• 응용통계연구
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• 제27권7호
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• pp.1269-1278
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• 2014

D-최적 실험은 실험의 이론적인 기초를 제공하는 이유로 비선형모형에 대해 실험설계 시 인기가 있지만 이러한 실험기준은 비선형인 경우 알려져 있지 않은 모수에 의존하는 모순적인 특징이 있다. 그러나 일부 비선형모형은 최적 실험이 비선형 모형의 일부 모수에만 의존하는 특징이 있는 부분비선형모형임 밝혀졌다. 일반적으로 비선형 모형인 경우는 maximin방법은 일반적으로 모수의 불확실성에 강건한 실험을 제공하지 못한다고 알려져 있으나 많은 부분비선형 모형인 경우 하나의 모수에만 최적실험이 의존하는 구조를 갖고 있어 최적실험의 구조를 밝히는데 매우 용이하다. 본 연구에서는 Michaelis-Menten 모형을 대상으로 모수의 불확실성에 대처하기 위한 maximin 방법을 D-최적 및 $D_s$-최적을 기준으로 살펴보았다.

• 한국경영과학회지
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• 제26권3호
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• pp.95-104
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• 2001

In this paper, we consider a maximin version of the linear programming knapsack problem with extended generalized upper bound (GUB) constraints. We solve the problem efficiently by exploiting its special structure without transforming it into a standard linear programming problem. We present an O(n$^3$) algorithm for deriving the optimal solution where n is the total number of problem variables. We illustrate a numerical example.

• 응용통계연구
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• 제28권3호
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• pp.561-571
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• 2015

실험영역을 벗어나는 점에 해당하는 반응값 예측을 위한 최적실험을 고려할 때 실험에 필요한 받힘점을 위한 실험기준을 선택하는 경우 매우 신중하여야 한다. 왜냐하면 가정한 모형과 오차구도가 실험영역을 벗어나도 타당하다는 가정을 하여야 되기 때문이다. 따라서 기존문헌의 외삽최적의 실험기준을 이러한 상황에 맞게 설계될 수 있도록 수정하였다. 본 연구에서는 maximin방법을 적용하여 새로운 실험기준의 특징 및 강건성을 단순회귀모형과 이차회귀모형을 기준으로 검정하였다.

• 지식경영연구
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• 제20권3호
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• pp.39-47
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• 2019

Response surface methodology (RSM) is a group of statistical modeling and optimization methods to improve the quality of design systematically in the quality engineering field. Its final goal is to identify the optimal setting of input variables optimizing a response. RSM is a kind of knowledge management tool since it studies a manufacturing or service process and extracts an important knowledge about it. In a real problem of RSM, it is a quite frequent situation that considers multiple responses simultaneously. To date, many approaches are proposed for solving (i.e., optimizing) a multi-response problem: process capability function approach, desirability function approach, loss function approach, and so on. The process capability function approach first estimates the mean and standard deviation models of each response. Then, it derives an individual process capability function for each response. The overall process capability function is obtained by aggregating the individual process capability function. The optimal setting is given by maximizing the overall process capability function. The existing process capability function methods usually use the arithmetic mean or geometric mean as an aggregation operator. However, these operators do not guarantee the Pareto optimality of their solution. Moreover, they may bring out an unacceptable result in terms of individual process capability function values. In this paper, we propose a maximin-based process capability function method which uses a maximin criterion as an aggregation operator. The proposed method is illustrated through a well-known multiresponse problem.

• Communications for Statistical Applications and Methods
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• 제14권3호
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• pp.531-540
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• 2007

When multiple objectives exist, there are three approaches exist. These are maximin design, compound design, and constrained design. Still, each of three design criteria has its own strength and weakness. In this paper Hybrid approach is suggested when multiple design objectives exist, which is a combination of maximin and constrained design. Sometimes experimenter has several objectives, but he/she has only one or two primary objectives, others less important. A new approach should be useful under this condition. The genetic algorithm is used for few examples. It has been proven to be a very useful technique for this complex situation. Conclusion follows.

• 산업경영시스템학회지
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• 제20권43호
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• pp.145-152
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• 1997

We present a continuous resource allocation problem with maximin objective functions under the generalized lower bound(GLB) and generalized upper bound(GUB) constraints. This problem is an extension for the problems of previous studies. An efficient algorithm is developed by exploiting extended structural properties, where n is the total number of variables. The worst computational complexity of the proposed algorithm is O(nlogn).

• 대한기계학회:학술대회논문집
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• 대한기계학회 2003년도 추계학술대회
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• pp.1846-1851
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• 2003

Approximate optimization has become popular in engineering field such as MDO and Crash analysis which is time consuming. To accomplish efficient approximate optimization, accuracy of approximate model is very important. As surrogate model, Kriging have been widely used approximating highly nonlinear system . Because Kriging employs interpolation method, it is adequate for deterministic computer simulation. Because there are no random errors and measurement errors in deterministic computer simulation, instead of classical DOE ,space filling experiment design which fills uniformly design space should be applied. In this work, various space filling designs such as maximin distance design, maximum entropy design are reviewed. And new design improving maximum entropy design is suggested and compared.

• 품질경영학회지
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• 제17권2호
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• pp.93-100
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• 1989

Nonlinear rank statistics for the simple tree alternatives problem are considered. Pitman efficiencies between several procedures are studied. A new maximin procedure is suggested and shown to have good efficiency properties. Additionally, it is desirable to terminate the experiment early comparing well known rank statistics or multiple comparison test statistics.

• 대한전자공학회논문지
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• 제27권1호
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• pp.120-129
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• 1990

In this paper, 3-dimensional object segmentation and classification are proposed. Planar object is segmented surface using jump boundary and internal boundary. Curved object is segmented surfaces by maximin clustering method. Segmented surfaces are classified by depth trends and angle measurement of normal vectors. Classified surfaces are merged according to adjacent surfaces and compared to Guassian curvature and mean curvature method. The proposed methods have been successfully applied to the synthetic range images and shows good classification.

• 대한기계학회논문집A
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• 제39권4호
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• pp.369-374
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• 2015

효율적인 최적설계를 위해 공학분야에 도입된 대체모델의 정확성은 표본점에 큰 영향을 받는다. 대체모델의 정확성을 높이는 방법으로 기 추출한 응답을 이용하는 순차실험계획이 제안되었다. 크리깅 대체모델의 상관계수를 가중치로 적용하여 대체모델의 정확성을 향상시킨 연구가 있었으나, 주어진 정보가 부족하거나 상관계수가 잘못 추정된 경우 표본점이 잘못 추출되어 대체모델의 강건성이 저하된다. 본 논문에서는 기존 순차실험계획의 여러 문제점을 제시하고, 이를 해결하기 위한 가중함수 기반 순차 최소거리최대화계획을 제안한다. 제안하는 순차실험계획의 효용성을 수학 함수에 적용하여 기존 순차실험계획들과 비교하여 정확성과 강건성이 향상됨을 예시한다.