• 한국경영과학회지
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• 제31권3호
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• pp.41-51
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• 2006

Clustering is to group similar objects into clusters. Until now there are a lot of approaches using Self-Organizing feature Maps (SOFMS) But they have problems with a small output-layer nodes and initial weight. For example, one of them is a one-dimension map of c output-layer nodes, if they want to make c clusters. This approach has problems to classify elaboratively. This Paper suggests one-dimensional output-layer nodes in SOFMs. The number of output-layer nodes is more than those of clusters intended to find and the order of output-layer nodes is ascending in the sum of the output-layer node's weight. We un find input data in SOFMs output node and classify input data in output nodes using Euclidean distance. The proposed algorithm was tested on well-known IRIS data and TSPLIB. The results of this computational study demonstrate the superiority of the proposed algorithm.

• 산업경영시스템학회지
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• 제21권48호
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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.

• 전자공학회논문지C
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• 제34C권11호
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• pp.95-103
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• 1997

A robust center estimation tecnique of n-fold engineering parts is presented, which use self-organizing neural networks with generating and merging learning for training neural units. To estimate the center of the n-fold engineering parts using neural networks, the segmented boundaries of the interested part are approximated to strainght lines, and the temporal estimated centers by thecosine theorem which formed between the approximaged straight line and the reference point, , are indexed as (.sigma.-.theta.) parameteric vecstors. Then the entries of parametric vectors are fed into self-organizing nerual network. Finally, the center of the n-fold part is extracted by mean of generating and merging learning of the neurons. To accelerate the learning process, neural network uses an adaptive learning rate function to the merging process and a self-adjusting activation to generating process. Simulation results show that the centers of n-fold engineering parts are effectively estimated by proposed technique, though not knowing the error distribution of estimated centers and having less information of boundaries.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1997년도 추계학술대회 논문집
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• pp.1032-1035
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• 1997

The purpose of this paper is to reduce the number of cutting tests and to predict the main cutting force and the specific cutting energy. By using the SOFM neural network, the most suitable cutting test conditions has been found. As a result, the number of cutting tests has been reduced to one-third. And by using MLP neural network and regression analysis, the main cutting force and specific cutting energy has been predicted. Predicted values of main cutting force and specific cutting energy are well concide with the measured ones.

• 산업경영시스템학회지
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• 제23권61호
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• pp.127-135
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• 2000

This paper provides a new method of initializing neurons used in self-organizing neural networks and sequencing input nodes for applying to Euclidean traveling salesman problem. We use a general property that in any optimal solution for Euclidean traveling salesman problem, vertices located on the convex hull are visited in the order in which they appear on the convex hull boundary. We composite input nodes as number of convex hulls and initialize neurons as shape of the external convex hull. And then adapt input nodes as the convex hull unit and all convex hulls are adapted as same pattern, clockwise or counterclockwise. As a result of our experiments, we obtain l∼3 % improved solutions and these solutions can be used for initial solutions of any global search algorithms.

• 산업경영시스템학회지
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• 제23권61호
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• pp.137-146
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• 2000

One of the problems faced in implementing cellular manufacturing systems is machine-part group formation. This paper proposes machine-part grouping algorithms based on Self-Organizing Map(SOM) neural networks and K-Means algorithm in cellular manufacturing systems. Although the SOM spreads out input vectors to output vectors in the order of similarity, it does not always find the optimal solution. We rearrange the input vectors using SOM and determine the number of groups. In order to find the number of groups and grouping efficacy, we iterate K-Means algorithm changing k until we cannot obtain better solution. The results of using the proposed approach are compared to the best solutions reported in literature. The computational results show that the proposed approach provides a powerful means of solving the machine-part grouping problem. The proposed algorithm Is applied by simple calculation, so it can be for designer to change production constraints.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 1999년도 가을 학술발표논문집 Vol.26 No.2 (3)
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• pp.318-320
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• 1999

