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5th International Conference on Information Technology and computer Communications, ITCC 2023

作者： Li, Ying Bing, Zhigang Zhao, Di Zhu, Yingzhuang Lai, Zhanhang School of Automation and Electrical Engineering Tianjin University of Technology and Education Tianjin Key Laboratory of Information Sensing & Intelligent Control Tianjin GongJiang Double Carbon Research Institute Tianjin China Engineering Teaching Practice Training Center Tiangong University Tianjin GongJiang Double Carbon Research Institute Tianjin300387 China

ISBN: (纸本)9798400700583

The manufacturing industry is gradually reforming in the direction of intelligent digitalization. The traditional automation control technology and virtual simulation technology cannot intuitively reflect the operation working state of the actual equipment in the intelligent manufacturing production line in order to solve the problems of low intelligence level, lack of 3D visualization function, and poor sense of interactive experience in the traditional manufacturing system. In this paper, we design a digital twin intelligent control training system based on digital twin technology and determine the overall framework of the system and the corresponding modules and functions. The static virtual model of the twin system is constructed using 3dMAX software, the UI visualization of the dynamic interactive twin system is built using the unity development platform, and the OPC UA based industrial IoT real-time data acquisition method is proposed. The digital twin module establishes the data acquisition framework to connect the actual production line with the virtual production line to realize real-time data signal acquisition and control and complete the digital twin and data fusion. The experimental results show that the system can realize intelligent control of production line visualization production and intuitively reflect the operation status of the equipment. The system is used to teach practical training courses in colleges and universities. Students can obtain the real-time operation status of production plants in the physical world through the models, movements, data, and other information displayed by the digital twin, which increases students' practical operation ability and improves the teaching effect, and lays the foundation for further expanding the application fields of digital twin technology. © 2023 ACM.

关键词： Data acquisition

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Joint Optimization of Compression, Transmission and Computation for Cooperative Perception Aided Intelligent Vehicular Networks

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8201-8214页

作者： Lu, Binbin Huang, Xumin Wu, Yuan Qian, Liping Zhou, Sheng Niyato, Dusit University of Macau State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science 999078 China Guangdong University of Technology School of Automation Guangzhou510006 China University of Macau State Key Lab of Internet of Things for Smart City 999078 China University of Macau State Key Laboratory of Internet of Things for Smart City Department of Computer Information Science 999078 China Zhuhai UM Science and Technology AQ2 Research Institute Zhuhai519301 China Zhejiang University of Technology Institute of Cyberspace Security Hangzhou310023 China Tsinghua University Beijing National Research Center for Information Science and Technology Department of Electronic Engineering Beijing100084 China Nanyang Technological University School of Computer Science and Engineering 639815 Singapore

Cooperative perception is a promising paradigm to tackle the perception limitations of a single intelligent vehicle (IV) to enhance the driving safety and efficiency in intelligent vehicular networks. However, the real-time transmission and computation-intensive fusion of raw sensing data raise new challenges for satisfying the stringent delay requirement of delay-sensitive applications. In this paper, we formulate a joint optimization problem of the cooperative IV selection, compression ratio selection, transmit power control, task offloading decision and computation allocation to minimize the end-to-end delay consisting of compression, transmission and computation. In particular, to meet the perception probability requirement, the quality and quantity-aware matching algorithm is proposed to optimize the cooperative IV selection. To guarantee the queue stability of offloading tasks, we develop the Lyapunov optimization algorithm to determine the upper bound of the offloading tasks and the corresponding optimal computation allocation. The Lyapunov aided deep reinforcement learning algorithm is further proposed to dynamically adjust the task offloading decision and compression ratio selection to minimize the end-to-end delay with the transmit power control being optimized by the successive convex approximation algorithm. Simulation results demonstrate that, compared to several benchmark algorithms, our proposed algorithm achieves the lowest end-to-end delay while guaranteeing the requirements on perception probability and queue stability effectively. © 1967-2012 IEEE.

关键词： Deep reinforcement learning

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Human Following Robot Based on Object Detection for Aerospace Application 1

Human Following Robot Based on Object Detection for Aerospac...

