• Journal of Korean Society of Industrial and Systems Engineering
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• v.28 no.1
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• pp.1-7
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• 2005

Data mining is widely used for turning huge amounts of data into useful information and knowledge in the information industry in recent years. When analyzing data set with continuous values in order to gain knowledge utilizing data mining, we often undergo a process called discretization, which divides the attribute's value into intervals. Such intervals from new values for the attribute allow to reduce the size of the data set. In addition, discretization based on rough set theory has the advantage of being easily applied. In this paper, we suggest a discretization algorithm based on Rough Set theory and SOM(Self-Organizing Map) as a means of extracting valuable information from large data set, which can be employed even in the case where there lacks of professional knowledge for the field.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.28B no.9
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• pp.739-746
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• 1991

This paper develops a modified multilayer perceptron architecture which speeds up learning as well as the net's mapping accuracy. In Phase I, a cluster partitioning algorithm like the Kohonen's self-organizing feature map or the leader clustering algorithm is used as the front end that determines the cluster to which the input data belongs. In Phase II, this cluster selects a subset of the hidden layer nodes that combines the input and outputs nodes into a subnet of the full scale backpropagation network. The proposed net has been applied to two mapping problems, one rather smooth and the other highly nonlinear. Namely, the inverse kinematic problem for a 3-link robot manipulator and the 5-bit parity mapping have been chosen as examples. The results demonstrate the proposed net's superior accuracy and convergence properties over the original backpropagation network or its existing improvement techniques.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.5
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• pp.75-82
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• 1998

Conventional vector quantization (VQ) codebook design methods have several drawbacks such as edge degradation and high computational complexity. In this paper, we first made activity coordinates from the horizonatal and the vertical activity of the input block. Then it is mapped on the 2-dimensional interconnected codebook, and the codebook is designed using kohonen self-organizing map (KSFM) learning algorithm after the search of a codevector that has the minumum distance from the input vector in a small window, centered by the mapped point. As the serch area is restricted within the window, the computational amount is reduced compared with usual VQ. From the resutls of computer simulation, proposed method shows a better perfomance, in the view point of edge reconstruction and PSNR, than previous codebook training methods. And we also obtained a higher PSNR than that of classified vector quantization (CVQ).

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.11
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• pp.153-162
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• 1995

When vector quantization is used in low rate image coding (e.g., R<0.5), the primary problem is the tremendous computational complexity which is required to search the whole codebook to find the closest codevector to an input vector. Since the number of code vectors in a vector quantizer is given by an exponential function of the dimension. i.e., L=2$^{nR}$ where Rn. To alleviate this problem, a multiple shell structure of hypercube feature maps (MSSHFM) is proposed. A binary HFM of k-dimension is composed of nodes at hypercube vertices and a multiple shell architecture is constructed by surrounding the k-dimensional hfm with a (k+1)-dimensional HFM. Such a multiple shell construction of nodes inherently has a complete tree structure in it and an efficient partial search scheme can be applied with drastically reduced computational complexity, computer simulations of still image coding were conducted and the validity of the proposed method has been verified.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.34C no.1
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• pp.59-64
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• 1997

By using a selective learnable self-organizing feature map(SOFM) a more practical and generalized mehtod is proposed in which the effective nonlinear shape restoration is possible regardless of the existence of the distortion modelss. Nonlinear mapping relation is extracted from the distorted imate by using the proposed selective learning SOFGM which has the special property of effectively creating spatially organized internal representations and nonlinear relations of various input signals. For the exact extraction of the mapping relations between the distorted image and the original one, we define a disparity index as a proximal nmeasure of the present state to the final idealy trained state of the SOFM, and we used this index to adjust the training of the mapping relations form the weights of the SOFM. Simulations are conducted on various kinds of distorted images with or without distortion models, and the results show that the proposed method is very efficeint very efficient and practical in nonlinear shape restorations.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.427-432
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• 1998

