu (W The BER of user k can be expressed as (Q (rk = xk )) Pe = Pr

ˆ k xk )|2 . |pk HkT Qu (W ∼ = 2Q K T ˆ k xi )|2 +N0 W p H Q | ( i u i=1,i=k k

4

(20)

Simulation

The channel estimation error factor ξ = 0.5, the learning rate is 0.04, and the maximum number of cycles is 30. Figure 1 shows the BER performance comparison between the ZF-PCEEP algorithm and traditional ZF precoding under different DAC conditions in a single cell. It can be seen from the simulation that the BER is very high when the traditional ZF precoding and 1-bit DAC is adopted. Meanwhile, the BER

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Fig. 2. The BER change of ZF precoding based on CEEP algorithm under unlimited DAC.

Fig. 3. The BER change of ZF precoding based on CEEP algorithm under 1-bit DAC.

of the user can be reduced by an order of magnitude on average with the ZFPCEEP algorithm is used, which can effectively improve the performance no matter in any SINR. The estimation error has a relatively small impact on the performance of the system and users when the BS uses an ideal unlimited DAC. However, when SINR is low, ZF-PCEEP algorithm can effectively reduce the BER. Figure 2 shows the performance of BER when unlimited DAC is used in BS with different error factors. The size of estimation error is various in the different communication system. The ZF-PCEEP algorithm under different estimation error is analyzed in Fig. 2. We can see that the proposed algorithm can significantly reduce the BER of the client, and it can still effectively limit the BER when the error factor is large. When SINR is greater than 0, multiple curves are almost overlapped. The reason is the performance of traditional ZF precoding is better when SINR is larger, and the loop will end when the BER reaches the optimization threshold of 10−4 . In Fig. 3, we verify the BER with SINR under different error factors adopting 1-bit DAC. Compared with the unlimited DAC mode in Fig. 2, the BER of

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the ZF-PCEEP algorithm under 1-bit DAC is relatively higher. However, the decrease is far less than the performance improvement. It shows that the ZFPCEEP algorithm can effectively improve the system performance with 1-bit DAC. Therefore, the ZF-PCEEP algorithm can achieve good performance even in low-power systems.

5

Conclusion

In this paper, we have investigated a high-performance precoding algorithm based on BGDRA and EEP for the massive MIMO system. We first estimate the error and generate the prediction channel with EEP, and add the prediction matrix to the channel matrix for precoding. Then BGDRA is used to reduce the MSE between the receiver and the transmitter in order to reduce the impact of estimation error and BER of the massive MIMO system. In particular, we compare 1-bit DAC with unlimited DAC. Finally, the simulation results illustrate that the proposed algorithm can effectively reduce the BER at low SINR under the ideal condition of unlimited DAC and the low bit DAC.

References 1. Hoydis, J., ten Brink, S., Debbah, M.: Massive MIMO in the UL/DL of cellular networks: how many antennas do we need? IEEE J. Sel. Areas Commun. 31(2), 160–171 (2013) 2. Mai, R., Le-Ngoc, T.: Nonlinear hybrid precoding for coordinated multi-cell massive MIMO systems. IEEE Trans. Veh. Technol. 68(3), 2459–2471 (2019) 3. Liu, A., Lau, V.K.N.: Two-stage subspace constrained precoding in massive MIMO cellular systems. IEEE Trans. Wirel. Commun. 14(6), 3271–3279 (2015) 4. Wang, T., Wen, C., Jin, S., Li, G.Y.: Deep learning-based CSI feedback approach for time-varying massive MIMO channels. IEEE Wireless Commun. Lett. 8(2), 416–419 (2019) 5. Jacobsson, S., Durisi, G., Coldrey, M., Goldstein, T., Studer, C.: Quantized precoding for massive MU-MIMO. IEEE Trans. Commun. 65(11), 4670–4684 (2017) 6. Moll´en, C., Choi, J., Larsson, E.G., Heath, R.W.: Uplink performance of wideband massive MIMO with one-bit ADCs. IEEE Trans. Wirel. Commun. 16(1), 87–100 (2017) 7. Vishwanath, S., Jindal, N., Goldsmith, A.: Duality, achievable rates, and sum-rate capacity of Gaussian MIMO broadcast channels. IEEE Trans. Inf. Theory 49(10), 2658–2668 (2003) 8. Wendler, F., Stein, M., Mezghani, A., Nossek, J.A.: Quantization-loss reduction for 1-bit BOC positioning. In: Proceedings of the ION International Technical Meeting, pp. 509-518, January 2013 9. Saxena, A.K., Fijalkow, I., Swindlehurst, A.L.: Analysis of one-bit quantized precoding for the multiuser massive MIMO downlink. IEEE Trans. Signal Process. 65(17), 4624–4634 (2017) 10. Loshchilov, I., Hutter, F.: SGDR: stochastic gradient descent with warm restarts. arXiv preprint arXiv: 1608.03983 (2016)

Radar Adaptive Interference Suppression Algorithm Based on MVDR Yumeng Zhang(B) , Jinliang Dong, Gan Wang, Lei Bian, and Huifang Dong Nanjing Glarun Defense System Co., Nanjing, China [email protected]

Abstract. Digital beamforming technology is an important part of modern radar. The phased array antenna can realize adaptive beam and sidelobe interference suppression through it, and realize beam scanning by controlling the phase shifter. This article introduces a variety of digital beamforming algorithms and improves them based on typical algorithms, and proposes a multi-beamforming method based on the MVDR (Minimum Variance Distortionless Response) criterion. The MVDR algorithm does not require iteration, and only needs to set multiple constraints, which can directly calculate the array weight and improve the radar’s adaptive anti-jamming ability. Keywords: Digital beamforming · Multi-beamforming · Minimum variance distortionless response

1 Introduction Phased array radars face changes in mission requirements at any time. Radars need to have the ability to quickly change the beam direction, complete beam reconstruction, and cover the mission airspace. For radar systems, radar systems often face various interferences from the ground or the air, and the system must have excellent anti-interference capabilities [1]. The interference is strong or weak according to the signal strength, and the interference frequency band is wide or narrow. Technologies such as adaptive frequency agility and residual clutter graphs can minimize the impact of most interferences. However, for high-power strong interference, generating nulls in the direction of the interference source is the optimal response method [2]. The adaptive beamforming technology based on smart antennas can well achieve spatial filtering and then interference suppression. Smart antenna refers to the antenna array that can adjust the weight of the antenna array according to the signal environment, generate the main beam in the desired signal direction to enhance the desired signal and generate nulls in the interference direction [3].

2 Typical Multi-beamforming Algorithm Based on Smart Array Antenna The digital adaptive beamforming technology can generate the desired pattern according to the signal information received by the antenna array and according to certain © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 439–446, 2022. https://doi.org/10.1007/978-981-19-0390-8_54

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beamforming optimal criteria. The information of the signal can be the arrival angle of the signal, the statistical characteristics of the signal, the specific form of the desired signal, and so on. The optimal beamforming criterion is the precondition of all adaptive algorithms, and each criterion is intended to optimize a certain performance of the array. Although they have different criterion descriptions and expressions, they are equivalent to each other under certain conditions. Which optimal criterion is selected also needs to be determined according to known conditions and requirements for array performance. 2.1 Multi-beamforming Based on LMS Algorithm The LMS(Least Mean Square) algorithm is an algorithm based on the MSE(Mean Squared Error) criteria described in Table 1. The algorithm needs to know the specific form of the desired signal. This algorithm was first proposed by Widrow, and Miller and Monzingo gave a good explanation. The algorithm constructs a performance surface with the square error between the actual output of the array and the expected signal, and the process of the algorithm to find the optimal weight is actually the process of finding the array weight corresponding to the lowest point of the performance surface. The principle of the algorithm is analyzed below [4]. When the signal is sampled at a specific moment, the error between the actual output of the array and the expected signal is: ε(k) = d (k) − wH (k)x(k)

(1)

The squared error can be obtained by squaring: |ε(k)|2 = |d (k) − wH (k)x(k)|2

(2)

Expand the right side of the above formula to get the cost function (that is, the performance surface function): J (w) = D − 2wH r + wH Rxx w

(3)

The LMS algorithm reaches the lowest point of the performance surface by finding the gradient of the cost function in the formula and iterating in the opposite direction of the gradient. The iterative formula of the algorithm is given by the following formula: 1 wN (k + 1) = wN (k) − μ∇J (wN (k)) 2

(4)

The LMS algorithm is valued for its simple principle and less calculation, and it occupies an important position in the field of self-adaptation. The final weight vector update formula is as follows: eN (k) = dN (k) − wNH (k)xN (k)

(5)

wN (k + 1) = wN (k) + μ xN (k)eN∗ (k)

(6)

0 < μ < Trace(R)

(7)

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Among them, the parameter μ is the step size factor that controls the convergence rate and stability, and the selection of μ has a great influence on the convergence performance of the algorithm. If the step size is too small, the algorithm will be overdamped. If the step size is too large, the algorithm will not converge and the optimal value will not be obtained. Therefore, the selection of parameters μ is very important [5]. The LMS algorithm has low complexity and small amount of calculation, and only 4 multiplications and 6 addition operations are required for each iteration. The disadvantage of this algorithm is that it is difficult to choose an appropriate step size to weigh the convergence speed and steady-state error performance, and the iteration speed is affected by the eigenvalue distribution. The algorithm implementation steps are as follows: (1) (2) (3) (4)

Initialize the weight value, set the array weight vector w(k); Signal sampling, sampling the element signal x(t), expected signal, denoted as d (t); Error estimation at sampling time: e(n) = d (n) − y(n) = d (n) − wˆ H x(n); Weight vector update: w(n ˆ + 1) = w(n) ˆ + μx(n)e∗ (n).

Based on the above theoretical analysis, single-beam and multi-beam simulation of the LMS algorithm. Compared with single beamforming, multi-beamforming takes into account the beam signals of other users as interference when considering one of the beams. In the pseudo-inverse multi-beam forming, we mentioned that the direction of each beam is controlled by a column of weights in the weighting matrix. Assuming that a total of p beams are generated, for controlling the weight of the i-th beam forming, it is hoped that while ensuring that the i-th desired signal direction generates the main lobe, it generates nulls in the directions of the other p-1 desired signals. Sometimes:  j=i s i,j = 1,2 · · · p (8) WjH ai (θ ,ϕ)si = i 0 j = i

2.2 Multi-beamforming Based on RLS Algorithm As mentioned in the previous section, the LMS algorithm has several disadvantages. The algorithm needs to go through many iterations before reaching convergence. This is because the LMS algorithm estimates the correlation matrix and through the signal snapshot data at a time. If the signal parameters change rapidly, the LMS algorithm cannot achieve satisfactory convergence. An effective way to deal with it is to use sampled data at multiple times and use recursive estimation and. The recursive least squares (RLS) algorithm adopts this form, and the RLS algorithm is also proposed based on the MSE criterion[6]. It can be seen from Table 1 that the optimal solution for the weight of the MSE criterion is: wopt = R−1 xx r

(9)

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Because the source parameters change slowly with time, early data should be given a smaller weight, and the most recent sampled data should be given a higher weight. Based on this idea, re-estimate Rxx and r: Rxx(n) =

n 

α n−i x(n)xH (n)

(10)

α n−i d ∗ (i)x(i)

(11)

i=1

r(n) =

n  i=1

In the above formula, α represents the forgetting factor. The size of the value α represents the degree of emphasis on the early data, and the value range is 0 ≤ α ≤ 1. When α = 1 means unlimited memory [7]. Decomposition can get: Rxx(n) = αRxx(n − 1) + x(n)xH (n), r(n) = αr(n − 1) + d ∗ (n)x(n)

(12)

Define the gain vector as: g(n) = R−1 xx (n)x(n)

(13)

Update the weight vector by the recursive relationship: w(n) = w(n − 1) + g(n)[d ∗ (n) − xH (n)wH (n − 1)]

(14)

2.3 Performance Comparison of Typical Algorithms Consider a linear array with N = 37, the element spacing is half a wavelength, two desired directions are set to 0° and 30°, the interference signal direction is 40°, and the signal-to-interference ratio S/I = 8 dB, RLS Algorithm forgetting factor is 0.95, 500 iterations.

Fig. 1. Comparison chart of direction map formed by typical algorithms

Fig. 2. Error vs. iteration number graph

As can be seen from Fig. 1 and Fig. 2. From the perspective of convergence speed, the RLS algorithm is higher than the LMS algorithm. Under the same simulation conditions,

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the LMS algorithm needs to iterate 150 times to converge, while the RLS algorithm only needs about 10 times. The reason is that the RLS algorithm passes through multiple rectangular windows. Sampling data is estimated recursively, and then the optimal value at each moment is selected during iteration. If the RLS algorithm is applied to an array with the number of elements N, the amount of multiplication calculation required 4N 2 + 7N for each iteration, although the convergence speed is faster, it also brings about the problem of higher computational complexity. From the final generated pattern, the RLS algorithm can better deal with interference, generate a deeper null in the corresponding direction, and the width of the main lobe is narrow, but its main and side lobe suppression ratio is not as good as the LMS algorithm. How to choose the compromise between convergence speed and computational complexity needs to be determined according to specific circumstances.

3 Extended Multi-beamforming Based on MVDR Criterion The above several kinds of multi-beamforming technologies based on adaptive algorithms need to set initial weights, and the weights are continuously updated and iterated through training signals until convergence [8]. If the radar system requires the beam direction to be changed quickly, or the signal source parameters change quickly, the algorithm may not be able to accurately form the beam in the coverage area. This section studies a multi-beam forming method based on the MVDR (Minimum Variance Without Distortion Response) criterion. This kind of algorithm does not need iteration, only needs to set multiple constraints, and the array weight can be directly calculated. The algorithm follows the minimum variance distortion-free response (MVDR) criterion. The MVDR criterion is described as “minimize the output noise variance”, which means that the total power is minimized while ensuring lossless transmission of signals in the desired direction. When the desired direction signal has a unity gain, the total power is the smallest, which means that the output power of interference and noise is the smallest. Based on the above considerations, first consider the formation of a single beam [9], and the constraints at this time can be expressed as: min wH Rx w

subject

to

wH a(θ0 ) = 1

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The Lagrangian multiplier method can solve the problem of solving equations under constraint conditions. Using this method to solve the above equations is as follows: J (w, λ) =

1 H w Rx w + λ(wH a(θ0 ) − 1) 2

(16)

Perform a gradient solution operation on the variable w in the above formula and make it zero: ∇w J = Rx w + λa(θ0 ) = 0

(17)

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Solving the optimal weight vector w at this time can be expressed as: wMVDR = −R−1 x a(θ )λ

(18)

The optimal weight vector in this algorithm can be obtained as: wMVDR =

R−1 x a(θ )

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a(θ )H R−1 x a(θ )

Considering the nature of single beamforming, it can be found that under the action of the objective function, it will gain the desired direction while suppressing the noise to form a null. When applied to a multi-beam forming network, it is necessary to meet the unity gain of multiple desired signals while suppressing interference[10]. At this time, the corresponding criterion of minimum variance without distortion can be expressed as: min wH (R + γ I )−1 w i =1···p subject to wH α(θi ) = 1 H subject to w α(θj ) = 0 j = p

(20)

According to the Lagrange multiplier method, there are: J (w, λ) =

j≥p p   1 H w Rx w + λ( wH a(θi ) − 1) + β( wH a(θj )) 2 i=1

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j

After deriving and substituting the constraints, the multi-beam weights can be expressed as: w = (Rˆ + γ I )C(C H (Rˆ + γ I )−1 C)−1 f

(22)

We name C as the steering constraint matrix formed by the steering vector in the desired direction and the interference direction, and f as the gain vector.

4 Simulation and Performance Analysis Consider a linear array with N = 37, the element spacing is half a wavelength, two desired directions are set to 0° and 30°, the interference signal direction is 40°, and the signal-to-interference ratio S/I = 8 dB. As can be seen from Fig. 3 and Fig. 4. It can be known from the simulation results that the main lobe can be generated in the desired direction with this method, and the null can be generated in the interference direction at 40°. This method does not need iteration, and does not need to find the correlation matrix of interference and desired signal, but it needs the inversion of the correlation matrix. Since there are multiple constraints at the same time, the robustness of the algorithm is increased. This algorithm generates a main beam in the desired direction, generates a null in the interference direction, and then forms a beam for coverage. It can be seen from the normalized pattern that the sidelobe level is very low relative to the main lobe, and the main and sidelobe suppression is better.

Radar Adaptive Interference Suppression Algorithm Based on MVDR

Fig. 3. Polarization pattern based on MVDR criterion

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Fig. 4. Directional map based on MVDR criteria

5 Conclusion This paper mainly focuses on the research of radar system interference suppression methods. First, the classic LMS and RLS algorithms are studied. Both of these algorithms use continuous iteration to obtain the optimal solution under the MSE criterion. After that, a multi-beamforming algorithm based on MVDR criterion without iteration is studied. The algorithm only needs to clarify the beam direction to perform beamforming in the desired direction. Simulation analysis shows that, compared with LMS and RLS algorithms, MVDR-based beamforming algorithms can achieve great improvements in convergence speed and final residual residuals. The characteristics of low complexity and fast convergence make it very suitable for high-precision phased array radar with real-time beam pointing adjustment, but it also needs to improve hardware performance to meet the needs of algorithm inversion.

References 1. Shang, H.: The analysis of interference suppression capability of mvdr algorithm based on microphone array. In: 2019 IEEE International Conference on Signal, Information and Data Processing (ICSIDP). IEEE (2019) 2. Geng, Z., Motahari, S., Deng, H., Himed, B.: Ground moving target detection for airborne radar using clutter doppler compensation and digital beamforming Microw. Opt. Technol. Lett. 60(1), 101–110 (2018) 3. Mitchell, M.A., Howard, R.L., Tarran, C.: Adaptive digital beamforming (ADBF) architecture for wideband phased-array radars. In: Proceedings of SPIE - The International Society for Optical Engineering (1999) 4. Godara, L.C.: Improved LMS algorithm for adaptive beamforming. IEEE Trans. Antennas Propag. 38(10), 1631–1635 (1990) 5. Moghaddam, S.S., Akbari, F.: Improving LMS/NLMS-based beamforming using ShirvaniAkbari array. American J. Signal Process. 2(4), 70–75 (2012) 6. Lei, W., Lamare, R.: Constrained constant modulus RLS-based blind adaptive beamforming algorithm for smart antennas. In: International Symposium on Wireless Communication Systems. IEEE (2007) 7. Hirikude, S.M., Patil, S.S.: Design of iRLS algorithm with/without pre-filter for antenna beam forming technique. Wireless Per. Commun. 118(4), 2785–2805 (2021)

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8. PengCheng, M., Li, D., Yin, Q.Y., Guo, W.: Robust MVDR beamforming based on covariance matrix reconstruction. Sci. China Inf. Sci. 56(4), 1–12 (2013) 9. Chen, H.-W., Zhao, J.-W.: Wideband MVDR beamforming for acoustic vector sensor linear array. IEE Proc. Radar Sonar Navig. 151(3), 158 (2004) 10. Mu, P, Li, D., Yin, Q.: A robust MVDR beamforming based on covariance matrix reconstruction. Int. Conf. Graphic Image Process. 56(4) (2011)

Research on Overall Design of Mobile Phased Array Radar for Field Vehicle Jinliang Dong(B) , Yumeng Zhang, Gan Wang, Lei Bian, and Huifang Dong Nanjing Glarun Defense System Co., Nanjing, China [email protected]

Abstract. Combined with a field vehicle mobile phased array radar project, this paper first introduces the operational requirements of field vehicle air defense weapon system; Then, combined with the characteristics of field vehicle mobile radar, the overall design content and key technologies of the system are analyzed; Finally, the development of the follow-up equipment is prospected, which can provide a good reference and help for the design of field vehicle mobile phased array radar. Keywords: Field vehicle · Phased array radar · Moving work · Overall design · Target classification

1 Introduction In the 21st century, electronic products are developing rapidly from digital to intelligent. The upgrading of weapons and equipment is not only the need of combat, but also the inevitable result of the continuous development of its own technology. Especially in recent years, new equipment such as stealth fighters and modern warships at home and abroad are all integrated with the most advanced electronic equipment in various countries. With the development of the future battlefield towards the diversification of combat tasks, equipment stealth unmanned, electromagnetic environment complex direction, the traditional single combat mission of military equipment is inevitably ushered in a new change[1, 2]. In the future war, the role of air strike will only be greater. The combat effectiveness and even survivability of the army are inseparable from the field air defense capability. Field air defense is an important part of the whole air defense system. Its main task is to protect the combat forces and other important targets and equipment in the combat area from enemy air attack under field conditions, so that the ground forces can successfully complete the combat tasks. With the rapid development of weapon technology, in order to protect the safety of ground combat forces, the target of field vehicle borne air defense weapon system has changed from traditional aircraft to low-cost throwing weapons and low, small and slow targets. The equipment needs to have the ability to detect and identify various types of targets, high dynamic moving ability and anti saturation attack ability, At the same time, it can sense the change of external electromagnetic environment in real time, and has the combat capability in complex electromagnetic environment [3–5]. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 447–456, 2022. https://doi.org/10.1007/978-981-19-0390-8_55

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2 Overall Design Content As an important part of field air defense weapon system strike force, field vehicle mobile phased array radar realizes radar detection function on limited platform, and cooperates with weapon system to complete a variety of combat tasks. The overall design of radar needs to have the characteristics of small volume, light weight, low power consumption, high mobility, high reliability and so on. 2.1 Working Mode Design In order to detect fixed wing aircraft, cruise missile, guided bomb, tactical air to ground missile, UAV and helicopter, and to facilitate radar state detection and maintenance, radar has designed a variety of working modes [6]. 2.1.1 Conventional Mode In the conventional mode, radar can detect flying targets in the whole airspace. Simultaneous multi beam technology can be used to enhance airspace coverage, monopulse system can be used to improve angle measurement accuracy, multi pulse accumulation MTD processing system can be used to improve the ability to distinguish small targets in strong clutter, and adaptive resource scheduling and management technology can be used to schedule radar system resources in real time and dynamically, In order to solve the problem of blind velocity of target detection, pulse group stagger method is used. Through the above working mode design, the radar can detect different types and characteristics of targets. The operator can load the radar target in the conventional way according to the combat task, and adjust the radar target recording mode, detection and filtering parameters, range calibration and video display according to the combat environment. The operator can also record the interested target data. The target data and working parameters are stored in the local display and control terminal equipment, and the recorded data can be played back to facilitate data analysis afterwards. 2.1.2 Self Check Mode As a basic part of radar, radar built-in self-test is developed synchronously with radar subsystems. There are three working modes of radar bit: put, PBIT and Mbit. a) Power on self-test: when the radar is turned on, power on self-test is carried out. Through processor diagnosis, check whether the radar data related processor works normally, and carry out overall check, memory read/write check and input/output cycle check. b) Periodic self check: after the radar works normally, some important working parameters of the radar are monitored periodically without interrupting the radar. c) The operator starts the self-test: according to the operator’s request, the minimum replaceable units of the radar are self checked, and the self-test results are displayed on the terminal.

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2.1.3 Monitoring Mode When the radar is in the state of array monitoring, the measured circuit works normally, other circuit components are closed, and the excitation signal is coupled by the directional coupler and sent to the monitoring channel through the power divider for subsequent processing and storage. The multi-channel received results are compared to complete the monitoring of the transmission channel. 2.2 Design of Moving Work Moving work is an important operation mode of field vehicle borne mobile phased array radar. The radar needs to have the ability to detect the target stably and provide the target range, azimuth, elevation, speed and other parameters in real time. Once the car body is in motion, the offset of the car body platform relative to the geodetic coordinate system and the change of the car body position relative to the geodetic coordinate system will affect the detection performance and measurement parameters of the target [7]. 2.2.1 Impact Analysis In the moving mode, the change of vehicle attitude will affect the beam pointing, moving target detection and target tracking of radar system. The vehicle body navigation and attitude measurement device will provide the offset angle of the vehicle body azimuth plane relative to the north direction, the offset angle of the vehicle body elevation plane relative to the horizontal zero degree, and the position information of the vehicle body. The radar system needs to adjust the radar in real time according to these inputs to ensure the detection effect of the radar. 2.2.2 Compensation Processing During Traveling The premise of radar moving is that the car body is equipped with complete navigation and attitude system, which can provide comprehensive and reliable car body information for radar. For radar, in addition to the real-time completion of the deviation compensation to ensure the normal work, we need to make full use of the original hardware resources, Radar system Vehicle attitude and navigation system

Pitch offset angle Roll offset angle Speed of movement True north offset angle Car body position

Beam control subsystem

Beam compensation

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Speed compensation

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Tracking compensation

Fig. 1. Working principle block diagram of field vehicle borne radar during moving

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try not to add additional equipment, and control the cost. Figure 1 shows the processing block diagram of the mobile radar in the field. a) Beam compensation The pitch phase scan radar has a wave control subsystem specially for beam pointing control, and its working principle is shown in Fig. 2. The wave control subsystem receives the car body sway angle provided by the car body attitude system ψ roll offset angle θ and the antenna azimuth information β provided by the servo subsystem. The pitch offset of radar pitch beam is obtained by formula ψ1 = arctan(cos β tan ψ + sin β tan θ ). The offset value is used to compensate the required beam pointing angle in the geodetic coordinate system to obtain the beam pointing angle in the car body coordinate system, and then combined with the radar working frequency to calculate the transmitting and receiving phase shift, which is provided to the phase shifter. After the above processing, even when the car body is inclined, the beam pointing angle can be designed based on the geodetic coordinate system. It not only ensures the airspace coverage of radar, but also ensures the accuracy of elevation measurement.

Working frequency

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Position Beam pitch coordinate offset transform ation

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Fig. 2. Principle block diagram of beam compensation

b) Speed compensation One way of speed compensation can be realized by modifying the coherent reference signal at the frequency synthesizer, which requires a certain amount of hardware resources. Another way is that the radar signal processing subsystem realizes the function with software to save equipment. The principle is shown in Fig. 3. Doppler frequency shift calculation unit of signal processing subsystem receives vehicle speed v and target azimuth β And the working frequency information f0, the doppler frequency shift generated by the target azimuth is fd = (2v/c)f0 cos β, and the Doppler frequency shift values of targets in different azimuth are calculated through the formula S 1 (t) = S(t) exp{j2π fd t}. After the pulse compression of the echo signal, the phase compensation is done through the formula S2 (t) = S1 (t) exp{−j2π fd t}, that is, the ground clutter will be compensated back to the zero frequency position. MTD processing can effectively suppress clutter and complete moving target detection in clutter background.

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Pulse compression

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Fig. 3. Principle block diagram of speed compensation

c) Tracking compensation If the radar wants to track the target steadily and continuously in the moving state, it needs to compensate the range and azimuth, which is mainly completed in the data processing subsystem. Figure 4 shows the workflow of tracking compensation. The data processing subsystem receives the target echo provided by the signal processing subsystem. After preprocessing, the position information (including distance, azimuth, elevation and other information) relative to the vehicle platform is sent to the compensation processing module. Vehicle position information

Azimuth compensation

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Fig. 4. Principle block diagram of tracking compensation

2.3 Narrowband Radar Target Classification Design Field vehicle mobile phased array radar is characterized by small size, light weight, low power consumption, high integration and less equipment, so as to meet the requirements of high mobility and various weapon platforms. Therefore, restricted by the above conditions, field vehicle mobile phased array radar is generally narrow band radar,. Narrow band radar has narrow bandwidth and wide beam, and does not have high resolution in radial and lateral direction. Radar echo itself can not carry rich information about target shape and structure for target recognition. In addition, the target detected by narrow-band radar can basically be regarded as a “point” target model, and the size and shape of the target can not be obtained directly from the echo data. In air defense operations, on the basis of identifying the attributes of the target, it is very important to identify the type of air target.

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a) According to the distance, course and type of the target, the commander can more accurately determine the threat degree of the target and timely command the troops to change the combat level state; b) According to the type of target, the commander can use the means and strength of air combat more pertinently. Therefore, it is very important to study a method of feature extraction and classification of narrowband radar target echo, which can distinguish fixed wing aircraft, helicopter, UAV, cruise missile and air to ground missile in a short time [8]. 2.3.1 Target Classification Method Conventional radar target recognition methods are mainly divided into artificial recognition based on waveform time domain characteristics and automatic recognition based on echo information processing. According to the time-domain characteristics of the waveform, the basis of artificial target recognition is the wave crest, wave width (wave belly), wave tissue and echo change rate of the target echo waveform. Using the frequency domain information carried by the target echo sequence to identify the target automatically, the conventional radar under the current technical system is difficult to meet the basic requirements of long echo sequence, and the practicability of the related methods is seriously limited; In addition, these methods only extract a certain technical feature to identify the target from a certain side, especially the robustness of classification and decision algorithm under complex and changeable conditions, which is still a long way from the operational requirements. In the face of air defense operational requirements, from the current technical conditions, comprehensive utilization of radar detection efficiency, target echo information, target tactical characteristics, etc., using the man in the loop decision strategy, can more robustly solve the problem of target classification. 2.3.2 Target Classification Design The number of echo pulses of a single scan target is limited when the radar scans in uniform azimuth, which does not meet the basic conditions of extracting target type features from coherent accumulated echo information. The main basis for recognizing/distinguishing aircraft, cruise missile and guided bomb from the terminal of target indication radar is discovery distance, flight speed, altitude, route, waveform characteristics, etc. Radar automatic recognition of target type adopts the automatic recognition strategy of manual intervention, that is, automatic recognition provides decision-making reference, and manual intervention confirms the recognition result. The basic flow of target recognition by conventional radar is shown in Fig. 5.

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Manual topping to identify target batch

According to the characteristics of the target, the automatic recognition decision-making reference is given

Adjust and display the linear velocity distribution, height distance curve and typical echo sequence waveform of the target to be identified

According to the echo sequence waveform, altitude time curve, linear velocity distribution, route characteristics and manual intervention identification results

End

Fig. 5. Basic flow chart of target recognition by conventional radar

Among them, the target’s route characteristics can be directly viewed on the PPI screen; Echo sequence waveform, time altitude curve and linear velocity distribution need to be displayed in another window. 2.3.3 Operation Steps of Target Classification After the radar finds the target, establishes the tracking system and calculates the linear velocity of the target, the steps of target classification at the display and control terminal are as follows: Step 1: Select the target to be identified; Step 2: View the identification reference, as shown in Fig. 6; Step 3: Confirm the recognition result.

Fig. 6. Schematic diagram of auxiliary identification target window.

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2.4 Modeling Design When the radar is installed on the field vehicle platform, the modeling design needs to go through a unified design planning, which organically connects the radar with the vehicle, weakens the sense of independence of the radar part, and makes the overall modeling of the command vehicle more complete with a sense of wholeness. a) The scattered unit structure or parts are hidden to cover the exposed part of the structure and reduce the number of product details, so as to weaken the visual complexity; b) Similar modeling elements or trends are used between each unit to make them echo each other, enhance the sense of continuity in vision and achieve the sense of wholeness; c) In order to highlight the strong sense of quality of radar, concave convex treatment is made on the box cover, turntable and support, so that people can directly feel the heavy sense of quality of radar products visually; d) Shielding measures shall be taken for exposed cables; e) The shape of the box is consistent with the appearance of other equipment in the platform.

3 Key Technology Analysis 3.1 EMC Design Technology Field vehicle mobile phased array radar is a complex and huge system integrated by many specifications and a large number of electronic devices. At the same time, the radar system usually needs to work in coordination with communication, command and control, navigation, Beidou and other systems. The incompatibility problems such as electromagnetic self disturbance and mutual disturbance are serious. Practice has proved that if the EMC design is carried out at the initial stage of product development, 80%–90% of the EMC problems can be solved. Before the design is put into operation, if the problems are solved when the whole machine is in joint commissioning, it will not only cause great technical difficulties, but also cause great waste of manpower and financial resources, Therefore, EMC measures must be considered in the design. The earlier they are considered, the lower the cost of solving the problem. The main contents of EMC design include grounding, lapping, shielding and filtering [9, 10]. 3.2 Miniaturization, High Efficiency and Low Sidelobe Antenna Technology Field radar platform has strict restrictions on antenna array aperture and weight, which is in contradiction with the requirement of radar power for antenna gain, and puts forward higher requirements for antenna radiation efficiency and link loss. At the same time, due to the relatively harsh environment and high reliability requirements of field radar vehicle platform, the antenna form and integrated installation mode are also put forward stringent requirements. The radiation array of the antenna must have enough rigidity and strength, so that it will not be damaged and deformed in case

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of vehicle vibration, impact, rainstorm and storm; The loss of the radome should be as low as possible, the shape should meet the requirements, and it can be tightly bonded with the slotted waveguide to ensure the tightness of the antenna. At the same time, there should be no deformation and bulge under the alternating high and low temperature. In order to achieve miniaturization and high efficiency of antenna and array performance, the following effective measures are adopted in antenna design a) b) c) d)

Design of miniaturized low sidelobe waveguide antenna; “Double resonance” broadband designl; Integrated low loss radome design; Cross polarization energy suppression;

3.3 High Reliability Field Vehicle Antenna Array Integration Technology When radar is used in the field environment, it is required to work while marching. The vibration impact of the system is large, and a large number of electronic equipment are concentrated in the box, so it can not be equipped with shock absorbers to resist vibration and shock. The system must take reliable equipment reinforcement measures to improve the anti vibration and shock ability of the antenna array. Therefore, the stiffness and strength design of the antenna array has high requirements, It needs to meet the requirements of vibration and shock to ensure that the radar system has the ability to work while marching. Limited by weight index, the box is made of light alloy material. In order to reduce the weight, ensure the internal closed environment and facilitate the installation and maintenance of the equipment, the following measures are taken in the specific design of the high frequency box: a) b) c) d)

Integrated design; Quick disassembly technology; Stiffness and strength design; Heat dissipation of equipment.

4 Conclusion In summary, as the terminal defense weapon, the field air defense weapon system is the last indispensable defense line for air defense. As an important part of the strike force of the field air defense weapon system, the performance of the field vehicle radar will affect the operational efficiency of the whole weapon system. With the further development of weapon technology, the future field air defense weapon system will have the ability to attack unmanned bee colony, ram and other targets in complex electromagnetic environment, which will also promote the further development of radar overall design technology.

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References 1. Liu, B., et al.: Analysis for characteristics of future air defense battlefield environment and development of air defense and antimissile equipment of US army. Aerospace defense 001(001), 8–12 (2018) 2. Wang, G., et al.: Operational effectiveness evaluation of integrated field electronic air defense system. Ship electronic countermeasures 42(05), 58–61 (2019) No.287 3. Dang, A., et al.: The impact of UAVs swarming fighting concept development on attack and defense in future battlefield. Tactical Missile Technol. (2019) 4. Xia, A., Zhang, X., Li, G.: Analysis of the influence of complex electromagnetic environment on the generation of system combat capability. Elec. Info. countermeasure Tech. 2017(3) 5. Pan, Z.D., Liu, F.X.: AFEU. Evaluation of deployment plan of field air defense combat with multi attribute decision making. Modern Def. Technol. (2018) 6. Dong, J., Han, C., Li, X.: Research on system design technology of sea clutter measurement radar. Radar & Ecm 4, 1–4 (2016) 7. Wu, J., Tao, J., Bian, L.: Research on vehicle borne search radar in motion. Modern radar 08, 8–10 (2010) 8. Dong, H., et al. A fast target classification method for narrow band radar. Foreign Elec. Measure. Technol. 02(v.37;No.279), 111–117 (2018) 9. Wei, J., Jin, Q., Chen, W.: Electromagnetic compatibility test method for a certain missile launch vehicle system. J. Bullet and Guidance 38(5), 31–34 (2018) 10. Kozan, M.D., Uysal, M.M., Usta, E.: EMC test requirements for combat vehicle. In: Electromagnetic Compatibility Conference. IEEE (2017)

Design of Rate Compatible Spatially Coupled LDPC Codes Liu Yang(B) , Cheng Shuangyi, Wang Bin, and He Xing Xi’an University of Science and Technology, Xi’an 710054, China [email protected]

Abstract. This paper presents one method of designing rate-compatible spatially coupled low-density parity-check (SCLDPC) codes. Rate compatibility is realized by partial repetition extending technique. Specially, given a high-rate SCLDPC mother code, the rate-compatible codes can be obtained by first selecting a certain fraction of variable nodes at each position and then repeating them certain times. By adjusting the selection fractions and repetition times, rate-compatibility can be achieved. Density evolution analysis shows that the proposed rate-compatible SCLDPC code ensembles display capacity approaching thresholds over the binary erasure channel. Keywords: Spatially coupled LDPC codes · Density evolution · Rate compatibility · Iterative decoding

1 Introduction For channel transmission, due to the continuous change of channel state information, different code rates are required. A rate-compatible code family is a set of codes with different rates. The higher rate codes are embedded in the lower rate codes [1]. One method of designing rate-compatible codes is to extending the parity matrix of a highrate code to obtain low-rate codes [2, 3]. Since the degree distribution of each member code in the rate-compatible code ensemble needs to be optimized to ensure that its BP threshold is close to Shannon limit, the complexity is very high and difficult to implement. In recent years, due to the threshold saturation of spatially coupled low-density parity-check (SCLDPC) codes [4, 5], a class of rate-compatible codes based on SCLDPC codes has attracted much attention. In [6], one method of constructing rate-compatible SCLDPC codes by coupling the pre-designed rate-compatible LDPC codes is proposed, and it is proved that all member codes are capable of achieving the capacity over the binary erasure channel (BEC). Based on the raptor-like code structure, a family of protograph-based rate-compatible LDPC convolutional code ensemble is proposed in [7]. The rate compatibility is achieved by adding an extra variable node and an extra check node in each extension. Compared with the codes in [6], the threshold performance is improved and the complexity is greatly reduced. In these two schemes, the Supported by the National Science Foundation of China (No. 61801371). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 457–464, 2022. https://doi.org/10.1007/978-981-19-0390-8_56

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rate-compatible SCLDPC codes are all constructed by spatial coupling rate-compatible LDPC codes. However, the extension matrix of each member code needs to be designed, which complicates both design and implementation. In this paper, we consider an alternative method of designing rate-compatible SCLDPC codes. We consider the SCLDPC codes as the mother code and then extend the mother code to obtain rate-compatible codes. Since SCLDPC codes have threshold saturation properties, a simple partial repetition extending technique is presented. To be specific, the lower rate codes are constructed by first randomly selecting s fraction of variable nodes at each position from the mother code with the highest rate and then repeating q times. By adjusting the selection fraction s and repetition time q, rate compatibility can be achieved. Then we analyzed the threshold performance of the proposed rate-compatible SCLDPC code ensembles over the BEC and the results show that the proposed rate-compatible SCLDPC display near capacity thresholds. In addition, we also compared the proposed rate-compatible SCLDPC codes with that of the proposed codes in [6], which show that the proposed codes have much better thresholds.

2 SCLDPC Codes Consider two different SCLDPC code ensembles: (dl , dr , L) code ensemble and (dl , dr , L, w) code ensemble. The first is mainly used for actual SCLDPC codes construction, and the second is mainly used for density evolution analysis of spatially coupled code ensemble. a) (dl , dr , L) code ensemble: the coupling chain is obtained by coupling L uncoupled (dl , dr ) regular LDPC protographs. Let w = gcd(dl , dr ) be the greatest common factor of d l and d r . There exists one pair of positive integers dl and dr satisfying dl = dl /w and dr = dr /w, where dl and dr denote the number of check nodes and variable nodes at each position [8]. To couple these L protographs, we need to spread the d l edges of each variable node to its adjacent check nodes. Specifically, each of d l edges of one variable node at position i is connected to the check node in the range {i, i + 1, · · · , i + w − 1}. To terminate the chain, w–1 extra check node positions need to be added at the end of the coupling chain. A regular (dl , dr , L, M) SCLDPC code can be obtained by taking an "M-lifting” operation on the (dl , dr , L) coupled chain [9]. b) (dl , dr , L, w) code ensemble: The (dl , dr , L, w) code ensemble is obtained by introducing a smoothing parameter w. We assume that each edge of a variable node at position i is uniformly and independently connected to the check nodes in the range {i, i + 1, · · · , i + w − 1}, where the parameter w is a positive integer. Correspondingly, each edge of a check node at position i is uniformly and independently connected to the variable node in the range {i − w + 1, · · · , i − 1, i}.

3 Construction of Rate Compatible SCLDPC Codes One method of constructing rate-compatible codes is the extending technique. Choose a highest rate code as the mother code and subsequent codes can be obtained by continuously extending the parity check matrix. Since repetition is a simple and effective method

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(b)

Fig. 1. (a) Protograph of CPR (dl , dr , L, s, q) (b) Partial repetition at each position

to obtain low-rate codes with lower encoding and decoding complexity, we propose a partial repetition extension method. The protograph of the rate-compatible SCLDPC codes constructed by partial repetition extending technique illustrated in Fig. 1(a). The protograph of the mother code is the (dl , dr , L) coupling chain. The operation at each position is shown in Fig. 1(b). The rate-compatible SCLDPC code ensemble constructed by partial repetition extension method is denoted as CPR (dl , dr , L, s, q). We first describe the extension method shown in Fig. 1(a). Below the dotted line in the figure is a (dl , dr , L) coupling chain. The corresponding SCLDPC code is defined as the mother code. At each position, there are M degree-dl variable nodes and (dl /dr )M degree-dr check nodes. For each position, randomly select s fraction of variable nodes, and then repeat them q times. By adjusting the parameters s and q, the rate-compatible SCLDPC codes can be obtained, and the rate of each member code is lower than that of the mother code. To be clear, the details of the proposed extension technique at each position are shown in Fig. 1(b). We first randomly select sM variable nodes from M variable nodes at each position (denoted as s-VN) and then repeat q times for each s-VN. The rest variable nodes at each position are denoted as Ns-VN. The proposed rate-compatible SCLDPC codes CPR (dl , dr , L, s, q) have the following three features: 1. The mother code is an SCLDPC code; 2. Achieve arbitrary code rate lower than the mother code rate; 3. Rate-compatibility can be realized. The rate of the proposed code ensmeble CPR (dl , dr , L, s, q) is shown as follows. RPR =

RM 1 + sq

(1)

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The code rate RPR can achieve arbitrary real number in the range (0, RM ]. Here RM is the rate of the mother code CPR (dl , dr , L, 0, 0).

4 Threshold Analysis In this section, we consider rate-compatible SCLDPC codes for the transmission over the BEC. We analyzed the iterative decoding thresholds of the rate-compatible SCLDPC code ensembles constructed by the partial repetition extension. For the transmission over the BEC, the channel model can be regarded as two cascaded BEC channels or equivalent to a single BEC channel with a modified channel erasure probability. Assume the erasure probability is ε. According to the partial repetition extension method, the channel erasure probability of each s-VN is εq+1 , while the channel erasure probability of each Ns-VN is still ε. The channel model is shown in Fig. 2(a). The channel model based on the partial repetition extension method can be regarded as two cascaded BEC channels. On average, it can be equivalent to a single BEC channel with the erasure probability of ε∗ , as shown in Fig. 2(b).

Fig. 2. (a) The channel model for partial repetition extension (b) An equivalent BEC

For these two channel models, since the number of the symbols correctly received at the receiving terminal is equal, the equation is shown as follows.     (2) 1 − εq+1 × sN + (1 − ε) × (1 − s)N = 1 − ε∗ × N where N = L × M and it represents the mother code length. From Eq. (2), we can obtain the relationship between the erasure probability of the equivalent BEC channel ε∗ and the erasure probability of the original BEC channel ε. ε∗ = s × εq+1 + (1 − s) × ε

(3)

where 0 ≤ s ≤ 1, q ≥ 0 and q is an integer. We assume that the iterative decoding M . The threshold of threshold of the mother code CPR (dl , dr , L, 0, 0) with rate RM is εBP

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M , we the proposed rate-compatible SCLDPC is εBP . Combining Eq. (3) and ε∗ = εBP can obtain εBP . The equation is shown as follows. M = s × (εBP )q+1 + (1 − s) × εBP εBP

(4)

According to Eq. (1), by adjusting the parameters s and q, the proposed ratecompatible SCLDPC can achieve an arbitrary real number rate in the range (0, RM ].

5 Simulation Results Based on the threshold analysis, the threshold of the rate-compatible SCLDPC code ensemble can be obtained. In this section, we calculate the thresholds of the rate-compatible SCLDPC code ensemble constructed by partial repetition expansion. Firstly, we investigated the influence of the parameters s and q on the thresholds of the proposed rate-compatible SCLDPC code ensemble. Consider the code ensemble CPR (4, 6, 100, s, q), where 0 ≤ s ≤ 1, q ≥ 1 and q is an integer. By adjusting the parameters s and q, the rate vs. gap is shown in Fig. 3, where the gap equals the difference between the BP threshold and Shannon limit. The starting point of all curves in this figure corresponding to the gap between the BP threshold of the mother code CPR (4, 6, 100, 0, 0) and Shannon limit. The rate of the mother code is 0.3212 and the gap is 0.0134. From Fig. 3, when q is fixed, the gap first increases and then decreases with increasing s, and the lowest code rate which can be achieved is RM /(1 + q). In addition, for the same design rate, a large s and a small q is a good choice.

Fig. 3. Rate vs. gap of the code ensemble CPR (dl , dr , L, s, q)

To be clear, we compared the threshold results of choosing the different parameters s and q, when reaching the same design rate. Consider the mother code M = CPR (4, 6, 100, 0, 0), which the code rate is RM = 0.3212 and the threshold is εBP 0.6654. Assuming the design rate to be 0.1606 with Shannon limit 0.8394, there exists

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many pairs of different s and q that can achieve this design rate. According to Eq. (1), the same design rate corresponds to the same product s and q. Table 1 lists the threshold results for each pair of s and q achieving the same design rate. The results show that as q increases and s decreases, the gaps between the BP threshold and Shannon limits become larger, which consists with the results shown in Fig. 3. Table 1. Comparison the thresholds for different s and q to achieve same design rate 0.1606 Design rate

q

s

εBP

Gap

0.1606

1

1

0.8152

0.0242

0.1606

2

0.5000

0.8058

0.0336

0.1606

3

0.3333

0.7960

0.0434

0.1606

4

0.2500

0.7860

0.0534

0.1606

5

0.2000

0.7760

0.0634

0.1606

6

0.1666

0.7663

0.0731

0.1606

7

0.1429

0.7572

0.0822

0.1606

8

0.1250

0.7488

0.0906

0.1606

9

0.1111

0.7412

0.0982

Then we show the thresholds of the proposed rate-compatible SCLDPC code ensembles constructed by the partial repetition extension method. Consider the mother code CPR (4, 6, 100, 0, 0). According to the Eq. (1) and the analysis shown in Table 1, the rate-compatible SCLDPC codes can be obtained by calculating the optimal select fraction s and repetition time q. The threshold results are listed in Table 2. It can be seen that the proposed rate-compatible SCLDPC code ensembles display excellent and stable performances for their achievable rate regions. Table 2. Threshold results of the rate-compatible SCLDPC code ensembles CPR (4, 6, 100, s, q) Design rate

q

s

Shannon limits

εBP

Gap

0.30

1

0.0707

0.70

0.6786

0.0214

0.25

1

0.2848

0.75

0.7205

0.0295

0.20

1

0.6060

0.80

0.7704

0.0296

0.15

2

0.5707

0.85

0.8178

0.0322

0.10

3

0.7373

0.90

0.8577

0.0245

0.05

6

0.9040

0.95

0.9370

0.0130

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Finally, we compared the threshold results of the proposed rate-compatible SCLDPC code ensembles with the rate-compatible SCLDPC code ensembles in [6]. Considering the mother code CPR (4, 6, 100, 0, 0), the rate-compatible SCLDPC codes are constructed using the partial repetition extension method with the same rates in [6]. The comparison results are shown in Table 3. It can be seen that the proposed rate-compatible SCLDPC code ensembles have much better thresholds than those in [6], especially in the lower rate region. Table 3. Compare the thresholds between the proposed rate-compatible SCLDPC code ensembles and the rate-compatible SCLDPC code ensembles in [6] q

s

Design rate

εBP

Gap

Gap [6]

εBP [6]

1

0.1984

0.268

0.7057

0.0263

0.0321

0.6999

2

0.5298

0.156

0.8114

0.0326

0.0442

0.7998

8

0.9342

0.038

0.9524

0.0096

0.0621

0.8999

6 Conclusion In this paper, we proposed one method of designing rate-compatible SCLDPC code ensembles. Rate compatibility is realized by partial repetition extension technique. For each position, randomly selecting s fraction of variable nodes and then repeating them q times, we can achieve arbitrary code rate lower than the mother code rate. Compared with the construction method which requires to design extension parity check matrix for each member code, the proposed method is easy to implement. Moreover, the threshold results show that the proposed rate-compatible SCLDPC code ensemble have excellent threshold performances.

References 1. Hagenauer, J.: Rate-compatible punctured convolutional codes and their application. IEEE Trans. Commun. 36(4), 389–400 (1988) 2. Yue, G.S., Wang, X., Madihian, M.: Design of rate-compatible irregular repeat accumulate codes. IEEE Trans. Commun. 55(6), 1153–1163 (2007) 3. Jacobsen, N., Soni, R.: Design of rate-compatible irregular LDPC codes based on edge growth and parity splitting. IEEE Vehicular Technol. Conf. 1052–1056, Sep.2007 4. Kudekar, S., Richardson, T.J., Urbanke, R.L.: Threshold saturation via spatial coupling: why convolutional LDPC ensembles perform so well over the BEC. IEEE Trans. Inf. Theory 57(2), 803–834 (2011) 5. Kudekar, S., et al.: Threshold saturation on BMS channels via spatial coupling. In: IEEE International Symposium on Turbo Codes and Iterative Information 309–313 (2010) 6. Si, Z., Thobaben, R., Skoglund, M.: Rate-compatible LDPC convolutional codes achieving the capacity of the BEC. IEEE Trans. Inf. Theory 58(6), 4021–4029 (2012)

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7. Nitzold, W., Lentmaier, M., Fettweis, G.P.: Spatially coupled protograph-based LDPC codes for incremental redundancy. In: 7th IEEE International Symposium on Turbo Codes and Iterative Information Processing 155–159, Aug.2012 8. Mitchell, D.G.M., Lenmaier, M., Costello, D.J.: Spatially coupled LDPC codes constructed from protographs. IEEE Trans. Inf. Theory 61(9), 4866–4889 (2015) 9. Thorpe, J.: Low-density parity-check (LDPC) codes constructed from protographs. INP Progress Report, 42–154, Aug.2003

Sensor Path Planning for Target Tracking XiaFei Huang(B) and Jing Liang School of Information and Communication Enginerring, University of Electronic Science and Technology of China, Chengdu, China [email protected] Abstract. Multimodal wireless sensor networks (MWSNs) consist of variable types of sensor nodes that can do many important tasks, such as target tracking, health care, and environment monitoring. Besides, as a typical application of flocking algorithm in mobile sensor networks, the coupling target and tracking (CTT) provides information on how to effectively utilize the mobility of sensor networks. In this paper, we construct a MWSNs model, using a fuzzy logic based flocking control scheme that run on a distributed Extended Kalman filter algorithm(EKF) for collaborative tracking of a target. The scheme based on fuzzy logic considers both the tracking performance and detection performance of each sensor comprehensively to obtain an accurate estimation of the target position, which is used to lead the tracking process of target. The simulation results show that under the proposed scheme, the sensor can form a stable tracking formation with low tracking error on the target in a short time, ensure support high-level applications. Keywords: MWSNs control

1

· Target tracking · Fuzzy logic · EKF · Flocking

Introduction

Mobile Targets Tracking (MTT) [1] has attracted the attention of many researchers in surveillance, security and information systems for monitoring the behavior of adversarial/friendly agents [2] over the past years. Wireless Sensor Networks (WSNs) have been proven to be the effective approach in solving the MTT problems [3,4]. Particularly, MWSNs consist of variable types of sensor nodes, which have been used for some applications such as ambient assisted living, health care [5,6]. Multiple modalities are characterized by the varied types of nodes deployed in WSNs. They supply different kinds of data together to solve the complex real-time applications. There are many advantages over WSNs. However, little research has been conducted on multimodal sensor networks. Based on these, a model of multimodal wireless sensor networks for MMT is proposed. There have been various methods proposed for MTT in WSNs. The authors in [7] propose and implement a new technique Ant Colony Optimization (ACO). [8] derives the posterior Cramer-Rao lower bound (CRLB) on the mean squared c The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022  Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 465–471, 2022. https://doi.org/10.1007/978-981-19-0390-8_57

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error (MSE) of target tracking in WSN with quantized ranging-only measurements. Flocking control algorithm can drive the sensors to the target and improve the connectivity of mobile sensors [9]. In practical applications, sensors not only have ranging error but also angle measurement error. It is difficult for each sensor to obtain consistent and accurate measurement results of the target. So the traditional flocking algorithm is not suitable for these scenarios. CTT [10] focus on how to effectively utilize the mobility of sensor networks. The main idea is to combine the flocking control algorithm and Kalman consistency filter algorithm (KCF), however, KCF is only suitable for linear measurement scenarios. To overcome the aforementioned problems, in this paper, we proposed a systematic analysis framework for MWSNs with a fuzzy logic based flocking control that run on a distributed EKF for collaborative tracking of a single target. Firstly, we apply EKF to get the estimated value of the target of each sensor, then fuzzy logic systems(FLSs) get the unique and accurate target estimated value based on different characteristics of sensors. Lastly the flocking algorithm controls the movement of each sensor. The main contributions of this paper can be summarized as follows: – Combine a fuzzy logic method and a flocking control algorithm that run on a EKF to track targets. – Propose a multimodal mobile sensor networks model for target tracking which more in line with actual application. The remaining of the paper is organized as follows. Some basic preliminaries and the proposed model are discussed in Sect. 2. Section 3 provides the simulation results and discussions. Section 4 concludes the paper and lists future.

2 2.1

DISTRIBUTED TARGET TRACKING ALGORITHM in MWSNs Algorithm Flow of the Proposed Method

Here, we introduce the algorithm of our method. The algorithm flow shows in Algorithm 1. There are three parts totally. Firstly, we get the target estimation of each sensors through EKF, the key point is to linearize the nonlinear observation equation through the first-order Taylor expansion. Then, fuzzy logic method helps us get the optimal estimation. Lastly, flocking control algorithm plans the movement of sensors. 2.2

Algorithm Details and Related Theories

The state equation of the uniform motion target is X(t + 1) = φX(t) + Γ W (t)

(1)

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Algorithm 1. The path planning of sensors for target tracking ˆ i (t − 1) Input: The estimated value of sensor i at last time t-1 X m×m ; The observation value of sensor transition matrix φ ∈ R Zi (t) ∈ Rn ; the variance of ranging error and angle error of sensor of sensor i-th Pi (t) ∈ Rm Here, F L means fuzzy logic algorithm, control algorithm. Output: the position of sensor i next time t+1 Pi (t + 1) ∈ Rm .

∈ Rm ; The state i current time t Ri ∈ Rn×n ; state F means flocking

ˆ i (t) ← EKF(X ˆ i (t − 1), φ, Zi (t)) Using EKF to get the target estimation of this 1: X time t, turn 3. If sensor can not sense target, then we get the value through state transition matrix, turn 2. ˆ i (t − 1) Turn 3. ˆ i (t) ← φX 2: X ˆ i (t) − Zi (t)||) According to fuzzy logic algorithm, we obtain the 3: Si ← FL(Ri , ||X score of each sensor estimation, turn 4. ˆ i (t)) Flocking control algorithm uses the optimal observation 4: Ui ← F(max(Si ), X results to get the acceleration velocity, turn 5. 5: return Pi (t + 1) The state of the sensor at the next moment

⎡ ⎡ 2 ⎡ ⎤ ⎤ ⎤ 1 dt 0 0 dt /2 0 Px ⎢0 1 0 0 ⎥ ⎢ dt ⎢ Vx ⎥ 0 ⎥ T ⎢ ⎢ ⎥ ⎥ ⎥ X=⎢ ⎣Py ⎦, φ = ⎣0 0 1 dt⎦, Γ = ⎣ 0 dt2 /2⎦. Suppose P1 (t) = [x(t), y(t)] 0 0 0 1 Vy 0 dt is the location of the target, P2 (t) = [x0 (t), y0 (t)]T is the location of a sensor. V is observation noise matrix. The observation of the target including ranging, azimuth angle as follows ⎤ ⎡ 2 2 (x(t) − x0 (t)) + (y(t) − y0 (t)) ⎦ + V (t) (2) Z(t) = ⎣ arctan((y(t)−y (t)) 0

(x(t)−x0 (t)))

According to Eq. (1) and Eq. (2), Linearize the nonlinear equation. x(t) ˙ and y(t) ˙ are the velocity in x dimension, y dimension respectively. H (t) =

∂Z(t) ∂Z(t) ∂Z(t) ∂Z(t) ∂Z(t) =[ , , , ] ∂X(t) ∂x(t) ∂ x(t) ˙ ∂y(t) ∂ y(t) ˙

(3)

To simplify the representation, we define x1 = (x(t) − x0 (t))2 + (y(t) − y0 (t))2 , x2 = (x(t) − x0 (t))2 + (y(t) − y0 (t))2 . Then we get x(t)−x (t)

(t) √ 0 √ 0 , 0, y(t)−y , 0 x2 x2 H(t) = y0 (t)−y(t) (4) , 0, x0 (t)−x(t) , 0 x1 x1 Now, we can use H to get the estimation value of sensor by EKF. Before apply the flocking control algorithm, we need to assure a accurate virtual leader position which is the target in our method firstly. After get the target estimation of each sensor by EKF, we consider the sensor’s angle measurement accuracy, ranging measurement accuracy, and external interference that is related to the

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distance to the target comprehensively. Then use fuzzy logic algorithm to select the best estimation result [11]. “IF-THEN” rules of the form, Rl : IF x1 is Fl1 and x2 is Fl2 ... xp is Flp , Then y is Gl . Assume singleton fuzzification, when an input x1 = (x1 , ..., xp ) is applied, the degree of firing level corresponding to the l-th rule is computed as uF1l (x1 ) ∗ uF2l (x2 ) ∗ ... ∗ uFpl (xp ) = Tpi=1 uFil (xi )

(5)

uFil is the membership function of i-th input under the lth rule. so that M 

ycos (x ) =

p  l=1 cGl Ti=1 μFil (xi ) M p  l=1 Ti=1 μFil (xi )

(6)

In our fuzzy logic system, the three inputs are angle measurement accuracy, ranging measurement accuracy and the distance to the target according to estimation value this time, normalize the three parameters to [0, 1]. Here, Fig. 1 shows the input membership function and output membership function. We use triangular membership function and trapezoid membership function denote three inputs and one output. We prefer to choose the sensor that the angle measurement accuracy and ranging measurement accuracy is high, and the distance to the target is close. The observation value of the sensor with the highest output score is used as the information of the virtual leader later. Until now, we can apply flocking control algorithm to the get sensor motion control. [9] proposes a distributed controller for each agent, considers N agents moving in an n dimensional Euclidean space. The motion of each agent is described by q˙i = pi (7) p˙i = ui , i = 1, 2, ..., N

Membership Degree

1.2 1

0.6 0.4 0.2 0 −0.5

Membership Degree

moderate small/near high/far

0.8

0

0.5 Input membership function

1

1.5

1 low moderate high

0.8 0.6 0.4 0.2 0 −5

0

5 Output membership function

10

15

Fig. 1. Input and output membership function

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where pi , qi ∈ Rn are respectively the position and velocity vectors of agent i, and ui ∈ Rn is the control input acting on agent i. Under the control scheme, agent i is steered via its acceleration input ui that consists of two components as follows: (8) ui = fiα + fir The first contains a gradient term and a velocity consistency term, which are used to control the potential energy between agents (absorption attraction or repulsion) and relative speed.

∇qi ψα (qj − qi σ )+ aij (t)(pj − pi ) (9) fiα = − j∈Ni (t)

j∈Ni (t)

The second is navigation feedback, realizing distributed target tracking. fir = −c1 (qi − qγ ) − c2 (pi − pγ ), c1 , c2 > 0

(10)

Compare to our method, qγ and pγ is qˆγ and pˆγ which is the target estimation after EKF and fuzzy logic algorithm. We can get the next position of sensor i according to ui .

speed error in X(m/s)

2 0 −2 −4 −6 −8

0

5

10

15

10

15

t/s

speed error in Y(m/s)

2 0 −2 −4 −6 −8

0

5 t/s

Fig. 2. The speed error under the proposed scheme.

3

Simulations and Discussions

We suppose the initial location and velocity of target is xT = [10, 1], v = [2, 3] respectively. The transfer equation and observation equation are as described above (1), (2). The communication radius is 18, the sensing radius is 12, if the distance between sensor and target is farther than sensing range, the sensor can not detect the target, but it can acquire the information about target from other sensors. There are 30 sensors, the initial position obeys normal distribution, the

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mean is 1, and the variance is 25, the initial velocity obeys normal distribution, the mean is −4, and the variance is 9. Other parameters about flocking algorithm is the same as [10]. Sensors are divided into 10 groups, the detection performance of each group is the same. The range error of sensors is uniformly distributed in [1,10], angle error is uniformly distributed in [1*(pi/180), 10*(pi/180)], the parameters follows [13]. In this phase, the simulation step is 0.05s, and total steps is 500. Figure 2 shows the speed error between each sensor and the target. We simulate 1000 times. Use fuzzy logic choose a only one estimated value. We can see in Fig. 2 the speed of all sensors can match the target, and meet the consistency requirement of the flocking algorithm. Besides, we compare our method with the other method. Choose a estimated value of a sensor randomly. We can see from Fig. 3 we get a smaller MSE through the proposed scheme, and MSE can stabilize quickly. 6 Random Fuzzy−logic 5

MSE/m

4

3

2

1

0

0

5

10

15

20

25

t/s

Fig. 3. The comparison of MSE among two methods.

4

Conclusions

This paper proposes a MWSNs that using a distribute scheme to solve target tracking, improved flocking control algorithm based on fuzzy logic leads the sensors to track the target. Choosing suitable target estimation based on the heterogeneity of sensors take full advantage of the performance of multimodal sensors. Compared with random selection, tracking accuracy increases by around 32%. Although the fuzzy logic method can find a unique and accurate estimation, the setting of rules is subjective which is a problem to be studied in the future. In addition, the sensor is limited by energy or other performance constraints, and the tracking time is limited. How to replace the sensor that performs the tracking task and ensure the tracking accuracy is also the next issue to be considered.

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Acknowledgments. This work was supported by the National Natural Science Foundation of China (61731006, 61671138), and was partly supported by the 111 Project No. B17008.

References 1. Xu, Enyang, Ding, et al.: Target tracking and mobile sensor navigation in wireless sensor networks. IEEE Trans. Mobile Comput. (2013) 2. Peng, Y.F., Huang, X.F.: Research target tracking algorithm of wireless sensor networks. Comput. Simul. (2012) 3. Calafate, C., Lino, C., Diaz-Ramirez, A., et al.: An integral model for target tracking based on the use of a WSN. Sensors 13(6), 7250–7278 (2013). Kindly provide author given name, volume number and page range for Ref. [1], if applicable. 4. Liu, L., Zhang, X., Ma, H.D.: Dynamic node collaboration for mobile target tracking in wireless camera sensor networks. In: Proceedings of the IEEE INFOCOM: Rio De Janeiro. Brazil 19–25(2009), 1188–1196 (2009) 5. Tunca, C., Alemdar, H., Ertan, H., Incel, O.D., Ersoy, C.: Multimodal wireless sensor network-based ambient assisted living in real homes with multiple residents. Sensors 14(6), 9692–9719 (2014). Kindly provide volume number and page range for Ref. [2], if applicable. 6. Wu, J., Zeng, C., Sun, J.: Research and application of wireless intelligent network monitoring smog system based on STM32F407. J. Tianjin Normal Univ. (Natural Sci. Ed.), 37(6), 62–66 (2017) 7. Prachi, Sharma, S.: Target tracking technique in wireless sensor network. In: International Conference on Computing, Communication Automation, Greater Noida, India, pp. 486–491 (2015) ´ 8. Zhou, Y., Li, J., Wang, D.: Posterior CramEr-Rao lower bounds for target tracking in sensor networks with quantized range-only measurements. IEEE Signal Process. Lett. 17(2), 157–160 (2010) 9. Olfati-Saber, R.: Flocking for multi-agent dynamic systems: algorithms and theory. IEEE Trans. Autom. Control 51(3), 401–420 (2006). model”, in Comput. Graph. (ACM SIGGRAPH’87 Conf. Proc.), vol. 21, Jul. 1987, pp. 25–34 10. Olfati Saber, B.R., Jalalkamali, P.: Coupled distributed estimation and control for mobile sensor networks. IEEE Trans. Automatic Control 57(9), 2609–2614 (2012) 11. Gomide, F.: Uncertain rule-based fuzzy logic systems: introduction and new directions: Jerry M. Mendel; PrenticHall, PTR, Upper Saddle River, NJ, 2001, 555pp. ISBN 0-13-040969-3. Fuzzy Sets Syst. 133(1), 133–135 (2003) 12. Liang, Q.: Clusterhead election for mobile ad hoc wireless network. In: 14th IEEE Proceedings on Personal, Indoor and Mobile Radio Communications, 2003. PIMRC 2003, Beijing, China, 2003, vol. 2, pp. 1623–1628. https://doi.org/10.1109/PIMRC. 2003.1260389 13. Huang, X.P., Wang, Y., Miao, P.C.: Principle and Application of Target Positioning and Tracking-MATLAB Simulation. Electronic Industry Press (2018). ISBN 9787-121-35345-1

Noncontact Heartbeat Extraction Using UWB Impulse Radar Fangfei Jing(B) , Xiaoqing Tian, and Jing Liang School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China [email protected]

Abstract. Ultra-wide band impulse radar with high range resolution and penetration can be used to remotely extract the vital signs of human. Current researches have been relatively mature in extracting respiratory features, but there are challenges in extracting heartbeat signals. This paper proposes an algorithm based on the vital sign signal model, which uses the harmonic and intermodulation information of breathing and heartbeat to extract all possible heartbeat frequencies in the frequency spectrum. The corresponding frequency components are compared with the actual frequency spectrum, and the fuzzy logic method is applied to select the frequency with the highest score as the final heartbeat frequency. Experiments have proved that this method can achieve high accuracy. Keywords: Heartbeat extraction Intermodulation products

1

· UWB radar · Harmonics ·

Introduction

Ultra-wide band technology with extremely wide bandwidth has a high range resolution. UWB wireless system’s anti-interference performance is strong has super short-range detection capabilities. There have been many studies using ultrawideband radar to monitor human breathing and heartbeat. Venkatesh et al. [1] establish a mathematical model for UWB pulse radar to monitor respiration, heartbeat and its harmonics, and use FFT to estimate the respiratory frequency, occasionally the heartbeat signal can be estimated. They also implement wall-through experiments, which proved that UWB pulsed radar can achieve through-wall monitoring. Based on the above mathematical model, Lazaro et al. [2] used a harmonic trap filter to eliminate respiratory harmonics to extract the heartbeat signal. Nguyen et al. [3,4] use the harmonic path in the spectrum to determine the fundamental frequency to be measured, and combine it with the measurement of the time points before and after to improve the accuracy. Naishadham et al. [5] introduce a state space method(SSM) to extract respiration and heartbeat frequencies without producing harmonics and intermodulation products. Ren et al. [6] extend the complex signal demodulation (CSD) and c The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022  Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 472–479, 2022. https://doi.org/10.1007/978-981-19-0390-8_58

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arctangent demodulation (AD) techniques to UWB pulse radars and combine SSM with CSD and AD. Duan et al. [7] apply the variational mode decomposition (VMD) algorithm to extract respiratory signals and heartbeat signals and adopt Hilbert transform to obtain the time-frequency information. Wang et al. [8] use convolutional sparse coding to model heartbeat signals and exploit the sparsity of the heartbeat signal in the time domain to obtain the heartbeat rate. Rong et al. [9] develop an idea of using the second heartbeat component to recover the fundamental heartbeat component. Zhang et al. [10] propose a Harmonic Multiple Loop Detection (HMLD) Algorithm, which extracts the fundamental frequency by detecting whether the second harmonic frequency is twice the fundamental frequency. However, the existing methods seldom consider the intermodulation products. They either ignore the influence of harmonics and intermodulation products, or eliminated the breathing harmonics, or used the harmonic frequency to calculate the fundamental frequency. In this paper, the information of fundamental products, harmonics and intermodulation products are used to extract the possible frequencies, and each of the heart frequencies is scored by the fuzzy logic method. A measurement experiment is conducted, which proves the accuracy of the algorithm is high. The rest of the paper is arranged as follows. In Sect. 2, a mathematics model of vital sign signal for UWB impulse radar is described. In Sect. 3, the algorithm of extracting the heartbeat frequency is proposed. In Sect. 4, an experiment is conducted and experimental results were analyzed. In Sect. 5, conclusion and future work are provided.

2

Vital Signs Model for UWB Impluse Radar

Respiration and heartbeat cause vibration of the chest cavity which can be regarded as a sine wave periodic motion. The distance from the radar to the human chest cavity changes over time, which can be written as: d (t) = d0 + mr sin (2πfr t) + mh sin (2πfh t)

(1)

where d0 is the nominal distance. mr and mh are the displacement of respiration and that of heartbeat respectively. fr and fh are respiration and heartbeat frequency respectively. The received vital signs signal via UWB impulse radar is the convolution of the transmitted pulse and the channel impulse response, including the echo of the human body under and the static clutter:  αi s (τ − τi ) (2) r (t, τ ) = s (τ ) ∗ h (t, τ ) = αd s (τ − τd (t)) + i

where t, τ denotes the slow and fast sampling times respectively. αd and αi denotes the magnitude of the human body response and that of the background reflections. τd (t) = 2d(t)/c where c is the speed of light, and τi is the delays of static reflections.

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The considering signal is the echo of the human body, denoted as: y (t, τ ) = αd s (τ − τd (t))

(3)

Using 2D Fourier transform, the first kind Bessel functions and the inverse Fourier transform, the Fourier transform of (3) at slow time t can be expressed as: +∞ +∞   Gk,l (τ ) δ (f − kfr − lfh ) (4) Y (f, τ ) = αd k=−∞ l=−∞

where  Gk,l (τ ) =



+∞

−∞

S (ν) Jk

4πmr ν c



 Jl

4πmh ν c



ej2πν (τ −

2d0 c

) dν

|Gk,l (τ ) | is the maximized at τ = 2dc 0 :     +∞    2d0 4πmr ν 4πmh ν Ck,l ≡ Gk,l S (ν) Jk = Jl dν c c c −∞

(5)

(6)

Consequently,  Y

2d0 f, c

 = αd

+∞ 

+∞ 

Ck,l δ (f − kfr − lfh )

(7)

k=−∞ l=−∞

The above formula (7) shows that the strength of the fundamental components, harmonics and intermodulation components is determined by Ck,l . The fundamental components, harmonics and intermodulation components can be expressed as fk,l = kfr + lfh , where k and l are the order of respiration and that of heartbeat. The fundamental respiration frequency is denoted as fr and f1,0 , and that of heartbeat is denoted as fh and f0,1 . For instance, fk,0 is the kth respiration harmonics when k = 0, l = 0. f0,l is the lth heartbeat harmonics when k = 0, l = 0. fk,l is the intermodulation products when k = 0, l = 0. Ck,l can be approximately calculated as:     4πmr fc 4πmh fc Ck,l ≈ BW · S (fc ) Jk Jl (8) c c where BW is the bandwidth of UWB impulse radar, and fc is the central Fourier transform of the transmitted pulse at the frequency. S(fc ) represents   center frequency. Jk 4πmc r fc is the kth-order Bessel function at 4πmc r fc , and   Jl 4πmch fc is the kth-order Bessel function at 4πmch fc . The relationship between the fundamental frequency, harmonic frequency and intermodulation frequency of respiration and heartbeat is fk,l = kf1,0 + lf0,1 . Ck,l is determined by the respiration displacement mr for a certain system. When the respiratory displacement is greater, the heartbeat fundamental component C1,0 is more likely to be masked by respiratory harmonics and noise, and

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the amplitude difference between the fundamental heartbeat and the intermodulation components is smaller, which causes challenge for extracting the heartbeat frequency. Therefore, this paper considers using the relationship between harmonics, intermodulation components and fundamental components, that is fk,l = kfr + lfh , to estimate the heartbeat more accurately.

3 3.1

Heart Rate Estimation Algorithm Preprocessing

Removal of Background Clutters. Respiration and heartbeat cause vibrations of the human body, so the moving target detection method can be used to remove the static background clutters. R [m, n] = R [m, n] −

M 1  R [m, n] M m=1

(9)

where M is the amount of the slow sample points. m and n are the slow and fast time sampling points respectively. Extraction of Vital Sign Signal in Time Domain. The fast time sampling points correspond to the echo signals at different positions, the energy in all the slow times is calculated at all the fast times. E [n] =

M 

|R [m, n] |2

(10)

m=1

The fast time sampling points corresponding to the maximum energy are selected, and the echo signals are extracted. Filtering and Smoothing. The respiration and heartbeat frequency for a quiet state human is within 0.15 Hz and 2 Hz, and their harmonics are considered. Therefore a Butterworth bandpass filter limited to [0.13 Hz, 4 Hz] is used to extract the vital signs signal and remove low-frequency noise and high-frequency noise. Since the ripple of spectrum is related to the time window used, a Kaiser window is used to smooth the spectrum. 3.2

Heart Rate Esimation Algorithm

Step 1. The spectrum of vital signs signal is obtained via Chirp Z-Transform, and take all the peak points f i in the spectrum. Step 2. Take the frequency corresponding to the largest peak among all the peak points as the respiratory frequency f1,0 .

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Step 3. Find out if there is a peak point larger than all other peak points within 0.95–1.8 Hz, if there is, then the corresponding frequency is considered to be the heart rate, and the algorithm ends, otherwise go to step 4. i i i i , f−1,1 , f1,1 and f0,2 in the four frequency bands Step 4. By finding f0,1 [0.95, 1.8], [0.95 − f1,0 , 1.8 − f1,0 ], [0.95 + f1,0 , 1.8 + f1,0 ] and [1.9, 3.6], calculate i according to all possible heartbeat frequencies f0,1

fk,l = kf1,0 + lf0,1

(11)

i and (11), calculate the corresponding Step 5. According to the obtained f0,1 i i i i i i i fk,l including f0,1 , f−1,1 , f1,1 , f0,2 , f−1,2 , f1,2 .

i in the real spectrum, and the frequency Step 6. Find the peak point close to fk,l i difference cannot exceed 0.02 Hz. Calculate their frequency difference |Δfk,l | and the corresponding peak amplitude aik,l .

i Step 7. Take this frequency difference |Δfk,l | and the corresponding peak amplii tude ak,l as the input of the fuzzy logic system, and find the score value scoreik,l . The fuzzy logic system is considered to be a 2-input 1-output system. The i | and the corresponding two inputs (x1 , x2 ) are the frequency difference |Δfk,l i amplitude ak,l , which includes three level: small, medium and large. The system output y denotes scoreik,l including 5 levels: low, low, medium, high, and very high. For all inputs, the output result y is calculated as follows: 9 p p p p=1 μF1 (x1 ) μF2 (x2 ) cg (12) y (x1 , x2 ) = 9 p p p=1 μF1 (x1 ) μF2 (x2 )

where Cgp is determined by the pth rule and the centroid of the membership functions. Step 8. scorei for each possible heartbeat frequency is obtained by weighting and averaging scoreik,l . The heartbeat frequency corresponding to the largest is considered to be the heartbeat frequency f0,1 of the human target.

4

Experiment and Result Analysis

In this experiment, P440 Ultra-Wideband impulse radar made by Time Domain is used. The center frequency of its transmitting waveform is 4.3 GHz, and the bandwidth is over 2 GHz. It has penetrability, strong anti-noise ability, and low power consumption. The pulse repetition frequency is set to 12.5 Hz, which can

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complete the detection of vital signs. The antenna is facing the human chest and the distance is 0.8 m. The experiment was completed in the hospital of the University of Electronic Science and Technology of China, and the ECG monitor was used for simultaneous monitoring. A healthy 24-year-old female participated in the experiment, and the experiment was measured continuously for 2 min. Figure 1(a) shows the echo received by the UWB radar, and the human body signal obtained after filtering out the static clutters is shown in Fig. 1(b).

Fig. 1. Signal preprocessing. (a) The received signal from UWB radar. (b) After filtering out static clutters.

Taking the fast time sampling point with the largest energy, the vital signs signal in time domain can be obtained as shown in Fig. 2.

Fig. 2. Vital signs sigal in time domain.

The respiration rate and heart rate obtained by this algorithm are shown in Fig. 3(a)(b). During these two minutes, the monitor was also used for monitoring,

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Fig. 3. Continuous 2-min respiration rate and heart rate monitoring. (a) Respiration rate monitering. (b) Heart rate monitering.

the accuracy of the respiration rate is 92.11% and the accuracy of the heart rate is 88.81%. CZT-MTI method is also conducted. For extracting breath, the two methods are the same. However, its accuracy of extracting heartbeat frequency is 78.77%, which is lower than the proposed method in this experiment. CZT-MTI method extracts the heartbeat frequency by notching the respiratory harmonics. When the respiratory amplitude is large, the intermodulation component may be misjudged as the heartbeat frequency, and when the noise is large, the pseudo peak may be misjudged as the heartbeat frequency. However, the proposed method also considers the intermodulation components, and makes full use of fundamental components, harmonics and intermodulation components. We give scores to each possible heartbeat frequency via fuzzy logic method, which makes the method’s anti-noise performance stronger. In future work, since the heart rate is unlikely to change suddenly, we plan to integrate the time before and after to meet the needs of actual monitoring.

5

Conclusion

In this paper, we realized the monitoring of human breathing and heartbeat through UWB impulse radar. A heartbeat estimation algorithm based on the vital signs model is proposed. The intermodulation component is considered, and the most suitable heartbeat frequency is selected by the fuzzy logic method. The actual measurement experiment was carried out for 2 min continuously, which proved that the method has high accuracy in heartbeat extraction. Compared with the CZT-MTI method that eliminates respiratory harmonics, the proposed method has higher accuracy. In future work, we will focus on improving the accuracy of the measurement by integrating the measurements at the before and after time points, and consider the measurement of multiple directions of the human body.

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Acknowledgments. This work was supported by the National Natural Science Foundation of China (61731006, 61671138), and was partly supported by the 111 Project No. B17008.

References 1. Venkatesh, S., Anderson, C.R., Rivera, N.V., Buehrer, R.M.: Implementation and analysis of respiration-rate estimation using impulse-based UWB, vol. 5, pp. 3314– 3320 2. Lazaro, A., Girbau, D., Villarino, R.: Analysis of vital signs monitoring using an IR-UWB radar. Progress Electromagnet. Res.-Pier 100, 265–284 (2010) 3. Nguyen, V., Javaid, A.Q., Weitnauer, M.A., IEEE: Harmonic Path (HAPA) algorithm for non-contact vital signs monitoring with IR-UWB radar. In: 2013 IEEE Biomedical Circuits and Systems Conference, Biomedical Circuits and Systems Conference, pp. 146–149 (2013) 4. Nguyen, V., Javaid, A.Q., Weitnauer, M. A.: Spectrum-averaged Harmonic Path (SHAPA) algorithm for non-contact vital sign monitoring with ultra-wideband (UWB) radar, pp. 2241–2244 5. Naishadham, K., Piou, J.E., Ren, L., Fathy, A.E.: Estimation of cardiopulmonary parameters from ultra wideband radar measurements using the state space method. IEEE Trans. Biomed. Circuits Syst. 10(6), 1037–1046 (2016) 6. Ren, L., Wang, H., Naishadham, K., Kilic, O., Fathy, A.E.: Phase-based methods for heart rate detection using UWB impulse doppler radar. IEEE Trans. Microw. Theory Tech. 64(10), 3319–3331 (2016) 7. Duan, Z., Liang, J.: Non-contact detection of vital signs using a UWB radar sensor. IEEE Access 7, 36888–36895 (2019) 8. Wang, P., et al.: Noncontact heart rate measurement based on an improved convolutional sparse coding method using IR-UWB radar. IEEE Access 7, 158492– 158502 (2019) 9. Rong, Y., Bliss, D.W., IEEE: Harmonics-based multiple heartbeat detection at equal distance using UWB impulse radar. In: 2018 IEEE Radar Conference, IEEE Radar Conference, pp. 1101-1105 (2018) 10. Zhang, Y., Li, X.P., Qi, R., Qi, Z.H., Zhu, H.: Harmonic multiple loop detection (HMLD) algorithm for not-contact vital sign monitoring based on ultra-wideband (UWB) radar. IEEE Access 8, 38786–38793 (2020)

Correlation Between Hail Diameter and VILD in Bayannur Yunong Xu1,2(B) , Debin Su1,2 , Ning Yang1,2 , Xiaoguang Sun1,2 , and Haijiang Wang1 1 Chengdu University of Information Technology, No. 24, Section 1, Xuefu Road, Southwest

Airport Economic Development Zone, Chengdu, China [email protected] 2 Key Laboratory of Cloud Physics Environment, Beijing, China

Abstract. The vertical integrated water liquid content density(VILD) can be calculated using the ratio of the vertical integrated liquid water content(VIL) to cloud thickness. This paper processes the reflectivity factor data of hail-days observed by the new generation of weather radar (CINRAD-CD) in Linhe area of Inner Mongolia from 2012 to 2020, and combines hail size data collected by the hailpad of each weather modification operation site, using the least squares method for regression fitting, and processing the VILD and the actual hail size. The results show that there is a good correlation between the VILD and the hail diameter on the ground. The power function model has the smallest cost value of 27.31, and using VILD as a recognition factor is more accurate than VIL. The power function model is fitted using hail-pad information, and the cost is 5.97, It can be concluded that the diameter of hail recorded by the hail-pad is more accurate, and the fitting result has certain reference significance for the estimation of hail particle diameter in Bayannur. Keywords: Vertical integrated liquid water content · Vertical integrated liquid water content density · Hail diameter · Hail-pad

1 Introduction In recent years, some scholars at home and abroad have conducted some studies on hail recognition, path tracking, regional distribution, generation mechanism and artificial hail prevention, and have drawn a large number of effective conclusions, but there are few studies on calculation of hail size. In the 1970s, Greene [1] proposed a method to calculate the vertical integrated liquid water content by using the radar reflectivity factor. The research results of Robert [2] also show that the VIL center is closely related to the precipitation center. Winston [3] also found that VIL has a good indicator effect on hail. In China, with the development of digital weather radar, the application research of VIL has also been developed. Wang, Fu et al. [4, 5] used VIL to predict hail, and Pan [6] et al. used VIL to carry out the study of estimate precipitation. Liu [7] used the 3D-Barnes scheme to interpolate the new generation weather radar reflectivity factor © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 480–487, 2022. https://doi.org/10.1007/978-981-19-0390-8_59

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contour plane data. The maximum diameter of hail is calculated by the ratio of VIL to the height of the cell, that is, the vertical integrated liquid water content density of the cell. The hail-pad is a commonly used hail measuring instrument at present. It is low in cost and suitable for field observation. By measuring the diameter of the pit, the size of the local hail can be estimated, and the spectral distribution, hail density and end velocity of the hail can also be calculated. It can also objectively measure the intensity of hail and the degree of damage to crops [8].

2 Study Area and Data Bayannur is located in the western part of Inner Mongolia, and the middle and high latitudes. As shown in Fig. 1. Affected by this special geographical environment, the weather in Bayannur is complex and changeable, Disasters occurs frequently. Among them, frequent hail disasters have caused serious impacts on agricultural production and people’s lives.

Fig. 1. Topography of Bayannur

The research area in this paper includes the new-generation weather radar centered in Linhe, Inner Mongolia (107°21 36 E, 40°43 48 N), and the effective scanning radius is 150 km. The actual hailfalls are from the hail disaster events reported by the county meteorological bureaus. The Table 1 shows part of the hail diameter data. In order to obtain more accurate and objective hail particle data, highlighting the accuracy of hail-pad, this paper uses the hail-pad data obtained during the hail prevention test in 2020 (Fig. 2).

3 Methodology 3.1 Three-Dimensional Barnes Interpolation The doppler radar data is located in the spherical coordinates centered on the radar and is not continuous in height. For the convenience of analysis and application, it is often necessary to interpolate the radar data to Cartesian coordinate grid points.

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Site Location

Longitude

Latitude

Hail Diameter

2012/6/23

Hanghou

107.51

40.99

5 mm

2014/6/29

Denkou Dukou

107.12

40.42

10 mm

2016/6/3

Linhe Xinhua

107.49

41.16

5 mm

2017/6/4

Taohai Hongsai

108.07

40.92

6.5 mm

Fig. 2. Hail drop marks on the Hail-pad

Currently, the adaptive Barnes scheme is defined in terms of polar coordinates. The defined weight function and analysis equation are as follows: w0 = exp[−

(rg − r0 )2 (ϕg − ϕ0 )2 (θg − θ0 )2 − − ] kr kϕ kθ  N N   f0 = wk fk wk k=1

(1)

(2)

k=1

Among them, w0 is the weight of a sampled echo value point; f0 is the interpolated echo value; fk is the i-th sampled echo value that affects the echo value; N is the total number of sampled echoes; rg , ϕg , θg are the polar coordinates of the interpolation grid points; r0 , ϕ0 , θ0 are the linear distance, elevation angle and azimuth angle of the radar sampling echo value point from the radar; kr , kϕ , kθ are Barnes interpolation parameters. 3.2 Radar Derived Parameters 3.2.1 VIL VIL is based on the assumption that the reflectivity factors obtained by the reflection of the solid and liquid water droplets inside the cloud meet the empirical derived relationship caused by the liquid water droplets. This paper uses the 3D-Barnes scheme to interpolate the PPI data of the radar volume scan to the 40-layer reflectivity factor Z (mm6 /m3 ) contour plane data (height interval

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is 0.5 km, horizontal grid is 0.01° × 0.01°). After the interpolation is completed, the calculation formula of VIL is: VIL = 3.44 × 10−6

n−1 

4

[(Zi + Zi+1 )/2] 7 h

(3)

i=1

Among them, Zi ,Zi+1 ,h and n are the reflectivity factor, vertical height and number of data layers of the two adjacent layers in the Contour plane data of reflectivity factor. In this paper, the vertical height h is 0.5 km, and the data layer number n is 40 layers. 3.2.2 VILD The meaning of VILD is the ratio of the VIL value to the height H of the hail cloud, the unit is g/m3 , and the formula is: VILD =

VIL H

(4)

Among them, H is the height of the hail cloud echo, and the unit is m. The final calculation formula of VILD can be obtained by bringing Eq. (3) into Eq. (4): VILD =

3.44 × 10−6

4 n−1  7 i=1 (Zi + Zi+1 )/2 h H

(5)

The cloud height H in formula (5) can be obtained from the 40 layers reflectivity factor grid after interpolation.

4 Results and Discussion This paper selects the reflectivity factor within 150 km from the Linhe CD radar site for interpolation, and selects two hailfall cases in the study area to discuss the results. As the VIL value above 20 kg/m2 may cause hail, it can be seen from the radar observation results in Fig. 3 that hail events may occur at these two moments.

Fig. 3. Shows the VIL values at 16:03 on August 9, 2019 and 15:59 on August 9, 2019.

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Fig. 4. The corresponding cloud height H and VILD at 16:03 on August 9, 2019 and 15:59 on August 9, 2019.

By judging the threshold value of the reflectivity factor in the vertical direction, obtaining the cloud top height H of the hail cloud, and the calculated VIL is divided by the cloud top height H of the corresponding position to obtain the VILD of the point. As shown in Fig. 4. 4.1 Human Evaluated Distribution According to the approximate relationship of VILD-R, we use the least square method to fit the data with linear, logarithmic, exponential and power functions. The fitting results are shown in Fig. 5. It can be obtained that there is a positive correlation between the VILD value of radar echo and the hail diameter. The function parameters after fitting are shown in Table 2.

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Fig. 5. Hail diameter and vertical integral liquid water density fitting results Table 2. Fitting function parameters Fitting type

θ1

Line type

0.26

Logarithmic Cubic curve model Exponential model

1.05

θ2

θ3

Cost

0.83

–

30.34

4.87

–7.64

–

32.36

5.06 * 10–4

–0.04

1.09

27.31

–

28.43

2.74

By directly fitting the result of VIL value and radius, as shown in Fig. 6:

Fig. 6. Hail diameter and vertical integral liquid water content fitting results

It can be seen from Fig. 5 and Fig. 6 that the regression equations established by the cubic curve model for VIL and VILD can be used to estimate the radius. And using VILD as the recognition factor is better than the recognition effect of VIL.

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Selecting 50 hailfall cases in Bayannur area, correspond the actual hail diameter to the diameter calculated by the cubic curve model with the smallest value of VILD, and divide the hail particle diameter into three intervals: R ≤ 10 mm, 10 mm < R < 20 mm, R ≥ 20 mm, find the mean square error (MSE) of each diameter interval, as shown in Table 3: Table 3. MSE of each hail diameter interval

MSE

R < = 10 mm

10 mm < R < 20 mm

R > = 20 mm

4.85

94.22

347.03

4.2 Hail-Pad Evaluated Spectrum The least square method is also used to fit the hail diameter and the VILD cubic curve model, as shown in Fig. 7 the fitting function is y = 1.17×10−3 x3 +0.06x2 −0.04x+3.45, The mean square error of the fitting result is 5.98, and the accuracy is significantly improved compared with the result of human recording the diameter of hail.

Fig. 7. Fitting results of hail-pad data

5 Summary There is a good correlation between the diameter of the hail on the ground and the density of the vertically integrated liquid water. It can be concluded that the result obtained by fitting the cubic curve model has the smallest cost function. It can be seen that the smaller the hail diameter, the more accurate the hail diameter calculated by VILD, which has certain reference significance for the estimation of the hail diameter in Bayannur area. Both VIL and VILD can use the regression equation established by the cubic curve model to estimate the hail radius, and using VILD as the identification factor is more accurate than VIL as the identification factor.

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In this paper, the relationship between hail diameter and VILD is fitted with the hailpad data of each site in Bayannur area, Inner Mongolia. The fitted cubic curve function model cost value is 5.97, which is significantly more accurate than the manual recording of hail diameter. In the future, it is necessary to collect the hail diameter information of hail-pad at more stations in order to obtain the real-time hail diameter size more accurately. Acknowledgement. 1. Weather Modification Capacity Construction Project of Northwest China(ZQC-R18217). 2. National Key R&D Program of China(2018YFC1506605).

References 1. Greene, D.R.: A comparison of echo predictability constant elevationvs. VIL radar data patterns. In: 15th Conf. on Radar Meteor Champaign-Urbana, IL:AMS 111–116 (1972) 2. Robert, A.C., Yates, J.C.: Applications of digital radar data in both meteorology and ydrology. In: 15th . Conf. on Radar Meteor. Champaign-Urbana, L:AMS 93–98 (1972) 3. Winston, H.A., Ruthi, L.J.: Evaluation of RADAP II severe storm detection algorithms. Bull Amer Meteor Soc 61(2), 142–150 (1986) 4. Wang, W., Jia, H.: Forecasting hail with radar vertical cumulative liquid water content data. Meteorological 28(1), 47–48 (2002) 5. Fu, S., et al.: Application of VIL in identifying hail clouds and analysis of estimation error. Plateau Meteorology 23(6), 810–814 (2004) 6. Jiang, P., Peichang, Z.: Precipitation estimation using vertical integral water content. J. Nanjing Ins. Meteorology 23(1), 87–92 (2000) 7. Liu, Z., et al.: Comparative analysis of the identification of the largest hail diameter in the central part of gansu. Arid Zone Res. 25(004), 592–599 (2008) 8. Fan, P.: Preliminary identification of hail board. Shanxi Meteorology 08, 1–5 (1980)

A Method for Damage Assessment of Orbital Targets Based on Radar Detection Information Zhengtao Zhang(B) , Qiang Huang, and Jianguo Yu Nanjing Research Institute of Electronics Technology, Nanjing 210000, China [email protected], [email protected], [email protected]

Abstract. Accurate damage assessment for orbital targets is of great significance for combat mission planning. Compared with infrared and other detectors, radar has the advantages of longer detection distance and less influence by weather. The damage assessment using radar detection information has certain application value. This paper uses theoretical modeling methods to extract damage features from the target’s motion, background radar plot, radar cross-sectional (RCS) and high resolution range profile (HRRP). A Bayesian network is used to fuse different types of damage features, and the performance of the proposed damage features is verified in the numerical experiment. The result shows that the target RCS and HRRP changes are the most effective features for damage assessment of orbital targets. Keywords: Damage assessment · Change detection · Empirical mode decomposition

1 Introduction Damage assessment refers to obtaining the damage level of a target by the detection information of some sensors after the target is damaged. The damage level of the target is generally determined by the change degree between the features before and after damage. Accurate and timely damage assessment is one of the important parts in general campaign process. This assessment not only provides a scientific basis for the implementation of correct decisions, but also helps to realize the optimal allocation of system resources. At present, scholars have done a lot of research on damage assessment. Some studies focus on the evaluation models used in damage assessment, which include graph model [1], Bayesian network model [2, 3], neural network model [4, 5], etc. Among these models, the Bayesian network model can combine prior knowledge with data information, as well as dependency relationships and probabilistic representations. It is an ideal model for damage assessment and has important applications in evaluation systems that deal with uncertain information. However, these studies did not describe in detail how to obtain the initial input of the assessment model based on radar detection information. In the aspect of damage feature extraction, most researches focus on the change detection of image data, such as damage assessment based on radar SAR images [6], change detection of remote sensing images [7–9], etc. These articles have deeply © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 488–497, 2022. https://doi.org/10.1007/978-981-19-0390-8_60

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studied how to accurately and efficiently extract damage features from image data, but have not discussed in detail about other types of data. There are also a few studies on damage assessment methods for radar detection information. The damage assessment by target course changes and acceleration changes are studied in [10]. Further considering of the target echo changes has been implemented in [11]. Based on these studies, this article proposes some new damage features about orbital targets and briefly verifies the performances of these damage features. The rest of this paper is organized as follows. Sect. 2 outlines the general characteristics of radar detection information changes after the orbital target is damaged. Sect. 3 and Sect. 4 discuss the extraction method of damage features from motion and background plots based on previous researches. Sect. 5 proposes a damage feature extraction method for RCS and HRRP. By utilizing Bayesian network for comprehensive damage assessment, the performance of the proposed damage features is presented in Sect. 6. Section 7 is devoted to conclusion.

2 Outline of Target Damage Features In addition to SAR, radar detection information generally includes the radar plot in the target’s neighborhood, the track obtained by association and filtering, and the RCS and HRRP. The physical phenomenon of target damage and the division of damage level have been detailed in [10] and [11], so this article will not repeat. When the orbital target is damaged, the radar detection information generally has the following changes. The orbital target’s track may change significantly. The target may burst into multiple fragments, which is reflected by the increase in the number of radar plots. Since the shape of the target may be incomplete and deformed, the RCS may decrease, and the posture movement may change drastically, resulting in more drastic changes in the RCS. The fragments generated after the target ruptures form multiple small scattering points with smaller amplitude on HRRP. The target wreckage and other fragments may roll irregularly and violently, which will lead to obvious changes in HRRP envelope. The size, period and other HRRP features change greatly and are generally unstable. It can be seen that the damage features provided by radar come from different forms of information sources, and can be summarized into three aspects: motion information, background plot information, RCS and HRRP information. Next this paper will introduce the specific method of extracting these damage features.

3 Motion Damage Feature 3.1 Acceleration Change Detection When damaged by impact or explosion, the motion damage features generated by different types of interceptor will be different. But in general, it is basically turns out to be a sudden change in the track, or to say the change of the orbital target’s acceleration. Segmented fitting is used to calculate the acceleration change in [10], but the number of segments and the fitting order are difficult to determine. The following is detailed analysis of this problem.

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The sudden change of acceleration caused by damage usually leads to the lag of filtering, which leads to inaccurate state estimation. Therefore, we carry out the least square fitting of adaptive order to estimate the acceleration of the orbital target using its original plots. Considering the stability of polynomial approximation, we uses orthogonal Chebyshev polynomial for fitting, and the time needs to be normalized to τ ∈ [−1, 1]. The specific form of Chebyshev polynomial is: P0 (τ ) = 1, P1 (τ ) = τ, Pk+1 (τ ) = 2τ Pk (τ ) − Pk−1 (τ ). Considering the short damage assessment time, the radar station center coordinate system can be directly selected to describe the movement, and the trajectory of the orbital target can be easily obtained by polynomial fitting. Assuming that the measurement error satisfies a normal assumption, then the goodness of fit can be calculated based on the fitting residual to determine the appropriate order. Denote the fitting error of distance, azimuth and pitching as (tl , rl , al , el ), l = 1, ..., n, the specific form of fit goodness C can be written as C = C(sr , βr , ηr , sa , βa , ηa , se , βe , ηe ), Br(k) =  sr =

1  rlk , k n σr,l n

l=1

3/2 2   (2) Br , βr = Br(3) / Br(2) , ηr = Br(4) / Br(2)

where sr , βr , ηr are respectively the normalized RMS, skewness and kurtosis of the distance residual. σr,l is the measurement error of the distance at time tl . The parameters for azimuth and pitching have the same form as the distance. When s deviates from 1, β deviates from 0, or η deviates from 3, the goodness of fit is considered to be poor. Further considering that when the order is low, s2 is generally greater than 1 and the measurement of distance, azimuth and pitching is independent, the specific form of C in this paper is:   n(η − 3)2 n(β)2 + , C = Qr · Qa · Qe , Q = s2 + wG , G = 6 24 where w is the weight coefficient. Here s2 satisfies a chi-square assumption with a degree of freedom of n − 1, G satisfies a chi-square assumption with a degree of freedom of 2. We select the order with smallest C as the optimal order and output the corresponding fitting result. Considering the overfitting problem, when the condition s2 < 1 is met, the order stops to increase. In this way, the appropriate fitting order is obtained. There will always be model errors when using polynomial to simulate the actual target trajectory. Generally, the model error increases with time. Therefore, for a given observation arc, the higher the segment number, the smaller the fitting model error. However, the larger the number of segments, the smaller the amount of data in each segment, which also leads to a larger estimation error. This brings the problem of how to determine the optimal number of segments. But the model error is unknown in actual

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scenarios, so it is hard to obtain the optimal number of segments. In this paper, the segment number is fixed. After using the above strategy to perform segmental fitting, the acceleration change features of the damaged target can be written as: 

 

a = max max ai ti,l − a1 l = 1, ..., n i = 2, ..., k ,

where ai ti,l is derived from the fitting polynomial, a1 is the estimated acceleration of the initial segment of the trajectory. The initial segment is directly fitted with a second-order polynomial. Using the above method, the movement direction change θ can also be obtained. When the target acceleration or course change exceeds the target maneuvering capacity, it can be considered that the target is damaged. 3.2 Collision Probability Miss distance is generally defined as the nearest distance rmin between the orbital target and the interceptor. When the miss distance is large, it’s obvious that no damage will occur. When the intersection occurs, it’s easy to determine the approximate time of the intersection by calculating the change of relative distance r. Denote the intersection time as tc and tk < tc < tk+1 , the relative distance r(t) during (tk , tk+1 ) can be easily obtained by predicting the state of the track before the intersection. By utilizing linear search technique the accurate intersection time tmin and miss distance rmin can be obtained. Then the position and the speed of the orbital target and interceptor at the intersection time and their position error matrixes can be given. Due to the different sizes of targets, it’s not accurate to directly use miss distance to assess the damage, and the collision probability should be adopted. At the intersection moment, denote the  1 , V1 , P1r , and that of position, the speed and the position error matrix of the target as R  2 , V2 , P2r , the unit vector of the encounter coordinate system used the interceptor as R to calculate the collision probability is: 2  −R V1 − V2 R  , ex =  1  , ey = ex × ez . ez =     1 − R  2  V1 − V2  R Considering the uncertainty of the target position in the direction of the relative speed does not affect the collision, the position error in the direction of ez can be ignored. Then the calculation of the collision probability can be simplified to the x-y plane. We have the relative position error matrix in the x-y plane as: T 

Pr = ex ey (Pr1 + Pr2 ) ex ey . The position of the error distribution center is (rmin , 0). Given the size of the orbital target as D0 and the kill radius of the interceptor as L0 , the calculation of the collision probability can be attributed to the calculation of the probability ρ that the relative vector is in the region x2 + y2 < (D0 + L0 )2 . The specific form is:   ¨  −1 T 1 1 dxdy. exp − x − rmin y Pr x − rmin y ρ= √ 2 2π |Pr | x2 +y2 n+ . The correlation coefficient of the two IMF components c− , c+ before and after damage can be written as:  ⎧ ⎫ n−  ⎨ ⎬ c · c + m) (j) (j  + − j=1 m = 1, 2, ..., n− − n+  R = max   n+ 2 n+ 2 m ⎩ ⎭  j=1 c+ (j) j=1 c− (j + m) Considering that the number of the intrinsic frequencies of target attitude movement generally does not exceed 2, we choose two low-frequency IMFs with larger modulus for change detection. After matching by frequency, damage features based on modal changes can be obtained by calculating correlation coefficient and modulus difference. For the target HRRP sequence, we can also use NA-MEMD [13] for modal change analysis, but it’s generally difficult to meet the real-time requirements. Considering that the size of HRRP can reflect the main modal features of the target to a certain extent, we use the size feature to obtain the approximate changes of target HRRP modal. So far, the extraction method of damage features has been elaborated, and the performance of the features proposed will be verified in the following numerical experiments. The assessment model is constructed by the method in [3] to fuse different features, and the specific process is omitted here. The number of damages levels is simplified to 2, and sigmoid function is used to map the damage feature to the corresponding input damage probability for Bayesian networks.

6 Numerical Results Multiple damage scenarios are investigated in this study, one of which is used as a test scenario, and the parameters of the assessment model are determined based on other scenarios and empirical knowledge. In the test scenario, there are multiple orbital targets in free flight, and another target intercepts one of them from the side. The intersection time is T = 62 s. We process damage assessment on the radar detection information of the orbital target in a time sliding window, and briefly compare the results of assessment

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under the following four conditions: a) only the motion damage features is considered; b) only the background plot damage features; c) only the RCS and HRRP damage features; d) all damage features. The results are as follows:

Fig. 1. Damage probability variation curve considering different factors

It can be seen from the above figure that the correct damage evaluation results can be obtained by the damage features proposed in this paper. In the first three ways (a, b, c), the RCS and HRRP damage features give the highest damage probability, followed by the motion damage features, and the damage probability given by plot damage features is the lowest. The damage probability given by motion damage features is not as high as expected. The track of the damaged target is usually associated to the wreckage whose motion state is close to the original target. At the same time, due to the low accuracy of the radar angle measurement, the accuracy of the motion damage feature will inevitably be low. These factors lead to a decrease in the accuracy of the assessment. For the radar plot damage features, the plot information is usually related to detection methods. When collision generates a large number of fragments, in order to maintain the main target tracking, the threshold of detection methods will be raised and the number of detection plots may be reduced, resulting in the low discriminability of the plot damage features. However, Fig. 1 shows that the stability of plot damage features is a little higher than that of the motion damage features, which indicates that the plot damage feature has a certain advantage. Then it’s suggested to use an additional fixed threshold to obtain the target plots when extracting the plot damage features. Figure 1 clearly shows that target RCS and HRRP change significantly after being damaged, and their damage features have high discriminability and stability, which can be used as reliable features for damage assessment. Finally, the comprehensive damage probability given by Bayesian network is the highest and have good stability as expected. The applicability of these features is verified.

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7 Conclusion This article briefly descries the damage features of the radar detection information of orbital targets after being damaged. On the basis of previous studies, we discuss the damage feature extraction methods in three aspects: track change detection, dot change detection, target RCS and HRRP change detection. The improvements mainly include: motion change detection method with adaptive fitting order; using collision probability instead of miss distance for damage assessment; Design radar plot entropy damage feature; Design modal change damage features based on EMD. Finally, the performance of the above damage features is verified by numerical experiment. The results show that the features proposed in this paper are feasible and have certain engineering application value. Since the actual damage physical process is more complicated, the target damage level is more detailed, and the damage effects of different interceptors for different types of orbital targets are more diverse, the research on damage evaluation still needs a lot of work. In addition, considering the scarcity of damage data, it’s necessary to comprehensively use theoretical modeling and machine learning methods to design more accurate damage assessment model.

References 1. Qianfu, Q., Zheng, X., Cungen, C. Military effect evaluation of operational plan based on graph theory. Computer Science 32(7), 148–151 (2005). (in Chinese) 2. Yan, J., et al.: Electomagnetic damage assessment of airborne radar based on bayesian networks. Transactions of Beijing Inst. Techno. 23(9), 960–975 (2012). (in Chinese) 3. Yijin, P.: Study on battle damage assessment based on Bayesian network. Modern Elec. Technique 41(11), 22–26 (2018). (in Chinese) 4. Zhenglong, W., Zhongshi, Z.: Research on fire damage assessment model based on adaptive fuzzy neural network system. Acta Armamentarii 33(11), 1352–1357 (2012). (in Chinese) 5. Meruane, V., Mahu, J.: Real-time structural damage assessment using artificial neural networks and antiresonant frequencies. Shock. Vib. 62(7), 118–124 (2014) 6. Ssu-Hsin, Y., Anuj, S., Raman, K..M.: Automatic battle damage assessment based on laser radar imagery. Proc. SPIE - The Inter. Soc. Opt. Eng. 3707, 210–221 (1999) 7. Dai, X., Khorram, S.: The effects of image misregistration on the accuracy of remotely sensed change detection. IEEE Trans. Geosci. Remote Sens. 36(9), 1566–1577 (1998) 8. Deren, L.: Remotely sensed images and GIS data fusion for automatic change detection. Int. J. Image Data Fusion 1(1), 99–108 (2010) 9. Wei, W., Cao, C.: A target damage information extraction method based on GLCM feature vector. Computer & Digital Engineering 43(11), 1946–1950 (2015). (In Chinese) 10. Yawen, Z., Xuezhi, X.: Intelligent target damage assessment based on the data fusion of radar and infrared imaging sensor. Ship Elec. Eng. 34(3), 111–115 (2014). (In Chinese) 11. Xiaoqing, N., Jiangguo, L., Yiwen, S.: Research on air targets hitting effect evaluation based on the trajectory change and radar echo change. Tactical Missile Techno. 1, 77–82 (2012). (In Chinese)

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12. Huang, N.E., et al.: The empirical mode decomposition and Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc Roy Soc London A 454, 903–995 (1998) 13. urRehman, N.P., Mandic, D.: Filter bank property of multivariate empirical mode decomposition. IEEE Trans. Signal Process. 59(5), 2421–2426 (2011) 14. Xianzong, B., Lei, C.: Research on calculational method of collision probability between orbital objects. Journal of Astronautics 29(4), 1435–1442 (2008). (In Chinese)

Speech Emotion Recognition Based on Henan Dialect Zichen Cheng1,2 , Yan Li1,2(B) , Mengfei Jiu1,2 , and Jiangwei Ge1,2 1 Tianjin Key Laboratory of Wireless Mobile Communications

and Power Transmission, Tianjin Normal University, Tianjin 300387, China [email protected] 2 College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin 300387, China

Abstract. In this paper, according to international standards, referring to CASIA Chinese phonetic library, recording the phonetic library based on Henan dialect. And use computer software to reduce noise. Use Matlab to preprocess the speech signal such as framing and windowing, and extract the short-term energy, formant frequency, pitch frequency, Mel cepstrum coefficient, and other characteristic parameters contained in the speech signal. Use KNN, BP neural network, PNN neural network, and LVQ neural network to recognize and process recorded dialect. It can be seen from the recognition rate of the LVQ neural network is the highest. For the five emotions, the recognition results of various algorithms are different. Keywords: Henan dialect · Speech recognition · LVQ neural network · Characteristic parameters

1 Introduction In recent years, with the rise of artificial intelligence technology, emotion recognition of speech signals is becoming more and more popular in this field. Many researchers have turned their attention to the field of emotion recognition. Speech emotion recognition technology determines the emotion information contained in speech signals according to some characteristic parameters contained in the speech signal. This technology involves many fields such as signal processing, feature extraction, and pattern recognition. These fields play an important role in problems of speech signal processing [1]. In China, many universities, technology companies, and some scientific research institutes have started recognition research on emotion problems contained in speech signals very early [2]. However, China is a country with a vast territory and a large population, and each province has its own dialect with local characteristics. Therefore, it’s very important to carry out research on the emotional information contained in dialects with strong local colors. However, due to the lack of speech databases of dialects, the research on dialect emotion recognition in China is still in its infancy. So, in this paper, referring to CASIA Chinese speech database, according to international standards © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 498–505, 2022. https://doi.org/10.1007/978-981-19-0390-8_61

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to establish a speech emotion database based on Henan dialect. KNN algorithm, BP neural network, LVQ neural network, and PNN neural network are used to identify and simulate the five emotions of happiness, neutrality, anger, surprise, and sadness. Finally, the identification results are explained.

2 Establishment of Emotional Database The international emotional databases are produced according to strict evaluation, production, and distribution norms. The emotional database of Henan dialect is collected and produced according to the CASIA Chinese phonetic database established by the Institute of Automation, Chinese Academy of Sciences, in this paper [3]. 2.1 Establishment of Speech Library Because the simulation experiment is for the needs of practical application, the establishment of the speech database must be true and reliable. Instead of collecting data in the traditional way of acting, it guides the recording person’s emotional induction by inducing the data to be as close as possible to the real emotional data [4]. The process of building a practical speech emotion database is shown in Fig. 1.

Formulate emoonal inducement scheme

Speech acquision

Data inspecon and supplement

Sentence segmentaon and annotaon

Listening test of emoonal speech

Fig. 1. The recording process of the speech library

2.2 Recording Process We chose students with household registration in Henan, clear speaking, and pure dialect. A total of six people, three men and three women were selected according to the experimental requirements. This article selected 10 texts that did not have any emotional color or clear semantic orientation through the Internet; The recording time of each person was controlled within 4 s. The recording environment was selected in a quiet laboratory. The beats headphone and Adobe Audition software were used for recording. The 16 K sampling frequency and 16 bit sampling accuracy were selected. Before recording the speech, firstly induced emotions, and then let the recorder use the five emotions of surprise, neutrality, happiness, anger, and sadness to read 10 sentences prepared in advance. At the end of the recording, AU software was used to reduce noise and arranged for 6 students who did not participate in the recording to check the recorded sentences. If a sentence was rejected by more than 4 students, they would be arranged to record again. Finally, 250 of them were selected as simulation samples. The chosen sentences are shown in Table 1.

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The holiday is coming

2

They support China

3

I have to go even if it rains

4

The Soviet Union represents the world

5

Rain delays work

6

Xiaobao catches the mouse

7

Sun Ying flies the plane

8

The billiards are interesting

9

The world becomes peaceful

10

I have to work

3 Principle and Technology of Emotion Recognition 3.1 Basic Principle of Emotion Recognition There are some characteristic parameters in a speech signal that can reflect the speaker’s emotional behavior characteristics, and these characteristics are often manifested by the prosody changes of the speech. Changes in speech emotion can usually be reflected in changes in speech feature parameters [5]. The pros and cons of emotional characteristics will have a great impact on the simulation results of the final experiment. Generally speaking, the main characteristic parameters used in the field of speech signal emotion recognition are short-term energy, pitch frequency, formant, Mel cepstrum coefficient, and their derivative parameters [6]. The emotion recognition algorithm for speech signals is shown in Fig. 2:

Training sample set

Feature vector extraction

Results Sample to be tested

Feature vector extraction

Feature vector set Classification recognition output Feature vector

Fig. 2. The principle diagram of speech signal emotion recognition algorithm

3.2 Speech Signal Preprocessing Before the experimental simulation, it is necessary to pre-emphasize, framing and windowing the recorded audio files. In this simulation experiment, the frame length is set

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to 25 ms, and the Hamming window is used. Secondly, we need to extract the feature parameters including short-time energy, pitch frequency, formant frequency, and Mel cepstrum coefficient and use them to form 140 speech emotion feature parameters for recognition. Among the 250 sentence samples used for simulation, 125 sentences are used as training samples and 125 sentences are used as test samples. 3.3 K-Nearest Neighbor Algorithm The K-near classification algorithm is a relatively simple and intuitive classification method, which is relatively mature in theory and has no obvious learning process. KNN algorithm is easy to implement, which is more in line with the distribution characteristics of speech emotion data and has a higher fitting ability for speech emotion data [7]. The classification idea of the KNN classification algorithm is that if there is a sample to be classified in a feature space, and the K nearest samples around belong to a certain category, then the current sample to be tested also belongs to this category. Suppose the feature parameter of the sample to be classified is X, and the feature parameter set of the training sample of the known category is {X1 , X2 , · · · Xn }; For the sample X to be tested, calculate the Euclidean distance D(X, Xl ), l = 1, 2, · · · , n between the sample to be tested and each sample in {X1 , X2 , · · · Xn }.   N  D(X, Xl ) =  (1) (X (i) − Xl (i))2 , l = 1, 2, · · · , n i=1

Among them, N represents the dimension of the feature vector, min{D(X, Xl )} is called the nearest neighbor of X, and the first K values of D(X, Xl ) arranged from small to large are called the K nearest neighbor of X. Analysis of K nearest neighbor belongs to which kind of the largest number, the X belongs to the class. 3.4 Artificial Neural Network At present, in the field of speech emotion recognition, the neural network has the characteristic of self-organization, self-learning, and self-adaptation. The information distribution of the neural network is stored in the connection weight coefficient, so that the network has high fault tolerance and robustness, and the neural network can successfully solve the noise interference caused by pattern recognition or partial missing of input patterns [8]. The commonly used neural network algorithm include BP neural network, PNN network, LVQ network, SOFM network, and RBF network. In this paper, the BP network, PNN network, and LVQ network are used. The Backpropagation neural network is based on the multi-layer perceptron model. It is an error back-propagation algorithm used for the learning and training of forward neural networks. It is an efficient calculation method for training multi-layer perceptions. The main characteristic of this kind of network is that the signal is transmitted forward and the error is propagated backward. In the forward transmission process, the input signal propagates layer by layer from the input layer through the hidden layer to the output layer, and the neuron state of each layer only affects the neuron state of the next

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layer. If the output layer does not get the desired output, then the backpropagation is used to adjust the network weights and thresholds according to the prediction error [9]. The specific algorithm structure is shown in Fig. 3.

N System modeling

Build the right network

Network initialization

Network training

Network classification

Data testing

End

Y

Fig. 3. Algorithm procedure

The learning vector quantization neural network is an input forward neural network used to train the supervised learning method of the competitive layer. The LVQ network has a very wide range of applications in the field of pattern recognition and optimization. The LVQ network contains 3 layers of neurons, namely the input layer, the output layer, and the competition layer. And the LVQ network has two algorithms, namely LVQ1 and LVQ2. This network structure is simple, only through the interaction of internal units can complete very complex classification processing. The Probabilistic neural network was proposed in 1989. It is a parallel algorithm based on the Bayesian classification rule and the Parzen window estimation method of the probability density function. It is a kind of artificial neural network with a simple structure, simple training, and wide application. As a kind of radial network, the PNN network is very suitable for pattern classification. Compared with the BP neural network, the PNN network process has simple convergence speed and good stability and robustness. The PNN network has four layers, namely, the input layer, the model layer, the summation layer and, the output layer. The basic structure of probabilistic neural networks is shown in Fig. 4.

… Input layer

… Pattern layer

… Summation layer

Output layer

Fig. 4. The basic structure of the probabilistic neural network

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4 Simulation Results In this paper, we used matlab2016a as the experimental simulation platform. Firstly, the feature extraction function was used to extract the five emotional feature vectors of surprise, happiness, anger, sadness, and neutrality, and the.mat file was stored in the directory of the main program. Because the experimental results of the K nearest neighbor algorithm were closely related to the K value, the K value and the simulation results of each emotion were summarized as shown in Table 2. Table 2. K value and emotion recognition rate K

Surprised

Happiness

Neutrality

Sadness

Anger

Mean value

1

0.56

0.64

0.68

0.92

0.8

0.72

2

0.32

0.6

0.56

1

0.88

0.672

3

0.44

0.62

0.72

0.8

0.84

0.684

4

0.52

0.52

0.76

0.88

0.8

0.696

5

0.64

0.56

0.68

0.72

0.84

0.688

6

0.52

0.56

0.76

0.84

0.88

0.712

7

0.52

0.56

0.72

0.8

0.8

0.68

8

0.56

0.56

0.8

0.72

0.8

0.688

9

0.48

0.56

0.72

0.6

0.8

0.632

10

0.48

0.6

0.8

0.64

0.84

0.672

According to this table, when the K value took different values, the recognition rate of the K nearest neighbor algorithm for various emotions would also change. If the K value was set too small, the classification accuracy would be reduced, and if the K value was set too large, the classification effect would be reduced. In summary, the K value of 6 could be selected as the K value of the simulation, and the mean value of emotion recognition reached the highest, indicating that the recognition effect was the best. For

Fig. 5. KNN algorithm identification results

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the Henan speech database, the recognition rates of four algorithms used in this paper are shown in Fig. 5, Fig. 6, Fig. 7, Fig. 8.

Fig. 6. BP neural network recognition results

Fig. 7. PNN neural network recognition results

Fig. 8. LVQ neural network recognition results

Table 3 can be obtained by sorting and summarizing the data.

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Table 3. Recognition results of each algorithm

KNN

Surprised

Happiness

Neutrality

Sadness

Anger

Mean value

0.52

0.56

0.76

0.84

0.88

0.712

BP network

0.85

0.45

085

0.65

0.85

0.73

PNN network

0.75

0.90

0.70

0.95

0.65

0.79

LVQ network

0.95

0.75

0.85

0.9

0.65

0.82

5 Conclusion As can be seen from Table 3, for the Henan dialect speech database, the average recognition rate of the LVQ neural network is the highest, and the average recognition rate reaches 82%. For the emotion of surprise, the recognition rate of the LVQ neural network is the highest, reaching 95%. For neutral, the recognition rate of the LVQ neural network and BP neural network are the highest. For sadness, the recognition rate of the PNN network is up to 95%; On the emotional recognition of anger, the K-nearest neighbor algorithm has the highest recognition rate, reaching 88%. For the Henan speech database, the recognition rates of the four algorithms are all above 70%, indicating that the establishment of the speech database is effective and has met the expectations of the experiment.

References 1. Hu, Y.: Artificial intelligence and speech recognition. Elec. Eng. Product World. 24(4), 23– 25+27 (2016) 2. Zhang, C., Wei, P., Lu, X., Shi, X.: The design and implementation of Chongqing dialect speech recognition system. Comp. Measure. Control. Jan 26(01), 256–259+263 (2018) 3. Ji, C., Cheng, L., Li, F.: Dialect emotion recognition based on improved BP_Adaboost and HMM hybrid model. J. Chengdu Univ. Info. Technol. Oct 34(5), 495–500 (2019) 4. Zhao, L., Liang, R., Wei, J., Xi, J.: Speech signal processing, 3rd edn. China Machine Press, Beijing (2018) 5. Lu, T., Wang, C., Han, X.: Research on Chinese speech emotion recognition based on SVM. Elec. Measure. Technol. Mar 30(3), 20–21+56 (2007) 6. Liang, R., Zhao, L., Wei, X.: Experimental Course of Speech Signal Processing, 2nd edn. China Machine Press, Beijing (2018) 7. Sun, X., Li, H.: Overview of speech emotion recognition. Comp. Eng. Appl. 56(11), 1–9 (2020) 8. Li, Q.: Overview of artificial neural networks. Kexue Yu Xinxihua 6(7), 181–182 (2021) 9. Wang, X., Shi, F., Yu, L., Li, Y.: MATLAB neural network 43 cases analysis, 9th edn. Beihang University Press, Beijing (2021)

Design and Simulation of an Anti-collapse CMUT Structure Jiajun Liu1,2 , Kaixin Li2 , Benxian Peng1 , and Fengqi Yu1(B) 1 Shenzhen Institutes of Advanced Technology, Chinese Academy of Science, Shenzhen, China

{jj.liu,bx.peng,fq.yu}@siat.ac.cn 2 Guangdong Power Exchange Center Co., Ltd., Guangzhou, China

Abstract. Compared with traditional piezoelectric ultrasonic transducers, CMUT (Capacitive Micro-machined Ultrasonic Transducer) has advantages of wider bandwidth, better dielectric impedance matching, easier for its arraying and higher compatibility with standard IC processes. The CMUT made with CMOS-MEMS technology can be directly integrated with circuits. However, the collapse of top electrode causes the device failure during the manufacturing process. Therefore, we propose a new MEMS process which is not only self-stopping but also prevent the top electrode from collapsing. In addition, COMSOL Multiphysics and Matlab are used in the simulations to verify the CMUT structure. Keywords: CMUT · Anti-collapse · Self-stopping · MEMS · CMOS

1 Introduction CMUT, Capacitive Micro-machined Ultrasound Transducer, has characteristics of wide bandwidth, good dielectric impedance matching, easy for its arraying and high compatibility with standard IC processes. In medical imaging and contact applications, CMUT has become a powerful alternative to piezoelectric transducers. The air-coupled CMUT array that we developed does not need to be immersed in water or coated with any coupling agent. It can be applied to automobile reversing radar and low-frequency magnetoelectric coupled ultrasound imaging [1]. The receiver and transmitter of CMUT share a same structure. As shown in Fig. 1, it can be used as a receiver as well as a transmitter. At the receiving mode, the incident ultrasonic wave will cause the deformation of the membrane, thus affecting the capacitance between the top electrode and bottom one. A suitable circuit is applied to convert the current signal caused by the change of the capacitance into a voltage signal. In other words, the transducer realizes the energy conversion from ultrasonic energy to mechanical energy and then to electrical energy [2]. In the transmission mode, the DC voltage is firstly applied between the top and bottom electrode of CMUT, where the membrane will be attracted by the electrostatic force. And then AC signal will be applied. The membrane will vibrate at a certain frequency, which emits ultrasound wave at the same time. At the transmission mode, CMUT realizes the energy conversion from electrical energy to ultrasonic energy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 506–515, 2022. https://doi.org/10.1007/978-981-19-0390-8_62

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Fig. 1. Transmitting mode and receiving mode of CMUT

2 The Compatibility Between CMOS and MEMS Technology In this section, the integration between CMOS and MEMS process is discussed. A special CMUT structure with three cantilevers supporting a circular membrane is proposed. In addition, 0.18 um CMOS standard process, layout and MEMS processing method are introduced. 2.1 Standard CMOS Process The standard CMOS process is based on the 0.18 um standard CMOS process at Global Foundries. As shown in Fig. 2.(a), the materials, their distribution and dimension of each layer in this standard process have been determined already [3]. Here Cadence Virtuoso is used as a layout tool. In this EDA tool, every metal layer can be operated. In other words, these layers can be used by users to do layout. For unused layers, they are filled with insulating materials. Considering the feasibility of MEMS process and the availability of poly layer, SiO2 and Al are taken as the material of CMUT membrane where Al in metal2 is applied as sacrificial layer. The overall layered structure is as shown in Fig. 2. (b).

Fig. 2. (a) Cross section of 0.18 um cmos process; (b) Cross section of each layer

2.2 The Design of the Air-Coupled CMUT Considering the dielectric loss among air, the frequency of air coupled ultrasonic transducer is generally 40–200 kHz. The low-frequency CMUT usually occupies a large area on chip. However, under CMOS standard process, the available area in a wafer is limited. In order to improve the area utilization on chip, we propose a special CMUT structure

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Fig. 3. The evolution of the design of our CMUT structure

with three cantilevers supporting a circular membrane. Figure 3 shows the evolution of the design of our CMUT structure. In CMOS standard process, the thickness of metal2 as sacrificial layer is 0.55 um only. Due to the existence of liquid surface tension in wet etching process, the top electrode and the bottom one often stick together, which leads to device failure [4]. Obviously, the increase of Metal2 thickness can solve the problem. However, the Metal2 thickness is fixed in standard process. Thus, a central column between the top electrode and the bottom one is introduced. This column will be removed by MEMS process later. 2.3 The Structure of the Air-Coupled CMUT In this section, the process steps are demonstrated by using the cross-sectional view combined with CMOS layout and MEMS technology. In MEMS process, we use twice dry etching and one wet etching. It is worth noting that the process has self-stopping capability. Firstly, after COMS process, we obtain the die from foundry, as shown in Fig. 4.(a) From the bottom side to the top, M1 is designed as the bottom electrode. M2 is designed as the gap filled with air. M3 is designed as the top electrode. M4 layer is designed as the protection layer of the second dry etching, which acts as a mask. M5 layer is designed as the protection layer for the first dry etching, which also acts as a mask. In addition, it is noted that there are two vertical dotted lines and a circle in the middle of the diagram, which is the structure of the central column. Secondly, MEMS process is carried out in a micro-nano lab. In this step, we use ICP (inductively coupled plasma) to do deep etching. The ICP does the etch from top to bottom, with its characteristics of good verticality and uniform rate [5]. The ICP etching has good selectivity because it can only etch non-metallic compounds with a proper

Fig. 4. (a) Initial cross section of DIE; (b) First ICP etching

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power. It will stop etching when encountering metal such as Al. The etching result is shown in Fig. 4.(b). Thirdly, the aim of this process is to corrode the sacrificial layer. Here, we use a corrosive solution mixed acetic acid nitric acid. In MEMS process, the wet etching of a certain material can only corrode the exposed metal without passing through the protective layer by controlling the concentration [6]. The wet etching result is shown in Fig. 5.(a). It can be seen that the central column provides additional support for the membrane. After the wet etching, the samples need to be dried in supercritical dryer. Fourth, the second dry etching is applied. There are two purposes of the etching. One is to expose the electrode so that the device can be connected with an offchip test circuit through wire bonding. On the other hand, the central column needs to be removed so that the membrane can vibrate. In the later process, liquid corrosion is no longer involved, so it is better to remove the central column at this time. The cross section after the second ICP etching is shown in Fig. 5.(b).

Fig. 5. (a) Wet etching; (b) Second ICP etching

So far, the complete anti-collapse, self-stopping MEMS processing for CMUT is presented where the reliability and fault tolerance of the whole process are both considered.

3 CMUT Simulation with FEM In this chapter, the COMSOL Multiphysics 5.3a is adopted to do CMUT simulation with finite element method. Three aspects like model setup, steady state simulation and natural mode simulation are demonstrated as follows. 3.1 Model Setup First of all, we build a 3-D model using COMSOL. We select Solid Mechanics (solid), Electrostatic (ES) as the coupling field. Then, we select the characteristic frequency to complete the setup of the physical field of the model. Next, we enter the main interface of the software, and complete the configuration, material, physical field, multi physical field, and mesh generation in the main interface. The geometric model setup will be done as follows. There are two ways to create a circular membrane with a central hole. The first one is graph Boolean operation where the big membrane subtracts the central column. The other is, in the 2-D plane, shifting the rotation plane for a certain distance, which is equal to the radius of the hole. In the first method, Boolean operation not only leads to the

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disorder of labels but also increase the difficulty of mesh generation in narrow areas. Therefore, the second way is adopted. A work plane (WP1) is established, which is selected from X-Z plane as the research plane. Using the rectangle in plane geometry, the rectangle is moving right by a small distance x0 to generate a small hole later on. Thus, a five-layer sandwich is generated. Then we use the rotation function to rotate the plane 360° around the Z axis. The result is shown in Fig. 6.

Fig. 6. (a) The cross section of CMUT circular structure; (b) CMUT 3-D structure after the rotation

A new work plane 2 (WP2) is generated to create 3 cantilevers. Using the rectangle in plane geometry, a 3-layer sandwich structure is created, which is the same as the middle three layers of rectangle in (WP1). The stretch function in COMSOL is called, and the parameter “distance to surface” is specified, then a cantilever is obtained automatically. In order to form a 3-beam structure, we use the rotation function to set the rotation angle as range (0, 120, 360) with the rotation axis as Z axis. Figure 7 shows the generated CMUT cantilever.

Fig. 7. The generation of CMUT cantilever

3.2 Stead-State Simulation In solid mechanics, the “fixed constraint” boundary condition is that each end of the three CMUT cantilevers is pinned. In electrostatic (ES), metal3 is set as the terminal and metal1 as the ground. When the displacement of the CMUT film reaches 1/3 of the cavity height, the sound pressure can be obtained without absorption [7].

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After setting the terminal into a DC voltage named VDC, its parameters are scanned. The simulation results show that when the DC bias of CMUT is 12.35 V, the maximum displacement in –Z direction is 0.165 um, which is about 1/3 height of the air gap. Figure 8 shows the Z displacement with DC bias. As expected, the membrane has the largest displacement near its centre. Figure 11 shows the potential of the cross section produced by the DC bias. It can be seen that the electric potential decreases from top metal to the other surface of SiO2 .

Fig. 8. (a) CMUT steady-state analysis; (b) potential contour of the gap between electrodes

3.3 Natural Mode Simulation This chapter we will perform a modal analysis of the model with and without applying a DC bias. Modal analysis is based on CMUT steady-state analysis. Bias is already present as a parameter in the model, so the prestressed eigenfrequency solver does not need to adjust the physics settings of COMSOL. To calculate the unbiased characteristic frequency, we adjust the solver settings to solve structural mechanics problems only. Figure 9 shows two modes of the CMUT under unbiased conditions. In the firstorder mode, the membrane can vibrate regularly. Only the first-order vibration mode is suitable for receiving and transmitting as an ultrasonic transducer. The frequency of one dimensional vibration along z axis is 200.35 kHz and the frequency of two dimensional vibration along z axis is 307.12 kHz. The second-order frequency of the CMUT is 1.5 times that of the first-order frequency. In this way, the membrane can keep vibrating up and down in a wide frequency range without being disturbed by other modes. It can be seen that the CMUT has a wider bandwidth.

Fig. 9. Two orders of CMUT and its frequency without biased voltage

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4 CMUT Simulation with FEM MATLAB is applied to our simulation, including its additional tool set Field II and Focus. In addition, the method of generating anisotropic transducer is introduced. 4.1 Stead-State Simulation In Field II package, there is no generation command for transducer with hole. Thus, function zero(x,y) is applied to control the on and off of the squared elements in the transducer array to form an approximate ring. In Fig. 10, the grids crossed by the circle with radius R are turned on (orange grid), and the others are turned off (white grid).

Fig. 10. Method of opening and closing the transducer unit

First, we use Xdc_get() function to get the coordinates (x, y) of the physical element, and set the radius of the outer circle as R, and the radius of the inner circle as r. According to formula: r < x2 + y2 < R, if it is within the range, the physical element remains working. Under the guidance of formula (1), the Xdc_ring_array_um() function is written. According to the parameters analyzed in the previous section, the transducer unit and the transducer array is as shown in Fig. 11. Here, we set the size of each small square to 1 um so that it is relatively close to circular.

Fig. 11. Transducer with a hole and its array

4.2 Comparison of Transducer Ultrasonic Pressure In this section, we setup a simulation to test the echo signal in CMUT. The so-called self transceiver means that the same transducer probe is used for not only transmitting but also receiving at the same time. The transmitter needs to be configured with Xdc_execution

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(emit aperture, execution). The frequency of the sine wave with 8 cycles is 200 kHz. The receiver needs to call the xdc_impulse() function, and the rest remain the default. It can be seen from Fig. 12.(a) that there is little difference between the transducer unit with and without a hole in normalized ultrasonic pressure. Figure 12.(b) shows that the normalized ultrasonic pressure of CMUT array with a hole is similar to that of the transducer without a hole. However, the echo ultrasonic pressure of 3 * 3 transducer array is 5 times higher than that of its transducer unit. It can be seen that the formation of transducer array is a necessary condition for obtaining a large echo ultrasonic pressure.

Fig. 12. (a) Normalized ultrasonic pressure comparison of transducer unit; (b) Comprehensive comparison of normalized pressure of transducer and its array

4.3 Comparison of Transducer Ultrasonic Pressure in Its Unit In this section, the ultrasonic field of CMUT array is simulated by Focus package, which can be seen as a supplement to Field II. In Focus software, we simulate the ultrasonic field of CMUT unit with the help of the CW_Pressure() function. It can be seen from Fig. 13 that the transducer unit has certain directivity, but it does not have the focusing ability.

Fig. 13. Acoustic field of transducer unit

As shown in Fig. 14, CMUT array can be focused and deflected. However, with the deflection of the beam, the side lobes produced by the array also increase. The presence of side lobes will produce artifacts and reduce the quality of ultrasonic imaging [8]. Thus, if we intend to make an ultrasonic imaging with high signal-noise-ratio, we can make some effort to decrease the side lobes.

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Fig. 14. (a) acoustic beam focusing; (b) acoustic beam deflection

5 Summary Based on CMOS-MEMS technology, CMUT can be directly integrated with circuits. It is found that the upper electrodes is easy to collapse, resulting in device failure in MEMS process. A novel CMUT process is presented in this paper. Combined with the simulation of the processing steps, we describe in detail how the process is compatible with the standard IC process, how to complete the self-stopping, and how to prevent the top electrode from collapsing. In addition, with the help of COMSOL, the CMUT model is established. Using the simulation tool FEA, the steady state and natural mode of the model are simulated. Furthermore, the ultrasonic field is simulated with MATLAB. Next, we will apply the simulating results to the process manufacturing CMUT while the model simulation will be modified according to the experimental data. Acknowledgement. This work was supported in part by National key R&D program of China (Grant number 2016YFC0105002, 61674162, 2018YFB2100904, U1913601, 2018YFF01012500), Shenzhen Key Lab for RF Integrated Circuits, Shenzhen Shared Technology Service Center for Internet of Things, Shenzhen government funds (Grant numbers JCYJ20180305164616316).

References 1. Savoia, A.S., Caliano, G., Pappalardo, M.: A CMUT probe for medical ultrasonography: from microfabrication to system integration. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 59(6), 1127–1138 (2012). https://doi.org/10.1109/TUFFC.2012.2303 2. Zhang, H., He, C., Miao, J., Li, Y., Zhang, W., Xue, C.: A method of the CMUT array design and imaging simulation based on MATLAB. Chinese J. Sensors Actuators 27(4), 490–494 (2014). https://doi.org/10.3969/j.issn.1004-1699.2014.04.013 3. Xu, P., Yu, T., Yu, F.: Investigation of residual stress influence on CMUT in standard CMOS process. In: International Conference on Electric Information and Control Engineering, pp. 495–498 (2011) 4. Khanghah, M.M., Sadeghipour, K.D.: A 0.5 V offset cancelled latch comparator in standard 0.18 um CMOS process, Analog Integr. Circuits Signal Process. 79(1) (2014) 5. Wodnicki, R., et al.: Multi-row linear cMUT array using cMUTs and multiplexing electronics, Proc. - IEEE Ultrason. Symp. 2696–2699 (2009). doi: https://doi.org/10.1109/ULTSYM.2009. 5442074

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6. Zhang, R., Ren, Z., Yu, S.: Loss-reduction in flexibly vertical coupled ring lasers through asymmetric double shallow ridge and ICP / ICP cascade etching 20(22), 1821–1823 (2008) 7. Hongliang, W., Xiangjun, W., Changde, H., Chenyang, X.: Design and performance analysis of capacitive micromachined ultrasonic transducer linear array. Micromachines 5(3), 420–431 (2014) 8. Zhang, Y., Zhu, Q.S., Itoh, T., Maeda, R., Toda, A.: High-resolution wet etching technology of thick electroless nickel alloy film for MEMS devices and packaging, In: Proc. - 2013 14th Int. Conf. Electron. Packag. Technol. ICEPT 2013, pp. 396–399 (2013), https://doi.org/10.1109/ ICEPT.2013.675649

An Efficient Learning Automaton Scheme for Massive-Action Environments Hongyu Zhu1 , Jianwei Tian1 , Chong Di2(B) , Zheng Tian1 , Yizhen Sun1 , Qiyao Li1 , and Shenghong Li2 1

State Grid Hunan Electric Power Information and Communication Corporation, Changsha, China 2 Shanghai Jiao Tong University, Shanghai, China [email protected]

Abstract. As a primary tool in reinforcement learning, learning automaton (LA) has been widely applied in the area of electronic engineerings such as signal processing and wireless communications. The scenario that an LA operates may have a large number of actions. The existing LA schemes, which are designed in a fixed manner, are inefficient in massive-action environments. In this paper, we propose an efficient LA (ELA) scheme based on a novel learning framework. The welldesigned framework includes a brand-new action set adjustment strategy, a convergence judgment strategy, and an exploration-exploitation strategy. Experimental results in various massive-action environments indicate that the proposed ELA scheme achieves significant improvements in both convergence rate and convergence time, especially in environments with an extremely large number of actions. Keywords: Learning automata · Massive-action environments Variable structure stochastic automaton

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Learning automaton (LA) is a foundational problem in computational learning theory and reinforcement learning [1], which has been widely applied in the area of engineerings such as signal processing pattern recognition, information security, computer vision, and wireless communications [2–6]. A broad range of LA and their applications are reviewed in [7,8]. An LA can adaptively explore the optimal action among all possible actions by interacting with a random environment [9]. The LA schemes are divided into various genres according to different definitions of the action set, different structures of the LA, and different types of feedback from environments. As a typical case, the variable structure stochastic automaton (VSSA) in P-model environments has raised great attention from researchers, and plentiful theoretical achievements and learning schemes for VSSA problems emerge in the last decades. In general, the framework of VSSA is mathematically formulated as a quadruple , where A is the finite action set, B is the feedback set, and c The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022  Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 516–524, 2022. https://doi.org/10.1007/978-981-19-0390-8_63

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the feedback is binary in P-model environments where 0 represents a reward and 1 represents a penalty, C is the set of reward probabilities of actions in the random environment, and ci = P r{action ai is rewarded}, P is the action probability vector. P plays a dual role in VSSA, including the action selection and the convergence judgment. At each interaction with the environment, the LA randomly selects an action according to the probability distribution of P and updates P in view of the feedback from the environment. The LA gets converged when the maximum action probability in P is greater than a predefined threshold. The aim of VSSA is to identify the optimal action that possesses the highest reward probability in the environment. The estimator algorithms are the state-of-the-art family of VSSA, which estimate the reward probabilities of actions and adopt unique strategies to update the action probability vector P utilizing the estimates. The discretized pursuit (DPRI ) scheme [10] is the most classic estimator algorithm, which increases the action probability of the estimated optimal action and decreases others. The discretized generalized pursuit algorithm (DGPA) [11] is a variant of DPRI . For the update strategy of P in DGPA, the action probabilities of actions that have higher estimates than currently selected action are increased while others are decreased. The last-position elimination-based learning automata (LELA) [12] is one of the latest estimator algorithms. It penalizes the action with the lowest estimate of reward probability by decreasing the corresponding action probability and eliminates an action when its action probability is decreased to zero. However, in massive-action environments, the existing schemes for VSSA problems are not perfectly applicable, preventing the LA from the large-scaleaction oriented applications. The predicaments are mainly embodied in two aspects: the exploration stage and the action probability vector update stage. In the exploration stage, the time complexity of selecting an action by the action probability vector P is O(r), where r is the total number of actions. It is timeconsuming in massive-action environments since the number of actions can be exceedingly large. Besides, in the action probability vector update stage, the 1 , where n ∈ N∗ is a resolution step size Δ for updating P is obtained by Δ = rn parameter for discretizing the step size. It is obvious that the step size varies inversely with the number of actions. In massive-action environments, the step size is extremely small, and thus the scheme will be inefficient. In this paper, we propose an efficient learning automaton (ELA) scheme to address the challenges of learning in massive-action environments. We evaluate the performance of ELA in various massive-action environments, and the experimental results show that ELA outperforms the state-of-the-art estimator algorithms in both convergence rate and convergence time.

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The Proposed Scheme

For a massive-action environment, there exists a large number of non-optimal actions that can interfere an LA to chase down the optimal action. It is desirable to design a strategy to adaptively narrow the action set during the learning process. Based on this idea, we propose a novel learning framework for LA in massive-action environments. The learning framework consists of three parts: the action set adjustment strategy, the convergence judgment, and the explorationexploitation strategy. For the action set adjustment strategy and the convergence judgment, a welldesigned mechanism based on the probability theory and statistics is proposed to adaptively remove the non-optimal actions from the action set. At each iteration during the learning process, the estimated reward probability cˆi of each action ai ∈ A can be calculated by collecting the historical information including the selected times Zi and the rewarded times Wi , cˆi =

Wi . Zi

(1)

Suppose am is the action that has the highest estimated reward probabilities cˆm , i.e., ci }. (2) m = arg max{ˆ i

F Pmi

is defined to evaluate the confidence of the claim that A false probability action am is better than action ai ∈ A, i = m. That is, F Pmi =

Zm  x  x=1 y=0

f (y, Zm , cˆm )f (x, Zm , cˆi )

   Zm  x   Zm y Zm −y Zm = (ˆ cm ) (1 − cˆm ) (ˆ ci )x (1 − cˆi )Zm −x , y x x=1 y=0

(3)

where f (·, ·, ·) is the probability mass function of Binomial distribution. WhenF ever the false probability Pmi is less than a predefined threshold H, the action ai will be removed from the action set. The convergence judgment is based on the action set adjustment strategy. That is, an LA gets converged if there is only one action left in the action set. The exploration-exploitation strategy is one of the most attractive research subjects in the field of reinforcement learning, which has been intensively studied by mathematicians. In traditional schemes for VSSA, the exploration of the environment is implemented by an action probability vector P. Since the action probability vector is inefficient in the massive-action environments as discussed above, we aim to design an economical exploration-exploitation strategy with low time complexity. Specifically, whenever the action set has been adaptively adjusted, a series of next iterations will be assigned to actions by following strategy:

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– exploration allocation: all actions in current action set get an opportunity to interact with the environment; – exploitation allocation: the actions with correspondingly high estimated reward probabilities get an extra interaction with the environment. In detail, the implementation of the exploration-exploitation strategy can be formulated by cm − cˆi )] , (4) Ni = 1 + [1 − (ˆ where Ni is the number of interactions with the environment of action ai in next iterations, and [·] is the round function. The proposed exploration-exploitation strategy is time efficient and can be achieved with the time complexity O(1) at each iteration. We summarize the overall process of the proposed ELA scheme in Algorithm 1.

Algorithm 1. The Efficient LA Scheme for Massive-Action Environments Require: The convergence threshold: H. 1: Initialize Convergence Flag: F = 0. 2: Initialize Iteration: t = 0. 3: Initialize Rewarded times: Wi = 1, ∀ai ∈ A. 4: Initialize Selected times: Zi = 2, ∀ai ∈ A. 5: Initialize Number of interactions in next iterations: Ni = 1, ∀ai ∈ A. 6: repeat 7: ∀ai ∈ A, interact with the environment for Ni times. 8: Update rewarded times Wi and selected times Zi , ∀ai ∈ A according to the feedback β(t) from the environment in each interaction at time t. 9: Update the estimation of rewards cˆi , ∀ai ∈ A by (1). 10: Get the estimated optimal action am by (2). 11: for ∀ai ∈ A, i = m do F using (3). 12: Compute the false probability Pmi F 13: if ∀i = m, Pmi ≤ H then 14: Remove action ai from action set A. 15: end if 16: Update Ni , ∀ai ∈ A according to (4). 17: end for 18: t = t + 1. 19: if |A| == 1 then 20: Convergence Flag F = 1. 21: end if 22: until F = 1 23: Output: the optimal action am which has the highest estimation of rewards.

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Experimental Results

In the area of LA, the complexity of an environment is embodied in two aspects: the number of actions r and the minimum difference δ of the reward probabilities between the optimal action and non-optimal actions. In this paper, without loss of generality, we consider two kinds of massive-action environments: normal environments and uniform environments. In normal environments, given the number of actions r, the reward probability cm of the optimal action and the minimum difference δ, the probability distribution of (r − 1) non-optimal actions is a standard normal distribution with normalization to the interval [0, cm − δ]. Similarly, in uniform environments, the probability distribution of (r − 1) nonoptimal actions is a uniform distribution within the interval [0, cm − δ]. The number of actions r varies from 1,000 to 20,000, evaluating the schemes with different degrees of challenges in massive-action environments. All simulations are compiled by C++ programming language, under Visual Studio, Windows 10, Intel(R) Xeon(R) CPU E5 @2.10 GHz, 64.00 GB RAM. For an algorithm of LA, it is inevitable to make a trade-off between the accuracy and the convergence rate. The accuracy is the probability of a correct convergence, and the convergence rate is the average iterations for an LA to get converged. Specifically, in estimator algorithms, there is a manual tuning process of the resolution parameter n ∈ N∗ , which is utilized to dominate the step size Δ and then balances the accuracy and the efficiency. In this paper, we set the values of resolution parameters following the instructions in [12]. As for the proposed ELA scheme, the predefined threshold H is set to 0.001. Table 1 lists the accuracy of contenders. The results indicate that all schemes can converge to the optimal action with quite high accuracy and corroborate the -optimality of LA schemes experimentally. Table 1. Accuracy (number of correct convergence/number of experiments) (cm = 0.8) δ = 0.3 δ = 0.2 1000 5000 20000 1000 5000 20000 Normal environment DPRI

0.997 0.996 0.996 0.996 0.996 0.995

DGPA 0.998 0.998 0.999 0.998 0.998 0.999 LELA 0.999 0.999 0.999 0.999 0.999 0.999 ELA

0.999 0.999 0.999 0.999 0.999 0.999

Uniform environment DPRI

0.998 0.997 0.997 0.997 0.997 0.996

DGPA 0.998 0.999 0.999 0.998 0.999 0.999 LELA 0.999 0.999 0.999 0.999 0.999 0.999 ELA

0.999 0.999 0.999 0.999 0.999 0.998

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In massive-action environments, the efficiency of an LA scheme is reflected both in the convergence rate and the convergence time. The convergence time is the average time for an LA to get converged, which intuitively represents the time complexity of a scheme. Figure 1 and Fig. 2 present the simulation results of the convergence rate in normal environments and uniform environments respectively. It is shown that the proposed ELA scheme achieves significant improvement in both types of massive-action environments. With the increase in the number of actions, the superiority of the ELA scheme is also increasingly obvious. Take the environments with 20,000 actions (δ = 0.2) as examples, the convergence rates of DPRI , DGPA and LELA are improved by ELA with 73.58%, 71.01%, 80.57% in normal environments and with 29.66%, 52.21%, 43.85% in uniform environments, respectively.

Fig. 1. Convergence rates of schemes in normal environments (cm = 0.8)

The simulation results of the convergence time in normal environments and uniform environments are separately illustrated in Fig. 3 and Fig. 4. Similarly, the proposed ELA scheme outperforms all of the estimator algorithms in convergence time. Especially in normal environments with 20,000 actions, ELA saves hundreds of times as long as estimator algorithms.

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Fig. 2. Convergence rates of schemes in uniform environments (cm = 0.8)

Fig. 3. Convergence time of schemes in normal environments (cm = 0.8)

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Fig. 4. Convergence time of schemes in uniform environments (cm = 0.8)

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Conclusion

This paper focuses on the problem that existing LA schemes can not well applicable in massive-action environments. We design a novel learning framework, including a brand-new action set adjustment strategy, a convergence judgment strategy, and an efficient exploration-exploitation strategy. Based on the learning framework, we propose an efficient LA scheme. We evaluate the proposed ELA scheme in various massive-action environments and compare with state-ofthe-art estimator algorithms. Experimental results indicate that the proposed ELA scheme achieves significant improvements in both convergence rate and convergence time, especially in environments with an extremely large number of actions. Acknowledgement. This research work is funded by the National Nature Science Foundation of China under Grant 61971283 and 2020 Industrial Internet Innovation Development Project of Ministry of Industry and Information Technology of P.R. China “Smart energy Internet security situation awareness platform project”.

