• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1995년도 Proceedings of the Korea Automation Control Conference, 10th (KACC); Seoul, Korea; 23-25 Oct. 1995
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• pp.146-149
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• 1995

A new method for grouping of relevant and equivalent inputs of a logical circuit was proposed by the authors by making use of pseudorandom M-sequence correlation. The authors show in this paper that it is possible to estimate the input grouping from a part of correlation functions when we admit small percentage of error, whereas it is impossible to reduce the data necessary to estimate the grouping by use of the truth table method. For example in case of 30-input logic circuit, the number of correlation functions necessary to calculate can be reducible from 1.07 * 10$^{9}$ to 465.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 Proceedings of the Korea Automatic Control Conference, 11th (KACC); Pohang, Korea; 24-26 Oct. 1996
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• pp.279-282
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• 1996

The authors have already proposed a method for grouping of inputs of a logical circuit under test (LCUT) by use of M-sequence correlation. We call this method as input grouping (IG) method. In this paper, the authors propose a new method to estimate the faulty part in the circuit by use of IG when some information on the candidate of faulty part can be obtained beforehand. The relationship between IG and fault probabilities of a LCUT, and undetected fault ratios are investigated for various cases. Especially the investigation was made in case where the IG was calculated by use of n correlation functions (I $G_{inp}$). From the theoretical study and simulation results it is shown that the estimation error ratio of fault probabilities and undetected fault ratio of LCUT are sufficiently small even when only a part of correlation functions are used. It is shown that the number of correlation functions which are to be memorized to calculate IG can be considerably reducible from 2$^{n}$ - 1 to n by use of I $G_{inp}$. So this method would be very useful for a fault diagnosis of actual logic circuit.uit.

• 한국전산구조공학회논문집
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• 제24권2호
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• pp.141-148
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• 2011

본 논문에서는 기둥축소량 보정의 효율성을 증진시키기 위한 방안으로서 유사한 축소 경향을 보이는 기둥들을 동일 그룹으로 묶는 기둥의 최적그루핑기법에 대하여 연구하였다. 기둥의 최적그루핑은 무감독학습에 의해 입력데이타의 패턴을 스스로 분류할 수 있는 코호넨의 자기조직화 형상지도 알고리즘을 이용하였다. 본 연구에 적용된 코호넨 네트워크는 두 개의 입력뉴런과 분류할 기둥그룹 개수만큼의 출력뉴런으로 구성된다. 입력뉴런에는 기둥축소량의 정규화된 평균과 표준편차가 입력되며, 출력뉴런에는 각 기둥이 속하게 될 기둥그룹이 출력된다. 제안된 알고리즘을 실제 축소량 해석이 수행된 두 개의 건물에 적용하여 그 적용성을 평가하였다. 적용결과 동일 그룹으로 분류된 기둥들은 서로 인접하고 있으며 서로 다른 기둥그룹끼리는 교차하지 않는 등 유사한 축소 경향을 보였다. 이로부터 본 연구의 기둥축소량의 최적그루핑 알고리즘은 충분한 실무적용성이 있음을 확인하였다.

• 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.

• 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.

• 한국디자인학회:학술대회논문집
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• 한국디자인학회 2004년도 춘계 학술발표대회 논문집
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• pp.98-99
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• 2004

• KSBB Journal
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• 제27권1호
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• pp.40-44
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• 2012

The laboratory animal group separation is dividing animal population into subgroups, which have similar average and standard deviation values among the subgroups, based on the biological characteristics such as body weight, glucose level in blood, etc. Although group separation is very important and initial step in experimental design, it needs a labor intensive process for researchers because of making similar average and standard deviation values among the subgroups using the raw biological characteristics. To reduce the labor cost and increase the efficiency of animal grouping, we developed a web program named as laboratory animal group separation (LAGS) program. This LAGS uses biological characteristics of population, number of group, and the number of elements per each subgroup as input data. The LAGS automatically separates the population into each subgroup that has similar statistical data such as average and standard deviation values among subgroups. It also provides researchers with the extraordinary data generated in the process of grouping and the final grouping results by graphical display. Through our LAGS, researchers can validate and confirm results of laboratory animal group separation by just a few mouse clicks.

• 대한산업공학회지
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• 제30권3호
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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.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 2006년도 추계학술대회
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• pp.656-658
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• 2006

This study deals with the part-machine grouping (PMG) that considers realistic manufacturing factors, such as the machine duplication, operation sequences with multiple visits to the same machine, and production volumes of parts. Basically, this study is an extension of Won(2006) that has adopted fuzzy ART neural network to group parts and machines. The proposed fuzzy ART neural network algorithm is implemented with an ancillary procedure to enhance the block diagonal solution by rearranging the order of input presentation. Computational experiments applied to large-size PMG data sets with a psuedo-replicated clustering procedure show effectiveness of the proposed approach.