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보수적 근사모델을 적용한 신뢰성기반 강건 최적설계 방법 기초연구
심형민(Hyoung Min Sim),송창용(Chang Yong Song),이종수(Jongsoo Lee) 대한기계학회 2011 대한기계학회 춘추학술대회 Vol.2011 No.10
The methods of robust design optimization (RDO) and reliability-based robust design optimization referred as RBRDO are implemented in the present study. The RBRDO is an integrated method that accounts for the design robustness in objective function and for the reliability in constraints. The objective function in RBRDO is expressed in terms of mean and standard deviation of an original objective function so that the multi-objective formulation is to be employed. The regressive approximate models are generated via moving least squares method (MLSM) and constraint-feasible moving least squares method (CF-MLSM) that is possible to realize the feasibility regardless of the multimodality/nonlinearity of the constraint function during the approximate optimization processes. The regression model based RBRDO is newly devised and its numerical characteristics are explored using the design of an actively controlled ten bar truss structure.
보수적 근사모델을 적용한 신뢰성 기반 강건 최적설계 방법
심형민(Hyoung Min Sim),송창용(Chang Yong Song),이종수(Jongsoo Lee),최하영(Ha-Young Choi) 한국해양공학회 2012 韓國海洋工學會誌 Vol.26 No.6
The methods of robust design optimization(RDO) and reliability-based robust design optimization (RBRDO) were implemented in the present study. RBRDO is an integrated method that accounts for the design robustness of an objective function and for the reliability of constraints. The objective function in RBRDO is expressed in terms of the mean and standard deviation of an original objective function. Thus, a multi-objective formulation is employed. The regressive approximate models are generated via the moving least squares method(MLSM) and constraint-feasible moving least squares method (CF-MLSM), which make it possible to realize the feasibility regardless of the multimodality/nonlinearity of the constraint function during the approximate optimization processes. The regression model based RBRDO is newly devised and its numerical characteristics are explored using the design of an actively controlled ten bar truss structure.
디젤 엔진 연료 분사량 예측을 위한 HCS기반 신경망 근사모델링
심형민(Hyoung Min Sim),박재인(Jae In Park),이준규(Joon Kyu Lee),이수홍(Soo Hong Lee),이종수(Jongsoo Lee) 한국자동차공학회 2011 한국자동차공학회 부문종합 학술대회 Vol.2011 No.5
In this paper, we proposed the use of hermit cubic spline and back-propagation neural networks to predict rates of injection in diesel engine fuel injection system. The rate of injection in the diesel engine is described in terms of energizing time and rail pressure, and its time integration corresponds to the total fuel quantity. All results verified the possibility of neural network based rates of injection prediction and hermite cubic spline interpolation method as well.