References 1. Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press (2018) 2. Kumar, N., et al.: Intelligent mobile video surveillance system as a Bayesian coalition game in vehicular sensor networks: learning automata approach. IEEE Trans. Intell. Transp. Syst. 16(3), 1148–1161 (2015) 3. Thathachar, M.A.L., Sastry, P.S.: Learning automata for pattern classification. In: Networks of Learning Automata, pp. 139–176. Springer, Boston (2004). https:// doi.org/10.1007/978-1-4419-9052-5 4

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4. Daliri Khomami, M.M., Rezvanian, A., Bagherpour, N., Meybodi, M.R.: Minimum positive influence dominating set and its application in influence maximization: a learning automata approach. Appl. Intell. 48(3), 570–593 (2017). https://doi.org/ 10.1007/s10489-017-0987-z 5. Cuevas, E., Wario, F., Osuna-Enciso, V., Zaldivar, D., P´erez-Cisneros, M.: Fast algorithm for multiple-circle detection on images using learning automata. IET Image Proc. 6(8), 1124–1135 (2012) 6. Di, C., Li, F., Li, S.: Sensor deployment for wireless sensor networks: a conjugate learning automata-based energy-efficient approach. IEEE Wirel. Commun. 27(5), 80–87 (2020) 7. Narendra, K.S., Thathachar, M.A.: Learning automata-a survey. IEEE Trans. Syst. Man Cybern. 4, 323–334 (1974) 8. Thathachar, M.A., Sastry, P.S.: Varieties of learning automata: an overview. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 32(6), 711–722 (2002) 9. Narendra, K.S., Thathachar, M.A.L.: Learning Automata: An Introduction. Courier Corporation (2012) 10. Oommen, B.J., et al.: Discretized pursuit learning automata. IEEE Trans. Syst. Man Cybern. 20(4), 931–938 (1990) 11. Agache, M., Oommen, B.J.: Generalized pursuit learning schemes: new families of continuous and discretized learning automata. IEEE Trans. Cybern. 32(6), 738– 749 (2002) 12. Zhang, J., Wang, C., Zhou, M.C.: Last-position elimination-based learning automata. IEEE Trans. Cybern. 44(12), 2484–2492 (2014)

A Descriptor System Approach to Fault Estimation and Compensation for T-S Fuzzy Discrete-Time Systems Yu Chen1(B) , Xiaodan Zhu2 , and Qian Sun2 1

2

School of Chemistry and Materials Science, Ludong University, Yantai 264025, China [email protected] School of Mathematics and Statistics Science, Ludong University, Yantai 264025, China

Abstract. This paper presents a fault estimation and compensation problem for T-S fuzzy model in term of a descriptor system approach. Set the fault as a state, then a new system named augmented descriptor system is established. An observer is given to show state and fault’s estimates, simultaneously. Fault compensation is also obtained according to the estimated sensor fault information above, which make this system can normally work when faults occur. A numerical example is used to prove that this method has effectiveness. Keywords: Observer design · Fault estimation Descriptor system approach · T-S fuzzy model

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Introduction

Fuzzy logic control was introduced to analyze highly complicated nonlinear systems. Fuzzy if-then rules were used to establish T-S fuzzy model [1]. The modeling method is used to make that analyses and syntheses of nonlinear systems be simplified. By applying the concept of T-S fuzzy, different state space’s local dynamics are used to describe linear systems by means of a membership function mixing local models together smoothly, nonlinear dynamics are described as the overall fuzzy model. H∞ control problem [2] for network T-S fuzzy systems was solved. Fuzzy delay compensation control scheme was proposed in [3]. For networked fuzzy model, [4] and [5] studied H∞ controller. A scheme of affine fuzzy model [6] was proposed to detect the faults. In finite-frequency rang, [7] designed H∞ control problem for continuous fuzzy models. In 1971, Niederlinski presented firstly the concept of “integrity control”, which marked the beginning of fault-tolerant control [8]. In 1980, Siljak proposed reliable stabilization control scheme [9], which was one of the earliest study. Fault-tolerant control has been widely concerned, and a great deal of innovation has been achieved. At present, fault tolerant control includes passive c The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022  Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 525–532, 2022. https://doi.org/10.1007/978-981-19-0390-8_64

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and active types. [10] addressed two types problems at the same time. In [11] and [12], the passive type methods were presented, respectively. Active type is that the gains of controller are controlled to reset or reconfigure structural units in case of system failure so that system fault adjustment is obtained, that is, controlled systems have fault tolerance. The fault detection method [13] was presented by applying the descriptor system method. For T-S fuzzy systems, a fault detection filter was proposed in [14]. [15] showed a scheme about designing robust fuzzy observer for nonlinear systems. At the same time, there were also many active type results. The active type methods were proposed in nonlinear multi-agent [16] and stochastic systems [17]. For fuzzy system with sensor faults [18], the fault estimation and compensation problem was solved by using the low-frequency method. By applying a descriptor system approach, some new active type strategies for fuzzy model [19] and nonlinear systems [20] were given, respectively. Therefore, there are few results on a fault estimation and compensation method for T-S fuzzy models. This paper’s contributions are listed: The control system is expended into a descriptor system by faults as state. Considering this system, an observer is given to get state and fault’s estimates, simultaneously. State error system’s stable and H∞ performance are analyzed. By considering the estimate information, the fault compensate scheme is designed so that this system can normally work when faults happen. A numerical example testifies that the proposed method has effectiveness.

2

Problem Formulation

Consider the system: Plant Rule i: If M1κ is Fi1 , M2κ is Fi2 , . . ., Mnκ is Fin , then χκ+1 =

N 

ηi (Mκ )[A1i χκ + Bi μκ + Di dκ ],

(1)

i=1

yk = A2 χκ + Fs fκ ,

(2)

where i = 1, . . . , N, N is the number of IF-THEN rules, M1κ , M2κ, . . ., Mnκ are n Fi (M ) n l i lκ , the premise variables, Fi , Fi2 , . . ., Fin are fuzzy set, ηi (Mκ ) = N l=1 i=1 l=1 Fl (Mlκ ) N g gy gd gu is output. dκ ∈ R , μ(k) ∈ R and i=1 ηi = 1. χκ ∈ R is state, yκ ∈ R fκ ∈ Rgf are disturbance, input, and sensor fault, respectively, which belong to L2 [0, ∞). A1i , Bi , Di , A2 , and Fs are known matrices. Hypothesis that (A1i , A2 ) is observable and Fs has full rank. T  =χ ¯κ , then the dynamic global model is Set χTκ fκT ¯χ E ¯κ+1 =

N 

¯i μκ + D ¯ i dκ ], ηi (Mκ )[A¯1i χ ¯κ + B

(3)

i=1

y¯κ = A¯2 χ ¯κ , (4)          ¯ i = Di , A¯2 = A2 Fs . ¯i = Bi , D ¯ = I 0 , A¯1i = A1i 0 , B where E 0 0 0 0 00 

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For system (3)–(4), construct the fuzzy observer: sκ+1 =

N 

¯i μκ + L1i yκ ], ˆ ηi (Mκ )[(A¯1i − L1i A¯2 )χ ¯κ + B

(5)

i=1

¯ + L2 A¯2 )−1 (sκ + L2 y¯κ ), χ ¯ˆκ = (E (6) ˆ¯κ . yˆ¯κ = A¯2 χ (7)     ˆ¯κ is χ ˆ¯κ is χκ ’s estimate, fˆκ = 0 I χ ˆ where χ ¯κ ’s estimate, χ ˆκ = I 0 χ ¯κ is fκ ’s ˆ estimate, y¯ is yκ ’s estimate, and L1i , L2 are the observer gains. By applying transformation, observer (5)–(7) is ¯2 )χ ¯ + L2 A ˆ (E ¯κ+1 =

N 

¯2 )χ ¯i μκ + L1i y¯κ + L2 y¯κ+1 ], (8) ˆ ηi (Mκ )[(A1i − L1i A ¯κ + B

i=1

ˆ yˆ ¯κ = A¯2 χ ¯κ .

(9)

Adding L2 y¯κ+1 on both sides of system (3), then ¯ + L2 A¯2 )χ (E ¯κ+1 =

N 

¯i μκ ηi (Mκ )[(A1i − L1i A¯2 )χ ¯κ + B

i=1

¯ i dκ + L1i y¯κ + L2 y¯κ+1 ]. +D

(10)

ˆ¯κ , ϑκ = y¯κ − yˆ¯κ , then the error system is that Define θκ = χ ¯κ − χ θκ+1 =

N 

¯2 )−1 (A1i − L1i A¯2 )θκ + (E ¯ i dκ ], (11) ¯ + L2 A ¯ + L2 A¯2 )−1 D ηi (Mκ )[(E

i=1

ϑκ = A¯2 θκ .

(12)

T  Remark 1. The gain L2 is that L2 = 0 QT , where non-singular matrix Q ∈ ¯ +L2 A¯2 is non-singular. R. Q = I is given to deal with the problem. Hence S = E To simplify, definitions are given: ¯1η = A

N 

¯2 )−1 (A1i − L1i A ¯2 ), D ¯ + L2 A ¯η = ηi (Mκ )(E

i=1

N 

¯2 )−1 D ¯ i. ¯ + L2 A ηi (Mκ )(E

i=1

For system (1)–(2), observer (5)–(7) is presented to make error system meet H∞ performance, i.e., ϑκ ∞ < γdκ ∞ holds.

3

Main Results

Theorem 1. For γ > 0, observer (5)–(7) is designed so that stability and H∞ disturbance attention level γ of system (11)–(12) are analyzed if matrices P = P T > 0 and L1i exist for all i ∈ {1, . . . , N} such that (13) is obtained.

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⎡

−P ⎢ ∗ ⎢ ⎣ ∗ ∗

φ12 φ13 −P 0 ∗ φ33 ∗ ∗

⎤ A¯T2 0 ⎥ ⎥ < 0, 0 ⎦ −I

(13)

¯ i , φ33 = where φ12 = (S −1 (A1i − L1i A¯2 ))T P, φ13 = (S −1 (A1i − L1i A¯2 ))T PS −1 D 2 −1 ¯ T −1 ¯ −γ I + (S Di ) P(S Di ). Proof. Define a Lyapunov function: Vκ = θκT Pθκ , then ΔVκ =

N 

ηi2 (Mκ ))θκT [(S −1 (A1i − L1i A¯2 ))T P(S −1 (A1i − L1i A¯2 )) − P]θκ .(14)

i=1

From (13), one has −P + (S −1 (A¯1i − L1i A2 ))T P × (S −1 (A¯1i − L1i A2 )) ¯ i )(γ 2 I − (S −1 D ¯ i )T P(S −1 D ¯ i ))−1 +(S −1 (A¯1i − L1i A2 ))T P(S −1 D ¯ i )T + A¯T A¯2 < 0, ×(S −1 (A¯1i − L1i A2 ))T P(S −1 D 2

(15)

so it is ΔVκ = −

N 

¯ i )(γ 2 I − (S −1 D ¯ i )T ηi2 (Mκ ))θκT [(S −1 (A¯1i − L1i A2 ))T P(S −1 D

i=1

¯ i ))−1 (S −1 (A¯1i − L1i A2 ))T P(S −1 D ¯ i )T + A¯T2 A¯2 ]θκ < 0. (16) ×P(S −1 D Therefore ΔVκ < 0 for θκ = 0, i.e., (11)–(12)’s stability is proven when dκ = 0. To prove the condition, one has T T ¯T T ¯T T ¯T ¯T ¯1η θκ + θT A ¯ ¯ ¯ A1η P A θκ+1 Pθκ+1 = θκ κ 1η P Dη dκ + dκ Dη P A1η θκ + dκ Dη P Dη dκ .

(17)

Consider (13), then ¯ η (γ 2 I − D ¯ T PD ¯ η )−1 × D ¯ T P A¯1η < 0, (18) −P + A¯T1η P A¯1η + A¯T2 A¯2 + A¯T1η P D η η so T ¯ ηT P A¯1η ]θκ ¯ η (γ 2 I − D ¯ ηT P D ¯ η )−1 D θκ+1 Pθκ+1 < θκT [P − A¯T2 A¯2 − A¯T1η P D ¯ ηT P A¯1η θκ + dTκ D ¯ ηT P D ¯ η dκ + dTκ D ¯ η dκ . +θκT A¯T1η P D (19)

Add −θκT Pθκ + ϑTκ ϑκ to (19), one has T θκ+1 Pθκ+1 − θκT Pθκ + ϑTκ ϑκ ¯ T P A¯1η ]θκ ¯ η (γ 2 I − D ¯ T PD ¯ η )−1 D < −θT [A¯T P D κ

1η

η

η

T ¯ T P A¯1η ]θκ+1 + 2dT D ¯T ¯ η (γ 2 I − D ¯ T PD ¯ η )−1 D +θκ+1 [A¯T1η P D η η κ η Pθκ+1 T ¯ ηT P A¯1η ]θκ+1 − dTκ D ¯ ηT P D ¯ η (γ 2 I − D ¯ ηT P D ¯ η )−1 D ¯ η dκ , (20) −θκ+1 [A¯T1η P D

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thus T ¯ T P A¯1η ]θκ ¯ η (γ 2 I − D ¯ T PD ¯ η )−1 D θκ+1 Pθκ+1 − θκT Pθκ + θκT [A¯T1η P D η η T ¯ η (γ 2 I − D ¯ ηT P D ¯ η )D ¯ ηT P A¯1η ]θκ+1 + ϑTκ ϑκ −θκ+1 [A¯T1η P D T ¯ T P A¯1η ]θκ+1 + 2dT D ¯T ¯ 2I − D ¯ T PD ¯ η )−1 D < −θκ+1 [A¯T1η P D(γ η η κ η Pθκ+1 ¯ T PD ¯ η )dκ + dT (γ 2 I − 2D ¯ T PD ¯ η )dκ ≤ dT γ 2 Idκ . −dT (γ 2 I − D κ

η

κ

η

κ

(21)

Sum up two sides of (21) and consider χκ  → 0 as κ → 0, then ϑκ ∞ < γdκ ∞ holds. This proof is finished. Theorem 2. For γ > 0, the form of observer (5)–(7) is shown to guarantee that system (11)–(12) is stable and has H∞ disturbance level γ if matrices P = P T > 0, Yi exist for all i ∈ {1, . . . , N} in order that (22) holds. ⎡ ⎤ −P ϕ12 ϕ13 A¯T2 ⎢ ∗ −P 0 0 ⎥ ⎢ ⎥ (22) ⎣ ∗ ∗ ϕ33 0 ⎦ < 0, ∗ ∗ ∗ −I ¯ i − A¯T Y T S −1 D ¯ i , ϕ33 = where ϕ12 = A¯T1i S −T P − A¯T2 YiT , ϕ13 = A¯T1i S −T PS −1 D 2 i  T 2 −1 ¯ T −1 ¯ −1 −γ I + (S Di ) P(S Di ). Observer’s gains are L1i = SP Yi , L2 = 0 I . Remark 2. For Theorem 1, it is difficult to deal with (13). Give a definition that Yi = LT1i S −T P, (22) is equivalent to (13), then L1i , L2 are given. Because  T L2 = 0 I in (22) is to deal with this paper’s problem. In the following, N a fault compensation method is proposed. Set μκ = v=1 ηv (Mκ )Kv yκ , Kv are controller gain. By subtracting fault’s   estimate from yκ , then ycκ = yκ − F2 fˆκ = A2 χκ + F2 0 I θκ . N Use ycκ to substitute yκ , then μcκ = v=1 ηv (Mκ )Kv ycκ so that the closedloop system is χκ+1 =

N  i=1

ηi (Mκ )

N 

  ηv (Mκ )[(A1i + Bi Kv A2 )χκ + Bi Kv F2 0 I θκ + Di dκ ]

v=1

  ycκ = A2 χκ + F2 0 I θκ , then this system has certain fault tolerance by adopting the proposed scheme.

4

A Numerical Example

The system considered in this article has two rules. 1 Rule 1: if χ1k is F1 , where F1 (χ1κ ) = 1+exp(−2χ ) , then parameters are 1κ      0.3 0 0.1 0 −2.0 A11 = , B1 = , D1 = , A2 = −0.1I, Fs = I. 0 0.7 0 −0.2 0.5

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Rule 2:if χ1κ is F2 , where  F2 (χ1κ) = 1 − F1 (x1κ ),  then parameters are 0.7 0 0.1 0 −0.8 A12 = , B2 = , D2 = , A2 = −0.1I, Fs = I. 0 0.5 0 −0.8 −2.0   T  Choose γ = 0.03, χ0 = χ10 χ20 = −0.5 0.8 , Fig. 1, 2 depict estimate of fκ , where f1 o is f1κ ’s estimate and f2 o is f2κ ’s estimate. The trajectory of ycκ is given in Fig. 3, where ycκ is denoted by yc . 0.06 f1 f1o

0.05

Magnitude

0.04

0.03

0.02

0.01

0

-0.01 0

50

100

150

Time step

Fig. 1. f1κ and its estimation 0.06 f2 f o 2

0.04

Magnitude

0.02

0

-0.02

-0.04

-0.06 0

50

100

Time step

Fig. 2. f2κ and its estimation

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0.08 yc1 y

0.06

c2

Magnitude

0.04

0.02

0

-0.02

-0.04

-0.06 0

50

100

150

Time step

Fig. 3. Compensated output ycκ

5

Conclusion

This paper has discussed a fault estimation and compensation strategy of T-S fuzzy model. By a descriptor system approach, a fuzzy observer is designed to give fault’s estimation, which guarantees the stability of error system with H∞ performance. A compensation controller is shown so that systems have certain fault-tolerance. A numerical example gives this method’s effectiveness. Acknowledgments. The work was supported by the Natural Science Foundation of Shandong Province under Grant ZR2019PF009.

References 1. Takagi, T., Sugeno, M.: Fuzzy identification of systems and its application to modeling and control. IEEE Trans. Syst. Man Cybern. smc–15, 116–132 (1985) 2. Aslam, M., Dai, X.: H∞ control for network T-S fuzzy systems under time varying delay for multi-area power systems. Int. J. Control Autom. Syst. 18, 2774–2787 (2020) 3. Zhang, J., Shi, P., Xia, Y.: Fuzzy delay compensation control for T-S fuzzy systems over network. IEEE Trans. Cybernet. 43, 259–268 (2013) 4. Lu, A., Zhai, D., Dong, J., Zhang, Q.: Network-based fuzzy H∞ controller design for T-S fuzzy systems via a new event-triggered communication scheme. Neurocomputing 273, 403–413 (2018) 5. Chaibi, R., Aiss, H., Hajjaji, A., Hmamed, A.: Stability analysis and robust H∞ controller synthesis with derivatives of membership functions for T-S fuzzy systems with time-varying delay: input-output stability approach. Int. J. Control Autom. Syst. 18, 1872–1884 (2020) 6. Liu, Y., Yang, H., Li, X.: Event-triggered fault detection observer design for affine fuzzy systems. Neurocomputing 267, 564–571 (2017)

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7. Wang, H., Peng, L., Ju, H., Wang, Y.: H∞ state feedback controller design for continuous-time T-S fuzzy systems in finite frequency domain. Inf. Sci. 223, 221– 235 (2013) 8. Niederlinski, A.: A heuristic approach to the design of linear multivariable interacting control systems. Automatica 7, 691–701 (1971) 9. Siljak, D.: Reliable control using multiple control systems. Int. J. Control 31, 303– 329 (1980) 10. Jiang, J., Yu, X.: Fault-tolerant control systems: a comparative study between active and passive approaches. Annu. Rev. Control. 36, 60–72 (2012) 11. Gao, Z., Jiang, B., Shi, P., Liu, J., Xu, Y.: Passive fault-tolerant control design for near-space hypersonic vehicle dynamical system. Circuits Syst. Sig. Process 31, 565–581 (2012) 12. Su, L., Zhu, X., Zhou, C., Qiu, J.: An LMI approach to reliable H∞ guaranteed cost control for uncertain non-linear stochastic Markovian jump systems with modedependent and distributed delays. ICIC Express Lett. 5, 249–254 (2011) 13. Zhu, X., Xia, Y.: Fault detection for discrete systems based on a descriptor system method. IET Control Theory Appl. 8, 697–704 (2014) 14. Zhu, X., Xia, Y.: H∞ descriptor fault detection filter design for T-S fuzzy discretetime systems. J. Franklin Inst. 351, 5358–5375 (2014) 15. Li, L., Ding, S., Yang, Y., Zhang, Y.: Robust fuzzy observer-based fault detection for nonlinear systems with disturbances. Neurocomputing 174, 767–772 (2016) 16. Deng, C., Yang, G.: Adaptive fault-tolerant control for a class of nonlinear multiagent systems with actuator faults. J. Franklin Inst. 354, 4784–4800 (2017) 17. Lin, Z., Lin, Y., Zhang, W.: H∞ stabilisation of non-linear stochastic active faulttolerant control systems: fuzzy-interpolation approach. IET Control Theory Appl. 4, 2003–2017 (2010) 18. Chen, Y., Zhu, X., Gu, J.: Fault estimation and compensation for fuzzy systems with sensor faults in low-frequency domain. In: Liang, Q., Wang, W., Liu, X., Na, Z., Li, X., Zhang, B. (eds.) Communications, Signal Processing, and Systems. CSPS 2020. LNEE, vol. 654, pp. 799–807. Springer, Singapore (2021). https://doi. org/10.1007/978-981-15-8411-4 106 19. Liu, M., Cao, X., Shi, P.: Fault estimation and tolerant control for fuzzy stochastic systems. IEEE Trans. Fuzzy Syst. 21, 221–229 (2013) 20. Gao, Z., Ding, S.: Sensor fault reconstruction and sensor compensation for a class of nonlinear state-space systems via a descriptor system approach. IET Control Theory Appl. 1, 578–585 (2007)

Intelligent Water and Fertilizer System Based on NB-IoT Zhifeng Jia and Peidong Zhuang(B) College of Electronics Engineering, Heilongjiang University, Harbin 150080, Heilongjiang, People’s Republic of China [email protected]

Abstract. Water and fertilizer integration technology is one of the important means of saving water and fertilizer in modern greenhouse agriculture. However, the lightweight water and fertilizer systems currently on the market rarely have remote control functions, which has a certain impact on the efficiency of agricultural production. With the rapid development of modern control technology and mobile Internet technology, a lightweight and simple integrated water and fertilizer control system has been designed using these technologies to promote the realization of the dream of the interconnection of everything in contemporary society. Compared with the water and fertilizer system on the market, this remote indoor environment monitoring system not only has an LCD, but the biggest highlight is the realization of hardware remote control and information transmission. It can be used as long as there is NB-IoT base station signal coverage. With the remote control function of this system, users can conveniently and accurately fertilize crops through mobile phones. Keywords: STM32F103RCT6 · NB-IoT · F-S201 flow sensor · HK1100C pressure sensor · Remote control APP

1 Overall Design of the Intelligent Water and Fertilizer System Water and fertilizer integration technology refers to a new agricultural technology that integrates irrigation and fertilization. The integration of water and fertilizer is to use the pressure system or the natural drop of the terrain to mix the soluble solid or liquid fertilizer according to the soil nutrient content and the fertilizer law and characteristics of the crop type, and the fertilizer solution and the irrigation water are mixed, and the water is supplied through a controllable pipeline system., Fertilizer supply, after the water and fertilizer are melted, drip irrigation is formed through the pipe and the dripper, which evenly, regularly, and quantitatively infiltrates the growth area of the crop root system, so that the main root-soil always maintains a loose and suitable water content, and at the same time according to the needs of different crops Fertilizer characteristics, soil environment and nutrient content conditions, crops need water in different growth periods, and the law of fertilizer needs. Design requirements for different growth periods, and

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 533–540, 2022. https://doi.org/10.1007/978-981-19-0390-8_65

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provide water and nutrients to crops directly in proportion. This technology saves fertilizer and water, saves labor and effort, reduces humidity, reduces disease, and increases production efficiency. This paper designs a smart water and fertilizer system based on NB-IoT. This design has low manufacturing cost, accurate fertilizer control, and accurate and intuitive data presentation. Compared with other products of the same type, it has the advantages of wireless transmission control, non-contact, precision fidelity, etc., and supports data upload to the cloud. The YF-S201 flow rate sensor obtains the flow rate of water and fertilizer in real-time and calculates the real-time flow rate based on the flow rate. The HK1100C pressure sensor obtains the water pressure to protect the hardware and prevent excessive water pressure from damaging the hardware. The information is displayed on the LCD screen, and the data is uploaded to the record through the NB-IoT module. This subject is dedicated to the realization of intelligent agricultural production and to promote the realization of the dream of the interconnection of everything in contemporary society [1].

2 Main Hardware Introduction 2.1 F-S201 Flow Sensor YF-S201 water flow sensor is mainly composed of a valve body, water flow rotor assembly, and Hall sensor. When water passes through the water flow rotor assembly, it drives the magnetic rotor to rotate and the Hall sensor outputs a corresponding pulse signal. The water flow can be judged by detecting the pulse signal. The output waveform is shown in Fig. 1:

Fig. 1. YF-S201 output waveform

Its flow rate—pulse frequency relationship: pulse(Hz) = [7.5 × flowrate Q(L/min)] ± 3%

(1)

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2.2 HK1100C Pressure Sensor Its working principle is based on the piezoresistive effect. Using the principle of piezoresistive effect, the pressure of the measured medium directly acts on the diaphragm of the sensor, causing the diaphragm to produce a micro-displacement proportional to the pressure of the medium, which changes the resistance value of the sensor. The electronic circuit detects this change and converts and outputs a standard measurement signal corresponding to this pressure. The compensation of the zero point drift of the piezoresistive pressure sensor adopts the method of series-connected resistance [2].

Fig. 2. Correction circuit

In the series-related resistance compensation principle diagram shown in Fig. 2, the compensation principle is to adjust the zero-point drift of the sensor through resistances connected in series and parallel to the bridge. Among them, the series resistance RS mainly plays the role of zero, and the parallel resistance Rp mainly plays the role of compensation. The key to this method is to accurately calculate the series and parallel resistances RS and RP . For the solution of the compensation resistance, the constant current source power supply method is adopted. Taking Fig. 2 as an example, the constant current source supplies power to the sensor after following the above connection. The bridge should meet the following requirements when the compensation resistor is not connected. V0 = I

R1 R3 − R2 R4 R1 + R2 + R3 + R4

(2)

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When the compensation resistor shown in Fig. 2 is added to the bridge string, to achieve the compensation effect and make the zero temperature drift of the bridge zero, the following requirements must be met.     R1 //Rp [R3 //Rs ] − R2 R4 R1 (T)//Rp [R3 (T)//Rs ] − R2 (T)R4 (T) I− I=0 R1 (T)//Rp + R3 (T) + Rs + R2 (T) + R4 (T) R1 //Rp + R3 + Rs + R2 + R4 (3) In formula (2), Ri (T) is the resistance value of the four bridge resistors at temperature T1 , and R is the resistance value of the four bridge resistors at temperature T0 , (T1 > T0 ). At the same time, the zero drift of the bridge should be zero after compensation. which is:   R1 //Rp [R3 + Rs ] − R2 R4 I=0 (4) R1 //Rp + R3 + Rs + R2 + R4 To simplify the calculation, the average value of the resistance of the four bridge resistances is taken as the resistance of the four resistances. We use Taylor expansion for R1 //RP and omit higher-order terms. Available: R1 //Rp ≈ R1 −

R21 Rp

(5)

The same can be obtained: R1 (T)//Rp ≈ R1 (T ) −

R21 (T ) Rp

(6)

Through experiments, we can measure that the sensor outputs V1 and V0 at the zero points of T1 and T0 before the sensor is compensated, and the zero point drift between the temperatures T1 and T0 can be represented by V = V1 – V0 . Therefore, based on the above analysis, the following two formulas can be obtained by simplifying formulas (2) and (3): (V1 − V0 ) +

RS −

V0 +

R2 (T ) Rp

4 RS − 4

I−

R2 Rp

RS −

R2 Rp

4

I =0

I =0

(7)

(8)

Simultaneous formulas (6) and (7) can be solved for RS , and the size of RP is: RS =

4R · R(T )[V0 R(T ) − V1 R]   V R2 − R2 (T )   I R2 (T ) − R2 Rp = 4(V1 − V0 )

(9) (10)

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2.3 NB-IoT Module The cellular-based Narrow Band Internet of Things (NB-IoT) of the NB-IOT module has become an important branch of the Internet of Everything. NB-IoT is built on a cellular network and only consumes about 180 kHz of bandwidth. It can be directly deployed on a GSM network, UMTS network, or LTE network to reduce deployment costs and achieve smooth upgrades. The cellular data connection that supports low-power devices in the wide-area network is also known as the low-power wide-area network (LPWA). NB-IoT supports the efficient connection of devices with long standby times and high network connection requirements. It is said that the battery life of NB-IoT devices can be increased to at least 10 years, while also providing very comprehensive indoor cellular data connection coverage [3].

3 Intelligent Water and Fertilizer System Design The core component of the intelligent water and fertilizer system is STM32F103RCT6. The hard circuit includes a power module, reset circuit module, liquid crystal circuit module, crystal oscillator module, NB-IoT base module, independent button module, YF-S201 flow sensor base module, HK1100C pressure sensor base module, and L298N motor drive module (Figs. 3, 4, 5 and 6). 3.1 Overall Hardware Circuit Schematic Diagram All hardware circuit design drawings use Altium Designer15.

Fig. 3. Single-chip minimum system

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Fig. 4. Sensors and other modules

3.2 Sensors and Other Modules 3.3 NB-IoT Data Transmission Module After the NB-IoT module is powered on, it will search for the NB signal, attach to the network, and then log in to port 183.230.40.39:6002 to connect to the OneNET platform. The APP side also needs to connect to the OneNETplatform server through TCP, and then they must use the MQTT protocol Log in to the device, subscribe to each other’s devices, and then you can communicate with data remotely. The MQTT protocol supports many-to-many data communication [4] (Table 1).

Fig. 5. MobileTerminal Interface

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Fig. 6. System design block diagram

Table 1. Test results of smart water and fertilizer system Number

A

B

C

D

1

10

50

2.3

0.0093

2

20

55

2.7

0.0188

3

30

60

3.2

0.0279

4

40

65

3.8

0.0371

5

50

70

4.1

0.0466

6

60

75

4.7

0.0559

7

70

80

5.2

0.0652

8

80

85

5.4

0.0748

9

90

90

6.0

0.0844

10

100

95

6.3

0.0940

A— Flow, L.B— Flow rate, L/min.C— Pressure, Kg.D— error, L.

4 Results Display 4.1 System Function Design Drawing 4.2 Test Outcome

5 Conclusion Judging from the test results, the design can meet the required accuracy in the case of a large flow rate, the error per liter is controlled within 0.001 L, the main functions meet the requirements, the overall design functions are complete and stable, and the product

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has achieved the set goals. In the future, the combination with intelligent agricultural greenhouses can achieve better performance and improve the intelligence of agricultural production.

References 1. Li, N., Liu, T.: Software test of water fertilizer integrated machine system platform based on embedded network. J. Agri. Mechanization Res. 44(02), 241–244 (2020) 2. Yan, W., Chen, H., Chen, H.: Research on on-line compensation method for the measurement error of silicon piezoresistive pressure sensor. Chinese J. Sci. Instr. 41(06), 59–65 (2020) 3. Tian, F.: Discussion on the application of internet of things technology in intelligent construction site. Intell. Build. Smart City 84–85 (2021) 4. Song, L.: Computer room power and environment monitoring system based on MQTT protocol. Microcontrollers & Embedded Systems 20(08), 83–86 (2020)

Deep Reinforcement Learning for Multi-user Resource Allocation of Wireless Body Area Network Xiuzhi Xu, Jiasong Mu(B) , and Tiantian Zhang Tianjin Normal University, TianJin 300387, China [email protected]

Abstract. The development of wireless body area network technology allows it to be widely used in various fields such as medical monitoring, sports, and entertainment, but the lack of spectrum resources also makes the interference between networks worse. Aiming at the optimization problem of multi-user resource allocation in wireless body area network, an allocation algorithm based on deep reinforcement learning is proposed. Facing the unknown and complex dynamic network environment and co-frequency interference between channels, Q-learning can effectively improve communication efficiency with the advantages of strong adaptability and no need to model the external environment. Regarding energy use efficiency as rewards and punishments by training agents constantly interacts with the external environment to gain experience, dynamically adjusting policy of allocation and decision, so as to obtain a nearly optimal allocation strategy. The communication efficiency and performance are significantly improved with the algorithm proposed. Keywords: Wireless body area network · Resource allocation · Deep reinforcement learning

1 Introduction The improvement of the performance and efficiency of bioelectronic chips and the development of wireless communication have promoted the application of wireless body area network (WBAN) technology in medical monitoring, personal health, sports, entertainment, and life [1] By implanting wireless sensor equipment inside the human body or on the surface of the human body, collecting important biological information of the human body such as blood pressure, heart rate, ECG, etc., the sensor node transmits the collected data to the HUB, and then to the terminal device and at last to the medical institution cloud. 24-h dynamic real-time monitoring can be achieved. However, with the development and application of WBAN technology, the probability of multiple or even a large number of WBANs appearing in the same scenario will increase [2, 3]. Which will cause interference in the multi-user WBANs. Reasonable planning on the allocation of network resources is an effective way to reduce the interference [4] between networks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 541–547, 2022. https://doi.org/10.1007/978-981-19-0390-8_66

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This paper designs a multi-user resource allocation algorithm for a wireless body area network based on deep reinforcement learning. The node acts as an agent, by the interaction with the external channel environment, and regarding energy efficiency as rewards, the system continuously gains experience, autonomously selects channels and makes decisions, and finally obtains a near-optimal resource allocation strategy.

2 System Model Assuming that each WBAN user has a HUB and several sensor nodes, due to the different functions and importance of sensor nodes, some nodes need to transmit data in real-time, while some nodes do not require high data transmission frequency. This article only considers one kind of node, which collects the human body’s ECG, heart rate, and other biological information. This information is essential for monitoring the human body’s health and needs to be transmitted in real-time. However, due to the lack of available channel resources and the signal interference generated when multiple users gather, it is difficult to guarantee the real-time, efficient, and accurate transmission of information. We assume that the relative position of each user’s Hub and Sensor remains constant. The abstract model is shown in Fig. 1

Fig. 1. Multi-link signal interference in the same channel

Where H represents the Hub center of the user, and S represents the sensor node [5]. Figure 1 only shows the interference between two users at a fair close range. The thick line is the user’s normal node-Hub link communication, and the thin line represents interference from other users. In real situations, more users tend to gather, and the communication link and co-frequency interference are more complicated. Suppose there are N WBAN users in a confined space of L2 a square meter, there are a total of K available channels in this environment, and the channel set is denoted as Cn = [c1 , c2 , . . . , cK ]. where, ck represents the occupancy state of the kth channel: when ck = 1, the channel is busy and occupied; when ck = 0, the channel is idle and accessible, and the data transmission power is denoted as p, and the agent allocates the channel to users dynamically. As shown in Fig. 1, there may be multiple communication links in the same channel during the communication process. The nth user’s Hub may be interfered by other communication links, which in turn affects the communication quality. From the knowledge

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of communication principles, we can use the Signal to Interference plus Noise Ratio (SIRN) to describe the channel conditions. For user n, its SINR can be expressed as SINRn = 

gnn · pn g m=n mn · pm + BN0

(1)

g represents the channel gain [6], p represents the transmission power, B represents the channel bandwidth, and N0 represents the noise power. The channel model used in this article includes both large-scale and small-scale fading. Among them, large-scale fading includes path loss and human shadowing effect; small-scale fading is Rayleigh fading of mean unit. Therefore, the above factors can be expressed by a mathematical formula  PL(d ) −ε ξn,m (2) g = 10− 10 dn,m PL(d ) is the path loss of the channel, ε is the path loss index, and ξn,m is the smallscale fading power gain. The path loss [7] of the channel can be expressed as PL(d ) = a lg d + b + N

(3)

Furthermore, according to Shannon’s theorem, we can get the maximum information transmission rate of the user n, that is, the channel capacity Cn Cn = B log2 (1 + SINRn )

(4)

To measure the transmission efficiency of the wireless body area network communication system and the optimization level of resource allocation, we define the energy efficiency ηn of the nth user, that is, the maximum amount of information that can be transmitted per unit of energy ηn =

C p

(5)

3 Deep Q-Network The using of reinforcement learning to solve the problem of resource allocation in the communication field and the Markov dynamic decision-making process has a fair wonderful effect. Figure 2 shows the interaction process with the environment of reinforcement learning. In the communication scenario, this paper uses Deep Q Network (DQN) to solve the problem of resource allocation in wireless body area networks [8]. The agent observes the current state, executes actions according to the original strategy, gets rewards and new states, and adjusts and updates the strategy based on this, and iterates by this way until obtains the best resource allocation strategy. The deep Q network uses the value network as the evaluation module and based on which, it traverses various actions in the current observation state, conducts real-time learning with the environment. The network stores the obtained states, action taken, and reward value in the pool and trains the value network by Q-learning. And finally, the action which obtains the largest reward is to be output.

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Fig. 2. Reinforcement learning process

Iterate according to the following formula Qk+1 (s, a) = Qk (s, a) + αEk

(6)

  Ek = R + γ max Qk s , a − Qk (s, a)

(7)

A

α represents the learning rate, in the process of reinforcement learning: the larger the α, the larger the proportion of results obtained by using new attempts while the smaller the proportion of maintaining the old results; γ represents the discount factor, which determines the importance of returns between current and future. The smaller the γ , the more inclined the algorithm is towards short-term returns. On the contrary, the larger the γ , the more inclined to long-term returns. The data of reinforcement learning is non-static, non-independent, and uniformly distributed, and the states are highly correlated [9]. Small deviations in the value function may affect the entire decision-making strategy. Therefore, the DQN network using the experience playback method to save the trained data in the experience pool, and each time it is randomly extracted as the input of the neural network for training, which makes the training data more independent and uniformly distributed. To achieve the value function approximation, then Ek needs to be close to 0, that is   R + γ max Qk s , a − Qk (s, a) → 0 (8) Define     min E = min R + γ max Qk s , a − Qk (s, a)

(9)

as the error function. Therefore, the deviation between the target Q value and the original network Q value can be minimized by the gradient descent method, and the optimal solution of the Q function can be finally obtained.

4 Resource Scheduling Algorithm Proposed State space: the state space includes the nth user’s current observation of the access state of the channel and its SINR, so the state space can be expressed as Sn = [Cn , SNRn ]. Action space: the user’s action to access the channel or get to the allocated channel CCn = (cc1 , cc2 , . . . , ccK ), where cck = 0 or 1, which indicates whether the kth channel

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is selected or allocated. The user can only select one channel at a time or no channel can be selected. Reward: the goal of this paper is to optimize resource allocation to improve energy efficiency, so the Reward function is: R = 0, if η < threshold; else R = η. If the energy efficiency is less than the set threshold, the reward is 0, otherwise, the reward is the average value of the energy efficiency of all users.

5 Simulation Results In this part, this article tests the proposed resource allocation algorithm based on deep reinforcement learning. In this WBAN scenario, we believe that the distance between the user’s own node and Hub is of a certain constant, and a certain number of WBAN users are in a closed environment. During the communication process, Users are randomly located in any position in this space. Each user node runs the proposed algorithm independently as an agent to find the best allocation strategy that maximizes energy efficiency. We set up 50 time slots for each episode, and the reward is to maximize the average energy efficiency of all users. The simulation platform is HP G4 Workstation Intel(R) Core(TM) i7–8700 CPU @ 3.19 GHz, and the software is based on Pycharm2020.1.1. The specific parameters of the communication process are set as follows (Table 1). Table 1. Parameters setting Parameter value Space size

100 mm2

Number of users

40

Number of channels

10

Channel bandwidth B 300 kHz Transmit power P

5 uw

Channel loss function a = 3, b = 34.6, N ~ (0,4.632 ) Path loss exponent

2

Figure 3 shows the change in the average energy efficiency of users with episodes increasing. Since the Reward is not a percentage, the times of threshold is used to measure. It can be seen from the figure that after 50 episodes, the energy efficiency quickly converges, and the energy efficiency also reaches a high value. Figure 4 shows the impact of the number of WBAN users on energy efficiency. We have increased the number of users. From the blue, orange, and green lines, the density gradually increases and the communication environment becomes more complicated. It can be seen that the energy efficiency has decreased to a certain extent, but the proposed algorithm still has a good convergence speed and reward value within the acceptable range.

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Fig. 3. Average energy efficiency of users

Fig. 4. Comparison of different numbers of users

6 Conclusion Aiming at the crowded scene of the wireless body area network, a resource allocation algorithm based on deep reinforcement learning is proposed. With energy use efficiency as rewards and punishments, the agent is trained to gain experience through interactive learning with the external communication environment, and dynamically adjust to obtain the best Optimal distribution strategy. The simulation results also show that the proposed algorithm has a positive effect on the improvement of the energy efficiency of wireless body area networks.

References 1. Riccardo, C., et al.: A survey on wireless body area networks: technologies and design challenges. IEEE Commun. Surv. Tutorials 16(3), 1635–1657 (2014) 2. Mahapatro, J., Misra, S., Manjunatha M., Islam, N.: Interference-aware channel switching for use in WBAN with human-sensor interface, In: 2012 4th International Conference on Intelligent Human Computer Interaction (IHCI), Kharagpur, pp. 1–5 (2012) 3. Boumaiz, M., El Ghazi, M., Bouayad, A., Fattah, M., El Bekkali M., Mazer, S.: The impact of transmission power on the performance of a WBAN prone to mutual interference, In: 2019 International Conference on Systems of Collaboration Big Data, Internet of Things & Security (SysCoBIoTS), Casablanca, Morocco, pp. 1–4 (2019) 4. Thachayani, M.: Interference mitigation scheme to support WBANs Co-exist|ing with WLANs, In: 2019 International Conference on Communication and Signal Processing (ICCSP), Chennai, India, pp. 0041–0044 (2019)

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5. Chen, Q., Su, C., Zhang, H., Chai, R.: User service oriented power allocation algorithm for wireless body area sensor networks, In: 5th IET International Conference on Wireless, Mobile and Multimedia Networks (ICWMMN 2013), Beijing, pp. 37–40 (2013) 6. Reusens, E., et al.: Characterization of on-body communication channel and energy efficient topology design for wireless body area networks. IEEE Trans. Inf Technol. Biomed. Nov. 13(6), 933–945 (2009). https://doi.org/10.1109/TITB.2009.2033054 7. Jiao, X. Research on wireless body area network channel model. South China University of Technology (2013) 8. Xu, Y.-H., Yu, G., Yong, Y.-T.: Deep reinforcement learning-based resource scheduling strategy for reliability-oriented wireless body area networks, In: IEEE Sensors Letters 9. Tan, H., Zhao, L., Liu, W., Niu, Y., Zhao, C.: Adaptive congestion avoidance scheme based on reinforcement learning for wireless sensor network, In: IET International Conference on Communication Technology and Application (ICCTA 2011), 2011, pp. 228–232, https://doi. org/10.1049/cp.2011.0664 10. Xuedong, L., Balasingham, I., Byun, S.-S.: A reinforcement learning based routing protocol with QoS support for biomedical sensor networks. In: Applied Sciences on Biomedical and Communication Technologies, 2008. ISABEL’08. First International Symposium on. IEEE, 2008 11. Yang, Y., Smith, D.B., Seneviratne, S.: Deep Learning Channel Prediction for Transmit Power Control in Wireless Body Area Networks, In: ICC 2019 - 2019 IEEE International Conference on Communications (ICC), Shanghai, China, 2019, pp. 1–6

Financial Risk Prediction Based on Stochastic Block and Cox Proportional Hazards Models Xiaokun Sun1 , Jieru Yang1 , Junya Yao1 , Qian Sun1 , Yong Su2 , Hengpeng Xu2(B) , and Jun Wang1(B) 1

College of Mathematics and Statistics Science, Ludong University, Yantai 264025, China [email protected] 2 Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin 300387, China [email protected], [email protected]

Abstract. Since 2019, the sudden outbreak of COVID-19 has made huge impacts on various aspects of society, especially the financial industries that are closely related to the national economy and people’s livelihood. Finance is a data-intensive field and its traditional research models include supervised and unsupervised models, state-based models, econometric models, and stochastic models. However, the above models are prone to lose their effectiveness in the situation of an extremely complex financial ecosystem with a large number of nonlinear unpredictable effects, such as those caused by COVID-19. To address this issue, we comprehensively explore and fuse Stochastic Block Model (SBM) and Cox Proportional Hazards Model (COX) for a reliable and accurate financial risk prediction. Specifically, SBM, which is popular in social network analysis, is employed to capture the impact factors on the financial industry in public emergencies, and COX is then leveraged to determine the duration of the impact factors. An extensive experimental evaluation validates the effectiveness of our framework in predicting financial risk. Keywords: Financial risk prediction · Stochastic block model proportional hazards model · Public emergency

1

· Cox

Introduction

The Corona Virus Disease 2019, named COVID-19, has been unexpectedly maintained for nearly half a year, which has brought a great impact on retail industry, tourism, catering industry, clothing industry, and etc. [1]. Market funds as well X. Sun and J. Yang—These authors are equally contributed to this work. c The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022  Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 548–556, 2022. https://doi.org/10.1007/978-981-19-0390-8_67

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as consumer demands have been suppressed, and the financial market has been dramatically impacted [2]. Compared with the global epidemics, such as Asian influenza and MERS, COVID-19 has made more severe impacts on the financial market has always been relatively weak due to its high rate of transmission and fatality [3,4]. The quantitative measurements of these impacts deserve an in-depth exploration and discussions. To address the above issue, a novel financial risk prediction framework is proposed in this paper. In specific, Stochastic Block Model (SBM) [5] is employed to explore the impact factors on the financial industry during COVID-19, and Cox Proportional Hazards Model (COX) [6] is leveraged to study the duration of the impact factors. In terms of SBM, complex random blocks with a network group structure are constructed respectively based on the financial market of a certain country (representative as China and the United States in this paper). Based on our pilot study, we select four macro factors that may have great impacts on the financial market as the communities of SBM, that is, urban unemployment, inflation level (CPI), central bank interest rates, and consumer price fluctuations. Then, we select some representative companies as the nodes of SBM and construct the SBM network. By examining the weights of the edges of SBM, we can determine the financial risk of a certain industry (representative as the clothing industry in this paper) in a country during COVID-19. Based on the output scores of SBM, we build COX with time and survival time as the independent variables and the macro-influencing factors during the time period as the covariates. We analyze the impact time limit of these factors on the financial industry via the relation between explanatory variables and risk of COX. Furthermore, we can add more time-based factors and learn their time limits via the survival analysis of COX. The structure of this paper is organized as follows. SBM is constructed and analyzed in Sect. 2, and COX is built and evaluated in Sect. 3. Finally, conclusions are drawn in Sect. 4.

2

Stochastic Block Model (SBM)

SBM was first proposed as a specific hidden space model for social network analysis [7]. Since the community can be well defined in SBM, the community discovery problem can be modeled as a statistical estimation, which can be reformulated as the structural equivalence problem. In this section, we will build SBM to explore the degree of economic radiation and major factors on the clothing industry during COVID-19. 2.1

SBM Construction

According to our pilot study, we take four macro factors as the SBM communities, that is, urban unemployment, inflation level, central bank interest rates,

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and consumer price fluctuations. In order to research the complex global economic system more specifically, we select the clothing industry of China and the United States as the analysis objects, which represent the developing and developed financial markets, respectively.

Fig. 1. Correlation coefficient matrix for the China market

Given the four macro factors as the communities and representative companies as the nodes, SBM generates the network A as follows: 1) Each node i in A is assigned to the numbered community k with a certain probability θk , which constitutes the probability vector θ. The community variable Gi satisfies the multiple distribution Gi ∼ M ult(θ). 2) Let Aii represent the connection probability of each pair of nodes i and j. Then, Aii is assumed to obey the Bernoulli distribution as Aii ∼ Bernoulli(ψGi Gj ),

(1)

where ψ is a k × k matrix whose each element represents the probability of a connection between the community and the node. 3) Finally, the SBM network can be constructed as follows:

P (A|Gi , ψ) =

 Aij  ψ Gi Gj i t) represents the probability that an individual survives at time T since time t. 2) Probability density function. This function determines the probability of a particular event occurring within an interval as follows: p (t < T < t + t) . t→0 t

f (t) = lim

(4)

3) Risk function. This function is mainly used to describe the possibility of an individual dying in a certain period of time, which is formulated as follows: f (t) =

p (t < T < t + t| T > t) f (t) = lim . S(t) t→0 t

(5)

Fig. 5. Survival and Hazard variance of the China market

3.2

COX Analysis

We use the time (January–December, 2020) as independent variables, and the SBM scores of the four macro factors in each month as covariates. The valid SBM score is recorded as the state“1” and “0” otherwise. Then, we can build

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Fig. 6. Survival and Hazard variance of the United States market

COX on the basis of the three functions in Sect. 3.1 respectively for the China and United States markets, as shown in Fig. 5 and Fig. 6. In Fig. 5(a), we can observe that the overall survival curve shows a declining trend over time. In August, there is a sharp decline in a cliff-like manner, where the cumulative survival value dropped from about 0.7 to near 0, and the overall survival curve shows two linear declines. In contrast, we can see that the hazard value begins to rise in October in Fig. 5(b), and it rises sharply in a linear manner in November. We assume that the impact of COVID-19 on the China clothing industry may probably still last eight to ten months. It is observed in Fig. 6(a) that the overall survival curve shows a declining trend over time, starting from March and remaining this trend till June. Then, it tends to be stable. The overall survival curve has experienced five linear declines. By contrast, we can see that the overall hazard value rises over time in Fig. 6(b). In June, the cumulative hazard value is largely increased and it has increased in a linear manner in November. Accordingly, we infer that the impact of COVID-19 on the United States clothing industry may probably still last about six months. Comparing the survival values of two countries, we can see that survival curve of China has fallen three times, while that of the United States has fallen five times. Taking into account the financial systems and policy systems of two countries, we assume that China is more competent for tackling the underlying financial crisis brought by COVID-19 than the United States.

4

Conclusions

In this paper, a novel financial risk prediction framework is proposed, which innovatively fuses the stochastic block and Cox proportional hazards models. SBM is utilized to determine the critical impact factors on the financial industry during COVID-19, and COX is explored to estimate the duration time. An extensive experimental evaluation reveals that the urban unemployment rate and inflation level are the main macro-influencing factors for the China clothing

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industry, which will last about eight to ten months. In contrast, urban unemployment rate, inflation level, and the rise and fall of consumer prices are the main influencing factors for the United States clothing industry, which will last about six months. Acknowledgments. This work was partially supported by the National Natural Science Foundation of China (No. 62106091), Doctoral Foundation of Tianjin Normal University (Nos. 52XB2104, 52XB2102), the Key R&D Program of Shandong Province (No. 2020RKB01772), and the Shandong Provincial Natural Science Foundation (Nos. ZR2021MF054, ZR2019MA049).

References 1. Lan, B., Zhang, L.: Research on the impact of the new crown pneumonia epidemic on the financial market. Stat. Decis. 5, 129–133 (2021) 2. Albulescu, C.T.: COVID-19 and the United States financial markets’ volatility. Finan. Res. Lett. 38, 101699 (2021) 3. Goodell, J.W.: COVID-19 and finance: agendas for future research. Finan. Res. Lett. 35, 101512 (2020) 4. Chang, C.L., McAleer, M., Wong, W.K.: J. Risk Finan. Manag. 13(5), 102 (2020) 5. Li, W., Ahn, S., Welling, M.: Mixed membership stochastic blockmodels. J. Mach. Learn. Res. 9, 1981–2014 (2008) 6. Kleinbaum, D.G., Klein, M.: The cox proportional hazards model and its characteristics. In: Survival Analysis. SBH, pp. 97–159. Springer, New York (2012). https:// doi.org/10.1007/978-1-4419-6646-9 3 7. Heimlicher, S., Lelarge, M., Massouli´e, L.: Community detection in the labelled stochastic block model. arXiv:1209.2910 (2012)

Learning Residents’ Consumption Structures Based on the ELES Model Zhenkuan Jiang1,2 , Zixiao Wang1 , Xuan Liu1 , Kun Liu1 , Yong Su3 , Hengpeng Xu3(B) , and Jun Wang1(B) 1

College of Mathematics and Statistics Science, Ludong University, Yantai 264025, China [email protected] 2 College of Statistics, Shandong University of Finance and Economics, Jinan 250000, China 3 Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin 300387, China [email protected], [email protected]

Abstract. The worldwide spread of COVID-19 has greatly hit global economy by now. The world’s major economies including both developed and developing countries have felt the resulting impact on their financial markets. Accordingly, learning residents’ consumption structure is significant for boosting consumption demand and recovering financial market. In this paper, the Extend Linear Expenditure System (ELES) model is explored to learn both urban and rural residents’ consumption structures of China during COVID-19. In specific, the indices of marginal propensity to consume, income elasticity of demand, and price elasticity can be yielded via the ELES model based on the disposable income and the consumption data. Furthermore, the consumption structures before and during the corona virus epidemic can be quantitatively compared. Extensive experimental results demonstrate that the epidemic has made profound impacts on the consumption structure of residents. Among them, the marginal propensities on food and medical services have greatly increased, while the proportions of other expenditures have been decreased.

Keywords: Consumption structure system · Machine learning

1

· Extend linear expenditure

Introduction

The corona virus disease 2019, named as COVID-19, is spreading rapidly all over the world, making crisis centered principally on the financial field [1]. Epidemic prevention policies, such as large-scale quarantine, travel restrictions, and social Z. Jiang and Z. Wang—These authors are equally contributed to this work. c The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022  Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 557–565, 2022. https://doi.org/10.1007/978-981-19-0390-8_68

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distancing, have led to a sharp decline in personal and corporate consumption, which in turn has triggered an economic recession [2]. People begin to radically alter their typical spending across a number of major consumption categories [3]. Accordingly, it deserves an in-depth exploration on how consumption responds to an outbreak by using transaction-level financial data. This paper tackles this issue via the Extended Linear Expenditure System (ELES) model [4]. ELES is a popular consumption structure learning model that can quantitatively describe the relationship of the linear demand of commodities with residents’ income and price factors [5]. ELES model has been successfully employed for the consumption structure analysis in economics, industry, and other sectors [6–8]. This paper comprehensively explores the ELES model to learn the consumption structures of urban and rural residents before and during COVID19. Specifically, per capita disposable income (PCDI) and various consumption expenditures of the urban and rural residents in China are qualitatively analyzed and quantitatively compared. Furthermore, the marginal propensity to consume, income elasticity, and price elasticity of urban and rural residents before and during COVID-19 are estimated via ELES for an in-depth analysis of the consumption structure. Based on the extensive experimental evaluations, practical and feasible suggestions that may help promote economic growth and development are put forward.

2

Constructing the ELES Model

The ELES model w.r.t. a given commodity i can be formulated as Vi = pi qi + βi (I −

n 

pj qj ),

(1)

j=1

where Vi is the actual consumption expenditure of the commodity i, pi and qi are its actual selling price and consumer demand, respectively, n is the number of commodities, and I is the consumer’s total disposable income. βi is the marginal propensity to consume the commodity i, which is constrained as 0 ≤ βi ≤ 1. For  all of the commodities, we have βi ≤ 1. Let αi = pi qi − βi

n 

pj q j .

(2)

j=1

Then, we can reformulate Eq. (1) as follows: Vi = αi + βi I + μi ,

(3)

where αi and βi are the learnable parameters, and μi stands for the stochastic error. In this work, we use the Stochastic Gradient Descent (SGD) to estimate the above parameters. Then, the marginal propensity to consume the commodity i can be calculated as ∂ Vi , s.t. 0 ≤ βi ≤ 1. βi = (4) ∂I

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Based on the optimal solution of βi , we reformulate Eq. (2) and calculate αi as follows: n n n    αi = (1 − βi ) pj q j . (5) i=1

i=1

j=1

Based on the optimal βi in Eq. (4) and optimal αi in Eq. (5), the total basic consumption expenditure for purchasing all commodities, i.e., the basic consumption expenditure, can be obtained as n j=1 αj  pi qi = αi + βi . (6) n 1 − j=1 βj Finally, the income elasticity ηi and price elasticity ξi can be calculated as follows: I (7) ηi = βi · , Vi pi q i − 1. (8) ξi = (1 − βi ) · Vi The income elasticity describes the customers’ demands for a commodity varying with their incomes. When ηi ≤ 0, i can be deemed as a low-end one commodity, implying customers’ demands will decrease when their incomes increase. 0 < ηi < 1 indicates a necessity and ηi ≥ 1 indicates a luxury. On the other hand, the price elasticity represents the customers’ demands varying with the commodity price. When 0 < ξi < 1, the variance of customers’ demands are smaller than that of commodity price. ξi ≥ 1 denotes a dominating customers’ demands.

3

Experiments

3.1

Pre-processing Data

We collect the consumption data of China in 2019 and 2020 from the National the Bureau of Statistics1 . The data include the per capita consumption expenditure and per capita disposable income for the following eight categories: (i) food, tobacco, and alcohol; (ii) clothing; (iii) housing; (iv) household goods and services; (v) transportation and communication; (vi) education, culture, and entertainment; (vii) medical care; and (viii) other supplies and services. The data of 2019 and 2020 correspond to those before and during COVID-19, respectively. The data in one year is further divided into two parts: the first half year and the second half year, and the ELES model is established on them to analyze the consumption structure of urban and rural residents in China.

1

http://www.stats.gov.cn/english/Statisticaldata/AnnualData/.

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Second half of 2019

p value βi

R2

p value

Food

0.272 0.998 0.021

0.302 0.832 0.076

Clothing

0.021 0.991 0.018

0.040 0.808 0.093

Housing

0.043 0.999 0.021

0.085 0.965 0.019

Household goods, ...

0.040 0.998 0.024

0.045 0.845 0.056

Transportation, ...

0.135 0.998 0.028

0.153 0.801 0.068

Education, culture, ... 0.121 0.985 0.076

0.119 0.999 0.011

Medical care

0.020 0.996 0.022

0.038 0.723 0.092

Other supplies, ...

0.020 0.992 0.018

0.029 0.837 0.042

First half of 2020

Second half of 2020

βi

3.2

R

2

R2

p value βi

R2

p value

Food

0.312 0.855 0.093

0.369 0.856 0.066

Clothing

0.012 0.921 0.081

0.037 0.93

0.086

Housing

0.066 0.936 0.062

0.061 0.949 0.093

Household goods, ...

0.048 0.719 0.07

0.055 0.869 0.068

Transportation, ...

0.093 0.765 0.047

0.06

Education, ...

0.125 0.867 0.085

0.122 0.997 0.032

Medical care

0.129 0.956 0.028

0.126 0.853 0.066

Other supplies, ...

0.012 0.969 0.089

0.012 0.805 0.061

0.891 0.072

Marginal Propensity to Consume

We separately construct the ELES model to estimate the marginal propensity to consume before and during COVID-19 in urban and rural areas, which is defined in Eq. (4). The estimation results are shown in Table 1 and Table 2, where βi stands for the marginal propensity to consume (a larger βi implies that residents are more willing to buy the commodity i) and R2 stands for the coefficient of determination. According to the p values in both tables, it can be seen that the fitting performance of the ELES model for each part is statistically significant, which passes the t-test at the significance level of 10%. In Table 1, we can observe that the highest marginal propensity of the urban residents before COVID-19 are the consumption of food, tobacco, and alcohol, followed by transportation, communications, education, and entertainment, indicating that these basic needs receive strong social purchasing wills from the urban residents. Comparing the results of the first half of 2019 and the first half of 2020, the marginal propensity to consume increases most significantly in health care, followed by food, tobacco and alcohol, while the most significant decline is in the transportation and communications. The highest increase in the second half of 2020 is still medical and food, while clothing, transportation, and

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Table 2. Marginal propensity to consume of the rural residents First half of 2019 βi

R

2

Second half of 2019

p value βi

R2

p value

Food

0.210 0.986 0.075

0.267 0.994 0.045

Clothing

0.033 0.996 0.036

0.038 0.884 0.027

Housing

0.162 0.886 0.092

0.175 0.999 0.018

Household goods, ...

0.071 0.999 0.006

0.069 0.997 0.011

Transportation, ...

0.179 0.998 0.027

0.183 0.998 0.000

Education, culture, ... 0.085 0.863 0.024

0.094 0.855 0.099

Medical care

0.077 0.837 0.263

0.099 0.981 0.066

Other supplies, ...

0.018 0.998 0.024

0.015 0.992 0.019

First half of 2020

Second half of 2020

βi

R2

p value βi

R2

p value

Food

0.311 0.798 0.703

0.296 0.989 0.064

Clothing

0.031 0.878 0.513

0.035 0.872 0.514

Housing

0.135 0.98

0.089

0.151 0.978 0.094

Household goods, ...

0.068 0.894 0.211

0.075 0.967 0.115

Transportation, ...

0.171 0.922 0.179

0.199 0.977 0.095

Education, culture, ... 0.076 0.912 0.191

0.023 0.834 0.266

Medical care

0.115 0.957 0.132

0.114 0.999 0.008

Other supplies, ...

0.003 0.938 0.092

0.017 0.996 0.037

communication decline. In general, the pandemic have made a great impact on the consumption expenditure structure of the urban residents. The significantly increasing propensities before versus during COVID-19 are healthcare, while the declining ones are clothing, transportation, and communication. Table 2 gives the estimation results of the ELES model for the rural residents. It can be observed that similar with the consumption of the urban residents, the highest marginal propensity for the rural residents is also food, tobacco, and alcohol, respectively reaching 0.210 and 0.267 in the first and second half of 2019, which is followed by housing, transportation, and communication. The marginal propensities for other commodities are relatively lower, especially in terms of clothing. During COVID-19, this index mostly increases in food, tobacco, and alcohol, followed by transportation, communications, and medical care, while it is decreased in education, culture, and entertainment. Generally, the rural residents’ consumption requirements for some non-essential commodities have been greatly reduced during COVID-19, indicating an apparent consumption structure changing due to the epidemic.

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Second half of 2019

First half of 2020

Second half of 2020

1.0

0.5

0.0

Food

Clothing

Housing

Household goods Trans & com

Edu & cul

Medical care

Other

(a) Income elasticity of urban residents First half of 2019

Second half of 2019

First half of 2020

Second half of 2020

1.5

1.0

0.5

0.0

−0.5 Food

Clothing

Housing

Household goods Trans & com

Edu & cul

Medical care

Other

(b) Income elasticity of rural residents

Fig. 1. Income elasticity

3.3

Income Elasticity of Demand

Based on the optimal value of the marginal propensity to consume (i.e., βi ) in Table 1 and Table 2, we can obtain the income elasticity of demand ηi defined in Eq. (7), which is demonstrated in Fig. 1. The figure illustrates that affected by the pandemic, the demand income elasticity of both urban and rural residents on medical care significantly rises during COVID-19. This kind of consumption has caused a certain burden on residents’ lives in a short time. As income increases, the urban residents’ demands for clothing increase slower than their income. This reveals that the urban residents’ purchasing tendency on clothing is decreased during the epidemic. On the other hand, with the increasing income of the rural residents, the commodities of education, culture, and entertainment have changed from the essential ones to the low-end ones. It can also be seen in the figure that the epidemic has different effects on the consumption structures of urban residents and rural residents. 3.4

Price Elasticity of Demand

Based on the optimal βi in Table 1 and Table 2, we can also yield the price elasticity ξi defined in Eq. (8). As shown in Fig. 2, the demands for most

Learning Residents’ Consumption Structures First half of 2019

Second half of 2019

First half of 2020

563

Second half of 2020

0.0

−0.5

−1.0

−1.5

−2.0

−2.5 Food

Clothing

Housing

Household goods Trans & com

Edu & cul

Medical care

Other

(a) Price elasticity of urban residents First half of 2019

Second half of 2019

First half of 2020

Second half of 2020

0

−1

−2

−3 Food

Clothing

Housing

Household goods Trans & com

Edu & cul

Medical care

Other

(b) Price elasticity of rural residents

Fig. 2. Price elasticity

commodities by both urban and rural residents increase faster than their prices. According to the absolute value of ξi , it can be found that the change of demand for each consumption item in urban areas doesn’t exceed the change of price in the first half of 2019. This index for all of the consumption items increase in the second half of 2019, except food, tobacco, and alcohol. While in 2020, its values on housing, household goods, services, transportation, communication, and medical insurance in urban areas are all greater than 1. The price elasticity for most commodities in rural areas before and during the epidemic are both relatively high. Compared with 2019, the absolute values of the consumer price elasticity on education, culture, and entertainment in rural areas drop to a certain extent in 2020, while those on food, tobacco, alcohol, and medical greatly rise. This observation is consistent with the income elasticity analysis in Sect. 3.3.

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Conclusions and Suggestions

Through a comprehensive comparative analysis of the marginal propensity to consume, price elasticity, and income elasticity of eight major commodities before and after the epidemic, the following conclusions can be drawn: 1) the changes in the marginal propensity to consume of the urban and rural residents are disparate before and during COVID-19, which is related to the general income, consumption concept, and consumption habits; 2) the marginal propensity is closely related to the average disposable income. That is, a higher level of the per capita disposable income corresponds to a lower consumption tendency; 3) the rapid consumption increase on food and medical suppresses the basic living expenses of the residents in other aspects; and 4) due to the impact of COVID19, the actual economic growth of China slows down in 2020, considering the rapid increase in food and medical consumption that will reduce the basic living expenses of residents in other aspects. Based on the aforementioned observations, we give some suggestions as follows: 1) advancing the prosperity and development of the rural culture and education is necessary. Promoting this kind of consumption can improve the corresponding consumption level; more importantly, it can improve the overall income and optimize the consumption structure of the rural residents in a longterm view; 2) the consumption focus of Chinese residents has changed during COVID-19, and the current supplies of most middle- and high- end commodities are insufficient. To further deepen the supply-side reforms as well as satisfy consumers’ demands in the new commercial markets is suggested; and 3) making some adjustments on the epidemic prevention and control policies is imperative. For example, on the basis of satisfying the safe production requirements, relaxing the restrictions of farm products and restoration enterprises are practicable. Acknowledgments. This work was partially supported by the National Natural Science Foundation of China (No. 62106091), Doctoral Foundation of Tianjin Normal University (Nos. 52XB2104, 52XB2102), the Key R&D Program of Shandong Province (No. 2020RKB01772), and the Shandong Provincial Natural Science Foundation (Nos. ZR2021MF054, ZR2019MA049).

References 1. Chen, H.C., Yeh, C.W.: Global financial crisis and COVID-19: industrial reactions. Finan. Res. Lett. 42(1), 101940 (2021) 2. Dong, D., Gozgor, G., Lu, Z., Yan, C.: Personal consumption in the United States during the COVID-19 crisis. Appl. Econ. 53(11), 1311–1316 (2021) 3. Baker, S.R., Farrokhnia, R.A., Meyer, S., Pagel, M., Yannelis, C.: How does household spending respond to an epidemic? Consumption during the 2020 COVID-19 pandemic. Rev. Asset Pricing Stud. 10(4), 834–862 (2020) 4. Liuch, C.: The extended linear expenditure system. Eur. Econ. Rev. 4, 21–32 (1973) 5. Chen, J., Rong, S., Song, M., Shi, B.: Evaluation of the rural minimum living standard line in China. Emerg. Market. Fin. Trade vil. 56(9), 1971–1988 (2020)

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6. Li, Y., Yuan, Z.: Changes in consumption structure of urban residents in Sichuan since the ten years of the western development in China. Consum. Econ. 5, 3–6 (2010) 7. Wijesooriya, K., Mohotti, D., Chauhan, K., Dias-da-Costa, D.: Numerical investigation of scale resolved turbulence models (LES, ELES and DDES) in the assessment of wind effects on supertall structures. J. Build. Eng. 25, 100842 (2019) 8. Zhang, H., Mou, J., Yin, H.: Empirical analysis on rural household energy consumption demand in forest region: based on dual extended linear expenditure system model. Chin. Rural Econ. 7, 64–74 (2010)

Observer-Based Fault Estimation for Discrete T-S Fuzzy Systems Yu Chen1(B) , Xiaodan Zhu2 , Weifang Yan2 , and Dandan Han2 1

2

School of Chemistry and Materials Science, Ludong University, Yantai 264025, China [email protected] School of Mathematics and Statistics Science, Ludong University, Yantai 264025, China

Abstract. In this paper, the problem of fault estimation (FE) for discrete T-S fuzzy system is considered. An observer-based fault estimator is designed by applying the extended state method and the error dynamic system is asymptotically stable and the disturbances’ effect meets the H∞ performance. Sufficient conditions and observer gains for the FE problem are shown. An example shows the feasibility of the proposed FE scheme. Keywords: Fault estimation · Observer T-S fuzzy system · H∞ performance

1

· Extended state method ·

Introduction

The T-S fuzzy systems [1] as a very important tool are used to depict the complex nonlinear systems. For this type of system, by means of a compact set, a weight sum of linear system describes them. Then the traditional linear systems and the fuzzy logical control theory can be employed into this class of nonlinear model. Therefore T-S fuzzy systems attracted many researchers’ attention [2–7]. [2,3] discussed T–S fuzzy systems’ robust stabilization and membershipfunction-dependent stability. For complex robot model, [4] designed a fuzzy neural network–backstepping control scheme. [5] proposed a integral sliding mode control based on event-trigger method. A based-event method was proposed to solve T-S fuzzy networked systems’ control problem [6]. For T-S fuzzy Markov jump model, [7] was provided a H∞ switched fuzzy filter approach. As an important means to improve the safety and reliability of the system, the fault diagnosis and fault-tolerant control (FTC) technology [8] has been paid more and more attentions, and become another main content of control theory, which is an involved in automatic control theory, computer science, signal processing, optimization theory, artificial intelligence and other multi-disciplinary new edge discipline. Fault diagnosis is a prerequisite for FTC, which has fault c The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022  Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 566–573, 2022. https://doi.org/10.1007/978-981-19-0390-8_69

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detection (FD), fault isolation (FI) and fault identify, where fault estimation (FE) is one of main contents. Recently, there are many literature reports about this content. [9,10] considered the FD programs for discrete systems by applying a descriptor system method. [11,12] provided two different design schemes to solve FE and FTC problem for discrete systems. Meanwhile, the results on FD and FTC of T-S Fuzzy models have been reported in [13–19]. [13,14] respectively designed robust FD observer of continuous and discrete T-S fuzzy models. In finite frequency domain, [15,16] provided FD design method for T-S fuzzy systems. [17] discussed the adaptive fuzzy FTC problem with sensor faults and dead zone input. [18] was concerned with FTC problem by resetting an observer. For fuzzy switched singular model containing actuator fault, [19] gave a FTC plan. In particular, there are many achievements on FE of T-S fuzzy model. [20] solved actuator and sensor FE for switched T-S fuzzy model. With digital communication constraints, [21] proposed a FE method by designing a sliding mode observer. A modified FE technique was introduced for nonlinear model [22]. The observer-based FE and FTC technique provided for stochastic fuzzy model with Brownian parameter perturbations [23]. [24] applied a finite-frequency method to provide a sensor FE scheme for fuzzy system. For switched fuzzy stochastic systems [25,26], the different FE and FTC strategies were shown. There are a lot of good works out but still many problems will be solved. However, to our knowledge, the problem of sensor FE for discrete T-S fuzzy model has not been fully investigated yet, which motivates us to make the effort in this paper.

2

Problem Formulation

Consider discrete T-S fuzzy system: xk+1 =

N 

φi (k)[Ai xk + Bi uk + Ci dk + dk wk ],

i=1

yk =

N 

φi (k)[Dxk + fsk ],

(1)

i=1

where xk ∈ Rn is state, yk ∈ Rr is measured output, fsk ∈ Rr denotes sensor fault. uk ∈ Rs denotes control input. dk ∈ Rp denotes unknown bounded disturbance. uk , dk belong to L2 [0, ∞). Ai ∈ Rn×n , Bi ∈ Rn×s , Ci ∈ Rn×p , D ∈ Rr×n are real constant matrices. wk is a stochastic process on a probability space (Υ, F, P) refer to (Fk )k∈S of υ-algebras Fk ⊂ F generated by (wk )k∈S and wk is independent with E{wk } = 0, E{wk2 } = d > 0. Assume that (Ai , D) is N observable. The weight of the ith fuzzy rule is φi (k) > 0 and i=1 φi (k) = 1.

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Define

          I0 Ai 0 ¯i = Bi , C¯i = Ci , I¯1 = 0 , E¯0 = , A¯i = ,B 0 0 00 I 0 −I        ¯  xk I ¯ = D I ,D = D 0 ,x ¯k = D , , I¯2 = 0 fsk

then the augmented system is that ¯0 x E ¯k+1 =

N 

¯i uk + C¯i dk + I¯1 fsk + I¯2 dk wk ], φi (k)[A¯i x ¯k + B

i=1

yk =

N 

¯ xk ] = φi (k)[D¯

i=1

N 

¯ x + f ], φi (k)[D¯ k sk

(2)

i=1

where the components of x ¯k ∈ Rn+r are states xk and faults fsk . To obtain the estimation of xk , yk , fsk , simultaneously, we design a FE observer. For system (2), design the following FE observer: N 

ˆ¯k = zk + Qyk , x N  ¯i uk ], φi (k)Hi zk+1 = φi (k)[Ri zk + B

i=1

i=1

¯x ˆ¯k , yˆk = D

(3) 

T

T where zk ∈ Rn+r is the auxiliary state and x ¯ˆk = x is the estimation of ˆTk fˆsk x ¯k . Matrices Q, Ri and Hi are the observer’s parameters to be determined. ¯ − R = 0, I + R Q = 0, then ¯ − Hi = 0, A¯i + Ri QD ¯0 + Hi QD Set E i i     Ai 0 0 Ri = ,Q = , (4) −D −I I

and design

 Hi =

 I + S i D Si , Ti D Ti

(5)

ˆ¯k , rk = y¯k − yˆ¯k , then global dynamic error system is that ¯k − x define sk = x sk+1 =

N  i=1

φi (k)[Hi−1 Ri sk + Hi−1 C¯i dk + Hi−1 I¯2 dk wk ],

¯ xk − x ¯ k. ˆ¯k ) = Ds rk = y¯k − yˆ¯k = D(¯

(6)

Remark 1. Si and Ti are free matrices to be determined in order to guarantee ¯0 . Similar to [9,10,13], the design method of Si the non-singularity of matrix E and Ti are shown. Therefore, the proposed FE observer design can be obtained based on matrices Ti and Si to guarantee that system (6) be asymptotically stable with H∞ performance.

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For γ > 0, consider system (1), observer (3) is a H∞ fault estimator such that system (6) is asymptotically stable with dk = 0, and for dk = 0, then (7) holds. E{

∞ 

rkT rk } < γ 2 E{

k=0

3

∞ 

dTk dk }.

(7)

k=0

Main Results

Theorem 1. For given that ⎡ −Pi ⎢  ⎢ ⎢  ⎢ ⎣  

scalar γ > 0, there exists matrix Pi = PiT > 0 such 0 −γ 2   

⎤ ¯ T (H −1 Ri )T D 0 i 0 (Hi−1 C¯i )T (Hi−1 I¯2 )T ⎥ ⎥ ⎥ 0 are non-singular, it has that (Pj − Φi )T Pj−1 (Pj − Φi ) ≥ 0, that is, −ΦTi Pj−1 Φi ≤ Pj − (ΦTi + Φi ), then ⎡ ⎤ ¯ 1 0 −Pi 0 D ⎢  −γ 2 0 2 3 ⎥ ⎢ ⎥ ⎢   −I 0 0 ⎥ (11) ⎢ ⎥ < 0. ⎣    4 0 ⎦     d1 4 where 1 = (Hi−1 Ri )T Φi , 2 = (Hi−1 C¯i )T Φi , 3 = (Hi−1 I¯2 )T Φi , 4 = Pj − (ΦTi + Φi ). Define Pi = diag{Pi1 , Pi2 }, Φi = diag{Φi1 , Φi2 }, and then they are substituted into (11), combined with (5), one has ⎡ ⎤ 0 Ψ15 Ψ16 0 −Pi1 0 0 DT ⎢  −Pi2 0 0 ⎥ Ψ25 Ψ26 0 I ⎢ ⎥ T 2 ⎢   −γ I 0 Ci Φi1 Ψ36 Φi1 Ψ38 ⎥ ⎢ ⎥ ⎢  0 0 0 0 ⎥   −I ⎢ ⎥ < 0, (12) ⎢  0 0 ⎥ Ψ55 0    ⎢ ⎥ ⎢  0 ⎥  Ψ66 0    ⎢ ⎥ 1 ⎣  ⎦   d Ψ55    1        d Ψ66 where Ψ15 = (Ai + Si Ti−1 D)T Φi1 , Ψ25 = (Si Ti−1 )T Φi1 , Ψ36 = −CiT DT Φi2 , Ψ16 = (−DAi − (Ti−1 + DSi Ti−1 )D)T Φi2 , Ψ55 = Pj1 − (ΦTi1 + Φi1 ),

Ψ26 = −(Ti−1 + DSi Ti−1 )T Φi2 , Ψ38 = −DT Φi2 , Ψ66 = Pj2 − (ΦTi2 + Φi2 ).

Set ΓiT = Ti−1 SiT Φi1 , ΥiT = Ti−T Φi2 + Ti−T SiT DT Φi2 , then (12) becomes ⎡ ⎤ 0 Λ15 Λ16 0 −Pi1 0 0 DT ⎢  −Pi2 0 0 ⎥ ΓiT −ΥiT 0 I ⎢ ⎥ T 2 ⎢   −γ I 0 Ci Φi1 Ψ36 Φi1 Ψ38 ⎥ ⎢ ⎥ ⎢  0 0 0 0 ⎥   −I ⎢ ⎥ < 0, (13) ⎢  0 0 0 ⎥ Ψ55    ⎢ ⎥ ⎢  ⎥ 0 ⎥  Φ66 0    ⎢ ⎣  ⎦   d1 Ψ55           d1 Ψ66

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where Λ15 = ATi Φi1 + DT ΓiT , Λ16 = −ATi DT Φi2 − DT ΥiT . Based on what has been discussed above, we show the following theorem. Theorem 2. For a given scalar γ > 0, system (6) is asymptotically stable with dk = 0 and satisfies (7), if there exist symmetric matrices Pi1 , Pi2 , matrices Φi1 , Φi2 , Γi and Υi such that (13) holds. Then Si and Ti satisfy −1 −1 T Ti = (Υi − ΦTi2 DΦ−T Φi2 , Si = Φ−T i1 Γi ) i1 Γi Ti .

(14)

Based on the aforementioned analysis, the FE problem for system (1) can be viewed as the appropriate fault estimator design so that system (9) is asymptotically stable and the effect of disturbances meets (7).

4

An Example

To prove the feasibility of the proposed H∞ FE scheme, consider that system contains two rules, where membership function is that φ1 (k) = 15 esin(k−0.5) and φ2 (k) = 1 − φ1 (k) with the parameters:         0.9 0.5 0.2 0 0.9 0.2 −0.5 0 , B1 = , C1 = , D= , A1 = 0 0.4 0.2 −0.1 0.3 0.8 0 0.8        T 0.2 0.4 0.3 0 0.2 0.5 0010 A2 = , B2 = , C2 = , Q= . 0.1 0.5 0 −0.4 0 0.7 0001 For γ = 2.4 and d = 0.9, according to Theorem 2, the gains in observer (3) are shown: ⎡ ⎡ ⎤ ⎤ 0.9 0.5 0 0 0.6906 0.2438 0.6187 0.3048 ⎢ 0 0.4 0 0 ⎥ ⎢ −0.0839 1.0492 0.1677 0.0615 ⎥ ⎢ ⎥ ⎥ R1 = ⎢ ⎣ 0.5 0 1 0 ⎦, H1 = ⎣ −0.5868 0.5665 1.1736 0.7082 ⎦, 0 −0.8 0 1 −0.0916 0.1985 0.1832 0.2481 ⎡ ⎡ ⎤ ⎤ 0.2 0.4 0 0 0.8263 −0.0043 0.3474 −0.0054 ⎢ 0.1 0.5 0 0 ⎥ ⎢ −0.0776 0.8501 0.1551 −0.1873 ⎥ ⎢ ⎥ ⎥ R2 = ⎢ ⎣ 0.5 0 1 0 ⎦, H2 = ⎣ −0.9170 0.2073 1.8339 0.2592 ⎦. 0 −0.8 0 1 0.7863 0.4996 −1.5725 0.6244 To show the effectiveness of the designed FE observer, consider T  T  T  uk = 0 0 , dk = 0.2rand − 0.1 0.4rand − 0.2 , fsk = fsk1 fsk2 ,  0.5 100 < k ≤ 200 sin(0.2k − 0.2) 100 < k ≤ 200 , fsk2 = . 0, other 0, other  T Set xk = 0.8 −0.6 , fault estimation fˆk is shown in Fig. 1, in which f so1 and f so2 denote estimations of fsk1 and fsk2 , respectively. where

fsk1 =



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1

Magnitude

0.5

0

-0.5

-1

-1.5 0

50

100

150

200

250

300

Time step k

Fig. 1. Fault estimation fˆsk

5

Conclusions

The FE problem for discrete T-S system is considered. An observer-based fault estimator is designed by applying the extended state method and the error dynamic system is asymptotically stable and meets H∞ performance. Sufficient conditions and observer gains of the FE problem are shown. An example proves that the proposed method’s effectiveness. Acknowledgments. The work was supported by the Natural Science Foundation of Shandong Province under Grant ZR2019PF009.

References 1. Takagi, T., Sugeno, M.: Fuzzy identification of systems and its application to modeling and control. IEEE Trans. Syst. Man Cybern. SMC-15, 116–132 (1985) 2. Zhao, L., Li, L.: Robust stabilization of t-S fuzzy discrete systems with actuator saturation via PDC and non-PDC law. Neurocomputing 168, 418–426 (2015) 3. Zheng, H., Xie, W., Lam, H., Wang, L.: Membership-function-dependent stability analysis and local controller design for T-S fuzzy systems: a space-enveloping approach. Inf. Sci. 548, 233–253 (2021) 4. Zheng, K., Zhang, Q., Hu, Y., Wu, B.: Design of fuzzy system-fuzzy neural networkbackstepping control for complex robot system. Inf. Sci. 546, 1230–1255 (2021) 5. Fan, X., Wang, Z., Shi, Z.: Event-triggered integral sliding mode control for uncertain fuzzy systems. Fuzzy Sets Syst. 416, 47–63 (2020). https://doi.org/10.1016/ j.fss.2020.09.002 6. Chen, Z., Zhang, B., Stojanovicc, V., Zhang, Y., Zhang, Z.: Event-based fuzzy control for T-S fuzzy networked systems with various data missing. Neurocomputing 417, 322–332 (2020) 7. Tian, Y., Wang, Z.: A switched fuzzy filter approach to H∞ filtering for TakagiSugeno fuzzy markov jump systems with time delay: the continuous-time case. Inf. Sci. 557, 236–249 (2021)

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8. Steven, X.: Model-Based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools. Springer, Berlin (2008). https://doi.org/10.1007/978-3-540-76304-8 9. Zhu, X., Xia, Y.: Fault detection for discrete systems based on a descriptor system method. IET Control Theory Appl. 8, 697–704 (2014) 10. Zhu, X., Xia, Y., Wang, M., Ma, S.: H∞ fault detection for discrete-time hybrid systems via a descriptor system method. Circuits Syst. Signal Process. 33(9), 2807– 2826 (2014). https://doi.org/10.1007/s00034-014-9779-4 11. Yang, X., Li, T., Wu, Y., Wang, Y., Yue, L.: Fault estimation and fault tolerant control for discrete-time nonlinear systems with perturbation by a mixed design scheme. J. Franklin Inst. (2020). https://doi.org/10.1016/j.jfranklin.2020.12.024 12. Zhu, X., Xia, Y., Fu, M.: Fault estimation and active fault-tolerant control for discrete-time systems in finite-frequency domain. ISA Trans. 104, 184–191 (2020) 13. Bouattour, M., Chadli, M., Hajjaji, A., Chaabane, M.: Robust fault detection observer design for takagi-sugeno systems: a descriptor approach. In: Proceedings of 18th Mediterranean Conference on Control and Automation, pp. 255–260 (2010) 14. Zhu, X., Xia, Y.: H∞ descriptor fault detection filter design for T-S fuzzy discretetime systems. J. Franklin Inst. 351, 5358–5375 (2014) 15. Yan, J., Yang, G., Li, X.: Fault detection in finite frequency domain for T-S fuzzy systems with partly unmeasurable premise variables. Fuzzy Sets Syst. (2020). https://doi.org/10.1016/j.fss.2020.08.014 16. Chibani, A., Chadli, M., Steven, X., Braiek, N.: Design of robust fuzzy fault detection filter for polynomial fuzzy systems with new finite frequency specifications. Automatica 93, 42–54 (2018) 17. Zhang, J., Tong, S.: Adaptive fuzzy output feedback FTC for nonstrict-feedback systems with sensor faults and dead zone input. Neurocomputing 435, 67–76 (2021) 18. Pourdehi, S., Karimaghaee, P.: Reset observer-based fault tolerant control for a class of fuzzy nonlinear time-delay systems. J. Process Control 85, 65–75 (2020) 19. Shen, H., Xing, M., Wu, Z., Park, J.: Fault-tolerant control for fuzzy switched singular systems with persistent dwell-time subject to actuator fault. Fuzzy Sets Syst. 392, 60–76 (2020) 20. Liu, Y., Wang, Y.: Actuator and sensor fault estimation for discrete-time switched T-S fuzzy systems with time delay. J. Franklin Inst. 358, 1619–1634 (2021) 21. Feng, X., Wang, Y.: Fault estimation based on sliding mode observer for TakagiSugeno fuzzy systems with digital communication constraints. J. Franklin Inst. 357, 569–588 (2020) 22. Xie, X., Yue, D., Park, J.: Robust fault estimation design for discrete-time nonlinear systems via a modified fuzzy fault estimation observer. ISA Trans. 73, 22–30 (2018) 23. Liu, X., Gao, Z., Zhang, A.: Observer-based fault estimation and tolerant control for stochastic Takagi-Sugeno fuzzy systems with brownian parameter perturbations. Automatica 102, 137–149 (2019) 24. Chen, Yu., Zhu, X., Gu, J.: Fault estimation and compensation for fuzzy systems with sensor faults in low-frequency domain. In: Liang, Q., Wang, W., Liu, X., Na, Z., Li, X., Zhang, B. (eds.) CSPS 2020. LNEE, vol. 654, pp. 799–807. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-8411-4 106 25. Han, J., Zhang, H., Wang, Y., Zhang, K.: Fault estimation and fault-tolerant control for switched fuzzy stochastic systems. IEEE Trans. Fuzzy Syst. 26, 2993–3003 (2018) 26. Sun, S., Wang, Y., Zhang, H., Xie, X.: A new method of fault estimation and tolerant control for fuzzy systems against time-varying delay. Nonlinear Anal. Hybrid Syst. 38, 1–23 (2020)

A Simple Design of Microstrip Patch Antenna for WLAN Application Using 5.4 GHz Band Prince Mahmud Ridoy(B) , Khan Md. Elme(B) , Rezauddin Shihab, Pranta Saha, Md. Jawad-Al-Mursalin, and Nowshin Alam American International University-Bangladesh, 408/1, Kuratoli, Khilkhet, Dhaka 1229, Bangladesh [email protected], [email protected]

Abstract. In this paper, a microstrip patch antenna for 5.4 GHz band has been designed using Computer Simulation Technology (CST) Microwave studio, choosing copper for constructing patch and ground plane, Flame Retardant 4 (FR-4) material for substrate and microstrip line for feeding. The small sized simple design offers high directivity as evident from a main lobe magnitude of 6.16 dBi and an impressive −53.189 dB S11 value at center resonant frequency of 5.38 GHz. Additionally, the efficiency is calculated to be 41.938% and the bandwidth an adequate value of 200.6 MHz. The Voltage Standing Wave Ratio (VSWR) value of 1.0044 indicates almost no mismatch between antenna and feedline. These simulated results confirm that the proposed antenna performs admirably despite the simple design and can be used for wireless local area network (WLAN) applications. Keywords: Microstrip patch antenna · FR-4 material · CST microwave studio · Return loss · WLAN applications

1 Introduction Modern wireless communication systems have undergone rapid growth in the last few decades, with the demand for wider bandwidth growing to support massive amounts of data necessary for high data rate applications. The radio frequency (RF) spectrum is becoming overcrowded with the evolution of wireless technologies like Bluetooth, WiFi, WiMAX, GSM, satellite, Ultra-Wide Band (UWB) etc. [1]. The common frequency bands used for wireless local area network (WLAN) applications are 2.4 (2.417–2.497) GHz and 5 (5.15–5.35, 5.475–5.725) GHz. [2]. The 5 GHz networks are becoming more attractive compared to the traditional 2.4 GHz ones since 2.4 GHz spectrum is also shared by many other devices like cordless phones, microwave ovens, Zigbee devices, wireless peripherals, radars, audio-visual devices and so on. Efforts on antenna design for WLAN applications have persisted for the last few decades, but antenna engineering keeps encountering new challenges to meet the more rigid requirements for bandwidth, size, performance, cost and installation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 574–581, 2022. https://doi.org/10.1007/978-981-19-0390-8_70

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The choice for Antenna type is mostly determined by its purpose. Microstrip patch antennas have gained popularity in wireless communication applications due to their low cost, simplicity of fabrication, low weight, feed line flexibility, omnidirectional field pattern and integrability with solid-state devices [3]. The trade-off for the miniaturization in microstrip antenna is a small bandwidth, low gain and efficiency and possibility of fabrication error [4]. Researchers have employed a number of approaches to alleviate these problems, but these methods come with their own issues, such as increasing the substrate height (size increase and design is not low profile), defected ground structure (reduction of front to back ratio and field pattern change) [4], non-contacting feeding methods and metamaterials [5] (fabrication difficulty increase), PBG structure and multiple resonator effect (thickness increase) [6] etc. The aim of this work is to propose a design for a microstrip patch antenna that uses the 5.4 GHz frequency band with a better performance over the most recent available work. Using CST Microwave studio, a simple design has been simulated and the working process has been documented in this paper in six sections. Section 2 covers the literature review of microstrip patch antenna for wireless communications using 5 GHz band. In Sect. 3, the design specifications and the geographical model architecture is explained, followed by Sect. 4 where the design equations used for the calculations are listed. Section 5 explains and analyses the simulated design in terms of calculated antenna characteristics, and offers a comparative study against existing works. Finally, the conclusion of the work is discussed in Sect. 6.

2 Literature Review The first microstrip antenna was introduced in the 1950s. Its development accelerated in the early 1980s and is currently ongoing [7]. In 2012, Alam et al. [8] proposed a design of an EBG single band microstrip patch antenna for WLAN and HIPERLAN applications where various values of return loss and bandwidth were attained by varying the length of the L-type slot. Additionally, a maximum return loss of −21.69 dB has been achieved at 5.4 GHz resonant frequency by 3 × 3 EBG structure [8]. In 2014, Khan et al. [9] designed an microstrip slot antenna at 5.4 GHz resonance frequency for WiMAX applications that achieved −15 dB return loss. Yang et al. [10] devised miniaturized structures of two dual band microstrip antennas for WLAN communication. The monopole antenna operated in the 4.5 GHz–7.5 GHz and 4.5 GHz–7.5 GHz frequency bands, while the microstrip antenna covered the 2.4 GHz–2.46 GHz and 5.16 GHz–5.4 GHz ranges. A rectangular microstrip array antenna was designed in 2017 by Parameswari et al. [11] for 5.4 GHz Wi-Fi applications. The design achieved 15 dB, 20 dB and 40 dB reflection coefficients through the use of 1 × 1, 1 × 2 and 2 × 2 array antennas, respectively. A triple-band monopole antenna was designed by Kulkarni et al. [12] in 2019 for various wireless applications where 34 dB return loss was obtained at 5.52 GHz cut-off frequency. In the next year, Vahora et al. [3] proposed a design of triple-band array antenna sporting − 23.5 dB return loss with a bandwidth of 220 MHz at 5.4 GHz cut-off frequency. In 2021, Kumar et al. [13] designed a 1 × 2 adaptive microstrip array antenna which operated at 5.4 GHz and 2.45 GHz resonant frequencies. The return losses and the bandwidths were −15.74 dB and 60 MHz for the 2.45 GHz band, and −49.09 dB and 200 MHz for the

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5.4 GHz scenario. In the same year, Thatere et al. [14] designed a modified rectangular microstrip antenna for wireless communication purposes that achieved −21.41 dB reflection coefficient and 236 MHz bandwidth at 5.302 GHz operating frequency. The existing literature indicates that there is a trade-off between gain and bandwidth. Additionally, designs tend to get more complex and bigger in size to improve return loss. This paper strives to balance the conflicting parameters, improving return loss in particular, while offering a simpler approach.

3 Design and Modelling To make the antenna suitable for the wireless applications at 5.4 GHz an appropriate measurement has been chosen first. Then the antenna has been designed in CST STUDIO SUITE software based on that measurement. Copper (Cu) material has been used to construct both ground and patch layer of the antenna. Each of these layers contain a thickness of 0.03 mm. Apart from this, 1 mm thick FR-4 material has been used to construct the substrate layer of the antenna. The reason for choosing Copper (annealed) and FR-4 (lossy) materials inside the software is to make the design realistic, as no materials are completely pure. The geometrical architecture of the antenna is shown in the figure given below. Figure 1 (a) shows the frontal geometrical architecture of the antenna in free space of the software window. The (c) and (d) marked portion of Fig. 1 (a). represents the slots cut and transmission line or feeding line of the designed antenna respectively. Those two slots at the lower portion of the patch layer have a width of 0.075 mm, and transmission line has a width of 0.775 mm only. The Fig. 1 (b) shows the layers of the designed antenna and its specification. The view of the antenna from the bottom is shown by Fig. 2 (a). Line feeding technique has been used and the antenna is fed via the port that is portrayed by a red region denoted by the number “1”.

Fig. 1. Design Specification: (a) feeding line and slots of the antenna, and (b) layers of the antenna

Figure 2 (b) depicts the antenna from a 3D viewpoint. The perspective view of the antenna in X-Y-Z plane in free space of CST software window is shown.

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Fig. 2. (a) Bottom view, and (b) Perspective view of the Antenna in X-Y-Z plane

Table 1 lists the parameters that were specified in the software to simulate the antenna. The total dimension of the antenna is 38 × 28 × 1.06 mm3 whereas the patch layer dimension is 17 × 12.8 × 0.03 mm3 . Table 1. Design Specifications Representation

Wg

Lg

HG or HP

HS

Value (mm)

38

28

0.03

1

Specification

Ground Width

Ground Length

Ground & Patch Height

Substrate Height

Representation

Wp

Lp

Wf

INSw

Value (mm)

17

12.8

0.775

0.075

Specification

Patch Width

Patch Length

Transmission Line Width

Width of Insert (lower slot)

4 Design Equations Formulas that have been used in this work are listed in the following table (Table 2) [3]. Table 2. Used formula Calculation of the Width of patch (W)

w=

c 2f0

εr+1 2

Here, c = free space velocity of light = 3 x 108 m/s; r = the relative permittivity of dielectric substrate = 4.3; f0 = resonant frequency = 5.4 GHz (continued)

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Calculation of actual length of patch (L)

L=

C 2f0

εr +1 2

−

  εreff +0.3 Wh +0.264  Here, h = 0.824h   εreff −0.258 Wh +0.8



Calculation of Effective dielectric constant (εreff ) Inset feed depth determination (y0 )

substrate’s height, w = patch’s width, εreff = effective dielectric constant

0.5 εreff = εr 2+1 + εr 2−1 1+12h w    z cos−2 R0 L in y0 = π

Here, z0 = line impedance, Rin = input impedance, L = length of patch

5 Simulation and Result Analysis Different parameters such as S-parameter/Return Loss, Far-Field pattern, Bandwidth, Voltage Standing Wave Ratio (VSWR), Efficiency of the antenna have been analyzed after completing the simulation.

Fig. 3. (a) Antenna’s S-parameter determination, and (b) Data measurement for bandwidth

Figure 3 (a) depicts the s-parameter S11 of the antenna at resonant frequency. At 5.38 GHz the return loss of the antenna is found to be 53.189 dB. Figure 3 (b) illustrates the necessary information to calculate the bandwidth of the frequency band. Depending on the information provided by the above figure, the calculated bandwidth = upper cutoff frequency – lower cutoff frequency = (5.4798 – 5.2792) GHz = 0.2006 GHz = 200.6 MHz, which is adequate for WLAN application.

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Figure 4 (a) indicates the Three Dimensional (3D) Far-Field pattern of the antenna. The figure illustrates the radiation efficiency and the total efficiency of the antenna. Both the radiation efficiency and total efficiency of the antenna at resonant frequency are 3.632 dB, and the directivity is 6.156 dBi. Figure 4 (b) shows the polar Far-Field pattern of the antenna, and also depicts that the major beam of the antenna has a magnitude of 6.16 dBi. It also illustrates that the antenna’s main beam has an angular width of 97.9° that is pointing at 3°.

Fig. 4. (a) Antenna’s 3D Far-field pattern, and (b) Antenna’s polar far-field radiation pattern

The value of antenna gain is extracted from Fig. 5 (a) and is found to be 2.5817 dBi at resonant frequency. Efficiency is one of the major factors used to analyse or measure antenna efficiency, which is mostly affected by two parameters- antenna gain and directivity. Following the given formula, the efficiency of the antenna can be determined as Gain(dBi) 2.5817 Directivity(dBi) 100 = 6.156 × 100 = 41.938%.

Fig. 5. (a) Antenna gain, and (b) Voltage Standing Wave Ratio (VSWR) indication

VSWR is one of the main factors in antenna design. VSWR, which is assumed to be a generally acceptable margin, has a value of between zero and two. Here the value of

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VSWR of the designed antenna almost tends to 1 at resonant frequency that is illustrated by Fig. 5 (b). Various antenna parameters such as return loss and resonance frequency were analyzed in this paper using data from previous research papers. In comparison to the references in Table 3, a better return loss was obtained at the operating frequency of 5.38 GHz from the CST simulation of the suggested design. So, the proposed design performs better in that perspective. The bandwidth is also adequate for WLAN application. Table 3. Comparative Study Resonance frequency (GHz)

S1,1 (dB)

Bandwidth (MHz)

Ref.

5.4

−17.04

230

[8]

5.4

−15

–

[9]

5.2

−24.5

240

[10]

5.4(Single patch antenna)

−10

–

[11]

5.4(1 × 2 array antenna)

−20

–

[11]

5.4(2 × 2 array antenna)

−40

–

[11]

5.52

−34

–

[12]

5.4

−23.5

220

[3]

5.45(Single patch antenna)

−47.59

240

[13]

5.45(1 × 2 array antenna)

−49.097

200

[13]

5.284

−19.778

239.3

[14]

5.302

−21.41

236.7

[14]

5.38

−53.189

200.6

This work

6 Conclusion A rectangular microstrip patch antenna with a size of 38 × 28 mm2 has been successfully simulated and designed on a substrate with a dielectric constant of 4.3 using CST software in this paper. This project aims to achieve the desired results by maintaining the antenna design simple, keeping in mind the challenges faced in fabrication. Several considerations were taken into account for improving the efficiency of the antenna, including operating frequency, measurements, and substrate selection. This designed antenna is proposed for WLAN applications that operate in the 5.38 GHz frequency range. This antenna followed the WLAN communication standards with a bandwidth of 200.6 MHz in the frequency spectrum of 5.4798 to 5.2792 GHz. The return loss value of 53.189 dB, higher than the values found in existing literature, indicates that the impedance matching is excellent. This antenna achieved an efficiency of 41.938% with a VSWR of 1.0044. A section of this paper shows a comparison between the results of previous research papers featuring antennas designed for WLAN applications, and

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the results of the proposed antenna design are also discussed. The comparative analysis shows that the designed antenna has been able to achieve better return loss with adequate bandwidth. The results also indicate that the proposed antenna can be used successfully for WLAN communication.

References 1. Kaur, A., Malik, P.K.: Multiband elliptical patch fractal and defected ground structures microstrip patch antenna for wireless applications. Prog Electromagn Res B 91, 157–173 (2021) 2. Arai, H.: Antennas in access points of WLAN/WiFi. In: Chen ZN, Liu D, Nakano H, Qing X, Zwick T (eds) Handb. Antenna Technol. Springer Singapore, Singapore, pp 2579–2588 (2016) 3. Vahora, A., Pandya, K.: Implementation of cylindrical dielectric resonator antenna array for Wi-Fi/wireless LAN/satellite applications. Prog Electromagn Res M 90, 157–166 (2020) 4. Bhanumathi, V., Swathi, S.: Bandwidth enhanced microstrip patch antenna for UWB applications. ICTACT J Microelectron 04, 669–675 (2019) 5. Balanis, C.A.: Antenna theory: analysis and design, 3rd edn. John Wiley, Hoboken, NJ (2005) 6. Elsheakh, D., Abdallah, E.: Compact multiband printed IFA on electromagnetic band-gap structures ground plane for wireless applications. Int J Microw Sci Technol 2013, 1–9 (2013) 7. Peixeiro, C.: Microstrip patch antennas: an historical perspective of the development. In: 2011 SBMOIEEE MTT- Int. Microw. Optoelectron. Conf. IMOC 2011. IEEE, Natal, Brazil, pp 684–688 (2011) 8. Alam, M.S., Yeh, K.M., Islam, M.T., Misran, N., Hasbi, A.M.: An EBG microstrip antenna for 5.4 GHz WLAN/HIPERLAN applications. In: 2012 IEEE Stud. Conf. Res. Dev. SCOReD. IEEE, Pulau Pinang, Malaysia, pp 144–147 (2012) 9. Khan, A.: Simulation of microstrip slotted patch antenna at frequency in upper band (5.25.8 GHz) for WiMAX application in Ie3d software. Int J Softw Hardw Res Eng 2, 18–21 (2014) 10. Yang, J., Wang, H., Lv, Z., Wang, H.: Design of miniaturized dual-band microstrip antenna for WLAN application. Sensors 16, 983 (2016) 11. Parameswari, M.S., Ajithkumar, A.: Design and simulation of patch antenna array for 5.4 GHz Wi-Fi application. Int J Eng Res 5, 1–5 (2017) 12. Kulkarni, J., Seenivasan, R., Abhaikumar, V., Subburaj, D.R.P.: Design of a novel triple band monopole antenna for WLAN/WiMAX MIMO applications in the laptop computer. Int J Antennas Propag 2019, 1–11 (2019) 13. Kumar, P., SY, P., Kumari, H.K.K. Design of adaptive array antenna for wireless communications. https://doi.org/10.21203/rs.3.rs-207963/v1 (2021) 14. Thatere, A., Khade, S., Lande, V.S.: A modified rectangular mid-band microstrip slot antenna for WLAN, WiFi and 5G applications. J Univ Shanghai Sci Technol 23, 1–6 (2021)

Estimation of Chlorophyll Concentration for Environment Monitoring in Scottish Marine Water Yijun Yan1 , Yixin Zhang2 , Jinchang Ren1(B) , Madjid Hadjal3 , David Mckee3 , Fu-jen Kao4 , and Tariq Durrani2 1 National Subsea Centre, Robert Gordon University, Aberdeen, UK

[email protected]

2 Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, UK 3 Department of Physics, University of Strathclyde, Glasgow, UK 4 National Yang Ming Chiao Tung University, Taipei, Taiwan

Abstract. Marine Scotland is tasked with reporting on the environmental status of Scottish marine waters, an enormous area of water extending from the shoreline to deep oceanic waters. As one of the most important variables, chlorophyll concentration (Chl) plays an important role in the seawater quality monitoring. Currently, the Chl observation is mostly done by expensive ship-based surveys that have very limited spatio-temporal coverage. Satellite based ocean colour remote sensing has the potential to significantly enhance monitoring capabilities but this opportunity has not been widely adopted by statutory reporting bodies across Europe due to concerns over satellite data quality. To break through this bottleneck, in this paper, we explore to implement advanced machine learning techniques to automatically estimate the Chl via the historic time series of ocean colour remote sensing data during from July 2002 to September 2019. Keywords: Environment monitoring · Scottish marine water · Chlorophyll · Multispectral remote sensing

1 Introduction Scotland is a maritime nation with territorial with a combined areal extent six times greater than our land area. This massive area ranges from the seashore to deep abyssal ocean depths, and supports a diverse range of industries including energy production, oil and gas, fisheries and aquaculture and tourism, bringing contribution of tens of billions to the Scottish economy annually. These waters are protected under various pieces of legislation including the EU Marine Strategy Framework directive [1], the Water Framework Directive [2] and other international obligations such as the OSPAR convention. Marine Scotland (MS) is the government Directorate with responsibility to report the water quality regularly. However, such a vast area of ocean can only be sampled at relatively sparse spatial and temporal resolution using traditional in situ sampling © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 582–587, 2022. https://doi.org/10.1007/978-981-19-0390-8_71

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approaches. For example, the Scottish Coastal Observatory consists of only 11 monitoring sites, from which conditions around Scotland are extrapolated. The Chlorophyll concentration (Chl) is commonly used as an indicator for observing primary productivity (PP), formation of harmful algal blooms (HABs) and monitoring the occurrence of eutrophication events (EE) [3]. It can be retrieved from satellite-based ocean colour remote sensing data with daily, global resolution (subject to cloud cover and daylight restrictions) [4]. Unfortunately, uncertainty and variability of data quality has proved to be a major hurdle hindering widespread uptake of ocean colour data by MS and other public environmental monitoring bodies. In this paper, we will use existing machine learning techniques to estimate the Chl on the optical signals from satellite data. This will inform evaluation of the historic ocean colour time series, with Marine Scotland’s existing database of in situ Chl data used to determine Chl algorithm performance. The outline of this paper is as follows: Sect. 2 introduces the data and method in this work. Section 3 presents some preliminary experimental results. Finally, some concluding remarks and future work are summarized in Sect. 4.

2 Methods 2.1 Data Source The North Sea area near Scottish coastal is used as study case in this paper. The in situ Chl data and remote sensing optical data that includes 15 spectral bands are sourced from Marine Scotland. There are 3684 spectral samples in total covering the period from July 2002 to September 2019. For the in situ Chl data, they are measured at different depth (1–30 m). An illustration of the dataset is shown in Fig. 2. In this work, all Chl data are categorized into three classes, i.e. Chl-30, Chl-20 and Chl-10, which represents the Chl at the depth of 30,20 and 10 meters, respectively. For example, when the in situ measurement depth is less than 10 meter, Chl-10, Chl-20 and Chl-30 have the same value. While the in situ measurement depth is higher than 10 meters, the Chl-10 will be zero and the Chl-20 will equal to Chl-30. To this end, each spectral sample has three associated Chl values. 2.2 Statistical Analysis In this work, four machine learning (ML) models i.e. Lasso regression, Ridge regression [5], Support Vector Machine (SVM) [6, 7] and Random Forest (RF) [8] are used to evaluate the prediction performance of chlorophyll concentration. In the process of model training, the data set is randomly divided into a training set and test set at a ratio of 8:2. The training set is used for model training and fitting, and the test set is used to evaluate the accuracy of the model.

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Fig. 1. Illustration of Chl v.s. Depth

To simplify the computation and facilitate the convergence speed and accuracy of the training models, all data X in this work is normalized into [0,1] through the min-max normalization processing (Eq. (1)). Min − Maxnormalization : Xnor =

X − min(X ) max(X ) − min(X )

(1)

During the test, R-square (R2 ) score is used as evaluation criteria (Eq. (2)). The definition of R2 score is shown below. It reflects the proportion of the total variation of the dependent variable that can be explained by the independent variable through the regression relationship. The numerator is the mean square error, and the denominator is the variance. The range of R2 is within [0,1], with 1 indicating 100% prediction accuracy.  N   2 yi − yi i=1 R2 = 1 −    2 N i=1 yi − yi 

(2)

where yi and yi represent the actual value and predicted value of the ith Sample,  respectively; yi is the mean of all yi values.

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3 Results and Discussion In this section, we plot the Chl estimation results objectively and subjectively in Table 1 and Fig. 2, respectively. It can be seen that two linear regression methods (i.e. Lasso regression and Ridge regression) have very fast computation speed but yield the relatively poor estimation accuracy. SVM has better prediction accuracy than linear regression methods but it has the highest computation cost. Among these ML models, random forest yields the highest R2 score and has much reduced computation cost than SVM. For better visualizing the estimated result, we plot the objective results in Fig. 2. Here we mainly show the predicted Chl results in the range from 0 to 10 as most in-situ Chl measured value is lower than 10 (Fig. 1). As seen in Fig. 2, linear regression cannot predict the Chl very well as the blue dots are far from red diagonal line which means most predicted results different against actual value. For SVM, when the Chl is lower (e.g. < 1), it has good prediction results where the blue dots are close to the red diagonal line. However, when the Chl is larger (i.e. > 4), the blue dots and the red line have a great distance, which indicates poor prediction accuracy. For random forest, when the Chl is lower than 4, it has the lowest prediction bias which will also increase with the growth of Chl. The main reason is that there is imbalance problem in the data, for instance, most spectral samples have lower Chl value ( a[i][0] + a[0] j (a[i][0] + a[0] j means “the energy  consumption by the first vertex between i and j”), then a[i] j = a[i][0]+ a[0] j . In   the k th update,if the energy consumption of a[i] j > a[i][k] +  the same way, a[k] j , then a[i] j = a[i][k] + a[k] j . After N updates, the operation is completed.

Fig. 1. A network

In a network, Fig. 1 shows the energy consumption relationship of each node.

1

2

3

1⎡0 2 ⎢⎢ 3⎢7 ⎢ 4⎣5

2 0

6 3 0 12

4 4⎤ ⎥ ⎥ 1⎥ ⎥ 0⎦

Fig. 2. Adjacency matrix

The adjacency matrix generated by the F algorithm is Fig. 2. In the case that only vertex 1 is allowed to pass through, the minimum energy consumption between any two points is updated as Fig. 3; If only vertices 1 and 2 are allowed to pass through, the minimum energy consumption between any two points is updated as Fig. 4; Similarly, when only vertices 1, 2, and 3 are allowed to transit, the minimum energy consumption between any two points is updated to Fig. 5; Finally, all vertices are allowed to be used as a transit, and the final minimum energy consumption between any two points is Fig. 6.

Cooperative Routing Algorithm Based on Data Priority

1 1⎡0 2 ⎢⎢ 3⎢7 ⎢ 4⎣5

2

3

4

1

2 3

4

2 6 4⎤ ⎥ 0 3 ⎥ 9 0 1⎥ ⎥ 7 11 0 ⎦

1⎡0 2 ⎢⎢ 3⎢7 ⎢ 4⎣5

2 5 0 3 9 0 7 10

4⎤ ⎥ ⎥ 1⎥ ⎥ 0⎦

Fig. 3. Pass Node 1

1 1⎡ 0 2 ⎢⎢10 3⎢ 7 ⎢ 4⎣ 5

2

3 4

2 5 0 3 9 0 7 10

4⎤ 4 ⎥⎥ 1⎥ ⎥ 0⎦

Fig. 5. Pass Node 1, 2, 3

605

Fig. 4. Pass Node 1, 2

1 1⎡ 0 2 ⎢⎢10 3⎢ 7 ⎢ 4⎣ 5

2

3 4

2 5 0 3 9 0 7 10

4⎤ 4 ⎥⎥ 1⎥ ⎥ 0⎦

Fig. 6. Pass all nodes

Therefore, when the sensor nodes of WBAN transmit information according to the path of F algorithm, the energy consumed is the least, which can greatly reduce the overall energy consumption of WBAN, and thus we can efficiently utilize the energy resources of the network.

3 Simulation Environment In short-distance free space communication, the transmission power domain is proportional to the transmission distance. So energy consumption can be expressed as E = λd 2 , λ is a fixed parameter. (Fig. 7 and Fig. 8).

Fig. 7. The hops set according to data priority

Since it is a simulation verification of the WBAN routing algorithm, the position of each node on the human body does not need to be accurately measured, only needs to be defined by itself according to the approximate ratio. In order to facilitate the comparison

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Fig. 8. Experimental parameters

of the position and distance between the various wireless sensors, the distance of each node is enlarged in equal proportion and rounded to facilitate the comparison of the multiple relationship between the various distances. According to the distribution of sensor nodes on the human body, the position coordinates of each wireless sensor node are now defined according to Fig. 9.

Fig. 9. Node coordinates

The position of each body node is shown in Fig. 10. Due to the movement of the human body, the sink node 14 may not receive the information of the node 10 or the node 9.

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Fig. 10. Position of each body node

4 Simulation Results First, data packets with priority 1, 2, 3 and 4 are randomly input to node 1. Simulation results of energy consumption of each node are shown in the figure below.

Fig. 11. Energy consumption of each node

As can be seen from Fig. 11, node 7 has the fastest energy consumption, while nodes 2, 3, 9 and 13 have no energy consumption. The remaining energy of nodes 4, 5, 10

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and 12 remains unchanged for a period of time because the priority of the data changes. The energy of nodes closer to node 1 is consumed first. The nearest or farthest node is consumed at last. The packets of priority 1 and 2, priority 3 and 4, and priority 1, 2, 3 and 4 are sent to node 1 respectively. The life cycle of the whole network is as follows:

Fig. 12. Network life cycles of different priorities

It can be seen from Fig. 12 that when node 1 sends data packets with data priorities 1, 2, 3, and 4, the whole network has longer lifetime.

5 Conclusion When node 1 sends data packets with priority 1, 2, 3, and 4, the lifetime of the entire network is longer than that of sending only packets with priority 1 and 2, and priority 3, and 4. However, when node 1 dies, most of the energy of some nodes is not consumed, which needs to be improved in future studies.

References 1. Morabia, A., Abel, T.: Preventing chronic diseases: a vital investment. Int. J. Public Health 51(2), 74 (2006) 2. Monton, E., et al.: Body area network for wireless patient monitoring. IET Commun. 2(2), 215–222 (2008) 3. Patel, M., Wang, J.: Applications, challenges, and prospective in emerging body area networking technologies. IEEE Wireless Commun. 17(1), 80–88 (2010) 4. Seyedi, M.H., Kibret, B., Lai, D.T.H., Faulkner, M.: A survey on intrabody communications for body area network applications. IEEE Trans. Biomed. Eng. 60(8), 2067–2079 (2013) 5. Gravina, R., Alinia, P., Ghasemzadeh, H., Fortino, G.: Multi-sensor fusion in body sensor networks: State-of-the-art and research challenges. Information Fusion 35, 68–80 (2017)

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6. Wei, Y., Qingsong, F., Lijuan, H.: Sports motion analysis based on mobile sensing technology. In: International Conference on Global Economy, Finance and Humanities Research (GEFHR 2014) (2014) 7. Agusti, S., et al.: Smart health: a context-aware health paradigm within smart cities. IEEE Commun. Mag. 52(8), 74–81 (2014) 8. Hiremath, S., Geng, Y., Kunal, M.: Wearable internet of things: concept, architectural components and promises for person-centered healthcare. In: Wireless Mobile Communication and Healthcare (Mobihealth), 2014 EAI 4th International Conference on. IEEE (2014) 9. Melanie, S.: Sensor mania! the internet of things, wearable computing, objective metrics, and the quantified self 2.0. J. Sens. Actuator Netw. 1(3), 217–253 (2012) 10. Chen, M., Gonzalez, S., Vasilakos, A., Cao, H., Leung, V.C.M.: Body area networks: A survey. Mobile Netw. Appl. 16(2), 171–193 (2011)

Comparison of Walking Structure of High Voltage Line Deicing Robot Jin Li1(B) , Li Wang1 , and Hanfang Zhang2 1 College of Engineering and Technology, Tianjin Agricultural University, Tianjin 300384,

China [email protected] 2 College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin 300387, China

Abstract. Icing on a large area of high-voltage lines brings damage to the power grid, and many countries in the world suffer from it, and China is one of them. However, the current de-icing methods of high-voltage lines mostly have problems of low efficiency and low safety rate. In the analysis of the advantages and disadvantages of the high voltage line inspection work, the status quo and development trend of the high voltage line patrol robot, and on the basis of adopting other methods to clean the ice, this paper designed a high voltage line de-icing robot based on STC89C52 microcontroller. The robot can complete the functions of walking on the high voltage line, de-icing and real-time data monitoring through Bluetooth communication, SMS communication and self-detection. Through laboratory test and adjustment, the expected function has been realized well. Keywords: Transmission line · Mechanical deicing · Light intensity detection · High-voltage lines

1 Introduction With the continuous development of science and technology, the power industry has developed greatly in China, and the coverage rate of the power grid is gradually expanded, followed by an increasing number of natural disasters on transmission lines. The disaster caused by snow and ice to power grid is a common problem faced by many countries in the world. The importance of electricity network security is of great importance to a country. Most countries attach great importance to the technology of ensuring the safety of power lines, especially the research on de-icing technology is also actively strengthened, such as the research and development of robots. At present, there are more than 30 kinds of deicing methods in the world [1], according to the different deicing mechanism, can be divided into two kinds, one is mechanical deicing method, the other is thermal melting ice method. The first kind consumes less energy, deicing efficiency is not high, and needs manual operation; The second method is relatively fast and has a high safety factor, but it is not suitable for wide application, © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 610–619, 2022. https://doi.org/10.1007/978-981-19-0390-8_75

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because too many factors limit its practical application [2]. Manual de-icing of the circuit has the best effect on serious de-icing, but it is relatively dangerous [3] and inefficient. Therefore, this design topic is about the design of high-voltage line de-icing robot [4], which can effectively eliminate the serious threat of snow and ice weather to power lines, so as to achieve the desired effect.

2 Overall Design Scheme The main goal of this project is to complete the comparative study of the deicing robot walking mechanism and get the optimal high-voltage line deicing robot walking mechanism. When the robot walks on the high voltage cable, it should be stable first and then fast. The testing section, the de-icing section and the driving section should be reasonably arranged. In order to meet the practical requirements of removing icing of high-voltage wires, this robot is composed of three parts, including the de-icing part, the obstacle crossing part and the self-balancing part, as shown in Fig. 1.

Fig. 1. Obstacle crossing section

3 Design of Power Line Deicing Robot 3.1 Obstacle Crossing Mechanism Design In order to ensure that the deicing robot can walk on the high-voltage power transmission line and cross obstacles [5], sliding gear, lifting nut, screw, spiral gear and tensioning mechanism are adopted in the design to realize the function of walking wheel. Figure 2 shows the principle of the robot obstacle traversing process.

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Fig. 2. Obstacle crossing process of robot.

3.2 System Design and Module Functions 3.2.1 System Design The system logic of the high-voltage line de-icing robot can be divided into four parts, which are the detection part, the walking mechanism, the de-icing mechanism and the upper computer. The overall structure block diagram is shown in Fig. 3, and the overall physical diagram is shown in Fig. 4. Detection part: according to the principle that icing affects light intensity, the icing condition of high voltage line is detected by laser and photosensitive sensor, and the signal is collected to the core controller by AD chip PCF8591. Walking mechanism: a hanging walking mechanism is adopted to drive the de-icing robot to move forward and backward freely on the high-tension wire through the positive and negative rotation of the stepping motor [6].

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Fig. 4. Overall Physical Diagram

De-icing mechanism: using the method of mechanical percussion, using the rotation of high-speed motor to drive the deicing scissors to remove the ice on the high voltage line. Upper computer: under laboratory conditions, mobile phone is used to communicate with the robot through Bluetooth module to control and monitor the robot’s data. Short message communication module can be considered under remote conditions. 3.2.2 Detection Module Using PCF8591 8-bit ADDA conversion chip, the analog-to-digital conversion of optical signals collected by two photoresistive resistors is carried out. The strength of the digitalized light signal is compared with the expected value, so as to determine whether the robot conducts autonomous de-icing. The expected value comes from the average of many trials. The circuit diagram of PCF8591 is shown in Fig. 5.

Fig. 5. PCF8591 circuit diagram

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3.2.3 Driver Module LM298 motor drive chip is used to control the motor to complete the control of the robot’s walking direction and the start and stop of de-icing. Since the LM298 drive module is a 2-way H-bridge drive, two motors can be driven at the same time. After ENA and ENB are enabled, the speed and direction of motor 1 can be driven by input PWM signals from IN1 and IN2 respectively. The speed and direction of motor 2 can be driven by input PWM signal from IN3 and IN4 respectively. The physical diagram of the driver module is shown in Fig. 6.

Fig. 6. LM298 motor drive module

3.2.4 Bluetooth Module The HC-06 Bluetooth module is connected with the mobile phone, and the real-time detection data is transmitted to the mobile phone. At the same time, the robot’s movements can also be controlled by the mobile phone. Compared with the original equipment using serial connection, the Bluetooth module in this design can make the device is no longer bound by the cable, to achieve wireless serial communication, the range is controlled within 10 m. This module does not need to understand the complex Bluetooth underlying protocol, just a few steps, and relatively simple, you can feel the convenience brought by wireless communication. The Bluetooth module is shown in Fig. 7.

Fig. 7. Bluetooth module

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3.3 Mechanism Design 3.3.1 Design of Testing Mechanism Since the high-voltage wire is relatively thick, there is no suitable concave wheel and groove in the market. After physical measurement of the high-voltage wire, we drew the appropriate concave wheel and groove mechanical structure, and printed the components that met our requirements by using 3D printer. A certain number of light-emitting diodes and laser generators are installed in the grooves of the detection part as light sources, and a photosensitive sensor is installed to collect the light intensity data in real time. The actual drawing is shown in Fig. 8.

Fig. 8. Physical diagram of testing institutions

3.3.2 Walking Mechanism Design Using double groove pulley suspension type walking mechanism, using the left and right pulleys to suspend the robot on the high voltage line, driving the pulley on both sides of the stepper motor distribution, in order to increase the stability. The grooved pulley moves forward and backward through the forward and reverse rotation of the stepper motor. In the same way, we use 3D printer to print the high-voltage line grooved pulley device that meets the requirements. At the same time, in order to ensure the stability of the pulley walking on the overlying ice, the contact surface between the pulley and the high tension line is designed into a non-smooth concave and convex surface to increase the friction force. The physical picture is shown as Fig. 9.

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Fig. 9. Physical drawing of walking mechanism

3.3.3 Design of De-icing Mechanism The deicing structure adopts a sciss-like structure. Two aluminum alloy tubes are intersected and fixed, the upper part is connected with a strong elastic spring, and the bottom part is driven by a high-speed motor to carry out circular movement of an elliptical disk of appropriate size, and the head is connected with a sharp deicing blade [7]. By alternating the long and short sides of the elliptical disc in circular motion, the deicing scissors hand structure is made to open and close with the tension generated by the strong spring deformation. During the action, the sharp deicing blade strikes and cuts the ice of the high-voltage line to achieve the removal of the ice of the high-voltage line [8]. The physical diagram is shown in Fig. 10.

Fig. 10. Physical drawing of de-icing mechanism

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4 Software Design The de-icing robot detects the light intensity change of ice-covered high-voltage line through the detection structure [9]. In order to improve the accuracy of detection, a laser generator is added to the detection module to collect light intensity information through a photosensitive sensor. The detection information is input to the single chip microcomputer, and after comparing with the preset value of the single chip microcomputer, the output signal drives the driving part and the de-icing part of the robot to start de-icing the frozen high-voltage line [10]. The de-icing robot has two working modes [11]. The first is manual operation. The light intensity information is collected by mobile phone APP, and the movement of the robot and the opening and closing of the de-icing mechanism are controlled manually.

Fig. 11. Operation flow chart

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The second is the automatic mode, in which the robot is started to inspect on the highvoltage line, and the collected light intensity value is compared with the preset value to make self-judgment for de-icing. The operation flow chart is shown in Fig. 11. In order to monitor the robot in real time and control the robot manually, we use Bluetooth module to carry out external communication. Through the Bluetooth module, mobile APP is used to give instructions to the robot to control it to make corresponding actions, and real-time monitoring and detection of data from part of the line can be used to artificially judge the icing situation of the line [12]. Figure 12 shows the interface of mobile phone APP.

Fig. 12. Physical picture of mobile APP

Because the Bluetooth module communication distance is relatively close, the laboratory test can fully meet the requirements. Considering the long communication distance in the actual operation, the Bluetooth module can be replaced by the SMS communication module to meet the requirements of the actual operation.

5 Conclusion High voltage line de-icing robot with STC89C52 microcontroller as the core processor, the detection structure real-time detection of light intensity data, the data obtained into the core processor for processing, the core processor after processing output instructions to drive the movement structure and de-icing structure action. In the laboratory, the Bluetooth module can be used for communication, so as to monitor data in real time and issue action commands. In the case of remote distance, the SMS communication module can be used to replace the Bluetooth module. This design has realized the monitoring

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of high tension line ice and ice cleaning, high efficiency of the work instead of manual de-icing, reduce the burden of the staff, ensure the safety of the staff, at the same time guarantee the normal operation of the power facilities, improve the stability of industrial power, and power consumption of daily life, to improve the economic and social benefits obvious.

References 1. Hai, L., Ji, Z., Ruzhou, Y., Lei, T.: Design of deicing robot for overhead high voltage transmission line. Sc. Technol. Info. China 19, 111–112 (2010) 2. Jianlin, Z., Suoxian, Y., Lumiao, C., Jianjun, L.: Design of hanging and walking high voltage line deicing and obstacle-crossing robot. Devt. Innov. Mech. Elec. Prod. (06):22–23+15 (2011) 3. Qinrong, C., Yanhe, H., Tingbiao, C., Haitang, C.: Line deicer in deep mountain. Guangxi Electric Industry Z1, 63 (2013) 4. Yi, Z.: High-voltage transmission line deicing robot mechanism design. J. Three Gorges Univ. Natural Sci. Edi. 30(6), 54–60 (2008) 5. Wu, J.-F., et al.: The high tension line detection of the boom type obstacle surmounting of robot mechanism research, Robot Technology (11), 35–36 (2006) 6. Zhou, G.X., Yanze, Z.: Patrol robot crawling design. Robot Techniq. Appl. 4, 19–21 (2002) 7. Xiashu, S., Qiu, N., Research on deicing measures of overhead transmission line. High Voltage Technology 8. Yang, Y., Hongliang, G., Suimin, M., Cheng, L., Ma, X.-Q., Structure design of de-icing robot for overhead transmission line. Electric Power Construction 30(3), 93–96 (2009) 9. Wu, G.-P., et al.: with functions of automatic obstacle of the high tension line patrol car. Hydraulic and electric machinery. 46–1999 (2), 50 10. Yi, H.: Analysis and countermeasures for large-scale ice disaster on the power grid. High voltage technology 34(2), 215–219 (2008) 11. Lan, C.: Overview of transmission line icing influence and de-icing technology. Zhejiang Electric Power (4), 29–32 (2008) 12. Tugang, S.: Review of research progress on anti-icing and deicing technology for power transmission lines. Mech. Elec. Eng. 7, 72–75 (2008)

Study of FlexRay Bus Test Methods Ruina Zhao1 , Linghui Zhang2(B) , Shao Li2 , Ming Gao2 , and Weizheng An2 1 Beijing North Vehicle Group Corporation, Beijing, China

[email protected]

2 China North Vehicle Research Institute, Beijing, China

{zhlh,lishao,anwzh}@noveri.com.cn

Abstract. FlexRay bus is widely used in applications due to its efficient network utilization and system flexibility characteristics, but variations in physical transmission characteristics in complex usage environments can lead to unstable communication quality. In this paper, the test methods for FlexRay physical layer testing are analysed, test evaluation items such as insertion loss, return loss, crosstalk, characteristic impedance and eye diagram are proposed, a test equipment is designed and the metrics are verified. Keywords: FlexRay · Physical layer testing · Tester

1 Introduction FlexRay is a high-speed, time-deterministic, fault-tolerant bus technology for vehicles, featuring efficient network utilization and system flexibility. Due to its advanced nature, it has not only become the unified special vehicle bus, but is also widely used in other systems and fields such as weapons, aerospace, communications and civilian vehicles. Due to the high rate of FlexRay bus and the complex environment of special vehicles, e.g., with many branches, walls and relays, limited wiring space and paths, and a harsh electromagnetic environment, waveform distortions and distortions can occur in physical transmission. FlexRay’s datalink protocol layer is also complex. Comprehensive testing of its physical layer parameters is required to accurately assess its performance. A typical use is shown in Fig. 1.

2 Physical layer test items 2.1 Insertion loss Insertion loss is the amount of signal attenuation caused by the introduction of other devices between the transmitter and receiver. Insertion loss determines the perfect design of the system transmission path and plays a decisive role in the transmitting performance of the whole machine.

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interface interface interface

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Fig. 1. Typical FlexRay bus architecture for special vehicles

2.2 Return loss Return loss is a parameter that indicates the reflection performance of a signal. It indicates that a portion of the incident power is reflected back to the signal source. Return loss is an important indicator of a digital cable product. Return loss combines the effects of both reflections, including deviations from nominal impedance, and structural effects to characterize the performance of the link or channel. It is caused by the non-uniformity of the characteristic impedance over the length of the cable and is ultimately due to the non-uniformity of the cable structure. Due to reflections caused by the signal at different locations in the cable, the signal arriving at the receiving end corresponds to a multipath effect in the propagation of the wireless channel, thus causing time spread and frequency selective fading of the signal, with the time spread leading to pulse spreading and making the signal pulses at the receiving end overlapping and undecided. Multiple reflections of the signal in the cable also lead to attenuation of the signal power, which affects the signal-to-noise ratio at the receiving end and courses an increase in the BER, thus also limiting the transmission speed. During the production of digital cables, the return loss indicator of the cable is prone to failure. 2.3 Crosstalk Crosstalk is the coupling between two signal lines, mutual inductance and mutual capacitance between signal lines causing noise on the line. Capacitive coupling causes coupling currents, while inductive coupling causes coupling voltages. Crosstalk is probably the single most important influence on the transmission of data at high speeds. It is an undesirable energy value generated by the coupling of one signal to another. According to Maxwell’s law, as long as there is a current, there will be a magnetic field, and interference between magnetic fields is the source of crosstalk. This induced signal can lead to loss of data transmission and transmission errors.

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2.4 Characteristic Impedance Characteristic impedance is the ratio of input voltage to current when an AC signal voltage is applied at the input. The characteristic impedance of a line and the DC resistance of a line are two completely different concepts. The DC resistance of a line is proportional to the length of the line. The characteristic impedance of a line is determined entirely by the construction and material of the line and is independent of the wire length, it’s a common factor considered in high-speed connectors and cable assemblies. 2.5 Eye Chart An eye diagram is a graphic that is shaped like an eye.” An eye diagram is the result of accumulating bits of the acquired serial signal in an afterglow overlay that looks like an eye. There are various shapes of eyes and diagrams. The shape of the eye diagram allows a quick determination of the quality of the signal.

3 Tester Solutions Wireless tester module 1

Wireless tester module 2

Wireless tester module n

FlexRay bus Tester host

Fig. 2. FlexRay tester test solution

The tester consists of a control terminal and a number of wireless test terminals, as shown in the block diagram above. The control terminal is an industrial tablet PC and the wireless test terminals use a software radio architecture to transmit and measure the bus test signals and report the test results to the tablet PC via a wireless link. To start the measurement, the control terminal initiates broadcast signaling to the wireless test terminals to make sure that all the wireless test terminals are online. The tester then sends a control command to the control terminals to select the wireless test terminal to be tested to carry out a topology test, i.e., determining whether the link can communicate properly and displaying the communication rate. When the network topology is confirmed, the wireless test terminal is selected two by two and the eye diagram template test and Sparameter test are completed for the bus between the wireless test terminals and the test results are recorded and the relevant parameters are calculated to estimate the insertion loss, return loss, crosstalk, characteristic impedance and eye diagram test for the bus (Fig. 2).

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3.1 Overall hardware solution The FlexRay Bus Tester consists of a host computer and several wireless test modules. The block diagram of the mainframe is shown in the Fig. 3. FlexRay bus

Bus under test

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Fig. 3. Schematic diagram of the FlexRay bus tester host

The mainframe can be used independently or with a wireless test module for testing. The mainframe is equipped with bus test signal transmission and measurement functions, as well as data analysis, processing, display and storage functions, and can complete the testing of physical layer and protocol layer indicators, carry out online monitoring, and control the wireless test module. The block diagram of the wireless test module is shown in Fig. 4. The wireless test module, in conjunction with the host computer, is used in a vehicle field environment. Bus under test

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Fig. 4. Schematic diagram of the wireless test module

The host computer and the wireless test module are linked using a software radio architecture, bus test signals are sent and measured, and the test results are reported to the test host computer via the wireless link. To start a measurement, the host control terminal initiates broadcast signaling to the wireless test terminals to establish that all wireless test terminals are online. The tester then sends a control command to the host control terminal selecting the wireless test terminal to be tested to perform a topology test, i.e., to

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determine whether the link can communicate properly and to display the communication rate. When the network topology is confirmed. The wireless test terminal is selected two by two and the eye diagram template test and S-parameter test are completed for the bus between the wireless test terminals and the test results are recorded. According to the hardware scheme, the FlexRay bus tester comprises a host computer and a wireless test module, structural form is shown in Fig. 5 and Fig. 6.

Fig. 5. Outline drawing of the mainframe

Fig. 6. Wireless test die profile

3.2 Overall software solution The control terminal of the host tester is based on the software architecture of an industrial tablet PC, which realizes the operation and control of the wireless test terminal through a wireless private network, and reads the test data to be displayed on the user interface, software architecture is shown in Fig. 7. The control terminal mainly completes the human-machine interface of the system, database management, analysis and processing of test data, remote control and management of the wireless test module. The control terminal adopts a layered software architecture and the terminal software architecture is shown in Fig. 8. 3.3 Test verification The physical layer of the FlexRay bus was tested by means of a typical special vehicle test prototype, as shown in Fig. 9, Fig. 10, Fig. 11 and Fig. 12„ which is able to easily display the values of the parameters and basically reflect the communication status of the vehicle FlexRay bus.

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Fig. 9. Insertion loss test schematic

Fig. 10. Schematic diagram of return loss test

Fig. 11. Crosstalk test schematic

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Fig. 12. Eye diagram as observed by oscilloscope

4 Summary In this paper, the physical layer parameters affecting the communication quality of the FlexRay bus are analysed and a hardware and software solution for a tester is designed, which allows the communication quality of FlexRay to be evaluated easily and quickly. The physical layer test methods and testers for the FlexRay bus can also be extended to the test and evaluation of in-vehicle buses such as the CAN bus.

References 1. Josef, B., et al.: FlexRay - The communication system for advanced automotive control systems. In: SAE 2001 World Congress (2001) 2. Inseok, P., Myoungho, S.: FlexRay network parameter optimization method for automotive applications. IEEE Trans. Ind. Electron. 58(4), 1449–1459 (2011)

Three-Dimensional Measurement Method for Tube’s Endpoints Based on Multi-view Stereovision Xiaoyu Hou, Mingzhou Li, Qi Lv, Jiang Zheng, and Jian Wang(B) School of Electronic Science and Engineering, Nanjing University, 163 Xianlin Road, Nanjing 210023, Jiangsu, China [email protected], {njucn,wangjnju}@nju.edu.cn

Abstract. In order to achieve the accurate measurement of pipeline’s end position, a method based on centerline vector with the help of multivision is proposed. This method uses pictures taken by eight industrial cameras to extract the pipeline’s centerline. And location of the center of end plane is determined with the aid of two data, the centerline vector and the pipeline’s outer diameter. The deep learning method is used for obtaining the camera calibration matrix which is used to reconstruct the space coordinates of endpoints. The relative position of two endpoints is calculated. The measurement experiment shows that the maximum relative deviation between the measured value and the standard value of this method is 0.6%, and the method is simple and measurement efficiency is high. Keywords: Multi-vision

1

· Endpoint measurement · Centerline vector

Introduction

The piping system is widely used in nuclear reactors, aerospace, shipbuilding and automobile industries. It plays an important role in transportation of gas, liquid and other media. Most pipelines are processed by CNC pipe benders. In the process of installation, due to springback, wrinkling and cross-sectional distortion, there will be a certain machining error between processed parts and design requirements. In order to ensure stress-free assembly, it is necessary to accurately measure and reconstruct the geometry of bent pipeline, and make corrections on this basis. The reflective and textureless characteristics of metal pipes make detection and reconstruction methods of surface speckle or natural texture no longer applicable. The method based on manpower, 3D measuring instrument and laser CCD devices are inefficient when measuring complex pipelines. And there are problems with the elastic deformation of pipelines under manual operation. The non-contact visual measurement method can be effective solve these problems. At present, scholars at home and abroad have done a lot of research on reconstruction of the whole pipeline [1–3], but few researches work on the end of pipelines. There are three main ideas. c The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022  Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 628–635, 2022. https://doi.org/10.1007/978-981-19-0390-8_77

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The first method is to fit the end’s contour. Zhang [4] use the property of closest distance between arc’s midpoint at the end of the pipeline and the end of centerline to obtain the arc of pipeline’s end. Others [5] locate the image coordinates of endpoints by fitting circles’ or ellipticals’ arc at the end of pipeline. The difficulty is to obtain a complete elliptical edge contour cannot. The second is to use a 2D point target for endpoint measurement. A method is proposed to measure pipeline ends with the aid of a 2D points target, which converts the recognition of the pipeline ends into identification of mark points of target [6,7]. The disadvantage of this method is the weak adaptability for a variety of objects. The third is a method based on centerline. Jin [8] used the centerline of pipeline to assist in positioning the center of pipeline ends. This method is based on the assumption that the pipeline end projections are mostly ellipses. In this paper, with the help of the third idea, aiming at the circular crosssection catheter, the camera takes the shape of pipeline to obtain centerline. The centerline vector and the outer diameter(OD) of pipeline are used to find the pixel position of endpoints.

2

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The geometric structure of pipeline is relatively simple. It can be regarded as a cylinder, and described with centerline and OD, as shown in Fig. 1.

Fig. 1. A pipeline described by centerline and OD.

The end of pipeline can be described as an ellipse with centerline as the normal and the major axis as the OD of pipeline, centerline passes through the center of ellipse. Thus, the precise position of endpoints can be solved by the intersection of centerline and end plane. According to the projection principle of camera, the projection of end plane in the image is an ellipse. The length of major axis of ellipse is equal to the length of pipeline diameter. The projection of cylinder centerline in the image is centerline of projection area, so the projection of pipe centerline in the image is centerline of pipeline’s ROI. Therefore, the center of end plane circle can be determined by searching for the ellipse’s major axis.

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Multivision-Based Measurement for Endpoints

As described in the previous section, the projection of pipeline’s end plane in the image is an ellipse, and the pipeline’s centerline of is perpendicular to the major axis of ellipse. The pixel position of endpoints in the image is located according to the OD of pipe, so as to reconstruct the spatial coordinates of endpoints. The algorithm consists of four sections, as shown in Fig. 2.

Fig. 2. The process of endpoints’ measurement

3.1

Centerline Generation

(a) Original picture

(b) ROI picture

(c) Bifurcated center- (d) Final centerline line

Fig. 3. Centerline generation

Firstly, control cameras to capture pictures and save the grayscale images for the following process, as shown in Fig. 3(a). A backlight illumination can enhance the contrast between pipe and background, and collected image shows a clear outline. Under such lighting conditions, all pixels in the image compared with a certain threshold that can classify them as background or foreground based on the Eq. 1. Since only the area where the pipeline located is of interest, and background interference will have a great impact on image processing, all interference must be removed. The pipeline area

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which is called ROI area can be easily found taking advantage of the characteristic that pipeline is located in the center of each view and is the largest connected domain except for background in the image, as Fig. 3(b).  255 threshold ≤ i ≤ 255 grayvalue(i) = (1) 0 0 ≤ i < threshold Through the thinning method of continuously stripping off edge pixels, the smooth centerline skeleton and the coordinates of centerline endpoints formed by the connection of single pixels are obtained, as shown in Fig. 3(c). If the contour of extracted ROI region is not smooth, there may be burrs or bifurcations in thinning result. To solve this problem, the idea of BFS can be used. The burrs generally intersect with contour which means that the distance from centerline’s end to contour is bigger. The distance transformation method is used to calculate the distance from point to edge of ROI contour. The points of top two distance are judged to be the endpoints of centerline. Then the Dijkstra algorithm is used to search for the shortest path between two ends of centerline, as shown in Fig. 3(d). It starts from one end of centerline and extends along the path of centerline, traversing all nodes, until it reaches to the other end. Finally, the shortest end-to-end sequence of centerline is obtained. 3.2

Endpoint Extraction

(a) Parallel part at the end of the pipeline

(b) Details of parallel part

(c) Midpoint of lipse’s major axis

el-

Fig. 4. Outer diameter calculation and positioning of endpoints

The accurate calculation of endpoints needs to be carried out on the basis of obtaining OD of pipeline. Under normal circumstances, segments of pipelinel’s end bent by CNC pipe bender are straight. Therefore, a section of parallel line segment near the end of centerline are intercepted, the line perpendicular to the centerline direction is maked and each parallel line have an intersection point, the distance between intersection points is calculated, as shown in Fig. 4(a) 4(b).

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The average value of the distance between multiple intersections is used as the OD of pipeline. On the basis of obtaining the centerline point sequence, N points near two ends are extracted. The least square method, shown as Eq. 2 and 3, is used to fit a straight line, and the Eq. 4 shown below is to minimize error. ⎧ ⎨aˆ = 0 ⎩aˆ1 = err =

y = a0 + a1 x    x2i y − x x y  i2 i 2 i i N xi  −(  xi )  N  xi yi − xi yi N x2i −( xi )2

(2)



N 

[yi − (a0 + a1 xi )]2

(3)

(4)

i=1

The centerline of pipeline’s end and the major axis of projected ellipse are perpendicular to each other, the vertical line move to the opposite direction to obtain two intersection points of vertical line and pipeline contour which can calculate OD. When the difference between distance of two points and OD is greater than a certain threshold value Δd, the position of major axis’s midpoint of ellipse is the endpoint, as shown in Fig. 4(c). 3.3

Camera Calibration

An important problem in the field of stero vision is camera calibration. The problem needs to be solved is that to establish the relationship between object point in world coordinate system and image point in image coordinate system. By analyzing the parameter matrix obtained from the traditional camera calibration, it can be seen that the relationship between the world and image coordinate system is complicated and non-linear, so the function should take full account of nonlinear information [9]. After repeated tests and comparisons, the function set in this project is described as follows: X is a matrix of order (p × m), Y is a matrix of order (p × n), and there are intermediate matrices of order (m × n), such as diag1, diag2, diag3, diag sqrt. √ ans = X × diag1 + X 2 × diag2 + X 3 × diag3 + X × diag sqrt. (5) cost = (Y − ans)2 .

(6) √ X 2 is the matrix X squared, X 3 is the matrix X cubed, X is the matrix X square root of each element, ans is the output of prediction function, and cost is the loss function, representing the difference between predicted value of the prediction function and matrix Y. Parameter optimization is to optimize the intermediate matrices diag1, diag2, diag3 and diag sqrt, so that the cost decreases continuously. And when the value is less than setted-threshold, it can be considered that the output of predictive function is almost equal to matrix Y, meaning that the predictive function can describe the relationship between matrix X and matrix Y.

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The back propagation method is used to update the parameters of each intermediate matrix according to learning rate and gradient calculation in each iteration cycle, so that the loss function moves towards a smaller direction. Through the adjustment of learning rate and the selection of iteration period, intermediate matrices is finally obtained. The prediction function with this set of intermediate matrices as parameters describes the relationship between the world coordinate system and the image coordinate system. For the multi-view measurement platform of 8 cameras, the point-point pixel data obtained is a matrix of order (n × 16). n is the total number of data, 16 is the pixel coordinates generated by each camera view are two numbers. The world coordinate’s matrix is n × 3, where 3 is xyz of the world coordinate system. Through the above fitting function and parameter optimization method, three intermediate matrices of 16 × 3 can be obtained. 3.4

Endpoint Reconstruction

After obtaining the endpoints pixel coordinate data, it should be matched one by one for the same endpoint in different pictures. Then two 1×16 matrices are generated, called X1 and X2 . The two matrices are respectively multiplied by calibrated mapping matrix to obtain the 3D position coordinates of endpoints. ansn = Xn × diag1 + Xn2 × diag2 + Xn3 × diag3.(n = 1, 2)

(7)

Xn is a (1 × 16) matrix composed of the horizontal and vertical coordinates of each particle point, Xn2 is the matrix obtained by squaring each element in X, Xn3 is the matrix obtained by the cube of elements in X. diag1, diag2, diag3 are the (16 × 3) intermediate matrices obtained by camera calibration. The (1 × 3) matrix calculated from Eq. (7) is the coordinates of endpoints that are reconstructed. The final result obtained by calculating the Euclidean distance.

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Test Results and Analysis

The hardware of pipeline multi-vision measurement system is composed of a camera group (eight 16-megapixel Medvision Gigabit Ethernet industrial cameras), a light source, a mechanical platform, a switch, a movable bracket, etc. Due to the problem of camera’s shooting angle and distance of the object being photographed, the focal length and view of cameras need to be adjusted so that all cameras have a common coverage area as large as possible. In this paper, we select the components with different diameters and lengths, and components with different shapes for testing, as shown in Fig. 5.

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(a) Components1

(b) Components2

(c) Components3

(d) Components4

Fig. 5. Pipeline used in the experimental measurement.

The experiment selected pipelines with diameters of 5 mm, 10 mm, and 25 mm for testing. It can be seen that the smaller the diameter, the smaller the measurement error, and the maximum measurement error does not exceed 0.6% of the actual distance. The measurement efficiency is high, and it can meet the needs of engineering measurement (Table 1) (Fig. 6). Table 1. Pipeline measurement results Spatial coordinates A

Experimental distances/mm B

Components1 (122.81, 57.44, 3.41)

(28.38, 86.80, 3.41)

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272.26

Components4 (368.93, 99.79, 3.41) (392.46, 300.03, 3.41)

201.61

Fig. 6. Comparison of experimental distance and actual distance

Analyzing the measurement error of endpoint positioning method based on centerline vector, the main factors affecting accuracy of measurement result are as follows. The first, the processing quality of end plane. The important premise of method proposed in this paper is that the centerline is perpendicular to the

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pipeline’s end plane and end plane is smooth. When the end plane is severely chamfered or it’s not smooth, the accuracy of measurement will decrease. The second, The extraction effect of pipeline’s ends in ROI area. Due to the reflection of pipeline, the extracted ROI is sometimes not completely smooth at the ends of pipeline, which will cause the centerline to have a Y-shaped bifurcation.

5

Conclusion

In this paper, centerline vector and OD are used to locate the pipe’s endpoints, reconstruct their spatial position. This method does not use any auxiliary tools except calibration board, the max relative error of experimental results is 0.6%, and the operation is simpler than manpower-based method, which can meet the needs of engineering measurement. Acknowledgments. This work was supported by Jiangsu Key R&D Plan. (Industry Foresight and Common Key Technology (BE2018114)) and Science and Technology Planning Project of Qixia District (GC201806).

References 1. Kazuaki, K., Satoshi, K., Hiroaki, D.: As-built modeling of piping system from terrestrial laser-scanned point clouds using normal-based region growing. J. Comput. Des. Eng. 1(1), 13–26 (2014) 2. Gong, M., Zhang, Z., Zeng, D., Peng, T.: Three-dimensional measurement method of four-view stereo vision based on gaussian process regression. Sensors (Basel) 19, 4486 (2019) 3. Tang, Y.C., Li, L.J., Feng, W. X., Liu, F., Zou, X.J., Chen, M.Y., et al.: Binocular vision measurement and its application in full-field convex deformation of concretefilled steel tubular columns. Measurement, 130, 372–383 (2018) 4. Tian, Z.: Research on mullivision-based digitized measurement method for pipelines, Beijing Institute of Technology (2014). (in Chinese) 5. Wade, P.P., Moran, D.H., Graham, J., et al.: Robust and Accurate 3D Measurement of Formed Tube Using Trinocular Stereo Vision (1997) 6. Peng, J., Jian, H.L., Liu, S., et al.: A multi-vision-based system for tube inspection. Assembly Autom. 38(1), 34–50 (2018) 7. Liu, S., Jin, P., Liu, J., et al.: Accurate measurement method for tube’s endpoints based on machine vision. Chin. J. Mech. Eng. (2017). https://doi.org/10.3901/ CJME.2016.0516.066 8. Peng, J., Jianhua, L., Shaoli, L., et al.: Centerline-based measuring method for endpoints of tube. Comput. Integr. Manuf. Syst. 22(10), 2284–2293 (2016). (in Chinese) 9. Anchini, R., Liguori, C., Paciello, V., et al.: A comparison between stereo-vision techniques for the reconstruction of 3-D coordinates of objects. IEEE Trans. Instrum. Meas. 55(5), 1459–1466 (2006)

Deep Image Classification Model Based on Dual-Branch Haoyu Chen1 , Qi Lv1 , Wei Zhou2 , Jiang Zheng1(B) , and Jian Wang1(B) 1 School of Electronic Science and Engineering, Nanjing University, 163 Xianlin Road, Nanjing

210023, Jiangsu, China {njucn,wangjnju}@nju.edu.cn 2 Unit 31106 of PLA, Nanjing 210016, Jiangsu, China

Abstract. The performance of deep learning in the field of computer vision is better than traditional machine learning technology, and the image classification problem is one of the most prominent research topics. The methods of computer vision are used in industry production. We proposed an image classification model which segments the image at different scales on the basis of Deep-ViT to obtain image information of different scales. When this model is applied to the classification of tube head shapes, the accuracy rate can reach more than 90%, and towards the classification of tube head materials, the accuracy is about 98%. Keywords: Image classification · Deep-learning · Deep-ViT

1 Introduction In this part, we introduced the background of the project and analyzed the shortages of the image classification methods in the traditional way. 1.1 Background At present, with the production of complex equipment in many areas such as the nuclear industry and aerospace, the measurement of the formed catheter is the most effective and direct method to ensure the quality of pipeline manufacturing and to achieve accurate and stress-free assembly. In the process of matching the tube head, the selection of matching features is very critical, which directly determines the accuracy and speed of the matching. With the introduction of the self-attention mechanism, the transformer model has been widely and effectively applied in the field of Natural Language Processing (NLP). Recently, the transformer model has begun to be applied to the field of image classification, and the Vision Transformer (ViT) model proposed by Google can classify images well. Based on the ViT model, many improved image classification models have been proposed, such as Deep-ViT, Cross-ViT, T2T-ViT, etc. Inspired by Deep-ViT, we proposed 2Deep-ViT which performed Deep-ViT processing on images of different patch-sizes to obtain deeper image features at different scales. We applied this method © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 636–643, 2022. https://doi.org/10.1007/978-981-19-0390-8_78

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to the classification task of tube heads in order to carry out tube head matching work. For different shapes of tube heads, the classification accuracy reached more than 90%, and for different materials, the accuracy was about 98%. 1.2 Shortages Pipelines usually contain multiple heads, and these heads have different shapes and materials. The lack of versatility and reliability often happened in specific algorithms for measurement and three-dimensional reconstruction through feature points or geometric primitives on the surface. Compared with the manually designed matching method, the deep learning method is more stable and accurate, and the extraction method is more flexible. Deep-ViT aims to obtain different features from images in the deeper level of different transformer modules. For different types of images, a fixed number of transformer modules will prevent the model from obtaining image features at different scales. It is also difficult to achieve better results even if Deep-ViT is used. Therefore, we use dualbranch Deep-ViT to make up for the shortcomings of Deep-ViT in this respect, so as to obtain features of different scales and deeper features at the same scale at the same time.

2 Related Work Computer vision classification algorithms have undergone a transition from manual design to deep learning. In the late 1990s and early this century, Support Vector Machine (SVM) [1] and K-nearest neighbors methods were used more. After that, the LeNet network was born in 1994, and the LeNet5 [2] in 1998 is a widely known version. AlexNet [3] is the first deep network in the true sense, its number of layers was 8 and the number of parameters of the network has also increased greatly. VGGNet [4] is a good demonstration of how the performance of the network can be improved by simply increasing the number and depth of the network based on the previous network architecture. The core of GoogleNet [5] is the Inception Module, which uses a parallel approach. In 2015, ResNet [6] won the classification task championship. It surpassed the recognition level of humans with an error rate of 3.57%. Without the dependence of convolution neural network (CNN), the ViT [7] model can be compared to the current optimal convolutional network. Computing resources required for training are greatly reduced. On the basis of ViT, people have proposed Deep-ViT [8] and other improvement methods. Our method has two mainly advantages: (1) we introduced 2Deep-ViT and used features of different scales to detect more information of images; (2) we used ViT in classifying tube heads, which is stable and can achieve high accuracy.

