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IEEE Conference on Decision and Control

作者： Dong Yue Engang Tian Qing-Long Han Department of Control Science and Engineering and the Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology Hubei 430074 China School of Electrical and Automation Engineering Nanjing Normal University 78 Bancang Street Nanjing 210042 China Center for Intelligent and Networked Systems and the School of Information and Communication Technology Central Queensland University Rockhampton Qld 4702 Australia

ISBN: (纸本)9781612848006

The event-triggered H_(infinity) control design is investigated for networked control systems with uncertainties and transmission delays. A novel event-triggering scheme is proposed, which has some advantages over traditional ones with a continuous detector. Considering the effect of the transmission delay, a delay system model for the analysis is firstly constructed. Then, based on the model and Lyapunov functional method, criteria for the stability with an H_(infinity) norm bound and criteria for the co-design of both the feedback gain and the trigger parameters are derived. In order to solve the feedback gain and the trigger parameters, the linear matrix inequality technique is employed. From the simulation example, it can be concluded that the proposed event-triggering scheme is superior to some other event-triggering schemes in some existing literature.

关键词： feedback gain Networked control systems Event trigger Delay systems Scheme Co-design control systems design control

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Sparsely Encoded Local Descriptor for face recognition

Sparsely Encoded Local Descriptor for face recognition

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International Conference on Automatic Face and Gesture Recognition

作者： Zhen Cui Shiguang Shan Xilin Chen Lei Zhang School of Computer Science and Technology Huaqiao University Xiamen China Chinese Academy and Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computer Technology Chinese Academy and Sciences Beijing China Chinese Academy of Sciences Beijing Beijing CN Department of Computing Hong Kong Polytechnic University Hong Kong China

In this paper, a novel Sparsely Encoded Local Descriptor (SELD) is proposed for face recognition. Compared with K-means or Random-projection tree based previous methods, sparsity constraint is introduced in our dictionary learning and sequent image encoding, which implies more stable and discriminative face representation. Sparse coding also leads to an image descriptor of summation of sparse coefficient vectors, which is quite different from existing code-words appearance frequency(/histogram)-based descriptors. Extensive experiments on both FERET and challenging LFW database show the effectiveness of the proposed SELD method. Especially on the LFW dataset, recognition accuracy comparable to the best known results is achieved.

关键词： Face Dictionaries Pixel Training Feature extraction Face recognition Databases

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A multiobjective immune clustering ensemble technique applied to unsupervised SAR image segmentation 10

A multiobjective immune clustering ensemble technique applie...

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ACM International Conference on Image and Video Retrieval, ACM-CIVR 2010

作者： Liu, Ruochen Zhang, Wei Jiao, Licheng Liu, Fang Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an China

ISBN: (纸本)9781450301176

In the past few years, multiobjective clustering has been one of the most successful techniques in the field of computer vision and data clustering. This paper proposes a novel unsupervised approach for synthetic aperture radar (SAR) image segmentation, namely, multiobjective immune clustering ensemble technique (MICET). The new technique first divides the image into several regions, and a certain number of pixels are picked out from these regions to form the clustering dataset. Second, artificial immune system (AIS) and multiobjective optimization (MOO) are introduced to generate multiple clustering results, which are then combined together for the following ensemble process. Multiple runs of the multiob-jective clustering method with different randomly selected image features are performed to ensure high quality components as well as necessary diversity for an efficient ensemble. Finally, each datum is assigned to one cluster according to the relationship with the clustering dataset. Experimental results show that interesting segmentation performances on SAR images can be achieved by the proposed technique despite its completely unsupervised nature. Copyright © 2010 ACM.

