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An Effective On-line Surface Particles Inspection Instrument for Large Aperture Optical Element

International Journal of automation and computing 2017年第4期14卷 420-431页

作者： Wen-Dong Ding Zheng-Tao Zhang Da-Peng Zhang De Xu Hai-Bing Lv Xin-Xiang Miao Guo-Rui Zhou Hao Liu Research Center of Precision Sensing and Control Institute of Automation Chinese Academy of Sciences School of Computer and Control Engineering University of Chinese Academy of Sciences Laser Fusion Research Center China Academy of Engineering Physics

Surface particles growing in large aperture optical element （LAOE） have significant impact on LAOE＇s stable operation. It is a challenge for the online system to inspect the particles with long working distance, enough precision and high efficiency because of the system constraints. In this paper, an effective and portable inspection instrument is designed based on dark-field imaging principle. A Nikon lens and an industrial high definition （HD） camera are selected to construct the vision system to inspect particles of microns size spreading over hundreds of millimeters. Using two motors and other mechanical structure, the system can realize auto-focus and image rectification functions. The line light sources are installed on both sides of the LAOE in a sealed box while the vision system is portable and working outside the box. An adaptive binarization method is proposed to process the captured dark-field image. The distribution of particles on the LAOE＇s surface is investigated. Because of the high resolution of the captured image, the SSE2 instructions optimization method is used to reduce the time cost of the algorithm. Experiments show that the instrument can inspect LAOE effectively and accurately.

关键词： Dark-field imaging image rectification adaptive binarization particle inspection large aperture optical elements（LAOE）.

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PDP: Parallel Dynamic Programming

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IEEE/CAA Journal of Automatica Sinica 2017年第1期4卷 1-5页

作者： Fei-Yue Wang Jie Zhang Qinglai Wei Xinhu Zheng Li Li IEEE State Key Laboratory of Management and Control for Complex Systems(SKL-MCCS) Institute of AutomationChinese Academy of Sciences(CASIA) School of Computer and Control Engineering University of Chinese Academy of Sciences Research Center for Military Computational Experiments and Parallel Systems Technology National University of Defense Technology State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences(SKL-MCCSCASIA) Qingdao Academy of Intelligent Industries Department of Computer Science and Engineering University of Minnesota Department of Automation Tsinghua University

Deep reinforcement learning is a focus research area in artificial intelligence. The principle of optimality in dynamic programming is a key to the success of reinforcement learning methods. The principle of adaptive dynamic programming ADP is first presented instead of direct dynamic programming DP , and the inherent relationship between ADP and deep reinforcement learning is developed. Next, analytics intelligence, as the necessary requirement, for the real reinforcement learning, is discussed. Finally, the principle of the parallel dynamic programming, which integrates dynamic programming and analytics intelligence, is presented as the future computational intelligence. © 2014 Chinese Association of automation.

关键词： Artificial intelligence Neural networks Reinforcement learning

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Deep learning for object detection and grasping: A survey

Deep learning for object detection and grasping: A survey

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2018 IEEE International Conference on Information and automation, ICIA 2018

作者： Jia, Qun Cai, Jun Cao, Zhiqiang Wu, Yelan Zhao, Xionglei Yu, Junzhi 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 Beijing101408 China School of Computer and Information Engineering Beijing Technology and Business University Beijing100048 China

ISBN: (纸本)9781538680698

Detecting and grasping objects in unstructured environments is an important yet difficult task. Fortunately, the breakthroughs from deep convolutional networks stimulate the development of object detection and grasping. The survey aims to serve as a comparison for region-based and region-free detection framework based on deep learning, and supplies the latest research results of object grasping with deep learning. Firstly, we briefly analyze the object detection and grasping. Then, the representative object detection methods based on deep learning are overviewed. Thirdly, we introduce the application of convolutional neural networks in object grasping. Finally, the potential trends in object detection and grasping based on deep learning are discussed. © 2018 IEEE.

关键词： Object detection

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Integrating semi-supervised label propagation and random forests for multi-atlas based hippocampus segmentation 15

Integrating semi-supervised label propagation and random for...

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15th IEEE International Symposium on Biomedical Imaging, ISBI 2018

作者： Zheng, Qiang Fan, Yong Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA19104 United States School of Computer and Control Engineering Yantai University Yantai264005 China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781538636367

A novel multi-atlas based image segmentation method is proposed by integrating a semi-supervised label propagation method and a supervised random forests method in a pattern recognition based label fusion framework. The semi-supervised label propagation method takes into consideration local and global image appearance of images to be segmented and segments the images by propagating reliable segmentation results obtained by the supervised random forests method. Particularly, the random forests method is used to train a regression model based on image patches of atlas images for each voxel of the images to be segmented. The regression model is used to obtain reliable segmentation results to guide the label propagation for the segmentation. The proposed method has been compared with state-of-the-art multi-atlas based image segmentation methods for segmenting the hippocampus in MR images. The experiment results have demonstrated that our method obtained superior segmentation performance. © 2018 IEEE.

