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Parallel Vision ■ Image Synthesis/Augmentation

IEEE/CAA Journal of Automatica Sinica 2024年第3期11卷 782-784页

作者： Wenwen Zhang Wenbo Zheng Qiang Li Fei-Yue Wang the School of Computer Science Shaanxi Normal UniversityXi’an 710119China the School of Computer Science and Technology Wuhan 430070China the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

Dear Editor,Scene understanding is an essential task in computer *** ultimate objective of scene understanding is to instruct computers to understand and reason about the scenes as humans *** vision is a research fram... 详细信息

关键词： instru explanation computer

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Parallel Learning:Overview and Perspective for Computational Learning Across Syn2Real and Sim2Real

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IEEE/CAA Journal of Automatica Sinica 2023年第3期10卷 603-631页

作者： Qinghai Miao Yisheng Lv Min Huang Xiao Wang Fei-Yue Wang School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China School of Artificial Intelligence Anhui UniversityHefei 266114China Qingdao Academy of Intelligent Industries Qingdao 230031China State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China IEEE

The virtual-to-real paradigm,i.e.,training models on virtual data and then applying them to solve real-world problems,has attracted more and more attention from various domains by successfully alleviating the data shortage problem in machine *** summarize the advances in recent years,this survey comprehensively reviews the literature,from the viewport of parallel ***,an extended parallel learning framework is proposed to cover main domains including computer vision,natural language processing,robotics,and autonomous ***,a multi-dimensional taxonomy is designed to organize the literature in a hierarchical ***,the related virtual-toreal works are analyzed and compared according to the three principles of parallel learning known as description,prediction,and prescription,which cover the methods for constructing virtual worlds,generating labeled data,domain transferring,model training and testing,as well as optimizing the strategies to guide the task-oriented data generator for better learning *** issues remained in virtual-to-real are ***,the future research directions from the viewpoint of parallel learning are suggested.

关键词： Machine learning parallel learning parallel systems sim-to-real syn-to-real virtual-to-real

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AI4S Based on DeSci: Reference Model and Research Issues

AI4S Based on DeSci: Reference Model and Research Issues

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

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

The rise of Artificial Intelligence for Science (AI4S) has highlighted the importance and urgency of ensuring open-ness, fairness, impartiality, diversity, and sustainability in scientific systems. Existing scientific systems, referred to as Centralized Science (CeSci), are built on centralized organizational structures and top-down institutional frameworks, which are lagging behind the development and practical requirements of AI4S. To address these limitations, AI4S needs to embrace a new scientific organizational and operational paradigm, namely Decentralized Science (DeSci). It can provide strong support to AI4S via effectively addressing issues such as information silos, biases, unfair distribution, and monopolies and promoting multidisciplinary, interdisciplinary, and trans disciplinary cooperation in science. Based on these considerations, this paper presents the framework of AI4S based on DeSci and explores its potential application scenarios and research issues. The research can provide effective guidance for the development of scientific systems. © 2023 IEEE.

关键词： Artificial Intelligence for Science (AI4S) ChatGPT Decentralized Autonomous Organizations and Operations (DAOs) Decentralized Science (De-Sci) Generative Artificial Intelligence (GAI) intelligent Science

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Urban Traffic control Meets Decision Recommendation System:A Survey and Perspective

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IEEE/CAA Journal of Automatica Sinica 2024年第10期11卷 2043-2058页

作者： Qingyuan Ji Xiaoyue Wen Junchen Jin Yongdong Zhu Yisheng Lv Zhejiang Lab Hangzhou 311121China College of Engineering and Architecture Zhejiang UniversityHangzhou 310013China College of Electrical Engineering Zhejiang UniversityHangzhou 310013China the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China IEEE

Urban traffic control is a multifaceted and demanding task that necessitates extensive decision-making to ensure the safety and efficiency of urban transportation *** approaches require traffic signal professionals to manually intervene on traffic control devices at the intersection level,utilizing their knowledge and ***,this process is cumbersome,labor-intensive,and cannot be applied on a large network *** studies have begun to explore the applicability of recommendation system for urban traffic control,which offer increased control efficiency and *** a decision recommendation system is complex,with various interdependent components,but a systematic literature review has not yet been *** this work,we present an up-to-date survey that elucidates all the detailed components of a recommendation system for urban traffic control,demonstrates the utility and efficacy of such a system in the real world using data and knowledgedriven approaches,and discusses the current challenges and potential future directions of this field.

