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The architecture and implementation of resource space model system

4th International Conference on Grid and Cooperative Computing - GCC 2005

作者： Shi, Peng Xing, Yunpeng Yao, Erlin Wang, Zhen Yuan, Kehua Zhang, Junsheng Wang, Jianzeng Guo, Fei Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China Graduate School Chinese Academy of Sciences Beijing China Hunan Knowledge Grid Lab Hunan University of Science and Technology Hunan China

ISBN: (纸本)3540305106

Resource Space Model (RSM) is a semantic model to manage and share heterogeneous resources on the Internet. This paper focuses on the general architecture, physical implementation and application of RSM. The RSM system adopts Client/Server mode for users to utilize resource spaces through the Internet. The resource spaces are designed based on domain knowledge and stored as XML files in the server. The tool 3DBrowser provides a friendly interface with three-dimensional scene to display resource spaces, locate and operate on resources. Based on the architecture and tools, a Dunhuang RSM system has been developed as a collaboration platform to organize and share the resources of Dunhuang Culture Grid. The system provides an efficient resource locating way based on the classification of resources and improves the efficiency of research. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Semantics

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Using fuzzy cognitive map to effectively classify e-documents and application

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4th International Conference on Grid and Cooperative Computing - GCC 2005

作者： Wang, Jianzeng Xing, Yunpeng Shi, Peng Guo, Fei Wang, Zhen Yao, Erlin Yuan, Kehua Zhang, Junsheng Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100080 China Hunan Knowledge Grid Lab Hunan University of Science and Technology Hunan China Graduate School Chinese Academy of Sciences Beijing China

ISBN: (纸本)3540305106

In the current Web, e-document has been the most common vehicle for delivering and exchanging information. As the amount of e-documents has grown enormously, effective classification facilities are urgently needed to classify and query e-documents users want. In this paper, we propose a method to classify e-documents into a set of predefined categories based on Fuzzy Cognitive Map (FCM). The e-documents are collected from Internet by a metasearch engine. FCM has been employed to capture the semantic relationships between keywords of e-documents. Experiments with a set of local e-documents have proved that this approach has high performance and can help users getting the e-documents efficiently and effectively. The proposed method has been implemented and integrated into the Dunhuang Feitian System to manage and classify e-documents. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Electronic document exchange

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Pushing scientific documents by discovering interest in information flow within E-science knowledge grid

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4th International Conference on Grid and Cooperative Computing - GCC 2005

作者： Ding, Lianhong Li, Xiang Xing, Yunpeng Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China Graduate School of Chinese Academy of Sciences 100080 Beijing China Hunan Knowledge Grid Lab. Hunan University of Science and Technology Hunan China

ISBN: (纸本)3540305106

The Knowledge Grid is an intelligent and sustainable Internet application environment that enables people and roles to effectively capture, publish, share and manage explicit knowledge resources. As an important function of the e-science Knowledge Grid, this paper proposes an approach to effectively push scientific documents within research teams by detecting the social characteristics in self-organized network, discovering interest in information flow, and capturing dynamic changes of interests over time. The proposed approach can be used in any cooperative organizations. © Springer-Verlag Berlin Heidelberg 2005.

关键词： information analysis

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Collaborating semantic link network with resource space model

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4th International Conference on Grid and Cooperative Computing - GCC 2005

作者： Xing, Yunpeng Liu, Jie Sun, Xiaoping Yao, Erlin Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100080 China Graduate School Chinese Academy of Sciences Beijing China Hunan Knowledge Grid Lab. Hunan University of Science and Technology Hunan China

ISBN: (纸本)3540305106

The Semantic Link Network model SLN and Resource Space Model RSM are semantic models proposed separately for effectively specifying and managing versatile resources across the Internet. Collaborating the relational semantics of SLN with the classification semantics of the RSM can support richer semantic modeling in applications. This paper introduces the Resource Class Hierarchies into the SLN and investigates to cooperatively model with the RSM. The proposed approach can be used as a normalized semantic overlay of the Knowledge Grid environment. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Mathematical models

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Investigating manifold learning algorithms based on magnification factors and principal spread directions

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Jisuanji Xuebao/Chinese Journal of computers 2005年第12期28卷 2000-2009页

