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PICMET 2009 - 2009 Portland International Conference on Management of engineering and Technology

作者： Maike, Scherrer-Rathje Arnoscht, Jens Egri, Péter Braun, Eric Csáji, Balázs C. Schuh, Günther University of St. Gallen Institute of Technology Management St. Gallen Switzerland Aachen University Laboratory for Machine Tools and Production Engineering Aachen Germany Hungarian Academy of Science Computer and Automation Research Institute Budapest Hungary

ISBN: (纸本)1890843202

The importance of joining collaborations and maintaining relationships has significantly increased for industrial companies due to the globalisation of markets and the ongoing specialisation of companies. Consequently, adjustments in organizational structures are required by companies to fit the characteristics of industrial collaborations. With this, the complexity of collaborations in highly dynamic environments is increasing but often underestimated. This research is based on an EU founded project with the main goal to enlarge the knowledge of complexity in collaborative networks. In this paper we show the results of a questionnaire based survey identifying problems arising in such collaborative networks. Furthermore we link these problems to different system characteristics (e.g. network structure, trust, degree of commitment, coordination, change, etc.) and show how the problems and system characteristics influence each other based on case studies in two different collaborative networks. The research results in a generic model for complexity (GeMoC) in collaboration networks which is based on Beer's viable system model and identifies white spots in the existing complexity research literature. © 2009 PICMET.

关键词： Beer

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Distributed H∞ consensus control for networks of multiple agents with linear coupling dynamics

Distributed H∞ consensus control for networks of multiple a...

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ICROS-SICE International Joint Conference 2009, ICCAS-SICE 2009

作者： Liu, Yang Jia, Yingmin Du, Junping Yu, Fashan 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 454000 Henan China

ISBN: (纸本)9784907764333

This paper is devoted to the consensus control for a network of autonomous agents with high-dimension linear coupling dynamics and subject to external disturbances. By transforming the consensus control problem into an H∞ control problem, we propose a distributed state feedback protocol, and obtain conditions in terms of linear matrix inequalities (LMIs) to ensure the consensus with a prescribed H∞ performance level for networks with zero and nonzero communication delays, respectively. Furthermore, the undetermined feedback matrix of the proposed protocol is also solved. A numerical example is included to validate the theoretical results. © 2009 SICE.

关键词： Multi agent systems

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A novel interacting multiple model algorithm based on multi-sensor optimal information fusion rule

A novel interacting multiple model algorithm based on multi-...

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2009 American Control Conference, ACC 2009

作者： Fu, Xiaoyan Jia, Yingmin Du, Junping Yuan, Shiying Beijing 100083 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

ISBN: (纸本)9781424445240

In this paper, a novel interacting multiple model (IMM) algorithm is proposed, which utilizes a multi-sensor optimal information fusion rule to combine multiple models in the linear minimum variance sense instead of famous Bayes' rule. Furthermore, the diagonal matrices are used as the updated weights of models, which are applied to distinguish the effects produced by different dimensions of state, so the new algorithm is named as diagonal interacting multiple model (DIMM) algorithm. Extensive Monte Carlo simulations indicate that the proposed DIMM algorithm has better accuracy of estimation than the IMM algorithm with no increase in the execution time, which confirm that the DIMM algorithm is a competitive alternative to the classical IMM algorithm. © 2009 AACC.

关键词： Monte Carlo methods

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Research on answer extraction method for domain question answering system(QA)

Research on answer extraction method for domain question ans...

