• 한국신뢰성학회지:신뢰성응용연구
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• 제4권1호
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• pp.39-64
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• 2004

Taguchi's multi-characteristic experiments seek proper choice of levels of contollable factors which satisfy that all reponses of characteristics in a desirable range simultaneously. This aim can be achieved by response surface techniques that allow more flexible in modeling than traditional Taguchi's parameter design. In this article, a multi-response surface modeling and analysis techniques is proposed to deal with the multi-characteristic optimization problem in experimentation with Taguchi's controllable and noise factors.

• Design & Manufacturing
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• 제15권2호
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• pp.17-22
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• 2021

The shape of the blank greatly affects the formability and quality of the product after the stamping process. In this study, the geometry of the B-Pillar blank in the stamping process was optimized using design of experiments. The geometry of the blank for the B-pillar was simplified with the two length values and two radius values. The effects of design variables were studied through the Design of experiments. The stamping process of the B-pillar was predicted with the Finite Element Analysis (FEA). The optimized blank geometry was obtained. It results in the reduced maximum equivalent plastic strain. The local necking and the wrinkling did not occurred with the optimized blank geometry.

• ETRI Journal
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• 제46권3호
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• pp.367-378
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• 2024

We propose a number-theory-based quantized mathematical optimization scheme for various NP-hard and similar problems. Conventional global optimization schemes, such as simulated and quantum annealing, assume stochastic properties that require multiple attempts. Although our quantization-based optimization proposal also depends on stochastic features (i.e., the white-noise hypothesis), it provides a more reliable optimization performance. Our numerical analysis equates quantization-based optimization to quantum annealing, and its quantization property effectively provides global optimization by decreasing the measure of the level sets associated with the objective function. Consequently, the proposed combinatorial optimization method allows the removal of the acceptance probability used in conventional heuristic algorithms to provide a more effective optimization. Numerical experiments show that the proposed algorithm determines the global optimum in less operational time than conventional schemes.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 신호처리소사이어티 추계학술대회 논문집
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• pp.99-102
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• 2003

Computing environments of Embedded Systems are different from those of desktop computers so that they have resource constraints such as CPU processing, memory capacity, power, and etc.. Thus, when a desktop S/W is ported into embedded systems, optimization should be seriously considered. In this paper, we investigate several S/W optimization techniques to be considered for porting H.263 encoder into a high performance DSP, TMS320C6711. Through experiments, it is found that optimization techniques employed can make a big performance improvement.

• 대한기계학회논문집A
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• 제24권4호
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• pp.894-900
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• 2000

Optimization of chassis frame is performed according to the minimization of eleven responses representing one total frame weight, three natural frequencies and seven strength limits of chassis frame that are analyzed by using each response surface model from D-optimal design of experiments. After each response surface model is constructed form D-optimal design and random orthogonal array, the main effect and sensitivity analyses are successfully carried out by using this approximated regression model and the optimal solutions are obtained by using a nonlinear programming method. The response surface models and the optimization algorithms are used together to obtain the optimal design of chassis frame. The eleven-polynomial response surface models of the thirteen frame members (design factors) are constructed by using D-optimal Design and the multi-disciplinary design optimization is also performed by applying dual response analysis.

• 한국자동차공학회논문집
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• 제22권2호
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• pp.115-122
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• 2014

Injection pressure is one of factors that influence part quality. In this paper, injection pressure was minimized by optimizing valve gate locations. In order to perform design optimization, MAPS-3DTM (Mold Analysis and Plastic Solution-3D) was used for injection mold analysis and PIAnOTM (Process Integration, Automation and Optimization) was used as process integration and design optimization. Also we adapted meta models based on design of experiments for efficiency. By using introduced methodology, we were able to obtain a result so that maximum injection pressure reduced by 28% compared to the initial design. And the validity of the proposed method could also be demonstrated.

