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IEEE/ASME (AIM) International Conference on Advanced Intelligent Mechatronics

作者： Enhao Zheng Jinchen Zeng Dongfang Xu Qining Wang Hong Qiao State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences No. 95 of Zhongguancun East Road Beijing China School of Automation and Electrical Engineering University of Science and Technology Beijing 30 Xueyuan Road Beijing Robotics Research Group College of Engineering Peking University Beijing China

ISBN: (数字)9781728167947

ISBN: (纸本)9781728167954

Noncontact captive sensing is a new sensing strategy that we proposed previously to compensate for the limitations of existing surface electromyography studies for exoskeleton control. It has been validated on locomotion mode recognition and gait phase estimation. However, our previous studies addressed the tasks of periodic ambulation based on machine learning algorithms. The performances of the capacitive sensing on non-periodical lower-limb motion recognition have never been evaluated. In this preliminary study, we designed a motion recognition method by fusing the capacitive sensing and the inertial sensors. The recognition algorithm was designed based on the combined logics, which freed the system from burdensome training procedures in the recognition tasks. The method was validated on three healthy subjects in performing 6 lower-limb motions and the transitions between them (10 in total). The capacitance-inertial fusion method produced an average precision/recall of >0.92 in static motions and >0.86 with transitions. The most prominent improvement of using the capacitance signals is that it increases the time-response ability during the motion transitions. Compared with purely using inertial sensors, the sensor fusion method reduced more than 100-ms latency on average. The pilot study extends the scope of the new sensing method in human motion recognition. Future efforts will be paid in this direction to get more promising results.

关键词： Robot sensing systems Capacitance Electrodes Task analysis Legged locomotion Exoskeletons

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Effect of heterogeneous risk perception on information diffusion, behavior change, and disease transmission

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Physical Review E 2020年第4期102卷 042314-042314页

作者： Yang Ye Qingpeng Zhang Zhongyuan Ruan Zhidong Cao Qi Xuan Daniel Dajun Zeng School of Data Science City University of Hong Kong Hong Kong SAR China Institute of Cyberspace Security Zhejiang University of Technology Hangzhou Zhejiang China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Shenzhen Artificial Intelligence and Data Science Institute Shenzhen Guangdong China

Motivated by the importance of individual differences in risk perception and behavior change in people's responses to infectious disease outbreaks (particularly the ongoing COVID-19 pandemic), we propose a heterogeneous disease-behavior-information transmission model, in which people's risk of getting infected is influenced by information diffusion, behavior change, and disease transmission. We use both a mean-field approximation and Monte Carlo simulations to analyze the dynamics of the model. Information diffusion influences behavior change by allowing people to be aware of the disease and adopt self-protection and subsequently affects disease transmission by changing the actual infection rate. Results show that (a) awareness plays a central role in epidemic prevention, (b) a reasonable fraction of overreacting nodes are needed in epidemic prevention (c) the basic reproduction number R0 has different effects on epidemic outbreak for cases with and without asymptomatic infection, and (d) social influence on behavior change can remarkably decrease the epidemic outbreak size. This research indicates that the media and opinion leaders should not understate the transmissibility and severity of diseases to ensure that people become aware of the disease and adopt self-protection to protect themselves and the whole population.

关键词： Epidemic Spreading Multilayer & multiplex networks Social networks

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Research on Track and Positioning Estimation of Autonomous Vehicles Based on Multi-sensor Fusion

Research on Track and Positioning Estimation of Autonomous V...

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第六届国际仿生工程学术大会暨2019年自然启迪科技国际研讨会

作者： Haibin Huang Yun Pei Wuling Huang Yaxia Liu Beijing Institute of Fashion Technology Jilin University The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences

Path estimation of unmanned vehicles is relative positioning. Relative positioning mainly depends on internal somatosensory sensors such as odometers and gyroscopes. The current position of the unmanned vehicle is determined by the displacement of the unmanned vehicle relative to the initial position. Target tracking based on driverless vehicles. This paper proposes an iterative iteration of integration, that is, a linear distance of movement in a very short time instead of a section of the trajectory of the vehicle. At the same time, to solve the problem that a single sensor has its own limited amount of information. Multi-sensor information data combining odometer(ie track derivation) data with the number of attitudes of the vehicle through extended Kalman filter fusion, estimating optimal vehicle trajectory to achieve positioning. The algorithm is validated by a miniature version of a driverless vehicle experiment platform equipped with a ROS(Robot Operating System) robot operating system.

关键词： Unmanned Cycling kinematics model Multi-sensor information EKF ROS

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Analyzing the Evolutionary Characteristics of the Cluster of COVID-19 under Anti-contagion Policies

Analyzing the Evolutionary Characteristics of the Cluster of...

