• 한국응용과학기술학회지
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• 제40권6호
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• pp.1363-1372
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• 2023

This study is concerned with the optimization of the manufacturing process of a hot water extract containing antioxidant activity from Lycium barbarum, traditionally known to have various physiological activities. For the establishment of the optimization process, the central composite design of response surface methodology(RSM) was used. Thirteen extraction processes were performed by encoding the independent variables, extraction temperature (65.9℃-94.1℃) and extraction time (2.59 hr-5.41 hr). As a result of the experiment, the optimal manufacturing conditions for the extract were 340.0 mg/100 g of GAE at an extraction temperature of 94.1℃ and an extraction time of 5 hr. The maximum yield of flavonoids was 22.44 mg/100 g of HES at an extraction temperature of 94.1℃ and an extraction time of 4 hr. The conditions for producing the extract with the maximum antioxidant capacity (DPPH 92.12%) were 90℃ and 4.5 hr extraction time. Therefore, the optimal manufacturing process conditions for extracts containing total phenol content, flavonoid content, and DPPH radical scavenging activity, which are dependent variables, were extraction temperature of 90-95℃ and extraction time of 4 hr, which were not significantly different from the actual values. Therefore, Lycium barbarum extract rich in total phenol and flavonoid content related to antioxidant function is expected to be used as a functional food and cosmetic material.

• JSTS:Journal of Semiconductor Technology and Science
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• 제11권3호
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• pp.135-145
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• 2011

Accurate process characterization and optimization are the first step for a successful advanced process control (APC), and they should be followed by continuous monitoring and control in order to run manufacturing processes most efficiently. In this paper, process characterization and recipe optimization methods with multiple outputs are presented in high density plasma-chemical vapor deposition (HDP-CVD) silicon dioxide deposition process. Five controllable process variables of Top $SiH_4$, Bottom $SiH_4$, $O_2$, Top RF Power, and Bottom RF Power, and two responses of interest, such as deposition rate and uniformity, are simultaneously considered employing both statistical response surface methodology (RSM) and neural networks (NNs) based genetic algorithm (GA). Statistically, two phases of experimental design was performed, and the established statistical models were optimized using performance index (PI). Artificial intelligently, NN process model with two outputs were established, and recipe synthesis was performed employing GA. Statistical RSM offers minimum numbers of experiment to build regression models and response surface models, but the analysis of the data need to satisfy underlying assumption and statistical data analysis capability. NN based-GA does not require any underlying assumption for data modeling; however, the selection of the input data for the model establishment is important for accurate model construction. Both statistical and artificial intelligent methods suggest competitive characterization and optimization results in HDP-CVD $SiO_2$ deposition process, and the NN based-GA method showed 26% uniformity improvement with 36% less $SiH_4$ gas usage yielding 20.8 ${\AA}/sec$ deposition rate.

• Journal of Electrical Engineering and Technology
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• 제12권2호
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• pp.830-839
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• 2017

Support vector machine (SVM) is introduced as an effective fault diagnosis technique based on dissolved gases analysis (DGA) for oil-immersed transformers with maximum generalization ability; however, the applicability of the SVM is highly affected due to the difficulty of selecting the SVM parameters appropriately. Therefore, a novel approach combing SVM with improved imperialist competitive algorithm (IICA) for fault diagnosis of oil-immersed transformers was proposed in the paper. The improved ICA, which is proved to be an effective optimization approach, is employed to optimize the parameters of SVM. Cross validation and normalizations were applied in the training processes of SVM and the trained SVM model with the optimized parameters was established for fault diagnosis of oil-immersed transformers. Three classification benchmark sets were studied based on particle swarm optimization SVM (PSOSVM) and IICASVM with four multiple classification schemes to select the best scheme for transformer fault diagnosis. The results show that the proposed model can obtain higher diagnosis accuracy than other methods. The comparisons confirm that the proposed model is an effective approach for classification problems.