3 Method In this part, we introduced several methods including the original transformer model, the ViT model based on the transformer model and the improved model we proposed.

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3.1 Transformer The Transformer model is a classic NLP model proposed by the Google team in 2017. It uses a self-attention (Self-Attention) mechanism instead of the sequential structure of Recurrent Neural Networks (RNN), so that the model can be trained in parallel and can have global information. The model is mainly composed of encoder and decoder. 3.2 Vision-Transformer Based on the Transformer framework, ViT [7] was proposed for image classification work. Figure 1 shows the model architecture diagram. ViT directly divide the images into fixed-size patches, and then obtain the patch embedding through linear transformation, which is analogous to words and word embedding in NLP. Since the input of the transformer is a sequence of token embeddings, the patch embeddings of the image can be sent to the transformer to perform feature extraction and then distinguish the kind.

Fig. 1. Vision Transformer model overview.

3.3 Deep-ViT As the transformer progresses to the nth layer, the attention maps will no longer change significantly, which tend to be the same. It means that when ViT goes to a deeper level, the self-attention mechanism will not be able to learn from the data effectively, which hinders the performance of the model. Therefore, [8] proposed a model named DeepViT, as the structure showed in Fig. 2, which adds a heavy attention layer to the attention mechanism to obtain different attention maps (Eq. (1)), thereby improving the diversity of the feature maps of the transformer in different layers, and improving the performance of the entire model.     QKT V (1) Re − Attention (Q, K, V) = Norm θT Softmax √ d

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Fig. 2. Deep-ViT model overview.

3.4 2Deep-ViT We proposed a dual-branch ViT technology that performs Deep-ViT transformation on images at different scales and linearly merges the output results to improve the Deep-ViT results. The structure is shown in the Fig. 3.

Fig. 3. 2Deep-ViT model overview.

The specific process is as follows: y1 = deepViT(data|small - size)

(2)

y2 = deepViT(data|big - size)

(3)

  y = a ∗ y1 : b ∗ y2

(4)

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Among them, data|small-size and data|big-size are respectively different sizes of segmentation of the image, a and b are the weight coefficients of different outputs, which can be changed according to the size ratio of the accuracy of the two branches during the training process.

4 Experiments We conducted experiments with different tube heads and different image classification models, and finally found that the model we proposed can classify tube heads better. 4.1 Datasets When collecting the tube head dataset, we firstly took the video of tube heads in different orientations, and then the video stream was decomposed into 20,228 pictures by using cv2. The standard dataset included the cat vs dog dataset published in 2013 and the CIFAR-10 compiled by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton. We first cleaned the dataset, removed the interference pictures in the dataset, and used the cv2.shape() algorithm to detect the images, then deleted the damaged pictures, and finally got the dataset that can be used for training and detection, as shown in the Fig. 4. The pictures in the dataset were named by the serial number of the tube header, and the naming method is shown in Table 1, which was convenient for identifying the label of the picture in the subsequent processing. The pictures in the dataset were divided into the final training set and the testing set at a ratio of 8:2. Table 1. Named list of different tube head shapes. Picture number

Tube head type

0

Two-way tube

1

Three-way tube

2

Connector

3

Elbow

Table 2. Named list of different tube head materials. Picture number

Tube head type

0

Plastic tube head

1

Metal tube head

For different classification granularities, we also carried out classification tests for different materials of tube heads. There were 12,703 pictures in total. Here we have selected metal tee tube heads and plastic tee tube heads for training. The corresponding methods of categories are shown in Table 2. An example of the dataset is shown in Fig. 5.

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Fig. 4. Representative examples of metal tube head.

Fig. 5. Representative examples of tee tube head.

4.2 Pre-training First, we adjusted the size of pictures of different sizes, converted the input picture into a 224 × 224 input feature map, then cut the image into 224 square-sized pictures according to the center, and randomly flipped the image horizontally with a probability of 50%. The productions were finally converted to tensor format, which is convenient to be processed by using neural network. 4.3 Models We used Deep-ViT and our proposed 2Deep-ViT to train the data set respectively. 64batch was used for training, the learning rate was 3e−5, and the experimental result was the accuracy of classifying the test set with the obtained model. 4.4 Results The experiment used a 3090 graphics card and the model was trained under the Ubuntu18 system. In this environment, each model took about 6 h to train. The model which was used to classify the tube heads in different materials only needed about half an hour because material was easier to classify and the training needed fewer iterations. We first used Deep-ViT for training and testing, which performed better than the original ViT model. Then we used the 2Deep-ViT model we proposed for training and

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testing, and found that the performance can be improved. For the standard data set cats_and_dogs, since the data are all real-life pictures and there are more interferences, the accuracy of classification was about 72%. The CIFAR-10 data set is not only pictures from life, but the pictures are small and distinguishable. The problem of low image resolution had become the main reason that restricted the improvement of model accuracy, so that the accuracy was around 49% (Table 3). Table 3. Comparison with state of the art on different image classification benchmarks. Data set

Deep-ViT

2Deep-ViT

Improvement

cats_and_dogs

72.1%

72.75%

+0.65%

CIFAR-10

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49.23%

+0.92%

Tube head in different shapes

91.42%

91.93%

+0.51%

Tube head in different material

98.19%

98.43%

+0.24%

But for different tube head shapes, because the picture background in the industrial production line was relatively simple and the interference was small, the resolution of the camera for identifying the parts was higher, so a higher accuracy model can be obtained. The accuracy rate of our proposed 2Deep-ViT reaches 91.93%, which was 0.51% higher than the original Deep-ViT. For different tube head materials, we can obtain a higher classification accuracy in a few training times, that is, 2Deep-ViT reached 98.43%, which was 0.24% higher than the original Deep-ViT.

Fig. 6. The classification accuracy of tube heads. Left: Classification accuracy of tube heads in different shapes. Right: Classification accuracy of tube heads in different materials.

It can be seen in Fig. 6 that in the early stage of the iteration of the tube head classification in different shapes, the accuracy rase rapidly, and in the later stage the accuracy rase more slowly. The accuracy rase faster when classifying tube head in different materials, which showed that the model only needed less time to obtain a more accurate classification model.

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5 Conclusion The model we proposed can improve the ability of classification effectively, especially in the classification of tube heads in different shapes and materials. In the future, more deep-learning methods like this can be used in the industry, which will promote the development of automated production. Acknowledgements. This project was supported by Jiangsu Key R&D Plan. (Industry Foresight and Common Key Technology) (BE2018114) and Science and Technology Planning Project of Qixia District (GC201806).

References 1. Platt, J.C.: Sequential Minimal Optimization. A Fast Algorithm for Training Support Vector Machines. Microsoft Research Technical Report (1998) 2. Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998). https://doi.org/10.1109/5.726791 3. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet Classification with Deep Convolutional Neural Networks (2012). https://proceedings.neurips.cc/paper/2012/file/c399862d3b9d 6b76c8436e924a68c45b-Paper.pdf 4. Simonyan, K., Zisserman, A.: Very Deep Convolutional Networks for Large-Scale Image Recognition. CoRR abs/1409.1556 (2015) 5. Szegedy, C., et al.: Going deeper with convolutions. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–9 (2015). https://doi.org/10.1109/CVPR.2015. 7298594 6. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90 7. Dosovitskiy, A.: An image is worth 16×16 words: transformers for image recognition at scale. In: 2021 International Conference on Learning Representations (ICRL) (2021) 8. Zhou, D., et al.: DeepViT: Towards Deeper Vision Transformer (2021)

Surface Defect Detection System of Condenser Tube Based on Deep Learning Siyu Chen1 , Qi Lv1 , Yuzhe Zhang2 , Jiang Zheng1(B) , and Jian Wang1(B) 1 School of Electronic Science and Engineering, Nanjing University, 163 Xianlin Road, Nanjing

210023, Jiangsu, China [email protected], {njucn,wangjnju}@nju.edu.cn 2 Unit 31106 of PLA, Nanjing 210016, Jiangsu, China

Abstract. Condenser tube is a common component with a wide range of applications. During the production process, there are unqualified samples with defects on the surface. It is of great significance to realize the automatic and efficient detection of the condenser tube’s defects. In view of the thread characteristics of the metal condenser tube’s surface and the characteristics of high-speed movement on the production line, we build an image acquisition device including three cameras and LED tubes. Then, we apply the classic neural network Resnet50 to extract low-, middle-, and high-level features of the condenser tube image. The trained model finally realizes the binary classification of the condenser tube image (images with defects vs. normal images). The result of the experiment shows that the precision value reaches 81% and the recall rate exceeds 94%. The detection speed is about 0.011 s/frame. In addition, the image acquisition device is used to shoot the same defect multiple times when the tube is moving. So there can be multiple consecutive detection results for the same defect in time series, which greatly improves the detection accuracy. Keywords: Condenser tube · Defect detection system · Image acquisition device · Deep learning · Resnet50

1 Introduction The condenser tube is a common metal component with a wide range of application scenarios. Tubes with defects on the surface will have a reduced service life and even cannot be put into use. Therefore, surface defect detection is very important for the quality control of the condenser tube. Usually, it is necessary to combine machine vision and deep learning techniques. People build product testing equipment on the production line to evaluate quality according to production conditions [1]. In the case of the condenser tube, the metal surface exhibits thread characteristics, and the defect parts will have brightness changes under the condition of external lighting. The condenser tube maintains a high-speed motion state on the production line. Given the conditions above, we design a three-camera image acquisition device under LED lighting conditions, and use the Resnet50 to realize an automatic defect detection and classification system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 644–650, 2022. https://doi.org/10.1007/978-981-19-0390-8_79

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2 Related Works At present, there are two types of methods commonly used in the field of surface defect detection: defect detection methods based on traditional machine vision and DP-based detection methods [2]. In traditional algorithms, X. Liu [3] proposed the local binary pattern (LBP) method. J. Hang [4] designed a targeted defect detection method for ceramic products with complex surface geometry based on the neighbourhood pixel gray threshold. Others methods such as pruning decision trees, region screening and segmentation, random forest, K-means clustering algorithm, etc., all rely on the process of manual feature extraction, which cannot be applied to defects whose features are difficult to be quantified. They cannot build and transfer defect feature models, so they are not universal. Deep learning techniques [5] have stood out in recent years. It performs well in the image field due to its robustness, high detection accuracy, fast speed, and deep feature level. Due to the various types of defects on the surface of metal workpieces and irregular distribution, the deep learning method is superior to the traditional algorithm [6]. In our experiment, we need to distinguish between defective parts and non-defective parts, that is, complete the classification task and return the label value of the predicted category. The standard network models commonly used in the field of deep learning for image classification are: Lenet [7] (the first one of CNN to be applied to image recognition), Alexnet (AlexNet uses two GPUs to achieve information interaction, breaking the limitation on network size. Facing the problem of over-fitting, the strategy of data expansion and dropout was adopted.), Vgg [8] (VGGNet is very similar to the Alexnet framework, but expands the depth of the model. It uses multiple scales for data enhancement during training and testing.), Resnet (This is the network selected in this article, which will be explained in detail later.), etc.

3 Corrugated Condensation Tube Defect Classification Detection System In this chapter, we introduce the main modules of the surface defect detection system of condenser tube and the generation process of the data set first. Secondly, we explain the basic principles of the selected deep learning framework resnet50. Moreover, the main evaluation indicators of the classification model are proposed at the end of the chapter. 3.1 The Design of Overall System As shown in Fig. 1, the entire defect detection system needs to go through the following processes: image acquisition, data set generation, model training and defect detection. We divide the entire complex system into two major modules: image acquisition and defect detection. As the input of the defect detection algorithm, image acquisition has a vital impact on the accuracy of the final result and the performance of the entire system undoubtedly. The image acquisition module mainly includes the imaging part and the image processing part. The imaging part is completed in the detection box. The tubes enter the detection

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box from the conveyor belt. At the same time, the raspberry pie camera is used to obtain the initial sample video image under the illumination of the LED tube. Three cameras work simultaneously to obtain images from multiple viewing angles for covering the surface area of the cylindrical pipe. Then the image processing part completes tasks such as image cropping and image enhancement. We first extract frame pictures of a specified size from the initial sample video. In order to expand the number of samples and avoid over-fitting, we adopt the following data enhancement methods: horizontal and vertical flipping, brightness adjustment and saturation adjustment. We finally obtain 9377 sample images with defects and 9322 normal images. They are three-channel RGB images with an original resolution of 380 × 380, saved in different directories of the dataset in jpg format. Defect images encompass four types of defects: protrusions, depressions, scratches, and stains. The numbers of images of each type are roughly the same. We divide the training set and the validation set at a ratio of about 8:2. The schematic diagram of the defect part of the collected image is as follows (Figs. 2 and 3): protrusions

depressions

stains

scratches

no defect

Fig. 2. Schematic diagram of common surface defects of condenser tubes

Fig. 3. Data set production process

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3.2 resnet50 The Resnet (Deep residual network) network is an important achievement of CNN in the image field proposed by Kai Ming He [5]. The basic principle of Resnet is to use residual learning to solve the degradation problem caused by the increase of network depth. In order to reduce the amount of parameters and calculations of ResNet50, unlike ResNet34 and other networks with shallower layers, ResNet50 uses a “bottleneck design” structure as shown in Fig. 4. If 256-dimensional data is input, first 1 × 1 convolution reduces the dimensionality of the data to 64, and then restores it to 256-dimensional in the same way. If this structure is not used, two 3 × 3 × 256 convolutions are required. The latter has dozens of times more parameters than the former. Therefore, we define the bottleneck function to build Resnet50. Studies have shown that the ResNet50 algorithm also has excellent performance in identifying the surface damage of wind turbine blades [9].

Fig. 4. “Bottleneck design” (right) and building block (left) structure comparison diagram

3.3 Evaluation Indicators The detection model takes precision and recall rate as evaluation indicators. The calculation formulas are as follows: Precision =

TP TP , Recall = TP + FP TP + FN

(1)

where TP, FP, and FN denote the number of correctly detected images with defects, false detected images with defects, and undetected images with defects, respectively. Precision is the percentage of positive predictions that are correct in all predictions and recall rate is the proportion of all positive samples that are correctly predicted to be positive. Speed evaluation index is the average detection time for predicting each frame of image. The unit is second/frame.

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4 Experiments and Results 4.1 Model Training Process In the model training process, the optimizer chooses the Adam algorithm. The initial learning rate is set to 0.0001. The size of the input image is resized to 224 × 224.The batch size is set to 64, and the network is trained for 15 epochs. We draw a line graph to record the loss value and accuracy change during the training process. The obtained image is as follows (where the abscissa is the number of cycles of training iterations, and the ordinate represents the accuracy and loss values respectively) (Fig. 5):

Fig. 5. Train history: accuracy/loss

It can be seen from the image that the entire network model finally converges. Due to the small size of the data set and the large correlation, there will be a certain degree of over-fitting. The visualization of the convolutional network is realized by defining and calling the output visualization function visual (model, data, num_layer). By visualizing the output response of the convolutional layer, we can intuitively observe the process of feature extraction. The following are the visualization results of the output responses to the 10th, 20th, and 30th layers (Fig. 6).

Fig. 6. The output response of num_layer = 10, 20, 30

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From the above several visualization graphs, it can be learned that the underlying convolutional network has extracted some information such as edge contours. The feature map obtained by the output response is more similar to the original input image. With the deepening of the network level, the features extracted by the convolutional layer become more and more abstract while the number of features that can be observed by the human eye is reduced. This is because the multi-layer convolution further fuses the basic features of the shallow layer to obtain higher-level deep abstract features. 4.2 Results and Analysis A. Image test set The test sample dataset contains 1794 sample images, including 900 defects (positive samples) and 894 defect-free images (negative samples). We load the detection model to forecast the dataset. Table 1 shows the test result. Table 1. Result parameter table Method

Precision

Recall

Time (s/frame)

TP/FP

TN/FN

Resnet50

81.3%

94.1%

0 is the passive decay rate of kl kl (t) ≥ 0, Dpq ≥ 0 are the connection or coupling strength the cell activity, Cpq of postsynaptic activity of the cell transmitted to Cpq , f, g : C → C are the continuous functions representing the output or firing rate of the cell Cpq , τ (t) ≥ 0 is the transmission delay, and σpq (t) denotes the transmission delay kernels. The initial conditions associated with system (1) are the form xpq (s) = ϕpq (s), s ∈ (−∞, 0], pq ∈ Λ, I R I where ϕpq (s) = ϕR pq (s) + iϕpq (s), ϕpq , ϕpq ∈ (−∞, 0] → R are bounded continuous functions. √ I I −1, xR Let xpq (t) = xR pq (t) + ixpq (t) ∈ C, where i = pq , xpq ∈ R. Assume that f, g : C → C, and the external input Lpq : C → C of (1) can be depicted as I I R I R R I I R I f (xpq ) = f R (xR pq , xpq ) + if (xpq , xpq ), g(xpq ) = g (xpq , xpq ) + ig (xpq , xpq ) and R I Lpq (t) = Lpq (t) + iLpq (t). In what follows, we view (1) as the drive system, and the corresponding response system is expressed as   kl Cpq (t)f (ykl (t − τ (t)))ypq (t) − y˙ pq (t) = −apq (t)ypq (t) −

 kl Dpq (t)

Ckl ∈Nr (p,q)

0

+∞



Ckl ∈Ns (p,q)

σpq (u)g(ykl (t − u))du ypq (t) + Lpq (t) + Upq (t), (2)

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R I where pq ∈ Λ, ypq (t) = ypq (t) + iypq (t) represents the state of the response R I system, Upq (t) = Upq (t) + iUpq (t) is a controller, the rest notations are the same as those in system (1) and the initial condition is

ypq (s) = ψpq (s), s ∈ (−∞, 0], pq ∈ Λ, R I where ψpq (s) = ψpq (s) + iψpq (s) are complex-valued bounded continuous functions on (−∞, 0]. From (1) and (2), we can rewrite in the following forms:   R kl x˙ R Cpq (t) f R (xkl (t − τ (t)))xR pq (t) = − apq (t)xpq (t) − pq (t) Ckl ∈Nr (p,q)

 − f I (xkl (t − τ (t)))xIpq (t) − R

g (xkl (t − u))du −

xIpq (t)

  kl Dpq (t) xR (t) pq



+∞

σpq (u) 0

Ckl ∈Ns (p,q)



+∞

 σpq (u)g I (xkl (t − u))du + LR pq (t),

0

x˙ Ipq (t) = − apq (t)xIpq (t) −

Ckl ∈Nr (p,q)

 + f I (xkl (t − τ (t)))xR pq (t) − g R (xkl (t − u))du + xR pq (t) R y˙ pq (t)

=−

R apq (t)ypq (t)



−

f I (ykl (t − τ (t))) − I − ypq (t) I y˙ pq (t)

=−

 0

I apq (t)ypq (t)



−

f I (ykl (t − τ (t))) −

0

 Ckl ∈Ns (p,q)

+∞



+∞ 0

0

σpq (u)

(4)

 σpq (u)g I (xkl (t − u))du + LIpq (t),

 R I f R (ykl (t − τ (t)))ypq (t) − ypq (t) +∞

0

σpq (u)g R (ykl (t − u))du

(5)

 kl I R Cpq (t) f R (ykl (t − τ (t)))ypq (t) + ypq (t)

Ckl ∈Nr (p,q)



Ckl ∈Ns (p,q)

+∞

 R σpq (u)g I (ykl (t − u))du + LR pq (t) + Upq (t),



R + ypq (t)

  kl Dpq (t) xIpq (t)

  kl R Dpq (t) ypq (t)

 Ckl ∈Ns (p,q)

+∞



kl Cpq (t)

Ckl ∈Nr (p,q)



(3)

 kl Cpq (t) f R (xkl (t − τ (t)))xIpq (t)



  kl I Dpq (t) ypq (t)

0

+∞

σpq (u)g R (ykl (t − u))du

(6)

 I σpq (u)g I (ykl (t − u))du + LIpq (t) + Upq (t),

For the above system (3)–(6), we define modules as   2 I 2 R )2 + (y I )2 , pq ∈ Λ. ρ(xpq ) = (xR (ypq pq ) + (xpq ) , ρ(ypq ) = pq Then, by Euler’s formula eiθ = cos θ +i sin θ, we have xpq (t) = ρ(xpq )eiθ(xpq ) and ypq (t) = ρ(ypq )eiθ(ypq ) , where θ(xpq ), θ(ypq ) are phases. So errors of modules and phases are epqρ (t) = ρ(ypq ) − ρ(xpq ) and epqθ (t) = θ(ypq ) − θ(xpq ), respectively.

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Throughout this paper, we make the assumptions as follows. I R I R I R I (H1) There exist positive constants LR f , Lf , Lg , Lg , Mf , Mf , Mg , Mg such that for all u, v ∈ R, I I I |f R (u) − f R (v)| ≤ LR f |u − v|, |f (u) − f (v)| ≤ Lf |u − v|, I I I |g R (u) − g R (v)| ≤ LR g |u − v|, |g (u) − g (v)| ≤ Lg |u − v|,

|f R (u)| ≤ MfR , |g R (u)| ≤ MgR , |f I (u)| ≤ MfI , |g I (u)| ≤ MgI . (H2) α := sup τ˙ (t) and 0 < α < 1. t∈R

(H3) The delay kernels σpq (u) : [0, +∞) → R are continuous integral functions  +∞ with 0 |σpq (u)|du = kpq , and |σpq (u)|eλu are integrable on [0, +∞) for λ > 0. Definition 1 [5]: The drive-response systems (1) and (2) are said to be modulephase synchronized if for neurons xpq and ypq , pq ∈ Λ in systems (1) and (2), such that limt→∞ epqρ (t) = 0 and limt→∞ epqθ (t) = 0, pq ∈ Λ. For presentation convenience, in the following, we denote f¯ = sup |f (t)|, f = inf |f (t)|, zpq (t) = ypq (t) − xpq (t), t∈R

t∈R

y − x = max{sup |ypq (t) − xpq (t)|}, pq∈Λ t∈R   kl kl ¯ pq C¯pq D MfI + kpq MgI , A1 := Ckl ∈Nr (p,q) Ckl ∈Ns (p,q)   kl ¯ kl kpq M R − a , C¯pq D MfR + A2 := pq g pq Ckl ∈Nr (p,q) Ckl ∈Ns (p,q)   R kl R I ¯R + C¯pq Wpq := (MfR Bpq + MfI Bpq )+L pq

¯ kl kpq (M R B R + D pq g pq

I ), MgI Bpq I Wpq :=

kl I ¯ pq D kpq (MgR Bpq +

Ckl ∈Nr (p,q)



Ckl ∈Nr (p,q)

R ), MgI Bpq

R = where Bpq

Ckl ∈Ns (p,q)

kl I R ¯ Ipq + C¯pq (MfR Bpq + MfI Bpq )+L

¯ I A1 −L ¯ R A2 L pq pq 2 A2 −A21

I and Bpq =

 Ckl ∈Ns (p,q)

¯ R A1 −L ¯ I A2 L pq pq . 2 A2 −A21

I Lemma 1: Assume that (H1)–(H3) hold, for any ϕR pq , ϕpq ∈ (−∞, 0] → R with R I I |ϕR pq (s)| ≤ Bpq , |ϕpq (s)| ≤ Bpq ,

we have

3

R I I |xR pq (t)| ≤ Bpq , |xpq (t)| ≤ Bpq ,

t ≥ 0.

Main Results

In this section, the main results of the paper will be given. We will discuss the module-phase synchronization of complex-valued SICNNs with distributed delays.

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zR For ∀z ∈ C, we denote Vec(z) := and get zI T lpq (ypq ) = Vec (Lpq (t))ρ(ypq )Vec(eiθ(ypq ) ), lpq (xpq ) = VecT (Lpq (t))ρ(xpq )Vec(eiθ(xpq ) ),  ¯lpq (ypq ) = VecT Lpq (t)e− π2 i ρ(ypq )Vec(eiθ(ypq ) ),  ¯lpq (xpq ) = VecT Lpq (t)e− π2 i ρ(xpq )Vec(eiθ(xpq ) ), upq = VecT (Upq (t))ρ(ypq )Vec(eiθ(ypq ) ),  π u ¯pq = VecT Lpq (t)e− 2 i ρ(ypq )Vec(eiθ(ypq ) ), F R (ekl (t − τ (t)))epqρ (t) = f R (ykl (t − τ (t)))ρ(ypq ) − f R (xkl (t − τ (t)))ρ(xpq ), F I (ekl (t − τ (t))) = f I (ykl (t − τ (t))) − f I (xkl (t − τ (t))), GR (ekl (t − u))epqρ (t) = g R (ykl (t − u))ρ(ypq ) − g R (xkl (t − u))ρ(xpq ), GI (ekl (t − u)) = g I (ykl (t − u)) − g I (xkl (t − u)). ¯pq as follows We design upq and u ⎧ ρ(ypq ) ⎪ ⎪ upq = − VecT (Lpq (t))ρ(ypq )Vec(eiθ(ypq ) ) + VecT (Lpq (t))ρ(xpq ) ⎪ ⎪ ρ(x ) ⎪ pq ⎪ ⎪ ⎪ ⎨ Vec(eiθ(xpq ) ) − dpq1 (t)ρ(ypq )epqρ (t),   ⎪ π ρ2 (ypq ) ⎪u T −π iθ(ypq ) 2 i ρ(y ⎪ VecT Lpq (t)e− 2 i L ¯ = − Vec (t)e )Vec(e ) + ⎪ pq pq pq 2 ⎪ ρ (x ) ⎪ pq ⎪ ⎪ ⎩ iθ(xpq ) 2 ) − dpq2 (t)ρ (ypq )epqθ (t), ρ(xpq )Vec(e where dpq1 (t) and dpq2 (t) are feedback control gains. So the feedback controller as follows: ⎧ upq cos θ(ypq )upq − sin θ(ypq )¯ R ⎪ ⎪ , ⎨ Upq (t) = ρ(ypq ) (7) ⎪ upq ⎪ U I (t) = sin θ(ypq )upq − cos θ(ypq )¯ ⎩ , pq ∈ Λ. pq ρ(ypq ) Let epq (t) = (epqρ (t), epqθ (t))T , based on (3)–(6), the error systems can be translated into  ⎧ kl ⎪ Cpq (t)F R (ekl (t − τ (t)))epqρ (t) ⎪ e˙ pqρ (t) = − apq (t)epqρ (t) − ⎪ ⎪ ⎪ Ckl ∈Nr (p,q) ⎪ ⎪ ⎪  +∞ ⎪  ⎪ ⎪ kl ⎪ − Dpq (t) σpq (u)GR (ekl (t − u))epqρ (t)du ⎪ ⎪ ⎪ 0 ⎪ Ckl ∈Ns (p,q) ⎨ − dpq1 (t)epqρ (t), ⎪ ⎪   ⎪ ⎪ kl kl ⎪ Cpq (t)F I (ekl (t − τ (t))) − Dpq (t) e˙ pqθ (t) = − ⎪ ⎪ ⎪ ⎪ C ∈N (p,q) C ∈N (p,q) ⎪ r s kl kl ⎪ ⎪  +∞ ⎪ ⎪ ⎪ ⎪ ⎩ σpq (u)GI (ekl (t − u))du − dpq2 (t)epqθ (t). 0

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Then we have the following theorem. Theorem 1: Under assumptions (H1)-(H3), the module-phase synchronization of complex-valued SICNNs (1) and (2) can be achieved with the controller (7) and the following assumption (H4), ⎧ ⎨ ξ = max{ξpq } < 0, pq∈Λ (H4) ⎩λ − d pq2 < 0,     R I I sup |ϕR , κ sup where κ1 = max (s)|, B = max |ϕ (s)|, B 2 pq pq pq pq , pq∈Λ s∈(−∞,0] pq∈Λ s∈(−∞,0]   R eλ¯τ   kl I κ = κ21 + κ22 , ξpq = λ − (apq + dpq1 ) + C¯pq Mf + 1−α (κLR f + Lf ) + Ckl ∈Nr (p,q)  R    kl +∞ λu I ¯ Dpq 0 |σpq (u)| Mg + e (κLR g + Lg ) du.

Ckl ∈Ns (p,q)

Proof: Consider the following Lyapunov functional   |epqρ (t)|eλt + |epqθ (t)|eλt + Ξpq (t) , V (t) = pq∈Λ

where Ξpq (t) =  Ckl ∈Ns (p,q)

kl ¯ pq D

 +∞ 0

 Ckl ∈Nr (p,q)

τ kl eλ¯ C¯pq 1−α

|σpq (u)|eλu

t t−u

t t−τ (t)

I λθ [κLR f |zkl (θ)| + Lf |zkl (θ)|]e dθ +

I λθ [κLR g |zkl (θ)| + Lg |zkl (θ)|)]e dθdu.

Calculating the derivative of V (t) along the trajectories of the error systems, we get       V˙ (t) = λeλt |epqρ (t)| + |epqθ (t)| + eλt |e˙ pqρ (t)| + |e˙ pqθ (t)| + Ξ˙ pq (t) . pq∈Λ

From Lemma 1 and |epqρ (t)| ≤ |ypq (t) − xpq (t)| ≤ y − x, we obtain V˙ (t)  ≤eλt {λ − (apq + dpq1 ) + pq∈Λ

 I × (κLR f + Lf ) +

 Ckl ∈Nr (p,q)

 Ckl ∈Ns (p,q)

kl ¯ pq D



+∞

 R eλ¯τ kl C¯pq Mf + 1−α

 |σpq (u)| MgR

0

  I + eλu (κLR + L ) du}y − x + (λ − d )|e (t)| . pqθ g g pq2

(8)

Based on (H4) and (8), we derive V˙ (t) ≤ 0. Hence, (1) and (2) are module-phase synchronized from Definition 1. The proof is completed.

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Numerical Examples

In this part, a numerical example is given to verify the theoretical result. We select some initial values and get the following two pictures, they show the tranR I = B11 = sient states of epqρ (t) and epqθ (t). By a simple calculation, we get B11 R I R I R I B22 = B22 ≈ 32.6087, B12 ≈ 50.7134, B12 = 21.8672 B21 = B21 ≈ 24.1935. Hence, κ ≈ 60.2924. Moreover, we have ξ11 ≈ −1.3144, ξ12 ≈ −0.3244, ξ21 ≈ −2.3244, ξ22 ≈ −1.8144, ξ ≈ −0.3244 < 0 and λ − dpq2 < 0. Thus, (H4) is satisfied, (1) and (2) are module-phase synchronized. 2.5 2 1.5 1 0.5 0 -0.5 -1 -1.5 -2 -2.5

0

5

10

15

20

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Fig. 1. Transient states of epqρ (t), p, q = 1, 2 2.5 2 1.5 1 0.5 0 -0.5 -1 -1.5 -2 -2.5

0

5

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20

Fig. 2. Transient states of epqθ (t), p, q = 1, 2

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Conclusion

In this paper, we discuss synchronization of complex-valued SICNNs with distributed delays via a module-phase-type controller. For SICNNs, most of the existing work is about the synchronization in state space [1], this paper investigates its synchronization from the module-phase, which can provide us with new ideas and methods. In addition, our results present an effective module-phasetype control scheme whose control efficiency is greatly improved. There are still many interesting problems to be studied about module-phase synchronization of CVNNs on hybrid time domains, we leave it in the future.

References 1. Li, Y., Wang, H.: Almost periodic synchronization of quaternion-valued shunting inhibitory cellular neural networks with mixed delays via state-feedback control. PLoS ONE 13(6), e0198297 (2018) 2. Yuan, M., Wang, W., Wang, Z., et al.: Exponential synchronization of delayed memristor-based uncertain complex-valued neural networks for image protection. IEEE Trans. Neural Networks Learn. Syst. 32(1), 151–165 (2020) 3. Hu, J., Zeng, C.: Adaptive exponential synchronization of complex-valued CohenGrossberg neural networks with known and unknown parameters. Neural Netw. 86, 90–101 (2017) 4. Feng, L., Yu, J., Hu, C., et al.: Nonseparation method-based finite/fixed-time synchronization of fully complex-valued discontinuous neural networks. IEEE Trans. Cybernet. 51(6), 3212–3223 (2021) 5. Nian, F., Wang, X., Niu, Y., et al.: Module-phase synchronization in complex dynamic system. Appl. Math. Comput. 217(6), 2481–2489 (2010) 6. Zhang, H., Wang, X., Lin, X.: Topology identification and module-phase synchronization of neural network with time delay. IEEE Trans. Syst. Man Cybernet. Syst. 47(6), 885–892 (2017) 7. Huang, Z., Mohamad, S., Bin, H.: Multistability of HNNs with almost periodic stimuli and continuously distributed delays. Int. J. Syst. Sci. 40(6), 615–625 (2009) 8. Peng, D., Li, X.: Leader-following synchronization of complex dynamic networks via event-triggered impulsive control. Neurocomputing 412, 1–10 (2020) 9. Chen, H., Shi, P., Lim, C.: Synchronization control for neutral stochastic delay Markov networks via single pinning impulsive strategy. IEEE Trans. Cybernet. 50(12), 5406–5419 (2020)

Evaluation System and Data Analysis Method of Low-Temperature Cold Start for Hydraulic Winch Oil Xudong Wang1 , Hua Li1 , Junbiao Hu2 , Shizhan Li1 , Weihua Zhang1(B) , and Yao Nie1 1 Institute of System Engineering, Academy of Military Science, Beijing 100300, China

[email protected] 2 Second Representative Office of the Bureau of Representatives, Xi’an, China

Abstract. In order to evaluate the influence of hydraulic oil and hydraulic transmission oil on the low-temperature cold start performance of the winch, a hydraulic winch low-temperature cold start performance evaluation test bench was developed. The overall structure and working principle of the test rig are described, and the key subsystems of the test rig are designed, including the ultra-low temperature freezing chamber subsystem design, and the motion control and data acquisition subsystem. The cold start performance and working parameters of the hydraulic winch were analyzed and measured on the developed test rig, and the overall performance of the test rig was tested and evaluated. The results show that the test bench can simulate the actual operating conditions of the hydraulic winch, and the bench has good stability, and can evaluate the low-temperature cold start performance of the hydraulic oil. Keywords: Hydraulic winch · Hydraulic oil · Low-temperature · Cold start performance

1 Introduction Hydraulic winch has the advantages of high power density, strong load-bearing capacity and continuous working capacity [1, 2], etc. It is widely used in the winching and winding device of heavy machinery equipment such as national defense equipment, construction machinery, vehicles, construction, ships and metallurgy [3]. The basic working principle of hydraulic winch is to use the hydraulic oil pump to generate high pressure hydraulic oil, after the high pressure oil pipe and control valve system to drive the hydraulic motor rotation, and through the reducer to reduce the hydraulic motor to increase the torque, so that the winch cylinder rotation to drag the load. In the low-temperature conditions to assess the performance of the hydraulic winch cold start [4, 5], the analysis of the best range of low-temperature viscosity of hydraulic oil, and then guide the design of hydraulic oil index, is one of the key means to improve the performance of the hydraulic winch system. At present, the domestic hydraulic winch R&D and production units in the field of hydraulic oil cold start performance assessment technology research is still blank, © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 719–726, 2022. https://doi.org/10.1007/978-981-19-0390-8_89

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foreign countries have not proposed the corresponding assessment device and assessment standards. In this paper, according to the working principle and structural characteristics of hydraulic winch system, the overall scheme and key component subsystems of the test bench were designed, the supporting motion control and data acquisition software were developed, the hydraulic winch oil low-temperature cold start evaluation test bench was developed, and finally the performance of the test bench was tested and evaluated.

2 Working Principle of Hydraulic Winch System The hydraulic winch system is mainly composed of oil pump, radial piston low-speed high-torque hydraulic motor, one-way or two-way balance valve, manual or pneumatic three-way four-way directional valve, shuttle valve, safety relief valve, planetary reducer, brake, winch drum, rope rower and other components[6].

1 - oil pump, 2 - safety relief valve, 3 - manual or pneumatic three-way four-way reversing valve, 4 - shuttle valve, 5 - hydraulic motor, 6 - brake, 7 - lineshifter, 8 - reducer winch rotor, 9 - load, 10 - balance valve, 11 - pneumatic valve Fig. 1. Working principle of hydraulic winch system

Figure 1 shows the working principle of the hydraulic winch system, its working principle is as follows: when the manual or pneumatic three-way four-way reversing valve 3 is manipulated by the handle or valve 11, the hydraulic system pressure oil provides pressure to the hydraulic motor 5 and brake 6 through the balance valve 10. When the pressure of the hydraulic oil is greater than the braking force of the brake 6, the hydraulic motor 5 is unlocked, at which time the pressure oil drives the hydraulic motor 5 to produce rotary motion, driving the reducer winch barrel 8 to rotate to drag the load 9 movement. When the reducer winch barrel 8 rotates, through the slider on the rope row 7 back and forth movement, to achieve the effect of forced rope row. Change the operation direction of the manual or pneumatic reversing valve 3 to make the reducer winch barrel 8 reverse rotation. When the manual or pneumatic reversing valve 3 is in the neutral position, the system unpressurized, the piston cavity corresponding to the balance valve 10 and the hydraulic motor 5 is in a closed state, and the brake 6 locks the hydraulic motor 5, playing a braking role.

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Hydraulic oil in the hydraulic winch system flow process, will flow through the oil pump, hydraulic motor, hydraulic control valves, pipes and other major components. Due to the role of internal friction in the flow of liquid in the oil channel, will produce along-range pressure loss, in the control valve mouth and other places due to sudden changes in flow rate and direction, etc., will cause mutual collision between the fluid mass and violent friction and local pressure loss. The formula for calculating along-range pressure loss and local pressure loss is. pλ = 32μlv/d 2

(1)

pε = ερv2 /2

(2)

Where: pλ - along-range pressure loss, μ - dynamic viscosity of the liquid, l - pipe length, v - flow rate, d - pipe diameter, pε - local pressure loss, ε - local resistance coefficient, ρ - liquid density. From the formula (1) and formula (2) can be seen, the pressure loss of the hydraulic winch system and viscosity, the greater the viscosity, the greater the pressure loss along the pressure loss and local pressure loss. High viscosity problems of low temperature conditions, also affect the performance of the hydraulic power system of armored vehicles and engineering vehicles hydraulic power unit, making mechanical action is slow or difficult to move. Therefore, the viscosity of the hydraulic oil in low temperature conditions is not the smaller the better, which requires analysis in low temperature to meet the conditions of cold start, the best range of hydraulic oil low temperature viscosity.

3 Overall Design Plan of the Test Bench Yj10HK hydraulic winch system has wide representation in test components and test conditions; meanwhile, Yj10HK hydraulic winch system is widely used in the third generation military off-road vehicles. Therefore, the Yj10HK hydraulic winch system is selected as the test component, and the main technical parameters of the hydraulic winch system are shown in Table 1. Table 1. Working parameters of YJ10HK hydraulic winch system Rated pressure P (MPa)

Rated flow rate Q (L/min)

Maximum power P (kW)

Rated pull F (KN)

Rotor diameter D (mm)

Working speed V (m/min)

20

75

45

98

240

6–8

By analyzing the working process of hydraulic winch system, combined with the influence of low temperature viscosity of hydraulic oil on the cold start performance of hydraulic winch, the overall scheme of the design of hydraulic winch system cold start performance evaluation device is shown in Fig. 2.

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Fig. 2. Hydraulic winch system oil low temperature cold start evaluation test device

Through the motor 1 to simulate the engine driven oil pump 2 rotation, the generated high pressure hydraulic oil under the control of the hydraulic winch 5 through the high pressure oil pipe 7, drive the hydraulic motor 17 high-speed rotation, and then use the planetary reducer 16 to the hydraulic motor 17 to reduce the torque increase, so that the planetary reducer 16 and the brake hub 13 to generate torque between the hydraulic winch system to simulate the towing capacity. During operation, the data acquisition and control platform 10 is used to obtain torque, rotational speed, pressure, temperature, voltage and current values from the torque sensor 14, pressure sensor 11, temperature sensor 21 and power distribution cabinet 3. The hydraulic oil’s ability to deliver power under ultra-low temperature conditions (ultra-low temperature freezing chamber 8) is evaluated by comprehensive analysis of torque and speed magnitude, pressure drop, temperature rise, power loss and other parameters.

4 Ultra-low Temperature Freezing Chamber Sub-system Design In the ultra-low temperature conditions, it is necessary to focus on the evaluation of the components of the device to the degree of low temperature tolerance, the design is as follows: First, the hydraulic winch system test components are all placed in the freezer compartment, including gear pumps, hydraulic motors, planetary reducer, control valve body, hydraulic oil tank, planetary reducer, high-pressure resistant oil pipe, etc.. Second, the other supporting components in addition to the test parts are placed outside the freezer compartment, mainly including motors, brake hubs, pressurization stations, control devices, sensors, etc. Therefore, the ultra-low temperature environment freezing chamber mainly for the hydraulic winch system to provide ultra-low temperature environment, the ambient temperature of the freezing chamber can be controlled at 0 ~ −50 °C, the cooling power of 20 kW. detailed parameters are shown in Table 2.

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Table 2. Hydraulic winch system detailed parameters table Type

Motor speed (r/min)

Motor power (kW)

Rated torque (N·m)

Rotational speed ratio

Rated speed (r/min)

Parameters

1500

36

11760

96:1

20

5 Motion Control and Data Acquisition Subsystem The motion control of the evaluation device mainly includes the start and stop of the hydraulic oil pump, the control of the cryogenic cooling of the refrigerated environment chamber, the control of the pressurization of the back load system, and the automatic safety protection under abnormal working conditions. The Visual Studio platform is used to develop the supporting motion control and data acquisition subsystem, which is capable of automatic, semi-automatic and manual control of hydraulic oil pump, refrigeration and loading, etc. It is capable of collecting,

Fig. 3. Client operating platform

Table 3. Parameter acquisition table Name

Pump outlet pressure

Valve group inlet pressure

Outlet pressure of valve group

Motor inlet pressure

Air pump pressure

Symbols

P1

P2

P3

P4

P5

Name

Fuel tank oil temperature

Pipeline oil temperature

Torque

Rotational speed

Voltage-current

Symbols

T1

T2

J

n

U·I

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displaying and saving the electrical signals transmitted by each sensor in the system during the operation of the device, and forming the relationship curve between each parameter and time, Fig. 3 shows the interface of the developed client platform. The parameter acquisition table is shown in Table 3.

6 Test Bench Testing and Evaluation 6.1 Test Procedure (1) Commissioning equipment. Check the safety of the equipment, to exclude leakage of electricity, liquid, valve is not tight and other safety hazards; open the hydraulic winch test stand control program and PC computer; open the three-way valve to adjust the valve position of the nitrogen gas cylinder, so that the pressure is 0.5 MPa. (2) Clean the oil circuit. Turn on the power of the test device, add 20 L test oil to the tank, set the three-way valve to the middle position, do not connect the load, turn on the data collector, start the motor, so that the hydraulic oil in the oil circuit for 30 s; then set the three-way valve to the left and right position, each cycle for 10 s; check the client data collection, display, storage for abnormalities, restore the three-way valve to the middle position, stop. (3) Change of oil used. Add 20 L test oil to the waste oil drum, add 20 L test oil to the tank, do not connect the load, adjust the three-way valve to the left and right position, start the motor, after 3 min of each cycle, restore the three-way valve to the middle position, stop the machine, drain the oil to the waste oil drum; add 20 L test oil to the tank, do not connect the load, adjust the three-way valve to the left and right position, start the motor, after 3 min of each cycle After 3 min, restore the three-way valve to the middle position, stop the machine, and drain the oil in the tank to the waste oil drum; add 20 L of test oil to the tank; add oil sample to the cold storage. (4) Temperature setting: set the temperature of the cold storage in accordance with the “instructions for use of cold storage”, and constant temperature of not less than 4h. (5) Equipment operation. ➀Turn on the data collector, start the motor and run for 10 s; ➁set the three-way four-way valve to the right position, case one: no speed output within 3 s, set the three-way four-way valve to the middle position, stop the machine and end the test; case two: if there is speed output, set the three-way four-way valve to the middle position after 10 s, then set it to the left position to offset the torque, end the data collector and stop the machine to cool the oil circuit. ➂When the temperature sensor value is less than 1 °C from the temperature difference before the test starts, start the air compressor to make the pressure in the air tank reach 0.85 MPa and connect the load. Repeat step ➀➁ and relieve the pressure. (6) Adjust the temperature to 0 °C, −20 °C and −40 °C respectively according to step (3) above, and run it once according to step (4) after each temperature adjustment, and after doing the test at −40 °C, the whole test is considered completed.

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6.2 Design of Test Conditions The test conditions include the setting of low temperature ambient temperature, the setting of drag force, and the setting of high temperature hydraulic oil heating temperature. The temperature gradient set for the test is 25 °C, 0 °C, −20 °C and −40 °C, which makes the test results distinguishable and the peak and inflection points of the test data curve are different for different oils. Select oil samples for testing in three areas. (1) Military hydraulic fluid: No. 15 aviation hydraulic fluid. (2) Civilian hydraulic oil: L-HM 46 hydraulic oil. (3) Alternative to hydraulic fluid: 8H hydraulic transmission fluid. 6.3 Analysis of Test Results The pump outlet pressure data values for 8H hydrodynamic fluid are shown in Fig. 4.

Fig. 4. Pump outlet pressure data curve of 8H hydraulic transmission oil

Analysis of the above graph shows that the same oil in different temperature gradient environment for the test, the resulting data is different, the law presents as follows: 25 °C pump outlet pressure data value (here refers to the maximum value) the smallest, 0 °C next, −20 °C maximum. As for the data of −40 °C, the data of −40 °C cannot be measured normally because the oil itself cannot have good fluidity in −40 °C environment.

Fig. 5. Three kinds of oil pump outlet pressure data curve graph

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Figure 5 shows the output power curves of No. 15 aviation hydraulic fluid, L-HM No. 46 hydraulic fluid, and 8H hydraulic transmission fluid with sampling time at an ambient temperature of 0 °C. It can be seen from the above figure that under the same ambient temperature, the test data of different oils are different. Each oil product adjusts the three-position four-way reversing valve to input the torque of the hydraulic winch system at 5 s. Similarly, after a short delay of about 5 s, the output power begins to rise to the maximum value, and the measured maximum values of different oil products are also different. The maximum value of No. 15 hydraulic oil is the largest, followed by 8H hydraulic transmission oil, and L-HM46 hydraulic oil is the smallest. The reason is that the low temperature tolerance limits of different oil products are different, which is mainly related to the physical and chemical properties of the oil. Therefore, it can be considered that selecting the output power as one of the indicators of the test is representative and feasible.

7 Conclusion By analyzing the test temperature limits of different oils, it can be seen that the developed hydraulic winch low temperature cold start performance evaluation test bench can truly simulate the actual operating conditions of hydraulic winch low temperature cold start. The test results of the test bench show that the test bench operation is stable and reliable, and the test results obtained are of good scientificity, which can evaluate the effect of low temperature cold startability on the pressure, power and temperature rise of the hydraulic winch. The development of the hydraulic winch low-temperature cold start performance evaluation test bench provides a technical means to evaluate the low-temperature cold start performance of hydraulic oil and hydraulic transmission oil. In the subsequent work, the group will establish a standard test method for evaluating the low-temperature cold start performance of hydraulic winches on this basis.

References 1. Yongzhe, L., Dawei, L.: Research on reliability assessment technology of weapon system in battle test stage. Ship Electronic Eng. 40(02), 104–108 (2020) 2. Wanli, X., et al.: Development of test-bed used for evaluating friction characteristics of manual transmission lubricant. J. Mech. Eng. 52(23), 176–181 (2016) 3. Yemin, D., Xiaowu, K.: Research on new constant power electro-hydraulic winch fluid. Power Transmission Control 05, 16–19 (2008) 4. Lu, Z., Fu, J.: Design and analysis of 30 kN hydraulic winch. Jiangsu Ship 06, 27–29 (1999) 5. Ming, L.: Design and research of hydraulic winch for heavy off-road vehicle. Industry Technol. Forum 15(23), 66–67 (2016) 6. Chi, Y.: The “Optimal Viscosity” of hydraulic oil. Mining Equipment 09, 52–53 (2015)

Primary Battery Electrical Performance Data Collection and Analysis Chunhua Xiong1 , Lei Xu1 , Weishan Wang2 , Hui Sun1 , Weigui Zhou1(B) , Bingchu Wang1 , and Wanli Xu1 1 Institute of System Engineering, Academy of Military Science, Beijing 100300, China

[email protected] 2 Troops of the People’s Liberation Army of China, Beijing 69008, China

Abstract. Due to the urgent needs of military electronic products, especially space technology and new defense equipment for primary batteries with high mass and energy density. In this paper, the electrical properties of metal-air battery and lithium primary battery were tested. The results show that in terms of output stability, the voltage of lithium primary battery is very stable, and the voltage fluctuation of metal-air battery which needs electrolyte is generally fierce. In terms of mass power density, 1 # and 2 # lithium-carbon fluoride batteries have obvious advantages over metal-air batteries. Keywords: Primary battery · Lithium battery · Metal-air battery · Electrical property

1 Introduction With the development of new energy technology, power system is more and more used to replace the traditional energy system. Charging stations, electric vehicles and power propeller systems have become the hot direction of the application of new energy technology, and power supply devices are the key to the development of new energy technology [1]. Military battery is used in army (single soldier system, army combat vehicle, military communication equipment), navy (submarine, underwater vehicle), air (unmanned reconnaissance aircraft), air force (satellite, spacecraft) and many other types of weapons, which are not only the power supply of military information, but also the power of military equipment. In peacetime training, batteries can be replaced with other power sources to reduce training costs; But if the actual combat exercises or war, only primary battery can be used. Several wars in the United States over the past decade have repeatedly proved that there were no conditions and no time to recharge batteries during the war [2]. Therefore, military electronic products, especially space technology and new national defense equipment (such as new satellites, spaceships, high-power laser weapons, digital soldier systems, etc.), are more urgent primary batteries with light weight, small size, high specific energy, high specific power, safe, reliable, and non-pollution [3]. Lithium primary batteries include lithium-carbon fluoride battery, lithium thionyl chloride battery, lithium manganese dioxide battery, etc. In 2017, Zhao Huihui et al. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 727–734, 2022. https://doi.org/10.1007/978-981-19-0390-8_90

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analyzed various factors affecting the performance of lithium-manganese dioxide (LiMn) primary battery from the aspects of anode material, binder and collector. The results show that the contact resistance of the battery can be reduced by corona aluminum foil in a certain period of time, thereby improving the rate performance and cycle life of the battery [4]. At present, the test of metal-air battery and lithium primary battery is basically concentrated on the performance test of materials, and there is no direct data support for the selection of batteries. Therefore, we need to test the electrical performance of the battery module and make a comparative analysis. In this paper, the electrical properties of lithium primary battery were tested at 25 °C. The lithium primary battery include lithium-carbon fluoride battery, lithiumsulfinyl chloride battery and lithium-manganese dioxide battery, respectively. The voltage and current of the battery in the discharge process were analyzed, and the mass and energy density of the battery was compared. The preliminary theoretical preparation and experimental study were made for the battery selection of field equipment.

2 Test Method 2.1 Test Equipment and Data Acquisition Platform Test Equipment.

(1) SDJ710FA high and low temperature humidity and heat box. The temperature control equipment used for the test is the SDJ710FA high and low temperature humidity and heat chamber produced by Chongqing Sida Test Equipment Co as shown in Fig. 1. The basic technical parameters of the equipment are shown in Table 1.

Fig. 1. SDJ710FA high and low temperature humidity and heat chamber

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The test device can simulate the solar illumination up to 80 ms. During this period, the data acquisition platform can measure and record about 4800 data and automatically plot a volt-ampere characteristics curve. Specific measurable n parameters acquired are shown in Table 1.

Table 1. Basic technical parameters of SDJ710FA Projects

Technical parameters

Maximum temperature (°C)

150

Minimum temperature (°C)

− 70

Accuracy (°C)

±1

(2) Electronic balance. The balance used for the test was PL6001-S electronic balance manufactured by Mettler Toledo, Switzerland, as shown in Fig. 2, and the main parameters of the balance are shown in Table 2.

Fig. 2. PL6001-S Electronic Balance Table 2. Basic parameters of PL6001-S electronic balance Projects

Technical parameters

Maximum range (g)

6100

Accuracy (g)

0.1

Data Acquisition Platform. The equipment used for the test is the energy recovery type battery module test system 17020 manufactured by Chroma Taiwan, as shown in Fig. 3. The basic technical parameters of the equipment are shown in Table 3.

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Fig. 3. Energy recovery type battery module test system 17020

Table 3. Basic technical parameters of energy recovery battery module test system 17020 Projects

Technical parameters

Number of test channels available

8

The maximum voltage that can be tested in a single channel (V)

20

Voltage test accuracy (V)

± 0.0001

Single channel can test the maximum current (A)

50

Current test accuracy (A)

± 0.0001

2.2 Test Objects Lithium primary batteries include lithium-fluorocarbon batteries, lithium-thiocarbonyl chloride batteries, and lithium-manganese dioxide batteries, and the basic parameters are shown in Table 4. Table 4. Basic parameters of primary battery for this test Serial number

Battery type

Battery number

Total mass of discharged part (g)

1

Lithium-fluorocarbon batteries

1#

783.4

Battery photos

(continued)

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Table 4. (continued) Serial number

Battery type

Battery number

Total mass of discharged part (g)

2

Lithium-fluorocarbon batteries

2#

791.0

3

Lithium thionyl chloride 1# batteries

31.3

4

Lithium-manganese dioxide battery

24.8

1#

Battery photos

2.3 Test Conditions Under the condition of ambient temperature 25 °C, after connecting the battery output to the battery test system, the electrical performance is tested according to the specified test conditions to obtain the battery voltage and current change curve during the discharge process, and calculate the mass energy density according to the battery discharge energy and mass, and then analyze the data. The test conditions of lithium primary battery are shown in Table 5. Table 5. Lithium primary battery electrical performance test conditions Serial number

Battery type

Battery number

Discharge method

Cut-off voltage (V)

7

Lithium-fluorocarbon batteries

1#

25 W fixed power

9.0

8

Lithium-fluorocarbon batteries

2#

25 W fixed power

9.0

9

Lithium thionyl chloride batteries

1#

0.4 A constant current

2.0

10

Lithium-manganese dioxide battery

1#

1 A constant current

2.0

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3 Analysis of the Electrical Performance of Lithium Primary Batteries 3.1 Electrical Performance Analysis of Lithium-Fluorocarbon Batteries The voltage and current variation curves of 1# and 2# lithium-fluorocarbon cells with discharge time are shown in Fig. 4 respectively.

(a) 1# lithium-fluorocarbon cell

(b) 2# lithium-fluorocarbon cell

Fig. 4. Variation of voltage and current

As can be seen from Fig. 4 1# and 2# lithium-fluorocarbon batteries will first have a peak voltage during the 25 W constant power discharge process, then the voltage drops sharply, then rises back to the normal operating voltage, then drops slowly with the discharge process, until near the end of the discharge drops rapidly to the cut-off voltage of 8 V, and the current changes with the change of voltage. Among them, the peak voltage of the battery of 1# is smaller than the peak voltage of the battery of 2#, and the output is smoother. 3.2 Electrical Performance Analysis of Lithium-Thiocarbonyl Chloride Batteries The voltage variation curve of 1# lithium-thiocarbonyl chloride cell with discharge time is shown in Fig. 5. As can be seen from Fig. 5, during the 0.4 A constant current discharge, the voltage of the 1# lithium-thiocarbonyl chloride cell first shows a sudden increase, then begins to drop gently until the end of the critical discharge, when the voltage drops rapidly to the cutoff voltage of 2 V. The voltage is maintained relatively smoothly throughout the discharge process. 3.3 Electrical Performance Analysis of Lithium-Manganese Dioxide Batteries The voltage variation curve of 1# Li-MnO2 battery with discharge time is shown in Fig. 6.

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Fig. 5. Voltage variation curve of 1# lithium-thiocarbonyl chloride cell with discharge time

Fig. 6. Voltage variation curve of 1# Li-MnO2 battery with discharge time

As can be seen from Fig. 6, during the 1 A constant current discharge process, the 1# Li-MnO2 battery voltage has a peak at the beginning, then returns to the normal operating voltage, declines slowly with the discharge process, and drops sharply to the cutoff voltage of 2 V near the end of the discharge. 3.4 Comparative Analysis of the Electrical Performance of Lithium Primary Batteries The discharge energy and mass energy density obtained from the lithium primary battery test are shown in Table 6. From Table 6, it is clear that the mass-energy density of the tested lithiumfluorocarbon cells is higher than that of the lithium-thiocarbonyl chloride and lithiummanganese dioxide cells, probably because the lithium-fluorocarbon cells are modules than the small cylindrical monoblocks of the lithium-thiocarbonyl chloride and lithiummanganese dioxide cells. The mass-energy density of the lithium-fluorocarbon cells of 1# is significantly higher than that of 2#. In the small cylindrical monoblocks, the

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Serialnumber

Battery type

Battery number

Discharge energy (Wh)

Battery mass (g)

Mass energy density (Wh/kg)

1

Lithium-fluorocarbon batteries

1#

419.1

783.4

534.9757

2

Lithium-fluorocarbon batteries

2#

381.8

791.0

482.6802

3

Lithium thionyl chloride batteries

1#

8.5

31.3

271.57

4

Lithium-manganese dioxide battery

1#

4.2

24.8

169.35

lithium-thiocarbonyl chloride battery of 1# has higher mass-energy density than the lithium-manganese dioxide battery.

4 Conclusion 1. Among the lithium primary batteries tested, the discharge voltages of all batteries remained basically smooth and slowly declined; among the lithium-fluorocarbon battery modules, the mass energy density of 1# was higher than that of 2#; among the small cylindrical monoblocks, the mass energy density of 1# lithium-thiocarbonyl chloride battery was higher than that of 1# lithium-manganese dioxide battery. 2. The results show that the output voltage stability of lithium primary batteries are very smooth, slowly decreasing without much fluctuation; the mass power density of 1# and 2# lithium-fluorocarbon batteries have obvious advantages compared with the rest of the batteries.

References 1. Tengfei, S., et al.: Research on the application of metal-air batteries in dual-use field. Dual-use Technol. Products Military Civilian Appl. 05, 64–67 (2018) 2. Yidao, W.: Review: status and outlook of lithium primary battery industry in China in the past two decades. New Mater. Industry 03, 15–19 (2019) 3. Yuqing, C., et al.: Research status and outlook of lithium-sulfur primary batteries. Energy Storage Sci. Technol. 6(03), 529–533 (2017) 4. Huihui, Z., Yanlei, Z., Lijuan, Z.: Research progress of lithium-manganese dioxide primary batteries. Battery 47(03), 188–190 (2017) 5. Yang, R.: Preparation and Performance Study of Al Metal-air Battery Cathode. China University of Petroleum, Beijing (2019) 6. Gong, H.: Development and Performance Testing of Metal-air Batteries for Electric Vehicles. Xi’an University of Petroleum (2019)

Volt-Ampere Characteristic Acquisition and Analysis of Thin Film Solar Cells Under Different Incident Angles Hua Li1 , Yanli Sun1 , Junbiao Hu2 , Hui Sun1 , Changbo Lu1(B) , Bingchu Wang1 , and Xudong Wang1 1 Institute of System Engineering, Academy of Military Science, Beijing 100300, China

[email protected] 2 Second Representative Office of the Bureau of Representatives, Xi’an, China

Abstract. Since the use location of man-portable photovoltaic power supply, field mobile photovoltaic system and other equipment will change at any time, the impact of light incidence angle on its power generation capacity is extremely significant. This paper mainly studies the volt-ampere characteristics of solar cells of two material systems, thin silicon and copper-indium-gallium-selenide, under different incidence angle conditions, and the results show that: with the increase of light incidence angle, the open-circuit voltage of the various types of solar cells tested decreases slightly, but the change is not obvious; the short-circuit current, maximum working power and photoelectric conversion efficiency decrease more and more rapidly, and when the light incidence angle is in the range of 0°–30°, the changes are not significant. Keywords: Solar cell · Thin film · Volt-ampere properties · Light incidence angle

1 Introduction China’s long national borders, there are many border posts are located in remote mountainous areas, scattered islands or vast grasslands, supply inconvenience, living and communication with electricity has been difficult to solve, while the army in the field training, exercises or combat, to ensure the supply of electricity is also a top priority [1]. At present, the field power supply is still using the traditional diesel generator power supply, this power supply method has the difficulties in oil resupply, utilization efficiency is not high, pollution of the environment, noise, poor concealment and other problems [2]. Solar photovoltaic technology is a power generation technology that converts solar energy directly into electricity through the photovoltaic effect, which is a clean and green energy source of renewable use with abundant available resources, reliable technology, simple and convenient maintenance, long service life, and reasonable economy [3]. A highly efficient, resistant to strong light radiation, the actual photoelectric conversion rate under low light is high, suitable for field environment use of mobile photovoltaic power, can effectively reduce the dependence on logistical supplies [4]. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 735–742, 2022. https://doi.org/10.1007/978-981-19-0390-8_91

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Because the irradiance of light received by solar cells varies greatly with the angle of light incidence, which greatly affects the power generation capacity. Under natural conditions, light is certain, so the angle of light incidence can be changed by adjusting the tilt angle of the solar cell. 2007, Chang, Zehui and Tian, Rui et al. conducted local real-world measurements on two fixed solar cell arrays, and adopted the comparative experimental method and the maximum power comparison method to find the optimal tilt angle of the fixed solar cell arrays near 40°N latitude. The results showed that among the six inclination angles with similar solar radiation tested in July 2006, the solar cell array with an inclination angle of 45° had the highest output power, and the best inclination angle of the stationary solar cell array near 40°N could be determined to be near 45° [5]. In 2012, Jiajia Tu and Guowei Liang conducted a test experiment on the effect of light incidence angle on the output power of three common solar cells, monocrystalline silicon, polycrystalline silicon and amorphous silicon, under natural conditions, and the results showed that: the effect of light incidence angle on the output power of the three solar cells is basically the same, but the test curves of monocrystalline silicon and polycrystalline silicon are closer. In 2014, Huang Lifang and Lin Xiaofeng designed a PV power generation efficiency testing device to collect the output of 12 solar modules with different orientations and inclination angles, and conducted a detailed analysis and study of the collected data. The results show that the Nanning area has a higher capacity of modules facing 22° inclination to the south, and the capacity of modules placed to the west is greater than that of modules placed to the east [6]. It can be seen that, at present, the experimental research and analysis on the optimal inclination angle of solar cell installation generally focus on the change of photoelectric conversion efficiency and maximum working power of fixed PV arrays in a particular region with the change of light irradiance and solar irradiation angle in all seasons, which is important for the local solar cell installation and use, but for the use locations including single-armed portable PV power, field mobile Photovoltaic systems and other equipment whose use location will change at any time, we need to do some experiments with more general significance to study and analyze the change law of the power of solar cells with the angle of light incidence and derive the relationship between them. In this paper, solar cells made of thin silicon and copper-indium-gallium-selenide (CIGS) were tested under different light incidence angles, and the volt-ampere characteristics of the same cells under different conditions were compared and investigated. The changes of open circuit voltage, short circuit current, maximum working power and photoelectric conversion efficiency with light incidence angle were analyzed and summarized, and the optimal light incidence angle of the tested solar cells was initially explored.

2 Test Method 2.1 Test Equipment and Data Acquisition Platform 2.2 Test Equipment The test needs to change the light incidence angle of the solar cell, and the light from the solar simulator shines vertically on the solar cell from the bottom up, so it is not easy to

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change the angle, so the light incidence angle can be adjusted by changing the tilt angle of the solar cell. The solar cell tilt angle characteristic test device is shown in Fig. 1, and the equipment parameters are shown in Table 1.

Fig. 1. Solar cell tilt angle characteristic test device

The components of the equipment are: 1 - bracket, 2 - angle disc, 3 - rotating handle, 4 - spring handle, 5 - positioning pin, 6 - tilting back plate. Table 1. Performance parameters of the solar cell tilt characteristics test device Name

Solar cell tilt angle characteristics test device

Adjustable angle (°)

90, 75, 60, 45, 30, 15, 0

2.3 Data Acquisition Platform The equipment used for the test is SPI-SUN Simulator 5600 SLP type solar simulator photovoltaic component tester produced by Spire Company of the United States as shown in Fig. 1. (Fig. 2)

Fig. 2. SPI-SUN simulator 5600SLP photovoltaic component tester

The test device can simulate the solar illumination up to 80 ms. During this period, the data acquisition platform can measure and record about 4800 data and automatically plot a volt-ampere characteristics curve. Specific measurable parameters acquired are shown in Table 2.

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Unit

Open circuit voltage (Voc)

V

Short-circuit current (Isc)

A

Maximum working power (Pmax)

W

2.4 Test Objects The solar cells tested are thin Si solar cells, CIGS solar cells and PSCs (Perovskite Solar Cell) respectively. The basic parameters are shown in Table 3. Table 3. Basic parameters of solar cells Serial number

Photovoltaic material

Effective area (cm2 )

1

Si

711. 5

2

CIGS

996. 0

Solar cell photo

2.5 Test Conditions Using the solar cell tilt angle characteristic test device, the solar cell is fixed flatly on the tilted back plate by gluing and I-beam nailing, etc. The tilt angle of the solar cell is changed by rotating the back plate to test the effect of different light incidence angles on the photovoltaic performance of the cell, and the volt-ampere characteristic curve of the cell is drawn and analyzed for data, and the test conditions are shown in Table 4. Table 4. Data acquisition conditions for the effect of light incidence angle on the photovoltaic performance of solar cells Temperature (°C)

25

25

25

25

25

25

Light irradiance (W/m2 )

1000

1000

1000

1000

1000

1000

Light incident angle (°)

0

15

30

45

60

75

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3 Results This time, the tilt angle characteristics of the thin silicon solar cell coarse and fine grid surfaces and the copper indium gallium selenide solar cell were tested, and their volt-ampere characteristics curves were obtained, as shown in Fig. 3. From the figure below, it can be seen that with the change of light incidence angle, the open-circuit voltage, short-circuit current and maximum working power of the tested solar cells will change, therefore, it is necessary to extract the key parameters of open-circuit voltage, short-circuit current, maximum working power and photoelectric conversion efficiency to further analyze their change laws.

(a) rough grid surface of Si solar cell

(b)fine grid surface of Si solar cell

(c) CIGS solar cell

Fig. 3. Volt-ampere characteristic curve.

4 Discussion 4.1 Open circuit voltage The open-circuit voltages of the tested types of solar cells were normalized with the variation of light incidence angle as shown in Fig. 4 respectively. From the above figure, it can be seen that the open-circuit voltage of the tested solar cells is maximum when the light incidence angle is 0°, after which the open-circuit voltage decreases slightly with the increase of the light incidence angle, but the change is not significant.

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Fig. 4. Variation of open circuit voltage.

4.2 Short circuit current The variation of short-circuit current with light incidence angle for the various types of solar cells tested are shown in Fig. 5 respectively.

Fig. 5. Variation of short circuit current.

From the above figure, it can be seen that the short-circuit current of the tested solar cells is the largest when the light incidence angle is 0°; when the light incidence angle is within the range of 0°–30°, the change is not significant; after the light incidence angle is greater than 30°, the short-circuit current decreases more and more with the increase of the light incidence angle. 4.3 Maximum working power The variation of the maximum working power of each type of solar cell with the angle of incidence of light is shown in Fig. 6 respectively. As can be seen from the above graph, the maximum working power of the tested solar cells is maximum when the light incidence angle is 0°; the light incidence angle

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Fig. 6. Variation of maximum working power of various solar cells.

varies less in the range of 0°–30°; after the light incidence angle is greater than 30°, the maximum working power decreases more and more as the light incidence angle increases. 4.4 Photoelectric Conversion Efficiency The variation of the photoelectric conversion efficiency of each type of solar cell with the angle of incidence of light is shown in Fig. 7 respectively.

Fig. 7. Variation of photoelectric conversion efficiency of various solar cells.

From the above figure, it can be seen that the photoelectric conversion efficiency of all kinds of solar cells tested is the largest when the light incidence angle is 0°; the light incidence angle changes in the range of 0°–30°, the change is smaller; after the light incidence angle is greater than 30°, the photoelectric conversion efficiency decreases more and more with the increase of the light incidence angle.

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5 Conclusion 1. With the increase of light incidence angle, the open-circuit voltage of the tested solar cells slightly decreases, but the change is not obvious; the short-circuit current, maximum working power and photoelectric conversion efficiency decrease more and more quickly, and they do not change much when the light incidence angle is in the range of 0°–30°. 2. During the use of solar cells, the light incidence angle should be kept in the range of 0°– 30° to ensure that the short-circuit current, maximum working power and photoelectric conversion efficiency of solar cells are less affected by the light incidence angle and improve the efficiency of solar energy utilization.

References 1. Xuezhi, H.: Research and application of mobile photovoltaic power supply technology. China High-Tech Enterprise 26, 39–40 (2016) 2. Yuan, Y., et al. Design calculation method of mobile photovoltaic system. Equipment manufacturing technology (08), 82–85+97 (2020) 3. Wang, M.: Analysis of the application of solar photovoltaic technology in national parks. Electrical Technol. 21(07),112–115+124 (2020) 4. Hu Junzhi, X., et al.: Design of portable thin-film solar photovoltaic field multifunctional emergency power supply. China Medical Equipment 9(09), 18–20 (2012) 5. Chang, Z.-H., Tian, R.: Experimental study on the optimal tilt angle of fixed solar cell square array. Power Technol. 31(04), 312–314+324 (2007) 6. Huang, L., Lin, X.: Study on the effect of tilt azimuth of photovoltaic array on the performance of photovoltaic system in Nanning area. Equipment Manufacturing Technol. (04),115–117+131 (2014)

Metal-Air Battery System Design and Electrical Performance Analysis Lei Xu1 , Weigui Zhou1 , Xuhui Wang2 , Hui Sun1 , Yaohui Wang1(B) , Bingchu Wang1 , and Wanli Xu1 1 Institute of System Engineering, Academy of Military Science, Beijing 100300, China

[email protected] 2 Tianjin Branch of Construction and Administration Bureau of South-To-North Water

Diversion Middle Route Project, Tianjin 300380, China

Abstract. Due to the urgent needs of primary batteries with high mass and energy density the search for a resourceful and environmentally friendly green energy source is a pressing issue. In this paper, the electrical properties of metal-air battery were tested. The results show that in terms of output stability, the voltage fluctuation of metal-air battery which needs electrolyte is generally fierce. In terms of mass power density, 1 # and 2 # zinc-air batteries have obvious advantages over other metal-air batteries. Keywords: Primary battery · Metal-air battery · Electrical property

1 Introduction Due to the urgent needs of primary batteries with high mass and energy density the search for a resourceful and environmentally friendly green energy source is a pressing issue. Metal air cell is a power generation device that converts the chemical energy of metal materials directly into electrical energy, similar to fuel cells, also known as metal fuel cells, with high theoretical energy density and energy utilization, which is one of the ideal solutions for developing new high-performance green power sources [1, 2]. Therefore, metal air batteries used as a portable power supply, can significantly reduce the weight and volume of the battery, easy to carry a single soldier, in the field of portable electronic devices has a broad application prospects. The types of metal air battery include aluminum-air battery, zinc-air battery, magnesium-air battery, lithium-air battery, sodium-air battery and iron-air battery [3]. In 2017, Niu Fu assembled the zinc-air battery designed by himself and conducted constant current charging and discharging tests. The results showed that the horizontal zinc-air battery had a stable discharge voltage of about 1.1 V and the discharge capacity can be maintained at about 700 mAh/g at the current densities of 5 mA/cm2 , 10 mA/cm2 and 20 mA/cm2 [4]. In 2019, Yang Rui used the optimized air electrode formula and experimental conditions to assemble aluminum metal air battery products, and its discharge performance was tested. The results showed that the specific energy of small size aluminum double-sided electrode structure battery could reach 1187.80 Wh/kg; Large size © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 743–750, 2022. https://doi.org/10.1007/978-981-19-0390-8_92

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aluminum double-sided electrode battery has good stability and discharge energy up to 311.16 Wh [5]. In 2019, Gong Hao prepared two kinds of magnesium-air battery packs according to the size of the air electrode area. The results show that the rated voltage of the battery is related to the material of the battery itself and the number of series, and is not related to the size of the positive and negative electrode area of the battery. For the output power of each battery, it is basically the sum of the output power of the single battery [6]. At present, tests on metal-air batteries focus on the electrical properties of the materials, and there is not enough test data on the discharge performance of the batteries. Therefore, we need to compare and analyze the discharge performance of battery modules with different material systems. In this paper, the electrical properties of metal-air battery were tested at normal temperature conditions. Metal-air batteries include magnesium-air batteries, aluminumair batteries, and zinc-air batteries. According to the experimental data, the trend of voltage and current changes during battery discharge are analyzed and the mass energy density of the battery is compared. Preliminary data preparation and experimental studies were done for battery selection of power-using equipment.

2 Test Method 2.1 Test Equipment and Data Acquisition Platform 2.1.1 Test Equipment The balance used for the test was PL6001-S electronic balance manufactured by Mettler Toledo, Switzerland, as shown in Fig. 1, and the main parameters of the balance are shown in Table 1.

Fig. 1. PL6001-S Electronic Balance

Table 1. Basic parameters of PL6001-S electronic balance Projects

Technical parameters

Maximum range (g)

6100

Accuracy (g)

0.1

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2.1.2 Data Acquisition Platform The equipment used for the test is the energy recovery type battery module test system 17020 manufactured by Chroma Taiwan, as shown in Fig. 2, and the main parameters of the platform are shown in Table 2.

Fig. 2. Energy recovery type battery module test system 17020

Table 2. Basic technical parameters of energy recovery battery module test system 17020 Projects

Technical parameters

Number of test channels available

8

The maximum voltage that can be tested in a single channel (V)

20

Voltage test accuracy (V)

± 0.0001

Single channel can test the maximum current (A)

50

Current test accuracy (A)

± 0.0001

2.2 Test Objects The primary batteries tested include metal-air batteries and lithium primary batteries, the metal-air batteries tested include magnesium-air batteries, aluminum-air batteries, zinc-air batteries, and lithium primary batteries include lithium-fluorocarbon batteries, lithium-thiocarbonyl chloride batteries, and lithium-manganese dioxide batteries, and the basic parameters are shown in Table 3. 2.3 Test Conditions At normal temperature conditions, the battery output is connected to the battery test system, and then the electrical performance is tested according to the specified test conditions to obtain the voltage and current change curves during battery discharge, and the mass energy density is calculated based on the battery discharge energy and mass. The test conditions of metal-air battery are shown in Table 4.

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Serial number

Battery type

Battery number

Total mass of discharged part (g)

1

Magnesium-air batteries

1#

6071.2

2

Magnesium-air batteries

2#

2469.3

3

Aluminum-air batteries

1#

6883.2

4

Aluminum-air batteries

2#

13162.0

5

Zinc-air batteries

1#

3902.4

6

Zinc-air batteries

2#

1714.1

Battery photos

Table 4. Electrical performance test conditions for metal-air batteries Serial number

Battery type

Battery number

Liquid change conditions

Number of fluid changes

Cut-off conditions

1

Magnesium-air batteries

1#

After 12h discharge

1

Voltage drop to 8V

2

Magnesium-air batteries

2#

Continuous liquid refill

After 1 h discharge (continued)

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Table 4. (continued) Serial number

Battery type

Battery number

Liquid change conditions

Number of fluid changes

Cut-off conditions

3

Aluminum-air batteries

1#

Voltage drop to 12 V

2

Voltage drop to 10 V

4

Aluminum-air batteries

2#

Voltage drop to 12 V

1

Voltage drop to 10V

5

Zinc-air batteries 1#

None

Voltage drop to 10V

6

Zinc-air batteries 2#

None

Voltage drop to 8V

3 Analysis of the Electrical Performance of Metal-Air Batteries 3.1 Analysis of the Electrical Performance of Magnesium-Air Batteries The curves of voltage and current variation with discharge time for 2# magnesium-air battery are shown in Fig. 3(a), and the curves of voltage variation with discharge time for 1# magnesium-air battery are shown in Fig. 3(b).

(a) Magnesium-air cell 1#

(b) magnesium-air cell 2#

Fig. 3. Variation curves of voltage and current with discharge time

As can be seen from Fig. 3(a), 1# magnesium-air battery, in the process of 2A constant current discharge, there will be a peak voltage at the beginning of the discharge, after that the battery voltage decreases slowly; after 12h of discharge, adding new electrolyte, the voltage has a significant increase but can’t reach the voltage at the beginning, after that the voltage decreases slowly until the end of the discharge. As can be seen from Fig. 3(b), 2# magnesium-air battery, firstly, discharges with a step current of 2A until the current reaches 30A and starts to discharge at a fixed current, during the discharge process, the electrolyte needs to be replenished continuously, and the battery voltage

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decreases rapidly with the increase of current; when the current is constant, the voltage changes drastically with the replenishment and consumption of electrolyte. 3.2 Analysis of the Electrical Performance of Aluminum-Air Batteries The voltage variation curves of 2# and 1# aluminum-air cells with discharge time are shown in Fig. 4.

Fig. 4. Voltage variation curves of 1# and 2# aluminum-air cells with discharge time

As can be seen from Fig. 4, the voltage of 1# and 2# aluminum-air batteries has been fluctuating up and down during the 2A constant current discharge process, and the voltage will rise rapidly after adding electrolyte, among which, the voltage fluctuation of 1# aluminum-air battery is the most drastic. 3.3 Analysis of the Electrical Performance of Zinc-Air Batteries The curves of voltage and current variation with discharge time for 1# zinc-air battery are shown in Fig. 5(a), and the curves of voltage variation with discharge time for 2# zinc-air battery are shown in Fig. 5(b). As can be seen from Fig. 5(a), the voltage of 1# zinc-air battery decreases gently and the current rises gradually during the 25W constant power discharge process, and at the end of the discharge process, the voltage drop rate becomes larger and the corresponding current rise rate also becomes larger. From Fig. 5(b), it can be seen that the voltage of 2# zinc-air battery has been decreasing gently during 2A constant current discharge. 3.4 Comparative Analysis of Electrical Performance of Metal-Air Batteries The discharge energy and mass energy density obtained from the metal-air cell test are shown in Table 5. As can be seen from Table 5, among the metal-air batteries tested so far, the zinc-air battery of 1# has the highest mass-energy density; although the aluminum-air battery of 2# has the highest discharge energy, it requires too much cell and electrolyte mass to discharge this energy, resulting in no advantage in mass-energy density.

Metal-Air Battery System Design

(a) 1# zinc-air battery

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(b) 2# zinc-air battery

Fig. 5. Variation curves of voltage and current with discharge time

Table 5. Test mass energy density of metal-air batteries Serial number

Battery type

Battery number

Discharge energy (Wh)

Total mass of discharged part (g)

Mass energy density (Wh/kg)

1

Magnesium-air batteries

1#

871.1

6071.2

143.5

2

Magnesium-air batteries

2#

57.9

2469.3

23.4

3

Aluminum-air batteries

1#

466.1

6883.2

67.7

4

Aluminum-air batteries

2#

3071.7

13162.0

233.4

5

Zinc-air batteries 1#

1117.7

3902.4

286.4

6

Zinc-air batteries 2#

413.9

1714.1

241.5

4 Conclusion 1. Among the metal-air batteries tested, the discharge voltage of 1# and 2# zinc-air batteries is relatively stable and decreases slowly, while the discharge voltage of other batteries fluctuates more drastically; meanwhile, 1# zinc-air battery has the largest mass energy density and performs the best. 2. The discharge voltage fluctuations of metal-air batteries that need to be filled with electrolyte are generally more drastic. 3. In terms of mass power density, 1# and 2# zinc air batteries are obviously better than other metal air batteries.

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References 1. Zongqiang, M.: Fuel Cell. Chemical Industry Press, Beijing (2005) 2. Egan, D.R., Ponce de León, C., Wood, R.J.K., Jones, R.L., Stokes, K.R., Walsh, F.C.: Developments in electrode materials and electrolytes for aluminum–air batteries. J. Power Sources 236(25), 293–310 (2013) 3. Tengfei, S., et al.: Research on the application of metal-air batteries in dual-use field. Dual-use Technol. Products Military Civilian Applications 05, 64–67 (2018) 4. Niu, F.: Design and Performance Study of Zinc-Air Battery Modules for Electric Vehicles. Dalian University of Technology (2017) 5. Yang, R.: Preparation and Performance Study of Al Metal-Air Battery Cathode. China University of Petroleum, Beijing (2019) 6. Gong, H.: Development and Performance Testing of Metal-Air Batteries for Electric Vehicles. Xi’an University of Petroleum (2019)

Review of Bias Point Stabilization Methods for MZ Modulator Mingzhu Zhang1,2 , Yupeng Li1,2(B) , Shuangxi Sun1,2 , Liang Han1,2 , Xiaoming Ding1,2 , and Xiaocheng Wang1,2 1 Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, Tianjin

Normal University, Tianjin 300387, China [email protected] 2 College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin 300387, China

Abstract. Communication is a high-speed communication method that uses optical fiber as the transmission medium and optical wave as the carrier wave. Compared with other communication technologies, communication has many outstanding advantages: large transmission capacity, long transmission distance, high security, strong anti-interference ability, etc.. However, due to factors such as material and temperature, it can lead to the drift of the bias point of its transmitter modulator, thus causing the instability of communication. Therefore, by compiling and synthesizing the bias point stability control methods of MZ modulator, this paper hopes to play a positive role in further research on the bias point stability control in the future. Keywords: Bias point stabilization · MZ (Mach-Zehnder) modulator · Dither signal · Power

1 Introduction Fiber optic communication is a high-speed communication method with optical fiber as the transmission medium and optical wave as the carrier wave. Fiber optic communication system consists of information source, optical transmitter, fiber optic cable and optical receiver [1, 2]. The information source is generally an electrical signal containing specific information, which is the signal to be transmitted. The optical transmitter modulates the information from the electrical signal to the optical signal, and the modulated signal is transmitted to the receiving end through the fiber optic line [3]. The receiver side performs steps such as amplification and demodulation to restore the original electrical signal [4, 5]. The signal needs to be modulated before it is transmitted. However, due to the material of the modulator and the temperature, the bias point will be shifted when the modulator is operating, which will lead to problems such as distortion of the modulated signal [6, 7]. Once the signal is distorted, the signal we get at the receiver side loses its meaning. Therefore, it is important to control the stability of the modulator bias point in fiber optic communication. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 751–757, 2022. https://doi.org/10.1007/978-981-19-0390-8_93

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There are many types of modulators available for modulation, this paper will focus on the bias point stabilization method for the MZ modulator (Mach-Zehnder Modulator). In the MZ modulator, after the optical signal enters the input, it is divided equally into two signals, which are phase modulated and finally converted into intensity modulation to interfere at the output coupler at the output. In order to prevent the problem of output signal distortion due to bias point offset, the bias point is generally adjusted accordingly by certain characteristics of the output signal or input signal, thus ensuring that the bias point is always in the desired state.

2 Common Bias Point Stabilization Methods for MZ Modulators MZ modulator is a common optical modulator, which has many advantages over other modulators: high operating rate, low optical loss and small chirp. The methods of bias point stabilization for this modulator are relatively mature. It can be roughly summarized into the following two categories: the method based on the applied dither signal and the power-based method. The bias point stabilization method based on the applied low-frequency dither signal is a harmonic analysis of the modulated dither signal, which is commonly used to stabilize four common bias points, namely ± Quad point, Peak point, and Null point. The method can quickly determine the offset size of the bias voltage and is mainly used in engineering applications. The power-based method can be subdivided into the average optical power slope value, the input-to-output optical power ratio-based stabilization method, and the output optical power-based stabilization method. In general, the input-to-output optical power ratiobased method is based on the ratio of the optical power at the output of the modulator to that at the input to determine the bias point offset. The output optical power-based method is relatively simple and has a single output optical power as its feedback variable. It determines the bias point offset by calculating the first-order bias derivative of the output optical power with respect to the bias voltage. The study of modulator bias point offset is important to ensure the stability of fiber optic communication. There are more methods of bias point stabilization with different advantages, which are applicable to different scenarios. It is believed that the control of bias point offset will develop more efficient methods based on the existing ones in the future, which will bring great development for fiber optic communication.

3 Bias Point Stabilization Method Based on Applied Dither Signal Many researchers have done research on the bias point stabilization method of MZ modulator based on the applied dither signal. There is a correspondence between the harmonic components of the modulated dither signal and the bias point, and the bias point shift can be derived from this correspondence. It is generally required that the amplitude and frequency of the applied dither signal should not affect the original modulated signal. In 2008, Zou Hui et al. proposed that a dither signal with a much smaller amplitude than the amplitude of the modulating signal can be added to the modulator bias voltage input port, and the bias operating point has a corresponding relationship with the harmonic component of the dither signal in the MZM output optical signal, and the error

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signal is detected using a photodetector, and then the amplitude of the control signal is adjusted according to the error signal amplitude, which makes the bias operating point change and gradually The position of the bias working point converges to the preset target position. At the same time, the total system error signal amplitude also gradually decreases to a position close to zero, at which time the MZM bias operating point is considered to have reached the preset position [8]. Figure 1 illustrates the structure of the Mach-Zehnder modulator bias control scheme using synchronous detector described in the paper. It can be seen that there are two signals at the input of the MZ modulator, one from the dither signal and RF signal after the driver, and the other from the dither signal after the frequency multiplier. According to the diagram, it can be seen that the dither signal after the frequency multiplier will enter the photodetector, then pass through the low-pass filter to get the second harmonic, and then pass through the synchronous detector to get a near DC error signal back to the MZ modulator’s input, thus controlling the stability of the bias point.

Fig. 1. The structure of the Mach-Zehnder modulator bias control scheme using synchronous detector described

In 2011, Yini Guo et al. from Tianjin University proposed a bias point control scheme based on the error value of the pilot signal, in which the direction and distance of the bias point drift is judged by detecting the magnitude and positive or negative of the output error signal of the electro-optical modulator. By adding a certain amplitude and frequency of perturbation signal to the input voltage, according to the characteristics of the MZ electro-optical modulator, if the bias voltage output to the electro-optical modulator makes it work just at the PEAK or NULL point, then this perturbation signal will not generate an error signal after being controlled by PID (especially the integral action). Conversely, if the control voltage deviates from these two particular operating points, then the perturbation signal will cause an error signal to be generated. At the PEAK point, the light intensity is already the strongest position that the laser can reach, so the light intensity will be weakened no matter the bias voltage increases or decreases, so the error signal generated by the perturbation signal should be negative in theory, and the magnitude of the error signal is proportional to the deviation from the PEAK

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point. The situation at the NULL point is just the opposite, so the error signal is positive. For the ± QUAD point, since the trend of light intensity with voltage is almost linear, it is necessary to extract the second harmonic of the disturbance signal, and the error signal caused by the second harmonic plays the same role of compensation as that of the fundamental wave at the PEAK and NULL points. Figure 2 illustrates the structure of the MZM zero bias point drift control scheme using the coherent self-mixing method described in the paper(Four common bias points).

Fig. 2. Structure of the MZM zero bias point drift control scheme using the coherent self-mixing method

In 2012, Zhenhua Feng et al. from Huazhong University of Science and Technology proposed a harmonic response feedback control method based on the fast Fourier transform algorithm and low-frequency small signal perturbation, based on the traditional LiNbO3 Mach-Zehnder modulator bias control theory, to realize a harmonic response feedback control method based on the fast Fourier transform (FFT) algorithm and lowfrequency small signal perturbation with A precision optoelectronic control system with modulator input optical power and insertion loss independent, biasable and lockable at any operating point is realized. By using the proportional-integral-derivative (PID) control method, a control accuracy of less than 0.5° offset at any operating point is achieved [9]. In 2013, Caixia Zhang et al. proposed to add a small-amplitude sinusoidal signal to the bias voltage side of the Mach-Zehnder modulator as a microturbulent signal, which will then participate in the modulation of the modulator, and then the output of the modulator contains the relevant components of the perturbed signal, and through a series of processing such as phase multiplication and integration of the output signal, an error signal is obtained that is closely related to the slope of the bias operating point on the transmission curve By multiplying and integrating the output signal, an error signal is obtained that is closely related to the slope of the position of the bias operating point on the transmission curve. By collecting, calculating and analyzing the change of this signal, the direction and the amount of change of the MZM drift can be judged, and then the modulator bias voltage is changed to make the corresponding correction, and finally the modulator operating point is stabilized at the preset bias position [10]. Figure 3 shows the structure of the Mach-Zehnder modulator bias control scheme using the integration of the micro-turbulent signal described in the paper.

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Fig. 3. The structure of the Mach-Zehnder modulator bias control scheme using the integration of the micro-turbulent signal

The bias point stabilization method based on the applied dither signal can accurately determine the offset of the bias point and perform bias point adjustment in the form of a feedback loop. However, its implementation is more complicated, and there are limitations in designing more circuit structures.

4 Power-Based Bias Point Stabilization Method There are many classifications of power-based bias point stabilization methods for MZ modulators, among which the stabilization method based on the average optical power slope value has become increasingly popular. However, it is not precise enough to control the bias point because it is easily influenced by the input optical power and so on. Gradually, a composite algorithm combining the average optical power slope value and other factors has been used to control the stabilization of the offset point, improving the shortcomings of the original single variable. In 2017, Chongzheng Hao et al. proposed a composite control algorithm based on the average optical power slope value and the residual cut value to implement an operating point stabilization control technique using FPGA technology [11]. Figure 4 illustrates the structure of the Mach-Zehnder modulator bias control scheme using the composite control algorithm of the average optical power slope value and the cotangent value described in that paper. The RF signal is modulated by the modulator and enters the 9:1 coupler. when the photodetector detects the light signal, after a series of amplification, AD and DA conversion, the corresponding voltage value V1 is read. then the value of V size is added to the bias voltage. The average optical power slope value d11 is calculated based on the two voltage values. d11 and d13 are calculated from d11 and d13, and the initial cotangent value R1 is calculated from d11 and d2; R2 is obtained by repeating the above steps, and when R2 > R1, the operating point is shifted to the left. The FPGA outputs the compensation voltage according to the interval range and inputs it to the MZM bias voltage input through DA conversion and amplification until R1 = R2 (R2 < R1 is the opposite of the compensation method). In 2019, Ding and Liang et al. proposed a bias point stabilization control scheme based on the detection of the average optical power slope value, which elaborates that the

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Fig. 4. The structure of the Mach-Zehnder modulator bias control scheme using the composite control algorithm of the average optical power slope value and the cotangent value

average optical power slope value obtains the minimum value at the negative orthogonal bias point Quad− and the maximum value at the positive orthogonal bias point Quad+, and the slope values at both Peak and Null points are zero, and the discrimination between Peak and Null points can be made by the point The distinction between Peak and Null points can be made by the voltage values around the point [12]. Figure 5 illustrates the structure of the Mach-Zehnder modulator bias control scheme using the average optical power slope value described in the paper.

Fig. 5. The structure of the Mach-Zehnder modulator bias control scheme using the average optical power slope value

The composite bias point stabilization method based on the average optical power slope value implements a simpler structure and has a higher accuracy for bias point control, which can be achieved for stabilization of arbitrary bias points. Both phase modulation and intensity modulation are available for relevant applications [13].

5 Conclusion MZ modulator is a widely used modulator, and the stability of modulator bias point is the key to ensuring the communication quality, so it is important to study the stability control of MZ modulator bias point. It is believed that there will be more and more advanced methods to ensure the stability of the bias point in the future, and the fiber optic communication will be developed significantly.

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Acknowledgment. This work was supported in part by the Natural Science Foundation of China under Grant 61901301, 62001328, 62001327 and in part by the Natural Science Foundation of Tianjin under Grant 18JCQNJC70900, 18JCZDJC31900 and in part by Doctor Fund of Tianjin Normal University under Grant 52XB2005.

References 1. Yu, J., Ze, D., Chien, H.-C., Shao, Y., Chi, N.: 7-Tb/s (7×1.284 Tbit/s/ch) signal transmission over 320 km using PDM-64QAM modulation. IEEE Photon. Technol. Lett. 24(4), 264–266 (2012). https://doi.org/10.1109/LPT.2011.2177454 2. Salvestrini, J.P., et al.: Analysis and control of the DC drift in LiNbO3 -based Mach-Zehnder modulators. J. Lightwave Technol. 29(10), 1522–1534 (2011) 3. Wang, L.L., Kowalcyzk, T.: A versatile bias control technique for any-point locking in lithium niobate Mach-Zehnder modulators. J. Lightwave Technol. 28(11), 1703–1705 (2010) 4. Sotoodeh, M., Beaulieu, Y., Harley, J., McGhan, D.L.: Modulator bias and optical power control of optical complex E-field modulators. J. Lightwave Technol. 29(15), 2235–2248 (2011). https://doi.org/10.1109/JLT.2011.2158291 5. Bhandare, S.P., et al.: Automatic bias stabilization of dual-polarization in-phase and quadrature optical modulator (2016) 6. Hao, Y., et al.: High-performance optical modulators bias control without dither. Optics 125, 1987–1989 (2014) 7. Kataoka, T., Hagimoto, K.: Novel automatic bias voltage control for travelling-wave electrode optical modulators. Electron. Lett. 27(11), 943–945 (1991) 8. Zou, H., Hu, Y., Tian, J., Xu, Y.: Stabilization control of the optimal bias point of M-Z external modulator. Opt. Commun. Res. 4(3), 30–32 (2008) 9. Feng, Z., Fu, S., Tang, M., Shen, P., Liu, D.: Investigation on agile bias control technique for arbitrary-point locking in lithium niobate Mach-Zehnder modulators. Opt. J. 32(12), 73–78 (2012) 10. Zhang, C., Zhang, Z., Xu, B., Li, Y.: A novel circuit design for bias controlling of MachZehnder electro-optic modulator. Optoelectron. Laser 24(8), 1461–1466 (2013) 11. Hao, C., Li, H., Sun, Q., Yang, Y., Wang, D.: Stable bias control technique for any-point locking in Mach-Zehnder modulator. Opt. J. 46(10), 71–79 (2017) 12. Ding, L., Wu, Z., Li, X., Gu, Y., Hu, J., Yin, J.: Ditherless bias control technique for MachZehnder modulator in laser communication. Infrared Laser Eng. 48(12), 175–181 (2019) 13. Bilal, S.M., Bosco, G.: Automatic bias control of Mach-Zehnder modulators for QPSK and QAM systems. J. Opt. Technol. 81(7), 403–407 (2014)

Research on Wireless Charging Queues Optimization Method of UAV Swarm Changfu Wang1 , Yaohui Wang1 , Bin Zhang2 , Mengyi Wang1 , and Xudong Wang1(B) 1 Institute of System Engineering, Academy of Military Science, Beijing 100300, China

[email protected] 2 94559 Troops of the People’s Liberation Army of China, Beijing, China

Abstract. UAV swarm inevitably have charging queues problems, especially when the number of UAVs is more than that of charging platforms. The charging queues make the system inefficient. In this paper, we study the charging queues problem of UAV swarm, analyze the queuing model, evaluation indexes and optimization algorithms from the basic principles of the queuing system, and focus on the principles of genetic algorithm, particle swarm algorithm and firefly algorithm with autonomous optimization function algorithms and their key applications in the queuing problem, in order to seek better solutions and improve the charging efficiency of UAV swarm. Keywords: UAV · Charging queues · Queuing theory · Optimization algorithm

1 Introduction UAV swarm refers to the formation of UAVs composed of a large number of UAVs, performing the same mission and having high autonomous cooperation capability [1]. Compared with a single UAV, the formation can make up the short board with a single performance by using quantity or combination, cooperate and adapt to the mission requirements and changing environment, and have the overall combat capability, which becomes the development direction of future war [2, 3]. The short endurance is a common problem of current UAVs, the reason for this is the small capacity of UAV’s battery, making it necessary to change or charge it shortly after takeoff. The battery exchange not only has high manual participation but also frequent plugging will cause irreversible mechanical damage and shorten the battery life. Therefore, it is generally agreed that charging is the best solution to the problem of UAV endurance. At the same time, with the development of Wireless Power Transmission (WPT) technology, wireless charging platforms are established on the ground, in this way, the UAVs that need to be charged can find and land to the platform for charging [4]. Generally, each charging platform has multiple parking space, which can supply power to multiple UAVs at the same time. However, the number of parking space is limited after all. When the parking space of a charging platform is occupied, if there is a UAV to be charged at this time, it will not be able to provide charging service for that UAV, and the UAV can only wait or leave, which is the charging queues problem of the UAV swarm. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 758–765, 2022. https://doi.org/10.1007/978-981-19-0390-8_94

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There are many disadvantages in the charging queues of UAV swarm, such as the decrease of effective working time of UAVs and the decrease of the number of UAVs in the same working state at the same time. This not only leads to the overall efficiency of the system becoming lower but also more serious, the lack of UAVs manning a certain airspace in military operations may lead to the loss of air control and huge losses. Therefore, it is urgent to solve the charging queues problem of UAV swarm. The charging queues of UAV swarm belongs to a multi-objective optimization problem under multi constraints, and one of its effective solutions is to establish queuing model based on queuing theory and obtain the global optimal solution by using an independent iterative optimization algorithm.

2 Queuing Theory Queuing theory, also known as random service system theory, originated from the telephone call problem in the early 20th century. Queuing theory is a statistical study of the input pattern of customers and the probability distribution of service time of service agencies to derive the operating indicators of the queuing system, and the queuing system is analyzed according to the operating indicators. The queuing theory refers to the object that makes the demand as the customer, the tool that realizes the service as the service organization, and the customer and the service organization form the queuing system.

Total customers

Customer arrive Input

Queueing mechanism

Service rules

Service organization

Output

Queueing design

Fig. 1. Structural model of queuing system

As shown in Fig. 1, customers arrive at the service organization from the total customers, wait for the service according to certain queuing rules until they leave the queuing system after receiving service. Customers can be divided into input process, queuing process and service process from departure to departure, and the characteristics of each part are as follows. 2.1 Input Process • • • • •

The composition of the total number of customers may be finite or infinite. Customers may arrive one by one or in batches. The interval of customers’ successive arrival can be fixed or random. Customer arrivals are independent of each other. The input process is stable, that is, the distribution of successive arrival intervals is independent of time.

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2.2 Queuing Rules • There are immediate and waiting queuing methods. The difference is that when the customer arrives and cannot get the service immediately, the customer of immediate system chooses to leave, while the customer of waiting system chooses to wait. The order of services in the waiting system are: first-come-first-served, last-come-firstserved, random service, service with priority, etc. • The queuing capacity can be finite or infinite. • The number of queues can be single or multiple. According to the different ways of queuing, queuing models can be divided into waiting-based queuing models and loss-based queuing models. In the charging queues problem of UAV swarm, since the State of Charge (SoC) is often used as the basis to judge whether it needs to be charged, the power of the UAV that needs to be charged is generally low and insufficient to support its flight to the next charging platform, so the UAV swarm charging queuing model generally belongs to the wait-based queuing model. 2.3 Service Organization • The service organization may have one or more service desks, and the multi-service desk queuing system is connected in series, parallel and mixed connections. • The service mode can be carried out one by one or in batches. • The service time can be determined or random, and if random, its probability distribution needs to be determined. • The distribution of service time is stable, that is, the distribution mean, variance, etc. are independent of time. According to the quantity characteristics of the service desk, it can be divided into two charging models: distributed charging model and centralized charging model. Wang Yunzhe et al. [4] used M/M/1/M and M/M/n/M models to describe centralized charging and distributed charging respectively (M means the arrival time interval and service time between customers follow an exponential distribution, n represents the number of servers in the queuing system, M represents the capacity of the queuing system). Through comparative analysis of the two charging models, it is concluded that centralized charging is better when the service intensity is weak, On the contrary, the conclusion that distributed charging is better provides a basis for the selection of UAV charging mode.

3 Optimization Algorithm There are little researches on the optimization of UAV charging queues, but the problem is essentially a multi-objective optimization problem under multi constraint conditions, which is similar to the problems of vehicle routing problem (VRP), electric vehicle charging facilities optimization, taxi charging station queuing system, and the location and capacity of energy storage power station, so we can learn from the above solutions.

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The usual solution steps are: establish a queuing model based on queuing theory, list the equilibrium equation, get the evaluation indexes such as customer waiting time and queue length according to the equilibrium equation, and finally use the optimization algorithm to solve the optimal solution in the feasible region [5]. The research on building models and evaluating metrics has been relatively mature, and optimization algorithms are the key to solving the problem. Optimization algorithms can be divided into two categories, gradient-based local search algorithm and probabilitybased global exploration algorithms [7]. Among them, gradient-based local search algorithms include the augmented Lagrange multiplier method, sequential quadratic programming method and feasible direction method; probability-based global exploration algorithms, also known as swarm intelligence algorithm, such as genetic algorithm (GA), particle swarm optimization (PSO), algorithm bee colony (ABC), etc. this kind of algorithm uses selection, crossover, mutation and other operations to simulate the natural evolution process, and guide the population gradually to the optimal solution. In solving complex combinatorial optimization problems, swarm intelligence algorithms usually can get the optimization effect quickly, so it is the best choice to study optimization problems [6]. 3.1 Genetic Algorithm Genetic algorithm is an optimization method that was proposed by Professor J. Holland of the University of Michigan in the 1990s to simulate the biological evolution law. It has been widely used in the fields of job shop scheduling, image processing, transportation, signal processing, finance, and artificial intelligence [8].

Fig. 2. Genetic algorithm framework diagram

The algorithm has five core elements, which are coding and decoding, population initialization, fitness function, genetic operator, and genetic parameter setting. Its basic framework is shown in Fig. 2. Li Jin et al. used this algorithm to deal with the problem of electric vehicle freight routing optimization, established the model to minimize the power consumption and

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travel time cost, and proposed an adaptive genetic algorithm based on hill-climbing optimization and neighborhood search, which can adaptively adjust the crossover and mutation probability as the change of population fitness, not only has faster calculation speed but also has higher efficiency, Moreover, it can effectively prevent the local optimization problem and improve the global search ability [9]. Zhu Zeguo et al. used the algorithm to deal with the multi-vehicle logistics distribution path optimization problem. Taking the minimum total cost and distribution time as the multi-objective model, a multi-objective evolutionary genetic algorithm was proposed. It was verified that the multi-vehicle combination optimization distribution has better economic benefits than single-vehicle [10]. This idea can be used for reference in the charging queues problem of UAV swarm by considering UAVs as individuals in a population and evaluating individual in the population to produce the optimal population. 3.2 Particle Swarm Optimization Particle swarm optimization was first proposed by Kennedy and Eberhart in 1995 [11]. Originating from the study of bird feeding behavior, the simplest and most effective strategy for birds to find food while feeding is to search the area around the bird that is currently closest to the food. Particle swarm optimization is a random search algorithm, which is often used to solve optimization problems. The algorithm is characterized by: 1. Extensiveness and expandability. The particles cooperate with each other by sharing information indirectly; 2. It is self-organizing and self-adaptive. Particles improve their adaptability through selflearning in the uncertain complex changing environment; 3. Robustness. The algorithm does not depend on the continuity, differentiability, and other mathematical properties of the optimization problem itself, as well as the structural characteristics of the objective function and constraints, and only the fitness function of the response needs to be modified when using the particle swarm algorithm. Each particle in the algorithm represents a potential solution to the problem, and each particle corresponds to a fitness value determined by the fitness function, which is used to evaluate the superiority of the particle position. The velocity of a particle determines the direction and distance of the particle moves, and the velocity is dynamically adjusted with the movement experience of itself and other particles. The implementation process of the particle swarm optimization algorithm is shown in Fig. 3. First initialize the population, then calculate the fitness value of each particle in the population according to the particle’s position, update the individual historical optimal and group historical optimal position of the particle according to the fitness value, update the velocity and displacement of the particle, judge whether the termination condition is satisfied, if it is satisfied, stop, if not, continue the loop iteration until the termination condition is satisfied. Shen Liangsheng et al. [12] established a multi-objective optimization model with the total length of stay and waiting time of patients as the objective function, solved the problem by particle swarm optimization, and obtained the non-inferior set of the problem. When he Jianxin and others studied the trajectory optimization problem of the industrial robot [13], taking the robot running time, energy consumption and pulsating impact as the objective function, the multi-objective particle swarm optimization algorithm

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Initial population Employed bees update food-source position

Initial population

Employed bees share food-source position with scout bees, then scout bees calculate food-source profitability

Initialization iterations Termination condition?

Yes

No Calculate the fitness function Update individual and group history optimal value

Scout bees update food-source position No

Not updated for a long time?

Yes Employed bees become scout bees to find new food-source

Update speed Iterations + 1 Output optimal solution

Reach termination conditions?

No

End

Yes Output optimal solution

Fig. 3. Flow chart of particle swarm algorithm

Fig. 4. Flow chart of swarm algorithm

was used to optimize the robot trajectory. If this algorithm is applied to deal with the charging queues problem of the UAV swarm, the UAVs can be regarded as particles, and the particles can independently find the optimal charging platform of the individual, and then realize the population-optimal charging strategy. 3.3 Artificial Bee Colony Algorithm Artificial bee colony algorithm was proposed by Karaboga [14]. Its inspiration comes from the foraging behavior of bees in nature, and the algorithm is used to simulate the honey collecting behavior of bees. The bees perform the task of nectar collection by dividing the work according to the class to which they belong, and they perform their tasks independently while exchanging signals with each other in order to share the nectar information and obtain the optimal solution of the problem to be solved through mutual collaboration. Artificial bee colony algorithm mainly includes employed bees, onlooker bees and scout bees. Employed bees are mainly responsible for algorithm exploration (searching for food source), onlooker bees are mainly responsible for algorithm development ability (selecting food source). Scout bees are converted from employed bees or scout bees and are mainly responsible for jumping out of local optimization, to achieve a balance between algorithm exploration and development [15]. The main flow of the algorithm is shown in Fig. 4. Firstly, the population is initialized, and the employed bees share the food source position with the onlooker bees, the onlooker bees calculate the fitness value of the food source and keep the food source with the higher fitness value, if the food source has not been updated for a long time, the algorithm is determined whether the termination condition is reached, if the termination condition is reached, the optimal solution is output, if the termination condition is not reached, the employed bees are

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transformed into scout bees to find a new. If the termination condition is not reached, the employed bees are transformed into scout bees to find a new food source and start a new round of iterative updates. With the advantages of fast convergence and wide adaptation range, the ABC algorithm is widely used in combinatorial optimization problems. For example, in the UAV’s trajectory planning problem, swarm information interaction and superiority evolution of the population are used to improve the efficiency and stability of trajectory planning [16, 17]. The UAV swarm charging queues problem can be based on this idea, where the ground charging platform is regarded as a food source and the UAV as a bee, then the process of searching for a charging platform by the UAV can be equated to the process of searching for a food source by a bee. In addition to the above algorithms, the swarm intelligence optimization algorithm also includes fish swarm algorithm, gray wolf algorithm, firefly algorithm, etc. each algorithm has its advantages and disadvantages. When it is applied, it needs to make choices according to the actual tasks. This paper will not repeat the reasons for the length. Moreover, in addition to a single algorithm, the mixed-use of different algorithms is also one of the development trends, hybrid algorithms can learn from each other, play a more excellent effect.

4 Conclusion and Prospect This paper conducts a review study on the optimization of the UAV swarm charging queues problem, focusing on queuing theory as well as swarm intelligence algorithms, leading to the following conclusions. The charging queues problem of UAV swarm belongs to the multi-objective optimization problem under multi constraint conditions, which can be modeled by using the principle of queueing theory and then solved by using optimization algorithms to find the optimal solution in the feasible solution space, and a large amount of literature verifies the importance and usefulness of the optimization work. The solution to optimization problem is very complex, and the practical application can be solved by swarm intelligent optimization algorithms with autonomous iterative optimization seeking function, such as genetic algorithm, particle swarm algorithm and bee swarm algorithm, etc. With the application and development of UAV swarm, the problem of charging queues will become more and more prominent, and there is less research on the problem and not much experience to draw on, so we hope to draw attention to it as early as possible.

References 1. Yang, L., Cao, Z., Li, Y.: Research on combat composition and combat concept of UAV bee colony. Mod. Defense Technol. 48(4), 44–51 (2020) 2. Zhang, S., et al.: Analysis of drone swarm operations. Mod. Inf. Technol. 4(12), 22–24 (2020) 3. Dong, Y., et al.: Research progress and development trend of American bee colony UAV. Cruise Missile 9, 37–42 (2020)

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4. Luo, X.: Research on radio energy transmission of UAVs. Mod. Commerce Trade Ind. 37(34), 472–473 (2016) 5. Wang, Y., et al.: Optimization method for charging queue of bee colony UAV. Acta Aeronaut. Sin. 41(10), 292–302 (2020) 6. Chen, L.: Research on layout planning of electric taxi charging station. Beijing Jiaotong University (2015) 7. Li, G., et al.: Review of constrained evolutionary algorithm and its application. Comput. Sci. (2021) 8. Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975) 9. Li, J., Wang, F., Yang, S.: Optimization model and algorithm of electric vehicle freight routing under power exchange mode. Comput. Appl. (2021) 10. Zhu, Z., Guang, X., Guo, M.: optimization of multi vehicle logistics distribution path under uncertain environment. Transport. Technol. Economy 23(2), 6–12 (2021) 11. Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Network. IEEE Service Center, Piscataway, NJ, pp. 1942–1948 (1995) 12. Shen, L., Deng, Z.: Application of multi objective particle swarm optimization in hospital bed arrangement. J. Jiamusi Univ. Nat. Sci. Ed. 37(1), 135–138 (2019) 13. He, J., Li, L., Lin, G.: Optimal trajectory planning of industrial robot based on multi-objective particle swarm optimization. Manuf. Autom. 43(2), 57–62 (2021) 14. Karaboga, D.: An Idea Based on Honey Bee Swarm for Numerical Optimization. Technical Report-tr06, Erciyes University, Engineering Faculty, Computer Engineering Department (2005) 15. Li, H.: Research on Improved Artificial Bee Colony Algorithm and Related Applications. Nanjing University of Posts and Telecommunications (2020) 16. Yu, S., Li, D., Wu, H.: Path planning of UAV based on improved artificial bee colony algorithm. Electro Opt. Control 24(1), 19–23 (2017) 17. Zhang, L., Xu, L., Wu, M.: Real time path planning of UAV Based on improved artificial bee colony algorithm. Flight Mech. 33(1), 38–42 (2015)

Throughput Analysis of Slotted Aloha with Retransmission Limit in Fading Channels Peng Li1 , Yitong Li1(B) , Wen Zhan2 , Pei Liu3,4,5 , Yue Zhang6 , Dejin Kong7 , and Kehao Wang3,5 1

School of Information Engineering, Zhengzhou University, Zhengzhou 450001, China [email protected] 2 School of Electronics and Communication Engineering, Sun Yat-sen University, Shenzhen 510006, China [email protected] 3 School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China [email protected], [email protected] 4 Key Lab of Broadband Wireless Communication and Sensor Network Technology (Nanjing University of Posts and Telecommunications), Ministry of Education, Nanjing 210003, China 5 Hubei Key Laboratory of Broadband Wireless Communication and Sensor Networks, Wuhan University of Technology, Wuhan 430070, China 6 College of Engineering, Shantou University, Shantou 515063, China [email protected] 7 School of Electronic and Electrical Engineering, Wuhan Textile University, Wuhan 430200, China [email protected]

Abstract. In practical random access networks, a packet discard scheme is usually applied to alleviate the network congestion, where a packet will be discarded if its retransmission times reaches the limit K. In this paper, the effect of retransmission limit K on the throughput performance of slotted Aloha over Rayleigh fading channels is studied. Explicit expression of the maximum throughput is obtained as a function of the SINR threshold μ and mean received SNR ρ. The analysis shows that though the maximum throughput is independent of the retransmission limit K, the corresponding optimal initial transmission probability of nodes has to cautiously selected in accordance with the retransmission limit K and the backoff function {ωi }i=0,...,K−1 .

1

Introduction

In recent years, the rising of Internet of Things (IoT) paradigm facilitates the Machine-Type Communications (MTC), which enables automated information c The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022  Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 766–774, 2022. https://doi.org/10.1007/978-981-19-0390-8_95

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exchange among massive number of machine-type devices. Due to the short and bursty traffic nature of MTC, random access protocols, such as slotted Aloha and CSMA, have been regarded as the most competitive solution in Medium Access Control (MAC) layer. Specially, slotted Aloha provide a simple way for multiple nodes to share the channel, in which each node can transmit at the beginning of the time slot when it has packet to send, and retransmitted according to the adopted backoff policy if it involves in a collision. Due to its simplicity, Aloha-type random access methods have been widely applied in practical machine-machine (M2M) networks, such as the Low power Wide Area Networks (LPWAN) including NB-IoT, Weightless and LoRaWAN [1–3]. The early studies on slotted Aloha focused on the collision model [4], in which a packet can be correctly received only when there is no concurrent transmissions and the channel condition was assumed to be perfect. While with channel fading effect is taken into consideration, the capture model is more appropriate. Under the capture model, each node’s packet can be correctly recovered if its received SINR is above a certain threshold [5,6]. Therefore, capture model has the capability to correctly receive multiple packets if the SINR threshold is sufficiently low [5–7]. It has been long observed that random access may result in severe congestion when the network traffic load is heavy. With large offered load, huge amount of nodes will contend for the shared channel, which results in frequent collisions. The retransmission of the collided packets will further aggravate the channel contention level. Therefore, a packet discard mechanism is usually applied to alleviate the congestion, where a packet is discarded if its retransmission times reaches the retransmission limit K. With the collision model, the stability issue of slotted Aloha with retransmission limit was studied in [8,10]. [8] explored the effect of retransmission limit on the stability region for two asymmetric nodes. The effect of retransmission limit on the CSMA with collision model was studied in [10], in which the explicit expressions of both throughput and mean access delay are obtained as the functions of the retransmission limit. In this paper, the throughput performance of a buffered slotted Aloha with retransmission limit K in Rayleigh fading channels is studied under the capture model. First of all, both the steady-state point and throughput in unsaturated and saturated conditions are derived by analyzing the head-of-line (HOL) packets’ aggregate activities. Explicit expression of the maximum throughput is also derived as a function of μ and ρ, which is independent on the retransmission limit K. While the optimal initial transmission probability to achieve the maximum throughput is closely related to the retransmission limit K and the backoff function {ωi }i=0,...,K−1 . The paper is organized as follows. In Sect. 2, the system model is described. Section 3 demonstrates how to obtain the steady-state points in both unsaturated and saturated conditions. The throughput analysis is provided in Sect. 4. Conclusion and future work are presented in Sect. 5.

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System Model

In this paper, a slotted Aloha network with packet retransmission limit over Rayleigh fading channels is considered. Specifically, we assume the multiple access scenario where n nodes communicate with a single access point. Each node can only start the transmission attempt at beginning of a time slot and the transmission time of each packet is assumed to be one time slot. Each node has the same packet input rate of λ ∈ (0, 1] and an infinite buffer size. For the headof-line (HOL) packet of each node’s queue, it will be discarded if its transmission fails for K ∈ [1, ∞) consecutive times, where K denotes the retransmission limit. Moreover, we assume that the uplink power control is performed to mitigate the near-far effect. As a result, each node has the same mean received SNR ρ. With the capture model, a packet can be successfully received if its received SINR is no smaller than the threshold μ.

Fig. 1. Diagram illustration of the HOL-packet’s state transition process.

2.1

HOL-packet Model

The performance of Aloha networks is closely related to the aggregate activities of HOL packets. As illustrated in Fig. 1, the HOL packet’s behavior can be modeled as a discrete-time Markov process. Specifically, a newly generated HOL packet is in State T with probability q0 to transmit. If it is not transmitted, it will transfer to State 0 with probability 1 − q0 . A phase-i HOL packet has the transmission probability with qi for i = 1, 2, ..., K −1, where the phase defines the number of unsuccessful transmission of the HOL packet. Therefore, for a phase-i HOL packet, i = 1, 2, ..., K−2, it would stay in State i if it is not transmitted with probability 1−qi . Otherwise, it moves to State min(K −1, i+1) if its transmission attempt is unsuccessful, or State T if the transmission is successful. Specially, a HOL packet with phase K − 1 will transfer to State T if its retransmission, i.e., the (K−1)−th retransmission, is successful. If its transmission fails, it will be discarded and moves to State 0, indicating a new packet becomes the HOL packet and is ready for transmission. Note that in random access networks, when the nodes involve in frequent collisions, their transmission probabilities should

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be reduced to alleviate the channel congestion. For demonstration, assume that qi = q0 · ωi where q0 ≤ 1 is referred as the initial transmission probability and ωi is referred as the backoff function with ω0 = 1 and ωi ≥ ωi+1 , i = 0, 1, . . . , K − 1. Therefore, {qi }i=0,1,...,K−1 are monotonic non-increasing with i. The steady-state distribution {πi } of the Markov chain is given by πT = and

 π0 = πi =

1−(1−p)K (1−p)i qi i=0

K−1 

1−q0 (1−(1−p)K ) πT , q0 (1−(1−p)K ) (1−p)i π , for qi (1−(1−p)K ) T

,

i = 1, 2, ...K − 1,

(1)

(2)

where lim pt = p. Note that the HOL packet is discarded if its transmission is t→∞ unsuccessful for K times. Therefore, pd can be expressed as pd = (1 − p)K .

(3)

As each HOL packet has probability 1 − pd to be served with service rate of πT . The offered load of each node’s queue can be written as δ=

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Steady-State Points

As illustrated in Sect. 2.1, the network performance is determined by the steadystate probability of successful transmission of HOL packets p. By following the same derivation in [7], p can be expressed as p=

n−1 

ri · Pr{i concurrent transmissions}.

(5)

i=0

Specifically, ri denotes the conditional probability that the tagged HOL packet’s transmission is successful given that there exists i concurrent transmissions, which can be explicitly written as [6] ri =

 μ exp − ρ (μ+1)i ,

(6)

The probability that there exists i concurrent transmissions among the remaining n − 1 nodes is closely determined by the offered load δ, based on which the network condition can be divided into unsaturated condition and saturated condition. In the unsaturated condition, the offered load δ is lower than 1 and each node’s queue has the probability 1 − δ to be empty. On the other hand, in the saturated condition, each node is always busy with a HOL packet to serve. In this section, the steady-state points in these two conditions will be characterized.

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Steady-State Point in Unsaturated Condition pL

In the unsaturated condition, for each node, the probability that it has a HOL K−1  d) packet requesting transmission is written as δ(πT q0 + πi qi ) = λ(1−p . Therep i=0

fore, the probability that there exists i concurrent transmissions is given by   n−1  λ(1−pd )n−1−i λ(1−pd ) i 1− p Pr{i concurrent transmissions} = · . (7) p i By combining (5), (6) and (7), we can obtain p in the unsaturated condition as   n−1  λ(1−pd ) n−1−i  λ(1−pd ) i 1− p p= (μ+1)i · p i i=0 (8)   n−1 for large n

 ˆ μ μ λ(1−pd ) μ μ λ(1−p d) , = exp − ρ · 1− μ+1 · p ≈ exp − ρ − μ+1 · p n−1 

 μ exp − ρ

ˆ = nλ denotes the aggregate input rate. The explicit expressions of the where λ roots in (8) is hard to obtain. Following a similar approach in [10], it can be easily shown that (8) has either one single non-zero root 0 < pL ≤ 1 or two nonzero roots 0 < pS ≤ pL ≤ 1, which is closely determined by the SINR threshold μ, mean received SNR ρ and retransmission limit K. Only pL is the steady-state point in unsaturated condition. 3.2

Steady-State Point in Saturated Condition pA

As the node input rate increases, the network will eventually become saturated. In the saturated condition, each node always has a HOL packet to transmit. The K−1  πi q i = probability that a HOL packet is transmitted can be written as πT q0 + i=0

πT p

. By combining (5) and (6), we can obtain p in the saturated condition as ⎧ ⎫   n−1 ⎬ n−1−i i for large n ⎨ exp − μ n−1 K ρ μ nμ 1−(1−p) πT πT 1− p= · ≈ exp − − · . (9) (μ+1)i p p  p(1−p)i⎭ ⎩ ρ μ+1 K−1 i i=0

i=0

qi

With the similar approach proposed in [7,10], it can be easily shown that (9) has one single root 0 < pA < 1 when the transmission probabilities {qi }i=0,...,K−1 are monotonic non-increasing with i. Therefore, we can see that the slotted Aloha network has two steady-state points, pL and pA , which are referred as desired steady-state point and undesired steady-state point, respectively. 3.3

Absolute-Stable Region

From (8) and (9), it can be observed the desired steady-state point pL is deterˆ while insensitive to the transmission probamined by the aggregate input rate λ bilities {qi }i=0,...,K−1 . On the other hand, the undesired steady-state point pA is

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closely related to the transmission probabilities {qi }i=0,...,K−1 . In this subsection, an absolute-stable region SL = [ql , qu ] is derived. With the initial transmission probability q0 ∈ SL , the network is guaranteed to operate at the desired steady/ SL , the network may shift to the state point pL . On the other hand, if q0 ∈ undesired steady-state point pA . By following the similar approach proposed in [9], we can obtain that if (8) has one non-zero root pL , the absolute-stable region SL is given by  K−1   (1−p )i (1) L SL = λ ,1 . (10) ωi i=0

While if (8) has two non-zero roots, pS and pL , the absolute-stable region SL reduces to  K−1   (1−p )i (2) μ+1 L SL = λ , − μ+1 (11) ωi nρ − nμ ln pS , i=0

which is closely related to the node input rate λ and retransmission limit K. Figure 2 illustrates how the steady-state points vary with the initial transmission probability q0 ∈ (0, 1] under retransmission limit K = 1 and K = 20 when μ and ρ are fixed. When K = 1, (8) has only one non-zero root pL and the absolute-stable region is given by SLK=1 = [0.012, 1]. Therefore, the network operates at p = pL for q0 ∈ SLK=1 , and it is insensitive to the initial transmission probability q0 . As K increases, for instance, when K = 20, the absolute-stable region reduces to SLK=20 = [0.024, 0.063]. As the transmission probability q0 increases, when q0 ∈ / SLK=20 , the steady-state point will shift to the undesired steady-state point pA , which sharply diminishes as q0 increases, as illustrated in Fig. 2.

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Throughput Analysis

When the network is unsaturated, it is operating at the undesired steady-state point pL . The corresponding throughput is given by ˆ ˆ u = (1 − ppL )λ. λ out d

(12)

ppdL

= (1−pL )K is not only the packet discard ratio, but also denotes Specifically, the throughput loss ratio due to the packet discard. By combining   (8) and (12), μ μ+1 u u ˆ ˆ λ −pL ln pL − pL ρ . By taking the out can be further written as λout = μ fist-order derivative with respect to pL , we can then obtain that the maximum ˆ u in the unsaturated condition as throughput λ max   ˆ u = μ+1 exp −1 − μ , (13) λ max μ ρ   which is achieved when pL = exp −1 − μρ . As the input rate of each node λ grows and exceeds the service rate πT , each node’s queue is always busy

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and the network becomes saturated. In this case, the throughput is equal to the n(1−(1−p)K ) aggregate service rate nπTp=pA = K−1 , which varies with the transmission  i=0

(1−p)i /qi

ˆ s in the saturated condition probabilities qi . According to (9),the throughput λ out  μ μ+1 ˆs = is further written as λ ln p − p −p A A A ρ , which is maximized with out μ   ˆ s = μ+1 exp −1 − μ , (14) λ max μ ρ   when pA = exp −1 − μρ and the initial transmission probability q0 is set as K−1 

q0∗

=

μ+1 nμ

·

i=0

  μ i μ  1−exp −1− ρ exp −1− ρ /ωi   . μ K 1− 1−exp −1− ρ

(15)

According to (13) and (14), we can conclude  thatthe maximum throughput is μ+1 s u ˆ ˆ ˆ given by λmax = λmax = λmax = μ exp −1 − μρ , which is solely determined by the SINR threshold μ and the mean received ρ. While the optimal transmisˆ max is also determined by the retransmission limit sion probability q0∗ achieving λ K and the backoff function {ωi }i=0,1,...,K−1 .

Fig. 2. Steady-state points p versus initial transmission probability q0 for retransmisˆ = 0.6, μ = 1, ρ = 10 dB and ωi = 1 for sion limit K = 1 and K = 20. n = 50, λ i = 0, 1, ...K − 1.

Figure 3 illustrates the throughput performance versus the initial transmission probability q0 ∈ (0, 1] with retransmission limit K = 1 and K = 20. With K = 1, it can be observed that with a low aggregate input rate, the network operates at the desired steady-state point pL if q0 ∈ SLK=1 = [0.012, 1], and ˆ u is given by (12), which does not vary with the corresponding throughput λ out K=1 q0 ∈ SL . As retransmission limit K increases, for instance, from K = 1 to K = 20, as the throughput loss ratio due to packet dropping is reduced, the ˆ u in the unsaturated condition increases. throughput λ out

Throughput Analysis of Slotted Aloha with Retransmission Limit

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ˆ out versus initial transmission probability q0 . n = 50, λ ˆ = 0.6, Fig. 3. Throughput λ μ = 1, ρ = 10dB and ωi = 1 for i = 0, 1, ...K − 1.

ˆ out versus initial transmission probability q0 . n = 50, λ ˆ = 2, Fig. 4. Throughput λ 1 μ = 0.5, ρ = 10 dB and ωi = 2i for i = 0, 1, ...K − 1.

Figure 4 illustrates the throughput performance versus the initial transmission probability q0 for retransmission limit K = 1 and K = 20 when the input rate of each node is pushed to the limit. Here, the network is fully saturated for q0 ∈ (0, 1]. It can be clearly seen that the maximum throughput can be achieved for different retransmission limit K remain the same. Yet the corresponding optimal transmission probability q0∗ varies according to retransmission limit K.

5

Conclusion

This paper presents the throughput analysis of slotted Aloha networks with retransmission limit K under the capture model. The maximum throughput ˆ max is derived as an explicit expression of SINR threshold μ and mean received λ ˆ max SNR ρ. The optimal setting of initial transmission probability q0 to achieve λ is also obtained, which is closely determined by the retransmission limit K and the backoff function {ωi }i=0,1,...,K−1 . Note that the effect of retransmission limit on the delay performance of random access is also significant. Intuitively, with a

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small retransmission limit, a collided HOL packet would has a large probability to be dropped. Therefore, the time for retransmission would be shortened, and thus both the access delay and queueing delay performance can be improved. In the future work, we will focus on the effect of retransmission limit on the delay performance of random access networks. Acknowledgments. This work was supported in part by National Natural Science Foundation of China under Grant 61801433, Grant 62001524, Grant 62001336, Grant 62001333, and Grant 61911540481, in part by the Science, Technology and Innovation Commission of Shenzhen Municipality under Grant 2021A04, in part by the open research fund of Key Lab of Broadband Wireless Communication and Sensor Network Technology (Nanjing University of Posts and Telecommunications), Ministry of Education under Grant JZNY202105, in part by the Fundamental Research Funds for the Central Universities, Sun Yat-sen University, No. 2021qntd11, and in part by the Fundamental Research Funds for the Central Universities under Grant WUT: 2021IVB018.

References 1. Laya, A., Alonso, L., Zarate, J.: Is the random access channel of LTE and LTEA suitable for M2M communications? A survey of alternatives. IEEE Commun. Surveys Tuts. 16(1), 4–16 (2014) 2. Laya, A., Kalalas, C., Vazquez-Gallego, F., Alonso, L., Alonso-Zarate, J.: Goodbye, ALOHA! IEEE Access 4, 2029–2044 (2016) 3. Yang, W., et al.: Narrowband wireless access for low-power massive internet of things: a bandwidth perspective. IEEE Wireless Commun. 24(3), 138–145 (2017) 4. Abramson, N.: Packet switching with satellites. In: Proceedings of the National Joint Computing Conference, vol. 42, pp. 695–702, Montvale 1973 5. Luo, J., Ephremides, A.: Power levels and packet lengths in random multiple access with multiple-packet reception capability. IEEE Trans. Inf. Theory 52, 414–420 (2006) 6. Dua, A.: Random access with multi-packet reception. IEEE Trans. Wireless Commun. 7(6), 2280–2288 (2008) 7. Li, Y., Dai, L.: Maximum sum rate of slotted Aloha with capture. IEEE Trans. Commun. 64(2), 690–705 (2016) 8. Seo, J.B., Hu, J., Leung, V.C.M.: Impacts of retransmission limit on stability and throughput regions of S-ALOHA systems. IEEE Trans. Veh. Technol. 11(66), 10296–10306 (2017) 9. Dai, L.: Stability and delay analysis of buffered Aloha networks. IEEE Trans. Wireless Commun. 11(8), 2707–2719 (2012) 10. Sun, X., Dai, L.: Performance optimization of CSMA networks with a finite retry limit. IEEE Trans. Wireless Commun. 15(9), 5947–5962 (2016)

Optimization of NB-IoT Uplink Resource Allocation via Double Deep Q-Learning Han Zhong1,2 , Runzhou Zhang1,2 , Fan Jin3 , and Lei Ning1(B) 1

2

College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China [email protected] College of Applied Technology, Shenzhen University, Shenzhen, China 3 Shenzhen Winoble Technology Co., Ltd, Shenzhen, China

Abstract. Recently, with the development of Internet of Things (IoT) technology, the devices with the various features of traffic and mobility are increasing exponentially, and now the existing traditional resource allocation algorithms are becoming more and more difficult to meet the ever-increasing demand for terminal transmission. Aiming at the problem of radio resource fragment for complex access users of existing traditional algorithms, this paper proposes a dynamic scheduling algorithm based on Double Deep Q-learning Network(DDQN). At the same time, we design and simulate the NPUSCH transmission environment of the NB-IoT as the interactive environment of the agent. After training iterations, the resource utilization rate of the dynamic scheduling algorithm based on DDQN can be stabilized above 81%, which is better than traditional scheduling algorithms.

Keywords: NB-IoT allocation

1

· DDQN · Reinforcement learning · Resource

Introduction

With the progress and development of science, the Internet of Things (IoT) technology has become an important part of communication technology. After the revolution of computer and the Internet technology, the IoT is also known as the wave of the third scientific and technological revolution. [1] The Narrow Band Internet of Things(NB-IoT) technology is a low power solution that begins with the post 4G and integrated in the 5G era. With its deep and wide coverage, low power consumption, and the characteristics of massive connectivity, NBIoT technology becomes one of the best communication methods of Internet of L. Ning—Sponsored by the Project of Cooperation between SZTU and Enterprise (No. 2021010802015 and No. 20213108010030) and Experimental Equipment Development Foundation from SZTU (No. 20214027010032). c The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022  Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 775–781, 2022. https://doi.org/10.1007/978-981-19-0390-8_96

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Things devices [2]. It is foreseeable that in the future, there will be more and more Internet of Things devices communicate with NB-IoT. As the amount of access is increasing, how to coordinate the data transfer of each device within a limited frequency domain resource has become a major problem [3]. In recent years, with the development of artificial intelligence(AI), AI algorithms have transcended traditional algorithms in many fields [4]. Among them, there is a lot of successful applications based on Reinforcement Learning(RL). For example, Wu et al. [5] proposed a Deep Q-learning Network(DQN) method used to control the Hand-Over (HO) procedure of the User Equipments (UEs) by well capturing the characteristics of wireless signals/interferences and network load. In [6], a multi-agent Deep Q-Network (DQN)-based dynamic joint Spectrum Access and Mode Selection (SAMS) scheme is proposed for the SUs in the partially observable environment. Zhang et al. [7] proposed a two-step deep reinforcement learning based algorithm to solve non-convex and dynamic optimization problem. DRL as a large branch of Machine Learning, effectively solving the high calculation needs of huge state space, strengthening the exploration and learning ability of unknown environments [8]. According to the characteristics of NB-IoT’s uplink resources, the available resources of NB-IoT are very limited. In [9], NB-IoT is optimized from the perspective of energy saving, which greatly improves the endurance of the devices. But from the perspective of the eNB, how to solve fragmentation and make better use of frequency domain resources is still a problem we need to consider. In order to solve this problem, we proposed a NB-IoT wireless uplink resource scheduling algorithm based on DRL. The algorithm can choose behavior in complex unknown environments, and make decisions and constantly optimize. Finally, it can get close to the optimal wireless channel resource allocation strategy.

2

Proposed of Environment Model and Agent Model

In this section, we will introduce the environment model of NB-IoT uplink data transmission based on realistic communication protocol constraints. After that, intelligent model will be defined. The Double Deep Q-Network(DDQN) also will be introduced as follow. 2.1

The Environment Model of NB-IoT Uplink

NB-IoT uplink frequency domain resources are the same as downlink, frequency domain resources 180 kHz, using SC-FDMA [10]. Taking into account the lowcost requirements of NB-IoT devices, it is necessary to support single frequency (Single Tone) transmission in the uplink. In addition to the original 15 kHz, a new subcarrier spacing of 3.75 kHz has been set for a total of 48 subcarriers. Considering that 3.75 kHz is rarely used in real commercial environments, this article only considers the case of 15 kHz, which is divided into 12 sub-carriers in the frequency domain. For the uplink, NB-IoT defines two physical channels:

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NPUSCH (narrowband physical uplink shared channel) and NPRACH (narrowband physical random access channel), and demodulation reference signal (DMRS). NPRACH allocation in the frequency domain is periodically allocated by the Evolved Node B(eNB). The UE transmits the NPRACH on the fixed frequency domain resources allocated by the eNB, and the remaining channels are used for NPUSCH transmission. NPUSCH is used to transmit uplink data and control information. NPUSCH transmission can use single tone or multi-tone transmission. Compared with the Physical Resource Block(PRB) as the basic resource scheduling unit in Long Term Evolution(LTE), the resource unit of the NB-IoT uplink shared physical channel NPUSCH is scheduled with a flexible combination of time-frequency resources. The basic unit of scheduling is called Resource Unit (RU). NPUSCH has two transmission formats, the corresponding resource units are different, and the content of transmission is also different. NPUSCH format 1 is used to carry the uplink shared transmission channel UL-SCH, to transmit user data or signaling, and the UL-SCH transmission block can be scheduled and sent through one or several physical resource units. The occupied resource unit includes two formats, which are single tone and multi-tone. NPUSCH format 2 is used to carry uplink control information, such as ACK/NAK response. 2.2

Design of Dynamic Resource Allocation Algorithm Based on DDQN

Reinforcement learning is one of the important tools in the field of machine learning. It is widely used to deal with Markov dynamic programming problems. The agent obtains the state of the environment by observing the environment, and takes corresponding actions according to the state to feed back to the environment. The environment will change according to the action and return to a new state and reward the agent. The agent optimizes and adjusts the strategy through the rewards of environmental feedback and repeats this process until the optimal strategy is finally obtained. In reinforcement learning, Q learning is a very effective learning method and is widely used in various fields. Different from the SARSA algorithm and other on-policy algorithms, Q learning is updated according to the improved strategy of q(st+1 , ∗), so as to achieve closer target value. Its goal-formula can be defined as: Ut = Rt+1 + γRt+2 + · · · + γ n−1 Rt+n + γ n

max

a∈A(St+n )

q(St+n , a)

(1)

Q learning updates the action value based on the above formula, which easily leads to maximization deviation and makes the estimated action value too large. Therefore, double Q learning is introduced. The double Q learning algorithm uses two independent action value estimates q (0) (·, ·) and q (1) (·, ·), and replaces maxa q(St+1 , a) in Q learning with q (0) (St+1 , argmaxa q (1) (St+1 , a)) or q (0) (St+1 , argmaxa q (1) (St+1 , a)). Since q (0) and q (1) are independent estimates, there is

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E[q (0) (St+1 , A∗ )] = q(St+1 , argmaxa q (1) (St+1 , a)))

(2)

In the process of double learning, both q (0) and q (1) are updated gradually, and each step of learning can select any one of the following two to update with equal probability (0)

= Rt+1 + γq (1) (St+1 , argmaxa q (0) (St+1 , a))

(1)

= Rt+1 + γq (0) (St+1 , argmaxa q (1) (St+1 , a))

Ut Ut

(3)

The traditional Q learning algorithm can effectively obtain the optimal strategy when the state space and action space are small. However, in actual situations, the state space and action space of the agent are very large. At this time, it is difficult for the Q learning algorithm to achieve the ideal effect. Therefore, a deep Q network composed of a combination of Q learning and neural network can solve this problem well [11]. Reinforcement learning data is usually non-static, non-independent and uniformly distributed. One state of data may continue to flow in, and the next state is usually highly correlated with the previous state. Therefore, small deviations in the value of the Q function will affect the entire strategy. As a supervised learning model, deep neural networks require data to meet independent and identical distribution. In order to break the correlation between data, DQN adopts the method of experience replay, storing the past training data in the form of (st , at , rt , st+1 ) in the experience pool, and randomly extracts part of the data each time as the input of the neural network for training. Through the use of experience replay, the correlation between the original data is broken, and the training data becomes more independent and evenly distributed. The network composed of double Q learning and DQN is called double deep Q network(DDQN). In the DDQN, only the evaluation network is used to determine the action and the target network is used to determine the estimate of the return. The algorithm process of the DDQN is showed as follows: In view of the characteristics of NB-IoT uplink data transmission, the state observed by the agent is divided into 12 frequency domains, and the distribution of resource utilization in each frequency encounter on the time axis is used as the feature of each frequency domain. At the same time, the state also includes UE data size, NPUSCH format, transmission quality, number of repetitions, and the number of NRU . The corresponding action taken by the agent is to allocate the corresponding resources required by the UE in the frequency domain resources. Therefore, the action of the agent is composed of 12 actions, corresponding to the divided 12 frequency domain positions.

3

Performance Evaluation

In order to verify whether the DDQN algorithm can achieve better results in a complex network environment, this paper simulates the NB-IoT uplink data link NPUSCH to establish an environment model. Every second, an average of

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Algorithm 1. Procedure of Double Deep Q Network Initialize the evaluation network q(·, ·; w) and target network q(·, ·; wtarget ) for episode in episodes do initialize and choose state S while episode not end do sampling q(S, ·; w) and get action A observation and get reward R and next state S  store experience (S, A, R, S  ) → D D → (Si , Ai , Ri , Si )(i∈B) Ui = Ri + γq(Si , argmaxa q(Si , a; w); wtarget ) update w S ← S if batch size ¿ memory capacity then update wtarget ← w end if end while end for

1000 UEs need to establish a connection to transmit data, and the communication quality of each UE is randomly selected. According to the distribution of communication quality in real scenarios [12], this article divides the communication quality into three types: good, medium, and poor, and their probability Corresponds to 60%, 30%, and 10%. When each UE establishes a connection, the communication quality is randomly determined according to the probability. According to the characteristics of NB-IoT data transmission in real life, the data transmission volume is generally 50-250 bytes, and the data volume of the UE in the simulation is also randomly generated based on this range. The simulation model and deep reinforcement learning constructed in this paper are implemented by Python, and the DDQN algorithm is designed and trained based on PyTorch. The values of the network parameters in this experiment are shown in Table 1. The neural network used for training is a fully connected neural network, which contains a hidden layer, and the hidden layer contains 50 neurons. The activation function used by each neuron is ReLU. The size of the discount factor determines how much the algorithm attaches importance to current returns and future returns. The smaller the discount factor, the more the Table 1. NPUSCH parameters Parameter name

Parameter value

Channel bandwidth

180 kHz

Subcarrier spacing

15 kHz

IT BS

1,2,3,4,5,6,7,8,9,10

NPUSCH repetitions 0,8 Data size

Range (50,250)

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algorithm tends to have short-term high returns. Since a series of actions need to be made in this experiment, in order to obtain a longer-term high return, this article sets the discount factor to 0.9. Based on the parameters of Table 1, the experiment scenario is simulated. In this experiment, dynamic resource scheduling is performed on the 180 kHz NBIoT uplink data link NPUSCH. On average, 1,000 UEs are scheduled per second, and the number of iteration rounds is 100,000. Calculate the average resource utilization rate and average transmission speed after each dynamic scheduling as the evaluation index. As shown in Fig. 1, after repeated iterations and updates, resource utilization has gradually increased, reaching a maximum of 83%. Compared with traditional scheduling algorithms, resource utilization has been improved by 7%. As shown in Fig. 2, with the repeated training of the algorithm, the data transmission speed gradually converges to between 60–70 kbs, and its data transmission rate is increased by 5% compared with the traditional algorithm.

Fig. 1. Resource utilization per round

Fig. 2. Resource utilization per round

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Conclusion

In this paper, we propose an NB-IoT uplink data transmission optimization algorithm based on deep reinforcement learning. The algorithm uses the timefrequency domain resources of NB-IoT as the state space, the frequency domain position as the action, the neural network as the error function, and the resource utilization as the reward and punishment value. DDQN is designed to interact with the environment and iteratively train the algorithm model. Compared with the traditional algorithm, the simulation result has effectively improved the resource utilization rate, and at the same time, the data transmission rate has also been greatly improved. Therefore, this algorithm model can effectively solve the dynamic scheduling problem of NB-IoT under multi-device data transmission.

References 1. Haridas, A., Rao, V.S., Prasad, R.V., Sarkar, C.: Opportunities and challenges in using energy-harvesting for NB-IoT. SIGBED Rev. 15(5), 7–13 (2018) 2. Feltrin, L., Tsoukaneri, G., Condoluci, M., Buratti, C., Mahmoodi, T., Dohler, M., Verdone, R.: Narrowband IoT: a survey on downlink and uplink perspectives. IEEE Wireless Commun. 26(1), 78–86 (2019) 3. Sinche, S., Raposo, D., Armando, N., Rodrigues, A., Boavida, F., Pereira, V., Silva, J.S.: A Survey of IoT management protocols and frameworks. IEEE Commun. Surv. Tutorials 22(2), 1168–1190 (2020) 4. Yang, N., Zhang, H., Long, K., Hsieh, H., Liu, J.: Deep neural network for resource management in NOMA networks. IEEE Trans. Veh. Technol. 69(1), 876–886 (2020) 5. Wu, M., Huang, W., Sun, K., Zhang, H.: A DQN-based handover management for SDN-enabled ultra-dense networks. In: 2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall), pp. 1–6, November 2020. ISSN: 2577–2465 6. Yang, N., Zhang, H., Berry, R.: Partially observable multi-agent deep reinforcement learning for cognitive resource management. In: GLOBECOM 2020–2020 IEEE Global Communications Conference, pp. 1–6, December 2020. ISSN: 2576–6813 7. Zhang, Y., Wang, X., Xu, Y.: Energy-efficient resource allocation in uplink NOMA systems with deep reinforcement learning. In: 2019 11th International Conference on Wireless Communications and Signal Processing (WCSP), pp. 1–6, October 2019. ISSN: 2472–7628 8. Luong, N.C., et al.: Applications of deep reinforcement learning in communications and networking: a survey. IEEE Commun. Surv. Tutorials 21(4), 3133–3174 (2019) 9. Sultania, A.K., Delgado, C., Famaey, J.: Implementation of NB-IoT power saving schemes in ns-3. In: Proceedings of the 2019 Workshop on Next-Generation Wireless with ns-3, WNGW 2019, pp. 5–8, New York, June 2019. Association for Computing Machinery 10. Miao, Y., Li, W., Tian, D., Hossain, M.S., Alhamid, M.F.: Narrowband internet of things: simulation and modeling. IEEE Internet Things J. 5(4), 2304–2314 (2018) 11. Jiang, N., Deng, Y., Nallanathan, A., Chambers, J.A.: Reinforcement learning for real-time optimization in NB-IoT networks. IEEE J. Select. Areas Commun. 37(6), 1424–1440 (2019) 12. Zhang, R., Zhong, H., Zheng, T., Ning, L.: Trajectory mining-based city-level mobility model for 5G NB-IoT networks. Wireless Commun. Mobile Comput. 2021, 1–12 (2021)

Study on Application of Wireless Sensor Network for Spacecraft Yukun Chen(B) , Guiming Zeng, Jinlong Li, Jiankang Wang, and Feng Qiu China Academy of Launch Vehicle Technology, Beijing 100076, China [email protected]

Abstract. With the development of spacecraft intelligence, the number and class of parameters measured are increasing continually. According to traditional means, the sensors power and signal are translated by cable, thereby the launch capacity will be occupied by the heavy cable, and the correlative design and equipments will be recomposed if scheme changes. In order to solve these problems, application scheme of wireless sensors network for spacecraft was presented, including network topology, wireless sensor architecture, wireless communication mode and time synchronization, and the key technologies were analyzed finally. Keywords: Spacecraft · WSN · Network topology · Time synchronization

1 Introduction With the development of aircraft intelligence, the demands of spacecraft state parameters and environment data must meet the needs of requirement. Generally speaking, launch vehicle has hundreds of sensors. Those sensors collect all kinds of parameters when launch vehicle tests or flights [1]. The cable supplies power and signal transmission according to traditional design methods, so the cable weight is heavy and the launch capacity also decreases [2]. The sensors are distributed all over the rocket and thereby the cable layout is complex. The relative cable designs and products must be modified when sensors change position. According to the requirement of decreasing aircraft’s weight, wireless sensor network was introduced. The spacecraft state parameters can be inspected through low power and short distance wireless communication technology, and the weight of telemetry and communication system can decrease 30% than the wire sensor.

2 The Concept of Wireless Sensor Network Technology In recent years wireless sensor network widely applied in many fields has become popular topic with the development of wireless communication technology and intelligent sensor technology [3]. Wireless Sensor Network (WSN) is a network application system distributed in the surveillant region. Wireless sensor network is the combination in the © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 782–790, 2022. https://doi.org/10.1007/978-981-19-0390-8_97

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process of sensor technology and communication technology and computer technology, which integrates the information collection and transmission and achieves the fusion of sensor and communication and computer. At present many countries have carried out the application research of wireless sensor network on military and environment and so on [4]. Because wireless sensor network has the advantage of real time and accuracy, which can meet the information demand of battlefield and equipment configuration and occupies the hot topic in the military region.

3 The Development Status of Wireless Sensor Network Companying with rapid development of information technology and semiconductor technology, the study of wireless sensor network has made great progress since 21th century. The American department of defense has launched the research of wireless sensor network early, and identified it as indispensable component in information system [5]. The American Defense Advanced Research Projects Agency (DARPA) has helped universities to develop “Smart Dust” sensor technology. The American army has presented the plan of smart sensor network communication to collect and transmit information for partners through distributing a great deal of sensors in the battlefield. The project of unattended sensors cluster has been established to deploy sensors to any regions for military commander [6]. The study on sensors network system was developed to manage sensors from different levels, and the core of the study was a set of real-time database manage system. Study on application of wireless sensor network in China was synchronously explored with developed countries. Wireless sensor network was first depicted on the report of knowledge innovation engineering region direction research, and the report was presented by the Chinese academy of science. The center of micro system research and development was founded by Chinese academy of science and Shanghai institute of micro system in 2001, focusing on the research of wireless sensor network. The researchers have built the research platform through hard work and make great progress on relative technology.

4 The Framework of Wireless Sensor Network Because infrastructure is different and type of wireless sensor is various, the framework of wireless sensor network is diverse, such as the American two level framework and the EU five level framework [7]. Chinese researchers put forward three level framework and present relative standards, as depicted in Fig. 1.

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High Level Wireless Sensor Network

Middle Level Wireless Sensor Network

Low Level Wireless Sensor Network

Fig. 1. The three level framework of wireless sensor network

5 The Design of Wireless Sensor Network for Aircraft 5.1 The Star Topology Framework of Wireless Sensor Network Network topology framework describes the connected methods between network nodes, such as bus type, loop type, tree type, ad hoc type and star type. The star topology framework is the basic architecture for all kinds of network, and it has the advantages of simplicity and facility. The star topology framework has been widely applied under the condition of close distances, interferential environment and real-time processing. Star topology framework is adopted on the aircraft after simplifying the three level framework of wireless sensor network, and it can decrease the protocol complexity and reduce the network power need. To simplify information flow, the data collected by sensors is transformed to sinks through single leap. Figure 2 shows the topology framework between low level and medium level, including some sensors and single sink. Each sink manages some sensors, including sensor 1, sensor 2, and so on, as depicted in Fig. 1. All sensors are deployed within the range that sink can receive data. The sink is

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Fig. 2. The topology framework between low level and medium level

responsible for cooperating with whole network. Firstly it receives data from attaching sensor nodes, then it transform information to data process equipments through other methods, so sink must be supplied with sufficient power. The architecture of whole aircraft wireless sensor network includes data process equipment, sinks and wireless sensor nodes, as depicted in Fig. 3. The data process equipment accomplishes the data exchange, time synchronization and dada management between different sinks. Sink is responsible for data collection and channel configuration within the same cluster. Sensors deployed all over the aircraft collect different parameter information. The digital signal can be achieved and transmitted through direct bus mode.