关键词： Image segmentation

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Model reduction of linear hybrid systems

arXiv

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arXiv 2020年

作者： Gosea, Ion Victor Petreczky, Mihaly Leth, John Wisniewski, Rafael Antoulas, Athanasios C. Group Max Planck Institute for Dynamics of Complex Technical Systems Sandtorstrasse 1 Magdeburg39106 Germany UMR CNRS 9189 CNRS Ecole Centrale de Lille France Department of Electronic Systems Automation and Control Aalborg University Fredrik Bajers Vej 7C Aalborg Denmark Department of Electrical and Computer Engineering Rice University 6100 Main St MS-366 HoustonTX77005 United States DRI Group Max Planck Institute for Dynamics of Complex Technical Systems Sandtorstrasse 1 Magdeburg39106 Germany Baylor College of Medicine 1 Baylor Plaza HoustonTX77030 United States

The paper proposes a model reduction algorithm for linear hybrid systems, i.e., hybrid systems with externally induced discrete events, with linear continuous subsystems, and linear reset maps. The model reduction algorithm is based on balanced truncation. Moreover, the paper also proves an analytical error bound for the difference between the input-output behaviors of the original and the reduced order model. This error bound is formulated in terms of singular values of the Gramians used for model reduction. Copyright © 2020, The Authors. All rights reserved.

关键词： Linear systems

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A 3D Display Parallel System: Light Field Re-rendering and Depth Sense Optimization

A 3D Display Parallel System: Light Field Re-rendering and D...

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International Conference on Display Technology, ICDT 2018

作者： Pei, Renjing Ma, Kui Wang, Feiyue State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences East Zhongguanchun Road No. 95 Haidian District Beijing China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China Parallel Optics Technology Innovation Center Qingdao Academy of Intelligent Industries Qingdao China

ISBN: (纸本)9781510867659

The main benefit of 3D display over 2D display is the obvious ability to create a more lifelike character with high depth sense. However, the limitation of human eye’s visual mechanism, unartful 3D scene structure design, or bad viewing condition always emerges poor depth perception experience or even physiological discomfort during the watching time, which is often sub-optimal for mass high-quality 3D display productions. To solve this problem, we propose a novel 3D display parallel system for depth sense optimization and it empirically guides how the light field should be re-rendered. Structurally, the parallel system consists of an artificial perception measurement system, a display evaluation model and a light field display rendering system, which includes the display calibration, scene capture, light field data processing and display. Particularly, the system can systematically analyze and model various factors affecting the depth sense which learned through the measurement system, like scene structure, objects’ speeds in 3D video and so on. And those sense factors can be personally modified or increased according to the viewer’s demands or technical improvement. Moreover, the light field could be real-time re-rendered, based on some image processing technology, optical flow analysis and object segmentation (or tracking) (especially the one-shot video segmentation). Theory and algorithms are developed and experimental validation results show a superior performance. © 2018 SID.

关键词： Three dimensional displays

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Primitives Generation Policy Learning without Catastrophic Forgetting for Robotic Manipulation

Primitives Generation Policy Learning without Catastrophic F...

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IEEE International Conference on Data Mining Workshops (ICDM Workshops)

作者： Fangzhou Xiong Zhiyong Liu Kaizhu Huang Xu Yang Amir Hussain State Key Lab of Management and Control for Complex Systems Institute of Automation Chinese Academy of Science China School of Computer and Control University of Chinese Academy of Sciences (UCAS) China CAS Centre for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences China Department of EEE Xi’an Jiaotong-Liverpool University China School of Computing Edinburgh Napier University U.K.

Catastrophic forgetting is a tough challenge when agent attempts to address different tasks sequentially without storing previous information, which gradually hinders the development of continual learning. Except for image classification tasks in continual learning, however, there are little reviews related to robotic manipulation. In this paper, we present a novel hierarchical architecture called Primitives Generation Policy Learning to enable continual learning. More specifically, a generative method by Variational Autoencoder is employed to generate state primitives from task space, then separate policy learning component is designed to learn torque control commands for different tasks sequentially. Furthermore, different task policies could be identified automatically by comparing reconstruction loss in the autoencoder. Experiment on robotic manipulation task shows that the proposed method exhibits substantially improved performance over some other continual learning methods.

关键词： Task analysis Robots Neural networks Learning systems Trajectory Decoding Aerospace electronics

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Adaptive-Learning-Based H∞ Optimal Tracking control Design for a Class of Constrained Nonlinear Systems

Adaptive-Learning-Based H∞ Optimal Tracking Control Design ...