作者： He, Li Zhang, Jun-Ping Zhou, Zhi-Hua Shanghai Key Laboratory of Intelligent Information Processing Department of Computer Science and Engineering Fudan University Shanghai 200433 China Key Laboratory of Complex Systems and Intelligence Science Chinese Academy of Sciences Shanghai 200433 China Department of Computer Science and Engineering School of Mathematical Sciences Fudan University Shanghai 200433 China National Laboratory for Novel Software Technology Nanjing University Nanjing 210093 China

As a new unsupervised learning technique, manifold learning has captured the attention of many researchers in the field of machine learning and cognitive sciences. The major algorithms include Isometric mapping (ISOMAP) and Locally Linear Embedding (LLE). The approaches can be used for discovering the intrinsic dimensions of nonlinear high-dimensional data effectively and aim researchers to analyze the data better. How to quantitatively analyze the relationship between the intrinsic dimensions and the observation space, however, has fewer reports. And thus further works in manifold learning may have suffered some difficulties. The paper focuses on two kinds of manifold learning algorithms (ISOMAP, LLE), and discusses magnification factors and principal spread directions from the observation space to the intrinsic low-dimensional space. Also the corresponding algorithm is proposed. Experiments show the effectiveness and advantages of the research.

关键词： Learning algorithms

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Space-time multiuser detection combined with adaptive wavelet networks over multipath channels

Space-time multiuser detection combined with adaptive wavele...

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International Symposium on Neural Networks, ISNN 2004

作者： Wang, Ling Jiao, Licheng Tao, Haihong Liu, Fang Institute of Intelligent Information Processing Xidian University Xi'an710071 China Key Lab for Radar Signal Processing Xidian University Xi'an710071 China School of Computer Science and Technology Xidian University Xi'an710071 China

ISBN: (纸本)3540228438

The capacity and performance of code division multiple access (CDMA) systems are limited by multiple access interference (MAI) and "nearfar" problem. Space-time multiuer detection combined with adaptive wavelet networks over frequency selective fading multipath channels is proposed in this paper. The structure of the multiuser detector is simple and its computational complexity mostly lies on that of wavelets networks. With numerical simulations and performance analysis, it is shown that the proposed detector can converge at the steady state rapidly and offer significant performance improvement over the space-time matched filtering detector, the conventional RAKE receiver and matched filter detector only at the time domain. Therefore, it can suppress the MAI and solve the "near-far" problem effectively. © Springer-Verlag Berlin Heidelberg 2004. All rights reserved.

关键词： Frequency selective fading

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Performance analysis of recurrent neural networks based blind adaptive multiuser detection in asynchronous DS-CDMA systems

Performance analysis of recurrent neural networks based blin...

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International Symposium on Neural Networks, ISNN 2004

作者： Wang, Ling Tao, Haihong Jiao, Licheng Liu, Fang Institute of Intelligent Information Processing Xidian University Xi'an710071 China Key Lab for Radar Signal Processing Xidian University Xi'an710071 China School of Computer Science and Technology Xidian University Xi'an710071 China

ISBN: (纸本)3540228438

With dynamics property and highly parallel mechanism, recurrent neural networks (RNN) can effectively implement blind adaptive multiuser detection at the circuit time constant level. In this paper, the RNN based blind adaptive multiuser detection is extended to ubiquitous asynchronous DSCDMA systems, and the performance of the output signal to interference plus noise ratio, asymptotic multiuser efficiency, computational complexity, operating time, and mismatch of the detector are quantitatively analyzed. With performance analysis and numerical simulations, it is shown that RNN based blind adaptive multiuser detection can converge at the steady quickly and offer significant performance improvement over some existing popular detectors in eliminating multiple access interference and "near-far" resistance. © Springer-Verlag Berlin Heidelberg 2004. All rights reserved.

关键词： Recurrent neural networks

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A Multi-layers Neural Network Model based on Characteristics of Ganglions' Receptive Fields in Retina and an Algorithm for Watchfulness-keeping

A Multi-layers Neural Network Model based on Characteristics...

