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3rd IFAC Workshop pn Cyber-Physical and Human systems (CPHS)

作者： Chen, Yuanyuan Lv, Yisheng Ye, Peijun Zhu, Fenghua Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Traffic prediction is an elemental function of Intelligent Transportation systems (ITS), and accurate and timely prediction is significant for both proactive traffic control and providing traveler information. In this paper, we focus on investigating ensemble leaning that benefits from different base models, and propose a traffic condition-awareness ensemble approach. We apply graph convolution on the network of traffic detectors to capture the spatial patterns embedded in traffic flow. Then, the extracted features are used to formulate a weight matrix to ensemble the predictions of base models according to their performances under a certain condition. We performed a series of experiments on a real dataset to compare the proposed methods with several competitive models, including ensemble methods: Weight Regression model and Gradient Boosting Regression Tree model, and single model approach: Support Vector Regression (SVR), Long Short-term Memory (LSTM) model and Historical Average Model model. Experimental results demonstrate that our method can significantly improve the performances of traffic flow prediction. Copyright (C) 2020 The Authors.

关键词： Intelligent road transportation Urban mobility

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POLSAR IMAGE FEATURE EXTRACTION BASED ON CO-REGULARIZATION

POLSAR IMAGE FEATURE EXTRACTION BASED ON CO-REGULARIZATION

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Huang, Xiayuan Nie, Xiangli Qiao, Hong Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

ISBN: (纸本)9781728163741

Wishart distance of covariance matrices and Euclidean distance of polarimetric features are two important similarity measurements for polarimetric synthetic aperture radar (PolSAR) image classification. This paper proposes a feature extraction method by combing the two distances for PolSAR image classification. Firstly, two weight graphs are constructed based on the two distances to represent the local information of the data. Specifically, the neighbouring samples are sought in a local region to reduce the computation burden and utilize the spatial information. Then the dimensionality reduction model is constructed based on the two weight graphs and co-regularization. The co-regularization aims to minimize the dissimilarity of low-dimensional features corresponding to two graphs. Finally, the obtained low-dimensional features are used for Po1SAR image classification. Experimental results on real PolSAR datasets demonstrate the effectiveness and superiority of the proposed method.

关键词： Wishart distance Euclidean distance of polarimetric features dimensionality reduction PolSAR image classification feature extraction

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Impacts of Export Restrictions on the Global Personal Protective Equipment Trade Network During COVID-19

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ADVANCED THEORY AND SIMULATIONS 2022年第4期5卷 2100352页

作者： Ye, Yang Zhang, Qingpeng Cao, Zhidong Chen, Frank Youhua Yan, Houmin Stanley, H. Eugene Zeng, Daniel Dajun City Univ Hong Kong Sch Data Sci Hong Kong Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100864 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Shenzhen Artificial Intelligence & Data Sci Inst Shenzhen Peoples R China City Univ Hong Kong Coll Business Dept Management Sci Hong Kong Peoples R China Boston Univ Dept Phys Boston MA 02215 USA

The COVID-19 pandemic has caused a dramatic surge in demand for personal protective equipment (PPE) worldwide. Many countries have imposed export restrictions on PPE to ensure the sufficient domestic supply. The surging demand and export restrictions cause shortage contagions on the global PPE trade network. Here, an integrated network model is developed, which integrates a metapopulation model and a threshold model, to investigate the shortage contagion patterns. The metapopulation model captures disease contagion across countries. The threshold model captures the shortage contagion on the global PPE trade network. Due to the Pareto distribution in global exports, the shortage contagion pattern is mainly determined by the export restriction policies of the top exporters. Export restrictions exacerbate the shortages of PPE and cause the shortage contagion to transmit even faster than the disease contagion. To some extent, export restrictions can provide benefits for self-sufficient countries, at the sacrifice of immediate economic shocks at not-self-sufficient countries. With export restrictions, a large amount of PPE is hoarded instead of being distributed to where it is most needed, particularly at the early stage. Cooperation between countries plays an essential role in preventing global shortages of PPE regardless of the production level. Except for promoting global cooperation, governments and international organizations should take actions to reduce supply chain barriers and work together to increase global PPE production.

