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International Conference of Electrical, Electronic and Networked Energy Systems, EENES 2024

作者： Liang, Ruofei Su, Jingjing College of Electrical Engineering and Automation Fuzhou University Fuzhou350108 China Zhejiang Institute of Mechanical and Electrical Engineering Co. Ltd. Hangzhou310051 China School of Computer and Big Data Minjiang University Fuzhou350108 China

ISBN: (纸本)9789819618514

Traditional method is difficult to be efficiently applied to series arc fault detection of complex electricity environment because of the complex home load working mechanism and its variety. For such a problem, an effective method based on multidimensional feature fusion and principal component analysis in this paper. Firstly, current waveforms of various loads are analyzed from the perspectives of time domain, frequency domain and time- frequency domain to extract arc fault features. Then, the root mean square of current time domain, harmonic amplitude of frequency domain, frequency domain energy of 2k–5 kHz and decomposition energy entropy of wavelet packet 3 layer are selected as the multidimensional characteristics for arc fault. And principal component analysis is used for dimensionality reduction for multidimensional features. Finally, the multidimensional features are input to the neural network to establish arc fault diagnosis model to realize the fault detection. The results of the experiments demonstrate that the proposed method realizes the multi-dimensional fusion and arc fault diagnosis model can effectively identify faults and realize load identification, with an accuracy of 99%. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Wavelet decomposition

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Dynamic Event Triggering Control for Matching Uncertain Systems 11

Dynamic Event Triggering Control for Matching Uncertain Syst...

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11th International Forum on Electrical engineering and automation, IFEEA 2024

作者： Hong, Yishen Xue, Shan Xu, Bo Liu, Derong Wang, Yonghua School of Automation Guangdong University of Technology Guangzhou510006 China School of Information and Communication Engineering Hainan University Haikou570228 China School of Automation and Intelligent Manufacturing Southern University of Science and Technology Shenzhen518055 China University of Illinois Chicago Department of Electrical and Computer Engineering ChicagoIL60607 United States

ISBN: (纸本)9798331516611

In this paper, a new dynamic event-triggering (DET) adaptive dynamic programming (ADP) is developed to deal with the robust control problem of an uncertain system with the matching condition. First, this paper transforms the robust control problem of a nonlinear robust system into the optimal control problem of a nominal system by designing a suitable cost function. In order to improve resource utilization, the DET scheme has been developed. A single critic network approximates the unknown optimal cost function. Moreover, it is proven that the neural networks (NNs) weight estimation error and network weight are bounded. Finally, an experiment demonstrates that the DET algorithm has the best resource-saving effect while guaranteeing learning performance. © 2024 IEEE.

关键词： Cost functions

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An Efficient Privacy-Preserving Asynchronous Federated Approach for Intelligent Decision Making in Equipment Maintenance 7

An Efficient Privacy-Preserving Asynchronous Federated Appro...

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7th IEEE International Conference on Big Data and Artificial Intelligence, BDAI 2024

作者： Dou, Kangkang He, Fan Shang, Fanshu Li, Xingyi Dong, Yuhao Liu, Jiayu Jiangsu Automation Research Institute Nanjing University of Aeronautics and Astronautics Department of Information Technology Research Lianyungang China Harbin Engineering University College of Computer Science and Technology Harbin China

ISBN: (纸本)9798350352009

Federated learning enables collaborative training of a high-quality global model across multiple participants without sharing raw local data. Its advantages, such as decentralization, data isolation, and high computational performance, have made it a popular research direction in various fields. However, federated learning faces privacy risks during model parameter transmission. Additionally, reducing communication overhead and computational resource consumption while ensuring data privacy protection remains a critical challenge. To address these issues, this paper proposes an efficient privacy-preserving asynchronous federated learning method tailored for equipment maintenance scenarios. Leveraging asynchronous federated learning techniques, this method enables efficient collaborative training among multiple participants. It also employs homomorphic encryption to protect local data information. By utilizing time-series models, we construct a predictive model for equipment spare parts consumption, providing valuable insights for intelligent maintenance decision-making. Experimental results demonstrate that our proposed method has faster convergence. More importantly, the root mean square error decreases by at least 0.004, the mean square error decreases by at least 0.024, and the relative error decreases by at least 0.008 compared to the baseline. © 2024 IEEE.

