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IEEE JOURNAL OF RADIO FREQUENCY IDENTIFICATION 2022年 6卷 779-782页

作者： Chen, Yizhu Wang, Xiao Tian, Yonglin Wang, Fei Yue Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Qingdao Acad Intelligent Ind Parallel Intelligence Res Ctr Qingdao 266000 Peoples R China

To further promote the implementation and application of artificial intelligence technology under data silos, federated ecology has defined a set of standardized and systematic solutions from upstream data to downstream applications from a systematic perspective. As the core of the federated ecology, federated control can transform the abstract tasks of federated management into concrete execution rules. Clustered training is a common cooperation rule for the personalized federated training task of multi-party cooperation. However, it is difficult to generalize in large-scale cooperation scenarios because of increased data transmission pressure and server storage burden. Therefore, this paper designs a personalized federated control method based on similarity measures(pFedSim) so that the server can accurately distribute the cluster model to clients, thereby reducing the transmission cost. Compared with the existing clustered training method IFCA, pFedSim transfers the selection rights of cluster models from the client to the server. Model selection preference, accurate distribution of cluster models. Experiments show that in the personalized training task with s clusters, the method can obtain the same model level with 1/s data transmission. It is better than the current optimal clustering training method Macro-Acc increased by 2.25%.

关键词： Federated control personalized federated training clustered training

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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,lab.r-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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System Dynamics-Based Analysis on Factors Influencing Artificial Intelligence Talents Training

IEEE JOURNAL OF RADIO FREQUENCY IDENTIFICATION

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IEEE JOURNAL OF RADIO FREQUENCY IDENTIFICATION 2022年 6卷 753-757页

作者： Jing, Sifeng Liu, Xiwei Gong, Xiaoyan Zhao, Hongxia Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Qingdao Acad Intelligent Ind Inst Smart Educ Syst Qingdao 266044 Peoples R China

There are gaps between education supply and industrial demand of talents in intelligent era. Thus, the shortage of artificial intelligence(AI) talents becomes the top challenges facing China's companies. Hence it is essential to implement educational reform to improve quality of education supply talents. However, educational reform is affected by multiple and intertwined factors. This research, therefore, developed a system dynamics-based model of education reform to sort out relationships among all factors and predict the influence of several factors on AI talents output. Then, based on this model, series of experiments are carried out to find main factors affecting cultivation of AI talents. Results show that: 1)this model could accurately simulate trends of output ratio AI talents under different educational reform measures;2) Measures including teaching mode reform, improving the application rate of new teaching model, speeding up the construction of new disciplines in higher education, developing AI, improving ethics of AI, and strengthening the integration of AI and education could play significant roles in promoting cultivation of AI talents.

关键词： System dynamics education reform simulation artificial intelligent talents cultivation

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Consensus control of Leader-Following Multi-Agent systems in Directed Topology With Heterogeneous Disturbances

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IEEE/CAA Journal of Automatica Sinica 2021年第2期8卷 423-431页

作者： Qinglai Wei Xin Wang Xiangnan Zhong Naiqi Wu the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049and also with Qingdao Academy of Intelligent IndustriesQingdao 266109China the Department of Computer and Electrical Engineering and Computer Science Florida Atlantic UniversityBoca Raton FL33431 USA the Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and SystemsMacao University of Science and TechnologyMacao 999078China

This paper investigates the consensus problem for linear multi-agent systems with the heterogeneous disturbances generated by the Brown *** main contribution is that a control scheme is designed to achieve the dynamic consensus for the multi-agent systems in directed topology interfered by stochastic *** traditional ways,the coupling weights depending on the communication structure are static.A new distributed controller is designed based on Riccati inequalities,while updating the coupling weights associated with the gain matrix by state errors between adjacent *** introducing time-varying coupling weights into this novel control law,the state errors between leader and followers asymptotically converge to the minimum value utilizing the local *** the Lyapunov directed method and It?formula,the stability of the closed-loop system with the proposed control law is *** simulation results conducted by the new and traditional schemes are presented to demonstrate the effectiveness and advantage of the developed control method.