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The Third Asian Conference on Vision (ACV2004)

作者：危辉陆汝钤 Department of Computer Science Lab of Algorithm for Cognitive Model Brain Science Research Center Fudan University Shanghai 200433 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing 100080 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing 100080 China

Machine vision is an active branch of Artificial Intelligence. An important problem in this area is the balance among efficiency, accuracy and huge computing. The visual system of human can keep watchfulness to the perimeter of visual field while at same time their central attention is focused to the center of visual field for fine information processing. This mechanism of computing resource assignment could ease the demand for huge and complex hardware structure. Therefore designing computer model based on biological visual

关键词： A Multi-layers Neural Network Model based on Characteristics of Ganglions Receptive Fields in Retina and an Algorithm for Watchfulness-keeping Model

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Advances in Multimedia information processing - PCM 2010 1

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丛书名： Lecture Notes in computer science

1000年

作者： Guoping Qiu Kin Man Lam Hitoshi Kiya Xiang-Yang Xue C.-C. Jay Kuo Michael S. Lew

ISBN: (数字)9783642157028

ISBN: (纸本)9783642157011

The 2010 Pacific-Rim Conference on Multimedia (PCM 2010) was held in shanghai at Fudan University, during September 21–24, 2010. Since its inauguration in 2000, PCM has been held in various places around the Pacific Rim, namely Sydney (PCM 2000), Beijing (PCM 2001), Hsinchu (PCM 2002), Singapore (PCM 2003), Tokyo (PCM 2004), Jeju (PCM 2005), Zhejiang (PCM 2006), Hong Kong (PCM 2007), Tainan (PCM 2008), and Bangkok (PCM 2009). PCM is a major annual international conference organized as a forum for the dissemination of state-of-the-art technological advances and research results in the fields of theoretical, experimental, and applied multimedia analysis and processing. PCM 2010 featured a comprehensive technical program which included 75 oral and 56 poster presentations selected from 261 submissions from Australia, Canada, China, France, Germany, Hong Kong, India, Iran, Italy, Japan, Korea, Myanmar, Norway, Singapore, Taiwan, Thailand, the UK, and the USA. Three distinguished researchers, Prof. Zhi-Hua Zhou from Nanjing University, Dr. Yong Rui from Microsoft, and Dr. Tie-Yan Liu from Microsoft Research Asia delivered three keynote talks to the conference. We are very grateful to the many people who helped to make this conference a s- cess. We would like to especially thank Hong Lu for local organization, Qi Zhang for handling the publication of the proceedings, and Cheng Jin for looking after the c- ference website and publicity. We thank Fei Wu for organizing the special session on large-scale multimedia search in the social network settings.

关键词： computer Applications Multimedia information Systems computer Communication Networks information Systems Applications (incl. Internet) information Storage and Retrieval User Interfaces and Human computer Interaction

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Cross-Region Graph Convolutional Network with Periodicity Shift Adaptation for Wide-area SST Prediction

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ACM Transactions on intelligent Systems and technology 1000年

作者： Han Peng Wengen Li Chang Jin Yichao Zhang Jihong Guan Hanchen Yang Shuigeng Zhou Department of Computer Science and Technology Tongji University China Department of Computer Science and Technology Tongji University China and Department of Computing The Hong Kong Polytechnic University China Shanghai Key Lab of Intelligent Information Processing and School of Computer Science Fudan University China

Accurate prediction of sea surface temperature (SST) is of high importance in marine science, benefiting applications ranging from ecosystem protection to extreme weather forecasting and climate analysis. Wide-area SST usually shows diverse SST patterns in different sea areas due to the changes of temperature zones and the dynamics of ocean currents. However, existing studies on SST prediction often focus on small-area predictions and lack the consideration of diverse SST patterns. Furthermore, SST shows an annual periodicity, but the periodicity is not strictly adherent to an annual cycle. Existing SST prediction methods struggle to adapt to this non-strict periodicity. To address these two issues, we proposed the Cross-Region Graph Convolutional Network with Periodicity Shift Adaptation (RGCN-PSA) model which is equipped with the Cross-Region Graph Convolutional Network module and the Periodicity Shift Adaption module. The Cross-Region Graph Convolutional Network module enhances wide-area SST prediction by learning and incorporating diverse SST patterns. Meanwhile, the periodicity Shift Adaptation module accounts for the annual periodicity and enable the model to adapt to the possible temporal shift automatically. We conduct experiments on two real-world SST datasets, and the results demonstrate that our RGCN-PSA model obviously outperforms baseline models in terms of prediction accuracy. The code of RGCN-PSA model is available at https://***/ADMIS-TONGJI/RGCN-PSA/.

关键词： Wide-area SST prediction graph neural network diverse SST patterns periodicity shift adaptation

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