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31st Chinese control Conference

作者： Li Hongliang Liu Derong Wang Ding Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

ISBN: (纸本)9789881563811

In this paper, we propose a novel adaptive dynamic programming (ADP) scheme based on general value iteration to obtain near optimal control for discrete-time nonlinear systems with continuous state and control space. First, the selection of initial value function is different from the traditional value iteration, and a new method is introduced to demonstrate the convergence property and convergence speed of the value function. Then, the control law obtained at each iteration can stabilize the system under some conditions. At last, three neural networks with Levenberg-Marquardt training algorithm are used to approximate the unknown nonlinear system, the value function and the optimal control law. One simulation example is presented to demonstrate the effectiveness of the present scheme.

关键词： Adaptive dynamic programming approximate dynamic programming optimal control value iteration neural networks reinforcement learning

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A Clustering based Hierarchical Image Feature Location Method for complex Shaped Work-piece

A Clustering based Hierarchical Image Feature Location Metho...

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31st Chinese control Conference

作者： Qi Yuhan Jing Fengshui Tan Min Diao Wenchao Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

ISBN: (纸本)9789881563811

This paper addresses an image feature location method for complex shaped work-piece. The "rough estimation first and then precise location" hierarchical feature location method is proposed to roughly estimate the work-piece's pose first and then precisely locate it in 2D image space. The proposed method has the following main steps: (1) simple features are detected first then more complex features are examined later, using the locations of the previously found features;(2) an improved local adaptive threshold algorithm is used to binarize the work-piece's gray image;(3) a size adjustable binary template is designed to match hole features;(4) a work-piece is finally located in image space according to its CAD model using the nearest neighbor clustering algorithm. The method has been tested on a sort of complex shaped automobile engine cylinder head as an example and the location results are satisfactory.

关键词： hierarchical feature location adaptive threshold adjustable binary template nearest neighbor clustering

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Designs for high performance PAL-based imaging systems

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APPLIED OPTICS 2012年第21期51卷 5310-5317页

作者： Hui, Dong Zhang, Mei Geng, Zheng Zhang, Yunfang Duan, Jingyuan Shi, Ancun Hui, Li Fang, Qing Liu, Yuliang Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Chinese Acad Sci Inst Semicond Optoelect Syst Lab Beijing 100083 Peoples R China

In this study, we investigate methods to optimize the design of a panoramic annular lens (PAL) system. The design details of a PAL surveillance system, an anamorphic PAL surveillance system, a phone camera with a PAL attachment, and a PAL endoscope system are described. All these designs are optimized using a standard optical software package (Zemax). The results combine very good image quality with a modulation transfer function above 0.3, which is within the cutoff frequency of sensor chips. (C) 2012 Optical Society of America

关键词： SHCBP1 gene programmable logic arrays HIGH-PERFORMANCE toroidal lens Image quality SURVEILLANCE systems PAL(LANGUAGE) Cutoff frequency LRIT1 gene Imaging systems Software packages modulation transfer function

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ENVIRONMENT COUPLED METRICS LEARNING FOR UNCONSTRAINED FACE VERIFICATION

ENVIRONMENT COUPLED METRICS LEARNING FOR UNCONSTRAINED FACE ...

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19th IEEE International Conference on Image Processing (ICIP)

作者： Cai, Xinyuan Wang, Chunheng Xiao, Baihua Zhou, Ji Chen, Xue Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

ISBN: (纸本)9781467325332;9781467325349

Making recognition more reliable under unconstrained environment is one of the most important challenges for real-world face recognition. In this paper, we propose a novel approach for unconstrained face verification. First, we use a spectral-clustering method based on Structural Similarity index to estimate the captured environments of facial images. Then for each pair of environments, we learn two coupled metrics, such that facial images captured in different environments can be transformed into a media subspace, and high recognition performance can be achieved. The coupled transformations are jointly determined by solving an optimization problem in the multi-task learning framework. Experimental results on the benchmark dataset (LFW) show the effectiveness of the proposed method in face verification across varying environments.

关键词： Face verification metric learning unconstrained environment multi-task learning

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International Joint Conference on Neural Networks (IJCNN)

作者： Song, Shuangyong Li, Qiudan Zheng, Xiaolong Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100080 Peoples R China

ISBN: (纸本)9781467314909

The rapid increasing popularity of micro-blogging has made it an important information seeking channel. By detecting recent popular topics from micro-blogging, we have opportunities to gain insights into internet hotspots. Generally, a topic's popularity is determined by two primary factors. One is how frequently a topic is discussed by users, and the other is how much influence those users have, since topics shown in the influential users' posts are more likely to attract others' attention. However, existing approaches interpret a topic's popularity with only the number of keywords related to it, which neglect the importance of the user influence to information diffusion in micro-blogging. In this paper, drawing upon the Cognitive Authority Theory and Social Network Theory, we propose a novel model that detects the most popular topics in micro-blogging with a user interest-based method. The proposed model first constructs a topic graph according to users' interests and their following relationship, and then calculates the topics' popularity with a link-based ranking algorithm. The popular topics detected by the method can reflect the relationship among users' interests, and the topics in the posts of influential users can be highlighted. Experimental results on the data of Twitter, a well-known and feature-rich micro-blogging service, show that the proposed method is effective in popular topic discovery.