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1st International Conference of Adisutjipto on Aerospace Electrical engineering and Informatics, ICAAEEI 2024

作者： Sumari, Arwin Datumava Wahvudi Aryandika, Ahnaf Bagus Purnami, Ndaru Atmi Dermawan, Denny Syamsiana, Ika Noer State Polytechnic of Malang Cognitive Artificial Intelligence Research Group Department of Electrical Engineering Malang Indonesia Adisutjipto Institute of Aerospace Technology Electrical Engineering Study Program Yogyakarta Indonesia Adisutjipto Institute of Aerospace Technology Aerospace Navigation Instrumentation Control Automation and Robotics System Research Group Electrical Engineering Study Program Yogyakarta Indonesia Adisutjipto Institute of Aerospace Technology Electrical Engineering Study Program Aerosp. Digit. Syst. Microprocessor Microcontroller and Programmable Logic Devices Research Group Yogyakarta Indonesia

ISBN: (纸本)9798331507930

In this research, the Human Following Robot (HFR) prototype has been designed and implemented using YOLO v3-Tiny and Tensor Flow Lite on Raspberry Pi hardware named Rewang. The HFR Rewang is designed to assist aircraft technicians by bringing maintenance tool kits to aircraft shelters. Rewang's movement is based on a camera as a vision sensor following the movement of human entities. Image input is processed by YOLO v3-Tiny through the determination of the bounding box, the calculation of Euclidean Distance, and the determination of the pixel value of the camera as the basis of Rewang's movement which is regulated by an L298N driver motor that controls two Direct Current (DC) motors for the left and right wheels. The test results show that Rewang can move at an average speed of 0.18 m/s at 2 m with a travel time of 11 seconds with a light intensity of more than 250 lux. The minimum and maximum distance of object detection is as far as 120 cm and 400 cm, with an object detection accuracy of 95.8%. Rewang allows the HFR to have a larger carrying capacity to bring more complete aircraft maintenance tools and is prospective for other aerospace and general applications. © 2024 IEEE.

关键词： Travel time

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Enabling Few-Shot Learning with PID control: A Layer Adaptive Optimizer 41

Enabling Few-Shot Learning with PID Control: A Layer Adaptiv...

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41st International Conference on Machine Learning, ICML 2024

作者： Yu, Le Li, Xinde Zhang, Pengfei Zhang, Zhentong Dunkin, Fir School of Automation Southeast University Nanjing China Nanjing Center for Applied Mathematics Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China Southeast University Shenzhen Research Institute Shenzhen China

Model-Agnostic Meta-Learning (MAML) and its variants have shown remarkable performance in scenarios characterized by a scarcity of labeled data during the training phase of machine learning models. Despite these successes, MAML-based approaches encounter significant challenges when there is a substantial discrepancy in the distribution of training and testing tasks, resulting in inefficient learning and limited generalization across domains. Inspired by classical proportional-integral-derivative (PID) control theory, this study introduces a Layer-Adaptive PID (LA-PID) Optimizer, a MAML-based optimizer that employs efficient parameter optimization methods to dynamically adjust task-specific PID control gains at each layer of the network, conducting a first-principles analysis of optimal convergence conditions. A series of experiments conducted on four standard benchmark datasets demonstrate the efficacy of the LA-PID optimizer, indicating that LA-PID achieves state-of-the-art performance in few-shot classification and cross-domain tasks, accomplishing these objectives with fewer training steps. Code is available on https://***/yuguopin/LA-PID. Copyright 2024 by the author(s)

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Health level verification for electric control valve based on BiLSTM with multi-head attention mechanism

Health level verification for electric control valve based o...