Fig. 3. The model of wireless sensor network for spacecraft

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When wireless sensor network begin to work, data process equipment configures parameters for all sinks and assigns collection time period for one single sink. Sink sends the information and sets the unique address to relative wireless sensor nodes. After accomplished configuration, wireless sensor nodes transmit data to corresponding sink, and then the sink sends configuration information to data process equipment. Wireless sensor network begins to collect and transmit information after achieving shake hand. Because the work condition for wireless sensors on aircraft is special, the topology framework of wireless sensor network is fixed. When sensor nodes cannot connect wireless communication link, sink will generate new collection time period and reestablish collection frame. 5.2 The Structure of Wireless Sensor

Fig. 4. The structure of wireless sensor nodes

Sensor is the core component of wireless sensor network, and in fact it is a micro embedded system. Wireless sensor consists of four modules, and they are data collection module, process control module, wireless communication module and power supply module, as depicted in Fig. 4. Data collection module includes sensor and A/D converter, accomplishing information collection and data transfer. Process control module includes microprocessor and memory, which is responsible for operating the whole sensor node and storing data from itself to other nodes. Wireless transceiver is the key component for wireless communication module, fulfilling wireless communication and information exchange with other sensors. Power supply module provides necessary energy for sensor nodes. 5.3 The Mode of Wireless Communication Transceiver can carry out the function of signal transform, modulation and demodulation. Nodes exchange control information and collection data with other nodes through

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wireless communication chip. Some factors should be taken into account, such as carrier frequency, data rate and signal modulation mode. At present the popular communication mode in the wireless sensor network are Bluetooth, Wi-Fi, and ZigBee. Table 1 compares the performance of different communication mode. Table 1. The performance of different communication mode Communication mode

Bluetooth

Wi-Fi

ZigBee

Carrier band

2.4G

2.4G

868M/915M/2.4G

Transmission rate

1 Mbps

11/54 Mbps

20–250 kbps

Transmission range

2–10 m

30–100 m

10–100 m

Application domain

Wireless entertainment equipment

Wireless local area network image

Inspection and control data

Characteristics

Convenient application; Smaller coverage; Larger power

High speed transmission; Flexibility; Larger power

Reliability; Lower power; Cheapness; Low transmission rate

Power

1–100 mW

100 mW

1–3 mW

Node number

7

32

255/most 65,000

According to the performance of different communication mode, Bluetooth and Wi-Fi and ZigBee respectively have their own characteristics. To meet the demand of collecting and transmitting data for aircraft, there are dozens of nodes, even to hundreds of nodes. There are many pressure and temperature parameter sensors on the aircraft, and node number is the important than data transmission rate, so ZigBee mode is adopted based on communication requirement. 5.4 Time Sequence Synchronization The star topology framework belongs to distributed architecture, all nodes need to synchronization signal. Synchronization signal can be achieved on every smart sensor by embedding GPS unit, and then absolute accuracy can reach to 1 µs. But the method increases the price and volume. A modified time sequence synchronization and control module is presented based on the solution of MicroStrain Company, as depicted in Fig. 5. The scheme first sends time information provided by GPS unit to time sequence synchronization and control module, and then sends relative time information provided by the crystal oscillator of data process equipment if GPS unit fails to work. Time sequence synchronization and control module synchronously provides accurate clock pulse signal for all network controllers by means of external clock trigger. According to synchronization mechanism, all network controllers release clock information through wire or wireless modes. Wireless controller

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Fig. 5. Time sequence synchronization and control module

regularly broadcasts clock signal to nodes by wireless synchronous beacon mechanism to ensure time sequence synchronization between wireless and wire network. The time shift of the wireless sensor can be modified by central broadcast synchronization beacon mechanism. Wireless network controller sends data package to all nodes, the address of the data package can be set by single broadcast or multiple broadcast. Because wireless communication technology based on IEEE 802.15.4 can provide addressing by broadcast and changing address can be achieved by turning on the function. Synchronous data package need to fixed time sequence. The GPS unit in the data process equipment is appointed as the accurate source for time sequence so as to trigger data package transmission. Finally hardware is introduced to transmit and receive data package to minimize communication time delay and hold the value as possible. Before transmission interrupt request reaches to the main receiver processor, wireless equipment decodes and verifies received data package by hardware state machine to maximize estimation efficiency.

6 The Key Technology Analysis of Wireless Sensor Network for Aircraft 6.1 Time Synchronization Wireless sensor nodes have local clock, and local time sequence information is tightly relative to target track, data process and data communication. Local clock information alters mutually between nodes to keep up with global time and support upper cooperation mechanism. The influence of random time delay should be taken into account specially [8]. Time synchronization becomes difficult as temperature change leads to parameter shift. To achieve data transmission synchronization, the design should be optimized from software to hardware.

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6.2 Energy Efficiency Wireless sensor nodes are driven by battery, therefore energy is limited. Energy supply is almost impossible for most application. Energy efficiency directly determines the survival time, so it is preferred constraints than other factors. At present the energy mostly exhausts in the course of information detecting, data communication and data process. Dynamic voltage adjustment and dynamic energy management is adopted on the sensor nodes to increase energy efficiency. The low power protocol can also be implemented, such as µIP, 6LowPAN, Rime. At the same time the network also have energy management mode, therefore the nodes can enter sleep mode when it needn’t to work. 6.3 Electromagnetic Compatibility Electromagnetic compatibility is a common question for aircraft electrical system. Aircraft structure design becomes complicated with the development of aerospace technology. Because inner space is small and cable coupling is complicated, the interference is various. The factor of board frequency band and bigger transmission equipment and high sensitivity receiver equipment lead to complexity for electromagnetic compatibility. As the number of radiant frequency equipment between different systems increases, the interference mutual becomes more and more. Wireless sensor is also influenced by the complex electromagnetic environment on the aircraft. Choosing adequate frequency band and testing electromagnetic compatibility are inevitable difficulty to solve.

7 Conclusions The design of wireless sensor network for aircraft was developed based on the star topology architecture. The paper introduced network architecture, communication mode and time sequence synchronization, and finally the key technology analysis for wireless sensor network on the aircraft was analyzed. Wireless sensor technology is a platform integrating network, data collection and transmission, and represents the development direction for future detecting technology. The manufacture process of novel aircraft can be accelerated by adopting wireless sensor network technology.

References 1. Arms, S.W., Townsend, C.P., Churchill, D.L.: Energy harvesting, wireless, structural health monitoring and reporting system. In: 2nd Asia Pacific Workshop on SHM, Melbourne, pp. 220– 234 (2008) 2. Arampatzis, T., Lygeros, J.: A survey of applications of wireless sensors and wireless sensor networks. In: Proceedings of the 2005 IEEE International Symposium on Mediterranean Conference on Control and Automation, pp. 719–724 (2005) 3. Jiang, C., Chen, W., Hao, C.: Experimental study on electromagnetic protection of signal lines of wireless sensor nodes. J. Ordnance Equip. Eng. 40(1), 163–167 (2019) 4. Xiaopeng, L.: Survey on key technologies of wireless sensor networks. Intell. Comput. Appl. 5(1), 81–83 (2014)

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5. Li, Y., Wang, L., Zou, C.: Technology of wireless sensor network and its application in avionics system. Aeronaut. Comput. Tech. 48(5), 301–304 (2018) 6. Song, W., Wang, B., Zhou, Y.: The Technology and Application of Wireless Sensor Network. Electronic Industry Press, Beijing (2007) 7. Yuan, Z., Liu, K., Xu, Z., Wang, H., Liu, Y., Tang, X.: Development of micro-radioisotope thermoelectric power supply for deep space exploration distributed wireless sensor network. Adv. Astronaut. Sci. Technol. 3(2), 157–163 (2020). https://doi.org/10.1007/s42423-020-000 62-1 8. Zhang, J., Zeng, Y., Zheng, H.: Application of wireless sensor network in ship communication system. Ship Sci. Technol. 42(9A), 133–135 (2020)

Preliminary Study on the Mosaic Algorithm of Weather Radar Network in Sichuan Province Wei Zhang1,2 , Jie Yang1,2(B) , Yi Huang1,2 , Min Sun1,2 , and Haijiang Wang1,2 1 CEC Jinjiang Information Industry Co. Ltd, Chengdu 610500, China

[email protected] 2 Chengdu University of Information Technology, Chengdu 610225, China

Abstract. The detection range of the ground-based unmovable single radar is limited, which is not enough to cover the weather system above the meso-β scale, and can not track the convective system moving from one radar detection area to another. When observing with a single radar, many problems will occur due to its geometric reasons, such as static cone region, beam broadening, beam height, and beam blocking. In the regional early warning and forecast, the role of a single weather radar has some limitations. In this paper, the data of multiple radars are unified into a unified coordinate system, and the vertical linear interpolation method (NVI) is used to grid the data of multiple radars. Finally, the distanceweighted method is used to fuse the spliced grid data to realize the high-precision weather radar network mosaic. The networking experiment with eight Doppler weather radars in Sichuan shows that the networking mosaic algorithm based on vertical linear interpolation method and distance weight fusion method can achieve high precision and reliable mosaic effect, which is beneficial for the better observation of the mesoscale weather system. Keywords: Networking mosaic · Vertical linear interpolation · Distance weight fusion

1 Introduction In 1989, Zhang Peichang discussed the problem of coordinate system transformation of monostatic CAPPI data [1]. Due to the non-uniformity of the vertical distribution of precipitation echoes, the echo data at the same height can better reflect the precipitation intensity in the radar detection area. It is helpful to show the distribution, nature, intensity, mobility, and other information of a wide range of weather systems, to greatly improve the role of weather radar in regional forecasting. Trapp et al. [2] studied the smoothing and filtering characteristics of these simple analysis methods by using theoretical method approximation and observation system simulation experiments. Huang Yunxian et al. [3] make a comparative analysis of these commonly used interpolation methods and mosaic methods. The spatial interpolation technique should minimize the smoothness in the radar data and retain the original echo structure features that exist in the single radar data as much as possible [4]. In addition, the weather information obtained by radar © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 791–797, 2022. https://doi.org/10.1007/978-981-19-0390-8_98

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detection of overlapping areas is more accurate than that obtained by a single radar sampling, and the problems caused by the beam geometry observed by a single radar can be solved to a great extent. The networking mosaic data has high temporal and spatial resolution and wide coverage, so it can be used in strong weather analysis algorithms, regional precipitation estimation, radar data assimilation in medium and small-scale numerical weather models.

Radar 1

Radar 2

NVI Interpolation

NVI Interpolation

CAPPI product

CAPPI product Fusion splicing Network puzzle

Radar 3

Radar N

NVI Interpolation

NVI Interpolation

CAPPI product

CAPPI product

Fig. 1. Flow chart of Networking mosaic

As shown in Fig. 1, this paper first unifies all radars into the same coordinate system by analyzing the geographical coordinates and scanning range information of radar stations involved in radar networking. Then the NVI interpolation algorithm is used to interpolate each radar to the two-dimensional grid. Finally, the grid data obtained by each radar interpolation are fused and stitched according to the exponential distance weight method, and the final networking mosaic is obtained.

2 Mosaic Method 2.1 Establishment of the Coordinate System of the Networking Mosaic Before carrying on the network mosaic, it is necessary to determine the Cartesian coordinate system that contains the multi-radar detection effective range, that is, the start and end longitude and latitude and the start-end height layer of the coordinate system (Fig. 2). Starting longitude and latitude, the longitude of the horizontal detection range of the radar is the smallest, and the initial longitude can be determined through the longitude of this radar station. The horizontal and longitudinal detection range of the radar reaches the minimum latitude, and the initial latitude can be determined through the latitude of this radar station. End longitude and latitude, the longitude of the horizontal detection range of the radar is the largest, and the end longitude can be determined through the longitude of this radar station. The horizontal and longitudinal detection range of the radar reaches

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Lat2

Radar1 Lon1

Radar1 Radar1

Lon2

Radar1

Lat1

Fig. 2. Schematic diagram of network mosaic scope

the largest latitude, and the ending latitude can be determined through the latitude of this radar station. Start-end height layer, the starting height layer is set to 0.5 km, the end height layer is set to 10 km, and the resolution is 0.5 km in the middle. Users can select the desired height layer to carry out the network puzzle. 2.2 Grid Processing The gridding of radar echo intensity data means that the radar data with uneven spatial resolution in the spherical coordinate system are interpolated into a unified Cartesian coordinate system to form grid data with uniform spatial resolution. And the original echo intensity structure characteristics of the original volume scan data are retained as far as possible in interpolation. The elevation, azimuth, and oblique distance of the grid point in the spherical coordinate system are calculated by using the longitude, latitude, and height of the grid point in the Cartesian coordinate system. By calculating the position of the elevation, azimuth, and oblique distance in the radar spherical coordinates, the value of the Descartes grid point is calculated by the interpolation method. Gird lattice adopts the nearest neighbor method in radial and distance gate data, as shown in Fig. 3.The azimuth angle of the grid point to be interpolated determines the two adjacent radials of the grid point. According to the distance between the grid point and the radar, it can be determined that the grid point falls between two adjacent distance libraries. According to the trapezoidal region of the grid point, its adjacent radial direction and its distance library can be judged. Set Car(E, A, D) as the position of any point in the spherical coordinates of the cartesian coordinate system, as shown in Fig. 4. Point Car(E1 , A, D) and point Car(E2 , A, D) are the intersection points of the vertical line of the grid point Car(E, A, D) and the central axis of the upper and lower elevation Angle beam in the radar base data respectively. Where E is the radar elevation and A is the radar azimuth angle (the north is zero), D is the oblique distance between the grid point and the radar station. Which CRCar(E,A,D) is set as the value of echo intensity obtained by Cartesian grid point interpolation.SRCar(E1 ,A,D) and SRCar(E2 ,A,D) are the echo intensity values in the corresponding radar spherical

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Fig. 3. Monostatic radar vertical section

Fig. 4. 3D gridding schematic diagram

coordinates. The interpolation of CRCar(E,A,D) grid point is obtained by vertical linear interpolation of SRCar(E1 ,A,D) and SRCar(E2 ,A,D) . CRCar(E,A,D) =[Wd 1 × SRCar(E1 ,A,D) + Wd 2 × SRCar(E2 ,A,D) ]/(Wd 1 + Wd 2 )

(1)

Where Wd 1 and Wd 2 are the interpolation weight values of SRCar(E1 ,A,D) and SRCar(E2 ,A,D) , which are related to the elevation angle of the radar data. Wd 1 = |(E2 − E)/(E2 − E1 )|

(2)

Wd 2 = |(E − E1 )/(E2 − E1 )|

(3)

The vertical line method is used to search the intersection of the central axis of the upper and lower elevation beam in the radar, which needs to get the real observation value of the radar. Adopting the radial and azimuth nearest neighbor method, Ac−1 , Ac

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and Ac+1 are three adjacent azimuths respectively, Dr−1 , Dr and Dr+1 are three adjacent range resolution units respectively. The dotted line in the middle of the adjacent azimuth is the half-power line of the radar beam, and the dotted line in the middle of the adjacent range resolution unit is the half-resolution line. The trapezoidal area surrounded by two dotted lines is represented by the observations on the radar Dr . The radar observation value SRCar(E,A,Dr ) falls on the dotted trapezoidal area Car(E, A, Dr ) in the radial and azimuth direction, which can directly use the radar processing observations of the range resolution unit (Fig. 5). Ac+1

Ac

Dr+1 Dr Dr-1

Ac-1

Fig. 5. Schematic diagram of nearest echo data

2.3 Fusion Splicing The latticed data of multiple radars are mapped to a unified Cartesian coordinate system, and there will be overlapping areas of grid data of multiple radars. The synthetic echo intensity of the point in the unified coordinate system is weighted by the echo intensity of multiple radar grid points. MR(Lat,Lon,HGT ) =

Nr  nr =0

ωnr × CRnr /

Nr 

ωnr

(4)

nr =0

MR(Lat,Lon,Hgt) is the synthesis of the echo intensity value of any lattice in the unified coordinate system, and Lat is the radial number of the lattice, and Lon is the zonal number of the lattice, and Hgt is the height of the lattice. N it represents the total number of radar data covering this point. ωnr is the synthetic weight of a single piece of data, and CRnr is the echo intensity value of the corresponding radar grid point. When N = 0, it means that there is no radar data to cover the point, and the synthetic echo intensity value of

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the point is invalid. When N = 1, it means that only one radar covers the point, and the echo intensity value of the point is directly equal to the echo intensity value of the radar grid point. When N > 1, indicating that multiple radars are covering the point, and the echo intensity is synthesized using the following synthetic weights, and the index weight method is used to synthesize the synthetic weights. ω = exp(−d 2 /D2 )

(5)

Where ω is the weight of the echo intensity of the single radar grid. D is a moderate distance ratio, which can be adjusted to take a temporary value of 150 in the software. d is the distance between the grid point and the radar.

3 Mosaic Effect When selecting data, we need to pay attention to the position distribution of multiple radars, and the consistency of the data time. A mesoscale squall line weather process occurred in Sichuan on July 28, 2015, which was simultaneously observed by radar in Nanchong and Yibin. The limited detection range of a single radar can not fully show the weathering process. Through the multi-radar network mosaic, the whole squall line process is better presented in space, and the monitoring and early warning of typical weather processes can be improved. In this networking experiment, 8 new-generation weather radars of China Meteorological Administration in Sichuan, namely Chengdu, Mianyang, Nanchong, Dazhou, Yibin, Guangyuan, Leshan, and Xichang radar stations were tested. Radar-related information is shown in the Table 1 (Fig. 6). Table 1. Radar station information table Site name

Radar model Network detection range Longitude (°) Latitude (°) Height (m) (Km)

Chengdu

SC

300

104.02

30.67

597

Mianyang

SC

300

104.77

31.45

562

Guangyuan SC

300

105.82

32.41

977.5

Dazhou

SC

300

107.53

31.17

760

Nanchong

SC

300

106.68

30.82

380

Leshan

SC

300

103.82

29.41

478

Yibin

SC

300

104.57

28.82

517.2

Xichang

CD

300

102.41

27.83

2703

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Fig. 6. Weather Radar Network mosaic in Sichuan Area,on July 28, 2015.

4 Conclude In this paper, the vertical linear interpolation algorithm is used to interpolate each radar scan data into the grid in the same coordinate system. And the distance weight method is used to merge multiple grid data. Experiments are carried out on the observation data of eight Doppler weather radars in Sichuan at the same time. The results show that the vertical linear interpolation method can not only accurately interpolate the reflectivity data, but also effectively solve the problem of uneven distribution of scanning data and the unified coordinate system of multiple radars. The grid data fusion algorithm based on distance weight can effectively stitch the grid data of each radar. The splicing edge is smooth and excessively flat, which is in line with the real situation.

References 1. Zhang, P., Du, B., Dai, T.: Radar Meteorology. Meteorological Press, Beijing (2010) 2. Trapp, R.J., Doswell, C.A.III.: Radar data objective analysis. J. Atmos. Ocean. Technol. 17(2), 105–120 (2000) 3. Huang, Y.X., Zhang, Y.: Comparison of interpolation schemes for the Doppler weather radar data. Remote Sens. Inf. 21(2), 39–45 (2008) 4. Barnes, S.L.: A technique for maximizing details in numerical weather map analysis. J. Appl. Meteorol. 3(4), 396–409 (1964)

Research on Low Temperature Discharge Characteristics of Ternary Lithium Batteries for Unmanned Systems Weihua Zhang1 , Yaohui Wang1 , Weishan Wang2 , Lijie Zhou1 , and Wanli Xu1(B) 1 Institute of System Engineering, Academy of Military Science, Beijing 100300, China

[email protected] 2 69008 Troops of the People’s Liberation Army of China, Beijing, China

Abstract. In order to study the effect of low temperature on the discharge characteristics of high energy density ternary lithium batteries for UAV, two types of ternary lithium batteries, energy type and power type, were used to conduct discharge tests at 25 °C, 0 °C and −25 °C with energy recovery type charge/discharge test equipment . The test results show that the energy density of energy type ternary lithium battery can reach 266.11 Wh/kg at room temperature, 237.38 Wh/kg at 0 °C and 180.20 Wh/kg at low temperature; the energy density of power type ternary lithium battery can reach 244.72 Wh/kg at room temperature, 221.13 Wh/kg at 0 °C and 181.20 Wh/kg at low temperature. The energy density at low temperature is 181.57 Wh/kg. Keywords: High energy density · Ternary lithium · UAV · Low temperature

1 Introduction The range of the drone is very sensitive to the size and weight of the battery, so the ternary lithium battery with a small volume and light mass and high energy density has become the focus of attention. The current lithium-ion batteries for UAVs mainly use lithium cobaltate, which has a high specific energy, as the cathode material, and the corresponding specific energy is 180–190 Wh/kg when the upper voltage reaches 4.2 V [1–3]. The current rapidly developing UAV field requires lithium-ion batteries with high energy density to ensure the endurance performance requirements of UAVs. Since the energy density of traditional lithium-ion batteries has shown a weak trend and cannot serve well in the field of UAV power batteries, it is urgent to find a new lithium-ion battery with high energy density [4, 5]. At the same time, the current operating temperature range of lithium-ion batteries is 0–55 °C. As the application fields of lithium-ion batteries continue to expand, special fields such as plateau and border defense require lithium-ion batteries that can operate normally under −20 °C or even lower temperature conditions. Therefore, this paper tests two new high energy density ternary lithium batteries designed for UAVs.

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 798–804, 2022. https://doi.org/10.1007/978-981-19-0390-8_99

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2 Experimental Methods 2.1 Test Device and Data Acquisition Platform The structure of high and low temperature charge/discharge test system is shown in Fig. 1. The battery charging and discharging test equipment in the figure is energy recovery type battery test system Chroma 17020, which can test voltage, current, energy, capacity and temperature at the same time, the maximum voltage is 20 V, the maximum current is 400 A, and the test accuracy is 0.001; the ultra-low temperature environment simulation test chamber is SDJ710FA high and low temperature humidity and heat chamber, the maximum temperature is 150 °C, the minimum temperature is −70 °C, the accuracy is ±1 °C.

Fig. 1. Schematic diagram of the structure of high and low temperature test system

2.2 Experimental Data Collection Objects The experimental object is two (energy type 270 and power type 240) ternary lithium batteries, whose main technical parameters are shown in Table 1. Table 1. Technical parameters Projects

Parameters

Nominal capacity

27 Ah

Nominal voltage

3.7 V

Quality

360 g ± 10 g (270), 410 g ± 10 g (240) (continued)

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Parameters

Charge cut-off voltage

4.2 V

Charge cut-off current

1.35 A

Max. charging multiplier 1 C Discharge cut-off voltage 2.75 V Maximum discharge rate 3 C

2.3 Experimental Procedure and Data Collection Content The battery is connected to the test interface and sampling interface of the battery charge/discharge test equipment through the special battery fixture and sensor, and the discharge experiments are conducted at different temperatures in the high and low temperature humidity and heat chamber. During the test, the battery discharge voltage, charge and discharge current and battery temperature data can be detected, the specific test and data collection as shown in Table 2. Table 2. Ternary lithium battery discharge performance test procedures at different temperatures Steps

Temperature

Experiment content

Data Acquisition

1

25 °C

1 C battery discharged to 2.75 V and – left for 1 h

2

25 °C

Charge to 4.2 V with 1 C battery and Voltage, current, temperature, then switch to constant voltage capacity charge to 0.05 C

3

25 °C

Resting for 12 h

4

25 °C

1 C battery discharged to 2.75 V at Voltage, current, temperature, the corresponding temperature and capacity left to stand at room temperature for 5h

5

25 °C

Charge to 4.2 V with 1 C battery and Voltage, current, temperature, then switch to constant voltage capacity charge to 0.05 C

6

0 °C

Resting for 12 h

7

0 °C

1 C battery discharged to 2.75 V at Voltage, current, temperature, the corresponding temperature and capacity left to stand at room temperature for 5h

–

–

(continued)

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Table 2. (continued) Steps

Temperature

Experiment content

Data Acquisition

8

25 °C

Charge to 4.2 V with 1 C battery and Voltage, current, temperature, then switch to constant voltage capacity charge to 0.05 C

9

−25 °C

Resting for 12 h

10

−25 °C

1 C battery discharged to 2.75 V at Voltage, current, temperature, the corresponding temperature and capacity left to stand at room temperature for 5h

–

3 Results and Analysis 3.1 Battery Voltage Data Processing and Analysis In order to study the effect of low temperature environment on the battery discharge capacity, we conducted experiments at 0 °C as well as −25 °C ambient conditions, while adding 25 °C room temperature for comparative analysis. Because only the temperature is different, the experimental system in Fig. 1 is still used to adjust the high and low temperature chamber temperature to 25 °C, 0 °C and −25 °C respectively. The relationship between discharge voltage and depth of discharge at different temperatures

Fig. 2. Relationship between discharge voltage and depth of discharge at different temperatures (power type)

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Fig. 3. Relationship between discharge voltage and depth of discharge at different temperatures (energy type)

was obtained, and the power type is shown in Fig. 2 and the energy type is shown in Fig. 3. The experimental results show that the discharge curves of the two ternary lithium batteries at different temperatures are relatively smooth and stable and reliable. For the power type ternary lithium battery, the depth of discharge of the battery at 1 C multiplier in 0 °C environment decreases about 5% compared with room temperature 25 °C, the depth of discharge in −25 °C environment decreases about 15% compared with room temperature, and the discharge voltage platform decreases 0.1 V at 0 °C compared with room temperature 25 °C and 0.4 V at −25 °C compared with room temperature 25 °C; for the energy type ternary lithium battery, the depth of discharge of the battery at 1 C multiplier in 0 °C environment The depth of discharge of the battery at 1 C multiplier drops about 8% compared to room temperature 25 °C, the depth of discharge at −25 °C environment drops about 20% compared to room temperature, the discharge voltage platform 0 °C drops 0.2 V compared to room temperature 25 °C, −25 °C drops 0.6 V compared to room temperature 25 °C. Overall, both the depth of discharge and the discharge voltage plateau decreased with the decrease of temperature, and in terms of discharge capacity, the discharge capacity decreased with the decrease of temperature, and the discharge capacity was 86% and 76% of the nominal capacity at −10 °C and −20 °C, respectively, and the low temperature environment seriously affected the utilization rate of the battery [6–9]. And the depth of discharge and the discharge voltage plateau decreased rapidly after the temperature was lowered to below zero, especially at −25 °C when the initial stage of discharge showed an obvious phenomenon of sudden voltage drop and then rebound.

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The performance of power-type batteries is significantly better than that of energytype batteries, both in terms of discharge depth and discharge voltage plateau at low temperatures, so power-type batteries can be used preferentially at low temperatures. 3.2 Energy Density Data Processing and Analysis In order to study the effect of low temperature environment on the battery discharge capacity, we used the experimental system in Fig. 1 to test the energy density under 1 C discharge conditions of two batteries in 0 °C as well as −25 °C environment respectively, while adding 25 °C room temperature for comparative analysis to obtain the energy density at different temperatures, as shown in Table 3. Table 3. Energy density at different temperatures 25 °C

0 °C

−25 °C

Power type

244.72

221.13

181.57

Energy type

266.11

237.38

180.20

The experimental results show that the energy density of both batteries decreases significantly with the decrease of temperature. For the power type battery, the energy density at 0 °C is 90.36% of room temperature, while the energy density at −25 °C is only 74.19% of room temperature; for the energy type battery, the energy density at 0 °C is 89.2% of room temperature, and the energy density at −25 °C is only 67.72%. On the whole, the activation performance of the battery decreases at low temperature, resulting in an overall decrease in the performance of the battery, and the energy density shows a trend of gradual decrease with decreasing temperature [10–13]. In comparison, the energy density of energy type is significantly greater than that of power type battery at room temperature, and the difference of energy density decreases gradually when the temperature decreases, and even the energy density of power type battery achieves the opposite when it reaches −25 °C. Therefore, it further indicates that it is better to choose power type battery when it is used at low temperature, and it is better to choose energy type battery when it is used at room temperature.

4 Conclusion In this paper, through the study of the discharge characteristics of two high-energy density ternary lithium batteries, it is found that they perform well in improving the range of UAVs and are greatly improved over previous power batteries. The following conclusions are drawn from the analysis. (1) The power type ternary lithium battery is relatively good in low temperature, and the discharge voltage platform is relatively stable, with relatively low energy density at room temperature and relatively high energy density at low temperature.

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(2) The energy type ternary lithium battery has relatively high energy density at room temperature, stable voltage platform, and slight deficiency in low temperature and low temperature multiplier. (3) It is better to choose energy type battery when used under normal temperature conditions and power type battery when used under low temperature conditions.

References 1. Liu, H., Wu, J., Chen, H.: Research progress of high specific energy nickel-rich ternary cathode materials. Chem. Eng. 34(7), 58–64 (2020) 2. Zhao, H., et al.: Research progress of ternary cathode materials for lithium-ion batteries. China Ceram. 56(5), 10–15 (2020) 3. Bao, K., Cai, Y., Park, H.: Research progress of low-temperature lithium-ion batteries. Battery 49(5), 435–439 (2019) 4. Wang, Z., et al.: Factors affecting the ultra-low temperature discharge performance of lithiumion batteries. Battery 48(4), 262–266 (2018) 5. Wu, Y.: Status and development trend of ternary cathode materials for lithium-ion batteries. World Nonferrous Met. 8, 1–3 (2020) 6. Chen, Y., Zhao, Z., Yang, T.: Experimental analysis of factors influencing the discharge capacity of ternary lithium batteries. J. Nanjing Eng. College (Nat. Sci. Ed.) 18(1), 60–63 (2020) 7. Hu, Y.: Analysis and Research on the Factors Affecting the Low Temperature Performance of Lithium-ion Batteries. Hunan University (2013) 8. Yang, Y.Y., Wei, X.Z., Liu, Y.F.: Study on the low temperature performance of automotive lithium-ion batteries. Mechatronics 22(6), 30–35+46 (2016) 9. Lai, C., et al.: Research progress of NCM ternary cathode materials for lithium-ion batteries. J. Shanghai Electric Power Inst. 36(1), 11–16 (2020) 10. Zhao, S.X., et al.: Advances in the study of low temperature characteristics of lithium-ion batteries. J. Silic. 44(1), 19–28 (2016) 11. Xie, X., Xie, J., Xia, B.: Study on the low-temperature charge and discharge performance of lithium-ion batteries. Chem. World (10), 581–583+614 (2008) 12. Guo, X., et al.: Research progress on modification of nickel-rich ternary cathode materials. Eng. Sci. Technol. 52(1), 9–17 (2020) 13. Zhang, C., Ni, Z.: Comparative analysis of ternary lithium batteries and lithium iron phosphate batteries for vehicles. Automot. Pract. Technol. (23), 28–29+65 (2019)

Design and Analysis of 1.5 GHz LNA for L-Band Hai Wang1 , Peng Sun2 , Guiling Sun1 , Ying Zhang1,3 , Xiaomei Jiang1 , and Zhihong Wang1(B) 1 Teaching Center for Experimental Electronic Information, College of Electronic Information

and Optical Engineering, Nankai University, Tianjin 300350, China [email protected] 2 Tianjin Key Laboratory of Optical Thin Film, Tianjin Jinhang Technical Physics Institute, Tianjin 300308, China 3 Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology, Tianjin 300071, China

Abstract. With the development of economic society and the progress of science and technology, wireless communication technique has also been rapidly developed. The transmission characteristics of radiofrequency (RF) module is greatly improved, but it has smaller size and lighter weight. The low noise amplifier (LNA) can amplify the signal, suppress the noise interference and improve the sensitivity of the whole system. The bias circuit and the input-output matching circuit are designed in this paper. A low noise amplifier with 1.5 GHz center frequency, 400 MHz bandwidth and 18 dB gain for L-band is designed with the help of simulation software. Keywords: LNA · Matching circuit · Center frequency · L-Band

1 Introduction According to IEEE 521–2002 standard, L-band refers to the radio wave band with frequency of 1–2 GHz. The amplifiers for L-band have been widely applied in our daily life at present [1–3]. For example, they are used to amplify the required signals in global position system (GPS), satellite TV and microwave communication systems [4, 5]. Although many L-band LNAs have been researched and produced, their parameters are not universal for RF front-end systems [6]. Therefore, it is necessary to design a low noise amplifier with specific parameters for a given RF system [7, 8]. Researchers at home and abroad have been on the L-band low noise amplifier for continuous research. LNA is a kind of amplifier with low noise figure, which is generally utilized as the preamplifier of high frequency or intermediate frequency of various radio receivers and the amplifier circuit of high sensitivity electronic detection equipment [9, 10]. It usually plays an important leading role in all kinds of remote sensing microwave detection, wireless communication, telephone radar and various electronic countermeasures between developed countries [11–13]. The low noise amplifier is one of the core components that constitute the important receiving part of microwave wireless communication electronic © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 805–811, 2022. https://doi.org/10.1007/978-981-19-0390-8_100

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system [14, 15]. The noise figure and harmonic gain of this circuit module have great influence on the whole system [16]. The central frequency, bandwidth and noise coefficient are primary indexes of LNA, which depends on the structure of amplifier circuit and the parameters of components [17, 18]. The device can amplify and process small signal, and the gain can directly reflect whether the system performance meets the design requirements [19]. In addition, it is necessary to avoid self-excited oscillation in the circuit system. The common methods to improve the circuit stability are to add resistive load at the drain or to add inductance between the source and ground. The former is generally applied to the output, the latter can form a series feedback, i.e. source feedback. Inductors or short transmission lines can be used in the circuit to improve the overall stability, but also reduce the gain of the device to a certain extent.

2 LNA Design Transistor, which working mechanism is essentially the same as a controlled voltage source or current source, is the core device of amplifier. Due to the high gain and low noise characteristics, Avago ATF54143 is adopted as the core chip of circuit in this design. The transistor can work normally under the condition of appropriate static working point, and the function of bias circuit is to provide this working environment for the transistor. Compared with the common low frequency amplifier, the RF amplifier adds a matching circuit between the input and output. The purpose of the matching circuit is to make the input and output impedance of the whole system match to 50 . So it is convenient to connect with other systems, such as filter circuit, frequency doubling circuit and so on. 2.1 Bias Circuit Design The offset frequency curve of ATF54143 is shown in Fig. 1, which illustrates that VDS and IDS are separately 4 V and 0.037 mA when the center frequency is about 1.5 GHz in normal operation. After determining the static working position of the transistor, the voltage divider bias circuit is established, and the resistor divider circuit is applied to

Fig. 1. Offset curve of ATF54143

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provide DC power. Two pairs of bypass filter capacitors with larger capacitance value are utilized for power protection. When PCB is welded, in addition, a feed through capacitors is added to the DC power supply to prevent the leakage of AC signal.

Fig. 2. System stability curve

Fig. 3. Circuit gain curve

Resistance and inductance are connected in parallel to improve the Q value and stability of the circuit system. The circuit is stable in the working frequency band by negative feedback regulation. The simulation results of DC bias circuit are shown in Fig. 2 and Fig. 3. It can be seen that the bias circuit operates stably in the frequency range of 1 and 2 GHz, and the maximum gain can be approximately 18.8 dB at the center frequency of 1.5 GHz. 2.2 Design of Input-Output Matching Circuit

Fig. 4. Input matching circuit

After the bias circuit scheme is determined, the input matching network (shown in Fig. 4) should be designed to make the input impedance of the whole circuit 50 . The whole electrical system, therefore, is convenient to connect with other circuits. Furthermore, it is necessary to ensure that the performance of the matching circuit will not be affected, that is, the S21 is close to zero and the S11 and S22 are as small as

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Fig. 5. S Parameters of input matching circuit

possible near the operating frequency. According to the simulation results illustrated in Fig. 5, both S11 and S22 are less than −40 dB at 1.5 GHz. The output matching circuit is indicated in Fig. 6. The simulation results also show that the gain will not affect the performance of the bias circuit, and the S parameters meet the design requirements.

Fig. 6. Output matching circuit

2.3 Overall Simulation Design of Circuit Module Since the amplifier is a two-port network, the index of the input impedance will be influenced by the impedance of the output matching circuit. Therefore, it is necessary to adjust the relevant parameters of the deviation when the circuit is simulated as shown in Fig. 7. Resistor divider circuit is applied in bias circuit to provide DC power for the transistor. C2 and C4 , C9 and C10 , as two pairs of bypass filter capacitors, are used for power protection. Resistor R4 and inductance L2 are connected in parallel to improve the Q value and stability of the whole circuit system. The equivalent input impedance is (21.3 + j13.2) ohm and the output impedance is (112.8 + j3.2) ohm near the center frequency. In addition, microstrip lines are applied to connect the discrete devices in the circuit, which is convenient for subsequent platemaking. As illustrated in Fig. 8 and

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Fig. 7. Circuit diagram of LNA

Fig. 9, the low noise amplifier works stably in the range of 1.3 to 1.7 GHz, and the gain is between 17 dB and 18.209 dB. Both S11 and S22 are less than −5 dB and noise figure is less than 1, which meets the design requirements.

Fig. 8. Stability factor curve

Fig. 9. Gain curve of LNA

3 Conclusion Based on the research of low noise amplifier, the main design process is completed, including the part of bias circuit, input-output matching circuit and the overall system. A low noise amplifier with center frequency of 1.5 GHz, maximum gain of 18 dB, bandwidth of 400 MHz, good stability and low noise figure is successfully designed

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with ATF54143 chip. Since LNA plays a significant role in modern communication system, it is the future research direction to further improve the performance of LNA, such as by the excellent performance of substrate integrated waveguide structure. Acknowledgement. This work was supported by the 2021 Self-made Experimental Teaching Instrument and Equipment Project Fund of Nankai University (21NKZZYQ01); the 1st batch of industry university cooperative education projects in 2021 (202101186002 and 202101186014); the 2nd batch of industry university cooperative education projects in 2021 (202102296002); Tianjin Science and Technology Project (20YDTPJC00760); the 2020 Rolling Cultivation Project Fund of Self-made Experimental Teaching Instrument and Equipment of Nankai University; the 2020 Undergraduate Education Reform Project Fund (NKJG2020326); Transformation and Extension of Agricultural Scientific and Technological Achievements in Tianjin (201901090) and Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology.

References 1. Liu, H.F., Jin, C.J., Cao, Y., et al.: High linearity, low noise, L-band cryogenic amplifier for radio astronomical receivers. Microw. Opt. Technol. Lett. 59(3), 500–505 (2017) 2. Paul, S., Heinrich, W., Bengtsson, O.: L-B and floating-ground RF power amplifier for reversetype envelope tracking systems. In: 2020 IEEE/MTT-S International Microwave Symposium (IMS), 2020-August, pp. 849–852 (2020) 3. Eron, M., Lin, S., Wang, D., et al.: L-band carbon nanotube transistor amplifier. Electron. Lett. 47(4), 265–266 (2011) 4. Cui, C., Yuk, K., Wu, H.M., et al.: A high power inverse class-F GaN amplifier for L-band GPS applications. In: 2018 IEEE 19th Wireless and Microwave Technology Conference (WAMICON), 2018, pp. 1–4 (2018) 5. Murthy, B.V., Rao, I.S.: Highly linear dual capacitive feedback LNA for L-band atmospheric radars. J. Electromagn. Waves Appl. 30(5), 612–625 (2016) 6. Li, S.S., Chi, T.Y., Wang, H.: Multi-feed antenna and electronics co-design: an E-band antenna-LNA Front end with on-antenna noise-canceling and G-boosting. IEEE J. Solid-State Circ. 55(12), 3362–3375 (2020) 7. Elkholy, M., Shakib, S., Dunworth, J., et al.: A wideband variable gain LNA with high OIP3 for 5G using 40-nm bulk CMOS. IEEE Microw. Wirel. Compon. Lett. 28(1), 64–66 (2018) 8. Sayginer, M., Rebeiz, G.M.: A W-band LNA/phase shifter with 5-dB NF and 24-mW power consumption in 32-nm CMOS SOI. IEEE Trans. Microw. Theory Tech. 66(4), 1973–1982 (2018) 9. Ghanem, H., Lepilliet, S., Danneville, F., et al.: 300-GHz intermodulation/noise characterization enabled by a single THz photonics source. IEEE Microw. Wirel. Compon. Lett. 30(10), 1013–1016 (2020) 10. Sattar, S., Zulkifli, T.: A 2.4/5.2-GHz concurrent dual-band CMOS low noise amplifier. IEEE Access 5, 21148–21156 (2017) 11. Belostotski, L., Veidt, B., Warnick, K.F., et al.: Low-noise amplifier design considerations for use in antenna arrays. IEEE Trans. Antennas Propag. 63(6), 2508–2520 (2015) 12. Wang, H., Wang, Z.H., Sun, G.L., et al.: Design of a third-order filter with 10 GHz center frequency. In: The 9th International Conference on Communications, Signal Processing, and Systems, 2020, vol. 654, pp. 225–233 (2020) 13. Liang, Q., Liu, X., Na, Z., Wang, W., Mu, J., Zhang, B. (eds.): CSPS 2018. LNEE, vol. 516. Springer, Singapore (2020). https://doi.org/10.1007/978-981-13-6504-1

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14. Johansen, D.H., Sanchez-Heredia, J.D., Zhurbenko, V., et al.: Association and dissociation of optimal noise and input impedance for low-noise amplifiers. IEEE Trans. Microw. Theory Tech. 66(12), 5290–5299 (2018) 15. Meghdadi, M., Piri, M., Ali, M.: A highly linear dual-gain CMOS low-noise amplifier for X-band. IEEE Trans. Circ. Syst. II Express Briefs 65(11), 1604–1608 (2018) 16. Varonen, M., Reeves, R., Kangaslahti, P., et al.: An MMIC low-noise amplifier design technique. IEEE Trans. Microw. Theory Tech. 64(3), 826–835 (2016) 17. Mahmoudidaryan, P., Medi, A.: Codesign of ka-band integrated limiter and low noise amplifier. IEEE Trans. Microw. Theory Tech. 64(9), 2843–2852 (2016) 18. Turkmen, E., Burak, A., Guner, A., et al.: A SiGe HBT D-band LNA With butterworth response and noise reduction technique. IEEE Microw. Wirel. Compon. Lett. 28(6), 524–526 (2018) 19. Kooi, J.W., Reck, T.J., Reeves, R.A., et al.: Submillimeter InP MMIC low-noise amplifier gain stability characterization. IEEE Trans. Terahertz Sci. Technol. 7(3), 335–346 (2017) 20. Pan, G.C., Karymy, A., Yu, P.P., et al.: Novel low noise amplifier for neural signals based on STT-MTJ spintronic device. IEEE Access 7, 145641–145650 (2019)

Transmission Optimization for Mobile Scenarios in 5G NB-IoT Networks Tongyi Zheng1,2 , Qingsong Ye1 , Runzhou Zhang1,2 , Fan Jin3 , and Lei Ning1(B) 1

2

College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China [email protected] College of Applied Technology, Shenzhen University, Shenzhen, China 3 Shenzhen Winoble Technology Co., Ltd, Shenzhen, China

Abstract. Since Narrow Band Internet of Things (NB-IoT) will produce network fluctuations in mobile scenarios and affect data transmission performance, this paper studies how to improve the data transmission performance of NB-IoT in mobile scenarios. We move in the real scene and collect the characteristic data of network signals, and at the same time calculate the Round-Trip Time (RTT) of data transmission. Machine learning method is used to analyze the network signal characteristics and to predict the RTT taken as Retransmission Time Out (RTO). The simulation results of the collected data show that compared with the traditional algorithm, the machine learning method can locally optimize the network resource consumption and transmission delay of NB-IoT transmission.

Keywords: NB-IoT

1

· RTT · RTO · Data transmit

Introduction

The Internet of Things (IoT) uses local network or Internet and other communication technologies to connect sensors, controllers, machines, personnel and other things together in a new way, forming the connection between people and things, things and things, to achieve information and remote management and control. From the perspective of technical architecture, the Internet of Things can be divided into four layers, the perception layer, the network layer, the processing layer and the application layer [1]. This paper focuses on how to optimize the prevailing communication mode in the network layer. The network layer of the Internet of Things is mainly based on Internet Protocol (IP) to realize the cell data communication between different devices and the cloud. There are many communication protocols at different protocol stack Sponsored by the Project of Cooperation between SZTU and Enterprise (No. 2021010802015 and No. 20213108010030) and Experimental Equipment Development Foundation from SZTU (No. 20214027010032). c The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022  Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 812–818, 2022. https://doi.org/10.1007/978-981-19-0390-8_101

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level, which can form a variety of different communication modes. The combination of different communication protocols is suitable for different application scenarios under the Internet of Things. Choosing different communication modes at the physical layer of the protocol stack can make a big performance difference. In this paper, NB-IoT technology, a low power wide area network technology, is chosen to communicate, which can use the existing Long Term Evolution (LTE) network to achieve cell data transmission. NB-IoT is one of the cornerstones of the large-scale IoT technology that forms the basis of 5G. Sigfox and Lora are also low-power wide area network technologies that run on unlicensed spectrum, but NB-IoT runs on licensed spectrum. All of these technologies need to communicate through protocols at the transport layer, such as Transmission Control Protocol (TCP) and User Datagram Protocol (UDP). UDP is more suitable for NB-IoT data collection and reporting because UDP is lightweight and connectionless [2]. There are many ways to optimize NB-IoT by using network signal characteristics, such as adjusting the transmission power of NB-IoT [3], optimizing the random access between NB-IoT and LTE network [4] and study the network status of NB-IoT in mobile scenarios [5]. In this paper, machine learning method is combined with network signal characteristics. This method improves the performance of NB-IoT data transmission based on UDP in mobile scenarios

2

Transmission Model

UDP has two problems that need to be solved. Some real-time applications need to use UDP without congestion control. However, when many source hosts send real-time data streams with high speed to the network at the same time, the network may be congested, causing everyone to be unable to receive normally. On the other hand, some real-time applications that use UDP need to make appropriate improvements to the unreliable transport of UDP to reduce data loss. The application process can add some measures to improve the reliability without affecting the real-time performance of the application, such as retransmitting lost messages. To solve these two problems, most application layer transport protocols based on UDP use sequential transmission and timed retransmission to solve them. Referred to several UDP model [6–8], this paper designed a simple and general UDP transmission model to meet the reliability, which was assumed as the simulation environment. – Sequential T ransmission: Specifies a message queue of length N. Each time a message is sent, the ID is bound to identify the order in which the message is sent. After each message is sent, the message queue stores the corresponding ID and the queue length is incremented by one. After receiving an Acknowledge character (ACK) from Server, the corresponding ID in the message queue is removed and the queue length is reduced by one. The message is sent at a fixed time interval T. But when the message queue length is N, that is, there are N messages to be acknowledged, client stops sending messages and waits

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for an ACK from server for the last message until the message queue length is less than N. – T imed Retransmission: After each message is sent, a timer will be set. When the timer exceeds the specified Retransmission Time Out(RTO) and does not receive an ACK from Server about the corresponding message ID, the message will be sent again. The time between the message being sent and the corresponding ACK being received is called Round-Trip Time (RTT). If the RTO is less than the RTT, client will recognize the message loss before receiving the corresponding ACK and will send the message again, resulting in many unnecessary retransmissions. If the RTO is much larger than the RTT, the message cannot be sent again in time when the message is sent and the ACK is lost, resulting in a certain transmission delay in the message transmission. In order to take into account both network resource consumption and transmission delay, RTO should be slightly larger than RTT. There are many different ways to determine the RTO, but most of them are base on traditional TCP algorithm. The TCP specification proposes two algorithms to determine RTT and RTO, which are the classical algorithm [9] and the standard algorithm [10]. In addition, CoAP-Eifel is an RTO algorithm for Constrained Application Protocol(CoAP) [11], which is an application layer protocol based on UDP. This paper will compare and analyze the above three algorithms with machine learning algorithms.

3

Proposed UDP-XGB

In order to better adapt to the large RTT fluctuations caused by scene switching during the movement of NB-IoT, such as from outdoor to indoor, this paper use machine learning(ML) method to forecast the RTT and take the predicted RTT as the RTO. In this paper, Pearson correlation coefficient method is used to analyze the characteristics of the acquired network signals. As shown in the Fig. 1, the absolute values of Reference Signal Receiving Quality (RSRQ), Reference Signal Receiving Power (RSRP), Signal to Interference plus Noise Ratio (SINR) and Time To Live (TTL) in the column of RTT are the largest, indicating that the four features of RSRQ, RSRP, SINR and TTL have a high correlation with RTT. These four features are selected as the input features of machine learning method. Extreme Gradient Boosting (XGBoost), is an algorithm or engineering implementation based on Gradient Boosting Decision Tree (GBDT) [12]. In this paper, the data of the above four dimensions were used as the characteristic input. RTT was used as the target output. Root Mean Square Error (RMSE) was selected as the error measurement function. Train 1000 trees and integrate them into a model. The maximum depth of each tree was 5. Input the characteristics of all the data into the model and predict the RTT as the RTO. The UDP-XGB algorithm in this paper is shown in Algorithm 1.

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Fig. 1. Network characteristics and RTT Pearson correlation coefficient

Algorithm 1. UDP-XGB algorithm to predict RTO Input: The four features with the highest correlation, RSRQ, RSRP, SINR and TTL, are taken as X;RTT as the target output Y;Set the error measure function to RMSE;Sets the number of trees for model integration to num=1000;Set the maximum depth of each tree to maxdepth = 5 Output: Model the mapping relationship between features and RTT, and use the model to predict RTT 1: Model = XGBoost model training(X,Y,RMSE,num,maxdepth) 2: RTTpredicted = Model predict(X) 3: RTO = RTTpredicted 4: return RTO

4

Simulation Results

The simulation model in this paper adopts static data simulation analysis, and carries out simulation analysis among four groups of different algorithms on the collected 942 network signal features to ensure the fairness of simulation data among different algorithms. Assume that each piece of data is treated as a round of data sent, and each round is sent at an interval of T. First of all, we need to count the number of packets sent. When RTT is 0, the data sent fails. RTT>RTO represents false retransmission; in these two cases, data need to be sent again after experiencing RTO round. RTTτ Sτ (x) = . 0 x≤τ

(12)

For matrix X ∈ Cm×n , the singular value decomposition is X = UVH , the singular value shrinkage operator is expressed as follows Dτ (X) = US τ ()VH

(13)

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where τ is the threshold, for τ > 0 and Y ∈ Cm×n , the singular value shrinkage operator can be described as

1 min τ Y∗ + Y − X2F . (14) Dτ (X) = arg Y 2 Then (9) and (10) can be further recast as Hk+1 = arg

  2 min  −1 μ H∗ + 21 H − M − Ek + μ−1 Yk F , H   = Dμ−1 M − Ek + μ−1 Yk

(15)

 2  min E − M − Hk+1 + μ−1 Yk F , P (E) = 0  −1 = P  M − Hk+1 + μ Yk

(16)

Ek+1 = arg

where  represents the complement of , and proposed IALM-based channel estimation algorithm as show in Algorithm 1.

4 Simulation Results In this section, we investigate the performance of the proposed and reference algorithms, which are OMP, SVT, LINALM and Proposed. The simulation parameters are set as follows: the working frequency of mmWave channel is 90 GHz, NT = 64, NR = 64, L = 6, d = λ/2, all the simulation results are the average of 100 Monte Carlo realizations. We evaluate the performance of the channel estimation algorithm using the normalized mean square error (NMSE) and the achievable spectrum efficiency (ASE) in bits/second/hertz. The NMSE is expressed as follows



2

H − HF NMSE  E H2F

 (17)

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where, H is the estimated value of H, and the expression of ASE is as follows

    −1 2 H HH ASE  E log2 det INR + NT N R σn + NMSE

(18)

Fig. 2. Curves of NMSE versus algorithm iterations: (a) T = 400 and (b) T = 800

In Fig. 2, we present NMSE versus number of algorithm iterations when signalto-noise (SNR) are set to 25 dB. As we can see, SVT requires the least number of iterations to converge, unfortunately the performance of NMSE is poor. The NMSE of LINALM and Proposed is much the same, however the number of iterations required for the convergence of Proposed less than LINALM. For example, in (b), the number of iterations when LINALM converges is 200, while that required for the Proposed is 68. Comparing (a) and (b), it can be seen that the NMSE performance of the three algorithms is positively correlated with T, owing to the number of known elements in the matrix has a significant influence on the result of matrix completion. The more training symbols sent by the transmitter, the more number of known elements in the matrix, thereby improving the accuracy and convergence speed of channel estimation.

Fig. 3. Curves of ASE versus SNR

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Figure 3 shows the simulation curve of ASE performance when T = 800, the dashed line represents the ASE of perfect channel estimation. As show, the ASE of each algorithm increases with the increase of SNR. This is consistent with our expectations, since as the increase of SNR, the less the signal received by the receiver is interfered by noise. What’s more, in all cases, the ASE performance of the proposed algorithm exceeds the other two algorithms.

5 Conclusion In this paper, we considered the problem of channel estimation for switch-based mmWave system and proposed IALM-based channel estimation algorithm. Firstly, we utilize the incoherence and low rank of MIMO channel to reconstruct the channel estimation problem into the nuclear norm optimization problem. Then we use IALM algorithm to solve this problem, and finally obtain the IALM-based channel estimation scheme. The simulation results show that the IALM-based channel estimation algorithm has excellent NMSE performance and requires less iterations when converging. Moreover, under the entire SNR, ASE performance precedes other algorithms compared.

References 1. Feng, C., Jing, Y., Jin, S.: Interference and outage probability analysis for massive MIMO downlink with MF precoding. IEEE Signal Process. Lett. 23(3), 366–370 (2016) 2. Méndez-Rial, R., Rusu, C., Alkhateeb, A., González-Prelcic, N., Heath, R.W.: Channel Estimation and Hybrid Combining for mmWave: Phase Shifters or Switches? 2015 Information Theory and Applications Workshop (ITA) (2015) 3. Donoho, D.L., Tsaig, Y., Drori, I., Starck, J.L.: Sparse solution of underdetermined systems of linear equations by stagewise orthogonal matching pursuit. IEEE Trans. Inf. Theory 58(2), 1094–1121 (2012) 4. Myers, N.J., Heath, R.W.: Message passing-based joint CFO and channel estimation in mmWave systems with one-bit ADCs. IEEE Trans. Wireless Commun. 18(6), 3064–3077 (2019) 5. Hu, R., Tong, J., Xi, J., Guo, Q., Yu, Y.: Robust Channel Estimation for Switch-Based mmWave MIMO Systems. In: 2017 9th International Conference on Wireless Communications and Signal Processing (WCSP) (2017) 6. Candès, E.J., Tao, T.: The power of convex relaxation: near-optimal matrix completion. IEEE Trans. Inf. Theory 56(5), 2053–2080 (2010) 7. Eliasi, P.A., Rangan, S., Rappaport, T.S.: Low-rank spatial channel estimation for millimeter wave cellular systems. IEEE Trans. Wireless Commun. 16(5), 2748–2759 (2017) 8. Hu, R., Tong, J., Xi, J., Guo, Q., Yu, Y.: Matrix completion-based channel estimation for MmWave communication systems with array-inherent impairments. IEEE Access 6, 62915– 62931 (2018) 9. Lin, Z., Chen, M., Ma, Y.: The Augmented Lagrange Multiplier Method for Exact Recovery of Corrupted Low-Rank Matrices. Eprint Arxiv (2010) 10. Méndez-Rial, R., Rusu, C., González-Prelcic, N., Alkhateeb, A., Heath, R.W.: Hybrid MIMO architectures for millimeter wave communications: phase shifters or switches? IEEE Access 4, 247–267 (2016)

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11. Vlachos, E., Alexandropoulos, G.C., Thompson, J.: Massive MIMO channel estimation for millimeter wave systems via matrix completion. IEEE Signal Process. Lett. 25(11), 1675– 1679 (2018) 12. Li, X., Fang, J., Li, H., Wang, P.: Millimeter wave channel estimation via exploiting joint sparse and low-rank structures. IEEE Trans. Wireless Commun. 17(2), 1123–1133 (2018) 13. Cai, J., Candès, E.J., Shen, Z.: A singular value thresholding algorithm for matrix completion. SIAM J. Optim. 20(4), 1956–1982 (2010) 14. Yang, J., Yuan, X.: Linearized augmented lagrangian and alternating direction methods for nuclear norm minimization. Math. Comput. 82(281), 301–329 (2012)

Resource Allocation for Full-Duplex V2V Communications Underlaying Cellular Networks Fang Qu1 , Fei Wu2 , and Liang Han1(B) 1 College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin

300387, China [email protected] 2 China Electronics Technology Avionics Co., Ltd., Chengdu 610097, China

Abstract. Applying a two-way full-duplex (FD) mode to the vehicular networks can not only improve spectrum efficiency but also reduce the latency of vehicular communications. However, the residual self-interference degrades the performance of vehicular communication links in some way. In this paper, we investigate the resource allocation for FD vehicle-to-vehicle (V2V) communications underlaying cellular networks. Specifically, we formulate the resource allocation problem as the maximization of the sum ergodic capacity of all vehicle-to-infrastructure (V2I) links while fulfilling the quality-of-service (QoS) requirement of the V2V link. Then on the basis, we analyze the sum ergodic capacity of all V2I links and that of all V2V links, respectively. The simulation results show that the sum ergodic capacity of all V2I links in half-duplex (HD) mode is larger than that in FD and the sum ergodic capacity of all V2V links is reversed, and the presence of residual self-interference renders the channel states worse so that more channels are unable to achieve resource allocation to maximize the ergodic capacity of total V2I links. Keywords: Vehicular communications · Full-duplex · Resource allocation · Self-interference · Ergodic capacity

1 Introduction Wireless terminals enabled by full-duplex (FD) are characterized by transmitting and receiving simultaneously over the same frequency band and thus FD offers the potential to double their spectral efficiency [1]. The large self-interference (SI) from a node’s own transmission can be effectively attenuated by self-interference cancellation (SIC) methods, including propagation-domain SI suppression, analog-domain SI cancellation, and digital-domain SI cancellation [1, 2]. There are many existing related works concerned with resource management to achieve communication network optimization. In [3], a three-step scheme was proposed to maximize the overall network throughput while guaranteeing the quality-of-service (QoS) requirement for both device-to-device users and cellular users. Inspired by [3], the © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 842–849, 2022. https://doi.org/10.1007/978-981-19-0390-8_105

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authors in [4] approached the optimization problem in two steps, and jointly considered the heterogeneous performance of vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) links. In [5], the authors developed a novel decentralized resource allocation mechanism for V2V communications based on deep reinforcement learning. Most of the existing papers about vehicular communications are based on halfduplex (HD) mode, which transmits and receives either at different times or over different frequency bands. In this paper, the deployment of FD mode is to further improve spectrum efficiency, meanwhile, it also leads to the residual SI, so the impacts of FD vehicular networks on link capacity are investigated in this paper.

2 System Model Figure 1 shows a FD vehicular network system model, where actually consists of a base station (BS), a set of M = {1, 2, 3, . . . , M } cellular vehicle users denoted as CUEs, forming M V2I links with the BS, and a set of K = {1, 2, 3, . . . , K} vehicle-to-vehicle pairs which constitutes 2K V2V links. Each V2V pair deployed with the FD technique forms two V2V links and consists of two adjacent vehicle users, denoted as VUE1 and VUE2 , respectively. Assume each vehicle is equipped with two antennas, which are used to transmit and receive signals, respectively. To improve spectrum utilization, underlay mode is assumed, i.e., the V2I link and V2V link share the same spectrum resources. For simplicity, each orthogonally allocated uplink spectrum of V2I is reused by a V2V pair, i.e., two V2V links.

Fig. 1. A simple full-duplex vehicular network system model

Due to the fast channel variations caused by high mobility in a vehicular environment, fast fading and slow fading caused by path loss and shadowing effect both need to be taken into account. The channel power gain between the mth CUE and the BS can be expressed as hm,B = gm.B · αm,B ,

(1)

where gm,B and αm,B denote fast fading channel power gain with Rayleigh distribution and slow fading channel power gain, respectively. Similarly, we can denote the channel

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power gain of the kth V2V pair as hk , the channel power gain from the mth CUE to the kth VUE1 and VUE2 as h1m,k and h2m,k , respectively, and the channel power gain from the kth VUE1 and VUE2 to the BS as h1k,B and h2k,B , respectively. For simplicity, we assume the V2V pairs equipped with FD are hardware symmetry, meanwhile the transmit powers at kth VUE1 and VUE2 are the same. According to the described system model, the received signal-to-interference-plus-noise-ratio (SINR) at the BS for the mth CUE can be written as c = γm,FD

ch Pm m,B  ,  2 σ + ρm,k Pkv h1k,B + Pkv h2k,B

(2)

k∈K

and the received SINR at the kth VUE1 and VUE2 can be written as v,1 γk,FD =

v,2 γk,FD =

σ2 + σ2 +

 m∈M

 m∈M

Pkv hk c h1 + P v β ρm,k Pm m,k k

Pkv hk c h2 + P v β ρm,k Pm m,k k

, (3) ,

c and P v represent the transmit powers at mth CUE and kth respectively, where Pm k VUE1 /VUE2 , σ 2 denotes the noise power, β denotes the SIC coefficient (0 ≤ β ≤ 1), and ρm,k can be interpreted as

 ρm,k =

1, the kth V2V pair reuses the spectrum of the mth V2I link . 0, otherwise

(4)

To measure the performance of the full-duplex vehicular network, we regard the sum ergodic capacity of all V2I links as the optimization objective, which can be expressed as   E log 1 + γ c  max (5) v c 2 m,FD {ρ } {P } {P } m,k

s. t.

m

k

m∈M

  c E log2 1 + γm,FD ≥ c0 , ∀m ∈ M

(5a)

 

v,1 v,2 Pr γk,FD ≤ γ0 ≤ p0 , Pr γk,FD ≤ γ0 ≤ p0 , ∀k ∈ K

(5b)

c ≤ Pc , 0 ≤ Pv ≤ Pv 0 ≤ Pm max max ∀m ∈ M , k ∈ K k

(5c)



ρm,k ∈ {0, 1}, ρm,k ∈ {0, 1} , ∀k ∈ K

(5d)

ρm,k ∈ {0, 1}, ρm,k ∈ {0, 1} , ∀m ∈ M ,

(5e)

m∈M

 k∈K

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where E{·} and Pr{·} represent the expectation and probability operations, respectively, c0 denotes the minimum capacity requirement of each V2I link, γ0 and p0 denote the minimum SINR and the tolerable outage probability to guarantee the reliability requirec and ment, (5b) represents the outage probability limits at the kth VUE1 and VUE2 , Pmax v Pmax represent the maximum transmit powers at CUE and VUE1 /VUE2 , respectively. (5d) and (5e) represent the spectrum of each V2I link is reused by only one V2V pair and each V2V pair only accesses the spectrum of one V2I link.

3 Resource Allocation Scheme The optimization objective in Sect. 2, described as the formula (5), is a nonconvex optimization problem. According to [3, 4], we split the entire optimization problem into two steps— power allocation and spectrum sharing. Assume the spectrum of the mth V2I link is reused by the kth V2V pair, then the optimization of mth V2I link capacity can be written as  

c max v E log2 1 + γm,FD , ∀m ∈ M , (6) { Pc } { P } m

k

which is constrained by formula (5b) (5c). From the analysis of formula (5b), we can get 

v,1 ≤ γ0 = 1 − Pr γk,FD

Pr

v,2 γk,FD



≤ γ0 = 1 −

thus

Pkv αk c α1 + Pv α γ0 Pm m,k k k

Pkv αk c α2 + Pv α γ0 Pm m,k k k



 1 ≤ exp − 1 1 − p0 γ0 αm,k  Pkv αk 1 c exp Pm ≤ − 2 1 − p0 γ0 αm,k

c Pm

Pkv αk

 exp − 

exp −

  γ0 σ 2 +Pkv β Pkv αk



  γ0 σ 2 +Pkv β v Pk αk



≤ p0 , (7) ≤ p0 ,

  γ0 σ 2 +Pkv β v Pk αk



  − 1 = f1 Pkv ,

  γ0 σ 2 +Pkv β v Pk αk



  − 1 = f2 Pkv .

And the constraints (8) can be written as  P v αk 1 c 

k Pm ≤ exp − 1 , α2 1 − p0 γ0 max αm,k m,k

  γ0 σ 2 +Pkv β v Pk αk



  − 1 = f Pkv .

(8)

(9)

According to the SINR expression of a single V2I link sharing the spectrum with a V2V pair as c γm,FD =

ch Pm m,B , v 1 2 σ + Pk hk,B + Pkv h2k,B

(10)

it is obvious that the capacity of the V2I link is monotonically increasing c , thus the optimal power that can be obtained on the curve P c with Pm m =

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     Pv α γ0 σ 2 +Pkv β 1

k k  − 1 , where exp − v 1−p0 1 ,α 2 Pk αk γ0 max αm,k m,k with Pkv . Substituting this equation into (10), we can get c = γm,FD

αk hm,B  2



1 , α2 γ0 max αm,k m,k

σ Pkv

+ h1k,B + h2k,B



c monotonically increases Pm

1 exp − 1 − p0

γ0

σ2 +β Pv k αk



−1 , (11)

where the SINR of the V2I link sharing the spectrum with a V2V pair is monotonically c and P v in (5c), the optimal increasing with Pkv . Combining the power constraints of Pm k power allocation should be discussed as     c  v   v  v v , Pc f Pmax Pmax , Pmax > f Pmax > f Pmax c∗ v∗ max  c  , Pk = Pm = , (12) −1 c Pmax , otherwise f Pmax , otherwise c∗ and P v∗ denote the optimal CUE and VUE transmit powers that maximize the where Pm k capacity of each V2I link, respectively, the expression of function f is shown in formula (9), and f −1 represents the inverse function. After achieving optimal power allocation, then through the Hungarian algorithm [6], the optimal spectrum matching pattern to maximize the sum capacity of all V2I links can be obtained. The capacity of each V2I link sharing the spectrum with the optimal V2V pair can be expressed as





c∗ , P v∗ ) = E log 1 + γ c Cm,k (Pm 2 m,FD k



=







= E log2 1 +



c∗ h Pm m,B

σ 2 + Pkv∗ h1k,B + Pkv∗ h2k,B

1 1 1 a 1 1 1 a b c e a E1 ( ) + e b E1 ( ) + e c E1 ( ) , ln 2 (a − b)(a − c) a b c (b − a)(b − c) (c − a)(c − b)

(13)

P c∗ α

where the details of the derivation process can refer to [4], a = mσ 2m,B , b = 1  ∞ −t Pkv∗ αk,B P v∗ α 2 , c = k σ 2k,B , and E1 (x) = x e t dt. σ2 Based on Eq. (13), the maximum capacity of the V2I link that satisfies the requirement ∗ (P c∗ , P v∗ ) thus of formula (5a) and achieves the optimal matching is recorded as Cm,k m k  ∗ c∗ , P v∗ ). Cm,k (Pm the sum capacity of all V2I links can be written as k m∈M

Compared with HD mode, there are two V2V links between a V2V pair enabled by FD mode for simultaneous data transmission, thus the capacity of a single V2V pair can be expressed as

 

  v,1 v,2 Ck,FD = E log2 1 + γk,FD + E log2 1 + γk,FD       Pkv hk Pkv hk = E log2 1 + + E log 1 + 2 c h1 + P v β c h2 + P v β σ 2 + Pm σ 2 + Pm m,k k m,k k 1



1 1 1 1 1 1 1 1 1 μ1 e μ1 E1 − e μ2 E1 + e μ1 E1 − e μ3 E1 = ln 2 μ1 − μ2 μ1 μ2 μ1 − μ3 μ1 μ3

(14)

Resource Allocation for Full-Duplex V2V Communications Pv α

Pc α1

847

Pc α2

k k where μ1 = σ 2 +P μ2 = σ 2m+Pm,k μ3 = σ 2m+Pm,k v , v , v . Notably, the V2V link capacity kβ kβ kβ to be obtained is based on maximizing the sum of all V2I links.

4 Simulation Results As the high mobility in the vehicular network, the rapid change of the vehicle channels leads to more channel states in a given period. To make the simulation results closer to the actual scenario, we analyze the system performance based on ergodic capacity. Notably, in Fig. 3 (a) (b), the calculations of ergodic capacity are based on 20,000 realizable channels. The simulation follows the setup for the freeway case detailed in 3GPP TR 36.885 [7], vehicle drop model subjects to Spatial Poisson distribution and we set carrier frequency f = 2 GHz, cell radius r = 500 m, BS antenna height HBS = 25 m, BS antenna gain hBS = 8 dBi, BS receiver noise figure NBS = 5 dB, the distance between BS and highway D = 35 m, vehicle antenna height Hv = 1.5 m, vehicle antenna gain hv = 3 dBi, vehicle receiver noise figure Nv = 9 dB, c0 = 0.5 bps/Hz, γ0 = 5 dB, c v = Pmax = 23 dBm, σ 2 = −114 dBm. The slow fading parameters can refer to [7, Pmax 10]. Figure 2 shows that when the total number of channels is 104 , 105 and 106 , respectively, the number of channels that can be matched between V2I links and V2V pairs decreases sharply with the increase of SIC coefficient β, which indicates the fewer channels can achieve resource allocation to maximize the sum ergodic capacity of all V2I links when the SIC capability is weak. This is because weaker SIC capability renders the received SINR at VUE1 /VUE2 smaller so that the outage probability is more likely to exceed the tolerable outage probability p0 .

Fig. 2. The number of matched channels under different SIC coefficients β, M = 10, K = 10, v = 60 km/h, p0 = 0.01.

Figure 3 (a) shows the sum ergodic capacity of all V2I links in HD mode is larger than that in FD, which can be explained that compared with HD, there are two VUE transmitters in each V2V pair simultaneously producing interference to the V2I link when sharing spectrum with matched V2V pair so that the SINR at the BS for each CUE degrades. Figure 3 (b) shows the sum ergodic capacity of all V2V links in FD mode is larger than that in HD, which can be interpreted that there are two V2V links between

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each V2V pair to transmit safety-related messages simultaneously in FD mode, thus the data transmission rate between each V2V pair is increased. In Fig. 3 (a), the sum ergodic capacity of all V2I links both in HD and FD modes increases with the increase of p0 and that of all V2V links is reversed in Fig. 3 (b). Since larger tolerable outage probability renders VUEs more tolerable to interference from CUEs, thus prompting CUEs to increase their transmit powers, then VUEs receive more interference from CUEs.

Fig. 3. V2I (a)/V2V (b) capacity with different p0 both in FD and HD modes, M = 10, K = 10, v = 60 km/h.

5 Conclusion In this paper, we achieve the maximation of the sum ergodic capacity of all V2I links in the network through a resource allocation scheme, and on this basis, we investigate the impacts of FD vehicular networks on link capacity. The results show that the sum ergodic capacity of all V2I links in HD mode is larger than that in FD and the sum ergodic capacity of all V2V links is reversed.The presence of residual SI renders the channel states worse so that more channels are unable to achieve resource allocation. FD mode enables real-time two-way communication between vehicles, which helps to reduce transmission delay and to increase the reliability of safety-related message dissemination. The limitation of this paper is that it does not deploy the FD technique in the V2I links to achieve greater capacity between BS and CUEs, which is also the direction we need to work hard in the future. Acknowledgment. This work was supported by the National Natural Science Foundation of China (61701345), Natural Science Foundation of Tianjin (18JCZDJC31900), and Tianjin Education Commission Scientific Research Plan (2017KJ121).

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References 1. Sabharwal, A., Schniter, P., Guo, D., Bliss, D.W., Rangarajan, S., Wichman, R.: In-band fullduplex wireless: challenges and opportunities. IEEE J. Sel. Areas Commun. 32(9), 1637–1652 (2014) 2. Duarte, M., Dick, C., Sabharwal, A.: Experiment-driven characterization of full-duplex wireless systems. IEEE Trans. Wirel. Commun. 11(12), 4296–4307 (2012) 3. Feng, D., Lu, L., Yuan-Wu, Y., Li, G.Y., Feng, G., Li, S.: Device-to-device communications underlaying cellular networks. IEEE Trans. Commun. 61(8), 3541–3551 (2013) 4. Liang, L., Li, G.Y., Xu, W.: Resource allocation for V2V-enabled vehicular communications. IEEE Trans. Commun. 65(7), 3186–3197 (2017) 5. Ye, H., Li, G.Y., Juang, B.F.: Deep reinforcement learning based resource allocation for V2V communications. IEEE Trans. Veh. Technol. 68(4), 3163–3173 (2019) 6. West, D.B., et al.: Introduction to Graph Theory, vol. 2. Prentice-Hall, Upper Saddle River, NJ, USA (2001) 7. 3rd Generation Partnership Project: Technical Specification Group Radio Access Network: Study LTE-Based V2X Services: (Release 14), Standard 3GPP TR 36.885 V2.0.0 (Jun 2016) 8. WINNER II Channel Models, Standard IST-4-–027756 WINNER II D1.1.2 V1.2 (Sep 2007)

Research on Multi-modal Sensor Data Acquisition and Processing System for USV Lin Cao1 , Hao Gao1,2 , Puyu Yao1(B) , Junhe Wan1 , Hui Li1 , Jian Yuan1 , and Hailin Liu1 1 Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong

Academy of Sciences), 37 Miaoling Road, Laoshan District, Qingdao 266061, China [email protected], [email protected] 2 College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China

Abstract. Autonomous obstacle detection technology is the premise to realize autonomous navigation in the Marine environment. The fusion sensing of multimodal sensors can improve the accuracy and stability of obstacle detection. In this paper, on the premise of analyzing and comparing the performance of sensors, two sensors with different modes are selected to be used, specifically, LIDAR sensor and vision sensor. The system selects the unmanned vehicle open source framework MOOS as the development framework, and realizes the real-time reading of webcam video stream through RTSP and multi-threading. At the same time, the real-time data acquisition of LIDAR is realized through UDP communication and multi-threading architecture. Finally, a set of multi-modal data real-time acquisition system is developed. It has the characteristics of high localization rate, low cost, easy expansion and stable performance, which lays a foundation for the subsequent research on multi-modal data fusion technology. Keywords: USV · MOOS · Multi-modal sensor · Multi-threading architecture

1 Introduction With the continuous and in-depth exploration of the ocean, the technology of marine environmental exploration is required to be safer and more intelligent. In the marine environment, the surface unmanned vehicle (USV) can replace the manual autonomous completion of obstacle avoidance, navigation, maritime surveillance, marine environment mapping and other work, which greatly improves the safety and intelligence of marine environmental detection work. Environment awareness technology is the first link and key technology to realize USV autonomy. Marine environment sensing technology applied to USV specifically refers to the detection, locating, classification and recognition of obstacles on the sea, which can obtain the category information and coordinate information of ship, reef, buoys and other floating objects. It could provide obstacle information for the USV autonomous obstacle avoidance and tracking. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 850–856, 2022. https://doi.org/10.1007/978-981-19-0390-8_106

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Many research institutions and scholars at home and abroad have carried out research on marine environment sensing technology. The sensors used mainly include automatic ship identification system (AIS), vision sensor, LIDAR and so on. AIS [1] is only capable of detecting ships with AIS transponders and cannot sense obstacles without transponder. The mode of the data collected by the vision sensor [2] is two-dimensional image data, and the classification and recognition of obstacles can be realized through the processing of image pixel information. The mode of data collected by LIDAR [3] is three-dimensional point cloud data, which can obtain the location information of the surrounding obstacles, but cannot distinguish the category information. However, the combination of the LIDAR sensor and the vision sensor can be integrated with each other to make up for the shortcomings and improve the overall robustness and security of the system. At present, the research of marine environment sensing technology applied to USV is mostly focused on a single sensor, while the research of multi-modal fusion sensing technology is still lacking [4]. The main reason is that compared with the land environment, the marine environment is characterized by fog, high salinity, high humidity, large wave fluctuation, and high cost and difficulty of data set acquisition [5]. This leads to the lack of effective marine data set for algorithm research. Furthermore, it is impossible to carry out in-depth research on multi-modal fusion sensing technology. Therefore, it is of great significance to build a stable and reliable multi-modal sensor data acquisition and processing system on the USV and complete the acquisition and production of multi-modal data sets in the offshore environment. In this paper, on the premise of analyzing and comparing the performance of sensors, two sensors with different modes are selected to be used, specifically, LIDAR sensor and vision sensor. The system selects the unmanned vehicle open source framework MOOS [6] as the development framework, and realizes the real-time reading of data through RTSP and multi-threading. Finally, a set of multi-modal data real-time acquisition system is developed, which has the characteristics of high localization rate, low cost, easy expansion and stable performance and lays a foundation for the subsequent research on multi-modal data fusion technology.

2 System Hardware Architecture In the actual project, this paper selected Dahua anti-corrosion star-class webcam and Leishen intelligent 16-line LIDAR to complete the hardware construction of the multimodal sensor acquisition system, and realized the localization and low-cost hardware platform. The physical diagram of the hardware system is shown in Fig. 1. The red rectangular box the LIDAR, and the blue rectangular box is the camera. The vision sensor can collect the two-dimensional image information of obstacles, and the LIDAR can collect the three-dimensional point cloud data information of obstacles. Because the data collected by the two sensors have different data structures, it is impossible to superimpose and fuse the two data information directly. The challenge of realizing the data fusion of two sensors is how to solve the data synchronization and data alignment of multi-modal sensors.

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Fig. 1. The physical diagram of the hardware system

3 Software MOOS Module Paul Michael Newman’s proposed MOOS is an open source C++ module for Marine robots. It has the functions of motion state control, high-precision navigation path planning, concurrent task arbitration, execution safety control task recording and playing, etc. MOOS adopts modular design, regards each component unit of the ocean robot as an independent application, by use of the core module MOOSDB to communicate with each connecting Apps. Different Apps do not communicate with each other, but exchange information through publishing and subscribing operations with MOOSDB. At the same time, MOOS has abundant resources related to autonomous operation of Marine robots and sensor acquisition, which is convenient for researchers to quickly build platforms and carry out algorithm research. MOOS has the advantages of crossplatform, publicly available free and easy to expand. Therefore, it has been widely used in the related research of marine robots by research institutions and scholars.

4 Sensor Data Acquisition System Implementation Compared with more expensive imported LIDAR and industrial-grade cameras, the lowcost and home-made sensors selected in this paper lack mature secondary development software development kit (SDK) for real-time applications. Therefore, it is necessary to

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independently develop the corresponding data acquisition program, which is also one of the valuable tasks of this paper. In the actual research and development of the project, due to the limitations of USV cabin space and the requirements of low power consumption, this paper selected PC104 industrial computer board as the control module. It is compatible with serial port and network port, supporting hard disk expansion, with small size and low power consumption, easy to expand the development and other advantages. The Leishen intelligent 16-line LIDAR uses UDP protocol to transmit data. The Dahua webcam transmits video streaming data through a network port. The LIDAR and webcam can communicate with the PC104 industrial computer through the network bridge, while the USV and the upper computer can communicate through wireless communication. The time of flight measurement method is used in the Leishen intelligent 16-line LIDAR. The LIDAR sends out the laser pulse to start the timing (t1). When the laser pulse meets the target object and the laser light returns, the receiving module stops the timing (t2). So we could calculate the distance. The Leishen intelligent 16-line LIDAR is equipped with 16 pairs of laser transmitting and receiving modules, and the motor is driven by 5 Hz speed to scan 360°. LIDAR data output and configuration use UDP/IP communication protocol. There are three UDP packet protocols, namely data packet, device packet and configuration packet. The data packet outputs the angle value, distance value, strength value, timestamp and other measurement data of the point cloud. In order to facilitate the use of point cloud data, the original data in the data packet needs to be transformed into three-dimensional coordinate data. In the process of implementation, a separate thread is set up to complete the data acquisition, in order to realize the real-time data acquisition and data conversion. The video stream captured by the webcam needs to be decoded to obtain image data. Real Time Streaming Protocol (RTSP) is an application-layer protocol in the TCP/IP protocol system, which is used to initiate and end streaming media transmission, exchange streaming media meta information, and has the advantages of real-time and expansibility. The webcam video encoding format selected in this paper is H.264, which supports RTSP transmission through TCP. Based on FFMPEG, OpenCV integrates the function of directly reading video stream through RTSP protocol and decoding to get image frames. The advantages of this method do not need additional research on FFMPEG and other multimedia processing tools, which reduces the development cost of researchers. Figure 2 is the flow chart of the webcam data acquisition program. The key point is to set two threads to carry out image data acquisition and subsequent image data processing algorithm based on OpenCV respectively. Through the application of multi-thread, the data jam and memory overflow caused by the long time consuming of image processing algorithm are avoided.

5 MOOS Function Interface Implementation The framework of MOOS takes MOOSDB (MOOS Database) as the core, and various application programming interfaces (API) are connected with the same MOOSDB. Sensor information collected by each sensor communicates with MOOSDB at a certain frequency. Currently open source APIs of MOOS related to marine robot sensor

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Fig. 2. The flow chart of the webcam data acquisition program

including Idvl, iCompass, iINS, iGPS, pNav, iRemote, uMS, etc., each have the corresponding.moos file suffix, realize the corresponding function. Currently, the open source resources of MOOS have no API for LIDAR and webcam, so it needs to be independently developed based on the MOOS framework. The class library of MOOS, which named after CMoosApp, defines the base classes that implement the data processing and implementation methods of each sensor module. The LIDAR sensor module collects data through UDP communication, and publishes the data after analyzing and coordinate transformation to MOOSDB. The API program is implemented as follows:

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bool LidarPublish::OnStartUp( ) { /* initialize settings */ LidarDriver::udp_init( ); LidarDriver::Lidar_init( ); /* Read radar data in circles */ LidarDriver::ProduceLidarData( ); return(true); } bool LidarPublish::Iterate( ) { LidarDataParser( ); /* Analyze the radar data and transform the coordinates*/ PublishData ( ); /* Publish radar data to MOOSDB*/ return(true); }

Where, LidarDriver:: udp_init() configures the Socket according to the IP address of the LIDAR to realize UDP communication. LidarDriver::Lidar_init() sets parameters such as the port mode and vertical Angle of the LIDAR. PublishData () function sends the LIDAR data to MOOSDB, and finally, the newly added LIDAR module is configured in the configuration file of.moos, and the software is started with pAntler command to realize the collection of LIDAR data. The webcam module collects video data through the network communication based on RTSP, and obtains image data frames through video decoding, which is finally published to MOOSDB. The framework of the API program is similar to that of LIDAR, which will not be described here.

6 Conclusion On the premise of analyzing and comparing the performance of sensors, this paper adopts two sensors of different modes: LIDAR sensor and vision sensor. The open source framework of unmanned vehicle MOOS is selected as the development framework. The real-time reading of webcam video stream is realized through RTSP and multi-threading, and the real-time data acquisition of LIDAR is realized through UDP communication and multi-threading. Finally, the hardware and software platform of the data acquisition system is built. In the future, based on this platform, the collection and labeling of two modal data sets will be executed, and the research of multi-modal sensor fusion sensing algorithm will be further carried out. Acknowledgments. This work was supported in part by the Youth Innovation and Technology Support Program in Colleges and Universities of Shandong Province under Grant 2019KJN009, in part by the Training Fund in Institute of Oceanographic Instrumentation of Shandong Academy of Sciences under Project HYPY202108, in part by the State Key Laboratory of Tropical Oceanography in South China Institute of Oceanology under Project LTO2017 and in part by the Open Research Fund of the State Key Laboratory of Estuarine and Coastal Research under Project SKLEC-KF202001.

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References 1. Mazzarella, F., Vespe, M., Alessandrini, A., et al.: A novel anomaly detection approach to identify intentional AIS on-off switching. Expert Syst. Appl. 78, 110–123 (2017) 2. Chen, J., Wang, H.: Wide-baseline obstacle mapping using monocular camera for unmanned surface vehicle. In: 2019 IEEE 15th International Conference on Control and Automation (ICCA), pp. 512–517. Edinburgh, United Kingdom (2019). https://doi.org/10.1109/ICCA. 2019.8899983 3. Wang, S., Xie, L., Ma, F., Wu, C., Xu, K.: Research of obstacle recognition method for USV based on laser radar. In: 2017 4th International Conference on Transportation Information and Safety (ICTIS), pp. 343–348. Banff, AB (2017. https://doi.org/10.1109/ICTIS.2017.8047787 4. Zhu, J., Yu, M., Yang, Y.: Overview of development of unmanned-surface-vehicle sensing technology. J. Harbin Eng. Univ. 41(10), 1486–1492 (2020). https://doi.org/10.11990/jheu. 202007111 5. Muhoviˇc, J., Mandeljc, R., Bovcon, B., Kristan, M., Perš, J.: Obstacle tracking for unmanned surface vessels using 3-D point cloud. IEEE J. Oceanic Eng. 45(3), 786–798 (2020). https:// doi.org/10.1109/JOE.2019.2909507 6. https://oceanai.mit.edu/moos-ivp/pmwiki/pmwiki.php?n=Main.HomePage

An Improved Filtering Method of Superfluous Relationship in Domain Model Based on Random Forest Mengyuan Yu(B) , Lisong Wang, Buzhan Cao, and Yang Hong Nanjing University of Aeronautics and Astronautics, Nanjing, China [email protected]

Abstract. Domain model is a beneficial tool to make the interpretation and refinement of natural language necessities extra accurate. Natural Language Processing (NLP) makes it viable to mechanically extract most of the data associated to domain modeling from requirements. However, in addition to the relevant information, NLP additionally extracts a giant quantity of records unrelated to the domain model from the requirements. The motive of this paper is to enhance the current domain redundancy filtering method. Therefore, this paper designs a technique primarily based on dual cost sensitive random forest. According the analysis of the statistics set, the training features are designed. According to the analyst’s comments on the relevance and redundancy of the extracted foremost model elements, the redundant relationships are categorized through the use of the comments information. In this paper, the demand of satellite components is taken as an instance to consider the method. The accuracy of experiment is 91.2%, which is 2.1% greater than the present methods. Keyword: Requirements · Domain model · Dual cost sensitive random forest · Feature selection · NLP

1 Introduction Requirement description refers to the document that specifies the functions and modules that the target system must have in the early stage of system development. Natural language (NL) is used prevalently for expressing systems and software requirements [1]. The recent improvement of NLP makes it viable to mechanically extract domain models from requirement texts. And we optimized the approach [2] to make it greater correct in the particular area (airborne display and control requirements) [3]. Arora et al. proposed an active-learning-based approach that iteratively learns from analysts’ feedback over the relevance and superfluousness of the extracted domain model elements and uses this feedback to provide recommendations for filtering superfluous elements This work is supported in part by the National Basic Research Program of China under Grant No. 2014CB744900. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 857–862, 2022. https://doi.org/10.1007/978-981-19-0390-8_107

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[4]. The accuracy of random forest classification is 89.2% in his experiment. In this paper, we use dual cost sensitive random forest (DCSRF), and advocate new training features, supplemented through super parameter optimization, the closing experimental accuracy is 91.2%.

2 Domain Model The system requirements are reprocessed by adopting the results of parse tree. We utilize Stanford Parser to mark symbols. These symbols will be removed or transformed without affecting the semantics. Figure 1 shows an overview of model extraction methods.

Fig. 1. An overview of model extraction methods

The pipeline in Fig. 2 deals with the transformed requirements. Afterwards, experts evaluate the results of domain model. Figure 3 shows the domain model extraction result of the requirement statement “The Engine HF shall provide a normal and compressed Engines layout”.

Fig. 2. Parsing pipeline

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Fig. 3. Sample extraction result

3 Improved DCSRF Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. This paper uses a random selection of features to split each node yields error rates that compare favourably to Gadabouts [5]. A basic characteristic cost sensitive decision system can be summarized as follows: S = (U , F, D, Va |a ∈ F ∪ D, Ia |a ∈ F ∪ D, C∗)

(1)

U is a global finite set of objects. F is the feature set and D is the decision set. V a is the value set of every a ∈ U. I a : → V a is the information function of every a ∈ U. C*: → R+ ∪ 0 is the characteristic cost function [6]. Compared with ordinary random forest, DCSRF introduces feature selection and sequential analysis in feature vector generation stage. The Gini coefficients of DCSRF are adjusted in the classification algorithm of regression tree (CART), and the Gini coefficients are used in the CART to construct binary decision trees. In the binary classification problem, the Gini coefficient is approximately half the entropy, but the calculation is simpler. Gini(D) = 1 −

n 

pi2

(2)

i=1

D is the whole sample. pi is the probability of each category. P = 1 is Gini = 0 in the extreme case, and impurity is the lowest and most stable. As shown in Table 1, some of the features are label independent and some are label dependent. Table 1. Feature matrix Id

Definition

IF1

Relationship types (Association, aggregation, attribute, generalization)

IF2

Rules of relation extraction

IF3

If the relationship conforms to the link path (LP) rule, then IF3 represents the depth of LP, otherwise IF3 does not hold (continued)

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Id

Definition

IF4

The cumulative length of all tokens in the relationship

IF5

The minimum number of tokens in the associated source concept and target concept

IF6

The Maximum number of tokens in the associated source concept and target concept

IF7

Any concept in a relationship is a marker in the definition dictionary

IF8

Any ending concept in a relationship contains a conjunction

DF1

The number of relevant relations in the training set

DF2

The number of relationships in the training set that end with the source concept of the relationship in question

DF3

The number of relationships in the training set that end with the target concept of the relationship in question

DF4

For a given relation r, S(r) denotes the union of all noun phrases and verbs that appear at the end of r and in the name of r. Let q be the relation in question, and DF4 be the number of relations r in the training set, where S(r) ⊆ S(q)

DF5

Correlation coefficient of two end points shared by relevant relations in training set

DF6

The training set shares the same correlation coefficient with correlation (only applicable to association)

DF7

Replace “relevant” in DF1 with “superfluous”

DF8

Replace “relevant” in DF2 with “superfluous”

DF9

Replace “relevant” in DF3 with “superfluous”

DF10

Replace “relevant” in DF4 with “superfluous”

DF11

Replace “relevant” in DF5 with “superfluous”

DF12

Replace “relevant” in DF6 with “superfluous”

DF13

For a given relation r, S(r) denotes the union of all noun phrases and verbs that appear at the endpoint of r and the name of DF13. Let q be the relation in question, and DF13 be the number of relations r in the training set, where S(q) ⊆ S(r)

DF14

Replace “relevant” in DF13 with “superfluous”

DF15

The number of relevant relations extracted from the same requirement by the training set

DF16

Replace “relevant” in DF15 with “superfluous”

NF1

For a given relation, whether there are duplicate relations in the existing relation set (true/false)

4 Experimental Process This section introduces the experimental process of domain model redundant relationship filtering. As shown in Fig. 4, the input of the experiment is the relation set extracted by NLP. NLP processing pipeline uses the natural language analysis plug-in provided by GATE.

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DCSRF

Relation set extracted from NLP

Experts decide relevance

The relationship is marked

Label relation is used to train classifier

Fig. 4. Experimental process

The experimental object is the requirement of satellite components. There are 110 demands, and 33 demands are sampled. Experts provided feedback for 157 relationships, that is, relevant or superfluous judgment. Table 2 shows the accuracy comparison of DCSRF and other experiment results. The accuracy of this method has been improved. Table 2. Optimal parameter setting of DCSRF Method

Accuracy

Random forest

69.7%

Arara’s method

89.1%

DCSRF

91.2%

In addition to the optimization of random forest, our optimization of super parameters also improves the accuracy of redundant relation filtering. Table 3 is the best super parameter setting of DCSRF. Table 3. Optimal parameter setting of DCSRF Parameter name

Parameter meaning

Value

n_estimators

The number of subtrees in forest

9

max_depth

Maximum depth of tree

– 1

min_samples_split

The minimum number of samples required for node splitting is less than this value, and nodes terminate splitting

6

min_samples_leaf

The minimum sample number of leaf nodes, less than which leaves are merged

2

min_split_gain

The minimum gain required for splitting is less than this value, and nodes terminate splitting

0.0

colsample_bytree

The proportion of random sampling of features in tree building

Sqrt

subsample

Subsampling scale

0.7

random_state

Seed of random number generator

81

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M. Yu et al.

5 Conclusion This paper provides the description and selection of features, parameter adjustment and accuracy evaluation. The features that we defined and employed for machine learning are targeted specifically at detecting the relevance and superfluousness of domain model elements. We improve and evaluate a method to filter redundant elements from the output of domain model extraction tools. The experimental results show that the filtering accuracy of our method reaches 91.2%. For the future work, we would like to further improve our method to improve the accuracy of domain model expression while trying to improve the filtering accuracy. Another problem to be improved is whether we can build a set of automatic tools to improve the efficiency in the annotation of the initial data set. The existing method is to collect experts’ feedback and label the data set in a semi-automatic way. Additionally, we hope to have a complete feedback collection system to improve work efficiency.

References 1. Pohl, K., Rupp, C.: Requirements Engineering Fundamentals. Rocky nook, San Rafael (2011) 2. Arora, C., Sabetzadeh, M., Briand, L., Zimmer, F.: Extracting domain models from naturallanguage requirements: approach and industrial evaluation. In: ACM/IEEE International Conference on Model Driven Engineering Languages & Systems. ACM (2016) 3. Mengyuan, Y., Lisong, W., Jiexiang, K., Zhongjie, G., Buzhan, C.: Automatic generation method of airborne display and control system requirement domain model based on NLP. In: 2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS). IEEE (2021) 4. Arora, C., Sabetzadeh, M., Nejati, S., Briand, L.: An active learning approach for improving the accuracy of automated domain model extraction. ACM Trans. Softw. Eng. Methodol. 28(1), 1–34 (2019) 5. Breiman, L.: Random forest. Mach. Learn. 45, 5–32 (2001) 6. Zhou, Q., Zhou, H., Li, T.: Cost-sensitive feature selection using random forest: selecting low-cost subsets of informative features. Knowl. Based Syst. 95, 1–11 (2016) 7. GATE NLP Workbench. http://gate.ac.uk/

Hidden Markov Model and Its Application in Human Activity Recognition and Fall Detection: A Review Tingting Xue(B) and Hui Liu University of Bremen, Bibliothekstraße 1, 28359 Bremen, Germany {xue,hui.liu}@uni-bremen.de

Abstract. This paper firstly describes the research framework of Human Activity Recognition and Fall Detection, as well as Hidden Markov Model and its extension with continuous observations and hierarchical topology, namely the Continuous Density Hidden Markov Model and the Hierarchical Hidden Markov Model. Subsequently, we introduce how to apply Hidden Markov Models to the human activity modeling in Human Activity Recognition and Fall Detection based on previous literature. Finally, famous research work for smart home technology and elderly care based on Hidden Markov Models is reviewed. Keywords: Hidden Markov model · Continuous density hidden Markov model · Hierarchical hidden Markov model · Human activity recognition · Fall detection · Activity modeling · Smart home

1 1.1

Background: Human Activity Recognition and Fall Detection Research Framework of Human Activity Recognition (HAR) and Fall Detection (FD)

The essential research aspects in the HAR and FD study, which compose a pipeline of the research framework, are introduced as follows: Recording Equipment and Data Acquisition. All recognition task is based on data and usually big data. So, we should first choose suitable devices and equipment for sensing the signals emitted from the human body and then use these devices to acquire enough data for the following research. Data Segmentation and Annotation. After we have the acquired data, we need to segment them into pieces, such as single motions, activities, or activity sequences. The annotation or labeling on the segments is also required since we want to train our machine learning model with the data segments. c The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022  Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 863–869, 2022. https://doi.org/10.1007/978-981-19-0390-8_108

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Digital Signal Processing (DSP) and Feature Extraction. The recorded data, or the segmented data pieces, are so-called raw data, which contain sequences of original values/vectors within a duration. In order to fit the feature extraction, DSP is a necessary step to polish these values. For example, we could apply signal normalization, amplification, or filtering to the data to make them more suitable for training and recognition. Subsequently, we can extract several features from the processed data. Simple example features of the human activity signals can be average values, variance, among others. More complex feature calculations can also be obtained directly from public feature libraries, such as [1]. So as to optimize the feature extraction and model training, sometimes feature space reduction and feature vector stacking are researched [2,3]. Human Activity Modeling of Machine Learning (ML). After preparing the data for the recognition system, we need to have a machine learning model for training and decoding. Here we can apply, for example, artificial neural networks (ANNs) [4]. As a further deepening of ANNs, Deep Neural Networks (DNNs) [5] play a cornerstone role in image recognition. On this basis, many research works have shown the ability of Convolutional Neural Networks (CNN) for image-based HAR [6]. Recently, Residual Network (ResNet) [7] shows its ability of HAR [8]. Moreover, Hidden Markov Model [9,10] is another useful human activity modeling tool that may better explain the internal structure of human activities than neural networks [11], which is the focus of this paper. Model Training, Evaluation, Tuning and Model Application. We apply the ML model and the labeled data to train the created models, recognize new input data, evaluate the recognition system, and tune parameters and models based on the experimental analysis. If the recognition system works well for our purpose, it could be put into use for real-world application. 1.2

Sensing Technology for HAR and FD

There are many types of sensing technologies for HAR and FD in a home environment, as categorized in Fig. 1. Basically, sensing technologies can be divided into two approaches, namely external and internal sensing methods. In external sensing, the devices are fixed in predetermined points of interest, so the inference of activities entirely depends on the voluntary interaction of the users with the sensors. For example, an intelligent home usually applies sensors to sense the signals emitted from the human body, while video-based activity and fall recognition try to apply image processing and recognition algorithms on the recorded video or image data, such as [12]. In internal sensing, the devices are attached to the user, which leads to the research topic of signal-based activity recognition using wearable sensors, such as smartwatches and smartphones.

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Fig. 1. Categories of sensing technologies for HAR and FD.

2 2.1

Hidden Markov Model and its Extension Hidden Markov Model (HMM)

We denote T as the length of the observation sequence (i.e., t = 1, 2, 3, ..., T ), the number of states S in the model by N and the number of observation symbols by M . A discrete observation HMM is then formally defined as 5-tuple λ = (S, V, π, A, B) [9]: S = {s1 , s2 , ..., sN }: the set of all possible states; V = {v1 , v2 , ..., vM }: the discrete set of possible symbol observations; π = {π(si )}, π(si ) = P (q1 = si at t = 1): the initial state distribution; A = {aij }, aij = P (qt+1 = sj |qt = si ), 1 ≤ i, j ≤ N : state transition probability distribution, usually represented as a matrix of probabilities, called transition matrix; – B = {bi (k)}, bi (k) = P (Vk at t|qt = si ), 1 ≤ i ≤ N, 1 ≤ k ≤ M : observation symbol probability distribution, usually also represented as a matrix of probabilities, called emission matrix.

– – – –

There are three basic problems of interest that can be solved for the model to be useful in real-world applications [9]: Problem 1: Evaluation Problem. Given the observation sequence O = O1 , O2 , ..., OT and a model λ, how do we efficiently compute P (O|λ), the probability of the observation sequence, given the model?

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Problem 2: Decoding Problem. Given the observation sequence O = O1 , O2 ..., OT , and the model λ, how do we choose a corresponding state sequence Q = q1 , q2 , ..., qT which is optimal in some meaningful sense (i.e., best “explains” the observation O)? HAR and FD are related to the solution of the second problem, for which a famous approach is the Viterbi algorithm [10]. Problem 3: Optimization Problem. How do we adjust the parameters A, B and π of the model λ to maximize P (O|λ)? 2.2

Continuous Density Hidden Markov Model (CDHMM)

In the HAR or FD task, the observations are the signals/segments/features instead of a finite set of discrete symbols. An obvious extension to the basic HMM model is to allow continuous observation space instead of a finite number of discrete symbols. In this model, parameters B (emission matrix) cannot be described as a simple matrix of point probabilities but rather as a complete Probability Distribution Function (PDF) over the continuous observation space for each state. Therefore, the values of bi (k) in the 5-tuple of HMM must be replaced with a continuous conditional probability distribution [13]: bj (x(t)) = P (x(t)|qt = si ), ∀x(t), i.

(1)

Equation 1 is called CDHMM. The conditional distributions (PDF) bj (x(t)) can, theoretically, be arbitrary, but usually, they are restricted to be finite mixtures of simple parametric distributions, like Gaussians. 2.3

Hierarchical Hidden Markov Model (HHMM)

The HHMM applied state transition in a hierarchy-based structure. Thus, the definitions of states, observations, emission probabilities, and initial distribution in the 5-tuple of HMM or CDHMM remain while the transition pattern changes. In a traditional HMM, the transition reveals the relationship between two activities with probabilities, which could be regarded as a “linear” transition. In an HHMM, the entire structure is like a tree. In a high level of activity transition, some activities could have their low-level sub-activities. The sub-activities also have their transition relationship. Taking the HMM modeling in [14] as an example, the “take a break” and “alternate workstation work” activities are at the same level. Thus, they have a transition relationship of probabilities with each other. Furthermore, the “take a break” activity could be sub-divided into several low-level sub-activities, such as “going on break” and “going off break”, forming their individual transition topology. There is no direct path from the “going on break” activity to the “alternate workstation work” activity because they are neither in the same hierarchy nor in the same inheritance relationship.

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HMM for Activity and Fall Modeling State-of-the-Art Topology Study of HMM Human Activity and Fall modeling for Internal Sensing

HMM is powerful for modeling human activities, such as single motions like “walk,” “stand,” “sit,” “jump,” and “fall.” Each activity can be modeled using a single HMM state [15,16], or a fixed number (greater than one) of HMM states [2,16,17], and both topologies work but with shortcomings. Single-state. The single-state model for the “stand” activity may be enough, but for a more complicated motion like the “walk” and “fall” activities, the single-state may not be sufficient. The single-state modeling has also been applied successfully in a real-time HAR system [16]. Fixed Number of States. Some literature models each single motion activity with six [2] or ten [17] states, which achieves good recognition accuracy in different applications. However, these six or ten states are not interpretable, similar to neural network modeling. Moreover, there may exist redundancy (e.g., six HMM states of the “walk” activity may work well, but applying six HMM states to the “sit” activity should be over-engineering). Distinct Number of States for Different Activities. Recently, state-ofthe-art research tries to model different activities with different numbers and meanings of phases and states, called phase and state partitioning [11]. For example, one gait of the “walk” activity contains two phases and five states: the grounding-contact phase (three HMM sates) and the swing phase (two HMM states), as seen from one leg’s view. HMM State Generalization. Moreover, based on the above-introduced phase and state partitioning, some states can be shared among activities, like the situation in speech recognition. The generalized states are called Motion Units [11]. By applying this topology, falls can also be efficiently modeled with state generalization, and different types of falls [18] can be well recognized instead of only being detected as one activity, “fall.” 3.2

Application of HMM for External Sensing

HMM also works for the external sensing, such as in an activity monitoring system for elderly care [19]. In this work, five types of wireless sensors, such as pressure mats, float sensors, and temperature sensors, are applied in the sensor network. The activities in this research are motion sequences rather than single motions, such as “leave,” “shower,” “sleep,” and “drink.” Four datasets are used to evaluate the activity recognition for elderly care. The HMM-based recognition results prove the capability of HMM modeling for external sensing of motion sequences, which helps assist elderly people.

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HHMM in the Smart Home Application

HHMM, as introduced in Sect. 2.3, can also be well applied in an external sensing system for smart home technology. For example, the so-called MavHome project equips the rooms with several external sensors [14]. The system can control all lights, appliances, fans, heaters, and window blinds as an interactive result based on activity recognition called a data-driven approach for an intelligent environment. However, there are many challenges to solve for such an environment, such as sensor network failure, power outages, and slow and unreliable human behaviors.

4

Summary

This paper goes through the research framework and topics about HAR and FD, then briefly introduces HMM and its extension: CDHMM and HHMM, followed by the HMM modeling topologies for human activities in real applications with the description of several research works and projects.

References 1. Barandas, M., et al.: TSFEL: time series feature extraction library. SoftwareX 11, 100456 (2020) 2. Hartmann, Y., Liu, H., Schultz, T.: Feature space reduction for multimodal human activity recognition. In: Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies, vol. 4, pp. 135–140. SciTePress, INSTICC (2020) 3. Hartmann, Y., Liu, H., Schultz, T.: Feature space reduction for human activity recognition based on multi-channel biosignals. In: Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies, pp. 215–222. SciTePress, INSTICC (2021) 4. Hopfield, J.J.: Artificial neural networks. IEEE Circ. Dev. Mag. 4(5), 3–10 (1988) 5. Miikkulainen, R., et al.: Evolving deep neural networks. In: Computing Research Repository (CoRR), abs/1703.00548, arXiv (2017) 6. Yang, J., Nguyen, M.N., San, P.P., Li, X., Krishnaswamy, S.: Deep convolutional neural networks on multichannel time series for human activity recognition. In: International Joint Conference on Artificial Intelligence 2015, vol. 15, pp. 3995– 4001. PMLR, Buenos Aires (2015) 7. He, K., Zhang, X., Ren,S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition 2016, pp. 770–778. IEEE, Las Vegas (2016) 8. Tuncer, T., Ertam, F., Dogan, S., Aydemir, E., Plawiak, P.: Ensemble residual network-based gender and activity recognition method with signals. J. Supercomput. 76(3), 2119–2138 (2020) 9. Rabiner, L.R.: A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE 77(2), 257–286 (1989) 10. Rabiner, L.R., Juang, B.H.: An Introduction to Hidden Markov Models. IEEE ASSP Mag. 3(1), 4–16 (1986)

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11. Liu, H., Hartmann, Y., Schultz, T.: Motion units: generalized sequence modeling of human activities for sensor-based activity recognition. In: 29th European Signal Processing Conference. IEEE, Dublin (2021) 12. Yang, L., Ren, Y., Zhang, W.: 3D depth image analysis for indoor fall detection of elderly people. Digital Commun. Netw. 2(1), 24–34 (2016) 13. Continuous Density Hidden Markov Model. http://www.cs.helsinki.fi/u/ahonkela/ dippa/node35.html/ 14. Youngblood, G.M., Cook, D.J.: Data mining for hierarchical model creation. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 37(4), 561–572 (2007) 15. Liu, H., Schultz, T.: ASK: a framework for data acquisition and activity recognition. In: Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies, vol. 3, pp. 262–268. SciTePress, INSTICC (2018) 16. Liu, H., Schultz, T.: A wearable real-time human activity recognition system using biosensors integrated into a knee bandage. In: Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies, vol. 1, pp. 47–55. SciTePress, INSTICC (2019) 17. Rebelo, D., Amma, C., Gamboa, H., Schultz, T.: Human activity recognition for an intelligent knee orthosis. In: 6th International Conference on Bio-inspired Systems and Signal Processing, pp. 368–371. SciTePress, INSTICC (2013) 18. Micucci, D., Mobilio, M., Napoletano, P.: UniMiB SHAR: a dataset for human activity recognition using acceleration data from smartphone. Appl. Sci. 7(10), 1101 (2017) 19. Van Kasteren, T.L.M., Englebienne, G., Kr¨ ose, B.J.A.: An activity monitoring system for elderly care using generative and discriminative models. Pers. Ubiq. Comput. 14(6), 489–498 (2010)

Spectral–Spatial Feature Completely Separated Extraction with Tensor CNN for Multispectral Image Compression Tongbo Cao1(B) , Ning Zhang2 , Shunmin Zhao1 , Kedi Hu1 , and Kang Wang1 1 College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing

210016, China [email protected] 2 National Key Laboratory of Science and Technology on Aircraft Control, AVIC Xian Flight Automatic Control Research Institute (Facri), Xian 710065, China [email protected]

Abstract. Considering the rich spectral and spatial information of multispectral image, a learned multispectral image compression method named spectral-spatial feature completely separated extraction with tensor CNN is proposed. In encoder, two parallel modules, spectral module and spatial module, are applied to extract spectral and spatial features respectively. Then in order to reduce the loss in the downsampling process, two tensor layers that integrate tucker decomposition and neural network are utilized to decompose the multiway features. The decomposed feature tensors are concatenated along the channel dimension, saving the spectral and spatial features separately. To recover the features, the concatenated tensor is split in symmetric decoder after quantizer and entropy codec. Finally, postprocessing module is utilized to remove the compression artifacts of reconstructed image fused by recovered feature tensors. Besides, rate-distortion optimization is embedded to balance the trade-off between the rate loss and the distortion. Experiments on eight-band dataset from the WorldView-3 satellite demonstrate our proposed method outperforms JPEG2000 and 3D-SPIHT in PSNR. Keywords: Multispectral image compression · Spectral-spatial feature · Completely separated extraction · Tensor layer · Rate-distortion optimization

1 Introduction Multispectral image compression can compress data dozens of times, greatly relieving the pressure of storage and transmission. Different from RGB image compression, which only needs to focus on removing spatial redundancy in general, spectral redundancy should be taken into account in multispectral image compression. Traditional image compression standards, including JPEG, JPEG2000 and 3DSPIHT [1], transform the image from the spatial domain to the frequency domain, concentrating the energy and compressing the data furtherly. However, this will cause © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 870–875, 2022. https://doi.org/10.1007/978-981-19-0390-8_109

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compression artifacts especially when the compression ratio is high. While Convolutional Neural Network (CNN) based image compression, normally with autoencoder as the main body, completely avoids this problem. Zhang et al. [2] proposed a non-local attention network to capture the long-range dependencies between spatial pixels due to the limitation that general convolution operation can only learn the local feature of image. To remove the compression artifacts, Zhou et al. [3] introduced a residual blocks based post-processing module. Some other models incorporates a hyperprior to efficiently capture the spatial correlation [4, 5], which has shown great results in image compression. Nevertheless, they do not consider the dependency and redundancy between channels of image. In this paper, we propose a novel architecture composed of two parallel modules, spectral module and spatial module, learning spectral and spatial features separately. Instead of inserting multiple traditional pooling layers into the network, we utilize a tensor layer to decompose the features after each module, reducing the loss caused by pooling. Notably, the spectral and spatial features are concatenated into a feature tensor at the end of encoder which is split at the beginning of decoder, and the recovered feature tensors are fused to reconstructed image at the end of that, that is, spectral–spatial feature extraction is completely separated. Since then, we operate 2D group convolution in different dimensions of image to extract spectral and spatial features pertinently, and a simplified attention module is introduced into the spatial module to focus on the complex texture. In order to remove the compression artifacts of reconstructed image, a post-processing module is embedded into the network. The proposed architecture leads to a parallel processing mode, which extracts and compresses spectral and spatial features completely separately, and the experimental results demonstrate that our method achieves a better performance on PSNR than JPEG2000 and 3D-SPIHT.

2 Proposed Method Our proposed multi-spectral image compression network is shown in Fig. 1, which is an end-to-end architecture with an encoder and a roughly symmetrical decoder. The main characteristic of the architecture is that the spectral and spatial feature extraction is parallel and independent, which means that, unlike the other researchers’ parallel model [6], we recover the two kinds of features respectively and do not need to consider the impact on feature recovery in decoder after features fusion in encoder. Therefore, what we need to focus on is the extraction, compression and final fusion of two features. 2.1 Spectral Module and Spatial Module As mentioned above, one of the most typical points is feature extraction. The spectral module and spatial module are designed for spectral and spatial feature extraction respectively. As illustrated in Fig. 1, the backbone of modules consists of convolution layers that have proved excellent performance in image processing. The GDN/IGDN implement is applied to activate neurons in spatial module, while LeakyReLU in spectral module. In the spatial module, we operate group convolution with groups equal to the number of image bands, i.e. 8 groups in this paper, to capture spatial feature only. A simplified

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Fig. 1. Illustration of the proposed multi-spectral image compression autoencoder. Group convolution parameters are denoted as (output channels, kernel size, stride, padding, groups), and size of 3 indicates 3 × 3, while (3, 1) denotes 3 × 1. Permute (B, W, 64, H) denotes that the dimension order of input is permuted to (Batch size × feature width × 64 × feature height)

attention module [7] is adopted to pay more attention to complex regions. we furtherly simplify and elaborately design it for our spatial module, as shown in Fig. 2. The 1 × 1 convolution in the attention branch is removed, and the original convolution layers are replaced with group convolution layers. In the spectral module, the dimension order of input feature is permuted to (batch size × feature width × channel × feature height) after a 1 × 1 group convolution, so that we can operate group convolution with kernel size of 3 × 1 on the dimension of (channel × feature height) to extract spectral feature. At the end, the dimension order of feature is changed back to the original order so as to concatenate two features along the channel dimension after tensor layer. 2.2 Tensor Layer Since the intermediate feature is a multiway tensor after the Batch dimension is added to the multispectral cube, which leads to the curse of dimensionality and huge amount

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Fig. 2. Illustration of the simplified attention module

of data. Although traditional pooling layer can alleviate these problems, multiple pooling operations will cause much more information loss. Chien et al. [8] presented a Tensor-Factorized Neural Networks, which integrates tucker decomposition and Neural Networks, to learn multiway data representation. Tucker decomposition, a form of higher-order PCA, decomposes a N-way tensor X ∈ RI1 ×I2 ×···×IN into a smaller core tensor G ∈ RJ1 ×J2 ×···×JN multiplied by a factor matrix M (n) ∈ RIn ×Jn along n-mode with a little loss, which can be defined as X ≈ G ×1 M (1) ×2 M (2) ×3 · · · ×N M (N )

(1)

where ×n denotes n-mode product. And the elementwise representation form is xi1 i2 ···iv ≈

J2 J1   j1 =1 j2 =1

···

JN  jN =1

(1)

(2)

(N )

gji j2 ···jN mii ji mi2 j2 · · · miN jN

(2)

where in = 1, 2, · · · , IN and JN is smaller than IN . We adopt backpropagation to train and update the factor matrices to reduce the information loss. In other words, we can replace the pooling layer with this called tensor layer with less information loss. In the three-way case, the Tucker decomposition is illustrated in Fig. 3.

Fig. 3. Illustration of Tucker decomposition of a three-way tensor

While in this paper, the feature tensor is a four-way tensor, that is, N is equal to 4, and we decompose all the dimensions except the Batch dimension. Thus, for this case,

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the factor matrix for the mode of the Batch dimension is set to be an identity matrix I ∈ RI1 ×I1 , which can be formulated as X ≈ G ×1 I ×2 M (2) ×3 M (3) ×4 M (4) = G ×2 M (2) ×3 M (3) ×4 M (4)

(3)

We utilize Eq. (3) to recover the feature tensor in the decoder, that is, G ∈ RB×Cm ×Wm ×Hm represents the output of Entropy decoding, X ∈ RB×64×W ×H denotes the input of spectral or spatial module, M (2) , M (3) and M (4) are learnable parameters. While in the encoder, the inverse operation of Tucker decomposition is used to decompose the output of two modules, which can be calculated by G ≈ X ×2 M (2)† ×3 M (3)† ×4 M (4)†

(4)

where M (n)† = (M (n)T M (n) )−1 M (n) is the pseudoinverse of M (n) . In practice, we initialize M (n)† directly at the beginning of training. 2.3 Post-processing In order to reconstruct the image, the outputs of two modules are simply fused by element-wise addition. To remove the compression artifacts of reconstructed image, a module named post-processing, as illustrated in Fig. 4, is embedded into the network, which is composed of five residual blocks that are the same as the simplified attention module.

Fig. 4. Illustration of post-processing module

3 Experiments 8700 8-band images with a size of 128 × 128 cropped from the WorldView-3 satellite images are used for training and the other 100 images with the same size are for test. The output size of tensor layers in encoder is set to Cm × Wm × Hm = 24 × 16 × 16, that is, the output size of the encoder is 48 × 16 × 16. Batch size is set to 24 and learning rate decreases from 1 × 10−4 to 1 × 10−5 . Adam optimizer is utilized to train the network with the MSE loss function. And rate-distortion optimization is used to balanced the trade-off between the rate loss LR and the distortion LD by adjusting the hyperparameter λ, which is defined as L = LD + λLR

(5)

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The experimental results that contain 5 models with different λ values are reported in Table 1. Since we perform JPEG2000 on each channel of multispectral images, the bit rate is the average value of each channel, i.e. bit rate per channel. From the table, our proposed method increased PSNR by 6 dB to 8.5 dB compared with JPEG2000, and by 3.5 dB to 5 dB compared with 3D-SPIHT. It is obvious that our proposed method is more robust than JPEG2000 and 3D-SPIHT. Table 1. Average PSNR on 100 8-band images at different bit rate per channel λ

5e-4

Bit rate per channel PSNR

JPEG2000

0.179 27.64

5e-5 0.264 29.13

5e-6 0.335 30.14

5e-7 0.342 30.24

5e-8 0.479 31.78

3D-SPIHT

31.04

32.58

33.74

33.84

35.58

Proposed

34.60

37.56

37.89

37.91

37.90

4 Conclusion This paper has presented a parallel architecture, which extracts and compresses the spectral and spatial features completely separately. This architecture replaces the traditional multiple downsampling layer and upsampling layer with one tensor layer respectively. The experimental results demonstrate that our proposed method outperforms traditional image compression standards, JPEG2000 and 3D-SPIHT, in PSNR.

References 1. Dragotti, P.L., Poggi, G., Ragozini, A.R.P.: Compression of multispectral images by threedimensional SPIHT algorithm. IEEE Trans. Geosci. Remote Sens. 38, 416–428 (2000) 2. Zhang, Y., Li, K., Li, K., Zhong, B., Fu, Y.: Residual non-local attention networks for image restoration. In: International Conference on Learning Representations (ICLR), pp. 1–18 (2019) 3. Zhou, L., Cai, C., Gao, Y., et al.: Variational autoencoder for low bit-rate image compression. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops 2018, pp. 2617–2620 (2018) 4. Ballé, J., Minnen, D., Singh, S., Hwang, S.J., Johnston, N.: Variational image compression with a scale hyperprior. arXiv preprint arXiv:1802.01436 (2018) 5. Minnen, D., Ballé, J., Toderici, G.: Joint autoregressive and hierarchical priors for learned image compression. In: Advances in Neural Information Processing Systems (2018) 6. Kong, F., Hu, K., Li, Y., Li, D., Zhao, S.: Spectral-spatial feature partitioned extraction based on CNN for multispectral image compression. Remote Sens. 13(1), 9 (2020) 7. Cheng, Z., Sun, H., Takeuchi, M., et al.: Learned image compression with discretized gaussian mixture likelihoods and attention modules. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2020, pp. 7939–7948 (2020) 8. Chien, J., Bao, Y.: Tensor-factorized neural networks. IEEE Trans. Neural Netw. Learn. Syst. 29(5), 1998–2011 (2018)

Deep Inter-spectrum Prediction Network for Multispectral Image Compression Yuxin Meng(B) , Kedi Hu, Tongbo Cao, and Mengyue Chen College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China [email protected]

Abstract. Multispectral image is one of the information carriers, which contains rich spatial and spectral information, and uncompressed multispectral images pose huge challenges for the storage and transmission of information. With the appearance and development of traditional image compression methods such as JPEG, JPEG2000 and 3D-SPIHT, image compression has exerted its performance to the extreme but brings block artifact and algorithm complexity. With the rapid development of deep learning, a deep inter-spectrum prediction network is proposed in this paper using the strong spatial and spectral correlation of multispectral image, which can enhance the reconstruction image quality. In proposed framework, the decoder ‘D’ uses spatial information extracted by the conditioning network ‘Cond’ and the spectral information extracted by the inter-spectrum encoder ‘E’ to predict original image. Our proposed approach is compared with JPEG2000 and 3D-SPIHT in objective evaluation index PSNR. And the experimental results show that our proposed framework have the significant quality improvements. Keywords: Multispectral image compression · Convolutional neural network · Inter-spectrum prediction

1 Introduction With the development of space exploration technology, remote sensing technology and communication technology, multispectral images are widely used in many fields like agriculture and military [1] because they contain rich spatial and spectral information. But abundant information poses great challenges for the storage and transmission because of huge data volume. In short, it is necessary for multispectral images to compress. Traditional image compression methods such as JPEG, JPEG2000 and 3D-SPIHT have already exerted their performance to the extreme. On the other hand, these methods bring have obvious insufficient like block artifact and algorithm complexity. Other traditional compression methods for multispectral image are based on prediction, transformation and vector quantization [2]. However, these methods have some defects like higher coding algorithm complexity and lower compression rate. Recently, with the wild application of deep learning, some researches have shown deep learning has great potential in lossy compression. For multispectral image compression, Kong et al. proposed an © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 876–880, 2022. https://doi.org/10.1007/978-981-19-0390-8_110

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end-to-end convolution neural network [3] and the experimental results show the better performance than traditional methods. This article indicates it is feasible to use deep learning in multispectral image compression. However, it uses spatial information but ignores numerous spectral information in multispectral images. Inspired by video deep inter-frame prediction in [4], we propose a deep interspectrum prediction network in order to take full advantage of spatial and spectral information. As Fig. 1 shown, in the encoding end, an encoder “E” learns how to produce hidden spatial and spectral information by 3D convolutional layer and “Cond” block learns spatial information from inference spectral images by 2D convolutional layer. In the decoding end, a decoder “D” predicts full image according to the spectral information from “E” and spatial information from “Cond”. The experimental results indicates our methods can store more spectral information and exceed JPEG2000 and 3D-SPIHT about 4 dB and 2 dB respectively at the same bitrate.

2 Proposed Method As above, we proposed the deep inter-spectrum prediction network to compress the multispectral images. Inspired by the use of traditional image codec as I-frame codec in [4], we consider residual network proposed in [3] as inference spectrum image codec in our method. In this section, we introduce the composition of the framework.

Fig. 1. The framework of deep inter-spectrum prediction network

2.1 The Encoder The encoder includes the inter-spectrum encoder, the quantizer, and the entropy encoder. And we focus on inter-spectrum encoder shown as Fig. 2 in this part. The input multispectral images are divided into two parts including inference spectrum images and to-be-predicted images. The inference spectrum images and the whole image are fed into the conditioning network and the inter-spectrum encoder respectively. The conditioning network uses 2D convolutional layer to extract a group of spatial information with different size. And the inter-spectrum encoder uses 3D convolutional layer to obtain hidden spatial and spectral information. The results of conditioning network

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Fig. 2. The structure of the prediction network

can be expressed as,  32

: x32 = Covn2D(xi ); : x64 = Covn2D(x32 ); hi = {  } 128 : x128 = Covn2D(x64 );  256 : x 256 = Covn2D(x128 );  64

(1)

In the processing of backward propagation, stochastic gradient descent is used to optimize network parameters. Since the quantization function is not differentiable [5], the quantization function is approximated as follows,    (2) y = round 2Q − 1 × Sigmoid (x) where Q indicates the quantization level, which is 8 in proposed method. The optimized quantization function only works during forward propagation, but it is skipped to pass the gradient to the upper layer directly during backward propagation. Compared with the training work, an entropy coding is introduced to the testing work. And ZPAQ [6] is used on account of its strong compression ability. 2.2 The Decoder The decoder includes he entropy decoder, the inverse quantizer and the spectrum decoder D. At the decoder D, inference spectrum image features extracted by the conditioning network ‘Cond’ are transformed based on the information held in the inter-spectrum encoder to predict the other spectrum images. Figures 2 illustrate our deep inter-spectrum prediction network in greater detail. Equations (3) summarise the inter-spectrum prediction processes depicted in Figs. 2.     (3) B˜ 1,...,8 = D E B1,2,3 , I4 , I5 , B6,7,8 , Cond (I4 ), Cond (I5 )

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3 Experimental Results The multispectral images from WorldView-3 satellite are adopted in our experiments as training and testing datasets, which contain 8 bands. In training dataset, there are 5000 images with a size of 128 × 128 having the rich and complete feature. And according to the same standard, we find 14 images with a size of 512 × 512 for testing. The batch size is set to 16. The Adam [7] optimizer is used in our experiment and the start learning rate is 0.0001.

Fig. 3. The PSNR and bitrate of test images in three methods

PSNR is adopted to display the objective quality of the proposed framework. As the results in Fig. 3 listed, the proposed method leads to great improvements in PSNR compared with JPEG2000 and 3D-SPIHT. As shown in figure, the deep inter-spectrum prediction network exceeds JPEG2000 and 3D-SPIHT about 4–5 dB and 1–2 dB respectively at the same bitrate.

4 Conclusion This paper presents a deep inter-spectrum prediction network. The experimental results show that the proposed framework makes obvious improvements in objective quality compared to the traditional codec JPEG2000 and 3D-SPIHT at the lower bitrate. Future work will make the network to adapt its bitrate according to fluctuations in image complexity by assigning fewer bits to simplistic image regions and vice versa.

References 1. Luo, M., Bu, Y., Xu, J.H., et al.: Optical element surface defect measurement based on multispectral technique. Chin. J. Laser 2017(01), 204–213 (2017) 2. Zhou, Z.L.: Research on Hyperspectral Image Compression Method. Nanjing University of Science and Technology, Nanjing (2008)

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3. Kong, F.Q., Zhou, Y.B., Shen, Q., et al.: An end-to-end multispectral image compression method based on convolutional neural network. Chin. J. Lasers 46(10), 285–293 (2019) 4. Nortje, A., Engelbrecht, H.A., Kamper, H.: Deep motion estimation for parallel inter-frame prediction in video compression (2019) 5. He, K., Zhang, X., Ren, S., et al.: Delving deep into rectifiers: surpassing human-level performance ImageNet classification. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 1026–1034 (2015) 6. Sogaard, J., Krasula, L., Shanid, M., et al.: Applicability of existing objective metrics of perceptual quality for adaptive video streaming. Electron. Imaging 2016(13), 1–7 (2016) 7. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. Comput. Sci. (2014). https:// doi.org/10.48550/arXiv:1412.6980

Spectral-Spatial Hyperspectral Unmixing Method Based on the Convolutional Autoencoders Fanqiang Kong(B) , Mengyue Chen, Tongbo Cao, and Yuxin Meng College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China [email protected]

Abstract. Considering that hyperspectral images have lavish spectral information, they also have spatial correlation information, a spectral-spatial unmixing method based on two-dimensional convolution autoencoder is proposed in this article. The hyperspectral image first passes through the convolutional layers to obtain the spatial features, and then concatenated to itself to retain the spectral information, at this time, a spectral-spatial tensor is acquired, finally it passes through the fully connected layers to realize the linear unmixing of the hyperspectral image. For the loss function, introduce reconstruction error constraints and abundance sparsity constraints to ensure that the abundance result has physical meaning. Experimental results demonstrate that the proposed method is superior to other sparse algorithms in terms of SRE results and visual effects. Keywords: Hyperspectral unmixing · Spectral-spatial feature · 2D convolution · Deep learning

1 Introduction Hyperspectral images (HSIs), owing to their characteristic of high spectral resolution, have a wide range of applications in many fields, such as environmental monitoring, military exploration, and biotechnology. However, limited by factors such as optical imaging equipment, the spatial resolution of hyperspectral images is comparatively low, and one pixel often contains distinct spectral features of ground substance, which is commonly known as “mixed pixels”. Hyperspectral unmixing technology aims to extract substance features (endmembers) from these mixed pixels, and obtain the corresponding proportions (abundance) of endmembers, thereby improving the accuracy of subsequent hyperspectral image processing. With the vigorous development of deep learning in the past few years, deep learning based methods have begun to perform well in the field of remote sensing. A series of unmixing methods based on AE networks have been proposed, showing the great potential of deep learning in hyperspectral unmixing. These methods are mainly divided into two categories, spectral only methods and spectral-spatial methods. The methods based © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 881–886, 2022. https://doi.org/10.1007/978-981-19-0390-8_111

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on spectral information only take the single pixel’s spectral information into consideration, such as DAEN [1], SNSA [2], uDAS [3] and so on, using variational autoencoder, sparse autoencoder, and denoising autoencoder respectively to process a single pixel. The spectral-spatial methods, considering that the hyperspectral image has spatial structure features, such as cube-based DCAE [4], MTAEU [5], etc., mainly use convolutional networks to extract both spatial and spectral feature of the image. Nevertheless, the input of the existing network is mostly pixel patches, which can only extract local spatial information, and there is still room for improvement. Based on the above analysis, a spectral-spatial unmixing algorithm based on convolutional autoencoders is proposed in this paper, two-dimensional convolution layers are used to extract global spatial information of the image, and dense layers are used to extract spectral information to improve the accuracy of unmixing.

2 The Unmixing Model The method is based on the linear mixing model (LMM), assuming that the mixed pixels are linear combinations of pure pixels (endmembers) with stable spectral characteristics. LMM model is expressed as the following equation: y = Ex + n

(1)

in which y ∈ RL×1 is the observed value of mixed pixel in L bands, E ∈ RL×p is the spectral characteristics of p endmembers contained in the mixed pixel, x ∈ Rp×1 is the corresponding abundance coefficient, n is the additive noise vector. Since the abundance reflects each endmember occupied in the proportion of mixed pixel, it needs to satisfy the non-negativity constraint (ANC) and the sum-to-one p constraint (ASC). The ANC and ASC are respectively expressed as xj ≥ 0, ∀j and j=1 xj = 1. Two-dimensional (2D) convolution weights and sums the neighborhood of each pixel of the image to obtain the output value of the pixel, it is applied in the network to obtain deep spatial feature information. For the value of the neuron at the position (x, y) on the j th feature map in the i th layer, the calculation formula is defined as: ⎛ ⎞ P i −1 i −1 Q   xy pq (x+p)(y+q) wijm v(i−1)m + bij ⎠ (2) vij = g ⎝ m p=0 q=0 pq

in which Pi and Qi are the length and width of the convolution kernel separately, wijm represents the weights connected to m layer at position (p, q). Considering that in real hyperspectral images, adjacent pixels have similar substance and their abundance have spatial correlation, 2D convolution layers are added to the network in this paper to extract high-dimensional spatial information in the hyperspectral image.

3 The Proposed Unmixing Network Unlike other deep learning based unmixing methods, most of which are based on a single pixel or pixel cube, that is, the input of the network is a pixel vector or a pixel patch, the

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proposed method in this paper takes the entire image as the input of the network in order to obtain the global spatial information of the hyperspectral image and to better perform subsequent processing of the abundance. The proposed network is shown in Fig. 1 and contains two parts: an encoder and a decoder.

Fig. 1. The proposed spectral-spatial unmixing network structure

The encoder contains four two-dimensional convolutional layers and two fully connected layers. First, a filter size of 5 × 5 is used to perform a convolution operation, and then three 3 × 3 size kernels are used to further extract the spatial information. For the convolutional layers, use the activation function of leaky_relu instead of the relu to maintain a small gradient, which preventing neurons from being activated. Although 2D convolution layers can extract image spatial feature, it cannot effectively extract feature from spectral dimension. In order to ensure that the information of the spectral dimension can be effectively utilized, after four convolutional layers, the obtained data is concatenated to the input original data, the data tensor formed at this time contains the spectral-spatial feature of the hyperspectral image. The spectral-spatial tensor then becomes one-dimensional vectors after the flatten layer, and passes through two fully connected layers to acquire the image abundance result. In addition, the activation function of relu and softmax are used on the FC layers to ensure the ANC and the ASC separately on the abundance result. The encoder stage can be summarized as the following: X=GE (Y)

(3)

where X is the abundance output of the encoder. So as to meet the linear spectrum mixing model, the decoder is only composed of a linear fully connected layer without bias. After the encoder outputs the abundance result to the decoder, the reconstructed hyperspectral image is obtained through the fully

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connected layer. The decoder is defined as: ˆ Y=E · GE (Y)=E · X

(4)

where Yˆ denotes the reconstructed input and E indicates the weight of the decoder. Comparing to (1), we can find that the weight is corresponding to the endmember matrix. For the purpose of improving the accuracy of result, the weight of the decoder is no longer initialized randomly, but the endmember in the spectral library is selected. Since the input of the network is an entire image, the result obtained is also the entire image. Therefore, we use the reconstruction error constraint and the abundance sparsity constraint as the loss function of the network: 1 L= Y − EGE (Y)2F +λGE (Y)2,1 2

(5)

where L2,1 constraint on abundance is to make the rows as sparse as possible. Furthermore, we use Adam optimizer to minimize the loss for it has the advantages of both AdaGrad and RMSProp and the learning rate is set as 0.0001.

4 Experimental Results In this part, we apply a synthetic hyperspectral data with 9 endmembers to test the performance of the proposed method, the results are compared with SMP [6], SUnSAL [7] and SUnSAL-TV [8]. Signal reconstruction error (SRE) is introduced as performance evaluation owing to the original abundance of synthetic image is known. SRE is defined as: 2     (6) SRE = E X22 /E X − Xˆ  2

in which X is original abundance matrix and Xˆ denotes the obtained abundance through network. Table 1. SRE (dB) values of abundance under 10 dB, 20 dB, 30 dB Gaussian noise SNR (dB)

SMP

SUnSAL 2.5494

SUnSAL-TV 5.1173

Proposed

10

4.8606

5.8014

20

14.5157

7.5880

11.2029

17.3035

30

22.2355

14.2254

18.4639

22.6664

From Table 1, it can be discovered that the presented method gets better results than other sparse unmixing methods under three different levels of Gaussian noise.

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True abundance

SMP

SUnSAL-TV

885

SUnSAL

Proposed

Fig. 2. Abundance map of 5th endmember under 20 dB Gaussian noise

Figure 2 shows one of abundance maps of four methods. By comparing with the true abundance map, the abundance map obtained by the method proposed in this paper has fewer noise points and better visual effect.

5 Conclusion In this paper, a 2D convolutional AE based method for hyperspectral unmixing is put forward to solve the limitation of results accuracy which owing to low spatial resolution of images. The experimental results illustrate that the method we raised in this paper has a more excellent performance than other sparse unmixing methods.

References 1. Su, Y., Li, J., Plaza, A., Marinoni, A., Gamba, P., Chakravortty, S.: DAEN: deep autoencoder networks for hyperspectral unmixing. IEEE Trans. Geosci. Remote Sens. 57(7), 4309–4321 (2019) 2. Su, Y., Marinoni, A., Li, J., Plaza, J., Gamba, P.: Stacked nonnegative sparse autoencoders for robust hyperspectral unmixing. IEEE Geosci. Remote Sens. Lett. 15(9), 1427–1431 (2018) 3. Qu, Y., Qi, H.: uDAS: an untied denoising autoencoder with sparsity for spectral unmixing. IEEE Trans. Geosci. Remote Sens. 57(3), 1698–1712 (2019) 4. Khajehrayeni, F., Ghassemian, H.: Hyperspectral unmixing using deep convolutional autoencoders in a supervised scenario. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 13, 567–576 (2020) 5. Palsson, B., Sveinsson, J., Ulfarsson, M.: Spectral-spatial hyperspectral unmixing using multitask learning. IEEE Access 7, 148861–148872 (2019)

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6. Shi, Z., Tang, W., Duren, Z., Jiang, Z.: Subspace matching pursuit for sparse unmixing of hyperspectral data. IEEE Trans. Geosci. Remote Sens. 52(6), 3256–3274 (2014) 7. Afonso, M., Bioucas-Dias, J., Figueiredo, M.: An augmented lagrangian approach to the constrained optimization formulation of imaging inverse problems. IEEE Trans. Image Process. 20(3), 681–695 (2011) 8. Iordache, M., Bioucas-Dias, J., Plaza, A.: Total variation spatial regularization for sparse hyperspectral unmixing. IEEE Trans. Geosci. Remote Sens. 50(11), 4484–4502 (2012)

Multispectral Image Compression Algorithm Based on Silced Convolutional LSTM Kang Wang1(B) , Ning Zhang2 , Kedi Hu1,2 , and Tongbo Cao1,2 1 College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing

210016, China [email protected] 2 National Key Laboratory of Science and Technology on Aircraft Control, AVIC Xian Flight Automatic Control Research Institute (Facri), Xian 710065, China [email protected]

Abstract. The multispectral imaging which are used for remote sensing imaging has a large amount of data, so this paper proposed a deep learning method which is based on sliced convolutional LSTM for multispectral image compression. Compared with other algorithms, the proposed algorithm further compresses the multispectral images by considering the similarity between the spectra and removing the inter-spectral redundancy. The proposed algorithm is based on end to end framework which is consist of encoder, decoder, entropy coding and quantizer. In experiments, the PSNR of proposed model is compared with that of JPEG2000 to evaluate the performance of our algorithm at several different bit rates. Keywords: Sliced convolutional LSTM · Multispectral image · Spectral features

1 Introduction Multispectral image is composed of 4–20 wavelengths which are mainly visible, infrared and ultraviolet light. Therefore, the multispectral image induces more information redundancy between bands which results in the amplification of the data volume of the multispectral image, which cause a great challenge to the image storage and transmission and consume more channel resources. So higher compression efficiency is the target of multispectral image compression. Traditional image compression technology has approached maturity with years of development, a typical example is the JPEG [1], which is still the most widely used image compression format. Besides, JPEG2000 [2] which is an updated version of JPEG is often used for remote sensing image storage. Deep learning methods based on neural network have made great achievements in image compression, especially the proposed of the end to end framework [3] which is utilized as the infrastructure in various image compression structures. However, the adjacent bands of multispectral image have a high degree of similarity, so in this paper, we proposed a module which remove the capture the similarities and characteristics of adjacent channels. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 887–891, 2022. https://doi.org/10.1007/978-981-19-0390-8_112

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Fig. 1. The architecture of proposed method

2 Method The main architecture of proposed method for multispectral image compression is shown in Fig. 1. This model is based on the end to end optimized framework which can be divided into several parts: forward network, quantizer, entropy coder, entropy decoder, inverse decoding network. 2.1 Forward Network The forward network which is shown in Fig. 2 takes the data of each channel of the multispectral image as input, and it includes 3 modules: convLSTM module, downsampling module and channel conversion module. The convLSTM which is proposed by Xingjian Shi [4] is used to predicted spatiotemporal sequence, the proposed model takes the spectrum segments of multispectral image as a sequence to replace the time because of the similarity between the channels. Besides, a sliced framework which is proposed to extract the features between adjacent spectral segments. The downsampling module is designed to reduce the size of image features, and it is made up of convolutional neural networks with 3*3 convolution kernels. The channel conversion module includes two parts: conv1 and conv2 the which is convolutional neural networks (the parameters conv1 and conv2 is shown in Table 1). 2.2 Inverse Decoding Network The inverse decoding network is the reverse process of the forward network. But the downsampling modules are replaced by upsampling modules which include PixelShuffle [5] and convolutional neural networks. and the kernel size of convolutional neural networks is 3 × 3. The upsampling module, which is contrary to downsampling module, is designed to recover the original size of multispectral image. and the kernel size of convolutional neural networks is 3 × 3. Besides, the concat function which mean merge inputs along channel directions is replaced by spilt function.

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Fig. 2. The forward network

2.3 Entropy Coding and Quantizer Quantization is usually introduced to improve the compression rate for image compression.in this paper, a method of uniform quantization is used for proposed model:    (1) XQ = Round XE 2N − 1 Where Round[.] represents the round function, X E denotes the output feature of encoder, the N represents the quantization level. X Q represents the quantified features. It is necessary to utilize entropy coding which is a lossless compression technique to compress to data further. So the APZQ [6] is used to compress the output features of quantizer in this paper. 2.4 Rate-Distortion Optimization The goal of rate-distortion optimization (RDO) is to reduce image distortion under the limitation of a certain bit rate, and the So the formula of RDO can be represented as follows: L = D + λR

(2)

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Convolution kernels

Step

Conv1

3*3

1

Conv2

3*3

1

Downsampling module

4*4

2

ConvLSTM

3*3

1

Where L represented the total loss between the input image and the reconstructed image, D denotes the distortion, and R denotes the bit rate, λ is the hyperparameter.

3 Experiment and Result 3.1 Dataset and Setup The landset 8 is selected as the training set and test set in our experience. The multispectral image of landset 8 contains 7 bands with a spatial resolution of 30 m (we copy the seventh band In order to satisfy the number of slices). We crop 17 pictures of 512 * 512 size as the test set and crop the rest data into 128 * 128 size. Besides, we set the output channels of conv1 to 7 and the output channels of conv2 to 48 to obtain the right bit rates. With the Adam optimization [7], the process of training includes two phases, the first phase is shown in Fig. 1, the RDO is added to the architecture of proposed method in the second phase. 3.2 Results We compare the PSNR of proposed model with that of JPEG2000 to evaluate the performance of our algorithm at several different bit rates. The bit rate is measured by the ABPP(average of bands bpp), so the bit rate of 0.6 is equal to about 80 times compressed. And we see the compression performance of our algorithm is outperform that of JPEG2000 at tested bit rates in Table 2, even the performance of our algorithm is better at low bit rate compared with JPEG2000. Table 2. Average PSNR of proposed model and JPEG2000

Proposed JPEG2000

Rate

PSNR

Rate

PSNR

Rate

PSNR

Rate

PSNR

0.56

46.82

0.49

49.68

0.36

45.94

0.14

40.81

43.49

43.23

42.32

39.33

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4 Conclusion We proposed a multispectral image compression algorithm based on sliced convolutional LSTM, which capture the correlation between the spectra of the multispectral image. Besides, and we compared our algorithm with that of JPEG2000 to validate the performance of proposed model, and the results show the superiority of the proposed algorithm.

References 1. Brown, B., Aaron, M.: The politics of nature. In: Smith, J. (ed.) The rise of modern genomics, 3rd edn. Wiley, New York (2001) 2. Dod, J.: Effective substances. In: The dictionary of substances and their effects. Royal Society of Chemistry. Available via DIALOG. http://www.rsc.org/dose/title of subordinate document. Cited 15 Jan 1999 (1999) 3. Slifka, M.K., Whitton, J.: Clinical implications of dysregulated cytokine production. J. Mol. Med. 78(2), 74–80 (2000). https://doi.org/10.1007/s001090000086 4. Smith, J., Jones, M., Jr., Houghton, L., et al.: Future of Health Insur. N Engl J Med 965, 325–329 (1999) 5. Shi, Z., Caballero, W.J., Huszar, F., et al.: Real-time single image and video superresolution using an efficient sub-pixel convolutional neural network. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 27–30 June 2016, Las Vegas, NV, USA, New York, pp. 1874–1883. IEEE (2016) 6. Mahoney, M.: The ZPAQ open standard format for highly compressed data-level 2 (2016) 7. Kingma, D., Ba, J.: Adam: A method for stochastic optimization. In: ICLR (2015)

Automatic Generation Method of Temporal Fault Tree Based on AltaRica3.0 Qin Zhang(B) , Lisong Wang, and Jun Hu Nanjing University of Aeronautics and Astronautics, Nanjing, China [email protected]

Abstract. Altarica3.0 is a high-level modeling language for security analysis, and its basic mathematical form is Guardian Transformation System (GTS). At present, GTS (or Altarica3.0 model) can be compiled into a fault tree or critical event sequence. There are several reasons for turning GTS model into a fault tree: automatically generating a fault tree from a high-level model is easier and less time consuming than creating one from scratch; Second, the high level model greatly improves the model design, sharing and maintenance; Finally, the evaluation tools of the Boolean model are more effective than the state/transition model. In general, however, the cost is the loss of sequence between events: the sequence of events is compiled into the connection of events, so there may be potential omissions throughout the system reliability analysis process and analysis results. To remedy this problem, in this paper we outline an analysis approach that converts failure behavioural models (GTS) to temporal fault trees (TFTs), which can then be analysed using Pandora a recent technique for introducing temporal logic to fault trees. The approach is compositional and potentially more scalable, as it relies on the synthesis of large system TFTs from smaller component TFTs. We verify, by using a redundant system, the effectiveness of the proposed method. Keywords: AltaRica3.0 · GTS · Accessibility diagram · Pandora · Temporal fault tree

1 Introduction With the wide application of critical safety system, its complexity is increasing. How to ensure the safety of the system and avoid the occurrence of catastrophic accidents, such as property loss or even casualties, has become a research hotspot in the field of system safety analysis. The Altarica modeling language is designed to solve this problem. Altarica [1–3] is a high-level safety analysis modeling language, which has a good layered system structure and dynamic description mechanism, and can provide fault behavior modeling elements based on fault. As a result, it is beginning to be widely used in safety-critical fields such as aerospace [4]. In research [5], an algorithm for compiling Altarica data streams (or pattern automata) into fault trees is proposed. However, because the event sequence is compiled into the connection of events, the timing relationship between the events is lost, which leads to the potential error in the analysis of the system dynamic timing fault. Therefore, considering the timing state of system component © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 892–900, 2022. https://doi.org/10.1007/978-981-19-0390-8_113

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failure events, Pandora timing gate is introduced to describe the timing relationship. In this paper, an automatic compilation and generation method of Pandora timing fault tree based on Altarica3.0 modeling language is proposed.

2 Background 2.1 Guardian Transformation System (GTS) Altarica3.0 is a new version of the Altarica language [6]. Its new underlying mathematical model is the guard transformation system (GTS) [7], which is a state/transition form specifically designed for security analysis. The Guardian Transition System (GTS) is an automaton whose state is represented by a variable assignment, that is, the variable and its value. A change in state is represented by an event-triggered transition. It can also represent the flow through the network and synchronize events to describe remote interactions between components of the system under study. The formal definition of flattened GTS is as follows: Definition 1: GTS is composed of a quintuple < V, E, T, A, ι >, where V is the set of variables, divided into two groups of disjoint state variable S and flow variable F, that is, V = S ∪ F; E is a set of events; T is a set of transformations, which are represented as a triple < e, G, P >, where e is an event in E; G is a guard condition (a Boolean expression) built on variable V, and P is an instruction built on variable V, known as an action or postcondition. Thus, in general, the transformation T is expressed as e: G → P; A is a set of assertions, assertions are instructions built on the variable V; ι is the assignment of the variable V, which is the initial or default assignment. To elaborate on the composition of the GTS, Fig. 1 shows a graphical representation of a cooling system and its flattened model representation.

Fig. 1. Cooling system

Concrete components in the cooling system are abstracted into classes. Tank, Pump and Reactor are respectively. These classes interact with each other through variables. Tank instantiates object T outFlow coolant as outFlow variable of the whole system, and two instantiated objects P1 and P2 of Pump have two variables respectively: outFlow from Tank, Pump and Reactor. InFlow of input variable, inFlow of Reactor is the same as outFlow of P1 and P2. The system has to keep at least one pump running so that the

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coolant can be used to cool the reactor properly, but the pump could fail or the tank could be empty, which could cause the entire system to fail. The part of GTS model obtained by flattening the hierarchical information of the cooling system is shown below (Table 1). Table 1. Part of the GTS model of the cooling system

block CoolingSystem Boolean T.isEmpty (init = false); Boolean T.outFlow (reset = false); RepairableState LineOne.POne.s,LineTwo.PTwo.s(init= WORKING); … event T.getEmpty; … transition T.getEmpty: not T.isEmpty -> T.isEmpty := true; … assertion T.outFlow := if not T.isEmpty then true else false; … end

2.2 Pandora Temporal Fault Tree Pandora defines three time gates: Priority-AND gate (PAND), Priority-OR gate (POR), and Simultaneous-AND gate (SAND) to extend the classic fault tree [8]. The temporal fault tree symbols of the three gates are shown in Fig. 2. PAND gates are not new and have been used in FTA [9] since the 1970s. The symbol “ < “ used to represent the PAND gate in the logical expression, such as A < B represents A PAND Y, where both A and B are failure events. the Priority-OR (POR, symbol ‘|’), which as described earlier models a priority situation where one event must occur first and other events may or may not occur subsequently.The Simultaneous-AND (SAND, ‘&’) gate, which represents simultaneous ocurrence of events, e.g. as a result of a common trigger. In this article, we use “ + “ for OR gate and “.“ for the AND gate. Pandora allows more than one form of reduction. As well as removal of redundancies, e.g. A < B + A|B ⇔ A|B (where A and B are fault tree events), Pandora also allows reduction via the recognition and removal of contradictions and by means of ‘completion’ — conversion of temporal expressions into static Boolean expressions. For full details about Pandora’s temporal laws we refer the reader to [12], but four laws in particular will be useful: (A|B).B ⇔ A < B; A.A|B ⇔ A|B; (A|B). (A|C) ⇔ A|B|C; A|(B + C) ⇔ A|B|C.

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3 A Framework for Automatic Generation of Pandora Temporal Fault Tree Based on GTS and Core Algorithm Set In general, this section gives the method framework and several steps of how to automatically generate the Pandora temporal fault tree based on the flattened GTS model of Altarica3.0. The algorithm GTS to Pandora Temporal Fault Tree (GTS2PTFTA) design involved in each step is then explained in detail. The GTS2PTFTA method framework is generally divided into the following five main steps: design involved in each step is then explained in detail. The GTS2PTFTA method framework is generally divided into the following five main steps: (I) Read the flattened GTS model description file and construct the corresponding GTS object. (II) The input GTS object is divided into a set of independent GTS and an independent assertion. (III) Iterate the independent GTS set to obtain the corresponding reachable graph (state machine graph) of each independent GTS, so as to facilitate the subsequent generation of event sequences with timing characteristics according to the state machine graph design algorithm. (IV) Compile each reachable graph and obtain the logical formula of the independent GTS with the temporal relationship. find all possible paths between two nodes in the reachable graph and design the algorithm to update the possible priority relationship into the path. The algorithm described in step 4 is corresponding to the logical formula containing time sequence relation of the Sect. 3.3. The AND gate operator is further annotated as: search all paths from state s0 to the other states of the graph. Events occurring along path π are transformed into the relationship of events. Each state of the graph is first associated with a sequence disjunction obtained through the compile path., then each pair (variable values) associated with a sequence of disjunction, including variable uses this value, secondly, traverse to search path, looking for the same end node status on different paths to share events, update priority or temporal sequence in the original sequence of disjunction, after exhaustive recursion and, at the same time, and preference or conversion between matching rules, Generates a sequence of events with a temporal relationship. (V) Synchronize the independent assertions obtained by partition with the formulas obtained by compilation of each independent GTS reachable graph, and obtain the set of timing relational event sequences of the whole model, which is composed of the Pandora timing fault tree of this GTS model.

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3.1 GTS Model Preprocessing and Partitioning A partition operation on a GTS instance object. Considering the large scale of the system model, after the GTS instance object is obtained, in order to simplify the subsequent steps, a more efficient partition algorithm is adopted here to improve the efficiency of the algorithm, so as to cope with the large scale of the system model. The partitioning algorithm divides a model into multiple components and modules, and then processes each component and module individually. When the fault tree with time sequence relationship is finally obtained, it is processed together with the data results, which simplifies the intermediate steps of the whole algorithm framework process. 3.2 Construction of the Accessibility Diagram of the Sub-GTS Model The corresponding reachable graphs of each independent GTS were obtained. In this step, we mainly process each independent GTS to obtain the relevant reachable chart. A reachable graph contains a set of nodes, and a node exists in the form of a set of variables, and the current system state is represented by the values of the current variables. 3.3 The Accessibility Graph of the Sub-GTS Model Is Compiled to Generate a Temporal Expression An accessibility graph is a state machine with a finite number of states. It may change state as an event occurs, but at each moment, it is only in one state. Definition 2: Reachability graph (RG) A Reachability graph is a quadruple (S, , δ, s0) where: • S is a finite set of states. •  is a finite set of events, such that S ∩  = ∅. • δ is a partial function: S ×  → S s.t. for (u, u’) ∈ S2 , and e ∈ , u’ = δ(u, e) iff e is incident from u to u’, and we write it as: u → u’(e). • s0 is the initial state. Definition 3: Paths set Let P be the set of all paths in the RG, P = {π| u → u’(π), (u, u’) ∈ S2 },We write u → u’ iff ∃π ∈ P s.t. u → u’(π). Definition 4: Forward and backward incidence sets For any state u ∈ S, let uI (resp. uJ) be the set of events incident from u (resp. incident to u), uI = {e ∈ | ∃u’ ∈ S s.t. u → u’(e)},uJ = {(e, u’) ∈  × S| u’ → u(e)}. Definition 5: Set of fifinal states Let F be the set of the fifinal states, F = {f ∈ S| fI = ∅}. The algorithm and pseudocode of compiling the accessible graph of independent GTS to obtain the time-series relation formula are as follows:

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Generates a set  of Pandora formulae:  = {ϕs|s ∈ F} one formula ϕs for each final state s. These expressions can then be analysed by Pandora. Transformation algorithms are biased toward increasingly dynamic systems. The best case complexity for checking the necessity of chronological order is O(n), and the worst case complexity is O(n2 ), where n is the number of paths from the initial state to the final state in the reachable graph. In the best case, for each divergent path, there exists a shareable event that is related to the direct reachable state of the connection state when the path diverges. In this case, the cost of adding j to each state is the order time of an O (m2 ) operation, where m is the degree of j.

