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Digger: A Graph Contraction Algorithm for Patrolling Games

IEEE TRANSACTIONS ON RELIABILITY 2024年第2期73卷 1104-1116页

作者： Han, Jinpeng Wang, Zhen Chen, Xiaoguang Yang, Manzhi Wang, Fei-Yue Xi An Jiao Tong Univ Sch Software Engn Xian 710049 Peoples R China Hangzhou Dianzi Univ Sch Cyberspace Hangzhou 310018 Peoples R China Macau Univ Sci & Technol Macau Inst Syst Engn Macau 999078 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Macau Univ Sci & Technol Macau Inst Syst Engn Macau 999078 Peoples R China

In security games, the patrolling problem is usually modeled as a Stackelberg game to obtain patrol schemes. However, solving Stackelberg games is challenging, as the player strategy space grows exponentially with patrolling scenarios that expand in space and time. Recent work on reducing the player strategy space does not consider adversarial graph features, making the process of solving the Stackelberg game inefficient. To address this issue, we propose a novel algorithm called "Digger," and the following important findings are made: the defender's optimal strategy can prevent an attacker from reaching a target, and this is achieved by a set of available interdiction vertices that separate the attacker from the target. The defender can arrive at the interdiction vertices earlier than the attacker. After arriving at the set of interdiction vertices, the next defender action subgraphs are not all useful, and the expected utility of the related player's pure strategy is not optimal. Finally, we build a player support set that does not contain useless strategies to improve the speed of the security game algorithm. An experimental evaluation of warehouse graphs demonstrates that Digger dramatically improves the existing algorithms.

关键词： Games Security Game theory Resource management Runtime Roads Cyberspace Graph contraction minimum vertex cut patrolling game security game Stackelberg game

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Securing the Future after Pager Bombs:Lifecycle Protection of Smart Devices via Blockchain Intelligence

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IEEE/CAA Journal of Automatica Sinica 2024年第12期11卷 2355-2358页

作者： Fei Lin Qinghua Ni Jing Yang Juanjuan Li Nan Zheng Levente Kovács Radu Prodan Mariagrazia Dotoli Qing-Long Han Fei-Yue Wang the Department of Engineering Science Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao 999078China the State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the School of Artificial Intelligence University of Chinese Academy of SciencesBei-jing 100049China IEEE Obuda University John von Neumann Faculty of InformaticsBecsi Str.96/bH-1034 BudapestHungary the Institute of Information Technology University of Klagenfurt9020 Klagenfurt am WortherseeAustria the Department of Electrical and Information Engineering Polytechnic of Bari70126 BariItaly the School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia the State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190

THE Lebanese wireless device explosion incident has drawn widespread attention,involving devices such as pagers,walkie-talkies,and other common devices[1].This event has revealed and highlighted the security vulnerabilities in global supply chains from raw material manufacturing and distribution to the usage of devices and equipment,signaling the onset of a new wave of"supply chain warfare"[2].

关键词： Smart cycle chains

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Hand Acupoint Detection from Images Based on Improved HRNet

Hand Acupoint Detection from Images Based on Improved HRNet

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IEEE International Conference on Fuzzy systems (FUZZ-IEEE) / IEEE World Congress on Computational Intelligence (IEEE WCCI) / International Joint Conference on Neural Networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Sun, Shiying Xu, Hongduo Sun, Lingyao Fu, Yuanbo Zhang, Yujia Zhao, Xiaoguang Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Capital Med Univ Beijing Hosp Tradit Chinese Med Beijing Peoples R China

ISBN: (数字)9781728186719

ISBN: (纸本)9781728186719

As an important component of Traditional Chinese Medicine (TCM), the acupoint therapy has achieved significant success in clinical practice. However, at present, the effect of acupoint therapy heavily depends on the skills of doctors and the acupuncture medical resources are seriously insufficient. The introduction of artificial intelligence technology in acupoint therapy can reduce the workload of doctors and ensure the consistency of acupoint operations, which is of great significance. The key process of acupoint therapy is acupoint detection. In this paper, we apply the deep learning method in automatic acupoint detection using images and propose an improved High-Resolution Network (HRNet) method for hand acupoint detection. What's more, we build a hand acupoint detection dataset and propose an evaluation metric. Experiments on the proposed dataset verify the effectiveness of the proposed method.

