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EvoWorkshops 2006: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, and EvoSTOC

作者： Tianzhu, Wang Wenhui, Li Yi, Wang Zihou, Ge Dongfeng, Han Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Ministry of Education Changchun 130012 China

ISBN: (纸本)3540332375

In this paper, we present an efficient method for detecting collisions between highly deformable objects, which is a combination of newly developed stochastic method and Particle Swarm Optimization (PSO) algorithm. Firstly, our algorithm samples primitive pairs within the models to construct a discrete binary search space for PSO, and in this way user can balance performance and detection quality. Besides a particle update process is added in every time step to handle the dynamic environments caused by deformations. Our algorithm is also very general that makes no assumptions about the input models and doesn't need to store additional data structures either. In the end, we give the precision and efficiency evaluation about the algorithm and find it might be a reasonable choice for complex deformable models in collision detection systems. © Springer-Verlag Berlin Heidelberg 2006.

关键词： Intelligent agents

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Modeling and refining directional relations based on fuzzy mathematical morphology

Modeling and refining directional relations based on fuzzy m...

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High-Power Diode Laser Technology and Applications IV

作者： Sun, Haibin Li, Wenhui Key Laboratory of Symbol Computation Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Jilin University Changchun 130012 China

ISBN: (纸本)0819461466

In this paper, we investigate the deficiency of Goyal and Egenhofer's method for modeling cardinal directional relations between simple regions and provide the computational model based on the concept of mathematical morphology, which can be a complement and refinement of Goyal and Egenhofer's model for crisp regions. Based on fuzzy set theory, we extend Goyal and Egenhofer's model to handle fuzziness and provide a computational model based on alpha-morphology, which combines fuzzy set theory and mathematical morphology, to refine the fuzzy cardinal directional relations. Then the computational problems are investigated. We also give an example of spatial configuration in 2-dimensional discrete space. The experiment results confirm the cognitive plausibility of our computational models. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Fuzzy sets

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Identification of transcription factor binding sites using GA and PSO

Identification of transcription factor binding sites using G...

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6th International Conference on Intelligent Systems Design and Applications (ISDA 2006)

作者： Chang, Xiao-Yu Zhou, Chun-Guang Li, Yan-Wen Hu, Ping Key Laboratory of Symbol Computation and J Knowledge Engineering Jilin University Ministry of Education China Department of Computer Science Northeast Normal University Changchun 130012 China

ISBN: (纸本)0769525288

Identification of transcription factor binding sites from the upstream regions of genes is a highly important and unsolved problem. In this paper, we propose a novel framework for using evolutionary algorithm to solve this challenging issue. Under this framework, we use two prevalent evolutionary algorithms: Genetic, Algorithm (GA) and Particle Swarm Optimization (PSO) to find unknown sites in a collection of relatively long intergenic sequences that are suspected of being bound by the same factor. This paper represents binding sites motif to position weight matrix (PWM) and introduces how to code PWM to genome for GA and how to code it to particle for PSO. We apply these two algorithms to 5 different yeast Saccharomyces Cerevisiae transcription factor binding sites and CRP binding sites. The results on Saccharomyces Cerevisiae show that it can find the correct binding sites motifs, and the result on CRP shows that these two algorithms can achieve more accuracy than MEME and Gibbs Sampler.

关键词： Binding sites

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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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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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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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Fuzzy neural network optimization by a particle swarm optimization algorithm

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

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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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Diagnosis of breast cancer tumor based on ICA and LS-SVM

Diagnosis of breast cancer tumor based on ICA and LS-SVM

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2006 International Conference on Machine Learning and Cybernetics

作者： Wang, Chao-Yong Wu, Chun-Guo Liang, Yan-Chun Guo, Xin-Chen Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Ministry of Education Changchun 130012 China Department of Fundamental Sciences Jilin Teacher's Institute of Engineering and Technology Changchun 130021 China Key Laboratory of Information Science and Engineering Beijing Jiaotong University Railway Ministry Beijing 100044 China

ISBN: (纸本)1424400619

An efficient method for the diagnosis of breast cancer tumor is proposed based on Independent Component Analysis (ICA) and Least Square Support Vector Machine (LS-SVM). In order to save the expense of detection, firstly, variables are selected based on the theory of statistics. Then the ICA is introduced in a concise way and followed by extracting the ICA component from these selected variables. Finally the processed data are classified by the LS-SVM. Experimental and analytical results show that in the diagnosis of breast cancer tumor the proposed method is superior to the classical B-P algorithm. © 2006 IEEE.

关键词： Biomedical engineering

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Temporal texture synthesis algorithm using genetic algorithm

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Journal of computational Information Systems 2006年第2期2卷 847-853页

作者： Meng, Yu Li, Wenhui Guo, Wu Wang, Yan Wang, Tianzhu Key Laboratory of Symbol Computation and Knowledge Engineering College of Computer Science and Technology Jilin University Changchun 130012 China

Texture synthesis is a very active research area in computer vision and graphics, and temporal texture synthesis is one subset of it. A new temporal texture synthesis algorithm is presented, in which genetic algorithm is introduced into the processes of synthesizing videos. In the algorithm, by analyzing and processing a finite source video clip, infinite video sequences that are played smoothly in vision can be obtained. Comparing with many existing temporal texture synthesis algorithms, this algorithm can get high-quality video results without complicated pre-processing of source video while it improves the efficiency of synthesis. This paper analyzes to determine the population size and the Max number of generations which influence the speed and quality of synthesis.

关键词： Computer vision

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Support vector machine regression algorithm based on chunking incremental learning

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ICCS 2006: 6th International Conference on computational Science

作者： Jingqing, Jiang Chuyi, Song Chunguo, Wu Maurizio, Marchese Yangchun, Liang Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Ministry of Education Changchun 130012 China College of Mathematics and Computer Science Inner Mongolia University for Nationalities Tongliao 028043 China Key Laboratory of Information Science and Engineering Beijing Jiaotong University Railway Ministry Beijing 100044 China Italy

ISBN: (纸本)3540343792

On the basis of least squares support vector machine regression (LSSVR), an adaptive and iterative support vector machine regression algorithm based on chunking incremental learning (CISVR) is presented in this paper. CISVR is an iterative algorithm and the samples are added to the working set in batches. The inverse of the matrix of coefficients from previous iteration is used to calculate the regression parameters. Therefore, the proposed approach permits to avoid the calculation of the inverse of a large-scale matrix and improves the learning speed of the algorithm. Support vectors are selected adaptively in the iteration to maintain the sparseness. Experimental results show that the learning speed of CISVR is improved greatly compared with LSSVR for the similar training accuracy. At the same time the number of the support vectors obtained by the presented algorithm is less than that obtained by LSSVR greatly. © Springer-Verlag Berlin Heidelberg 2006.

关键词： Learning systems

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