关键词： Consensus control directed topology external disturbance multi-agent(MA)systems

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Event-Triggered Deep Reinforcement Learning Using Parallel control: A Case Study in Autonomous Driving

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON intelligent VEHICLES 2023年第4期8卷 2821-2831页

作者： Lu, Jingwei Han, Liyuan Wei, Qinglai Wang, Xiao Dai, Xingyuan Wang, Fei-Yue Qingdao Acad Intelligent Ind Parallel Intelligence Innovat Res Ctr Qingdao 266109 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Anhui Univ Sch Artificial Intelligence Hefei 230031 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266114 Peoples R China

This paper utilizes parallel control to investigate the problem of event-triggered deep reinforcement learning and develops an event-triggered deep Q-network (ETDQN) for decision-making of autonomous driving, without training an explicit triggering condition. Based on the framework of parallel control, the developed ETDQN incorporates information of actions into the feedback and constructs a dynamic control policy. First, in the realization of the dynamic control policy, we integrate the current state and the previous action to construct the augmented state as well as the augmented Markov decision process. Meanwhile, it is shown theoretically that the goal of the developed dynamic control policy is to learn the variation rate of the action. The augmented state contains information of the current state and the previous action, which enables the developed ETDQN to directly design the immediate reward considering communication loss. Then, based on dueling double deep Q-network (dueling DDQN), we establish the augmented action-value, value, and advantage functions to directly learn the optimal event-triggered decision-making policy of autonomous driving without an explicit triggering condition. It is worth noticing that the developed ETDQN applies to various deep Q-networks (DQNs). Empirical results demonstrate that, in event-triggered control, the developed ETDQN outperforms dueling DDQN and reduces communication loss effectively.

关键词： Autonomous vehicles Decision making Path planning Training Optimal control Deep learning complex systems Autonomous driving deep reinforcement learning deep Q-network event-triggered control parallel control

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On-road Vehicle Detection Method based on Multi-scale Active Basis Model

On-road Vehicle Detection Method based on Multi-scale Active...

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IEEE International Conference on Vehicular Electronics and Safety (ICVES)

作者： Yao, Yanjie Xiong, Gang Chinese Acad Sci Inst Automat Beijing Engn Res Ctr Intelligent Syst & Technol State Key Lab Management & Control Complex Syst Beijing Peoples R China Chinese Acad Sci Cloud Comp Ctr Dongguan Res Inst CASIA Dongguan Peoples R China State Key Lab Management & Control Complex Syst Beijing Peoples R China

ISBN: (纸本)9781479903801

This paper introduces a vehicle detection method based on multi-scale active basis model in traffic surveillance systems. Due to the effects of perspective, vehicles which are close to the camera are larger and more detailed than the far ones on individual video frames. Using camera calibration, we get the multi-scale information of vehicles, and then we learn the multi-scale active basis model from the target training sample set by using the shared sketch algorithm. The multi-scale ABM can detect vehicles in various poses, shapes, and sizes in a whole video frame. The experiment results show that this proposed method can fit the changes of vehicle size in images.

关键词： ABM multiscale camera calibration vehicle detection

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A novel defect detection and identification method in optical inspection

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NEURAL COMPUTING & APPLICATIONS 2014年第7-8期24卷 1953-1962页

作者： Xie, Liangjun Huang, Rui Gu, Nong Cao, Zhiqiang Nec Labs China Beijing 100084 Peoples R China Deakin Univ Ctr Intelligent Syst Res Waurn Ponds Vic 3216 Australia Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Optical inspection techniques have been widely used in industry as they are non-destructive. Since defect patterns are rooted from the manufacturing processes in semiconductor industry, efficient and effective defect detection and pattern recognition algorithms are in great demand to find out closely related causes. Modifying the manufacturing processes can eliminate defects, and thus to improve the yield. Defect patterns such as rings, semicircles, scratches, and clusters are the most common defects in the semiconductor industry. Conventional methods cannot identify two scale-variant or shift-variant or rotation-variant defect patterns, which in fact belong to the same failure causes. To address these problems, a new approach is proposed in this paper to detect these defect patterns in noisy images. First, a novel scheme is developed to simulate datasets of these 4 patterns for classifiers' training and testing. Second, for real optical images, a series of image processing operations have been applied in the detection stage of our method. In the identification stage, defects are resized and then identified by the trained support vector machine. Adaptive resonance theory network 1 is also implemented for comparisons. Classification results of both simulated data and real noisy raw data show the effectiveness of our method.

