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IEEE International Geoscience and Remote Sensing Symposium

作者： Xiayuan Huang Xiangli Nie Hong Qiao Bo Zhang State Key Lab of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China State Key Lab of Scientific and Engineering Computing and Institute of Applied Mathematics AMSS Chinese Academy of Sciences Beijing China

Spatial information helps improve the performance of polarimetric synthetic aperture radar (PolSAR) image classification. Some existing methods have combined the spatial information and polarimetric features by the third-order tensor representation for feature extraction. They describe a pixel with the patch centered on this pixel. But they neglect the spatial heterogeneity, which may influence the classification performance. Therefore, we firstly seek k nearest samples based on the polarimetric feature similarity for each pixel to construct the second-order tensor, whose first order denotes the nearest samples and the second order denotes the polarimetric features. Moreover, k nearest samples are searched in a spatial local region rather than the full image, which can exploit the spatial information and reduce the computational burden. Then we employ tensor principal component analysis (TPCA) to extract low-dimensional features. Experimental results demonstrate that the proposed method can improve the classification performance compared with other methods.

关键词： Feature extraction Principal component analysis Visualization Euclidean distance Eigenvalues and eigenfunctions Roads

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A Novel Method for Parkinson's Disease Classification and Dyskinesia Quantification Using Wearable Inertial Sensors

A Novel Method for Parkinson's Disease Classification and Dy...

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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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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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A Brief Research and Lookback of the Development of Iris Recognition

A Brief Research and Lookback of the Development of Iris Rec...

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

作者： Xiaoshuang Li Shenhua Hu Chengkan Lv Haojun Qin Fenghua Zhu Feiyue Wang State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Research Center of Precision Sensing and Control Institute of Automation Chinese Academy of Sciences Beijing China School of electrical engineering and aotumation Wuhan University Wuhan China

Personal identification has been used more and more widely in information society. Iris recognition, as one of numerous identification methods, has already been applied in many situations, because the iris patterns have stable, unique and non-invasive features. We analyze the general process of iris recognition in this paper, including image preprocessing, feature extraction, classification and recognition. We also study the progress in iris recognition brought by deep learning method. It is our belief that traditional approaches play a very significant role in iris recognition field, while deep learning methods will make a great difference to iris recognition.

关键词： Iris recognition Feature extraction Iris Image edge detection Wavelet transforms automation

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AI education system for primary and secondary schools 126

AI education system for primary and secondary schools

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126th ASEE Annual Conference and Exposition: Charged Up for the Next 125 Years, ASEE 2019

作者： Gong, Xiaoyan Zhao, Liang Tang, RenHao Guo, YaNa Liu, Xiwei He, Jian Wang, Fei-Yue Tang, Ying Shi, Wanruo Niu, Xiaojie Wang, Xinzhu Institute of Smart Education Qingdao Academy of Intelligent Industries China Faculty of Information Technology Beijing University of Technology China Qingdao Academy of Intelligent Industries Institute of Automation Chinese Academy of Sciences China National Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences China Rowan University United States Beijing Normal University China

This paper presents an AI education system and related AI curricula specifically designed for primary and secondary students at different cognitive levels. In this system, complicated AI algorithms are encapsulated and modularized with friendly and easy-to-use interfaces. AI curricula are developed based on constructivism, project-based learning and multidisciplinary integration. Typical teaching cases, such as speech recognition, text recognition, image recognition, intelligent transportation, smart home, intelligent robots, etc., which are used to enhance comprehension of AI concepts and applications, are also discussed. © American Society for Engineering Education, 2019

关键词： Curricula

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A Vision-Based Path Planning and Following System for a Miniature Robotic Fish

A Vision-Based Path Planning and Following System for a Mini...

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IEEE International Conference on Real-time Computing and Robotics (RCAR)

作者： Yue Lu Xingyu Chen Zhengxing Wu Yan Yan Junzhi Yu University of Chinese Academy of Sciences Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China State Key Laboratory for Turbulence and Complex System BIC-ESAT College of Engineering Peking University Beijing China

ISBN: (数字)9781728137261

ISBN: (纸本)9781728137278

Most existing path following algorithms for middle-or large-sized underwater vehicles minimize yaw difference of the control object. Breaking the canonical manner, this paper presents a vision-based path planning and following system for miniature robotic fish. As for path planning, the proposed system finds a gird path based on the AStar algorithm and selects a series of target points in the grid path. By connecting these points in order, a curve path can be planned. In terms of path following, the proposed system forgoes to give a specific yaw and only uses a global camera. It obtains coordinates of the robotic fish in real time by KCF tracking algorithm to calculate an approximate motion vector. A PID controller is employed to drive the difference angle between the motion vector and the target vector (the vector from the robotic fish to the target point) to zero. By swimming to target points one by one, the robotic fish can move to the terminus following the planned path. Finally, aquatic experiments are conducted to verify the effectiveness of the vision-based path planning and following system. The results reveal that the developed system can generate a safe and smooth path, and the robotic fish can follow the path with acceptable deviation.

关键词： Fish Robot kinematics Path planning Robot sensing systems Solenoids Magnetic resonance imaging

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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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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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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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