• 산업공학
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• 제16권spc호
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• pp.123-126
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• 2003

In this paper, we use three cluster algorithms (K-means, Self-Organizing Map, and Fuzzy K-means) to find proper graded stock market brokerage commission rates based on the cumulative transactions on both stock exchange market and HTS (Home Trading System). Stock trading investors for both modes are classified in terms of the total transaction as well as the corresponding mode of investment, respectively. Empirical analysis results indicated that fuzzy K-means cluster analysis is the best fit for the segmentation of customers of both transaction modes in terms of robustness. We then propose the rules for three grouping of customers based on decision tree and apply different brokerage commission to be 0.4%, 0.45%, and 0.5% for exchange market while 0.06%, 0.1%, 0.18% for HTS.

• 산업경영시스템학회지
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• 제28권1호
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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.

• 전자공학회논문지B
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• 제28B권9호
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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.

• 전자공학회논문지S
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• 제35S권5호
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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).

• 전자공학회논문지B
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• 제32B권11호
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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.

• 전자공학회논문지C
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• 제34C권1호
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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.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 추계학술대회 학술발표 논문집
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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.

• 대한전기학회논문지:시스템및제어부문D
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• 제54권6호
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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.

• 지능정보연구
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• 제8권2호
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• pp.159-175
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• 2002

웹 마이닝은 e-Business 환경하에서 존재하는 대량의 웹 데이터에 데이터 마이닝 기법을 적용하여 유용하고 이해 가능한 정보를 추출해내는 과정을 의미하는데, 성공적인 e-Business전개를 위한 핵심적인 기술이다. 본 논문은 인공지능 기법에 기반한 웹마이닝 기술을 활용하여 e-Business상의 온라인 고객의 특성을 분석할 수 있는 data visualization system과 구매 판매 예측시스템의 효과적인 구조와 핵심적인 분석절차를 제안하였다.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2006년도 한국컴퓨터종합학술대회 논문집 Vol.33 No.1 (B)
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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.