• 한국전산구조공학회논문집
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• 제23권5호
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• pp.501-507
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• 2010

본 논문에서는 중력하중 및 풍하중, 지진하중을 받는 지상 8층, 지하 3층의 RC(Reinforced Concrete) 빌딩 시공에 필요한 부재의 재료비를 줄이기 위해 중량을 감소시키는 구조 최적설계를 수행한다. 이를 위해 설계요구사항을 바탕으로 부재의 부피를 최소화하는 설계변수값을 찾기 위한 설계문제를 정식화한다. 최적설계 수행을 위해 상용 PIDO(Process Integration and Design Optimization) 툴인 PIAnO(Process Integration, Automation and Optimization)에서 제공하는 다양한 설계기법들을 이용한다. 먼저 실험계획법(Design of Experiments; DOE)을 이용하여 실험계획을 세우고, 실험점에 따라 건축분야 범용 구조해석 프로그램인 MIDAS Gen을 사용하여 구조해석을 수행한다. 그리고 해석결과를 바탕으로 각 응답에 대한 근사모델을 생성한 후 근사모델의 예측성능을 평가한다. 예측성능이 검증된 근사모델과 최적화기법을 이용하여 최적설계를 수행하고, 설계조건을 만족하면서 부재의 부피를 최소화하는 최적 설계변수값을 도출함으로서 본 논문에서 제안된 설계방법의 유효성을 보이고자 한다.

• Journal of Microbiology and Biotechnology
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• 제16권6호
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• pp.863-869
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• 2006

The behavior of submerged membrane bioreactor (SMBR) filtration systems utilizing rapid air backpulsing as a cleaning technique to remove reversible foulants was investigated using a genetic algorithm (GA). A customized genetic algorithm with suitable genetic operators was used to generate optimal time profiles. From experiments utilizing short and long periods of forward and reverse filtration, various experimental process parameters were determined. The GA indicated that the optimal values for the net flux fell between 263-270 LMH when the forward filtration time ($t_f$) was 30-37 s and the backward filtration time ($t_b$) was 0.19-0.27 s. The experimental data confirmed the optimal backpulse duration and frequency that maximized the net flux, which represented a four-fold improvement in 24-h backpulsing experiments compared with the absence of backpulsing. Consequently, the identification of a region of feasible parameters and nonlinear flux optimization were both successfully performed by the genetic algorithm, meaning the genetic algorithm-based optimization proved to be useful for solving SMBR flux optimization problems.

• 한국지능시스템학회논문지
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• 제19권3호
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• pp.432-436
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• 2009

We have developed a new optimization algorithm termed simple bacteria cooperative optimization (sBCO) based on bacteria behavior patterns [1]. In [1], we have introduced the algorithm with basic operations and showed its feasibility with some function optimization problems. Since the sBCO was the first version with only basic operations, its performance was not so good. In this paper, we adopt a new operation, rank replacement, to the sBCO for improving its performance and compare its results to those of the simple genetic algorithm (sGA) which has been well known and widely used as an optimization algorithm. It was found from the experiments with four function optimization problems that the sBCO with rank replacement was superior to the sGA. This shows that our algorithm can be a good optimization algorithm.

• 한국지능시스템학회논문지
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• 제18권3호
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• pp.392-400
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• 2008

This paper proposes a novel optimization algorithm inspired by bacteria behavior patterns for foraging. Most bacteria can trace attractant chemical molecules for foraging. This tracing capability of bacteria called chemotaxis might be optimized for foraging because it has been evolved for few millenniums. From this observation, we developed a new optimization algorithm based on the chemotaxis of bacteria in this paper. We first define behavior and decision rules based on the behavior patterns of bacteria and then devise an optimization algorithm with these behavior and decision rules. Generally bacteria have a quorum sensing mechanism that makes it possible to effectively forage, but we leave its implementation as a further work for simplicity. Thereby, we call our algorithm a simple bacteria cooperative optimization (BCO) algorithm. Our simple BCO is tested with four function optimization problems on various' parameters of the algorithm. It was found from experiments that the simple BCO can be a good framework for optimization.