정보시스템의 보호를 위한 침입탐지의 방법으로 오용탐지와 비정상행위 탐지방법이 있다. 오용탐지의 경우는 알려진 침입 패턴을 이용하는 것으로 알려진 침입에 대해서는 아주 높은 탐지율을 나타내지만 알려지지 않은 침입이나 변형패턴에 대해서는 탐지할 수 없다는 단점이 있다. 반면 비정상행위 탐지는 정상행위 모델링을 통해 비정상패턴을 탐지하는 것으로 알려지지 않은 패턴에 대해서도 탐지할 수 있는 장점이 있는데 국내외적으로 아직까지 널리 연구되어 있지 않다. 본 논문에서는 BSM으로부터 얻은 다양한 정보를 추출하고 이러한 정보를 자기조직화 신경망을 이용하여 축약함으로써 고정된 크기의 순서정보로 변환하는 방법을 제안한다. 이렇게 생성된 고정크기의 순서정보는 비정상행위 탐지에 효과적으로 사용될 수 있을 것이다.

• Asia pacific journal of information systems
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• 제16권3호
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• pp.49-65
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• 2006

This study proposes a model for customer segmentation using the psychological characteristics of Internet banking customers. The model was developed through two phased clustering method, called SONN-HC by integrating self-organizing neural networks (SONN) and hierarchical clustering (HC) method. We applied the SONN-HC method to internet banking customer segmentation and performed an empirical analysis with 845 cases. The results of our empirical analysis show the psychological characteristics of Internet banking customers have significant differences among four clusters of the customers created by SONN-HC. From these results, we found that the psychological characteristics of Internet banking customers had an important role of planning a strategy for customer segmentation in a financial institution.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2000년도 제13회 춘계학술대회 및 임시총회 학술발표 논문집
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• pp.680-685
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• 2000

최근 그 심각성이 커지고 있는 해킹피해를 줄이기 위한 한 방법으로 시스템에 침입한 불법적 사용을 탐지하는 연구가 활발히 진행되고 있다. 침입을 탐지하는 방법으로는 오용탐지와 비정상행위 탐지가 있는데 비정상행위 탐지를 위해서는 정보수집의 정확성, 신속성과 함께 다량의 정보들로부터 필요한 정보를 추출하고 축약하는 것이 중요하다. 본 논문에서는 감사기록 도구인 BSM으로부터 정보를 추출하고 자기조직화 신경망을 이용하여 다차원의 정보를 저차원정보로 축약.변환하는 방법에 대한 실험적인 검증을 시도하였다. 또한 BSM에서 얻을 수 있는 데이터의 유용성을 조사하기 위하여 축약된 감사자료에 의한 탐지성능을 살펴보았다. 실험결과, 시스템 호출 및 파일관련 정보의 축약이 탐지성능향상에 크게 기여하는 중요한 척도임을 알 수 있었으며 각 척도마다 탐지성능이 좋은 맵의 크기가 다름을 알 수 있었다. 이러한 축약된 정보는 여러 정상행위 모델링방법에 의해 유용하게 사용될 수 있을 것이다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2006년도 추계학술대회 학술발표 논문집 제16권 제2호
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• pp.179-182
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• 2006

현재 감정을 인식할 수 있는 방법으로는 음성, 뇌파, 심박, 표정 등 많은 방법들이 존재한다. 본 논문은 이러한 방법 중 음성 신호를 이용한 방법으로써 특징들은 크게 피치, 에너지, 포만트 3가지 특징 점을 고려하였으며 이렇게 다양한 특징들을 사용하는 이유는 아직 획기적인 특징점이 정립되지 않았기 때문이며 이러한 선택의 문제를 해결하기 위해 본 논문에서는 특징 선택 방법 중 Multi Feature Selection(MFS) 방법을 사용하였으며 학습 알고리즘은 Self Organizing Map 알고리즘을 이용하여 음성 신호의 감정 특징 패턴을 분류하는 방법을 제안한다.