关键词： Federated learning

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Joint Class-Balanced Client Selection and Bandwidth Allocation for Cost-Efficient Federated Learning in Mobile Edge Computing Networks

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IEEE Transactions on Mobile Computing 2025年第7期24卷 5681-5698页

作者： Tang, Jian Li, Xiuhua Li, Hui Li, Penghua Wang, Xiaofei Leung, Victor C. M. Chongqing University School of Big Data & Software Engineering Chongqing400000 China Chongqing University of Posts and Telecommunications School of Automation Chongqing400065 China Tianjin University College of Intelligence and Computing Tianjin300072 China Shenzhen MSU-BIT University Artificial Intelligence Research Institute Shenzhen518172 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T1Z4 Canada

Federated Learning (FL) has significant potential to protect data privacy and mitigate network burden in mobile edge computing (MEC) networks. However, due to the system and data heterogeneity of mobile clients (MCs), client selection and bandwidth allocation is key for achieving cost-efficient FL in MEC networks with limited bandwidth. To address these challenges, we investigate the issue of joint client selection and bandwidth allocation for reducing the cost (i.e., latency and energy consumption) of FL training. We formulate the problem and decompose it into a holistic subproblem to reduce the number of rounds and a partial subproblem to reduce the costs of FL each round. We propose a joint class-balanced client selection and bandwidth allocation (CBCSBA) framework to address the whole problem. Specifically, for the holistic subproblem, CBCSBA combines MCs into groups, each having data distribution as close as possible to class-balanced distribution;For the partial subproblem, CBCSBA reduces costs by exploratively selecting a group and sequentially optimizing the latency and energy consumption of MCs within the group. Experimental results show that CBCSBA outperforms the baseline frameworks in reducing latency by 28.2% and energy consumption by 25.3% on average in the considered four datasets. © 2002-2012 IEEE.

关键词： Federated learning

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Modeling the Processes of Distribution of Disinformation in the Internet Environment

Modeling the Processes of Distribution of Disinformation in ...

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International Conference on Information Technologies in engineering Education (Inforino)

作者： Alexandr Maximov Anatoliy Shvetcov Department of Automation and Computer Engineering Vologda State University Vologda Russia

ISBN: (数字)9798350371369

ISBN: (纸本)9798350371376

The article addresses the issue of the spread of misinformation in the online environment. It proposes the use of agent-based modeling to study and analyze this process. A model of an information agent for the process of spreading misinformation has been developed, represented by a graph of states and transitions of a probabilistic finite automaton. The structure is determined and the relationships between the transition probabilities of this automaton are established. An agent-based simulation model of the processes of dissemination of misinformation in the Internet environment has been developed using the NetLogo 6.3 multi-agent modeling system. The results of simulation experiments and assessment of the adequacy of the software model are presented. Options for using abstract and simulation models in the educational process of the direction “Information Systems and Technologies” are proposed.

关键词： Network topology Navigation Automata Probabilistic logic Mathematical models Software Internet

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Dimensionality Reduction for Classification Using Divide-and-Conquer Based Genetic Programming 13

Dimensionality Reduction for Classification Using Divide-and...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Wang, Peng Xue, Bing Liang, Jing Zhang, Mengjie School of Electrical and Information Engineering Zhengzhou University Zhengzhou China Center for Data Science and Artificial Intelligence School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand School of Electrical Engineering and Automation Henan Institute of Technology Xinxiang China

