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Fault pattern classification using modified dempster-shafer(D-S) combination rule

Zhendong Ceshi Yu Zhenduan/Journal of Vibration, Measurement and Diagnosis 2011年第2期31卷 144-149页

作者： Su, Xiaoyan Deng, Yong Wu, Ying Jiang, Wen School of Electronics Information and Electric Engineering Shanghai Jiao Tong University Shanghai 200240 China Key Laboratory of Digital Agricultural Early-Warning Technology of Ministry of Agriculture Beijing 100081 China Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan 411105 China College of Computer and Information Sciences Southwest University Chongqing 400715 China School of Electronics and Information Technology Northwestern Polytechnical University Xi'an 710072 China

A modified method of conventional D-S evidence combination rule was presented. The consensus index of evidence based on distance measures of evidence was introduced into modifying the BPAs of hypotheses with the evidence sufficiency and evidence importance. By using the D-S combination and new decision rules, the different sets of BPAs for each hypothesis were calculated. A numerical example of the fault diagnosis is used to demonstrate the validity of the method by comparison with other methods.

关键词： Failure analysis

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A new dynamic method of machine learning from transition examples

Journal of Software

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Journal of Software 2011年第10期6卷 2064-2067页

作者： Zhang, Xiao-Dan Zhang, De-Gan Zhao, De-Xin Kang, Xue-Jing Qiao, Xiao-Dong Institute of Scientific and Technical Information of China Beijing 100038 China Tianjin Key Lab of Intelligent Computing and Novel software Technology Tianjin University of Technology China Key Laboratory of Computer Vision and System Tianjin University of Technology China

It's well known machine learning from examples is an effective method to solve non-linear classification problem. A new dynamic method of machine learning from transition example is given in this paper. This method can improve the traditional method ID3 which learns from static eigenvalues of examples. The limits of the traditional method ID3 lie on no comprehension and no memory, especially, no the varieties and dynamic correlation of eigenvalues. In the new method, it can learn from dynamic eigenvalues, the change of data can be learned because the training data is the initial eigenvalue and the end eigenvalue in the interval. All eigenvalue's varieties and correlation can be understood and remembered in application. By test experiments, the new method can be used as classifier when the multi-parameters are dynamic correlation, and it has special use in the many kinds of information fusion fields. © 2011 ACADEMY PUBLISHER.

关键词： Classification (of information)

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Roughness in vector spaces

Roughness in vector spaces

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IEEE International Conference on Granular computing (GRC)

作者： Mingfen Wu Xiangyun Xie School of Computer Science Wuyi University Guangdong China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Mathematics and Computational Science Wuyi University China

Rough set theory proposed by Pawlak, is a complementary generalization of classical set theory. The relations between rough sets and algebraic systems endowed with two binary operations such as rings, groups and semigroups have been already considered. Wu, Xie and Cao defined a pair of rough approximation operators based on a sub-space. In this paper we do the further study about the upper (lower)approximations. We propose concepts of W-upper(lower) rough subspaces in an approximation space and give some properties of the upper(lower) and rough subspaces. Some characterizations of the upper(lower) rough subspace are expressed with addition and intersection operations of subspaces. At the same time, some counterexamples are offered to illustrate the relevant results.

关键词： Approximation methods Rough sets Vectors Educational institutions Laboratories

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A Novel Dependency-to-String Model for Statistical Machine Translation 11

A Novel Dependency-to-String Model for Statistical Machine T...

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Conference on Empirical Methods in Natural Language processing

作者： Jun Xie Haitao Mi Qun Liu Key Laboratory of Intelligent Information Processiong Institute of Computing Technology Chinese Academy of Sciences P.O. Box 2704 Beijing 100190 China

ISBN: (纸本)9781618391100

Dependency structure, as a first step towards semantics, is believed to be helpful to improve translation quality. However, previous works on dependency structure based models typically resort to insertion operations to complete translations, which make it difficult to specify ordering information in translation rules. In our model of this paper, we handle this problem by directly specifying the ordering information in head-dependents rules which represent the source side as head-dependents relations and the target side as strings. The head-dependents rules require only substitution operation, thus our model requires no heuristics or separate ordering models of the previous works to control the word order of translations. Large-scale experiments show that our model performs well on long distance reordering, and outperforms the state-of-the-art constituency-to-string model (+1.47 BLEU on average) and hierarchical phrase-based model (+0.46 BLEU on average) on two Chinese-English NIST test sets without resort to phrases or parse forest. For the first time, a source dependency structure based model catches up with and surpasses the state-of-the-art translation models.

关键词： machine translation Resorts Belgo-Luxembourg Economic Union reduced mass Translations BASES(STRUCTURES) Semantics Heuristics

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Stereoscopic learning for disparity estimation

Stereoscopic learning for disparity estimation

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Zhebin Zhang Yizhou Wang Tingting Jiang Wen Gao National Engineering Laboratory for Video Technology Peking University Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China Key Laboratory of Machine Perception (MoE) School of EECS Peking University Beijing China Peking University Beijing Beijing CN

In this paper, we propose a learning based approach to estimating pixel disparities from the motion information extracted out of input monoscopic video sequences. We represent each video frame with superpixels, and extract the motion features from the superpixels and the frame boundary. These motion features account for the motion pattern of the superpixel as well as camera motion. In the learning phase, given a pair of stereoscopic video sequences, we employ a state-of-the-art stereo matching method to compute the disparity map of each frame as ground truth. Then a multi-label SVM is trained from the estimated disparities and the corresponding motion features. In the testing phase, we use the learned SVM to predict the disparity for each superpixel in a monoscopic video sequence. Experiment results show that the proposed method achieves low error rate in disparity estimation.

