• Title, Summary, Keyword: 순차적 실험계획

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순차적 혼합물 실험계획을 평가하기 위한 그래픽방법

  • 장대홍;박상현
    • Communications for Statistical Applications and Methods
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    • v.2 no.1
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    • pp.64-73
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    • 1995
  • 혼합물 실험계회긔 확장시 기존의 연구들은 D-최적계획을 중심으로 전개되어 왔다. 이러한 판정기준들은 실험계획의 전 영역에 걸친 성능의 정도를 알아내는 데는 한계가 있다. 이러한 단점을 극복하기 위하여 하나의 그래픽 방법을 제안하였다. 혼합물 실험 계획에서 결측값이 발생하는 경우에도 이 그래픽 방법을 이용할 수 있다.

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Development of Optimization Algorithm Using Sequential Design of Experiments and Micro-Genetic Algorithm (순차적 실험계획법과 마이크로 유전알고리즘을 이용한 최적화 알고리즘 개발)

  • Lee, Jung Hwan;Suh, Myung Won
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.38 no.5
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    • pp.489-495
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    • 2014
  • A micro-genetic algorithm (MGA) is one of the improved forms of a genetic algorithm. It is used to reduce the number of iterations and the computing resources required by using small populations. The efficiency of MGAs has been proved through many problems, especially problems with 3-5 design variables. This study proposes an optimization algorithm based on the sequential design of experiments (SDOE) and an MGA. In a previous study, the authors used the SDOE technique to reduce trial-and-error in the conventional approximate optimization method by using the statistical design of experiments (DOE) and response surface method (RSM) systematically. The proposed algorithm has been applied to various mathematical examples and a structural problem.

Sequential Feasible Domain Sampling of Kriging Metamodel by Using Penalty Function (벌칙함수 기반 크리깅메타모델의 순차적 유용영역 실험계획)

  • Lee Tae-Hee;Seong Jun-Yeob;Jung Jae-Jun
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.30 no.6
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    • pp.691-697
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    • 2006
  • Metamodel, model of model, has been widely used to improve an efficiency of optimization process in engineering fields. However, global metamodels of constraints in a constrained optimization problem are required good accuracy around neighborhood of optimum point. To satisfy this requirement, more sampling points must be located around the boundary and inside of feasible region. Therefore, a new sampling strategy that is capable of identifying feasible domain should be applied to select sampling points for metamodels of constraints. In this research, we suggeste sequential feasible domain sampling that can locate sampling points likely within feasible domain by using penalty function method. To validate the excellence of feasible domain sampling, we compare the optimum results from the proposed method with those form conventional global space-filling sampling for a variety of optimization problems. The advantages of the feasible domain sampling are discussed further.

Weight Function-based Sequential Maximin Distance Design to Enhance Accuracy and Robustness of Surrogate Model (대체모델의 정확성 및 강건성 향상을 위한 가중함수 기반 순차 최소거리최대화계획)

  • Jang, Junyong;Cho, Su-Gil;Lee, Tae Hee
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.39 no.4
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    • pp.369-374
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    • 2015
  • In order to efficiently optimize the problem involving complex computer codes or computationally expensive simulation, surrogate models are widely used. Because their accuracy significantly depends on sample points, many experimental designs have been proposed. One approach is the sequential design of experiments that consider existing information of responses. In earlier research, the correlation coefficients of the kriging surrogate model are introduced as weight parameters to define the scaled distance between sample points. However, if existing information is incorrect or lacking, new sample points can be misleading. Thus, our goal in this paper is to propose a weight function derived from correlation coefficients to generate new points robustly. To verify the performance of the proposed method, several existing sequential design methods are compared for use as mathematical examples.

Sensitivity Approach of Sequential Sampling for Kriging Model (민감도법을 이용한 크리깅모델의 순차적 실험계획)

  • Lee, Tae-Hee;Jung, Jae-Jun;Hwang, In-Kyo;Lee, Chang-Seob
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.28 no.11
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    • pp.1760-1767
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    • 2004
  • Sequential sampling approaches of a metamodel that sampling points are updated sequentially become a significant consideration in metamodeling technique. Sequential sampling design is more effective than classical space filling design of all-at-once sampling because sequential sampling design is to add new sampling points by means of distance between sampling points or precdiction error obtained from metamodel. However, though the extremum points can strongly reflect the behaviors of responses, the existing sequential sampling designs are inefficient to approximate extremum points of original model. In this research, new sequential sampling approach using the sensitivity of Kriging model is proposed, so that new approach reflects the behaviors of response sequentially. Various sequential sampling designs are reviewed and the performances of the proposed approach are compared with those of existing sequential sampling approaches by using mean squared error. The accuracy of the proposed approach is investigated against optimization results of test problems so that superiority of the sensitivity approach is verified.