关键词： coronavirus disease 2019 export restrictions network personal protective equipment severe acute respiratory syndrome coronavirus 2

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Mixed Motivation Driven Social Multi-Agent Reinforcement Learning for Autonomous Driving

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IEEE/CAA Journal of Automatica Sinica 2025年第6期12卷 1272-1282页

作者： Long Chen Peng Deng Lingxi Li Xuemin Hu State Key Laboratory of Multimodal Artificial Intelligence Systems and the State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing WAYTOUS Inc. Beijing Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China School of Computer Science and Information Engineering Hubei University Wuhan China Purdue School of Engineering and Technology Indiana University-Purdue University Indianapolis Indianapolis IN USA School of Artificial Intelligence Hubei University Wuhan Key Laboratory of Intelligent Sensing System and Security (Hubei University) Ministry of Education Wuhan China

Despite great achievement has been made in autonomous driving technologies, autonomous vehicles (AVs) still exhibit limitations in intelligence and lack social coordination, which is primarily attributed to their reliance on single-agent technologies, neglecting inter-AV interactions. Current research on multi-agent autonomous driving (MAAD) predominantly focuses on either distributed individual learning or centralized cooperative learning, ignoring the mixed-motive nature of MAAD systems, where each agent is not only self-interested in reaching its own destination but also needs to coordinate with other traffic participants to enhance efficiency and safety. Inspired by the mixed motivation of human driving behavior and their learning process, we propose a novel mixed motivation driven social multi-agent reinforcement learning method for autonomous driving. In our method, a multi-agent reinforcement learning (MARL) algorithm, called Social Learning Policy Optimization (SoLPO), which takes advantage of both the individual and social learning paradigms, is proposed to empower agents to rapidly acquire self-interested policies and effectively learn socially coordinated behavior. Based on the proposed SoLPO, we further develop a mixed-motive MARL method for autonomous driving combined with a social reward integration module that can model the mixed-motive nature of MAAD systems by integrating individual and neighbor rewards into a social learning objective for improved learning speed and effectiveness. Experiments conducted on the MetaDrive simulator show that our proposed method outperforms existing state-of-the-art MARL approaches in metrics including the success rate, safety, and efficiency. More-over, the AVs trained by our method form coordinated social norms and exhibit human-like driving behavior, demonstrating a high degree of social coordination.

关键词： Measurement Reinforcement learning Safety Driver behavior Autonomous vehicles Optimization

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Parallel Societies: A Computing Perspective of Social Digital Twins and Virtual-Real Interactions

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2020年第1期7卷 2-7页

作者： Wang, Fei-Yue Qin, Rui Li, Juanjuan Yuan, Yong Wang, Xiao 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

Welcome to the first issue of the IEEE Transactions on Computational Social systems (TCSS) of 2020, and Happy New Year to You! We would like to take this opportunity to express our sincere thanks to our editors, reviewers, authors, and readers for your great support and effort devoted to the TCSS, along with our best wish and hope that everyone has a happy, healthy, and fruitful 2020.

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Toward a Maneuverable Miniature Robotic Fish Equipped With a Novel Magnetic Actuator System

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IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2020年第7期50卷 2327-2337页

作者： Chen, Xingyu Yu, Junzhi Wu, Zhengxing Meng, Yan Kong, Shihan Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

Most existing robotic fish have a large body size driven by servo motor system, while conventional small-sized actuators hardly generate a high swimming performance. This paper reports a miniature untethered robotic fish, whose body length is 69 mm. In particular, a newly designed magnetic actuator system (MAS) is equipped, which guarantees both small-sized dimension and flexibility of the robot. More specifically, the magnetic field generated by a permanent magnet is first investigated based on Biot-Savart law. Then, a novel tail-beating rhythm called magnetically actuated pulse width modulation (MAPWM) is modeled for the new actuator system. Further, an MAPWM-based control method is presented, in which the duty ratio of MAPWAM is innovatively utilized to realize the turning maneuvers for the first time. In addition, Lagrangian method is employed to establish the dynamic model to assess the MAPWM-based control method and the turning performance of the robotic fish. To further improve the maneuverability, the effect of a shape-variable caudal fin is analyzed based on computational fluid dynamics and the built dynamic model. Finally, combined with the MAS, the MAPWM-based control method, and the optimally selected caudal fin, extensive aquatic experiments are conducted on the robotic prototype. The results indicate that the developed miniature robotic fish achieves a considerably higher level of maneuverability in terms of turning radius when compared to swimming robots with equivalent dimensions.

关键词： Magnetosphere Actuators Turning Vibrations Robot kinematics Solenoids Dynamic modeling magnetic actuator system (MAS) maneuverability robotic fish underwater robot

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Blockchain-based consensus study on distributed control systems 1

Blockchain-based consensus study on distributed control syst...