4 Case Studey To better illustrate the effectiveness of our approach, we use a generic three-module redundancy (GTR) system, as shown in Fig. 3. Elements X1, X2, and X3 are abstract representations of any input, control, or drive, arranged in redundant series, with X1 as the primary element, X2 as the secondary element, and X3 as the third element. Two monitoring sensors S1 and S2 detect leakage from X1 and X2 outputs, respectively, activating the next backup in the series. O is just an abstraction of the GTR output, and I represents the input to the system. System failure events are Omission of input at I, all of X1, X2, and X3 fail, Both X1 and S1 fail (X2 will not be activated) and all of X1, X2, S2 fail (X3 is not activated) and failure of O [11]. If there is no input to the system, then it cannot operate; If all three main components fail, then the system will likewise fail as well. However, the GTR system exhibits dynamic behaviour: its true failure behaviour depends on the chronology of events. Different sequences of failure events can lead to the system failing in different ways, and not all of them are correctly represented by the results. For example, suppose

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X2 fails first, then X1 fails again; In this case, X2 cannot be activated when sensor S1 detects the output leakage of X1, and sensor S2 cannot activate X3 because X2 has never been activated. Sensor S2 can detect the output leakage only when the monitored component is activated. So, failure of sequence X2 before sequence X1 will result in system failure, regardless of the status of X3 and S2. In this experiment, Altarica3.0 was used to convert the model file built by the system into the GTS model of the flattened system, and the description file was taken as the input file of this experiment. Because the system contains many variables, transformations and assertions, it generates many accessibility graphs. This paper presents only partial resultsd. For component X3 (reachability graph of Fig. 4), there are two final states, both with omission of output from X3 (O-X3) as common effect. The state permanently OFF means that X3 will never be activated and is caused by O-S2 (i.e. S2 has prematurely failed and both X1 and X2 have failed in sequence). The other final state is a state where X1 and X2 have failed in sequence and where X3 has also failed (caused by the event X3 fails). Omission from X3 is the third cause of failure for output component O, leading to Omission O and system failure. Note that X1 and O are each initially in state ON, i.e. the GTR system is initially working with its primary component, and the output is being delivered by the system. Backup components have their initial states set to OFF and sensors are initially set to Monitoring.

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Compile the accessibility graph into an expression with a time series relationship, and generate the corresponding Pandora timing fault tree based on the expression, as

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shown in Fig. 5. for the sake of clarity, any component symbol (e.g. X1) abbreviates the failure (e.g. X1 fails) of the corresponding component. O

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5 Conclusion This paper proposes a method to transform the GTS model into a Pandora temporal fault tree, which allows more detailed analysis of different sequences of events to better capture dynamic behavior. In the following work, we will further combine the previous work with the present work to build a complete system security modeling and analysis tool for Altarica3.0, and conduct a large scale application case study in the typical aviation field.

References 1. Jun, H., Song, C., Mingming, W.: Transformation method for AltaRica 3.0 to Promela and its verification. Comput. Eng. Sci. 39(4), 708–716 (2017) 2. Prosvirnova, T.: AltaRica 3.0: a model-based approach for safety analyses. Ph.D. diss., Ecole Polytechnique (2014) 3. Prosvirnova, T.: The AltaRica 3.0 project for model-based safety assessment. In: IEEE International Conference on Industrial Informatics, IEEE (2013) 4. Latif-Shabgahi, G., Bass, J.M., Be Nnett, S.: A taxonomy for software voting algorithms used in safety-critical systems. IEEE Trans. Reliab. 53(3), 319–328 (2004) 5. Zhipeng, W.U., Jun, H.U., Chen, S., Shi, J.: Safety verification methodology of embedded system based on altarica model. J. Front. Comput. Sci. Technol. 11(1), 24–36 (2017) 6. Batteux, M., Prosvirnova, T., Rauzy, A.: Advances in the simplification of fault trees automatically generated from AltaRica 3.0 models (2018) 7. Rauzy, A.B.: Guarded transition systems: a new states/events formalism for reliability studies. Proc. Inst. Mech. Eng. Part O J. Risk Reliab. 222(4), 495–505 (2008) 8. Walker, M., Bottaci, L., Papadopoulos, Y.: Compositional temporal fault tree analysis. In: Saglietti, F., Oster, N. (eds.) SAFECOMP 2007. LNCS, vol. 4680, pp. 106–119. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-75101-4_12

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9. Rauzy, A.: Mode automata and their compilation into fault trees. Reliab. Eng. Syst. Saf. 78(1), 1–12 (2002) 10. Mahmud, N.: Dynamic model-based safety analysis: from state machines to temporal fault trees. University of Hull (2012) 11. Walker, M.D.: Pandora: a logic for the qualitative analysis of temporal fault trees. Computer Science the University of Hull (2009) 12. Walker, M.D.: Pandora: a logic for the qualitative analysis of temporal fault trees. Ph.D. diss., The University of Hull (2009)

A Simulation Framework for VRM Requirement Model WanLi Zhan(B) , Jun Hu, LiSong Wang, and JiaRun Lv College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China [email protected] Abstract. Model simulation is a research method that can discover and improve system design problems through various experiments without the participation of the actual system. In the field of security critical systems and aeroelectronics, a series of security problems are often caused by large and complex system requirements. In this paper, a simulation framework for VRM formal requirement model is proposed to find and solve the problems that occur during the design phase of system requirements. The work includes: comprehensively analyzing each component of VRM model, relying on SysML model to build simulation sequence, executing simulation event propagation system constraints, and displaying system model status in real time. The framework solves the ambiguity of natural language requirements by using VRM formal requirement model, and designs some SysML modeling methods to help emulators build and refine simulation behaviors through case diagrams. Keywords: Model simulation behavior

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Introduction

In the field of safety-critical systems [5] and avionics, the system has complex requirements in order to achieve a large number of functions. When the content and quantity of the requirements are complex, the first thing to face is the safety of the system. In recent years, model-based formal modeling, security analysis, and formal verification methods have gradually been applied to various fields to realize automatic or semi-automatic security analysis and verification. How to make full use of the system model to ensure system security is a hot research issue in the field of system modeling and analysis. This paper proposes a simulation framework based on the VRM (Variable Relation Model) [3], and checks whether the requirement model implies some problems from the perspective of simulation.

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The model simulation framework in this paper is aimed at the formal requirement model of VRM, and at the same time uses the SysML model in system engineering to construct the simulation sequence. c The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022  Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 901–909, 2022. https://doi.org/10.1007/978-981-19-0390-8_114

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2.1

VRM Model

VRM model, which is based on the four-variable model [1], oriented to natural language description of the requirement items in the avionics field, constructing including variables, conditions, events, patterns, and environmental interactions The modeling method of engineering practical formal requirement model with other elements. The VRM model has both tabular and formal semantics, similar to the way of data storage and processing in a relational database. The intuitive form of the VRM model is to use a two-dimensional table to establish a requirement model. The two-dimensional relational data in the relational database is actually a mathematical model strictly defined by the relational calculus logic system. 2.2

SysML Model

SysML[7] is a standard modeling language for systems engineering. It is a reuse and extension of a subset of UML2.0 to build a well-designed, clear-level and maintainable complex system. SysML can describe the hierarchy, structure, behavior, attributes, and complex relationships between components of the system. The use case diagram, activity diagram, sequence diagram, and state machine diagram in the SysML model are used in the framework to extract the system simulation sequence [4].

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In the previous section, it was introduced that the framework relies on the two models of VRM and SysML. The VRM model is built by system requirements modelers, and the SysML model is built by system simulation modelers. The two models play different roles. For this reason, certain requirements should be met in the model construction. 3.1

Build a VRM Model

Since system simulation [2] is an extremely complex process, VRM model should be logically clear when modeling, and strictly follow the modeling standards: When building the VRM model, we make the following standards: • In the domain concept library in the system model, each input variable, output variable, intermediate variable type, value range, etc. should be reasonably defined, and all variables should have initial values. • In the domain concept library in the system model, it should be reasonable Define the content of the pattern set, each pattern should not be repeated, and the transition between the patterns should not conflict or cyclically depend, nor should there be a situation where a pattern reaches different target patterns through the same event.

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• In the standardization requirements in the system model, standardization should be carried out according to the content of the original requirements, and the condition table and the event table should be selected reasonably. In the standardized requirements in the system model, the content of the requirements should not be cyclically dependent. 3.2

Build a SysML Model

The SysML model mainly plays the role of extracting the simulation sequence in the simulation framework[9]. When building the SysML model, we make the following standards: • The use case diagram mainly includes Actor and UseCase. A single-role system can use only one Actor. Different use cases can be connected to the Actor to represent the simulation events that the Actor can execute. At the same time, Actors can also be used in a multi-role simulation system. Different Actors connect the use cases that need to be executed to realize the problem of multi-role execution event rights limitation. The same use case name should not be repeated in the use case diagram. • The activity diagram mainly includes the initial node, Action, and the End Node. Each activity diagram starts with Initial Node and ends with End Node. The middle simulation behavior is used to describe what is done in a simulation event, and each simulation behavior must be atomic and cannot be combined. Each of the simulation behaviors must be connected in sequence, and the sequence cannot be wrong. • The sequence diagram includes Actor, and its main function is to adjust the disordered UseCase in the use case diagram. It can also be used to execute all events with one click and must be in accordance with the sequence diagram timing requirements. • The state machine diagram includes states, events, guards and action sets. The transition process between states is triggered by events. The guard judges whether the state transition conditions are met, and if so, executes the preset action set, and completes the transition between system states. After extracting the simulation sequence from the above SysML model, the system can be simulated by the simulation framework. The simulation sequence mainly contains a collection of simulation events [6]. Each simulation event contains a collection of simulation action.

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Design of Simulation Framework

System engineering simulation is often the most complicated item in simulation engineering, and from the six-tuple content of VRM, the most important part of the VRM model is its variable set and pattern set, including input variables, intermediate variables, and output variables. Mode conversion part, in addition, because the VRM requirement model is a requirement modeling for a system, it

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contains various requirement constraints or constraint events, which form a set of constraints for the system. In the subsequent simulation process, the above five Part as an important part of the simulation process. Similarly, because the VRM model is only a system requirement model and cannot provide any simulation sequence, this simulation tool uses the SysML model to provide simulation sequence information, which is mainly based on use case diagrams, activity diagrams, sequence diagrams, and the state machine diagram. The architecture diagram of the simulation framework is shown in Fig. 1.

Fig. 1. VRM model simulation framework architecture diagram

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Analyze Model File

Analyze the VRM Model. From the VRM model file, you can parse all the original requirement sets and standardized requirement sets in the model according to the content, the input variable set, the possible mode of each condition table or event table under the tag, and the middle Variables, output variables, the initial state and end state of all mode sets under the tag, the conditions that need to be met for state transition, or the events that need to be triggered.

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Analyze the SysML Model. The SysML model file generally contains the following parts: From the SysML use case diagram model file, the Actor in the system, the set of use cases, the relationship between the Actor and each use case can be parsed according to the content. From the SysML activity diagram model file, the Transition in the activity diagram model can be parsed according to the content, or it can be understood as the edge. ActionState is the intermediate behavior state of the entire activity diagram, and PseudoState is the activity The initial state or end state node of the graph. From the SysML sequence diagram model file, the sequence of events in the sequence diagram model can be parsed according to the content to help the simulation program control the sequence of events. When the user does not use the sequence diagram to assist the simulation, the simulation system will The simulation event is executed step by step, and the simulation sequence is completely operated by the simulation personnel. Since the VRM model contains the content of state transitions, the SysML state machine diagram is generally used to assist in identifying the state changes of the simulation system. 4.2

Initialize

The simulation framework does not verify and process the initial state of the VRM model file, that is, the simulation framework believes that the currently imported VRM model file is a correct and stable system state. If the VRM model file is wrong, the error should be found before the simulation And deal with it. When the framework is initialized, it will analyze the VRM model file, display all the variable information in it on the graphical view, and assign initial values. 4.3

Trigger Simulation Event Sequence

When the user triggers a simulation event, the simulation system will use the current project state as the initial test state, execute the simulation behavior, modify the corresponding input variable value or perform certain operations, and then traverse the various condition tables in the VRM model in turn. Event table, when a certain condition is met or a certain event is completed, the variable or mode in the project changes and then transitions to a new state, until the system reaches a steady state, that is, the execution of a simulation event ends. Every time a state transition occurs in the project, that is, after the variable changes, the simulation system will update the content in the graphical view in time to inform the simulation personnel of the current system changes. The standardization requirement constraint of the condition table in the VRM model is shown in the Fig. 2(a). The ‘conditionTable’ tag represents that this is a condition table, and the content of the condition is stored in the form of a logical expression under the ‘condition’ tag. The conditions shown in the figure mean that when the value of “ipSYNCSwitch” is “off” and the value of “ipSYNCSwitchPressedHighestPriority” is “true”, the variable “tHDGSwitchPressedHighestPriority” will be assigned the value true.

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(a) ConditionTable

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The standardization requirement constraint of the event table in the VRM model is shown in the Fig. 2(b), where the ‘eventTable’ tag represents that this is a condition table, and the content of the condition is stored in the form of a logical expression under the ‘event’ tag. The meaning of the event shown in the figure means that when the result of “AP=Engaged” was “False” last time and this time is “True”, the variable “When(AP=Engaged)” will be assigned “True”. There are multiple categories of events in the VRM model. Due to space limitations, not all types are shown here. 4.4

Engineering Status Transition Record

The simulation framework has a thread private set corresponding to each variable or pattern set in the model, which is used to store the state change record of the current variable or pattern set, and is used to help the simulator view the change of a single variable during the simulation process, which is convenient Analyze the simulation results.

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Case Analysis

In this section, the FGS flight control system [8] is selected as a simulation example. The flight navigation system (FGS) is an integral part of the entire flight control system (FCS). The flight crew mainly uses FGS to select switches for different flight modes, such as vertical speed (VS) mode switch, horizontal navigation (NAV) mode switch, heading selection (HDG) mode switch, etc. These flight instruction commands generated by FGS are displayed graphically on the primary flight display (PFD).

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Fig. 3. Main view of simulation tool

First, import the VRM model and SysML simulation sequence model of the FGS flight control system respectively, and select the simulation options for the system. The tool will open the simulation data view, the simulation event view. The simulation data view mainly contains the input variables, intermediate variables, output variables, and pattern sets in the VRM model, while the simulation event view mainly contains the items in the SysML model Simulation events and simulation behaviors. The main interface of the simulation tool is shown in the Fig. 3. The simulator triggers a simulation event, and each variable or mode in the simulation data view will change accordingly, and finally a final state will be obtained through system constraint propagation. Choose to execute “Start HDG mode”, the simulation behaviors under this simulation event are divided into “ipSYNCSwitchPressedHighestPriority=true” and “ipHDGSwitch=on”, and start to execute the first simulation behavior. “ipSYNCSwitchPressedHighestPriority” When the variable changes, its value becomes true and is marked as red. After a series of system constraints are propagated, the system reaches a steady state and starts to perform the “ipHDGSwitch=on” behavior. After the same modification of the value of the “ipHDGSwitch” variable, The system obtains a steady state through constraint propagation, and the final result of system variable change is shown in Fig. 4.

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Fig. 4. The result after executing the ‘launch HDG mode’ event

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Conclusion and Future Work

From the case in the previous section, we can see that the simulation of the VRM model by the simulation framework is reasonable, and the expected results can be obtained every time the simulation event is executed. Each variable included in the VRM will change in turn according to requirement constraints, and finally the system steady state is obtained. After the work of this paper, in order to further improve the scalability of the simulation framework and the diversity of simulation sequence design, it will be open to more kinds of SysML models, so as to facilitate the simulation personnel to design simulation sequences more in line with their own requirements.

References 1. Buckl, C., Knoll, A., Schieferdecker, I., Zander, J.: 10 model-based analysis and development of dependable systems. In: Model-Based Engineering of Embedded Real-Time Systems - International Dagstuhl Workshop, Dagstuhl Castle, Germany, 4–9 November 2007, Revised Selected Papers (2010) 2. Chow, J., Pfaff, B., Garfinkel, T., Christopher, K., Rosenblum, M.: Understanding data lifetime via whole system simulation (awarded best paper!). In: Proceedings of the 13th USENIX Security Symposium, San Diego, CA, USA, 9–13 August 2004 (2004) 3. Hu, J., Hu, J., Wang, W., Kang, J., Gao, Z.: Constructing formal specification models from domain specific natural language requirements. In: 2020 6th International Symposium on System and Software Reliability (ISSSR) (2020) 4. Kaufmann, M.: Systems engineering with sysml/uml. Computer (6), 83 (2008) 5. Moser, L.E., Melliar-Smith, P.M.: Formal verification of safety-critical systems. Softw. Pract. Exp. 20(8), 799–821 (2010)

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6. Peduzzi, P., Concato, J., Kemper, E., Holford, T.R., Feinstein, A.R.: A simulation study of the number of events per variable in logistic regression analysis. J. Clin. Epidemiol. 49(12), 1373–9 (1996) 7. Sandy, F.: Systems modeling language (sysml) specification. Insight 7(3) (2015) 8. Wang, X., Yi, G., Wang, Y.: An empirical study of flight control system model checking integrated with FMEA. In: 2020 IEEE 20th International Conference on Software Quality, Reliability and Security Companion (QRS-C) (2020) 9. Yue, C., Liu, Y., Paredis, C.: System-level model integration of design and simulation for mechatronic systems based on sysml. Mechatronics 21(6), 1063–1075 (2011)

A Coupling Analysis Method Based on VRM Model Zhipeng Qiu(B) , Lisong Wang, and Jun Hu College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China [email protected]

Abstract. Due to the scale and complexity of the embedded airborne software in the aviation field, the modules of the airborne software will influence each other in ways unexpected by the designer under certain specific circumstances. Therefore, software coupling analysis has become an indispensable part of the airborne software design process. At present, most of the coupling analysis in the aviation field is carried out on R-BT (Requirements-Based Testing). The VRM model is a formal requirement model, which can be well modeled at the system requirement level. According to the definition of coupling and the requirements of coupling analysis in DO-178C(Airworthiness Standards for Civil Aviation Airborne Software), this article provides a set of coupling definition and coupling degree measurement methods based on the VRM(Variable Relation Model). At the same time, the coupling analysis of the flight guidance system in the field of avionics under this set of methods is given to verify the feasibility of this set of methods. According to the coupling definition and coupling degree measurement method proposed in this paper, a set of prototype tools supporting coupling analysis has also been developed. Keywords: Avionics system · Requirement model · Coupling analysis

1 Introduction Avionics software systems are composed of modules, but due to the scale and complexity of embedded airborne software programs, the software modules cannot influence each other in the way the software designer wants under abnormal circumstances. So in order to ensure the correctness of the interactions and dependencies of these modules, it is necessary for us to do a coupling analysis of the software program. Coupling is the basic characteristic of software system. It is closely related to the quality of software design and has a great influence on program operation. The coupling between software components also affects the maintenance of the software. In order to make the maintenance and correction process more efficient, we must evaluate the degree of coupling in the software system. This work is supported in part by the National Basic Research Program of China under Grant No. 2014CB744900. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 910–918, 2022. https://doi.org/10.1007/978-981-19-0390-8_115

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At present, manufacturer does not have a unified conclusion on coupling analysis. The main research on coupling degree analysis in academia is as follows: Denys Poshyvanyk [4] reported the results of a case study in the source code of the Mozilla browser, in which the conceptual coupling metric was compared with nine existing structural coupling metrics. Istvan Gergely Czibula [3] and others introduced a new aggregate coupling measure. It can capture the structural and conceptual characteristics of coupling between software components. It can combine the text information contained in the source code with the structural relationship between the software components. DO-178C requires that the analysis of the coupling degree of software components should start from R-BT (Requirements-Based Testing) [2], because requirements-based software testing can ensure that the requirements of the software and the interaction between the components are correct from the perspective of software design. Here select the VRM (Variable-Relation Model) as the requirement model. The VRM model describes the required system behavior and software behavior at the system requirements level, and limits the software requirements by specifying the system requirements and the input and output hardware interfaces of the system. The VRM model can also describe the interaction behavior between the system and the outside world, as well as the behavior characteristics of the system’s internal state and mode changes. Therefore, the VRM model can be used and can well describe the requirement characteristics of the fields such as avionics software systems and flight control systems.

2 Coupling Definition Based on VRM Model 2.1 Definition of VRM Model The specific description of the VRM model is in this article [1]. VRM model is a tablebased model, which is a six-tuple composed of state variables, variable types, variable range, conditions, events, and table functions. At the same time, the table functions in the VRM model include three categories: condition table functions, Event table functions and mode transformation table functions, and each category has a corresponding formal semantic definition. The symbolic representation of the VRM formal requirement model is: {SV , C, E, F, TS, VR}. The dependency relationship between the various state variables of VRM is shown in Fig. 1. Dependency diagram of VRM model.

Fig. 1. Dependency diagram of VRM model

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2.2 Definition of VRM Model Component DO-178C puts forward the requirements of data coupling and control coupling for software testing. Among them, DO-178c defines data coupling and control coupling as follows: A component is a self-contained component, a combination of sub-assemblies and units that performs a clear function of the system. Data coupling is the dependence of a software component on data that is not completely under the control of this component. Control coupling is the way or degree by which one software component affects the operation of another software component. VRM describes the behavior of the software system through table functions and state variables. Among them, the change of the system state is described by the mode transition table. According to the requirements of D0-178C, we propose the following definitions of data coupling and control coupling based on the VRM model. The first is the definition of model component.

I

F ModeTransion Table Funcon

I N P U T

Condion Table Funcon IV MV TV

O MC

O U T P U T

Event Table Funcon

Fig. 2. VRM model components

Definition 1: (VRM model components) We argue that the VRM model consists of a set of model components, with MoC = {moc1 , moc2 , . . . , mocm }.VRM model components are represented by triple {I , O, F}, where I represents the input set of model components MoC and I = {IV , MV , MC, TV , E}. O represents the output set of the model component MoC and has O = {OV , CV , MC, TV }. F represents the table function in the model component MoC and has F = {MTF, EF, CF}, where MTF = {mtf 1 , mtf 2 , . . . , mtf m } represents the mode transition table, CF = {cf 1 , cf 2 , . . . , cf n } represents the condition table, EF = {ef 1 , ef 2 , . . . , ef l } represents the event table. As shown in the Fig. 2 is a VRM model components. 2.3 Definition of Control Coupling In the VRM model, the change in the state of the entire system is represented by changing the variables (mode class) that describe the state of the system. For example, in a lighting control system, mcStatus is a mode class used to describe whether the room is in an occupied state, and mcStatus = occupied represents that the room is currently in an

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occupied state. @T (mcStatus = temp_empty)WHENmcStatus = occupied represents the current room status has changed from occupied status to temporarily empty status. Between the two model components, the value of the mode class is changed through the mode transition table, so here we give the following definitions to describe the control coupling: Definition 2: (Control coupling between two mode transition tables) Two model mode transition tables describe the behavior of system state changes by changing the values of state variables, which is called the coupling between the mode transition tables. Therefore, we define the control coupling between the two mode transition tables as CCoMTF(mtf k , mtf j ), where mtf k and mtf k represent two mode transition tables with a coupling relationship. Definition 3: Suppose there are mock ∈ MoC and mocj ∈ MoC as two different model components in the VRM model (mock = mocj ). Each components has a set of mode transition tables, namely MTF(mock ) = {mtf k1 , mtf k2 , . . . , mtf kr } and r = |MTF(mock )|, MTF(mocj ) = {mtf j1 , mtf j2 , . . . , mtf jt } and t = |MTF(mocj )|. Between each pair of mode transition tables (mtf k , mtf j ) there is a coupling CCoMTF(mtf k , mtf j ) between mode transition tables. Therefore, we define the coupling relationship CCoMTF(mtf k , mtf j ) between a mode transition table mtf k and model component mocj as follows: t   q=1 CCoMTF(mtf k , mtf jq ) CCoMTFC mtf k , mocj = t Definition 4: (Control coupling between two VRM model components) We define the coupling between two VRM model components mock ∈ MoC and mocj ∈ MoC as follows: r   l=1 CCoMTFC(mtf kl , mtf j ) CCoCC mock , mocj = r The mathematical meaning of the coupling between the model components mock ∈ MoC and mocj ∈ MoC is expressed as the average of the coupling degree between all pairs of disordered pattern transition tables from mock and mocj . This definition ensures that the coupling components is symmetrical,  betweenthe two model   measurement namely CCoCC mtf k , mtf j = CCoCC mtf j , mtf k . 2.4 Definition of Data Coupling VRM model components exchange value of system variables between condition tables and event tables to describe the interaction and dependence between VRM model components. Similar to control coupling, we give the definition of VRM model data coupling in this chapter. Here, the condition table (CF) and event table (EF) are collectively called the data transition table (DTF). Definition 5: (Data coupling between two data transition tables) We define the control coupling between the two data transition tables as DCoDTF(dtf k , dtf j ), where dtf k and dtf j represent two data transition tables with a coupling relationship.

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Definition 6: We define the coupling relationship DCoDTFC(dtf k , mocj ) between a data transition table dtf k and model component mocj as follows: t   q=1 DCoDTF(dtf k , dtf jq ) DCoDTFC dtf k , mocj = t Definition 7: (Data coupling between two VRM model components) We define the coupling between two VRM model components mock ∈ MoC and mocj ∈ MoC as follows: r   l=1 DCoDTFC(dtf kl , dtf j ) DCoCC mock , mocj = r

3 Coupling Degree Measurement Method Based on VRM Model The coupling between the table function pairs is the smallest unit of measurement for us to perform coupling analysis, but the calculation method of the coupling degree between the tables is not given, and we will give the calculation method below. 3.1 Method of VRM Model Control Coupling Degree Measurement The method used here is based on singular value decomposition. Singular value decomposition [5] is widely used in data compression, feature extraction and other fields. In order to calculate the coupling degree, we give the following definition: Definition 8: (mode transition table-variable matrix) The mode transition tablevariable matrix Am×n is composed of the frequency of all the variables related to the mode transition table in a certain VRM model, where m represents the number of mode transition tables in the model, n represents the number of variables in the model. Aij corresponds to the frequency of the j-th variable in the i-th mode transition table. Definition 9: (Characteristic vector of the mode transition table) Perform singular value decomposition on the mode transition table-variable matrix Am×n to obtain the corresponding k-rank approximation matrix Vm×n . The i-th row in the Vm×n matrix represents the feature vector of the i-th mode transition table, denoted as vi . Definition 10: (variable correlation between model transition tables) Suppose there are two model transition tables mtf k and mtf k . The correlation of variables is determined by calculating the cosine value between the characteristic vector corresponding to the two mode transition tables, so the formula is given as follows:   vk T vj   Cos mtf k , mtf j = |vk |vj    Cos mtf k , mtf j ∈ [−1, 1], but according to the non-negative nature of the coupling degree, then there is:  Cos(mtfk , mtfj ) if Cos(mtfk , mtfj ) ≥ 0 CCoMTF(mtfk , mtfj ) = 0 else

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Based on the above definition, we divide the method of calculating the control coupling between the mode transition tables into the following steps: Read the source VRM model file to construct the mode transition table-variable matrix. perform singular value decomposition on the mode transition table-variable matrix, and take its k rank to approximate the matrix. Take the cosine value of the angle between the variable characteristic vector corresponding to two specific mode transition tables as the variable correlation between them. Determine the value of the control coupling degree according to the relationship between the control coupling degree and the variable correlation degree. 3.2 Method of VRM Model DataCoupling Degree Measurement The measurement of the degree of data coupling between the data transition tables is similar to the measurement of the degree of control coupling. However, since the data transition table is composed of a condition table and an event table, the events used in the event table are essentially a combination of conditions. Therefore, additional processing is required for the events in the event table. Therefore, for the calculation of the data coupling measurement between the data transition tables, we give the following definitions: Definition 11: (data transition table-variable matrix) The data conversion tablevariable matrix is similar to the mode conversion table-variable matrix, where m represents the number of data transition tables in the model, Definition 12: (Characteristic vector of the data transition table) The i-th row in the Vm×n matrix represents the feature vector of the i-th data transition table, denoted as vi . Definition 13: (variable correlation between data transition tables) The correlation of variables is determined by calculating the cosine value between the characteristic vector corresponding to the two data transition tables, so the formula is given as follows:   vk T vj Cos dtf k , dtf j = |vk ||vj | By definition, there are:  DCoMTF(dtfk , dtfj ) =

Cos(dtfk , dtfj ) if Cos(dtfk , dtfj ) ≥ 0 0 else

4 Case Study The following is an example of coupling analysis using the VRM model. The Table 1 shows the mode transition table and corresponding model components involved in the flight control system modeled by the VRM model. The flight control system uses four model components: horizontal mode, vertical mode, flight mode, and edge components to describe the flight state transition of the aircraft. Among them, ROLL in horizontal mode means roll, HDG means heading selection; in vertical mode, PITCH means pitch, VS means vertical speed; Mode in flight mode

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Model component

Horizontal mode

Vertical mode

Flight mode

Edge component

Mode transition table

ROLL

PITCH

Mode

AP

HDG

VS

Onside_FD

Fig. 3. The source file of the mode

means whether the aircraft is currently in flight; AP means autopilot in edge component, Onside_FD means flight Guidelines. According to our calculation method of control coupling degree, we must first construct the mode transition table-variable matrix. As shown in the Fig. 3, it is the modeling code of the mode transition table Mode, and the corresponding table function is as follows:

It can be seen that there are three paths in the mode transition table of the mode type Mode, among which there are two paths for Mode = on and one path for Mode = off. In the corresponding program implementation of this part, there are three calculations involving Mode. Therefore, the frequency of Mode in the mode transition table of Mode is 3, and the frequencies of other variables are as show in the Table 2.

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Table 2. Frequency of variables related to Mode cAP_Lamp

cAP_Lamp

cOnside_FD_Lamp

cOnside_FD_Lamp

Mode

_Mode

…

2

2

2

2

3

3

0

After obtaining the frequencies of all the mode transition tables, the mode transition table-variable matrix of the flight control system is constructed. Then perform singular value decomposition on the mode transition table-variable matrix, and find the k-rank approximation matrix. Table 3 shows the degree of control coupling between the various mode transition tables in the flight control system. The explanation of the first line is: after an error occurs in the program implementation of the mode transition table ROLL, the order that may cause problems in other mode transition tables is PITCH, AP, Onside_FD, HDG, VS, Mode. Table 3. Control coupling of flight guidance system CCoMTF

ROLL

HDG

PITCH

VS

Onside_FD

AP

Mode

ROLL

1

0

0.9661

0

0.0998

0.2739

0

HDG

0

1

0

0

0

0.6284

0.8673

PITCH

0.9661

0

1

0

0.1724

0.0702

0

VS

0

0

0

1

0.0009

0

0

Onside_FD

0.0998

0

0.1724

0.0009

1

0

0

AP

0.2739

0.6284

0.0702

0

0

1

0.7861

Mode

0

0.8673

0

0

0

0.7861

1

5 Conclusion According to the requirements in DO-178C, coupling analysis needs to be analyzed at the software requirements level, and the VRM model can describe the behavior of the system well at the system requirements level. The VRM model can be used and can well describe the demand characteristics in fields such as avionics software systems and flight control systems. Based on the VRM model, this article provides a set of definitions of coupling types that conform to the DO-178C standard and the corresponding coupling measurement methods. An example of the flight control system was given to verify the feasibility of the method, and a set of prototype tools was developed to support the coupling analysis. However, this method also has some shortcomings, such as the timing of input and output variables, how to define the coupling between different levels, etc. These issues will also become the next research content.

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References 1. Hu, J., Hu, J., Wang, W., et al.: Constructing formal specification models from domain specific natural language requirements. In: 2020 6th International Symposium on System and Software Reliability (ISSSR) (2020) 2. Zhipeng, Q., Lisong, W., Jiexiang, K., et al.: A method of test case generation based on VRM Model. In: 2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS). IEEE (2021) 3. Czibula, I.G., Czibula, G., Miholca, D.L., et al.: An aggregated coupling measure for the analysis of object-oriented software systems. J. Syst. Softw. 148, 1–20 (2018) 4. Poshyvanyk, D., Marcus, A., Ferenc, R., et al.: Using information retrieval based coupling measures for impact analysis (2008). https://doi.org/10.1007/s10664-008-9088-2 5. Cruz, R.J.D.L.: On the Two-sided perplectic Singular Value Decomposition and perplectically diagonalizable matrices. Asian-Eur. J. Math. (2021)

A Framework for System Safety Modeling and Simulation Based on AltaRica 3.0 Jun Hu1 , Jian Qi1(B) , Lisong Wang1 , Yanhong Dong1 , Qingfan Gu2 , and Hao Rong2 1

2

College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China {hujun,flagship}@nuaa.edu.cn Department of Software, China National Aeronautic Radio Electronics Research Institute, Shanghai 200233, China

Abstract. AltaRica 3.0 is a high-level modeling language for safety critical systems, which can be used for system modeling and safety analysis. At present, there are some tools for AltaRica 3.0, such as OpenAltaRica, which can realize system modeling and step simulaiton. However, they can’t support graphical modeling and display. Therefore, this paper aims to design and implement a modeling and simulation tool with graphical modeling function based on AltaRica 3.0. The main work includes the following aspects: Firstly, a text editor for modeling is constructed. Fault model compilation, fault tree generation and fault tree analysis are implemented based on OpenAltaRica’s engines and Arbre Analyste tool. Secondly, a graph editor is constructed, and the functions of loading, modifying and saving graph models are realized. Then, a simulation method based on step simulation engine is designed and implemented. Finally, the correctness and effectiveness of the modeling and simulation method are verified through the analysis of an example system.

Keywords: AltaRica 3.0 simulation

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Introduction

For safety critical systems [5], the occurrence of system failure often brings huge casualties and property losses. Therefore, the reliability and safety evaluation of the system is extremely important. The core idea of Model-Based Safety Assessment(MBSA) [8] is to write models in the form of high-level description to make it closer to the function and physical architecture of the studied system. On this basis, safety assessment and reliability analysis of the system are carried out. AltaRica is a high-level modeling language based on MBSA, which is dedicated to safety analysis. AltaRica 3.0 [2] is the latest version of the language, which is composed of System Structure Modeling Language (S2ML) and c The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022  Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 919–927, 2022. https://doi.org/10.1007/978-981-19-0390-8_116

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Guarded Transition System [12] (GTS). S2ML is used to describe the hierarchical structure of the system, and GTS is used to analyze the safety of the system. In order to effectively evaluate and verify the safety of the system, it is necessary to transform S2ML model with hierarchical structure into GTS model with semantic equivalence [7]. Based on GTS model, the fault tree [14] can be generated and analyzed, or the system can be simulated step by step. Simfia [6] is based on AltaRica data-flow [4], which can realize model-based airworthiness analysis of complex systems and support graphical modeling. OpenAltaRica [3], based on the latest AltaRica 3.0 language, is committed to the safety analysis of complex systems. The purpose of this study is to design and implement a tool based on AltaRica 3.0, which has the functions of graphical modeling and dynamic step simulation. The structure of this paper is as follows: Sect. 2 outlines the linguistic basis of this study. Section 3 describes the specific design of the tool, and focuses on the implementation of graphical modeling and step simulation. In Sect. 4, an example is given to verify the effectiveness of the modeling and simulation method. Finally, the work of this paper is summarized and the future work is prospected.

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Background

AltaRica is a high-level modeling language for safety analysis, which has been developed in three versions. The semantics of the first version is based on the definition of constraint automata [11], and the corresponding evaluation tools are developed for this version, but it is very resource-consuming for industrial scale systems. The second version is based on mode automata [9], which can evaluate industrial scale systems, but does not support systems with loops and acausal components. The latest version is AltaRica 3.0, which introduces the concepts of GTS and object-oriented. It can handle real-time loop systems and support the reuse of components. The evaluation tools developed around this version include fault model compiler, fault tree generator, step simulator, etc. AltaRica 3.0 consists of System Structure Modeling Language (S2ML) and Guarded Transition System (GTS). S2ML is a system model with explicit hierarchical structure for modeling, and GTS is a formal [1] semantic model for safety analysis. By compiling the fault model, S2ML model can be transformed into GTS model, and then the safety of the system can be analyzed. AltaRica 3.0 model is composed of basic components class and block. Each basic component is mainly composed of variables, events, transitions and assertions: (1) Variable: including state variables and flow variables. (2) Event: triggering event will cause changes of state variables. (3) Transition: it is usually expressed as e: G→P, where e is an event, G is a guard (boolean expression built on variable set), and P is an action executed on state variables to calculate the new state of the system. The meaning of e: G→P is when G is true, execute instruction P.

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(4) Assertion: an instruction based on variable set, used to calculate the value of flow variables according to the value of state variables.

3 3.1

System Modeling and Simulation Method Architecture Design

The modeling and simulation tool A-MAST aims to realize the modeling and step simulation function based on AltaRica 3.0. Its architecture is shown in Fig. 1, which is mainly divided into six modules, namely: modeling module, safety analysis module, simulation module, file system module, GUI module and error handling module. The modeling module supports the construction of AltaRica 3.0 models and graph models of the system. The safety analysis module is based on AltaRica 3.0 models for safety analysis, such as fault model compilation, fault tree generation and fault tree analysis. Based on GTS models and graph models, the simulation module can simulate the fault injection and dynamically display the system state and fault propagation path. File system is used to manage text files in tools, such as project files, graph model files, etc. The GUI module aims to design a good visual interface to simplify user operation, which is used for text display, graph model display and step simulation results demonstration. The error handling module can respond to the error to guide the user to operate correctly. GUI Use r interaction

File display

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Simulation results display

interact Safe ty analysis

Modeling

Simulation

Compile S 2M L

Build S2ML m odel

response Generate fault tree Build graph model

read

Ana lyse fault tree

write File System

error

Step simulation

response

error Error Processing

Fig. 1. Architecture diagram of A-MAST

3.2

Data Flow Design

The data processing flow of A-MAST is shown in Fig. 2, and the main steps are as follows: (1) The user writes the S2ML model of the system through the text editor of GUI module. (2) Safety analysis module reads S2ML model and outputs GTS model. (3) Safety analysis module reads the GTS model and outputs the fault tree file.

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(4) In safety analysis module, fault tree files are read and analyzed by the Arbre Analyste tool, and then analysis results are presented to the user through GUI module. (5) The user draws the graph model of the system through the graph editor of GUI module. (6) Simulation module reads the GTS model and graph model files, performs step simulation, and outputs the result file. (7) Simulation module reads and processes the result file, and displays the dynamic step simulation result to the user through GUI module.

GUI

Error Processing

Fig. 2. Data processing flow

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Core Function Modules

Graphical Modeling. Graphical modeling is the basic function to realize the explicit view of system graphic structure and dynamic step simulation results. The specific steps to realize this function are as follows: First of all, we need to use SWT [10] (Standard Widget Toolkit), GEF [13] (Graphical Editing Framework) and Draw2d technology to realize a graph editor with the functions of creating or deleting components and connecting lines. Then, in order to realize the persistence of graph models, we need to design corresponding XML files to save relevant information, including directory information of graph model files, structure information of systems and basic information of graphics systems or components. Each time a model is created or modified, local files which save graph models will be modified accordingly. The graph model of the system can be loaded by reading local XML files. Finally, the business logic of graphics operation is realized, that is to modify model files after graphics operation. The implementation of this function is based on GEF technology, and its working principle is shown in Fig. 3: Firstly, the user’s operation on a model in the editor is captured by EditPartViewer and converted into a request by GEF. Then, the request is sent to an EditPolicy group in the controller EditPart corresponding to the model. Then, the model and local model files are modified by the corresponding command object in EditPolicy. Finally,

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EditPolicy1 Command1 Command2

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SWT Event

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Modify

EditPolicy2 Request

Command3 Command4

Refresh

View

Tool

Fig. 3. Working principle diagram of GEF

model changes are notified to the corresponding EditPart, which refreshes the corresponding graphics to keep the information synchronized. Step Simulation. Step simulation is the core function of this method. Based on GTS models and graph models of the system, with the help of gtsstp.exe, the step simulation engine of OpenAltaRica, step simulation of the system can be carried out. The dynamic step simulation method is implemented using multithreading technology. The main thread is responsible for starting the step simulation engine, receiving the execution results of each thread’s commands and saving them to result files, and then performing different operations according to the command results, so as to ensure the coordination between the threads. The processing flow of step simulation is shown in Fig. 4. (1) Start the step simulation engine. (2.1) Execute the command of printing all trigger events and update the list of trigger events. (2.2) Execute the command of printing all variables of the system, and update the variable table. (3) Execute the command to trigger a fault event. (4.1) Execute the command of printing all variables of the system. (4.2) Execute the command of printing all trigger events. (5) Analyze the result file which saves all variables of the system, dynamically demonstrate the fault propagation path and update the variable table.

Fig. 4. Step simulation processing flow

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Case Study Overview of Cooling System

A reactor cooling system is shown in Fig. 5(a). Its function is to pump the coolant from the tank to the reactor in order to cool the reactor. The system consists of the following parts: (1) One tank: store coolant. (2) Four valves: two valves to control the input and two valves to control the output. (3) Two pumps: deliver coolant from one end to the other. (4) One reactor: cooling target of the system.

Line1 Valve VI

Pump P

Valve VI

Pump P

Valve VO

Valve VO

Reactor

Line2

Tank T

(a) Cooling System

block CoolingSystem Tank TK (evGetEmpty.delay = Dirac(0.0)); block Line1 embeds main.TK as t1; Valve VI, VO; Pump P; assertion VI.vfLeftFlow := t1.vfOutFlow; P.vfInFlow := VI.vfRightFlow; VO.vfLeftFlow := P.vfOutFlow; end block Line2 embeds main.TK as t2; Valve VI, VO; Pump P; assertion VI.vfLeftFlow := t2.vfOutFlow; P.vfInFlow := VI.vfRightFlow; VO.vfLeftFlow := P.vfOutFlow; end block Reactor Boolean vfInFlow (reset = false); end observer Boolean oCooledReactor = Reactor.vfInFlow; parameter Real pPumpsCCF = 1.0e-6; event evPumpsCCF (delay = exponential(pPumpsCCF)); transition evPumpsCCF: !Line1.P.evFailure & !Line2.P.evFailure; assertion Reactor.vfInFlow := Line1.VO.vfRightFlow or Line2.VO.vfRightFlow; end

(b) S2ML Model

Fig. 5. Cooling system and S2ML model

4.2

Cooling System Modeling

Firstly, the S2ML model of Cooling System is built with AltaRica 3.0 language. The system consists of a tank, two coolant delivery lines (each contains two valves and a pump) and a reactor. The S2ML models of tank, valve, pump and the whole system should be built respectively. The S2ML model corresponding to the main block is shown in Fig. 5(b). Then, based on the S2ML model of the Cooling System, the graph model of the system is constructed through the graph editor. The graph model needs to be consistent with the hierarchical structure of the S2ML model and the system name or component name. The graph model of the system is shown in Fig. 6.

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Fig. 6. Graph model

4.3

Step Simulation

The simulation workbench interface of A-MAST tool is divided into eight parts: menu bar, toolbar, graph model list area, graph model display area, variable table, event list area, event record area and property page. The following describes the step simulation function in combination with the Cooling System. In the initial state of the system, the tank is full and all components are in normal working state. The triggering events are as follows: tank becomes empty; failure of valve VI1 and VI2; failure of pump P1 and P2; failure of valve VO1 and VO2; a common cause failure event which indicates that two pumps failed simultaneously. Firstly, failure of pump P1 is triggered, and the graphic node corresponding to the component will turn red after flashing (indicating failure), and then the valve VO1 will turn orange after flashing (indicating blocking). Since line 2 is still unblocked, the system can still deliver coolant from the tank to the reactor. Then, failure of valve VI2 is triggered, and the graphic node corresponding to the component will turn red after flashing, then the pump P2 will turn orange after flashing, then the valve VO2 will turn orange after flashing, and finally

Fig. 7. System status after triggering events

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the reactor will turn orange after flashing. Because both line 1 and line 2 are blocked, the system cannot deliver coolant from the tank to the reactor. After triggering the above two events, the state of the system is shown in Fig. 7. After that, we can continue to trigger other events or reset the state of the system.

5

Conclusion

In this paper, a system modeling and simulation method based on AltaRica 3.0 is proposed. Firstly, the framework of the tool is built based on RCP and SWT technology, and the text editor is created. Secondly, the graph editor is created based on GEF and Draw2d technology. Next, the S2ML model of the system is written and the corresponding graph model is drawn. Then, based on OpenAltaRica’s engines and Arbre Analyste tool, fault model compilation, fault tree generation and fault tree analysis are implemented. Finally, based on GTS models and graph models, and with the help of step simulation engine, the graphical dynamic step simulation function is realized. The further research work includes: Firstly, implement an autonomous and controllable fault model compilation algorithm to complete the transformation from S2ML model to GTS model. Secondly, based on GTS model, implement the step simulation algorithm independently. Thirdly, verify effectiveness of the modeling and simulation method through larger scale systems, and solve the problems to improve the versatility of the method.

References 1. Arcaini, P., Gargantini, A., Riccobene, E., Scandurra, P.: A model-driven process for engineering a toolset for a formal method. Softw. Pract. Exp. 41(2), 155–166 (2011) 2. Batteux, M., Prosvirnova, T., Rauzy, A.: Altarica 3.0 language specification. AltaRica Association (2015) 3. Batteux, M., Rauzy, A., Labrogere, P.: The openaltarica project. In: Short & Tutorial Proceedings of the 4th International Symposium on Model-Based Safety Assessment, IMBSA 2014 (2014) 4. Boiteau, M., Dutuit, Y., Rauzy, A., Signoret, J.P.: The altarica data-flow language in use: modeling of production availability of a multi-state system. Reliabil. Eng. Syst. Saf. 91(7), 747–755 (2006) 5. Bozzano, M., Villafiorita, A.: Design and Safety Assessment of Critical Systems. CRC Press, Boca Raton (2010) 6. Gao, P., Liu, C., Dong, H., Zheng, W.: A dynamic fault tree based CBTC onboard ATP system safety analysis method. In: 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), pp. 1–7. IEEE (2020) 7. Hu, J., Chen, S., Chen, D., Kang, J., Wang, H.: An ANTLR-based flattening framework for altarica 3.0 model. Int. J. Perf. Eng. 15(9), 2462 (2019) 8. Lisagor, O., Kelly, T., Niu, R.: Model-based safety assessment: review of the discipline and its challenges. In: The Proceedings of 2011 9th International Conference on Reliability, Maintainability and Safety, pp. 625–632. IEEE (2011)

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9. Maraninchi, F., R´emond, Y.: Mode-automata: a new domain-specific construct for the development of safe critical systems. Sci. Comput. Program. 46(3), 219–254 (2003) 10. Northover, S., Wilson, M.: SWT: The Standard Widget Toolkit, vol. 1. AddisonWesley Professional, Boston (2004) 11. Pourvatan, B., Sirjani, M., Arbab, F., Bonsangue, M.M.: Decomposition of constraint automata. In: Barbosa, L.S., Lumpe, M. (eds.) FACS 2010. LNCS, vol. 6921, pp. 237–258. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3642-27269-1 14 12. Rauzy, A.B.: Guarded transition systems: a new states/events formalism for reliability studies. Proc. Inst. Mech. Eng. Part O J. Risk Reliabil. 222(4), 495–505 (2008) 13. Rubel, D., Wren, J., Clayberg, E.: The Eclipse Graphical Editing Framework (GEF). Addison-Wesley Professional, Boston (2011) 14. Ruijters, E., Stoelinga, M.: Fault tree analysis: a survey of the state-of-the-art in modeling, analysis and tools. Comput. Sci. Rev. 15, 29–62 (2015)

A Summary of 5G WiFi Security Issues Cui Zhuohan, Li Zheng, Liu Enfei, and Ai Xiaoxi(B) School of Computer, Dalian Neusoft University of Information, Dalian 116032, China [email protected]

Abstract. With the continuous innovation of wireless communication technology, 5G WiFi devices has been popularized in the average families, and its highspeed transmission characteristics are widely favored by the public. Since many technologies in 2.4G WiFi are used in 5G WiFi, such as (WPA/PSA) authentication, which leads to many security problems and defects left by 2.4G WiFi in 5G WiFi. Although the IEEE has introduced technologies such as WPA3 as authentication methods for 5G WiFi devices, which are still not widely available, the maximum transmission speed supported by 5G WiFi devices in the circulating market is growing, few devices use the latest encryption authentication and other technologies. In this paper, it is reviewed the emergence of problems and solutions in 5G WiFi, based on the test of the common security problems appeared. Keyword: 5G WiFi · Security issues · Authentication

1 Introduction With the popularity of 5G technology and optical fiber transmission data, 5G WiFi and its supporting facilities are emerging in the market. Among the 5G-based WiFi devices, due to the innovation of transmission technology, the speed has broken the original 300 megabits maximum transmission speed of WiFi at 2.4 GHz. At the present, the most prominent problem is that many manufacturers do not have the appropriate new technologies to protect the security of 5G WiFi and its security authentication method still used WPA/WPA2 PSK authentication method, which there is a risk of hacker cracking because of the same as 2.4G. Many 5G WiFi management mechanisms still use the 2.4 GHz management mechanism, which there are also security issues due to no updating. The physical change between 5G WiFi and 2.4G WiFi is that 2.4G WiFi has a longer transmission distance and a stronger penetration ability to objects, but a slower transfer rate, while 5G WiFi has a closer transmission distance and a poor penetration ability to objects, but a faster transmission speed.

2 Mainstream Issues 2.1 5G WiFi Authentication Security Issues There are three standards for WPA, WPA2, and WPA3 for WPA (Wi-Fi Protected Access). When it comes to wireless security technology, we can trace it back to the © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 928–936, 2022. https://doi.org/10.1007/978-981-19-0390-8_117

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earliest wireless encryption system, Wired Privacy Wired Peer Secrecy, which is easy to crack due to algorithmic flaws. The WPA that followed, and its derived WPA2, WPA3. Without upgrading the hardware, WPA uses TKIP (Temporal Key Integrity Protocol Temporary Key Integration Protocol) to enhance the security of WEP by implementing Wi-Fi access control, key management, and data encryption. In WPA, identity authentication, data transmission encryption authentication, data integrity authentication three authentication algorithms combined. 5G WiFi follows 2.4G WPA/WPA2 PSK authentication technology, there are no major vulnerabilities, but its use time is long, hackers can use the Deauthentication attack or use Flooding attack to disconnect the owner and WiFi has established a connection, such as the two devices to establish a connection between the two devices when intercepting the connection to establish a handshake package. Handshake package is the medium encapsulated in WPA-PSK transmission. The “handshake pack” contains encrypted WiFi passwords, from which hackers cannot decrypt WiFi passwords directly, but hackers can create a dictionary of commonly used passwords through social engineering to enumerate on WiFi passwords. However, the enumerate attack need more time, for the symbol, letter, number composed of more than 10 bits of complex passwords, enumerate time cost is difficult to measure by formula, which directly led to this kind of attack effect is not good. Although enumerate have been criticized for their time-consuming, the issue of time-consuming is no longer a problem as graphics computing power is updated. Hackers can use powerful graphics cards to perform operations and compare them one by one with redactions in messages, which greatly improves the efficiency of WiFi password cracking. According to the test, the laptop can attempt about 180,000 WiFi passwords per second using the GTX1650 graphics card in an SSD storage dictionary environment, as shown in the figure (Figs. 1, 2 and 3):

Fig. 1. GTX 1650 attempt result

RTX 3080Ti graphics cards can attempt about 90.4W WiFi passwords per second, as shown in the figure: After analyzing the passwords of the extracted 171 WiFi password databases, the data showed that about 56.7% of those who used a pure phone number as a password

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Fig. 2. RTX 3080Ti attempt result

or two to three-digit letter plus a phone number, a format of 2021063 or an abbreviation with a pure number date, the letters with a one to three and with the numbers with five to eight digits. After the corresponding crack dictionary of the corresponding city is produced by social engineering, take Dalian as an example: the expectation of cracking success is 3.063, if the cracking is done using RTX 3080Ti, the average time to crack is 5.4 h, and the examples of 5G WiFi enumerate are as follows:

Fig. 3. Grabbing “Handshake package”

In this experiment, the router is using a slightly more complex common WiFi password: the first two lowercase letters and local mobile phone number, according to the internet search will be all the mobile phone segment to obtain, a total of 2521 number segments, if not excluded from using the number segment, before the mobile phone number plus two lowercase The letters generate a total of 17,041,960,000 passwords, which can be cracked for an attacker up to a maximum of 26.30 h with the computational power of the notebook GTX1650. In most cases, WiFi users only use the phone number as the WiFi password, in this case, the time of the experiment is about 2.34 min. If the password or initials are used for the pure number date and the WiFi password is used in the format of the pure number date, the crack time is shorter, with a total of 29,767 dates from 1 January 1940 to 30 June 2021 Possible passwords, with 773,942 possible passwords added to one letter and 20, 122,492 possible passwords with two letters, with a maximum crack time of approximately 1.86 min. If the dictionary is filtered and modified using the initials statistical distribution data, verifying the availability of mobile phone number, etc., can greatly reduce the size of

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the dictionary, the cracking speed will be faster, in addition to the attacker’s dictionary content also includes some commonly used password data, which can greatly improve the success rate of cracking. Therefore, setting up complex passwords that are unique to device individuals or changing them regularly is the best way to prevent a enumerate attack. However, this way requires users to have a higher sense of network security and prevention awareness, 5G WiFi as a high-speed communication equipment, should be timely improve its authentication methods to prevent or increase the enumerate difficult to achieve, so as to ensure the user’s network information security, otherwise 5G WiFi will become a way for hackers to obtain user information faster. A feasible solution to the current 5G WiFi certification: Save and bind the MAC address of the authenticated device in WiFi. A new device first connected to WIFI needs to use WIFI password authentication, after which WIFI will record the MAC address of the device, after each time the device is connected to WIFI is connected with a real-time updated scroll key, to ensure the security of the original WiFi password at the same time, even if the scroll key is hijacked and the attacker forged the original device MAC address, and the timeliness of the scroll key guarantees that only one connection to the MAC address device can be maintained, the next connection will use a new scroll key, so that The way an attacker uses a truncated connection to intercept a “handshake pack” while waiting for the device to connect to fail. The disadvantage of this approach is that it takes up some resources of the device to record the MAC address of the connected device and the corresponding scrolling algorithm, but also takes up some bandwidth to transmit these scroll keys, but as a 5G device, the bandwidth occupied does not affect the normal use of the user. 2.2 Co-sharing WiFi Information Leakage Security Problem Many well-known “co-sharing software of WiFi”, which provides users with different geographical locations with password WiFi connection services, while silently sharing in the background in the user’s phone saved WiFi password and other information, the information will then “share” to other users. In recent years, user passwords and other information leakage events, such companies also leaked a large number of user WiFi passwords and other information, these disclosure information to users externally caused the risk of password leakage, attackers through the download of this information database to the user’s WiFi password or other passwords of users to guess, these wellknown “sharing”, “sharing” effect for the time being, it has caused users a great risk of password, geographical location and other privacy disclosure. 2.3 Fakeability of 5G WiFi 5G WiFi also inherits a huge weakness of 2.4G WiFi: forgeability, which means devices of WiFi consumers with the same name, the same band, and the same channel only displaying and connecting the strongest signals by default. If an attacker creates a WiFi with the same name, the same band, the same channel, and fake WiFi transmits much more power than the real WiFi, the user will first connect to the attacker’s WiFi and mistakenly think he or she is connected to the real WiFi. Attackers often refer to this attack as “phishing” counterfeit attacks, which are particularly suitable for 5G bands,

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because of the 5G band penetration, transmission distance is short characteristics, the attacker will use high-power 5G WiFi devices to attack, usually targeting public 5G WiFi, such attacks are widespread. Attackers can often fake WiFi can be connected to the Internet, allowing users to mistakenly think they are connected to a real public WiFi, so as to do some networked operations: such as payment, login to some accounts, etc. the attacker through packet software to collect all this information for use, the user’s information will be leaked. Solution: Users should avoid connecting unknown WiFi when they are on business and avoid some relationship privacy and property operations once connecting some public WiFi. As a 5G WiFi device itself, new device identification should also be developed to ensure that users connect to real devices. 2.4 Authentication Package Flooding For 2.4G WiFi, authentication package flooding attacks are a common attack that paralyzes WiFi connections. Experimented, in 5G WiFi compared to 2.4G WiFi from the coverage is not easy to be floodinged, but in the range of receiving certification package flooding attack will be WiFi connection messages full, so that WiFi devices mistakenly think that there is a device to initiate a connection, thereby consuming resources to process these certification packages. Certification package flooding attacks can easily cause a brief paralysis of WiFi devices, preventing users from connecting to WiFi. The certification package flooding attack process is as follows (Fig. 4):

Fig. 4. A 5G WiFi frozen status after attacked

When the echo of a 5G WiFi attack is Frozen, no other device can connect to the WiFi. To avoid such attacks, device need to include an authentication package filtering mechanism in the 5G WiFi connection process that allows 5G WiFi to automatically filter fake connection messages when a device is connected and is not affected by the attack. 2.5 EAPOL Flooding The EAP (Extensible Authentication Protocol) authentication mechanism is a commonly used authentication mechanism commonly used in wireless networks or point-to-point connections. EAPOL (EAP OVER LAN) is an extended certification protocol based on

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the LAN 802.1X certification protocol. And the EAPOL flooding attack is: Send EAPOL Start and Logoff Packet Injection. Floods an AP with EAPOL Start frames to keep it busy with fake sessions and thus disables it to handle any legitimate clients. Or logs off clients by injecting fake EAPOL Logoff messages. This type of attack is generally applicable to EAP WiFi devices using authentication mechanisms, attacks send packets will cause the consumer to be unable to connect to WiFi devices properly, the attack process is shown below (Fig. 5):

Fig. 5. EAPOL flood attack

The start frame and log-out frame interaction of the EAPOL authentication mechanism are easily injected by attack, resulting in WiFi devices unable to handle normal interaction such as normal connection, and the cumbersome process of connection interaction is the root cause of EAPOL flooding attack, although the EAPOL mechanism provides a good connection device management control function, but its cumbersome intermediate interaction process needs to be optimized. 2.6 WiFi Firmware Back Door Security Issues The above security issues have a strong scope, only within the 5G WiFi signal coverage can be achieved, and 5G WiFi brush into the back firmware this attack method can be WiFi device networking in any network location for remote packet capture and other operations, whose attack method is similar to the Trojan virus in the computer, and more secret than Trojan virus, because in the antivirus software can not detect if WiFi has been implanted in the back door. This security issue also exists as early as 2.4G WiFi, the difference is that if the user’s network speed is faster, the implanted backdoor takes up traffic to the attacker to forward data is more difficult for the user to discover. As a result, high-speed data transfer features of 5G WiFi also increase the concealment of backdoor attacks. But to implant the back door, device first need to connect to WiFi and get the WiFi administrator’s user password to achieve, because of user habits, most users use the WiFi password or default password as the administrator password, that is, usually as long as the attacker can connect to the WiFi, it is equivalent to the ability to upload the backdoor firmware through the administrator page without hindrance. Most 5G WiFi devices do not have good transport confidentiality, even though users

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have different administrator passwords than WiFi passwords. In the case of an attacker connecting to 5G WiFi and turning on packet-grabbing software, users can simply log on to the administrator interface, some passwords can be easily intercepted without encryption, and some encrypted password redactions can be restored to clear text by common decryption methods, as follows (Figs. 6 and 7):

Fig. 6. WIFI administrator login information HTTP request message

From the figure, device can see that the login username and the password of the redacted text are intercepted.

Fig. 7. Decrypted password

The above example reproduces an attacker’s process of stealing the router administrator’s account password by grabbing a packet. Other WiFi devices have vulnerabilities in their own web pages that allow attackers to bypass the administrator’s login process and go directly to the management interface without an administrator account password. Once in the WiFi management interface, an attacker can use firmware unzipped tools such as binwalk to extract and implant the backdoor code from the firmware website, then use the tool to package it, upload the rewritten firmware to the WiFi device on the administrator page, which is equivalent to updating the original firmware, as shown below (Figs. 8 and 9):

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Fig. 8. An example of 5G WiFi upgrade interface

Fig. 9. A successful example of 5G WiFi firmware decompression

Unzipped files include WiFi background code, web files, data files, etc., which can be modified to implant a backdoor into any WiFi device. Once successful, this type of attack is difficult for users to detect. Therefore, this kind of attack is harmful and hidden, but the conditions required for its implementation are also more demanding. This type of attack requires manufacturers of 5G WiFi devices to use unique signature and encryption techniques to prevent firmware source code from being acquired by attackers, and signature technology ensures the recognition of uploading firmware to prevent such attacks in both ways. For consumers, always use the updates that come with 5G WiFi to keep the firmware up to date. At the same time enhance the awareness of prevention, in their own network speed significantly slow down when the timely detection of problems, if not hardware failure needs to reset WiFi or from the WiFi device manufacturer’s official website to download and update to the latest firmware.

3 Conclusion With the continuous innovation of wireless high-speed transmission technology such as 5G, many authentication technologies used in 5G WiFi cannot guarantee the information security of users. For different user groups, 5G WiFi devices should be made personalized improvements and appropriate improvements, according to the needs of users and security issues, which promotes 5G WiFi products to meet the needs of different scenarios.

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References 1. Khalil, N., Najid A.: Performance Analysis of 802.11ac with Frame Aggregation Using NS3. Int. J. Electr. Comput. Eng. (IJECE) 10(5), 5368–5376 (2020) 2. Mobile Computing. Studies from Ben-Gurion University of the Negev Have Provided New Data on Mobile Computing (Analysis of Different Approaches To Distributed Multiuser Mimo In the 802.11ac). Comput. Weekly News (11), 1047 (2020) 3. Marques, N., Zúquete, A., Barraca, J.P.: EAP-SH: An EAP authentication protocol to integrate captive portals in the 802.1X security architecture. Wirel. Personal Commun. (4), 1–25 (2020)

An WiFi-Based Human Activity Recognition System Under Multi-source Interference Jiapeng Li1 , Ting Jiang1(B) , Jiacheng Yu1 , Xue Ding1 , Yi Zhong2 , and Yang Liu3 1

Beijing University of Posts and Telecommunications School of Information and Communication Engineering, Beijing, China {lijp,tjiang,yujiacheng333,dxue}@bupt.edu.cn 2 Beijing Institute of Technology School of Information and Electronics, Beijing, China [email protected] 3 Aisino Co., Ltd, Beijing, China [email protected]

Abstract. WiFi-based human activity recognition in simple scenes has made exciting progress driven by deep learning methods, but current applications are focused on recognition without interference. When the channel state information(CSI) matrix of the receiver contains both the features of the target activities and other interference, the neural network often needs a deeper model structure if deep features of the activities are desired. But a deep network model is often difficult to converge, resulting in a decline in accuracy. And the model size is too large to be deployed in the real world. In this study, an ultra-lightweight neural network recognition system with a group communication(GC) named GC-LSTM is proposed. This design can easily convert a large model into a lightweight counterpart and improve network performance under multi-source interference via reducing network size and complexity. The experimental results show that the optimal recognition rate of the proposed method is 98.6% in the classification of four kinds of activities under six different interferences. By further adjusting the parameters, the model size is reduced to 4.1% of that of plain Long Short-Term Memory(LSTM), while the identification accuracy remains at 96.4%. Keywords: Activity recognition · Channel state information (CSI) · Group Communication (GC) · Multi-source interference · Model size

1

Introduction

In recent years, activity recognition based on Wi-Fi signals has attracted wide attention due to its advantages of non-line of sight, non-contact, and ubiquitous devices. It has been applied in many fields, such as activity recognition c The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022  Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 937–944, 2022. https://doi.org/10.1007/978-981-19-0390-8_118

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[1–7], daily behavior detection [8,9], fall detection [10,11], identity recognition [12,13], breathing and heartbeat detection [14,15], etc. In these studies, activity recognition is closely connected with human-computer interaction, making it a hot field of Wi-Fi perception. The mainstream Wi-Fi sensing system generally uses signal processing or machine learning methods to extract activity features from channel state information and then maps them to different activity for recognition. At present, deep learning is widely used in WiFi-based activity recognition. CSI samples are directly input into the neural network model, and a neural network model can accomplish two tasks simultaneously: feature extraction and sample classification. Long Short-Term Memory network(LSTM) is a kind of temporal cyclic neural network, which can extract the characteristics of samples in the time dimension. The convolutional kernel of the Convolutional Neural Network(CNN) slides on the time dimension and can also process temporal signals. The above two network models are most widely used in deep learning in the field of activity recognition based on WiFi. SignFi [16] measures CSI by WiFi packets and uses a 9-layer CNN as the classification algorithm to recognize frequently used sign gestures. Zhang et al. [17] use LSTM algorithm to classify data from CSI. The preliminary result shows that the systems achieve an average accuracy of 96% with 5 states of arm movement. Deepnum [18] uses a 7-layer CNN to achieve automatic feature extraction and accurate gesture classification which recognizes 10 finger gestures with an overall accuracy of 98% in three typical indoor scenarios. However, most WiFi recognition applications can only work without interference. When there are other people around to perform interference actions, such as walking, waving, and standing up, the recognition accuracy will be greatly reduced. This is because every user in the space is a signal reflection source, and the CSI matrix at the receiving end is an aggregation of multiple sources. We can use samples with various interference to train a deeper neural network, making the model to extract deeper motion features. But a deep network model is often difficult to converge because of large model size, resulting in a decline in accuracy. And too large model size will takes up more memory and computing resources, hindering the application in reality. In response to the above questions, this paper proposes a robust WiFi recognition system, which is an activity recognition system anti-interference in multisource scenes. We have improved an ultra-lightweight LSTM network called GCLSTM by introducing a Group Communication(GC) [19,20] before the LSTM layer. GC is a general network design method which can convert a large model into a lightweight counterpart. It can be implemented with a small CNN, Recurrent Neural Network(RNN), or full connection layer. GC decomposes an Ldimensional long vector into K groups of D-dimensional short vectors and then establishes relationships between K groups. Then the K groups of M dimension vectors will be processed in parallel by a subsequent module. The size of this 1 because the dimension of its input vector is reduced module is reduced to K 1 to K . The subsequent processing module is implemented by LSTM, because of its excellent performance in processing time-series data and invariance of time

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shift. The experimental results show that the network can not only achieve a high recognition accuracy under multi-source interference but also greatly reduce the model size. The rest of this article is organized as follows. Section 2 introduces the preparation work. Section 3 provides the detailed design of the system. Section 4 presents the evaluation process and the analysis of the results. We conclude our work in Sect. 5.

2

Experiment Setup

We collected experimental data in the living room. As shown in Fig. 1. The receiver and the transmitter which are two PCs equipped with Intel 5300 network interface card(NIC) and three antennas are placed close to two walls, respectively. The NICs work at 5 GHz, 20 MHz bandwidth. There are three transmitting antennas and three receiving antennas, a total 9 pairs of communication links. The sampling rate of the device is 200 Hz, and the time length of each activity sample is 4 s. The target user execute four kinds of activity, “UP” (Raise hand forward.), “O” (Draw a circle with arm.), “PO” (Raise arms and spread them out to the side.) and “LR” (Wave arm from side to side), on one side of the table. While the other user performs “sit still”, “stand up”, “node”, “wave hands”, “strike keyboard”, and “write on paper” on the other side. There are four categories, and each category has six combinations.

Fig. 1. Experimental environment.

3 3.1

Fig. 2. Overview of system.

Methedology Overview of the System

An improved GC [19,20] is used in our model to transform complex modules into their lightweight counterparts. Reduce the size and complexity of the model while

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Fig. 3. Process of TAC.

ensuring accuracy. The whole GC-LSTM is divided into GC-module and LSTMmodule. The GC-module divides high dimensional features into low dimensional feature sets and establishes connections between groups. The LSTM-module shares parameters between all groups and processes the vector of each group parallel. Before the GC-LSTM module, an encoder is applied to reduce the dimension of the input CSI subcarrier number. At the end of the model, a classifier is implemented to process the features extracted by LSTM to complete the final classification task. We flatten the subcarriers between the nine transceiver pairs and extend the real and imaginary parts of CSI to the subcarrier dimension so that the phase information can be utilized. And band pass butterworth is used to remove high frequency noise and static path. One input CSI sample is x ∈ RC×L , where C represents the number of subcarriers and L represents the length of time. We use a 1-D convolution to encode it in the time domain and eliminate the correlation between the subcarriers. H = E(x)

(1)



E is a 1-D convolutional layer and H is a real-valued representation. At the end of the network, we choose 1-D convolution and FC layer as the classifier after the GC-LSTM module. The overview of the system is shown in Fig. 2. 3.2

Group Communication

In a processing module equipped with GC [19,20], the large L-dimensional input eigenvectors are divided into K groups and each group contains a small Ddimensional eigenvector. We use Transform-Average-Concatenate(TAC) [21] to implement the GC, which captures dependencies between groups. A small LSTM is then applied in parallel to the output of the group. Multiple such GC-LSTM processing layers can be stacked to form a deep architecture.

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Group communication is a process that works for any vector. For a L K dimensional vector v ∈ RL , we can divide it into K groups wi i=1 with wi ∈ RD , where L = KD and K, D ∈ Z+ . Let G() represents the network  K ˆ i ∈ RD module applied on wi i=1 to build the inter-group relationships, and w i ˆ is then processed as an input to is the depth feature vector of group i. Each w a shared network module.  i K  K  ˆ i=1 = G wi i=1 w (2) The TAC is used as the actual architectures for GC. The whole process which is divided into three steps is shown in Fig. 3.  K Transform. Each of the wi i=1 is passed to a fully-connected(FC) layer  with parametric rectified linear unit(PReLU) activation. Let T represent the transform-step defined by the FC layer and ui ∈ RM denotes the output of it.   ui = T w i (3) Average.  The second FC layer with PReLU is applied on theMaverage of all the ˆ ∈ R is the output of ui . A denotes the second FC layer with PReLU and u this step. K  1 ˆ =A (4) ui u K i=1 Concatenate. We finally concatenate the output of the transform-step, ui , with ˆ . The third FC layer with PReLU is applied on the concatenation to calthe u ˆ i  finally. [a; b] is the operation of the concatenation between culate the output w vector a and b and C is the mapping function defined by the third FC layer.   ˆ i = C [ui ; u ˆ] w (5) After the GC module, we apply an LSTM layer  and a linear layer to be the ˆ i parallel. Let L be the mapping function of back-end module which process w back-end module and li ∈ RD be the output of it.  i ˆ (6) li = L w

4 4.1

Evaluation Performance Compared with Other Networks

We compared the GC-LSTM we proposed with a 3-layer CNN and a 4-layer LSTM to show the difference among them. We collected 50 samples in each

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case for a total of 1400 samples, using 80% of them as the training set and the remaining 20% as the test set. All models are trained for 50 epochs with the Adam optimizer with an initial learning rate of 0.001. Four classification accuracy is used as the training objective for all models. In this part of the experiment, all six kinds of interference are used for network training and testing. The relevant parameters of GC-LSTM are shown in Table 1. The GC-LSTM we choose the one where K = 2, Gi = 64, Gh = 128, L = 4. According to the experimental results in Table 2, GC-LSTM enables the network to converge to a higher recognition accuracy by reducing the model size and complexity. Table 1. Parameter of GC-LSTM in experiment Hyperparameter

Notation

Number of groups (‘K’ in Fig. 3)

K

Input channel of GC (‘C’ in Fig. 3)

Gi

Hidden dimensions of GC (‘M’ in Fig. 3) Gh Number of GC-LSTM layers

L

Kernel size of encoder

W

Table 2. Comparison of different network performance Model

4.2

Model size Memory access cost Accuracy

GC-LSTM 506.948K

310.249M

98.57%

LSTM

739.844K

594.740M

78.93%

CNN

2.548M

498.161M

85.35%

Research on Ultra-Lightweight

There is a lower limit to the size and complexity of the model because a too-small model can lead to underfitting problems. We adjust the parameters of GC-LSTM to research the limit size of the model while maintaining the performance. The experimental configuration is described in the previous section. As can be seen from the overall trend of experimental results, with the increase of the number of groups, the model size greatly decreases, but at the same time, the recognition accuracy also decreases slightly, which is mainly caused by the decrease of the number of model parameters. However, the recognition accuracy can be improved by increasing the number of GC-LSTM layers.(K = 8, Gi = 8, Gh = 16, L = 4 compared with K = 8, Gi = 8, Gh = 16, L = 6) Therefore, we can conclude that too small a model size will lead to under-fitting and lower recognition accuracy, but compared with the benefits of reduced model size, the loss of accuracy is completely acceptable (Table 3).

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Table 3. Performance of GC-LSTM under different parameters Model

K Gi Gh L Model size Memory access cost Accuracy

LSTM

–

–

–

– 739.844K

594.740M

78.9%

CNN

–

–

–

– 2.548M

498.161M

85.4%

128 128 64 32 32 16 16 16 16 16 16

4 1 4 4 4 4 4 6 4 4 6

310.249M 129.296M 96.437M 57.312M 33.547M 23.358M 13.105M 15.347M 23.358M 13.105M 15.347M

98.6% 97.8% 97.5% 97.5% 96.7% 95.7% 94.6% 96.4% 96.1% 92.5% 92.8%

GC-LSTM 2

64 64 4 32 32 8 16 16 8 8 16 16 8 8

5

506.948K 257.156K 171.588K 117.06K 65.348K 51.396K 27.588K 30.484K 51.396K 27.588K 30.484K

Conclusion

For the recognition of activity under different interference, the CSI variation characteristics of the same activity under different interference may vary greatly. If the neural network is used for the recognition, the shallow layer network can not learn the deep characteristics within CSI, and the deep layer network has the problem of too large size and can not converge. To solve the above problems in the case of multi-sources, we introduce group communication before LSTM and construct it as a GC-LSTM module so that it can be stacked at multiple layers. Experiments show that this structure can achieve very high recognition accuracy at a very low cost, and its ultra-lightweight model size makes it possible to actually deploy in the mobile terminal. Acknowledgment. This work is supported in part by the National Natural Sciences Foundation of China under Grant 62071061, 61671075, and 61631003.

References 1. Feng, C., et al.: Wi-multi: a three-phase system for multiple human activity recognition with commercial WiFi devices. IEEE Internet Things J. 6(4), 7293–7304 (2019) 2. Pu, Q., et al.: Whole-home gesture recognition using wireless signals. In: Proceedings of the 19th Annual International Conference on Mobile Computing & Networking (2013) 3. Islam, M.T., Nirjon, S.: Wi-fringe: leveraging text semantics in WiFi CSI-based device-free named gesture recognition. In: 2020 16th International Conference on Distributed Computing in Sensor Systems (DCOSS). IEEE (2020)

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4. Zhang, J., et al.: Data augmentation and dense-LSTM for human activity recognition using WiFi signal. IEEE Internet Things J. 8, 4628–4641 (2020) 5. Ding, X., et al.: A new method of human gesture recognition using Wi-Fi signals based on XGBoost. In: 2020 IEEE/CIC International Conference on Communications in China (ICCC Workshops). IEEE (2020) 6. Ding, X., et al.: Improving WiFi-based human activity recognition with adaptive initial state via one-shot learning. In: 2021 IEEE Wireless Communications and Networking Conference (WCNC). IEEE (2021) 7. Ding, X., et al.: Wi-Fi-based location-independent human activity recognition via meta learning. Sensors 21(8), 2654 (2021) 8. Wang, W., et al.: Understanding and modeling of WiFi signal based human activity recognition. In: Proceedings of the 21st Annual International Conference on Mobile Computing and Networking (2015) 9. Wang, Y., et al.: E-eyes: device-free location-oriented activity identification using fine-grained WiFi signatures. In: Proceedings of the 20th Annual International Conference on Mobile Computing and Networking (2014) 10. Hu, Y., et al.: A WiFi-based passive fall detection system. In: ICASSP 2020– 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE (2020) 11. Cardenas, J., Gutierrez, C.A., Aguilar-Ponce, R.: Effects of antenna orientation in fall detection systems based on WiFi signals. In: 2020 IEEE Latin-American Conference on Communications (LATINCOM). IEEE (2020) 12. Fei, H., et al.: Multi-variations activity based gaits recognition using commodity WiFi. IEEE Trans. Veh. Technol. 69(2), 2263–2273 (2019) 13. Zhang, L., et al.: WiDIGR: direction-independent gait recognition system using commercial Wi-Fi devices. IEEE Internet Things J. 7(2), 1178–1191 (2019) 14. Zhang, D., et al.: BreathTrack: tracking indoor human breath status via commodity WiFi. IEEE Internet Things J. 6(2), 3899–3911 (2019) 15. Zhang, D., Hu, Y., Chen, Y.: Breath Status tracking using commodity WiFi. In: 2018 IEEE Global Communications Conference (GLOBECOM). IEEE (2018) 16. Ma, Y., et al.: Signfi: sign language recognition using WiFi. In: Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 2, no. 1, pp. 1–21 (2018) 17. Zhang, P., et al.: Complex motion detection based on channel state information and LSTM-RNN. In: 2020 10th Annual Computing and Communication Workshop and Conference (CCWC). IEEE (2020) 18. Zhou, Q., et al.: From signal to image: enabling fine-grained gesture recognition with commercial Wi-Fi devices. Sensors 18(9), 3142 (2018) 19. Luo, Y., Han, C., Mesgarani, N.: Ultra-lightweight speech separation via group communication. In: ICASSP 2021–2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE (2021) 20. Luo, Y., Han, C., Mesgarani, N.: Group communication with context codec for ultra-lightweight source separation. arXiv preprint arXiv:2012.07291 (2020) 21. Luo, Y., et al.: End-to-end microphone permutation and number invariant multichannel speech separation. In: ICASSP 2020–2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE (2020)

Target Detection and Recognition System Based on PYNQ Platform Youzhong Wang1 , Yanping Zhu1(B) , Zhiyuan Qi2 , and Jinli Chen1 1 School of Electronic and Information Engineering, Nanjing University of Information Science

and Technology, Nanjing, China [email protected] 2 Chang Wang School of Honors, Nanjing University of Information Science and Technology, Nanjing, China

Abstract. Target detection and recognition is a very important part in artificial intelligence field. In recent years, how to achieve target detection with low power consumption has become a research hotspot. In this paper, based on the PYNQ platform, the neural network single step multi-frame detection algorithm (SSD) is adopted, the overall function planning and system construction are carried out through software and hardware cooperation. Firstly, the convolutional neural network (CNN) training model is used for training and then weight parameter files is generated. After that we use HLS to design custom IP core and build system access. The last step is calling PC program. The test results under Jupyter Notebook environment show that the system can achieve effective target classification with low power consumption, which proves the feasibility of the whole system design. Keyword: CNN · SSD · HLS · PYNQ

1 Introduction With the development of Internet artificial intelligence, neural network has gradually become to the hotspot in many fields. CNN is a feedforward network including convolution calculation, which is a typical application of deep learning. Target recognition is an extremely basic and important process in the field of computer image processing [1]. In the past decades, many researchers have done in-depth research in this field. The development of neural network brings a lot of challenges [2]. The gradual increase of weight parameters and the amount of calculation makes it difficult to transplant relatively complex models to embedded devices [3]. Embedded devices have strict constraints on power consumption and storage. Based on ARM platform, embedded devices have the problem of insufficient computing power, while GPU is more dependent on the environment and library. In order to solve the problem of insufficient computing power of CNN computing devices, researchers design the hardware platform from computing unit structure, memory access mode, memory access mode, memory access mode and so on to. Low power design and other aspects put forward various © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 945–952, 2022. https://doi.org/10.1007/978-981-19-0390-8_119

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CNN [4, 5] accelerator technologies and solutions, the PYNQ [6] can give full play to its advantages. Because of its unique architecture and parallelism, PYNQ provides huge computing power for CNN. In the next ten years, the development of artificial intelligence technology in embedded mobile terminals is the hotspot of national development planning. Whether it is to efficiently complete the application of artificial intelligence technology, or to deploy flexible and low-power embedded mobile terminal devices, the system based on PYNQ platform can control energy consumption. This paper is organized as follows: Sect. 2 describes the system principle and design scheme in details. Section 3 gives the SSD algorithm structure and steps. Section 4 covers the experimental configuration. Test results show that the this proposed platform and algorithm can achieve accurate target classification with low power consumption. Section 5 provides the conclusion and the future work.

2 System Principle and Design Scheme 2.1 Overall Platform Design The whole design is divided into CNN algorithm and hardware platform implementation. The CNN part mainly involves a model training process. It will generate relevant weight file. After file format conversion, it becomes binary files. Then the hardware implementation involves HLS [7]. The IP core of function layer in CNN can be designed and the hardware path can be connected through RTL. Finally, the bitstream file is generated. The specific process is shown in Fig. 1.

CNN

training

Weight bais bin device

RTL HLS

IP core

Vivado design

overlay

bit

Fig. 1. Overall design framework

2.2 Software and Hardware Architecture The cooperative work idea is to make overall consideration of software algorithm and hardware design, and optimize the design at the best conjunction point to improve the effect of practical work. This idea is widely used in embedded design. For PS part, it is a Python programming operating system, including the process from image input to result output. The PL part is the connection and information transmission between AXI bus and ZYNQ Soc (system on chip). The architecture is powered by dual-core ARM (DRAM). The traditional design usually only includes hardware or software. The cooperation can be more easily integrated with programs and has strong portability. The architecture is shown in Fig. 2.

Target Detection and Recognition System Based on PYNQ Platform Processing system(PS)

Python Application

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ZYNQ Soc

CNN

AXI

DRAM

Fig. 2. Simplified overview architecture

2.3 Development Comparison The design of FPGA involves the configuration and connection properties of hardware units, and even extends to LUT and FF. Designers can control every detail module by controlling the overall design. However, this kind of operation has strict requirements for designers, which requires hardware personnel to deeply understand the execution details of the underlying hardware. The design is extremely difficult and inefficient to achieve optimal integration. In order to overcome the above problems, we use Xilinx’s vivado HLS [8] tool to optimize the design of the image classification and recognition system. Unlike RTL development, which needs to focus most of the design work on the back end of the design process, HLS uses high-level assembly language (such as C, C++ or system c) to complete the whole bottom programming, defines the port connection circuit and other operations, converts to the RTL simulation on Xilinx FPGA [9] platform, and finally completes the construction of the hardware path. This design can obviously shorten the research cycle. 2.4 HLS The design of HLS can be divided into input file, simulation synthesis file and output file. In the input file, manually writing and adding three files are needed, which are test bench, C function file and header file. Constraints can be added to the clock and port. To improve the efficiency, test bench is used to verify whether the function meets the design requirements. The header file enhances the portability and readability of the function. The simulation synthesis in HLS design process consists of two parts: C program simulation and C code synthesis. The simulation of C program mainly verifies errors in the program. The synthesis of C code needs to optimize the data structure such as for loop and array, synthesize C code into RTL for simulation, and generate IP core module for subsequent system construction after the correct results. The implementation process is shown in Fig. 3. After the development, the upper computer runs the program.

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Test Bench

C simulation

Comparasio n

RTL simulation

Constrains

C synthesis

VHDL/ Verilog

HLS

Packaged IP

Fig. 3. HLS design process

3 SSD Algorithm This system selects the SSD algorithm structure of CNN. SSD is modified according to the VGG16 model. It mainly converts the full connection layer into the ordinary convolution layer, removes the dropout layer, and adds several convolution layers. Figure 4 shows the modified SSD structure. Single stage target detection is to detect and recognize the target at the same time and regression. Different scales for different locations of dense sampling process is a main process of SSD algorithm. The whole process only needs one step.

Fig. 4. SSD network structure

The comparison results between SSD and other detection algorithms are shown in Table 1. SSD has the same accuracy as Faster R-CNN and the same speed as YOLO.

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Table 1. Comparision of models under VOC2007 Method

mAP

Faster R-CNN(VGG16)

73.2

Fast YOLO YOLO(VGG16) SSD300 SSD512

FPS

Batch size

Boxes

Input resolution

7

1

~6000

~1000 × 600

52.7 66.4

155 21

1 1

98 98

448 × 448 448 × 448

74.3 76.8

59 22

8 8

8732 24564

300 × 300 512 × 512

Each detection layer of SSD has different attribute prior boxes, as shown in Fig. 5. Different detection layers have different number of prior boxes, which have four or six types. These boxes are generated for each pixel in the feature graph. Here SSD generates 8732 bounding boxes in total.

Fig. 5. SSD feature extraction network

The next key process is to train the network. It includes the following steps: 1) Put picture data into the SSD network; 2) Preprocess the picture data through Auxiliary convolution layer and Basic convolution layer; 3) Give prediction boxes by prediction convolution layer. 4) Calculate the final loss and iterate continuously to make the loss value small or stable. The variation of the loss function value finally stays about 0.11.

4 Results The hardware configuration of the PYNQ-Z2 board is first needed. Here is an Ethernet cable, micro USB data cable and SD memory card (the capacity is no less than 8 GB), which are respectively used for data communication, power supply for the implementation board and storage of PYNQ-Z2 image files.Then, install the win32disk imager

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software and burn the image file of the software into the SD card.The router will automatically assign an IP address for the PYNQ board, and access it through the browser PYNQ: 9090 to enter the Jupyter notebook interface for debugging. Figure 6 shows the connecting method. After the SSD backbone network structure is built, we run the host computer program to detect and identify the image target. The training model defines 20 kinds of types, which can be totally identified in the actual detection.

Fig. 6. Connecting method

Fig. 7. Testing results of software and hardware platform

Figure 7 shows that the designed function has been realized. At the same time, when the system path is built, the power report can be generated to monitor the power

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consumption of the system. Figure 8 shows that the power consumption is ideal. The image test results and test power data show that the whole system has a high target classification with low power consumption.

Fig. 8. Power consumption details

5 Conclusion This paper designs an image classification and recognition system based on the PYNQ development platform. We lays out the PS driver and PC program on the PYNQ-Z2 platform based on the ZYNQ architecture, and designs the custom IP core module in the PL part. Finally, the function and performance of the system are tested on the Jupyter notebook platform. The test results show that the system can realize accurate classification and recognition under the premise of low power consumption. Although the system has achieved the expected goal, it still has many deficiencies. Compared with the traditional FPGA system design, the acceleration of the system is not particularly significant. The number of on-chip APIs of PYNQ is limited. In the future, we will consider designing on a more powerful hardware platform to speed up the processing. In terms of algorithm, YOLO remains as our future work to improve the overall performance. Acknowledgment. The project is supported by the National Natural Science Foundation of China under Grant No.61801231 and 62071238.

References 1. Li, B., He, Y.: An improved res net based on the adjustable shortcut connections. IEEE Access 6, 18967–18974 (2018) 2. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)

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3. Macias-Bobadilla, G., Rodríguez-Reséndiz, J., Mota-Valtierra, G., Soto-Zarazúa, G., MéndezLoyola, M., Garduño-Aparicio, M.: Dual-phase lock-in amplifier based on FPGA for lowfrequencies experiments. Sensors 16(3), 379 (2016) 4. Chen, Y., Emer, J., Sze, V., et al.: Eyeriss: a spatial architecture for energy-efficient dataflow for convolutional neural networks. In: Proceedings of the 2016 International Symposium on Computer Architecture, Washington, DC, pp. 367–379. IEEE Computer Society (2016) 5. Jouppi, N.P., Young, C.S., Patil, N., et al.: In-datacenter performance analysis of a tensor processing unit. In: Proceedings of the 2017 International Symposium on Computer Architecture, New York, vol. 45, no. 2, pp. 1–12. ACM (2017) 6. Venieris, S.I., Alexandros, K., Christos-Savvas, B.: Toolflows for mapping convolutional neural networks on FPGAs. ACM Comput. Surv. 51(3), 1–39 (2018) 7. Xilinx UG902, Vivado Design Suite: User Guide High-Level Synthesis (2014) 8. Sharma, A., Singh, V., Rani, A.: Implementation of CNN on Zynq based FPGA for real-time object detection. In: 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Kanpur, India, pp. 1–7 (2019) 9. Qiu, J., Wang, J., Yao, S., et al.: Going deeper with embedded FPGA platform for convolutional neural network. In: Proceedings of the 2016 ACM/SIGDA International Symposium on Field Programmable Gate Arrays, New York, pp. 26–35. ACM (2016)

Secure Transmission for Intelligent Reflecting Surface Assisted MISO Systems: Achievable Rate and Optimal Time Allocation Xiaoming Liu1,2,3(B) , Xiaokai Liu1,2,3 , Jianwei Tian1,2,3 , Bin Li1,2,3 , and Chenglin Zhao1,2,3 1

Beijing University of Posts and Telecommunications (BUPT), Beijing, China [email protected] 2 Beijing Cyberstar Technology Co., Ltd., Beijing, China 3 State Grid Hunan Electric Power Co., Ltd., Changsha, China

Abstract. In this paper, we study the Physical Layer Security (PLS) problems in intelligent reflecting surface (IRS) multiple-input singleoutput (MISO) communication systems. A novel transmission scheme aimming to minimize the system energy consumption is proposed by jointly optimizing the phase shift matrix at the IRS and the beamforming vectors at the base station (BS). Furthermore, the maximum secrecy capacity is obtained by designing the time allocation between channel training and information transmission. Theory analysis and numerical results validate that the time ratio value depends on the signal-to-noise ratio (SNR) at the intended receiver. Keywords: Physical layer security Secrecy capacity · Time allocation

1

· Intelligent reflecting surface ·

Introduction

Security has been one of the critical considerations in wireless communications. In the past few years, many researchers have paid attention to the field of physical layer security (PLS) [1]. Recently, intelligent reflecting surface (IRS) has drawn wide attention because it can deal with weak link problem in scattering environment and enhance the PLS by controlling the reflecting coefficients [2,3]. [4] first studies the PLS in IRS-MISO systems, where IRS is deployed to provide virtual line-of-sight (LoS) links between the base station (BS) and a legitimate user. Based on the block coordinate descent (BCD) and minorization maximization (MM) techniques, approximate solutions for the beamformer at the BS and This work has been supported by 2020 Industrial Internet Innovation Development Project of Ministry of Industry and Information Technology of P.R. China “Internet Security Situational Awareness Platform Project of Smart Energy” (Hunan Electric Power Co., Ltd.). c The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022  Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 953–960, 2022. https://doi.org/10.1007/978-981-19-0390-8_120

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the phase shifts at the IRS is developed [5] considers a downlink multiuser MISO system, and then designs a jointly optimization of the phase shift matrix at the IRS and the beamforming vectors and AN covariance matrix at the BS scheme to maximize the system sum secrecy rate. We note that most studies of PLS in IRS-MISO systems assume the CSI is constant. It is well documented that larger training time will generate a more accurate channel state information (CSI), but it will reduce the transmission time in a given time range [6]. To the best of our knowledge, none of these existing works considers the influence of channel estimation on transmission performance in IRS assisted systems. Motivated by the aforementioned aspects, this paper studies the PLS in IRSMISO systems, and then designs a novel transmission scheme by jointly optimize the phase shift matrix at the IRS and the beamforming vectors at BS, which can minimize the energy consumption. Furthermore, a closed-form expression of the achievable secrecy rate for IRS-assisted MISO systems is derived, which is used to find the optimal time allocation to achieve the maximum secrecy capacity. Our results show that improving the SNR at the intended receiver could reduce the time consumption on channel training.

2

System Model and Joint Precoding and Coefficient Design

Fig. 1. A block diagram of IRS-MISO systems.

Consider the IRS-MISO system proposed in [4] as shown in Fig. 1, where the BS (Alice) and the legitimate user (Bob) equips with multiple antennas (M ) and one antenna, respectively. The eavesdropper (Eve) has a single antenna. The direct link between Alice and Bob/Eve is blocked by an obstacle. IRS is used to guarantee the data stream sent from Alice to Bob, which employs L reflective units. CSI remains static in a given time (i.e., wireless channels are ˆ Iz ∈ CL×1 , z = b, e characterized by block fading). Let HˆaI ∈ CM ×L and h denotes the channel estimation result of the forward and the scatter channel, respectively. The CSI of backscatter channel fˆz could be obtained by cascading

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M ×1 ˆ ˆ Θ h the forward channel and the scattering channel, i.e., fˆz = H , aI z Iz ∈ C where Θz = diag{exp(jθ1 ), . . . , exp(jθL )} ∈ CL×L is the the reflection coefficient. θk , k = 1, . . . , L represents the kth reflective phase of the IRS. The variance of each element of fz is normalized to be unity. P is referred to as the transmit power of the information-bearing signal s. The signal received at Bob and Eve can then be expressed as

yb = P αb fˆbH ws + nb , y = P αe fˆH ws + ne , e

(1)

e

where αb and αe are the channel fading coefficient for the legitimate user and the eavesdropper, respectively. w ∈ CM ×1 represents the corresponding transmit precoding/beamforming vector. nb ∈ CN (0, σb2 ) and ne ∈ CN (0, σe2 ) express the white noise. Accordingly, the secret rate of the received signals at Alice and Bob can be expressed as     + P αb  ˆH 2 P αe  ˆH 2 R = log2 1 + 2 fb w , (2) − log2 1 + 2 fe w σb σe where [x]+ = max{0, x}. In order to facility the operation, the variance is normalized to unity, i.e., σb2 = σe2 = 1. We aim to devise the precoding vector w and the diagonal phase shift matrices Θ to ensure transmission safety and reduce energy consumption, which satisfies the following conditions min P, w,Θ

  2  2    s.t. log2 1 + P αb fˆbH w − log2 1 + P αe fˆeH w ≥ ,

(3)

2

w ≤ 1, where  denotes the minimum secure capacity requirement of the system. ˆ ar = abH can be well approximated as a matrix of rank 1, Base on [3], H H where a and b represent the normalized array response vector associated with the IRS and the BS. The Eq. (3) is equivalent to 2 − 1 P ≥ 2  2 2 .  aH w αb ˆ hH Θb − 2 αe ˆ hH Θb Ib

(4)

Ie

The optimal transmit beamformer to Eq. (4) is given by w = a/a, 2 s.t.w = 1, then the optimization problem is accordingly given by 2  H 2

 H hIb Θb − γ ˆ hIe Θb max f (Θ) = ˆ Θ



H = θ H hb hH γhe hH b θ−θ e θ

H = θ H hb hH b − γhe he θ, H

s.t. θ = [exp (jθ1 ) , . . . , exp (jθL )] ,

(5)

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     where γ = 2∇ αe αb , hb = diag hˆH = diag hˆH b, h e Ie b. Denote g = Ib H H hb hH b − γhe he , and there is the following relationship g = ga ga , s.t. ga = [ga,1 , . . . , ga,L ] due to g is a diagonal matrix. It is clear that the optimization of θ can be solved via  2 (6) max f (θ) = θ H ga  . θ

It can be easily verified that the optimal solution can be obtained when θˆ = {exp [−j arg (ga,1 )] , . . . , exp [−j arg (ga,L )]} .

3

(7)

The Closed-Form Expression

Assume T is the total time budget, and φ ∈ (0, 1) is the time ratio. The length of channel estimation and data transmission are expressed as φT and (1 − φ)T , respectively. The secrecy capacity in a given time T is C = (1 − φ)T R.

(8)

Considering that a transmission process consists of two stages: channel estimation and signal transmission. In the first stage, the CSI is obtained through σ2 the pilot training process, i.e., fˆz = fz + vz , where vz ∈ CN (0, Nz IM ) denotes the estimation error of fz , N indicates the length of pilot sequences. Since the larger the training length N , the higher the accuracy of CSI, i.e. lim fˆz = fz . N →+∞

Cascaded channel estimation result can be expressed in another equivalent form1 √ as fˆz = ρfz , where ρ = 1/(1 + σz2 /φT ). To facilitate calculation, we consider a special scenario, i.e., γ → 0. Then 2 we obtain the loose upper bound of the secrecy rate. Denoted x  fbH w , the ergodic secrecy rate between Alice and Bob in integral form is



˜ = Ex log2 1 + ρP αb ·x . R (9) Based on [7], the PDF of x is f (t) = 4tK0 (2t), where K0 is the modified zeroorder Bessel function of the second kind. Generally, it is not straightforward to obtain the closed-form expression. Using the asymptotic form of K0 [8], which 1

T{Channel training} = T{pilot transmission} +T{channel estimation} , T{channelestimation}  complexity of channel estimation algorithm × calculation speed of SOC chip. Taking LS channel estimation algorithm as an example, the complexity of which is about O(Ma Mb N + Ma N 2 ). it is assumed that the number of antennas at Alice and Bob is Ma = 4 and Mb = 1, respectively. And the length of pilot sequence is N = 100. It is clear that the upper bound of the maximum calculation complexity is 104 . Due to the current SOC computing speed reaching GHz level, the order of magnitude of time required to compute 104 units is about 10−6 s; In generally, the time required for pilot transmission is about 10−3 s. Therefore, channel training time ≈ pilot transmission time.

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π satisfy the following conditions, i.e., K0 (t) = 2t exp (−t) , t >> 0.25; K0 (t) = t − ln( 2 ) − C, 0 < t > 1, there is ln(1 + ρP t) ≈ ln(ρP t). The preceding equation is equivalent to √    0.4π 0.64  0.8 ˜= ln(0.4ρP ) R ln(0.4ρP )(J1 − J3 ) + (J2 − J4 ) − ln 2 ln 2    ln(0.4ρP ) ln(0.4) + C + ln(0.4) + C J5 + J6 + , (11) 2 where

 J1 =

∞

 exp(−0.8t)t0.5 d(t),

J2 =

0



exp(−0.8t)t0.5 d(t),

ln(t) exp(−0.8t)t0.5 d(t),

(12)

0  1

1

ln(t)tdt, 0

1

J4 =

0

J5 =

ln(t) exp(−0.8t)t0.5 d(t),

0



1

J3 = 

∞

J6 =

ln(t) ln(t)tdt. 0

Based on the equations given in [9], which are respectively shown by 

∞

1 −1 xv1 exp (−u1 x)dx = Γ (v1 + 1)u−v , s.t.Re[u1 , u2 ] > 0, 1

0 ∞ 2 xv2−1 exp(−u2 x)ln(x)dx = u−v 2 Γ (v2 )[ψ(v2 )−ln (u2 )], s.t.Re[v1 , v2 ] > 0, (13) 0

where ψ(·) is the Euler psi-function, and Γ (·) represents the Gamma-function. Then we have J1 = Γ (1.5)0.8−1.5 ,   J2 = Γ (1.5)0.8−1.5 ψ(1.5)−ln(0.8) . With the help of Taylor series, wherein ex =

∞ 

xk Γ (k+1) k=0

(14) and ln(x) =

and the following equations given in [9], i.e.,  1 xv3 −1 (1 − x)u3 −1 dx = B(u3 , v3 ), s.t. Re[u3 , u4 ] > 0,

∞  (1−x)k k=1

−k

,

0



0

1

xv4−1 (1−x)u4−1 ln(x)dx = B(v4 , u3 )[ψ(v4 )−ψ(u4 +v4 )], Re[v3 , v4 ] > 0 (15)

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where B(·, ·) is Beta-function, we have J3 = J5 =

∞  k=0 ∞  k=1

∞

∞

  (−0.8)k B(k + 1, l + 1.5) (−0.8)k , J4 = − Γ (k + 1)(k + 1.5) k · Γ (l + 1) k=1 l=0

−

B(k + 1, 2) , k

J6 =

∞ 

−

k=1

B(2, k + 1)[ψ(2) − ψ(k + 3)] . k

(16)

To make the problem tractable, we obtain the asymptotic numerical solution of (15), which are J1 ≈ 1.24, J2 ≈ 0.32, J3 ≈ 0.42, J4 ≈ −0.34, J5 ≈ −0.25, J6 ≈ 0.25, respectively. Accordingly, the closed-form expression of Eq. (11) is approximately equal to ˜ ≈ 1 [ln(ρP αb ) − C]. R (17) ln(2)

4

Optimal Time Allocation

In this section, we study the optimal time allocation between training and transmission under total time constraint. The approximated expressions of the ergodic secrecy rate are given, which will be used to obtain analytical results and useful insights on the optimal time allocation. ˜ can be respectively approximated by Considering large T regime, R     1 1 ˜ appr ≈ Ex ln (1 + P αb · x) R σ2 ln(2) 1 + φTz

  σz2 1 ln(P αb ) − C − = . (18) ln(2) φT Substituting (18) into (8), we obtain the secrecy capacity C. Note that the optimal value satisfies ∂C/∂φ = 0 and the solution is φ= 

σz2 T [ln(P αb ) − C]

.

(19)

It can be shown that ∂ 2 C/∂φ2 < 0. As a result, (19) is the optimal time allocation to achieve the maximum secrecy capacity.

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Simulation Result

In this section, we provide numerical results to illustrate the theoretical analysis. In the simulations, the noise variance at Alice is set to σz2 = 1, and the fading coefficient is αb = αe = 1. The number of antennas at Alice is M = 8 if there is no other specification. The length of total time is T = 300. In the following simulations, we will study the impact of time allocation coefficient on the secrecy capacity.

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Figure 2 displays the performance of ergodic secrecy rate R via the simulation, theory, and approximate results. With the increasing of T and P , the simulation and theory results of R match very well, and they are close to the approximate result. The above results verify the accuracy of our theoretical analysis. Figure 3 shows that the performance of ergodic secrecy capacity C versus the total time T . In a small T range (e.g., T < 50), by comparing the performance of C under P = 20 dB, Φ = 0.1 with the performance of C under P = 20 dB, Φ = 0.5, it is observed that the version of the former is lower than the latter. This is because a considerable training length will generate a more accurate CSI, which will result in a higher SNR in the subsequent transmission phase. With the increase of P (e.g., P = 30 dB, Φ = 0.1 and P = 30 dB, Φ = 0.5), the performance

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of the former is higher than that of the latter. It is indicated that increasing the transmit power can reduce the training length within a given time range. In a large T range (e.g., T > 500), the performance of C under P = 20 dB, Φ = 0.1 is better than that under P = 30 dB, Φ = 0.5. It is shown that a larger time ratio allocated to training will reduce transmission performance in a broader time range. This is because less gain of CSI would be achieved when the training length overflows the optimal ratio φ in a given T , which will result in less time for transmission. In other words, when the available transmit power increases, less time should be allocated in the channel estimation phase.

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Conclusion

In this letter, we had studied the optimal time allocation of the IRS-assisted MISO systems. First, a joint design scheme of beamforming and IRS reflection coefficient was proposed. Then the closed form expression of the scheme was derived, with which the optimal time allocation scheme was obtained. Extensive simulation results verified our theoretical analysis and illustrated that more time should be allocated to the transmission in a broad time range as well as in a large transmit power.

References 1. Mukherjee, A., Fakoorian, S.A.A., Huang, J., Swindlehurst, A.L.: Principles of physical layer security in multiuser wireless networks: a survey. IEEE Commun. Surveys Tuts. 16, 1550–1573 (2014) 2. Liang, Y., Long, R., Zhang, Q., Chen, J., Cheng, H.V., Guo, H.: Large intelligent surface/antennas (LISA): making reflective radios smart. J. Commun. Inf. Netw. 4, 40–50 (2019) 3. Wu, Q., Zhang, S., Zheng, B., You, C., Zhang, R.: Intelligent reflecting surface aided wireless communications: a tutorial. IEEE Trans. Commun. 69, 3313–3351 (2021) 4. Yu, X., Xu, D., Schober, R.: Enabling secure wireless communications via intelligent reflecting surfaces. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp. 1–6. IEEE Press, New York (2019) 5. Xu, D., Yu, X., Sun, Y., Ng, D.W.K., Schober, R.: Resource allocation for secure IRS-assisted multiuser MISO systems. In: 2019 IEEE Globecom Workshops (GC Wkshps), pp. 1–6. IEEE Press, New York (2019) 6. Liu, X., Li, B., Zhao, F.: Secrecy capacity and optimal time allocation for MISO wiretap channel with a multiple-antennas jammer. Electron. Lett. 56, 741–743 (2020) 7. Alhassoun, M., Durgin, G.D.: Spatial fading in backscatter channels: theory and models. In: 2019 16th IEEE Annual Consumer Communications Networking Conference (CCNC), pp. 1–6. IEEE Press, New York (2019) 8. Abramowitz, M., Stegun, I.A. (eds.): Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables. US Government printing office, New York (1948) 9. Steven K.M.: Fundamentals of Statistical Signal Processing. Prentice Hall PTR, Upper Saddle River (1993)

Formal Verification for VRM Requirement Models Yang Zhang1(B) , Jun Hu1 , Lisong Wang1 , Qingfan Gu2 , and Hao Rong2 1

College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China [email protected] 2 Department of Software, China National Aeronautic Radio Electronics Research Institute, Shanghai 200233, China

Abstract. At the requirements level, formal verification and analysis are the focus of task’s attention which is developing complex systems by formal methods. Model checking is a technique for analysis and automated verification of complex safety-critical software systems. In this paper, a requirement model verification method based on formal technology is proposed to practice the model checking activity into the development process. Firstly, this essay analyzes syntax and semantics of models, which are defined by tabular expressions in VRM (variables relationship model). Then we preprocess the VRM model to classify into events tables, conditions tables and model class tables, and transform the VRM model into the automaton state transfer diagram with the help of semantic complementary work. Finally, we design an automatic model transformation framework from the VRM model to the model verification tool (nuXmv) and implement a translator between the formal specification language VRM and the symbolic model checker nuXmv. In this paper, we discuss our translation and abstraction approach in some depth and illustrate its feasibility with some preliminary examples. Keywords: VRM model · nuXmv translation · Safety verification

1

· Model checking · Model

Introduction

Safety critical system (SCS) [1] is a type of software which is applied in the field of safety-critical areas, such as aviation and aerospace. Due to the requirements of application scenarios, it is complex to design software about these systems. Therefore, the reliability and security of the computer system software are the prerequisites for the normal operation of SCS. Model-based security assessment (MBSA) [7] method can describe complex system behavior as accurate and readable mathematical expressions by building models of safety-critical systems, and then, analyze relevant requirements specifications, check the consistency and completeness of requirements models to verify the correctness of the system. c The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022  Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 961–969, 2022. https://doi.org/10.1007/978-981-19-0390-8_121

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At present, there are many methods of formal modeling, but few of them originate from actual engineering and can be applied to actual engineering field. VRM is a kind of modeling method based on four variables model and using two dimensional table to establish the requirement models. Different from other theoretical models, VRM model does not have complex and unfamiliar symbols, but uses the most common tables in the field of systems engineering as the core model elements. Therefore, it is a kind of formal method for systems engineers that is suitable for practical engineering. VRM [4] model uses formal mathematical symbols such as mode class table, condition table and event table to describe the different values of state variables under different modes, conditions and states of the system, so as to realize formal description of system behavior. nuXmv [2] is a traditional symbolic model checker. nuXmv is an extension of SMV, originally developed by K. L. McMillan at Carnegie Mellon University, which was the first BDD-based model checker. After that, the nuXmv, which was expanded and re-implemented, added a verification engine based on the SAT algorithm, which was able to perform formal verification of finite state [5] and infinite state systems. The modeling language XMV of nuXmv builds the model of systems through the description of the state transition of the finite state machine (FSM), and uses the state transition to define the change of the value of the system variables triggered by the events. The main contributions of our work rest on the following points: (1) The translation must to the largest extent preserve the full power of the source language VRM; (2) The translation must require little user interaction; (3) The translation must closely match the structure of VRM, so that counterexamples are easy to interpret without detailed knowledge of the translation process; (4) Our work proposes a feasible method to help system engineers build models and verify attributes; (5) Our work shows that we have implemented a translator to support the practical application of automated verification of software specifications. The rest of the paper is organized as follows. Section 2 discuss our general verification framework and the languages involved, such as VRM and the specification langauge for nuXmv. We describe our general translation approach in Sect. 3 and discuss some abstractions necessary to map an VRM specification to nuXmv with ANTLR4 [6]. A preliminary example with industry appears in Sect. 4 followed by brief discussion and conclusion.

2

Framework

In this section, we first describe the VRM model system security verification framework to give a basic introduction about the important processes in the framework. Figure 1 shows an overview of our verification framework. The user models the required behavior of a system in the formal and executable specification language VRM. The VRM model is transformed into a nuXmv model and described by the XMV program, which is mainly divided into the following three steps:

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Preprocessing work VRM model files input condition tables

condition grammar files

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build condition syntax trees

events tables

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types of mode class

conditions of mode class

events of mode class

build conditions or events syntax trees

Generate nuXmv constraints of mode classes

Generating nuXmv models

Fig. 1. Overview of verification framework

(1) preprocessing of VRM model; (2) parsing condition table, event table and mode class table which have been preprocessed into syntax tree; (3) generating the nuXmv model; Firstly, the preprocessing work divides the VRM model into condition tables, event tables, and mode class tables. For conditional tables, this step extracts input variables, output variables and type information from these tables. For the event tables, it constructs the prior variables of events; For mode class tables, it defines additionally modes as new types. Secondly, the syntax parsing work to generate the extracted information into a grammar tree with the help of ANTLR4 tool. For the condition table, this step defines the constraint grammar corresponding to the condition to construct the syntax tree of the condition. For the event table, it defines the constraint grammar corresponding to the events to construct the event syntax tree. Finally, on the basis of the above process, the model generation step translates the VRM model into the nuXmv model by defining the transformation functions according to the extracted conditions, events and mode class information against the transformation rules. For model checking the system model, users specify required properties in a temporal logic, CTL or LTL, and use nuXmv for the analysis. To make it easier for the reader to understand our translation approach, we provide a short overview of our source language VRM as well as the target language of nuXmv. 2.1

VRM

A VRM specification consists of a collection of input/term/output variables, types, table functions. Input variables are used to record the values observed in the environment; Term variables are organized in a hierarchical fashion and

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are used to model various states of the control mode; Output variables are used to represent the output of the system. In the VRM model, table functions are divided into three categories: condition tables, event tables, and mode class tables. A condition refers to the value of a variable used to indicate the state of the system, which can be connected by the logical operators AND, OR, NOT, such as a condition can be described as the current height of aircraft is maintaining at 1000 m, etc. However, in the real world, a simple table of conditions cannot describe the entire system requirements, so we have introduced a new function called the event table, such as asking to raise an alarm when the height suddenly falls below 500. The format of the event is as follows: eventType(S)helpType(D) There are three types of EventType: @T, @F, and @C. There are three types of helpType operators: WHEN, WHILE, and WHERE. The details are as follows: (1) @T: An event which the last state S is FALSE and the current state S is TRUE. The event semantics are NOT S AND S; (2) @F: An event which the last state S is TRUE and the current state S is FALSE. The event semantics are S AND NOT S; (3) @C: An event where the value of the previous state S is not equal to the value of the current state S. The event semantics are S! = S; Mode class table represents transformations between independent properties of the system, which can be converted by events or conditions. The figure shown in the example we gave in Sect. 4 is a description of the mode class table which has 6 states, such as start, waiting etc. 2.2

nuXmv

The input language of nuXmv is designed to allow the description of synchronous or asynchronous finite state machines at various levels of abstraction. The language of nuXmv is structured using keywords MODULE, VAR, IVAR, DEFINE, ASSIGN, and SPEC; MODULE represents a unit of a reusable component, VAR and IVAR are for variable declaration, ASSIGN is used to specify transition relations, and SPEC is to specify system properties in temporal logic. Different from the VRM model, the XMV model is driven by state, which reflects the dynamic behavior of the model through state migration, and the state consists of the minimum unit state variable (VAR).

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Translation from VRM to nuXmv

This section is split into three. In the first part, we introduce the basic translation of condition tables to give the reader a flavor of our approach. This part is

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mainly about transforming the condition table in the VRM model directly to the migration between states in nuXmv. In the second part and third part, we introduce the translation of event and mode class tables . This part is mainly about how to add semantic which is the lost of states in VRM to make semantics of the model remain unchanged after transformation. 3.1

Translation of the Condition Tables

The condition table of VRM defines the condition constraint, that is, after meeting a specific condition, the term variables or the output variables get its corresponding value. The nuXmv uses state transitions to describe the changes that occur in the system, so there is no direct definition that corresponds to the condition table one to one. Therefore, we transform the condition tables to state migration in nuXmv. The state transition takes the state as the unit. When a certain condition is satisfied, the value of the next state is migrated to a specific value. Figure 2 shows the specific translational rules of the condition tables.

Vout value1 value2

condition conditionName1 conditionName2

ASSIGN next(vout):case conidtionName1 value1 condtionName2 value1 translational rules of the condition

Fig. 2. Translational rules of the condition tables

3.2

Translation of the Event Tables

The event table of VRM defines the event constraint, that is, after meeting a specific event, the term variables or the output variables get its corresponding value. Events have temporal semantics and represent a jump in state. Therefore, the event table in the VRM is transformed into a state transition after adding the prior state information. When an event occurs, the value of the next state is migrated to a specific value. Figure 3 shows the specific translational rules of the event tables.

Vout value1 value2

event eventName1 eventName2

ASSIGN next(vout):case eventName1 valuepre1 eventName1_pre value1 eventName2 valuepre2 eventName2_pre value2 translational rules of the event

Fig. 3. Translational rules of the event tables

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Translation of the Modeclass Tables

The translation of the mode class tables need to add semantics of the modes. The specific supplements are as follows: First, a mode class table contains multiple modes, so we define the entire mode class table as a variable, and each mode is defined as the value that it can get, that is, to supplement each mode class table with a custom enumerated type. Second, We replace the semantics of each source mode to target mode as when the current state of the variable is the source mode and the system meets the specified constraints, then the intermediate variable will migrate to the target mode. Third, the specified constraints also include two special descriptions such as conditions and events. The conditions in the mode constraints are transformed according to the translation rules of the above specified condition table, and the events in the mode constraints are transformed according to the translation rules of the event table. Figure 4 shows the specific translational rules of the mode class tables.

source value1 value2 value3

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destination value2 value3 value1

ASSIGN next(vout):case vout=value1 & conditionName1 value2 vout=value2 & eventName2 value3 vout=value3 & conditionName2 value1 Translation rules of mode class table

Fig. 4. Translational rules of the mode class tables

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Preliminary Transformation Cases

In order to verify the rationality, comprehensiveness and completeness of the above-mentioned model translation algorithm, taking a simple module of the airborne software as an example, we establish a system VRM model, which has condition tables, event tables, and a mode class table. This system includes 6 states, such as start, waiting, searching, ready, catching and success, so we can build a mode class table. The state transition relationship of the system is as follows: When the power is turned on and the start switch is pressed, the system enters the waiting state. When there are no other tasks in the waiting queue and the search button is pressed, the system enters the searching state. When an interruption occurs, it returns to the waiting state. When the target is selected, it enters the ready state. If everything is ready, it enters the catching state; if there is an interruption in this process, it returns to the waiting state. If the target is caught, it enters the success state.

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According to the above description, we can extract the condition table and event table from it. The condition table shows the change of the condition variable when states of system are transforming. The specific content is shown in the following table. The event table shows instantaneous change of variables that occur during the transition of the system state, and the specific content is shown in the following table (Table 1 and 2). According to the extracted table, we can use the transition algorithm mentioned above to transform them into the corresponding nuXmv model. The specific state transition diagram and its corresponding model are shown in the following Fig. 5. Verifying security attributes is an important method to ensure the security and correctness of the system. This article extracts the system requirements specifications from the system and adds it to the smv files to verify the security of the system. Table 1. Condition table Assignment of output variables Transition of modes Conditions waitLamp = on

start to waiting

power = on

waitLamp = on

searching to waiting interruption = true

waitLamp = on

catch to waiting

searchLamp = on

waiting to searching isEmptyQueue = true

catchLamp = on

ready to catching

interruption = true readyFlag = true

Table 2. Event table Assignment of output variables Transition of modes Events waitingLamp = on

start to waiting

searchLamp = on

waiting to searching @T(searchSwitch = on)

@T(startSwitch = on)

readyLamp = on

searching to ready

@T(hasTarget = true)

successLamp = on

catching to success

@T(catchFlag = true)

Positive example verification: this paper selects constraint of the state transition of the system mode. Temporal logic expression is described as follows: CTLSPEC EF(state = waiting->state = searching). It means that system may transition from the waiting state to the searching state, the specific positive example verification is shown in the Fig. 6. Negative example verification: We inject error constraints in the attributes that need to be verified. Temporal logic expression [3] is described as follows: CTLSPEC AG (state = ready).

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@T(hasTarget = true)

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power = on @T(startSwitch = on)

interruption=true readyFlag = true

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MODULE main VAR state:{start,waiting,searching,ready,catching,success}; power:{on,off}; startSwitch :{on,off}; searchSwitch :{on,off}; interruption :boolean; isEmptyQueue :boolean; readyFlag :boolean; hasTarget :boolean; catchFlag :boolean; _startSwitch :{on,off}; _searchSwitch :{on,off}; _hasTarget:boolean; _catchFlag:boolean; ASSIGN init(state):=start; init(power):=on; next(state):= case state=start & power=on & _startSwitch=off & startSwitch=on: waiting; state=waiting & _searchSwitch=off & searchSwitch=on & isEmptyQueue=TRUE: searching; state=searching & interruption=TRUE: waiting; state=searching & _hasTarget=FALSE & hasTarget=TRUE:ready; state=ready & readyFlag=TRUE:catching; state=catching & interruption=TRUE: waiting; state=catching & _catchFlag=FALSE & catchFlag=TRUE:success; TRUE : state; esac; next(_startSwitch):=searchSwitch; next(_searchSwitch):=searchSwitch; next(_hasTarget):=hasTarget; next(_catchFlag):=catchFlag;

(a)

(b)

Fig. 5. State transition diagram and nuXmv model

Fig. 6. Experimental results

It means that the system state will always be in the ready state, which is obviously an incorrect nature constraint. The specific counterexample path is shown in Fig. 6. It can be seen that this algorithm can transform the VRM model into a state machine, and then traverse the entire state space to verify the security attributes of the system.

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Conclusion

We have implemented a translator to support the practical application of automated verification of software specifications. The initial version of the translator maps the source language (VRM) quite closely onto the target language (nuXmv) with only minor abstractions. We have largely satisfied the goals we set at the beginning of the project; the translation is fully automated and preserves (almost all of) the expressive power of VRM. Our next step is to anticipate future problems as the size of the specification grows. We have the following two areas of improvement: The model mapping

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rules presented in this paper still have room for improvement in the constraint elements, and the complex constraints such as time will be further studied. Security attributes still need to be artificially transformed from natural language into standard constraint sentences, which has certain limitations.

References 1. Bernardeschi, C., Cassano, L., Domenici, A.: SRAM-based FPGA systems for safetycritical applications: a survey on design standards and proposed methodologies. J. Comput. Sci. Technol. 30(2), 373–390 (2015) 2. Biernacki, J.: Alvis models of safety critical systems state-base verification with nuXmv. In: Computer Science & Information Systems, pp. 1701–1708 (2016) 3. Cook, B., Khlaaf, H., Piterman, N.: Verifying increasingly expressive temporal logics for infinite-state systems. J. Acm 64(2), 1–39 (2017) 4. Hu, J., Hu, J., Wang, W., Kang, J., Gao, Z.: Constructing formal specification models from domain specific natural language requirements. In: 2020 6th International Symposium on System and Software Reliability (ISSSR), pp. 52–60 (2020) 5. King, J.M., Bergeron, C.A., Taylor, C.E.: Finite state machine implementation for left ventricle modeling and control. BioMed. Eng. OnLine 18(1), 1–24 (2019) 6. Parr, J.B.: ANTLRworks: an ANTLR grammar development environment. Softw. Pract. Experience 38(12), 1305–1332 (2010) 7. Rauzy, A.B., Haskins, C.: Foundations for model-based systems engineering and model-based safety assessment. Syst. Eng. 22(2), 146–155 (2019)

A Model-Based System Safety Analysis Tool and Case Study Yanhong Dong1,2 , Jun Hu1,2(B) , Jian Qi1,2 , Qingfan Gu3 , and Hao Rong3 1

3

College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China {yanhong.dong,hujun}@nuaa.edu.cn 2 Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing, China China National Aeronautic Radio Electronics Research Institute, Shanghai, China

Abstract. As the scale and complexity of the system tend to be increasing, model-based safety assessment (MBSA) has gradually become a research hotspot in system modeling and analysis. This paper independently designs and implements a model-based system safety analysis tool, and carries out a case study with landing gear system (LGS) as an example, including system hierarchy modeling, component interaction modeling and fault behavior modeling for LGS using AltaRica 3.0, as well as system simulation analysis and fault tree analysis for LGS on the platform. Finally, it is proved that the effectiveness of the tool and the accuracy of modeling and system safety analysis using AltaRica 3.0. Keywords: Model-based safety assessment AltaRica 3.0 · Safety analysis

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· Landing gear system ·

Introduction

Currently, the failures in system operation often have the potential to cause significant loss of life and property [1]. Therefore, the safety and reliability of safety-critical systems need to be effectively verified and analyzed during the system engineering design. Traditional system engineering is a document-based method, and the system safety analysis techniques that it adopts need to be implemented by experienced safety analysts, such as fault tree analysis (FTA) [2] and failure mode effect analysis (FMEA) [3], etc. These will consume lots of human and financial resources. For model-based systems engineering (MBSE) [4], the central task of the complex system engineering implementation process is to construct various perspectives of system models and analysis. MBSA is an extension of MBSE methodology. Besides, the core of MBSA is to model the whole system and then perform qualitative and quantitative analysis to discover safety defects and potential system risks in the model [5]. In recent years, the safety-critical system analysis method based on MBSA has gradually attracted wide attention in c The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022  Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 970–979, 2022. https://doi.org/10.1007/978-981-19-0390-8_122

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the industry, and MBSA has also been adopted as a recommended method in the revised appendix of the SAE-ARP4761 standard. Moreover, some tools have been launched internationally: OpenAltaRica [5], Simfia [6]. However, domestic research on the AltaRica and MBSA is mainly focused on the theoretical method. Therefore, software based on MBSA needs to be established. AltaRica [7,8] is a multi-level model language that supports MBSA, and it has the grammatical and semantic features of formal definitions. In the AltaRica 3.0 model, each component behaviour represented by Class and Block consists of the following parts:Variable: state and flow variable; Event: it describes the events that may occur in the system; Transition: it can describe the change of the component state after an event is fired. Besides, it is represented by a triple e, G, P, that is, e: G → P. e illustrates the event that may occur in the system; G shows that the guard needs to meet the condition in order to complete transition after the event is triggered; P represents the new state of a component in the system; Assertion: it is an instruction used to calculate the value of the flow variable after the transition occurs. The main work of this paper is to develop software that supports MBSA based on the AltaRica 3.0 and perform case analysis. The specific content is as follows: Sect. 2 introduces the structure and function of A-MAST (modeling and simulation tool), which is independently designed and implemented in the paper. Section 3 shows the process of modeling and safety analysis for a typical safety-critical system - landing gear system [9] (LGS) based on MBSA. Section 4 is related work and summary.

2

A-MAST Tool Framework

This section proposes a modeling and simulation tool (A-MAST) that can support MBSA by combining the modeling and safety analysis ideas of MBSA and AltaRica 3.0. Besides, it introduces the functions, the framework and the data processing flow of this platform. A-MAST realizes the modeling and safety analysis of safety-critical systems and it is mainly for the three types of user groups: familiar with SysML modeling users, familiar with AltaRica 3.0 modeling users, and general users without related knowledge of SysML and AltaRica 3.0. The primary function of this tool includes: SysML model files import; the conversion of SysML model to AltaRica 3.0 model; AltaRica 3.0 modeling; fault model compilation; fault tree generation; single-step simulation and formal verification; fault tree analysis; fault modeling includes: graphic modeling and loading, display and modify the graphic model, and create new subsystem. Figure 1 shows the use case of this tool. Figure 2a displays the main interface of the A-MAST tool, and Fig. 2b illustrates the graphical simulation interface of this tool. Taking into account the above functional requirements, the framework of tool is divided into three layers: application layer, kernel layer and data layer.

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(1) Application layer: including GUI module, it can display related data and data analysis results. (2) Kernel layer: including five modules: model transition module, fault modeling module, fault simulation module, safety analysis module and formal verification module. The model transition module is mainly used to convert the SysML model to the corresponding AltaRica 3.0 model, and the method of this design adopts the method in [10,11]. The fault modeling module includes various fault mode libraries, fault extension models and processing of various graphic models. The fault simulation module performs fault simulation analysis based on the flattened AltaRica 3.0 model. The flattened file in this tool is created by this method in [12,13], which converts the AltaRica 3.0 model into a semantics equivalent GTS file based on the ANTLR (another tool for language recognition). The safety analysis module performs the safety analysis based on AltaRica 3.0 model, such as fault model compilation, fault tree generation, fault tree analysis, dynamic sequence fault tree analysis and calculation. Besides, the method of fault tree generation adopts the method in [14], which the flattened AltaRica 3.0 model to automatically generate the corresponding fault tree. For the fault tree analysis, the paper adopts the method from [2,15], that is, perform safety analysis (calculating the minimum cut set) of the safety-critical system based on generated fault tree. The formal verification module includes the transformation of the fault model into the corresponding NuXMV, the verification property construction and NuXMV verification. (3) Data layer: including file system module and error processing module. The file system module manages all kinds of files, and the error processing module responds to errors that may occur during the use of the tool. Figure 3 shows the architecture of A-MAST. Besides, A-MAST involves lots of data and data transfer. Figure 4 shows the data processing flow of the tool.

Fig. 1. Use case of A-MAST

Fig. 2. A-MAST diagram

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Fig. 4. Data processing flow diagram

The tool takes the interactive function of the GUI module as the entrance. There are two options: open the system project and create a new system project. The process of creating a new system project is as follows: (1) Select the model type (AltaRica 3.0 model or SysML model). For the SysML model, first import the corresponding SysML model file, and then the tool will convert it to the corresponding AltaRica 3.0 model according to the transition rules; For AltaRica 3.0 model, create AltaRica 3.0 model to obtain the corresponding model file; Draw the system model: edit the component information and generate the AltaRica 3.0 file; (2) Generate the graphics file based on the AltaRica 3.0 file; (3) Flatten the AltaRica 3.0 model into the GTS file, and then generate the fault tree, NUXMV model and system model graphic based on the GTS file; (4) These results are displayed by the GUI module, including the fault tree analysis, the fault dynamic behavior analysis and the NuXMV verification result. Open the system project: including modifying the system model and the AltaRica 3.0 model file. Other data processing procedures are the same as (2) (3) (4) in the process of creating a new system project.

3

Case Analysis of LGS

This section describes the system structure of landing gear system (LGS) and designs the AltaRica 3.0 model of LGS by combing with this structure; At the same time, it illustrates in detail how to perform model construction and safety analysis with AltaRica 3.0 on the A-MAST platform; Finally, it is proved that the A-MAST is effective, and modeling complex system and safety analysis are accurate with AltaRica 3.0 through the case analysis of LGS. 3.1

LGS System Structure

The paper chooses the case of LGS, which is proposed in the ABZ conference to carry out the research. Furthermore, this case proposes is to hope that more

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researchers can use it as a research standard for the tool, thereby performing the functional characteristics of the tool analysis [9]. At the same time, LGS is one of the most critical components of the aircraft, and its primary function is to make the plane take off and land safely. Therefore, its reliability and safety directly affect the flight safety of aircraft. LGS comprises three parts: the man-machine interface subsystem, LGSsoftware subsystem and hydraulic actuation subsystem. Figure 5 shows the system structure of LGS. The man-machine interface subsystem is composed of lights and handle: lights give the current position of gears and doors; handles is manipulated by the pilot to issue commands to the whole system. The LGSsoftware subsystem is composed of two computing module, which are responsible for controlling the gears and doors, detecting abnormal conditions and timely feedback to the pilot [9]. The hydraulic actuation system is composed of the analogical switch, various electronic valves, hydraulic cylinders and various sensors, and its function is mainly to perform a series of physical manipulations under the action of hydraulic pressure. Table 1. LGS structure modeling Model level

System module

Sub-components

First level

LGS

Software module Physical module Control module

Second level Control module

Handle module

Second level Software module

Computing module1 Computing module2

Second level Physical module

Analogical module Electronic valve module Hydraulic cylinder module Hydraulic circuit module

Third level

Electronic valve module

General electronic valve Close door electronic valve Open door electronic valve Retract gear electronic valve Extend gear electronic valve

Third level

Hydraulic cylinder Front door cylinder Left module (right) door cylinder Front gear cylinder Left (right) gear cylinder

Fig. 5. The structure of LGS

3.2

LGS Modeling Design

LGS Function Module Modeling Design. The LGS is divided into three module: control module, software module, physical module, and Table 1 shows the hierarchical structure modeling of LGS in detail. The software module is shown as a typical example in this section, and other modules can obtain their corresponding AltaRica 3.0 model through similar methods. Software module

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modeling includes the following steps: firstly, determine the state and flow variables that may be involved in this module; Secondly, use the assertion of AltaRica 3.0 to describe the input and output of the software module and introduces the concept of sensors to more accurately describe the calculation process of the system. Sensors can feedback the status of the various components and aircraft in real time, such as SoftwareGearShockAbsorberFrontState can describe the state of the aircraft (GROUND or FLIGHT). Furthermore, this variable can illustrate this condition accurately: when the aircraft is on the ground, gear cannot be retracted or extended. Part of the Altarica 3.0 modeling of the software module is shown in Fig. 6, and Table 2 shows the number of the state variables, flow variables, transition and assertion used by the function module modeling. LGS Interaction Behavior Modeling Design. The system requires the interaction between various components to complete the behavior of the entire system. The component interaction of LGS is divided into 5 modules: the interaction between the whole system module (LandingModule); the analogical switch, general electronic valve and hydraulic circuit (PhysicsGeneral); the interaction between three doors (PhysicsDoor); the interaction between three gears (PhysicsGear), the interaction between the whole physical module (PhysicsModule). LandingModule module completes the signal transmission between the handle module, software module and physical module; PhysicsGeneral module can complete the transmission of hydraulic pressure; PhysicsDoor module and PhysicsGear module describe how LGS can simultaneously open (close) three doors and retract (extend) three gears. PhysicsModule module combines all the physical components to complete all the behaviors of the physical module. This section takes the PhysicsGear module as an example and Fig. 7 shows the interaction between gears in AltaRica 3.0 modeling. Besides, Table 3 shows the number of the state and flow variables, transition and assertion in the process of interaction module modeling.

Fig. 6. Software module model

Fig. 7. Gear interaction model

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Table 2. Function module modeling pcs

Table 3. Interactive module modeling pcs

System module

Interaction module

Flow State Transition Assertion variable variable

Handle module

1

1

2 5

1

Computing 38 module

5

8

Software module

41

–

–

71

Analogical module

4

2

8

2

Electronic valve module (parent class)

1

2

8

–

Electronic valve module (child class)

2

–

–

1

Hydraulic circuit module

3

2

4

1

Hydraulic cylinder (front gear (cylinder)

5

2

8

2

Flow Instance Tran- Assertion variable variable sition

LandingModule –

3

–

36

PhysicsGeneral 8

3

–

11

PhysicsGear

18

5

2

24

PhysicsDoor

15

5

2

21

PhysicsModule 36

3

–

40

LGS Failure Behavior Modeling Design. This section considers the abnormal condition that may occur during the system operation. The failure condition is subdivided into four types: electronic valve failure, analogical switch failure, hydraulic cylinder failure, and hydraulic circuit failure. This section takes the electronic valve failure as an example to describe, other fault modeling process is similar. During the AltaRica 3.0 modeling, the design defines three state variables include vsKO (the failure state of electronic valve), vsState (the state of electronic valve) and evE (the command signal of electronic valve); and two failure events include evFailClosing and evFailOpening; the transition corresponding to two events: (1) Closing electronic valve stuck failure: evFailClosing: vsKO == false and vsState == OPEN and evE == false → vsKO := true; (2) Opening electronic valve stuck failure: evFailOpening: vsKO == false and vsState == CLOSE and evE == true → vsKO := true; 3.3

LGS Safety Analysis

Single-Step Simulation. It is mainly used to detect whether the established model can operate normally and fault detection. The analogical switch failure simulation is as follows: The pilot issues the command (DOWN) to extend gears, and then the analogical switch should be closed. However, analogical switch failed

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to close, and then general electronic valve and hydraulic circuit will be blocked. It can be seen from Fig. 8 this assumption is accurate, and this tool is practical. Fault Tree Analysis (FTA). FTA [2] is based on the fault tree model and analyzes the system safety by processing a series of events step by step through logic. Due to system’s complexity, the paper chooses the failure of M1 computing module failure as the top event. When the M1 computing module fails, it can be seen from Fig. 9 that it may be caused by the analogical switch failure, or the hydraulic circuit sensor failure, or the failure of one of the five electronic valves, or the failure of one of the six hydraulic cylinders.

Fig. 8. Single-step simulation

Fig. 9. Fault tree of M1 computing module

In the fault tree analysis, qualitative analysis (calculate the minimum cut set) [2] can find the cause that leads to the occurrence of top events. However, larger minimum cut sets indicate a less safety system, so LGS has two computing modules, one of them can be used as spare parts. This design is mainly to prevent the one computing module failure from affecting the entire system. Through the fault tree analysis, it is proved that the AltaRica 3.0 model can effectively analyze the system failure situation and assess the safety of the system.

4

Conclusion

In order to improve the safety and reliability of the system, researchers in the field of aviation and avionics have proposed different formal verification methods to verify the safety and reliability of the system. Reference [16] proposes to use ASM (Abstract State Machine) modeling method to perform a case study on LGS. However, these formal verification methods do not have mature tools for effective safety analysis. Besides, for AltaRica 3.0, there are some relatively mature tools for safety analysis, whereas the research in this field is still blank in China. MBSA has attracted the attention of various research fields, and scholars have studied on AltaRica and MBSA: Reference [5] proposes a safety assessment method of Integrated Modular Avionics (IMA) based on MBSA and designs a tool that can generate ALT files (AltaRica 3.0 files) based on XML configuration

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files of the IMA system (provided by system engineers), and then OpenAltaRica software is used to perform system safety assessment. Reference [6] illustrates how to apply MBSA to advanced driver assistance systems and mainly uses AltaRica modeling language and Simfia to model a complex system. However, there is no relatively mature MBSA-based system safety analysis and simulation tool developed currently. Compared with the existing work, the main work of this paper includes: The paper designs a modeling and simulation tool (A-MAST) and describes how to model the system in AltaRica 3.0, perform safety analysis and single-step simulation on the A-MAST platform. Finally, from the analysis in Sect. 3, it can be seen that AltaRica 3.0 can accurately describe various functions and failure situations of the system, and system safety analysis based on the model can effectively improve the safety and reliability of the system. Further research includes: quantitative analysis will be performed to optimize the model, thereby improving system’s reliability.

References 1. Martins, L.E.G., Gorschek, T.: Requirements engineering for safety-critical systems: overview and challenges. IEEE Softw. 34(4), 49–57 (2017) 2. Weilin, L., Wei, O., Mingyu, H.: Computing minimal cut sets of fault tree using SAT solver. Comput. Eng. Sci. 39(04), 725–733 (2017) 3. Nguyen, D.: Failure modes and effects analysis for software reliability. In: Annual Reliability and Maintainability Symposium, Proceedings. International Symposium on Product Quality and Integrity (Cat. No. 01CH37179), vol. 2001, pp. 219– 222. IEEE (2001) 4. Kaslow, D., et al.: A model-based systems engineering (MBSE) approach for defining the behaviors of CubeSats. In: 2017 IEEE Aerospace Conference. IEEE (2017) 5. Dong, H., Gu, Q., Wang, G., et al.: Availability assessment of IMA system based on model-based safety analysis using altarica 3.0. Processes 7(2), 117 (2019) 6. Tlig, M., Machin, M., Kerneis, R., et al.: Autonomous driving system: model based safety analysis. In: 2018 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W). IEEE, pp. 2–5 (2018) 7. Kloul, L., Prosvirnova, T., Rauzy, A.: Modeling systems with mobile components: a comparison between AltaRica and PEPA nets. Proc. Inst. Mech. Eng. Part J. Risk Reliab. 227(6), 599–613 (2013) 8. Zhai Zhipeng, H., Jun, C.S., et al.: Embedded system security verification method for AltaRica model. J. Comput. Sci. Technol. 11(1), 24–36 (2017) 9. Boniol, F., Wiels, V.: The landing gear system case study. In: International Conference on Abstract State Machines, Alloy, B, TLA, VDM, and Z, pp. 1–18. Springer, Cham (2014) 10. Hongying, T., Hu, J., Chen, S., Shi, M.: System safety analysis tool for SysML and case study. Comput. Sci. 47(05), 284–294 (2020) 11. Li, W., Hu, J., Chen, S., Zhang, W.: Method of system safety analysis and verification for SysML methods. Comput. Sci. 46(11), 100–108 (2019) 12. Chen, S., Hu, J., Wang, L.: Design and implementation of flattening algorithm for AltaRica 3.0 model based on ANTLR. J. Chin. Comput. Syst. 41(07), 1476–1487 (2020)

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13. Hu, J., Qi, J.: A Class Flattening Method for AltaRica Model. Jiang Su: CN111984233A,2020-11-24 14. Hu, J., Zhan, W.: A System safety assessment method of model-based fault tree automatic generation. Jiang Su: CN111709133A,2020-09-25 15. Mingyu, H., Wei, O., Weilin, L.: System safety analysis method using fault feature model. J. Chin. Comput. Syst. 38(09), 1950–1955 (2017) 16. Arcaini, P., Gargantini, A., Riccobene, E.: Modeling and analyzing using asms: the landing gear system case study. In: International Conference on Abstract State Machines, Alloy, B, TLA, VDM, and Z, pp. 36–51. Springer, Cham (2014)

Joint CSI Acquisition Based on Deep Learning for FDD Massive MIMO Systems Mengxin Li, Jing He(B) , and Yuan Cheng Communication University of China, Beijing, China {lmx56011271,hejing,chengyuan2018}@cuc.edu.cn

Abstract. To fully utilize the spatial multiplexing gains and the array gains of massive multiple-input multiple-output (MIMO), the channel state information (CSI) must be acquired at the transmitter side. However, conventional solutions involve overwhelming overhead both for downlink channel training and uplink feedback in frequency-division duplexing (FDD) massive MIMO systems. In this paper, we propose a joint CSI acquisition solution based on deep learning (DL) to achieve the goal of reducing the overhead. Particularly, we consider a millimeterwave (mmWave) massive MIMO system based on lens antenna array, in which dispatching users directly feedback the pilot observation to the base station (BS), and then joint CSI can be recovered at the BS. We further take advantage of the beamspace channel to transform the joint CSI recovery problem at the BS into a sparse signal recovery problem, and then we propose a learned-approximate message passing (LAMP) network based on deep neural network (DNN) to obtain CSI. Simulation results demonstrate that the proposed scheme can provide accurate CSI with lower overhead compared with the existing conventional CSI acquisition schemes. Keywords: Massive MIMO · CSI · FDD · Deep learning · mmWave

1 Introduction With the explosive growth of mobile data demand, the fifth-generation mobile communication network innovatively uses a large number of spectrums in the mmWave frequency band to achieve the purpose of increasing communication capacity [1]. However, in addition to the advantages of wider bandwidth, mmWave has the advantages of high frequency, short wavelength and greater penetration loss, which makes it very easy to be affected by the atmosphere and rain and lead to more serious channel attenuation. Therefore, a massive MIMO system is proposed to compensate the loss [2]. The key advantage of massive MIMO is its potential gain in energy efficiency, which makes it one of the key technologies in 5G and beyond [3]. The excellent performance of the massive MIMO systems relies on accurate CSI, so accurate CSI at the transmitter is the most crucial [4]. The BS needs an independent CSI feedback process from the user to the BS to obtain the CSI in the frequency division duplexing (FDD) which will bring more pilot overhead. But due to some unique © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 980–987, 2022. https://doi.org/10.1007/978-981-19-0390-8_123

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advantages of FDD in the communication system [5], it is therefore of great interest to come up with effective approaches for obtaining CSI for FDD massive MIMO systems. The conventional acquisition of CSI for FDD MIMO systems is divided into two stages: channel estimation in the downlink and feedback of CSI in the uplink. At the same time, the overhead of downlink pilot signal and uplink feedback are problems to be solved urgently. Recently, varies effective approach have been proposed to address this issues. In [6] and [7], authors specially design pilots to reduce the overhead of downlink [8] proposed a hybrid low-rank matrix completion algorithm based on the singular value projection (SVP). In [9], an approach based on distributed compressive sensing (CS) is proposed to perform the CSI recovery at the BS jointly. But all of these schemes above [6–9] is promising when the channel in high signal-to-noise (SNR) regions. Recently, the amazing success of DL in other fields like object detection and image processing has greatly inspired scholars to use this powerful tool to solve problems in wireless communications [10]. This paper proposed a joint CSI acquisition scheme based on DL for FDD massive MIMO systems. Specifically, a mmWave MIMO system based on lens antenna array is used in which the spatial channel can be transformed to beamspace. First, the BS transmits pilots for downlink channel training, and the observations of the pilots are directly fed back to the BS at the users. Then, the CSI requisition at the BS can be formulate as a sparse signal recovery problem, which can be solved by the proposed LAMP network. In this way, the overhead of CSI acquisition can be reduced, which will be verified by simulation results. The rest of this paper is organized as follows. Section 2 presents the system model. Section 3 provided the details of the proposed LAMP network. The simulation results are provided in Sect. 4. Finally, Sect. 5 concludes this paper.

2 System Model In this section, we first introduce the system model we used, and then we formulate the beamspce CSI acquisition as a sparse signal recovery problem.

RF chains

Lens Antenna Array U

Dimension Reduced Digital Precoding

Selecng Network

User 1

User 2

User K

Fig. 1. mmWave massive MIMO systems with lens antenna array.

We consider the downlink of FDD mmWave massive MIMO system with lens antenna array, as shown in Fig. 1 [12], in which the BS employs a lens antenna array with M antennas to simultaneously serve K single-antenna users. For the purpose of formulate the beamspace CSI acquisition issue, we begin with the conventional mmWave massive

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MIMO channel in the spatial domain. In this paper, we divide the pilot signal into T time blocks for transmission. In the t th block, the transmission pilot is φt ∈ CM×1 , and then collect the received signal yDL k,t = hK φk,t + nk,t for all the K users. The received DL K×1 signal vector yt ∈ C for all K users can be presented by ytDL = HD φt + nt ,

(1)

where HD ∈ CK×M is the downlink channel matrix, HD = [hd 1 , hd 2 , ..., hdK ]H , hdk ∈ CM×1 is the channel vector between the BS and the k-th (k = 1,2, ... ,K)user,  2 . The nt is the additive white Gaussian noise (AWGN) vector following CN 0, σDL channel vector hdk ∈ CM×1 can be equivalently written as hdk =

P 

  gk,p a θp ,

(2)

p=1

where P is the number of resolvable physical paths, gk,p is the propagation gain of the  (p = 1, 2,...,P) path, θp is the angle-of-departure (AoD) of the p-th path, and  p-th a θp ∈ CM×1 is the steering vector. In  paper, we consider the simple uniform linear  this arrays model, so the corresponding a θp expression is T    D D a θp = 1, e−j2π λ cos(θp ) , ..., e−j2π λ (M −1) cos(θp ) ,

(3)

where D is the antenna spacing and λ is the carrier wavelength. In channel model (2), rich scattering is assumed at the user side, and almost clusters around the BS are accessible for   P all users. That is to say, the channel vectors have the same steering vectors a θp p=1 . Then, we can present the downlink channel matrix as follow: HD = GAT ,

(4) 

 where G ∈ CK×P with the (k, p)-th entry being gk,p , A = a(θ1 ), a(θ2 ), ..., a θp ∈ CM×P . The lens antenna array can be described by a discrete fourier transform (DFT) matrix U to further illustrate: U = [a(ϕ1 ), a(ϕ2 ), ..., a(ϕM )]H ∈ CM×M , (5)   where ϕM = M1 m − M 2+1 , m = 1, 2, ..., M are the spatial directions predefined by the lens antenna array. It can be seen from the corresponding steering vector that each column of the DFT matrix U is orthogonal to each other. The received signal vector DL y˜ t ∈ CK×1 for all K users in beamspace can be presented by ˜ y˜ DL t = HD Uφt + nt = HD φt + nt , ˜ D can be presented by The beamspace channel H H  H  ˜ D = HD U = h˜ d 1 , h˜ d 2 , ..., h˜ dK H = UH hd 1 , UH hd 2 , ..., UH hdK ,

(6)

(7)

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  ˜ DL ˜ DL ∈ After T instants of pilot transmission, the received signal YUE = y˜ DL 1 , y 2 , ...., y T CK×T at the user terminal can be given by ˜ D  + ND , YUE = H

(8)



where  = φ1 , φ2 , ..., φT is an M × T dimensional matrix constituted by the pilots during T time blocks, and ND = [n1 , n2 , ..., nT ] ∈ CK×T is the AWGN of downlink with zero mean and unit variance. In our scheme, each user directly feeds back its own pilot observation to the BS for the joint MIMO channel recovery of all users. We directly return the signal YUE to the BS. At this time, the received signal of the BS is [13]: YBS = UH HU YUE + NU ˜ D  + ND + NU =UH HU H ˜ D  + UH HU ND + NU =UH HU H H ˜ D  + N, =U HU H

(9)

where HU ∈ CM×K is the uplink Rayleigh fading channel matrix whose entries following CN(0, 1), HU = [hu1 , hu2 , ..., huK ]H and N = UH HU ND + NU . Let W = UH HU , then we summarize the formula to get the signal received by the BS as: ˜ D  + N, YBS = WH

(10)

Then reformulate the problem, and first we vectorized (10) as  ˜ D  + vec(N) y = vec WH   ˜ D + vec(N) = T ⊗ W vec H

(11)

= ˜ψh + n,  ˜ D , n = vec(N), note that each where y = vec(YBS ), ψ = T ⊗ W, h˜ = vec H element of the beamspace channel h˜ in (11) can be regarded as the sum of all paths at each spatial angle. There are only a few dominant propagation paths with large gains at mmWave frequencies, so the beamspace channel h˜ is sparse. That is to say, the CSI acquisition problem can be formulated as a sparse signal recovery problem





min h˜ , s.t. y − ψh˜ ≤ ε, (12) 0

2



˜ ε is the error tolerance parameter. where h˜ is the number of non-zero elements of h, 0

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3 LAMP for CSI 3.1 LAMP Network In the mmWave massive MIMO system, the number of antennas is usually large, so the dimension of sparse signal in formula (10) is high. As a combination of iterative algorithm and deep learning in CS, the LAMP network can be applied to the recovery of high-dimensional sparse signals. In this section, we introduce how the LAMP network estimates the beamspace channel. As shown in Fig. 2, each iteration of the classical AMP algorithm is mapped to each layer of the LAMP network.

Fig. 2. LAMP network structure (the i th layer is explained in datail)

The network consists of I layers each with the same structure. Specifically,   the input of ith layer are the output of previous (i−1)th layer. In the LAMP network, λ, ψH are replaced by (λi , Bi ) at i−th iteration. Moreover, the LAMP network uses the powerful learning ability of the neural network to select different coefficients Bi and the non-linear shrinkage parameters λi for each layer i. Therefore, for the i-th layer of LAMP, downlink channel is estimated as follows: vi = y − ψhˆ˜ i - 1 + bi - 1 vi - 1 ,   vi 22 ˆh˜ = η hˆ˜ , i i - 1 + Bi vi ; √ M

(13)

(14)

with the inputs of first layer is v0 = 0, b0 = 0, hˆ˜ 0 = 0. The details about LAMP network process the signal as follows, which is shown in Algorithm 1.

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3.2 Proposed Joint CSI Acquisition Scheme The proposed joint CSI acquisition scheme mainly works in two phases: offline training and online estimation. We use supervised learning to train the network. We first generate D  according to (2), (4), (9) and (11), where yˆ d is the the training data set yˆ d , h˜ d d =1

input of LAMP network with the corresponding label h˜ d and D is the sample size of the data set. Then we use layer-by-layer training to optimize Bi and λi . After all trainable variables of T layers are optimized, we save I ={Bi , λi }Ii=1 as data set. In online estimation phase, the new measurements are fed into LAMP network with already trained coefficients to output the recovered signal of the last layer.

4 Simulation Results In this section, we first introduce the settings in the simulation experiment, then we provide the performance comparison among proposed LAMP network and others. The parameters in our experiments are set as: M = 64, θp = −π /2 + π (p − 1)/P, D/λ=0.3, K = 4, T = 75, P = 3, I = 4. Since the true channel noise level is generally unknown or can be time-varying, we build the training data set by generating 2 * 104 and 103 for each SNR level in the range [5, 10, 15, 20] dB and [0, 20] dB respectively. The learning rate are set to be 10–6 . In Fig. 3, we present simulation results. • AMP, OMP: Traditional CS recovery algorithms. • LS: Traditional channel estimation algorithm • SVP: A singular value decomposition method based on low-rank characteristics. The performance evaluation standard is normalized MSE (NMSE), which can be defined as follow:   K K 2

2  

ˆ˜

ˆ (15) NMSE = E

hk − h˜ k /

h˜ k , k=1

k=1

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Fig. 3. NMSE comparison of the CSI acquisition against different SNR

Fig. 4. Performance comparison of the CSI acquisitions against pilot T

It can be observed from the Fig. 3 that the proposed LAMP outperforms the conventional channel estimation scheme, the traditional channel estimation scheme based on CS and the scheme based on SVP. Even in low SNR area, the LAMP network can still achieve better NMSE performance. We next investigate the overhead reduction of the proposed joint CSI acquisition scheme against pilot length. In Fig. 4, we set the SNR as 15 dB, then we can observe the NMSE with pilot length T as the abscissa. It be seen that pilot length required for the LAMP is much smaller than others. The results indicate that the proposed LAMP network achieved satisfactory results in reducing the overhead of downlink channel training and uplink channel feedback.