关键词： Recommendation system traffic control traffic perception traffic prediction

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Open Set Domain Adaptation with Zero-shot Learning on Graph 23

Open Set Domain Adaptation with Zero-shot Learning on Graph

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4th International Conference on Computing, Networks and Internet of Things, CNIOT 2023

作者： Zhang, Xinyue Yang, Xu Liu, Zhiyong School of Artificial Intelligence University of Chinese Academy of Sciences China and State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798400700705

Open set domain adaptation focuses on transferring the information from a richly labeled domain called source domain to a scarcely labeled domain called target domain, while classifying the unseen target samples as one unknown class in an unsupervised way. Compared with the close set domain adaptation, where the source domain and the target domain share the same class space, the classification of the unknown class makes it easy to adapt to the real environment. Particularly, after the recognition of the unknown samples, the model can either ask for manually labeling or further develop the classification ability of the unknown classes based on pre-stored knowledge. Inspired by this idea, we propose a model for open set domain adaptation with zero-shot learning on the unknown classes in this paper. We utilize adversarial learning to align the two domains while rejecting the unknown classes. Then the knowledge graph is introduced to generate the classifiers for the unknown classes with the employment of the graph convolution network (GCN). Thus the classification ability of the source domain is transferred to the target domain, and the model can distinguish the unknown classes in detail with prior knowledge. We evaluate our model on digits datasets and the result shows superior performance. © 2023 Owner/Author.

关键词： Zero-shot learning

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Short-Term Wind Power Prediction Based on KNN-BiGRU 5

Short-Term Wind Power Prediction Based on KNN-BiGRU

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5th International Conference on Power Engineering, ICPE 2024

作者： Fan, Xinyu Wu, Zhenlong Bian, Guibin Liu, Yanhong Zhengzhou University School of Electrical and Information Engineering Zhengzhou China State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798331531140

Wind power generations have received widespread concern recently, however, due to the continuity of time series, ordinary machine learning models cannot learn the dependencies of continuous time series data well. To better predict the time series and improve the prediction effect of wind power, a shortterm wind power prediction method based on K-Nearest Neighbor(KNN) and Bidirectional Gated Recurrent Unit(BiGRU) is proposed. Firstly, KNN is used to detect outliers in the wind speed-power sequence in the field collected data, and then the wind speed value of all abnormal data is modified to the mean value of wind speed. Then the modified data is input to the bidirectional gated recurrent unit for prediction. The bidirectional gated recurrent unit can efficiently learn the context information of the data and improve the prediction effect. The experimental results show that the proposed prediction method based on KNN-BiGRU obtains a good prediction effect on a single unit. Compared with other methods, it has the best prediction effect on multi-unit where the accuracy rate is 8 4. 3 0 4 %. © 2024 IEEE.

关键词： Wind forecasting

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Vision-based control in the open racing car simulator with deep and reinforcement learning

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Journal of Ambient Intelligence and Humanized Computing 2023年第12期14卷 15673-15685页

作者： Zhu, Yuanheng Zhao, Dongbin State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China

With decades of development, computer intelligence has now reached a really high level. Especially deep learning (DL) and reinforcement learning (RL) endow computers the perception and decision abilities. This paper aims to design a vision-based system that is able to play The Open Racing Car Simulator (TORCS) like a human player that uses images. With the DL-trained perception module, useful and low-dimensional information is extracted from first-person images. Based on that, the RL-trained module further manipulates the simulated car in the middle of the lane. The two modules are separately trained, and both DL and RL advantages are maximally utilized. Experiments on different tracks show the promising performance of the method. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Reinforcement learning