• Industrial Engineering and Management Systems
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• 제15권2호
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• pp.156-172
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• 2016

Complex and uncertain issues in supply chain result in integrated decision making processes in supply chains. So decentralized (distributed) decision making (DDM) approach is considered as a crucial stage in supply chain planning. In this paper, an uncertain DDM through coordination mechanism is addressed for a multi-product supply chain planning problem. The main concern of this study is comparison of DDM approach with centralized decision making (CDM) approach while some parameters of decision making are assumed to be uncertain. The uncertain DDM problem is modeled through fuzzy mathematical programming in which products' demands are assumed to be uncertain and modeled using fuzzy sets. Moreover, a CDM approach is customized and developed in presence of fuzzy parameters. Both approaches are solved using three fuzzy mathematical optimization methods. Hence, the contribution of this paper can be summarized as follows: 1) proposing a DDM approach for a multi-product supply chain planning problem; 2) Introducing a coordination mechanism in the proposed DDM approach in order to utilize the benefits of a CDM approach while using DDM approach; 3) Modeling the aforementioned problem through fuzzy mathematical programming; 4) Comparing the performance of proposed DDM and a customized uncertain CDM approach on multi-product supply chain planning; 5) Applying three fuzzy mathematical optimization methods in order to address and compare the performance of both DDM and CDM approaches. The results of these fuzzy optimization methods are compared. Computational results illustrate that the proposed DDM approach closely approximates the optimal solutions generated by the CDM approach while the manufacturer's and retailers' decisions are optimized through a coordination mechanism making lasting relationship.

• 지식경영연구
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• 제19권2호
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• pp.151-162
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• 2018

Response surface methodology (RSM) is one of popular tools to support a systematic improvement of quality of design in the product and process development stages. It consists of statistical modeling and optimization tools. RSM can be viewed as a knowledge management tool in that it systemizes knowledge about a manufacturing process through a big data analysis on products and processes. The conventional RSM aims to optimize the mean of a response, whereas dual-response surface optimization (DRSO), a special case of RSM, considers not only the mean of a response but also its variability or standard deviation for optimization. Recently, a posterior preference articulation approach receives attention in the DRSO literature. The posterior approach first seeks all (or most) of the nondominated solutions with no articulation of a decision maker (DM)'s preference. The DM then selects the best one from the set of nondominated solutions a posteriori. This method has a strength that the DM can understand the trade-off between the mean and standard deviation well by looking around the nondominated solutions. A posterior method has been proposed for DRSO. It employs an interval selection strategy for the selection step. This strategy has a limitation increasing inefficiency and complexity due to too many iterations when handling a great number (e.g., thousands ~ tens of thousands) of nondominated solutions. In this paper, a TOPSIS-based method is proposed to support a simple and efficient selection of the most preferred solution. The proposed method is illustrated through a typical DRSO problem and compared with the existing posterior method.

• 한국전산구조공학회논문집
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• 제20권2호
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• pp.165-179
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• 2007

이 논문에서는 철근콘크리트 구조물의 최적설계를 위해 기둥과 보 부재 설계 단면의 데이터베이스를 구성하고 이로부터 단면 번호와 단면 저항 능력간의 관계를 나타내는 회귀분석식을 구성하여, 직접 탐색법으로 빠르게 최적해를 검색하는 효율적인 알고리즘을 제안하였다. 설계 실무에서 가격을 고려하여 설계하기보다는 성능 최적화에 가까운 설계를 수행한다는 사실로부터 제안된 알고리즘을 이용하여 성능 최적화와 가격 최적화를 모두 수행하여 그 결과를 비교 검토하였고, 예제 구조물을 대상으로 적용성과 효율성을 검토하였다. 본 알고리즘은 목적 함수 구성시 제한 조건이 없고 전개 과정이 매우 단순하면서도 빠른 수렴성을 보이며 선택된 해가 설계 규준과 실무상의 제한 조건에 부합하므로 바로 적용 가능하다는 장점이 있다. 전체 구조물의 최적화는 개별 부재의 최적화를 통해 이루어진다.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2006년도 춘계학술대회 논문집
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• pp.107-108
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• 2006

This paper shows the design optimization of the paper feeding mechanism under dynamic behavior by using commercial codes of RecurDyn/MTT2D and RecurDyn/AutoDesign which are developed by functionBay, Inc. A virtual mockup for dynamics analysis of the paper feeding mechanism is build on RecurDyn/MTT2D and is simulated. Flexible paper is represented as a series of rigid bars connected by revolute joints and rotational spring dampers. Paper is fed by a contact and friction mechanism on rollers or guides. The slip of the paper and nip force of rollers are measured to estimate the system performance. After a simulation, these performances are automatically send to RecurDyn/AutoDesign which is a sequential approximate optimization tool based on the response surface modeling. RecurDyn/AutoDesign makes the approximate objective function and computes the optimized design points of the design variables and gives them to analysis tool. And then the simulation is repeated with the updated design variables. These processes are repeated until finding a tolerable design optimization. In this paper, a paper feeding mechanism is introduced and it is optimized with the proposed algorithms.

