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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Chen Wang Liang Peng Zeng-Guang Hou Weiqun Wang Tingting Su State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China Department of Mechanical and Electrical Engineering North China University of Technology Beijing P.R. China

ISBN: (数字)9781728124858

ISBN: (纸本)9781728124865

Upper-limb rehabilitation robots are being increasingly included in the rehabilitation after stroke. Clinical studies suggest that the patient's active participation can maximize effectiveness of robot-assisted rehabilitation and result in fastest possible recovery. In order to promote active involvement of the patient, we develop a subject-adaptive controller within the assist-as-needed (AAN) paradigm. The controller employs Recursive Least Square (RLS) algorithm to identify human arm impedance parameters, with aims to quantify the residual motor capability of the patient. According to the upper-limb impedance of the patient in the direction along movements, the reference trajectory can be generated based on the motion patterns in healthy humans. In addition, the fuzzy logic control strategy is implemented in the direction perpendicular to movements, which is capable of adjusting the robotic assistance level by considering both upper-limb impedance of the patient and the variations of deviation between the reference trajectory and actual trajectory. Furthermore, the proposed subject-adaptive AAN controller was validated in the simulation study, and the results demonstrated the capability of the controller to modulate the assistance level in accordance with the subject's instantaneous requirements. Future works will focus on the implementation of the control scheme with post-stroke patients.

关键词： Impedance Trajectory Fuzzy logic Training Rehabilitation robotics Task analysis

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Hindsight generative adversarial imitation learning

arXiv

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arXiv 2019年

作者： Liu, Naijun Lu, Tao Cai, Yinghao Li, Boyao Wang, Shuo State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Techology Chinese Academy of Sciences

Compared to reinforcement learning, imitation learning (IL) is a powerful paradigm for training agents to learn control policies efficiently from expert demonstrations. However, in most cases, obtaining demonstration data is costly and laborious, which poses a significant challenge in some scenarios. A promising alternative is to train agent learning skills via imitation learning without expert demonstrations, which, to some extent, would extremely expand imitation learning areas. To achieve such expectation, in this paper, we propose Hindsight Generative Adversarial Imitation Learning (HGAIL) algorithm, with the aim of achieving imitation learning satisfying no need of demonstrations. Combining hindsight idea with the generative adversarial imitation learning (GAIL) framework, we realize implementing imitation learning successfully in cases of expert demonstration data are not available. Experiments show that the proposed method can train policies showing comparable performance to current imitation learning methods. Further more, HGAIL essentially endows curriculum learning mechanism which is critical for learning policies. Copyright © 2019, The Authors. All rights reserved.

关键词： Demonstrations

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Modal regression based structured low-rank matrix recovery for multi-view learning

arXiv

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arXiv 2020年

作者： Xu, Jiamiao Wang, Fangzhao Peng, Qinmu You, Xinge Wang, Shuo Jing, Xiao-Yuan Philip Chen, C.L. School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute China State Key Laboratory of Software Engineering School of Computer Wuhan University China Department of Computer and Information Science Faculty of Science and Technology University of Macau 99999 China Dalian Maritime University Dalian116026 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100080 China

Low-rank Multi-view Subspace Learning (LMvSL) has shown great potential in cross-view classification in recent years. Despite their empirical success, existing LMvSL based methods are incapable of well handling view discrepancy and discriminancy simultaneously, which thus leads to the performance degradation when there is a large discrepancy among multi-view data. To circumvent this drawback, motivated by the block-diagonal representation learning, we propose Structured Low-rank Matrix Recovery (SLMR), a unique method of effectively removing view discrepancy and improving discriminancy through the recovery of structured low-rank matrix. Furthermore, recent low-rank modeling provides a satisfactory solution to address data contaminated by predefined assumptions of noise distribution, such as Gaussian or Laplacian distribution. However, these models are not practical since complicated noise in practice may violate those assumptions and the distribution is generally unknown in advance. To alleviate such limitation, modal regression is elegantly incorporated into the framework of SLMR (term it MR-SLMR). Different from previous LMvSL based methods, our MR-SLMR can handle any zero-mode noise variable that contains a wide range of noise, such as Gaussian noise, random noise and outliers. The alternating direction method of multipliers (ADMM) framework and half-quadratic theory are used to efficiently optimize MR-SLMR. Experimental results on four public databases demonstrate the superiority of MR-SLMR and its robustness to complicated noise. Copyright © 2020, The Authors. All rights reserved.