关键词： Twitter micro-blogging topic ranking user interest social network theory PageRank

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Optimal control of unknown nonaffine nonlinear discrete-time systems based on adaptive dynamic programming

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AUTOMATICA 2012年第8期48卷 1825-1832页

作者： Wang, Ding Liu, Derong Wei, Qinglai Zhao, Dongbin Jin, Ning Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Illinois Dept Elect & Comp Engn Chicago IL 60607 USA

An intelligent-optimal control scheme for unknown nonaffine nonlinear discrete-time systems with discount factor in the cost function is developed in this paper. The iterative adaptive dynamic programming algorithm is introduced to solve the optimal control problem with convergence analysis. Then, the implementation of the iterative algorithm via globalized dual heuristic programming technique is presented by using three neural networks, which will approximate at each iteration the cost function, the control law, and the unknown nonlinear system, respectively. In addition, two simulation examples are provided to verify the effectiveness of the developed optimal control approach. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Adaptive critic designs Adaptive dynamic programming Approximate dynamic programming Globalized dual heuristic programming Intelligent control Neural network Optimal control

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Pure-Strategy Nash Equilibria of GSP keyword Auction

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JOURNAL OF THE ASSOCIATION FOR INFORMATION systems 2012年第2期13卷 57-87页

作者： Li, Linjing Zeng, Daniel Zhao, Huimin Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing Peoples R China Univ Wisconsin Milwaukee Sheldon B Lubar Sch Business Milwaukee WI USA

Despite the tremendous commercial success of generalized second-price (GSP) keyword auctions, it still remains a big challenge for an advertiser to formulate an effective bidding strategy. In this paper, we strive to bridge this gap by proposing a framework for studying pure-strategy Nash equilibria in GSP auctions. We first analyze the equilibrium bidding behaviors by investigating the properties and distribution of all pure-strategy Nash equilibria. Our analysis shows that the set of all pure-strategy Nash equilibria of a GSP auction can be partitioned into separate convex polyhedra based on the order of bids if the valuations of all advertisers are distinct. We further show that only the polyhedron that allocates slots efficiently is weakly stable, thus allowing all inefficient equilibria to be weeded out. We then propose a novel refinement method for identifying a set of equilibria named the stable Nash equilibrium set (STNE) and prove that STNE is either the same as or a proper subset of the set of the well-known symmetrical Nash equilibria. These findings free both auctioneers and advertisers from complicated strategic thinking. The revenue of a GSP auction on STNE is at least the same as that of the classical Vickrey-Clarke-Groves mechanism and can be used as a benchmark for evaluating other mechanisms. At the same time, STNE provides advertisers a simple yet effective and stable bidding strategy.

关键词： keyword Advertising Stability Generalized Second-Price Sponsored Search Nash Equilibrium

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Neural-Network-Based Optimal control for a Class of Unknown Discrete-Time Nonlinear systems Using Globalized Dual Heuristic Programming

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2012年第3期9卷 628-634页

作者： Liu, Derong Wang, Ding Zhao, Dongbin Wei, Qinglai Jin, Ning Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Illinois Dept Elect & Comp Engn Chicago IL 60607 USA

In this paper, a neuro-optimal control scheme for a class of unknown discrete-time nonlinear systems with discount factor in the cost function is developed. The iterative adaptive dynamic programming algorithm using globalized dual heuristic programming technique is introduced to obtain the optimal controller with convergence analysis in terms of cost function and control law. In order to carry out the iterative algorithm, a neural network is constructed first to identify the unknown controlled system. Then, based on the learned system model, two other neural networks are employed as parametric structures to facilitate the implementation of the iterative algorithm, which aims at approximating at each iteration the cost function and its derivatives and the control law, respectively. Finally, a simulation example is provided to verify the effectiveness of the proposed optimal control approach.

关键词： Adaptive dynamic programming approximate dynamic programming globalized dual heuristic programming intelligent control neural networks optimal control

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Click frauds and price determination models

Click frauds and price determination models

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2012 10th IEEE International Conference on Intelligence and Security Informatics, ISI 2012

作者： Li, Xiarong Zeng, Daniel Wang, Lei State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences China University of Arizona United States

ISBN: (纸本)9781467321037

Click fraud (CF) has become a serious problem in the online advertising, making the anti-CF issue quite important. In this paper, we analyze the effects of the price determination model on the CF situations in online advertising. Our theoretical results show that the flat-rate model can induce more click frauds than the real-time-bidding model. Our finding is validated with a real advertising dataset. © 2012 IEEE.

关键词： Marketing

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Listwise approaches based on feature ranking discovery

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Frontiers of Computer Science 2012年第6期6卷 647-659页

作者： Yongqing WANG Wenji MAO Daniel ZENG Fen XIA Commercial Products Development Department Alibaba Inc.Hangzhou 310052China State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Department of Management Information Systems University of ArizonaTucsonAZ 85721USA Union Research and Development Department Baidu Inc.Beijing 100085China

Listwise approaches are an important class of learning to rank, which utilizes automatic learning techniques to discover useful information. Most previous research on listwise approaches has focused on optimizing ranking models using weights and has used imprecisely labeled training data; optimizing ranking models using features was largely ignored thus the continuous performance improvement of these approaches was hindered. To address the limitations of previous listwise work, we propose a quasi-KNN model to discover the ranking of features and employ rank addition rule to calculate the weight of combination. On the basis of this, we propose three listwise algorithms, FeatureRank, BL-FeatureRank, and DiffRank. The experimental results show that our proposed algorithms can be applied to a strict ordered ranking training set and gain better performance than state-of-the-art listwise algorithms.

关键词： learning to rank listwise approach feature＇s ranking discovery

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