5 Conclusion This paper investigated a novel CSI acquisition scheme for FDD mmWave massive MIMO system with lens antenna array. A novel LAMP network was presented, which inherits the superiority of data-driven. We formulate the CSI acquisition as a sparse signal recovery problem. Simulation have verified that the proposed LAMP network can obtain accurate CSI at the transmitter compared with other algorithms, which validates the effectiveness and developability of DL in wireless communication.

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References 1. Wei, Y., Zhao, M.-M., Zhao, M., Lei, M., Yu, Q.: An AMP-based network with deep residual learning for mmWave beamspace channel estimation. IEEE Wirel. Commun. Lett. 8(4), 1289– 1292 (2019) 2. Han, S., Xu, C.I.Z., Rowell, C.: Large-scale antenna systems with hybrid analog and digital beamforming for millimeter wave 5G. IEEE Commun. Mag. 53(1), 186–194 (2015) 3. Mumtaz, S., Rodriquez, J., Dai, L.: MmWave Massive MIMO: A Paradigm for 5G. Academic, New York, NY, USA (2016) 4. Wu, Y., Wang, M., Xiao, C., Ding, Z., Gao, X.: Linear precoding for MIMO broadcast channels with finite-alphabet constraints. IEEE Trans. Wirel. Commun. 11(8), 2906–2920 (2012) 5. Rusek, F., et al.: Scaling Up MIMO: opportunities and challenges with very large arrays. IEEE Sig. Process. Mag. 30(1), 40–60 (2013) 6. Choi, J., Love, D.J., Bidigare, P.: Downlink training techniques for FDD massive MIMO systems: open-loop and closed-loop training with memory. IEEE J. Select. Top. Sig. Process. 8(5), 802–814 (2014) 7. Noh, S., Zoltowski, M.D., Love, D.J.: Downlink training codebook design and hybrid precoding in FDD massive MIMO systems. IEEE Glob. Commun. Conf. 2014, 1631–1636 (2014) 8. Shen, W., Dai, L., Gao, Z., Wang, Z.: Joint CSI acquisition based on low-rank matrix recovery for FDD massive MIMO systems. In: 2015 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pp. 83–84 (2015) 9. Rao, X., Lau, V.K.N.: Distributed compressive CSI estimation and feedback for FDD multiuser massive MIMO systems. IEEE Trans. Sig. Process. 62(12), 3261–3271, 15 June 2014 10. Li, R., et al.: Intelligent 5G: when cellular networks meet artificial intelligence. IEEE Wirel. Commun. 24(5), 175–183 (2017) 11. Wei, X., Hu, C., Dai, L.: Deep learning for beamspace channel estimation in millimeter-wave massive mimo systems. IEEE Trans. Commun. 69(1), 182–193 (2021) 12. Gao, X., Dai, L., Han, S., Wang, C.I.X.: Reliable beamspace channel estimation for millimeterwave massive MIMO systems with lens antenna array. IEEE Trans. Wirel. Commun. 16(9), 6010–6021 (2017) 13. Shen, W., Bu, X., Gao, X., Xing, C., Hanzo, L.: Beamspace precoding and beam selection for wideband millimeter-wave MIMO relying on lens antenna arrays. IEEE Trans. Sig. Process. 67(24), 6301–6313 (2019)

3D Terrain Mapping and Object Detection Using LiDAR S. Bharath(B) , S. Vinay, and S. Srividhya BNMIT, #543, “Sri Sai Nivas”, Vinayaka Layout, 2nd Stage, Nagarbhavi, Bangalore, Karnataka, India [email protected], [email protected], [email protected]

Abstract. Distance is a vital information for any autonomous or guided system to maneuver around or across an obstacle. Senor which is capable of providing distance information with a 3D representation of the obstacle is LiDAR, which provides Point Cloud Data (PCD). PCD provides a flexible geometric representation of the environment, the data is basically rows of coordinates of points with the 1st 3 columns x, y, z values respectively. Here we provide a novel solution to perform object detection with LiDAR using Dynamic Graph Convolutional Neural Network (DGCNN). The DGCNN network was originally trained on ModelNet40 dataset, as per our requirements we retrained it on data recorded from Quanergy M8 LiDAR. The solution is capable of classifying 4 different classes of objects. This solution can be integrated with other existing autonomous system solutions to make a much better efficient system. Keywords: Point Cloud Data (PCD) · Dynamic Graph Convolutional Neural Network (DGCNN) · LiDAR · Autonomous system · Object identification

1 Introduction Humans have always strived to achieve a state of full automation and one of the most interesting fields of automation is autonomous or guided systems. The vision assistance systems used in most of the currently available guided system uses a camera to show the immediate scene present in front of the system to the person controlling or guiding the said system which works out fine, but when an autonomous system is to be designed, the system must analyze the scene and detect any object in front of it and take the appropriate actions such as maneuvering around or across the detected object. This whole procedure to take place, it is essential to determine the distance between the system and the interested object, determining this is arduous, inefficient and requires high computational power even after considering more than one camera. When LiDAR is used, we can obtain accurate distance between the system and object. This is a much faster process and requires less processing, unlike cameras that provide a closed view range LIDAR provide a 360-degree view enabling the autonomous system to know what object is present not just in front of it but also in the whole surrounding. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 988–995, 2022. https://doi.org/10.1007/978-981-19-0390-8_124

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In this paper, we have implemented a plug and play application that uses DGCNN [1, 2] method to detect objects, the solution has a well-defined pipeline that first cleans the data along with isolating the object of interest, followed by a distance determiner. Once the distance is calculated the cleaned normalized data is then sent to the object identification model, which is the DGCNN [1, 4] network but retrained on our data. The DGCNN is originally based on PointNet [3, 8] and PointNet++ [3, 4] which tries to classify each point in a point cloud as an object but DGCNN [1, 5] uses the same philosophy but with extra components of grouping points together. This is system is capable of identifying objects in both outdoor [10] and indoor [11] environments.

2 Methodology (System Design) (See Fig. 1)

Fig. 1. The system design consists of mainly 7 components starting from LiDAR, where the raw data is obtained. The raw data is sent to a Visualizer and Data processor component, the visualizer is used to view the raw data and the data processor component processes the data. The processed data is sent to object identification model to classify the object present along with a distance determiner function that determines the distance following which all the obtained result is sent to voice module as output.

2.1 Processing Pipeline An uncleaned and non-normalized data is a big hurdle while training the model so a preprocessor is designed which parses through all the point cloud files and removes

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the NaN values. A single object will consist of several thousand points but only a fraction of that is required to train the model so we only collect the first N points. The bigger the N the better quality of data is obtained. We have found that Ideally 61440 points are sufficient after several trial and errors. The obtained data is stored as a Numpy array of size (N, 3). The obtained data is a 360 Degree view of the surrounding, so the next step is to isolate the object which is achieved using the Euclid’s Distance formula. The shortest point T from LiDAR is identified which acts like tracer point which will help in isolation of the object from surrounding points. Then considering the point T as the initial point we apply the Euclid’s distance formula to every point in the data and check whether this distance lies within a 1-m range. This way all the points related to the object are clustered together and the isolation of object is achieved. Hence, we obtain a clean data C of size (M, 3) where M < N. Once the object is isolated its crucial to normalize the data, the normalization technique that we have used is based on Mean normalization. The mean of X, Y and Z points are calculated and they are subtracted with the original value, this results in a translated point cloud data. Then the point which is farthest from the LiDAR is extracted and every point is divided by this. This normalization method ensures that the data of isolated object is within the range of (1.−1) and it also centers the object within a 3D frame. Hence, we obtain a normalized data K of size (M, 3). Data augmentation is the process of increasing the size of data set by changing certain properties of the data, in images the size, color contrast and orientation properties are changed to augment data, but point cloud data is just X, Y and Z co-ordinates so we rotate each data in certain angle θ in X, Y and Z planes to change the orientation of the normalized data (K) as approached in [7]. Here K is a matrix of size (M, 3) which is transposed and dot multiplied with the rotation matrix Rx(θ), Ry(θ) and Rz(θ) to obtain new orientation of the normalized data K. This way each data can be augmented in different angles in all 3 planes. The value of θ can be in range (A1, A2) in the steps of D degrees. It is ideal to keep a medium size step as exceedingly small step sizes resulted in overfitting of data. We have found that the ideal range is (30, 60) with a step size of 30°. ⎡ ⎡ ⎤ ⎤ 1 0 0 cos(θ ) 0 − sin(θ ) ⎦ Rx (θ ) = ⎣ 0 cos(θ ) − sin(θ ) ⎦Ry (θ ) = ⎣ 0 1 0 0 sin(θ ) cos(θ ) sin(θ ) 0 cos(θ ) ⎡ ⎤ cos(θ ) − sin(θ ) 0 Rz (θ ) = ⎣ sin(θ ) cos(θ ) 0 ⎦ 0 0 1

3 Implementation The object detection model consists of the Dynamic graph convolutional neural network. The network consists of both classification and segmentation model, here we are only utilizing the classification model. Initially the model was trained on ModelNet40

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dataset but when evaluating on our data the model performed poorly. Hence our approach was to retrain the network to understand our data. The data we used was raw PCD of objects which did not provide data similar to the modelnet40 dataset the Fig. 2 shows how our data when visualized on Open3D [9] library looks like. Using Quanergy M8 LiDAR with Q-view application we recorded the object data, we mainly used general indoor objects like Chair and table, and added two more classes Human and two-wheeler. While recording the objects were all kept at a minimum distance of 2 m away from the LiDAR. The LiDAR was placed at an optimum height of 3.7ft to obtain maximum number of points with respect to all the classes. As mentioned earlier these data were sent through the processing pipeline and then fed directly into DGCNN [1, 6]. The hyperparameters used in the training were Epoch: 150, learning rate: 0.001, batch size: 24 and the rest were kept as default. The model obtained after training had an overall accuracy of 91.66%, the solution can isolate the object, classify to which class it belongs to and accurately determine how far the object is from the LiDAR, then the result obtained is sent to a voice module, for which we have used gTTS python library (Figs. 2, 3, 4, 5 and 6).

Fig. 2. Depicts the uncleaned, unnormalized data of a two-wheeler. As seen the data consists of almost minimum of 150,000 points, the points land from 2 m to 200 m away from the LiDAR.

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Fig. 3. Depicts the uncleaned, unnormalized data of a man running in sideways.

Fig. 4. The fig on left depicts the learning accuracy of the model during its training, with hyperparameters epoch value 150, batch size 24, learning rate 0.001 and other hyperparameters as default. The fig on right depicts the loss rate of the model during its training, with hyperparameters epoch value 150, batch size 24, learning rate 0.001 and other hyperparameters as default.

Fig. 5. Depicts the cleaned, normalized data of a man running in sideways.

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Fig. 6. Depicts the result of training the DGCNN model with our data.

4 Result (See Figs. 7 and 8).

Fig. 7. Depicts the execution of the model where it classifies the object as two-wheeler and also outputs the distance as 2.96 m away from the LiDAR.

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Fig. 8. Depicts the cleaned, normalized data of a two-wheeler.

5 Conclusions The result achieved is a plug and play application that uses LiDAR data to detect the immediate object and its respective distance. The solution currently can classify 4 different classes of objects. The preprocessor can be used to increase the number of classes which will enable the system to perform better in real time and is also one of the future enhancements. The solution can be integrated with other computer vision projects to create a much efficient solution in the field of automation and as an aid for the visually impaired too.

References 1. Wang, Y., Sun, Y., Liu, Z., Sarma, S.E., Bronstein, M.M., Solomon, J.M.: Dynamic graph CNN for learning on point clouds (2018). arXiv:1801.07829 2. Qi, C.R., Liu, W., Wu, C., Su, H., Guibas, L.J.: Frustum pointnets for 3d object detection from rgb-d data. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 18–22 June, pp. 918–927 (2018) 3. Qi, C.R., Su, H., Mo, K., Guibas, L.J.: Pointnet: deep learning on point sets for 3d classification and segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, 21–26 July, vol. 1, p. 4 (2017) 4. Qi, C.R., Yi, L., Su, H., Guibas, L.J.: Pointnet++: deep hierarchical feature learning on point sets in a metric space. In: Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA, 4–9 December, pp. 5099–5108 (2017) 5. Xu, M., Zhang, J., Peng, Z., Xu, M., Qi, X., Qiao, Yu.: Learning Geometry-Disentangled Representation for Complementary Understanding of 3D Object Point Cloud (2020) 6. Li, Y., Bu, R., Sun, M., Wu, W., Di, X., Chen, B.: PointCNN: convolution on X-transformed points. In: Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada, 3–8 December (2018), pp. 828–838 (2018)

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7. Lu, M., Guo, Y., Zhang, J., Ma, Y., Lei, Y.: Recognizing objects in 3D point clouds with multi-scale local features. Sensors 14, 24156–24173 (2014). https://doi.org/10.3390/s14122 4156 8. Chen, C., Zanotti Fragonara, L., Tsourdos, A.: Go wider: an efficient neural network for point cloud analysis via group convolutions. Appl. Sci. 10, 2391 (2020). https://doi.org/10.3390/ app10072391 9. Zhou, Q.-Y., Park, J., Koltun, V.: Open3D: A Modern Library for 3D Data Processing. arXiv:1801.09847 (2018) 10. Golovinskiy, A., Kim, V.G., Funkhouser, T.: Shape-based recognition of 3D point clouds in urban environments. In: 2009 IEEE 12th International Conference on Computer Vision, 2009, pp. 2154–2161 (2009). https://doi.org/10.1109/ICCV.2009.5459471 11. Armeni, I., Sener, O., Zamir, A.R., Jiang, H., Brilakis, I., Fischer, M., Savarese, S.: 3d semantic parsing of large-scale indoor spaces. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 26 June–1 July, pp. 1534–1543 (2016) 12. Manay, S., Cremers, D., Hong, B.-W., Yezzi, A.J., Soatto, S.: Integral Invariants for Shape Matching.Trans. PAMI28 10, 1602–1618 (2006) 13. Fan, H., Su, H., Guibas, L.J.: A point set generation networkfor 3D object reconstruction from a single image. In: Proceedings of the CVPR (2017) 14. Mao, J., Wang, X., Li, H.: Interpolated convolutional networks for 3D point cloud understanding. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2019) 15. Liu, X., Han, Z., Liu, Y.-S., Zwicker, M.: Point2Sequence: learning the shape representation of 3D PointClouds with an attention-based sequence to sequence network. In: Thirty-Third AAAI Conference on Artificial Intelligence (2019)

Fast Beam Tracking for Base Station and UAV Communications Yiwen Tao1(B) , Tao Feng2 , Bin Li1 , and Chenglin Zhao1 1

School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China [email protected] 2 China National Tendering Center of Machinery and Electric Equipment, Beijing, China

Abstract. The Unmanned Aerial Vehicle (UAV) is considered more and more often to assist the existing terrestrial communication systems. For UAV assisted cellular system, a critical challenge is the real time beam tracking for the UAV, introduced by the high mobility of it. The beam pointing deviation would result in the severe transmission performance reduction problem. This paper considers the beamforming from the base station to the moving UAV, and proposes a novel localization assisted fast beam tracking mechanism. The location of the UAV is first estimated by the base station with a Bayesian inference based algorithm. Then the beam direction is calibrated using a recursive searching method. Simulation results prove that the proposed beam tracking method can achieve accurate beam alignment within several adjustments. The convergence performance is superior to the existing technique.

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The Unmanned Aerial Vehicle (UAV), as a promising tool to realize flexible and efficient communications, is attracting more and more attention. UAVs are proved useful in various application scenarios, typically in unsatisfied transmission circumstances, e.g. mountainous scenarios and dense environments [1]. In these circumstances, benefiting from the UAV’s high mobility and flexibility, the UAV-assisted communication system can easily establish satisfied and costefficient data links, thereby promising highly flexible and reliable communication networks [2,3]. More recently, it is widely considered to employ UAV to assist the conventional terrestrial communication infrastructure, e.g. the existing cellular communication network. Here, a critical challenge lies in the real time beam tracking problem. Different from the terrestrial communication systems, the UAV may be highly dynamical. This will result in the drastic time variant property of the channels [4]. Therefore, fast beam tracking technique with short processing time would be necessary. On the other hand, the beam tracking accuracy is also important for narrow main-lobe width based beamforming between the base station and the UAV, which can benefit high data rate transmission performance [5]. The above contradiction makes the beam tracking in the considered scenario a tough challenge. c The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022  Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 996–1003, 2022. https://doi.org/10.1007/978-981-19-0390-8_125

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Various methods have been proposed to cope with the beam tracking problem. For instance, in [6], the sparse characteristic of the Millimeter-wave channels is considered and a Compressed Sensing (CS) reconstruction method is developed for fast channel recovering. The authors in [7] regard the optimal beam pair search problem as a global optimization task in a 2-dimension map, and solve the beam alignment via numerical searching techniques. The reference [8] proposed a hierarchical codebook is designed to reduce training overhead. In [9], a fast UAV beam training technique relying ont movement pattern is derived by the flight control system. While in [10] a blind beam tracking method that adjusts the beam pointing by mechanical method and refines via electrical algorithms. However, the existing methods would still be inadequate to deal with the highly dynamical scenarios. Most of the existing methods use only the current measurement, ignoring the correlations in time domain. Besides, the training overhead of the existing methods is still redundant (Requiring multiple training times for convergence). This paper aims at the beam tracking problem between the base station and the moving UAV, and proposes a novel localization assisted fast beam tracking algorithm. Specifically, the locations of the UAV is sequentially estimated by the base station via the Bayesian sequential inference based filtering algorithm. Subsequently, the beam direction is further calibrated via a perturbation method, with the help of the location estimation results. Numerical simulations prove that the proposed method can achieve satisfied beam tracking performance, which is superior to the existing state-of-the-art technique.

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In this paper, we consider the beamforming based communication between the base station and the moving UAV, as shown in Fig. 1. In this 3-Dimension Cartesian Coordinate System (CCS), the base station is located in the coordinate of (x0 , y0 , z0 ), and equipped with a Uniform Planar Array (UPA) with M × N antennas. The moving UAV is equipped with single antenna with the coordinate at discrete time t denoted by (xt , yt , zt ).

Fig. 1. The considered beam tracking scenario with a base station and a moving UAV.

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Received Signal Model

We  consider that the distance between the base station and the UAV, i.e., d = (xt − x0 )2 + (yt − y0 )2 + (zt − z0 )2 , is far larger than the antennas spacing ε, the UAV is then regarded as a far field target. Denote the transmitted signal as s(t) with E[s(t)s(t)H ] = 1, the received signal is given by  (1) y(t) = PR wH α (θt , ϕt )s(t) + wH nt , where PR is the received power, which is expressed in dB manner as 10 lg PR = PT − Pref (d0 ) − 10β lg(d/d0 ).

(2)

PT is the transmitted power, Pref (d0 ) is the fading at the reference distance d0 . β is the fading coefficient. w ∈ CM N ×1 is the receiving weight vector and α (θt , ϕt ) ∈ CM N ×1 is the column vector expanded by the steering vector α(θt , ϕt ) ∈ CM ×N corresponding to the azimuth and elevation angle θt , ϕt . The {m, n}th element of α(θt , ϕt ) is given by α(θt , ϕt )(m,n) = ej

2πε[(m−1) cos θt cos ϕt +(n−1) cos θt sin ϕt ] λ

,

(3)

In this paper, for analysis simplicity, we employ the main-lobe of the received beamforming pattern, which can be modeled as a Gaussian distribution [11]. Denote gR the beamforming gain, it can be expressed as   (4) gR = g0 exp −ξΔφ2t , where g0 is the maximum gain, ξ is the beam-width factor and Δφt denotes the beam angle deviation. Then the received power in dB form can be written as Yt = 10 lg (PR gR ) + nt ,

(5)

nt is the noise modeled by nt ∼ CN (0, σ 2 ). And the instantaneous channel capacity between the base station and the UAV is given by    PR wH α (θt , ϕt )αH (θt , ϕt )w , (6) R(t) = log 1 + σ2 2.2

UAV Moving Pattern

We then investigate the moving pattern of the UAV. Considering the moving property along with the three axes of the CCS, we assign a position state vector Lt at time t for the UAV, which is fully given by Lt = [xt , vx,t , yt , vy,t , zt , vz,t ]T , where vx,t , vy,t , vz,t are the velocities corresponding to the three axes. We assume that the dynamical behavior of the UAV’s position state vector is governed by a first-step Makovian process. That is, the current state of Lt is

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only related with its state at the last time slot. By denoting φ(·) the transitional density of the position state vector, the transitional behavior is expressed as Lt ∼ φ (Lt |Lt−1 ) .

(7)

And the UAV’s moving patterns corresponding to the three axes are independent to the others and share the same behavior. Taking the X-axis for an example, the moving pattern along with the X-axis is modeled as

    xt xt−1 xt ∼N ;F ,Γ , (8) vx,t vx,t vx,t−1 where

 1 ΔT F = , 0 1

Γ =

κΔT 3 κΔT 2 3 2 κΔT 2 κΔT 2

,

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ΔT is the time interval and κ is the noise intensity of the transitional process.

3 3.1

Localization Assisted Fast Beam Tracking Algorithm UAV Localization

The sequential UAV localization is based on the Maximum A Posterior (MAP) conception. Suppose that at time k − 1 the posterior position state vector is obtained and shown as Lt−1 . Then in the MAP point of view, the position state vector at time t can be estimated via the following equation: ˆ t = arg maxf (Lt |Y1:t ) |φ (Lt |Lt−1 ) , Lt−1 , L Lt

(10)

where f (Lt |Y1:t ) is the posterior distribution of Lt at time t. For acquiring the posterior distributions, we refer to the Unscented Kalman filter. Briefly speaking, the real-time state vector and the corresponding covariance matrix Pt is kept. At time t, a set of Sigma points are sampled as ⎧ i = 1, ⎨ Lt−1 ,  (i) (11) χt−1 = Lt−1 + (δ + μ) Pt−1 , i = 2, . . . , δ + 1, ⎩ Lt−1 − (δ + μ) Pt−1 , i = L + 2, . . . , 2δ + 1, where δ is the state vector dimension and μ is a scaling factor. By assigning (i) (i) the state weight ωS and the covariance weight ωC for the ith Sigma point, the position state and the covariance matrix can be predicted by: Lt|t−1 = Pt|t−1 =

2L+1 i=1 2L+1 i=1

(i)

(i)

ω S χt ,   H (i) (i) (i) ωC χt − Lt|t−1 χt − Lt|t−1 + Γ,

(12) (13)

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(i)

where χt = F χt−1 . Relying on Lt|t−1 , the base station can adjust the beam direction corresponding to the azimuth angle and elevation angle as follows: ⎞ ⎛ y − y 0 t ⎠, (14a) θt = arccos ⎝  2 2 d − (z t − z0 )

 z t − z0 ϕt = arccos , (14b) d where xt , y t and z t are the coordinates in Lt|t−1 , and d is the distance between the base station and the predicted position of the UAV. Once the beam adjustment is accomplished, the base station obtains the power Yt . (i) Then, we predict the signal measurement Yt from the ith Sigma point by substituting the corresponding beam direction into Eq. (4). As such, the system measurement prediction can be obtained by Yt|t−1 =

2L+1 i=1

(i) (i) ωS Yt .

(15)

On the basis of signal measurement prediction, we compute the cross covariance matrix PL,Y between the state vector and the measurement, and the auto covariance matrix PY,Y of the measurement itself. The UAV position state vector and the corresponding covariance matrix can then be updated via   ˆ t = Lt|t−1 + PL,Y P −1 Yt − Yt|t−1 , (16) L Y,Y  T −1 T Pˆt = Pt|t−1 − PL,Y PY,Y PL,Y , (17) Subsequently, according to the posterior position state vector Lt , the corresponding azimuth angle and elevation angle θˆt , ϕˆt can be computed with the same equation as shown in Eq. (14), which is not repeated here. 3.2

Beam Calibration

As the above section is devoted to estimating the position of the UAV, instead of the direction. Therefore the estimated azimuth and elevation angles may be deviated from their true values. Besides, the sequential localization can be regarded as a trajectory smoothing procedure, which may also introduce instantaneous estimation errors. As a consequence, the beam direction calibration is necessary. In order to realize fast beam direction estimation, we refer to the stochastic optimization conception, which has been widely utilized in various applications, e.g. physical research, social sciences, etc. [12]. Specifically, the optimization procedure relies on the measurements, instead of the loss function itself. We start a0 c0 from the parameter initialization. The parameters ak = (A+k) α and ck = kγ are first computed, where a0 , A, α, c0 , γ are all small perturbation parameters. k is

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the iteration number. Denote Φ = [θt , ϕt ]T . At every iteration, the gradient of the received power is approximated via k (Φk ) =

Y (Φk + ck Λk ) − Y (Φk − ck Λk ) , 2ck Λk

(18)

where Λk is a random perturbation vector. Then the beam direction can be updated at every iteration as Φk+1 = Φk − ak k (Φk ) .

(19)

The specific calibration process is given in Algorithm 1. Algorithm 1. Beam Calibration Algorithm Require: θˆt , ϕ ˆt after UAV Localization; Ensure: Accurate directions θt , ϕt ; ˆt ]T 1: Initialize a0 , A, α, c0 , γ, k = 1, Φk = [θˆt , ϕ 2: repeat a0 c0 3: Compute ak = (A+k) α and ck = kγ ; 4: Approximate gradient via eq. (18); 5: Update beam direction via eq. (19); 6: k = k + 1; 7: until The beam direction changes little

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Simulation Results

This section validates the proposed beam tracking method via numerical simulations. The base station is equipped with a 20 × 20-element UPA. The channel is dominated by line-of-sight component. For the signal model we configure PT = 20 dBm, d0 = 1 m and Pref (d0 ) = 3 dB. The path loss factor is 2 while beam width factor is 0.886. The maximum beam gain is g0 = 20 dB. For the UAV moving pattern, we set ΔT = 0.1 s, the transitional process noise intensity κ = 0.02. The initial state vector of the UAV is [85, 1.5, 70, 0, 30, 0]T . First, we set the noise variance to be σ 2 = 0.5 and evaluate the instantaneous direction estimation performance, as shown in Fig. 2-(a). It is seen that the instantaneous estimation performance can be improved after each step. After the beam calibration step, the direction estimation error can be reduced from above 1◦ to below 0.3◦ . Then the averaged direction estimation performance is shown in Fig. 2-(b). As can be seen the direction estimation performance is greatly reduced with the decrease of noise variance. The errors after prediction and calibration are 0.5◦ and below 0.1◦ in small σ 2 cases. Subsequently, the convergence performance of the proposed method is validated, which is compared with the existing state-of-the-art method, i.e. simultaneous perturbation [10]. The result is shown in Fig. 3. It is seen that the proposed

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method can significantly improve the convergence performance, by feeding the localization result in the optimization procedure. The existing method would require more than 8 times for algorithm convergence. While our method needs merely 4 times.

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Conclusion

This paper investigates the beam tracking problem in UAV assisted cellular network and proposes a fast beam tracking method for the base station to track the moving UAV via beamforming. The real time locations of the UAV is taken into account, by which a sequential localization method is designed. Based on the localization results, a beam calibration algorithm is further proposed to refine the beam directions. Numerical simulations prove that the proposed method achieves accurate direction estimation, the performance is superior to the existing method. Acknowledgments. This paper is supported by National Natural Science Foundation of China (NSFC) under Grant U1805262.

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References 1. Zeng, Y., Zhang, R., Lim, T.J.: Wireless communications with unmanned aerial vehicles: opportunities and challenges. IEEE Commun. Mag. 54(5), 36–42 (2016) 2. Xiao, Z., Xia, P., Xia, X.: Enabling UAV cellular with millimeter-wave communication: potentials and approaches. IEEE Commun. Mag. 54(5), 66–73 (2016) 3. Zeng, Y., Zhang, R., Lim, T.J.: Throughput maximization for UAV-enabled mobile relaying systems. IEEE Trans. Commun. 64(12), 4983–4996 (2016) 4. Zhang, C., Zhang, W., Wang, W., Yang, L., Zhang, W.: Research challenges and opportunities of UAV millimeter-wave communications. IEEE Wirel. Commun. 26(1), 58–62 (2019) 5. Xiao, M., et al.: Millimeter wave communications for future mobile networks. IEEE J. Select. Areas Commun. 35(9), 1909–1935 (2017) 6. Zhang, D., Li, A., Shirvanimoghaddam, M., Cheng, P., Li, Y., Vucetic, B.: Codebook-based training beam sequence design for Millimeter-wave tracking systems. IEEE Trans. Wirel. Commun. (2019) 7. Li, B., Zhou, Z., Zou, W., Sun, X., Du, G.: On the efficient beam-forming training for 60GHz wireless personal area networks. IEEE Trans. Wirel. Commun. 12(2), 504–515 (2013) 8. Li, B., Zhou, Z., Zhang, H., Nallanathan, A.: Efficient beamforming training for 60 GHz millimeter-wave communications: a novel numerical optimization framework. IEEE Trans. Veh. Technol. 63(2), 703–717 (2014) 9. Zhao, J., Gao, F., Kuang, L., Wu, Q., Jia, W.: Channel tracking with flight control system for UAV mmWave MIMO communications. IEEE Commun. Lett. 22(6), 1224–1227 (2018) 10. Zhao, J., Gao, F., Wu, Q., Jin, S., Wu, Y., Jia, W.: Beam tracking for UAV mounted SatCom on-the-move with massive antenna array. IEEE J. Select. Areas Commun. 36(2), 363–375 (2018) 11. Fan, C., Li, B., Zhao, C., Guo, W., Liang, Y.: Learning-based spectrum sharing and spatial reuse in Mm-wave ultradense networks. IEEE Trans. Veh. Technol. 67(6), 4954–4968 (2018) 12. Spall, J.C.: Implementation of the simultaneous perturbation algorithm for stochastic optimization. IEEE Trans. Aerosp. Electron. Syst. 34(3), 817–823 (1998)

Superpixel Based Sea Ice Segmentation with High-Resolution Optical Images: Analysis and Evaluation Siyuan Chen1 , Yijun Yan2 , Jinchang Ren2(B) , Byongjun Hwang3 , Stephen Marshall1 , and Tariq Durrani1 1 Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow,

Scotland 2 National Subsea Centre, Robert Gordon University, Aberdeen, Scotland

[email protected] 3 Department Biological and Geographical Sciences, University of Huddersfield, Huddersfield,

England

Abstract. By grouping pixels with visual coherence, superpixel algorithms provide an alternative representation of regular pixel grid for precise and efficient image segmentation. In this paper, a multi-stage model is used for sea ice segmentation from the high-resolution optical imagery, including the pre-processing to enhance the image contrast and suppress the noise, superpixel generation and classification, and post-processing to refine the segmented results. Four superpixel algorithms are evaluated within the framework, where the high-resolution imagery of the Chukchi sea is used for validation. Quantitative evaluation in terms of the segmentation quality and floe size distribution, and visual comparison for several selected regions of interest are presented. Overall, the model with TS-SLIC yields the best results, with a segmentation accuracy of 98.19% on average and adhering to the ice edges well. Keywords: Sea ice segmentation · Superpixel · Satellite remote sensing

1 Introduction With the trend of global warming, the global climate changes in the last few decades significantly affect the Arctic Ocean area [1], leading to the decline of the Arctic sea ice extent. According to the Arctic amplification phenomenon, the trends of temperature variation are more obvious in the Arctic region than that of the northern hemisphere or the globe as a whole [2]. To understand the ongoing sea ice changes, it is valuable to investigate the dimension of the marginal ice zone (MIZ). MIZ is defined as the transitional region between the open sea and dense drift ice, and it is closely correlated to the representation of sea ice in the climate and weather models [3]. Nevertheless, MIZ J. Ren---This work was supported in part by the Natural and Environmental Research Council under the Grant NE/S002545/1, U.K and the University of Strathclyde PhD Scholarship. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 1004–1012, 2022. https://doi.org/10.1007/978-981-19-0390-8_126

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is vulnerable to open ocean processes such as waves and wind [4], which makes its size unpredictable and hard to be detected correctly. High-resolution optical (HRO) imagery, the satellite image with a relative high spatial resolution, offers the potential for precisely identifying the location of the boundary between sea ice and open water, i.e. so called ice edges [5], and accurately determining the floe size distribution (FSD). Recent years, as the HRO images have become more accessible, various approaches have been developed for sea ice segmentation, including the combination of the maximum cross-correlation techniques and multi-sensor HRO images [6], the combination of the watershed transformation and random forest classification [7], and the texture-sensitive superpixel based segmentation [8]. To tackle the high computation complexity of the HRO imagery, superpixel algorithms provide an alternative representation of regular pixel grid by grouping the similar pixels into over-segmented small regions. The generated superpixels can accordingly reduce the complexity for subsequent processing whilst adhering well to the boundaries of ice floes and open water. For instance, the simple linear iterative clustering (SLIC) superpixel is combined with the minimum spanning tree in [9], and the random forest is used for grouping the superpixels produced by the SLIC algorithm in [10]. Motivated by the aforementioned challenges and inspired by some existing works, this paper therefore, aims at evaluating the performance of different superpixel algorithms for sea ice segmentation from HRO imagery within a multi-stage model framework, which includes contrast enhancement and noise removal, superpixel generation and grouping, followed by a morphological opening process as post-processing. To evaluate the model, the selection of the superpixel number is investigated. The proposed modal is also compared against two state-of-the-art image segmentation methods quantitatively and qualitatively.

2 Superpixel Based Sea Ice Segmentation 2.1 The Framework The framework of the multi-stage model is presented in Fig. 1, which includes three main stages. Firstly, the image is pre-processed via the Top-Hat and Bottom-Hat transforms, the widely used detail enhancing approach in medical image processing [11], which allows the segmentation of the small ice floes from the ice-water mixed regions with low contrast. The bilateral filter [12] is then employed for noise removal whilst preserving the edge information. By adding the Top-Hat and subtracting the Bottom-Hat on the original image, the processed image shows enhanced foreground and suppressed background, which is beneficial for the following-on segmentation process. Next, superpixel segmentation is generated. In this study, we have employed four algorithms for comparison, including the SLIC [13], Texture Sensitive SLIC (TS-SLIC) [8], Water Pixel (WP) [14], and Bayesian Adaptive Superpixel Segmentation (BASS) [15]. Next, k-means is applied to group the superpixels into two classes, namely water and ice, based on the mean intensity and standard deviation of each individual superpixel. Then the clustered superpixels are merged to form the segmented result. Finally, the segmented result is refined by morphological opening as post-processed to smooth the ice shapes and also separate the connected floes. For the morphological operations, the

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results vary largely according to the size of the structuring element (SE). In this study, the disk-shaped SE was chosen, where the radius is set to R = 5 in the pre-processing stage for better noise removal whilst preserving enough texture information, and R = 3 was used in the post-processing stage for refining the segmented result whilst retaining the small floes.

Fig. 1. Workflow of the multi-stage segmentation model

2.2 Superpixel Algorithms and Implementation As one of the most popular superpixel algorithms, SLIC groups the pixels with the local k-means clustering that searches the pixels in a limited region centered at each cluster center based on the distance, D, that considering both the colour and spatial Euclidean distances, namely dc and ds , as defined below:   2 2 2   dc = lj − li + aj − ai + bj − bi (1)  ds =

 2 2  xj − xi + yj − yi

(2)

where the vector (l, a, b) denotes the values of three colour components in the CIELAB colour space, and (x, y) is the spatial coordinate of each pixel. Let N be the total number of pixels in the input image, and K √ is the superpixel number, the sampling interval, S, accordingly can be derived, S = N /K [13]. By further introducing the compactness coefficient, m, the final distance can be determined as:  2  ds 2 m D = (dc ) + (3) S The distance in TS-SLIC contains a texture descriptor dt , and a local directional zigzag pattern (LDZP [16]). As a result, the generated superpixel become more sensitive to the texture information, where the dt is given by:  Nr  2 LDZPi,n − LDZPj,n dt = (4) n=1

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where Nr is the number of neighbouring pixels in a selected local window (i.e. Nr = 8 for a 3 by 3 window). Similarly, the compactness refined distance, D , is derived as:   2 ds  2 D = (dc ) + m2 + dt (5) S The Water Pixel algorithm first converts the original image to a gradient image and generates the initial seeds. This is achieved by defining a regular hexagonal grid according to the predefined grid step, σ , then investigating the regions with minimum gradient values inside the individual cells. Next, the image is distance transformed based on the seed regions and added to the gradient image with multiplying a regularisation parameter, kwp , enforcing the compactness of the latter superpixels generation. Consequently, the superpixels are determined by performing the watershed transforms on the regularised gradient image. The BASS algorithm is a refinement of the Direchlet-Process Gaussian Mixture Model (DPGMM), which is a Bayesian Non-Parametric (BNP) mixture model. By introducing the Potts term to the Bayesian estimation of the spatial covariances, the resulting superpixels respect more spatial coherence than the DPGMM. Moreover, BASS can produce size-adaptive superpixels without predefining the superpixel number by iteratively evaluating the split and merged supeprixel proposals through the Hastings ratios, encouraging the superpixels to retain only the connected regions. For implementation, we adopted the compactness coefficient m = 10 for both SLIC and TS-SLIC, and the regularisation parameter kwp is set to 8 for WP. The gradient image used for WP is produced by performing a basic morphological gradient using the SE with the radius R = 3. Additionally, since BASS does not allow manual parameter setting, we directly take the published results for comparison. Regarding the superpixel number, we analyse its effect on the segmentation results in Sect. 3.3.

3 Experiments and Discussions 3.1 Source Data and Ground Truth (GT) The studied area is centered at around 69◦ 56 N /170◦ 00 W in the Chukchi Sea. The source data [17] is provided by the U.S. Geological Survey (USGS), which is available in the Global Fiducials Library, a long-term archive of the images from U.S. National Imagery Systems. The image is in grayscale where the acquired date was 31 May 2013. In this study, the GT data is generated via the methodology proposed in study [18], where the interested region was firstly cropped out and downsized, then the environment for visualizing images (ENVI) software was used to produce automated segmentation result, followed by manual correction by domain experts. In addition, since the superpixel algorithms e.g. SLIC require a RGB imagery as the input, the grayscale HRO image is duplicated into the three colour bands for meeting the need. 3.2 Evaluation Metrics To subjectively evaluate the segmentation results, several popularly used metrics are selected, including pixel accuracy (ACC), Matthews correlation coefficient (MCC), and

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conformity coefficient (CC) [19–21]. In addition, to evaluate the FSD, the segmented floes are quantified into nine categories according to the size ranged 

with 8 connectivity in 10i−1 , 10i pixels, where i = [1, 9]. The occurrence frequencies of the floes in nine categories can be seen as a vector. Then, the person correlation coefficient (PCC) is used for measuring the linear correlation between vectors A and B, the vectors of normalised floe occurrence frequencies from the GT and the segmented results, respectively. The definitions of these metrics are summarised as follows: ACC = (TP + TN )/(TP + TN + FP + FN ) MCC = √

TP · TN − FP · FN (TP + FP) · (TP + FN ) · (TN + FP) · (TN + FN )

(6) (7)

CC = 1 − (FP + FN )/TP

(8)

   Ai − A Bi − B PCC(A, B) =   2  2 , where i = [1, 9] Ai − A Bi − B

(9)

where TP, TN, FP and FN denote respectively the pixel level true positive, true negative, false positive, and false negative from the confusion matrix. A and B are the mean values of vectors A and B. 3.3 Evaluation of the Number of Superpixels To evaluate the effect of different numbers of superpixels to the three superpixel number adjustable algorithms, we change the K from 1 × 104 to 10 × 104 with an increment of 1 × 104 for SLIC and TS-SLIC algorithms, and adjust the grid step, σ , in the WP algorithm to change the superpixel number with an increment of around 1 × 104 . Worth noting that WP only support uint16 format as the output, resulting in the superpixel number being limited. The results are presented in Fig. 2.

Fig. 2. (a) MCC and (b) PCC scores versus the superpixel number

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As seen in Fig. 2(a) and (b), the TS-SLIC algorithm shows a better invariance against the superpixel number variation than the others in both segmentation quality and FSD, and the algorithm can be seen achieves higher MCC when superpixel number is low. The MCC scores of SLIC and WP by contrast firstly increase rapidly as the superpixel number increases and become stable after certain values. The PCC scores of WP similarly firstly shows a growing trend and maintains a high level. The highest PCC scores are achieved when the superpixel number is K = 7 × 104 for both SLIC and TS-SLIC, and σ = 20 (corresponding K = 47212) for WP. Hence, in the following experiments, we adopt K = 7 × 104 for SLIC and TS-SLIC, and σ = 20 for WP for simplicity. 3.4 Results and Analysis Figure 3 presents the visual comparison of three cropped regions of interest (ROIs). In the green bounding box, the floes are concentrated with poor boundary, where SLIC and TS-SLIC outperform the other two approaches in terms of relatively successfully separated connected floes whilst adhering well the boundary. In the low contrast waterice mixed region, as highlighted in the blue bounding box, TS-SLIC shows a superior performance in identifying these small floes. However, BASS in yellow has a better performance in adhering the boundaries of melt ponds inside the floes even than the GT. Figure 4 shows the detailed performance of the model when integrating different algorithms. Although all algorithms have yielded overall good performance, the TSSLIC can be seen to slightly outperform the competitors in terms of the accuracy, MCC and CC, which is therefore the best method in terms of segmentation quality. However, it is somewhat surprising that TS-SLIC has the lowest floe size distribution similarity to the ground truth. To investigate the reason, the relative distribution histogram is derived and shown in Fig. 5, where TS-SLIC tends to extract more small floes within the size ranging from 10 to 100 pixels than the other three algorithms. This can also be obtained directly from the blue bounding box in Fig. 3. This, however, may not be accounted as the drawback of this algorithm as extracting small floes are extremely valuable for FSD.

Fig. 3. ROI visual comparison

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Fig. 4. Quantitative results of four superpixel algorithms

Fig. 5. Histogram comparison of the FSDs.

To further evaluate the segmentation quality and model efficiency, the segmentation result is compared with the Open Source Sea-ice Processing (OSSP) method [7] and the algorithm [22] based on the hidden Markov random field (HMRF) model and its expectation-maximization (EM) with the iteration number set to 10. All the experiments were executed on a computer with 4.1 GHz CPU and 16 GB RAM. The results are given in Table 1, where the SLIC result slightly outperforms the others in terms of the segmentation quality, which validates the efficacy of the model. Our model, however, takes around 46 s longer than OSSP, in which the bilateral filtering takes around 42 s. The efficiency thus needs to be further improved in the future work. Additionally, the running time will be extended when using the BASS algorithm to generate superpixels due to the algorithm complexity, which is considered as a drawback in the HRO image processing. Table 1. Quantitative comparison to other segmentation methods using the SLIC result. Method

Platform

ACC

MCC

CC

PCC

Time (s)

OSSP

Python

98.26

90.94

98.04

98.45

41

HMRF-EM

MATLAB

93.47

76.26

92.07

95.22

359

Our model

MATLAB

98.30

91.27

98.07

99.14

87

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4 Conclusions This paper presents the sea ice segmentation from HRO imagery using a superpixel based multi-stage model, where the effect of the superpixel number and the performance of four superpixel algorithms are evaluated. Consequently, the algorithms showed an overall good performance, among which the TS-SLIC outperforms three others slightly in terms of the segmentation quality and extraction of small floes. The BASS performs the best in adhering the boundary of the melting ponds, however, it is computationally extensive has limited its application in HRO imagery processing. In the future work, we plan to apply the superpixel based method to the HRO images even with a low contrast and complex environmental issues (i.e. cloudy), which are the main issues that the optical images usually have. To accurately investigate ice regions in such images, the superpixel, as a compact intermediate image representation, can be combined with the supervised models such as the deep learning methods to further improve the performance whilst speeding up the computation.

References 1. Stroeve, J., et al.: Arctic sea ice decline: faster than forecast. Geophys. Res. Lett. 34(9), p. n/a-n/a (2007) 2. Serreze, M.C., Barry, R.G.: Processes and impacts of Aarctic amplification: a research synthesis. Glob. Planet. Change 77(1–2), 85–96 (2011) 3. Batrak, Y., Müller, M.: Atmospheric response to kilometer-scale changes in sea ice concentration within the marginal ice zone. Geophys. Res. Lett. 45(13), 6702–6709 (2018) 4. Meylan, M.H., et al.: Dispersion relations, power laws, and energy loss for waves in the marginal ice zone. J. Geophys. Res. Oceans 123(5), 3322–3335 (2018) 5. Scheuchl, B., et al.: Potential of RADARSAT-2 data for operational sea ice monitoring. Can. J. Remote Sens. 30(3), 448–461 (2004) 6. Hyun, C.-U., Kim, H.-C.: A feasibility study of sea ice motion and deformation measurements using multi-sensor high-resolution optical satellite images. Rem. Sens, 9(9), 930 (2017) 7. Wright, N.C., Polashenski, C.M.: Open-source algorithm for detecting sea ice surface features in high-resolution optical imagery. Cryosphere 12(4), 1307–1329 (2018) 8. Chai, Y., et al.: Texture-sensitive superpixeling and adaptive thresholding for effective segmentation of sea ice floes in high-resolution optical images. IEEE JSTARS 14, 577–586 (2021) 9. Wang, M., et al.: Optimal segmentation of high-resolution remote sensing image by combining superpixels with the minimum spanning tree. IEEE TGRS 56(1), 228–238 (2018) 10. Csillik, O.: Fast segmentation and classification of very high resolution remote sensing data using SLIC superpixels. Remote Sens. 9(3), 243 (2017) 11. Kushol, R., Kabir, M.H., Salekin, M.S., Rahman, A.B.M.A.: Contrast enhancement by tophat and bottom-hat transform with optimal structuring element: application to retinal vessel segmentation. In: Karray, F., Campilho, A., Cheriet, F., (eds.) Image Analysis and Recognition. ICIAR 2017. LNCS, vol. 10317 (2017). Springer, Cham. https://doi.org/10.1007/978-3-31959876-5_59 12. Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: Proceedings of ICCV, pp. 839–846 IEEE (1998) 13. Achanta, R., et al.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE TPAMI 34(11), 2274–2282 (2012)

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14. Machairas, V., et al.: Waterpixels. IEEE Trans. Image Process. 24(11), 3707–3716 (2015) 15. Uziel, R., Ronen, M., Freifeld, O.: Bayesian adaptive superpixel segmentation. In: Proceedings of ICCV, pp. 8470–8479 (2019) 16. Roy, S.K., et al.: Local directional ZigZag pattern: a rotation invariant descriptor for texture classification. Pattern Recogn. Lett. 108, 23–30 (2018) 17. Survey, U.S.G.: Chukchi_20130531_2. 2013. https://lta.cr.usgs.gov/gfl/index.php?img:3858: 124&PTAGNAME=ArcticSea 18. Hwang, B., et al.: A practical algorithm for the retrieval of floe size distribution of Arctic sea ice from high-resolution satellite synthetic aperture radar imagery. Elementa 5 (2017) 19. Chang, H.-H., et al.: Performance measure characterization for evaluating neuroimage segmentation algorithms. Neuroimage 47(1), 122–135 (2009) 20. Chicco, D., Jurman, G.: The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics 21(1) (2020) 21. Taha, A.A., Hanbury, A.: Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med. Imaging 15(1), 1–28 (2015) 22. Wang, Q.: HMRF-EM-image: Implementation of the Hidden Markov Random Field Model and its Expectation-Maximization Algorithm. arXiv pre-print server (2012)

A U-Net Based Multi-scale Feature Extraction for Liver Tumour Segmentation in CT Images Ming Gong1(B) , John Soraghan1 , Gaetano Di Caterina1 , and Derek Grose2 1 Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, UK

[email protected] 2 Beatson West of Scotland Cancer Centre, Glasgow, UK

Abstract. A new method for automatic liver tumour segmentation from computed tomography (CT) scans based on deep neural network is presented. Two cascaded deep convolutional neural networks are used to segment the CT image of the abdominal cavity. The first U-net is used for coarse segmentation to obtain the approximate position of the liver and tumour. Using this as a prediction the original image is cropped to reduce its size in order to increase the segmentation accuracy. The second modified U-net is employed for accurate segmentation of the actual liver tumours. Residual modules and dense connections are added to U-net to help the network train faster while producing more accurate results. In addition, multi-dimensional information fusion is introduced to make the network more comprehensive in restoring information. The Liver Tumour Segmentation (LiTs) dataset is used to evaluate the relative segmentation performance obtaining an average dice score of 0.665 based our method. Keywords: CT data · Liver tumour · U-Net · Multi-scale feature fusion

1 Introduction In recent years, the incidence and mortality of liver cancer have maintained a world-wide upward trend. Proper liver image segmentation can yield accurate liver volume, which has an important impact on liver surgery and patient’s postoperative recovery evaluation. However, due to highly variable shape, close proximity to other organs and diverse pathologies, the liver tissue in the abdominal CT image will have the same in-tensity value as the adjacent organs like stomach, heart, pancreas, kidney [1]; and furthermore its shape will be deformed. In clinical applications, liver seg-mentation is usually done by experienced radiologists. This remains an extremely time-consuming task that can reach 90 min per patient, with a degree of inter and intra segmentation errors. Recently threshold segmentation regional growth algorithm, active contour model and level set methods have also been applied to the segmentation of liver tumours [2]. However, the above methods all have disadvantages of taking significant time. Consequently liver segmentation is a challenging task in the field of medical image processing. In the past few years, Deep Learning has become the main research direction of computer vision problems. Due to the achievements of the Convolutional Neural Network in image classification, a growing number of researchers choose to use deep neural © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 1013–1020, 2022. https://doi.org/10.1007/978-981-19-0390-8_127

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networks to solve a series of image processing tasks, such as image recognition, object detection, and segmentation [3]. A convolutional neural network for end-to-end and pixel-to-pixel semantic segmentation [4] led to a new direction for image segmentation. In particular, U-net is a popular modified segmentation network based on fully convolutional neural network that is widely used in biomedical image segmentation, such as pulmonary nodule, sclerotic metastases, lymph node and colonic polyp [5]. Benefitting from the symmetrical encoding-decoding structure and the skip-connection of feature map, U-net can extract features at different levels and obtain high-resolution prediction. Due to the lighter network structure of U-net and the high accuracy performance of medical image segmentation, U-net has become the baseline for many medical image segmentation challenges. A symmetric encoder-decoder architecture called SegNet with pooling indices to segment liver tumour was presented in [6]. Two cascaded U-net separate the liver and the tumour independently was presented in [7], and the tumour segmentation is based on the results of liver segmentation. They subsequently used a 3D-CRF to post- process the segmentation results to improve the accuracy of the segmentation. A joint cascaded U-Net is used to segment liver and tumour simultaneously was proposed in [8]. They cascaded the predicted image of liver and the original scan to segment the tumour. The above methods all use a U-Net similar structure for tumor segmentation, but they are not sufficient for feature extraction in different dimensions. Moreover, the cascaded network will lead to huge network parameters, excessive training time and over-fitting problems. The method we propose will improve these problems. The rest of this article is organized as follows, the second part introduces the new tumour segmentation including cascade segmentation method and a modified U-Net. The third part shows the performance of this method on public data sets. The last part summarizes the full paper.

2 Methods 2.1 Data Preprocessing The data set used [9] contains scanned images of the patient’s entire abdominal cavity. We only select slices containing liver tissue for training and testing and save them locally for batch processing. We perform denoising, smoothing and contrast enhancement of the data set to increase the final prediction accuracy. In order to reduce the serious class imbalance problem caused by relatively small size tumours in the prediction problem the original image is divided into 4 slices with size 256 * 256. Slices containing the tumour according to the tumour label are selected. In addition, we also specially cut a scan that only contains the liver for training. Data augmentation is employed using rotation, flip and zoom for original CT scan to obtain more variant tumour shapes.

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2.2 Methodology Our proposed segmentation workflow is shown in Fig. 1. The workflow consists of three major steps. The first step includes data selection and data preprocessing for network. In a second step, using a cascade of a U-Net followed by a modified U-Net to jointly segment the liver and tumor, where the tumor segmentation is based on the seg-mentation result of the liver. In the final step, the predicted tumor lesions need to be restored to the original image size according to the boundary of the cropping.

Fig. 1. A flow diagram of the proposed U-Net Cascade algorithm

A classic U-Net mainly composes a down-sampling path, up-sampling path, and skip connection. The down-sampling path is the same as an ordinary deep convolutional network, which usually includes convolution operation, pooling operation, and dropout to obtain features at different levels. The purpose of up-sampling is to restore the extracted high-level features to the size of the input image. This process is mainly done by deconvolution or un-pooling. Using skip connection to introduce feature information on the corresponding scale into the up-sampling process, providing multi-scale and multi-level information for later image segmentation. At the end of the network, the sigmoid function is used to classify the feature map between 0–1 to obtain the prediction distribution prediction map. An original U-net is used for roughly segmenting liver and tumour to ensure the approximate location of them. The purpose of segmenting the liver is to exclude those tumour predictions out of liver organ. We use a U-Net that includes four down-sampling and up-sampling. The number of convolution kernels is increased from 64 to 512 in a double increment and the size of the convolution kernel is set to 3 * 3. Use same padding to ensure that the final prediction result matches the input image.

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Using the prediction results of the first U-net, we crop the input image to obtain a smaller input image to increase the ratio of foreground to background, which can improve the class imbalance problem. Through the four boundary points of the tumour, we can calculate the centre position of the tumour distribution and crop a 256 * 256 image with this centre point. The boundary of the tumour will be recorded and used to restore the prediction result to the original size at the end (512 * 512).

Fig. 2. The architecture of proposed neural network for the second segmentation task

As illustrated in Fig. 2, the fine segmentation process comprises a modified U-Net structure. The initial U-net designed 4 or 5 pooling layers for extracting deeper features. However excessive pooling process will inevitably lead to the loss of information, which is non negligible in small target segmentation. The size of earlier liver tumours is usually less than 5 cm, and it only accounts for approximately 5%−8% of the entire CT image. After several times of down-sampling, this important tumour information will be reduced or even disappear completely. We have experimentally determined that using three pooling layers allows the network to obtain sufficient features, while retaining enough detailed information. The input image first passes through a 7 × 7 convolutional layer to obtain some low-level semantic information with a larger receptive field. Then use a convolution block to generate the feature map of this layer. The pooling operation is used to halve the dimension of the feature map, and convolution block is used repeatedly to obtain a higher-level feature map. We used pooling operation three times and obtained four levels of semantic features, such as the blue, orange, gray, and green blocks in Fig. 2. These different levels of semantic information are fused together in their respective level. This part will be explained in the later part. Use the feature maps of these information fusions to perform feature restoration with skip connection. Figure 3 shows the structure of the residual module and the dense module, respectively. In each feature extraction block, we use dense module, respectively. In each feature extraction block, we use dense module [10] to increase the depth of the network and

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Fig. 3. (a) represents a basic residual connection, (b) represents a dense block

reduce the risk of gradient vanish. Each feature layer is cascaded with previous features to form a new input, and all feature layers are cascaded to form the final output. Dense connection emphasizes the repeated use of features, and the features of the previous layer will be used as part of the input in the subsequent feature extraction. This allows the network to learn more features with limited layers. Furthermore, we use the residual connection throughout the whole network [11]. This process is realized by adding the identity mapping of the input and the output. Because residual learning is easier than learning the original features directly. The mapping after introducing the residual is more sensitive to the change of the output, which is conducive to the spread of the network. As illustrated in Fig. 2 the features obtained at four different scales are reused in up-sampling to obtain sufficient information through skip connections, because deconvolution is used to expand the size of the feature map and cannot create information. How-ever, the transmission of information between corresponding scales is not sufficient, the shallow features will not be used to restore deep features. In order to make full use of the features in each scale, we concatenate these feature maps to ensure the richness of information in each scale. Due to the different sizes of feature maps between different dimensions, we need to adjust them to the same size through maximum pooling and up-sampling. In the last step, we need to restore the prediction map (256 × 256) to its original size (512 × 512). This part is completed by adding 0 around the prediction map, according to the boundary when the image is cropped.

3 Experiments The data set used for training and testing is the Liver Tumour Segmentation Challenge (LiTs), which comes from clinical sites around the world [9]. The data set contains CT scans of 130 patients. All scans have been provided in nii format with an axial size of 512 * 512. For each patient, it contains hundreds of cross-sectional scans of the abdominal cavity and marked images provided by professional radiologists. The new algorithms were implemented in Anaconda with Python, running on PC with 32G RAM, 3.8 GHz AMD Ryzen7 3800X 8-core CPU, and a NVIDIA RTX2080 GPU with 8GB memory. The 130 patients provided are not all liver cancer patients. After simplifying the data set, we only retained CT scan images containing tumors, for a total of 73 patients. Considering the difference in tumour size, we require the ratio of the number of large tumours and small tumours to be 1:1 when selecting the data to ensure the generalization of the network. So, we select 60 patients for training and testing. The training set and test set contained 40 patients and 20 patients, respectively.

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This work is implemented using Keras based on the TensorFlow backend. The first liver and tumour segmentation U-Net is trained for 60 epochs using Adam optimizer with learning rate 1e-4. For the second tumour segmentation network, we used data augmentation to improve the robustness of the network, including rotation (0.2 range), shift on horizontal (0.05) and vertical (0.05) direction, shear (0.05), zoom (0.05), and flip (horizontal). Similarly, we still use the Adam optimizer for the second network, but the learning rate is set to 1e-5 with 150 epochs. Table 1. Dice score for five different tumour segmentation method Author

Method

Dice per case

Dey, Hong [12]

Hybrid Cascaded Neural Network

0.681

He et al. [13]

Multi-scale Attention U-Net

0.596

Bellver et al. [14]

Fully Convolutional Network with bounding box sampling 0.590

Vorontsov et al. [8] Joint U-Net

0.661

Proposed method

0.665

Proposed U-Net with multi-scale feature fusion

Table 1 shows the dice scores of five different tumour segmentation methods. Our method ranks second, followed by the hybrid cascade neural network. We got a 0.665 average dice score based our method, which is better than most segmentation network. Compared with cascaded network, our network structure is more concise and lighter because of fewer network layers. And use the residual structure to reduce over-fitting. Compared with the attention mechanism, it is more convenient to use multi-layer information fusion because there is no need to select each feature map, including the addition, multiplication, and extension of the feature map. Our result is not as good as hybrid cascade U-Net. It may be because we did not use 3d CNN and thus lost a certain amount of information in the vertical space. After all, there is a connection between slices, and the features extracted from a series of slices are much richer than the features obtained from an image. However, the computational cost is also huge. Figure 4 shows some visualization results of tumour segmentation from three patients. Our method has good performance for large, medium, and small tumours. Ordinary U-Net has a good segmentation ability for large and medium-sized liver tumours, we mainly discuss the segmentation performance of small tumours. Generally speaking, the network is very insensitive to small targets due to the serious class imbalance problem, such as Fig. 4(c). Changing the loss function like dice loss or adding attention mechanism is the method to solve the class imbalance problem. However, the effects of these methods are not as obvious as directly reducing the size of the target area. After our test, the accuracy of fine segmentation using the cropped image of the target area is about 12% higher than segmenting original scan directly.

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Fig. 4. Visualization result of three different sizes of tumours. The first column is original CT scans, the second column is our prediction result, and the third column is the ground truth.

4 Conclusion In this paper, we presented a new method to segment liver tumours from CT scans, this method has good performance even for small tumours. We first use a U-net to lock the target area on the liver, and then used a second improved U-net to finely segment the tumour. Due to the reduced input image, the problem of class imbalance is im-proved. In addition, the use of residual structure and dense links makes information extraction more effective and sufficient. Multi-scale information fusion allows images to obtain more information when restoring features. This method can also be applied to CT image segmentation of other organs. In the future, we will extend this method to a 3d convolutional network to enhance temporal features. It is also possible to introduce dilatated convolution while reducing the pooling layer to obtain high-level information.

References 1. Sijens, P.E., Edens, M.A., Bakker, S.J., Stolk, R.P.: MRI-determined fat content of human liver, pancreas and kidney. World J. Gastroenterol. WJG 16(16), 1993 (2010) 2. Gotra, A., et al.: Liver segmentation: indications, techniques and future directions. Insights Imaging 8(4), 377–392 (2017). https://doi.org/10.1007/s13244-017-0558-1

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3. Shinde, P.P., Shah, S. (eds.): A review of machine learning and deep learning applications. In: 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA). IEEE (2018) 4. Long, J., Shelhamer, E., Darrell, T. (eds.): Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015) 5. Ben-Cohen, A., Diamant, I., Klang, E., Amitai, M., Greenspan, H.: Fully convolutional network for liver segmentation and lesions detection. Deep learning and data labeling for medical applications, pp. 77–85. Springer, Chem (2016).https://doi.org/10.1007/978-3-319-469 76-8_9 6. Almotairi, S., Kareem, G., Aouf, M., Almutairi, B., Salem, M.A.-M.: Liver tumor segmentation in CT scans using modified SegNet. Sensors 20(5), 1516 (2020) 7. Christ, P.F., et al.: Automatic liver and tumor segmentation of CT and MRI volumes using cascaded fully convolutional neural networks. arXiv preprint arXiv:170205970 (2017) 8. Vorontsov, E., Tang, A., Pal, C., Kadoury, S. (eds.): Liver lesion segmentation informed by joint liver segmentation. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). IEEE (2018) 9. Bilic, P., et al.: The liver tumor segmentation benchmark (lits). arXiv preprint arXiv:190104056 (2019) 10. Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q. (eds.): Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017) 11. He, K., Zhang, X., Ren, S., Sun, J. (eds.): Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016) 12. Dey, R., Hong, Y. (eds.): Hybrid cascaded neural network for liver lesion segmentation. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI). IEEE (2020) 13. He, F., Zhang, G., Yang, H., Jiang, Z. (eds.): Multi-scale attention module U-Net liver tumour segmentation method. In: Journal of Physics: Conference Series. IOP Publishing (2020) 14. Bellver, M., Maninis, K.-K., Pont-Tuset, J., Giró-i-Nieto, X., Torres, J., Van Gool, L.: Detection-aided liver lesion segmentation using deep learning. arXiv preprint arXiv:171111069 (2017)

Design of Test Platform for Fuel Cell Power Supply of Small UAV Changfu Wang, Yaohui Wang, Yanli Sun, Guang Hu, and Youjie Zhou(B) Institute of System Engineering, Academy of Military Science, Beijing 100300, China [email protected]

Abstract. Based on a small multi-rotor UAV, a mobile test platform of fuel cell power source for field tests is designed in this paper. The platform can carry a fuel cell power supply system with power less than 3 kW, the maximum current of 60 A and weight less than 4 kg for flight test in 1000 m altitude airspace, providing technical support for evaluating the working ability of fuel cell power supply of small UAV in actual airspace conditions. Keywords: UAV · Test platform · Fuel cell power supply

1 Introduction Nowadays, in the battlefield dominated by modern information technology, UAVs, as consumables, are favored by militaries due to their high mobility, low cost, and ability to reduce casualty rate effectively. As one of the essential components of UAVs, the power supply has become a primary concern. In addition to ensuring small size and high energy density, designing a battery that satisfies the flight tasks is also a research focus [1]. Generally, the flight phase of UAVs is divided into take-off, climb, cruise, landing, etc. [2, 3]. The power output characteristics of corresponding batteries vary greatly in different flight stages. In the military field, the mission design of UAVs is more complicated. Taking the U.S Navy as an example, Table 1 summarizes the mission areas for UAVs [4]. At present, the performance test of the UAV battery is usually conducted by software simulation or on the laboratory bench. However, the static basic electrical performance test method and semi-dynamic laboratory bench cannot approach the discharge process of the battery under real working conditions, so the reference value of these test methods is limited. Therefore, it is of great significance to design a specific UAV test platform for the power system. According to the power performance requirements of fuel cells, the overall technical requirements and performance indicators of the test platform for 3 kW fuel cell power supply system are proposed. The functions and specific designs of the subsystems, such as the power supply subsystem, hydrogen source guarantee subsystem, power control subsystem, data acquisition and safety monitoring subsystem, are given in detail. Moreover, the future work is prospected at last. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 1021–1026, 2022. https://doi.org/10.1007/978-981-19-0390-8_128

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Mission area

Combat mission

Above the horizon

Detection, classification, target orientation, battle loss assessment

Amphibious operations support

Naval fire support, sea mines, landmines

Command and control

Communication/Radio

Intelligence

Weather, electronic support measures, signal intelligence, nuclear weapons, biochemical weapons sensors, surveillance, reconnaissance

Mixed job

Attack, deception, psychological warfare, search, and rescue operations

Electronic warfare/confrontation

Electronic countermeasures, electronic support measures, jammers, air decoys

2 Design of Test Platform 2.1 Technical Requirements of Test Platform Design The mobile test platform is designed to develop and test the power supply system of kilowatt class fuel cell UAV. The main design tasks are given in the table below (Table 2). Table 2. Technical requirements of fuel cell power supply test platform for small UAV No

Design content

Requirements

1

Test power range

0–3 kW

2

Test voltage range

20–40 V

3

Maximum current

≥60 A

4

Maximum mount capacity

4 kg

5

Self sustaining endurance

≥30 min

6

Interface specification

XT60

7

Cruise speed

0–6 m/s

8

Communication distance

0–2 km

9

Wind resistant operation

Normal operation under 0–4 m/s wind speed

2.2 System and Composition The small UAV test platform is composed of a power supply subsystem, hydrogen source guarantee subsystem, power control subsystem, data acquisition and safety monitoring subsystem. The schematic system diagram is shown as follows (Fig. 1):

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Fig. 1. Schematic design of fuel cell UAV test platform

2.3 Function Design of Subsystem 2.3.1 UAV Basic Carrying Platform According to the technical requirements for the mobile test platform, this paper selects the small eight-rotor UAV as the basis for the platform transformation, and the relevant parameters are as follows: 1. Loading Capacity: ≥7.0 kg; 2. Power Supply: 6S 20000 mAh LiPo battery for UAV 3. Power consumption: 1500 W Hovering Power Consumption, 4000 W Maximum Power Consumption; 4. Wind resistance grade: Grade 6; 5. Cruise speed: ≥8 m/s 2.3.2 Energy Storage Battery Subsystem A Li-ion battery is used as a reliable power supply in the test platform to ensure the UAV flight test process. When the power supply of the test object is uncertain, it is used as the base power supply for the safe flight of the test platform. At the same time, it is also used as the output power supplement of the fuel cell test object to provide auxiliary power during the flight test. In addition, the Li-ion batteries need to provide energy for each monitoring module and cooling system to ensure the normal operation of the entire system. BMS management unit is set in the power supply subsystem, which can monitor the voltage, current and SOC of Li-ion battery in real-time and provide a protection mechanism at the same time. The mobile test platform is also equipped with a charger for the Li-ion battery, ensuring that the fuel system can charge the Li-ion battery through DC/DC. The reverse diode is installed at the energy input to prevent the reverse voltage during the charging process.

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2.3.3 Hydrogen Supply Subsystem A high-pressure hydrogen cylinder with a pressure of 35 MPa and a volume of 3.5 L is designed in the hydrogen supply subsystem. The cylinder is made of aluminum alloy and carbon fiber winding. Both sides of the bottle valve are provided with a high-pressure air inlet and a decompression air outlet. The pressure-reducing valve on the top of the cylinder can reduce the high-pressure hydrogen in the cylinder to 0.03–0.07 MPa, which can give the hydrogen pressure required for the normal operation of the fuel cell. The pressure sensor of the cylinder is also set on the pressure-reducing valve, which can sense the pressure in the cylinder. The measuring range is 0–35 MPa, and it can be output in the form of an analog signal (Fig. 2).

Fig. 2. Schematic diagram of the hydrogen supply system

2.3.4 Hybrid Control Subsystem The power system is controlled by the DC/DC voltage stabilizing circuit, MCU microcontrol unit, and other software. Through the real-time monitoring of UAV flight status, the central control system can give different control commands to the flight and power characteristics of UAV and fuel cell during the flight. Before starting, MCU could automatically diagnose whether the Li-ion battery system, fuel cell control system, DC/DC are ready, whether the pressure sensors and temperature sensors are abnormal. If everything is normal, MCU will instruct the fuel cell control board to open the hydrogen solenoid valve to supply hydrogen to the battery. In addition, the exhaust valve is also opened to discharge the water vapor in the battery stack, and the contactor is closed after the fuel cell is stable. Then the fuel cell starts up. The whole startup process lasts about 5–10 s. When the UAV is idle, descent, cruise and other lower power consumption conditions, the fuel cell charges the Li-ion batteries, and the charging current is controlled by MCU. On the other hand, under high power conditions such as take-off, climbing, and acceleration, the peak power is jointly provided by the fuel cell and Li-ion batteries. The related functions and parameters of MCU are summarized as follows:

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1. Control DC/DC: MCU can control the output of DC/DC according to the load of the small UAV; 2. H2 high and lower pressure sensor: MCU can detect whether the hydrogen pressure in UAV is too high or too low through hydrogen pressure sensor to avoid potential risks and protect the normal use of the fuel cell; 3. Contractor: MCU can realize the alarm function by controlling the contactor to prevent various unpredictable problems in the system; 4. Remote control: MCU supports a remote-control system. The UAV is able to achieve take-off, acceleration, and other functions through the remote control by drivers; 5. CAN communication protocol: MCU is compatible with CAN communication protocol, which can control fuel cell, DC/DC, Li-ion battery, etc.; 6. FC protection: MCU can protect the fuel cell at the main system level. 2.3.5 Data Acquisition and Transmission System The mobile test platform is equipped with data sensors that can collect and monitor the key parameters of fuel cell discharge in real-time. The data collected by voltage, current and hydrogen concentration sensors can be fed back to MCU through the CAN bus in real-time. The system can adjust the power output according to the actual flight situation. The PT100 temperature sensor is installed in the stack storage cabin, which can monitor the working temperature of the stack. The stack temperature can be controlled by changing the exhaust speed of the stack fan to prevent the working temperature from being too high. Table 3 summarizes the important parameters that can be collected by the system. Table 3. The parameters collected by each data collection device Gas part 1. Pressure in the cylinder 2. Inlet pressure of the stack 3. Temperature of cylinder

Electric part Fuel Cell

Li-ion Battery

1. Voltage 2.Current 3. Inlet pressure of the stack

1. Voltage 2. Current 3. SOC 4. Temperature

Environmental monitor 1. GPS 2. Flight altitude 3. Environmental temperature 4. Concentration of oxygen

The data is collected every 200 ms and sent to the ground side through the airborne data transmission radio displayed in real-time by the ground side computer and saved locally in the form of a file. At the same time, a storage unit (SD card) is added to the UAV. The flight data of the UAV and the discharge data of the fuel cell and Li-ion battery can be saved in the SD card and can be viewed after the UAV returns. 2.3.6 Safety Management System When the cylinder pressure is too high, alarm information will pop up to inform users of the risk of taking off in this situation. Moreover, the alarm message of “insufficient

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hydrogen remaining” will pop up if the residual gas volume is less than 10%. When the residual gas volume is less than 5%, the UAV will be forced to return to the take-off point for autonomous landing to facilitate recovery. At the same time, the real-time alarm will be given for abnormal temperature, abnormal internal pressure and low output voltage of the fuel cell.

3 Conclusion and Prospect In order to achieve the discharge characteristics of the UAV power supply in the actual working environment, a mobile test platform for UAV is designed based on a small multi-rotor UAV. The platform can be equipped with a fuel cell power supply system with a power of 3 kW or less, a maximum current of 60 A, and a weight of not more than 4 kg. It can conduct a continuous 15 min flight test at the height of 1000 m and a measurement and control distance of less than 2 km. The data acquisition and monitoring system of the platform can obtain the flight environment temperature, the internal pressure of hydrogen storage bottle and stack, and the discharge data of the battery under real working conditions. It provides a reliable basis for the design of the fuel cell UAV, the research on the working efficiency of UAV power supply, and the evaluation of the adaptability of the battery to UAV. In the next step, according to the different mission types of UAV, the working mission profile of UAV and the discharge power spectrum in the later mission phase will be deeply analyzed and studied through flight tests to provide reliable data support for the future design and development of electric UAV.

References 1. Wen, X., Ying, L.: Unmanned air vehicle’s applied foreground in future warfare from the American development. Modern Defence Technol. 31(5), 1–4, 27 (2003) 2. Raymer, D.P.: Aircraft Design. A Conceptual Approach. American Institute of Aeronautics and Astronautics Inc., Washington, D. C. (1992) 3. Li, Y.: Conceptual Design for Solar/Hydrogen Hybrid Powered Small-scale UAV. Beijing Institute of Technology, Beijing (2014) 4. Fahlstrom, P.G., Gleason, T.J.: Introduction to UAV Systems, 4th edn. Willey, Chichester (2021)

Design and Implementation of UAV Based on Fuel Cell Power Supply Youjie Zhou1 , Hua Li1 , Chun Wu2 , Mengyi Wang1 , and Changfu Wang1(B) 1 Institute of System Engineering, Academy of Military Science, Beijing 100300, China

[email protected] 2 Institute Beijing Institute of High Technology, Beijing 10094, China

Abstract. According to the requirement of a long-endurance power supply for small electric UAVs, this paper proposes a conceptual design of a fuel cell/Liion battery hybrid power system. Based on the in-depth analysis of power supply system configuration, the design scheme of the UAV power supply system is given, and the field patrol flight test at 200 m altitude is carried out. The results show that the fuel cell/ Li-ion battery power source can increase the range of UAV by more than four times, which has guiding significance for the future research of small UAV power systems. Keywords: Fuel cell · Hybrid power · Multi-rotor UAV

1 Introduction 1.1 Background In the modern information war, UAV combat equipment has gradually emerged in the military field and become one of the important military equipment components to break through the enemy frontier defense. Due to its outstanding performance in the Middle East, Gulf, Kosovo and other wars, UAV has fully proved its military value [1]. At present, most UAVs use traditional batteries as power supplies. Although this kind of battery has the feature of high-power density and can provide the necessary energy demand for various UAV missions in a short time, its limited energy density is always one of the main reasons hindering the development of UAV. The endurance time of less than half an hour cannot meet the requirements of a long-distance cruise mission. Therefore, it is particularly important to find a new UAVpower supply that has high energy density to replace the traditional battery and ensure the long-time fight of the UAV. Fuel cells, as the high energy density power supply, naturally become the first choice to solve endurance problems [2, 3]. 1.2 UAV Mission Profile Analysis The flight mission profile of UAV can be divided into the following stages: take-off, climb, cruise, descent, and landing. Among them, the cruise phase accounts for the © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 1027–1032, 2022. https://doi.org/10.1007/978-981-19-0390-8_129

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largest proportion of the total flight section time, and the working condition is relatively stable, which is very suitable for the fuel cell to provide stable electrical output. In the take-off and climb stages, the UAV needs a large power supply, and the battery often needs to be discharged at a large rate to support the relevant demand. Although fuel cell has high energy density, its instantaneous discharge rate is insufficient. In order to make up for the lack of power density, it is often necessary to equip an auxiliary power to form a hybrid power system. In order to solve the problem of short endurance of the UAV powered by the traditional battery, a fuel cell based on a small multi-rotor UAV is designed. According to the flight demand, the fuel cell is equipped with a Li-ion battery, forming a hybrid power output mode with the fuel cell as the main power and the Li-ion battery as the auxiliary power.

2 Design of UAV Power Supply System 2.1 Structure of Fuel Cell Hybrid Power System The topology of fuel cell hybrid power can be divided into two types: direct connection with load and indirect connection with load after DC/DC voltage transformation [4– 6]. In the form of the indirect connection, parallel convert and parallel connection are compared and studied in this paper [3]. The topological structure is as follows:

Fig. 1. The topology of fuel cell hybrid power

2.1.1 Direct Connection Model The direct connection topology is relatively simple (as shown in Fig. 1a), which can relieve the working pressure of the Li-ion battery to a certain extent. However, as for the load that has high requirements for output power changes, such as small UAVs, the discharge characteristics of the fuel cell cannot meet the power demand of the load.

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2.1.2 Parallel Connection Model The components of the parallel model (see Fig. 1b) include a fuel cell, Li-ion battery, DC/DC converter, controller, and UAV load. The fuel cell is connected to the bus through a DC/DC converter in order to output stable energy for the UAV, and the Li-ion battery is directly connected to the bus. In the case of low energy requirements for the load, the fuel cell can charge the Li-ion battery but also supply power to the load. When the UAV requires high instantaneous power, the Li-ion battery can supply remain power the part that the fuel cell cannot provide. 2.1.3 Parallel Convert Model The parallel convert model (as shown in Fig. 1c) is composed of a fuel cell, Li-ion battery, Li-ion battery charger, and parallel convert device. Similar to the working of the parallel model, at low power consumption, the fuel cell can charge the Li-ion battery through the charger. At high power consumption, the fuel cell and the Li-ion battery work together, and the power is delivered to the load through the parallel converter. Among them, the parallel converter can implement energy management on the hybrid power system to meet the flight mission requirements of the UAV. Through analysis, the parallel topology design is simple, and the complementary effect of fuel cell Li-ion battery discharge power can also meet the flight requirements for the small UAV. Therefore, this paper adopts the parallel mode as the topology of the fuel cell hybrid power system. 2.2 Design of Power Supply for UAV The power supply of the UAV designed in this paper adopts the electricity hybrid technology (fuel cell/Li-ion battery) for power output. The rated power of the UAV motor is 1.2 kW, the rated voltage is 24 V, and the maximum current is 60 A. The fuel cell and Li-ion battery are connected in parallel [3]. The schematic diagram of the system is as follows (Fig. 2):

Fig. 2. The schematic diagram of the electrical system

The main control system is installed inside the UAV, which can constantly monitor the operational status of the fuel cell and the UAV. The main control system can respond according to the flight characteristics and power characteristics under different working conditions. In the take-off and climb phases, the fuel cell and the Li-ion battery work together, and extra power is provided by the Li-ion battery. In the cruise (stable flight), landing, and other low-power flight phases, the fuel cell becomes the only source of

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electrical output. The Li-ion battery is converted into the form of energy storage which is charged by the fuel cell. When the hydrogen in the storage cylinder is exhausted, the Li-ion battery can be used as an emergency power source so that the drone can return to the take-off point for recovery. 2.2.1 Fuel Cell Selection As the main power source, the rated power of the fuel cell stack is 800 W, and it is matched with a 3.5 L & 35 MPa hydrogen cylinder to store the high-pressure hydrogen. Moreover, a pressure sensor is equipped at the valve, which can continuously monitor the pressure in the cylinder. A pressure regulating valve installed downstream (outlet) is used to reduce the hydrogen pressure to the range of 30 kPa–70 kPa, and regulated hydrogen is delivered to the fuel cell for reaction. Due to fluctuations in fuel cell output voltage, a DC/DC converter is equipped at the output end of the fuel cell to stabilize the voltage and provide energy for the controller through the bus. 2.2.2 Li-ion Battery Selection The Li-ion battery is directly connected with the bus, which can supply power for the system and be used as the receiving end of the fuel cell power output to achieve energy storage function. In the aspect of type selection, this paper adopts 6 Ah/24 V special Li-ion battery for UAV, the discharge rate is 10C, and the maximum current is not less than 60 A. 2.3 Monitoring Design of UAV Power Supply System 2.3.1 Data Monitoring System The UAV designed in this paper also includes a data monitoring system. The system can monitor the data generated by the UAV in the process of executing tasks. The data obtained are analyzed and calculated by the software. Then the control system independently issues instructions to the UAV and makes some corresponding task adjustments according to the analysis results. 2.3.2 Data Abnormality Alarm Function In the process of filling the hydrogen cylinder, if the hydrogen overpressure occurs, the system will pop up an alarm message to inform the user that there is a risk of taking off when the pressure of the cylinder is too high. In the process of UAV flight, if the pressure of the cylinder is less than 5%, the central control system will give the UAV instructions to force it to return to the starting point and land autonomously. At the same time, the system will give a real-time alarm to prompt the user to check the fuel cell stack in case of abnormal working temperature, abnormal pressure and low output voltage.

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3 Flight Test A small mobile UAV test platform was used to conduct a 200 m altitude cruise flight test on the fuel cell/Li-ion hybrid power source. The test results verified the functional ability of the power supply system. – Under the Li-ion battery power supply mode, the 200 m altitude cruise flight time is only 41 min. – Under the power supply mode of the fuel cell/Li-ion battery hybrid power system, the UAV can cruise for 170 min. Figure 3 records the changes in the output current, voltage and power of the hybrid power during flight. The results show that it is feasible to adopt the hybrid power as the power source of UAV, which can not only adapt to the selected small rotor UAV but also provide support for long cruise flight. Among them, the fuel cell can provide stable energy output for the UAV during the cruise. The Li-ion battery provides part of power compensation for the UAV in the process of takeoff and wind resistance.

Fig. 3. (a) Current characteristic curve (b) Voltage characteristic curve (c) Power characteristic curve

4 Conclusion Aiming at the requirement of long endurance power supply for small low altitude electric UAVs, this paper puts forward the conceptual design of a fuel cell/ Li-ion battery hybrid power system. Based on the in-depth analysis of power supply system configuration, the design scheme of the UAV power supply system is given, and the fight test is carried out. The final design of the UAV power system meets the overall design requirements in the weight and output power. The specific conclusions are as follows:

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1. The design of fuel cell/Li-ion battery hybrid power can give full play to the advantages of high-power discharge of Li-ion battery and high energy density of fuel cell, and improve the flight range of UAV; 2. As the main power output in the cruise stage, fuel cell power supply can greatly increase the range by more than four times; 3. Under the joint action of the main control system and the data monitoring module, the rationalization of the distribution and utilization of electric energy can be promoted to the maximum, and the purpose of range extension can be realized.

References 1. Jinjing, J., Minle, W., Bin, J.: Research on operational application of UAV. Aerodyn. Missile J. 01, 41–44 (2019) 2. Li, Y.: Conceptual Design for Solar/Hydrogen Hybrid Powered Small-scale UAV. Beijing Institute of Technology, Beijing (2014) 3. Zhang, Z.: Design of Multi-rotor Unmanned Aerial Vehicle Hybrid Power System for Fuel Cell. Zhe Jiang University, Hangzhou (2018) 4. Liu, T.: Design of Air-cooled Fuel Cell/Lithium Battery Hybrid Power System. Southwest Jiaotong University, Chengdu (2017) 5. Ming-Gao, O., Li, J., Yang, F., Lu, L.: Automotive New Powertrain: Systems Models and Controls. Tsinghua University, Beijing (2008) 6. Guo, D.: Modeling and Simulation of the Fuel Cell Hybrid Powertrain. Wuhan University of Technology, Wuhan (2010)

Design and Trial Manufacture of Methanol Reforming Hydrogen Generator Changbo Lu1 , Lei Xu1 , Yan Qin2 , Guang Hu1 , Weigui Zhou1(B) , and Youjie Zhou1 1 Institute of System Engineering, Academy of Military Science, Beijing 100300, China

[email protected] 2 Beijing Institute of High Technology, Beijing 10094, China

Abstract. Based on the self-heating reforming hydrogen production technology, a hydrogen production prototype is trial-produced in this paper through the functional and integrated design of the regenerator, combustion chamber, catalytic bed, purifier, control execution module. The test results show that the maximum hydrogen production capacity of the hydrogen generator is close to 80 slpm, and the hydrogen purity is over 99.99%, of which the CO impurity content is 0.07 ppm, which meets the relevant national standards for hydrogen used in fuel cells. The research results have important reference value significance for promoting the development of methanol reforming hydrogen production technology. Keywords: Methanol reforming · Hydrogen generator · Self-heating reforming

1 Introduction With the depletion of fossil fuels, hydrogen fuel cells, as a new type of power generation device, have gradually become a popular research direction depending on their high efficiency and environmental friendliness. As a key component for the fuel cells, the hydrogen source plays a decisive role in developing fuel cells [1, 2]. There are two main types of hydrogen sources for fuel cells: the storage of pure hydrogen and hydrogen production by reforming technology. Although pure hydrogen has advantages in storage and refilling, it has not been widely accepted because of its unstable chemical properties. Compared with pure hydrogen, the technology of reforming hydrogen by liquid fuel has more advantages in security and economy. It is an ideal hydrogen supply method besides pure hydrogen storage methods. At present, methanol not only has a wide range of sources and relatively low price but also has the advantages of relatively low reaction temperature and high concentration of hydrogen production. Hence, it is an ideal raw material for hydrogen production by reforming [3, 4]. Based on the self-heating methanol reforming hydrogen production technology [3, 5], the key modules such as combustor and purifier are designed in this paper. By integrating these modules, a methanol reforming hydrogen generator is trial-produced. Relevant indicators of the prototype, such as hydrogen production rate and hydrogen purity, are tested in the paper. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 1033–1039, 2022. https://doi.org/10.1007/978-981-19-0390-8_130

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2 Scheme Design and Trial Production 2.1 Scheme Design The methanol reforming hydrogen generator is mainly composed of a regenerator, a combustion chamber, a catalytic bed, a purifier, and a control execution module. The basic structure diagram is shown in Fig. 1:

Fig. 1. The structure layout diagram of the methanol reforming hydrogen generator

Each module is described as follows: (1) Regenerator The regenerator is used to recover waste heat and improve fuel efficiency. (2) Combustion chamber The combustion chamber consists of a shell and combustion catalyst, which is used to generate heat for the system to maintain the operation of the system through the catalytic combustion of undialyzed gas (such as a small amount of hydrogen, CO, CO2 etc.) and air. Meanwhile, the harmful gases such as CO in the exhaust gas are fully burned and converted into CO2 and water vapor. (3) Evaporation chamber The evaporation chamber is used to vaporize the fuel and raise the temperature for the subsequent reforming reaction. (4) Catalytic bed The catalytic bed is used to generate H2 , CO and CO2 through the catalytic reaction of fuel (methanol aqueous solution). (5) Purifier The purifier is used to separate and purify the hydrogen from the mixed gas after the reforming reaction. Then the purified hydrogen is supplied to the hydrogenconsuming device such as fuel cells. The remaining exhaust gas returns to the combustion chamber for generating heat again. (6) Control execution module The control execution module includes a control board auxiliary machine (liquid pumps, fans, solenoid valves, etc.), pressure and temperature sensors, etc. The module controls the parameters within the set range and responds to changes in the load.

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2.2 Trial production

Fig. 2. The physical drawing of the core components

Figure 2 shows the core components of the prototype. The outer part is thermal insulation material, and the inner part consists of a regenerator, a burner, a catalytic bed, and a purifier. The auxiliary part includes a control board, liquid pump, valve block, fan, sensor, etc. The main fuel for the system is the methanol aqueous solution. As for pure methanol, it is only used in the initial heating stage.

3 Experiment and Results Analysis 3.1 Start-Up Test The completion sign of the start-up of the hydrogen generator is usually judged according to the stable conditions of the combustion chamber, purifier and catalytic bed temperature. The temperature change of each area during the start-up is shown in Fig. 3.

Fig. 3. Heating curves

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As shown in Fig. 3, the x-axis represents time, and the y-axis representing temperature includes three parts: combustion chamber temperature (Tb), purifier temperature (Tp), and catalytic bed temperature (Tc). In addition, the system pressure (Psys) is also shown in Fig. 3 in order to provide extra information. Position 1 is the start point. At this stage, high-pressure Ar around 400 ~ 450 kPa will be flushed into the whole system to eliminate impurity atmosphere; Position 2 is the ignition point, the combustion chamber temperature (Tb) will rise to 500 °C from the ambient temperature after the methanol solution is pumped into the system is ignited. From position 2 to position 3, Tb is maintained in the range of 550 ~ 650 °C, while Tp and Tc rise from ambient temperature to 320 °C and 270 °C, respectively. Due to system sealing problems, Psys drops from 400 kPa to about 100 kPa at this stage. Position 3 is the stage for the completion of the initial heating. It takes around 2.5 h from the start point (position 1) to position 3. After that, the methanol aqueous solution starts to be pumped into the system, Psys gradually rises to 800 kPa, and hydrogen is produced during this process. Although the initial hydrogen yield is low, the hydrogen production will gradually increase as the fuel pumping volume and system temperature (Tp, Tc, etc.) increase. After the system reaches the set temperature (such as Tp = 400 ~ 420 °C), it will enter the steady-state. 3.2 Hydrogen Production Rate Test The relationship between fuel consumption and hydrogen production rate is shown in Table 1, which shows the proportional relationship between hydrogen production rate and pump times. When the pump time is 528 times/min, the hydrogen production rate reaches the maximum value (76.9 slpm) which the corresponding fuel efficiency is 76.9% (1.35 L fuel is consumed to produce 1 m3 hydrogen); When the hydrogen production rate is 40.3 slpm, the hydrogen production efficiency of methanol reaches the maximum of 80.5% (1.29 L fuel is consumed to produce 1 m3 hydrogen); When the hydrogen outlet rate is relatively low (4.9 slpm), the methanol consumption per 1 m3 of hydrogen is about 2.08 L corresponding 49.8% efficiency. The low efficiency is due to the increase in the proportion of energy consumed to maintain the system temperature. Table 1. The relationship between hydrogen production and fuel consumption No

Pump times (time/min)

Hydrogen production (slpm)

Fuel efficiency (%)

1

50.9

4.9

49.8

2

152.1

14.5

49.3

3

169.5

17.5

53.4

4

210.1

23.3

57.4

5

202.7

28.5

72.7

6

259.1

40.3

80.5 (continued)

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Table 1. (continued) No

Pump times (time/min)

Hydrogen production (slpm)

Fuel efficiency (%)

7

334.4

47.5

73.5

8

377.2

56.3

77.2

9

422.1

60.6

74.3

10

477.0

67.7

78.4

11

528.0

76.9

76.9

3.3 Hydrogen Purity Test In the process of prototype design and manufacturing, the purity of the produced hydrogen is a key point that needs to be concerned. Because CO has a poisoning and corrosive effect on hydrogen fuel cell catalysts, the CO impurity content in the hydrogen is usually required not to exceed 10 ppm [6, 7]. The mixture gas (hydrogen, methanol aqueous solution, CO2 , CO, etc.) generated by methanol reforming catalytic reaction is separated and purified by palladium tube in the purifier to obtain the final hydrogen product. After characterization, the purity of hydrogen produced by the prototype is 99.99%. The contents of each impurity component are listed in Table 2. Among them, the CO concentration of the produced hydrogen is 0.07 ppm, which has met the design requirements. Table 2. The results of the hydrogen purity test No

Test condition

Impurity components

Content (mol/mol)

1

Temperature 23.5 °C

O2

6.02 × 10–6

N2

98.2 × 10–6

CO

0.07 × 10–6

4

CO2

1.98 × 10–6

5

Methanol

1.88 × 10–6

2 3

Humidity 28.0% RH

3.4 Continuous Operation Test Figure 4 shows the continuous operation test curve of the prototype. In this test, the equipment has been running continuously for about 124 h. As shown in Fig. 4, phase 1 is the heating stage which lasts for about 2 h. The system pressure (Psys) and hydrogen discharge pressure (PH2 ) rise from atmospheric pressure to about 850 kPa and 60 kPa, respectively, while Tb, Tp and Tc rise to 620 °C, 320 °C and 280 °C respectively from room temperature. Phase 2 is the stable operation stage. In this stage, Psys and PH2 are stabilized at 830–870 kPa, 60–65 kPa respectively, and Tb, Tp, Tc are stable at

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580–650 °C, 370–410 °C and 310–330 °C, which lasts for 122 h. After that, in stage 3, the system is shut down, and the internal pressure is released. Since the fuel supply is stopped, Tb decreased greatly, Psys and PH2 gradually fall to atmospheric pressure. The shutdown process will last 15 min, and then the system will be on standby.

Fig. 4. Continuous running curves

4 Conclusions Based on the self-heating reforming hydrogen production technology, this paper designs and develops a methanol reforming hydrogen production prototype. The functional components of the regenerator, combustor, catalytic bed, purifier and controller are given. In addition, the start-up time, hydrogen production rate and reliability tests are carried out. The results show that the start-up time of the prototype is 2.5 h, the hydrogen production is close to 80 slpm, and the hydrogen production rate is 1.35 L/m3 . In addition to the stable output pressure of the system, the purity of the produced hydrogen also meets the requirements of relevant national standards. The trial prototype meets the design requirements, which can be used as a supporting hydrogen source device for a movable fuel cell power station. The details are as follows: (1) It takes about 2.5 h from the start to the completion of the initial heating. The temperature of the combustion chamber is maintained at 550 ~ 650 °C. As the temperature of the purifier and catalytic bed increases, the system gradually enters a stable hydrogen production state; (2) The maximum hydrogen production rate of the system can reach 76.9 slpm, and the combustion consumption of 1 m3 hydrogen is about 1.35 L; (3) The concentration of CO in the produced hydrogen is 0.07 ppm, which meets the index requirements in GB/T 37244-2018 “Proton Exchange Membrane Fuel Cell Vehicle Fuel Hydrogen”; (4) In the continuous test, the system pressure and hydrogen output pressure are stable at 830 ~ 870 kPa and 60 ~ 65 kPa, respectively. The temperature of the combustion chamber, purifier and catalytic bed are stable in the range of 580 ~ 650 °C, 370 ~ 410 °C and 310 ~ 330 °C, respectively.

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References 1. Hongliu, X.: Application status and development trend of hydrogen fuel cell technology. Sci. Technol. Innov. 02, 160–161 (2019) 2. Suzhen, R., Hongmin, D., Zhiyu, S.: Application of methanol reforming to hydrogen production in fuel cell. Sol. Energy 02, 30–33 (2008) 3. Liwei, P.: Studies on a plate-fin reformer for methanol steam reforming in fuel cell systems. Chinese Acad. Sci. 108, 51–58 (2005) 4. Su, X.: Design and Research of Miniaturized Methanol Fuel System for Hydrogen Production. China University of Petroleum, Beijing (2018) 5. Yi, Z.: Development of a Self-heated Methanol Steam Reforming Reactor for PEMFC. ZheJiang University, ZheJiang (2019) 6. Choudhary, T.V., Goodman, D.W.: CO-free fuel processing for fuel cell applications. Catal. Today 77(1), 65–78 (2002) 7. Wu, X.: Study of Sorption Enhanced Reforming Process of Methanol for Hydrogen Production. ZheJiang University, ZheJiang (2015)

Analysis of the Development Status of Micro Gas Turbine Generation Technology at Home and Abroad Lianling Ren1 , Liang Wen1 , Huakui Han2 , Ruiguo Zhu3(B) , Yongcheng Huang1 , and Youjie Zhou1 1 Institute of System Engineering, Academy of Military Science, Beijing 100300, China 2 College of Energy and Power Engineering, Shandong University, Jinan, China 3 Quartermaster Energy Technology Service Center, Beijing, China

[email protected]

Abstract. There is no complicated valve and timing mechanism in the micro gas turbine with a total power density of about 0.8–1 kW/kg and more than 31% power generation efficiency. In actual use, the comprehensive power generation efficiency is able to increase to more than 80% if the waste gas can be reused through cogeneration technology, which can increase the. In addition, due to the characteristics of compact mechanical structure and durable work, the micro gas turbine has strong application prospects in civil and military fields. This paper mainly states the development status of micro gas turbine generation technology at home and abroad. Keywords: Micro gas turbine · Power generation · Brayton cycle · Development status

1 Introduction The micro gas turbine (MGT) is a small heat engine that uses gaseous or liquid substances as fuel. It has gradually attracted public attention with the advantages of fuel diversification, low consumption rate, low noise, low emissions, and low maintenance rate [1]. The MGT has modular components with a capacity of more than 1000 kW can be provided to meet larger load requirements through flexible integration and parallel stacking of multiple units [2]; at the same time, when it is used in cogeneration, the heat energy of the micro gas turbine exhaust can be recovered to generate heated water or low-pressure steam which can be used effectively with absorption cooling equipment [3, 4]. At present, there are more than 360 sites in the United States using micro gas turbines for cogeneration with a total capacity of 92 MW, accounting for more than 8% of the total number of cogeneration sites in the United States. It is the best way for multi-purpose small distributed power generation and micro-cogeneration [5].

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 1040–1047, 2022. https://doi.org/10.1007/978-981-19-0390-8_131

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2 Advanced Micro Turbine Series Plan in the U.S. The United States is at the dominant level in the development and application of micro gas turbines. As early as 2000, the U.S. Department of Energy Decentralized Energy Office commissioned the United Technology Research Center to carry out the project called “Micro Gas Turbine System Plan 2000”, which was implemented from the perspectives of performance and application to promote the improvement the strategy of effective distributed. The specific targets include that the system cost shall be $500/kW, electrical efficiency shall be 40%, overhaul interval shall be 11,000 h, Ox emission shall be 7 PPM@15%O2 , and it is accessible for gas-liquid fuels [6]. At that time, about 50% of the electricity in the United States was provided by coal-fired power plants, the average power efficiency of the grid was about 32%, and it was affected by line losses, especially during peak power periods when the losses were as high as 15% [3]. At the same time, the average value of nitrogen oxides based on grid power output in the United States in 2000 was 3.0 lb/MWh. According to the electrical efficiency of 32%, it is about 70 PPM NOx@15%O2 , which is five times higher than the level of gas turbine generator sets with natural gas [7]. Moreover, the high cost of building a new transmission and distribution network and relocation challenges in densely populated urban areas have forced the US Department of Energy to formulate a complete research and development plan for micro gas turbines. The United Technology Research Center initially adopted an integrated method to realize the R&D plan. The integrated micro-turbine enhanced operational performance, and its electrical efficiency reaches about 33%. The organic Rankine cycle subsystem was able to convert exhaust gas into additional electricity. Among them, the prototype of the micro-turbine is the 400 kW micro turbine ENT400 powered by the ST5 engine of Pratt & Whitney Canada. However, the development of ENT400 was stopped in 2002 due to technical and financial issues. Subsequently, the United Technology Research Center shifted its focus to Capstone’s C200 micro gas turbine (Fig. 1) [8, 9].

Fig. 1. Appearance (left) and internal structure (right) of C200 micro gas turbine from Capstone, USA

In 2016, the Office of Advanced Manufacturing of the U.S. Department of Energy summarized the technical performance characteristics of micro gas turbine cogeneration systems with a scale between 65 kW and 1,000 kW (Table 1). It is believed that an onboard gas compressor is applied in most micro gas turbines to provide all required gas pressure, the inlet fuel pressure of the micro gas turbine usually needs to be close to 75 psig, the overall cogeneration efficiency of the micro gas turbine is 64%–72%, and the power-to-heat ratio is 0.53–0.77 [10]. The basic components of all commercial

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Table 1. The technical performance of cogeneration system for the US micro gas turbine (according to the Office of Advanced Manufacturing of the US Department of Energy, 2016) Described content

Representative products 1

2

3

4

5

Rated power (kW)

65

200

250

333

1000

Gas compressor load (kW)

4

10

8

10

50

Net electric power (kW)

61

190

242

323

950

Fuel input (MMBtu/hr, HHV)

0.84

2.29

3.16

3.85

11.43

Effective heat energy (MMBtu/hr)

0.39

0.87

1.20

1.60

4.18

Work-to-heat ratio

0.53

0.75

0.69

0.69

0.77

Electric efficiency (%, HHV)

24.7

28.4

26.1

28.7

28.3

Thermal efficiency (%, HHV)

46.9

38.0

38.0

41.6

36.6

Total efficiency (%, HHV)

71.6

66.3

64.0

70.2

64.9

Net electric power (kW)

61

190

242

323

950

Complete component ($)

2120

2120

1830

1750

1710

Construction and installation ($)

110

1030

870

800

790

Installation cost ($)

3220

3150

2700

2560

2500

Operation and maintenance (excluding fuel, /kWh)

1.3

1.6

1.2

0.8

1.2

Table 2. Waste emission characteristics of micro gas turbine systems (according to the Office of Advanced Manufacturing of the U.S. Department of Energy, 2016) Content Net electric power (kW)

System 1

2

3

4

5

61

190

242

323

950

Emission (PPM when oxygen content is 15%) NOx

9

9

5

5

9

CO

40

40

5

5

40

VOC

7

7

5

5

7

NOx

0.46

0.40

0.24

0.22

0.40

CO

1.24

1.08

0.15

0.13

1.08

VOC

0.12

0.11

0.08

0.08

0.11

Emission (lbs/MWh)

Emission of carbon dioxide (lbs/MWh) Only generates electricity

1613

1406

1529

1392

1407

Cogeneration

667

739

804

668

764

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micro gas turbines include micro gas turbines and power electronic equipment with interconnection and parallel functions. Most micro gas turbine cogeneration systems achieve heat recovery through an integrated heat exchanger with basic components. The maintenance cost varies with scale, fuel type and technology (air bearing or lubricating bearing) (including fixed and random maintenance based on 6000 h/year), and most of them are in the range of 0.8–1.6$/kWh. In addition, the test results of the exhaust gas quantity showed in Table 2 indicate that the maximum NOx , CO, and VOC emissions are 9 PPM, 40 PPM, and 7 PPM, respectively. Moreover, the emissions range of CO2 used for power output and the entire cogeneration system is from 667 lbs/MWh to 804 lbs/MWh. In contrast, the emissions of a typical natural gas combined cycle power plant range from 800 lbs/MWh to 900 lbs/MWh, while the carbon dioxide emissions of coal-fired power plants are close to 2000 lbs/MWh. Based on the performance of the commercial products mentioned above, the United Technology Research Center first recognized that the combination of high-performance micro-turbines and organic Rankine cycle systems are useful methods for micro-gas turbine systems to achieve high performance and low emissions. The electrical efficiency is more than 32%, and the NOx and CO emissions under are less than 10 (15% oxygen condition). In addition, the use of ceramic materials and environmentally friendly coatings can extend the durability of the micro-ceramic turbine and reduce the cooling airflow without affecting the turbine performance [7, 11, 12]; On the other hand, the stable micro turbine combustor taking natural gas as fuel can achieve ultra-low emissions in a wide adjustment range [13–15], and a high-performance and economical organic Rankine cycle system can be realized through heating, ventilation and air conditioning components [16–19]. In order to further accelerate the improvement and innovation of the micro gas turbines, the Ministry of Energy jointly released the report in April 2020 with other government agencies, industry and academia called “Gas Turbine Advanced Technology Plan 2030”, which is more inclined to explore new theories, new technologies and new manufacturing processes. The plan advocated complete innovation rather than inheriting the achievements from “Micro Gas Turbine System Plan 2000”. It focused on the power generation capacity of combined cycle gas turbines (large stationary turbines), which supply power to the grid; the simple cycle gas turbine generation capabilities include large stationary turbines (power supply), stationary turbines (oil and gas transmission), and micro gas turbines (power generation); gas turbines for commercial and military aircraft propulsion, including narrow and wide turbines for aircraft propulsion, micro gas turbines for UAV/unmanned vehicle power, medium and small and medium-sized gas turbines for vehicle power/vehicle hybrid power [20].

3 Current Status of Typical Foreign Products The US Department of Energy has successfully incubated a series of micro gas turbines such as Capstone C series, AE-T100, MT70/MT250, etc., through the “Micro Gas Turbine System Plan 2000”. Capstone in the United States, as the most famous manufacturer of MGT energy systems, the power of its products is mostly 30 kW–10 MW, which are widely used in

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energy efficiency management, renewable energy, critical power sources, transportation and various micro-grids. So far, Capstone has more than 9,000 systems and nearly 10,000 equipment in 83 countries. Capstone’s micro gas turbines integrate aviation turbine engines, magnetic generators, advanced power electronic equipment and air bearing technology with low maintenance. A variety of fuels, including natural gas, associated gas, biogas, liquefied petroleum gas/propane and liquid fuels (diesel, kerosene and, including aviation fuel) are used. It can be installed separately or configured in multiple groups. It can run in parallel or independently with the local power grid. The product feature is that there is no problem with cooling system maintenance and coolant treatment. The emission of NOx is less than 5 PPM, and only 65 dB @10 m of noise are generated during operation. It has multi-fuel capability with expandable monitoring functions for remote equipment status performance [21]. The Ingersoll Rand Energy Systems subsidiary, which once belonged to Ingersoll Rand, has released an MT70 micro gas turbine that takes natural gas as fuel with the power generation output of 100 kW, the power factor of 0.82, the heat output of 85.3 btu/h– 119.485.3 btu/h when in the open air. The average annual NOx emission is less than 2 t of power plants with the same power [22]. It is compatible with oil-free rotary screw, contact cooling rotary screw and centrifugal air compressor. It can be used in wastewater treatment and agricultural facilities, landfills, oil production facilities, hospitals and factories. However, the Ingersoll Rand Energy Systems subsidiary was sold. Therefore, the further development of the micro gas turbine business was stopped. Different from the organic Rankine cycle adopted by the U.S. Advanced Micro Turbine System Plan, the British Bowman Company applied the electric turbine compound system. Its products improve the power density and fuel efficiency by recovering the waste energy in the exhaust gas of the generator set, thereby making the gas and diesel generator set work more efficiently. Compared with the organic Rankine cycle, the electric turbine compound system is more compact, cheaper, and easier to install. It is suitable for more than 800 common generators in the range of 150 kW to 2.5 MW, such as CAT 3516 TA /LE, Cummins KTA50-G3, KTA50-G8, QSK60, QSK78, Jenbacher J312 and J320, MAN 2842 and D2676, Mitsubishi 6R41, MTU 4000, Scania DC 12, Volvo TAD1642, rtsilä W20V34SG and W18V50SG, etc. Bowman has also developed customized high-speed motors for large engines equipped with wastegate turbine chargers, such as the 9 MW rtsilä W20V34SG gas turbine. The fuel consumption of the products with an electric turbine compound system is between 4% and 7%, and only 10% of the extra power is generated. More than 800 systems have been sold worldwide, and the cumulative stable operation has exceeded 2 million hours. The Italian Ansaldo Energy Company mainly produces AE-T100 micro gas turbines for thermal power generation and three-heat power generation using natural gas as fuel. The pressure of the combustor is 4.5 bar, the turbine inlet temperature is 950 °C, the fuel pressure is (0.02–0.1) bar, fuel temperature is 0 °C–60 °C, electrical efficiency is about 30%. It is low in maintenance requirements with an interval of 6000 working hours. It can be installed indoors or outdoors and can be remotely monitored. It is mostly used in the food processing industry, chemical preparation manufacturing, laundry, paper machine, logistics center, hotels, hospitals, office/commercial centers and other places [23, 24].

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4 Current Status of Domestic R&D In contrast, the level of research and development and manufacturing for domestic MGT power generation technology is relatively low. The commercial models used on a large scale are all from the U.S. Capstone Company. Some scholars and research institutions have achieved certain results in basic research theories and certain models of prototypes. However, the overall manufacturing process, materials and equipment maturity are far behind those of developed countries. With the distributed power generation and cogeneration systems are more and more widely used in industry, China’s demand for micro gas turbines has also grown rapidly where products with a power of less than 100 kW account for 56%, and products with a power of 100 kW–300 kW accounts for 29%. The research and development and manufacturing for micro gas turbines with independent intellectual property rights are still in the initial stage. The products are either OME of foreign companies or co-production with foreign companies. For example, Aero Engine Corporation Hunan Power Machinery Research Institute had self-developed 75 kW micro gas turbine by referencing the micro gas turbines experience in Germany, France, Britain and other countries in the 1990s, which has a market share of 100% in the domestic vehicle power market; Shanghai Jiao Tong University had co-developed 30 kW and 50 kW low-calorific value micro gas turbine generation devices with the Commonwealth Research Institute of Australia, and produced a small batch of micro gas turbines for water pumps with 37 hp; Shanghai Astronautics Energy Co., Ltd. and U.S. Capstone jointly manufactured parts and components; Harbin Dongan Technology Development Company, successfully developed the first domestic 18 kW micro gas turbine generator set with independent intellectual property rights in 2015, which can be used for auxiliary power devices of vehicles and emergency backup power supply for satellite communication base stations; Shanghai Fanzhi Energy Equipment belonging to ENN Group Co., Ltd. completed the 100 kW micro gas turbine development project in March 2017 and handed it over to ENN Power for small-scale production; in August 2020, the major key scientific and technological project of the Municipal Science and Technology Commission called “Research on the Key Technologies of High-Efficiency Transmission and Combustion of Micro Gas Turbines” was accepted by the Shanghai Municipal Science and Technology Commission which was led by ENN Power and participated by Tongji University and Shanghai GENE Automation Technology Co., Ltd. in Shanghai. Other research institutions and manufacturing companies are still in the laboratory prototype stage without industrialized complete machine products with independent intellectual property rights. Among the domestic micro gas turbines mentioned above, small-batch industrialized products with a high degree of independent intellectual property rights were represented by ENN100 micro gas turbines developed by ENN Energy Power. Since the operation of the cogeneration system of Lanxi Best Aluminum Products Co., Ltd. in March 2018, the system has been operating stably, and the cost is about 8,000 RMB/kW, which is greatly reduced compared to imported micro-gas turbines. On the other hand, the thermoelectric ratio can be adjusted flexibility, and the exhaust temperature can be continuously adjusted between 270 °C and 650 °C. Compared with a single regenerative cycle unit, it is more widely applicable. However, there are still problems such as high maintenance rate, frequent shutdowns, and multiple failures of equipment components.

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Given the fact that the R&D and manufacturing capabilities of China’s gas turbine (including micro gas turbine) are far behind those of developed countries in Europe and the United States, in September 2019, the National Energy Administration issued Response of The National Energy Administration on the Inclusion of 24 Projects Including the Gas Turbine Power Generation Project of Huaneng Nantong Power Plant in the First Batch of Gas Turbine Innovation and Development Demonstration Projects which clearly demonstrates 22 gas turbine models and two operation and maintenance service projects covering heavy gas turbines and small and medium-sized gas turbines developed by major domestic gas turbines development companies such as Harbin Power Plant Equipment Corporation, Dongfang Electric Corporation, Shanghai Electric Group, Aero Engine Corporation, and China State Shipbuilding Corporation.

5 Conclusion Micro gas turbine generation technology has significant advantages. It can guarantee the safety and efficiency of electric energy in various environments compared with the existing diesel generator sets with inherent disadvantages such as large volume, high noise, high vibration, obvious target characteristics, single fuel, and poor power adjustment ability. Therefore, it has strong application potential in the civilian and military fields. Compared with developed countries, China’s manufacturing process, materials and equipment maturity are backward. Therefore, it is necessary to carry out more research to solve the problems such as high maintenance rate, frequent shutdown and multiple faults of equipment components.

References 1. do Nascimento, M.A.R., et al.: Micro gas turbine engine: a review. IntechOpen (2014). https:// doi.org/10.5772/54444 ˙ 2. Zywica, G., Kaczmarczyk, T.Z., Bre´nkacz, Ł: Investigation of dynamic properties of the microturbine with a maximum rotational speed of 120 k rpm–predictions and experimental tests. J. Vibroeng. 22(2), 298–312 (2020) 3. Anda, M.F.D., TerMaath, C., Fall, N.K.: Distributed energy technology simulator microturbine demonstration. Energ. Rep. 10, 1–17 (2001) 4. Arrieta, F.R.P., Sodré, J.R., Herrera, M.D.M., Zárante, P.H.B.: Exergoeconomic analysis of an absorption refrigeration and natural gas-fueled diesel power generator cogeneration system. J. Nat. Gas Sci. Eng. 36, 155–164 (2016) 5. Advanced manufacturing office: Improving the operating efficiency of microturbine-based distributed generation at an affordable price. DOE/EE-0432 (2017). http://www.osti.gov/sci tech 6. Energy and Environmental Analysis, Inc.: Advanced microturbine system: market assessment. Oak Ridge National Laboratory, Washington, DC (2003) 7. Elgowainy, A., Wang, M.Q.: Fuel cycle comparison of distributed power generation technologies. Argonne National Laboratory. ANL/ESD/08-4, 1~16 (2008) 8. Staunton, R.H., Ozpineci, B.: Microturbine power conversion technology review. Oak Ridge National Laboratory, ORNL/TM-2003/74 (2003)

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9. Borbely-Bartis, A.-M., DeSteese, J.G., Somasundaram, S.: U.S. installation, operation, and performance standards for microturbine generator sets. Prepared for the U.S. Department of Energy under Contract DE-AC06-76RL01830 (2000) 10. Advanced Manufacturing Office: Microturbines. DOE/EE-1329 (2016) 11. Han, D., Tang, C., Yang, J., Hao, L.: Experimental research on instability fault of high-speed aerostatic bearing-rotor system. Vibroeng. Procedia 3, 93–99 (2014). ISSN 2345-0533 12. Geiling, D.W.: DOE Advanced Ceramic Microturbine. U.S. Department of Energy National Energy Technology Laboratory, Cooperative Research and Development for Advanced Microturbine Systems. Cooperative Agreement No DE-FC2G-00CH-11059 (2005) 13. Król1, D., Poskrobko, S., Go´scik, J.: Micro cogeneration - rich-methane gasifier and micro gas turbine. Energy Fuels 14, 01024 (2016). https://doi.org/10.1051/71401024 14. Talib, A.R.A., Gires, E., Ahmad, M.T.: Performance evaluation of a small-scale turbojet engine running on palm oil biodiesel blends. J. Fuels 2014, 1–9 (2014). Article ID 946485 15. Tan, E.S., Anwar, M., Adnan, R., Idris, M.A.: Biodiesel for gas turbine application—an atomization characteristics study (2013). https://doi.org/10.5772/54154 16. Capata, R., Saracchini, M.: Experimental campaign tests on ultra micro gas turbines, fuel supply comparison and optimization. Energies 11(4), 799 (2018). https://doi.org/10.3390/ en11040799 17. Rossi, I., Traverso, A.: Ambient temperature impact on micro gas turbines: experimental characterization. Acta Polytech. CTU Proc. 20, 78–85 (2018). https://doi.org/10.14311/APP. 2018.20.0078 18. U.S. Environmental Protection Agency: Powering microturbines with landfill gas. EPA430F-02-012, 1~4 (2002) 19. Rosfjord, T., Tredway, W., Chen, A., Mulugeta, J., Bhatia, T.: Advanced microturbine systemsFinal Report for Tasks 1 Through 4 and Task 6. Prepared for The U.S. Department of Energy, Office of Distributed Energy Award No. DE-FC26-00CH11060, Report Number DOE/CH/11060-1 (2007) 20. Committee on Advanced Technologies for Gas Turbines Aeronautics and Space Engineering Board Division on Engineering and Physical Sciences: Advanced technologies for gas turbines. The National Academies Press, Washington, DC (2020) 21. Nourse, J.: High efficiency microturbine with integral heat recovery. U.S. DOE Industrial Distributed Energy Portfolio Review Meeting (2011) 22. U.S. Environmental Protection Agency: Environmental Technology Verification ReportIngersoll-Rand Energy Systems IR PowerWorksTM 70 kW Microturbine System. SRI/USEPA-GHG-VR-21 (2003) 23. Mahmood, M.: Study of hybrid solar gas turbine system: T100 modeling and dynamic analysis of thermal energy storage. Università degli Studi di Genova, Ph.D. Thesis in Fluid Machinery Engineering 24. Haugwitz, S.: Modelling of microturbine systems. Department of Automatic Control, Lund Institute of Technology, Master Thesis. ISRN LUTFD2/TFR-5687-SE (2002)

Biased-Infinity Laplacian Applied to Depth Completion Using a Balanced Anisotropic Metric Vanel Lazcano1(B) , Felipe Calderero2 , and Coloma Ballester3 1

N´ ucleo de Matem´ atica F´ısica y Estad´ıstica., Universidad Mayor, Santiago 08544, Chile [email protected] 2 Farmac´eutica, 28033 Madrid, Spain [email protected] 3 Universitat Pompeu Fabra, 08018 Barcelona, Spain [email protected]

Abstract. This paper studies an anisotropic interpolation model that can fill in-depth data in a largely empty region of a depth map. We suppose the image is endowed with an anisotropic metric gij considering spatial and photometric information. The interpolation model is the biased Infinity Laplacian or biased Absolutely Minimizing Lipschitz Extension (bAMLE). Given an implementation based on the “eikonal” operator, we investigate different metrics to improve the performance of bAMLE in the depth completion task. To perform this completion, we suppose we have a reference color image and a depth map at our disposal. We used first a standard metric that considers spatial and photometric differences, second, the same metric but using different exponents in each term, and third a new metric that considers a balance term between intensities and gradients of the image. Using this new metric, we outperform our version of the model and other contemporary models in KITTI dataset. Keywords: Biased AMLE Balance term

1

· Eikonal operator · Anisotropic metric ·

Introduction

This paper discusses an anisotropic depth data completion operator applied to the interpolation of depth maps in large empty regions. Our proposal aims to fill the empty data region of a depth image using two elements: i) the data from the self depth image and ii) a corresponding color image or reference image. This interpolation operator is the biased Absolute Minimizing Lipschitz Extension, also known as the biased Infinity Laplacian or bAMLE, first appeared in [2]. The AMLE was introduced in [3] as a completion method from a theoretical point of view. AMLE operator emerges as a straightforward operator that satisfies a small set of mathematical axioms, as explained in [2]. c The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022  Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 1048–1055, 2022. https://doi.org/10.1007/978-981-19-0390-8_132

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Related Works

Depth interpolation aims to recover complete depth maps from sparse acquired or estimated data. Depth interpolation methods, on the one hand, use only the depth available data, and on the other hand, use additional information such as a color reference image. Nowadays, many methods use CNN to recover depth maps, for example, [4,6]. In [4] is proposed a method that simultaneously estimates depth map and reconstructs a gray level image of the scene. This method performs better than other methods that use only depth information in the KITTI Depth Completion Suite [5]. The authors of [6] evaluated convolutional networks in a database containing depth images captured by a Kinect sensor. The authors used CNN to complete the data after down-sampling these depth images every other 8 pixels square. Application to depth completion of AMLE filter was performed in either elevation models [7] or optical flow [8]. Another approach such as [15] proposed to complete depth maps using a binary anisotropic diffusion tensor. Also, it is proposed an image-guided nearest neighbor search, needed by the binary tensor. The proposed model is an anisotropic variational scheme that includes a data term and a regularization term. This proposal can preserve discontinuities present in the scene. That is to say, it generates piece-wise constant depth maps. Obtained results show that the methods can recover flat surfaces. Previously, we presented in [9] the numerical solution of the biased infinity Laplacian applied to the depth interpolation problem. The proposal uses a reference image and an anisotropic metric to complete sparse depth data. Considering the image endowed with an anisotropic metric, we solved the biased infinity Laplacian numerically, evaluating different rationals exponents for the terms of the considered metric. In this paper, we present an extension of our previous work considering this new metrics. The contribution of this work is two-fold: i) The exponents of the terms of metrics can be real positive values. ii) A new metric that contains a weight map, which is a balance term between intensities and gradients of the image is proposed. In this work in Sect. 2 we present the model we used to interpolate depth map and the new metric. In Sect. 3 we present the used dataset and the experiments we performed. Results and conclusions are presented in Sect. 4 and 5, respectively.

2

Interpolation Model

The bAMLE (or biased infinity Laplacian) appeared in the axiomatic analysis of interpolators in manifolds presented in [2]. The interpolation problem considers to solve, (1) Δ∞,g u + β |∇u|ξ = 0 ∈ Ω, with the constraint u|∂Ω = θ, where Ω ⊂ R2 is the problem domain, gij is the metric and, ξ is the direction of the gradient, β ∈ R+ , ∇u gradient of the depth map, Δ∞,g u is the depth map infinity Laplacian given the metric gij and

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 ∇u ∇u with M = (Ω, g) the manifold. Considering Δ∞,g u := , |∇u|ξ |∇u|ξ the discrete image domain as a graph and taking a pair of grid points x, y and let dxy be their distance. q  dxy = κx |x − y|2 + κc |I(x) − I(y)|2 . (2) 

2 DM u

where q ∈ Q. 2.1

A Numerical Implementation for the Biased Infinity Laplacian

We recall the numerical implementation presented in [9] based on the “eikonal” operator: β+ dxz u(y) + β− dxy u(z) . (3) u(x) = β+ dxz + β− dxy where y and z are a locations in a neighborhood N (x) of x such that the eikonal u(y) − u(x) u(z) − u(x) positive and the eikonal negative operator are maxidxy dxz mum and minimum, respectively. The numerical implementation for the iterative discretized biased Infinity Laplacian is: β+ dxz uk (y) + β− dxy uk (z) , k = 0, 1, ... β+ dxz + β− dxy   u(y) − u(x) + βsgn max and β− = 12 . dxy y∈N (x)

uk+1 (x) =

with β+ =

2.2

1 2

(4)

Considered Metrics

Given the model and the metric, we proposed to evaluate the interpolator using different Metrics. We will first consider the standard metric presented in Eq. 2. A more general metric (or exponential metric) can be stated as: q

dxy = (κx |x − y|p + κc |I(x) − I(y)|p ) .

(5)

where the exponent p and q are positive ∈ R. Image acquired in real scenarios presents illumination changes, shadows, and other effects. In real scenarios, only comparing intensities is not robust enough to determine similarities with a high confidence level. Given these facts, a new metric is proposed, which is robust to illumination changes. The gradient of the image used to compare pixels performs better in these scenarios than intensities of color. We added to the metric in Eq. 5 a gradient component and a weight map that balances between gradients and intensities: q

dxy = (κx |x − y|p + κc [γ(x)|I(x) − I(y)|p + (1 − γ(x))|∇I(x) − ∇I(y)|p ]) . (6)

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where γ(x) is a weight map that balance between intensities and gradients as in [10,11], 1 γ(x) = , (7) 1 + eb(DI −τ D∇ ) where DI is the difference between intensities and |I(x) − I(y)|p and D∇ is the difference between gradients |∇I(x) − ∇I(y)|p , b and τγ are positive constants. In one hand, if DI >> D∇ means that DI − D∇ > 0 so that eb(DI −D∇ ) should be large and γ(x) ≈ 0, thus the distance is more confident in gradients. In the other hand, if D∇ >> DI means that DI − D∇ < 0 so that eb(DI −D∇ ) should be small and γ(x) ≈ 1, thus the distance is more confident in intensities. 2.3

Proof of Concept

As a proof of concepts, we evaluated the γ(x) weight map in a neighborhood of size 1 pixel in the color image presented in Fig. 1.

Fig. 1. Weight map, for example, of the KITTI dataset. (a), (b), (c) color reference images of the KITTI dataset. (d) Corresponding γ(x) weight map for color image in (a). The weight map values are represented in gray levels: white represents 1, and black 0. We observe that in shadows present in the color image, the gradient is compared. (e) We observe that in the tree’s shadows, the gradient is used. (f) Saturated region on the road uses both intensities and gradients (γ(x) = 0.5).

2.4

Particle Swarm Optimization

To estimate the parameter of the model, we used the Particle Swarm Optimization (PSO) algorithm. It is possible to evaluate the MSE (Mean Square Error) and MAE (Mean Absolute Error), given a set of the incomplete depth map, their ground truth, and the given results. We computed the MSE + MAE in this dataset, and using the PSO algorithm; we iteratively determined the parameters model that minimized the total error. The estimated parameter are radius (neighborhood size), κx , κc , β, n iter (number of iterations), exponents p, q, and b and τγ .

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Experiments and Dataset

To evaluate the performance of the bAMLE using the proposed metrics, we used the KITTI Depth Completion Suite [17]. KITTI dataset consists of 1000 color images and 1000 sparse depth images. The ground truth for each image pair is available on the KITTI webpage. In Fig. 2 we show two examples of the KITTI data set.

Fig. 2. Examples of KITTI Depth Completion Suite. (a) Reference images. (b) Sparse depth data.

In Fig. 2 (a), we show a reference color image where a person rides a bike. In (b), we present the available sparse depth data values which are represented using MATLAB jet colormap. (d) Other reference images and (d) show the sparse data (yellow lines) superimpose to the reference image. We estimated the model parameter using the PSO algorithm. We trained using three references images and their corresponding sparse depth map. The used images to train are already presented in Fig. 1. We evaluate the model performance in the 997 images not considered to train and then compute MSE and MAE for each completed depth map.

4

Results

We trained both models using the AMLE and the bAMLE using the PSO algorithm. In order to train the model, we used 50 individuals and 30 iterations. 4.1

Parameters Values

We show in Fig. 3 the evolution of the best parameters in our training dataset. As we see in Fig. 3 (a) and (c) at the beginning of the iterations, the PSO is oscillatory, and as the iteration goes, the method converges. We present in Table 1 the final MSE+MAE values for these models: We selected the models in bold in Table 1 and with these models and the estimated parameter we evaluated in the complete KITTI Depth Completion Suite.

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Fig. 3. Training stage of the model. (a) Evaluation of the training stage for AMLE in 30 iterations, using exponential metric in Fig. 5. (b) Evolution of the fitness of the best parameters. (c) Evolution for bAMLE using balanced metric. (d) Evolution of the fitness for bAMLE, and we show the obtained fitness value for iteration 21. Table 1. Final results obtained by PSO using AMLE Model. Model

Metric

Parameters

MSE+MAE

Infinity Laplacian

Exponential

p = 1, q = 1

1.692

Infinity Laplacian

Standar Metric p = 2, q = 1

Infinity Laplacian

Exponential

Infinity Laplacian

Balanced

p = 5.47, q = 0.19 1.408

biased Infinity Laplacian Balanced

p = 5.16 q = 22.85 1.393

p = 3, q = 1

1.686 1.810

Table 2. Validation results in the KITTI dataset. Model

Metric

Parameters

DeepLiDAR [13]

–

–

Infinity Laplacian

Exponential p = 2 q = 1

1.785 0.468

biased Infinity Laplacian

Exponential p = 2 q = 1

1.728 0.446

Infinity Laplacian

Balanced

p = 5.47 p = 0.19

biased Infinity Laplacian

Balanced

p = 5.16 p = 22.85 1.381 0.317

biased Infinity Laplacian [9] Exponential p =

MSE MAE 0.687 0.215

1 2

q=1

1.410 0.402 1.692 0.457

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Results in KITTI Depth Completion Suite

We have evaluated in 997 images the KITTI dataset, obtained results are presented in Table 2. As we show in Table 2 best results were obtained by biased Infinity Laplacian using Balanced metric. We observe that the proposal outperforms our previous results presented in [9]. We observe that other methods such as DeepLidar presents very small MSE and MAE, the authors use Deep Learning which is far more complex than our proposal and it is hard to train. In Fig. 4 we show a comparison of the results obtained by the different models.

Fig. 4. Examples of results obtained by Infinity Laplacian and biased Infinity Laplacian.

In Fig. 4 we show the reference color image in (a) and the sparse data in (b). In (c), we present the results obtained by Infinity Laplacian. In (d), we show the result obtained by the biased Infinity Laplacian. We observe that the result in (d) preserves the edges better and it is less diffuse than the results in (c). In this case AMLE obtained MSE = 0.937 MAE = 0.278 and bAMLE obtained MSE = 0.775 and MSE = 0.202.

5

Conclusions

We have evaluated the Infinity Laplacian and biased Infinity Laplacian using a new metric called the balanced metric. We have used a numerical implementation that produces a weighted average-based numerical model. We observe that other models based on Deep Leaning present better performance than ours, but these models are far more complex than ours, and they are hard to train. In future work, we think that by using tools like balanced terms, temporal information, and patches comparison, we can reach more competitive results. We will also investigate the interpolation of sparse depth data without using a reference color image. The AMLE and bAMLE are very simple interpolators that are easy to implement and easy to compute, these facts make them suitable for these kinds of applications.

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References 1. Caselles, V., Morel, J-M, Sbert, C.: An axiomatic approach to image interpolation, IEEE Trans. Image Process. 7(2), 376–386 (1998) 2. Caselles, V., Igual, L., Sander, O.: An axiomatic approach to scalar data interpolation on surfaces. Numerische Math. 102(3), 383–411 (2006) 3. Aronsson, G.: Extension of functions satisfyng Lipschitz conditions. Aktiv fuer Mathematik 6(6), 551–561 (1967) 4. Lu, K., Barnes, N., Anwar, S., Zheng, L.: From depth what can you see? depth completion via auxiliary image reconstruction. In: IEEE Computer Vision and Pattern Recognition,vol. 2020, 11306–11315 (2020) 5. Uhrig, J., Schneider, N., Schneider, L., Franke, U., Brox, T., Geiger, A.: Sparsity Invariant CNNs. In: International Conference on 3D Vision (3DV), pp. 11–20 (2017) 6. Li, Y., Huang, J.-B., Ahuja, N., Yang, M.-H: Joint image filtering with depth convolutional networks. Trans. Pattern Anal. Mach. Intell. 41(8), 1909–1923 (2019) 7. Almansa, A., Cao, F., Gousseau, Y., Roug´e, B.: Interpolation of digital elevation models using AMLE and related methods. IEEE Trans. Geosci. Rem. Sens. 40(2), 314–325 (2002) 8. Raad, L., Oliver, M., Ballester, C., Haro, G., Meinhardt, E.: On anisotropic optical flow inpainting algorithms. Image Process. On Line, 10, 78–104 (2020). https:// doi.org/10.5201/ipol.2020.281 9. Lazcano, V., Calderero F., Ballester, C.: Comparing different metrics on an anisotropic depth completion model. Int. J. Hybrid Intell. Syst. Pre-press, no. Pre-press, 1–13 (2021). https://doi.org/10.3233/HIS-210006 10. Lazcano, V.: An empirical study of exhaustive matching for improving motion field estimation. Information 9(12), 320 (2018). https://doi.org/10.3390/info9120320 11. Xu, L., Jia, J., Matsushita, Y.: Motion detail preserving optical flow estimation. IEEE Trans. Pattern Anal. Mach. Intell. 34, 1744–1757 (2012) 12. Oberman, A. M.: A convergent difference scheme for the infinity Laplacian: construction of absolutely minimizing lipschitz extensions. Math. Comput. 74(251), 1217–1230 (2005) 13. Qiu, J., et al.: DeepLiDAR deep surface normal guided depth prediction for outdoor scene from sparse LiDAR data and single color image. In: The IEEE Conference on CVPR, June 2019 14. Nan, Z., Zhiyu, X., Yiman, Ch., Shuya, Ch., Chengyu, Q.: Simultaneous semantic segmentation and depth completion with constraint of boundary. Sensors, 20(3), 1– 25 (2020). https://www.mdpi.com/1424-8220/20/3/635, https://doi.org/10.3390/ s20030635 15. Yao, Y., Roxas, M., Ishikawa, R., Ando, S., Shimamura, J., Oishi, T.: Discontinuous and smooth depth completion with binary anisotropic diffusion tensor. IEEE Robot. Autom. Lett. 5(4), 5128–5135 (2020). https://doi.org/10.1109/LRA.2020. 3005890 16. Bai, L., Zhao, Y., Elhousni, M., Huang, X.: DepthNet: Real-Time LiDAR point cloud depth completion for autonomous vehicles. IEEE Access 8, 227825–227833 (2020). https://doi.org/10.1109/ACCESS.2020.3045681 17. Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? the kitti vision benchmark suite. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3354–3361. IEEE (2012)

A Study on the Effects of Temperature Changes on the Rheological Properties of Lithium Lubricating Grease and Data Processing Weigui Zhou, Long Huang(B) , Changfu Wang, Weihua Zhang, Jinmao Chen, Xudong Wang, Youjie Zhou, and Jing Wang Institute of System Engineering, Academy of Military Science, Beijing 100300, China [email protected]

Abstract. In order to study the rheological properties of lubricating grease, poly A olefins (PAO) synthetic oil as base oil and 12 - hydroxy stearic acid lithium soap as the thickener were used to prepare lithium lubricating grease. The effects of temperatures on the thixotropic ring areas, storage modulus, loss modulus, strain amplitudes and apparent viscosities of lithium grease were investigated, and the mechanism was discussed accordingly. The results showed that lithium-based grease was a yield pseudoplastic fluid, and its apparent viscosity was dependent on shear rate and time, which exhibited a typical viscoelasticity. With the increase of temperature, the structure of lithium grease became unstable. When the temperature was higher than 100 °C, the structure of lithium grease will be destroyed seriously, and its performance will change significantly. At 75 °C, the viscoelasticity of lithium grease was remarkable; At 0 °C, the lithium grease showed an obvious anti-thixotropy. Keywords: Lithium lubricating grease · Thixotropy · Modulus · Apparent viscosity · Strain amplitude · Temperature

1 Introduction Rheology is a science to study the deformation and flow of materials. It mainly studies non-Newtonian fluid and Newtonian fluid from the aspects of strain, stress, temperature, shear rate and time. Lithium lubricating grease is a kind of lubricating material with special structure and typical viscoelasticity, whose research belongs to the category of rheology. Lithium lubricating grease with colloidal structure is a typical two-phase dispersion system, which has special rheological properties. Under low load and normal temperature, it keeps a certain shape with micro deformation without flowing, and adheres to the contact surface without sliding. When it reaches its yield stress or the temperature rises, the lithium lubricating grease changes from elasticity to viscosity and begins to flow, so it has good sealing, protection and lubrication effect [1]. Domestic scholars Wang Xiaoli, Yao Lidan and Xu Jun made a preliminary study on the rheological properties of © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 1056–1063, 2022. https://doi.org/10.1007/978-981-19-0390-8_133

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grease [2–4]. Zhou Weigui et al. [5] analyzed the influence of base oil on the soap fiber structure of 12 hydroxy lithium stearate soap, and pointed out that under the same process conditions, the higher the viscosity of base oil, the longer the soap fiber of lithium lubricating grease, and the larger the effective oil storage space. Zeng Hui [6] and others also observed that the structure of composite lithium lubricating grease soap fiber with high viscosity of base oil was loose. At present, there isn’t any systematic study about the rheological properties of grease, which brings some limitations for correct understanding of grease and guiding practical usages. In this paper, the rheological properties of lithium lubricating grease, such as apparent viscosity, yield stress, thixotropy, storage modulus and loss modulus, were studied based on the practical conditions of stress, strain, shear rate and temperature.

2 Experimental Part 2.1 Preparation of Test Materials and Lithium Lubricating Grease Raw Materials Thickener: 12 hydroxy lithium stearate soap. Base oil: Poly- A Olefin synthetic oil (The kinematic viscosities of PAO4, PAO8, PAO25 and PAO100 at 100 °C were 3.9, 8.039, 26.0 and 104 mm2 s–1 , respectively). Preparation and Performance Evaluation of Lithium Lubricating Grease. Using PAO8 as the base oil, lithium lubricating grease with different consistency grades of 3, 2, 1, 0, 00 and 000 was prepared, respectively. The effect of thickener content on the rheological properties of lithium lubricating grease was investigated. The physical and chemical parameters were shown in Table 1. The effect of the base oil viscosity on the fluidity of lithium lubricating grease was mainly investigated. Four PAO base oils with different viscosities were used to prepare lithium lubricating grease with consistency grades of 2. The physical and chemical parameters were shown in Table 2.

Table 1. Physical and chemical parameters of lithium lubricating grease with different soap content Serial number

Consistence

Soap content/%

Worked cone penetration/(1/10 mm)

Dropping point/°C

Oil steel mesh points/%

1

3

8.99

245

205

0.0103

2

2

8.46

279

205

0.0351

3

1

6.98

331

206

0.0375

4

0

5.47

357

206

0.0687

5

00

4.60

417

206

–

6

000

4.55

459

206

–

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Table 2. Physical and chemical parameters of lithium lubricating grease with different viscosities base oil Serial Base oil Number

Consistence Soap Worked cone Dropping Oil steel content/% penetration/(1/10 mm) point /°C mesh points/%

a

PAO4

2

9.99

281

204

0.0693

b

PAO8

2

8.46

279

205

0.0351

c

PAO25

2

7.06

271

209

0.0519

d

PAO100 2

8.97

279

206

0

2.2 Rheological Test Experimental Instruments. The experiment was carried out with a rotary rheometer and PP system. The distance between the rotor and the flat plate was 1 mm. The rotary rheometer was equipped with Peltier temperature control system, which can ensure the uniformity of the temperature distribution of the sample in the whole temperature range and make the experimental data more reliable. Main Contents of the Experiment. (1) Dynamic rheological experiment under strain control: the angular velocity was constant at 10 rad. s−1 , and the stress, storage modulus and loss modulus of lithium lubricating grease at six different temperatures were studied (2) Steady state rheological experiment under the control of shear rate: ➀ the shear rate increases from 0.01 s−1 to 1000 s−1 . The shear stress and apparent viscosity of lithium lubricating grease at six different temperatures were studied, and the rheological equation was fitted and the parameters of the equation were analyzed accordingly. ➁ The cyclic method of shear rate 2 s−1 ~ 50 s−1 ~ 2 s−1 was used to study the change process of stress and apparent viscosity of lithium lubricating grease with shear rate and analyze its thixotropy. Rheological Parameters. Under a certain shear rate, the consistency of lubricating grease decreases with the increase of shear time after shear action, and increases again after shear action stops. The phenomenon of partial recovery of its structure is called thixotropy [7]. The speed of grease structure recovery can be studied by the area of thixotropic loop. It can also be used to measure the stability of grease structure. The minimum shear force that destroys the colloidal structure of grease is called yield stress, which is related to the sealing property of grease [8]. Under the condition of dynamic experiment, the stress and storage modulus (G’) and loss modulus (G”) with strain amplitude (γ). G’ reflects the internal elastic potential energy of lithium lubricating grease, which is related to its retention ability G”; The energy consumed in the form of heat during deformation, namely, the energy lost could be attributed to irreversible viscous deformation, reflects the viscosity of lithium lubricating grease [13]. When G’ > G”, lithium lubricating grease shows recoverable elastic deformation; When G’ = G”, some deformation can be recovered; The intersection point of G’ = G” is also called flow point. At this time, the lithium lubricating grease changes

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from elastic to viscous; When G’ < G”, lithium lubricating grease shows irrecoverable viscous deformation. γ index is a key index to measure the difficulty of lithium lubricating grease to start flowing, the larger the value of γ, the longer the time and mileage of the external force needed for the lithium lubricating grease to change into flow form under the action of external force [9].

3 Results and Discussion 3.1 Effect of Temperature on the Rheological of Lithium Lubricating Grease Effect of Temperature on Thixotropy of Lithium Lubricating Grease. The thixotropic loop area of six lithium lubricating greases with different consistencies at different temperatures is shown in Fig. 1. It can be seen from Fig. 1 that the thixotropic loop area of lithium lubricating grease decreased with the increase of temperature. With the increase of temperature, the larger the consistency was, the larger as the change range of thixotropic loop area. The higher the consistency was, the more sensitive the thixotropy was to temperature. The hydrogen bond and van der Waals force of lithium lubricating grease were more flexible due to the high temperature and violent molecular motion, and the structure of lithium lubricating grease recovered quickly at high temperature. However, it should be noted that the thixotropic loop area of lithium lubricating grease was negative near 0 °C, and lithium lubricating grease showed obvious anti thixotropic effect on time. Temperature, as an important factor of anti thixotropy of materials [10], possessed a problem θ, the results showed that the lithium lubricating grease exhibited obvious anti thixotropy θ = 0 °C, the recovery rate of lithium lubricating grease was obviously higher than the failure rate while the structure recovery was dominant effect. Effect of Temperature on Stress and Modulus of Lithium Lubricating Grease. The strain, stress and storage modulus of six kinds of lithium lubricating grease with different 88000 #

3 # 2 # 1 # 0 # 00 # 000

thixotropic loop area/(Pa/s)

77000 66000 55000 44000 33000 22000 11000 0 -40

-20

0

20

40

60

80

100

120

140

T/℃

Fig. 1. Thixotropic loop area of lithium lubricating grease in different temperature

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soap contents at yield point and flow point at different temperatures are shown in Fig. 2. It can be seen from Fig. 2 (a) that the strain at the yield point of lithium lubricating grease with different soap contents first decreased with the increase of temperature, reached the minimum at 0 °C and then increased, followed by reached to the maximum at 75 °C and then decreased, while the strain values at the yield point were all less than 1.2%. The results showed that temperature possessed a great influence on the viscoelastic response of lithium lubricating grease. The viscoelastic response speed increased with the increase of temperature; At 0 °C, the response was the fastest and tended to be ideal elastomer; When the temperature continued to increase, the viscoelastic response speed decreased, and the response speed was the slowest at 75 °C; After that, the response speed of lithium lubricating grease increased with the increase of temperature, and the viscoelastic response of lithium lubricating grease was faster. The reason was that the soap fiber cross-linking of lithium lubricating grease was the largest near 75 °C, and the larger the deformation reached its yield point, and the viscoelastic characteristics were obvious at this temperature. Figure 2 (b) shows that the stress is large near – 30 °C; from – 30 °C to ~10 °C, the stress of lithium lubricating grease with different soap contents decreased; The stress increased from 0 °C to 80 °C, and reaches the maximum value near 75 °C; However, when the temperature was above 100 °C, the stress dropped sharply. At –30 °C, the soap fiber of lithium lubricating grease was highly entangled and disordered, which made it move directionally under the action of external force, so the stress was high; When the temperature increased from –30 °C to –10 °C, the soap fiber did not fully unfold, and the viscosity of base oil played a leading role, and the viscosity and stress of base oil decreased; When the temperature increased from 0 °C to 80 °C, the soap fiber extended and stretched, and the fiber crosslinked and strengthened, which became the dominant factor of the stress, so the stress increased. At 75 °C, the soap fiber crosslinked was the largest, so the stress was the largest; However, when the temperature was higher than 100 °C, the structure of lithium lubricating grease changes and the stress dropped sharply. Figure 2 (c) shows that the strain at the flow point increases with the increase of temperature. When the strain reached the maximum value near 25 °C, it began to decrease. When it was higher than 80 °C, it decreases sharply, even from the maximum of 60% to the minimum of 5%. The strain value at the flow point was the response parameter of the viscosity of lithium lubricating grease to the external force. With the increase of temperature, the response speed of viscosity slowed down obviously, and it was more obvious at 25 °C. The results showed that the flow zone of lithium lubricating grease at this temperature was the smallest, and it showed a longer yield zone and linear viscoelastic zone, and its elasticity was more obvious at this temperature. With the increase of temperature, the structure of lithium lubricating grease became more loose and its viscosity responded faster to the outside. It should be noted that when the temperature was higher than 100 °C, the strain value was reduced to 5%, and the viscosity response to the outside was too fast. That is to say, when the temperature was higher than 100 °C, the linear viscoelastic zone of lithium lubricating grease changed rapidly to the flow zone, and the viscosity characteristics played a dominant role, which was not conducive to the maintenance of grease on the

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contact surface, and even the phenomenon of loss occurred. It may not be able to protect and seal, and its lubrication effect was not ideal. Figure 2 (d) shows that the storage modulus of the flow point decreases with the increase of temperature. The reason is that the 12-hydroxystearate soap fiber is usually twisted disorderly at normal and low temperature. With the increase of temperature, the fiber molecules grew and the unwinding probability decreased, the elastic potential energy stored in the fiber molecules decreased and the storage modulus decreased obviously. #

3 # 2 # 1 # 0 # 00 # 000

1.2

0.8

0.6

0.4

#

3 # 2 # 1 # 0 # 00 # 000

160

Stress of total LVE range/Pa

strain of LVE range %

1.0

180

140 120 100 80 60 40 20

0.2

0

-40

-20

0

20

40

60

80

100

120

140

-40

-20

0

20

T/℃

40

60

(a)

strain of flow point/(%)

50 40 30 20 10 0 40

60

T/℃

(c)

80

100

120

140

3 # 2 # 1 # 0 # 00 # 000

6000

4000

2000

0 20

140

#

Storage modulus(G'=G'')/Pa

60

0

120

8000

#

3 # 2 # 1 # 0 # 00 # 000

-20

100

(b)

70

-40

80

T/℃

-40

-20

0

20

40

60

80

100

120

140

T/℃

(d)

Fig. 2. Rheological parameters yield point and flow point of lithium lubricating grease in different temperature

Effect of Temperature on Apparent Viscosity and Yield Stress of Lithium Lubricating Grease. In order to study the apparent viscosities and yield stress of lithium lubricating grease, Herschel Bulkley rheological equations of lithium lubricating grease at different temperatures were fitted by using the data processing software of rotary rheometer, as shown in Table 3. The relationship between apparent viscosity and temperature is shown in Fig. 3. According to Table 3 and Fig. 3, at the same temperature, the higher the yield stress and apparent viscosities of lithium lubricating grease with higher soap contenst, the smaller the flow indexes. With the increase of temperature, the yield stress and apparent viscosities of lithium lubricating grease decreased, and the flow indexes increased. With the increase of temperature, the greater the activation energy of lithium soap fiber, the smaller the resistance to produce directional flow, the smaller the apparent viscosity and the higher the flow index. The higher the temperature, the more obvious the effect was.

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Temperature

Serial number

τ = τy + ηγ˙ n

Temperature

Serial number

τ = τy + ηγ˙ n

– 30 °C

1

τ = 1144.6 + 997.28γ˙ 0.3292

75 °C

1

τ = 263.6 + 39.9γ˙ 0.9916

2

τ = 843.81 + 430.88γ˙ 0.4340

2

τ = 201 + 31.59γ˙ 1.178

3

τ = 400.84 + 339.26γ˙ 0.4499

3

τ = 77.66 + 11.66γ˙ 1.115

4

τ = 214.07 + 251.12γ˙ 0.4574

4

τ = 52.24 + 2.61γ˙ 1.547

5

τ = 157.47 + 189.89γ˙ 0.4669

5

τ = 38.16 + 4.41γ˙ 1.341

6

τ = 179.87 + 109.31γ˙ 0.5433

6

τ = 34.26 + 3.278γ˙ 1.512

1

τ = 301.2 + 48.58γ˙ 0.5822

1

τ = 97.36 + 17.002γ˙ 0.7797

2

τ = 488 + 28.8γ˙ 0.5771

2

τ = 75.74 + 13.78γ˙ 0.7271

3

τ = 234.8 + 20.85γ˙ 0.5986

3

τ = 36.54 + 6.267γ˙ 0.8873

4

τ = 141.5 + 11.62γ˙ 1.007

4

τ = 19.83 + 3.485γ˙ 0.373

5

τ= 138.9+4γ˙ 0.7513

5

τ = 14.7 + 5.767γ˙ 0.5767

6

τ = 103.64 + 9.705γ˙ 0.5918

6

τ = 12.84 + 6.436γ˙ 0.5658

130 °C

#

3 # 2 # 1 # 0 # 00 # 000

1000

apparent viscosity/(Pa.s)

25 °C

500

0

-40

-20

0

20

40

60

80

100

120

140

T/℃

Fig. 3. Apparent viscosity of the lithium lubricating grease in different temperature

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4 Conclusion Rheology is the macroscopic manifestation of the microstructure of grease. It is of great significance to the formulation design and practical use of grease. The main conclusions are as follows: (1) With the increase of temperature, the yield stress and apparent viscosity of lithium lubricating grease decreased, but the flow index increased; The area of thixotropic ring decreased as a whole, and its structure recovered quickly. At 0 °C, the lithium lubricating grease showed anti-thixotropy to time. (2) The storage modulus of the flow point of lithium lubricating grease decreased with the increase of temperature. When the temperature was higher than 100 °C, the linear viscoelastic zone of lithium lubricating grease changed rapidly to the flow zone, and the structure of lithium lubricating grease damaged seriously, which was not conducive to the sealing and protection of lithium lubricating grease. (3) Lithium lubricating grease was belonged to yield pseudoplastic fluid. When the shear rate was very small, lithium lubricating grease reached yield stress and its apparent viscosity decreases rapidly; When the external force exceeded the yield stress, the apparent viscosity of lithium lubricating grease finally tended to a stable value.

References 1. Zheng, H., et al.: Discussion on the mechanism of boric acid and phosphoric acid to improve the hardening of complex calcium lubricating grease. China Pet. Process. Petrochemical Technol. 21(4), 112–118 (2019) 2. Wang, X.L., Gui, C.L., Zhu, T.S.: Study on rheological parameters of domestic lubricating grease. Tribology 17(3), 232–237 (1997) 3. Yao, L.D., Yang, H.N., Sun, H.W.: Research of rheology for the lithium lubricating grease. Acta Petrolei Sinica (Petroleieum Process. Sec.) 27, 1–5 (2011) 4. Xu, J., Xunan, N., Wang, X.B.: Rheology of lithium lubricating greases under iced water and room air. Tribology 33(4), 406–412 (2013) 5. Zhou, W., Guo, X., Jiang, M., et al.: Study on rheology of lithium lubricating grease prepared from paraffin-base mineral lube base oil. Petroleum Process. Petrochemicals 45(6), 90–95 (2014) 6. Zeng, H., Chen, X.N., Chen, Z.: Influence of composition of complex lithium-based lubricant grease on the microstructure. Lubr. Eng. 36(8), 42–47 (2011) 7. Zhou, W., et al.: Influence of base oil on rheological property of lithium grease. Lubr. Eng. 41(9), 88–92 (2016) 8. Xun, G.Y., Zhang, Y.Z.: Kinetic energy correction factor of lubricating grease flowing in pipes with wall-slip. J. China Univ. Min. Technol. 14, 352–355 (2005) 9. Mezger, T.G.: The Rheology Handbook. Vincentz, Hannover (2002) 10. Draft DIN 51810-2, Testing of lubricants-Testing rheological properties of lubricating greases; Part 2: Determination of the flow point using oscillatory rheometer with a parallel-plate measuring system, July 2009, Beuth Verlag, GmbH, Berlin (2009) 11. Zhu, T.S.: Grease Technology, p. 369. China petrochemical press, Beijing (2005)

On-Chip Generation of PAM-4 Signals Based on a 3 × 3 MMI Architecture for Optical Interconnect and Computing Systems Duy Tien Le and Trung Thanh Le(B) International School (VNU-IS), Vietnam National University (VNU), Hanoi, Vietnam [email protected]

Abstract. We propose a new structure for multilevel pulse amplitude modulation (PAM-4) signal generation for on-chip optical interconnects and data center networks. Our proposed architecture use only one 3 × 3 multimode interference (MMI) Based ring resonator with two segmented phase shifters. Two segmented phase shifters applied the plasma dispersion effect in silicon waveguide are used in the feedback ring resonator waveguide. The structure provides a very steep slope compared with the conventional structure based on Mach Zehnder Interferometer (MZM). As a result, an extreme reduction of power consumption is achieved. Based on this structure, an extreme high bandwidth and compact footprint are also achieved. The device is designed and analyzed using the Beam Propagation Method (BPM) integrated with the Finite Difference-Time Difference (FDTD) simulations. Keywords: Multimode interference (MMI) · Microring resonator · Integrated optics · PAM-4 · High performance computing systems · Data center networks · Optical interconnects

1 Introduction Over the years, on off keying (OOK) modulation is often used for the optical links of optical interconnect and data center networks. However, when the high bit rate and bandwidth are required, the OOK modulation cannot meet. The complexity of the system also increases. As a result, an efficient higher order modulation methods such as M-QAM, PAM-M and M-PSK are considered [1]. Multi-level pulse amplitude modulation (PAM-4) has shown as a good candidate for high data rate transmissions with suitable cost and complexity, especially for applications in optical interconnects and data center networks. It is because that compared with other higher modulation such as M-PSK, M-QAM, PAM-4 requires only a limited resource of digital signal processing (DSP) requirements. The PAM-M modulation allows for direct direction of optical intensity signals without requiring complex DSP, although good signal-to-noise ratio is required [2].