关键词： Multiobjective optimization

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An improved radius of gyration similarity measure between generalized fuzzy numbers and its application to multisource data fusion

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Journal of Computers 2011年第8期6卷 1732-1739页

作者： Yong, Deng Dong, Sun Wen, Jiang Jianling, Xu College of computer and information sciences Southwest University Chongqing 400715 China Hangzhou Key Lab of E-Business and Information Security Hangzhou Normal University Hanzhou Zhejiang 310036 China School of Electronics and Information Technology Shanghai Jiao Tong University Shanghai 200240 China Key Laboratory of Intelligent Computing Information Processing of Ministry of Education Xiangtan University Xiangtan Hunan 411105 China Department of Electronics Engineering Northwestern Polytechnical University Xían 710072 China Grain Economics Institute Nanjing University of Finance and Economics Nanjing 210003 China

The degree of similarity between fuzzy numbers plays an important role in fuzzy information fusion. In this paper, improved ROG-based similarity measure developed from the current ROG method is presented. It is shown that the new method can overcome some shortcomings of the existing similarity measures. Based on the proposed similarity measure, a more flexible and intelligent algorithm is developed to deal with multisensor data fusion problems. A numerical example is used to illustrate the efficiency of the proposed method. © 2011 ACADEMY PUBLISHER.

关键词： Numerical methods

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Algorithm of partition based network boosting for imbalanced data classification

Algorithm of partition based network boosting for imbalanced...

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2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010

作者： Shuiping, Gou Hui, Yang Licheng, Jiao Xiong, Zhuang Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an 710071 China

ISBN: (纸本)9781424469178

Network Boosting (NB) is an ensemble learning method which combines weak learners together based on a network and can learn the target hypothesis asymptotically. NB has higher generalization ability compared to Bagging and AdaBoost. But, when datasets are class-imbalanced, the performance of NB will decrease quickly. In order to solve this problem, we present a Partition based Network Boosting method (PNB) to classify imbalanced data. For PNB method, every classifier node of the classifier network is provided with the same number of training data which are all of same weights. The classifier in the network is built by the balanced training set sampled from the training data according to the weights record of the training data it holds. And then, the weights of the instances of every node classifier are updated based on the classification results of self-node and its neighbor nodes. The classifier network is trained repeatedly in such a way. Weight factor of hypothesis in the training progress is introduced to improve the performance. The final classification is formed by all the hypotheses of the classifier network learned during the training progress so that the label of new instances can be decided by the weight voting. The experimental results on UCI data and imbalanced biomedical data show that the PNB algorithm has better AUC and recall performance compared with NB learning machine. © 2010 IEEE.

关键词： Adaptive boosting

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Aggregation of interval numbers in group decision making

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ICIC Express Letters 2011年第4 A期5卷 1057-1062页

作者： Deng, Yong Wu, Jiyi Sun, Xiaohong Xu, Peida Zhang, Yajuan College of Computer and Information Sciences Southwest University Tiansheng Road Beibei District Chongqing 400715 China Hangzhou Key Lab of E-Business and Information Security Hangzhou Normal University No. 16 Xuelin Street Xiasha Higher Education Zone Hanzhou 310036 China Key Laboratory of Intelligent Computing and Information Processing Ministry of Education Xiangtan University Xiangtan 411105 China College of Food Science and Technology Shanghai Ocean University Hu Cheng Loop-road Lingang New City Shanghai 201306 China School of Electronics and Information Technology Shanghai Jiao Tong University Dongchuan Rd. Shanghai 200240 China

A group of decision-makers may differ in their choice of alternatives while taking a decision. So, in any decision-making problem concerning decisions made by a group, the question arises how best we can aggregate individual choices into a general consensus choice. Due to the inherent uncertain property of the problems in the ambiguous decision domain, the decision makers may use interval numbers to represent their opinions in some situations. In this paper, a simple method is proposed to aggregate interval numbers. Some desirable properties of this method are also discussed. Finally, a numerical example is given to show the use of the proposed method. ICIC International © 2011.

