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作者： Yang, Yi Nie, Feiping Xiang, Shiming Zhuang, Yueting Wang, Wenhua College of Computer Science Zhejiang University Hangzhou 310027 China Department of Computer Science and Engineering University of Texas Arlington 76019 United States National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9781577354642

Over the past few years, a large family of manifold learning algorithms have been proposed, and applied to various applications. While designing new manifold learning algorithms has attracted much research attention, fewer research efforts have been focused on out-of-sample extrapolation of learned manifold. In this paper, we propose a novel algorithm of manifold learning. The proposed algorithm, namely Local and Global Regressive Mapping (LGRM), employs local regression models to grasp the manifold structure. We additionally impose a global regression term as regularization to learn a model for out-of-sample data extrapolation. Based on the algorithm, we propose a new manifold learning framework. Our framework can be applied to any manifold learning algorithms to simultaneously learn the low dimensional embedding of the training data and a model which provides explicit mapping of the out-of-sample data to the learned manifold. Experiments demonstrate that the proposed framework uncover the manifold structure precisely and can be freely applied to unseen data. Copyright © 2010, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Mapping

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Feedback iterative learning control for time-delay systems based on 2D analysis approach

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控制理论与应用（英文版） 2010年第4期8卷 457-463页

作者： Deyuan MENG Yingmin JIA Junping DU Shiying YUAN The Seventh Research Division Beihang University Beijing 100191 China Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia School of Computer Science and Technology Beijing University of Posts and Telecommunications Beijing 100876 China School of Electrical Engineering and Automation Henan Polytechnic University Jiaozuo Henan 454000 China

This paper deals with the iterative learning control （ILC） design for multiple-input multiple-output （MIMO）,time-delay systems （TDS）.Two feedback ILC schemes are considered using the so-called two-dimensional （2D） analysis *** shows that continuous-discrete 2D Roesser systems can be developed to describe the entire learning dynamics of both ILC schemes,based on which necessary and sufficient conditions for their stability can be provided.A numerical example is included to validate the theoretical analysis.

关键词： Iterative learning control Time-delay systems 2D analysis approach

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AI supported maintenance and reliability system in wind energy production

AI supported maintenance and reliability system in wind ener...

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European Wind Energy Conference and Exhibition 2010, EWEC 2010

作者： Viharos, Zsolt János Monostori, László Erdós, Gábor Kovács, András Computer and Automation Research Institute Laboratory on Engineering and Management Intelligence Hungarian Academy of Sciences Kende utca 13-17 1111 Budapest Hungary Department on Manufacturing Science and Technology Budapest University of Technology and Economics Hungary

ISBN: (纸本)9781617823107

The paper describes the result of the research in the fields of supervision, failure detection and prognosis, control, maintenance planning and decision support performed for ensuring high level availability of wind turbines and wind farms. This activity is realized in the frame of the EU 7th Framework project ReliaWind: Reliability focused research on optimizing Wind Energy systems design, operation and maintenance: Tools, proof of concepts, guidelines & methodologies for a new generation. Wind turbines are relatively complex electro-mechanical systems, their smooth functioning is an important economical factor. The handling of this complexity is supported by various, applied artificial intelligence techniques and solutions as described in the paper.

关键词： Wind turbines

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Scheduling fuzzy job shop using random key genetic algorithm

Scheduling fuzzy job shop using random key genetic algorithm

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International Conference on Machine Learning and Cybernetics

作者： Zheng, You-Lian Li, Yuan-Xiang Lei, De-Ming Ma, Chuan-Xiang State Key Laboratory of Software Engineering Wuhan University Wuhan 430072 China Faculty of Mathematics and Computer Science Hubei University Wuhan 430062 China School of Automation Wuhan University of Technology Wuhan 430070 China

ISBN: (纸本)9781424465262

Genetic algorithm has been successfully applied to fuzzy job shop scheduling problem, however, the coding and decoding strategies of the problem aren't fully investigated. This paper presents an efficient random key genetic algorithm (RKGA) for the problem to minimize the maximum fuzzy completion time. RKGA uses a novel random key representation, a new decoding strategy and discrete crossover. RKGA is applied to some fuzzy scheduling instances and compared with a genetic algorithm and particle swarm optimization with genetic operators. Computational results demonstrate that RKGA has the promising advantage on fuzzy scheduling. © 2010 IEEE.