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 1064–1071, 2022. https://doi.org/10.1007/978-981-19-0390-8_134

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In order to generate PAM-4 signal, significant works have been studied and demonstrated. For example, structures based on microring modulators (MRMs), travellingwave Mach-Zehnder modulators (TWMZMs) [3], segmented electrode MZI [4], silicon hybrid modulators, electro-optic polymer modulators and LiNbO3 modulators have been presented [3]. One of the disadvantages of these approaches is to require complex circuits and has low fabrication tolerance. In addition, these structures use directional couplers, so it is very difficult to control the directional coupler to achieve exact coupling ratios. Another limitation of the published approaches is to generate PAM-4 levels with high power consumption due to the requirement of the required phase shifts. In recent years, we have presented some new structures based on only one 3 × 3 MMI coupler for electromagnetically induced transparency (EIT), Fano effect generation, high sensitive biosensors [5, 6]. However, the 3 × 3 MMI coupler applied to optical integrated circuits for high speed, on-chip optical interconnects in high performance computing systems and data center networks have not been realized. Therefore, in this study, we propose a new architecture to implement a PAM-4 signaling system by using a 3 × 3 MMI coupler based ring resonator. Photonic circuits with large scale integrated circuits become feasible and practical with silicon photonics. Therefore, we use the CMOS existing technologies for the design [7].

2 PAM-4 Structure Based on 3x3 MMI Ring Resonator Figure 1 is the schematic of the proposed PAM-4 architecture based on a 3 × 3 MMI based ring resonator. In literature, we have shown that microring structures based on a 3 × 3 GI-MMI coupler can be used for optical filtering, modulating and switching applications [8–10]. Silicon-on-insulator (SOI) rib waveguide structure using the PN junction to generate the phase shifts. The SOI rib waveguide has a width of 500 nm and a height of 220 nm, and it is on a 90-nm slab. We use two PN junction phase shifter segments at feedback waveguide as shown in the box of Fig. 1 for two bits b0 b1 , which use the plasma dispersion effect in silicon waveguides. The small PN doping concentrations are used reducing the optical scattering loss in the SOI rib waveguide. The widths of the P doped layer and N doped layer in the 90-nm slab waveguide are 1 μm. There are also P++ and N++ doped regions with a width of 6.35 μm, which are designed for ohmic contact with the metal pads. They are both 1 μm away from the edge of the rib waveguide. The simulated peak doping concentrations in are 7.8 × 1017 cm–3 for P, 2.1 × 1018 cm–3 for N, 3.9 × 1019 cm–3 for P++ and 9.7 × 1019 cm–3 for N++ [11].

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Fig. 1. PAM-4 generation architecture based on a 3 × 3 MMI coupler based ring resonator with two segmented phase shifters

The change in index of refraction is described by Soref and Bennett’s equations [12]. Around the operating wavelength of 1550 nm, the changes in refractive index and absorption are expressed by: n (at 1550 nm) = −8.8 × 10−22 N − 8.5 × 10−18 P0.8

(1)

α (at 1550 nm) = 8.5 × 10−18 N + 6 × 10−18 P [cm−1 ]

(2)

Figure 2 plots the simulated effective index changes (neff ) and the propagation losses when applying reverse bias voltages on the four PN junction designs in Fig. 1. By segmenting the length of the phase shifter into L1 and L2, where L2 = 2L1 with applied voltage V0 and V1 respectively, 4 levels of PAM-4 signal can be achieved. It is assumed that the phase shifter with the length L1 is for LSB bit and L2 is for MSB bit of input data bits b0 b1 = 00, 01, 10,11.The effective index change was achieved by the plasma dispersion effect in silicon waveguide due to the applied voltage. The total phase shift is calculated by: φ =

2L2 2L1 neff (V0 ) + neff (V1 ) λ λ

(3)

By carefully choose the positions of input waveguides and length of the 3 × 3 MMI coupler, we can achieve the characteristic matrix M = [mij ]3×3 (i, j = 1, 2, 3) of the 3 × 3 MMI coupler as follows [5]: ⎞ −1 −e−j2π/3 e−j2π/3 1 M = √ ⎝ e−j2π/3 −1 e−j2π/3 ⎠ 3 −1 e−j2π/3 −e−j2π/3 ⎛

(4)

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(a)

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(b)

Fig. 2. (a) Effective index change and loss factor at different bias voltage at the PN junction

The relationship between the output complex amplitudes bj (j = 1,2,3) and the input complex amplitudes ai (i = 1,2,3) of the coupler can be expressed by ⎛ ⎞ ⎞⎛ ⎞ ⎛ −j2π/3 −j2π/3 b1 e −1 a1 −e 1 ⎝ b2 ⎠ = √ ⎝ e−j2π/3 (5) −1 e−j2π/3 ⎠⎝ a2 ⎠ 3 b3 −1 e−j2π/3 −e−j2π/3 a3 Here the feedback waveguide connected from output port 3 to input port 2, a3 = [αexp(jθ )]b3 , and α = exp(−α0 L) is the transmission loss along the ring waveguide, where L = 2π R + LMMI and α0 (dB/cm) is the loss coefficient in the core of the optical waveguide; θ = β0 L is the phase accumulated over the racetrack waveguide, where β0 = 2π neff /λ and neff is the effective refractive index. The mode profile of the optical waveguide at 1550 nm is shown in Fig. 3, where the effective refractive index is neff = 2.61.

Fig. 3. (a) Field propagation through the 3 × 3 MMI coupler at the optimal length of 107.8 μm and (b) Normalized output powers at three ports

In order to work correctly as theoretical analysis using matrix method, the numerical simulations of the 3 × 3 MMI coupler need to be undertaken to achieve the optimal length of the MMI coupler. Figure 3 shows the field propagation and normalized powers at three

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output ports at the optimal length 107.8 μm. We can see that fabrication tolerance in MMI length of ±30 nm causes the fluctuation in output power of 0.05. For the existing CMOS fabrication technology with fabrication error of ±5 nm [13], the microring resonator based on 3 × 3 GI MMI coupler has an extremely large tolerance fabrication. The complex amplitudes at output ports 1 are given by b1 = (m11 +

m13 m31 αejθ )a1 1 − m33 αejθ

(6)

For the input signal presented at input port 1:   2   Eout 2  m13 m31 αejθ     = (m11 + ) T= Ein  1 − m33 αejθ 

(7)

The normalized output powers at output ports 1 and 2 compared with the MZM structure are shown in Fig. 4.

Fig. 4. Transmissions at output port 1 and 2

Figure 5 plots the normalized transmission at output port 1 of the proposed structure compared with the MZM structure used to generate the PAM-4 signal. We can see that in order to achieve four levels of PAM-4 of 0.2, 0.4, 0.6 and 0.8, a very small phase shift of the proposed structure is required. By applying two independent binary non-returnto-zero (NRZ) electrical signals V0 and V1 with different peak-to-peak voltages at two output waveguides, four distinct levels for data bits 00, 01, 10, 11 are obtained in the output power. Modeling can be used to find out the voltages required to reach 4 equally spaced power levels, while exploiting the full dynamic range of the output transmission. The required phase shifts to achieve four levels of PAM-4 signal are shown in Table 1 and Fig. 6. We can see that a the slope of the proposed architecture is nearly constant compared with the MZM architecture. Therefore, the transmission of the proposed architecture has a great linearity. The results also show that that power consumption to achieve multilevel PAM-4 is extremely low compared with the MZM based PAM-4 generation, but it provides a higher bandwidth and large fabrication tolerance compared with conventional structure based on the MZM in the literature. The simulation results in Fig. 6 show that for data bits 00, 01, 11, 10, the total phase difference between two

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arms of Fig. 1(a) must be –0.06π , –0.02π , 0.02π and 0.06π (rad), respectively. In order to achieve a linear region, four normalized output powers at 0.2, 0.4, 0.6 and 0.8 are selected.

(a)

(b)

Fig. 5. (a)Transmissions at output port 1 and 2 compared with the PAM-4generation architecture using the MZM and (b) Slope of the two transmissions

Table 1. PAM-4 levels based on two segmented phase shifters PAM-4 Bits

PAM-4 levels (normalized transmission)

Phase shifts required for the conventional MZM

Phase shifts required for this proposed structure

00

V0 (0.2)

2.23 rad

0.63 rad

01

V1 (0.4)

1.77 rad

0.50 rad

10

V2 (0.6)

1.37 rad

0.38 rad

11

V3 (0.8)

0.94

0.24 rad

The whole working principle of the device is verified by using the FDTD simulations. Figure 7 shows the FDTD simulations for 4 levels of PAM-4: 00, 01, 10 and 11 injected into the segmented phase shifters, respectively. The FDTD show a very good agreement with the theoretical analysis.

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Fig. 6. Phase shifts required for PAM-4 levels

(a) 00

(b) 01

(c) 10

(d) 11

Fig. 7. FDTD simulation of the whole device when input signal is at port 1

3 Conclusion We have presented a new architecture for achieving PAM-4 signals using only one 3 × 3 MMI coupler based on CMOS technology. The proposed configuration can provide a good fabrication tolerance and high bandwidth, which is particularly suitable for complex systems with some channels integrated on a chip. Very low power consumption is achieved. The proposed approach is suitable and useful for high performance computing, multicore and high speed data center systems. Acknowledgements. This research is funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 103.03-2018.354.

References 1. Wang, K., et al.: 200-Gbit/s PAM4 generation by a dual-polarization Mach-Zehnder modulator without DAC. IEEE Photonics Technol. Lett. 32(18), 1223–1226 (2020) 2. Binh, L.N.: Optical Modulation: Advanced Techniques and Applications in Tran. CRC Press, Boca Raton (2019) 3. Shi, W., et al.: Silicon photonic mod. J. Opt. 20(8), 083002 (2018)

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4. Kim, M., et al.: A low-power 28-Gb/s PAM-4MZM driver with level pre-distortion. IEEE Trans. Circuits Syst. II Express Briefs 68(3), 908–912 (2021) 5. Le, T.T.: Fano resonance based on 3x3 multimode interference structures for fast and slow light applications. Int. J. Microwave Opt. Technol. 12(5), 406–412 (2017) 6. Trung-Thanh, L.: Cascaded multimode interference-based microresonators for multiple Fano resonance engineering. Opt. Eng. 57(11), 1–7 (2018) 7. Tzintzarov, G.N., Rao, S.G., Cressler, J.D.: Integrated silicon photonics for enabling. Photonics 8(4), 131 (2021) 8. Le, T.T., Cahill, L.W.: The modeling of MMI structures for signal processing applications. In: Greiner, C.M., Waechter, C.A. (eds.) Integrated Optics: Devices, Materials, and Technologies XII. Proceedings of the SPIE, March 2008, vol. 6896, pp. 68961G–68961G-7 (2008) 9. Le, T.T.: Design and analysis of optical filters using 3x3 multimode interference couplers based microring resonators. J. Sci. Technol. Vietnam Acad. Sci. 12(13) (2009) 10. Le, T.T., Cahill, L.W.: Photonic signal processing using mmi coupler-based microring resonators. In: The 20th Annual Meeting of the IEEE Lasers and Electro-Optics Society (LEOS 2007). Lake Buena Vista, FL, USA (2007) 11. Samani, A., et al.: A silicon photonic PAM-4 modulator based on dual-parallel Mach-Zehnder interferometers. IEEE Photonics J. 8(1), 1–10 (2016) 12. Emelett, S.J., Soref, R.: Design and simulation of silicon microring optical routing switches. IEEE J. Lightwave Technol. 23(4), 1800–1808 (2005) 13. Bogaerts, W., Heyn, P.D., Vaerenbergh, T.V.: Silicon microring resonators. Laser Photonics Rev. 6(1), 47–73 (2012)

All-Optical XNOR and XOR Logic Gates Based on Ultra-Compact Multimode Interference Couplers Using Silicon Hybrid Plasmonic Waveguides Thi Hong Loan Nguyen1 , Duy Tien Le2 , Anh Tuan Nguyen2 , Le Minh Duong3 , and Trung Thanh Le2(B) 1 Hanoi University of Natural Resources and Environment (HUNRE), Hanoi, Vietnam 2 International School (VNU-IS), Vietnam National University (VNU), Hanoi, Vietnam

[email protected] 3 VNU University of Engineering and Technology (VNU-UET), Vietnam National University

(VNU), Hanoi, Vietnam

Abstract. We present a new structure based on ultra-compact cascaded multimode interference (CS-MMI) structures using silicon hybrid plasmonic waveguides (HPWG) for all-optical XOR and XNOR logic gates. An ultra-compact footprint of 3 × 16 μm2 can be achieved. We show that the contrast ratios for logic 1 and logic 0 for XOR, XNOR gates are about 20 dB for a ultra-high bandwidth of 70 nm, respectively. A large fabrication tolerance can be achieved by using this structure. Keywords: Optical logic gate · Multimode interference · Silicon on insulator · Silicon photonics · Hybrid plasmonic waveguide · Beam propagation method

1 Introduction In recent years, the plasmonic waveguide is a kind of novel structure enabling nanoscale optical confinement. It is suitable for ultra-compact devices. One of the most important plasmonic waveguides is silicon hybrid plasmonic waveguides (HPWGs), which is compatible with SOI (silicon-on-insulator) technology, but still has a strong field [1]. The propagation loss of such waveguide is also quite small, is at order of 0.01 dB/μm, which is much lower than those traditional metal nanoplasmonic waveguides. In particular, onchip photonic devices assisted locally by HPWGs can have acceptably low losses and ultra-compact footprints. As a result, a variety of HPWGs with modified structures have been proposed for realizing ultra-high compact devices, which is useful for photonic integrated circuits [2]. All-optical logic gates have many possible applications in optical signal processing systems and optical switching networks such as header recognition, parity checkers, and encryption systems [3]. In all-optical networks, there is a great need for implementing all-optical logic gates having small size, low power consumption and high-speed [4]. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 1072–1079, 2022. https://doi.org/10.1007/978-981-19-0390-8_135

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These requirements can be met by using photonic integrated circuits, especially silicon photonics. There have been some approaches to realize all-optical logic gates such as Mach-Zehnder interferometer with a nonlinear phase shifter [5], semiconductor optical amplifiers (SOAs) [6], microelectromechanical systems (MEMS) [7], MMI based photonic crystal, periodic waveguide [8], plasmonic waveguide [9] and multimode interference waveguide [10]. However, these methods require high power and/or complicated fabrication. Over the last few years, we have presented a general theory for implementing optical signal processing based on MMI elements [11]. For further development, we have proposed 2 × 2, 3 × 3 and 5 × 5 MMI based structures for implementing many optical logic gates including NAND, OR, AND, NOT, XNOR, NORgates [12]. In this paper, we present a new scheme for realizing all-optical logic XOR and XNOR gates based on only one 4 × 4 MMI cascaded with a 2 × 2 MMI coupler on HPWGs. The device has the advantage of ease of fabrication, large fabrication tolerance, high contrast ratio and compatibility with system-on-a- chip configuration.

2 Theory of XNOR and XOR Gates Based on Cascaded MMIs on HPWG Figure 1 shows a structure for realizing all-optical XOR and XNOR gates based on cascaded MMI couplers. The silicon hybrid plasmonic waveguide is shown in Fig. 1(b). For a low loss and compact size, we choose the layer thicknesses as follows: hSi = 230 nm, hSiO2 = 50 nm and hAg = 100 nm. It is noted that we choose the suitable thickness of SiO2 layer working as a slot region between Ag and Si layers to balance the loss and confinement factors. The refractive indices of silicon, SiO2 , and silver are nSi = 3.455, nSiO2 = 1.445 and nAg = 0.1453 + j11.3587 at around operation wavelength 1550 nm [13]. PMMA is chosen to cover the cladding with its refractive index of 1.481. The working principle of an MMI coupler is based on the self-imaging principle [14]. When the access waveguides with an identical width of Wa at the positions pi = (i + 21 ) WMMI N , the electrical field inside the MMI coupler can be expressed by [15] E(p, z) = exp(−jkz)

M  m=1

Em exp(j

mπ m2 π z)sin( p) 4 WMMI

(1)

In this work, phase encoding of information is used for input signals and amplitude encoding is used for output signals. We use the logic “1” is represented by 1ej0 and logic “0” is represented by 0ej0 . To determine the logic level at the output of the device, the power in the output waveguide needs to be compared to a threshold value. This can be done electronically by connecting output ports to a photo-detector and a decision circuit. Another approach is to use an optical threshold device based on active MMI couplers instead of using an electronic threshold device [16]. Figure 1(a) shows the proposed scheme for optical logic gate implementation based on 4 × 4 and 2 × 2 MMI structures. By properly choosing the positions of input and output waveguides, the complex amplitude at output port y2 can be expressed by:

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(a) PMMA

Ag SiO2 Si SiO2

(b)

(c) Wa=200nm

Fig. 1. (a) Proposed scheme for optical logic gates, (b) HPWG cross-sectional view and (c) Field profile of the waveguide

y2 = 0.5(jx1 + x2 + jx3 − x4 ) = 0.5(jx1 − x4 ) + 0.5(x2 + jx3 ) = f(x2 , x3 )

(2)

where x1 , x4 are local oscillators and x2 , x3 are input logic variables and y2 is the output logic variable. Here we assume that the wavelength and polarization of the local oscillation signals and information signals are the same. The widths of the HPWG single and multimode waveguides are chosen carefully to have a quality self-images at the output, but still guarantee the loss low enough. Due to the presence of the metal Ag in the hybrid plasmonic waveguide structure, the loss factor increases when the length is too long. We undertake the simulations to find the effective index for TE and TM modes as shown in Fig. 2. The simulations show that the widths of 2 × 2 and 4 × 4 MMI are chosen to be 700 nm and 2000 nm, respectively. Here the access waveguide width for single mode operation is Wa = 200 nm. To realize the XOR logic gate, we use the local oscillation inputs x1 = 1ejπ/2 and x4 = 1ej0 and we assume that a phase shifter of −π/2 is used at the input port x3 . The input signal is encoded by the phase information. This means that on input port x3 , π/2 phase corresponds to logic 1 and −π/2 corresponds to logic 0. For input port x2 , 0-phase corresponds to logic 0 and π -phase corresponds to logic 1. As a result, the truth table for the XOR gate is shown in Table 1. We use the local oscillation inputs x1 = 1ejπ/2 and x4 = 1ej0 and we assume that a phase shifter of π/2 is used at the input port x3 . For input port x3 , a π/2 phase corresponds to logic 0 and −π/2 corresponds to logic 1. For input port x2 , 0-phase corresponds to logic 0 and π -phase corresponds to logic 1. As a result, the truth table for the XNOR gate is shown in Table 2.

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Fig. 2. Effective refractive index of the HPWG waveguide at different widths Table 1. Truth table of the XOR logic gate Input logic

Output logic

x2 (phase)

x3 (phase)

y2 = f(x2 , x3 )

0 (0)

0 (−π/2)

0

0 (0)

1 (π/2)

1

1 (π )

0 (−π/2)

1

1 (π )

1 (π/2)

0

Table 2. Truth table of the XNOR logic gate Input logic

Output logic

x2 (phase)

x3 (phase)

y2 = f(x2 , x3 )

0 (0)

0 (π/2)

1

0 (0)

1 (–π/2)

0

1 (π )

0 (π/2)

0

1 (π )

1 (–π/2)

1

In order to achieve the required phase shift at the input waveguides of the MMI structure, one 1 × 1 MMI coupler is used. For an 1 × 1 MMI, the field at distance z along the multimode section can be written as [17]: ψ(y, z = L1×1MMI ) = e−jβ0M z ψ(y, z = 0)

(3)

The difference of the relative phase between two arms of the multimode waveguide and single waveguide is ϕ = (β0M − β0 )LM , where β0 and β0M are the propagation constants of the fundamental modes of the single and multimode sections. Figure 3 shows the simulations of the phase shift design using an 1 × 1 MMI coupler with a width of 1200 nm and 800 nm. Figure 1(a) shows the field propagation and the selfimage positions at different lengths are shown in Fig. 1(b). Figure 1(c) shows the phase shift obtained for both cases.

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(a)

(b)

(c)

Fig. 3. Effective refractive index of the HPWG waveguide at different widths

3 Simulation Results In this section, light propagation through the logic gates is investigated. The numerical methods are used for the simulations. Figure 2 shows the field distributions of the XOR logic gate at wavelength of 1550 nm for input logic values of 00, 01, 10 and 11, respectively (Fig. 4). The simulations show that there is good agreement with the theoretical analysis given by Table 1.

(a) Input 00

(b) Input 01

(c) Input 10

(d) Input 11

Fig. 4. XOR gate with input signals 00, 01, 10, 11

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Figure 3 shows the field distributions of the XNOR logic gate for input logic values of 00, 01, 10 and 11, respectively (Fig. 5). The simulations show that there is a good agreement with the theoretical results given by Table 2.

(a) Input 00

(b) Input 01

(c) Input 10

(d) Input 11

Fig. 5. XNOR gate with input signals 00, 01, 10, 11

The normalized output powers at output ports y3 and y4 for a signal at input port x4 are shown in Fig. 6(a). The overall transmission of the structure including the 4 × 4 MMI coupler cascaded with a 2 × 2 MMI coupler is shown in Fig. 6(b). We can see that the optimal length of the 4 × 4 MMI is 14.75 μm and the optimal length of the 2 × 2 MMI coupler is found to be 3 μm.

(a)

(b)

Fig. 6. Optimal designs for 4 × 4 and 2 × 2 MMI structures

Figure 7 shows the normalized output powers of the XNOR and XOR gates for bit 1 and 0 at different wavelengths, respectively. We can see that the output powers are quite constant over a large range of wavelength (about 70 nm). We evaluate the performance of the optical logic gates by using the contrast ratio (CR). For example, the optical XOR gate, the CR is expressed by CR = 10log10 (

Plogic1 )(dB) Plogic0

(4)

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As a result, the CR for the XOR and XNOR gates are shown in Fig. 8. It is shown that for a bandwidth of 70 nm from 1530 nm to 1600 nm, the CR varies from 16 dB to 22 dB.

Fig. 7. (a) Normalized output powers for logic 1 and 0 for (a) XOR and (b) XNOR gates

Fig. 8. Contrast Ratios of the XOR and XNOR gates

4 Conclusions We have presented a structure for implementing all-optical XOR, XNOR logic gates based on silicon hybrid plasmonic waveguide. The proposed structure is based on only one 4 × 4 MMI cascaded with a 2 × 2 MMI coupler and it has advantages of ease of fabrication, large fabrication tolerance, quite large contrast ratio and high bandwidth. This new structure can be useful for optical label swapping and recognition in optical packet switching networks or on-chip signal processing applications. Acknowledgements. This research is funded by Vietnam National University, Hanoi (VNU) under project number QG.19.58.

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References 1. Guan, X., et al.: Ultracompact and broadband polarization beam splitter utilizing the evanescent coupling between a hybrid plasmonic waveguide and a silicon nanowire. Opt. Lett. 38(16), 3005–3008 (2013) 2. Li, C., Zhang, M., Xu, H., Tan, Y., Shi, Y., Dai, D.: Subwavelength silicon photonics for onchip mode-manipulation. PhotoniX 2(1), 1–35 (2021). https://doi.org/10.1186/s43074-02100032-2 3. Le, T.-T., Multimode Interference Structures for Photonic Signal Processing. 2010: LAP Lambert Academic Publishing 4. Hardy, J., Shamir, J.: Optics inspired logic architecture. Opt. Express 15(1), 150–165 (2007) 5. Yabu, T., et al.: All-optical logic gates containing a two-mode nonlinear waveguide. IEEE J. Quantum Electron. 38(1), 37–46 (2002) 6. Connelly, M.J.: Semiconductor Optical Amplifiers. Springer, Chem (2002) 7. Lin, L.Y., Goldstein, E.L., Tkach, R.W.: Free-space micromachined optical switches with submilli-second switching time for large-scale optical crossconnects. IEEE Photonics Technol. Lett. 10(4), 525–527 (1998) 8. Zeng, S., et al.: Ultrasmall optical logic gates based on silicon periodic dielectric waveguides. Photonics Nanostruct. Fundam. Appl. 8(1), 32–37 (2010) 9. Ota, M., et al.: Plasmonic-multimode-interference-based logic circuit with simple phase adjustment. Sci. Rep. 6, 24546 (2016) 10. Li, Z., Chen, Z., Li, B.: Optical pulse controlled all-optical logic gates in SiGe/Si multimode interference. Opt. Express 13(3), 1033–1038 (2005) 11. Le, T.T., Cahill, L.: All-optical signal processing circuits using silicon waveguides. In: The 7th International Conference on Broadband Communications and Biomedical Applications. Melbourne, Australia (2011) 12. Le, D., Le, T., Cahill, L.W.: Optical signal processing based on 4×4 multimode interference structures. In: 2018 20th International Conference on Transparent Optical Networks (ICTON) (2018) 13. Palik, E.D.: Handbook of Optical Constants of Solids. Academic Press, New York (1985) 14. Bachmann, M., Besse, P.A., Melchior, H.: General self-imaging properties in N × N multimode interference couplers including phase relations. Appl. Opt. 33(18), 3905 (1994) 15. Heaton, J.M., Jenkins, R.M.: General matrix theory of self-imaging in multimode interference(MMI) couplers. IEEE Photonics Technol. Lett. 11(2), 212–214 (1999) 16. Rodgers, J.S., Ralph, S.E., Kenan, R.P.: Self guiding multimode interference threshold switch. Opt. Lett. 25(23), 1717 (2000) 17. Le, T.-T.: Multimode Interference Structures for Photonic Signal Processing: Modeling and Design. Lambert Academic Publishing, Germany, ISBN 3838361199 (2010)

Retrieval of Small and Medium-Scale Wind Field Based on Doppler Radar Liwei Zhang1 , Xu Wang1,2(B) , Caili Wang1 , and Haijiang Wang1,2 1 School of Electronic Engineering, Chengdu University of Information Technology,

Sichuan, China [email protected] 2 Key Laboratory of Atmospheric Sounding, China Meteorological Administration, Chengdu 610225, China

Abstract. Compared with the inversion of large-scale continuous wind field, the inversion of small and medium-scale wind field is more difficult. Through the inversion and analysis of small and medium-scale wind field, many disastrous weather can be predicted. It is of great significance to study the formation mechanism of small and medium-scale weather system and the complex structure of atmospheric boundary layer. The GUI interface of MATLAB is used to set the parameters of wind field simulation. Then the radial velocity fields of various small and medium-scale wind fields are simulated according to Rankine mode. Finally, use NIVAP (integration method in natural coordinate system) and EVAPTC (EVAP method for tropical cyclone inversion), wind field inversion methods to invert the simulated wind field, and compare the similarity coefficients of the two inversion results. Through comparison and analysis, we can draw the following conclusions: both these two inversion methods can get better inversion results, but EVAPTC method is slightly better than NIVAP method. Keywords: Rankine mode · Small and medium-scale cyclone · Inversion of wind field · Similarity coefficient

1 Introduction Doppler weather radar is an active remote sensing detection tool for measuring the occurrence of cloud, rain and various strong convective weather. Its working principle is to measure the velocity of the scatterer relative to the radar based on the Doppler effect, and under certain conditions inversely show the atmospheric wind field, the distribution of the vertical velocity of the airflow and the turbulence. This is of great significance to the warning of strong convective weather. From the 1960s to the present, scholars at home and abroad have made great achievements in research of wind field inversion. Lhermitte [1] et al. proposed the VAD method based on single Doppler radar. At present, the VAD method has been widely used in meteorological services. Then, Zuyu Tao [2] proposed the VAP method. Jie Bai [3] et al. performed two-dimensional filtering on the original data to improve the calculation accuracy of the VAP method. Zhenbo Zhou [4] proposed a method of extending VAP based © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 1080–1087, 2022. https://doi.org/10.1007/978-981-19-0390-8_136

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on the VAP method. The assumption that the change of local wind field is nonlinear is more close to the real wind field information. Xudong Liang [5] unified the VAD method and the VAP method, and proposed the IVAP method, and then introduced the natural coordinate system into the IVAP technology, and proposed the NIVAP method. Experiments have proved that the NIVAP method has higher accuracy for the inversion of vortex wind fields. According to the characteristics of tropical cyclones, Changrong Luo and Zhaobo Sun [6] improved the VAP method and proposed an EVAP method suitable for tropical cyclone inversion, namely the EVAPTC method. Experiments show that the EVAPTC method also has high accuracy for the inversion of the vortex wind field. This design is based on the small and medium-scale wind field inversion of Doppler radar. Firstly, the small and medium-scale wind field is simulated according Rankine mode, and then use the inversion method to invert it. Invert the simulated radial velocity field, then calculate and compare the similarity coefficients between the inversion results of different wind fields and the simulated true values to find out more suitable inversion methods for small and medium-scale wind fields.

2 Rankine Mode Rankine mode can be used to simulate wind fields for small and medium-scale cyclones. The size of the core radius (Rmax ) is a key parameter in the simulated wind field, and the maximum rotation velocity (Vmax ) at this radius is also a key parameter in the simulated wind field. The velocity distribution law of Rankine mode is shown in Fig. 1. It can be seen from Fig. 1 that from the center of the cyclone where the rotation velocity is zero to the radius of Rmax , the rotation velocity (Vr ) increases linearly with the radius. Vr = C1 r(r ≤ Rmax )

(1a)

Outside the radius of Rmax , Vr is inversely proportional to the radius. Vr = C2 /r(r ≥ Rmax )

Fig. 1. Cyclone velocity distribution in Rankine mode

(1b)

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Constants C1 and C2 are determined by Rmax and Vmax . C1 = Vmax /Rmax

(2a)

C2 = Vmax Rmax

(2b)

Two very important parameters describing the center motion of a cyclone are Rmax and Vmax .

3 Principle of Inversion Methods Principle of NIVAP method [7] (Fig. 3): Assuming that the wind velocity and wind direction in a range are uniform and constant, at any point (A) in the wind field, make a circle with the center of the vortex as the center (O). On the arcs on both sides of A, take two points of equal arc length to A, A and A respectively. By calculating the integral within the range of  A OA , the radial velocity and tangential velocity of the inversion point are obtained. According to these two parameters, draw the wind vane, and then calculate its wind vector. The following is the calculation formula (5a, 5b, 5c) of the NIVAP method, and the meaning of each parameter in the formula Table 3.

⎧ ⎪ ⎨ vT = ⎪ ⎩ vR =

Va = vT sin α + vR cos α

(5a)

α = arcsin[r0 sin(θ − θc )/rc ]

(5b)

 α2

 α2  α2  α2 Vα sin αd α α1 cos2 αd α− α1 Vα cos αd α α1 cosα sin αd α  α2 2  α2  α2 2 α−( α1 cos α sin αd α)2 α1 sin αd α α1 cos αd  α2   α2 2 αd α− α2 V sin αd α α2 cosα sin αd α V cos αd α sin α1 α α1 α α1  α2 2 α1  α2  α2 2 2 α1 sin αd α α1 cos αd α−( α1 cos α sin αd α) α1

(5c)

Table 3. The meaning of each parameter in formula 5a, 5b, 5c Parameter name

Parameter meaning

vT

Tangential velocity at point A

vR

Radial velocity at point A

[α1 , α2 ]

An assumed range of constant wind direction and wind velocity ( A OA )

Va

Radial velocity observed by radar

θc

The radial azimuth of the vortex center of the wind field

θ

Radar azimuth

r0

Distance from radar to vortex center of wind field

rc

The radius of circle ◦ O

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Fig. 3. The geometric relationship of the NIVAP method

Principle of EVAPTC method [7] (Fig. 4): Draw a circle with the distance from any point(B) in the wind field to the center of the vortex as the radius. The radial directions adjacent to the left and right of b intersect at A and C respectively. A and C are points in the radial direction adjacent to the left and right on the same distance circle as B. Use A instead of A and C instead of C. According to formula 6, the wind direction and wind vector are calculated. The meaning of each parameter in formula 6 is shown in Table 4.  2 −θ2 )−cos(β1 −θ1 ) tan α = bbcos(β sin(β2 −θ2 )+sin(β1 −θ1 ) (6) Vr1 V = cos(α−β 1 +θ1 )

Table 4. The meaning of each parameter in formula 6 Parameter name Parameter meaning θ1

Angle difference between A and B radial

θ2

Angle difference between C and B radial

Vr1

Radial velocity of A

Vr

Radial velocity of B

Vr2

Radial velocity of C

β1

 AOB

β2

 BOC

α

The angle between the actual wind direction and the radial wind direction of point B

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Fig. 4. The adjacent radial relationship (a), The geometric relationship of the EVAPTC method (b)

The calculation method of similarity coefficient is shown in formula 7, and the meaning of each parameter in the formula is shown in Table 5. ⎧

  ⎨ RR = N Vi V  /( N Vi 2 N V  2 ) i i i i i

  (7) ⎩ r = N δ δ  /( N δ 2 N δ  2 ) r i i i i i i i

Table 5. The meaning of each parameter in formula 7 Parameter name

Parameter meaning

RR

Similarity coefficient of wind velocity

rr

Similarity coefficient of wind direction

Vi

Wind velocity of simulated wind field

Vi

Wind velocity of inversion results

δi

Wind direction of simulated wind field

δi

Wind direction of inversion results

N

Number of inversion points

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4 Inversion Results a. Simulation and inversion of radial velocity of small and medium-scale cyclones The small and medium-scale cyclone is simulated, the radar center position is the origin of the coordinates, the elevation angle is 0.5°, the cyclone center is located 45° east of the radar center, it is 80 km from the radar center, and the cyclone core radius is 3 km. The rotating wind velocity on a circle with the core radius as the radius is 30 m/s. Figure 5a is the Doppler velocity simulation diagram of the cyclone. It can be seen from Fig. 5a that a pair of positive and negative radial velocities with the same value are located on both sides of the cyclone center. The component toward the center of the cyclone is on the left, and the component away from the center of the cyclone is on the right, which rotates counterclockwise. Therefore, Fig. 5a is a cyclone Doppler velocity image. It can be found that the inverted wind direction of the Small and medium-scale cyclone wind field is counterclockwise (as shown in Fig. 5b and Fig. 5c). In Fig. 5b and Fig. 5c, ‘*’ means that the velocity is 0. The farther away from the center, the shorter the arrow, that is, the lower the velocity. It is consistent with the velocity field characteristics of the Rankine model.

Fig. 5. Cyclone Doppler velocity image (a), NIVAP cyclone wind field inversion image (b), EVAPTC cyclone wind field inversion image (c)

b. The velocity simulation and inversion results of divergent wind field The Doppler radial velocity of the divergent wind field is simulated, the elevation angle is 0.5°, the divergence center is located at 45° east by north, and the distance from the radar center is 80 km. The radius of the divergent core is 3 km. The maximum wind speed on the circle is 30 m/s. Figure 6a is a divergent Doppler velocity image. The arrow of divergence wind field inversion points outward from the convergence center, and the shorter the arrow goes outward (Fig. 6b and Fig. 6c). In Fig. 6b and Fig. 6c, ‘*’ means that the velocity is 0. It is consistent with the characteristics of the velocity field of the Rankine mode.

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Fig. 6. Divergent Doppler velocity image (a), NIVAP divergent wind field inversion image (b), EVAPTC divergent wind field inversion image (c)

Compare the inversion accuracy of the two inversion methods by calculating the similarity coefficient of wind velocity between the simulated wind field and the inverted wind field, and the similarity coefficient of the wind direction. Then, which of the two methods is more suitable for the inversion of small and medium-scale wind field is compared. Table 6 shows the similarity coefficients calculated for the two inversion methods under different wind fields. Table 6. Similarity coefficient

Cyclone Divergence

NIVAP

EVAPTC

RR

0.9062

0.9038

rr

0.7765

0.8605

RR

0.9161

0.9154

rr

0.9518

0.9483

5 Conclusion By setting the values of these series of parameters, the corresponding radial velocity simulation diagram can be obtained. Then use the NIVAP inversion method and the EVAPTC inversion method to invert the radial velocity simulation map. The following conclusions can be drawn through comparison: a. From the perspective of the wind direction of the inversion results, the general wind direction of the inversion results of these two methods are consistent with the Doppler velocity images of cyclones and divergence. That is, the negative velocity zone is close to the radar center, and the positive velocity zone is far away from the radar center. In addition, the cyclone inversion result rotates counterclockwise.

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b. From the wind speed of the inversion results, the wind speed changes of the inversion results of these two methods are consistent with the Doppler speed image of cyclones and divergence, that is, the closer to the vortex at the center of the vortex, the greater the speed, the maximum speed appears at the center of the vortex. c. From the calculation results of the similarity coefficient, the wind speed similarity coefficients of the NIVAP method and the EVAPTC method are relatively close and relatively stable, and both are positively correlated with the wind speed of the simulated wind field, and the correlation is above 0.9. The wind direction similarity coefficient of the EVAPTC method is also relatively stable and positively correlated with the wind direction of the simulated wind field, and the correlation is basically above 0.9. The wind direction similarity coefficient of the NIVAP method is not stable. Based on the above analysis, the following conclusions can be drawn: Both inversion methods can obtain better inversion results, but the EVAPTC method is slightly better than the NIVAP method. Among the two methods, the EVAPTC inversion method is better and more suitable for higher accuracy requirements.

References 1. Lhermitte, R.M., Atlas, D.: Precipitation motion by pulse Doppler radar. In: Proceedings of the Ninth Weather Radar Conference, pp. 218–223 (1961) 2. Tao, Z.: VAP method for inversion of wind vector field from single Doppler velocity field. Acta Meteorol. Sci. 50(1), 81–90 (1992) 3. Bai, J., Tao, Z.: Data preprocessing of VAP retrieval from single Doppler radar wind field. Acta Meteorol. Sci. 11(1), 21–26 (2000) 4. Zhou, Z., Min, J., Peng, X., et al.: Extended VAP method for single-Doppler radar wind field inversion(I): method and comparative test. Plateau Weather 25(3), 516–524 (2006) 5. Liang, X., Wang, B.: Doppler radar integral VAP retrieval technology. Acta Meteorol. Sci. 65(2), 261–271 (2007) 6. Luo, C., Sun, Z., Wei, M., et al.: VAP extended application method for retrieving tropical cyclone near-center wind field with single Doppler radar. Acta Meteorol. Sci. 69(1), 170–180 (2011) 7. Chang, Y., Dai, J., Huang, X., Zheng, S.: Analysis of retrieving vortex wind field by single Doppler radar. Meteorol. Technol. 47(5), 720–730 (2019) 8. Liou, Y.-C., Bluestein, H.B., et al.: Single-Doppler velocity retrieval of the wind field in a tornadic supercell using mobile, phased-array, Doppler radar data. J. Atmos. Oceanic Technol. 08, 35 (2018) 9. Ueng, S.-K., Chiang, Y.-C.: Wind field retrieval and display for Doppler radar data. In: Bebis, G., et al. (eds.) ISVC 2008. LNCS, vol. 5358, pp. 171–182. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-89639-5_17 10. Zeng, Y., Zhou, H., Roarty, H., Wen, B.: Wind speed inversion in high frequency radar based on neural network. Int. J. Antennas Propag. 2016(Pt. 3), 1–8 (2016) 11. Yang, L., Wang, Y., Yang, Y.: Experimental study on wind field retrieval using WRF 3D-var assimilation Doppler radar. Glacial Permafrost 38(01), 113–120 (2016) 12. Lin, T., Yang, R., Pan, C., Xie, L.: Application of Doppler radar velocity data in short-term weather forecast. Abstract Ed. Nat. Sci. 2016(01), 228 (2016)

Energy-Efficiency of Full-Duplex-Enabled D2D Communications Underlaying Cellular Networks Ning Liang1 , Liang Han1,2(B) , and Yupeng Li1,2 1 College of Electronic and Communication Engineering,

Tianjin Normal University, Tianjin 300387, China [email protected] 2 Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, Tianjin Normal University, Tianjin 300387, China

Abstract. This paper investigates the energy efficiency (EE) of device-to-device (D2D) communications underlaying cellular networks. Two transmission modes, full-duplex (FD) underlay mode and half-duplex (HD) underlay mode are considered. For each mode, we maximize the system EE while fulfilling the minimum rate requirements and maximum transmit power constraints for both cellular and D2D users. The parametric Drinklbach method is adopted to transform the optimal functions into a more tractable form. Then, we use the concave-convex procedure (CCCP) algorithm to obtain the optimal solution as the optimization problem can be transformed into a difference of convex functions (D.C.) programming. Finally, numerical results reveal how the channel gains and self-interference (SI) cancellation ability influence the EE. Keywords: Device-to-device (D2D) communications · Energy efficiency (EE) · Full-duplex (FD) · Concave-convex procedure (CCCP)

1 Introduction Recently, the fifth-generation (5G) communication technology has gradually matured, and its application is more and more extensive [1]. In a traditional cellular network, users communicate with each other under a base station (BS). As one of the key technologies of 5G, device-to-device (D2D) communications technology enables adjacent D2D users to communicate directly. However, the utilization of limited spectrum resources has become one of the most concerned research contents of D2D communications. Full-duplex (FD) mode, which allows users to transmit and receive messages in the same frequency band, and recent research found that FD mode has a better communication performance than half-duplex (HD) mode, the spectrum efficiency (SE) and energy efficiency (EE) of users have also been significantly improved [2]. So far, most existing work focuses on the optimization of SE while the investigation on EE of D2D communications has rarely been considered. In [3], the author divides FD mode into time-division duplex (FDD) mode and frequency-division duplex (TDD) © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 1088–1095, 2022. https://doi.org/10.1007/978-981-19-0390-8_137

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mode, which is both studied to maximize the EE of a single node and all nodes with the sum SE constraint or individual SE constraint. In [4] and [5], three transmission modes, dedicated mode, reusing mode, and cellular mode, are respectively considered to maximize the overall SE and EE. In this paper, we investigate the problem of EE optimization for D2D communications under various duplex modes. The FD underlay mode and HD underlay mode are both considered. We give the system EE expressions of the two modes while fulfilling the minimum rate requirements and maximum transmit power constraints for both cellular users (CUs) and D2D users (DUs). For FD underlay mode, we use the parametric Drinklbach method to eliminate the fractional form of the objective function, then the part of the objective function can be transformed into the difference of convex functions (D.C.) structure and then solve by concave-convex procedure (CCCP) [6, 7]. By iterative algorithm, we obtain the optimal solution in fractional programming [8]. For FD underlay mode, the method of the search was considered first to determine the spectrum resource allocation of DUs and CUs. According to the maximum EE, the optimal mode can be selected. The rest of the paper is organized as follows. In Sect. 2, we describe the system model and formulate the optimal EE mode switching problem. In Sect. 3, we solve the EE optimization problems in two transmission modes. Then, series of simulation results are given in Sect. 4. Section 5 concludes this paper.

2 System Model In this section, we first introduce the system model and then formulate the EE optimization problems. We consider a single-cell scenario where a pair of DUs (DU1 and DU2) share the uplink spectrum resource with a CU. The total shared bandwidth is W HZ, which is divided into M resource blocks (RBs). DUs can select working on either FD underlay mode or HD underlay mode, and DUs reuse the spectrum of the CU. Figure 1 shows the two transmission modes. We denote BS, CU, DU1 and DU2 as b, c, 1 and 2, respectively. The channel is denoted as hi,j with i = {c, 1, 2} and j = {b, 1, 2}, where i = j. Otherwise, we denote the transmit power of the CU, DU1 and DU2 as Pc , P1 and P2 , respectively. The maximum transmission power of the CU and DUs are expressed as Pcmax and Pdmax , respectively. In our proposed model, the residual self-interference is still existing in-channel and it is subject to the zero-mean complex Gaussian random variables. So, the variance of residual self-interference (RSI) at DU1 and DU2 was denoted as βP1 and βP2 , respectively, where β is a constant that reflects the self-interference (SI) cancellation ability. Since the optimal goal is EE, we need to consider the total power consumption. For HD mode, the total power consumption includes two parts: power consumption of power amplifier (PA) and radio frequency (RF) circuit. We denote the power consumption of PA as PPA which depends on transmit power and drain efficiency, i.e., PPA = ωP

(1)

 where 1 ω represents drain efficiency and P represents transmit power. The power consumption of the RF circuit is independent of the transmit rate and is generally assumed

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as a constant P0 . For the node in FD mode, the total power consumption includes not only power consumption of the PA and RF circuits but also the power consumption of the self-interference cancellation (SIC), which is assumed as a linear function of transmit power, i.e., PSIC = ξ P + Ps0

(2)

where ξ represents the isolation factor and Ps0 represents the static SIC circuits power consumption. BS

BS Useful signal Interference

CU

Useful signal Interference CU

DU1

DU2

DU1

(a) FD Underlay Mode

DU2

(b) HD Underlay Mode

Fig. 1. System model

2.1 FD Underlay Mode In this mode, DUs reuse the entire spectrum resources of CU. Since they work on FD mode then RSI at DUs and mutual interference between CU and DUS have occurred. Because spectrum allocation is not required, the EE maximization problem of FD underlay mode can be formulated as: Rc + R1 + R2 EEFD,underlay = Pc ,P1 ,P2 (ω+ξ )(P1 + P2 ) + ωPc +3P0 +2Ps0   Pc |hcb |2 s.t. Rc = W log2 1 + ≥ Rmin c , 2 2 |h | |h | P1 1b + P2 2b + WN0   P2 |h21 |2 R1 = W log2 1 + ≥ Rmin 1 , Pc |hc1 |2 + βP1 + WN0   P1 |h12 |2 R2 = W log2 1 + ≥ Rmin 2 , Pc |hc2 |2 + βP2 + WN0 max

0 ≤ Pc ≤ Pcmax , 0 ≤ P1 , P2 ≤ Pdmax ,

(3)

min and Rmin denote the minimum rate requirement for CU, DU1 and where Rmin c , R1 2 DU2, respectively. The spectral density of the additive white Gaussian noise (AWGN) was denoted as N0 .

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2.2 HD Underlay Mode In this mode, DUs still reuse the spectrum of the CU, so there only exists mutual interference between CU and DUs. And DU1 and DU2 take up different RBs in two-way D2D communications, we need to think about the problem of spectrum allocation. The numbers of RBs allocated by DU1 and DU2 are denoted as m1 and m2 , respectively. The EE optimal problem can be formulated as follows: Rc + R1 + R2 ω(P1 + P2 + Pc )+3P0     m1 m1 Pc |hcb |2 m2 m2 Pc |hcb |2 W log2 1 + W log2 1 + s.t. Rc = + ≥ Rmin c , M M MP1 |h1b |2 + m1 WN0 MP2 |h2b |2 + m2 WN0 ⎛ ⎞ m2 MP2 |h21 |2   ⎠ ≥ Rmin R1 = W log2 ⎝1 + 1 , M m2 Pc |hc1 |2 + WN0 ⎞ ⎛ |h12 |2 m1 MP 1   ⎠ ≥ Rmin W log2 ⎝1 + R2 = 2 , M m1 Pc |hc2 |2 + WN0 max

m1 ,m2 ,P1 ,P2 ,Pc

EEHD,underlay =

m1 , m2 ∈ {1, 2, · · · , M }, m1 + m2 = M , 0 ≤ Pc ≤ Pcmax , 0 ≤ P1 , P2 ≤ Pdmax .

(4)

Let η1 and η2 denote the maximum EE in the FD underlay mode and HD underlay mode, respectively. So, the optimal transmission mode of the DUs can be selected as M ∗ = arg max {η1 , η2 }. M ∈{1,2}

(5)

3 EE Optimizations in Two Transmission Modes In this section, we present the methods to solve EE optimization problems under the conditions of minimum transmission rate requirements and maximum transmission power constraints for two transmission modes. 3.1 FD Underlay Mode Note that the objective function is non-convex, so it has no way to solve the optimization problem directly. But we found the numerator of objective function has a D.C. structure, and the denominator of it is linear. The objective function can be rewritten as EEFD,underlay =

f1 (P) − f2 (P) , (ω+ξ )(P1 + P2 ) + ωPc +3P0 +2Ps0

(6)

where P = [Pc , P1 , P2 ]T and superscript (·)T denotes the transpose operator. And f1 (P), f2 (P) can be expressed as    f1 (P) = W log2 Pc |hcb |2 + P1 |h1b |2 + P2 |h2b |2 + WN0 +W log2 P2 |h21 |2 + Pc |hc1 |2   (7) +βP1 + WN0 )+W log2 P1 |h12 |2 + Pc |hc2 |2 + βP2 + WN0 ,

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and

    f2 (P) = W log2 P1 |h1b |2 + P2 |h2b |2 + WN0 + W log2 Pc |hc1 |2 + βP1 + WN0   + W log2 Pc |hc2 |2 + βP2 + WN0 . (8) In addition, three constraints in (3) can be written as  min   Rc 2 W Pc |hcb | − 2 − 1 P1 |h1b |2 + P2 |h2b |2 + WN0 ≥ 0,  Rmin   1 W P2 |h21 | − 2 − 1 Pc |hc1 |2 + βP1 + WN0 ≥ 0, 2

 Rmin   2 P1 |h12 |2 − 2 W − 1 Pc |hc2 |2 + βP2 + WN0 ≥ 0,

(9)

respectively. Since the constraints are all linear, a convex set can be obtained from (9), which is denoted as 1 . To make the problem more tractable, we can use the parametric Dinkelbach algorithm (PDA) to eliminate the denominator of (6) [9]. So, the optimal problem can be written as

 max f1 (P) − f2 (P) − λ∗ ((ω+ξ )(P1 + P2 ) + ωPc +3P0 +2Ps0 ) = 0, (10) P∈1

where λ∗ denote the maximum EE. Obviously, f2 (P) is differentiable, so we can solve the optimization problem by CCCP.First, the first-order of f2 (P) at   Taylor expansion  point P (k) can be approximated as f2 P (k) + ∇f2 P (k) , P − P (k) where x, y = xT y   denotes the inner product between vectors x and y, and ∇f2 P (k) denotes the gradient of T  f2 (P) at P (k) = Pc(k) , P1(k) , P2(k) . The k denotes ordinal number of the iteration, after above deformation, the optimal function is given as the form that a concave function subtracts an affine function. Therefore, the optimal problem can be seen as the convex optimization problem which can be solved efficiently by the interior-point method [10]. In each iteration, the optimal solution can be obtained by the iterative algorithm as follow:       P (k+1) = arg max f1 (P) − ∇f2 P (k) , P − λ(k) ((ω+ξ )(P1 + P2 ) + ωPc +3P0 +2Ps0 ) , P∈1

(11)  where λ(k) = EEFD,underlay P=P(k) . The whole procedure above is illustrated in Algorithm 1.

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The optimal value obtained by each iteration is increased and unchanged ultimately so that the iterative terminates. 3.2 HD Underlay Mode In the FD underlay mode, we should consider the problem of spectrum allocation. Given the RBs are finite, m1 and m2 both have a minimum value according to the constraint in (2), which were rewritten as   MPdmax |h21 |2 m2 W log2 1 + ≥ Rmin 1 , M m2 WN0   MPdmax |h12 |2 m1 W log2 1 + (12) ≥ Rmin 2 . M m1 WN0 Because R1 and R2 are increasing functions, then we can obtain the minimum m1 and m2 min which were denoted as mmin 1 and m2 , respectively. Similar to FD underlay mode, we treat them as constants, then CCCP and fractional programming are combined to solve the optimization problem in (2) efficiently.

4 Numerical Results In this section, some numerical results are illustrated to confirm our results. The total bandwidth is set to be 1.8 MHz, which is divided into 10 RBs. To simplify the channel power gains, we define |h12 |2 = |h21 |2 = |hd |2 , |hc1 |2 = |hc2 |2 = |hc |2 , |h1b |2 = |h2b |2 = |hb |2 . The minimum rate requirement for DU1 and DU2 are the same and set to = Rmin = 10 Mbps, the minimum rate requirement for CU is set to Rmin = 5 Mbps, Rmin c 1 2 and the maximum transmits power of the CU and DUs are the same and set to Pcmax = of AWGN is set to be N0 = −174 dBm/Hz. The Pdmax = 100 mW. The spectral density  drain efficiency of PA is set as 1 ω = 25%. The power consumption of RF circuits is modeled as P0 = 200 mW, and the isolation factor and the static SIC circuits power consumption are set as ξ = 0.1 and Ps0 = 50 mW, respectively. In Fig. 2, we compare the maximum EE for two modes with the variation of the SI cancellation coefficient β. In general, the maximum EE of FD underlay mode decrease

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monotonously with the increase of β for two cases while it remains unchanged under HD underlay mode. The FD mode becomes infeasible when β > −98 dB in case I and β > −108 dB in case II respectively because of the poor SI cancellation ability.

Fig. 2. The effects of SI cancellation coefficient β on the maximum EE of two modes, where |hc |2 = |hb |2 − 120 dB, |hcb |2 = −100 dB, |hd |2 = −80 dB for case I and |hd |2 = −90 dB for case II.

Fig. 3. The effects of channel gain |hd |2 on the maximum EE of two modes, where |hd |2 = −100 dB, β = −120 dB, |hc |2 = |hb |2 = −120 dB for case I and |hc |2 = |hb |2 = −110 dB for case II.

Figure 3 illustrates the effects of channel gain |hd |2 on the maximum EE of two modes. We can see that the maximum EE of two modes increase with the increase of |hd |2 , and the EE of FD underlay mode performs better than HD underlay mode.

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5 Conclusion In this paper, we investigated the mode switching for D2D communications with the purpose of EE maximization. The FD underlay mode and HD underlay mode are proposed. In both two modes, we combined the D.C. program and fractional program to solve the non-convex optimization problem. By the iterative method, we use CCCP to obtain the maximum EE of FD mode and HD mode, so it is easy to select the best mode which has the greater performance. It is shown by the numerical results that the FD underlay mode has a better EE performance than HD underlay mode.

References 1. Agarwal, M., Roy, A., Saxena, N.: Next generation 5G wireless networks: a comprehensive survey. IEEE Commun. Surv. Tutor. 18(3), 1617–1655 (2016). 3rd Quart. 2. Duarte, M., Sabharwal, A.: Full-duplex wireless communications using off-the-shelf radios: feasibility and first results. In: Proceedings of the Asilomar Conference Signals, System and Computers, November 2010, pp. 1558–1562 3. Guo, W., Zhang, H., Huang, C.: Energy efficiency of two-way communications under various duplex modes. IEEE Internet Things J. 8(3), 1921–1933 (2021). https://doi.org/10.1109/JIOT. 2020.3016430 4. Feng, D.-Q., et al.: Mode switching for device-to-device communications in cellular networks. In: Proceedings of the IEEE GlobalSIP, December 2014, pp. 1291–1295 5. Feng, D., et al.: Mode switching for energy-efficient device-to-device communications in cellular networks. IEEE Trans. Wireless Commun. 14(12), 6993–7003 (2015). https://doi. org/10.1109/TWC.2015.2463280 6. Tuan, H.D., Hosoe, S., Tuy, H.: D.C. optimization approach to robust controls: the feasibility problems. Int. J. Control 73(2), 89–104 (2000) 7. Yuille, A.L., Rangarajan, A.: The concave-convex procedure (CCCP). In: Proceedings of the Advances in Neural Information Processing Systems, pp. 1033–1040 (2001) 8. Schaible, S.: Fractional programming. Zeitschrift für Oper.-Res. 27(1), 39–54 (1983). https:// doi.org/10.1007/BF01916898 9. Dinkelbach, W.: On nonlinear fractional programming. Manag. Sci. 13(7), 492–498 (1967) 10. Boyd, S., Vandenberghe, L.: Convex Optimization. Cambridge University Press, Cambridge (2004)

Air Channel Measurement in Free-Space Optical Communications Xiaorui Yuan1,2 , Ruotong Zou1,2 , Ming Li1,2(B) , Jiawei Han1,2 , and Xiaocheng Wang1,2 1 Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, Tianjin

Normal University, Tianjin 300387, China [email protected] 2 College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin 300387, China

Abstract. The performance of free space optical communication system is degraded severely over the atmospheric channel. In this paper, the relation model between wavefront error variance and phase structure function is established theoretically. The experimental setup has been built, and the Wavefront measurement data of optical signal is collected by the Shack-Hartmann Wavefront Sensor (SHWFS) of adaptive optics system in real time, and the correctness of the relational model is verified by experiments. The results can be used to estimate the atmospheric coherent diameter and refractive index structure constant of atmospheric optical link, which enables to provide guidance for free space optical communication. Keywords: Free-space optical communication · Adaptive optics · Atmospheric turbulence

1 Introduction Free-space optical communication has many advantages, such as strong anti-jamming capability, high reliability and confidentiality, etc. The use of atmospheric laser communication to obtain information is a hot research topic nowadays. But atmospheric turbulence still greatly affects the quality and transmission distance of communication. Turbulence is an irregular and chaotic movement, while atmospheric turbulence is a random movement in the atmosphere. When the temperature distribution is uneven, it will affect the change of wind speed and direction, forming a series of turbulent whirlpools with different sizes, and then lead to the uneven refractive index of the atmosphere, which is the atmospheric turbulent motion [1]. Small temperature fluctuations can change the refractive index of the atmosphere, and the refractive index of each point in the turbulence changes randomly with time and space [2]. It’s difficult to analyze the characteristics of atmospheric turbulence directly. Therefore, we need to establish a model to analyze the change of the refractive index and the characteristics of atmospheric turbulence. X. Yuan and R. Zou—Contribute equally to this work. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 1096–1102, 2022. https://doi.org/10.1007/978-981-19-0390-8_138

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The most representative and widely used atmospheric turbulence model is Kolmogorov turbulence model, which was proposed by Kolmogorov, a scholar of the former Soviet Union, to further study the formation reason of atmospheric turbulence and its influence on imaging. Kolmogorov argued that although the fluid as a whole is not isotropic, it can be approximated to be statistically uniform and isotropic at a given microscopic scale. Therefore, the statistical averaging method can be used to describe atmospheric turbulence is equivalent to the refractive index change. Random changes in the atmosphere produce different turbulent eddies, which are constantly created and lost under the influence of many factors, such as wind speed and direction. Under the influence of inertial forces, large whirlpools become smaller, small whirlpools gradually disappear and other energy is consumed in the form of heat, while new whirlpools are constantly being created. In other words, the vortex is constantly splitting from the larger to the smaller, which is the transfer of energy from the outer scale of the turbulence to the inner scale of the turbulence. In the inertial sub-range, C n 2 is the structure constant of atmospheric refractive index, and R is the distance between two points of turbulence characteristics. The structure function of atmospheric refraction can be expressed as: Dn (R) = Cn2 R2/3 , linner  R  Louter , where, L outer is the upper bound, the usual size of meters to the order of 100 m; linner is the lower bound, usually a few millimeters to centimeters in size [3]. The refractive index structure constant C n 2 (n is the atmospheric refractive index) is used to describe the intensity of refractive index fluctuation caused by temperature fluctuation in atmospheric turbulence, and this parameter can also be used to characterize the intensity of turbulence [4]. The structure constant of refractive index is an index of atmosphere, but it has many variables and is difficult to accurately predict and directly measure. Therefore, C n 2 can be described indirectly through other parameters, or other parameters can be directly used to describe the effect of atmospheric turbulence. The refractive index structure constant is related to temperature, altitude, pressure, humidity and other factors [5], and can be directly measured by professional measuring instruments. For example, Wu Xiaoqing et al. [6] calculated the near-ground refractive index structure constant of Taishan station through some conventional meteorological parameters observed at the station. At present, there are some models that can describe the refractive index structure constant approximately. For example, Tatarski established the relationship between the conventional meteorological parameters and through the outer scale parameters; Slc-Night model [7] and Hufnagel-Valley model [8] can be calculated theoretically, and the relationship between altitude and refractive index structure constant can be obtained. Through the existing model, we have a certain basis for the study of refractive index structure constant. Therefore, we tried to establish an estimation model of refractive index structure constant by Kolmogorov spectrum phase structure function and the direct relationship between wavefront error variance and coherent diameter to describe the refractive index structure constant, and set up experiments for verification.

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2 Coherent Diameter The phase structure function can be used to describe the effect of turbulence on laser in the atmosphere, assuming that the turbulence is statistically uniform and isotropic in a certain scale, which is demonstrated in Fig. 1.

Fig. 1. Turbulence cell within the inertial sub-range

We consider r as the spatial variance statistics phase structure function, and use r = |r 1 − r 2 | as the distance between two locations in polar coordinates, and using (r) to represent the phase of the wave at the position defined by the distance. The phase structure function D (r) can be expressed as: Dφ (r) = Dφ (r1 − r2 ) = [φ(r, t) − φ(0, t)]2 ,

(1)

To assess the amount of distortion caused by atmospheric turbulence, plane waves can be used as a reference. The instantaneous wavefront error variance at aperture R, that is, the average deviation between the distorted wavefront and the reference plane wave occupying the entire aperture. If the time average of both sides of Eq. (2) is now taken, a simple relationship between the wavefront error variance and the phase structure function can be obtained by simple operation. In other words, the time average of the wavefront error variance is equal to the space average of the phase structure function: ˜ Dφ (r)rdrd ϕ 2 2 σ (R, t) = (2) π R2 D(r) = Da (r) + Dφ (r) can be obtained by the known Kolmogorov spectrum phase structure function in Eq. (2), where D(r) represents the wave structure function, which is also one of the parameters describing the transmission of light waves in atmospheric turbulence [9]. While, Da (r) is a logarithmic amplitude structure function, and Dφ (r) is dominant in this formula. Therefore, it can be expressed as:  5/3 r , Dφ (r) ≈ 6.88 r0

(3)

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where r 0 represents the coherent diameter of atmospheric turbulence. Substitute the relation about in Eq. (3) into Eq. (2), and the average wavefront error variance can be obtained as follows:  5/3 R σ 2 (R, t)2 = 3.75 × . (4) r0 By simple transformation of Eq. (4), the functional relation of the coherent diameter can be obtained:  3/5 3.75 × R5/3 r0 = . (5) σ 2 (R, t)2 It is not difficult to see that the atmospheric coherent diameter can be calculated by calculating the time average of the wavefront error variance.

3 Experimental Setup Turbulence is subject to random changes due to many factors, which makes it very difficult to measure atmospheric turbulence. This phenomenon causes instability in channel transmission, which affects the quality of optical communication. Adaptive optics can solve this problem to a large extent, it has a wide range of use and far-reaching research value. Adaptive Optics (AO, adaptive optics) system is a real-time detection and correction of dynamic wavefront aberration caused by atmospheric turbulence to improve imaging capability [10]. The adopted AO system is presented below in Fig. 2. This experiment measures wavefront error and performs wave front correction by building an adaptive optical system. The wavefront sensor we use is the Shack-Hartmann wave front sensor, which occupies an important position in the AO system, has high research value [11], has the advantages of high energy utilization rate and simple structure. The time average of the wave front error variance required for coherent diameters are constructed by building adaptive optical system, and the following is the specific process of the experiment. This experiment uses laser as a light source, the instrument correction is carried out before the beginning of the experiment, to ensure that the laser and camera panel are vertical, adjust the position of the reflector and Shack-Hartmann wave front sensor, the laser through the mirror into the Shack-Hartmann wave front sensor. Connect the SH sensor and computer and start the Wavefront wave front computing software, which converges light on the camera panel to detect parallel beams of instantaneous φ(r, t). Then adjust the wave front detection time of the adaptive optical system, then the exposure time is controlled in millisecond magnitude, at this time the size of the plane reference wave is measured, this data as reference data. Those will be used to eliminate the phase difference caused by the experimental element. The temperature difference is generated by the heater, which simulates atmospheric turbulence and causes the change of refractive index and the change of phase. After the temperature difference is generated, no other variables are changed, and 50 sets of pre-wave measurement data are measured, making it easy to calculate the time average of the wave front error variance σ 2 (R, t)2 . Because components cause phase differences, the analysis of the data also requires

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removing the effects of reference waves without turbulence, by subtracting reference data measured when the heater is not in use. According to the relationship established above, and σ 2 (R, t)2 the coherent diameter r 0 can be calculated in a simple calculation to get C n 2 .

Fig. 2. Adaptive optics system.

4 Results and Discussion The Kolmogorov model is established within the range of Dn (R) = Cn2 R2/3 , linner  R  Louter . The size of the internal scale usually ranges from a few millimeters to a few centimeters, and its value will decrease as turbulence increases. According to the definition of wave front, we calculate the average time of wave front error. At the same time, the mapping between σ 2 (R, t)2 and the aperture radius R can be established in Eq. (4), and in Eq. (5) can be used to match the coherent diameter corresponding to the known wave front error variance time average and aperture radius R. However, due to experimental errors, we can roughly estimate the resulting coherent diameter. As can be seen from Fig. 3, the coherent diameter estimated by our experimental condition gradually reaches a gradient value of about 5.2 mm in different experiments. To test the effectiveness of this method, the approach value of the r 0 is replaced in Eq. (4). Then by r 0 the relationship between the C n 2 , we can calculate the value of the refractive index structural constant. Table 1. Experimental results σ 2 (R, t)2

3.769

Coherent diameter r 0

5.190

Cn2

5.03E-10

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estimated coherence diameter/mm

estimated coherence diameter 5.8 5.6 5.4 5.2 5 4.8 4.6 4.4 4.2

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49

The number of experiments

Fig. 3. Estimated coherent diameter based on 50 experimental measurements.

5 Results and Discussion To summarize, in this paper we used the optical adaptation system to obtain phase differences, using the corresponding formula step by step to calculate the refractive index structure constant we want. Due to the limitations of the experimental conditions, the coherent diameter corresponding to the different beam expansion rates was not measured, and will be supplemented in subsequent experiments. Communication technology for transmitting information in vacuum or atmosphere is an important free space optical communication technology. Laser communication has many advantages, such as large communication capacity, high transmission rate confidentiality and so on. However, the communication process is not only affected by atmospheric attenuation, but also greatly affected by atmospheric turbulence. Because of the turbulence motion of the atmosphere, the refractive index of the atmosphere has the nature of random fluctuations, which causes the phase to show random fluctuations in time and space, which seriously affects the reliability and stability of communication systems. Therefore, by measuring and calculating the refractive index structure constant in advance, and taking corresponding measures in practical applications to reduce the impact of atmospheric turbulence. Acknowledgement. This work was supported in part by the National Natural Science Foundation of China (Grant 61805176, Grant 62001327).

References 1. Gao, W., Cvijetic, M.: Evaluation of air turbulence impact based on wavefront reconstruction. In: Conference on Lasers and Electro-Optics, OSA Technical Digest (online). Optical Society of America (2017). paper AM4B.3 2. Li, M.: Orbital-angular-momentum multiplexing optical wireless communications with adaptive modes adjustment in Internet-of-Things networks. IEEE Internet Things J. 6(4), 6134–6139 (2019) 3. Li, M., Cvijetic, M., Takashima, Y., Zhongyuan, Y.: Evaluation of channel capacities of OAM-based FSO link with real-time wavefront correction by adaptive optics. Opt. Express 22, 31337–31346 (2014)

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4. Andreas, E.L.: Estimating Cn2 over snow and sea ice from meteorological data. J. Opt. Soc. Am. A 5, 481–495 (1988) 5. Larry Andrews, C., Phillip, R.L.: Laser Beam Propagation Through Random Media, 2nd edn. SPIE, Bellingham (2005) 6. Li, M., Gao, W., Cvijetic, M.: Slant-path coherent free space optical communications over the maritime and terrestrial atmospheres with the use of adaptive optics for beam wavefront correction. Appl. Opt. 56, 284–297 (2017) 7. Li, M., Cvijetic, M.: Coherent free space optics communications over the maritime atmosphere with use of adaptive optics for beam wavefront correction. Appl. Opt. 54, 1453–1462 (2015) 8. Tyson, R.K.: Principles of Adaptive Optics, 3rd edn. CRC, Boca Raton (2010) 9. Roddier, F.: Curvature sensing and compensation: a new concept in adaptive optics. Appl. Opt. 27, 1223–1225 (1988) 10. Li, M.: Phase corrections with adaptive optics and Gerchberg-Saxton iteration: a comparison. IEEE Access 7, 147534–147541 (2019) 11. Wittkopp, J.M., et al.: Comparative phase imaging of live cells by digital holographic microscopy and transport of intensity equation methods. Opt. Express 28, 6123–6133 (2020)

Design and Realization of Dual-Axis Follower Zhenzhen Huang and Peidong Zhuang(B) College of Electronics Engineering, Heilongjiang University, Harbin 150080, Heilongjiang, People’s Republic of China [email protected]

Abstract. With the rapid development of the national economy, the energy demand has become more and more urgent, and clean solar energy has become a good choice. However, low utilization efficiency is a bottleneck of solar energy. During solar power generation, whether the angle between the solar panel and the sun’s rays is perpendicular or not is a key factor in determining the power generation efficiency. This paper proposes that the dual-axis follower design mainly realizes the tracking function in solar power generation. Through the light sensor, the received light intensity change signal is processed by the analog-todigital conversion system and the tracking algorithm, and then passed through the STC89C52RC The single-chip microcontroller outputs control instructions and drives the corresponding motor to adjust the position. Keyword: Light tracker · Single chip microcontroller · Photosensitive resistance

1 Introduction In recent years, environmental protection has become the keyword of the times, and the speed of social development has increased rapidly. People are constantly trying to use more energy. Among the emerging energy, solar energy has become the development goal of more experts and scholars [1]. Our country has a large land area, and we have more advantages in the process of using solar energy. However, some shortcomings of solar energy, because the direction and intensity of sunlight are not fixed, rising from east to west is a constant operating law, so it is necessary to continue to research and develop new technologies to improve collection efficiency. But currently, our use of the sun is low. In the process of solar power generation, if real-time tracking of the sun can be achieved, the power generation efficiency will increase to an astonishing 35%. It can be seen that in the process of using solar energy, the use of tracking devices is essential [2]. By consulting a large number of relevant literature, it is found that single-axis tracking methods are commonly used in solar tracking research at home and abroad at this stage. However, with the continuous advancement of technology, the solar tracking device will approach the dual-axis tracking method to achieve real-time synchronous tracking of sunlight and further improve the power generation efficiency [3]. According to the above-mentioned actual situation, the main design this time is the dual-axis tracking method in solar tracking so that the real-time vertical effect of the light tracking device and the light source is achieved. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 1103–1110, 2022. https://doi.org/10.1007/978-981-19-0390-8_139

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2 The Overall Design of the System The main modules for this design include the single-chip central control module, the light source receiving module, the data conversion module, the display drive module, the clock timing module, and the motor drive module. The overall follow-up process is that when the photoresistor senses the change in light intensity, it converts the light intensity signal into an analog electric signal, and then inputs the analog-to-digital conversion circuit to realize the analog-to-digital conversion, and realizes the conversion of the analog electric signal into a digital electric signal After the system receives the signal, it is processed by the tracking algorithm, and then sends out instructions to control the driver chip to perform the rotation operation of the stepper motor. After the operated device, the vertical alignment of the light source will be carried out, and when the time reaches the set time Automatically switch between auto/manual mode at the alarm time, you can use the button alone to control the forward and reverse rotation of the dual motors and display it on the LCD screen in real-time. The following figure shows the overall design scheme of the system (Fig. 1):

Display system

Button control (Mode modification)

Time system

Button control (Time modification)

Automatic/ Manual

Light source

Button control (Motor driven)

Target time

MCU processing

Drive circuit

Photoelectric conversion

Voltage value analog to digital conversion

Fig. 1. System overall design plan

3 System Hardware Design In this tracking method, dual-axis tracking is used. It has many advantages. It can achieve real-time tracking of two angles. This time, two motors will be used to achieve independent adjustment of azimuth and altitude. With the LCD display, all control information can be easily understood, and the clock module is innovatively integrated into the follow-up system, which realizes the function of timing on and off, and solves the effect of energy saving in the dark environment [4]. The hardware system is mainly composed

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of a single-chip control circuit, photoelectric conversion circuit, AD conversion module circuit, stepper motor drive circuit, clock system, and single-chip peripheral circuit. The following mainly elaborates on these aspects. 3.1 MCU Control Circuit The STC89C52 single-chip microcontroller is selected this time. It has two functions: reset and crystal oscillator. It is very convenient in the process of use. At the same time, because of its powerful functions, many chip functions can be independently realized by it, reducing the number of chips used. Improve the stability of the system, can also serve as a read-only memory after erasing, the function is very diverse [5]. LRF-TXD and LRFRXD realize the serial connection, the program is burned into the program through this interface; LRF-KEY1 to LRF-KEY4 represent the function keys. In this design process, the key functions are nested to achieve large The switching of different functions and the adjustment of data increase and decrease under the framework; LRF-XTAL1 and LRF-XTAL2 are connected to a crystal oscillator system that can provide the required clock frequency for the entire system, so that the entire system can operate normally; D0 to D7 The interface used to connect the pull-up resistor, it is also connected to the driver chip, the purpose is to pull up the output level and improve the drive capability of the entire system for the motor (Fig. 2).

Fig. 2. The smallest single-chip microcontroller system

In the single-chip microcontroller system, the reset function is one of the general functions, or it can be said to be the most basic function. The reset function realizes the initialization of the current running state and returns to the initial running state of the program. The effective time of the reset function is at least two mechanical cycles (Fig. 3).

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Fig. 3. Reset circuit

The crystal oscillator is another indispensable part of the single-chip micro-controller The STC89C52RC has its oscillating circuit system, so in use, only a 12 M crystal oscillator and two capacitors with a capacitance between 15 pF–50 pF need to be provided from the outside to realize the crystal oscillator circuit. 3.2 Photoelectric Conversion Circuit The photoelectric conversion device converts the optical signal into an electrical signal, and the system is controlled by the signal according to the signal, which controls the corresponding stepper motor to rotate, so that the tracking device renders 90° of vertical face to the light source, and improves the efficiency of solar energy utilization.. Four photosensitive resistors were transmitted on the chasing board for optoelectronic detection, which accurately perceived small variations of light intensity, when the monitoring point C felt the change in the light intensity, compared the light intensity data for the on-side monitoring point D When the C intensity is greater than D, the light at C is stronger, and the chasing plate is driven towards the direction of C, until the C and D of the light intensity difference is small and the algorithm threshold, at this time, the chasing operation is completed. The monitoring point AB monitors changes in the horizontal direction of light, and the monitoring point CD monitors changes in the high direction of light [6]. The distribution and circuit structure of the photoresistor on the chasing board are shown below (Fig. 4).

Fig. 4. Photoresistor model

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3.3 AD Conversion Module Circuit This design uses the PCF8591 chip, which is an 8-bit analog-to-digital and digital-toanalog conversion chip with I2 C bus port. It is very convenient to use. It only needs to provide two interfaces, SCL and SDA, when the clock signal is synchronized, You can transfer data through. At the same time, compared with the traditional transmission mode, the I2 C bus simplifies the overall structure, facilitates the maintenance of the entire system, is very convenient to read and write during transmission, can expand many functions, is more integrated as a module, and has very high reliability, etc. PCF8591 uses the external reference voltage, through the successive comparison method, to realize the analog-to-digital conversion of 4 inputs through the I2 C bus serial port and 1 output, and to encode the address through the three pins of A0, A1, and A2. No need to connect other hardware to program the address. At the same time, the I2 C bus serial port is also responsible for transmitting control commands, hardware addresses, and data information, and the maximum conversion efficiency can reach 11 kHz (Fig. 5).

Fig. 5. AD conversion module

3.4 Stepper Motor Drive Circuit In order to complete the light-following function in the design, a mechanical adjustment mechanism is required to complete the angle adjustment, so this time a pulse motor, which is a stepper motor, is used. The principle of stepping motor operation is that when receiving a pulse signal, it will rotate at a certain angle. This angle can be controlled by adjusting the pulse and frequency. The speed and acceleration are also the same. The angular displacement of the step angle can be calculated by the following formula. ω=

360◦ mz · np

(1)

Among them, ω is the angular displacement, mz is the number of evidence teeth, and np is the number of running beats.

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3.5 Clock System The DS1302 is used because it is necessary to introduce a clock system in the system, which is realized, mainly to display time information. The DS1302 is mainly connected to the control of the data read and write, and the operation of the LRF_IO transmission data information and the RST implementation reset is connected to the microcontroller. In order to be able to implement the external power supply of Vcc2 in the chip In addition, there is also a VCC1 spare power to perform alternate power supply, X1 and X2 involve the external oscillator. When the DS1302 is operated, all time-related information is present in the internal registers, so the target of our operations is the register. Seven of 12 registers are related to time, the form of the BCD code is applied to store (Fig. 6).

Fig. 6. Clock chip circuit diagram

3.6 System Physical Platform Construction The final main part of the construction of the physical platform is the mechanical part related to the tracking light, which mainly includes the tracking board composed of photoresistors and stepping motors, and the main circuit board composed of the remaining components so that it can achieve full-angle rotation in the horizontal direction and vertical Rotate the direction at all angles. The overall control of the system is controlled by the single-chip microcontroller system. In order to build the mechanical structure, the horizontal motor is fixed on an empty circuit board with hot melt adhesive as the base of the overall follow-up part, and then the vertical motor is fixed on the top of the horizontal motor with a fixing screw, showing a vertical angle, and then the hot melt adhesive is used to fix the vertical motor on the top of the horizontal motor. The chase board is fixed on the vertical motor. When the power is on, the two motors are driven by the driver chip to control the continuous movement of the follower to adjust the angle.

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4 System Software Design In the process of software design, the chase algorithm is one of the difficult points. Relying on the single-chip microcontroller as the core component to organically combine each circuit module and cooperate with each other is also the focus of the design. The light-following algorithm is mainly based on the difference caused by the different acceptance of the light intensity of the photoresistor on the opposite side, which is used as the control source of the motor drive. For ease of understanding, U1, U2, U3, and U4 are used to replace the voltage values of the photoresistors in the four directions. In this design, the resolution accuracy is 10, that is, when the light intensity of the opposing two photoresistors exceeds 10, Just make adjustments (Fig. 7).

Start

Initialization

Read data U1 U2 U3 U4

U1>U2 or U1-U2>10

U2>U1 or U2-U1>10

U3>U4 or U3-U4>10

U4>U3 or U4-U3>10

Horizontal motor forward

Horizontal motor reverse

Vertical motor forward

Vertical motor reverse

Fig. 7. The working flow chart

5 Follower Overall Test We test the designed follower from the five aspects in the following Table 1. After testing, we got the following results: • The angle variation range of the received light is 270° from the front of the chasing plate. • After testing, in order to realize the normal tracking function, the reasonable operating range of the motor is 270° in the vertical direction and 360° in the horizontal direction. • Limited by the hardware conditions and the influence of ambient light, when using the mobile phone flashlight as the test light source, the expected effect can be obtained at about 15cm.

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Test points The range of angle of received light Reasonable driving range of two-axis motor The light distance that the chasing board can receive Normal start of timing function Simulate the light source’s movement to test the actual effect of the light follower

• Fourth, the timing function reaches the expected assumption, and the numerical modification of the two clocks can be realized, and after the timing is completed, the switching of the operating mode is successfully realized. • When using a flashlight as a light source, the minimum adjustment takes 0.8–1.8 s each time. The light source tracking function can be realized in the mode of simulating the translation and rotation of the light source.

6 Conclusion The smart follower is designed this time. It uses the STC89C52 single-chip microcontroller as the central control chip, uses the photoresistor to receive the light intensity information, and then realizes the AD conversion. After the follow-up algorithm is calculated, the motor is driven to rotate to realize the tracking of the light source. All the control information can be displayed on the LCD screen, and through the nested control logic, a small number of keys are used to achieve complex function control, and the innovative clock control display system is also introduced to realize the timing control switching operation. The function of the model has certain development value.

References 1. Philibert, J., Cedric, M.: The present and future use of solar thermal energy as a primary source of energy. IEA.Archived from the original, pp 49–51 (2005) 2. Sucharitha, P.: A Review and Implementatlrf_IOn of A Sun Positlrf_IOn Tracker With A Twin-Axis Control .lrf_IOP Conference Series (4), pp. 1–5 (2020) 3. Yang, M., Yu, S., Shen, J.: Maximum power point tracking controller of solar cell using single-chip microcontroller. Electron Technol. 12, 49–51 (2002) 4. Wang, X.: Design of automatic tracking control system for solar panels. J. Northwest Univ. (Natural Science Edition) 34(2), 163–164 (2004) 5. Xue, J.: The design of solar cell automatic tracking system based on single chip microcontroller. J. Changchun Normal Univ. 24(3), 26–30 (2005) 6. Yu, Z., Song, C.: The design of silicon photovoltaic panel automatic tracking solar mechanical device. J. Xinxiang Univ. (3), 70–71 (2008)

A Survey of Compatibility and Cooperative Construction Mode Between Public 5G Network and Smart Grid Wang Yudong1 , Liu Lirong1(B) , Xin Peizhe1 , and Li Yang2 1 State Grid Economic and Technological Research Institute Co., Ltd., Beijing 102209, China

[email protected] 2 Nari Group Corporation/State Grid Electric Power Research Institute Co., Ltd.,

Nanjing 211106, China

Abstract. This article discusses the advantages of introducing5G applications into Smart Grid, and provides a general framework of adapting 5G to Smart Grid services from multiple perspectives, such as the spatial distribution, the QoS performance, the application architecture and security isolation. On this basis, the overall structure and mode of Cooperative Construction between the Power Grid company and Telecom Operator was proposed. Theoretical analysis and tests proved the feasibility and effectiveness of the proposed schemes, which point out the direction for the forthcoming large-scale 5G application in Smart Grid. Keywords: Smart Grid service · 5G private network · 5G cooperative construction mode

1 Introduction 5G focuses on the interconnection of everything in the standard design concepts, especially in the Smart Grid and other industrial control fields. There are three typical application scenarios in 5G: eMBB, uRLLC and mMTC. 5G can enable resource isolation and performance certainty through slicing technology. Therefore, 5G will effectively satisfy different kinds of Smart Grid business performance, especially the 10ms level latency and 99.999% reliability requirements [1], and realize the physical isolation of power production control service, which cannot be completely realized in 4G LTE. According to the co-construction and sharing policy of 5G infrastructure, the 5G application in Smart Grid will mainly rely on the Public 5G Network in PNI-NPN (Public network integrated NPN) mode, which is defined by 3GPP TS23.501 [2]. In order to guarantee the performance and isolation demands of Smart Grid business, especially The paper is supported by the Science and Technology Project of State Grid Corporation of China: Comparative study on 5g power network mode and technical economy (No. 5700-202056376A0-0-00).

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 1111–1120, 2022. https://doi.org/10.1007/978-981-19-0390-8_140

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the power production control service which is traditionally carried by power private wired communication, it is necessary to analyze the matching degree of 5G and Smart Grid business from multiple perspectives, making 5G adapt to Smart Grid in security isolation, reliability and effectiveness.

2 Key Technologies of 5G Applications in Smart Grid 2.1 End-to-End Physical Isolation The 5G slicing technology can realize the physical isolation of air interface, transport network and core network in the form of radio frequency, time slot, and physical equipment. The physical isolation technology ensures the certainty of service performance by guaranteeing that resources are exclusive and not preempted by public network services. In Air interface, PRB resource isolation can reserve frequency resource block for private users while sharing the RAN with public users. Utilizing FlexE technology, the 5G transport network innovatively realizes the physical isolation based on time-slot in Ethernet system, while 4G LTE transport network can only segregate different services logically. Due to software-based architecture (SBA), 5G Core can be flexibly deployed, such as UPF sinking to implement edge computing and shorten the transmission delay. It can further support the exclusive use of SMF, AMF and other control plane elements to improve the controllability and security according to the Private user’s requirements. 2.2 Logical Isolation Based on QoS 5G QoS defines the mapping rules between IP Flow and QoS Flow, with the characteristic parameters of QoS Flow transmitted in network element [2]. The characteristic parameters are divided into standardized value and special value defined by Telecom Operators. From the perspective of architecture, 5G QoS Flow can achieve a finer guarantee than 4G QoS. Because 4G LTE has more user plane nodes, it is necessary to establish multi-segment load-bearing (wireless load-bearing, S1 load-bearing, S5/S8 load-bearing), while the 5G control plane and the user plane are completely separated. 2.3 Low Latency and High Reliability Technology 5G introduces low latency and high reliability capabilities, and provides system-level solutions for power relay protection and control applications, which require 10 ms level latency and “99.999%” communication reliability. By increasing the sub-carrier space(SCS), the physical layer frame length can be reduced. Through lower bit rate MCS [3] and PDU session redundancy connection mechanism [4], the reliability of 5G communication will be greatly improved.

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3 Analysis of the Matching Degree Between Public 5G Network and Smart Grid Applications 3.1 Matching Framework The matching framework between public 5G network and Smart Grid applications is shown in Fig. 1. First, lay out the 5G network according to the spatial distribution of Smart Grid applications, and determine the priority coverage areas, as well as business requirements. Then, deploy user plane elements according to the application architecture, and determine the UPF sharing mode and sinking position. After that, deploy control plane elements according to service security requirements, and determine the sharing mode of SMF/AMF and other 5G core network elements. Finally, match 5G scenarios and determine the slicing types and parameters according to the Smart Grid applications SLA (Service Level Agreement).

Fig. 1. The matching framework between Public 5G network and Smart Grid applications

3.2 Matching Degree Between the Spatial Distribution of Power Service and Operator 5G Network According to different voltage levels, Smart Grid services are divided into main network services operating in scenarios above 35 kV and distribution network service operating in scenarios of 35 kV and below, such as the distribution automation, power load control, etc. The matching degree between the spatial distribution of Smart Grid services and 5G network is shown in Table 1.

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Table 1. The matching degree between the spatial distribution of power services and 5G network Voltage grade

The status quo

5G network matching Status quo of 5G networks

Main network Above Optical fiber coverage 35 kV is perfect, the network resources are complete

With wired transmission as the main, 5G network as the auxiliary

5G network coverage shows a downward trend with the increase of voltage level. Due to the location and line corridor of 500 kV and above substations and transmission lines are mostly in sparsely populated suburbs, there are 5G coverage blind areas. The main network scene needs to be built in cooperation with operators and fill the blind spots in hot areas

35 kV and below distribution network

Focus on solving the problem through 5G network

Power grid distribution scene is densely staffed, and the public 5G network coverage is priority, which has good compatibility with 5G network coverage

Distribution network scenarios have the access requirements of massive terminal, high cost and difficulty of optical fiber coverage, and the most urgent demand on 5G wireless access

3.3 Matching Degree Between Smart Grid Application Architecture and Public 5G Network According to the correlation between Smart Grid application and 5G network architecture, we can classify the application into three categories, including point-to-point interactive service, local decision service and remote decision service. Point-to-point interactive application refers to pairwise interactive data of power terminals. The data stream does not need to be uploaded to the master station, but only need to be forwarded through UPF. Typical applications include power distribution line current differential protection and distributed FA (Feeder Automation).

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Local decision application refers to the local transmission and local processing of terminal data, and the data flow needs to be uploaded to the local master station through the UPF. The local master station processes the data and sends the control commands back to the power terminal. A typical application is substation robot patrol inspection. The remote decision application refers to the services that terminal data is uploaded to the master station deployed in the municipal or provincial companies through UPF, processed by the master station and sent to the terminal [5, 6]. Most applications in Smart Grid such as power distribution automation, electricity information collection, load control, etc. fall into this category.Data flow of the above three types of service architectures and the corresponding 5G network architectures are shown in Fig. 2.

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Fig. 2. Service architecture data flow

3.4 Matching Degree Between Public 5G Network and Smart Grid Service Isolation Demand It is significant to match the power monitoring system security requirement [7, 8] with the 3GPP standard [9–13], and analyze the security technologies adopted in the radio access, the core network and transport network of the 5G network. In the case of the sharing degree and deployment location of 5G UPF are determined, the sharing degree of 5G gNB and control plane elements is determined according to the isolation requirements of Smart Grid service security partition, so as to determine the mode of cooperation with Operators. Considering the Smart Grid application architecture and security requirements, the matching conclusion is shown in Table 2.

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Table 2. The matching degree of power service architecture and security requirements with 5G network Power service zoning

The production control zoning

Service type

Distribution automation

Service architecture

Remote decision

Precise load control

Base station

Shared or exclusive the base station

Core network User plane network element

Control plane network element

Degree of sharing

Deployment location

Degree of sharing

Deployment location

Exclusive user plane network element

UPF for municipal (prefectural) or provincial company

Exclusive access control AMF network element and session management SMF network element

Operator provincial or regional center room

Distributed power supply Distribution network - PMU Intelligent Distributed FA

Point-to-point interaction

Edge dedicated UPF

Distribution network differential protection The manage information zoning

Electricity information acquisition

Cooperation Mode of Operators

Independent private network

Edge or municipal (prefectural) dedicated UPF Remote decision

Shared the base station

UPF for municipal (prefectural) or provincial company

UAV patrol

Shared core network element

Mixed private network

Shared core network element

Virtual private network

Intelligent switchboard area Charging piles for electric vehicles Mobile field operation management Emergency field management

Internet zoning

Substation patrol robot

Local decision

Teleconferencing

Remote decision

Edge dedicated UPF Shared the base station

Shared user plane network element

UPF for municipal (prefectural) or provincial company

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3.5 Matching Degree Between Public 5G Network and Smart Grid Service Performance Demand 3.5.1 Analysis of 5G QoS Performance and Smart Grid Application Power services can be divided into typical application scenarios such as production control, information management, collection and mobility, and there are huge differences between them. Note that the Smart Grid application scenarios do not always correspond to the 5G three typical scenarios. For example, some production control application such as Distributed Generation requires not only high reliability, but also massive connections, which puts forward higher requirements for 5G application in the Smart Grid. 3.5.2 Analysis of the Time Synchronization Performance Time synchronization is widely required for power dispatching and fault analysis, especially in the field of real-time control. According to the accuracy requirements of power services, time synchronization can be divided into four levels: 1 us, 1 ms, 10 ms and 1 s. At present, time accuracy requirements of 5G network for eMBB scenes can reach 1.5 us, which satisfies the needs of most services with synchronization requirements. Meanwhile, the time reference parameters introduced by 3GPP standard will further improve the time synchronization solution.

4 Plans and Results of the Joint Construction of 5G Between Power and Operators In order to promote the 5G application in the industry, the Telecom Operators have launched several 5G private industry network schemes according to the exclusive degree of 5G infrastructure and different security isolation levels.The virtual private network is the mode that gNB and 5GC are sharing by private users and public users. The mixed private network is the mode that gNB and 5GC control plane are shared while 5GC user plane is exclusive to private uses. The independent private network is the mode that gNB, 5GC user plane and the pivotal control plane network element are exclusive to private users. Telecom Operators industry private network schemes are shown in Table 3. Table 3. Telecom Operators industry private network schemes The name of private network provided by Sharing degree of 5G network element operator China mobile

China unicom

China telecom

Optimal enjoy

Virtual private Zhiyuan network

gNB

5GC user plane 5GC control network element plane network element

Shared

Shared

Shared (continued)

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The name of private network provided by Sharing degree of 5G network element operator China mobile

China unicom

China telecom

gNB

5GC user plane 5GC control network element plane network element

Exclusive enjoy

Mixed private network

Bilin

Shared

Exclusive

Shared

Honour enjoy

Independent private network

Ruyi

Can be excluded by user

Exclusive

Pivotal network element exclusive

Combined with the above operators industry private network schemes, the typical ways for Power Grid companies and Telecom Operators to jointly construct 5G are as follows, which is shown in Fig. 3.

Fig. 3. Joint construction scheme combined with operators

4.1 Virtual Private Network Mode Virtual private network mode is similar to the 4G APN which is replaced by DNN in 5G. It is logical isolation, and is suitable for the business with low isolation requirement, especially the Smart Grid services in Internet zone and safety zone IV. The cooperation between the Power Grid company and Telecom Operators is the same as 4G customer leased line, and the Operator rents the logical virtual private network to Power Grid company. All the public 5G network physical resources are

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shared by Power Grid company and public users, and services are isolated by DNN in 5GC, or VPN in TN together if necessary. 4.2 Mixed Private Network Mode In the mixed private network mode, industrial users exclusively use the dedicated UPF while share the 5GC control plane and RAN with Operators. It is suitable for the services business with high security requirements, especially the Smart Grid services of management information zone. The dedicated UPF will be deployed in the data center of provincial or municipal Power Grid companies to offload the data of remote decision applications in the widearea scene, or deployed in substations or power plants for the landing of local decision applications, so as to ensure the isolation of data forwarding. 4.3 Independent Private Network Mode Independent private network mode is the scheme with the highest isolation and security. The industrial users shall not only monopolize the UPF, but also enjoy alone part of spectrum and even the gNB. In particular, the components of the 5GC control plane can also be specially used by industrial users on demand. This mode has the highest security isolation and is especially suitable for Smart Grid services in power production control areas. The cooperative construction mode of UPF is similar to the mixed private network mode, while the SMF and AMF elements are deployed in the 5GC room of the Telecom Operator. PRB isolation in spectrum and FlexE rigid pipe in TN shall be coordinated to structure the 5G hard slice for the most essential applications of power production control zone.

5 Conclusion This paper analyzes the advantages of 5G application in Smart Grid, and puts forward a system framework for matching public 5G network with smart grid application in many aspects. Theoretical analysis and tests show that the performance of 5G in latency, reliability and security isolation is highly matched with that of Smart Grid services, especially the production control services, which cannot be realized by 4G. According to different security isolation, application deployment architecture and performance requirements of Smart Grid services, this paper introduces three practical and effective cooperative construction modes between Operators and Power Grid companies, and will popularize them in the coming large-scale application of 5G in Smart Grid.

References 1. Dou, K.: Research on Communication Protocol and Network Technology of Distribution WAMS Network. Hefei University of Technology (2019)

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2. 3GPP. System architecture for the 5G system (5GS) Stage1 specification: TS 23.501[S], July 2020 3. 3GPP. Physical layer procedures for data specification: TS 38.214 [S], December 2020 4. 3GPP. System architecture for the 5G system (5GS) Stage2 specification: TS 23.502 [S], January 2021 5. Peng, D.U., Liang, Y.A.N., Baocheng, G.A.O., et al.: Wide-area data acquisition scheme based on power dispatching data network. Autom. Electric Power Syst. 043(013), 156–161 (2019) 6. Zheng, N., Yang, X., Wu, S., et al.: A survey of low-power wide-area network technology. Inf. Commun. Technol. 2017(1), 47–54 (2017) 7. Zhang, T., Zhao, D., Xue, F., et al.: Research framework of cyber-security protection technologies for smart terminals in power system. Autom. Electric Power Syst. 2019(19), 7–14+91 (2019) 8. McDaniel, P., McLaughlin, S.: Security and privacy challenges in the smart grid. IEEE Secur. Priv. 7(3), 75–77 (2009) 9. 3GPP. Services provided by the physical layer specification: TS 38. 202 [S], June 2020 10. 3GPP. Medium access control (MAC) specification: TS 38.321 [S], December 2020 11. 3GPP. Radio link control (RLC) specification: TS 38.322 [S], December 2020 12. 3GPP. Packet data convergence protocal (PDCP) specification: TS 38. 323 [S], September 2020 13. 3GPP. Radio resource control (RRC) protocol specification: TS 38. 331 [S], September 2020

Energy Efficiency Analysis for Sub-6G and mmWave Massive MIMO Systems Hao Gao1,2,3(B) , Xiaoting Xu1,2,3 , and Danpu Liu1,2,3 1 Beijing Laboratory of Advanced Information Networks, Beijing, China 2 Beijing Key Laboratory of Network System Architecture and Convergence, Beijing, China 3 Beijing University of Posts and Telecommunications, Beijing, People’s Republic of China

{ghao,dpliu}@bupt.edu.cn

Abstract. Energy efficiency (EE) has become a guideline for evaluating fifthgeneration (5G) green communication performance. So far, 5G frequency ranges (FR) that have been commercialized by operators are Sub-6G and millimeter wave (mmWave). In this paper, we first investigate the power consumption (PC) of macro base stations (BSs) in Sub-6G and mmWave FR. Subsequently, the throughputs and EE of a macro BS are analyzed under three architectures, i.e. digital beamforming (DBF) at Sub-6G band, as well as hybrid beamforming (HBF) and analog beamforming (ABF) both at mmWave band. Simulation results show if the construction cost and complexity are not considered, increasing the number of antennas is a powerful way to improve EE of mmWave HBF and ABF BSs. However, that is not the case for Sub-6G DBF BSs because the number of antennas has a linear relationship with the total PC in DBF architecture. Furthermore, it is not wise to increase the number of radio frequency (RF) links in a mmWave HBF system due to the high PC introduced by one RF link. An ABF BS with multiple antennas is the better choice to maximize EE in mmWave band.

1 Introduction 5G FR that have been operating are Sub-6GHz and mmWave frequency bands [1]. The Sub-6G signal FR is lower than 6 GHz, and the bandwidth is about 100 MHz. It has the advantage of wide coverage due to its strong anti-interference ability; the FR of mmWaves is 30–300 GHz, but high-frequency transmission brings serious path loss, which requires massive multi-input multi-output (MIMO) and beamforming technology to improve the anti-interference ability of the channel and enhance the reliability of the transmission signal. However, with the rapid increase in the number of antennas and the continuous expansion of the network scale, the energy consumption of the wireless mobile communication network is on the sharp rise, and the environmental pollutants (such as carbon dioxide) generated by the resource consumption of the wireless communication network system cannot be ignored [2]. EE is defined as the ratio of throughputs to total PC, representing the number of bits of information transmitted per unit of Joule energy, and much research focuses on analyzing H. Gao—Masterminded EasyChair and created the first stable version of this document. H. Gao—Created the first draft of this document. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 1121–1130, 2022. https://doi.org/10.1007/978-981-19-0390-8_141

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EE for the MIMO system and improving EE further while sacrificing SE as little as possible. The paper [3] analyzes the EE of multiple beamforming architectures, but the power model used in calculating EE is not comprehensive. In [4], the author investigates the EE for multiple cellular systems with large-scale antenna arrays under consideration of the circuit PC of each antenna and propose a new PC model that considers not only transmit power on the power amplifier but also circuit power dissipated by analog devices and residually lossy factors in BSs. In [5], a consumed power model for components designed for 60 GHz is given, and a comparison between fully DBF, 1-bit ADC, and ABF is given. In summary, the existing research on EE analysis mostly uses theoretical values in PC modeling, which are significantly different from the actual PC of macro BSs. More specifically, most of the PC modeling does not consider the influence of BS architecture and ignores practical factors such as cooling system, DC/AC loss, etc. The difference between the theoretical value and the actual PC of the BSs is dozens of times or even higher. To address the above issues, we propose a PC model of the macro BS in response to Sub-6G and mmWave BSs and analyze the impact of different configurations and beamforming architectures on EE. The main contributions and results can be summarized by: • We decompose the structure of the BS according to the function and carry out independent PC modeling for each functional module. The actual products designed by Analog Devices (ADI) are employed in the modeling to bring the modeling closer to the actual scenario. • Further, we simulate and analyze the throughputs and EE of Sub-6G DBF and mmWave HBF /ABF BSs against the numbers of users, RF links, and antennas. Simulation results show that: Increasing the number of antennas is a powerful way to improve EE in mmWave HBF and ABF BSs because of its light PC compared with the total BS. While that is not the case in the current Sub-6G DBF BS and there is an optimal number of antennas that maximizes EE. It is not wise to increase the number of RF links in a mmWave HBF system due to the high PC introduced by an RF link. In other words, ABF architecture has a better performance on EE than HBF and DBF in mmWave band. Notation: a is a scalar variable; a is a vector; A denotes a matrix. a2 denotes the l 2 norm of vector a. aF denotes norm of vector a. (·)H means the conjugate   the2Frobenius transpose of the matrix. CN 0, σ denotes a complex Gaussian distribution with zero mean and variance σ 2 . pinv(A) means the pseudo-inverse of matrix A. I denotes the identity matrix.

2 System Model 2.1 DBF System at Sub-6G Band Consider a single-cell multi-user (MU)-MIMO system wherein one BS communicates, on the same frequency slot, with K mobile users. The system structure of MU-MIMO in Sub-6G DBF architecture is shown in Fig. 1.

Energy Efficiency Analysis for Sub-6G Ns

RF Chain

Nr K×Ns

RF Chain Baseband Beamforming FBB

RF Chain

RF Chain

1123

Baseband Processing WBB

User 1

RF Chain

Nt Ns

RF Chain RF Chain

Nr

Sub-6G Macro Base Station

RF Chain

Baseband Processing WBB

User K

RF Chain

Fig. 1. MU-MIMO system with fully digital architecture in Sub-6G.

The Sub-6G BS is generally based on a fully digital beamforming architecture. The Nt denotes the number of transmit antennas at the BS, and Nr denotes the number of receiving antennas at the user’s device. Each RF chain is connected to an antenna which means NRF = Nt . Define that s = [s1 ; s2 ; . .. ; sK] ∈ CKNs is the vector of the data 1 IK×Ns . The signal received symbols intended for K users which satisfies E ssH = K×N s by the generic k-th user is expressed as the following Ns -dimensional vector. kH kH s˜k = WBB Hk FBB Ps + WBB nk ,

(1)

k ∈ CNr ×Ns where FBB ∈ CNt ×KNs is the joint precoding matrix for all users and WBB represents combining matrix for the  nk is additive white  k-th user, √ gaussian noise for k-th user, by the vector of i.i.d CN ∼ 0, δn2 INr . Denote that P = ρ/KNs I ∈ CKNs ×KNs is the power allocation matrix between different data streams, and ρ is the total transmitting power. H = [H1 ; H2 ;. . . ; HK ] ∈ CKNr ×Nt is modeled as an i.i.d Rayleigh channel with H(i, j) ∼ CN 0, β 2 , where β(dB) = −32.4 − 17.3lg(d3D ) − 20lg(f ) is the pass loss factor refer to TR 38.901. Let f and d3D represent signal center frequency and three-dimensional distance between the BS and the user. We will try to design low-complexity precoding and combining schemes to get the sum rates in order to evaluate the subsequent EE. The zero-forcing   algorithm can eliminate inter-stream interference between users, by FBB = pinv HH . To reduce the algorithm complexity, we simplify the combining process at the mobile users’ end by k = INs . Assuming Gaussian data symbols, the sum rates for the Sub-6G DBF WBB system can be shown as

RSub =

Ns K  

  log2 1 + SINRki ,

(2)

k=1 i=1

where SINRki is the signal-to-interference and noise (SINR) ratio for the i-th data stream of the k-th user, written as SINRki =

 ρ 2 k H (i, :)H F (:, k ) |WBB i k BB KNs | , 2   N N K 2 k   ρ 2 s k H  s k H ρ (:, i) WBB (i, :)Hk FBB :, kj KNs + WBB (i, :)Hk FBB (:, ml ) KNs + δn2 WBB

j=1,j =i

m=1,m =k l=1

(3)

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2.2 HBF/ABF Systems at mmWave Band Different from DBF architecture at Sub-6GHz, HBF or ABF is generally adopted at mmWave band due to the limitation of implementation complexity and power consumption at high frequency. Let’s consider a downlink mmWave MU-MIMO system based on the fully connected HBF structure shown in Fig. 2.

Nrf KNs

RF Chain

User 1

RF Chain Baseband Processing FBB

RF Chain

Analog Beamforming FRF

Nt

eSV channel

RF Chain

RF Chain

User K

mmWave Base Station

Fig. 2. mmwave MU-MIMO system with hybrid beamforming structure

The BS is equipped with Nt antennas and Nrf independent RF chains. In particular, the ABF architecture corresponds to the case that Nrf = 1. Each RF chain is connected to all of the antennas. Each of K mobile users is equipped with a single antenna. The are transmitted from the BS to K mobile user. The data streams are data streams precoded by a digital precoder F BB ∈ CNrf ×KNs at the baseband, followed by an analog precoder F RF ∈ CNt ×Nrf at the analog domain. After the analog precoding, each data stream is transmitted by all of the antennas. Generally, the received signal at the k-th user can be represented as. (4) where hk ∈ CNt is a narrow geometric mmWave channel for k-th user, which can be modeled as an Extended Saleh-Valenzuela (ESV) channel [6] as follows. hk = γ

ray,i Ncl N 

 t  αi,l β(f , d3D )atH φi,l .

(5)

i=1 l=1

Narrowband multi-ray MIMO channel is typically applied to mmWave system. Using the clustered channel model, the matrix channel hk is assumed to be a sum of the contributions of Ncl scattering clusters, each of which contributes Nray propagation paths to the channel matrix hk . Define that β is the path loss as same as the 2.1 section, and the variable ∅ti,l ∈ (0, 2π ) be the physical angle of departure (AoD). The variable αi,l ∼ (0, 1) denotes the complex path gain. The vector at ∈ CNt ×1 as follows are the antenna array response vectors at BS, where uniform linear arrays (ULA) are used as [6]. We employ a low-complexity HBF algorithm under a fully connected architecbased on the ture. In the analog domain, F√ RF is determined  maximum rate of pre coding (MRP) by F RF = 1/ Nt exp j  [h1 ; h2 ; . . . ; hk ]H . Zero-forcing precoding is used in the baseband processing as the same as the Sub-6G DBF system, by

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F BB = pinv([h1 ; h2 ; . . . ; hk ]FRF ). The sum rates for the mmWave system can be written as RMMW =

K 

  log2 1 + SINRk,MMW ,

(7)

k=1

where SINRk,MMW is the received SINR of the user k, which is specifically expanded as  |hk F RF F BB (:, k) Kρ |2 SINRk,MMW = , (8)  2 K  ρ hk F RF F BB (:, m) K + δn2 m=1,m=k

3 Power Consumption of Macro BS The EE of a macro BS can be defined by EE =

B×R , Ptotal

(9)

where B and R denote the bandwidth of the signal and sum rates for all users. Next, we will discuss Ptotal that is the total power model of the macro BS under the above two systems. In general, mobile communication BS is mainly a distributed structure. The traditional cabinet form integrated with the Base Band Unit (BBU) and the Radio Remote Unit(RRU) has been successively withdrawn from the network and replaced by the distributed BS structure [7, 8]. Distributed BSs are divided into two physical units: BBU and Active Antenna Unit(AAU). By splitting AAU into several basic modules, the following formula is used to the power model in the Sub-6G BSs: PAAU, Sub = PRF + PSam + PT /η + Nt PBH /2 + NRF PMS /2,

(10)

where PRF = NRF (Pmix + Ppa + PLNA ) is the PC of RF front end containing a mixer, Nt power amplifier (PA) and Nt low noise amplifier (LNA). It is noted that Ppa refers to static PC of power amplifier (PA) and does not include transmitted power. PSam = NRF (PADC + PDAC ) is the PC of analog to digital converter (ADC) and digital to analog converter (DAC). PT is the PC of the transmit power module. PBH is the backhaul power on the BS side. PMS is the supply power for 2 RF links. η is the amplification efficiency of the PA which is taken as 0.25 [9]. Because of the different architecture and central frequency, the AAU power modeling of the mmWave system is as follows PAAU, MMW = Nt × Ppa + Nt × NRF × PPS + Nrf × NCC × (Pmix + PADC + PDAC ) + PMS × NRF /2 + PT /η, (11) where NCC denotes the number of carrier components on the mmWave BS side, and PPS is the PC of the phase shifter.

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According to [7], one BBU can be cascaded with three AAU to form three sectors in both Sub-6G and mmWave BSs, thus the macro BS power model is. Ptotal =

(PBBU + 3PAAU ) , (1 − δcooling )(1 − δDC )

(12)

where δcooling and δDC  are the PC of the cooling module and DC conversion loss, which satisfy 1 − δcooling (1 − δDC ) ≈ 0.8 [10]. Note that the PC of BBU in Sub-6G and mmWave FR is similar because BBU is only responsible for processing baseband signals and has nothing to do with signal center frequency.

4 Simulation Results In this section, we provide numerical simulation results to show the performance of the Sub-6G and mmWave BSs in terms of throughputs and EE. In all the simulations, the carrier frequency for Sub-6G and mmWave systems is 3 GHz and 73 GHz respectively. The number of multi-path is Ncl = 2, NRay = 20. Note that there are K users using total bandwidth whose locations are random, with 100 m for mmWave FR and 500 m for Sub-6G FR maximum distance from the BS. The noise power δn2 = FN0 B, with F = 3 dB the receiver noise figure and N0 = − 174 dBm/Hz [11]. Define that C = B × R is the throughputs. The results come from an average of over 1000 independent realizations of users’ locations and propagation channels. Set that PMS = 10.89 W, PBBU = 293 W. For Sub-6G FR, Ppa = 25 mW, PLNA = 180 mW, Pmix = 1 W, PADC = 2.7 W, PDAC = 0.5 W is from ADI, and PBH = 0.5 W [9], B = 100 MHz. For mmWave FR, Ppa = 25 mW, Pmix = 1 W, PADC = 2.5 W, PDAC = 3.8 W is from ADI, and PPS = 75 mW [11], B = 800 MHz. Figure 3 (a) and (b) describe the performance of the Sub-6G system in terms of throughputs and EE versus the transmit power. Here, a BS with Nt = 64 antennas has been considered. Results show a trend that the SE grows with the transmit power (at (a)

(b)

Fig. 3. (a) Throughputs of the Sub-6G system versus transmitting power, under N t = 64. (b) Energy efficiency of the Sub-6G system versus transmitting power, under Nt = 64

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least in the considered range of values), while the EE reaches a maximum around 20 dBW. It is also observed that MU multiplexing can simultaneously improve EE and SE. Essentially increasing the number of users is equivalent to increasing the transmit data streams. In the Sub-6G FR, i.i.d. Rayleigh channels are full rank, so more data streams mean that the spatial gain of the MIMO channel can be effectively exploited to improve throughputs and EE. Figure 4(a) and (b) are devoted to reporting the performance of the Sub-6G system in terms of throughputs and EE versus the number of antennas. It is obvious that the number of antennas has a logarithmic effect on sum rates. When the number of antennas is small, the increase of antennas can effectively enhance the sum rates. However, when the number of antennas reaches a certain level, the increase in throughputs obtained by increasing the number of antennas is very limited. Meanwhile, the PC model has a linear relationship with the number of antennas in Sect. 3. Therefore, increasing the number of antennas is not beneficial to improve EE in the current Sub-6G BS configuration (Nt = 64). It is worth noting that the maximum EE of the red curve does not seem to be very obvious, and the reason is that the peak of the red curve appears around 50. If the number of antennas keeeps growing, the curve will decline. (a)

(b)

Fig. 4. (a) Throughputs of the Sub-6G system versus the number of antennas, under K = 2. (b) Energy efficiency of the Sub-6G system versus the number of antennas, under K = 2

Figure 5(a) and (b) report the performance for the mmWave system on EE and throughputs versus the transmit power. When the number of users is fixed, the number of RF links will determine how many users can access the system. If Nrf ≥ K, all users can access the BS at the same time in a service slot. While Nrf < K, only Nrf users will be served every time. The more Nrf , the more users will be served at the same time. In Fig. 5(a), the power is concentrated on fewer users means higher throughput at low SNR. Multi-user multiplexing gain can be revealed under high SNR. From an EE perspective, it is seen that the best performing beamforming structure is the ABF owing to its lower PC. To sum up, increasing the number of RF links can effectively improve throughputs, but due to the increase in PC determined by the architecture, EE will change along the opposite direction.

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(a)

(b)

Fig. 5. (a) Throughputs of the mmWave system versus the transmitting power, under K = 8, N t = 64. (b) Energy efficiency of the mmWave system versus throughputs, under K = 8, N t = 64.

Figure 6(a) and (b) report the impact of the number of antennas on EE and throughputs for the mmWave system. It is obvious that the BS with more antennas obtain better performance, both in terms of throughputs and EE. In Sect. 3, we analyzed the impact of Nt on the overall PC of the BS. The total PC of the phase shifter and PA will be affected by Nt , but it is very small compared to the overall PC of BS. Developing more antennas is equivalent to increasing the throughputs without changing the PC of BS and EE will naturally be increased. In a word, the extra PC due to the addition of more antenna units can be ignored for a fixed Nrf . If the cost is not considered, increasing the number of antennas as much as possible can effectively improve EE and throughputs. (a)

(b)

Fig. 6. (a) Throughputs of the mmWave system versus transmitting power with N rf = 1 under a different number of antennas (b) Energy efficiency of the mmWave system versus throughputs with N rf = 1 under a different number of antennas.

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5 Conclusion In the paper, we mainly study the power composition and performance analysis of BSs with different architectures. We decompose the BS architecture and conduct power modeling for each module respectively according to the existing product PC typical values and the modeling method in theoretical research. The DBF BS architecture is considered in the Sub-6G FR, and we evaluated the throughputs and EE of the BS in MU scenarios and analyzed the factors that affect EE. In the mmWave FR, we estimated the approximate power of the BS. Throughputs and EE of DBF, HBF, and ABF are compared in the simulation. The simulation shows that increasing the number of antennas and the number of RF links both can improve throughputs of the Sub-6G DBF and mmWave HBF/ABF BSs, but more RF links will lead to lower EE in mmWave HBF/ABF BSs. For maximizing EE, an ABF BS with multiple antennas is the better choice. Acknowledgement. This work is supported by the National Natural Science Foundation of China under Grant No. 61971069, 61801051, Beijing Natural Science Foundation under Grant No. L202003, the Key R&D Program Projects in Shanxi Province under Grant No. 2019ZDLGY07-10, and the Open Project of A Laboratory under Grant No. 2017XXAQ08.

References 1. Alkhateeb, A., Nam, Y.H., Zhang, J., Heath, R.W.: Massive MIMO combining with switches. IEEE Wireless Commun. Lett. 5(3), 232–235 (2016) 2. Li, G.Y., Xu, Z., Xiong, C., et al.: Energy-efficient wireless communications: tutorial, survey, and open issues. IEEE Wirel. Commun. 18(6), 28–35 (2011) 3. Buzzi, S., D’Andrea, C.: Energy efficiency and asymptotic performance evaluation of beamforming structures in doubly massive MIMO mmWave systems. IEEE Trans. Green Commun. Networking 2(2), 385–396 (2018) 4. Dong, S., Wang, Y., Jiang, L., Chen, Y.: Energy efficiency analysis with circuit power consumption in downlink large-scale multiple antenna systems. In: 2016 IEEE 83rd Vehicular Technology Conference (VTC Spring), pp. 1–5 (2016) 5. Han, S., Chih-Lin, I., Xu, Z., Rowell, C.: Large-scale antenna systems with hybrid analog and digital beamforming for millimeter wave 5G. IEEE Commun. Mag. 53(1), 186–194 (2015). Sun, X., Wu, X., Zhao, C., Jiang, M., Xu, W.: Slepian-wolf coding for reconciliation of physical layer secret keys. In: Proceedings of IEEE Wireless Communication Network Conference, Sydney, Australia, April 2010, pp. 1–6 (2010) 6. Buzzi, S., D’Andrea, C.: On clustered statistical MIMO millimeter wave channel simulation. CoRR, vol. abs/1604.00648 (2016) 7. Chih-Lin, I., Han, S., Bian, S.: Energy-efficient 5G for a greener future. Nat Electron 3, 182–184 (2020) 8. Vlachos, E., Thompson, J.: Energy-efficiency maximization of hybrid massive MIMO precoding with random-resolution DACs via RF selection. IEEE Trans. Wireless Commun. 20(2), 1093–1104 (2021)

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9. Wang, W., Li, X., Liu, D., Cai, Z., Gong, C.: Multilevel power modeling of base station and its ICs. China Commun. 12(5), 22–33 (2015) 10. Lin, C., Li, G.Y.: Energy-efficient design of indoor mmWave and Sub-THz systems with antenna arrays. IEEE Trans. Wireless Commun. 15(7), 4660–4672 (2016) 11. Dong, S., Wang, Y., Jiang, L., Chen, Y.: Energy efficiency analysis with circuit power consumption in downlink large-scale multiple antenna systems. In: 2016 IEEE 83rd Vehicular Technology Conference (VTC Spring), Nanjing, pp. 1–5 (2016)

Advanced Nonintrusive Load Monitoring System and Method for Edge Intelligence of Electric Internet of Things Xiande Bu1(B) , Shidong Liu1 , Chuan Liu1 , Wenjing Li2 , and Liuwang Wang3 1 Global Energy Interconnection Research Institute Co., Ltd., Beijing, China

[email protected]

2 State Grid Information and Communication Industry Group, Beijing, China 3 Electric Research Institute, State Grid Zhejiang Electric Power Co., LTD., Hangzhou, China

[email protected]

Abstract. Non-invasive load monitoring technology is to decompose the total load electricity information, get the information of individual load, and then extract the characteristics of these information respectively. After getting some unique features, you can identify what the load is. This technique is a great improvement over traditional intrusive monitoring techniques. This article mainly adopted factor hidden Markov model for load decomposition, using light REDD data set, refrigerator, washing machine, dishwasher and microwave oven, the five electrical data, on the median filtering methods for data preprocessing, after the total load modeling, obtained FHMM model, then for each load clustering, after that, Viterbi algorithm is used to determine the state of each load at each time, then a new coding method is used to code the state of the corresponding load. Finally, the accuracy of the results is calculated, and the conclusion is drawn that the accuracy of the onoff load is the highest, the accuracy of the finite state load is the second, and the accuracy of the continuous variable state load is the lowest. Keywords: Condenser tube · Defect detection system · Image acquisition device · Deep learning · Resnet

1 Introduction Edge computing, compared with cloud computing, meets the high-bandwidth and lowlatency requirements to support the rapid development of mobile networks, meanwhile reduces the load of the whole networking system. In addition to that, edge computing could also improve energy efficiency and enhance data security. Thus, edge computing has drawn close attention from the industry since 2016. Multiple organizations have not only actively promoted the formulation of multi-access edge computing (aka. MEC) standards, but also developed solutions based on MEC for different application scenarios [1]. Non-Intrusive Load Monitoring (aka. NILM), is one of the solutions based on edge computing. Compared with traditional intrusive load monitoring techniques, NILM features simple operation, high reliability, low cost, good data integrity and ease for fast © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 1131–1139, 2022. https://doi.org/10.1007/978-981-19-0390-8_142

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promotion, along with other advantages. Therefore, NILM in one hand enables electrical firms to manage and monitor electrical equipment conveniently and intelligently, in another hand helps users understand the usage of various electrical appliances at home, making it possible for users to rationally arrange the working time of each electrical appliances, thereby reducing energy consumption [2, 3]. The concept of non-intrusive load monitoring was proposed in the 1980s, which mainly includes load monitoring and electrical equipment identification. Load monitoring is to monitor the overall current waveform of the power inlet and decompose it into individual waveforms according to its characteristics; electrical equipment identification performs feature extraction on the decomposed waveforms provided by load monitoring, identifies to which electrical appliance the feature corresponds [4, 5]. This article first investigates hidden Markov models and one of its extended structures, the Factor Hidden Markov model (FHMM). Then a hidden Markov model is used to model a single load, after which the overall load is further modeled using FHMM. Non-intrusive load monitoring based on FHMM is proposed. The second step is to cluster the data by using K-means clustering and to encode load states by using a digit string. Finally, the article calculates the accuracy of the experiments using the accuracy formula, and evaluates the result of the analysis.

2 Hidden Markov Model and Its Extended Model Hidden Markov Model (HMM) is a probability model related to time series [6, 7], it can be seen from the figure below that the model includes two random process sequences in Fig. 1. The above is an unobservable random process sequence, the lower is an observable random process sequence. Since the above sequence is not observable, the state of the sequence at the same time is determined by the observed state value of the observable sequence. The hidden Markov model is determined by the state transition probability matrix A, the observation probability matrix B and the initial state probability vector C, which can be expressed as λ = { A,B,C}. Markov chain

q1

q2

q3

...

qT

Implicit State

O1

O2

O3

...

OT

Observation Sequence

Observation Process

Fig. 1. Hidden Markov Model

As shown in Fig. 2, factorial Hidden Markov Model (FHMM) is a multi-chain hidden Markov model, including multiple Markov chains and an observation sequence.

Advanced Nonintrusive Load Monitoring System

The first layer

(1)

q3

q2 (1)

(1)

(2) The second q1 layer

(2)

O3 (2)

q2 (2)

(1)

O2

O1

Observation sequence

(1)

(1)

q1

1133

q3 (2)

(2)

O1

O2

O3

O1

O2

O3

Fig. 2. Factorial Hidden Markov Model

It can be seen from the structure diagram that each layer of the factorial hidden Markov model contains an HMM model, and the state between each layer is independent. The characteristics of each layer only allow the transfer of the state of the same layer, and the HMM models between different layers do not affect each other. The only connection of these HMM models is that they jointly determine the state of the observation sequence at a time. Since FHMM decomposes the state into the sum of several layers, it can reduce the over-fitting problem of HMM [8].

3 Non-intrusive Load Monitoring Based on FHMM

Hidden Markov Model (HMM) single load modeling

Factorial Hidden Markov Model (FHMM) total load modeling

Load modeling

K-means clustering determine the number of load states

Viterbi algorithm determine the state of the load

Data median filtering

Load decomposition and accuracy analysis of results

Load state coding Load status analysis

Load decomposition

Fig. 3. Non-intrusive load monitoring based on FHMM

The work of this paper is mainly divided into three parts: load modeling, load status analysis and load decomposition in Fig. 3. Load modeling uses Hidden Markov Model

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to model a single load, and then further uses its extended result FHMM to model the total load. Load status analysis considers the use of K-means clustering to cluster the data used in the experiment, and find the average value of the corresponding active power under the same working status, so as to solve the problem that the continuous variable status load has no fixed number of statuses. Finally, the different The load status code is coded. In the load decomposition part, the experimental data set is firstly processed by median filtering, and then calculated according to the accuracy formula, the accuracy of different types of electrical appliances is obtained and the results are analyzed. 3.1 Load Model Based on HMM and FHMM A. Model of Single Load Because the working state of continuous variable state load has the characteristic of changing continuously and there is no relatively steady load characteristic, this type of load is not considered when establishing a single load model, only ON/OFF load and finite state load are considered. Since it is a single load, only a single Markov chain is needed to describe the running state of the load, and the hidden Markov model can be used to complete the modeling. Each working state of the load is represented by the set Q of all possible states, in the form of {Q1 , Q2 . . . , Qk }, where k is the number of working states of the load. The operation process of the load is represented by the set V of all possible observations, in the form of {V1 , V2 . . . , VT }, where T represents the time the load is running. Each running state in set V is contained in set Q as well in Fig. 4. Corresponding to set V , the observation sequence O represents the active power output of the load at each time, in the form of {O1 , O2 . . . , OT }. Therefore, a single load model can be expressed in the form of HMM, that is, λ = {A, B, C}. Where C is the probability that the load is in the state at T = 1, A is the load state transition probability matrix, and B is the output probability P(O|V = i) from the state to the observation.    −1   1 − 21 − 2p P(o|v) = |M | (2π ) ∗ exp − o − uq M o − uq (1) 2 In the formula, p is the dimension of the active power value o; uq is the mean vector; M is the covariance matrix of the observation vector.

V1

V2

...

VT

Operating state of the load

O1

O2

...

OT

Corresponding active power

Fig. 4. Single load model based on HMM

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B. Total Load Model Because the total load contains multiple loads, the HMM model can be used to describe each single load in the total load separately. First ofall, ith-load is represented by  Markov chain in the form of X i = X1i , X2i , · · · , XTi , and its load output is Oi =  i i  O1 , O2 , · · · , OTi in Fig. 5. However, because the power of the total load can only be observed in reality, the power output of a single load is unobservable, the Markov chain of a single load cannot be simply superimposed. With the help of FHMM, the model parameters of I loads are  defined as λ = {A, B, C}, where C is the initial state probability in the form of P X11 , X22 , · · · , XTI , and A is the load state transition probability matrix,  1 2 I P(Xt |Xt−1 ) = P Xt1 , Xt2 , · · · XtI |Xt−1 (1) , Xt−1 , · · · , Xt−1   B is the output probability matrix in the form of P Ot |Xt1 , Xt2 , · · · , XtI .

Load 1

X1(1)

Load 2

X1(2)

X (2) 2 X1( N)

Load N

Total active power of N loads

X 3(1)

X (1) 2

O1

X (1) T X 3(2) X 3( N)

X (2N)

O2

X (2) T

O3

X (TN)

OT

Fig. 5. Total load model based on FHMM

3.2 Analysis of Load Status Information Before load decomposition, it is necessary to analyze and determine the state information of load, including the state quantity of load and the state of load at a certain time. Because there is no constant mean value of continuous variable state load in the steady-state operation process, this paper uses the k-means method to cluster the data to determine the state number of loads. On this basis, Viterbi algorithm is used to determine the state of load at a certain time. In addition, to decompose the total load model using the FHMM model, it is necessary to code the different states of each load to distinguish the different states of different loads. Finally, in order to improve the accuracy of load decomposition, this paper uses median filter to denoise the Redd data set.

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A. K-means Clustering and Viterbi Algorithm Before carrying out load decomposition, it is necessary to know the state number of each load and its active power data. But for the continuously variable state load, because its power during steady-state operation does not have a constant mean value, but continuously changes within a range, K-means method is used to cluster all data. As is demonstrated in Table 1, through the above K-means clustering method to cluster the data of the five electrical appliances selected in this experiment: electric lamps, refrigerators, washing machines, dishwashers, and microwave ovens, Table 1 shows the results. Table 1. K-means clustering results Load Light

Number of states 2

Average level of active power State 1

State 2

State 3

State 4

State 5

0

330

–

–

–

Refrigerator

5

0

5

34

166

247

Washing machine

4

0

20

134

531

–

Dishwasher

3

0

122

768

–

–

Micro-wave oven

3

0

126

3266

–

–

After the K-means clustering method is used to determine the number of states owned by each load, it is necessary to determine the state of the load at a certain moment. In this paper, the Viterbi algorithm is used to determine the state of the load at each moment. B. Status Code To use the FHMM model to decompose the total load model, each different load needs to be coded to represent the state transition. Since the total load contains finite-state load, it is not possible to simply use sequence numbers such as 0, 1, 2, etc. for encoding. To this end, this article adopts a coding method, that is, first determine the state number H of a load, assign it a string of H digits, and then determine which state it is currently in. The position number character is represented by 1, and the rest is represented by 0 filling. For example, a load that has 4 states and is currently in state 3, its code is “0010”. This article selects five electrical appliances: electric light, refrigerator, washing machine, dishwasher, and microwave oven. The set of state numbers is Q{2, 5, 3, 3, 4}, if they are in the state respectively The set is V {1, 3, 2, 1, 4}, then the code is “10/00100/010/100/0001” (there is no “/” in the real code) IN Table 2.

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Table 2. State set and corresponding code State set V

Serial number

Corresponding code

{1, 1, 1, 1, 1}

1

10100001001001000

{1, 1, 1, 1, 2}

2

10100001001000100

{1, 1, 1, 1, 3}

3

10100001001000010

…

…

…

{2, 5, 3, 3, 4}

360

01000010010010001

3.3 Processing of Load Data In any method of non-intrusive load decomposition, the reliability and accuracy of the data largely determine the accuracy of load decomposition and electrical identification. Therefore, a data set with a considerable number of use is much more appropriate. This article uses the REDD data set and selects the data of five electrical appliances including electric lights, refrigerators, washing machines, dishwashers and microwave ovens. In order to ensure the accuracy of the results, the data set is preprocessed first to reduce the noise and interference factors. Currently, the commonly used data denoising methods include median filtering, mean filtering, etc. This article mainly uses median filtering to denoise the data of the five selected electrical appliances. The median filter has the advantages of simple principle and fast calculation speed. It has good performance in dealing with overload white noise and long tail overload noise, and has good adaptability.

4 Experimental Results and Analysis 4.1 Non-intrusive Load Monitoring Toolkit and Data Set The current main data sets include: NILMTK [9], REDD [10], UK-DALE data set [11], etc. Among them, REDD has data on 6 families in the United States, and each family basically has one to several months of data. At the same time, 15 kHz high-frequency data and second-level low-frequency data are provided. Low-frequency data meters use 1s sampling, and electrical appliances use 3s sampling. 4.2 Evaluation Indicators and Results A. Accuracy of Load Decomposition Results The decomposition accuracy Acc can be expressed as 

T  i i  ˙ t=1 At (mi ) − At (mi ) Acc = 1 −

T 2 ∗ t=1 Ait (mi )

(2)

In the formula, A˙ it (mi ) represents the power of the ith device at time t obtained after load decomposition, and Ait (mi ) represents the true value of the ith device in the original data set at time t.

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B. Experimental Results It can be seen from Table 3 that the accuracy of the load decomposition result for the lamp is the highest. Since the light is a simple on/off load, it only includes two states, so it is more accurate when determining the state. The accuracy of the load decomposition results of the refrigerator is not very high. The main reason is that the refrigerator is a continuous variable state load, and its power during steady-state operation does not have a constant average value, but continuously changes within a range. Table 3. Accuracy of each device Equipment

Electric light

Refrigerator

Washing machine

Dishwasher

Microwave

Accuracy

0.963

0.528

0.602

0.675

0.816

5 Conclusion This paper mainly uses the factor hidden Markov model to decompose the load, and uses the REDD data to collect the data of the five electrical appliances, namely, electric lamps, refrigerators, washing machines, dishwashers and microwave ovens, and conducts median filtering on them for data preprocessing. Then model the total load to obtain the FHMM model, then cluster each load, then use the Viterbi algorithm to determine the state of each load at each moment, and then use a new encoding method to perform the corresponding load state Encoding. Finally, the accuracy of the experiment is analyzed in detail. Acknowledgements. This work was supported by science & research project of SGCC. (Development and application of low delay, safe and reliable intelligent IOT agent device. Project No. 5700-202041163A-0-0-00).

References 1. Abbas, N., Zhang, Y., Taherkordi, A., Skeie, T.: Mobile edge computing: a survey. IEEE Internet Things J. 5(1), 450–465 (2017) 2. Hosseini, S.S., Agbossou, K., Kelouwani, S., Cardenas, A.: Non-intrusive load monitoring through home energy management systems: a comprehensive review. Renew. Sustain. Energy Rev. 79, 1266–1274 (2017) 3. Nalmpantis, C., Vrakas, D.: Machine learning approaches for non-intrusive load monitoring: from qualitative to quantitative comparation. Artif. Intell. Rev. 52(1), 217–243 (2018) 4. Liu, Y., Geng, G., Gao, S., Xu, W.: Non-intrusive energy use monitoring for a group of electrical appliances. IEEE Trans. Smart Grid 9(4), 3801–3810 (2016) 5. Zoha, A., Gluhak, A., Imran, M.A., Rajasegarar, S.: Non-intrusive load monitoring approaches for disaggregated energy sensing: a survey. Sensors 12(12), 16838–16866 (2012)

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6. Makonin, S., Popowich, F., Baji´c, I.V., Gill, B., Bartram, L.: Exploiting HMM sparsity to perform online real-time nonintrusive load monitoring. IEEE Trans. Smart Grid 7(6), 2575– 2585 (2016) 7. Kong, W., Dong, Z.Y., Hill, D.J., Luo, F., Xu, Y.: Improving nonintrusive load monitoring efficiency via a hybrid programing method. IEEE Trans. Ind. Inf. 12(6), 2148–2157 (2017) 8. Yao, W.: Non-intrusive load decomposition method based on factor hidden Markov model. North China Electric Power University, Beijing (2019) 9. Batra, N., et al.: NILMTK: an open source toolkit for non-intrusive load monitoring. In: 5th International Conference on Future Energy Systems (ACM e-Energy). ACM (2014) 10. Kolter, J.Z., Johnson, M.J.: REDD: a public data set for energy disaggregation research. Artif. Intell. 25, 1–6 (2011) 11. Kelly, J., Knottenbelt, W.: The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes. Sci. Data 2, 150007 (2015)

Design and On-Orbit Verification of Satellite-Borne AIS Chi Zhang1(B) , Tao Wang1 , Shuai Liu1 , Yawen Cai1 , Qingsong Li2 , and Xutao Hou2 1 Institute of Remote Sensing Satellite, Beijing 100094, China

[email protected] 2 Space Start Technology Co., Ltd., Beijing 100080, China

Abstract. This paper introduces the AIS system of HY-2C satellite, especially focusing on the electromagnetic compatibility design, Baseband Design and capture algorithm design of the AIS system. The application results of the AIS system in-orbit show that it is reasonable design and easy to operate. Also the AIS system can effectively operate under the complex environment of EMC, which can provide references for subsequent satellite-borne AIS. Keywords: Satellite-borne AIS · Complex EMC · In-orbit validation

1 Introduction In recent years, China has also begun to develop satellite-borne AIS (Automatic Identification System) technology, which can receive AIS signal from ships in hundreds of nautical miles or even thousands of nautical miles. AIS signal includes dynamic information (position, speed, heading, etc.) and static information (ship name, nationality, draft, etc.). Satellite-borne AIS can track the ship information around the nation and even around the whole world. It makes up the limitation of visual range of coast station AIS and achieves the global real-time monitoring. Therefore Satellite-borne AIS provides the widespread and efficient ship status data for maritime management organization and operator. AIS (Automatic Identification System) is a new type auxiliary navigation system based on GPS (Global Position System) technology, VHF (Very High Frequency) technology and SOTDMA (Self Organizing Time Division Multiple Access) technology. It has functions of identifying and tracking ship, avoiding impact, environmental protection, search and rescue. Satellite-borne AIS has developed greatly. The typical on-orbit AIS satellite platform includes VesselSat series of American ORBCOMM [1], AprizeSat series of American Space-Quest [2–5], Rubin series of German OHB [6], NTS of Canadian COM DEV [7] and AISSat-1 of Norwegian FFI. Chinese self-developed “Tiantuo-1” satellite can quickly detect and collect of ship position, heading, speed and other information, which makes up the defection that there is no satellite-borne AIS in China. The AIS system of HY-1C satellite, HY-2B satellite and HY-2C satellite has served the Marine Department. In the future, China will launch © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 1140–1146, 2022. https://doi.org/10.1007/978-981-19-0390-8_143

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several maritime satellite with AIS to develop our satellite-borne AIS technology and improve the maritime rights and interests. HY-2C satellite is capable of monitoring the global ocean and lands all the time and all the weather. The main tasks of HY-2C satellite are not only to observe sea surface height, effective wave height, sea wind field and sea surface temperature with high precision and resolution, but also to identify global ships automatically. This paper introduces the design and on-orbit verification of satellite-borne AIS based on HY-2C satellite.

2 AIS Subsystem Introduction 2.1 Main Function and Performance Requirements of AIS When satellite-borne AIS works in orbit, it has 4 frequency points (161.975 MHz, 162.025 MHz, 156.775 MHz, 156.825 MHz). Meanwhile it has the ability to detect, receive and analysis the AIS signal. 2.2 System Composition AIS system is constituted of receiving antenna, preamplifier, receiver and high frequency cable. It can complete the AIS signal receiving, processing and data forwarding. The VHF AIS signal from ship station is received by the satellite-borne AIS antenna. After amplification, filtering and frequency conversion in AIS receiver, VHF realizes the function of AIS signal acquisition, tracking and demodulation. The AIS payload can produce the AIS massage data based on commands. Meanwhile AIS can transmit the data to transceiver subsystem in real time (Fig. 1). LVDS

AIS Receiving unit 1553B

AIS antenna AIS Receiving unit master

RF signal

Remote control command

OBDH

Telemetry parameters

AIS Preamplifier AIS Receiving unit backup

Primary power supply

Power system

Fig. 1. The composition of AIS system

2.3 Main Work Mode In order to meet the work task of HY-2C satellite, the AIS subsystem has two work modes: real-time demodulation and transmission mode, real-time sampling and demodulation mode.

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3 AIS Subsystem Introduction 3.1 Complex EMC (Electromagnetic Compatibility) Design The AIS antenna works in the VHF frequency band so that its size is quite large. Considering such factors as satellite layout and EMC environment, we adopt the miniaturized antenna with M configuration. The satellite flies in the direction of +X and the AIS antenna is placed on the +Z plane. Using the gain coverage characteristics of the antenna, it can receive AIS signal in the area of 6362 thousand square kilometers ocean area (Figs. 2, 3 and 4).

Fig. 2. The structure of AIS antenna

Fig. 3. 3D directional image of AIS antenna

Fig. 4. Earth coverage of AIS antenna

3.2 Baseband Design and Verification The gain of AIS antenna is 1.5–4.5 dBi, and the receiving sensitivity of AIS is required to be no less than −111 dBm@85%.When the maximum radiation intensity of the satellite in the AIS operating frequency band is controlled at 4.26 dBuV/m, the Eb/N0 of the AIS signal demodulation is not greater than 11 db. When Eb/N0 is 9 dB, the corresponding unpacking rate is about 85%, which meets the requirement of the unpacking rate index. AIS payload baseband processing design signal acquisition module, frame head positioning module, frequency offset estimation compensation module to complete signal processing before signal demodulation (Fig. 5). 1. Signal acquisition: AIS signal recognition needs to have better acquisition performance of low signal-to-noise ratio to reduce false acquisition caused by satellite electromagnetic interference and missed acquisition of low signal-to-noise ratio signal caused by signal collision.

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Satellite jamming AD sampli ng

Signal acquisi tion

Frequen cy offset estimati on compens ation

Frame header position ing

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Fig. 5. Flow of baseband processing

2. Frame head positioning: accurate positioning of AIS signal frame head position ensures the performance of subsequent frequency offset estimation and compensation module, and screening of high-power signal in collision mode. 3. Frequency offset estimation and compensation: eliminate the frequency offset of the data to be demodulated and improve the performance of coherent demodulation. 3.3 Design of AIS Message Capture Algorithm Making full use of the 24 bit 0 and 1 alternating synchronization sequence in AIS message, the sequence after NRZI coding has 2T (T is a single bit period) half period characteristics or 4T period characteristics, while the electromagnetic interference form of electronic equipment is random, the design uses the characteristics of synchronization sequence itself to determine the arrival of AIS signal through signal autocorrelation, so as to reduce the impact of electromagnetic interference. The specific implementation mode is that after the power decision, 4T cycle decision and 2T half cycle decision of the sampling sequence, the AIS signal is considered to arrive, otherwise it is considered as electromagnetic interference. Specific treatment process (Fig. 6):

Cache data

Decision 2: periodic characteristic decision Decision1: Power decision

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Data Output control

period

Fig. 6. Signal acquisition algorithm based on signal characteristics

Decision 1: power decision The current sampling position is marked as pos, starting with pos, and a total of N1 sampling points are taken, of which the maximum power point is marked as Maxdp The accumulated value of power at point DP and N1 is recorded as Sumdp , conditions of judgment: Sumdp >X ∗ Maxdp

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Decision 2: periodic characteristic decision After encoding, the period of the sequence is 4T, starting from pos, which is conjugated and multiplied with the delay 4T data respectively, and the calculation results of N2 sampling points are added, conditions of judgment: Sum2I + Sum2Q >Y ∗ Sumdp

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Decision 3: half period characteristic decision After encoding, the half period of the sequence is 2T, starting with pos, which is conjugated and multiplied with the delay 2T data respectively, and the calculation results of N2 sampling points are added, conditions of judgment: Sum2I + Sum2Q < Z ∗ Sumdp

(3)

Simulation test: AIS signal Eb/N0 is 5 dB, parameters x, y, z are 6, ½, 1/8 respectively, AIS data is 0–255 random data, data sampling rate is 4 times of baseband information rate, the results are shown in Fig. 7, the decision algorithm can easily identify 150 points as the position of signal synchronization sequence, with better low SNR signal acquisition characteristics. The abscissa is the number of sampling sequence points, and the ordinate is the real-time power, autocorrelation peak and corresponding decision threshold.

Fig. 7. Simulation of AIS signal capture

4 On-Orbit Verification After the HY-2C satellite was put into orbit, the AIS system started up on September 25, 2020. It generates a global AIS situation map to obtain the density distribution of different types of ships. As of July 16, 2021, we have unpacked about 5.6 million messages and received about 19,0000 messages per day (Figs. 8, 9 and Table 1).

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Fig. 8. Message reception of 20210618~20210718

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Fig. 9. The amplitude and width of AIS in HY-2C satellite

Table 1. Message reception of each frequency point of AIS in HY-2C satellite Every daty

Total

Fre1

Fre 2

Fre 3

Fre 4

Average messages

191031

62260

82892

22447

23432

32.59%

43.39%

11.75%

12.26%

Receiving proportion

By processing the ship message received and demodulated by the HY-2C satellite AIS subsystem, the track tracking of a ship can be realized effectively (Figs. 10 and 11).

Fig. 10. A ship 1 navigation track 20210620-20210627

Fig. 11. A ship 2 navigation track 20210620-20210627

5 Conclusion This paper analyzes the AIS system composition, working mode, characteristics and in-orbit situation of HY-2C satellite. The AIS system has a large range of detection and reception and many messages. It draws a chart of AIS data of ships all over China and improves the global ship database of our country. At the same time, it can also fuse the AIS and SAR system data under the same satellite platform. It also provides reference for AIS - guided SAR satellite imaging function.

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References 1. Zhao, Z., Ji, K., Xing, X., et al.: Ship surveillance by integration of space-borne SAR and AIS-review of current research. J. Navig. 67(1), 177–189 (2014) 2. Schwehr, K.D., Foulkes, J.A., Lorenzini, D., et al.: Global coastal and marine spatial planning (CMSP) from space based AIS ship tracking. In: AGU Fall Meeting Abstracts, vol. 1, p. 1278 (2011) 3. Class B. Applications and limitations 4. Hardhienata, S., Trihaijanto, R.H., Mukhayadi, M.: LAPAN-A2: Indonesian near-equatorial surveillance satellite. In: 18th Asia-Pacific Regional Space Agency Forum, Singapore (2011) 5. Vu Trong, T., Dinh Quoc, T., Dao Van, T., et al.: Constellation of small quick-launch and self-deorbiting nano-satellites with AIS receivers for global ship traffic monitoring. In: 2nd Nano-Satellite Symposium, Tokyo, Japan, March 2011 (2011) 6. Holsten, S., Tobehn, C., Borowy, C.: Global maritime surveillance with satellite-based AIS. In: Proceeding of OCEANS-Europe, Bremen, German, pp. 1–4 (2009) 7. Posada, M., Greidanus, H., Alvarez, M., Vespe, M., Cokacar, T., Falchetti, S.: Maritime awareness for counter-piracy in the Gulf of Aden. In: Proceedings of 2011 International Geoscience & Remote Sensing Symposium, Vancouver, BC, pp. 249–252 (2011)

The Design and Application of Space to Space Communication System in Space Station Missions Lu Zhang(B) , Yuan Wen, Kai Ding, and Cai Huang Beijing Institute of Spacecraft System Engineering, Beijing 100094, China [email protected]

Abstract. Space to space communication system is the key support equipment of rendezvous and docking in manned spaceship and target spacecraft, it can effective guarantee the measure and control of relative distance, velocity, attitude between manned spaceship and target spacecraft. The design of space to space communication system is briefly described in this paper and its key technology such as CDMA, power autonomous switching technology, channel independent switching technology, dynamic data scheduling strategy is thoroughly summarized. The application of the system ensure rendezvous and docking mission successfully, and provide technology accumulation for formation fight of spacecraft in orbit and manned lunar landing. Keywords: Space to space communication system · Space rendezvous and docking · Power switch · Telemetry · Command · Relative measure

1 Introduction Space to space communication system is the key support equipment of rendezvous and docking in manned spaceship and target spacecraft, it is used for the transmission of forward remote control command, programmed instruction from manned spaceship to target spacecraft, and backward engineering telemetry and positioning data from target spacecraft to manned spaceship. It can strictly guarantee the measure and control of relative distance, velocity, attitude between manned spaceship and target spacecraft, and can support astronauts and ground commander to monitor the status of spacecraft in real-time [1]. Compared with the requirements on communication and data management when in a single spacecraft mission, the space rendezvous and docking mission increases requirements on the data communication, state discrimination, telemetry, command, and manual support for both the autonomous control and the manual control, particularly when two spacecraft perform the process of rendezvous, docking, and separating under different time series [2]. As the key support equipment of rendezvous and docking, how to design a high efficiency, reliable, safety and autonomy system, meeting the requirements of relative measure, tracking, control and key data communication in space rendezvous and docking. This paper will research the key technology of space to space communication and the application problem in space station mission. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 1147–1153, 2022. https://doi.org/10.1007/978-981-19-0390-8_144

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2 The Design of Space to Space Communication System 2.1 The Frame of Space to Space Communication Link To achieve autonomous rendezvous and docking, it is necessary to configure rendezvous sensors on target spacecraft and tracking spacecraft in order to measure the relative position, velocity and relative angle and angular velocity. It also needs to configure space to space communication system, helping to establish communication link when the distance between two spacecraft is 0–100 km, and to achieve relative measure function and multi-data two-way transmission in this distance. Space to space communication system is used for transmitting the real-time position measurement date of target spacecraft to manned spaceship, in order to establish relative navigation. At the same time, it can transmit the real-time engineering telemetry of target spacecraft to manned spaceship, and the manned spaceship forwards them to the ground station soon afterwards, in this way the TT&C ground station realizes the task of measurement of two spacecraft by one single station. The real-time engineering telemetry contain attitude control data of target spacecraft, which are transmitted to GNC subsystem finally to accomplish relative attitude control [3]. The information planning of the space to space communication link is shown in the Table 1. Table 1. The information planning of the space to space communication link Communication link

Communication content

Purpose

From target spacecraft to manned spaceship

Real-time position measurement date

Relative measure

Key data of attitude control

Relative measure

Real-time engineering telemetry data

Relay link

Autonomous command date

Relative control

Command and injection data

Relay link

From manned spaceship to target spacecraft

In order to realize the autonomous control of manned spaceship to target spacecraft, the space to space communication equipment A/B of manned spaceship send the autonomous command interoperated by CTU to target spacecraft; To expand the measurement and control capability in rendezvous and docking, space to space communication system can be used as relay link when only one spacecraft under the coverage of TT&C station or Relay satellites. In this way the TT&C ground station realizes the task of control of two spacecraft by a single station. 2.2 The Composition of Space to Space Communication Space to space communication system is consist of space to space communication equipment, communication interface and antenna [4]. The manned spaceship and target spacecraft separate installs two space to space communication equipment A/B, one Interface and two antennas. Space to space communication equipment A/B use the CDMA technology, working at the same time in order to backup for each other, and the communication

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interface is the control center exchanging data with other equipment such as GPS receiver, CTU, command system, telemetry system and GNC subsystem. The composition block diagram of space to space communication is shown in the Fig. 1. GPS antenna

GPS receiver command system

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position measurem ent date

command /injecon data autonomous command

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S-to-S communication equipment A autonomous command

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CTU

key data of attitude control

GNSS recevier

GNC subsystem

command /injecon data

command system

Manned Spaceship

Fig. 1. Block diagram of the space to space communication system

3 Research on Space to Space Communication Technology 3.1 CDMA Technology As the two-way key communication of rendezvous and docking, dual hot-standby and high reliability space to space communication system must be designed. In order to solve the same frequency electromagnetic compatibility, we use S frequency code division technology, polarization isolation technology and error control technology, in addition the system reliability and safety is improved. The space to space communication equipment use the CDMA technology, applying the spread spectrum and de-spread spectrum module, it is important to design the GLOD codes which have fine autocorrelation and fine self-correlation. Figure 2 and Fig. 3 is the spread gain of GOLD code. The length of GOLD code from manned spaceship to target spacecraft is 1023 chips, and the spread gain is 22 dB, while the length of GOLD code from target spacecraft to manned spaceship is 127 chips, and the spread gain is 18 dB. As two space to space communication equipment have the same frequency, so when they work at the same time at short range, it will produce near-far effect inevitably, which causing the receiver cannot be locked normally. To solve the near-far effect, the antenna uses rotary polarization isolation technology, the antenna A uses left-hand circular polarization, and the antenna B uses right-hand circular polarization, in this way polarization isolation is obtained [5]. 3.2 Power Autonomous Switching Technology The space to space communication link usually has the characteristics of long range coverage, high transfer data rate and good concealment [6]. In order to reduce mutual interference, near-far effect, and improve stealth performance of communication system, it is

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necessary to use power control of communication link. It can limit forward and backward link transmit power, while maintaining link quality in all the above applications. The relative distance of two spacecraft varies greatly from more than 100 km to docking, while the equivalent power variation is more than 100 dB, much higher than the usual receiver which dynamic maximum range is 80 dB. We need to use power control technology and allocate reasonable link index, preventing receiver from saturation when two spacecraft are close to each other. At the same time, in order to improve the autonomous control ability of spacecraft, we design power autonomous switching strategy. The system learns from mature and reliable power control algorithm of space to space communication, in which an open loop power control algorithm using distance information is adopted. We design the control switching strategy of large/small power setting, using a 30 dB programmable attenuator in transmitter. When the distance of two spacecraft is close to 400 m, the CTU switches transmit power to small power setting based on the relative distance between two spacecraft. The transmit power is reduced about 7–13 dBm of manned spaceship and about 1–5 dBm of target spacecraft almost simultaneous. While in docking mission, when the distance of two spacecraft is far away from 400 m, the CTU switches transmit power to large power setting, making sure the communication system is uninterrupted while the distance changes widely.