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IEEE International Conference on Intelligence and Security Informatics, ISI

作者： Pu Miao Hu Tian Xingwei Zhang Saike He Xiaolong Zheng Desheng Dash Wu Daniel Zeng The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Stockholm Business School Stockholm University Stockholm Sweden

ISBN: (数字)9781728188003

ISBN: (纸本)9781728188010

With the rampaging of Coronavirus disease 2019 (COVID-19) across the world, analyzing the dynamic characteristics and understanding the evolutionary patterns of clusters are becoming even more crucial for people and policymakers to make timely responses for avoiding injury caused by COVID-19. To solve the scarcity of the fine-grained spatiotemporal data, we construct a novel dataset about the spread of patients during the resurgent period of the COVID-19 epidemic at the Xinfadi Market in Beijing. Leveraging our self-build dataset, we analyze the evolutionary characteristics of the cluster of COVID-19 under anti-contagion policies and obtained some remarkable evolution patterns. These findings can provide significant insights for policymakers and researchers to understand the evolutionary characteristics regarding the cluster of COVID-19 and deploy effective anti-contagion policies.

关键词： COVID-19 Epidemics Simulation Sociology Market research Spatiotemporal phenomena Statistics

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Short-term wind power forecast with turning weather based on DBSCAN-RFE-LightGBM

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RENEWABLE ENERGY 2025年 251卷

作者： Wu, Zhenlong Fan, Xinyu Bian, Guibin Liu, Yanhong Zhang, Xiaoke Chen, Yangquan Zhengzhou Univ Sch Elect & Informat Engn Zhengzhou 450001 Peoples R China Zhengzhou Univ State Key Lab Intelligent Agr Power Equipment Luoyang 471000 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China State Grid Henan Elect Power Co Elect Power Res Inst Zhengzhou 450052 Peoples R China Univ Calif Merced Mechatron Embedded Syst & Automation MESA Lab Dept Mech Engn Sch Engn Merced CA 95343 USA

At present, the wind power prediction technology is becoming more and more mature, however, the turning weather brings a decrease in prediction accuracy due to its suddenness and instability. In this paper, a wind power prediction method considering the turning period and non-turning period respectively is proposed to weaken the adverse effects caused by turning weather. Firstly, the prediction effects of multiple models are compared, density-based spatial clustering of applications with noise (DBSCAN) is selected as the outlier processing method, recursive feature elimination (RFE) is used as the feature selection method, and Light gradient boosting machine (LightGBM) is used for prediction. The combined prediction method based on DBSCAN-RFE-LightGBM can reduce the influence of abnormal data and redundant features and improve the prediction effect. Then, the sliding window is set to detect the turning period. Considering that the turning weather is an emergency and does not happen frequently, the amount of data is small, which leads to the inability to train the model well. Generative adversarial networks (GAN) are applied to expand the turning period data. The LightGBM is trained and predicted by using the expended turning period data. Finally, the time-division prediction results are merged. Using the data collected from wind farms for short-term power prediction experiments, the time-segment prediction method proposed in this paper with GAN reduces MAE by 1.913 and RMSE by 3.351 on a single unit compared with the non-differentiated period.

关键词： Turning weather Combination model Generative adversarial networks Multi-unit Wind power forecast DBSCAN-RFE-lightGBM

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Effect of compliant passive joint on swimming performance for a multi-joint robotic fish

Effect of compliant passive joint on swimming performance fo...

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2019 IEEE International Conference on Robotics and Biomimetics, ROBIO 2019

作者： Chen, Di Wu, Zhengxing Yu, Junzhi Institute of Automation CAS State Key Lab Management and Control for Complex Systems Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Dept. Mech. Eng. Sci. BIC-ESAT College of Engineering Peking University Beijing100871 China

ISBN: (纸本)9781728163215

In this paper, a novel compliant passive joint with two torsion springs is designed for a multi-joint robotic fish to improve propulsive performance. The motion of the multi-joint robotic fish is governed by the central pattern generator (CPG) control method. Using this multi-joint robotic fish as a testbed, the effect of compliant passive joint on swimming speed with changes in spring constant and CPG parameters such as oscillation frequency and phase difference is explored. As for the multi-joint robotic fish with a compliant passive joint, the swimming speed increases directly with the increase of oscillation frequency and the swimming speed achieved at maximum oscillation frequency increases with the decrease of phase difference. In addition, the comparison of swimming performance between robotic fish with a compliant passive joint and a fixed passive joint is made. Remarkably, the maximum swimming speed after improvement increases approximately 9.43 cm/s, corresponding to 0.17 BL/s. Experimental results reveal that adding an extra compliant passive joint can not always bring an improvement in swimming performance. Appropriate stiffness of passive joint and control parameters are needed to achieve better swimming performance. © 2019 IEEE.