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2009 International Conference on Computational Intelligence and Security, CIS 2009

作者： Mao, Cun-Li Li, Li-Na Yu, Zheng-Tao Han, Lu Guo, Jian-Yi Lei, Xiong-Li School of Information Engineering and Automation Kunming University of Science and Technology Kunming 650051 China Institute of Intelligent Information Processing Computer Technology Application Key Laboratory of Yunnan Province Kunming 650051 China School of Computer Science and Technology Kunming College of Metallurgy Kunming 650093 China School of Architecture and Arts Kunming College of Metallurgy Kunming 650093 China

ISBN: (纸本)9780769539317

The domain knowledge has a direct impact on the result of question - answering (Q & A) in the restricted domain Question Answering System (QA). In this paper, a method of answer extraction for domain Chinese question-and-answer (Q&A) is proposed, which based on the analysis of interrogative sentence and the answer type, carrying on the text retrieval with the help of the domain knowledge and obtaining the relevant paragraphs of the question as the candidate answer. For the question of numeral or list entity type, extracted the question center related domain entity as the answers by adopting the naming entity recognition. For the definition questions, the sentences or paragraphs with higher relevance can be extracted to become the answer based on the relevance ranking between the candidate sentences or paragraphs and questions by combined the computing method of keywords weighting and the method of semantic similarity between the sentences and questions. Experimented on the answer extraction in Yunnan tourism domain, the results show that more remarkable effects have been achieved by adopting the method of answer extraction from domain Chinese question-and-answer. © 2009 IEEE.

关键词： Domain Knowledge

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Adaptive multi-class support vector machine for microarray classification and gene selection

Adaptive multi-class support vector machine for microarray c...

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ICROS-SICE International Joint Conference 2009, ICCAS-SICE 2009

作者： Li, Juntao Jia, Yingmin Du, Junping Yu, Fashan 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 454000 Henan China

ISBN: (纸本)9784907764333

This paper proposes an adaptive multi-class support vector machine for simultaneous microarray classification and gene selection. By evaluating the gene ranking significance, the adaptive multi-class support vector machine is shown to encourage an adaptive grouping effect in the process of building classifiers, thus leading a sparse multi-classifiers with enhanced interpretability. Based on a reasonable correlation between the two regularization parameters, an efficient solution path algorithm is developed for solving the proposed support vector machine. Experiments performed on the leukaemia data set are provided to verify the obtained results. © 2009 SICE.

关键词： Support vector machines

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Data preprocessing in SVM-based keywords extraction from scientific documents

Data preprocessing in SVM-based keywords extraction from sci...

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2009 4th International Conference on Innovative Computing, Information and Control, ICICIC 2009

作者： Wu, Chunguo Marchese, Maurizio Wang, Yufei Krapivin, Mikalai Wang, Chaoyong Li, Xitong Liang, Yanchun College of Computer Science and Technology Jilin University Changchun 130012 China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing 100080 China Italy Department of Fundamental Sciences Jilin Teacher's Institute of Engineering and Technology Changchun 130021 China

ISBN: (纸本)9780769538730

Scientific documents are unstructured data consisting of natural language and hard for scientists to read and manage. Keywords are very helpful for scientists to search the related documents and know about their contents in a prompt way. In this paper we investigate a kind of data preprocessing technique used in SVM-based keyword extraction from scientific documents. Four definitions of regular scientific documents are proposed, and the analysis on the experimental results is performed based on the proposed definitions. The experimental results confirm the intuition that abstract is important for keywords extraction. © 2009 IEEE.

关键词： Support vector machines

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Automatic Forward Model Selection Based on Leave-One-Out Cross-Validation

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IFAC Proceedings Volumes 2009年第10期42卷 874-879页

作者： Dajun Du Kang li Minrui Fei George W. Irwin Shanghai Key Laboratory of Power Station Automation Technology School of Mechanical Engineering and Automation Shanghai University Shanghai 200072 China School of Electronics Electrical Engineering and Computer Science Queen's University Belfast Belfast BT9 5 AH UK

This paper proposes a new automatic forward model selection method based on leave-one-out (LOO) cross-validation and a fast forward recursive algorithm. It can automatically select a sparse model by incrementally minimizing a LOO test error from a pool of candidate model terms, without the need to specify an additional termination criterion. By defining a proper regression context, the computational efficiency is ensured. A numerical example demonstrates the effectiveness of the proposed method.

