Lan Huang

Jilin University, Yung-chi, Jilin Sheng, China

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Publications (10)0 Total impact

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    ABSTRACT: Ant Colony Algorithm is a new bionic algorithm which adopts the positive feedback structure, combines parallel computing and heuristic factors. It showed remarkable performance in combinatorial optimization problem. One-dimensional Cutting Stock is one of the classic NP-hard combinatorial optimization problems. It is widely applied in engineering technology and industrial production. Aiming at the specific characteristics of the problem, a series of improvement strategies and the specific algorithm implementation steps are given. Then the parameters are analyzed in details. Through experiment analysis and results comparison, it is proved that the improvement strategies and adjusted parameters have advantages in implementation efficiency and solving ability.
    08/2009: pages 319-329;
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    ABSTRACT: This paper extracts automotive marketing information, constructs data warehouse, adopts an improved ID3 decision tree model and an association rule model to do data mining, and then obtains prediction information of automotive customers' behavior. Experimental and comparative results verify the validity and accuracy of the prediction results.
    Machine Learning and Applications, 2008. ICMLA '08. Seventh International Conference on; 01/2009
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    ABSTRACT: Advantages and disadvantages of two common algorithms frequently used in the moving target detection: background subtraction method and frame difference method are analyzed and compared in this paper. Then based on the background subtraction method, a moving target detection algorithm is proposed. The background image used to process the next frame image is generated through superposition of the current frame image and the current background image with a certain probability. This algorithm makes the objects which stay long time to be a part of the background after a certain period of time, but not be detected as a part of foreground. The experimental results show that this algorithm can detect moving targets more effectively and precisely.
    01/2009;
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    ABSTRACT: Clustering technology is the core technology of text mining. Through text clustering, a large number of text messages can be divided into several meaningful classes or clusters. According to the features of Chinese documents, this paper designs and implements the Chinese Text Clustering System to perform automatic clustering of Chinese documents. Firstly, this system will carry out Chinese word automatic segmentation for the input Chinese document sets by using reverse maximum matching method. Secondly, further text preprocessing is performed. Finally the K-means clustering algorithm is used to obtain the clustering results. The prototype system can also be used in clustering Chinese Web pages to search for user's interest model by search engines, which will improve the efficiency of searching the target content.
    International Conference on Networked Computing and Advanced Information Management, NCM 2009, Fifth International Joint Conference on INC, IMS and IDC: INC 2009: International Conference on Networked Computing, IMS 2009: International Conference on Advanced Information Management and Service, IDC 2009: International Conference on Digital Content, Multimedia Technology and its Applications, Seoul, Korea, August 25-27, 2009; 01/2009
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    ABSTRACT: Immune algorithm is a rising algorithm which simulates the organism immune system by computer. One of the immune algorithms named clonal selection algorithm is widely used due to its adaptability, implicit parallelism and diversity. A fitness function is constructed in this paper by using protein folding restrictions, such as amino acids' hydrophobicity rule, protein's secondary structure folding rule, degree and amount of contacts in contact map, and other special rules. The experiment results of the prediction of 200 non-homological proteins with different lengths show that, this improved clonal selection algorithm has good adaptability and high efficiency.
    International Conference on Networked Computing and Advanced Information Management, NCM 2009, Fifth International Joint Conference on INC, IMS and IDC: INC 2009: International Conference on Networked Computing, IMS 2009: International Conference on Advanced Information Management and Service, IDC 2009: International Conference on Digital Content, Multimedia Technology and its Applications, Seoul, Korea, August 25-27, 2009; 01/2009
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    ABSTRACT: One-dimensional cutting-stock is one of the classic NP-hard problems in combinatorial optimization. It is widely applied in engineering technology and industrial production. In this paper, an improved ant colony optimization is proposed based on the optimized one-dimensional cutting-stock model. Aiming at the specific characteristics of the problem, a series of improvement strategies are proposed, including part encoding, solution path, state transition probability and pheromone updating rules. Then the concrete steps of algorithm are described. Through the analysis and comparison of experimental results, this method is proved high efficiency.
    International Conference on Networked Computing and Advanced Information Management, NCM 2009, Fifth International Joint Conference on INC, IMS and IDC: INC 2009: International Conference on Networked Computing, IMS 2009: International Conference on Advanced Information Management and Service, IDC 2009: International Conference on Digital Content, Multimedia Technology and its Applications, Seoul, Korea, August 25-27, 2009; 01/2009
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    ABSTRACT: An Immune Particle Swarm Optimization (IPSO) for parameters optimization of Support Vector Regression (SVR) is proposed in this article. After introduced clonal copy and mutation process of Immune Algorithm (IA), the particle of PSO is considered as antibodies. Therefore, evaluated the fitness of particles by the Cross Validation standard, the best individual mutated particle for each cloned group will be selected to compose the next generation to get better parameters εC δ of SVR. It can construct high accuracy and generalization performance regression model rapidly by optimizing the combination of three SVR parameters at the same time. Under the datasets generated from sincx function with additive noise and forest fires dataset, experimental results show that the new method can determine the parameters of SVR quickly and the gotten models have superior learning accuracy and generalization performance.
    Advances in Neural Networks - ISNN 2009, 6th International Symposium on Neural Networks, ISNN 2009, Wuhan, China, May 26-29, 2009, Proceedings, Part II; 01/2009
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    ABSTRACT: Support vector regression is based on statistical learning theory under the framework of a new general-purpose machine learning method, which is a effective way to deal with nonlinear classification and nonlinear regression. Due to the comprehensive theoretical basis and excellent learning performance, The technology has become the current international machine learning research community hot spots, which can to better address the practical problem, such as the small sample and high dimension, nonlinear and local minima etc.. In the article, support vector regression (SVR) and the RBF neural network do function fitting tests, using simulation data, and the results are compared and evaluation. And use the SVR algorithm to solve practical problems in the area of real estate for predict housing values, with a view to consumers in the choice of housing to provide good guidance.
    2009 International Conference on Computational Intelligence and Security, CIS 2009, Beijing, China, 11-14 December 2009, Volume 2 - Workshop Papers; 01/2009
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    ABSTRACT: The paper presents a dynamic clustering method based on genetic algorithm. In order to obtain the perfect clustering results, the preprocessing such as primary component analysis or wavelet transformation is often used, but it is likely to result in distortions. In this paper, the essential associations between objects are modeled by their dissimilarity. The dissimilarity between objects is mapped into their Euclidean distance, and then the mapping is optimized by genetic algorithm, which means the coordinates of each object are optimized by genetic algorithm gradually, and thus makes the Euclidean distances among objects approximate to their dissimilarity. The primary advantages of the proposed method are that the clustering does not depend on the feature space distribution of the input objects while simplifying the clustering and improving the visualization. A numerical simulation illustrates its feasibility and availability.
    Machine Learning and Cybernetics, 2003 International Conference on; 12/2003
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    ABSTRACT: This paper proposes a new application of particle swarm optimization for traveling salesman problem. We have developed some special methods for solving TSP using PSO. We have also proposed the concept of swap operator and swap sequence, and redefined some operators on the basis of them, in this way the paper has designed a special PSO. The experiments show that it can achieve good results.
    Machine Learning and Cybernetics, 2003 International Conference on; 12/2003