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5th Asia Symposium on Image Processing, ASIP 2023

作者： Jia, Yuyu Han, Chunxiao Tong, Wenhui Pei, Yue Tianjin University of Technology and Education School of Automation and Electrical Engineering Tianjin Key Laboratory of Information Sensing & Intelligent Control Tianjin China

ISBN: (纸本)9798350323429

Emotion recognition is an important research directions in the field of artificial intelligence. In this paper, we adopted deep learning methods to try to improve the performance of emotion recognition. First, we designed an experiment to induce three different emotions by video footage and collected corresponding EEG signals. Then, CNN-SVM algorithms were adopted to classify positive, neutral and negative emotions. Finally, the recognition performance of CNN-SVM for three-class emotion recognition was verified. It is found that CNN-SVM achieves a highest recognition accuracy of 99.98% and an average recognition accuracy of 98.53%, which is better than CNN alone. © 2023 IEEE.

关键词： Emotion Recognition

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A Hierarchical Formation-Containment control Scheme for Second-Order Multi-agent Systems 7th

A Hierarchical Formation-Containment Control Scheme for Sec...

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7th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Cao, Yue Wang, Xiangyu Chen, Yangyang School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China

ISBN: (纸本)9789819733231

In this paper, a hierarchical formation-containment control scheme is proposed for second-order multi-agent systems. The proposed hierarchical scheme decouples the control task into two layers, i.e., reference signal generator and agents’ tracking controllers. First, a reference signal generator is designed in Layer 1 for leader agents and follower agents, respectively, to produce the formation reference signals and containment reference signals, whose outputs "achieve" formation-containment asymptotically. Second, tracking controllers are designed in Layer 2 for each agent to track the reference signal. The proposed hierarchical formation-containment scheme realizes formation-containment asymptotically. The simulations are performed to validate the effectiveness and practicality of the scheme. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Multi agent systems

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Multi-objective Optimal Operation of Centralized Battery Swap Charging System with Photovoltaic

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Journal of Modern Power Systems and Clean Energy 2022年第1期10卷 149-162页

作者： Yuanzheng Li Yihan Cai Tianyang Zhao Yun Liu Jian Wang Lei Wu Yong Zhao School of Artificial Intelligence and Automation Ministry of Education Key Laboratory of Image Processing and Intelligence ControlHuazhong University of Science and TechnologyWuhanChina School of Electrical and Electronic Engineering Nanyang Technological UniversitySingaporeSingapore College of Mechatronics and Control Engineering Shenzhen UniversityShenzhenChina Department of Electrical&Computer Engineering Stevens Institute of TechnologyHobokenUSA

Electric vehicles(EVs)are widely deployed throughout the world,and photovoltaic(PV)charging stations have emerged for satisfying the charging demands of EV *** paper proposes a multi-objective optimal operation method for the centralized battery swap charging system(CBSCS),in order to enhance the economic efficiency while reducing its adverse effects on power *** proposed method involves a multi-objective optimization scheduling model,which minimizes the total operation cost and smoothes load fluctuations,***,we modify a recently proposed multi-objective optimization algorithm of non-sorting genetic algorithm III(NSGA-III)for solving this scheduling ***,simulation studies verify the effectiveness of the proposed multi-objective operation method.

关键词： Multi-objective optimization electric vehicle battery swap charging system scheduling photovoltaic

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An Improved YOLOv5 Algorithm for Elderly Fall Detection 12

An Improved YOLOv5 Algorithm for Elderly Fall Detection

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12th International Conference on Innovation, Knowledge, and Management, ICIKM 2023

作者： Wu, Han Zhao, Xueling Qiao, Jianghe Che, Yanqiu School of Automation and Electrical Engineering Tianjin University of Technology and Education Tianjin Key Laboratory of Information Sensing & Intelligent Control Tianjin China

ISBN: (纸本)9798350303315

Accidental falls are one of the major threats to the elderly population. Older adults who are not caught in time after a fall may miss the best time to be rescued. We propose an improved YOLOv5 fall detection algorithm in this paper. Firstly, we use SIoU Loss instead of CIoU Loss to improve the speed of training and the accuracy of inference. Secondly, the SimAM attention mechanism is introduced into the YOLOv5 model to further improve the model's performance. Experiments show that the mAP@0.5 of the improved YOLOv5 fall detection model reaches 96.7%, which is 2.4% higher than the original YOLOv5. The overall detection effect is better than the original YOLOv5 model and other network algorithms. The algorithm used in this paper can detect fall behavior promptly, which largely alleviates the risk of death or long-term treatment. © 2023 IEEE.

