• 한국정밀공학회지
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• 제12권12호
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• pp.120-129
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• 1995

This study introduces a tool wear monitoring technology in face milling operation comprised of an unsupervised neural network. The monitoring system employs two types of sensor signal such as cutting force and acceleration in sensory detection state. The RMS value and band frequency energy of the sensor signals are calculated for te input patterns of neural network. ART2 neural network, which is capable of self organizing without supervised learning, is used for clustering of tool wear states. The experimental results show that tool wear can be effectively detected under various cutting conditions without prior knowledge of cutting processes.

• 산업경영시스템학회지
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• 제39권3호
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• pp.83-89
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• 2016

The group formation problem of the machine and part is a critical issue in the planning stage of cellular manufacturing systems. The machine-part grouping with alternative process plans means to form machine-part groupings in which a part may be processed not only by a specific process but by many alternative processes. For this problem, this study presents an algorithm based on self organizing neural networks, so called SOM (Self Organizing feature Map). The SOM, a special type of neural networks is an intelligent tool for grouping machines and parts in group formation problem of the machine and part. SOM can learn from complex, multi-dimensional data and transform them into visually decipherable clusters. In the proposed algorithm, output layer in SOM network had been set as one-dimensional structure and the number of output node has been set sufficiently large in order to spread out the input vectors in the order of similarity. In the first stage of the proposed algorithm, SOM has been applied twice to form an initial machine-process group. In the second stage, grouping efficacy is considered to transform the initial machine-process group into a final machine-process group and a final machine-part group. The proposed algorithm was tested on well-known machine-part grouping problems with alternative process plans. The results of this computational study demonstrate the superiority of the proposed algorithm. The proposed algorithm can be easily applied to the group formation problem compared to other meta-heuristic based algorithms. In addition, it can be used to solve large-scale group formation problems.

• 컴퓨터교육학회논문지
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• 제9권2호
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• pp.101-109
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• 2006

본 논문에서는 DCT 도메인에서 영상의 블록에 대한 분류에 따라 다른 블록들에 삽입될 워터마크의 강도를 적용적으로 조절하여 워터마크를 삽입하기 위해 인간 시각 시스템(HVS)과 선경회로망 중 SOM(Self-Organizing Map)을 이용한 적용적 디지털 이미지 워터마킹을 제안한다. 인간 시각 시스템을 기반으로 하여 블록의 특정벡터를 찾아낸다. 블록의 특정벡터를 입력으로 SOM에 의해 블록들은 4등급으로 분류된다. 이들 중 3개의 등급에 속하는 블록을 선택하여 DCT 계수들 중 DC성분을 제외한 저주파 성분을 가지는 6개의 계수들을 선택하여 워터마크를 삽입한다. 실험을 통해 새로 제안된 알고리즘은 좋은 화질을 얻을 수 얻을 수 있었고 JPEG 압축, 영상처리, 기하학적 변환과 잡음과 같은 공격에 아주 강인하였다.

• ETRI Journal
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• 제13권2호
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• pp.34-41
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• 1991

In this paper, a new distributed parallel algorithm for pattern classification based upon Self-Organizing Neural Network(SONN)[10-12] is developed. This system works without any information about the number of clusters or cluster centers. The SONN model showed good performance for finding classification information, cluster centers, the number of salient clusters and membership information. It took a considerable amount of time in the sequential version if the input data set size is very large. Therefore, design of parallel algorithm is desirous. A new distributed parallel algorithm is developed and experimental results are presented.

• Communications for Statistical Applications and Methods
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• 제10권2호
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• pp.507-517
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• 2003

Self-organizing map (SOM) has been developed mainly by T. Kohonen and his colleagues as a unsupervised learning neural network. Because of its topological ordering property, SOM is known to be very useful in pattern recognition and text information retrieval areas. Recently, data miners use Kohonen´s mapping method frequently in exploratory analyses of large data sets. One problem facing SOM builder is that there exists no sensible criterion for evaluating goodness-of-fit of the map at hand. In this short communication, we propose valid evaluation procedures for the Kohonen SOM of any size. The methods can be used in selecting the best map among several candidates.

• 소음진동
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• 제10권2호
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• pp.331-337
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• 2000

The necessity of diagnosis of the rotating machinery which is widely used in the industry is increasing. Many research has been conducted to manipulate field vibration signal data for diagnosing the fault of designated machinery. As the pattern recognition tool of that signal, neural network which use usually back-propagation algorithm was used in the diagnosis of rotating machinery. In this paper, self-organizing feature map(SOFM) which is unsupervised learning algorithm is used in the abnormal defect diagnosis of rotating machinery and then learning vector quantization(LVQ) which is supervised learning algorithm is used to improve the quality of the classifier decision regions.

• 한국콘텐츠학회:학술대회논문집
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• 한국콘텐츠학회 2015년도 춘계 종합학술대회 논문집
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• pp.3-4
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• 2015

A self-organizing map (SOM) is a good tool to visualize applied data in the form of a feature map. With the help of such functions, a disaster risk diagnosis based on the residential house structure is tried in this study. According to some computer simulations with actual residential data, it is found that overall tendencies in the developed feature map are acceptable. Then, it is concluded that the proposed method is an effective means to estimate disaster risk appropriately.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2006년도 춘계학술대회 학술발표 논문집 제16권 제1호
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• pp.381-384
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• 2006

Self Organizing Map(SOM) is a powerful neural network model for unsupervised loaming. In many clustering works with exploratory data analysis, it has been popularly used. But it has a weakness which is the poorly theoretical base. A lot more researches for settling the problem have been published. Also, our paper proposes a method to overcome the drawback of SOM. As compared with the presented researches, our method has a different approach to solve the problem. So, a hybrid SOM is proposed in this paper. Using Monte Carlo computing, a hybrid SOM improves the performance of clustering. We verify the improved performance of a hybrid SOM according to the experimental results using UCI machine loaming repository. In addition to, the number of clusters is determined by our hybrid SOM.

• The Journal of the Acoustical Society of Korea
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• 제13권1E호
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• pp.20-24
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• 1994

이 논문에서는 새로운 음성 인식 모델인 동적 국부 자기 조직 지도 모델과 그 학습 알고리즘을 제안한다. 동적 국부 자기 조직 지도 모델은 음성의 시간적, 공간적 왜곡을 프로그래밍 기법과 국부 자기 조직 지도로 각각 정규화 시킨다. 한국어 숫자음에 대한 실험 결과로 제안하는 모델이 예측 신경회로망 모델보다 적은 수의 연결을 갖고서도 약간 높은 인식률을 보여 효과적임을 알 수 있었다.

• 한국콘텐츠학회:학술대회논문집
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• 한국콘텐츠학회 2012년도 춘계 종합학술대회 논문집
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• pp.195-196
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• 2012

A self-organizing map (SOM) can be seen as an analytical tool to discover some underlying rules in the given data set. Based on such distinctive nature called topology-preserving projection, a new method for generating intermediate patterns was proposed. Then, following to this method, producing animation as a series of backward-projected patterns just like a flip book is tried in this article.