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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： He Hu Da-You Liu Sheng-Sheng Wang Open Symbol Computation and Knowledge Engineering Laboratory of State Education Committee College of Computer Science and Technology Jilin University Changchun China

ISBN: (纸本)0780378652

In this paper, we explore the links between two well-known models in spatial and temporal reasoning - Allen's interval algebra in temporal reasoning and the RCC model in spatial reasoning. We show that the temporal relations identified by interval algebra are actually a combination of RCC-8 relations with directional relations. The work divides RCC-8 relations into two distinct sets with respect to directional factors. This result can be utilized to combine topology and directional relations in particular applications.

关键词： Algebra Calculus knowledge engineering Laboratories Computer science education Educational institutions Computer science Educational technology Topology Rivers

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A new feature selection method based on clustering

A new feature selection method based on clustering

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2011 8th International Conference on Fuzzy Systems and knowledge Discovery, FSKD 2011, Jointly with the 2011 7th International Conference on Natural computation, ICNC'11

作者： Liu, Huawen Mo, Yuchang Wang, Jiyi Zhao, Jianmin Department of Computer Science Zhejiang Normal University Jinhua 321004 China Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Changchun 130012 China School of Computer Science and Engineering Southeast University Nanjing 211189 China

ISBN: (纸本)9781612841816

Feature selection is an effective technique to put the high dimension of data down, which is prevailing in many application domains, such as text categorization and bio-informatics, and can bring many advantages, such as improving efficiency and avoiding over-fitting, to learning algorithms. Currently, many efforts have been attempted in this field and various feature selection methods have been developed and proved to be very competitive. Unlike other selection methods, in this paper we propose a new method to select important features using a manner of feature clustering. The main character of our method is that it works like data clustering in an agglomerative way. In this method, each feature is considered as a data point clustered with between-cluster and within-cluster distances. As a result, the selected feature subset has minimal redundancy among its members and maximal relevance with the class labels. Our performance evaluations on seven benchmark datasets show that the classification performance achieved by our proposed method is better than other feature selection methods. © 2011 IEEE.

关键词： Feature Selection

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Region-based image retrieval using graph-cuts and global/local features

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Journal of Harbin Institute of Technology (New Series) 2007年第SUPPL. 2期14卷 68-71页

作者： Li, Wenhui Han, Dongfeng Zhang, Zhenhua Guo, Wu Wang, Yi Key Laboratory of Symbol Computation and Knowledge Engineering College of Computer Science and Technology Jilin University Changchun 130012 China

This paper describes a region-based retrieval system based on graph-cuts and global/local feature. We also use dynamic partial function (DPF), indexing by locality sensitive hashing (LSH) and learning feedback for improving system performance. Such a system is useful for finding a sub-object from a large image database. In order to obtain the sub-object from a sample image, we propose an efficient graph-cuts segmentation method to cut out the object. The system utilizes the segmentation results to capture the higher-level concept of images and gets a stable and accurate result. Also the feedback method is efficient. Experimental and comparison results, which are performed using a general purpose database containing 5,000 images, are encouraging.

关键词： Image retrieval

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3D reconstruction using particle swarm optimization

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Journal of computational Information Systems 2007年第2期3卷 541-547页

作者： Li, Wenhui Han, Dongfeng Key Laboratory of Symbol Computation and Knowledge Engineering College of Computer Science and Technology Jilin University Changchun 130012 China

A new 3D reconstruction algorithm based on particle swarm optimization (PSO) is proposed. This is the first time to introduce PSO to the multi-views problem. The proposed algorithm designs a scheme to represent the 3D parameters using a particle. Then the PSO algorithm is used to search the solution space and optimizes the fitness function. Finally, the algorithm can efficiently get the correct solution with less time and less complexity. Additionally, some details of implement are discussed which influence the performance of the algorithm. Experiments and comparisons are given in real data. The accuracy of the recovered solution is compared to the existing algorithms and outperformed them.

关键词： Image reconstruction

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Using improved particle swarm optimization to tune PID controllers in cooperative collision avoidance systems

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Frontiers of Information Technology & Electronic engineering 2017年第9期18卷 1385-1395页

作者： Xing-chen WU Gui-he QIN Ming-hui SUN He YU Qian-yi XU College of Computer Science and Technology Jilin University Changchun 130012 China MOE Key Laboratory of Symbol Computation and Knowledge Engineering Changchun 130012 China Department of Measurement and Controlling Engineering Changchun University Changchun 130012 China

The introduction ofproportional-integral-dorivative （PID） controllers into cooperative collision avoidance systems （CCASs） has been hindered by difficulties in their optimization and by a lack of study of their effects on vehicle driving stability, comfort, and fuel economy. In this paper, we propose a method to optimize PID controllers using an improved particle swarm optimization （PSO） algorithm, and to bettor manipulate cooperative collision avoidance with other vehicles. First, we use PRESCAN and MATLAB/Simulink to conduct a united simulation, which constructs a CCAS composed of a PID controller, maneuver strategy judging modules, and a path planning module. Then we apply the improved PSO algorithm to optimize the PID controller based on the dynamic vehicle data obtained. Finally, we perform a simulation test of performance before and after the optimization of the PID controller, in which vehicles equipped with a CCAS undertake deceleration driving and steering under the two states of low speed （≤50 km/h） and high speed （≥100 km/h） cruising. The results show that the PID controller optimized using the proposed method can achieve not only the basic functions of a CCAS, but also improvements in vehicle dynamic stability, riding comfort, and fuel economy.