An Improved Stochastic Algorithm Using Kriging for Practical Optimal Designs (크리깅을 이용한 개선된 확률론적 최적화 알고리즘)

  • 임종빈;박정선;노영희
    • Journal of the Korean Society for Aeronautical & Space Sciences
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    • v.34 no.9
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    • pp.33-44
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    • 2006
  • As many scientific phenomena are now investigated using complex computer models, the effective use of Kriging on physical problems has been expanded to provide global approximations for optimization problems. This paper is focused on the two types of strategies to improve efficiency and accuracy of approximate optimization models using Kriging. These methods are performed by the stochastic process, stochastic-localization method(SLM), as the criterion to move the local domains and the design of experiments(DOE), the classical design and space-filling design. The proposed methodology is applied to the designs of 3-bar truss, Sandgren's pressure vessel, and honeycomb upper platform of a satellite structure.

Optimum Design Based on Sequential Design of Experiments and Artificial Neural Network for Enhancing Occupant Head Protection in B-Pillar Trim (센터 필라트림의 FMH 충격성능 향상을 위한 순차적 실험계획법과 인공신경망 기반의 최적설계)

  • Lee, Jung Hwan;Suh, Myung Won
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.37 no.11
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    • pp.1397-1405
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    • 2013
  • The optimal rib pattern design of B-pillar trim considering occupant head protection can be determined by two methods. One is the conventional approximate optimization method that uses the statistical design of experiments (DOE) and response surface method (RSM). Generally, approximated optimum results are obtained through the iterative process by trial-and-error. The quality of results strongly depends on the factors and levels assigned by a designer. The other is a methodology derived from previous work by the authors, called the sequential design of experiments (SDOE), to reduce the trial-and-error procedure and to find an appropriate condition for using artificial neural network (ANN) systematically. An appropriate condition is determined from the iterative process based on the analysis of means. With this new technique and ANN, it is possible to find an optimum design accurately and efficiently.

Sensitivity Approach of Sequential Sampling Using Adaptive Distance Criterion (적응거리 조건을 이용한 순차적 실험계획의 민감도법)

  • Jung, Jae-Jun;Lee, Tae-Hee
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.29 no.9
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    • pp.1217-1224
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    • 2005
  • To improve the accuracy of a metamodel, additional sample points can be selected by using a specified criterion, which is often called sequential sampling approach. Sequential sampling approach requires small computational cost compared to one-stage optimal sampling. It is also capable of monitoring the process of metamodeling by means of identifying an important design region for approximation and further refining the fidelity in the region. However, the existing critertia such as mean squared error, entropy and maximin distance essentially depend on the distance between previous selected sample points. Therefore, although sufficient sample points are selected, these sequential sampling strategies cannot guarantee the accuracy of metamodel in the nearby optimum points. This is because criteria of the existing sequential sampling approaches are inefficient to approximate extremum and inflection points of original model. In this research, new sequential sampling approach using the sensitivity of metamodel is proposed to reflect the response. Various functions that can represent a variety of features of engineering problems are used to validate the sensitivity approach. In addition to both root mean squared error and maximum error, the error of metamodel at optimum points is tested to access the superiority of the proposed approach. That is, optimum solutions to minimization of metamodel obtained from the proposed approach are compared with those of true functions. For comparison, both mean squared error approach and maximin distance approach are also examined.

Optimum Design based on Sequential Design of Experiments and Artificial Neural Network for Heat Resistant Characteristics Enhancement in Front Pillar Trim (프런트 필라 트림의 내열특성 향상을 위한 순차적 실험계획법과 인공신경망 기반의 최적설계)

  • Lee, Jung Hwan;Suh, Myung Won
    • Journal of the Korean Society for Precision Engineering
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    • v.30 no.10
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    • pp.1079-1086
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    • 2013
  • Optimal mount position of a front pillar trim considering heat resistant characteristics can be determined by two methods. One is conventional approximate optimization method which uses the statistical design of experiments (DOE) and response surface method (RSM). Generally, approximated optimum results are obtained through the iterative process by a trial and error. The quality of results depends seriously on the factors and levels assigned by a designer. The other is a methodology derived from previous work by the authors, which is called sequential design of experiments (SDOE), to reduce a trial and error procedure and to find an appropriate condition for using artificial neural network (ANN) systematically. An appropriate condition is determined from the iterative process based on the analysis of means. With this new technique and ANN, it is possible to find an optimum design accurately and efficiently.

Development of Optimization Algorithm for Unconstrained Problems Using the Sequential Design of Experiments and Artificial Neural Network (순차적 실험계획법과 인공신경망을 이용한 제한조건이 없는 문제의 최적화 알고리즘 개발)

  • Lee, Jung-Hwan;Suh, Myung-Won
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.32 no.3
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    • pp.258-266
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    • 2008
  • The conventional approximate optimization method, which uses the statistical design of experiments(DOE) and response surface method(RSM), can derive an approximated optimum results through the iterative process by a trial and error. The quality of results depends seriously on the factors and levels assigned by a designer. The purpose of this study is to propose a new technique, which is called a sequential design of experiments(SDOE), to reduce a trial and error procedure and to find an appropriate condition for using artificial neural network(ANN) systematically. An appropriate condition is determined from the iterative process based on the analysis of means. With this new technique and ANN, it is possible to find an optimum design accurately and efficiently. The suggested algorithm has been applied to various mathematical examples and a structural problem.