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0.6 PN(a)自相䎔 PN(a)与PN(b)互相䎔 0.5

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3.3 Channel Independent Switching Technology The space to space communication equipment use dual hot-backup and cross hot-backup working mode. In order to ensure the backup function work timely and effectively, so we design optimization strategy and autonomous fault detection strategy to achieve channel independent switching. At the same time, in addition to prevent the equipment working normally due to the failure of autonomous switching function, we design the prohibition command and allow recovery command of autonomous switching function, which can improve the reliability of the equipment. The receiving modules of space to space communication interface can detect autonomously and switch adaptively based on voltage signal. Space to space communication equipment A/B receives communication link data, which will be despreaded and demodulated next, and finally the transmission packet header is determined. When the transmission packet header is correct, the space to space communication equipment A/B give interface normal Locked signal, while when the header cannot be determined, the Unlocked signal is given. The space to space communication interface has two exactly the same processing modules and receiving modules which are dual and cross hotbackup, and they priority choose the date form the same channel. When failure occurs,

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it can select the effective communication data independently according to the level of the Locked signal, in this way, to ensure the data continuity of rendezvous and docking. 3.4 Dynamic Data Scheduling Strategy There are many kinds of transmission date in rendezvous and docking, such as real-time position measurement date, real-time engineering telemetry data, key data of attitude control, autonomous command data and injection data. Affected by interface transmit protocol design and baseband processing delay of transmitter and receiver, the kinds of data have different time-delay. To meet real-time requirements of key data and burst data, and periodic requirements of general user data at the same time, we design asynchronous scheduling strategy, and the different priorities are designed for different users. We use sub-package transmission strategy and 1 MHz high bit rate transmission strategy to reduce the data buffer delay in transmitter and receiver, and on this basis we use dynamic data scheduling strategy. For example, when real-time engineering telemetry data and position measurement date arrive at the same time, the position measurement date is delivered priority, while the engineering telemetry data is delivered to a buffer to wait until the engineering telemetry data is completed. In this way, the max transmit time-delay of position measurement date is less than 0.2 s, which ensuring the real-time of relative measurement of rendezvous and docking.

4 Application Analysis and Prospect In rendezvous and docking mission, the stable communication link can be established when the distance of two spacecraft are more than 140 km, and the large/small power setting is switched in time according to its relative distance. All kinds of two-way communication date transmit correctly, and thanks to the relay by space to space communication, the ground TT&C station realizes the task of measurement and monitor of two spacecraft by a single station. Table 2 is the application analysis of space to space communication system in rendezvous and docking mission. Table 2. Application analysis of space to space communication system in flight mission Item

From manned spaceship to target spacecraft

From target spacecraft to manned spaceship

Transmit power setting

Large power: 32–34 dBm Small power: 1–5 dBm

Large power: 40–42 dBm Small power: 7–13 dBm

Sensitivity of receiver

−125 dBm

−115 dBm

The distance of stable communication link

More than 140 km

More than 140 km

In space station mission, there are many times of rendezvous, docking and transfer between the core capsule of space station and visiting spacecraft. The directions of

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docking include forward, backward and radial, and the working scenes include automatic, manual and handle control. All the missions and scenes need the support of space to space communication, which play a key role of transmit of real-time image data, position measurement date, command data and even voice data of astronauts. In order to adapt to all the working scenes, the space to space communication equipment should possess the two working modes simultaneously [7]. In automatic and manual rendezvous and docking, the direct sequence spread spectrum communication is applied, and while in handle control, the non-spread spectrum communication is applied.

5 Concluding Space to space communication system is the key support equipment of rendezvous and docking mission, which plays an important role in space station mission. This paper describes the design and key technology of space to space communication system, it can apply to missions of multi module assembly of space station, and it can provide technology accumulation for formation fight of spacecraft in orbit and manned lunar landing.

References 1. Zhou, J.: Space Rendezvous and Docking Technology. National Defense Industry Press, Beijing (2013) 2. Zhang, B.: Analysis and Design of Spacecraft Rendezvous and Docking Mission. Science Press, Beijing (2011) 3. Wang, J.: Data Management design for rendezvous and docking/complex of manned spaceship. Spacecr. Eng. 21(4), 70–71 (2012) 4. Shi, Y.: Space to space communication subsystem manned spaceflight and its key technology. Aerosp. Shanghai 28(6), 38–42 (2011) 5. Chen, R., Chen, X., Huang, B., et al.: Design and implementation of EMC in space to space communication equipment on target spacecraft. Radio Eng. 50(6), 499–503 (2020) 6. Qu, T.: Research on power control in the context of air-air communication. New Technol. 13, 90–91 (2013) 7. Huang, B., Chen, R., Cheng, Q.: Design and implementation of the space to space communication equipment used multiple mode. Space Electron. Technol. 15(6), 17–22 (2018)

Analysis and Simulation of Potato Combine Harvesting Machine Tizhi Cao1 , Yanwei Wang1,2(B) , and Jiaping Chen1 1 Heilongjiang Bayi Agricultural University, Daqing 163319, Heilongjiang, China

[email protected] 2 Mechanical Engineering, Heilongjiang University of Science and Technology, Harbin 150027,

Heilongjiang, China

Abstract. A new type of small potato combine harvesting machine, which is suitable for remote mountain hills and big flaw working, is designed in this paper. It can reduce potato breakage rate caused by digging shovel and improve the entry efficiency, achieving the maximum utilization of power. The design of control circuit of potato harvester is mainly to use GPS satellite positioning to control the route, hydraulic steering control, to complete the auxiliary driving of agricultural machinery, Solve the problem of fatigue driving, improve the personnel utilization rate and potato harvest efficiency. Result shows that it is tiny, agricultural machinery and strong adaptability. Thus, potato production efficiency will be improved and predominantly human labor will be reduced. Keywords: Potato combine harvesting machine · Potato breakage rate · Separation plant · Circuit design

1 Introduction Recently, manual operation is the main operation in China’s potato industry. Compared with developed countries mechanization, there is a deep gap between them. Actually, land farming, field mechanization management and conventional agricultural machinery just meet the mechanized production requirements, but the potato sowing, harvesting and other related machine are still imperfect [1]. Compared with the foreign potato joint harvester, the domestic harvest machinery research time is relatively early, the improvement speed is also relatively fast, and the mechanization level is relatively advanced [2]. In the late 1970s, many developed countries have realized the development and development of potato harvesting machinery. It did not gradually develop potato machinery in 1960s until the mid-1960s on potato machinery [3, 4]. At present, domestic potato harvesting machinery is mainly small harvesting machinery. Compared with foreign machinery and equipment, the driving force requirement is low, poor working efficiency and relatively single function. Most of the output is upward output for field potato harvest, and only a few machines have a lateral output, or a box of material on one side. The structure is relatively simple and the price is more reasonable. However, the selected integrated small and medium-sized excavator shovel may lead to the friction drag of the excavator © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 1154–1161, 2022. https://doi.org/10.1007/978-981-19-0390-8_145

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to move in the complex natural environment, with low coordination of the soil layer and harvest time and short period. Most harvesters have no secondary separation and transport equipment, which makes the potatoes dug through the foundation pit twice buried in the surface soil, and has a relatively low rate of open potato. So the mechanized potato harvesting process involves soil-machine-crop interactions [5]. Components of potato harvesting machinery are tended to eroded, damaged or broken by bad working environment. Thus many levels design is applied in this paper, which is structural characteristics, production technology and the pyrolysis of raw materials. It can improve the reliability of parts structure, further strengthen the safety of human operation.

2 Overall Structure and Working Principle Potato harvester mainly consists of excavation shovel, swing chain, rack, sprocket assembly, V belt assembly, reducer and other parts. As shown in Fig. 1. The potato harvester is connected to the auxiliary equipment of large tractors with 80–100 horsepower for related work, for collaborative work. The practical operation method of the connection is mainly the three-point suspension. Two anchor bolts mounted the shovel rack on two front baffle and secure the excavation shovel to the shovel frame. On the frame of the large tractor, two fixed parts for suspension were also welded, interconnecting with a two-point suspension on the harvester. A coupling mechanism with a gear drive control function is provided in the coupling frame part, which consists of two cylindrical-like gear drive boxes, a pair of conical-like gears, and a circular drive shaft. An eccentric wheel is provided at the two ends of the transmission linkage, and the eccentric wheel to the other end of the transmission link can do lateral rotary movement, and a lateral swing filter is carried on the other end of the link to produce lateral vibration, so that potato and other soil crops can achieve automatic separation [6]. Large tractors move the traction harvester forward according to the three-point suspension. Its drive output shaft is connected to the drive mechanism of the harvester according to the rotation shaft. After the harvester runs, integrate the drive output shaft to speed up rotation. By this transmission mechanism, the driving force is transmitted to rotate and swing the swing screen [7]. When working, the excavation shovel is dug into the soil and potato blocks, then with the excavation shovel into the rotary transport screen for the first separation, and then from the rotary sieve to the swing separation mechanism for secondary separation [8]. With the help of the rotating sieve, the potato block is separated from the soil, and the potato block is orderly thrown on the field behind the excavator, which can facilitate the manual inspection work [9].

3 Excavation Shovel Design Analysis Digging shovel is an important mechanism to ensure the excavation of potatoes. One of the key components of excavator is the movable triangular plane shovel. Therefore, it is necessary to consider the comprehensiveness of excavation and potato breakage rate, as well as the rationality of structural design and maximum use of power [10]. The structure is simpler and more convenient than the vibration excavator shovel and the active disc

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1-Digging shovel 2-reduce 3-V belt 4-Swinging chain assembly 5-rack 6-Small sprocket assembly

Fig. 1. Small jitter potato harvest

excavator, and it is more convenient to drive the system without power transmission. Its disadvantage is that the work may cause the accumulation of soil, the main reasons are: 1) the soil is stiff, there are large stones and weeds winding; 2) The excavation depth and advance speed exceed the expected value; 3) The size of the hanging connecting mechanism is wrong, and the working inclination of the digging bucket is too large or the deviation is too small. However, in places with better land conditions, proper use of excavators can effectively reduce or even avoid the occurrence of water blockage [11]. Fixed triangular planar multi-functional bucket has been widely used in this field [12]. The triangular flat spade has a flat surface, as shown in Fig. 2. Among γ is blade angle, α is the obliquity, L is the length of the shovel, β is the width of the shovel, h is height of back end of shovel. Therefore, this equipment is selected by 13 raw materials 65Mn triangle shovel excavation shovel. The installation method is to fix the flat shovel handle on the shovel frame, and leave a gap between each flat shovel.

Fig. 2. Excavating parameters

The stress analysis is shown in Fig. 3

(a)Stress analysis of shovel body

(b)Force analysis of shovel tip

Fig. 3. Excavation shovel force analysis

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By the formula Psin(90◦ − γ) > F

(1)

Among them P is the resistance encountered when the blade enters the soil (N), F is the friction force when the blade enters the soil (N), γ is the bevel Angle of the shovel blade. According to the law of friction F = Ntgϕ

(2)

N = Pcos(90◦ − γ)

(3)

Substitute (2) and (3) into Eq. (1) γ < 90◦ − ϕ Pcosα − F − G

(4)

a=0

(5)

N − Gcosa − Psinα = 0

(6)

F = tgϕ

(7)

Among them N is the reaction force of shovel on soil (N), G is the gravity of the soil on the shovel surface (N), F is the friction between soil and shovel (N), f is the friction coefficient generated by soil shovel, ϕ is the friction Angle between soil and shovel blade. By solving Eqs. (4), (5) and (6) simultaneously, we can obtain p − fG fP + g

(8)

P = Gtg(α + ϕ)

(9)

α = arctg

The resistance of the workpiece is not only caused by the movement of the soil along the direction of the shovel, but also caused by the mutual resistance of the cutting soil. Therefore, the compressive stress of this part is defined as P = ka

(10)

If the influence of soil moving speed along the surface of the shovel is not taken into account, the total resistance of the shovel is R = P + P1 = Gtg(∝ +ϕ) + KA = tgϕ

(11)

Among them is the friction Angle between soil and steel; K is the ratio resistance of furrow soil (N/m2 ).

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Fig. 4. The end distance height of the horizontal angle and shovel

While Light soil K = 16 000–20 000 N/m2 , Medium light soil K = 20 000–24 000 N/m2 , Moderately solid soil K = 24 000–30 000 N/m2 , A is the cross-sectional area of soil on the shovel surface (m2 ). The theoretical value of the horizontal inclination Angle of the shovel surface can be determined by measuring the force balance equation of the excavated material moving on the shovel surfaceα, as shown in Fig. 4. Pcosα − T − Gsinα = 0 R − Gcosα − Psin = 0 Among them P is the force required to move the excavated object along the shovel surface, R is the reaction force of the shovel against the soil, G is the gravity of the excavated object, T is the friction force of the shovel against the excavated object, T = Rf (f Is the friction coefficient of soil to steel). It can be concluded that α = arctg(P − G)/(P + G) If the excavation exceeds the above value, the excavated material is likely to immediately block the surface of the shovel, causing the potato block to slide from the side of the shovel and increase its working resistance. In practical activities, it is stipulated that the excavation shovel must increase the aspect ratio of the excavated material and loose the soil to clarify the α angle. If the expansion of Cape α can increase the fragmentation rate of soil excavation, but will increase the resistance during excavator operation. The angle of α if too small affects the depth of soil. According to the measurement experiments, α = 1825 was taken. The length of the shovel L can be calculated from the excavation depth h to the inclination angle α. That is L = h/sinα

4 Control Circuit Design To better achieve the above goals, a specialized integrated circuit chip control system for producing and manufacturing potato harvesters has the following main functions, The monitoring camera for collecting rural land situation images, the potato harvester

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receives the rural land landscape image collected by the monitoring camera, calculates and obtains the main parameters of the navigation strip parallel line, the potato harvester accepts the main parameters of the harvester navigation angle data signal installed on the steering wheel of the car and accepts the speed limit sensor installed on the potato harvester. After the harvester drive speed data signal of the harvester is issued, the potato harvester visual navigation steering control system integrates the parallel navigation path line main parameters, the harvester steering angle data signal and the harvester driving speed data signal, the control command output to the power circuit control system steering system software, and the full hydraulic equipment steering system software of the power circuit control system to adjust the steering angle to complete the steering control of the potato harvester. The key technical aspects involved in the design are image processing and fully automatic control, using DSP and MCU for the design. As shown in Fig. 5, The power circuit control system for the potato harvester described in this design comprises.

Fig. 5. Circuit control system

When the switch is driven manually and safely, the driver adjusts the steering wheel on the rear axle of the potato harvester’s original full-hydraulic device steering system software. Steering Angle, and finally complete the steering control of the potato harvester. When the auxiliary driver is turned on, the full hydraulic steering system software of the auxiliary equipment of the power circuit control system adjusts the steering angle of the rear axle steering wheel of the car according to the control command, and finally completes the steering control of the potato harvester. Based on the above technical specifications, the visual navigation path recognition technology of potato harvester received the rural land scenery images collected by the monitoring camera, and successively carried out colorful indoor space transformation, noise reduction, threshold segmentation and processing. Navigating the scene of rural land. Get the main parameters of the parallel line of the bar path, and finally get the main parameters of the parallel line of the navigation bar. The main parameters of the navigation bar parallel line are basically based on the above technical specifications. The servo motor is used to adjust the steering angle of the steering wheel in the rear axle of the car. Based on the above technical specifications, the key to the visual navigation and steering control of the potato harvester system is the STC microcontroller design. Among the above two technical solutions, the key to the visual navigation path recognition technology of the potato harvester is to choose the DM6446 CPU. The DM6446

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CPU includes a ARM kernel and a DSP kernel. The operation of the ARM kernel-based potato harvester uses a visual navigation path recognition technology to control the device before the video to collect the video data information of the surveillance camera 1 and to generate and distribute the DSP kernel for image processing. After solving the path identification and solution optimization algorithm and the shortest path algorithm decision support system, according to the main parameters of the navigation path parallel line, the main parameters of the navigation bar are finally obtained. The DSP kernel transmits the body forward error data to the ARM core data information indoor space, and transmits it to the STC single chip design according to the I2C system bus. This design introduces the potato harvester power circuit control system and GPS and SCM design closely integrated. System software function is low, machine miniaturization, easy installation. Suitable for marketing and application. It is suitable for visual navigation of potato harvester. After installing the system software, the driver can use the system software to assist driving at work, thus reducing the fatigue of safe driving and improving the efficiency and quality of driving. Keys to the potato harvester power circuit control system include h264 coding socket, video output socket, corner sensor socket, servo motor controller socket, monitoring cameras and TV. Power to the power system and its adjustment use serial communication and network cable ports. Small volume, convenient socket.

5 Conclusion A new small potato harvester machine is designed in this paper, analysing the structural parameters and theoretical calculation the its size. Results show that the digging shovel dip angle α is between 18 to 25°, which is composed of 13 triangular flat shovels. And the jitter wheel speed is 134 r/min, the conveying screen speed is 1.6 m/s, The swing screen working inclination is 15°, length (wheelbase) is 1 542 mm, width (two belt center distance) is 1 756 mm, screen surface inclination is between 10 to 15°. Therefore, working reliably, minor damaging and good performance are suitable for small potato harvesting. The driving circuit of the harvester is also designed, the path is manipulated by GPS satellite positioning, and the hydraulic steering is controlled by single chip microcomputer. Its small size and adaptability, which and can smoothly and orderly plow the farmland in hills circumstance and remote mountainous areas with high potato production efficiency and low labor fee.

References 1. Li, X., Shang, Q., Yin, F.: Current situation analysis of potato mechanization production technology. Sci. Technol. Econ. Guide 17, 69 (2017) 2. Du, M., Wang, J., Wang, W., Yu, W., et al.: Development status and trend of potato combine harvester. Agric. Mach. Use Maint. 08, 15–17 (2019). (in Chinese) 3. Guo, X.: Optimization and experimental study on key components of small potato harvester. Shihezi University (2016) 4. Lü, J., et al.: Design and experiment on 4U1Z vibrating potato digger. Trans. Chin. Soc. Agric. Eng. 31(12), 39–47 (2015)

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5. Li, H., Su, G., Sun, C., Wei, Z., et al.: Design and experiment of potato harvester with double buffer with low placement. Trans. Chin. Soc. Agric. Mach. 50(09), 140–152 (2019) 6. Yin, Y., Yang, R., Wang, Z., Wang, J., et al.: Simulation and parameter optimization of potato harvester conveyor Chain. Agric. Eng. 10(10), 79–83 (2020) 7. Xiao, J.: Study on the popularization mode of mechanized harvesting technology of winter potato in South China. Zhongkai University of Agriculture and Engineering (2016) 8. Dustkulov, A., Matmurodov, F., Matmurodov, F., Abdiyev, N.: Mathematical simulation of transfer mechanisms of crocheting potato harvesting machine. IOP Conf. Ser. Mat. Sci. Eng. 883(1), 012176 (2020) 9. Liang, R., Li, Q., Qin, X., Zhang, D., et al.: Development of a quantitative stacking potato harvester. J. Agric. Mech. Res. 41(12), 74–79+91 (2019) 10. Li, W., Wang, M., Yang, L.: Design of potato harvester. Intern. Combust. Eng. Accessories 15, 232–233 (2019) 11. Liu, C.: Design of key components of 4U–51 potato harvester. Contemp. Agric. Mach. 06, 76–79 (2016) 12. Guo, W., et al.: Design and experimental study on belt-tooth type collector for potato film residues. IOP Conf. Ser. Earth Environ. Sci. 252(4), 042092 (2019)

Research on Pesticide Fog Droplet Drift Detection Applied in UAV Jiaping Chen1 , Yanwei Wang1,2(B) , and Tizhi Cao1 1 Heilongjiang Bayi Agricultural University, Daqing 163319, Heilongjiang, China

[email protected] 2 Heilongjiang University of Science and Technology, Harbin 150027, Heilongjiang, China

Abstract. With the development of precision and efficiency, UAV is greatly developed in the field of plant protection to saving water and protecting, and is widely used in the future. However, drug liquid lossing and farmland environmental pollution are existing in the plant protection UAV spraying operation. Drug mist drops drifting, working in complex field operation environment, is vulnerable affected by temperature, humidity, UAV equipment, and natural wind. Thus it lead to utilization reducing, economic lossing and health problems. The characteristics, influencing factors, detection methods and detection technology are discussed in this paper. Recently results shows that the influence of fog droplets should be consider in plant protection UAV, and the new fog droplet drift detection technology is needed to provide guarantee, improving the efficiency of quality assurance operation efficiency, saving agricultural resources and protecting the crop growth environment. Keyword: Plant protection UAV · Fog droplet drift · Fog droplet particle diameter

1 Introduction With the continuous improvement of agricultural mechanization level in China, it is an important means of improving crop yield in the field of agricultural plant protection. The emergence of plant protection machinery can not only strengthen the governance capacity of diseases, insects and pests, but also reduce the waste of agricultural resources and protect the agricultural ecological environment. Plant protection UAV, because of its applicable farmland types, large operation scope, high application efficiency, water saving and considerable development [1]. The core form of the current plant protection UAV application technology is still spray. Recently research shows that the smaller size particle of atomized solution, the higher organism of drug solution [2]. But small fog droplets in the atmosphere have the problem of movement towards non-target regions, both drifting. And the fog-free droplet movement is affecting by uncontrollable meteorological factors in the natural environment. The drift of fog droplets in the field environment operation is inevitable and difficult to accurately predict and control. The ejected droplets settle down by their © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 1162–1167, 2022. https://doi.org/10.1007/978-981-19-0390-8_146

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own potential energy. The smaller of the droplet diameter, the lighter of the weight, and it is more likely to be affected and drift. Thus, increasing aerosol droplet size can reduce drift, but this is in conflict with the requirement of pharmacological bioefficacy for aerosol droplet size. In fact, not all fog droplets act accurately on the target crops. In this paper, and their factors causing the phenomenon of fog droplet drift are preliminarily analyzed, further more, the existing fog droplet drift detection methods are summarized, and the comprehensive application of fog droplet drift detection method in the future is reviewed.

2 Characteristics and Influencing Factors of Plant Protection UAV Plant protection UAV drug spray droplets drift is in the process of pesticide spraying and pesticide spray after the near time, drug spray droplets under the influence of application technology, application equipment, natural environment and other factors, produce drift phenomenon (namely, pesticide fog droplets or dust particles in the atmosphere from the target area to the non-target area of a physical movement [3]). This fog droplet drift movement, under the influence of various factors, can be divided into evaporation drift, drift with the wind. 2.1 Evaporation Drift Evaporative drift refers to the pesticide smoke particles attached to the surface of crops and the soil surface under the influence of the atmosphere after forming fog droplets. Regular movement spread to the surrounding non-target environment in the atmosphere, mainly caused by volatile components in the pesticide. The evaporation of pharmaceutical liquid drift, drifting distance, stranded floating in the air for a long time, large cover area and low harm. 2.2 Drift with the Wind Wind drift refers to the motion process [4] of the small fog droplets ejected from the nozzle with the UAV rotor wind field and the atmospheric airflow pressure. It is mainly related to pesticide application machinery, operator application technology, atmospheric environment and other factors. In most cases, the drift distance is related to the particle size of pesticide fog droplets, and most pesticide fog droplets have a short drift distance, which is small and harmful to the recover area but large density of non-target plots. 2.3 Impact Factors of Pesticide Fog Droplet Drift In the actual process of spraying liquid, many factors can cause the formation of droplet drift, such as droplet particle size, physical and chemical nature, application equipment, wind speed, temperature and humidity and other factors, among which the size of droplet particle volume is the main factor leading to the drift of atomization drug droplets. According to British scientist Matthews, the fog droplet size is shown in Table 1. The smaller the particle size of the fog droplets, the farther the drift distance with the wind,

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the higher the harm degree caused by the drift is [5, 6]. Gas droplets, fog droplets mass is very light, by the atmospheric resistance in the natural environment, invincible settlement speed will gradually reduce, its potential energy often can not meet the fog droplets successfully reach target crops, and the smaller the particle size of pharmaceutical liquid droplets more susceptible to temperature and relative humidity, under the action of solar temperature will continue evaporation, the quality of fog droplets will be lighter and under the action of wind. The quality of medium and coarse fog droplets is large, less affected by atmospheric resistance, and the settlement velocity is fast, but the application effect is not uniform. Table 1. The fog droplet size classification Medium-volume diameter of the fog droplets/µm

Fog drop size classification

400

Thick fog drops

For a long time, the problem of droplet droplets not only troubled the majority of agricultural workers, but also has an impact on the environmental health of non-target crops, fishing and animal husbandry and other related fields. It provides reference for protecting the farmland environment, improving the effective utilization rate of pesticides, optimizing the plant protection operation method of UAV plant protection, and the research of new plant protection technology in the field of plant protection researchers in the future. The detection method of fog droplet drift at home and abroad is of great significance to promoting the development of new plant protection technology suitable for China’s national conditions.

3 Detection Method of Fog Droplet Drift of Plant Protection UAV at Home and Abroad Compared with the backward research of plant protection and drug application technology in developed countries, there are many problems in the prevention and control of diseases, insects and pests in farmland operations [7]. In recent years, aviation plant protection equipment has developed rapidly, and the operation mode of plant protection UAV can improve the effective utilization rate of pesticides and ensure the safety of spraying operators. Compared with traditional drug application machinery, plant protection UAV has high operation efficiency and effective utilization rate of pesticides, and is widely used in controlling crop diseases [8]. Plant protection drones can operate efficiently in various geographical conditions, especially for paddy fields and difficult

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hilly areas, which not only reduces the risk of pesticide application, but also can reduce environmental pollution [9]. Plant protection drones mostly use ultra-low capacity spray, and due to the light fog droplets, they are more susceptible to ambient airflow and airflow around the rotor, with a higher risk of pesticide fog droplet drift compared to traditional application methods [10]. With the popularization and development of plant protection UAV, scholars at home and abroad have conducted a lot of experimental research on the liquid fog droplet drift of plant protection UAV. 3.1 Foreign Detection Method At present, foreign scholars study plant protection UAV drug droplets drift detection common test methods including field test, water sensitive paper image analysis, wind tunnel experiment, etc., Li [11] 4 will 4 rotor UAV droplets in the air and ground and the amount of comparative analysis, get the drug droplets drift influencing factors related to the height and spraying system. Brown et al. [12] tested the spray deposition and displacement of the Type R-Max II oily monomotor UAV in vineyards and found a correlation between deposition and displacement. Martinez-Guanter et al. [13]. In order to determine the drift of the ultra-low capacity variable spray UAV and the air sprayer, water sensitive paper is set between the fruit tree lines. The comparison test shows that the ultra-low spray UAV can effectively reduce the displacement and reduce the risk. WTDISP models by testing fog droplet diameter and flow fields under different conditions, Fritz [14, 15] et al. and Martin [16] et al. did tests in the wind tunnel to obtain the law of fog droplet drift of static nozzle under different side currents. Foreign scholars studied it indirectly through field testing or water-sensitive paper image analysis, and few are studied at present. 3.2 Current Status of Domestic Research Compared with foreign countries, domestic scholars have carried out a lot of research on the drug fog droplet drift of plant protection UAV, and the common test methods include field test, water sensitive paper image analysis method, model simulation calculation analysis, wind tunnel test analysis and drift test platform. The simulation analysis of the wind field in the front, middle and rear propeller spraying area of the multirotor UAV was conducted based on SolidWorks 2016 software by Haiba Fu [17] et al. In this wind field, by changing the nozzle installation position of the fuselage, the influence of the UAV rotor wind field on the effective spray amplitude of the two propellers in front of the fuselage is studied. The study shows that the UAV rotor spin direction and nozzle layout have a significant impact on the effective spray amplitude. Plastic filaments made of PE materials received fog droplets in the air tube test table equipped with sprinklers, through DIX (drift potential index) and analyzed the fog droplet deposition drift characteristics of commonly used sprinklers under different temperature and humidity and wind tunnel wind speed. Changling Wang, Xiongkui He and others [18] of plant protection UAV orchard spraying fog droplet drift of the simulation analysis, for the crown deposition, ground and air drift collection device, designed by the team simulation orchard test platform for three fog droplet collection device test, after testing evaluation indicators can effectively evaluate the UAV rotor downdirection of fog droplet drift characteristics

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Songchao Zhang [19, 20] et al. simulated the spatial drift of pesticide fog droplets in the field operation of the N-3 unmanned helicopter. By comparing the air drift with the ground droplet displacement, the results show that the CFD technology can qualitatively simulate the actual operation time and surface of the UAV. As can be seen from the study method of plant protection UAV at home and abroad, the detection adopts field test, model establishment, simulation, air tunnel test, water sensitive paper image analysis and colorimetric analysis. These methods are mostly detected after fog droplet deposition and conduct real-time detection in complex agricultural environment.

4 Conclusion Crop growth depends on plant protection operation, thus the quality of pesticide spray effect is closely related to agricultural quality and farmland environment, pesticide liquid droplet drift is one of the main factors affecting drug effect, in the process of plant protection UAV dosage displacement scientific detection can avoid liquid loss and the damage to the farmland environment. Previous results shows that a comprehensive detection of plant protection UAV operation drift movement rules not only to test the floating droplet distribution, but also settlement in the ground drift droplet distribution, especially in the process of natural wind speed, UAV rotor field, UAV flight altitude, UAV flight speed. Operation irradiation change of factors for comprehensive and objective analysis, to wait until the precise characteristics of plant protection UAV droplet drift distribution. At present, there is few research on drug fog droplet drift detection in plant protection UAV. Most methods are based on data derived from deposition and simulation tests. In conclusion, the direct detection of fog droplet status should be considered in the future study of the drug fog droplet drift detection method of plant protection UAV. And It combined with image technology, laser tracer technology, multi-UAV collaboration technology and 5G technology and other high and new technologies, the real-time detection and adjustment of the full-field drug fog droplet drift are realized.

References 1. Liu, C., Zhao, L.: Current situation and development suggestions of plant protection UAV in China. Agric. Eng. Technol. 38(12), 39–42 (2018) 2. Guo, Z.: Implement the regulations on crop disease and disease control to effectively protect food production safety. Hubei Plant Protect. 05, 3 (2020) 3. Zeng, A.: Technical studies on reducing pesticide fog droplet drift. China Agricultural University (2005) 4. Chen, B., Li, C., Lin, J., Liu, X., Lv, W., Wang, L.: Analysis of the influencing factors of fog droplet motion. Agric. Technol. 34(05), 190–191 (2014) 5. Smith, D.B., Bode, L.E., Gerard, P.D.: Predicting ground boom spray drift. Trans. ASAE 43(3), 547–553 (2000). https://doi.org/10.13031/2013.2734 6. Wang, X.: Study on pesticide fog droplet drift and drift reduction method. China Agricultural University (2017) 7. Landers, A., Muise, B.: The development of an automatic precision canopy sprayer for fruit crops. Asp. Appl. Biol. 99(1), 29–39 (2010)

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8. Fritz, B.K., Hoffmann, W.C., Lan, Y.B.: Evaluation of the EPA drift reduction technology (DRT) low - speed wind tunnel protocol. J. ASTM Int. 6(4), 12 (2009) 9. Teske, M.E., Bowers, J.F., Rafferty, J.E., et al.: FSCBG: an aerial spray dispersion model for predicting the fate of released material behind aircraft. Environ. Toxicol. Chem. 12(3), 453–464 (1993) 10. Zhou, H., Yang, B., Yan, H., et al.: Application status and development prospect of highefficiency plant protection machinery in China. Agric. Eng. 4(6), 4–6 (2014) 11. Li, L., Liu, Y., He, X., et al.: Assessment of spray deposition and losses in the apple orchard from agricultural unmanned aerial vehicle in China. In: 2018 ASABE Annual International Meeting, Detroit, USA (2018). Paper Number: 1800504 12. Brown, C.R., Giles, D.K.: Measurement of pesticide drift from unmanned aerial vehicle application to a vineyard. Trans. ASABE 61(5), 1539–1546 (2018) 13. Martinez-Guanter, J., Agüera, J., Agüera, P., et al.: Spray and economics assessment of a UAV-based ultra-low-volume application in olive and citrus orchards. Precis. Agric. 21(1), 226–243 (2020) 14. Fritz, B.K., Hoffmann, W.C., Lan, R.Y., Zollinger, A.R., Dean, S.W.: Evaluation of the EPA drift reduction technology (DRT) low-speed wind tunnel protocol. J. ASTM Int. 6(4), 102129 (2009). https://doi.org/10.1520/JAI102129 15. Zhu, Y., Chen, X.: On the current situation and development opportunities of plant protection machinery in China. Agric. Dev. Equip. 10, 9–11 (2007) 16. Martin, D.E., Carlton, J.B.: Airspeed and orifice size affect spray droplet spectrum from an aerial electrostatic nozzle for fixed-wing applications. Atom. Sprays 22(12), 997–1010 (2013) 17. Fu, H., Sun, H., Qi, L., Li, R., Zhang, W., Zhang, P.: Effect of wind field spraying of multirotor plant protection UAV on effective spray amplitude. Res. Agric. Mech. 43(08), 164–170 (2021) 18. He, X., et al.: Fog droplet drift test method and test of plant protection UAV based on simulated orchard test bench. Agric. Eng. J. 36(13), 56–66 (2020) 19. He, X., Zhang, S., Zeng, A.: Test and evaluation of drift characteristics of typical hydraulic nozzle in wind tunnel environment. J. Agric. Eng. 15, 78–81 (2005) 20. Oerke, E.C.: Crop losses to pests. J. Agric. 144(1), 31–43 (2006)

Simulation of OFDM Index Modulation in VDES System Zou Xu, Jie Yang(B) , Dong Wang, and Jiaqi Zhen College of Electronic Engineering, Heilongjiang University, Harbin 150080, China [email protected]

Abstract. OFDM is a multi carrier transmission technology widely used in various communication systems, such as 5G communication, underwater acoustic communication, power line communication and WLAN communication. However, OFDM technology itself has some shortcomings, such as high peak-toaverage power ratio, very sensitive to the frequency deviation of the system, etc., Taking VHF data exchange system (VDES) as the research background, aiming at the load problem of existing data link, this paper proposes to integrate high-speed transmission technology orthogonal frequency division multiplexing (OFDM) and index technology into VDES system, and proposes a digital OFDM index modulation method to improve the performance and communication efficiency of vdes system. Through the physical layer simulation modeling, different digital index modulation schemes are used to simulate the BER performance of real data communication on the sea, and the performance of its communication system is simulated and analyzed. The results show that compared with traditional OFDM, this method can achieve lower bit error rate and effectively realize offshore VHF data communication. Keywords: VDES · OFDM index modulation · Wireless communication · BER simulation · Simulation

1 Introduction Recently, with the rapid promotion of automatic ship identification system (AIS), the available frequency band of AIS system becomes very crowded, and there may be a high data link load, which leads to information congestion, loss of a large amount of important information, and affects navigation safety. In view of this serious problem, the International Navigation Mark Association (IALA) put forward the concept of VDES for the first time. The system has the functions of ship identification, position reporting and tracking, ship navigation data, search and rescue support, and to support the data exchange requirements of E-navigation [1]. Based on the original AIS function, VDES adds special application message (ASM) and broadband VHF data exchange (VDE), which can achieve higher data transmission rate and effectively alleviate the pressure of data communication in the existing AIS system [2]. Very high frequency (VHF) wireless communication equipment is the main equipment to realize short-distance wireless communication in offshore and inland waters, and its working frequency range is © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 1168–1175, 2022. https://doi.org/10.1007/978-981-19-0390-8_147

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156–174 MHz. However, because of its narrow working frequency band and small communication capacity, the International Telecommunication Union (ITU) put forward the development goal of new marine VHF radio technology in ITU recommendation ITUR-M.1842 [3]. Orthogonal Frequency Division Multiplexing (OFDM) is one of the main technologies of high-speed transmission [4, 5]. OFDM technology can not only improve the band efficiency, but also reduce the influence of multipath fading, and effectively eliminate multipath interference [6–8]. However, OFDM technology has some shortcomings, such as high peak-to-average power ratio and very sensitive to the frequency offset of the system. In order to further improve the system performance and spectral efficiency, index modulation is introduced to increase the hidden information bits. By adjusting the number of subcarriers of OFDM subblocks and the number of active subcarriers, different choices of spectral efficiency can be provided. Therefore, this paper takes the VDES system as the research background, proposes an index modulation OFDM technology, through in-depth analysis of the principle of modulation and demodulation and simulation, to complete the performance analysis of the VDES system.

2 Index OFDM System Model As a multicarrier modulation technology, compared with traditional OFDM systems, indexed OFDM does not activate all subcarriers and then send modulated symbols, but selects a part of subcarriers to activate to send modulated symbols, so as to divide the transmitted information bits into two parts, some of which are carried by activated subcarrier index combinations, called index bits. The other part of the bit is carried by the modulation symbol transmitted by the active subcarrier, which is called the symbol bit [9]. Figure 1 is the schematic diagram of transmitter structure of OFDM index system with N subcarriers. In each transmission block, M information bits are evenly divided into g groups, and each group includes p = m/g bits. Then, the p bits of each group are mapped to OFDM sub-blocks with n = N /g growth. Finally, g OFDM sub-blocks are merged to wait for OFDM transfer block of length N [10]. Since all OFDM subblocks are independent of each other and their operations at the transmitter are the same, the β subblock is illustrated here as an example, where β = {1, · · · , g}. In the β subblock, the p bit is divided into index bit p1 and symbol bit p2 . The index bit selects k of the n subcarriers as the active subcarriers through the index mapper, so the index combination of the activated subcarriers can be expressed as: I β = {iβ (1), · · · , iβ (k)}

(1)

Where iβ (α) ∈ {1, · · · , n}, β = {1, · · · , g}, α = {1, · · · , k}. If α1 = α2 , Then iβ (α1 ) = iβ (α2 ). On the other hand, the symbol bit p2 is mapped by the M -order modulation mapper to obtain a vector with k modulation symbols, and then transmitted using the activated k subcarriers. Therefore, the modulation symbol vector transmitted by the activated subcarrier is:  T sβ = sβ (1) · · · sβ (k) (2)

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P1 Bit

x(2)

P Bit P2 Bit

X(2)

Insert cyclic prefix. Parallel and serial change

Fourier transform

Packet interleaving. Creation of subblocks

Traditional constellation point mapping

Bit splitter

M Bit

X(1)

x(1)

Index selector

P1 Bit Index selector

P Bit P2 Bit

x(N)

Traditional constellation point

X(N)

mapping

Fig. 1. Transmitter block diagram of OFDM index modulation system

Order E{sβ (sβ )H } = k, That is, the average power of the signal constellation has been normalized to unit power. Therefore, in the first OFDM subblock, it can be represented as: X β = [xβ (1)xβ (2) · · · xβ (n)] xβ (η) =



sβ (η), η ∈ I β 0, η ∈ Iβ

(3)

 (4)

Finally, g OFDM subblocks are merged into a complete OFDM transmission block with N subcarriers. OFDM transport blocks can be represented as: X = [X 1 X 2 · · · X 9 ] = [x(1)x(2) · · · x(N )]T

(5)

Different from the traditional OFDM system, the X of the OFDM index modulation system contains a part of the zero element that carries information by its index. Therefore, in an OFDM transport block, the number of index bits carried by the active subcarrier index combination can be expressed as:   (6) m1 = p1 g = log2 (C(n, k)) = cg There . stands for downward rounding operation, c = log2 (C(n, k)), C(n, k) is the number of combinations. In addition, the number of symbol bits carried by the M -order modulation symbol can be expressed as: m2 = p2 g = k(log2 M )g

(7)

Finally, the total number of bits carried by an OFDM transport block is m = m1 + m2 . If the effect of cyclic prefix is not taken into account, then the spectral efficiency of the system can be expressed as:   log2 (C(n, k)) + k log2 M (bps) p m = = (8) F= N n n(Hz)

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Then, the inverse IFFT is applied to the OFDM transmission block, and the time domain expression of the OFDM transmission block can be obtained: n XT = √ IFFT {X } = [X (1) · · · X (N )] k

(9)

Where, the normalization factor √n is to ensure that the E{XTH XT } = N is satisfied after k the IFFT transformation. Then, after the parallel-serial change and digital-to-analog conversion, the OFDM transmits symbols through the frequency-selective Rayleigh fading channel. It is assumed that the coefficient of channel impulse response of Rayleigh fading channel is: hT = [hT (1) · · · hT (V )]T

(10)

At the receiving end, by removing the cyclic prefix of the receiving limit and performing N -point FFT changes, the relationship between the received signal and the transmitted signal of the system in the frequency domain can be expressed as: yF (α) = hF (α)x(α) + WF (α), α = 1, · · · , N

(11)

Where yF (α) is the received signal in the frequency domain, hF (α) is the fading coefficient of the channel, and wF (α) is the noise in the frequency domain. The distributions of hF (α) and wF (α) are CN (0, 1) and CN (0, N0 ),N0 , respectively, which are the noise variances in the frequency domain. The average SNR of each subcarrier is defined as ρ = nNk 0 [11].

3 The Mode of Index Modulation At the transmitter of the system, the index combination I β corresponding to the activated subcarrier corresponding to the index bit p1 is obtained by the index mapper. At the receiving end, the system can estimate the index combination Iˆ β of the active subcarrier through the mobile phone detection algorithm, and then through the inverse index mapper, the estimated value pˆ 1 of the index bit transmitted by the system can be obtained. it can be seen that there is an one-to-one mapping relationship between the index bit and the index combination of the activation subcarrier, and the function of the index mapper is to conceal the index bit of the transmitter into the index combination of the activation subcarrier, so it is possible to map the index bit into the index combination of the activation subcarrier. Index mapping of activated subcarriers is a very important part of OFDM indexed modulation system. The index mapping methods of two OFDM index systems are introduced in detail in reference [12]. (1) look-up table method: In this mapping method, the transmitter and receiver need to create and store tables of the same size c = 2p1 . At the same time, the table needs to give a detailed mapping relationship between the index bit p1 and the index combination I β of the active subcarrier. Here is an example, in Table 1, the parameters are as follows: the number of subcarriers of the subblock is n = 4, and the number of activated subcarriers

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k = 2, c = 2log2 (C(n,k)) = 4, Sχ Sξ ∈ S. Because of C(4, 2) = 6 > c = 4, there are two index combinations of unused (illegal) active subcarriers in the system. When the value of C(n, k) is small, the look-up table method is a very simple and effective method, but when the value of C(n, k) is large, the size of the table becomes very large, so it is very difficult to design and maintain such a table, which makes the system difficult to implement in engineering practice. Table 1. Mapping table of n = 4, k = 2, C = 4 Bit

Index combination of activated Sub block subcarriers

[0 0]

{1 2}

[Sχ Sξ 0 0]

[0 1]

{1 3}

[1 0]

{2 3}

[Sχ 0 Sξ 0] [0 Sχ Sξ 0]

[1 1]

{3 4}

[0 0 Sχ Sξ ]

Illegal {1 4}

[Sχ 0 0 Sξ ]

Illegal {2 4}

[0 Sχ 0 Sξ ]

(2) Combination method: For any natural number Z ∈ [0, C(n, k) − 1], the combinatorial method can map the natural number into a strict monotone decreasing sequence of length k. For fixed n and k, all natural numbers Z ∈ [0, C(n, k) − 1] can be mapped into a J sequence of length k, where J sequence is: J = {Ck , · · · , C1 } and Ck > · · · > C1 ≥ 0, {C1 , · · · , Ck } ∈ {1, · · · , n − 1}. In addition, the element value of J sequence can be given by the following formula: Z = C(Ck , k) + · · · + C(C1 , 1)

(12)

For all n and k, the method of obtaining J sequence can be simply described as follows: first, select the largest ck to satisfy C(ck , k) ≤ Z, then select the maximum ck−1 to satisfy C(ck−1 , K − 1) ≤ Z − ck , and so on until the J sequence of length k is obtained. In the concrete implementation of the OFDM index system, for each sub-block, at the transmitter, the system first maps the index bit p1 into a decimal natural number Z, then obtains the J sequence by the combination method, and finally adds 1 to all the element values in the J sequence, thus obtaining the index combination of the activated subcarriers, namely I β = J + 1. At the receiving end, the system obtains the index combination estimation Iˆ β of the active subcarrier through the detection algorithm, and then obtains the value of the natural number Z according to the formula (11), and finally converts it into a binary number, thus reaching the estimated value pˆ 1 of the index bit p1 .

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4 Receiver Detection Algorithm The task of the receiver is to detect the index combination of the active subcarrier and the modulation symbols transmitted on the active subcarrier. Different from the traditional OFDM system, the information carried by the index modulation OFDM system is carried on the OFDM sub-block rather than on a single sub-carrier. Therefore, the detection of the receiver needs to be carried out on the OFDM sub-block. The ML detector considers all possible subblock implementation [13] by searching all possible activation subcarrier combination I β and modulation symbol vector sβ , that is, by minimizing the value obtained by the following formula, the activation index combination estimate Iˆ β and modulation symbol vector sˆβ of each subblock are obtained. 2 n β β (13) (Iˆ β , sˆ β ) = arg min yF (α) − hF (α)xβ (α) α−1

β yF (α),

Where α = 1, · · · , n represents the received signal in the frequency domain of β the βth sub-block. hF (α), α = 1, · · · , n represents the fading coefficient of the channel β β of the β subblock yF = yF (n(β − 1) + α), hF = (n(β − 1) + α). For each sub-block, there are k different implementations of M -order modulation symbols by M k and there are I β implementations of index combination 2log2 C(n,k) for activating subcarriers. Therefore, the computational complexity of ML detection algorithm is o(2log2 C(n,k) M k ) [14]. This paper uses ML detection algorithm.

5 Simulation Process and Analysis This paper studies the relationship between SNR and BER of OFDM index modulation signal and traditional OFDM signal under different modulation schemes under the background of VDES. The main simulation parameters are: the number of subcarriers in the system N is 128, the length of cyclic prefix is 32, the number of multipaths of channel is 12, the interval between subcarriers is 378 Hz, and the bandwidth is 50 kHz. Except for labeling, the detection algorithm defaults to ML detection algorithm. As can be seen from Fig. 2, under the active mode configuration of n = 4 and k = 2, the BER performance of the system is improved because when the transmission power of index modulation OFDM and traditional OFDM system is the same, the ratio of total power allocated to single subcarrier in index modulation OFDM system is larger. When the transmitter limits the constant transmission power, it can be seen from Fig. 3, with the index modulation OFDM sub-block is to activate the subcarrier number increase, each activation subcarrier can be obtained by sending power [15] decreases, and activate the subcarrier number is smaller, the stronger the sparse nature of subcarrier, the better the performance of anti-jamming, the better bit error rate performance of the system. In Fig. 4, when using the same carrier sequence number modulation scheme, using 16-QAM and 16-PSK has closed Bit Error Rate (BER) performance at low signalto-noise ratio, but better BER performance when using 16-QAM at intermediate and higher signal-to-noise ratio. Therefore, when the order of the symbol modulated constellation is higher, the index modulated OFDM system using M-QAM constellation symbol modulation can achieve better BER performance.

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Fig. 2. n = 4, k = 2, M = 4 QAM and traditional OFDM bit error rate comparison chart

Fig. 4. n = 4, k = 2, M = 16 QAM and PSK bit error rate comparison chart

Fig. 3. n = 4, k = 1, 2, 3, M = 4 QAM bit error rate comparison chart

Fig. 5. BER comparison of traditional OFDM and of dm-im under the same spectral efficiency

Figure 5 shows that index OFDM has the same spectral efficiency as traditional OFDM, that is, using n = 4, k = 1 carrier sequence number modulation scheme and BPSK carrier sequence number modulation scheme, index modulated OFDM obtains 5 dB performance better than traditional OFDM under the condition of bit error rate of 10–3 order of magnitude. Because the bit error rate performance of the bits transmitted in the index domain is improved.

6 Conclusion In this paper, a digital OFDM index modulation method is proposed to solve the problem of data link load. The physical layer simulation modeling is carried out based on the index modulation OFDM system, and different activation modes and digital modulation schemes are used to simulate the BER performance of real data communication at sea. Simulation results show that the above index modulation OFDM technology can achieve good system performance, and can effectively overcome the shortcomings of OFDM system.

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References 1. Melki, R., Noura, H.N., Fernandez, J.H., Chehab, A.: Message authentication algorithm for OFDM communication systems. Telecommun. Syst. 76(3), 403–422 (2020) 2. Lu, X., Shi, Y., Li, W., Lei, J.: Encrypted subblock design aided OFDM with all index modulation. IET Commun. 14(17), 2924–2930 (2020) 3. ITU - R M. 1842-1: Characteristics of VHF radio systems and equipment for the exchange of data and electronic mail in the maritime mobile service RR Appendix 18 channels. ITU, Geneva (2009) 4. Xie, J., Hou, S., Zhang, Q.: Central frequency offset estimation based on multicarrier VDES system. In: 2019 IEEE 9th International Conference on Electronics Information and Emergency Communication (ICEIEC), pp. 76–80 (2019). https://doi.org/10.1109/ICEIEC.2019. 8784499 5. Hwang, T., Yang, C., Gang, W., Li, S., Li, G.Y.: OFDM and its wireless applications: a survey. IEEE Trans. Veh. Technol. 58(4), 1673–1694 (2009) 6. Noh, S., Jung, Y., Lee, S., Kim, J.: Low-complexity symbol detector for MIMO-OFDM-based wireless LANs. IEEE Trans. Circ. Syst. II Exp. Briefs 53(12), 1403–1407 (2006). https://doi. org/10.1109/TCSII.2006.884122 7. ITU - R M. 2092-0: Technical characteristics for a VHF data exchange system in the VHF maritime mobile band. ITU, Geneva (2015) 8. Taheri, T., Mohamad, M., Nilsson, R., van de Beek, J.: Joint spectral-spatial precoders in MIMO-OFDM transmitters. Sig. Process. 172, 107538 (2020) 9. Offiong, F.B., Sinanovi´c, S., Popoola, W.O.: Pilot-aided frame synchronization in optical OFDM systems. Appl. Sci. 10(11), 4034 (2020) 10. Pinto-Benel, F.A., Cruz-Roldán, F.: A bandpass wavelet OFDM system for power line communications. J. Franklin Inst. 357(11), 7211–7228 (2020) 11. Briar, E., Aygolu, U., Panayirci, E., et al.: Orthogonal frequency division multiplexing with index modulation. IEEE Trans. Sig. Process. 61(22), 5536–5549 (2013) 12. Mattera, D., Tanda, M.: Windowed OFDM for small-cell 5G uplink. Phys. Commun. 39, 100993 (2020) 13. Zeng, H.: Research on Low Complexity Receiver of Carrier Sequence Number Modulation System, pp. 51–57. South China University of Technology, Guangzhou (2018) 14. Atoui, S., Doghmane, N., Afifi, S.: Blind frequency offset estimator for OFDM systems. TELKOMNIKA (Telecommun. Comput. Electr. Control) 17(6), 2722 (2019) 15. Steeg, M., Exner, F., Tebart, J., Czylwik, A., Stöhr, A.: OFDM joint communication–radar with leaky-wave antennas. Electron. Lett. 56(21), 1139–1141 (2020)

Color Image Enhancement Algorithm on the Basis of Wavelet Transform and Retinex Theory Xinzhe Xia, Jie Yang(B) , Jinpeng Li, and Jiaqi Zhen College of Electronic Engineering, Heilongjiang University, Harbin 150080, China [email protected]

Abstract. For enhance the contrast of low-illuminance images, reduce image noise, and maintain image edge detail information, a color image enhancement algorithm on the basis wavelet transform and Retinex theory is proposed in this paper. First, convert the low-illuminance color image to be enhanced from RGB space to HSV color space, perform wavelet transformation on the brightness component V, and separate several high-frequency and low-frequency children; then use improved single-scale Retinex algorithm to enhance the low-frequency offspring, reduce the influence of external light factors on the image and retain the edge texture of the image; the high-frequency children are subjected to blur enhancement processing to achieve image denoising and enhancement. The saturation component S is enhanced by segmented exponential transformation to make the image color more suitable for human visual habits; finally, the processed image is changed back to a color RGB image. The experimental results show that the edge texture of the image processed by the algorithm are maintained well, the contrast has been significantly improved, color distortion is avoided, and it has a good visual effect. Keywords: Low-light image · Image enhancement · Wavelet transform · Bilateral filtering · Retinex algorithm · Fuzzy enhancement

1 Introduction In a low-illumination environment, the acquired image first brings visual discomfort to people, and it is also not conducive to subsequent image processing work. In order to better display the scene information and highlight the edge detail information, it is necessary to enhance the low-light color image. Traditional image enhancement methods include histogram equalization algorithms, Retinex algorithms, and wavelet transform based algorithms; the histogram equalization algorithm [1–4] can effectively improve the contrast of the original image, but image information will be lost, and the information entropy The improvement is not obvious. Retinex algorithms, such as SSR [5], MSR [6], MSRCR [7], etc. Although the Retinex algorithm improves the image effect to a certain extent, it still has shortcomings. The SSR algorithm cannot satisfy both compressed dynamic range and color invariance. The MSR algorithm will cause color distortion. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 1176–1183, 2022. https://doi.org/10.1007/978-981-19-0390-8_148

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The enhancement effect of MSRCR on the details of darker areas is not obvious. Image enhancement algorithms based on wavelet transform [8–12] will amplify high-frequency image noise. Aiming at the problems of the above-mentioned algorithms in processing images, a color image enhancement algorithm on the basis wavelet trans form and Retinex theory is designed. First, convert the original RGB color image to HSV space, perform wavelet transformation on component V, perform fuzzy enhancement on the high and low frequency subbands of the brightness component and use the Retinex algorithm to enhance processing to reduce the impact of lighting factors and image noise. Keep the image edge information; perform a piecewise exponential function processing on the component S to make the image conform to the viewing characteristics of the human eye; finally, the processed image is changed back to a color RGB image.

2 Retinex Basic Theory Retinex theory is a color theory that simulates the human visual system proposed by Land [13]. Retinex theory shows that the reflection ability of light on different objects determines the color displayed by the object, but the color of the object itself is constant and constant. Sexuality, and is not affected by external light. According to Retinex theory, the image model is expressed as follows: S(x, y) = L(x, y)R(x, y)

(1)

Where: S(x, y) is the original image; L(x, y) is the illumination component of the image; R(x, y) is the reflection component of the image. According to the color constancy theory, the reflection component determines the true color of the object R(x, y), so the essence of enhancement is to extract the reflection component in the original image. According to Retinex theory, first use a certain method to estimate the illumination component, and then turn Go to the logarithmic domain and subtract the illumination component from the input image to get the reflection component. As shown in formula (2): lg[S(x, y)] = lg[L(x, y)R(x, y)] = lg[L(x, y)] + lg[R(x, y)]

(2)

3 Algorithms in This Chapter The overall flow of the algorithm in this chapter is: convert the low-illuminance color image to be processed from RGB to HSV space, and use wavelet transform to resolve the component V into several low and high frequency subbands. The saturation component S, low frequency and high frequency subbands are respectively processed with a piecewise exponential function, an improved single-scale Retinex algorithm, and a fuzzy enhancement algorithm. The enhanced high-frequency and low-frequency subbands are  processed by wavelet inverse transform V , Fusion to obtain the enhanced luminance component. Finally, the processed image is changed back to a color RGB image.

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3.1 HSV Space Conversion The HSV space is a color space created based on the intuitive characteristics of colors, which can better present people’s perception and cognition of colors. It is composed of three attributes: hue H, saturation S, and brightness V [14]. In the RGB color space, the three channel components have relatively strong correlation. When any channel is enhanced, other channels will inevitably be affected and cause image distortion. In the HSV space, component H, component S, and component V are all separate channels. When the brightness channel is processed, the color of the image will not be affected, which avoids the problem of color image distortion. 3.2 Wavelet Transform Wavelet transform is developed on the basis of Fourier transform, which overcomes the resolution problem of Fourier transform which cannot analyze frequency and time. When performing two-dimensional signal processing such as digital images, it is necessary to discretize the wavelet transform, that is, the two-dimensional discrete wavelet transform. The principle of two-dimensional wavelet transform [15] is as follows:  f (x, y) ∈ L2 (R2 ) ⇐⇒

+∞  +∞ −∞

−∞

2

|f (x, y)| dxdy < ∞

(3)

Where: f (x, y) ∈ L2 (R2 ) is a two-dimensional image, x and y respectively represent the abscissa and ordinate of the pixel, L2 (R2 ) and represent the square integrable function space on the plane. ψ(x, y) Let be the two-dimensional wavelet basis function, then the two-bit continuous wavelet transform is defined as:   ¨ x − b1 y − b2 1 ∗ , dxdy (4) f (x, y), ψa,b1 ,b2 (x, y) = f (x, y)ψ a a a a,b1 ,b2 The decomposition and reconstruction process of the two-dimensional discrete wavelet transform is shown in Fig. 1.

Fig. 1. Image two-dimensional discrete wavelet decomposition and reconstruction process

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3.3 Wavelet Transform The center surround function of the traditional SSR algorithm is a Gaussian surround function, which is simple to operate and easy to implement, but it is difficult to estimate the edge illumination of the image when processing color images, resulting in a halo phenomenon and unable to maintain image edge information well. The bilateral filter function [16, 17] is a nonlinear filter function. Compared with the Gaussian filter function, the bilateral filter function can better maintain the image edge effect and effectively remove the halo phenomenon. The expression of bilateral filtering is as follows: 

G (x, y) = exp[−

(x − x0 )2 + (y − y0 )2 [f (x, y) − f (x0 , y0 )]2 − ] 2σr2 2σd2

(5)

Where: f (x, y) is the coordinates of the image center point; f (x0 , y0 ) is the gray value of the image center point; σd is the spatial geometric standard deviation parameter; σr is the brightness standard deviation parameter. This article takes σd = 5, σr = 0.001. The expression for replacing the surround function of the SSR algorithm with a bilateral filter function is as follows: 

lg[R(x, y)] = lg[S(x, y)] − lg[S(x, y) ∗ G (x, y)]

(6)

3.4 Blur Enhancement In this paper, the fuzzy enhancement algorithm is used to process the high-frequency subbands to achieve the purpose of suppressing noise. The membership function is as follows: μmn =

xmn − xmin xmax − xmin

(7)

Where: xmn is the high-frequency sub-band wavelet coefficients, xmax and xmin are the maximum and minimum high-frequency wavelet modulus, differentiate. The fuzzy enhancement function is as follows: 

μmn = Tr (Tr−1 (μmn )), r = 1, 2, · · ·  μ2 mn 0 ≤ μmn ≤ μc T1 (μmn ) = μc (1−μ )2 mn 1 − 1−μc μc ≤ μmn ≤ 1

(8)

The difference from the classic nonlinear transformation function is that different values can be used for different enhancement ranges in the image μc for blur enhancement, and the transformed μmn value range is [0, 1], which avoids the situation where the minimum value is less than zero.

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3.5 Piecewise Exponential Function The piecewise exponential function is a non-linear enhancement method, which can avoid the problem of over-saturation of the image color or insufficient saturation of the image with the linear transformation stretching method, and achieve better visual effects. The specific expression of the piecewise exponential function is as follows:

⎧ S(x,y) − 1 , S(x, y) ≤ 0.2 ⎨a e (9) S  (x, y) = eS(x,y) − 1,

0.2 < S(x, y) ≤ 0.7 ⎩ S(x,y) − 1 , else b e 

where, S (x, y) represents the saturation after enhancement; S(x, y) is the saturation of the original image; The parameters a and b are used to adjust the transformation scale. The enhancement effect is the best when it is a 1.2–1.5 and b 0.7–0.9. The a value of this article is 1.4 and 0.8.

4 Experimental Results Analysis For verify the enhancement effect of the proposed algorithm, three low-illuminance color images are selected from the network for experiments, and the measured results are compared with SSR, MSR and the MSRCR algorithm. Subjective evaluation and objective evaluation of experimental results are made, subjective evaluation uses visual observation; objective evaluation uses standard deviation (SD), information entropy, average gradient (AG) and peak signal-to-noise ratio (PSNR) for judgment. The experimental equipment is configured with Intel processor Core(TM) i7-10870H, 8-core 2.20 GHz, and memory 16 GB; the algorithm is implemented on the Matlab 2020a platform. Three scale parameters used in the experiment σ1 = 15, σ2 = 80, σ3 = 250. 4.1 Subjective Evaluation The results of the algorithm processing are shown in Fig. 2. In the figure (a) is the original image; (b) is the SSR algorithm; (c) is the MSR algorithm; (d) is the MSRCR algorithm; (e) is the algorithm in this paper; processed by the SSR algorithm After the image brightness and contrast are improved, the visual effect is poor, especially the excessive enhancement in the left area of the second image; after the MSR algorithm is processed, the overall image is improved, but there is halo phenomenon; after the MSRCR algorithm is processed, The overall image has been improved, but the overall image is whiter, and the definition has been reduced; and the algorithm in this paper improves the brightness and contrast of the image, and the edge detail details are better maintained, and the human visual effect is the best. 4.2 Objective Comment Compared with subjective evaluation, objective evaluation is more convincing. The following four quality indicators: standard deviation, information entropy, AG and PSNR

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(a)

(b)

(c)

(d)

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(e)

Fig. 2. Comparison of experimental results of different algorithms

are to appraise the results. The SD mirrors the comparison of the image, the larger the value, the image of the better the quality; the information entropy reflects the quantity of information in the image, the greater the information entropy, the richer the information and the richer the details; the average gradient reflects Image texture contour information, the larger the value, the clearer the image contour and texture; PSNR reflects the degree of distortion, the larger the value of PSNR, the smaller the distortion of the image. Table 1. Comparison of quality indicators of different algorithms Test image

Quality index

SSR

MSR

MSRCR

OUR

Image 1

SD

73.49

84.14

86.32

88.05

Image 2

Image 3

Entropy

6.85

7.03

7.14

7.22

AG

5.72

21.07

5.51

22.19

PSNR

26.01

26.14

26.08

26.19

SD

77.21

80.07

87.39

97.56

Entropy

7.05

7.12

7.35

7.16

AG

5.26

17.62

5.83

23.02

PSNR

24.03

24.06

24.05

24.07

SD

79.62

81.45

82.62

86.97

Entropy

6.90

7.03

7.17

7.20

AG

4.15

15.66

5.04

19.54

24.12

24.15

24.09

24.20

PSNR

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This article uses three low-illuminance color images as the test object, and each image uses 4 indicators to illustrate the image enhancement results. Judging from the experimental data in Table 1, in the 12 indicators, only one indicator of the algorithm in this paper is lower than the MSRCR algorithm, and the other indexes are better than other algorithms, indicating that the algorithm in this text has better enhancement effects than other algorithms.

5 Conclusion A color image enhancement algorithm on the basis wavelet transform and Retinex theory is proposed in this paper. First, the picture is transferred from RGB space to HSV space to settle the problem of color distortion caused by the correlation between each channel. The saturation component S is processed with a piecewise exponential function enable saturation conform to the human visual perception; Wavelet transform is performed on the luminance component V, the low frequency subband is processed by the improved SSR algorithm, the image contrast is improved, and the image edge is well maintained. The high frequency subband is processed by the fuzzy enhancement algorithm to suppress noise. This text, the experiment results are evaluated subjectively and objectively, and the results show the effectiveness of the algorithm in this text. Contrast with the traditional classical algorithm, it has a good advantage. However, the algorithm in this paper also has its shortcomings. Because of the application of multiple algorithms in this algorithm, the running time is longer, so the next step is to study how to reduce the running time.

References 1. Khodambashi, S., Moghaddam, M.E.: An impulse noise fading technique based on local histogram processing. In: 2009 IEEE International Symposium on Signal Processing and Information Technology. Ajman, United Arab Emirates, pp. 95–100 (2009) 2. Kim, Y.T.: Contrast enhancement using brightness preserving Bi-histogram equalization. IEEE Trans. Consum. Electron. 43(1), 1–8 (1997). https://doi.org/10.1109/30.580378] 3. Chen, S.D., Ramli, A.R.: Minimum mean brightness error bihistogram equalization in contrast enhancement. IEEE Trans. Consum. Electron. 49(4), 1310–1319 (2003). https://doi.org/10. 1109/TCE.2003.1261234 4. Shen, H.Y., Sun, S.F., Lei, B.J., et al.: An adaptive brightness preserving bi-histogram equalization. In: Proceedings of SPIE Volume 8005, MIPPR 2011: Parallel Processing of Images and Optimization and Medical Imaging Processing, Guilin, China (2011). 80050U 5. Jobson, D.J., Rahman, Z., Woodell, G.A.: Properties and performance of a center/surround Retinex. IEEE Trans. Image Process. 6(3), 451–462 (1997) 6. Jobson, D., Rahman, Z., Woodell, G.: A multiscale Retinex for bridging the gap between color images and the human observation of scenes. IEEE Trans. Image Process. 6(7), 965–976 (1997) 7. Liu, Y., Yan, H., Gao, S., Yang, K.: Criteria to evaluate the fidelity of image enhancement by MSRCR. IET Image Process. 12(6), 880–887 (2018) 8. Wu, R., Gong, L., Liu, J., Zhou, X.: A low distortion image defogging method based on histogram equalization in wavelet domain. J. Image Process. Theor. Appl. 3(1), 1–10 (2020)

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9. Demirel, H., Anbarjafari, G.: IMAGE resolution enhancement by using discrete and stationary wavelet decomposition. IEEE Trans. Image Process. 20(5), 1458–1460 (2011) 10. Demirel, H., Anbarjafari, G.: Discrete wavelet transform based satellite image resolution enhancement. IEEE Trans. Geosci. Remote Sens. 49(6), 1997–2004 (2011) 11. Mustafa, W.A., Yazid, H., Khairunizam, W., et al.: Image enhancement based on Discrete Cosine Transforms (DCT) and Discrete Wavelet Transform (DWT): a review. In: The 1st International Conference on Mechanical Electronic and Biosystem Engineering, Bogor, Indonesia, p. 012027 (2019) 12. Jiao, T.Y., Zhang, J.S., Wang, Y.: Wheel image enhancement based on wavelet analysis and pseudo-color processing. Autom. Instrum. 35(1), 47–51 (2020) 13. Land, E.: The Retinex. Am. Sci. 52(2), 247–264 (1964) 14. Liu, S., Long, W., He, L., Li, Y., Ding, W.: Retinex-based fast algorithm for low-light image enhancement. Entropy (Basel, Switz.) 23(6), 746 (2021) 15. Khan, S.S., Khan, M., Alharbi, Y.: Multi focus image fusion using image enhancement techniques with wavelet transformation. Int. J. Adv. Comput. Sci. Appl. (IJACSA) 11(5) (2020) 16. Das, A., Shylaja, S.S.: Efficient quality enhancement of gastrointestinal endoscopic video by a novel method of color salient bilateral filtering. Multimedia Tools Appl. 80(4), 6235–6245 (2020) 17. Mirmozaffaria, M.: Filtering in image processing. ENG Trans. 1, 1–5 (2020)

A Polarization Identification Method of Full Polarization Phased Array Radar and Active Decoy Yang Zhou(B) and Zhian Deng Harbin Engineering University, Harbin 150001, China {Zhouyangg,dengzhian}@hrbeu.edu.cn

Abstract. Aiming at the problem of too few identification methods for phased array radar and active decoys and the low recognition rate, this paper proposes a polarization identification algorithm combining polarization descriptor and Multivariate Long Short Term Memory Fully Convolutional Network (MLSTM-FCN) neural network. Firstly, this paper realizes the accurate modeling of fully polarized phased array radar and active decoys in detail, and analyzes the polarization characteristics in spatial domain. The polarization characteristics of full polarization phased array radar and active decoy are distributed differently in the same spatial domain. The sample datasets of polarization descriptors of polarized phased array radar and active decoys in spatial domain is obtained. Finally, we train the recognition model with MLSTM-FCN neural network. The simulation results show that the recognition rate is above 92% at a low signal-to-noise ratio (SNR) of 7 dB. Keywords: Fully polarized phased array radar · Radiation source identification · Active decoys · Polarization descriptors · Deep learning

1 Introduction The extensive use of various new radars and missiles leads to the complexity of the electromagnetic environment in modern warfare. Polarization radar, which has the ability of acquiring and processing polarization information, plays an important role in air defense and anti-missile, early warning, battlefield reconnaissance, precision guidance and countering complex electromagnetic environment. As the development trend of radar in the future, countries have carried out in-depth research on the theoretical analysis and system development of fully polarized active phased array radar. In order to cope with the anti-radiation missile threat, many radar systems are equipped with active decoying systems. In the process of searching and tracking the target emitter with wide beam, multiple signals with the same or similar frequency and signal style enter the passive radar seeker receiver. These signals make it difficult for passive radar seeker to identify and sort signals, and cannot achieve accurate direction finding and tracking of targets. How to quickly and accurately identify the new phased array radar as well as active decoys is one of the current research directions and a problem that needs to be solved at present. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 1184–1191, 2022. https://doi.org/10.1007/978-981-19-0390-8_149

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At present, there is little research on radar and decoy recognition using polarization information. In reference [1], polarization information is used to identify radar and decoy [1]. Since the standard deviation of dynamic polarization characteristics of decoy antenna is very small and the standard deviation of dynamic polarization characteristics of radar antenna is large, it is proposed to use the standard deviation of polarization descriptor as a feature for identification. Reference uses the position of phase and energy jump to identify radar and decoy signals [2]. The recognition rate of these two methods is low when the signal-to-noise ratio (SNR) is 5–10 dB. Both of these methods need to find their own characteristics and have high requirements. To solve the above problems, this paper studies the spatial polarization characteristics of fully polarized phased array radar and active decoy. According to this characteristic, a polarization recognition algorithm based on polarization descriptor and Multivariate Long Short Term Memory Fully Convolutional Network (MLSTM-FCN) is proposed in this paper. Firstly, we extract the spatial polarization characteristics, construct sample datasets, and train the recognition model with MLSTM-FCN neural network. The method trains the model offline and identifies online, thus meeting the real-time processing requirements of anti-radiation missiles. Finally, the simulation results prove the effectiveness of the method.

2 Theoretical Basis 2.1 Polarization Characterization Select the spherical coordinates centered on the antenna port surface (r, θ, ϕ). The radiation field of the antenna can be described as: E(r, θ, ϕ) = Eθ · aˆ θ + Eϕ · aˆ ϕ

(1)

Where aˆ θ and  aˆ ϕ are the unit vectors in the pitch and azimuth directions, respectively,  and aˆ θ , aˆ ϕ , aˆ r forms a right-handed coordinate system. Eθ and Eϕ are the polarization components in the aˆ θ and aˆ ϕ directions, respectively and (θ, ϕ) is the angular coordinate at the measurement point. The polarization ratio of radiating electromagnetic waves is ρ=

Eθ = tan γ ejη Eϕ

(2)

  Where (γ , η) is the spatial polarization descriptor of the antenna, tgγ = |Eθ | Eϕ , η = φθ − φ ϕ . 2.2 Basis of Neural Network According to literature, the proposed neural network is divided into three parts: preprocessing module, convolution neural network (CNN) and Long short-term memory neural network (LSTM) [3]. The nodes of adjacent layers of CNN are not fully connected, and are generally cascaded alternately by convolution layer and pooling layer [4]. The main purpose of

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the convolution layer is to extract features. We can set convolution kernels, then slide the convolution kernel with a certain step size, and obtain the corresponding local feature matrix based on the convolution between the convolution kernel and the input. The pool layer has feature dimensionality reduction to prevent over fitting function. Traditional RNNs are unable to use historical information effectively as time series increase. Long short-term memory network (LSTM) is a special kind of recurrent neural network, LSTM have been successful in classifying univariate time series [5]. They were introduced by Hochreiter & Schmidhuber and improved and popularized by many people [6]. It can complete the tasks of output prediction and identification according to the current and long-term past inputs. LSTM consists of recursively connected LSTM units, and each LSTM unit structure is shown in Fig. 1, which consists of forgetting gate, input gate, and output gate respectively. First, the input state Ct−1 at the previous moment is controlled by the forgetting gate σ f based on the input xt and the output ht−1 of the previous LSTM cell, losing the unimportant information, and then the control information σ f of the input gate decides to add useful information to obtain the current state Ct . Finally, the final output ht is obtained by influencing the output gate through the forgetting gate and the input gate, where tanh is the activation function to scale the state and input quantities. This structure allows the network to selectively remember the important information for the output and forget the unimportant information.

Fig. 1. LSTM network model

3 Algorithm 3.1 Polarization Characteristics of Fully Polarized Phased Array Radar Different antenna units have different spatial polarization characteristics. The fully polarized phased array radar takes the fully polarized microstrip patch antenna as the unit, and the decoy antenna is a wide beam pyramid horn antenna. The ideal fully polarized phased-array radar spatial polarization state is related to a single array element, so only a

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single fully polarized microstrip patch antenna is of interest. According to the literature [7], there exist two ports of the fully polarized microstrip patch antenna, as in Fig. 2. The excitation of port 1 is set to I1 and the excitation of port 2 is set to I2 , whose far field ρ is ρ=

Eθ (θ, ϕ) = tgγ ejη = Eϕ (θ, ϕ)

−(sin θ tan ϕ)−1 tan−1 θ +

  tan θ tan kW 2 cos θ I2   I1 tan kW 2 sin θ sin ϕ

(3)

The descriptor of fully polarized phased array radar is related to port excitation and spatial position. When the polarization state of the radar’s main beam is determined and the port excitation is determined, its descriptor is only related to the spatial position.

Fig. 2. Full polarization microstrip patch antenna model

3.2 Polarization Characteristics of Active Decoy According to literature [8], the far field of the pyramid horn ρ is: ρ=

Eθ (θ, ϕ) = tgγ ejη = tan ϕ Eϕ (θ, ϕ)

(4)

According to the above formula, the polarization descriptor (γ , η) of the pyramid horn is independent of the attitude in the horn antenna coordinate system. γ is related to ϕ of airspace (θ, ϕ), and η is independent of airspace (θ, ϕ). Theoretically, γl (θ, ϕ) = ϕ, ηl (θ, ϕ) = 0 (Fig. 3). According to the formula, the polarization descriptor (γ , η) of fully polarized microstrip antenna is related to the excitation of two ports. By comparing Eqs. (4) and (6), when the required polarization state of the main beam is obtained by using specific excitation, the polarization descriptor (γ , η) of radar and decoy will be different. Based

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Fig. 3. Broadband pyramid antenna model

on this characteristic, a method combining polarization descriptor and MLSTM-FCN neural network is proposed to identify fully polarized phased array radar and active decoy. 3.3 Datasets Establishment Eθ (θ, ϕ) is obtained by sampling within a specific spatial domain, and the sampling angle of Eϕ (θ, ϕ) is sorted from small to large. Assuming that only azimuth, sampling angle ϕ = N ◦ , sampling angle step ϕ = N ◦ and (θ0 , ϕ0 ) are the initial sampling position, then the corresponding:  

Eθ (θ0 , ϕ0 ) = Eθ (θ0 , ϕ0 ) Eθ (θ0 , ϕ0 + N ◦ ) · · · Eθ (θ0 , ϕ0 + N ◦ )

(5)  Eϕ (θ0 , ϕ0 ) = Eϕ (θ0 , ϕ0 ) Eϕ (θ0 , ϕ0 + N ◦ ) · · · Eϕ (θ0 , ϕ0 + N ◦ ) According to formula (3), the corresponding polarization descriptor is: ⎧     E (θ,ϕ) ⎪ ⎪ ⎨ γθ (θ0 , ϕ0 ) = arctan Eθ (θ,ϕ) ϕ     Eθ (θ,ϕ) ⎪ ⎪ ⎩ ηϕ (θ0 , ϕ0 ) = angle 

(6)

Eϕ (θ,ϕ)

In addition, it is necessary to normalize the data to ensure that the data of different dimensions have the same distribution scale, which is more conducive to network and can effectively shorten the training time. Due to (γ , η) ∈ optimization

0, π 2 × [0, 2π ], the normalized data are: ⎧    ⎨ γ ∗ (θ0 , ϕ0 ) = γ  (θ0 , ϕ0 ) π 2 θ θ  (7) ⎩ η∗ (θ0 , ϕ0 ) = η (θ0 , ϕ0 ) (2π ) ϕ ϕ Through the above normalization processing, the influence caused by the change of data characteristics is reduced under large noise. We construct the sample datasets required for the training model by traversing different angle starting points and different main beam polarization states.

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3.4 Network Structure Since the sampled spatial electric field data can be regarded as time series data, the corresponding polarization descriptor can also be trained as time series data. Therefore, CNN and LSTM, which are good at processing time series, are selected to solve the problem of radar emitter recognition. Global pooling is used to reduce classification parameters. The complete convolution block consists of three stacked time convolution blocks with filter sizes of 128, 256 and 128. Each block consists of a time convolution layer, followed by the ReLu activation function. In order to speed up the training and convergence speed of the network, prevent the network from over fitting, the BN layer is added before activating the convolution results. Finally, the output of the activation layer is input into the pooling layer for down sampling to reduce the dimension of the features. In addition, in the pooling layer, in order to further improve the generalization ability of the model, the Dropout operation is also carried out. At the same time, the time series enters the LSTM block, which includes the general LSTM layer, followed by dropout. The loss function used in this paper is the cross-entropy function. The optimization algorithm is a random gradient descent algorithm, and the learning rate is set to 0.001. During training, the size of batch processing is 500. After each iteration, check the loss value of the verification set. When it does not change for many consecutive times, it indicates that the model parameter training is sufficient. Stop training and obtain the trained radar emitter recognition network model.

4 Experimental Results 4.1 Comparison of Polarization Characteristics Assuming that the polarization state (γ , η) = (60◦ , 40◦ ) needs to be controlled in the direction of the fully polarized main beam. Figure 4 and Fig. 5 respectively show the distribution of the polarization descriptors of the fully polarized phased array and pyramid horn antenna after polarization control in the airspace of θ ∈ (0◦ , 180◦ ) ϕ ∈ (−90◦ , 90◦ ). The polarization descriptors of the two radars are inconsistent. The polarization descriptor η of the pyramid horn antenna is relatively flat, and γ is distributed at about 0°, −180° and 180°. The polarization descriptors of the polarization controlled fully polarized phased array have a large number of spikes. Therefore, we use neural network to train the polarization phase descriptor datasets to obtain the model for classification. 4.2 Identification Effect Analysis Monte Carlo experiments are carried out 100 times to obtain the corresponding average accuracy. The lowest average recognition accuracy on the 7 dB test sets is 92.22%, and the average recognition rate on the 10 dB test sets is 99.473%. The network has a good recognition effect on polarization phased array radar and active decoy. Under low signal-to-noise ratio, the effect of this method is better (Fig. 6).

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Fig. 4. Spatial polarization descriptors γ and η of fully polarized phased array

Fig. 5. Spatial polarization descriptors γ and η of pyramid horn antenna

accuracy of different SNR under main beam polarization state (γ , η) =  Fig.◦ 6. Average 55 , 55◦ .

5 Conclusion In this paper, a deep learning emitter recognition method is proposed. It bases on the combination of polarization descriptor and MLSTM-FCN. This method automatically extracts the polarization phase descriptor features of the radiation source. MLSTM-FCN network has a good recognition effect. The future work will further improve the network structure and improves the recognition rate under low SNR.

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References 1. Pei, T.H.: Research and implementation of radar signal and its active decoy identification method. University of Electronic Science and Technology, Chengdu (2020) 2. Zhang, L.: Research on Key Technologies of polarization anti decoy of passive radar seeker. National University of Defense Science and Technology, Changsha (2013) 3. Karim, F., Majumdar, S., Darabi, H., et al.: Multivariate LSTM-FCNs for time series classification. Neural Netw. 116, 237–245 (2018) 4. Kim, Y.: Convolutional neural networks for sentence classification. eprint arXiv (2014) 5. Karim, F., Majumdar, S., Darabi, H.: Insights into LSTM fully convolutional networks for time series classification. IEEE Access 7, 67718–67725 (2019) 6. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997) 7. Balanis, C.A.: Antenna Theory Analysis and Design, 3rd edn. Wiley, Hoboken (2005) 8. Collmann, R.R., Landstorfer, F.M.: Calculation of the field radiated by horn-antennas using the mode-matching method. IEEE Trans. Antennas Propag. 43(8), 876–880 (1995)

Effects of Fuselage Scattering and Posture on UAV Channel Boyu Hua1 , Qiuming Zhu1(B) , Cheng-Xiang Wang2,3(B) , Haoran Ni1 , Kai Mao1 , Tongtong Zhou1 , Weizhi Zhong1 , and Hengtai Chang3 1

Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China {byhua,zhuqiuming,nihaoran,maokai,zhoutongtong,zhongwza}@nuaa.edu.cn 2 National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China [email protected] 3 Purple Mountain Laboratories, Nanjing 211111, China hunter [email protected]

Abstract. To study the effects of unmanned aerial vehicle (UAV) fuselage scattering and posture variation, a novel non-stationary geometrybased stochastic model (GBSM) is proposed for multiple-input multipleoutput (MIMO) UAV channels. It consists of line-of-sight (LoS) and non-line-of-sight (NLoS) components. The factor of fuselage scattering effect (FSE) and posture are considered by introducing an FSE vector and a time-variant posture matrix into the channel model, respectively. Besides, temporal autocorrelation function (ACF) is derived and investigated in detail. Simulation results show that both FSE and posture have significant effects on the UAV channel characteristic. The agreements between theoretical and simulated results verify the correctness of the proposed model.

Keywords: UAV GBSM

1

· Fuselage scattering · Posture variation · Channel ·

Introduction

Owing to the versatility and high mobility, UAVs are promising to be used in scenarios including communication relay, hazardous exploration, and traffic control [1]. The UAV communication has some unique channel characteristics, that conventional channel models cannot describe appropriately. To better design and evaluate the future UAV communication systems, it is essential to establish a specialized and realistic UAV channel model [2]. In past decades, the GBSM has been widely accepted in UAV channel modelling due to its moderate complexity and accuracy compared with deterministic and other stochastic models [3,4]. For example, some GBSMs were proposed in [5,6] to study UAV channels, where the terminals were assumed to be fixed or c The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022  Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 1192–1200, 2022. https://doi.org/10.1007/978-981-19-0390-8_150

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move in a straight line. However, the vehicle may experience velocity variation in practice. Taking the three-dimensional (3D) speed variation of UAV into consideration, a modified channel model was proposed in [7], but the ground terminal is still fixed. Furthermore, the authors in [8] proposed a UAV MIMO channel model where both transmitter (Tx) and receiver (Rx) moved along 3D trajectories. However, the time-variant posture variation was not considered. Recently, the impact of drone pitch, i.e., an angle of posture variation of UAV was investigated in [9], and some corresponding statistical properties were derived as well. However, the scattering effect of fuselage, which cannot be neglected in the realistic scenario, was not involved. Note that both the FSE and posture variation (including roll, pitch, and yaw) of the UAV have a certain impact on the UAV system [10]. However, to the best of the authors’s knowledge, the thorough study of FSE and 3D posture for the realistic UAV channel modelling are still missing. In this paper, an improved UAV channel model incorporating the FSE and posture variation is proposed to fill this research gap. Furthermore, the channel parameters, i.e., the angles under FSE and the phases with posture matrix are calculated. The temporal ACF is derived and simulated, the correctness of the model and derivation is verified by simulation.

2

Channel Model Incorporating FSE and Posture

The proposed channel model for UAV communication with FSE and posture is shown in Fig. 1. The mobile Tx, i.e., the UAV is equipped with P antenna elements, while the mobile Rx, i.e., the terminal on the ground is equipped with Q antenna elements. The proposed model consists of the LoS component and NLoS components. It should be noted that the Tx and Rx have their own coordinate systems, which are respectively marked as x  - y - z and x - y - z coordinate. The original locations are at the center of Tx and Rx, respectively. In virtual link, the first-bounce scatterers (FBSs) are independent from the last-bounce scatterers (LBSs), and the angles of departure (AoDs) from fuselage are independent to angles of arrival (AoAs). To describe the UAV posture, three posture variation angles, i.e., roll, pitch, and yaw are introduced in the coordinate of Tx. The UAV MIMO fading channel between the UAV and the ground terminal can be defined as a complex matrix, i.e., H (t, τ ) = [hqp (t, τ )]Q×P , where hqp (t, τ ) denotes the complex channel impulse response (CIR) between the p-th transmitting antenna and the q-th receiving antenna. Moreover, the single CIR can be modeled as the superposition of LoS and NLoS components, i.e.,    K(t) hLoS (t) δ τ − τ LoS (t) hqp (t, τ ) = K(t) + 1 qp (1)  N Tx (t)·N Rx (t)    1 NLoS hNLoS (t) + qp,n (t) δ τ − τn K(t) + 1 n=1 where K (t) denotes the Ricean factor, τ LoS (t) and τnNLoS (t) denote the path delay for LoS and NLoS components, respectively. Note that N Tx (t) and N Rx (t)

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Fig. 1. Typical UAV-to-ground communication scenario. are the numbers of valid FBSs and LBSs, and N Tx (t) · N Rx (t) is the number of valid scattering paths. In the proposed model, N Tx (t) is mainly determined by the fuselage structure, which is also the decisive factor for AoDs. N Rx (t) is affected by the scattering environment around the ground receiver and independent with N Tx (t), which is different from the existing models [7–9]. Moreover, the complex channel coefficient NLoS hLoS qp (t) and hqp,n (t) can be further expressed as 

T  Tx  Tx M Tx 1  jΦLoS/NLoS (t) Fp,V αLoS/n,m (t) , βLoS/n,m (t)  Tx  e n,m Tx Tx Fp,H αLoS/n,m (t) , βLoS/n,m (t) M m=1

⎤ ⎡   Rx  Rx jΦVV jΦVH Rx LoS/n,m e LoS/n,m κ−1 n,m e Fq,V αLoS/n,m (t) , βLoS/n,m (t) ⎦ ⎣

· Rx Rx Rx jΦHV jΦHH αLoS/n,m Fq,H (t) , βLoS/n,m (t) e LoS/n,m κ−1 e LoS/n,m LoS/NLoS

hqp/qp,n

(t)=

(2)

n,m

LoS/NLoS

where M is the number of sub-paths within n-th path, Φn,m (t) is the total phase Tx Tx , Fp,H , shift of the LoS path or the m-th sub-path within the n-th path. Besides, Fp,V Rx Rx Fq,V and Fq,H denote the vertically and horizontally polarized field components of the Tx/Rx Tx/Rx Tx or Rx, respectively, αLoS/n,m (t) and βLoS/n,m (t) denote the azimuth and elevation VV

VH

HV

HH

AoDs and AoAs, respectively, ejΦn,m , ejΦn,m , ejΦn,m and ejΦn,m are random initial phases of four different polarization combinations, and κn,m is the cross polarization power ratio. Note that the LoS path can be regarded as a special case of NLoS path, where M = 1 and κ−1 n,m = 0.

3 3.1

Channel Parameters and Statistical Properties Analysis Angle Parameters Under FSE

Rx Tx In order to calculate the angle parameters under FSE, sTx LoS (t), sLoS (t), sn,m (t), and Rx sn,m (t) are defined as the angle unit vectors of the LoS path and NLoS path at both

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the Tx and Rx sides, respectively. They can be defined by the AoDs and AoAs in a Cartesian coordinate system as ⎤ Tx/Rx Tx/Rx cos βLoS/n,m (t) cos αLoS/n,m (t) ⎥ ⎢ Tx/Rx Tx/Rx Tx/Rx sLoS/n,m (t) = ⎣ cos βLoS/n,m (t) sin αLoS/n,m (t) ⎦ . Tx/Rx sin βLoS/n,m (t) ⎡

(3)

Based on the RT method, the FSE can be considered as several specific scatterers around the transmitting antenna. The instant location vector of UAV can be calculated by  t   vTx t dt (4) LTx (t) = LTx (t0 ) + t0

where Ltx (t0 ) denotes the initial location of UAV, vTx (t) is 3D velocity vectors of the Tx, and the FSE vector of UAV can be defined as FSEn,m

DTx

(t) = LTx (t) − LFSEn,m (t)

(5)

where LFSEn,m (t) is the location vector of the scattering point on the fuselage for each sub-paths. Then, the AoDs from fuselage can be deterministically calculated as   FSE ey · DTx n,m (t) Tx (6) αn,m (t) = arctan2 FSE ex · DTx n,m (t)   FSE ez · DTx n,m (t) Tx (7) βn,m (t) = arcsin n.m DFSE (t) Tx where arctan2 (·) denotes the four-quadrant inverse tangent operation, ex , ey and ez are the base vectors of the coordinate axis. Based on the FSE vectors offered by deterministic method such as RT, the proposed model can describe the scattering environment near the fuselage effectively. Note that the AoAs can also be calculated by the similar method, but the location vector of LBS LLBSn (t) is usually determined by the deterministic scatterers of specific scenario or random scatterers under specific statistical distribution. In the GBSMs, the scatterers around the Rx are normally generated in a stochastic way, which can be addressed in [8].

3.2

Phase Parameters Under Posture Variations LoS/NLoS

The total channel phase in the proposed model Φn,m (t) includes three items as LoS/NLoS LoS/NLoS LoS/NLoS LoS/NLoS ΦIn,m (t), ΦDn,m (t), and ΦAqp,n,m (t), where ΦIn,m (t) is the random uniLoS/NLoS

(t) is the Doppler phase caused by velocity, formly initial phase over (0, 2π], ΦDn,m which can be derived as  t         Rx    vTx t · sTx t · sRx dt (8) ΦLoS D (t) =k LoS t +v LoS t 0

NLoS,Tx/Rx

ΦDn,m

(t) = k



t



       vTx/Rx t − vnScatt t · sTx/Rx t dt n,m

0 vnScatt (t)

(9)

where vTx (t), vRx (t) and are 3D velocity vectors of the Tx, Rx, and scatterers, k = 2πf0 /c0 denotes the wave number, f0 and c0 represent the carrier frequency

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and the speed of light, respectively. Besides, ΦAqp,n,m (t) is the spatial phase related to movement direction and UAV posture variation, which can be expressed as   LoS/NLoS Tx Rx Rx (t)·RP (t)·sTx (t)·sRx (10) ΦAqp,n,m (t) = k rTx p ·R LoS/n,m (t)+rq ·R LoS/n,m (t) where rTx and rRx are the position vectors of the p-th transmitting antenna and the p q q-th receiving antenna, respectively, and the rotation matrix Ri (t), i ∈ {Tx, Rx} aims to amend the position vector of transmitting or receiving antenna in MIMO channels and can be expressed as ⎡ ⎤ cos θi (t) cos φi (t) − sin φi (t) − sin θi (t) cos φi (t) i i i i i i (11) R (t) = ⎣ cos θ (t) sin φ (t) cos φ (t) − sin θ (t) sin φ (t) ⎦ sin θi (t) 0 cos θi (t) where φi (t) and θi (t) are azimuth and elevation angle of the velocity vector, respectively. In this paper, we introduce a specific posture matrix RP (t) to modify the posture variation of UAV, which can quantify the influence of posture changes on the channel through defining the posture angles. The time-variant roll angle ω (t), yaw angle ϕ (t), and pitch angle γ (t) are the rotated Euler angles converting the world coordinate x ¯-¯ y -¯ z to the fuselage coordinate x ˜-˜ y -˜ z with respect to z axis, y axis, and x axis, respectively. In order to simplify the formulas, these time-variant parameters are marked as ω, ϕ, and γ. Note that roll angle rotates around an axis drawn through the body of UAV from tail to nose and ω ∈ [−π, π], the yaw angle describes the bearing with a counter-clockwise increasing mode and ϕ ∈ [0, 2π), the pitch angle rotates related to the horizontal plane and γ ∈ [−π, π]. Therefore, the rotation matrix between the fuselage coordinate and the world coordinate can be obtained. The transformation can be calculated as x y˜ z˜]T =Rz · Ry · Rx · [˜ x y˜ z˜]T . [¯ x y¯ z¯]T = RP (t) · [˜

(12)

Then, the transfer matrices from the fuselage coordinate system to the world coordinate system can be expressed as ⎡ ⎤⎡ ⎤⎡ ⎤ 1 0 0 cos (ϕ) 0 sin (ϕ) cos (ω) − sin (ω) 0 P 0 1 0 ⎦ · ⎣ sin (ω) cos (ω) 0⎦. R (t) = ⎣0 cos (γ) − sin (γ)⎦ · ⎣ (13) 0 sin (γ) cos (γ) − sin (ϕ) 0 cos (ϕ) 0 0 1 Based on the posture matrix, the new model takes the posture variation of UAV into account. Thus, it can describe the arbitrary UAV posture changes by setting the 3D rotational angles ω, ϕ, and γ.

3.3

Statistical Property

Due to the randomness of environment, the statistical properties are generally used to evaluate the channel quality. In this paper, the temporal ACF is analyzed since it is usually used to evaluate the time correlation of UAV channels and the channel fading sensitivity with respect to the delay can be estimated from the fluctuation of ACFs. The detailed temporal ACF expressions is as follows K(t)ρLoS qp (t; Δt) + ρqp (t; Δt) = K(t) + 1

N Tx (t)·N Rx (t)



n=1

ρNLoS qp,n (t; Δt) N Tx (t) · N Rx (t) · [K(t) + 1]

(14)

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where

LoS LoS j(ΦLoS (t)−τ LoS (t+Δt)) qp (t+Δt)−Φqp (t)) ej2πf (τ ρLoS qp (t; Δt) = e M    j ΦNLoS (t+Δt)−ΦNLoS (t) j2πf τ NLoS (t)−τ NLoS (t+Δt) NLoS ( ) qp,n,m qp,n,m n n ρqp,n (t; Δt) = E e e .

(15) (16)

m=1

4

Numerical Results and Discussions

In this section, we investigate the impact of FSE and posture variation rotation on the channel characteristics and then verify the proposed channel model by comparing the theoretical statistical properties with simulated results. In the simulation, the carrier frequency is 2.4 GHz. At the initial moment, the UAV is flying in a speed of 30 m/s at the height of 300 m without any posture variation. Firstly, it rotates around an axis drawn through the body from tail to nose with an angular velocity π/4 rad/s until the roll angle reaches π/2. Then, the UAV rotates upward related to the horizontal plane with an angular velocity π/4 rad/s until the pitch angle reaches π/2. The ground terminal is moving in a constant velocity as 10 m/s. It should be noticed that we take the first NLoS component as an example to analyze the influence of FSE and UAV posture on the channel characteristic. The channel coefficients, i.e., hNLoS qp,n (t) in (2), of the proposed model as well as the conventional model in [8] are shown in Fig. 2. Note that the conventional model did not consider the FSE and UAV posture, thus the channel coefficient generated by the proposed model has a more severe fluctuation in the envelop. It can be inferred that the FSE and UAV posture variation leads the channel characteristics more complicated and brings more obvious non-stationarity.

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The temporal ACFs of the proposed model and the model in [8] which did not consider the FSE, are shown in Fig. 3. In this paper, we choose four main scattering points on the fuselage. It can be found from the figure that FSE and posture variation have certain impacts on the ACF. Different scattering points result in different AoDs from fuselage, which leads to the variation between the conventional model and the

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proposed model. In addition, it can be seen that the simulated ACFs have a good consistency with the corresponding theoretical ones, which proves the correctness of proposed model. The temporal ACFs of the proposed model under different UAV postures and the model in [8] are shown in Fig. 4. We find that different roll and pitch angles result in different trends of ACFs in our proposed channel model, but the ACF of conventional model keeps constant since it does not consider the effect of posture variation of UAV. Moreover, it can be found that the pattern change of ACF has a regular relationship with both the variation of roll and pitch angle. When the posture variation become smaller, the output of proposed model approaches the result of the model in [8]. In addition, it can be seen that the simulated ACFs have a good consistency with the corresponding theoretical results, which prove the correctness of proposed channel model and ACF derivations.

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Conclusions

In this paper, a novel 3D non-stationary UAV channel model has been proposed to study the effects of fuselage scattering and posture variation. By assigning the independent UAV and ground terminal scattering conditions and applying deterministicstochastic-combined parameter generation method for angles, the effect of fuselage scattering has been introduced into the channel model. Meanwhile, the UAV posture variations have been considered in the model by transforming the coordinate system and introducing the time-variant posture matrix. The expression of ACF has been derived and verified by simulations as well. The analysis and simulation results have shown that both the FSE and posture have significant impacts on the ACF. It should be mentioned that the proposed GBSM can be applied to diverse UAV communication scenarios by adjusting parameters, i.e., UAV to vehicle, UAV to pedestrian, and UAV to base station. It is useful for the design, optimization, and evaluation of realistic UAV MIMO communication systems. Acknowledgments. This work was supported by the National Key Scientific Instrument and Equipment Development Project under Grant No. 61827801, CEMEE State Key Laboratory fund No. 2020Z0207B, National Defense Science and Technology Key Laboratory fund No. 6142001190105, Aeronautical Science Foundation of Chin, No. 201901052001, and the Fundamental Research Funds for the Central Universities No. NS2020026 and No. NS2020063.

References 1. Wang, C.-X., Huang, J., Wang, H., Gao, X., You, X., Hao, Y.: 6g wireless channel measurements and models: trends and challenges. IEEE Vehicle Technol. Mag. 15(4), 22–32 (2020) 2. Ullah, Z., Al-Turjman, F., Mostarda, L.: Cognition in UAV-Aided 5G and beyond communications: a survey. IEEE Trans. Cogn. Commun. Networking 6(3), 872–891 (2020) 3. Zhao, X.W., Du, F., Geng, S.Y., Sun, N.Y., Zhang, Y., Fu, Z.H., et al.: Neural network and GBSM based time-varying and stochastic channel modeling for 5G millimeter wave communications. China Commun. 16(6), 80–90 (2019) 4. Cheng, L.L., Zhu, Q.M., Wang, C.-X., Zhong, W.Z., Hua, B.Y., et al.: Modeling and simulation for UAV air-to-ground mmWave channels. In: 14th European Conference on Antennas and Propagation, Copenhagen, pp. 1–5 (2020) 5. Jiang, H., Zhang, Z.C., Wu, L., Dang, J.: Three-dimensional geometry-based UAVMIMO channel modeling for A2G communication environments. IEEE Commun. Lett. 22(7), 1438–1441 (2018) 6. Cheng, J.Q., Guan, K., Quitin, F.: Wireless channel characteristics for UAV-based radio access networks in urban environments. In: IEEE Vehicular Technology Conference, Honolulu, pp. 1–5 (2019) 7. Chang, H., Wang, C.-X., Liu, Y., Huang, J., Sun, J., Zhang, W., et al.: A novel nonstationary 6G UAV-to-ground wireless channel model with 3D arbitrary trajectory changes. IEEE Internet Things J. 8(12), 9865–9877 (2021) 8. Zhu, Q.M., Wang, Y.W., Jiang, K.L., Chen, X.M., Zhong, W.Z., Ahmed, N.: 3D non-stationary geometry-based multi-input multi-output channel model for UAVground communication systems. IET Microwaves Antennas Propag. 13(8), 1104– 1112 (2019)

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9. Ma, Z.F., Ai, B., He, R.S., Wang, G.P., Niu, Y., Yang, M., et al.: Impact of UAV rotation on MIMO channel characterization for air-to-ground communication systems. IEEE Trans. Veh. Technol. 16(11), 12418–12431 (2020) 10. Jang, J.T., Gong, H.C., Lyou, J.: Computed torque control of an aerospace craft using nonlinear inverse model and rotation matrix. In: International Conference on Control, pp. 1743–1746 (2015)

A UAV-Based Measurement Method for Three-Dimensional Antenna Radiation Pattern Tianxu Lan1,2 , Hongtao Duan3 , Qiuming Zhu1,2(B) , Qihui Wu1,2 , Yi Zhao1,2 , Jie Li1,2 , Xiaofu Du1,2 , and Zhipeng Lin1,2 1

Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space, Nanjing University of Aeronautics and Astronautics, 211106 Nanjing, China [email protected] 2 College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, 211106 Nanjing, China 3 The State Radio Monitoring Center, 100037 Beijing, China [email protected]

Abstract. An accurate and effective method is extremely important for antenna pattern measurement. This is because antenna pattern, which directly shows the ability of antenna radiation, can affect antenna working and the whole system performance. However, it is difficult to measure the radiation pattern of large outdoor antennas. This paper presents a new precise three-dimensional (3D) antenna radiation pattern measurement method based on the unmanned aerial vehicle (UAV) technique. Cylindrical cone and horn antennas are tested to verify the effectiveness of the method. Comparison results show that the 3D antenna radiation pattern can be quickly obtained by using the proposed method with high accuracy. Keywords: Radiation pattern construction

1

· UAV · Channel model · 3D

Introduction

Antenna is important for many application systems including broadcasting, communication, radar, navigation, electronic countermeasure, remote sensing, radio astronomy and so on. As an essential parameter of antenna [1], three-dimensional (3D) antenna radiation pattern reflects the radiation ability in all directions. However, radiation pattern of antennas can be easily affected by installations and environments, in particular, for large outdoor antennas. The realistic radiation pattern of antennas equipped in specific scenarios needs to be measured or evaluated [2,3]. Employing traditional methods to measure antenna radiation pattern is difficult. Paper [4] presents a method that obtains antenna radiation pattern by calculating antenna near-field measurement data. However, the computational c The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022  Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 1201–1208, 2022. https://doi.org/10.1007/978-981-19-0390-8_151

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complexity of this method is very high. [5] constructs a test field to test radiation pattern, but it costs a lot. With the development of unmanned aerial vehicle (UAV) technique in recent years, many researchers propose to use UAV for measurement in wireless communications [6,7]. [8–10] apply UAVs to test the antenna radiation pattern, but they do not consider the impact of environments on the measured data. This paper presents a new outdoor antenna pattern measurement device and a novel measurement method based on UAV. The method can achieve the acquisition of outdoor antenna pattern measurement data with high precision. A correction method based on dual path model is also proposed, which can eliminate the influence of ground reflection on the measurement data. To calculate the 3D antenna radiation pattern, a new approximate algorithm is next designed based on SA algorithm [11]. This algorithm significantly improves the accuracy of antenna radiation pattern.

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Measurement System and Strategy

The system consists of an air part and a ground part. The air part includes an UAV and a spectrum receiver. In this paper, the rotor UAV is employed, because it flies smoothly and can reduce the impact of UAV attitude on the measurement results and the spectrum receiver carried by the UAV. The UAV flies autonomously according to the preset trajectory, and it is equipped with a sky part of Real-time kinematic (RTK) to collect real-time centimeter-level position information. The spectrum receiver carried by the UAV receives the real-time signal power. The ground part consists of a data transmission antenna, a RTK ground part and a data processing computer terminal. The ground part is responsible for receiving and processing data. Note that the centimeter-level position data and flight accuracy provided by RTK are extremely important in measurement. The flow diagram of 3D antenna radiation pattern measurement procedure of is shown in Fig. 1. According to [12], during the antenna measurement, we keep the radiation pattern of a passive antenna unchanged in the receive mode and the emission mode. In our scheme, the Antenna Under Test (AUT) emission signal and the test antenna equipped by the UAV receives the signal. The received signal, which is affected by the polarization mismatch factor M between the antenna equipped by UAV and the measuring antenna, can be expressed as M = |ˆ ps · pˆ∗AU T | = |px s px AU T + pco s pco AU T | 2

2

(1)

where pˆs and pˆ∗AU T are complex unit vectors of the electric field of the antenna equipped by the UAV and the measuring antenna; the superscript ∗ indicates that the complex vector is conjugate and the superscripts co and x denote the co polarization and cross polarization components, respectively. In order to improve the measurement accuracy, the polarization matching between the two antennas should be maintained in the measurement process.

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Fig. 1. Measurement procedure of 3D antenna radiation pattern.

The UAV collects data along a circle, whose center is the antenna phase center and radius is equal to r on the E and H planes of the antenna. Note that when the flight altitude of UAV is low, it is impossible to obtain all data on the circle. In this case, the symmetrical characteristics of antenna pattern can be used to fill the data. The test antenna and the AUT must meet the far-field conditions [13], as given by 2D2 (2) rmin ≥ λ where rmin is the distance between auxiliary antenna and test antenna; D is the maximum aperture of the test antenna; and λ is the test wavelength. It is noted that the measurement trajectory is the trajectory of the test antenna rather than the trajectory at the sky part of RTK, and the planned GPS flight trajectory of UAV needs to be corrected.

3 3.1

Calculation and Reconstruction of 3D Radiation Pattern Measured Data Processing

In actual field test environments, there are complex multipath reflection interferences, resulting in that the field measurement method is difficult to eliminate the test error caused by multipath-reflected waves. Therefore, in this section,

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we propose the new antenna measurement method to minimize the impact of the main electromagnetic wave reflection path, especially the ground reflection path. Specially, a two-path transmission model [14] is established to reduce the impact of ground reflection, as shown in Fig. 2.

Fig. 2. Two path transmission model.

By taking the phase center of the antenna as the origin, a spherical coordinate system can be constructed. The electric field ER received by UAV is composed of a direct path electric field ELoS and a ground reflection path electric field ERef lect . The received electric field ER can be expressed as ER = ELoS + ERef lect 

where

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PT GT (f, θ1 , ϕ)GR η0 e−jkR1 2π R1

(3)

(4)

PT GT (f, θ1 , ϕ)GR η0 e−jk(R2 +R3 ) R (5) 2π R2 + R3 are the direct path electric field and the ground-reflection path electric field; PT is the emission power; GT (f, θ1 , ϕ) is the emission gain of the AUT in this direction; and GR is the reception gain in the receiving direction of the test antenna. Because the test antenna is omnidirectional antenna, the gain GR in each direction can be regarded as a constant; R is the reflection coefficient. Which is related to the polarization of incident wave, incident angle and dielectric constant of reflector εr . The calculation formula of R is expressed as:  cos(θ2 ) − εr − sin2 (θ2 )  R⊥ = (6) cos(θ2 ) + εr − sin2 (θ2 ) ERef lect =

A UAV-Based Measurement Method for Antenna Pattern

R =

εr cos(θ2 ) − εr cos(θ2 ) +

 

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εr − sin2 (θ2 )

where R⊥ and R are the reflection coefficients of vertically polarized and horizontally polarized waves, respectively, θ2 is the angle between the incident wave and the normal of the reflecting surface. The signal power PR received by the UAV in the measurement can be expressed as λ2 |ER |2 (8) PR = 8πη0 where the free space impedance η0 is 377, λ is the test wavelength. The ground reflected path power signal needs to be excluded from the received power PR to obtain the direct path power PLoS to calculate the three-dimensional antenna radiation pattern. The direct path power PLoS can be expressed as PLoS =

3.2

λ2 |ELoS |2 8πη0

(9)

Reconstruction of 3D Radiation Patternn

Place E plane and H plane on the horizontal plane and the vertical plane respectively. The horizontal plane is θ = π2 plane, vertical plane is ϕ = π2 plane. The interpolation method is used to interpolate the discrete direct path power PLoS to obtain to obtain the horizontal continuous power data hor(ϕ) and the vertical continuous power data vert(θ). Denote the maximum value of power data in two planes as Pmax . By normalizing the data, the decibel (DB) pattern of horizontal plane and vertical plane can be represented as  GH (ϕ) = 10 lg hor(ϕ) Pmax (10) GV (θ) = 10 lg vert(θ) Pmax For any point in the space, the 3D radiation pattern G(θ, ϕ) of the antenna can be approximated as G(θ, ϕ) =

GH (ϕ)v1 (θ, ϕ) + GV (θ)v2 (θ, ϕ)  (1 − u) + (GH (ϕ) + GV (θ))u v12 (θ, ϕ) + v22 (θ, ϕ) 

where  u=

v1 (ϕ, θ) = gV (θ)[1 − gH (ϕ)] v2 (ϕ, θ) = gH (ϕ)[1 − gV (θ)]

1 ϕ = 90◦ , 270◦ or θ = 90◦ 1 − hor(ϕ) else  GH (ϕ) gH (ϕ) = 10 10 GV (θ) gV (θ) = 10 10

(11)

(12) (13) (14)

Taking G(θ, ϕ) as the radius of the corresponding direction in the spherical coordinate system, the 3D radiation pattern of the antenna can be obtained.

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Measurement Results

In this paper, the 3D radiation patterns of 1G frequency points of a cylindrical cone antenna and a horn antenna are measured. The measurement results of the horn antenna are shown in Fig. 3. In Fig. 3(a), the red line is the theoretical antenna E-plane pattern, and the blue line is the measured antenna E-plane pattern. The red line in Fig. 3(b) is the theoretical antenna H-plane pattern and the blue line is the measured antenna H-plane pattern. Figure 3(c) is the theoretical 3D antenna radiation pattern, and Fig. 3(d) is the measured 3D antenna radiation pattern. E-plane pattern of antenna

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The measurement results of the cylindrical cone antenna are shown in Fig. 4. In Fig. 4(a), the red line is the theoretical antenna E-plane pattern, and the blue line is the measured antenna E-plane pattern. The red line in Fig. 4(b) is the theoretical antenna H-plane pattern, and the blue line is the measured antenna H-plane pattern. Figure 4(c) is the theoretical 3D antenna radiation pattern, and Fig. 4(d) is the measured 3D antenna radiation pattern. The measurement results show that two antenna patterns have very similar trend which verifies the effectiveness of proposed method. However, the measured pattern still has error

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especially in some directions, due to the random factors such as GPS errors of UAV and attitude changes of UAV.

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Conclusions

This paper has proposed a high-accuracy UAV-based measurement method for 3D antenna radiation pattern, where the UAV is used to collect the radiation data. We have used a two path transmission mode to correct the data and a new method to construct 3D antenna radiation pattern. We have measured the cylindrical cone antenna and the horn antenna to demonstrate the effectivity of the proposed method. In the future work, we will optimize the flight strategy and the approximation method of 3D antenna radiation pattern. Acknowledgements. This work was supported by the National Key Scientific Instrument and Equipment Development Project under Grant No. 61827801.

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References 1. N´ un ˜ez, J., Orgeira-Crespo, P., Ulloa, C., et al.: Analysis of the operating conditions for UAV-based on-board antenna radiation pattern measurement systems. PLoS ONE 16(2) (2021) 2. Committee, A.S.: IEEE standard test procedures for antennas. Electron. Power 26(9), 749 (1980) 3. Croswell, W.: Antenna theory, analysis, and design. In: IEEE Antennas and Propagation Society Newsletter, pp. 28–29 (1962) 4. Diakite, C., Lanteri, J., Migliaccio, C.: An outdoor measurement technique for large structures antennas. In: IEEE Conference on Antenna Measurements & Applications, pp. 16–19 (2017) 5. Kremer, D.: Outdoor far-field antenna measurement system for testing of large vehicles. In: European Conference on Antennas and Propagation, pp. 2938–2938 (2013) 6. Zhu, Q.M., Wang, Y.W., Jiang, K.L., Chen, X.M., Zhong, W.Z., Ahmed, N.: 3D non-stationary geometry-based multi-input multi-output channel model for UAVground communication systems. IET Microwaves Antennas Propag. 13(8), 1104– 1112 (2019) 7. Zhong, W.Z., Xu, L., Zhu, Q.M., Chen, X.M., Zhou, J.J.: A Novel Beam Design Method for mmWave Multi-Antenna Arrays with Mutual Coupling Reduction. China Commun. 16(10), 37–44 (2019) 8. Virone, G., et al.: UAV-based antenna and field measurements. In: IEEE Conference on Antenna Measurements & Applications, pp. 1–3 (2016) 9. Virone, G., Paonessa, F., Tibaldi, A., et al.: UAV-based radiation pattern verification for a small low-frequency array. In: IEEE Antennas and Propagation Society International Symposium, pp. 995–996 (2014) 10. Picar, A., M., Marque, C., Anciaux, M., et al.: Antenna pattern calibration of radio telescopes using an UAV-based device. In: International Conference on Electromagnetics in Advanced Applications, pp. 981–984 (2015) 11. Vasiliadis, T.G., Dimitriou, A.., Sergiadis, G.D.: A novel technique for the approximation of 3-D antenna radiation patterns. IEEE Trans. Antennas Propag., 2212– 2219 (2005) 12. Elliot, R.S.: Antenna theory and design (2021) 13. Yaghjian, A.: An overview of near-field antenna measurements. IEEE Trans. Antennas Propag, 30–45 (1986) 14. Zhu, Q.M., et al.: A General altitude-dependent path loss model for UAV-to-ground millimeter-wave communications. Front. Inf. Technol. Electron. Eng. 22(6), 767– 776 (2021)

Height-Dependent LoS Probability Model for A2G MmWave Communications Under Built-Up Scenarios Minghui Pang1 , Qiuming Zhu1(B) , Fei Bai1 , Zhuo Li2 , Hanpeng Li1 , Kai Mao1 , and Yue Tian1 1

Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China {pangminghui,zhuqiuming,baifei,lizhuo,lihanpeng,maokai,tian yue}@nuaa.edu.cn 2 The Key Laboratory of Radar Imaging and Microwave Photonics, Ministry of Education, College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China

Abstract. Based on the three-dimensional propagation characteristic under built-up scenarios, a height-dependent line-of-sight (LoS) probability model for air-to-ground (A2G) millimeter wave (mmWave) communications is proposed in this paper. With comprehensive considerations of scenario factors, this paper upgrades the prediction method of International Telecommunication Union-Radio (ITU-R) standard to both low altitude and high altitude cases. In order to speed up the LoS probability prediction, an approximate parametric model is also developed based on the theoretical expression. The simulation results based on ray-tracing (RT) method show that the proposed model has good consistency with existing models at the low altitude. Moreover, it has better performance at the high altitude. Keywords: Line-of-sight (LoS) probability · Air-to-ground (A2G) Unmanned aerial vehicle (UAV) · Millimeter wave (mmWave) communications · Height-dependent

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Introduction

Unmanned Aerial Vehicles (UAVs) have shown great growth in various fields due to their high mobility and low cost [1]. MmWave communications have the advantages of fast transmission rate, high security, and spatial multiplexing of spectrum resources [2]. The UAV-aided air-to-ground (A2G) mmwave communication technology is promising for the beyond fifth and sixth generation (B5G/6G) mobile communication systems [3]. It is well-known that the line-ofsight (LoS) path dominates the reliability of A2G communication link but it appears randomly due to UAV’s high mobility and maneuver [4]. Therefore, it is vital to build a LoS probability model for channel modeling and performance evaluation of A2G communication systems. c The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022  Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 1209–1217, 2022. https://doi.org/10.1007/978-981-19-0390-8_152

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There are limited theoretical or measurement studies in the literatures for the LoS probability modeling, [5–17]. Several standardized channel models, e.g., the Third Generation Partnership Project (3GPP) TR 38.901 [5], International Telecommunication Union-Radio (ITU-R) M.2135-1 [6], WINNER II [7] and 5G Channel Model (5GCM) [8], have their own LoS probability calculation methods, but most of them are based on the measurement results and only provide accurate results for specific environments. Since channel measurements are expensive and time consuming, some researchers studied the LoS probability based on the simulation data, obtained from the ray-tracing (RT) method [9,10] and point cloud [11]. However, a prior accurate knowledge of environment information is required for this kind of deterministic methods, which is unrealistic for most of applications. The analytical method based on the geometry can provide acceptable estimation with generality [12–17]. It describes the built-up scenarios by several statistical parameters and derived the LoS probability based on the geometric relationships. For example, a widely accepted analytical method was studied in the ITU-R Rec. P.1410 model [12], but the factor of building width was not included. By conducting simulations for massive locations of mobile terminal, the LoS probability function was fitted respect to the elevation angle in [13,16]. Note that these two models were only suitable for the high-altitude case in which the height was greater than 1000 m. The authors in [17] modeled the buildings by random distributed cylinders with random heights. The authors in [14,15] introduced the frequency factor into the LoS probability model. The method was general but it’s complicated to identify the LoS component within the LoS clearance zone. This paper aims to find a general LoS probability model for A2G mmWave communications with a good tradeoff between complexity and accuracy. The proposed model is suitable for both low and high altitudes, and includes the geometric factors such as the locations of terminals, building height distribution, building width, and building space. In addition, on this basis, we also derive a closed-form solution that can simplify the calculation with good accuracy.

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A2G Propagation Scenarios

The built-up scenario is one of the most common application scenarios for the future A2G communications, where buildings are referred as main obstacles. In order to achieve a more general LoS probability prediction method, the environment-dependent geometrical parameters are usually described in a stochastic way. The main characteristic of built-up area is the layout of buildings. A typical A2G communication link under built-up scenario is shown in Fig. 1. In the figure, hTX , hRX represent the heights of UAV as the transmitter (TX) and vehicle as the receiver (RX), respectively and dRX is the horizontal distance from the TX to the RX. A well-known classification for built-up scenarios, i.e., suburban, urban, dense urban and high-rise urban can be addressed in [13]. It describes different scenarios by three parameters ψ ∈ {α, β, γ} defined in

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[12], where α is the percent of land area covered by buildings, β represents the mean number of buildings per unit area, and γ is a random variable denoting the random building height with the probability density function (PDF) as   h2 h P (h) = 2 exp − 2 . (1) γ 2γ In Fig. 1, S, W represent the building space and width which can be obtained from the real data or the above scenario-dependent parameters can be expressed as  (2) W = 1000 α/β  √ S = (1000/ β)(1 − α). (3) The environment-dependent parameters ψ ∈ {α, β, γ} are (0.1, 750, 8), (0.3, 500, 15), (0.5, 300, 20), and (0.5, 300, 50), respectively.

3 3.1

Height-Dependent LoS Probability Model Theoretical LoS Probability Derivation

It should be mentioned that during the propagation of radio waves, radio radiation is not only concentrated on the optical path. The Fresnel zone is the area where the electric field strength is affected by obstacles. Theoretically, only when the Fresnel zone is completely blocked by obstacles can the LoS propagation be cut off. As shown in Fig. 2, we find that at a certain distance, the width of Fresnel zone decreases as the frequency increases and increases as the distance increases. In addition, the width of Fresnel zone at the mmWave band is within 2.5 m, but the distance between the receiver and the transmitter is several hundred meters. Therefore, for the mmWave frequency band, the width of Fresnel zone can be approximately ignored. When the positions of TX and RX are known, the probability of LoS path can be viewed as the probability that all building along the

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propagation path are below the height of connection line between TX to RX. The height of connection line at any point can be proved as hLoS = hTX −

dLoS (hTX − hRX ) dRX

(4)

where dLoS is the distance from the TX to the obstruction. Furthermore, the LoS probability can be defined as PLoS =

Nb 

Pi =

i=0

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P (hi < hLoS )

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i=0

are where hi is the building height. Assuming that all buildings   √ and streets equally distributed, the number of buildings Nb = floor dRX αβ/1000 , where the function floor represents the meaning of rounding down. The visibility from TX to RX means that the LoS path is not blocked by any building between the transceivers. Based on the environmental parameters, the probability Pi that a building height is smaller than height hLoS can be obtained by  Pi = P (hi < hLoS ) =

hLoS 0

  h2 P (h)dh = 1 − exp − LoS . 2γ 2

(6)

The LoS probability is obtained weighting each Pi with weights i dependent on the distance from the transmitter. It accounts for the number of buildings being greater at larger distance. By substituting (6) to (5), we can get the theoretical result related to width and height which can be expressed as (7). PLoS (dRX , hTX , hRX , ψ) =

N b

Pi i=1 ⎡

⎛ 

2 ⎞⎤ i − 0.5 W hTX − (hTX − hRX ) ⎟⎥ + ⎜ ⎢ Nb 2dRX ⎜ ⎢ ⎟⎥ = ⎢1 − exp ⎜− ⎟⎥ ⎝ ⎠⎦ 2γ 2 i=1 ⎣ N b

(7) As it shows that the LoS probability is fully determined by the position and environmental parameters. It should be mentioned that the new model is independent on the frequency and applicable for any heights of TX and RX. Moreover, the factor of building width is taken into account and the special case of W = 0 reduces to the same result as Eq. (4) in[16]. In order to observe the effect of building width on the LoS probability, Fig. 3 shows the LoS probability with the central frequency f = 28 GHz, hTX = 70 m, hRX = 1.5 m and γ = 15 at four different building width. It clearly shows that the LoS probability declines when building width increases. The reason is that the wider buildings would increase the block possibility.

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Simplified Parametric Expression

The analysis model proposed in this paper can be applied to A2G scenarios with different building heights, and also takes the environment information such as the width of the houses into account. However, due to the continuous multiplication process, the calculation of the LoS probability is slightly complicated. For solving this problem and deriving a closed-form solution of system performance and channel model, we need a simple and closed-form expression of LoS probability. In order to obtain a closed-form expression and reduce the computational complexity, we further approximate the theoretical model by a parametric simplified model for specific scenarios. 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

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30.07 38.63 0.4911

Urban

0.3891 1.098

23.92 21.31 0.4770

Dense urban

0.3475 1.018

20.15 18.87 0.4461

High-rise urban 0.1885 0.9723 17.31 15.70 0.4106

Noted that the LoS probability is normally modeled as exponential functions against the distance, which D1 is the breakpoint distance where LoS probability is no longer equal to 1, and D2 is a decay parameter. Based on the proposed theoretical model, we describe D1 and D2 as height-dependent parameters for universality and applicability. The approximate model can be obtained by the minimum mean square error (MMSE) criteria as (8),        D1 dRX dRX  , 1 · 1 − exp − + exp − PLoS (dRX , h ) = min D2 D2  

dRX      b1 dRX dRX a1 h + c1 , 1 · 1 − exp − + exp − = min dRX a2 h b2 a2 h b2 (8)

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where h = hTX −hRX and dRX is the horizontal distance from the TX to the RX. Note that the LoS probability is described by the distance and height difference where the ground plane is viewed as the height of hRX . Then, we link the model parameters to the height-dependent variables. The optimal parameters for four typical built-up scenarios are summarized in Table 1.

4 4.1

RT-Based Validation and Comparison RT-Based Simulation Method

The RT technique has been widely adopted to radio channels modeling, alleviating the burden of measurement campaigns. In the RT simulation, the electromagnetic wave departing from the source is considered to be a bunch of rays using a ray-optic approximation, so a geometric solution can be obtained based on the uniform theory of diffraction and geometric optics. In this paper, we only focus on the LoS probability characteristic. The proposed algorithm in this paper mainly includes two steps, i.e., reconstruction of scenario and probability computation by RT techniques. In this paper, we obtain the width, height and space of the buildings according to the environmental parameters ψ and reconstruct four built-up scenarios. In each scenario, we set up N transmitters of different heights. Afterwards taking TX as a center, the positions of RX are distributed in concentric circles where the distance between the transmitter and receiver is equal. In this paper, the simulations are performed at the central frequency of 28 GHz, with a bandwidth of 500 MHz. In order to obtain LoS probability data at different communication heights, we set both TX heights and RX distances start from 0 m to 1000 m with the step of 10 m and the RX height is fixed at 2 m. The number of buildings per square kilometer is 300 and the building width is 40.8 m. 4.2

Validation and Comparison

In this section, we run simulations for different heights and distances to theoretically validate the performance of proposed model of proposed model. For comparison purpose, we set hRX = 0 in the theoretical model to ensure that hTX and h represent the same physical meaning. In order to visually display the results, we set the absolute error function between the theoretical model and approximate model. It can be expressed as Aerf = [PLoS (dRX , hTX , hRX , ψ)−PLoS (dRX , h )]2

(9)

where PLoS (dRX , hTX , hRX , ψ) is the probability of the theoretical model and the PLoS (dRX , h ) is the the probability of the approximate model. The absolute error simulation results are shown in Fig. 4 and the range of dRX and Δh = hTX −hRX are from 0 m–1000 m. Figure 4 shows that the absolute error between the analytical model and approximate model is from 0–0.1. The good agreement between

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analytical and approximate results shows the applicability of simplified parametric model. In fact, this approximate model with lower complexity and closed-form expression may have wider application in the future channel modeling and communication performance evaluation.

0.15

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Fig. 5. Comparisons of different models with different heights.

In order to demonstrate the validity of proposed model in A2G scenarios, we also compare them with other previous models. Since measurement campaigns for A2G channels are difficult and high cost, we conduct tremendous RT simulations and obtained huge number of LoS probability data for comparison. Since some standard models such as 3GPP and 5GCM model are only suitable for low altitude, taking 30 m as an example, we compare our proposed model with them and RT data in Fig. 5(a). Good consistency across all models and RT data proves that our model can also achieve good prediction at low altitudes. For the high altitude case, taking 1000 m as an example, we compare our proposed model with

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other applicable models [16,17]. For comparison purpose, we use the elevation angle θ = arctan(h /dRX ) to describe the LoS probability as it did in [16,17]. Figure 5(b) shows that three models have similar trends but our proposed model is much closer to the RT data.

5

Conclusions

In this paper, we have derived a height-dependent LoS probability model based on the statistical characteristic and geometric parameters of built-up scenarios. In addition, a simplified parametric model has been proposed to speed up the prediction process as well as assist the closed-form solution derivation for channel model and system performance. The simulation results have shown that the proposed model demonstrates good versatility at both low and high altitudes and has a good agreement with RT-simulated data. The proposed model has a wide range of applications such as channel modeling, performance evaluation, and system optimization for A2G communications. Future work will include the channel measurement of LoS probability and incorporating the factor of antenna pattern. Acknowledgments. This work was supported by the National Key Scientific Instrument and Equipment Development Project (No. 61827801), Aeronautical Science Foundation of China (No. 201901052001), State Key Laboratory of Integrated Services Networks Funding (ISN22-11).

References 1. Zhu, Q., Jiang, K., Chen, X., Zhong, W., Yang, Y.: A novel 3D non-stationary UAV-MIMO channel model and its statistical properties. China Commun. 15(12), 147–158 (2018) 2. Cheng, X., Li, Y., Wang, C., et al.: A 3-D geometry-based stochastic model for unmanned aerial vehicle MIMO ricean fading channels. IEEE Internet Things J. 7(9), 8674–8687 (2020) 3. Li, B., Fei, Z., Zhang, Y.: UAV communications for 5g and beyond: recent advances and future trends. IEEE Internet Things J. 6(2), 2241–2263 (2019) 4. Zhu, Q., Li, H., Fu, Y., Wang, C.-X., Tan, Y., et al.: A novel 3D non-stationary wireless MIMO channel simulator and hardware emulator. IEEE Trans. Commun. 66(9), 3865–3878 (2018) 5. Zhu, Q., Wang, C.-X., Hua, B., Mao, K., Jiang, S., Yao, M.: 3GPP TR 38.901 Channel Model, The Wiley 5G Ref: The Essential 5G Reference Online. Wiley Press, Hoboken (2021) 6. ITU-R: Guidelines for Evaluation of Radio Interface Technologies for IMTAdvanced, Geneva, Switzerland, REP. ITU-R M.2135-1, December 2009 7. IST-4-027756 WINNER II D1.1.2 V1.2. WINNER II channel models, February 2008 8. 5GCM: 5G Channel Model for Bands up to 100 GHz. Technical report, October 2016

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9. Samimi, M.K., Rappaport, T.S., MacCartney, G.R.: Probabilistic omnidirectional path loss models for millimeter-wave outdoor communications. IEEE Wirel. Commun. Lett. 4(4), 357–360 (2015) 10. Lee, J., Choi, J., Kim, S.: Cell coverage analysis of 28 GHz millimeter wave in urban microcell environment using 3-D ray tracing. IEEE Trans. Antennas Propag. 66(3), 1479–1487 (2018) 11. Jarvelainen, J., Nguyen, S.L.H., Haneda, K., Naderpour, R., Virk, U.T.: Evaluation of millimeter-wave line-of-sight probability with point cloud data. IEEE Wirel. Commun. Lett. 5(3), 228–231 (2016) 12. ITU-R: Rec. P.1410-2 Propagation Data and Prediction Methods for The Design of Terrestrial Broadband Millimetric Aadio Access Systems. P Series, Radiowave propagation (2003) 13. Holis, J., Pechac, P.: Elevation dependent shadowing model for mobile communications via high altitude platforms in built-up areas. IEEE Trans. Antennas Propag. 56(4), 1078–1084 (2008) 14. Liu, X., Xu, J., Tang, H.: Analysis of frequency-dependent line-of-sight probability in 3-D environment. IEEE Commun. Lett. 22(8), 1732–1735 (2018) 15. Cui, Z., Guan, K., Briso-Rodriguez, C., Ai, B., Zhong, Z.: Frequency-dependent line-of-sight probability modeling in built-up environments. IEEE Internet Things J. 7(1), 699–709 (2020) 16. Al-Hourani, A., Kandeepan, S., Lardner, S.: Optimal LAP altitude for maximum coverage. IEEE Wirel. Commun. Lett. 3(6), 569–572 (2014) 17. Al-Hourani, A.: On the probability of line-of-sight in urban environments. IEEE Wirel. Commun. Lett. 9(8), 1178–1181 (2020)

Simulation Analysis of Multi-region Receiver Based on Orthogonal Transmitter Structure Xiu Zhang(B) , Yang Li, and Xin Zhang Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, Tianjin Normal University, Tianjin 300387, China [email protected]

Abstract. This paper analyzed an orthogonal wireless power transmission (WPT) model which can transmit energy in all directions in space. In this paper, the orthorhombic transmission structure was divided into eight regions, and the working mode of the receivers in different regions was explored to obtain the highest energy when the three inputs are in phase. The simulation results indicated that the receiving coils in different areas can achieve the best performance when the transmitting coils are in different working modes. Keywords: Magnetic induction intensity · Orthorhombic transmission structure · Wireless energy transmission

1 Introduction Wireless energy transmission has attracted much attention in recent years because it is more environmentally friendly and flexible than traditional wired transmission methods. In the past few years, the research direction focused on the study of wireless power transmission systems for plane transmission and reception, but its performance is greatly affected by the alignment conditions. When there is a deviation between the transmitting coil and the receiving coil, the efficiency will be severely reduced [1, 2], and the threedimensional wireless transmission technology has good prospects in this respect. So the omnidirectional wireless power transmission system has attracted the attention of researchers with better flexibility. Constructing a three-dimensional WPT can transmit power in multiple directions in space. A transmitter structure with a current phase difference of 90 degrees is proposed, which can perform omnidirectional transmission in two-dimensional space and provide power to multiple receivers [3]; The WPT system proposed in [4] uses a transmitter composed of a controllable coil array to transmit energy to multiple single-coil receivers to improve the transmission efficiency of the system; literature [5] proposes a new type of wireless charging bowl, Provide large energy and the magnetic field has uniformity and omnidirectionality, can position the transmitter freely, and can be charged in any direction; it just proposes a new type of coil topology that can compensate for changes in receiver position The power loss can ensure omni-directional wireless charging [6], this experiment verifies the feasibility of the orthogonal topology. In [7], a 3D molded device © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 1218–1224, 2022. https://doi.org/10.1007/978-981-19-0390-8_153

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is used to replace the traditional planar coil, when the receiver rotates 360 degrees, the transmission power changes little. It is proved that the 3D-MID technology can replace the planar coil for research; this paper [8] optimizes the design of the receiver in the omnidirectional wireless power transmission system to enhance the receiving effect, and can increase the transmission power under the same input and position conditions; it analyzes the relationship between the load and the input power, output power and energy efficiency of the two transmitting coils, and uses current control to generate the magnetic field vector to efficiently locate the position of the receiver [9]; it optimizes the magnetic structure and proposes four magnetic structure is compared, and the transmission efficiency can be improved without changing the size of the transmitter [10]. In this paper, a space wireless power transmission system is built. Its transmitting structure is composed of three independent coils, and the transmitter structure is divided into eight areas. Explore the best working mode of the transmitter coil for receiving the highest energy in different areas when the three coils are input in the same phase.

2 Theoretical Analysis of Current Vector and Magnetic Field Vector In the wireless power transmission system model, the transmitter structure adopts an orthogonal structure, and the mutual inductance of the coil is theoretically zero, which can be ignored in the calculation [11]. In order to facilitate subsequent research, three independent voltage sources are used to power the system. According to Biot-Savart’s law, it can be known that in a single circular coil, the coil magnetic field is [12]:  − μ0 I → → → → μ0 I − − → r (1) dl · l × − r = 2 l ×− B = 2 4π r 2r − → → Where, l and − r are the tangential unit vector and radial unit vector of the coil − → respectively [12]. The vector B is perpendicular to the coil plane. According to the formula, the magnetic field is proportional to the current, and the same is true for threedimensional coils. If three coils are orthogonal, the center of the circle is the same as the − → origin, and the three coils are placed on xy, yz, xz plane respectively. B can be calculated − → − → − → as the superposition of three coils and three magnetic field components B1 , B2 , B3 [12], such as: − → − → − → − → μ0  − → − → − → I 1 I + I 2 J + I3 K B = B1 + B2 + B3 = (2) 2r Then the vector direction of the resultant magnetic field is the same as the vector  − → − → − → I1 I + I2 J + I3 K . Therefore, the different working modes of the transmitting coil will result in different combined magnetic fields, and the direction of the focused magnetic field energy will also change. In this way, the working mode of the transmitter can be changed in different areas of the transmitter to allow the receiver to obtain higher energy and reduce the transmission energy loss.

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Parameter

Value

Transmit Start-Radius 100/98/96 (mm) coil1/coil2/coil3 Turns 10 Pitch Receive coil Fig. 1. WPT model

2

Start-Radius 75 (mm) Turns

11

Pitch

2

3 The System Model The established model is shown in Fig. 1, where in the red coil represents coil1, the green coil represents coil2, and the blue coil represents coil3. The yellow coil is the receiving coil, and the coil data is shown in Table 1. Among them, each transmitting coil will be connected to an independent voltage source, the input voltage is set to 50 V, the operating frequency is 100 kHz, the load is 10 ohms, and the external capacitors meet the resonance conditions. The transmitter is divided into eight zones according to the X, Y and Z axes Divide the transmitter into eight zones along the X,Y and Z axis, write X + Y + Z + , X + Y + Z − , X − Y + Z + , X − Y + Z − , X − Y − Z + , X − Y − Z − , X + Y − Z + , X + Y − Z − eight zones as part1, part2, part3, part4, part5, part6, part7, part8.

4 Simulation and Result After constructing the orthogonal wireless transmission model, this paper uses the finite element method to analyze the transient mode of the model, set the three voltage sources to be in phase, and divide the transmitter’s working mode into four, the first is the threecoil Working together, the second type is that coil1 and coil2 work together, the third type is that coil1 and coil3 work together, the fourth type is coil2 and coil3 work together, and the simulation time is set to 100us. 4.1 Effect of Different Working Modes on Received Current The receiver position is changed, and the receiving current in the eight regions is shown in Fig. 2. Under the same conditions, the maximum current of the three coils working in the first mode in part1 can reach 0.905 A, while the current in the second working mode can be up to 1.782 A, as shown in Fig. 2(a); part 2 is the first The maximum current of this mode can reach 0.882 A. Obviously, the receiving current of the third working mode is 1.792 A, as shown in Fig. 2(b); the working current of the first mode of part 3 can reach 0.765 A, while the fourth working mode The mode receiving current is 1.690 A,

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as shown in Fig. 2(c); part 4 obviously has the maximum working current of three coils, which is 2.632 A, as shown in Fig. 2(d); part 5 has a maximum working current of 0.876A for three coils, And the receiving current in the third working mode is 1.744 A, as shown in Fig. 2(e); part 6 The working current in the first mode can reach 0.908 A, and the receiving current in the second working mode is 1.786 A, as shown in Fig. 2(f); part 7 three-coil working current is the largest, up to 2.60 A, as shown in Fig. 2(g); part 8 first working mode working current up to 0.785 A, and fourth working mode receiving current Is 1.738 A, as shown in Fig. 2(h). 4.2 Effect of Different Working Modes on Receiving Magnetic Induction Intensity Then select the distance of the corresponding position, and simulate the magnetic induction intensity of the receiving coil in different positions and different working modes when the current is maximum, as shown in Fig. 3. Pick the middle point to record the value. As shown in Fig. 3(a), the magnetic induction intensity of the three coils working together in part1 is 74.312 uT, and the receiving magnetic induction intensity of the second cooperative mode is 142.297 uT; as shown in Fig. 3(b), the magnetic induction intensity of the three coils working together in part2 It is 74.202 uT, the third cooperative mode receives magnetic induction intensity of 139.423 uT; as shown in Fig. 3(c), the magnetic induction intensity of three coils working together in part3 is 62.322 uT, and the fourth cooperative mode receives magnetic induction intensity of 138.457 uT; In Fig. 3(d), the three coils working together in part4 have the strongest magnetic induction intensity, which is 187.654 uT; as shown in Fig. 3(e), the three coils working together in part5 have a magnetic induction intensity of 74.901 uT, and the third cooperative mode is used to receive The magnetic induction intensity is 147.364 uT; as shown in Fig. 3(f), the magnetic induction intensity of the three coils working together in part6 is 76.548 uT, and the receiving magnetic induction intensity of the second cooperative mode is 141.709 uT; as shown in Fig. 3(g), part7 The magnetic induction intensity of the three coils working together is 185.837 uT; as shown in Fig. 3(h), the magnetic induction intensity of the three coils working together in part8 is 64.473 uT, and the receiving magnetic induction intensity of the fourth cooperative mode is 140.582 uT. In general, the orthogonal transmitter structure can transmit energy in multiple directions in space, and the greater the current, the greater the received magnetic induction intensity. In the experiment, an adaptive sensor can be used to sense the position of the receiver, and the working mode can be adjusted as the position of the receiver changes. When the receiver is in part1 and part6, coil1 and coil2 work together; when in part2 and part5, coil1 and coil3 work together; when in part3 and part8, coil2 and coil3 work together; when in part4 and part7, the three coils work together to increase the received energy and reduce the loss. But it should be noted that this simulation also verified that three in-phase voltage sources are set to supply power, and the energy transmission in multiple directions lacks uniformity, and it cannot realize a true omnidirectional wireless power transmission system.

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Fig. 2. Receiving current

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Fig. 3. Receiving magnetic induction intensity

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5 Conclusion In order to ensure that wireless power transmission can be transmitted stably in multiple directions in space, it is necessary to study multi-mode and multi-application omnidirectional transmission technology. This paper studies that when the three inputs of the transmitting structure are in phase, the receiver has its best operating mode at different positions, which can ensure the highest current and received magnetic induction. At present, there is still a lot of room for development of omni-directional power transmission. In the future, research can be carried out in the aspects of receiver optimization, research on receiving special positions or different three-input designs, etc., and a true omni-directional power transmission scheme can be proposed.

References 1. Wang, J., Ho, S.L., Fu, W.N., Sun, M.: Analytical design study of a novel witricity charger with lateral and angular misalignments for efficient wireless energy transmission. IEEE Trans. Magn. 47(10), 2616–2619 (2011) 2. Beh, T.C., Kato, M., Imura, T., Oh, S., Hori, Y.: Automated impedance matching system for robust wireless power transfer via magnetic resonance coupling. IEEE Trans. Industr. Electron. 60(9), 3689–3698 (2013) 3. Che, B.-J., Meng, F.-Y., Wu, Q.: An omnidirectional wireless power transmission system with controllable magnetic field distribution. In: Proceedings of IEEE International Workshop Electromagnetic, Applications and Student Innovation Competition, May 2016, pp. 1–3 (2016) 4. Bai, T., Mei, B., Zhao, L., Wang, X.: Machine learning-assisted wireless power transfer based on magnetic resonance. IEEE Access 7, 109454–109459 (2019) 5. Feng, J., Li, Q., Lee, F.C., Fu, M.: Transmitter coils design for free-positioning omnidirectional wireless power transfer system. IEEE Trans. Ind. Inf. 15(8), 4656–4664 (2019) 6. Zhang, Z., Zhang, B.: Angular-misalignment insensitive omnidirectional wireless power transfer. IEEE Trans. Ind. Electron. 67(4), 2755–2764 (2020) 7. Sergkei, Z., Lombard, P., Semet, V., Allard, B., Moguedet, P., Cabrera, M.: The potential of 3d-mid technology for omnidirectional inductive wireless power transfer. In: 2018 13th In-ternational Congress Molded Interconnect Devices (MID), pp. 1–6 (2018) 8. Zhang, Z., Zhang, B., Wang, J.: Optimal design of quadrature-shaped pickup for omnidirectional wireless power transfer. IEEE Trans. Magn. 54(11), 1–5 (2018). Art no. 8600305 9. Lin, D., Zhang, C., Hui, S.Y.R.: Mathematical analysis of omnidirectional wireless power transfer—part-I: two-dimensional systems. IEEE Trans Power Electron. 32(1), 625–633 (2017) 10. Cha, H.-R., Park, K.-R., Kim, T.-J., Kim, R.-Y.: Design of magnetic structure for omnidirectional wireless power transfer. IEEE Trans. Power Electron. 36(8), 8849–8860 (2021) 11. Wlang, D., Zhu, Y., Zhu, Z., Mo, T.T., Huang, Q.: Enabling multi-angle wireless power transmission via magnetic resonant coupling. In: Proceedings of International Conference Computer Convergence Technology, pp. 1395–1400 (2012) 12. Zhang, C., Lin, D., Hui, S.Y.: Basic control principles of omnidirectional wireless power transfer. IEEE Trans. Power Electron. 31(7), 5215–5227 (2016)

An Improved Frequency Measurement Algorithm Based on Rife and Its FPGA Implementation Xin Hao(B) , Heng Sun, Lei Guo, and Chao Wang Beijing Aerospace Long March Vehicle Research Institute, Beijing, China [email protected]

Abstract. In order to improve the frequency measurement accuracy and speed of frequency measurement receiver, an improved rife frequency measurement algorithm is proposed. The implementation steps of the algorithm are described in detail based on the analysis of traditional rife frequency measurement algorithm, The simulation results show that the frequency measurement error of the algorithm is less than that of rife algorithm under the same signal-to-noise ratio. In order to be easy to implement in FPGA and improve the running speed of the algorithm, the division operation in the original algorithm is optimized, the division is realized by subtraction, and the logarithmic operation is realized by look-up table, so that the frequency measurement speed is as high as 240 MHz. Keyword: Frequency estimation · Improved rife algorithm · FPGA implementation

1 Introduction Sine wave frequency estimation is a classical topic in signal parameter estimation, and it is also one of the key topics in the field of electronic countermeasures. Literature [1] proposes a spectrum correction method using DFT for frequency estimation and adding a sinc like window function in the time domain. This method widens the window spectrum width and made the measurement frequency ambiguity of two adjacent DFT spectral lines. Therefore, a large number of DFT points was required to ensure the accuracy of frequency measurement. Literature [2] uses the method of long and short phase delay, Short time delay can be used to eliminate frequency ambiguity, and long delay can be used to obtain higher frequency resolution to improve the accuracy of frequency measurement. However, this method is greatly affected by noise and has large frequency measurement error at the edge of frequency band. The maximum likelihood frequency estimation method is given in literature [3]. The algorithm can reach the lower limit of frequency estimation: Cramer Rowe limit (CRB), but the algorithm has a large amount of calculation which is not conducive to engineering application. Based on the analysis of rife algorithm and from the perspective of FPGA implementation, this paper proposes an improved rife algorithm. The simulation and measured results show that the algorithm can realize the fast and accurate measurement of signal frequency under the condition of short data, and has high engineering application value. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 Q. Liang et al. (Eds.): CSPS 2021, LNEE 878, pp. 1225–1231, 2022. https://doi.org/10.1007/978-981-19-0390-8_154

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2 Rife Algorithm Analysis Literature [4, 5] uses the maximum spectral line and the adjacent sub-large spectral line for interpolation, and uses the interpolation results for frequency estimation. This algorithm is called rife algorithm. The principle of rife algorithm is as follows: Suppose that the received signal and sampled is: s(n) = x(n) + ω(n) which x(n) is the received pure signal ω(n) is the Gaussian white noise mixed into the signal, its mean value is 0 and its variance is σ 2 set x(n) = aej(2π f0 /fs +ϕ0 ) , which a Is the signal amplitude; fc is the received signal frequency, ϕ0 is the initial phase of the signal; fs is the sampling frequency of the signal. The sampled signal s(n) do N Point FFT operation, the maximum spectral line value is denoted as |X (k0 )|, the sub-maximum spectral line value is denoted as |X (k0 + r)|. Then the frequency estimation value obtained by rife algorithm is:   |X (k0 + r)| fs fc = k0 + r (1) |X (k0 )| + |X (k0 + r)| N 

Fig. 1. Rife Algorithm diagram

The r = −1 when |X (k0 + 1)| ≥ |X (k0 − 1)| On the contrary, the r = −1 when |X (k0 + 1)| < |X (k0 − 1)|. Figure 1 Rife Algorithm diagram shows r = 1 due to 0 ≤ |X (k0 +r)| 1 |X (k0 )| + |X (k0 +r)| ≤ 2 so the offset of the interpolation is within [k0 − 1/2, k0 + 1/2]. The frequency value of the input signal can be estimated by substituting the offset and into formula (1).

3 Analysis of Improved Rife Algorithm Rife algorithm determines the interpolation direction by judging the amplitude of spectral (k0 +r)| lines on both sides of FFT peak |X (k0|X)|+|X (k0 +r)| and select the offset size by calculation. Thus it can be seen that the division operation is required when using FPGA, and the division operation of FPGA is usually realized by calling IP core. The calculation result of IP core is usually quotient and remainder, and the accuracy is not high. In addition,

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the division operation needs to occupy more FPGA resources, and the operation cycle is cost long time, which limits the frequency measurement speed. After simulation, it is found that after windowing the input signal, the power difference between the two sub-large values on both sides of the maximum FFT value G = |G(k0 + 1)| − |G(k0 − 1| maintains a linear relationship with the offset [k0 − 1/2, k0 + 1/2] where k0 Is the subscript index value corresponding to the maximum FFT amplitude, G Represents the power difference. As shown in the Fig. 2. Therefore, only the linear slope of any section is required α and know the maximum subscript index value of FFT k0 , using power differenceG, the frequency of the input signal can be determined by the formula (2). fc =

fs × (k0 + α × G) N

(2)

Fig. 2. Relationship between FFT spectral line index k0 and the difference of power

In order to realize the above algorithm, we first need to calculate the slope α in the specific implementation, the results can be calculated by MATLAB, and the results can be used to realize the improved rife algorithm frequency measurement in hardware. The specific steps are as follows: 1. The sampling sequence is windowed, and the length of window is the same as the number of points for FFT; 2. FFT operation is performed on the windowed sequence, and takes the amplitude peak value |X (k0 )| the two values on both sides |X (k0 + 1)| and |X (k0 − 1)|; 3. Convert |X (k0 + 1)| and |X (k0 − 1)| into the corresponding power value and calculate the power difference: G = |G(k0 + 1)| − |G(k0 − 1| record G And k0 ; 4. In the frequency range of the input signal to be measured, input the signal and repeat steps 1–3 each time according to the same steps until the end; 5. Select any segment in [k0 − 1/2, k0 + 1/2] Between the frequency sections, recorded according to step 3. G and k0 and find the reciprocal of the slope of this section, I.E α The size of the value;

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6. Calculated by 5, α and the subscript index value of the amplitude peak of FFT k0 , and power difference G, the frequency value of the input signal can be obtained by substituting Eq. 2.

4 Simulation Analysis Set the simulation parameters as follows: input signal frequency range: fc = 300 + fc ,   fc take −fs /2N , fs /2N , the frequency interval is 0.05 MHz, the sampling frequency is 240 MHz, the number of sampling points is 64, and the initial phase of the signal is φ0 take [0, 2π ] Random number, noise mean 0, variance σ 2 = 0.5. Monte Caro simulation experiment is conducted for 1000 times for each frequency point. When the input signal is a real signal, the corresponding Cramer Luo limit (CRB) is determined by Eq. (3).  12fs  /2π CRB = (3) SNR ∗ N N 2 − 1

Fig. 3. Error of rife algorithm and improved rife

Figure 3 shows the simulation results of frequency measurement error of rife algorithm and improved rife algorithm. When SNR at 6 dB and |fc | ≤ 13 , it can be seen the frequency measurement error of rife algorithm is large, and the maximum mean square error is 0.7 MHz, while the frequency measurement error of the improved rife algorithm is small. On the contrary when |fc | > 13 the frequency measurement error of the improved rife algorithm becomes larger, and the maximum mean square error is less than 0.5 MHz.

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Fig. 4. The frequency RMSE under different input SNR

Figure 4 shows the simulation results of frequency measurement performance of improved rife algorithm and rife algorithm under different input signal-to-noise ratio. The SNR variation range is 0 to 20 dB. 1000 Monte Carlo tests are conducted under each signal-to-noise ratio,   and the input frequency of each test signal is fc = 300 + fc , fc is −fs /2N , fs /2N Random number. It can be seen from the figure that when the signalto-noise ratio increases, the frequency measurement accuracy of rife algorithm and the improved rife algorithm improves, but the frequency measurement error of improved rife algorithm is always less than that of rife algorithm.

5 FPGA Implementation of Improved Rife Algorithm The improved rife algorithm is implemented in Intel EP3SL200F1152I4 FPGA. The selected window function is Hamming window with a length of 64 points, which is stored in ROM after quantization. Use counter to generate ROM address and the corresponding window data in the ROM is read out sequentially on the rising edge of each clock. In QuartusII, the multiplier is invoked to complete the multiplication of input signal and window function. The results are sent to the FFT IP core, the number of FFT points is 64, the output results are converted into amplitude, then change to power use look up table, and the subscript index at the power peak is taken out k0 , and set the index value to k0 − 1 and k0 + 1, use the subtractor to make a difference to obtain the difference of power. Using the power difference as the address, look up the table in FPGA to calculate the frequency offset in an interval, and use the power peak subscript index k0 And slope α The frequency of the input signal can be calculated. In addition to the main modules, it also includes the control of FFT module timing sequence and delay control. The quantized bits of the selected window function are 11 bits, and the input mode of FFT module is streaming mode.

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5.1 Comparison Between Measured Data and Simulation Results The signal source uses E4438C, and the noise source NC6110A is used to add noise of corresponding power to the input signal. The ADC sampling rate is 240 MHz.The input signal frequency is 300 MHz, the input signal-to-noise ratio SNR is 0 dB, 3 dB, 6 dB and 9 dB respectively, and the pulse width is 2 μs, the frequency of 64 sampling points at the pulse front is estimated. The 4K depth output results are sampled by signaltapII, and the mean square error is calculated by MATLAB software. The test results are shown in Table 1. It can be seen from Table 1 that the measured mean square error is slightly larger than the theoretical mean square error. The reason is that the quantization error is introduced by quantifying the frequency. At the same time, some errors will be introduced by unsatisfactory ADC sampling, which will have a certain impact on the experimental results. Table 1. Measured results SNR/dB

Theoretical mean square error/MHz

Measured mean square error/MHz

0

0.4391

0.4953

3

0.3466

0.3891

6

0.2314

0.2760

9

0.1562

0.1983

5.2 FPGA Resource Consumption The project is established in QuartusII. The FPGA chip EP3SL200F1152I4 is selected. After synthesis and implement, the total resource consumption is 4%. The resource consumption of each part is shown in Table 2 below: Table 2. Resource consumption of improved rife algorithm Device for compilation

EP3SL200F1152I4

Combinational ALUTs

3106/15912 (2%)

Memory ALUTs

145/79560 (