关键词： Acupoint detection HRNet deep learning Traditional Chinese Medicine

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A Dissemination Model Based on Psychological Theories in complex Social Networks

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IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL systems 2022年第2期14卷 519-531页

作者： Luo, Tianyi Cao, Zhidong Zeng, Daniel Zhang, Qingpeng 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 City Univ Hong Kong Sch Data Sci Hong Kong Peoples R China

Information spread on social media has been extensively studied through both model-driven theoretical research and data-driven case studies. Recent empirical studies have analyzed the differences and complexity of information dissemination, but theoretical explanations of its characteristics from a modeling perspective are underresearched. To capture the complex patterns of the information dissemination mechanism, we propose a resistant linear threshold (RLT) dissemination model based on psychological theories and empirical findings. In this article, we validate the RLT model on three types of networks and then quantify and compare the dissemination characteristics of the simulation results with those from the empirical results. In addition, we examine the factors affecting dissemination. Finally, we perform two case studies of the 2019 novel Corona Virus Disease (COVID-19)-related information dissemination. The dissemination characteristics derived by the simulations are consistent with the empirical research. These results demonstrate that the RLT model is able to capture the patterns of information dissemination on social media and thus provide model-driven insights into the interpretation of public opinion, rumor control, and marketing strategies on social media.

关键词： Social networking (online) Psychology Integrated circuit modeling Attenuation Interference Resistance COVID-19 complex networks differential dissemination dissemination mechanism information dissemination psychology

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Federated control: A Trustable control Framework for Large-Scale Cyber-Physical systems

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第5期20卷 7986-7994页

作者： Zhu, Jing Yuan, Yong Wang, Fei-Yue Wang, Ge Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing 210016 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Macau 999078 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100023 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Macau 999078 Peoples R China Renmin Univ China Sch Math Beijing 100872 Peoples R China

To break the dilemma of data island, a distributed framework for trustable control is proposed toward information security and data privacy in large-scale cyber-physical systems. The federated control system consists of distinct blockchains, as such information security and data privacy are technologically guaranteed. Moreover, data are divided into private and nonprivate data. Only nonprivate data can be exchanged for a better global system performance, where the interblockchain communication is ensured by cross-blockchain technologies. Federated control framework establishes a trustable environment where each subsystem is willing to share data for optimal performance. The architecture, structure, and implementation process of federated control are discussed, together with the potential applications to smart buildings.

关键词： Blockchains control systems Encryption Data privacy Data models Process control Topology Cosmos cyber-physical system (CPS) distributed control federated ecology multiblockchain structure

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Role of Asymptomatic COVID-19 Cases in Viral Transmission: Findings From a Hierarchical Community Contact Network Model

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2022年第2期19卷 576-585页

作者： Luo, Tianyi Cao, Zhidong Wang, Yuejiao Zeng, Daniel Zhang, Qingpeng 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 City Univ Hong Kong Sch Data Sci Hong Kong Peoples R China

As part of ongoing efforts to contain the coronavirus disease (COVID-19) pandemic, understanding the role of asymptomatic patients in the transmission system is essential for infection control. However, the optimal approach to risk assessment and management of asymptomatic cases remains unclear. This study proposed a Susceptible, Exposed, Infectious, No symptoms, Hospitalized and reported, Recovered, Death (SEINRHD) epidemic propagation model. The model was constructed based on epidemiological characteristics of COVID-19 in China and accounting for the heterogeneity of social contact networks. The early community outbreaks in Wuhan were reconstructed and fitted with the actual data. We used this model to assess epidemic control measures for asymptomatic cases in three dimensions. The impact of asymptomatic cases on epidemic propagation was examined based on the effective reproduction number, abnormally high transmission events, and type and structure of transmission. management of asymptomatic cases can help flatten the infection curve. Tracing 75% of the asymptomatic cases corresponds to a 32.5% overall reduction in new cases (compared with tracing no asymptomatic cases). Regardless of population-wide measures, household transmission is higher than other types of transmission, accounting for an estimated 50% of all cases. The magnitude of tracing of asymptomatic cases is more important than the timing;when all symptomatic patients were traced, tested, and isolated in a timely manner, the overall epidemic was not sensitive to the time of implementing the measures to trace asymptomatic patients. Disease control and prevention within families should be emphasized during an epidemic.

关键词： COVID-19 Epidemics Pandemics Sociology Market research Mathematical models Timing Risk management Statistics Asymptomatic patients COVID-19 epidemic rebound strategy evaluation transmission model

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Decentralized Event-Driven Constrained control Using Adaptive Critic Designs

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING systems 2022年第10期33卷 5830-5844页

作者： Yang, Xiong Zhu, Yuanheng Dong, Na Wei, Qinglai Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

We study the decentralized event-driven control problem of nonlinear dynamical systems with mismatched interconnections and asymmetric input constraints. To begin with, by introducing a discounted cost function for each auxiliary subsystem, we transform the decentralized event-driven constrained control problem into a group of nonlinear H-2-constrained optimal control problems. Then, we develop the event-driven Hamilton-Jacobi-Bellman equations (ED-HJBEs), which arise in the nonlinear H-2-constrained optimal control problems. Meanwhile, we demonstrate that all the solutions of the ED-HJBEs together keep the overall system stable in the sense of uniform ultimate boundedness (UUB). To solve the ED-HJBEs, we build a critic-only architecture under the framework of adaptive critic designs. The architecture only employs critic neural networks and updates their weight vectors via the gradient descent method. After that, based on the Lyapunov approach, we prove that the UUB stability of all signals in the closed-loop auxiliary subsystems is assured. Finally, simulations of an illustrated nonlinear interconnected plant are provided to validate the present designs.

