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作者： Cai, Xinyuan Wang, Chunheng Xiao, Baihua Shao, Yunxue State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

Face verification is the task of determining whether two given face images represent the same person or not. It is a very challenging task, as the face images, captured in the uncontrolled environments, may have large variations in illumination, expression, pose, background, etc. The crucial problem is how to compute the similarity of two face images. Metric learning has provided a viable solution to this problem. Until now, many metric learning algorithms have been proposed, but they are usually limited to learning a linear transformation. In this paper, we propose a nonlinear metric learning method, which leams an explicit mapping from the original space to an optimal subspace using deep Independent Subspace Analysis (ISA) network. Compared to the linear or kernel based metric learning methods, the proposed deep ISA network is a deep and local learning architecture, and therefore exhibits more powerful ability to learn the nature of highly variable dataset. We evaluate our method on the Labeled Faces in the Wild dataset, and results show superior performance over some state-of-the-art methods.© 2013 The Institute of Electronics, Information and Communication Engineers.

关键词： Learning algorithms

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Non-Gaussian Approximation Iterative Receiver for MIMO-OFDM Vehicular Networks

Non-Gaussian Approximation Iterative Receiver for MIMO-OFDM ...

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17th International IEEE Conference on Intelligent Transportation systems & The Asia-Pacific Council on systems Engineering Conference 2014

作者： Shuangshuang Han Liuqing Yang Xiang Cheng Shichao Chen Fenghua Zhu Zhongdong Yu Haitao Xie Qingdao Academy of Intelligent Industries State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences School of Electronics Engineering and Computer Science Peking University Guang Zhou Communication Information Construction Investment and Operation Co.Ltd.

Multiple-Input Multiple-Output- Orthogonal Frequency Division Multiplexing(MIMO-OFDM) is adopted to vehicular networks to increase the capacity,reliability and *** this paper,iterative demodulation and decoding algorithms are studied to approach the capacity of MIMOOFDM vehicular *** analysing the drawbacks of the Gaussian approximation on the interference cancellation,NonGaussian approximation is proposed to enhance the performance of interference cancellation based detectors with large *** results demonstrate that the proposed non-Gaussian algorithm can achieve a significant performance gain over existing ones with high order constellations.

关键词： Gaussian Non-Gaussian Approximation Iterative Receiver for MIMO-OFDM Vehicular Networks

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Unmanned Aerial Vehicles (UAV) Heading Optimal Tracking control Using Online Kernel-based HDP Algorithm

Unmanned Aerial Vehicles (UAV) Heading Optimal Tracking Cont...

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International Joint Conference on Neural Networks

作者： Fuxiao Tan Derong Liu Xinping Guan Bin Luo School of Computer and Information Fuyang Teachers College State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University School of Computer Science and Technology Anhui University

ISBN: (纸本)9781479914821

UAV can work in places that are dangerous, or not easy to reach for humans. However, due to active control and operating difficulties, it is still a challenge to develop fully autonomous flight in complex environments. This paper applies a novel heuristic dynamic programming for the UAV heading optimal tracking controller design, using kernel-based heuristic dynamic programming (KHDP). Kernel-based HDP is developed by integrating kernel methods and approximately linear dependence (ALD) analysis with the critic learning of HDP algorithm. Compared with conventional HDP where neural networks are widely used and their features were manually designed, the proposed algorithm can obtain better generalization capability and learning efficiency through applying the sparse kernel machine into the critic learning process of HDP algorithm. Simulation and experimental results of UAV heading optimal tracking control problems demonstrate the effectiveness of the proposed kernel-based HDP algorithm.

关键词： Kernel Algorithm design and analysis Heuristic algorithms Equations Vectors Approximation algorithms Mathematical model

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Social manufacture cloud mode in high-end apparel, footwear and hats

Social manufacture cloud mode in high-end apparel, footwear ...

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World Congress on Intelligent control and Automation (WCICA)

作者： Xiuqing Shang Baoli Su Xiwei Liu Gang Xiong Zengbo You The State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China Dongguan Research Institute of CASIA Chinese Academy of Sciences Cloud Computing Center Dongguan Guangdong Province China Beijing Engineering Research Center of Intelligent Systems and Technology

This paper expounds the high-end apparel footwear and hats manufacture industry society mode of cloud services, achieve consumers actively participate in the high-end apparel, footwear and hats industry of mass customization services. Through social production theory and the combination of cloud computing services, use many kinds of 3D technology, new process technology of cooperative management, e-commerce, logistics and wisdom dynamic value chain analysis and so on the many kinds of technologies, constructed with size and cost of mass production, customization of new clothing, footwear and hats business model, and implements all participate in the design, all involved in manufacture, and everyone involved for “a new type of manufacture and service mode, will effectively drive the transformation and upgrading of industries, and bring new economic growth.