关键词： Cooperative collision avoidance system （CCAS） Improved particle swarm optimization （PSO） PID controller Vehicle comfort Fuel economy

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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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Genetic algorithm with affinity propagation

Genetic algorithm with affinity propagation

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2010 IEEE International Conference on Information and Automation, ICIA 2010

作者： Wu, Chunguo Gao, Hao Yu, Lianjiang Liang, Yanchun Xiang, Rongwu College of Computer Science and Technology Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry Jilin University Changchun 130012 China School of Basic Courses Shenyang Pharmaceutical University Shenyang 110016 China

ISBN: (纸本)9781424457021

Classical genetic algorithm suffers heavy pressure of fitness evaluation for time-consuming optimization problems, e.g., aerodynamic design optimization, qualitative model learning in bioinformatics. To address this problem, we present a combination between genetic algorithms and clustering methods. Specifically, the clustering method used in this paper is affinity propagation. The numerical experiments demonstrate that the proposed method performs promisingly for well-known benchmark problems in the term of optimization accuracy. ©2010 IEEE.

关键词： Genetic algorithms

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SVM classifier-guided for domain-specific resource discovery based on HTML tag tree

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Journal of computational Information Systems 2007年第3期3卷 981-986页

作者： Peng, Tao Zuo, Wanli Zhang, Changli Wang, Dong Key Laboratory of Symbol Computation and Knowledge Engineering College of Computer Science and Technology Jilin University Changchun 130012 China

In designing a focused crawler, the choice of strategy for prioritizing unvisited URLs is vital. The text surrounding a link or the link context on the HMTL page is a good summary of the target page. This paper investigates some alternative methods and advocates that the link-context derived from reference page's HTML tag tree can provide a wealth of illumination for domain-specific web resource discovery guided by SVM classifier with uneven margins, which is particularly helpful for small training datasets. Little work has been done to utilize the beneficial link context information about the seed URLs. In order that crawler can acquire enough illumination from link-context, we initially look for some referring pages by traversing backward from seed URLs. The method collects this kind of resources beforehand and then uses it to steer resource discovery. A comprehensive experiment has been conducted using multiple crawls over 10 topics covering thousands of pages allowing us to derive statistically strong results.

关键词： Websites

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An ontology-based schema matching on deep web

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Journal of computational Information Systems 2010年第4期6卷 1077-1084页

作者： Zhang, Aiqi Zuo, Wanli Wang, Ying Ji, Wenyan Peng, Tao Key Laboratory of Symbol Computation and Knowledge Engineering College of Computer Science and Technology Jilin University Changchun 130012 China

A large number of techniques have been implemented mostly based on abundance of data statistics and they usually do not harness the semantic relationships between attributes. This paper proposes a novel ontology-based schema matching approach. First, a core domain ontology has been built through analyzing pages from numerous domains. Then a two-step schema-matching process is presented to identify the correct matches between schema attributes and ontology information from the semantic perspective and this process can handle not only simple matches but also complex matches. Within the matching process, the ontology is real-time updated to make the subsequent matching processes easier. Finally, 100 interfaces about four different domains in the UIUC dataset are used for evaluating our approach and the experimental results show that it can improve the accuracy and the efficiency of schema matching among multiple interfaces. Copyright © 2010 Binary Information Press.

关键词： Ontology

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An hidden markov model-based transcription factor mining method

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Journal of Bionanoscience 2013年第1期7卷 66-71页

作者： Wu, Chunguo Li, Gaoyang Wu, Xiaozhou Han, Xiaosong Wang, Binghong Breis, Jesualdo Tomás Fernández Wang, Fei Liang, Yanchun Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Computer Science and Technology Jilin University Changchun 130012 China Business School University of Shanghai for Science and Technology Shanghai 200093 China Shanghai Key Laboratory of Intelligent Information Processing Shanghai 200433 China Department of Informatics and Systems University of Murcia 30100 Murcia Spain

A text mining algorithm named HMM-TFM (Hidden Markov Model based transcription factor name mining) is presented. The proposed algorithm does not need a dictionary of transcription factor names. A small verb set is defined to filter sentences. Transcription factor names are mined according to the part of speech tagged by hidden Markov model. Experimental results show that the recall and precision of HMM-TFM achieve 74.2% and 77.9%, respectively. Copyright © 2013 American Scientific Publishers.

关键词： Data mining

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