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dc.contributor.author이태희-
dc.date.accessioned2018-02-27T05:33:20Z-
dc.date.available2018-02-27T05:33:20Z-
dc.date.issued2011-05-
dc.identifier.citation한국자동차공학회 춘계학술대회. 2011(5) ,1777-1780en_US
dc.identifier.urihttp://www.dbpia.co.kr/Journal/ArticleDetail/NODE01755614-
dc.identifier.urihttp://hdl.handle.net/20.500.11754/40801-
dc.description.abstractSolutions to the deterministic design optimization cannot consider the uncertainties that may occur from the tolerance of design variables, noise and environmental factors, material properties, and uncontrollable operations. Therefore these solutions may cause the possibility of failure. However, reliability-based design optimization (RBDO) can provide the reliability of a system by mean of probabilistic design criteria, i.e., possibility of failure. To assure these criteria, many methods for the reliability analysis have been developed. However, these methods assume that data take one of continuous forms of distribution functions. In real problems, because real data may often provide discrete form, it is important to estimate the distributions from discrete data in the reliability analysis. In this paper, we introduce BIC method that can determine the best estimated distribution for discrete data. These distributions are used for reliability analysis in RBDO. Mathematical example is illustrated to verify the proposed method. In engineering example, bogie frame is illustrated to apply the proposed method. Solutions to the deterministic design optimization cannot consider the uncertainties that may occur from the tolerance of design variables, noise and environmental factors, material properties, and uncontrollable operations. Therefore these solutions may cause the possibility of failure. However, reliability-based design optimization (RBDO) can provide the reliability of a system by mean of probabilistic design criteria, i.e., possibility of failure. To assure these criteria, many methods for the reliability analysis have been developed. However, these methods assume that data take one of continuous forms of distribution functions. In real problems, because real data may often provide discrete form, it is important to estimate the distributions from discrete data in the reliability analysis. In this paper, we introduce BIC method that can determine the best estimated distribution for discrete data. These distributions are used for reliability analysis in RBDO. Mathematical example is illustrated to verify the proposed method. In engineering example, bogie frame is illustrated to apply the proposed method.en_US
dc.description.sponsorship본 논문은 국토해양부 국토해양기술연구개발사업 과제인 "파일럿 집광시스템의 신뢰성 기반 최적설계연구" 과제의 지원으로 수행되었습니다. 본 연구에 대해 지원해주신 국토해양부 해양연구원 관계자들께 감사의 말씀드립니다.en_US
dc.language.isootheren_US
dc.publisher한국자동차공학회en_US
dc.subjectbayesianinformationcriterion베이지안정보척도en_US
dc.subjectModel selection, maximumlikelihoodfunction최우량함수, probabilitydensityfunction확률밀도함수en_US
dc.subjectreliabilityanalysis신뢰성해석en_US
dc.subjectreliabilitybaseddesignoptimization신뢰성기반최적설계en_US
dc.subjectbogieframe대차틀en_US
dc.subjectBayesian Information Criterionen_US
dc.subject베이지안정보척도en_US
dc.subjectMaximum likelihood functionen_US
dc.subject최우량함수en_US
dc.subjectProbability density functionen_US
dc.subject확률밀도함수en_US
dc.subjectReliability analysisen_US
dc.subject신뢰성해석en_US
dc.subjectReliability-based designoptimizationen_US
dc.subject신뢰성기반최적설계en_US
dc.subjectBogie frameen_US
dc.subject대차틀en_US
dc.subjectbogieframe대차률en_US
dc.title베지안 정보 척도를 이용한 대차틀 강성의 신뢰성 기반 최적설계en_US
dc.title.alternativeReliability-based Design Optimization for Strength of Bogie Frame using Bayesian Information Criterionen_US
dc.typeArticleen_US
dc.relation.page1157-1162-
dc.contributor.googleauthor임우철-
dc.contributor.googleauthor이태희-
dc.sector.campusS-
dc.sector.daehakCOLLEGE OF ENGINEERING[S]-
dc.sector.departmentDEPARTMENT OF AUTOMOTIVE ENGINEERING-
dc.identifier.pidthlee-
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COLLEGE OF ENGINEERING[S](공과대학) > AUTOMOTIVE ENGINEERING(미래자동차공학과) > Articles
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