최근 산업발전에 따라 야기되는 문제점 중 전력수요의 증가에 의한 피해가 증대되고 있다. 여름철 하계부하등에 의한 과부하는 가정이나 대형건물의 정전을 발생시키거나 공장의 기계를 파손시키기도 하기 때문에 이를 미연에 방지할 수 있는 부하예측기법이 점차로 강조되고 있는 현실이다. 이에 본 논문에서는 초(sec)단위의 순시부하예측/제어를 위한 새로운 방법과 퍼지제어기를 제안한다. 제안한 순시부하예측/제어는 크게 과거의 데이터를 가지고 일정시간 후의 값을 예측하는 예측부와 이 결과의 신뢰도를 높여주기 위한 퍼지제어기로나눌 수 있다. 예측부는 SOFM (Self-Organizing Feature Map) 신경망을 이용하며, 예측된 출력값을 퍼지제어기의 입력으로 사용한다.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.6
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• pp.417-422
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• 2005

This paper presents an EMG pattern classification method to identify motion commands for the control of the artificial arm by SOM-TVC(self organizing map - tracking Voronoi cell) based on neural network with a feature parameter. The eigenvalue is extracted as a feature parameter from the EMG signals and Voronoi cells is used to define each pattern boundary in the pattern recognition space. And a TVC algorithm is designed to track the movement of the Voronoi cell varying as the condition of additive noise. Results are presented to support the efficiency of the proposed SOM-TVC algorithm for EMG pattern recognition and compared with the conventional EDM and BPNN methods.

• Journal of Intelligence and Information Systems
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• v.8 no.2
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• pp.159-175
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• 2002

Web mining is an emerging science of applying modem data mining technologies to the problem of extracting valid, comprehensible, and actionable information from large databases of web in e-Business environment and of using it to make crucial e-Business decisions. In this paper, we present the noble framework of data visualization system based on web mining for analyzing the characteristics of on-line customers in e-Business. We also propose the framework of forecasting system for providing the forecasting information of sales/purchase through the use of web mining based on artificial intelligence techniques such as back-propagation network, memory-based reasoning, and self-organizing map.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06b
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• pp.136-138
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• 2006

최근 이동통신 기술의 급격한 발전과 PPC(Pocket PC), 노트북 등의 휴대단말기의 보급 확산에 따라 위치기반 서비스(Location Based Service: LBS)가 주요한 응용분야고 부상하고 있다. 위치 정보에 대한 정확한 위치 추적 및 활용 방안에 대한 활발한 연구가 진행되고 있지만, 대부분 제공되는 서비스는 현재 사용자의 위치에 기반한 정적인 서비스를 제공하는 초보적인 단계에 있다. 이동경로는 사용자의 성향이나 상태를 반영하기 때문에 사용자의 이동패턴을 예측하거나, 사용자의 현재 상태를 추론하는데 도움을 줄 수 있다. 본 논문에서는 이동패턴에 따른 사용자의 의도를 예측하여 개별화 된 서비스 제공을 위해, RSOM(Recurrent Self Organizing Map)과 마르코프 모델을 단계적으로 구성하여 사용자의 이동패턴을 모델링하는 방법을 제안한다. 실제 연세대학교 캠퍼스 내에서 실제 대학원생의 생활을 모델로 GPS(Global Positioning System) 데이터를 수집하여. 이동패턴을 모델링하고 개별화된 서비스를 제공함으로써 제안하는 방법의 유용성을 검증하였다.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.4
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• pp.172-179
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• 2001

A component based method out of load modeling is to aggregate component load model according to the composition rate of each component load at load bus based on the circuit theory. But the most of component loads respond complex nonlinear characteristics respect to voltage and frequency variation due to the control techniques and semiconductor elements applied to component load. It needs to verify this approach through actual experiment of the aggregation of component load even if it can be down. To identify this aggregation method well known, this paper is proposed the classifying method of component load characteristics for component loads to group by quantitative analysis. The component load characteristics were divided into several types by KSOM (kohonen self organizing map), which can classify multi-dimension vector, component load pattern, into two-dimension vector. Some ambiguous cases happened from KSOM were classified by the proposed closing degree.