• Journal of Korean Society of Steel Construction
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• v.17 no.4 s.77
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• pp.385-394
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• 2005

In this paper, a discrete optimum design program was developed using the refined fuzzy-genetic algorithms based on the genetic algorithms and the fuzzy theory. The optimum design in this study can perform section and shape optimization simultaneously for planar and spatial steel structures. In this paper, the objective function is the weight of steel structures and the constraints are the design limits defined by the design and buckling strengths, displacements, and thicknesses of the member sections. The design variables are the dimensions and coordinates of the steel sections. Design examples are given to show the applicability of the discrete optimum design using the improved fuzzy-genetic algorithms in this study.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 1998.10a
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• pp.19-19
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• 1998

라이너를 포함한 필라멘트 와인딩 복합재 압력용기의 성능향상을 위한 효율적인 구조최적 설계방안을 제시하였다. 이를 위해 온도효과가 고려된 비선형 유한요소법을 이용하여 각 층에서의 응력해석과 파손해석을 수행하였다. 또한 설계변수로 라이너의 두께와 헬리컬 적층두께, 실린더를 따라 변하는 직각 적층두께를 설정하였다. 모든 설계변수들에 대한 강도비 민감도를 유도하였으며 헬리컬 적층두께에 대한 강도비 민감도를 유지하기 위해 측지 등장력 돔설계 방법을 고려하였다. 구조 최적설계결과, 실린더를 따라 직각 적층두께를 최적으로 분포시켜 강성을 효과적으로 배열하였으므로 적층두께를 일정하게 고려한 경우보다 더 경량화 시킬 수 있었다.

• The Korean Journal of Applied Statistics
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• v.9 no.1
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• pp.53-73
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• 1996

This paper compares two accelerated life for Weibull distribution. One is the optimum constant stress accelerated life test which minimizes the asymptotic variance of maximum likelihood estimator of a specified quantile at design stress, and the other is corresponding simple step stress test. The models and optimum designs of constant stress and step stress tests are reviewed. Behaviors of asymptotic variances, effects of design parameters to optimum tests, and expected numbers of failures and expected test times of the two tests are investigated. The efficiency of step stress test relative to constant stress test is studied in terms of variance ratio, and robustness to preestimates of design parameters are investigated.

• Journal of the Korea institute for structural maintenance and inspection
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• v.9 no.1
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• pp.259-269
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• 2005

This paper describes the application of genetic algorithm for the discrete optimum design of reinforced concrete continuous beams. The objective is to minimize the total cost of reinforced concrete beams including the costs of concrete, form work, main reinforcement and stirrup. The flexural and shear strength, deflection, crack, spacing of reinforcement, concrete cover, upper-lower bounds on main reinforcement, beam width-depth ratio and anchorage for main reinforcement are considered as the constraints. The width and effective depth of beam and steel area are taken as design variables, and those are selected among the discrete design space which is composed with dimensions and steel area being used from in practice. Optimum result obtained from GA is compared with other literature to verify the validity of GA. To show the applicability and efficiency of GA, it is applied to three and five span reinforced concrete beams satisfying with the Korean standard specifications.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.6
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• pp.821-826
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• 2013

When the values of design variables change, mode switching can often occur. If the mode of interest is not tracked, the frequencies and modes for design optimization may be miscalculated owing to modes that differ from the intended ones. Thus, mode tracking must be employed to identify the frequencies and modes of interest whenever the values of design variables change during optimization. Furthermore, reliability-based design optimization (RBDO) must be performed for design problems with design variables containing uncertainty. In this research, we perform RBDO considering the mode tracking of a compressive coil spring, i.e., a component of the joint spring that supports a compressor, with design variables containing uncertainty by using only kriging metamodels based on multiple responses approach (MRA) without existing mode tracking methods. The reliability analyses for RBDO are employed using kriging metamodel-based Monte Carlo simulation.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.8
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• pp.921-927
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• 2012

Reliability-based design optimization (RBDO) can be used to determine the reliability of a system by means of probabilistic design criteria, i.e., the possibility of failure considering stochastic features of design variables and input parameters. To assure these criteria, various reliability analysis methods have been developed. Most of these methods assume that distribution functions are continuous. However, in real problems, because real data is often discrete in form, it is important to estimate the distributions for discrete information during reliability analysis. In this study, we employ the Akaike information criterion (AIC) method for reliability analysis to determine the best estimated distribution for discrete information and we suggest an RBDO method using AIC. Mathematical and engineering examples are illustrated to verify the proposed method.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.11
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• pp.5306-5313
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• 2011

Most previous studies for finding optimal design parameters of a tuned mass damper(TMD) have been focused on the harmonic excited single-degree-of freedom system. In this study, optimal values of damping ratio and tuning frequency ratio of a TMD applied to control a seismically excited structure are investigated through numerical analyses. Considering that the structural responses due to earthquake loads are strongly dependent on the soil condition, the site effects on the optimal parameters of the TMD are studied and compared to those presented by previous studies. Numerical analyses results indicate that better control performance can be obtained by using the parameters proposed by this study in the seismic application of the TMD.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.2
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• pp.1066-1073
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• 2014

This paper presents a new design method of optimal control of system which are changed the system parameters. The method used for this purpose are the Lagrange interpolation method and Pole's Moving range method. We selects a system within the scope of the changing the system parameters. Using pole's moving range we calculated the state weighting matrix of optimal control. The optimal controller is designed by Lagrange interpolation method of the state weighting matrix. We are compared with a traditional optimal controller and proposed method by simulation. The simulation showed that the proposed method is better control performance than traditional method of optimal controller.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.9
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• pp.1305-1310
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• 2010

In a practical design process, the method of extracting the design space information of the complex system for verifying, improving, and optimizing the design process by taking into account the design variables and their shape tolerance is very important. Finite element analysis has been successfully implemented and integrated with design of experiment such as D-Optimal array; thus, a response surface model and optimization tools have been obtained, and design variables can be optimized by using the model and these tools. Then, to guarantee the robustness of the design variables, a robust design should be additionally performed by taking into account the statistical variation of the shape tolerance of the optimized design variables. In this study, a new approach based on the use of the response surface model is proposed; in this approach, the standard normal distribution of the shape tolerance is considered. By adopting this approach, it is possible to simultaneously optimize variables and perform a robust design. This approach can serve as a means of efficiently modeling the trade-off among many conflicting goals in the applications of finite element analysis. A case study on the robust optimal design of disc brakes under thermal loadings was carried out to solve multiple objective functions and determine the constraints of the design variables, such as a thermal deformation and weight.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.10
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• pp.1323-1328
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• 2011

In the current design process for the lower arm used in automobile parts, an optimal solution of its various design variables should be found through exploration of the design space approximated using the response surface model formulated with a first- or second-order polynomial equation. In this study, a multi-level computational DOE (design of experiment) was carried out to explore the design space showing nonlinear behavior, in terms of factors such as the total weight and applied stress of the lower arm, where a fractional-factorial orthogonal array based on the artificial neural network model was introduced. In addition, the tolerance robustness of the optimal solution was estimated using a tolerance optimization with six sigma constraints, taking into account the tolerances occurring in the design variables.