• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5A
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• pp.861-872
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• 2006

The element free Galerkin method is extended by the local partition of unity method to model the cohesive cracks in two dimensional continuum. The shape function of a particle whose domain of influence is completely cut by a crack is enriched by the step enrichment function. If the domain of influence contains a crack tip inside, it is enriched by a branch enrichment function which does not have the LEFM stress singularity. The discrete equations are obtained directly from the standard Galerkin method since the enrichment is only for the displacement field, which satisfies the local partition of unity. Because only particles whose domains of influence are influenced by a crack are enriched, the system matrix is still sparse so that the increase of the computational cost is minimized. The condition for crack growth in dynamic problems is obtained from the material instability; when the acoustic tensor loses the positive definiteness, a cohesive crack is inserted to the point so as to change the continuum to a discontiuum. The crack speed is naturally obtained from the criterion. It is found that this method is more accurate and converges faster than the classical meshless methods which are based on the visibility concept. In this paper, several well-known static and dynamic problems were solved to verify the method.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1338-1343
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• 2003

Data is the expression of the language or numerical values that show some characteristics. And information is extracted from data for the specific purposes. The knowledge is utilized as information to construct rules that recognize patterns and make decisions. Today, knowledge extraction and application of the knowledge are broadly accomplished to improve the comprehension and to elevate the performance of systems in several industrial fields. The knowledge extraction could be achieved by some steps that include the knowledge acquisition, expression, and implementation. Such extracted knowledge can be drawn by rules. Clustering (CU, input space partition (ISP), neuro-fuzzy (NF), neural network (NN), extension matrix (EM), etc. are employed for expression the knowledge by rules. In this paper, the various approaches of the knowledge extraction are examined by categories that separate the methods by the applied industrial fields. Also, the several test data and the experimental results are compared and analysed based upon the applied techniques that include CL, ISP, NF, NN, EM, and so on.

• Journal of the Korean Data and Information Science Society
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• v.26 no.1
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• pp.31-39
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• 2015

This paper deals with a method for estimating variance components on the basis of projections under the assumption of random effects model. It discusses how to use projections for getting sums of squares to estimate variance components. The use of projections makes the vector subspace generated by the model matrix to be decomposed into subspaces that are orthogonal each other. To partition the vector space by the model matrix stepwise procedure is used. It is shown that the suggested method is useful for obtaining Type I sum of squares requisite for the ANOVA method.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.2
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• pp.194-199
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• 2003

Data are an expression of the language or numerical values that show some features. And the information is extracted from data for the specific purposes. The knowledge is utilized as information to construct rules that recognize patterns or make a decision. Today, knowledge extraction and application of that are broadly accomplished for the easy comprehension and the performance improvement of systems in the several industrial fields. The knowledge extraction can be achieved by some steps that include the knowledge acquisition, expression, and implementation. Such extracted knowledge is drawn by rules with data mining techniques. Clustering (CL), input space partition (ISP), neuro-fuzzy (NF), neural network (NN), extension matrix (EM), etc. are employed for the knowledge expression based upon rules. In this paper, the various approaches of the knowledge extraction are surveyed and categorized by methodologies and applied industrial fields. Also, the trend and examples of each approaches are shown in the tables and graphes using the categories such as CL, ISP, NF, NN, EM, and so on.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.6
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• pp.102-110
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• 1996

A new design scheme of a binary decision tree is proposed. In this scheme a binary decision tree is constructed by using genetic algorithm and FCM algorithm. At each node optimal or near-optimal feature subset is selected which optimizes fitness function in genetic algorithm. The fitness function is inversely proportional to classification error, balance between cluster, number of feature used. The binary strings in genetic algorithm determine the feature subset and classification results - error, balance - form fuzzy partition matrix affect reproduction of next genratin. The proposed design scheme is applied to the tire tread patterns and handwriteen alphabetic characters. Experimental results show the usefulness of the proposed scheme.

• Journal of the Korean Institute of Intelligent Systems
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• v.5 no.2
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• pp.58-69
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• 1995

In this paper GFI (Generalized Fuzzy Isodata) and FI (Fuzzy Isodata) algorithms are studied and applied to the tire tread pattern classification problem. GFI algorithm which repeatedly grouping the partitioned cluster depending on the fuzzy partition matrix is general form of GI algorithm. In the constructing the binary tree using GFI algorithm cluster validity, namely, whether partitioned cluster is feasible or not is checked and construction of the binary tree is obtained by FDH clustering algorithm. These algorithms show the good performance in selecting the prototypes of each patterns and classifying patterns. Directions of edge in the preprocessed image of tire tread pattern are selected as features of pattern. These features are thought to have useful information which well represents the characteristics of patterns.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.290-293
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• 1996

This paper proposes a scheme of initial cluster center selection in FCM algorithm using the genetic algorithms. The FCM algorithm often fails in the search for global optimum because it is local search techniques that search for the optimum by using hill-climbing procedures. To solve this problem, we search for a hypersphere encircling each clusters whose parameters are estimated by the genetic algorithms. Then instead of a randomized initialization for fuzzy partition matrix in FCM algorithm, we initialize each cluster center by the center of a searched hypersphere. Our experimental results show that the proposed initializing scheme has higher probabilities of finding the global or near global optimal solutions than the traditional FCM algorithm.

• Proceedings of the IEEK Conference
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• summer
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• pp.137-142
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• 2004

In this paper, we propose a new scheme for geometry coding of three-dimensional (3-D) mesh models using a fixed spectral basis. In order to code the mesh geometry information, we generate a fixed spectral basis using the dual graph derived from the 3-D mesh topology. After we partition a 3-D mesh model into several independent sub-meshes to reduce coding complexity, the mesh geometry information is projected onto the generated orthonormal bases which are the eigenvectors of the Laplacian matrix of the 3-D mesh. Finally, spectral coefficients are coded by a quantizer and a variable length coder. The proposed scheme can not only overcome difficulty of generating a fixed spectral basis, but also reduce coding complexity. Moreover, we can provide an efficient multi-resolution representation of 3-D meshes.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.3
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• pp.337-340
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• 2004

The clustering problem can be formulated as the problem to find the number of clusters and a partition matrix from a given data set using the iterative or non-iterative algorithms. The author proposes a nearest neighbor and validity-based clustering algorithm where each data point in the data set is linked with the nearest neighbor data point to form initial clusters and then a cluster in the initial clusters is linked with the nearest neighbor cluster to form a new cluster. The linking between clusters is continued until no more linking is possible. An optimal set of clusters is identified by using the conventional cluster validity index. Experimental results on well-known data sets are provided to show the effectiveness of the proposed clustering algorithm.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07a
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• pp.892-894
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• 2005

Graph partitioning provides an important tool for data clustering, but is an NP-hard combinatorial optimization problem. Spectral clustering where the clustering is performed by the eigen-decomposition of an affinity matrix [1,2]. This is a popular way of solving the graph partitioning problem. On the other hand, semidefinite relaxation, is an alternative way of relaxing combinatorial optimization. issuing to a convex optimization[4]. In this paper we present a semidefinite programming (SDP) approach to graph equi-partitioning for clustering and then we use eigen-decomposition to obtain an optimal partition set. Therefore, the method is referred to as semidefinite spectral clustering (SSC). Numerical experiments with several artificial and real data sets, demonstrate the useful behavior of our SSC. compared to existing spectral clustering methods.