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arXiv 2017年

作者： Zhou, Wengang Li, Houqiang Fellow, Tian I.E.E.E. CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei230027 China Department of Computer Science University of Texas San Antonio San AntonioTX78249 United States

—The explosive increase and ubiquitous accessibility of visual data on the Web have led to the prosperity of research activity in image search or retrieval. With the ignorance of visual content as a ranking clue, methods with text search techniques for visual retrieval may suffer inconsistency between the text words and visual content. Content-based image retrieval (CBIR), which makes use of the representation of visual content to identify relevant images, has attracted sustained attention in recent two decades. Such a problem is challenging due to the intention gap and the semantic gap problems. Numerous techniques have been developed for content-based image retrieval in the last decade. The purpose of this paper is to categorize and evaluate those algorithms proposed during the period of 2003 to 2016. We conclude with several promising directions for future research. Copyright © 2017, The Authors. All rights reserved.

关键词： Content based retrieval

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Gap metric based offline Multiple Model Predictive control

Gap metric based offline Multiple Model Predictive Control

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Chinese control Conference (CCC)

作者： Dewen Li Xiangyuan Tao Ning Li Shaoyuan Li Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai CN

Multiple Model Predictive control (MMPC) method is an efficient strategy to deal with the strongly nonlinear system with a large operating range. But sub-model selection and time-consuming online calculation are two practical problems for MMPC method. This paper develops an offline multiple model predictive control method to solve such problem. First, we utilize the gap metric to measure the difference between two linear models and present a neighborhood estimation algorithm. Then a class of linear models is established to approximate the nonlinear system. Based on the robust constrained MPC algorithm, we design a local off-line model predictive controller for each sub-model. In the offline part, a sequence of discrete states is chosen and the corresponding feedback gains are obtained. In the online part, the control law is easily acquired by selecting the gain according to the current state. This offline approach can reduce the online computation burden and be suitable for the fast time-varying process. After that, a switching rule between each sub-model is proposed to guarantee the global stability. Finally, the presented procedure is illustrated with the simulation example of a continuous stirred-tank reactor (CSTR).

关键词： Measurement Nonlinear systems Predictive models Predictive control Algorithm design and analysis Prediction algorithms Switches

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Comparative deep learning of hybrid representations for image recommendations

Comparative deep learning of hybrid representations for imag...

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2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016

作者： Lei, Chenyi Liu, Dong Li, Weiping Zha, Zheng-Jun Li, Houqiang CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China

ISBN: (纸本)9781467388511

In many image-related tasks, learning expressive and discriminative representations of images is essential, and deep learning has been studied for automating the learning of such representations. Some user-centric tasks, such as image recommendations, call for effective representations of not only images but also preferences and intents of users over images. Such representations are termed hybrid and addressed via a deep learning approach in this paper. We design a dual-net deep network, in which the two subnetworks map input images and preferences of users into a same latent semantic space, and then the distances between images and users in the latent space are calculated to make decisions. We further propose a comparative deep learning (CDL) method to train the deep network, using a pair of images compared against one user to learn the pattern of their relative distances. The CDL embraces much more training data than naive deep learning, and thus achieves superior performance than the latter, with no cost of increasing network complexity. Experimental results with real-world data sets for image recommendations have shown the proposed dual-net network and CDL greatly outperform other stateof-the-art image recommendation solutions.

关键词： Pattern recognition

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Optimal power management for failure mode of MVDC microgrids in all-electric ships

arXiv

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arXiv 2017年

作者： Xu, Qimin Yang, Bo Han, Qiaoni Yuan, Yazhou Chen, Cailian Guan, Xinping Department of Automation Shanghai Jiao Tong University Shanghai200240 China Collaborative Innovation Center for Advanced Ship and Deep-Sea Exploration Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Institute of Electrical Engineering Yanshan University Qinhuangdao066004 China