关键词： Adaptive critic designs (ACDs) adaptive dynamic programming (ADP) decentralized event-driven control input constraint reinforcement learning (RL)

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A Parkinson's Bradykinesia Recognition System Based on Deep Learning Method 12

A Parkinson's Bradykinesia Recognition System Based on Deep ...

引用

12th International Conference on CYBER Technology in Automation, control, and Intelligent systems, CYBER 2022

作者： Tong, Lina Liu, Daisong Zhang, Mingjia Peng, Liang Department of State Key Laboratory of Management and Control for Complex Systems Institute of Automation China Academy of Science Beijing China School of Mechanical Electronic and Information Engineering Chinese University of Mining and Technology-Beijing Beijing China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

Parkinson's disease is a neurodegenerative disease common in middle-aged and elderly people, and bradykinesia is one of the most obvious symptoms. In this study, A deep learning-based Parkinson's bradykinesia recognition system is designed which has an inertial sensor-based data acquisition system. It can wirelessly collect bradykinesia kinematic information by wearing it on the wrist and ankle of Parkinson's patients. In addition, a method for identifying the symptom of Parkinson's bradykinesia based on Long Short Term Memory (LSTM) Network is proposed, with an accuracy of 98.6%. It provides a feasible method to identify Parkinson bradykinesia to assist the diagnosis and treatment. © 2022 IEEE.

关键词： Neurodegenerative diseases

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Design and control of a Two-Motor-Actuated Tuna-Inspired Robot System

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IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2021年第8期51卷 4670-4680页

作者： Du, Sheng Wu, Zhengxing Wang, Jian Qi, Suwen Yu, Junzhi 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 Shenzhen Univ Sch Med Dept Biomed Engn Shenzhen 518060 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Peking Univ Coll Engn State Key Lab Turbulence & Complex Syst Dept Mech & Engn SciBIC ESAT Beijing 100871 Peoples R China

This article presents the mechanical design and locomotion control of a novel tuna-inspired robot system for both fast swimming and high maneuverability. Mechanically, the developed robotic fish named CasiTuna comprises three important parts, i.e., an innovative two-motor-actuated propulsive mechanism, a buoyancy adjustment structure, and a pair of pectoral fins. Unlike most robotic fishes' multiple concatenated links-based propulsive mechanism, CasiTuna's two-motor-actuated one places both motors in the anterior body and utilizes a transmission system to achieve tuna-like lateral undulations. Meanwhile, the buoyancy adjustment mechanism in conjunction with pectoral fins endows the robot with the capability of three-dimensional maneuverability. Kinematic and dynamic analyses are further conducted to reveal the interactive hydrodynamic forces. Regarding the locomotion control method, a bio-inspired central pattern generator-based controller is adopted to achieve multimodal swimming. In particular, two kinds of turning maneuvers are implemented and discussed. Aquatic experiments, including straight swimming, circular turning, and nearly static pitching validate the effectiveness of proposed mechatronic design and locomotion control methods. Remarkably, CasiTuna achieved a peak forward speed of 0.8 m/s (corresponding to 1.52 body lengths per second) and a minimum turning radius of less than 0.3 body lengths.

关键词： Bio-inspired robot fast swimming maneuverability robotic tuna underwater robot

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Synergetic learning for unknown nonlinear H. control using neural networks

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NEURAL NETWORKS 2023年 168卷 287-299页

作者： Zhu, Liao Guo, Ping Wei, Qinglai Beijing Normal Univ Int Acad Ctr Complex Syst Zhuhai 519087 Guangdong Peoples R China Beijing Normal Univ Sch Syst Sci Beijing 100875 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 Macau Univ Sci & Technol Inst Syst Engn Taipa 999078 Macau Peoples R China

The well-known H. control design gives robustness to a controller by rejecting perturbations from the external environment, which is difficult to do for completely unknown affine nonlinear systems. Accordingly, the immediate objective of this paper is to develop an on-line real-time synergetic learning algorithm, so that a data-driven H. controller can be received. By converting the H. control problem into a two-player zero-sum game, a model-free Hamilton-Jacobi-Isaacs equation (MF-HJIE) is first derived using off-policy reinforcement learning, followed by a proof of equivalence between the MF-HJIE and the conventional HJIE. Next, by applying the temporal difference to the MF-HJIE, a synergetic evolutionary rule with experience replay is designed to learn the optimal value function, the optimal control, and the worst perturbation, that can be performed on-line and in real-time along the system state trajectory. It is proven that the synergistic learning system constructed by the system plant and the evolutionary rule is uniformly ultimately bounded. Finally, simulation results on an F16 aircraft system and a nonlinear system back up the tractability of the proposed method.

关键词： H. control Nonlinear systems Adaptive dynamic programming Temporal difference Neural network Data-driven

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