关键词： Gaussian noise (electronic)

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An image processing method for green apple lesion detection in natural environment based on GA-BPNN and SVM 15

An image processing method for green apple lesion detection ...

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15th IEEE International Conference on Mechatronics and Automation, ICMA 2018

作者： Tian, Yunong Li, En Yang, Lei Liang, Zize 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

ISBN: (纸本)9781538660720

The technologies of information processing, especially the image processing, are important technical means to realize intelligent management and control of modern orchard. In order to realize the intelligentization of orchard production, the orchard image database, especially the lesion image database needs to be set up. Because of the uneven illumination and the complex background under the natural light condition, traditional image segmentation methods cannot solve the adaptive threshold issue during the lesion image processing of green apples. This paper proposes an image processing method based on a BP neural network updated by genetic algorithm (GA-BPNN) and support vector machine (SVM) to realize lesion image processing of green apples in orchard. With this method, apple images can be processed in batch and the lesion image database can be consummated automatically. Furthermore, the recognition of the diseased apple is realized. The experimental results show that the proposed method can obtain green apple segmentation and lesion detection with good efficiency and robustness in complex natural orchard environment. © 2018 IEEE.

关键词： Support vector machines

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A vision-based robotic grasping approach under the disturbance of obstacles 15

A vision-based robotic grasping approach under the disturban...

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15th IEEE International Conference on Mechatronics and Automation, ICMA 2018

作者： Zhao, Xionglei Cao, Zhiqiang Jia, Qun Pang, Lei Yu, Yingying Tan, Min 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

ISBN: (纸本)9781538660720

This paper presents a vision-based robotic grasping approach with complex obstacles environments. A deep learning-based object detection algorithm is used to detect object in the image and obtain the object's category and position. Then the Euclidean cluster extraction algorithm is adopted to segment scenes composed of 3D point clouds to obtain the positions and size of obstacles. According to the acquired information of the object and obstacles, one can judge whether the object can be directly grasped. If there is no direct solution, the obstacles that interfere with the grasping shall be firstly moved to other positions, then the object is grasped. These new positions of interference obstacles are selected based on artificial potential field. The experimental results on the Kinova MICO2 arm demonstrate that the approach can effectively achieve the grasping of target object even with severe interference from obstacles. © 2018 IEEE.

关键词： Object detection

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Security Assessment of a Partially Decentralized Blockchain System

Security Assessment of a Partially Decentralized Blockchain ...

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IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI

作者： Yiwen Liu Qingqing Ji Quankai Zheng Huaiyu Wu Zhihai Wang Gang Xiong Faculty of Information The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing University of Technology Beijing China University of Chinese Academy of Sciences Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Economics Finance and Management University of Bristol Bristol UK The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Faculty of Information Beijing University of Technology Beijing China

As a new technology with subversive significance in the Internet field, blockchain has received extensive attention from research and applications sector in recent years. Its decentralized structure and unchangeable data nature have a wide application prospect in Internet transactions. However, it is difficult to implement supervision in a partially decentralized system. Therefore, a blockchain system with a single or a few central nodes has emerged during the development of blockchain. For such special partially decentralized blockchain systems, more scientific development is needed. This paper takes X insurance trading chain as an example to study this partially decentralized blockchain system, to make a more scientific security assessment of this kind of system, and put forward corresponding countermeasures to improve the security of this kind of system.

关键词： Blockchain Insurance Security Distributed databases Online banking Contracts

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Research and Application of Industrial Equipment management Service System Based on Cloud-Edge Collaboration

Research and Application of Industrial Equipment Management ...