ISBN: (纸本)9798350308365

Dimensionality reduction (DR) is to obtain meaningful low-dimensional representation concealed within high-dimensional data. Genetic programming (GP) has been used to achieve DR for classification because of the flexible representations and promising performance. However, one drawback of many current GP-based DR methods is the high computational demands, potentially limiting their utility in high-dimensional scenarios. To tackle this issue, this work develops a new GP algorithm to achieve DR for classification. A divide-and-conquer strategy based on feature clustering is proposed to split the original feature set into multiple small feature groups. Each tree in the multi-tree GP is used to learn one high-level feature from each feature group. The performance of the proposed method is examined on 10 classification datasets with varying difficulty. The results indicate that the new GP method achieves significantly better classification performance than commonly used feature construction and feature selection methods. More importantly, the proposed GP method uses fewer nodes but achieves better or comparable classification accuracy than the baseline multi-tree GP method. © 2024 IEEE.

关键词： Dimensionality reduction

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EFFICIENT MULTI-AGENT REINFORCEMENT LEARNING BY PLANNING 12

EFFICIENT MULTI-AGENT REINFORCEMENT LEARNING BY PLANNING

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12th International Conference on Learning Representations, ICLR 2024

作者： Liu, Qihan Ye, Jianing Ma, Xiaoteng Yang, Jun Liang, Bin Zhang, Chongjie Department of Automation Tsinghua University China Institute for Interdisciplinary Information Sciences Tsinghua University China Department of Computer Science & Engineering Washington University St. Louis United States

Multi-agent reinforcement learning (MARL) algorithms have accomplished remarkable breakthroughs in solving large-scale decision-making tasks. Nonetheless, most existing MARL algorithms are model-free, limiting sample efficiency and hindering their applicability in more challenging scenarios. In contrast, model-based reinforcement learning (MBRL), particularly algorithms integrating planning, such as MuZero, has demonstrated superhuman performance with limited data in many tasks. Hence, we aim to boost the sample efficiency of MARL by adopting model-based approaches. However, incorporating planning and search methods into multi-agent systems poses significant challenges. The expansive action space of multi-agent systems often necessitates leveraging the nearly-independent property of agents to accelerate learning. To tackle this issue, we propose the MAZero algorithm, which combines a centralized model with Monte Carlo Tree Search (MCTS) for policy search. We design a novel network structure to facilitate distributed execution and parameter sharing. To enhance search efficiency in deterministic environments with sizable action spaces, we introduce two novel techniques: Optimistic Search Lambda (OS(λ)) and Advantage-Weighted Policy Optimization (AWPO). Extensive experiments on the SMAC benchmark demonstrate that MAZero outperforms model-free approaches in terms of sample efficiency and provides comparable or better performance than existing model-based methods in terms of both sample and computational efficiency. Our code is available at https://***/liuqh16/MAZero. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Multi agent systems

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Terahertz image denoising via multiscale hybrid-convolution residual network

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CAAI Transactions on Intelligence Technology 2025年第1期10卷 235-252页

作者： Heng Wu Zijie Guo Chunhua He Shaojuan Luo Bofang Song Guangdong Provincial Key Laboratory of Cyber-Physical System School of AutomationGuangdong University of TechnologyGuangzhouChina School of Computer Guangdong University of TechnologyGuangzhouChina School of Chemical Engineering and Light Industry Guangdong University of TechnologyGuangzhouChina Wyant College of Optical Sciences The University of ArizonaTucsonArizonaUSA

Terahertz imaging technology has great potential applications in areas,such as remote sensing,navigation,security checks,and so ***,terahertz images usually have the problems of heavy noises and low *** terahertz image denoising methods are mainly based on traditional image processing methods,which have limited denoising effects on the terahertz *** deep learning-based image denoising methods are mostly used in natural images and easily cause a large amount of detail loss when denoising terahertz ***,a residual-learning-based multiscale hybridconvolution residual network(MHRNet)is proposed for terahertz image denoising,which can remove noises while preserving detail features in terahertz ***,a multiscale hybrid-convolution residual block(MHRB)is designed to extract rich detail features and local prediction residual noise from terahertz ***,MHRB is a residual structure composed of a multiscale dilated convolution block,a bottleneck layer,and a multiscale convolution *** uses the MHRB and global residual learning to achieve terahertz image *** studies are performed to validate the effectiveness of MHRB.A series of experiments are conducted on the public terahertz image *** experimental results demonstrate that MHRNet has an excellent denoising effect on synthetic and real noisy terahertz *** with existing methods,MHRNet achieves comprehensive competitive results.

