• Korean Management Science Review
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• v.21 no.3
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• pp.85-99
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• 2004

The retention of customer is an increasingly pressing issue in today's competitive environment. The proposes of this paper is a personalized defection detection and the procedure of prevention based on economic analysis of customer defection possibility, and behaviour state transition cost. This procedure is based on the observation that potential defectors have a tendency to take a couple of months or weeks to gradually change their behaviour before their eventual withdrawal. In this procedure, the SOM(Self-Organizing Map) is used to determine the possible states of customer behaviour from past behaviour data, and to prevent the defection of potential defectors, the proposed procedure recommends the desirable behaviour state for the next period based on the analysis of transition cost. and likelihood of defection. The case study has been conducted for a Korean on-line game provider to evaluate of this procedure.

• Journal of Korean Institute of Industrial Engineers
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• v.30 no.3
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• pp.175-180
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• 2004

The machine-part group formation is to group the sets of parts having similar processing requirements into part families, and the sets of machines needed to process a particular part family into machine cells using grid computing. It forms machine cells from the machine-part incidence matrix by means of Self-Organizing Maps(SOM) whose output layer is one-dimension and the number of output nodes is the twice as many as the number of input nodes in order to spread out the machine vectors. It generates machine-part group which are assigned to machine cells by means of the number of bottleneck machine with processing part. The proposed algorithm was tested on well-known machine-part grouping problems. The results of this computational study demonstrate the superiority of the proposed algorithm.

• Proceedings of the Korea Contents Association Conference
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• 2003.11a
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• pp.447-453
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• 2003

Emotion Technology is used in many field such as computer A.I., graphics, robot, and interaction with agent. We focus on the theory, the technology and the features in emotion application. Firstly in the field of theory, there are psychological approach, behavior-based approach, action-selection approach. Secondly in the field of implementation technologies use the learning algorithm, self-organizing map of neural network and fuzzy cognition maps. Thirdly in the field of application, there are software agent, agent robot and entrainment robot. In this paper, we research the case of application and analyze emotion architecture.

• Journal of Korea Multimedia Society
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• v.10 no.6
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• pp.699-706
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• 2007

This article is an approach to improving the clustering of Web images by using high-level semantic features from text information relevant to Web images as well as low-level visual features of image itself. These high-level text features can be obtained from image URLs and file names, page titles, hyperlinks, and surrounding text. As a clustering algorithm, a self-organizing map (SOM) proposed by Kohonen is used. To evaluate the clustering efficiencies of SOMs, we propose a simple but effective measure indicating the accumulativeness of same class images and the perplexities of class distributions. Our approach is to advance the existing measures through defining and using new measures accumulativeness on the most superior clustering node and concentricity to evaluate clustering efficiencies of SOMs. The experimental results show that the high-level text features are more useful in SOM-based Web image clustering.

• Journal of Microbiology and Biotechnology
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• v.16 no.5
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• pp.748-756
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• 2006

We propose a genome-scale clustering approach to identify whole genome relationships using the functional groups given by the Kyoto Encyclopedia of Genes and Genomes Orthology (KO) database. The metabolic capabilities of each organism were defined by the number of genes in each functional category. The archaeal, bacterial, and eukaryotic genomes were compared by simultaneously applying a two-step clustering method, comprised of a self-organizing tree algorithm followed by unsupervised hierarchical clustering. The clustering results were consistent with various phenotypic characteristics of the organisms analyzed and, additionally, showed a different aspect of the relationship between genomes that have previously been established through rRNA-based comparisons. The proposed approach to collect and cluster the metabolic functional capabilities of organisms should make it a useful tool in predicting relationships among organisms.

• Journal of Korea Multimedia Society
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• v.7 no.12
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• pp.1737-1744
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• 2004

In the compression methods widely used today, the image compression by VQ is the most popular and shows a good data compression ratio. Almost all the methods by VQ use the LBG algorithm that reads the entire image several times and moves code vectors into optimal position in each step. This complexity of algorithm requires considerable amount of time to execute. To overcome this time consuming constraint, we propose an enhanced self-organizing neural network for color images. VQ is an image coding technique that shows high data compression ratio. In this study, we improved the competitive learning method by employing three methods for the generation of codebook. The results demonstrated that compression ratio by the proposed method was improved to a greater degree compared to the SOM in neural networks.

• Proceedings of the KIEE Conference
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• 2000.11d
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• pp.800-802
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• 2000

In this paper, we propose the Self-Organizing Networks(SON) based on competitive Fuzzy Polynomial Neuron(FPN) for the optimal design of nonlinear process system. The SON architectures consist of layers with activation nodes based on fuzzy inference rules. Here each activation node is presented as FPN which includes either the simplified or regression Polynomial fuzzy inference rules. The proposed SON is a network resulting from the fusion of the Polynomial Neural Networks(PNN) and a fuzzy inference system. The conclusion part of the rules, especially the regression polynomial uses several types of high-order polynomials such as liner, quadratic and modified quadratic. As the premise part of the rules, both triangular and Gaussian-like membership functions are studied. Chaotic time series data used to evaluate the performance of our proposed model.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.21 no.48
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• pp.123-132
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• 1998

The group formation problem of the machine and part is a very important issue in the planning stage of cellular manufacturing systems. This paper investigates Self-Organizing Map(SOM) neural networks approach to machine-part grouping problem. We present a two-phase algorithm based on SOM for grouping parts and machines. SOM can learn from complex, multi-dimensional data and transform them into visually decipherable clusters. Output layer in SOM network is one-dimensional structure and the number of output node has been increased sufficiently to spread out the input vectors in the order of similarity. The proposed algorithm performs remarkably well in comparison with many other algorithms for the well-known problems shown in previous papers.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.737-741
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• 1993

In this paper, we design the fuzzy logic controller(FLC) for a nonlinear multivariable steam generating unit. Based on the knowledges of operator, the self-organizing controller(SOC) - a kind of FLC - is developed and tested. Both FLC and SOC based on linguistic rules have the advantages of not needing of some exact mathematical model for plant to be controlled. Beside, the SOC modifies the existing control rules by monitoring the control performance. The computer simulations have been carried out for the 200MW steam generating unit to show the usefulness of the proposed method and the effects of disturbances and parameter variations are considered.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.377-382
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• 2005

This paper presents a new method for the attitude control of planar space robots. In order to control highly constrained non-linear system such as a 3D space robot, the analytical formulation for the system with complex dynamics and effective control methodology based on the formulation, are not always obtainable. In the proposed method, correspondingly, a non-analytical but effective self-organizing modeling method for controlling a highly constrained system is proposed based on a polynomial data mining algorithm. In order to control the attitude of a planar space robot, it is well known to require inputs characterized by a special pattern in time series with a non-deterministic length. In order to correspond to this type of control paradigm, we adopt the Model Predictive Control (MPC) scheme where the length of the non-deterministic horizon is determined based on implementation cost and control performance. The optimal solution to finding the size of the input pattern is found by a solving two-stage programming problem.