关键词： Business Collaboration Clothing Crowdsourcing Three-dimensional displays Computational modeling

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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Behavior Modeling of Internet Water Army in Online Forums

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IFAC Proceedings Volumes 2014年第3期47卷 9858-9863页

作者： Ke Zeng Xiao Wang Qingpeng Zhang Xinzhan Zhang Fei-Yue Wang The School of Electronic and Information Engineering Xi'an Jiaotong University Xi'an 710049 China The State Key Laboratory of Management and Control of Complex Systems Institute of Automation Chinese Academy of Science Beijing 100190 China Tetherless World Constellation Rensselaer Polytechnic Institute Troy NY 12180 USA Software Engineering Xi'an Jiaotong University Xi'an 710049 China

The behavior patterns and strategies of Internet Water Army in online forums are investigated in this paper. Internet Water Army focuses on the controlling and steering of cyber collective opinions, and adjusts their behavior according to two principles: to avoid being exposed and to increase the ability to exert influence. To study how the ability of Internet Water Army to exert influence, we construct a multi-agent system with coevolution of topics and cyber collective behaviors and design the behavior patterns and strategies of Internet Water Army. Based on synthetic data and real data, we find that Internet Water Army dynamically adjusts their behavior strategy to maximize their influence and the effectiveness of strategy of Internet Water Army is closely related to the features of the users. Our work sheds insight on the design of viral marketing mechanism in e-commerce systems as well as on guiding collective behaviors in social media.

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Design and its visual servoing control of an inspection robot for power transmission lines

Design and its visual servoing control of an inspection robo...

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2013 IEEE International Conference on Robotics and Biomimetics, ROBIO 2013

作者： Zhao, Dezheng Yang, Guodong Li, En Liang, Zize State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9781479927449

A bi-branchiate robot is presented for the inspection of the high-voltage power transmission lines. With a symmetrical mechanical structure, the robot can roll along the power lines and negotiating all the obstacles in the lines to complete the inspection work. A hierarchical structure based on modular approach are used to design the control system of the robot. A combination mode composed of teleoperation via RF module and local intelligence based on visual servoing is developed to control the inspection robot. Using an image feature acquisition method based on Otsu adaptive threshold, the position and width of the power line in image plane are extracted as the basis of controller design. Experiments of obstacle negotiation and automatic line grasping in laboratory prove the effectiveness of the proposed robot prototype and the relative control method. © 2013 IEEE.

关键词： Inspection

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An iterative adaptive dynamic programming algorithm for optimal control of unknown discrete-time nonlinear systems with constrained inputs

An iterative adaptive dynamic programming algorithm for opti...

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作者： Liu, Derong Wang, Ding Yang, Xiong State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China

In this paper, the adaptive dynamic programming (ADP) approach is employed for designing an optimal controller of unknown discrete-time nonlinear systems with control constraints. A neural network is constructed for identifying the unknown dynamical system with stability proof. Then, the iterative ADP algorithm is developed to solve the optimal control problem with convergence analysis. Two other neural networks are introduced for approximating the cost function and its derivatives and the control law, under the framework of globalized dual heuristic programming technique. Furthermore, two simulation examples are included to verify the theoretical results. © 2012 Elsevier Inc. All rights reserved.

关键词： Iterative methods

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A new self-learning optimal control scheme for discrete-time nonlinear systems using policy iterative adaptive dynamic programming

A new self-learning optimal control scheme for discrete-time...

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3rd IFAC Conference on Intelligent control and Automation Science, ICONS 2013

作者： Wei, Qinglai Liu, Derong State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9783902823458

In this paper, a new self-learning method using policy iterative adaptive dynamic programming (ADP) is developed to obtain the optimal control scheme of discrete-time nonlinear systems. The iterative ADP algorithm permits an arbitrary admissible control law to initialize the iterative algorithm. It is the first time that the properties of the policy iterative ADP are established for the discrete-time situation. It proves that the iterative performance index function is nonincreasingly convergent to the optimal solution of the Hamilton-Jacobi-Bellman (HJB) equation. It also proves that any of the iterative control policy can stabilize the nonlinear systems. Neural networks are used to approximate the performance index function and compute the optimal control policy, respectively, for facilitating the implementation of the iterative ADP algorithm. Finally, a simulation example is given to illustrate the performance of the present method. Copyright © 2013 IFAC.

关键词： Reinforcement learning

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Flickr group recommendation based on user-generated tags and social relations via topic model

Flickr group recommendation based on user-generated tags and...

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10th International Symposium on Neural Networks, ISNN 2013

作者： Zheng, Nan Bao, Hongyun State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9783642390678

The boom of Flickr, a photo-sharing social tagging system, leads to a dramatical increasing of online social interactions. For example, it offers millions of groups for users to join in order to share photos and keep relations. However, the rapidly increasing amount of groups hampers users' participation, thus it is necessary to suggest groups according to users' preferences. By analyzing user-generated tags, one can explore users' potential interests, and discover the latent topics of the corresponding groups. Furthermore, users' behaviors are affected by their friends. Based on these intuitions, we propose a topic-based group recommendation model to predict users' potential interests and conduct group recommendations based on tags and social relations. The proposed model provides a way to fuse tag information and social network structure to predict users' future interests accurately. The experimental results on a real-world dataset demonstrate the effectiveness of the proposed model. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Social networking (online)

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