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Operational semantics of the seal calculus

Operational semantics of the seal calculus

引用

4th IFIP TCI2 International Conference on Intelligent Information Processing, IIP 2006

作者： Jing, Zhang Li-Cui, Zhang De-Gui, Guo College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education of China Jilin University Changchun 130012 China College of Communication Engineering Jilin University Changchun 130012 China

ISBN: (纸本)9780387446394

As a distributed process calculus with localities and mobility of computational entities, Seal calculus is playing an important role in expressing key features such as security and mobility of Internet programming directly. However, little implementation technique proposed for the calculus, partly due to the complication of inobile computation, which fusions three important techniques: concurrency, distribution and mobility at the same time. The abstract machine PSN for a distributed implementation of the Seal calculus is presented. In PSN the logical structure of a seal system and its physical distribution are separated which induces a more simple and clear implementation. Moreover, an operational semantics description of the Seal calculus based on PSN is given.

关键词： Calculations

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An efficient method for very large scale out-of-core terrain visualization

An efficient method for very large scale out-of-core terrain...

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16th International Conference on Artificial Reality and Telexistence - Workshops, ICAT 2006

作者： Zhang, Huijie Sun, Jigui Yu, Haihong Qi, Changsong College of Computer Science and Technology Jilin University College of Computer Science Northeast Normal University Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education

ISBN: (纸本)076952754X

Large scale terrain visualization with high-resolution has an increasing demand in many research fields. To realize the efficient rendering of terrain, this paper presents an out-of-core terrain visualization method based on multi-resolution storage techniques. In external memory, the terrain data set is subdivided from top to bottom to build a multi-resolution hierarchical structure based on a quad-tree. The hierarchical structure can decimate the elevation data that must be loaded into internal memory. Thus it can improve the efficiency of I/O access greatly. Moreover, in order to implement rapid data retrieval of the real time terrain flyover, an efficient indexing algorithm is proposed, in which those nodes in the hierarchical structure will be divided into several clusters in terms of the similarities of static error and the closed space constraint. In addition, a method for crack-free is also proposed here. The comprehensive experiment conducted on the GTOP30 data set shows that this approach outperforms the Block and the Hierarchy algorithms in the both ways of efficiency and simplification ratio. © 2006 IEEE.

关键词： Efficiency

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SLNN: A Neural Network for Fuzzy Neural Network's Structure Learning

SLNN: A Neural Network for Fuzzy Neural Network's Structure ...

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International Conference on Intelligent Systems Design and Applications (ISDA)

作者： Daxin Tian Yanheng Liu Jian Wang College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China

A novel structure learning algorithm for fuzzy neural networks (SLNN) is presented in this paper. The neurons of SLNN are created and adapted as online learning proceeds. The learning rule of SLNN is based on Hebbian learning and a kernel winner-take-all algorithm - KWTA. KWTA not only can let SLNN be able to learn from new data but also can prevent losing the knowledge which has been learned earlier. To obtain a concise fuzzy rule, a pruning algorithm is adopted in SLNN which doesn't disobey the basic design philosophy of fuzzy system. Simulations are performed on the primary benchmark: circle-in-the-square. Comparison with ARTMAP and BP neural network indicates that better performance is achieved

关键词： Neural networks Fuzzy neural networks Fuzzy systems Clustering algorithms Signal processing algorithms Computer networks Neurons Partitioning algorithms Learning Fuzzy control

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Research of Mobile IPv6 Application Based On Diameter Protocol

Research of Mobile IPv6 Application Based On Diameter Protoc...

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International Multi-Symposiums on Computer and computational Sciences (IMSCCS)

作者： Da Wei Yanheng Liu Xuegang Yu Xiaodong Li College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China

The diameter protocol is recommended by IETF as AAA (authentication, authorization and accounting) protocol criterion for the next generation network. Because the IPv6 protocol will be widely applied in the intending all-IP network, mobile IPv6 application based on diameter protocol will play more important role in authentication, authorization and accounting. In this paper, the implementation of mobile node's authentication and authorization is presented with PANA (protocol for carrying authentication for network access) protocol. It is based on diameter protocol for the application expansion of mobile IPv6, which provides the supports to the basic AAA process of mobile IPv6 nodes and dynamic home agent distribution in the visited network and the secret key distribution. Finally, the correctness of this application expansion is testified with developing the design of protocol based on opendiameter

关键词： Access protocols Authentication Cryptographic protocols Authorization Network servers Application software IP networks Peer to peer computing Transport protocols Computer science

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Adaptive support vector clustering for multi-relational data mining

Adaptive support vector clustering for multi-relational data...

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3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks

作者： Ling, Ping Zhou, Chun-Guang Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Ministry of Education Changchun 130012 China School of Computer Science Xuzhou Normal University Xuzhou 221116 China

ISBN: (纸本)354034439X

A novel self Adaptive Support Vector Clustering algorithm (ASVC) is proposed in this paper to cluster dataset with diverse dispersions. And a Kernel function is defined to measure affinity between multi-relational data. Task of clustering multi-relational data is addressed by integrating the designed Kernel into ASVC. Experimental results indicate that the designed Kernel can capture structured features well and ASVC is of fine performance. © Springer-Verlag Berlin Heidelberg 2006.