关键词： Pixel Three dimensional displays Cameras Feature extraction Histograms Support vector machines Optical imaging

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一种基于模型共享的半监督多标签图像学习法

一种基于模型共享的半监督多标签图像学习法

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第十二届中国虚拟现实大会

作者： ZHANG Da-peng 张大鹏 WEN Jia 闻佳 LIU Xi 刘曦 Institute of Information Science and Engineering Yanshan University Qinhuangdao 066004 China Guan 燕山大学信息科学与工程学院秦皇岛 066004 桂林电子科技大学广西可信软件重点实验室桂林 541004 Institute of Information Science and Engineering Yanshan University Qinhuangdao 066004 China 燕山大学信息科学与工程学院秦皇岛 066004 The Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese 中国科学院计算技术研究所智能信息处理重点实验室北京 100190

提出一种快速且有效的半监督多标签学习方法：模型共享半监督推举。该方法能发现、共享并组合多个基模型，每个基模型是在某个标签上利用半监督支持向量机(S3VM)上学习的。通过使用模型共享，标签关联被显示地利用且对于每个标签来说只... 详细信息

提出一种快速且有效的半监督多标签学习方法：模型共享半监督推举。该方法能发现、共享并组合多个基模型，每个基模型是在某个标签上利用半监督支持向量机(S3VM)上学习的。通过使用模型共享，标签关联被显示地利用且对于每个标签来说只需要少量的基模型即可生成最后的决策结果。在Core15k和Mediamill数据集上评估方法，实验结果显示的方法与当前流行的监督和半监督多标签学习方法是可比的。

关键词：信息处理半监督多标签学习算法模型共享支持向量机

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Robust and discriminative image authentication based on sparse coding

Robust and discriminative image authentication based on spar...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Luntian Mou Tiejun Huang Yonghong Tian Shiguo Lian Xilin Chen Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China Institute of Digital Media Peking University Beijing China France Telecom Research and Development Center (Beijing) Beijing China

Image authentication is usually approached by checking the preservation of some invariant features, which are expected to be both robust and discriminative so that content-preserving operations are accepted while content-altering manipulations are rejected. However, most of existing features have not obtained convincing performance due to insufficiency of experiments and over biasing of robustness. Motivated by the sparse coding strategy discovered in primary visual cortex, we explore the possibility of using sparse coding coefficients for image authentication. Through extensive experiments, we discover that the proposed feature bears great discrimination as well as robustness, which indicates the effectiveness of sparse coding as a new invariant feature for image authentication.

关键词： Authentication Image coding Robustness Feature extraction Discrete cosine transforms Encoding Visualization

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An Integrated High-throughput Workflow for Identification of Crosslinked Peptides from Complex Samples

An Integrated High-throughput Workflow for Identification of...

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第七届中国蛋白质组学大会暨第三届国际蛋白质组学论坛

作者： Bing Yang Yan-Jie Wu Ming Zhu Jinzhong Lin Kun Zhang Shu-Kun Luo Yue-He Ding Li-Yun Xiu She Chen Keqiong Ye Si-Min He Meng-Qiu Dong National Institute of Biological Sciences BeijingBeijing 102206People''s Republic of China Key Lab of Intelligent Information ProcessingInstitute of Computing TechnologyChinese Academy of SciencesBeijing 100190People''s Republic of China

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computing importance of 2D contour parts by reconstructability

Computing importance of 2D contour parts by reconstructabili...

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International Conference on Computer Vision Workshops (ICCV Workshops)

作者： Ge Guo Yizhou Wang Tingting Jiang Alan Yuille Wen Gao Graduate University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China National Engineering Laboratory for Video Technology Schl of EECS Peking University Beijing China Department of Statistics University of California Los Angeles CA USA

Part-based representation has become an essential representation for objects. Different parts of a shape have various importance to shape perception. Concerned by the contributions of local parts to the perception of global shapes, we introduce an importance measure of shape parts from the viewpoint of their “reconstructability”. Given one shape part as a local observation, we estimate the global object shapes through a proposed novel shape reconstruction method. The reconstruction quality is determined by part geometry variation and part uniqueness. Then a conditional entropy formulation is introduced to measure the part importance considering the reconstruction quality, part uniqueness and variation. Extensive experiments demonstrate the benefit of the proposed part importance to object detection applications. Moreover, psychological experiments are conducted to further support the consistency of our importance measure with human perception towards shape parts.

关键词： Shape Entropy Computational modeling Image reconstruction Object detection Shape measurement Psychology

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Exploiting associations between word clusters and document classes for cross-domain text categorization

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Statistical Analysis and Data Mining 2011年第1期4卷 100-114页

作者： Zhuang, Fuzhen Luo, Ping Xiong, Hui He, Qing Xiong, Yuhong Shi, Zhongzhi The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100039 China Graduate University of Chinese Academy of Sciences Beijing 100039 China Hewlett Packard Labs China China MSIS Department Rutgers University United States Innovation Works United States

Cross-domain text categorization targets on adapting the knowledge learnt from a labeled source domain to an unlabeled target domain, where the documents from the source and target domains are drawn from different distributions. However, in spite of the different distributions in raw-word features, the associations between word clusters (conceptual features) and document classes may remain stable across different domains. In this paper, we exploit these unchanged associations as the bridge of knowledge transformation from the source domain to the target domain by the non-negative matrix tri-factorization. Specifically, we formulate a joint optimization framework of the two matrix tri-factorizations for the source- and target-domain data, respectively, in which the associations between word clusters and document classes are shared between them. Then, we give an iterative algorithm for this optimization and theoretically show its convergence. The comprehensive experiments show the effectiveness of this method. In particular, we show that the proposed method can deal with some difficult scenarios where baseline methods usually do not perform well. Copyright © 2010 Wiley Periodicals, Inc.

关键词： Iterative methods

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