• Title/Summary/Keyword: optimization of experiments

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Multi-response Optimization by a Response Surface Approach for a Taguchi-Type Multi-characteristic Experiments (다중반응표면분석방법을 이용한 다꾸찌 다특성 실험에 대한 분석 방법)

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    • Journal of Applied Reliability
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    • v.4 no.1
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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.

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Optimization of blank geometry for the stamping process of B-pillar using design of experiments (실험 계획법을 사용한 B-Pillar 성형공정에서 블랭크 형상 최적화)

  • Youn, Hyung-Won;Choi, Yong-seok;Lee, Chang-Whan
    • Design & Manufacturing
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    • v.15 no.2
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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.

Numerical analysis of quantization-based optimization

  • Jinwuk Seok;Chang Sik Cho
    • ETRI Journal
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    • v.46 no.3
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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.

Optimization of H.263 Encoder on a High Performance DSP (고성능 DSP 에서의 H.263 인코더 최적화)

  • 문종려;최수철;정선태
    • Proceedings of the IEEK Conference
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    • 2003.11a
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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.

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Optimization of Chassis Frame by Using D-Optimal Response Surface Model (D-Optimal 반응표면모델에 의한 섀시 프레임 최적설치)

  • Lee, Gwang-Gi;Gu, Ja-Gyeom;Lee, Tae-Hui
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.24 no.4 s.175
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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.

Optimization of Valve Gates Locations Using Automated Runner System Modeling and Metamodels (유동 안내부 모델링 자동화 및 근사모델을 이용한 자동차용 도어트림의 밸브 게이트 위치 최적화)

  • Joe, Yong-Su;Park, Chang-Hyun;Pyo, Byung-Gi;Rhee, Byung-Ohk;Choi, Dong-Hoon
    • Transactions of the Korean Society of Automotive Engineers
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    • v.22 no.2
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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.

Structural Optimization of a RC Building for Minimizing Weight (중량 최소화를 위한 RC 빌딩의 구조 최적설계)

  • Park, Chang-Hyun;Ahn, Hee-Jae;Choi, Dong-Hoon;Jung, Cheul-Kyu
    • Journal of the Computational Structural Engineering Institute of Korea
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    • v.23 no.5
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    • pp.501-507
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    • 2010
  • Structural optimization is performed to minimize the weight of a RC building structure, which has eight floors above ground and three underground, under gravity, wind, and seismic loads. Design optimization problem is formulated to find the values of the design variables that minimize the volume while satisfying various design and side constraints. To solved the optimization problem posed, several design techniques equipped in PIAnO, a commercial PIDO tool, are used. DOE is used to generate training points and structural analysis is performed using MIADS Gen, a general-purpose structural analysis CAE tool. Then, meta-models are generated from structural analysis results and accuracies of meta-models are evaluated. Next, design optimization is performed by using the verified meta-models and optimization technique equipped in PIAnO. Finally, we obtained optimal results, which could demonstrate the effectiveness of our design method.

Flux Optimization Using Genetic Algorithms in Membrane Bioreactor

  • Kim Jung-Mo;Park Chul-Hwan;Kim Seung-Wook;Kim Sang-Yong
    • Journal of Microbiology and Biotechnology
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    • v.16 no.6
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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.

Simple Bacteria Cooperative Optimization with Rank Replacement

  • Jung, Sung-Hoon
    • Journal of the Korean Institute of Intelligent Systems
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    • v.19 no.3
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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.

A Novel Optimization Algorithm Inspired by Bacteria Behavior Patterns

  • Jung, Sung-Hoon;Kim, Tae-Geon
    • Journal of the Korean Institute of Intelligent Systems
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    • v.18 no.3
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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.