Optimal power management of shipboard power system for failure mode (OPMSF) is a significant and challenging problem considering the safety of system and person. Many existing works focused on the transient-time recovery without consideration of the operating cost and the voyage plan. In this paper, the OPMSF problem is formulated considering the mid-time scheduling and the faults at bus and generator. Two-side adjustment methods including the load shedding and the reconfiguration are coordinated for reducing the fault effects. To address the formulated non-convex problem, the travel equality constraint and fractional energy efficiency operation indicator (EEOI) limitation are transformed into the convex forms. Then, considering the infeasibility scenario affected by faults, a further relaxation is adopted to formulate a new problem with feasibility guaranteed. Furthermore, a sufficient condition is derived to ensure that the new problem has the same optimal solution as the original one. Because of the mixed-integer nonlinear feature, an optimal management algorithm based on Benders decomposition (BD) is developed to solve the new one. Due to the slow convergence caused by the time-coupled constraints, a low-complexity near-optimal algorithm based on BD (LNBD) is proposed. The results verify the effectivity of the proposed methods and algorithms. Copyright © 2017, The Authors. All rights reserved.

关键词： Relaxation processes

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Energy trading between microgrids individual cost minimization and social welfare maximization

arXiv

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arXiv 2017年

作者： Qiao, Zhenyu Yang, Bo Xu, Qimin Xiong, Fei Chen, Cailian Guan, Xinping Chen, Bei Department of Automation Shanghai Jiao Tong University Shanghai200240 China Collaborative Innovation Center for Advanced Ship and Deep-Sea Exploration Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Electric Group Co. Ltd Central Academe Shanghai China

High penetration of renewable energy source makes microgrid (MGs) be environment friendly. However, the stochastic input from renewable energy resource brings difficulty in balancing the energy supply and demand. Purchasing extra energy from macrogrid to deal with energy shortage will increase MG energy cost. To mitigate intermittent nature of renewable energy, energy trading and energy storage which can exploit diversity of renewable energy generation across space and time are efficient and cost-effective methods. But current energy storage control action will impact the future control action which brings challenge to energy management. In addition, due to MG participating energy trading as prosumer, it calls for an efficient trading mechanism. Therefore, this paper focuses on the problem of MG energy management and trading. Energy trading problem is formulated as a stochastic optimization one with both individual profit and social welfare maximization. Firstly a Lyapunov optimization based algorithm is developed to solve the stochastic problem. Secondly the double-auction based mechanism is provided to attract MGs' truthful bidding for buying and selling energy. Through theoretical analysis, we demonstrate that individual MG can achieve a time average energy cost close to offline optimum with tradeoff between storage capacity and energy trading cost. Meanwhile the social welfare is also asymptotically maximized under double auction. Simulation results based on real world data show the effectiveness of our algorithm. Copyright © 2017, The Authors. All rights reserved.

关键词： Optimization

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Medical Entity and Attributes Extraction system Based on Relation Annotation

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Wuhan University Journal of Natural Sciences 2016年第2期21卷 145-150页

作者： ZOU Yuwei GU Jinguang FU Haidong College of Computer Science and Technology Wuhan University of Science and TechnologyWuhan 430065HubeiChina Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan 430065HubeiChina

The abundant entities and entity-attribute relations in medical websites are important data resources for medical ***,the medical websites are usually characterized of storing entity and attribute values in different *** extract those data records efficiently,we propose an automatic extraction system which is related to entity and attribute relations（attributes and values）of separate *** system includes following modules：（1）rich-information interactive annotation page rendering;（2）separate storage attribute relations annotating;（3）annotated relations for pattern generating and data records *** paper presents the relations about the attributes which are stored in many pages by effective annotation,then generates rules for data records *** experiments show that the system can not only complete attribute relations of separate storage extraction,but also be compatible with regular relation extraction,while maintaining high accuracy.

关键词： relation annotation information extraction medical data relation extraction

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Structure Design for RBF Neural Network Based on Improved K-means Algorithm

Structure Design for RBF Neural Network Based on Improved K-...