• 대한조선학회논문집
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• 제33권1호
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• pp.161-172
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• 1996

최적화기법을 포함한 종래의 전산 프로그램들은 수치적 계산과정과 그 결과에만 중점을 두고 개발되어 왔으며, 설계모델의 개발과 최적화기법의 선택 및 결과의 판단 등은 설계 전문가에 의하여 수행되어 왔다. 반면에 전문가의 경험적지식을 처리하는 지식기반시스템은 기호처리에 중점을 두고 있기 때문에 수치적 계산을 효과적으로 할 수 없다. 본 논문에서는 수치적인 계산결과만을 제공하는 최적화기법의 한계와 기호처리에 중점을 두고 있는 지식기반시스템의 한계를 극복하여, 보다 현실적인 최적설계안을 도출할 수 있는 지식기반 다목적함수 최적설계 시스템을, 최적화기법과 LISP 언어로 개발한 지식기반시스템을 통합하여 구현하고, 이를 LNG선의 최적설계 모델에 적용하여 개발된 시스템의 유용성을 보였다.

• International Journal of Naval Architecture and Ocean Engineering
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• 제7권1호
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• pp.100-114
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• 2015

This paper deploys optimization techniques to obtain the optimum hull form of KSUEZMAX at the conditions of full-load draft and design speed. The processes have been carried out using a RaPID-HOP program. The bow and the stern hull-forms are optimized separately without altering neither, and the resulting versions of the two are then combined. Objective functions are the minimum values of wave-making and viscous pressure resistance coefficients for the bow and stern. Parametric modification functions for the bow hull-form variation are SAC shape, section shape (U-V type, DLWL type), bulb shape (bulb height and size); and those for the stern are SAC and section shape (U-V type, DLWL type). WAVIS version 1.3 code is used for the potential and the viscous-flow solver. Prior to the optimization, a parametric study has been conducted to observe the effects of design parameters on the objective functions. SQP has been applied for the optimization algorithm. The model tests have been conducted at a towing tank to evaluate the resistance performance of the optimized hull-form. It has been noted that the optimized hull-form brings 2.4% and 6.8% reduction in total and residual resistance coefficients compared to those of the original hull-form. The propulsive efficiency increases by 2.0% and the delivered power is reduced 3.7%, whereas the propeller rotating speed increases slightly by 0.41 rpm.

• 한국산학기술학회논문지
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• 제18권2호
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• pp.704-710
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• 2017

본 연구팀은 프레스 공정의 협소공간에서 작업이 가능한 6자유도 로봇을 개발하고 있으며, 본 논문은 개발된 로봇의 작업 시간을 최소화하기 위한 작업 시퀀스 최적화 방법을 제안하였다. 우선 6 자유도 로봇의 기구학을 모델링하고 작업 시간 예측 방법을 기술하였다. 그리고 작업 시퀀스 최적화를 위하여 수학적 모델을 제시하고, 이를 바탕으로 개미 집단 시스템(ant colony system), 시뮬레이트 어니일링(simulated annealing), 유전자 알고리즘(genetic algorithm)의 세 가지 최적화 방법을 적용하고 결과를 비교 분석하였다. 시뮬레이션 결과 유전자 알고리즘이 가장 좋은 결과를 보임을 확인할 수 있었으며, 계산 속도 측면에서도 가장 빨리 최적값에 수렴하였다. 또한, 개미집단시스템과 시뮬레이티드 어니일링의 경우 여러 파라미터 값들의 설정에 따라 수렴된 최적값의 편차가 비교적 큰 것에 비하여, 유전자 알고리즘은 파라미터 값에 상관없이 안정적으로 근사 최적값을 찾을 수 있었다. 마지막으로, 로봇의 작업시퀀스 최적화 방법을 시각적으로 검증하기 위하여 Mathworks 사의 Matlab과 Coppelia Robotics 사의 V-REP (virtual robot experimentation platform)를 사용한 시뮬레이션을 수행하였다.