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Chinese Automation Congress (CAC)

作者： Hua Zhang Shichao Chen Ping Zou Gang Xiong Hongxia Zhao Yicun Zhang Platform Research and Development Department Beijing Aerospace Smart Manufacturing Technology Development Co.Ltd Beijing China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Macau University of Science and Technology Beijing China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Product Department Zhejiang Hanshow Technology Co.Ltd Beijing China

With the integration development of Internet of Things, edge computing, cloud computing and artificial intelligence, the computing paradigm centered on traditional cloud computing has begun to integrate the edge computing paradigm, forming an industrial Internet of Things revolution based on Cloud-Edge collaboration. It not only can improve the response speed of industrial field system and reduce the load pressure of network bandwidth caused by data transmission, but also can realize the refined management of industrial devices and the deep mining of industrial data by on the cloud platform. In short, the cloud platform and the edge platform can play their respective advantages and continue to promote the industrial Internet of Things to the intellectualized development. In this paper, it started from the system architecture, and an industrial equipment management service system based on Cloud-Edge collaboration was discussed, including the design of equipment management service cloud platform and edge intelligent gateway in detail. The central airconditioning energy consumption monitoring and optimization application based on INDICS platform and SMART IOT edge intelligent gateway were introduced, to verify system availability.

关键词： Cloud computing Collaboration Logic gates Data models Memory Edge computing Internet of Things

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Comparative Study of Four Speed controllers of Brushless DC Motors for Industrial Applications

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IFAC-PapersOnLine 2020年第5期53卷 59-64页

作者： Tariku Sinshaw Tamir Gang Xiong Zhen Shen Xiaoyan Gong Sheng Liu Ehtisham Lodhi Li Wan Xisong Dong State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing 100049 China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Donggguan 523808 China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing 100190 China Ten Dimensions (Guangdong) Technology Co. Ltd. Foshan 528225 China

Direct current (DC) motors are one of the most important kind of motors and are widely used in robotic and industrial applications. Recently, there have been significant efforts to develop direct current (DC) motors in an attempt to control speed of motors. However, conventional controlling approaches perform undesirably in terms of stability and quick response. Therefore, this paper presents a hybrid intelligent controller configuration for optimized speed control of brushless direct current (BLDC) motors in a factory supervisory control data acquisition (SCADA) system. We compare this hybrid intelligent controller with a conventional PID controller, fuzzy logic controller (FLC), and artificial neural network model reference controller (ANNMRC) in MATLAB , and the results show that the hybrid (neuro-fuzzy) controller performs superior in terms of stability, speed trajectory tracking capability, fast response, and simplicity for implementation.

关键词： BLDC motor fuzzy logic controller (FLC) artificial neural network model reference controller (ANNMRC) hybrid (neuro-fuzzy) control

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A control chart approach to power system line outage detection under transient dynamics

arXiv

引用

arXiv 2019年

作者： Yang, Xiaozhou Chen, Nan Zhai, Chao Department of Industrial Systems Engineering and Management National University of Singapore Singapore117576 Singapore School of Automation China University of Geosciences Wuhan430074 China Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China

Online transmission line outage detection over the entire network enable timely corrective actions to be taken, which prevents a local event from cascading into a large scale blackout. Line outage detection aims to detect an outage as soon as possible after it happened. Traditional methods either do not consider the transient dynamics following an outage or require a full Phasor Measurement Unit (PMU) deployment. Using voltage phase angle data collected from a limited number of PMUs, we propose a real-time dynamic outage detection scheme based on alternating current (AC) power flow model and statistical change detection theory. The proposed method can capture system dynamics since it retains the time-variant and nonlinear nature of the power system. The method is computationally efficient and scales to large and realistic networks. Extensive simulation studies on IEEE 39-bus and 2383-bus systems demonstrated the effectiveness of the proposed method. Copyright © 2019, The Authors. All rights reserved.

关键词： Anomaly detection

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Correction: COVID-19 Vaccine Tweets After Vaccine Rollout: Sentiment-Based Topic Modeling

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Journal of medical Internet research 2022年第3期24卷 e37841页

作者： Luwen Huangfu Yiwen Mo Peijie Zhang Daniel Dajun Zeng Saike He Fowler College of Business San Diego State University San Diego CA United States. Center for Human Dynamics in the Mobile Age San Diego State University San Diego CA United States. The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China. University of Chinese Academy of Sciences Beijing China.

[This corrects the article DOI: 10.2196/31726.].

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