关键词： Robotics

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Finite-time Passive control of Networked Semi-Markov Jump systems with Time-varying Delays

Finite-time Passive Control of Networked Semi-Markov Jump Sy...

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第三十八届中国控制会议

作者： Yidao Ji Wei Wu School of Automation and Electrical Engineering University of Science and Technology Beijing State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences

This paper studies the finite-time control issue of semi-Markov jump systems with time-varying delays based under passivity framework. Firstly, sufficient criterion is derived to ensure that finite-time boundedness condition can be satisfied and the desired passivity performance can be achieved simultaneously. Then, state-feedback controller procedure is presented with help of linear matrix inequalities(LMIs). Finally, a simulation example is provided for showing the applicability of the developed method.

关键词： finite-time boundedness semi-Markov jump systems time-varying delays passivity

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A Two-Stage Learning Framework for Driver Lane Change Intention Inference

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IFAC-PapersOnLine 2020年第5期53卷 638-643页

作者： Yang Xing Bin Tian Chen Lv Dongpu Cao School of Mechanical and Aerospace Engineering Nanyang Technological University 639798 Singapore State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences and School of Artificial Intelligence University of Chinese Academy of Sciences Beijing 100190 China Department of Mechanical and Mechatronics Engineering University of Waterloo ON N2L3G1 Canada

With the rapid development of intelligent vehicles, human drivers are sharing the control authority with the automation functionalities. The mutual understanding between the intelligent vehicle and the human driver is the key to the efficient multi-agent teaming and the collaborative driving system. In this study, a two-stage learning framework for spatial-temporal driver lane change intention inference is developed. The two-stage learning framework contains two separate parts. First, an operational level driving behavior recognition system based on the deep convolutional neural network (CNN) is developed to recognize the driver behaviors, including mirror checking and normal driving. Then, based on the driver behavior features from the CNN, the sequential feature vector will be used for the construction of driver intention inference model based recurrent neural networks (RNN) and long short-term memory (LSTM). The proposed model achieved a state-of-the-art result on the driver lane change intention inference task. Based on the naturalistic driving dataset, the model achieved over 91% accuracy for lane change and lane-keeping intention prediction. The two-stage learning framework can significantly increase the model flexibility and accuracy, which makes it easier to be implemented and updated in intelligent vehicles.

关键词： Driver Behavior Driver Model Deep Learning LSTM Intelligent Vehicle

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Switching function-based optimal active fault-tolerant bipartite consensus control for UAV swarm

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Automatica 2025年 179卷

作者： Pei-Ming Liu Xiang-Gui Guo Jian-Liang Wang Hao Lu Zheng-Guang Wu Beijing Engineering Research Center of Industrial Spectrum Imaging School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing 100083 China Shunde Innovation School University of Science and Technology Beijing Foshan 528000 China Collaborative Flying Robotics Lab Hangzhou Innovation Institute of Beihang University Hangzhou 310051 China State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou 310027 China

This paper studies the problem of optimal active fault-tolerant bipartite consensus control for flying wing unmanned aerial vehicle (UAV) swarm with nonidentical and unknown direction faults (NUDFs) and disturbances by integrating switching and saturation functions. An observer–controller framework is employed to prevent error propagation among follower UAVs. Based on this framework, an active fault-tolerant control strategy is proposed to improve the system’s transient performance. This strategy prevents sudden shocks from excessive reverse inputs on the system and avoids state chattering caused by excessive adjustment inputs, which often occur in the Nussbaum function-based fault-tolerant control method. Additionally, the newly proposed switching criterion directly matches the desired operating mode, even in the presence of disturbances and multiple fault direction changes, thus avoiding ineffective switching and enhancing the robustness of the designed controller. To further improve the UAVs’ transient performance, a reinforcement learning (RL)-based preset performance backstepping control method is introduced. This method optimizes output regulation, saves energy consumption, and reduces the impact of soft saturation on system performance. Finally, simulation results validate the effectiveness and superiority of the proposed scheme.

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Optimal vaccination program for two infectious diseases with cross immunity

arXiv

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arXiv 2020年

作者： Ye, Yang Zhang, Qingpeng Cao, Zhidong Zeng, Daniel Dajun School of Data Science City University of Hong Kong Hong Kong Hong Kong The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Shenzhen China

There are often multiple diseases with cross immunity competing for vaccination resources. Here we investigate the optimal vaccination program in a two-layer Susceptible-Infected-Removed (SIR) model, where two diseases with cross immunity spread in the same population, and vaccines for both diseases are available. We identify three scenarios of the optimal vaccination program, which prevents the outbreaks of both diseases at the minimum cost. We analytically derive a criterion to specify the optimal program based on the costs for different vaccines. Copyright © 2020, The Authors. All rights reserved.

关键词： Vaccines

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