• 한국정밀공학회지
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• 제20권11호
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• pp.117-126
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• 2003

GA(genetic algorithm) has a powerful searching ability and is comparatively easy to use and to apply as well. By that reason, GA is in the spotlight these days as an optimization skill for mechanical systems.$^1$However, GA has a low efficiency caused by a huge amount of repetitive computation and an inefficiency that GA meanders near the optimum. It also can be shown a phenomenon such as genetic drifting which converges to a wrong solution.$^{8}$ These defects are the reasons why GA is not widdy applied to real world problems. However, the low efficiency problem and the meandering problem of GA can be overcomed by introducing parallel computation$^{7}$ and gray code$^4$, respectively. Standard GA(SGA)$^{9}$ works fine on small to medium scale problems. However, SGA done not work well for large-scale problems. Large-scale problems with more than 500-bit of sere's have never been tested and published in papers. In the result of using the SGA, the powerful searching ability of SGA doesn't have no effect on optimizing the problem that has 96 design valuables and 1536 bits of gene's length. So it converges to a solution which is not considered as a global optimum. Therefore, this study proposes ExpGA(experience GA) which is a new genetic algorithm made by applying a new probability parameter called by the experience value. Furthermore, this study finds the solution throughout the whole field searching, with applying ExpGA which is a optimization technique for the structure having genetic drifting by the standard GA and not making a optimization close to the best fitted value. In addition to them, this study also makes a research about the possibility of GA as a optimization technique of large-scale design variable problems.

• 제어로봇시스템학회논문지
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• 제6권2호
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• pp.207-216
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• 2000

This paper introduces an effective system design method to develop a customer oriented product using a design optimization process and to select a set of critical design paramenters,. The process results in the development of a successful product satisfying customer needs and reducing development risk. The proposed scheme adopted a five step QFD(Quality Function Deployment) in order to extract design parameters from customer needs and evaluated their priority using risk factors for extracted design parameters. In this process we determine critical design parameters and allocate them to subsystem designers. Subsequently design engineers develop and test the product based on these parameters. These design parameters capture the characteristics of customer needs in terms of performance cost and schedule in the process of QFD, The subsequent risk management task ensures the minimum risk approach in the presence of design parameter uncertainty. An application of this approach was demonstrated in the development of weld quality monitoring system. Dominant design parameters affect linearity characteristics of weld defect feature vectors. Therefore it simplifies the algorithm for adopting pattern classification of feature vectors and improves the accuracy of recognition rate of weld defect and the real time response of the defect detection in the performance. Additionally the development cost decreases by using DSP board for low speed because of reducing CPU's load adopting algorithm in classifying weld defects. It also reduces the cost by using the single sensor to measure weld defects. Furthermore the synergy effect derived from the critical design parameters improves the detection rate of weld defects by 15% when compared with the implementation using the non-critical design parameters. It also result in 30% saving in development cost./ The overall results are close to 95% customer level showing the effectiveness of the proposed development approach.

• Journal of Electrical Engineering and Technology
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• 제8권3호
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• pp.402-410
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• 2013

In this paper, a novel and robust Power System Stabilizer (PSS) is proposed as an effective approach to improve stability in electric power systems. The dynamic performance of proposed PSS has been thoroughly compared with Conventional PSS (CPSS). Both the Real Coded Genetic Algorithm (RCGA) and Particle Swarm Optimization (PSO) techniques are applied to optimum tune the parameter of both the proposed PSS and CPSS in order to damp-out power system oscillations. Due to the high sufficiency of both the RCGA and PSO techniques to solve the very non-linear objective, they have been employed for solution of the optimization problem. In order to verify the dynamic performance of these devices, different conditions of disturbance are taken into account in Single Machine Infinite Bus (SMIB) power system. Moreover, to ensure the robustness of proposed PSS in damping the power system multi-mode oscillations, a Multi Machine (MM) power system under various disturbances are considered as a test system. The results of nonlinear simulation strongly suggest that the proposed PSS significantly enhances the power system dynamic stability in both of the SMIB and MM power system as compared to CPSS.

• 경영과학
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• 제29권3호
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• pp.81-89
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• 2012

Since introduced by Vining and Myers in 1990, the concept of dual response approach based on response surface methodology has widely been investigated and adopted for the purpose of robust design. Separately estimating mean and variance responses, dual response approach may take advantages of optimization modeling for finding optimum settings of input factors. Explicitly assuming functional relationship between responses and input factors, however, it may not work well enough especially when the behavior of responses are poorly represented. A sufficient number of experimentations are required to improve the precision of estimations. This study proposes an alternative to dual response approach in which additional experiments are not required. An artificial neural network has been applied to model relationships between responses and input factors. Mean and variance responses correspond to output nodes while input factors are used for input nodes. Training, validating, and testing a neural network with empirical process data, an artificial data based on the neural network may be generated and used to estimate response functions without performing real experimentations. A drug formulation example from pharmaceutical industry has been investigated to demonstrate the procedures and applicability of the proposed approach.

• 제어로봇시스템학회논문지
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• 제22권5호
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• pp.326-331
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• 2016

This paper presents a novel robust adaptive AC8B exciter system against synchronous generators for ships. A PID (proportional integral derivative) control framework, which is a part of the AC8B exciter system, is simply composed of nominal and auxiliary control configurations. For selecting these proper parameter values, the former is conventionally chosen based on the experience and knowledge of experts, and the latter is optimally estimated via a neural networks optimization procedure. Additionally, we propose an online parameter learning-based auxiliary control to practically cope with deterioration of control performance owing to uncertainty in electric generator systems. Such a control mechanism ensures the robustness and adaptability of an AC8B exciter to enhance control performance in real-time implementation. We carried out simulation experiments to test the reliability of the proposed robust adaptive AC8B exciter system and prove its superiority through a comparative study in which a conventional PID control-based AC8B exciter system is similarly applied to our simulation experiments under the same simulation scenarios.