关键词： image processing multiscale hybrid-convolution residual learning terahertz image denoising

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Modeling and Analysis for the Propagation of DDoS Attacks in Microgrid System

Modeling and Analysis for the Propagation of DDoS Attacks in...

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2023 IEEE International Conference on Dependable, Autonomic and Secure Computing, 2023 International Conference on Pervasive Intelligence and Computing, 2023 International Conference on Cloud and Big Data Computing, 2023 International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Xiong, Lei Xu, Qichao Fang, Dongfeng Zeng, Hui School of Mechatronic Engineering and Automation Shanghai University Shanghai China California Polytechnic State University Department of Computer Science and Software Engineering San Luis ObispoCA United States

ISBN: (纸本)9798350304602

With the development and progress of electric power technology and information technology, microgrid has become an important and indispensable part of smart grid. The cyber-security of microgrids has a significant impact on the stability and security of smart grids. Distributed denial-of-service (DDoS) attack as one of the common types of cyberattacks in microgrids has formed a considerable threat to the network security of microgrids. In a DDoS event, attackers first use malware to infect few nodes in the microgrid and the infected nodes can generate large-scale malware propagation in the system. These infection lays the foundation for the flood attacks. Therefore, this paper establishes a model to analyse the DDoS attack propagation in microgrid. Firstly, this paper establishs a dynamic model based on the epidemiological theory to describe the propagation dynamics of DDoS attack in microgrid. Then we go on to compute the attack threshold $(R_{0})$ of the model and prove the stability of equilibrium point. Finally we carry out the experimental simulation based on the model, and the experimental results show that the proposed model can reflect the propagation of DDoS attack in microgrid effectively. © 2023 IEEE.

关键词： Denial-of-service attack

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Transforming the Future: An Innovative LiFi Framework to Elevate Sustainability and Security in UAE Smart Cities 10

Transforming the Future: An Innovative LiFi Framework to Ele...

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10th International Conference on engineering and Emerging Technologies, ICEET 2024

作者： Ati, Modafar Jamil, Yaqoob Qadir, Brzo Aziz Abu Dhabi University Department of Computer Science & Information Technology Abu Dhabi United Arab Emirates Erbil Polytechnic University Department of Automation Industrial Technology Engineering Erbil Iraq

ISBN: (纸本)9798331532895

The rapid advancement of smart cities calls for the implementation of cutting-edge communication solutions that are capable of effectively addressing the myriad challenges associated with sustainability and security. This comprehensive research presents the innovative concept of integrating Light-Fidelity (LiFi) technology as a pivotal element for significantly enhancing smart city infrastructures, with a strong emphasis on its application in the United Arab Emirates. By harnessing the exceptional capabilities of light-emitting diodes (LEDs), LiFi offers a significantly low-energy, high-speed alternative to traditional Wi- Fi systems by utilizing visible light as the medium for the transmission of data. This in-depth investigation delves into the various ways through which LiFi enhances security measures by restricting communication to designated areas that are illuminated while simultaneously minimizing overall energy consumption, thus strongly supporting the sustainable development goals aimed at fostering smart cities. The thorough framework assesses the practicality, economic viability, and comprehensive security measures associated with the potential implementation of LiFi in diverse urban settings. Additionally, it proposes a sustainable communication method that effectively combats escalating energy demands, strategically targets areas lacking adequate Wi-Fi coverage, and addresses the prevalent security concerns that are common in densely populated city environments. The detailed results emphasize LiFi's transformative potential to revolutionize urban communication networks, aligning seamlessly with the ambitious aspirations of the UAE towards innovative and sustainable smart city initiatives. © 2024 IEEE.

关键词： Sustainable city

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