关键词： Aggregates

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Optimal combination of conflicting evidence based on genetic algorithm

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ICIC Express Letters 2011年第4 A期5卷 1145-1150页

作者： Jiang, Wen Peng, Jinye Deng, Yong School of Electronics and Information Northwestern Polytechnical University No. 127 Youyixi Road Xi'an 710072 China Hangzhou Key Lab of E-Business and Information Security Hangzhou Normal University No. 16 Xuelin Street Xiasha Higher Education Zone Hanzhou 310036 China Key Laboratory of Intelligent Computing and Information Processing Ministry of Education Xiangtan University Xiangtan 411105 China School of Computer and Information Sciences Southwest University No. 2 Tiansheng Road Beibei District Chongqing 400715 China School of Electronics and Information Technology Shanghai Jiao Tong University No. 800 Dongchuan Road Shanghai 200240 China

The Dempster-Shafer (D-S) evidence theory is widely used in many fields of information fusion. However, counter-intuitive results may be obtained by the classical Dempster combination rule when collected evidences are highly in conflict with each other. So an optimal weighted average information fusion method is proposed. Firstly, the global distance of the weighted evidence is presented based on the distance function. Secondly, the optimal weight of every evidence body was acquired by genetic algorithm, while the global distance is the minimum. Finally, the weighted average of the masses is combined by classical Dempster's rule. Compared with existing methods, the proposed optimal method can effectively handle conflicting evidence with better performance of convergence. Numerical examples are illustrated the effectiveness of the proposed method. ICIC International © 2011.

关键词： Genetic algorithms

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Robust filtering for networked systems with random transmission delays and packet dropouts

Robust filtering for networked systems with random transmiss...

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作者： Cai, Yunze Wang, Hua O. Tanaka, Kazuo Xu, Xiaoming Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing 800 Dongchuan Rd. Shanghai 200240 China Department of Mechanical Engineering Boston University Boston MA 02215 United States Department of Mechanical Systems and Intelligent Systems University of Electro-Communications Tokyo 182-8585 Japan Department of Automation Shanghai Jiao Tong University 800 Dongchuan Rd. Shanghai 200240 China University of Shanghai for Science and Technology 16 Jun Gong Rd. Shanghai 200093 China

ISBN: (纸本)9781457700804

This paper addresses a robust H∞ filtering problem for networked systems that are subject to both random transmission delays and packet dropouts. To start with, a data transmission model is established by employing random series with Bernoulli distributions. A sufficient condition for robust stability with H∞ constraints is derived for the filtering error system. The robust filter is designed in terms of the feasibility of a linear matrix inequality (LMI). The numerical examples are provided to show the effectiveness of the data transmission model and the proposed filtering method. © 2011 AACC American Automatic Control Council.

关键词： Linear matrix inequalities

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Joint parsing and translation

Joint parsing and translation

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23rd International Conference on Computational Linguistics, Coling 2010

作者： Liu, Yang Liu, Qun Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China

Tree-based translation models, which exploit the linguistic syntax of source language, usually separate decoding into two steps: parsing and translation. Although this separation makes tree-based decoding simple and efficient, its translation performance is usually limited by the number of parse trees offered by parser. Alternatively, we propose to parse and translate jointly by casting tree-based translation as parsing. Given a source-language sentence, our joint decoder produces a parse tree on the source side and a translation on the target side simultaneously. By combining translation and parsing models in a discriminative framework, our approach significantly outperforms a forest based tree-to-string system by 1.1 absolute BLEU points on the NIST 2005 Chinese-English test set. As a parser, our joint decoder achieves an F1 score of 80.6% on the Penn Chinese Treebank.

关键词： Syntactics

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Dependency-based bracketing transduction grammar for statistical machine translation

Dependency-based bracketing transduction grammar for statist...

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23rd International Conference on Computational Linguistics, Coling 2010

作者： Su, Jinsong Liu, Yang Mi, Haitao Zhao, Hongmei Lü, Yajuan Liu, Qun Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China

In this paper, we propose a novel dependency-based bracketing transduction grammar for statistical machine translation, which converts a source sentence into a target dependency tree. Different from conventional bracketing transduction grammar models, we encode target dependency information into our lexical rules directly, and then we employ two different maximum entropy models to determine the reordering and combination of partial dependency structures, when we merge two neighboring blocks. By incorporating dependency language model further, large-scale experiments on Chinese-English task show that our system achieves significant improvements over the baseline system on various test sets even with fewer phrases.

关键词： Computer aided language translation

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