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第37届中国控制会议

作者： Jiaxu Hou Ding Wang Derong Liu School of Automation and Electrical Engineering University of Science and Technology Beijing The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences School of Computer and Control Engineering University of Chinese Academy of Sciences School of Automation Guangdong University of Technology

This paper, considering constrained inputs, focuses on designing the optimal controller for discrete-time H∞ tracking control problems. The neural-network-based iterative learning algorithm is deemed to be an excellent method to work. The iterative heuristic dynamic programming algorithm is employed for developing the formulated regulation of the tracking error. Strict convergence guarantees are given to support the optimality of the adaptive algorithm. A neural-network-based training scheme is adopted to implement the learning algorithm and stabilize the system in the optimal manner. Two numerical examples are provided to illustrate the applicability and good performance of the designed tracking control scheme.

关键词： Adaptive dynamic programming H∞ tracking control constraints convergence proof

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Universal enzymatic numerical P systems with small number of enzymatic variables

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Science China(Information Sciences) 2018年第9期61卷 38-49页

作者： Zhiqiang ZHANG Tingfang WU ANDrei P■UN Linqiang PAN Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Automation Huazhong University of Science and Technology Department of Computer Science Faculty of Mathematics and Computer ScienceUniversity of Bucharest Bioinformatics Department National Institute of Research and Development for Biological Sciences School of Electric and Information Engineering Zhengzhou University of Light Industry

Numerical P systems(for short, NP systems) are distributed and parallel computing models inspired from the structure of living cells and economics. Enzymatic numerical P systems(for short, ENP systems) are a variant of NP systems, which have been successfully applied in designing and implementing controllers for mobile robots. Since ENP systems were proved to be Turing universal, there has been much work to simplify the universal systems, where the complexity parameters considered are the number of membranes, the degrees of polynomial production functions or the number of variables used in the *** the number of enzymatic variables, which is essential for ENP systems to reach universality, has not been investigated. Here we consider the problem of searching for the smallest number of enzymatic variables needed for universal ENP systems. We prove that for ENP systems as number acceptors working in the all-parallel or one-parallel mode, one enzymatic variable is sufficient to reach universality; while for the one-parallel ENP systems as number generators, two enzymatic variables are sufficient to reach *** results improve the best known results that the numbers of enzymatic variables are 13 and 52 for the all-parallel and one-parallel systems, respectively.

关键词： bio-inspired computing numerical P system mobile robot membrane controller universality

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5G-ALLSTAR: An Integrated Satellite-Cellular System for 5G and Beyond

5G-ALLSTAR: An Integrated Satellite-Cellular System for 5G a...

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IEEE Wireless Communications and Networking Conference Workshops (WCNCW)

作者： Junhyeong Kim Guido Casati Antonio Pietrabissa Alessandro Giuseppi Emilio Calvanese Strinati Nicolas Cassiau Gosan Noh Heesang Chung Ilgyu Kim Marjorie Thary Jean-Michel Houssin Federico Pigni Sylvain Colombero Pierre Dal Zotto Leszek Raschkowski Stephan Jaeckel Future Mobile Communication Research Division ETRI Daejeon Korea Broadband and Broadcast Department Fraunhofer IIS Erlangen Germany Department of Computer Control and Management Engineering University of Rome “Sapienza” Rome Italy Space Research Group Consortium for the Research in Automation and Telecommunication (CRAT) Rome Italy CEA-Leti Grenoble France CEA-Leti MINATEC Campus Grenoble France Thales Alenia Space Toulouse France Department of Management Technology and Strategy Grenoble Ecole de Management - Univ Grenoble Alpes ComUE Grenoble France Wireless Communications and Networks Department Fraunhofer Institute for Telecommunications Heinrich Hertz Institute HHI Berlin Germany Stephan Jaeckel Consulting Germany

ISBN: (数字)9781728151786

ISBN: (纸本)9781728151793

This paper provides an overview of recent research activities of the 5G AgiLe and fLexible integration of SaTellite And cellulaR (5G-ALLSTAR) project which aims to develop Multi-Connectivity technology that integrates the cellular and satellite networks to provide seamless, reliable and ubiquitous broadband services. 5G-ALLSTAR also entails developing millimeter-wave (mmWave) 5G New Radio (NR)-based cellular access system and investigating the feasibility of NR-based satellite access for providing broadband and reliable 5G services. In addition, spectrum sharing between cellular and satellite networks is studied. With all the technologies developed, 5G-ALLSTAR will showcase the first fully integrated satellite and cellular prototype system for 5G and beyond 5G (B5G) services at a big event (e.g., sporting event like Roland-Garros) in 2021. This paper also provides a preliminary techno-economic analysis on potential use cases targeting vertical markets, and introduces recent standardization activities of relevance.

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