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International Joint Conference on Energy, Electrical and Power Engineering, CoEEPE 2021

作者： Ma, Youjie Jiang, Xinyu Zhou, Xuesong Tianjin Key Laboratory of Control Theory and Application for Complex Systems School of Electrical Engineering and Automation Tianjin University of Technology Tianjin300384 China

ISBN: (纸本)9789811919213

In order to improve the compensation accuracy and speed of the static synchronous compensator (DSTATCOM) for the unbalanced load, a novel linear active disturbance rejection controller is proposed in this paper. The controller is applied to the current tracking control of DSTATCOM, and its mathematical model is derived. The performance of the new linear active disturbance rejection controller is analyzed by frequency domain analysis to prove its correctness. Finally, the operation performance of DSTATCOM under the action of the new controller is verified by MATLAB/Simulink simulation platform, and compared with the traditional ADRC controller. The results show that the capacitive split DSTATCOM controller has better compensation performance and is more practical. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： controllers

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STGSA:A Novel Spatial-Temporal Graph Synchronous Aggregation Model for Traffic Prediction

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IEEE/CAA Journal of Automatica Sinica 2023年第1期10卷 226-238页

作者： Zebing Wei Hongxia Zhao Zhishuai Li Xiaojie Bu Yuanyuan Chen Xiqiao Zhang Yisheng Lv Fei-Yue Wang the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China the Shandong Key Laboratory of Smart Transportation(Preparation) Shandong Jiao Tong UniversityShanghai 200240China the College of Science China University of PetroleumQingdao 266580China the Department of Transportation Engineering School of Transportation Science and TechnologyHarbin Institute of TechnologyHarbin 150090China IEEE

The success of intelligent transportation systems relies heavily on accurate traffic prediction,in which how to model the underlying spatial-temporal information from traffic data has come under the *** existing frameworks typically utilize separate modules for spatial and temporal correlations ***,this stepwise pattern may limit the effectiveness and efficiency in spatial-temporal feature extraction and cause the overlook of important information in some ***,it is lacking sufficient guidance from prior information while modeling based on a given spatial adjacency graph(e.g.,deriving from the geodesic distance or approximate connectivity),and may not reflect the actual interaction between *** overcome those limitations,our paper proposes a spatial-temporal graph synchronous aggregation(STGSA)model to extract the localized and long-term spatial-temporal dependencies ***,a tailored graph aggregation method in the vertex domain is designed to extract spatial and temporal features in one graph convolution *** each STGSA block,we devise a directed temporal correlation graph to represent the localized and long-term dependencies between nodes,and the potential temporal dependence is further fine-tuned by an adaptive weighting ***,we construct an elaborated spatial adjacency matrix to represent the road sensor graph by considering both physical distance and node similarity in a datadriven ***,inspired by the multi-head attention mechanism which can jointly emphasize information from different r epresentation subspaces,we construct a multi-stream module based on the STGSA blocks to capture global *** projects the embedding input repeatedly with multiple different ***,the predicted values are generated by stacking several multi-stream *** experiments are constructed on six real-world datasets,and numerical results show that the proposed STGSA

关键词： Deep learning graph neural network(GNN) multistream spatial-temporal feature extraction temporal graph traffic prediction

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Context-Aided Semantic-Aware Self-Alignment for Video-Based Person Re-Identification

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IEEE Transactions on Circuits and systems for Video Technology 2025年第6期35卷 5307-5317页

作者： Ran, Zhidan Xiao, Zhiyao Lu, Xiaobo Wei, Xuan Liu, Wei Southeast University School of Automation Nanjing210096 China Ministry of Education Key Laboratory of Measurement and Control of Complex Systems of Engineering Nanjing210096 China

Video-based person re-identification (Re-ID) aims at associating the video sequences of the identical person across multiple cameras. The ubiquitous appearance misalignment poses a major obstacle for video person Re-ID. Existing alignment-based methods generally rely on off-the-shelf semantic parsing models to locate visible human parts, which ignore identifiable personal belongings and cannot handle various interferences (e.g., pedestrian detection errors and occlusions) in video clips. In this work, we propose a novel framework termed Context-Aided Semantic-Aware Self-Alignment (CSSA) for video-based person Re-ID. First, we propose to jointly learn pixel-level part-aligned representations and semantic-aligned global-level representations in an end-to-end manner. Unlike most existing approaches that depend on prior information in terms of pose for part estimation, CSSA can locate different body parts and achieve the pixel-level semantic alignment without extra human topology semantics. Second, a Context-Aided Region Enhancement (CARE) module is proposed to efficiently highlight macro-visual patterns associated with the target pedestrian and suppress noise caused by factors like background clutters and occlusions. Third, we propose a Semantic-Aware Global Feature Alignment (SGFA) method for generating pair-wise semantic-aligned global representations, which play an essential role in both the training and inference phases. Extensive experimental results on multiple challenging benchmarks indicate the superiority and effectiveness of the proposed CSSA. © 1991-2012 IEEE.