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1st IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2021

作者： Zhu, Jing Deng, Hongchang Li, Xiang Yuan, Yong Wang, Feiyue College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing China School of Mathematics Renmin University of China Beijing China Institute of Automation Chinese Academy of Sciences State Key Laboratory for Management and Control of Complex Systems Beijing China

ISBN: (纸本)9781665433372

In this paper, two blockchain-based consensus mechanisms are proposed for distributed control systems, those are the proof-of-work-based (PoW-based) and proof-of-stake-based (PoS-based) consensus mechanisms. The basic idea and process of these blockchain-based consensus mechanisms are introduced, giving rise to the dynamic, impartial and efficient consensus compared with the consensus protocol achieved by traditional control theories. © 2021 IEEE.

关键词： Multi agent systems

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Design and Tension Modeling of a Novel Cable-Driven Rigid Snake-Like Manipulator

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JOURNAL OF INTELLIGENT & ROBOTIC systems 2020年第2期99卷 211-228页

作者： Xu, Dawei Li, En Liang, Zize Gao, Zishu Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

In this study, a cable-driven snake-like manipulator with high load capacity and end-positioning accuracy is designed for applications in complex and narrow environments. The Hooke joint-like two degree-of-freedom joint design and the full actuation mode enhanced the rigidity of the robot. The modular link design increased the local flexibility of the robot. Because the cable tension cannot be ignored under high load on the basis of the kinematics model, a cable tension model is established based on rigid body static equilibrium to describe the relationship between posture and cable tension. This provided a foundation for follow-up studies on obstacle avoidance path planning with optimized tension. At the same time, in order to improve the response speed of the motor position controller to the tension change, this study introduces both the tension as the reference model input and the system state variable into the adaptive control method based on model identification and reinforcement learning. The kinematics model and the cable tension model were validated by experiments on the prototype. The practical results of the two adaptive control methods were compared and the result shows that the method based on model identification has a better effect.

关键词： Snake-like manipulator Cable-driven Kinematics Tension model Neural network Reinforcement learning

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Multi-Sensor Fusion Perception System in Train 10

Multi-Sensor Fusion Perception System in Train

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10th IEEE Data Driven control and Learning systems Conference, DDCLS 2021

作者： Gao, Hongfei Huang, Yongzhen Li, Haoran Zhang, Qichao State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Watrix Technology Beijing Co. Ltd. China

ISBN: (纸本)9781665424233

Environment perception is one of the most crucial modules in a self-driving system. In an open subway environment, pedestrians, equipment boxes and other unknown obstacles often appear. The perception module is required to quickly and accurately recognize the obstacle and measures the corresponding distance data. This paper proposes a detection and ranging fusion method based on one Lidar and two different focal length cameras, which be applied in the subway system, to alert drivers to possible obstacles and assist brake. First, the relative transformation between Lidar and camera is calibrated to find the intrinsic and extrinsic matrices. Modified SSD network is trained on the self-built dataset to detect the potential obstacle and produces the final detection bounding box. Then, rail track segmentation network RailNet is applied to obtain rail shape features, which offers a region of interest (ROI) for sensing systems in point cloud clustering algorithms. Further, the fusion information is utilized to estimate the distance of objects detected by the SSD detector. The proposed method can realize real-time pedestrian detection and range on the onboard embedded platform Jetson Xavier, which satisfies the subway environment's perception requirements. © 2021 IEEE.

关键词： Optical radar

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Evolution of cooperation in Snowdrift game with Multi-memory mechanism

Evolution of cooperation in Snowdrift game with Multi-memory...

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第42届中国控制会议

作者： Yaojun Liu Xingwen Liu Nyima Tashi Lab.of Complex Systems & Intelligent Control Key Lab.of Electronic Information of State Ethnic Affairs CommissionCollege of Electrical EngineeringSouthwest Minzu University Engineering Research Center for Tibetan Information Processing School of Information Science and TechnologyTibet University

Cooperative behavior has been of great concern in evolutionary game researches because of its important role in social life and natural *** memory can greatly influence the emergence of cooperative behavior,memory-based research on evolutionary games has proliferated in recent *** the present paper,inspired by the well-known "Stanford marshmallow experiment"(SME),we proposed a multi-memory mechanism in Snowdrift game(SDG) on spatial lattice networks whose core lies in what follows:Players are divided into two groups according to individual self-control;different memory lengths are set by group and by time in a single *** simulation results show that our proposed mechanism promotes cooperation level of the considered evolutionary *** specifically,as memory length increases to an intermediate length,the cooperation level increases as *** mechanism with short memory length,the effect of population structure is obvious and small values of adjusting factor can intensively facilitate high-frequency cooperative *** findings may be helpful to understand the connection between population heterogeneity and the emergence of high cooperation.

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