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IEEE International Conference on Cyber Technology in Automation, control, and Intelligent systems

作者： Rongkai Liu Liang Peng Lina Tong Yingjie Wu China University of Mining and Technology (Beijing) Beijing CO China State Key Laboratory of Management and Control for Complex Systems (SKLMCCS) Institute of Automation Chinese Academy of Science Beijing CO China

ISBN: (数字)9781728107707

ISBN: (纸本)9781728107714

This work introduces a novel method for classification and the dyskinesia quantification of Parkinson's disease patients. We merely utilized the data from two 9-axis inertial sensors (only use 3D gyroscope and 3D accelerometer) located at wrist and ankle. And an SVM classifier with RBF kernel was applied for classifying the data frame of PD subjects and controls and finally got an accuracy of 87.1%. Additionally, a pr value was proposed for quantification the dyskinesia into three levels in a fixed sample time period. Combining with the quadratic weighted Kappa coefficient, we evaluated the interrater reliability of artificial quantification and our proposed method in the end (k=0.961). The outcome indicates that our method is effective for identifying the PD patients and healthy individuals and quantitative evaluating the dyskinesia severity levels of PD patients, which has the application prospect of continual illness monitoring and levodopa dosing assessment for PD patients in the future.

关键词： Feature extraction Legged locomotion Gyroscopes Sensors Accelerometers Monitoring Support vector machines

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Parallel Adaptive Critic Designs of Optimal control for Ice-Storage Air Conditioning systems

Parallel Adaptive Critic Designs of Optimal Control for Ice-...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Zehua Liao Qinglai Wei Ruizhuo Song The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China

ISBN: (数字)9781728124858

ISBN: (纸本)9781728124865

As a kind of distributed energy storage system, ice-storage air conditioning (IAC) system is an effective way for transferring cooling load. A parallel adaptive critic design method is constructed for solving the optimal control problem of IAC systems. Adaptive critic design is also called adaptive dynamic programming (ADP). The initial value function of the parallel ADP method is obtained by using particle swarm optimization (PSO) method to pre-train the weights of the neural networks. Another PSO algorithm is utilized in each iteration to achieve the target control, which avoids solving a nonlinear equation in traditional ADP algorithms. Finally, the efficiency of the parallel ADP algorithm will be illustrated through the experiment results.

关键词： Optimal control Heuristic algorithms Air conditioning Neural networks Dynamic programming Particle swarm optimization

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Joint learning with keyword extraction for event detection in social media 16

Joint learning with keyword extraction for event detection i...

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16th IEEE International Conference on Intelligence and Security Informatics, ISI 2018

作者： Chen, Guandan Mao, Wenji Kong, Qingchao Han, Han State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences China University of Chinese Academy of Sciences China CNCERT/CC China

ISBN: (纸本)9781538678480

Event detection for social media is an important social media analytics task in security domain, which can provide valuable information for decision making, intelligence analysis and public management. Traditional event detection methods either rely on simple term frequency based features, or take the bag of words assumption. Recently, some deep neural network based event detection methods are proposed. However, these methods still have some drawbacks. Firstly, they do not provide an effective way to learn the connection between message representation and event representation, but simply use average of message representations as the event representation. Secondly, representations in the hidden space lack interpretability compared to the traditional event keyword representation. To deal with these weaknesses, we propose an event detection approach joint learning with keyword extraction. We provide an episode learning strategy to enable the training of event representation update. In addition, by joint learning with keyword extraction, the model is more explainable, and can achieve a better performance. As the selection of a set of keywords is a combinatorial problem and non-differential, we also employ reinforcement learning in our approach. Experiments on a public available dataset show the superiority of our approach compared with baseline methods. © 2018 IEEE.

关键词： Reinforcement learning

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A Blockchain-based Educational Digital Assets management System

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IFAC-PapersOnLine 2020年第5期53卷 47-52页

作者： Junqing Li Mo Lan Ying Tang Shichao Chen Fei-Yue Wang Wei Wei Shandong University of Science and Technology Qingdao China 266590 The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China 100190 Southwest University Chongqing China 400715 Beijing Achievelearn Education Technology Group Beijing China 100095 Electrical and Computer Engineering Rowan University Glassboro NJ 08003 USA Qingdao Academy of Intelligent Industries Qingdao China 266042 3E Smart Education Beijing China 100083

A variety of heterogeneous information is produced after the application of various emerging technologies in education, which is of great research value as educational assets. The traditional educational asset management system has emerged. In view of the disadvantages of traditional system centralization process, people begin to use decentralized blockchain technology for innovation. This paper proposes an educational digital asset management system architecture based on blockchain 3.0 technology system, which forms various data formed by students in and out of class learning process, courseware and experience data generated by teachers in the process of teaching into educational digital assets, and carries out the right confirmation and storage on the blockchain. The system can use a variety of heterogeneous data in digital assets to analyze the development status of school users, achieve the teaching purpose of teaching students according to their aptitude and different people.

关键词： blockchain blockchain system digital assets educational technology decentralized systems

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From Mind to Products:Towards Social Manufacturing and Service

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IEEE/CAA Journal of Automatica Sinica 2018年第1期5卷 47-57页

作者： Gang Xiong Fei-Yue Wang Timo R. Nyberg Xiuqin Shang Mengchu Zhou Zhen Shen Shuangshuang Li Chao Guo Key Laboratory for Management and Control of Complex Systems Institute of Automation Beijing 100190 China School of Science at Aalto University Aalto FI-00076 Finland Laboratory for Discrete Event Systems New Jersey Institute of Technology NJ-07102 USA Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Beijing 100190 China Qingdao Academy of Intelligent Industries Qingdao 266109 China Cloud Computing Center Dongguan 523808 China IEEE

After reviewing the development of industrial manufacturing, a novel concept called social manufacturing(SM) and service are proposed as an innovative manufacturing solution for the coming personalized customization era. SM can realize a customer's requirements of "from mind to products", and fulfill tangible and intangible needs of a prosumer, i.e., producer and consumer at the same time. It represents a manufacturing trend,and is expected to become popular in more and more ***, a comparison between mass customization and SM is given out, and the basis and motivation from social network to SM is analyzed. Then, its basic theories and supporting technologies,like Internet of Things(Io T), social networks, cloud computing,3 D printing, and intelligent systems, are introduced and analyzed,and an SM platform prototype is developed. Finally, three transformation modes towards SM and 3 D printing are suggested for different user cases.

关键词： Big data cloud computing intelligent system social manufacturing social networks 3D printing

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A Novel Assist-As-Needed controller Based on Fuzzy-Logic Inference and Human Impedance Identification for Upper-Limb Rehabilitation

A Novel Assist-As-Needed Controller Based on Fuzzy-Logic Inf...

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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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