关键词： Optical inspection Defect detection Classification Support vector machine Adaptive resonance theory network

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3-D Tracking for Augmented Reality Using Combined Region and Dense Cues in Endoscopic Surgery

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2018年第5期22卷 1540-1551页

作者： Wang, Rong Zhang, Mei Meng, Xiangbing Geng, Zheng Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100190 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266000 Shandong Peoples R China

An augmented reality (AR) technique has recently gained its popularity in minimally invasive surgery. Tracking is a crucial step to achieve precise AR. Besides optical tracking in traditional medical AR, visual tracking attracts a lot of attention due to its generality. Moreover, when the target organ's 3-D model can be obtained from preoperative images and under the model rigidity assumption, tracking is then converted into a problem of computing the six-degree-of-freedom pose of the 3-D model. In this paper, we introduce a robust tracking algorithm in our endoscopic AR system, where we combine the benefits of both region and dense cues in a unified framework. Each kind of cues alone may not be adequate for tracking in endoscopic surgery. However, they have complementary characteristics, with region cues being more robust to motion blur and fast motion, and dense cues being more accurate when motion is not large. We also propose an appearance model adaption method and an occlusion processing method to effectively handle occlusions. Experiments on both synthetic dataset and simulated surgical environment show the effectiveness and robustness of our proposed method. This work presents a novel tracking strategy in medical AR applications.

关键词： Augmented reality dense cues endoscopic surgery region cues tracking

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Consistent Matrix: A Feature Selection Framework for Large-Scale Datasets

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IEEE TRANSACTIONS ON FUZZY systems 2023年第11期31卷 4024-4038页

作者： Yang, Tian Li, Yuan-Jiang Qian, Yuhua Wang, Fei-Yue Hunan Normal Univ Hunan Prov Key Lab Intelligent Comp & Language Inf Changsha 410081 Peoples R China Shanxi Univ Inst Big Data Sci & Ind Taiyuan 030006 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100080 Peoples R China

Large-scale data processing based on limited computing resources has always been a difficult problem in data mining, where feature selection is often used as an effective data compressing mechanism. For granular computing of Big Data, discernibility matrix and dependency degree are the most representative methods for matrix-based and feature-importance-degree-based feature selection, respectively. However, their temporal and space complexities are high and often lead to poor performance. In this article, a novel feature selection framework for large-scale data processing with linear complexities was proposed for the first time. First, a much more concise fuzzy granule set, called fuzzy arithmetic covering, was introduced to reduce computational costs. Then, a new matrix-based feature selection framework, namely consistent matrix, was proposed for general rough set models. As a result, a heuristic attribute reduction algorithm, i.e., HARCM, was designed accordingly. Compared with six state-of-the-art algorithms for feature selection, the average running time of the newly proposed algorithm was reduced up to 2913 times, with a comparable or even better classification performance.

关键词： Consistent matrix feature selection fuzzy arithmetic covering fuzzy rough sets granular computing

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MetaSocieties in Metaverse: MetaEconomics and Metamanagement for MetaEnterprises and MetaCities

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2022年第1期9卷 2-7页

作者： Wang, Fei-Yue Qin, Rui Wang, Xiao Hu, Bin Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266109 Peoples R China Lanzhou Univ Sch Informat Sci & Engn Gansu Prov Key Lab Wearable Comp Lanzhou 730000 Gansu Peoples R China

Welcome to the first issue of the IEEE Transactions on Computational Social systems (TCSS) of 2022. We would like to take this opportunity to express our sincere thanks to our associate editors, reviewers, authors, and readers for your great support and effort devoted to IEEE TCSS. Happy New Year to you all, and cheers to health, happiness, and high-producing in 2022!

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