关键词： Genetic algorithms

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Scheduling flexible job shop with fuzzy processing time using co-evolutionary genetic algorithm

Scheduling flexible job shop with fuzzy processing time usin...

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Global Congress on Intelligent Systems

ISBN: (纸本)9780769543048

Fuzzy scheduling and flexible scheduling in job shop environment have been extensively considered;however, the problems with both flexible process plan and fuzzy processing conditions are seldom investigated for the high complexity. This paper addresses the scheduling problems of flexible job shop with fuzzy processing time. We developed an efficient co-evolutionary genetic algorithm (CGA) for the problems to minimize the fuzzy makespan. CGA uses two-string representation with a real string and an integer string, a new decoding strategy and the co-evolutionary technique applied to chromosome. In each generation, both the evolution of only one string for some individuals and the evolution of two strings for other individuals occur. We conduct numerical experiments by using some instances to show the effectiveness of CGA. Computational results show that CGA performs better than the existing methods from literature. © 2010 IEEE.

关键词： Genetic algorithms

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Scheduling jobs and preventive maintenance on fuzzy job shop using genetic algorithm

Scheduling jobs and preventive maintenance on fuzzy job shop...

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International Conference on Machine Learning and Cybernetics

作者： Zheng, You-Lian Li, Yuan-Xiang Lei, De-Ming State Key Laboratory of Software Engineering Wuhan University Wuhan 430072 China Faculty of Mathematics and Computer Science Hubei University Wuhan 430062 China School of Automation Wuhan University of Technology Wuhan 430070 China

ISBN: (纸本)9781424465262

Preventive maintenance (PM) has been considered on many scheduling problems, however, the problem of scheduling jobs and PM on fuzzy job shop are seldom investigated. This paper presents a random key genetic algorithm (RKGA) for the problem with resumable jobs and PM in the fixed time intervals. RKGA uses a novel random key representation, a new decoding strategy incorporating maintenance operation, and discrete crossover. RKGA is applied to some instances to minimize the maximum fuzzy completion time. Computational results show the optimization ability of RKGA on fuzzy scheduling with PM. © 2010 IEEE.

关键词： Preventive maintenance

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A swarm intelligence based searching strategy for articulated 3D human body tracking

A swarm intelligence based searching strategy for articulate...

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2010 IEEE computer Society Conference on computer Vision and Pattern Recognition - Workshops, CVPRW 2010

作者： Zhang, Xiaoqin Hu, Weiming Wang, Xiangyang Kong, Yu Xie, Nianhua Wang, Hanzi Ling, Haibin Maybank, Steve College of Mathematics and Information Science Wenzhou University Zhejiang China National Laboratory of Pattern Recognition Institute of Automation Beijing China School of Communication and Information Engineering Shanghai University Shanghai China School of Computer Science Beijing Institute of Technology Beijing China School of Computer Science University of Adelaide Australia Department of Computer and Information Science Temple University Philadelphia United States School of Computer Science and Information Systems Birkbeck College London United Kingdom

ISBN: (纸本)9781424470297

This paper proposes an annealed particle swarm optimization based particle filter algorithm for articulated 3D human body tracking. In our algorithm, a sampling covariance and an annealing factor are incorporated into the velocity updating equation of particle swarm optimization (PSO). The sampling covariance and the annealing factor are initiated with appropriate values at the beginning of the PSO iteration, and 'annealing' is carried out at reasonable steps. Experiments with multi-camera walking sequences from the Brown dataset show that: 1) the proposed tracker can effectively alleviate the problem of inconsistency between the image likelihood and the true model;2) the tracker is also robust to noise and body self-occlusion. © 2010 IEEE.

关键词： Swarm intelligence

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Collaborative filtering: Weighted nonnegative matrix factorization incorporating user and item graphs

Collaborative filtering: Weighted nonnegative matrix factori...