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2024 International Conference on Cloud Computing, Performance Computing, and Deep Learning, CCPCDL 2024

作者： Duan, Wenqiang Zhang, Jingyu Yang, Bing Wei, Baohong Li, Ying Tianjin Key Laboratory of Information Sensing and Intelligent Control Tianjin University of Technology and Education Tianjin China Tianjin Engineering Machinery Research Institute Co. Ltd. Tianjin China Automation Technology Co. Ltd. Tianjin China

ISBN: (纸本)9781510683044

The failure of electric valves represents a significant safety hazard in industrial systems. Traditional manual detection and regular replacement strategies are insufficient to address this issue. This study proposes an online verification method combining convolutional neural networks (CNN), two-way long short-term memory networks (BiLSTM) and multi-head attention mechanism (MHA), which is capable of identifying weak fault characteristics such as plugging and twitching caused by regulator wear. Transfer learning addresses the issue of data scarcity and enhances the model's adaptability in the target domain. The CNN-BiLSTM-MHA-TL model exhibited high prediction accuracy, with MAE, RMSE, and MAPE values of 0.4451, 0.5722, and 0.0132, respectively. This online verification method offers notable advantages and promising prospects for application in the field of valve health monitoring. © 2024 SPIE.

关键词： Convolutional neural networks

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Gradient Projection Differential Neural Solution for Quadratic Optimization with Quadratic Constraints: An ACP Perspective

Gradient Projection Differential Neural Solution for Quadrat...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Liufu, Ying Liu, Mei Jin, Long Wang, Feiyue School of Information Science and Engineering Lanzhou University Lanzhou China College of Computer Qinghai Normal University Xining China Institute of Automation The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350337020

In recent years, quadratic optimizations have become increasingly popular in engineering. However, conventional methods that investigate this problem from the perspective of a canonical form with linear constraints are not effective in dealing with the significant challenges posed by quadratic constraints in practice. This paper proposes a solution framework for the quadratic optimization with quadratic constraints (QOQC) based on innovative artificial societies, computational experiments, and parallel execution (ACP) framework. Then, a gradient projection differential neural solution (GPDNS) is proposed to address this. To illustrate the effectiveness of the GPDNS model in solving the QOQC system, numerical simulations are provided. Overall, this paper presents the potential of innovative approaches like the ACP framework to enhance our capabilities in addressing challenging optimization systems. © 2023 IEEE.

关键词： ACP constraints differential neural solution numerical simulation Quadratic optimization with quadratic

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Preventive Maintenance Strategy of Environmental Test Chamber Based on Particle Swarm Optimization Algorithm

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International Journal of Circuits, Systems and Signal Processing 2022年 16卷 618-627页

作者： Zhou, Xianwen Gu, Chaoyang Sun, Yuyu Han, Chengjing Gu, Wei Niu, Wangqiang Key Laboratory of Transportation Technology and Control Engineering Shanghai Maritime University Shanghai201306 China Jiangsu Automation Research Institute Lianyungang222021 China Shanghai Sizhong Experimental Equipment Co. Ltd Shanghai201615 China

—With the development of various physical industries, people pay more attention to reliability tests and test equipment. To solve the problem of making maintenance strategy of an environmental test chamber for reliability test, a periodic preventive maintenance strategy based on RCM(Reliability Centre Maintenance) is proposed. Firstly, a multi-objective optimization model of reliability and maintenance cost is established by combining reliability theory and life distribution theory, and two objectives of equipment reliability and maintenance cost are considered. Secondly, the actual environmental test chamber fault maintenance data is analyzed, and it is found the fault distribution meets the dual parameter Weibull. Finally, the particle swarm optimization algorithm is used to solve the multi-objective model optimization, and a series of Pareto optimal solutions are obtained, that is, the number of maintenance times and the corresponding time interval in the operation cycle of the environmental test chamber, and these solutions might be good references for maintenance management personnel. © 2022, North Atlantic University Union NAUN. All rights reserved.

关键词： Weibull distribution

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Fault diagnosis of Electro-hydrostatic actuators based on CWT and CNN-Transformer 3

Fault diagnosis of Electro-hydrostatic actuators based on CW...