• 동력기계공학회지
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• 제6권2호
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• pp.40-47
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• 2002

In this paper, friction welding optimization for 1Cr0.5Mo-STS304 (${\phi}14\;mm$), AE applications for the weld quality evaluation and the applications of various life prediction methods such as LMP (Larson-Miller Parameter) and ISM (initial strain method) were investigated : The creep behaviors of those steels and the friction welded joints under static load were examined by ISM combined with LMP at 400, 500, 550 and $600^{\circ}C$, and the relationship between these two kinds of phenomena was studied. The real-time predicting equations of elevated-temperature creep life (rupture time) under any creep stress at any elevated-temperature could be developed by LMP and LMP-ISM. It was confirmed that the life prediction equations by LMP and LMP-ISM are effective only up to 102 h and can not be used for long times of 103-106 h, but by ISM it can be used for long times creep prediction of more than 104 h with most reliability.

• 한국해양공학회:학술대회논문집
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• 한국해양공학회 2001년도 춘계학술대회 논문집
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• pp.307-313
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• 2001

In this Paper, friction welding optimization for 1Cr0.5Mo-STS304($\Phi$14mm), AE applications for the weld quality evaluation and the applications of various life prediction methods such as LMP(Larson-Miller Parameter) and ISM(initial strain method) were investigated : the creep behaviors of those steels and the friction welded joints under static load were examined by ISM combined with LMP at 400, 500, 550 and $600^{\circ}C$, and the relationship between these two kinds of phenomena was studied. The real-time predicting equations of elevated-temperature creep life(fracture time) under any creep stress at any elevated- temperature could be developed by LMP and LMP-ISM, It was confirmed that the life prediction equations by LMP and LMP-ISM are effective only up to 10$^2$hrs and can not be used for long times of 10$^3$-10$^{6}$ hrs, but by ISM it can be used for long times creep prediction of more than 10$^4$hrs with most reliability.

• 조명전기설비학회논문지
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• 제22권1호
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• pp.40-49
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• 2008

IPMSM 드라이브는 하중 비에 대한 출력이 우수하여 전기자동차 등 응용분야에서 관심이 증가하고 있다. 이러한 응용분야에서 최대 효율을 얻기 위하여 본 논문은 신경회로망 제어기법을 제시한다. 동손과 철손으로 구성된 제어가능한 전기적 손실은 신경회로망의 오류 역전파 알고리즘(EBPA)를 이용하여 최소화시킬 수 있다. 손실의 최소화는 IPMSM 드라이브의 효율 최적화 제어를 가능하게 한다. 본 논문에서는 신경회로망의 EBPA를 이용하여 전동기 구동에 대하여 d축 인덕턴스, 전기자 저항, 역기전력 상수 변화와 같은 파라미터 변동을 시간으로 계산하여 고성능 및 강인성 제어를 제시한다. 제시한 알고리즘은 IPMSM 드라이브 시스템에 적용하고 효율최적화 제어에 의해 제어된 동작특성을 분석하여 논문의 타당성을 입증한다.

• 한국시뮬레이션학회논문지
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• 제26권3호
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• pp.55-63
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• 2017

곡사포의 사격오차는 탄착의 분산도와 탄착중심오차(MPI)를 포괄하는 용어로, 본 연구에서는 사격시험을 수행하지 않고 정량적 분석을 통해 사격오차를 예측하는 기법에 대해 논하고자 한다. 기존에도 곡사포의 사격오차를 예측하기 위한 분석기법은 있었지만, 오차에 관여하는 영향요소들에 대한 정보가 부족하여 활용이 제한되었다. 본 연구에서는 이런 문제를 해결하기 위해 누적된 시험이 수행된 기존 무기체계 시험결과를 활용하여, 오차의 원인이 되는 각 요소 값들을 역으로 산출하는 방식을 제안한다. 이 과정에서 항공공학 분야에서 흔히 사용되는 최적화 알고리즘을 이용한 입력계수 추출 방식을 도입하였다. 최적화 알고리즘으로는 CMA-ES라는 진화적 기법을 소개하며, 적용 결과에 대하여 해설하였다. 이런 과정을 통해 얻은 사격오차요인 값은 향후 신규 무기체계 개발에 있어 성능요구사항 산출에 사용될 수 있으며, 야전에서의 곡사포 정확도 향상에도 기여할 것으로 보인다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2007년도 춘계학술대회 학술발표 논문집 제17권 제1호
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• pp.341-344
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• 2007

본 논문에서는 계층적 공정 경쟁 개념을 병렬 유전자 알고리즘에 적용하여 계층적 공정 경쟁 기반 병렬유전자 알고리즘 (Hierarchical Fair Competition Genetic Algorithm: HFCGA)을 구현하였을 뿐만 아니라 실수코딩 유전자 알고리즘(Real-Coded Genetic Algorithm: RCGA)에서 좋은 성능을 갖는 산술교배(Arithmetic crossover), 수정된 단순교배(modified simple crossover) 그리고 UNDX(unimodal normal distribution crossover)등의 다양한 교배연산자들을 적용, 분석함으로써 개선된 병렬 유전자 알고리즘을 제안하였다. UNDX연산자는 다수의 부모(multiple parents)를 이용하여 부모들의 기하학적 중심(geometric center)에 근접하게 정규분포를 이루며 생성된다. 본 논문은 UNDX를 이용한 HFCGA모델을 구현하고 함수파라미터 최적화 문제에 많이 쓰이는 함수들에 적용시킴으로써 그 성능의 우수성을 증명 한다.