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10th SIAM International Conference on Data Mining, SDM 2010

作者： Gu, Quanquan Zhou, Jie Ding, Chris State Key Laboratory on Intelligent Technology and Systems Department of Automation Tsinghua University Beijing 100084 China Department of Computer Science and Engineering University of Texas at Arlington 416 Yates Street Arlington TX 76019 United States

Collaborative filtering is an important topic in data mining and has been widely used in recommendation system. In this paper, we proposed a unified model for collaborative filtering based on graph regularized weighted nonnegative matrix factorization. In our model, two graphs are constructed on users and items, which exploit the internal information (e.g. neighborhood information in the user-item rating matrix) and external information (e.g. content information such as user's occupation and item's genre, or other kind of knowledge such as social trust network). The proposed method not only inherits the advantages of model-based method, but also owns the merits of memory-based method which considers the neighborhood information. Moreover, it has the ability to make use of content information and any additional information regarding user-user such as social trust network. Due to the use of these internal and external information, the proposed method is able to find more interpretable low-dimensional representations for users and items, which is helpful for improving the recommendation accuracy. Experimental results on benchmark collaborative filtering data sets demonstrate that the proposed methods outperform the state of the art collaborative filtering methods a lot. Copyright © by SIAM.

关键词： Collaborative filtering

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Computationally effective stereovision SLAM

Computationally effective stereovision SLAM

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IEEE International Conference on Imaging Systems and Techniques

作者： Nalpantidis, Lazaros Sirakoulis, Georgios Ch. Carbone, Andrea Gasteratos, Antonios Laboratory of Robotics and Automation Department of Production and Management Engineering Democritus University of Thrace University Campus Kimmeria GR-67100 Xanthi Greece Laboratory of Electronics Department of Electrical and Computer Engineering Democritus University of Thrace University Campus Kimmeria GR-67100 Xanthi Greece Autoagent Laboratory for Cognitive Robotics Department of Computer and Systems Science University of Rome La Sapienza Via Ariosto 25 00185 Rome Italy

ISBN: (纸本)9781424464944

In this paper a visual Simultaneous Localization and Mapping (SLAM) algorithm suitable for indoor area measurement applications is proposed. The algorithm is focused on computational effectiveness. The only sensor used is a stereo camera placed onboard a moving robot. The algorithm processes the acquired images calculating the depth of the scenery, detecting occupied areas and progressively building a map of the environment. The stereo vision-based SLAM algorithm embodies a custom-tailored stereo correspondence algorithm, the robust scale and rotation invariant feature detection and matching Speeded Up Robust Features (SURF) method, a computationally effective v-disparity image calculation scheme, a novel map-merging module, as well as a sophisticated Cellular Automata (CA)-based enhancement stage. The proposed algorithm is suitable for autonomously mapping and measuring indoor areas using robots. The algorithm is presented and experimental results for self-captured image sets are provided and analyzed. © 2010 IEEE.

关键词： Stereo vision

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Fast facial fitting based on mixture appearance model with 3D constraint

Fast facial fitting based on mixture appearance model with 3...

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Chinese Conference on Pattern Recognition

作者： Huang, Xiangsheng Gong, Lujin Wang, Xiaoyan Institute of Automation Chinese Academy of Sciences Beijing 100190 China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing AMSS Beijing 100190 China College of Computer Science and Technology Zhejiang University of Technology Hangzhou 310023 China

ISBN: (纸本)9781424472109

Fast facial points fitting plays an important role in applications such as Human-computer Interaction, entertainment, surveillance, and is highly relevant to the techniques of facial expression analysis, face recognition, 3D face model generation, etc. Active Appearance Models (AAMs) are generative models commonly used to fit face. They are sensitive to illumination and expression changes because they use only raw intensity to build observation models. In this paper, a real time facial points fitting approach using mixture observation models is presented. Furthermore, the 3D modes are used to constrain the AAM so that it can only generate model instances that can also be generated with the 3D modes. Finally, we give a derivative process for fast energy minimization using the inverse compositional algorithm. A coarse-to-fine fitting strategy is used for realtime and robust facial points fitting. We apply this algorithm to facial expression cloning of 3D Avatar system. Experimental results demonstrate that fitting the AAM with mixture observation models and 3D constraint outperforms other classical algorithms. ©2010 IEEE.

关键词： Mixtures

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