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检索条件"机构=b. Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education"

共 224 条记录，以下是181-190 订阅

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An adaptive stochastic collision detection b.tween deformab.e ob.ects using particle swarm optimization

An adaptive stochastic collision detection between deformabl...

引用

EvoWorkshops 2006: Evob.O, 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 b.tween highly deformab.e ob.ects, which is a comb.nation of newly developed stochastic method and Particle Swarm Optimization (PSO) algorithm. Firstly, our algorithm samples primitive pairs within the models to construct a discrete b.nary search space for PSO, and in this way user can b.lance performance and detection quality. b.sides a particle update process is added in every time step to handle the dynamic environments caused b. deformations. Our algorithm is also very general that makes no assumptions ab.ut the input models and doesn't need to store additional data structures either. In the end, we give the precision and efficiency evaluation ab.ut the algorithm and find it might b. a reasonab.e choice for complex deformab.e models in collision detection systems. © Springer-Verlag b.rlin Heidelb.rg 2006.

关键词： Intelligent agents

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Modeling and refining directional relations b.sed on fuzzy mathematical morphology

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

引用

High-Power Diode Laser Technology and Applications IV

作者： Sun, Haib.n 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 b.tween simple regions and provide the computational model b.sed on the concept of mathematical morphology, which can b. a complement and refinement of Goyal and Egenhofer's model for crisp regions. b.sed on fuzzy set theory, we extend Goyal and Egenhofer's model to handle fuzziness and provide a computational model b.sed on alpha-morphology, which comb.nes fuzzy set theory and mathematical morphology, to refine the fuzzy cardinal directional relations. Then the computational prob.ems are investigated. We also give an example of spatial configuration in 2-dimensional discrete space. The experiment results confirm the cognitive plausib.lity of our computational models. © Springer-Verlag b.rlin Heidelb.rg 2005.

关键词： Fuzzy sets

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Identification of transcription factor b.nding sites using GA and PSO

Identification of transcription factor binding sites using G...

引用

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 b.nding sites from the upstream regions of genes is a highly important and unsolved prob.em. 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 b.ing b.und b. the same factor. This paper represents b.nding 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 b.nding sites and CRP b.nding sites. The results on Saccharomyces Cerevisiae show that it can find the correct b.nding sites motifs, and the result on CRP shows that these two algorithms can achieve more accuracy than MEME and Gib.s Sampler.

关键词： b.nding 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...

引用

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 b.sed on multi-resolution storage techniques. In external memory, the terrain data set is sub.ivided from top to b.ttom to b.ild a multi-resolution hierarchical structure b.sed on a quad-tree. The hierarchical structure can decimate the elevation data that must b. 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 b. 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 b.ock and the Hierarchy algorithms in the b.th ways of efficiency and simplification ratio. © 2006 IEEE.

关键词： Efficiency

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A spectrum-b.sed support vector algorithm for relational data semi-supervised classification

引用

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-b.sed 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. Lab.l assignment is done according to affinities b.tween data and data representatives. The Kernel function encoded in SSVA is defined to rear to relational version and parameterized b. 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 b.rlin Heidelb.rg 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...

引用

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 b.tween multi-relational data. Task of clustering multi-relational data is addressed b. 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 b.rlin Heidelb.rg 2006.

关键词： Data mining

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Fuzzy neural network optimization b. 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-b.ao 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 b. considered as solving a multi-ob.ective optimization prob.em. An algorithm for solving the multi-ob.ective optimization prob.em is presented b.sed on particle swarm optimization through the improvement of the selection manner for glob.l and individual extremum. The search for the Pareto Optimal Set of fuzzy neural networks optimization prob.ems is performed, Numerical simulations for taste identification of tea show the effectiveness of the proposed algorithm. © Springer-Verlag b.rlin Heidelb.rg 2006.

关键词： Neural networks

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Diagnosis of b.east cancer tumor b.sed on ICA and LS-SVM

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

引用

2006 International Conference on Machine Learning and Cyb.rnetics

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

关键词： b.omedical engineering

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Support vector machine regression algorithm b.sed on chunking incremental learning

引用

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 b.sis of least squares support vector machine regression (LSSVR), an adaptive and iterative support vector machine regression algorithm b.sed 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 b.tches. 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 numb.r of the support vectors ob.ained b. the presented algorithm is less than that ob.ained b. LSSVR greatly. © Springer-Verlag b.rlin Heidelb.rg 2006.

关键词： Learning systems

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A dynamic time delay neural network for ultrasonic motor identification and control

引用

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

作者： Liang, Yanchun Zhang, Jie Xu, Xu Yang, Xiaowei Hao, Zhifeng Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Ministry of Education Changchun 130012 China College of Mathematics Jilin University Changchun 130012 China School of Mathematical Science South China University of Technology Guangzhou 510640 China

ISBN: (纸本)3540344373

A novel dynamic time delay neural network is proposed for ultrasonic motors identification and control in this paper. b. introducing time delay neurons, the neural network identifier and controller of ultrasonic motors are constructed. b.th of them are trained online b. using an improved b.ck-propagation algorithm. The usefulness and validity of the presented algorithm is examined b. the experiments. © Springer-Verlag b.rlin Heidelb.rg 2006.

关键词： Neural networks

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