关键词： Fall detection

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An Unsupervised Leak Detection Method via Assembling Twin Attention-based Prediction Models for Natural Gas Gathering Pipelines

An Unsupervised Leak Detection Method via Assembling Twin At...

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2024 China automation Congress, CAC 2024

作者： Zhang, Hao Zuo, Zhonglin Zheng, Li Liang, Shan Lü, Qingguo College of Automation Chongqing University Chongqing China College of Control Science and Engineering Zhejiang University Hangzhou China College of Computer Science Chongqing University Chongqing China

ISBN: (纸本)9798350368604

Accurately detecting leaks in natural gas gathering pipelines cannot only help enterprises and governments timely cope with the safety management problems, but also maintain the reliable operation of pipelines. The data-driven approaches for leak detection have become a preferred solution with the widely installation of sensors in pipeline network. However, these methods face challenges in achieving high identification accuracy due to the lack of labeled leak data and insufficient representation learning. To overcome this difficulty, an unsupervised leak detection method that leverages twin attention-based prediction models is proposed for these gathering pipelines in this article. First, attention-based point and sequence prediction approaches are designed by joint utilization of attention mechanism (AM) and the cascade of one-dimensional convolutional network (1D-CNN) and long short-term memory (LSTM) network, where the sequence one also employs the operation of sequence repetition and flipping for better prediction. Based on the designed attention-based point and sequence prediction approaches, an unsupervised twin attention-based prediction structure is then introduced to jointly establish the normal pipeline models. Specifically, the point prediction model is mainly used to learn the short-term dependency patterns whereas the sequence prediction model for the long-term ones from the multivariate time series. Next, a fusion strategy is presented to fuse the prediction errors generated from the twin attention-based models for the computing of overall leak scores as well as exploiting their complementary detection sensibility. To obtain clear status of pipelines without affected by abnormal points resulting from device anomalies or noises, the minimum covariance determinant (MCD) approach is adopted to attain the reliable leak scores of the fused errors. The experimental results on datasets derived from real-world gathering pipelines validate the effectiveness of

关键词： Long short-term memory

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Master-slave game-based optimal operation of microgrids containing customer-owned photovoltaics 8

Master-slave game-based optimal operation of microgrids cont...

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8th International Conference on Energy System, Electricity, and Power, ESEP 2023

作者： Liu, Xinyue Ji, Yuehui Shi, Shaobo Liu, Junjie Gao, Qiang Xu, Zhao Tianjin Key Laboratory of Complex System Control Theory and Application TJ 300384 China School of Electrical Engineering and Automation Tianjin University of Technology TJ 300384 China

ISBN: (数字)9781510679801

ISBN: (纸本)9781510679795

Due to the dispersed and disorderly feathers of distributed photovoltaic (PV) power generation on the user side, the difficulties of the low-carbon transformation of the power grid and sustainable development of large-scale distributed PV power generation is increased. Based on a master-slave game, this article proposes an optimizing scheme for the operation of microgrids, where the users built PV and shared energy storages. Firstly, this paper introduces the operational framework of the system and the functions of micro-network operator (MNO) and load aggregator (LA). The optimization operation models for MNO and LA are modelled in the framework of Stackelberg master-slave game. The game relationship between MNO and LA is analyzed. Yalmip tool with the CPLEX solver are introduced to solve the differential evolution algorithm. Final, the effectiveness of the proposed method is verified in numerical examples. © 2024 SPIE.

关键词： Energy storage

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Pantograph Structure Anomaly Detection Based on Computer Vision 6

Pantograph Structure Anomaly Detection Based on Computer Vis...

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6th International Conference on Electronics Technology, ICET 2023

作者： Liu, Libo Liu, Quanli Wang, Wei Yu, Zichen Zhao, Xiaoguang Dalian University of Technology School of Control Science and Engineering Dalian China Dalian Seasky Automation Co. Ltd Dalian China

ISBN: (纸本)9798350337693

Pantograph is the only equipment for trains to obtain power. As the core component of the train power supply system, real-time and accurate detection of the pantograph structure state is of great significance to ensure the safety of train operation. Aiming at the shortcomings of current pantograph structure anomaly detection method, such as high cost, narrow application range and strong dependence on abnormal data, this paper designs a pantograph structure anomaly detection method based on computer vision technology, which has a wide range of application and does not rely on abnormal data. Firstly, the improved YOLOv5s object detection model is used to locate the pantograph area in the vehicle pantograph surveillance camera in real time. Then, the semantic segmentation method based on UNet is used to accurately segment the pantograph structure. Finally, the state of the pantograph structure is judged by calculating the similarity with the normal state of the pantograph structure. The experimental results show that the detection method can realize the detection of pantograph structural anomalies with high accuracy and speed. © 2023 IEEE.