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第28届中国控制与决策会议

作者： Sun Yuqing Qiao Junfei Han Honggui College of Electronic Information & Control Engineering Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System

ISBN: (纸本)9781467397155

Aiming at the disadvantages of the traditional K-means clustering algorithm,a new algorithm based on density is proposed to remove the noises and outliers in this *** algorithm determines whether a point is a noise or not according to the density of the *** show that this algorithm can effectively eliminate the influence of the noises when the K-means algorithm searches cluster centers in the *** the subtractive clustering algorithm is used to initialize the clustering centers of the K-means algorithm,meanwhile the number of cluster centers is *** improved K-means algorithm is taken to optimize the structure of RBF neural network,and the results of experiments on the typical function approximation show that the proposed algorithm has the better approximation ability.

关键词： K-means algorithm Density Subtractive clustering algorithm RBF neural network

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Structure design for RBF neural network based on improved K-means algorithm 28

Structure design for RBF neural network based on improved K-...

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28th Chinese control and Decision Conference, CCDC 2016

作者： Yuqing, Sun Junfei, Qiao Honggui, Han College of Electronic Information and Control Engineering Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing100124 China

ISBN: (纸本)9781467397148

Aiming at the disadvantages of the traditional K-means clustering algorithm, a new algorithm based on density is proposed to remove the noises and outliers in this paper. This algorithm determines whether a point is a noise or not according to the density of the point. Experiments show that this algorithm can effectively eliminate the influence of the noises when the K-means algorithm searches cluster centers in the samples. Then the subtractive clustering algorithm is used to initialize the clustering centers of the K-means algorithm, meanwhile the number of cluster centers is gotten. The improved K-means algorithm is taken to optimize the structure of RBF neural network, and the results of experiments on the typical function approximation show that the proposed algorithm has the better approximation ability. © 2016 IEEE.

关键词： Density K-means algorithm RBF neural network Subtractive clustering algorithm

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Design of Flight Attitude control Model for the Mini-quadrotor

Design of Flight Attitude Control Model for the Mini-quadrot...

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IEEE International Conference on Mechatronics and Automation

作者： Ma Zhongli Li Huixin Li Qianqian Gu Yanming Li Zuoyong College of Automation Harbin Engineering University Heilongjiang 150001 China School of Astronautics Harbin Institute of Technology Heilongjiang 150001 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou 350121 China

ISBN: (纸本)9781509067602

Micro-quadrotor has recently been used in different areas even military field. However, controlling mini-quadrotor to flight attitude in the air has not been perfectly achieved due to mini-quad rotor's small size and light weight. The purpose of this paper is to introduce the flight attitude control system of mini-quadrotor, including design and simulation of calculations and controllers. By using distributed multi-sensors, the attitude and horizontal position information of mini-quadrotor is obtained. The simulation of the attitude controller is applied with the MATLAB Simulink library. In conclusion, the designed attitude controller can make mini-quadrotor have smooth flight and move in all directions and enable the mini-quadrotor to properly flight.

关键词： Mini-quadrotor Flight attitude control system Calculation controller

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Convex local sensitive low rank matrix approximation

Convex local sensitive low rank matrix approximation

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International Joint Conference on Neural Networks (IJCNN)

作者： Chong-Ya Li Lin Zhu Wen-Zheng Bao Yong-Li Jiang Chang-An Yuan De-Shuang Huang College of Electronics and Information Engineering Tongji University Shanghai China Ningbo Haisvision Intelligence System Co. Ltd. Ningbo Zhejiang China Science Computing and Intelligent Information Processing of GuangXi Higher Education Key Laboratory Guangxi Teachers Education University Nanning Guangxi China

ISBN: (纸本)9781509061839

The problem of matrix approximation appears ubiquitously in recommendation systems, computer vision and text mining. The prevailing assumption is that the partially observed matrix has a low-rank or can be well approximated by a low-rank matrix. However, this assumption is strictly that the partially observed matrix is globally low rank. In this paper, we propose a local sensitive formulation of matrix approximation which relaxes the global low-rank assumption, leading to a representation of the observed matrix as a weighted sum of low-rank matrices. We solve the problem by an efficient way based on the alternating direction method of multipliers (ADMM). Our experiments show improvements in prediction accuracy over classical approaches for recommendation tasks.

关键词： Kernel Recommender systems Convex functions Minimization Motion pictures Sparse matrices Smoothing methods

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