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International Conference on Neural Networks and Brain (ICNNB)

作者： Shifei Ding Zhongzhi Shi College of Information Science and Engineering Shandong Agricultural University Taian China Key Laboratory of Intelligent Information Processing Chinese Academy of Sciences Institute for Computing Technology Beijing China

Based on fuzzy association degree, a new pattern recognition algorithm is set up. First, some new concepts of fuzzy association coefficient (FAC), fuzzy association degree (FAD) and fuzzy relative weight (FRW) have been proposed for surveying data information. Second, on the basis of the concepts proposed here, a new pattern recognition algorithm has been set up. At last, the algorithm set up here is applied to surveying data. The results of simulation application show that the recognition algorithm presented here is feasible and effective

关键词： Pattern recognition Fuzzy sets Laboratories Fuzzy neural networks Educational institutions information science Agricultural engineering History Artificial neural networks Pattern analysis

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Analysis and Applications of PCA information Features

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通讯和计算机（中英文版） 2005年第9期2卷 25-31页

作者： Shifei Ding Zhongzhi Shi College of lnformation Science and Engineering Shandong Agricultural University Tai ＇an 271018 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beo＇ing 100080 China

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Service-oriented knowledge management on virtual organizations

Service-oriented knowledge management on virtual organizatio...

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International Conference on Computer and information technology (CIT)

作者： He Huang Zhongzhi Shi Yong Cheng Lirong Qiu Graduate School of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China

WWW is a repository of information mainly oriented to human consumption. The lack of explicit and formal expression of data semantics makes the Web increasingly difficult to use and exploit. To remedy it, knowledge management sphere (KMSphere), taking advantages of semantic Web, Web services and virtual organizations, is proposed to explore important aspects of service-oriented and ontology-driven knowledge management on the grid. By building two kinds of mappings, ontologies construct a knowledge space on top of data repositories. KMSphere, based on OGSA, emphasizes how to organize, discover, utilize, and manage the knowledge resources in that space. This paper describes in detail the architecture of KMSphere.

关键词： Knowledge management Ontologies Web services World Wide Web Resource management Humans Semantic Web Service oriented architecture Space technology Laboratories

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Theoretical study on a new information entropy and its use in attribute reduction

Theoretical study on a new information entropy and its use i...

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IEEE International Conference on Cognitive Informatics

作者： Ping Luo Qing He Zhongzhi Shi Graduate School of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China

The positive region in rough set framework and Shannon conditional entropy are two traditional uncertainty measurements, used usually as heuristic metrics in attribute reduction. In this paper first a new information entropy is systematically compared with Shannon entropy, which shows its competence of another new uncertainty measurement. Then given a decision system we theoretically analyze the variance of these three metrics under two reverse circumstances, Those are when condition (decision) granularities merge while decision (condition) granularities remain unchanged. The conditions that keep these measurements unchanged in the above different situations are also figured out. These results help us to give a new information view of attribute reduction and propose more clear understanding of the quantitative relations between these different views, defined by the above three uncertainty measurements. It shows that the requirement of reducing a condition attribute in new information view is more rigorous than the ones in the latter two views and these three views are equivalent in a consistent decision system.

关键词： information entropy Measurement uncertainty Set theory Algebra Analysis of variance Machine learning Merging Laboratories information processing Computers

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Metadata model in grid database and its application

Metadata model in grid database and its application

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International Conference on Computer Supported Cooperative Work in Design

作者： Liyan Liu Hua Li Zongkai Lin Graduate School of Chinese Academy of Sciences Beijing China Chinese Academy of Sciences Key Laboratory of Intelligent Information Processing Institute of Computing Technology Beijing China

ISBN: (纸本)184000025

Plenty of structured data existing on the grid need to be managed by databases, but there is little work available on grid-enabled databases. Two problems lie in current research: the first is the deficiency of a uniform management and access method to various kinds of databases; the second is that query efficiency decreases rapidly with the increase of the amount of data records. Metadata plays a critical role in grid databases integration, so a layered metadata model is put forward which comprises information of data storage, data properties and relations between data objects. Through the metadata model, the distributed, heterogeneous data resources can be accessed uniformly and efficiently. At last an example of hospital information database system proves the validity and feasibility of our model.

关键词： Conference management technology management Hospitals Database systems Distributed databases information management Deductive databases information processing Computers Distributed computing

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A novel feature extraction algorithm

A novel feature extraction algorithm

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Shi-Fei Ding Zhong-Zhi Shi Vun-Cheng Wang Shu-Shan Li College of Information Science and Engineering Shandong Agricultural University Taian China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China College of Information Science and Engineering Shandong University of Science and Technology Qingdao China

Feature extraction or selection is one of the most important steps in pattern recognition or pattern classification, data mining, machine learning and so on. In this paper, we introduce the information theory, propose a new concept of probability information distance (PID) and prove that the PID satisfies four requests of axiomatization of the distance. So the PID is a kind of distance measure, which can be used to measure the degree of variation between two random variables. We make the PID be separability criterion of the classes for information feature extraction, and call it PID criterion (PIDC). Based on PIDC, we design a novel algorithm for information feature extraction. Compared with principal components analysis (PCA), correlation analysis etc., the algorithm put forward in this paper had regarded for the class information, and so it is a kind of supervised algorithm of feature extraction. The experimental results demonstrate that the algorithm is valid and reliable, and it provides a new research approach for feature extraction, data mining and pattern recognition.