关键词： Forward regression leave-one-out cross-validation linear-in-the parameters model model selection system identification

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Adaptive kernel principal analysis for online feature extraction

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World Academy of science, engineering and Technology 2009年 59卷 288-293页

作者： Ding, Mingtao Tian, Zheng Xu, Haixia Department of Science Northwestern Polytechnical University Xi'an China Department of Science Department of Computer Science and Engineering Northwestern Polytechnical University Xi'an China National Key Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China Department of Computer Science and Engineering Northwestern Polytechnical University Xi'an China

The batch nature limits the standard kernel principal component analysis (KPCA) methods in numerous applications, especially for dynamic or large-scale data. In this paper, an efficient adaptive approach is presented for online extraction of the kernel principal components (KPC). The contribution of this paper may be divided into two parts. First, kernel covariance matrix is correctly updated to adapt to the changing characteristics of data. Second, KPC are recursively formulated to overcome the batch nature of standard *** formulation is derived from the recursive eigen-decomposition of kernel covariance matrix and indicates the KPC variation caused by the new data. The proposed method not only alleviates sub-optimality of the KPCA method for non-stationary data, but also maintains constant update speed and memory usage as the data-size increases. Experiments for simulation data and real applications demonstrate that our approach yields improvements in terms of both computational speed and approximation accuracy.

关键词： Covariance matrix

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Cluster-based Construction of Tree Crown from Scanned Data

Cluster-based Construction of Tree Crown from Scanned Data

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International Symposium on Plant Growth Modeling, Simulation, Visualization and Applications

作者： Chao ZHU Xiaopeng ZHANG Marc JAEGER Yinghui WANG Sino-French Laboratory LIAMA CAS Institute of Automation CIRADAMAP INRIA EPI DigiPlante Department of Computer Science & Engineering Xi'an University of Technology

ISBN: (纸本)9781424463299

It is a challenge task to reconstruct a real tree from scattered scanned points in virtual reality. Although many progresses have been made on main branch structures and overall tree shape, reconstructions are still not faithful in terms of silhouette and details. We push the idea that 3D reconstruction of the tree crown shapes may help to constrain reconstruction of complete real tree geometry. We propose here a new approach to reconstruct tree crown based on clusters of points, where the data are unevenly distributed points in a volume rather than lying on a surface. From this approach several extracted silhouette mesh models can be generated;every mesh model represents a crown section of the reconstructed tree crown. Experimental result shows that our technique works well on crown shape of real trees.

关键词： Virtual reality Geometry Tree graphs Surface reconstruction Shape measurement Skeleton Laboratories Content addressable storage automation Clouds

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Unsupervised Maximum Margin Feature Selection with manifold regularization

Unsupervised Maximum Margin Feature Selection with manifold ...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Bin Zhao James Kwok Fei Wang Changshui Zhang State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology Department of Automation Tsinghua University Beijing China Department of Computer Science and Engineering HKUST Hong Kong China

Feature selection plays a fundamental role in many pattern recognition problems. However, most efforts have been focused on the supervised scenario, while unsupervised feature selection remains as a rarely touched research topic. In this paper, we propose manifold-based maximum margin feature selection (M3FS) to select the most discriminative features for clustering. M3FS targets to find those features that would result in the maximal separation of different clusters and incorporates manifold information by enforcing smoothness constraint on the clustering function. Specifically, we define scale factor for each feature to measure its relevance to clustering, and irrelevant features are identified by assigning zero weights. Feature selection is then achieved by the sparsity constraints on scale factors. Computationally, M3FS is formulated as an integer programming problem and we propose a cutting plane algorithm to efficiently solve it. Experimental results on both toy and real-world data sets demonstrate its effectiveness.

关键词： Filters Pattern recognition Clustering algorithms Face recognition Laplace equations Laboratories Linear programming Feature extraction Unsupervised learning Supervised learning

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