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The Metaverse of Mind:Perspectives on DeSci for DeEco and DeSoc

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IEEE/CAA Journal of Automatica Sinica 2022年第12期9卷 2043-2046页

作者： Fei-Yue Wang IEEE The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing100190China Macao Institute of Systems Engineering Macao University of Science and TechnologyMacao999078China

First of all, I would like to take this opportunity to express my sincere and deep thanks to our Editor-in-Chief, Professor Meng Chu Zhou, who took over my position after I was drafted for rejuvenating IEEE Transactions on Computational Social systems in 2017. During the past five years, Meng Chu’s professional leadership and dedication has transformed IEEE/CAA Journal of Automatica Sinica(JAS) from its infancy to a young and high-impact publication in the world that is full of vitality and actively engaged by a group of talented and charged associate Ei Cs and editors, which is clearly demonstrated in Meng Chu’s farewell editorial [1]. I am very glad that Professor Qing-Long Han, an influential and leading scientist of the world-class in AI, control, automation, and intelligent science and technology from Australia, as well as a staunch supporter and great leader of this journal from its beginning, will take over the Ei C torch from Meng Chu next year, since I am extremely confident that our journal will reach a new high for its service and quality under his new leadership.

关键词： journal leadership Australia

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Cooperative Output Regulation of Heterogeneous systems over Directed Graphs:A Dynamic Adaptive Event-Triggered Strategy

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Journal of systems Science & complexity 2023年第3期36卷 909-921页

作者： SU Hang CHENG Bin LI Zhongkui State Key Laboratory for Turbulence and Complex Systems and Department of Mechanics and Engineering Science College of EngineeringPeking UniversityBeijing 100871China Department of Control Science and Engineering College of Electronics and Information EngineeringTongji UniversityShanghai 201804China Frontiers Science Center for Intelligent Autonomous Systems Shanghai 201210China

This paper investigates the cooperative output regulation problem of heterogeneous linear multi-agent systems over directed graphs with the constraint of communication *** that there exists an exosystem whose state information is not available to all agents,the authors develop distributed adaptive event-triggered observers for the followers based on relative information between neighboring *** should be pointed out that,two kinds of time-varying gains are introduced to avoid relying on any global information associated with the network,and dynamic triggering conditions are designed to get rid of continuous *** the basis of the designed observers,the authors devise a local controller for each *** with the existing related works,the main contribution of the current paper is that the cooperative output regulation problem for general directed graphs is solved requiring neither global information nor continuous communications.

关键词： Adaptive control consensus cooperative output regulation directed graph event-triggered control multi-agent systems

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eVTOL Performance Analysis: A Review From control Perspectives

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on intelligent Vehicles 2024年第5期9卷 1-13页

作者： Su, Jiangcheng Huang, Hailong Zhang, Hong Wang, Yutong Wang, Fei-Yue Department of Aeronautical and Aviation Engineering Hong Kong Polytechnic University Hong Kong China Co. Ltd. Guangzhou China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

Electric Vertical Takeoff and Landing (eVTOL) aircraft has gained significant attention as a basic element of urban air mobility (UAM), a potential solution for urban transportation challenges using low-altitude urban airspace. Ensuring the safe operation of eVTOL is crucial for UAM applications, which are related to various professional fields such as aerodynamics, control, structures, and power systems. This article systematically analyzes the characteristics of different design configurations, including multi-rotor, lift+cruise, and tilt-rotor types of eVTOL. The advantages and limitations of each type of eVTOL are analyzed. After that, the overall design problems are analyzed, and challenges of eVTOL control system design are discussed from aspects of overall control structure and subsystems, such as controller, sensors, actuators, and command generator. This article tries to fill the gap in the eVTOL design from a control perspective and provides some resolutions for the eVTOL application. IEEE

关键词： Air mobility

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