关键词： Data mining

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A spectrum-based support vector algorithm for relational data semi-supervised classification

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13th International Conference on Neural Information Processing, ICONIP 2006

作者： Ping, Ling Zhe, Wang Chunguang, Zhou Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Ministry of Education Changchun 130012 China School of Computer Science Xuzhou Normal University Xuzhou 221116 China

ISBN: (纸本)3540464794

A Spectrum-based Support Vector Algorithm (SSVA) to resolve semi-supervised classification for relational data is presented in this paper. SSVA extracts data representatives and groups them with spectral analysis. Label assignment is done according to affinities between data and data representatives. The Kernel function encoded in SSVA is defined to rear to relational version and parameterized by supervisory information. Another point is the selftuning of penalty coefficient and Kernel scale parameter to eliminate the need of searching parameter spaces. Experiments on real datasets demonstrate the performance and efficiency of SSVA. © Springer-Verlag Berlin Heidelberg 2006.

关键词： Learning algorithms

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Fuzzy neural network optimization by a particle swarm optimization algorithm

Fuzzy neural network optimization by a particle swarm optimi...

引用

3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks

作者： Ma, Ming Zhang, Li-Biao Ma, Jie Zhou, Chun-Guang Key Laboratory for Symbol Computation and Knowledge Engineering Jilin University National Education Ministry of China Changchun 130012 China Information Manage Center Beihua University Jilin 132013 China

ISBN: (纸本)354034439X

Designing a set of fuzzy neural networks can be considered as solving a multi-objective optimization problem. An algorithm for solving the multi-objective optimization problem is presented based on particle swarm optimization through the improvement of the selection manner for global and individual extremum. The search for the Pareto Optimal Set of fuzzy neural networks optimization problems is performed, Numerical simulations for taste identification of tea show the effectiveness of the proposed algorithm. © Springer-Verlag Berlin Heidelberg 2006.

关键词： Neural networks

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COMPUTING AND RAY TRACING POINT-BASED GEOMETRY

COMPUTING AND RAY TRACING POINT-BASED GEOMETRY

引用

2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

作者： YUN-JIE PANG BIN ZHOU YONG QUAN WEN-HUI LI Computer Graphics Laboratory College of Computer Science and Technology Jilin University Qianwei Key Laboratory for Symbolic Computation and Knowledge Engineering of Ministry of Education

In this paper a new technique for computing and ray tracing point based geometry is presented. It uses a novel point primitive that is called "Spherical Patch Point" (SPP) to approximate the vicinity of a surface point. Due to property of curvature, SPP can achieve similar visual quality compared with previous methods with much fewer primitives for the cost of a few additional bytes per point and thus makes a significant reduction in rendering time. During pre-process,important attributes are added to each SPP for the purpose of ray tracing. During rendering, an intersection algorithm different from previous ones has been demonstrated to get satisfied results. The proposed technique makes it possible to render high quality ray traced images with global illumination using SPPs. It offers a higher ray tracing speed in comparison with previous methods.

关键词： Points Curvature Sphere Radius Ray tracing

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A NEW POINT-BASED RENDERING METHOD FOR RAY TRACING

A NEW POINT-BASED RENDERING METHOD FOR RAY TRACING

引用

2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

作者： GUO-LIANG ZHOU YUN-JIE PANG ZHEN-HUA ZHANG YONG QUAN WEN-HUI LI Computer Graphics Laboratory College of Computer Science and Technology Jilin University Qianwei Key Laboratory for Symbolic Computation and Knowledge Engineering of Ministry of Education

In this paper a new point-based rendering method for ray tracing is presented. An oriented spherical patch that passes a surface point is used to approximate the vicinity of that *** this paper the spherical patch together with the surface point is called "Spherical Patch Point" (SPP). Due to property of curvature, SPP can achieve similar visual quality compared with previous methods with much fewer points. This paper defines new point attributes for the purpose of efficiently locating the intersection between incoming ray and ***, an algorithm of intersecting a ray with point geometry is proposed. The algorithm can achieve a higher rendering speed in comparison with previous methods. The presented technique deals well with shadow, reflection and refraction.

关键词： Point-based rendering Curvature Spherical patch Radius Ray tracing

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A Dynamic Growing Neural Network for Supervised or Unsupervised Learning

A Dynamic Growing Neural Network for Supervised or Unsupervi...

引用

World Congress on Intelligent Control and Automation (WCICA)

作者： Daxin Tian Yanheng Liu Da Wei College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun Jilin China

A dynamic growing neural network (DGNN) for supervised learning of pattern recognition or unsupervised learning of clustering is presented. The main ideas included in DGNN are growing, resonance, and post-prune. DGNN is called dynamic growing because it is based on the Hebbian learning rule and adds new neurons under certain conditions. When DGNN performs supervised learning, resonance will happen if the winner can't match the training example; this rule combines the ART/ARTMAP neural network and WTA learning rule. When DGNN performs unsupervised learning, post-prune is carried out to prevent over fitting the training data just like decision tree learning. DGNN's prune rule is based on the distance threshold. DGNN has some advantages: learning not only is stable because it grows under certain conditions; but also it is faster than back-propagation rules and favorable learned predictive accuracy in small, noisy, online or offline data sets. Three classes of simulations are performed on the primary benchmarks: circle-in-the-square and two-spirals-apart benchmarks are used to check DGNN's supervised learning and compare it with ARTMAP and BP neural networks; DGNN's unsupervised learning ability is checked on UCI Machine Learning Archive's Synthetic Control Chart Time Series data set

关键词： Neural networks Unsupervised learning Supervised learning Resonance Pattern recognition Hebbian theory Neurons Subspace constraints Training data Decision trees

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