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3rd International Conference on Intelligent Mechanical and Human-computer Interaction Technology, IHCIT 2024

作者： Li, Jiatong Tu, Chaofan Wang, Xingjian Wang, Zhaoyang Xiu, Wanbo School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Laboratory of Science and Technology on Marine Navigation and Control China State Shipbuilding Corporation Tianjin300131 China China Tianjin Navigation Instrument Research Institute Tianjin300131 China

ISBN: (纸本)9781510683105

Electro-hydraulic actuator (EHA) system is a key component of the ship's electro-hydraulic control system, but the internal failure mechanism of EHA is extremely complex, and it is limited by insufficient detection methods in practical systems, making fault diagnosis of EHA a great challenge. To address this issue, this paper proposes a fault diagnosis method based on Continuous Wavelet Transform (CWT), Convolutional Neural Network (CNN) and Transformer. Firstly, the collected one-dimensional time series signal of the sensor is converted into a two-dimensional time-frequency map through CWT. Then, a hybrid module of CNN-Transformer is used to extract local and global fault features, and finally the classification results are output through softmax. Conduct EHA fault simulation experiments to validate the proposed method. The research results indicate that the proposed method has a fault diagnosis accuracy of 96.33%, achieving high-precision diagnosis of EHA faults. © 2024 SPIE.

关键词： Wavelet transforms

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Physical Layer Authentication Using Ensemble Learning Technique in Wireless Communications

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computers, Materials & Continua 2022年第12期73卷 4489-4499页

作者： Muhammad Waqas Shehr Bano Fatima Hassan Shanshan Tu Ghulam Abbas Ziaul Haq Abbas Engineering Research Center of Intelligent Perception and Autonomous Control Faculty of Information TechnologyBeijing University of TechnologyBeijing100124China Faculty of Computer Science and Engineering GIK Institute of Engineering Sciences and TechnologyTopi23460Pakistan School of Engineering Edith Cowan UniversityJoondalup Perth6027WA Australia Faculty of Electrical Engineering GIK Institute of Engineering Sciences and TechnologyTopi23460Pakistan

Cyber-physical wireless systems have surfaced as an important data communication and networking research *** is an emerging discipline that allows effective monitoring and efficient real-time communication between the cyber and physical worlds by embedding computer software and integrating communication and networking *** to their high reliability,sensitivity and connectivity,their security requirements are more comparable to the Internet as they are prone to various security threats such as eavesdropping,spoofing,botnets,man-in-the-middle attack,denial of service(DoS)and distributed denial of service(DDoS)and *** methods use physical layer authentication(PLA),themost promising solution to detect ***,the cyber-physical systems(CPS)have relatively large computational requirements and require more communication resources,thus making it impossible to achieve a low latency *** methods perform well but only in stationary *** have extracted the relevant features from the channel matrices using discrete wavelet transformation to improve the computational time required for data processing by considering mobile *** features are fed to ensemble learning algorithms,such as AdaBoost,LogitBoost and Gentle Boost,to classify *** authentication of the received signal is considered a binary classification *** transmitted data is labeled as legitimate information,and spoofing data is illegitimate ***,this paper proposes a threshold-free PLA approach that uses machine learning algorithms to protect critical data from spoofing *** detects the malicious data packets in stationary scenarios and detects them with high accuracy when receivers are *** proposed model achieves better performance than the existing approaches in terms of accuracy and computational time by decreasing the processing time.

关键词： Physical layer authentication machine learning cyber-physical systems security

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Overview of bioinformatic tools to study viral infections

Overview of bioinformatic tools to study viral infections

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Zulian, Verdiana Garbuglia, Anna Rosa Fiscon, Giulia Paci, Paola National Institute for Infectious Diseases Lazzaro Spallanzani Irccs Virology Laboratory Rome Italy Sapienza University of Rome Department of Computer Control and Management Engineering Italy National Research Council Institute for Systems Analysis and Computer Science 'Antonio Ruberti' Rome Italy

ISBN: (纸本)9798350337488

microRNAs play an important role in post-transcriptional gene regulation. Recently, viral microRNAs have been discovered in several viruses, including Hepatitis B virus. This brief work explores bioinformatics tools for viral/host miRNA research and provides insights into the roles of miRNAs in HBV infection, offering an overview of this field, in order to facilitate the selection of the most suitable bioinformatics tools according to individual needs and research goals. © 2023 IEEE.

关键词： bioinformatics tools Hepatitis B virus microRNA target gene

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