关键词： Object detection

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Automatic Schizophrenia Detection from Scalp EEG Using CNN with Wide Convolutional Kernel and ELM 5

Automatic Schizophrenia Detection from Scalp EEG Using CNN w...

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5th Asia Symposium on Image Processing, ASIP 2023

作者： Wei, Chun Qin, Yingmei Liu, Jing Jiang, Lijie Che, Yanqiu Tianjin University of Technology and Education School of Automation and Electrical Engineering Tianjin Key Laboratory of Information Sensing & Intelligent Control Tianjin China

ISBN: (纸本)9798350323429

Detection of schizophrenia is of great significance in clinical practice. In conventional machine learning methods, manual feature extraction is required, which is a hard and time-consuming task. This paper proposes a hybrid model consisting of a convolutional neural network (CNN) with a wide convolution kernel and an extreme learning machine (ELM). By changing the size of the convolution kernel to form a suitable wide convolution kernel, and automatically extracting and optimizing the features of the scalp electroencephalogram (EEG), the performance of the model is effectively improved and accurate detection of schizophrenia is achieved. The input of this model is the original multi-channel EEG picture after short-time Fourier transform, and the output are two types of classification labels for schizophrenia and healthy people. In the end, the detection accuracy of schizophrenia reached 95.59%. This model has a powerful advantage in detecting schizophrenia. © 2023 IEEE.

关键词： Electroencephalography

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CAPACITY ALLOCATION METHOD OF BATTERY ENERGY STORAGE SYSTEM CONSIDERING MULTI-SERVICE TARGET LEVEL ASSESSMENT

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International Journal of Electrical engineering 2022年第5期29卷 141-153页

作者： Ma, Suliang Wu, Yiwen Jiang, Yuan Li, Yaxin Li, Jianlin School of Electrical and Control Engineering North China University of Technology Shijingshan District Beijing China Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Haidian District Beijing China

Rational planning of battery energy storage system is the key technology to solve the problem of high proportion of new energy consumption and the requirements of high performance power supply. Starting from the multiple application dimensions of power supply, power grid, load and battery energy storage system, this paper proposes a method of battery energy storage system capacity allocation based on multi-objective comprehensive evaluation. Firstly, the capacity configuration model of battery energy storage system is established from two aspects of service objectives and operation constraints;Then, under the two-level optimization framework, the capacity allocation scheme of the battery energy storage system is formed by using particle swarm optimization and multi- attribute decision-making methods;Finally, the configuration results of IEEE33 nodes with high proportion of distributed new energy access are analyzed, and the influence of model parameters and target orientation on the capacity of battery energy storage system is discussed. The simulation results show that the proposed method can flexibly and effectively configure the battery energy storage system, complete multiple different target service requirements, and promote the scientific application of the battery energy storage system. © 2022, Chinese Institute of Electrical engineering. All rights reserved.

关键词： Battery storage

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Sneak Circuit Analysis Covering Interface Circuits in Spacecraft Integrated Chips while Considering Faults 6

Sneak Circuit Analysis Covering Interface Circuits in Spacec...

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6th International Conference on System Reliability and Safety engineering, SRSE 2024

作者： Liu, Weiming Chen, Cen Xiao, Kaiwen Feng, Mingtao Zhang, Haonan Zhai, Guofu Harbin Institute of Technology Department of Electrical Engineering & Automation Harbin China Beijing Aerospace Automatic Control Institute Beijing China Harbin Institute of Technology Harbin China

ISBN: (纸本)9798350356083

Sneak circuit analysis is a crucial reliability design process. In the design of spacecraft electronic systems, it is challenging to identify sneak circuits related to integrated chip interface circuits, and there are few targeted researches. In extreme working conditions, the coupling sneak circuits caused by the failure of integrated chips are difficult to identify, affecting the design optimization. The paper proposes a typical interface information specification model for integrated chips to simulate the electrical information characteristics of interfaces. And the typical failure models of intrinsic components inside the chip are determined through multi-physics field analysis. Then, based on the digital model, a sneak circuit diagnosis process based on excitation and fault injection are proposed, and a sneak circuit screening method based on Python virtual prototyping is implemented in the case study. Finally, the design constraint clues for the chip's external circuit are obtained, which provides a reference for related fault diagnosis and reliability optimization works. © 2024 IEEE.

关键词： Integrated circuit design

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