关键词： Semantic Segmentation

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Collaborative Decision-Making Processes Analysis of Service Ecosystem: A Case Study of Academic Ecosystem Involution 19th

Collaborative Decision-Making Processes Analysis of Service ...

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19th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Yan, Xiangpei Xue, Xiao Peng, Chao Liu, Donghua Feng, Zhiyong Xiao, Wang School of Computer Software College of Intelligence and Computing Tianjin University Tianjin China China Waterborne Transport Research Institute Tianjin China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783031545306

With the collaboration of several intelligent services, a crowd intelligence service network has been formed, and a service ecosystem has gradually emerged. As a novel service organization model, the Service Ecosystem (SE) can provide more sophisticated, precise, and thorough services and has attracted widespread attention. However, it also brings negative effects such as involution, and information cocoon room. Thus, how to analyze the collaborative decision-making mechanism between the SE regulation algorithm and the crowd intelligence group, exploring the reasons behind the negative effects, and finding effective intervention strategies have become problems in this field. To solve the challenges, we propose a Computational Experiments-based method Decision-making processes Analysis model in SE, namely CEDA. The proposed CEDA model consists of three modules: the autonomous evolution mechanism module, the learning evolution mechanism module, and the collaborative decision-making analysis module. Among them, the computational experiments can provide a customized test environment for the analysis of collaborative decision-making processes and find out the appropriate intervention strategy. Finally, the validity of the CEDA model is verified through the case of academic ecosystem involution. The results show that computational experiments can provide new ideas and paths for collaborative decision-making processes analysis. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Ecosystems

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From DAO to TAO: Finding the Essence of Decentralization

From DAO to TAO: Finding the Essence of Decentralization

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

作者： Li, Juanjuan Liang, Xiaolong Qin, Rui Wang, Fei-Yue Institute of Automation State Key Laboratory of Multimodal Artificial Intelligence Systems Chinese Academy of Sciences Beijing100190 China Macau University of Science and Technology Faculty of Innovation Engineering Macau999078 China Institute of Automation State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9798350337020

Decentralized Autonomous Organizations (DAOs) have been gaining popularity in recent years due to their promise of realizing the decentralized Web 3.0. However, most DAOs rely heavily on token-centric value systems as well as allocate decision-making authority and yield-sharing rights according to the held tokens, which often lead to monopolization of power and rights. To address this issue, this paper contributes to propose a truly democratic organization model, named True Autonomous Organizations and Operations (TAOs), that does not count upon tokens and is guided by principles of contribution-based and on-demand allocation. We first discuss the design of TAOs, including their infrastructures, power structures, and value systems, and then provide a technical roadmap for implementing TAOs in the DeSci context. This research can provide a valuable guidance for the construction and application of TAOs. © 2023 IEEE.

关键词： Decentralized Autonomous Organizations and Operations (DAOs) governance systems open consortium blockchain True Autonomous Organizations and Operations (TAOs) value systems

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Civilian Autonomous Carriers for Peaceful Services: Intelligent Vehicle Carries Enabled Future Transportation and Logistics

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2023年 1-5页

作者： Wang, Fei-Yue The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

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Integrated Inspection of QoM, QoP, and QoS for AOI Industries in Metaverses

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IEEE/CAA Journal of Automatica Sinica 2022年第12期9卷 2071-2078页

作者： Yutong Wang Yonglin Tian Jiangong Wang Yansong Cao Shixing Li Bin Tian IEEE The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China Department of Engineering Science Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao 999078China North Automatic Control Technology Institute in Taiyuan ShanxiChina Qingdao Academy of Intelligent Industries Shandong 266109China

With the rapid development of information technologies such as digital twin, extended reality, and blockchain,the hype around "metaverse" is increasing at astronomical speed. However, much attention has been paid to its entertainment and social functions. Considering the openness and interoperability of metaverses, the market of quality inspection promises explosive growth. In this paper, taking advantage of metaverses, we first propose the concept of Automated Quality Inspection(Auto QI), which performs integrated inspection covering the entire manufacturing process, including Quality of Materials, Quality of Manufacturing(Qo M), Quality of Products, Quality of Processes(Qo P), Quality of systems, and Quality of Services(Qo S). Based on the scenarios engineering theory, we discuss how to perform interactions between metaverses and the physical world for virtual design instruction and physical validation feedback. Then we introduce a bottomup inspection device development workflow with productivity tools offered by metaverses, making development more effective and efficient than ever. As the core of quality inspection,we propose Quality Transformers to complete detection task,while federated learning is integrated to regulate data *** summary, we point out the development directions of quality inspection under metaverse tide.