关键词： Feature extraction Pattern recognition Data mining Algorithm design and analysis Principal component analysis Machine learning algorithms Pattern classification Machine learning information theory Random variables

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information feature analysis and improved algorithm of PCA

Information feature analysis and improved algorithm of PCA

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Shi-Fei Ding Zhong-Zhi Shi Yong Liang Feng-Xiang Jin College of Information Science and Engineering Shandong Agricultural University Taian China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China College of Geo-Information Science and Engineering Shandong University of Science and Technology Qingdao China

Principal component analysis (PCA) is an important method in multivariate statistical analysis, and its main idea is compression of dimensionality including variables and samples. In this paper, based on the ideas concerned with information function and information entropy of Shannon information theory, consider the inherent characteristic of eigenvalues of matrix, two new concepts of possibility information function (PIF) and possibility information entropy (PIE) are proposed firstly. On the basis of these, the formulae of information rate (IR) and accumulated information rate (AIR) are set up, by which the degree of information compression is measured. In the end, we improve the PCA algorithm called improved principal component analysis (PCA). Through simulated application in practice, the results show that the IPCA proposed here is efficient and satisfactory. It provides a new research approach of information feature compression for pattern recognition.

关键词： information analysis Algorithm design and analysis Principal component analysis information entropy Educational institutions Agricultural engineering Statistical analysis information theory information rates Geographic information Systems

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A logical foundation for ontology representation in NKI

A logical foundation for ontology representation in NKI

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IEEE International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE)

作者： Yu Sun Yuefei Sui Institute of Computer Science and Information Technology Yunnan Normal University Kunming China Graduate School Chinese Academy and Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China

A formal representation of ontologies is proposed, based on F-logic and O-logic; and the works in the building of ontologies in NKI. An ontology includes class frames, slot frames, class-slot frames, object frames and axioms. The value restrictions of slots are defined in slot frames. For each slot and each class, there is a class-slot frame representing the specific value restrictions of the slot when defining the class; and the relations between class-slot frames and slot frames are discussed. For a slot in a class frame, its values are inherited to its subclasses without blocking; and its default values are inherited to its subclasses taking overriding, revising and conflict resolution into account. After giving the formal representation of ontologies, the semantics of ontologies are discussed, and main results are presented.

关键词： Ontologies Databases Laboratories intelligent structures information processing Computer science information technology Vocabulary Logic

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R-Chord: A Semantic-based Peer Data Management Model

R-Chord: A Semantic-based Peer Data Management Model

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International Conference on Semantics, Knowledge and Grid (SKG)

作者： Jie Liu Hai Zhuge Graduate School of Chinese Academy of Sciences China China Knowledge Grid Research Group Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China

The key issue of peer data management systems (PDMSs) is how to efficiently organize and manage distributed resources in P2P networks to accurately route queries from the peer initiating the query to appropriate peers to avoid network flooding. This paper proposes a semantic-based PDMS model, called R-Chord, by deploying the resource space model above the Chord overlay for uniformly, normally and effectively organizing and managing resources distributed in P2P networks. Experiments show that, compared to the Chord model, the R-Chord model is more flexible to support semantic-based queries and can significantly decrease the average visiting number of and visiting times on peers for answering queries.

关键词： Resource management Peer to peer computing Knowledge management Computer network management technology management Large-scale systems Relational databases XML Prototypes intelligent networks

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Automatic gait recognition based on wavelet reflective symmetry moments

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Journal of information and Computational Science 2005年第2期2卷 421-428页

作者： Zhao, Guoying Cui, Li Xiang, Shiming Li, Hua Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100080 China Graduate School Chinese Academy of Sciences Beijing 100039 China School of Mathematics Science Beijing Normal University Beijing 100875 China

Gait is an identifying biometric feature. In recent years, Video-based gait recognition is becoming a new challenging problem in the field of computer vision. In this paper, wavelet reflective symmetry moments (WRSMs) have been proposed to describe and recognize gait automatically. WRSMs represent the appearance of people with merits of moments and wavelet analysis and reflect people's symmetrical walking habit. Moments have translation, scale and rotation invariant characteristics;while wavelet analysis is able to extract the multi-resolution features subtly and deal with noise. So combination of wavelet moments and reflective symmetry not only has characteristics of moments and wavelet analysis, but also is in accordance with one of the relative results in psychological researches which state that gait is a type of symmetrical model. Experiments based on USF's database have shown that the application of wavelet reflective symmetry moments in gait recognition leads to relatively high distinguishability of gait with effective noise handling.

关键词： Computer vision

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