关键词： Automated Optical Inspection Metaverses Quality Inspection Transformers

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Semi-asynchronous personalized federated learning for short-term photovoltaic power forecasting

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Digital Communications and Networks 2023年第5期9卷 1221-1229页

作者： Weishan Zhang Xiao Chen Ke He Leiming Chen Liang Xu Xiao Wang Su Yang College of Computer Science and Technology China University of Petroleum(East China)China Sichuan Energy Internet Research Institute Tsinghua UniversityChina School of Computer and Communication Engineering Beijing University of Science and TechnologyChina State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijingChina School of Computer Science Fudan UniversityChina

Accurate forecasting for photovoltaic power generation is one of the key enablers for the integration of solar photovoltaic systems into power *** deep-learning-based methods can perform well if there are sufficient training data and enough computational ***,there are challenges in building models through centralized shared data due to data privacy concerns and industry *** learning is a new distributed machine learning approach which enables training models across edge devices while data reside *** this paper,we propose an efficient semi-asynchronous federated learning framework for short-term solar power forecasting and evaluate the framework performance using a CNN-LSTM *** design a personalization technique and a semi-asynchronous aggregation strategy to improve the efficiency of the proposed federated forecasting *** evaluations using a real-world dataset demonstrate that the federated models can achieve significantly higher forecasting performance than fully local models while protecting data privacy,and the proposed semi-asynchronous aggregation and the personalization technique can make the forecasting framework more robust in real-world scenarios.

关键词： Photovoltaic power forecasting Federated learning Edge computing CNN-LSTM

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Management-Oriented Operating systems: Harnessing the Power of DAOs and Foundation Models

Management-Oriented Operating Systems: Harnessing the Power ...

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

作者： Qin, Rui Li, Juanjuan Liang, Xiaolong Wang, Fei-Yue Institute of Automation State Key Laboratory of Multimodal Artificial Intelligence Systems Chinese Academy of Sciences Beijing100190 China Macau University of Science and Technology Faculty of Innovation Engineering Macau999078 China Institute of Automation The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9798350337020

This paper presents Management-Oriented Operating systems (M2OS) that leverages the power of parallel intelligence theory, Decentralized Autonomous Organizations (DAOs) and foundation models to revolutionize the manner of management in Cyber-Physical-Social systems (CPSS). The parallel architecture on M2OS is proposed, including the parallel interactive actual M2OS and artificial M2OS. Among them, the artificial M2OSs provide digital infrastructures for organizations to operate, collaborate, and make decisions in the virtual space, and conduct computational experiments to evaluate management decisions and predict future states of the actual M2OS. Through parallel execution and closed-loop feedback between the artificial and actual M2OSs, the management and control, experimentation and evaluation, as well as learning and training of the actual M2OS can be realized. Moreover, the functional layers of M2OS, including the infrastructure layer, data layer, scenario layer, modeling layer, decision layer, and application layer, are discussed. These layers work together to support the intelligent, autonomous, collaborative, and adaptive nature of the M2OS, and facilitate data-driven decision-making, optimize business operations, and empower managers with real-time actionable insights. The proposed M2OS paradigm has great potential to transform the management paradigm and opens up new possibilities for intelligent and collaborative decision-making. © 2023 IEEE.

关键词： cyber-physical-social systems Decentralized Autonomous Organizations foundation models management-oriented operating systems parallel intelligence

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Adaptive SOM neural network algorithm 3

Adaptive SOM neural network algorithm

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3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing, AIAHPC 2023

作者： Wang, Xin Yan, Taishan School of Information Science and Engineering Hunan Institute of Science and Technology Hunan Yueyang China Hunan Institute of Science and Technology School of Information Science and Engineering China Key Laboratory of Hunan Province on Intelligent Control and Optimization of Complex Industrial Logistics System Hunan Yueyang China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

In order to improve the performance of traditional SOM model, an adaptive SOM neural network algorithm is proposed. By introducing the error square sum solution formula, the clustering error of the model sub-data set is calculated by using the method of cutting the data set, and the change trend of the clustering error is quickly obtained. Then the learning rate of the SOM network is adaptively adjusted according to this, and the convergence speed is accelerated on the basis of ensuring that the network is fully trained, thereby improving the performance of the SOM network. © 2023 SPIE.

关键词： Errors

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