• 한국정보통신학회논문지
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• 제10권12호
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• pp.2283-2288
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• 2006

최근 생명 정보학 기술의 발달로 마이크로 단위의 실험조작이 가능해짐에 따라 하나의 chip상에서 전체 genome의 expression pattern을 관찰할 수 있게 되었고, 동시에 수 만개의 유전자들 간의 상호작용도 연구 가능하게 되었다. 이처럼 DNA 마이크로어레이 기술은 복잡한 생물체를 이해하는 새로운 방향을 제시해주게 되었다. 따라서 이러한 기술을 통해 얻어진 대량의 유전자 정보들을 효과적으로 분석하는 방법이 시급하다. 본 논문에서는 실험용 데이터로 하버드대학교의 바이오인포메틱스 코어 그룹의 샘플데이터 이용하여 마이크로어레이 실험에서 다양한 원인에 의해 발생하는 잡음(noise)을 줄이거나 제거하는 과정인 표준화 과정을 거쳐 특징 추출방법인 베이지안 알고리즘 ASA(Adaptive Simulated Annealing) 방법을 이용하여 데이터를 2개의 클래스로 나누고, 정확도를 평가하는 시스템을 설계하고 구현하였다. Lowess 표준화 후 98.23%의 정확도를 보였다.

• 한국항공우주학회지
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• 제34권1호
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• pp.42-48
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• 2006

연구에서는 다분야 통합 최적설계를 위한 시스템 근사화 기법으로 RRSET (Repetitive Response Surface Enhancement Technique)를 제안하였다. 2차 다항식만으로는 어려운 반응면의 표현을 위해 RRSET는 설계공간을 변형할 수 있는 스트레칭 함수를 도입하고 전역 최적화 알고리즘인 담금질 모사기법을 이용하여 반응면을 최적화 하였다. 도출된 최적점은 반복적으로 다음 순기의 반응면의 구성에 이용하여 반응면의 신뢰도를 더욱 높일 수 있었다. 제안된 기법을 수치예제 등에 적용한 결과, 비교적 적은 수의 실험 회수로 비선형적인 반응면을 잘 표현하고 최적 설계점을 도출해낼 수 있음이 확인되었다. 정밀한 근사화 기법의 중요성이 강화되고 있는 현재, 본 연구에서 제시된 근사화 기법은 차후의 연구에서 다분야 통합 최적화 기법에의 적용이 가능하리라 사료된다.

• Geomechanics and Engineering
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• 제2권4호
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• pp.253-280
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• 2010

A new prediction model is derived for the uplift capacity of suction caissons using a hybrid method coupling genetic programming (GP) and simulated annealing (SA), called GP/SA. The predictor variables included in the analysis are the aspect ratio of caisson, shear strength of clayey soil, load point of application, load inclination angle, soil permeability, and loading rate. The proposed model is developed based on well established and widely dispersed experimental results gathered from the literature. To verify the applicability of the proposed model, it is employed to estimate the uplift capacity of parts of the test results that are not included in the modeling process. Traditional GP and multiple regression analyses are performed to benchmark the derived model. The external validation of the GP/SA and GP models was further verified using several statistical criteria recommended by researchers. Contributions of the parameters affecting the uplift capacity are evaluated through a sensitivity analysis. A subsequent parametric analysis is carried out and the obtained trends are confirmed with some previous studies. Based on the results, the GP/SA-based solution is effectively capable of estimating the horizontal, vertical and inclined uplift capacity of suction caissons. Furthermore, the GP/SA model provides a better prediction performance than the GP, regression and different models found in the literature. The proposed simplified formulation can reliably be employed for the pre-design of suction caissons. It may be also used as a quick check on solutions developed by more time consuming and in-depth deterministic analyses.

• 산업경영시스템학회지
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• 제46권3호
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• pp.139-147
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• 2023

The metal bush assembling process is a process of inserting and compressing a metal bush that serves to reduce the occurrence of noise and stable compression in the rotating section. In the metal bush assembly process, the head diameter defect and placement defect of the metal bush occur due to metal bush omission, non-pressing, and poor press-fitting. Among these causes of defects, it is intended to prevent defects due to omission of the metal bush by using signals from sensors attached to the facility. In particular, a metal bush omission is predicted through various data mining techniques using left load cell value, right load cell value, current, and voltage as independent variables. In the case of metal bush omission defect, it is difficult to get defect data, resulting in data imbalance. Data imbalance refers to a case where there is a large difference in the number of data belonging to each class, which can be a problem when performing classification prediction. In order to solve the problem caused by data imbalance, oversampling and composite sampling techniques were applied in this study. In addition, simulated annealing was applied for optimization of parameters related to sampling and hyper-parameters of data mining techniques used for bush omission prediction. In this study, the metal bush omission was predicted using the actual data of M manufacturing company, and the classification performance was examined. All applied techniques showed excellent results, and in particular, the proposed methods, the method of mixing Random Forest and SA, and the method of mixing MLP and SA, showed better results.

• 전자공학회논문지A
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• 제32A권1호
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• pp.206-221
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• 1995

Metal-Metal Matrix(M$^{3}$) layout is a recently proposed layout style which uses minimum amount of poly wires for high speed operation. In this paper we propose a method of generating functional modules in M$^{3}$ layout style. In the proposed method the transistors and the input/output lines of the given circuit are first placed in M$^{3}$ layout style and then they are interconnected using two metal layers. We develop a new placement method by simulated annealing, and we modify the well known channel routing method for the interconnections. When we applied our method to several logic circuits, the area of the generated layout is smaller than the ones by the previously known method. Our results also compares favorably to the other layout styles like gate matrix layout.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.1267-1270
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• 1997

An assembly sequence is considered to be optimal when it minimizes assembly cost while satisfying assembly constraints. to generate such sequences for robotic assembly, this paper proposes a method using a genetic algorithm (GA). This method denotes an assembly sequence as an individual, which is assigned a fitness related to the assembly cost. Then, a population consisting of a number of individuals evolves to the next generation through genetic operations of crossover and mutation based upon the fitness of the individuals. The population continues to repetitively evolve, and finally the fittest individual and its corresponding assembly sequence is found. Through case study for an electrical relay, the effectiveness of the proposed method is demonstrated. Also, the performance is evaluated by-comparing with those of previously presented approaches such as a neural-netowork-based method and a simulated annealing method.

• 산업경영시스템학회지
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• 제23권61호
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• pp.105-114
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• 2000

Job Shop Problem which consists of the m different machines and n jobs is a NP-hard problem of the combinatorial optimization. Each job consists of a chain of operations, each of which needs to be processed during an uninterrupted time period of a given length on a given machine. Each machine can process at most one operation at a time. The purpose of this paper is to develop the heuristic method to solve large scale scheduling problem using Constraint Satisfaction Problem method and Simulated Annealing. The proposed heuristic method consists of the search algorithm and optimization algorithm. The search algorithm is to find the solution in the solution space using CSP concept such as backtracking and domain reduction. The optimization algorithm is to search the optimal solution using SA. This method is applied to MT06, MT10 and MT20 Job Shop Problem, and compared with other heuristic method.

• International Journal of Control, Automation, and Systems
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• 제4권5호
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• pp.575-591
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• 2006

In a previous research, we presented the Grid Method and confirmed it as a systematic and efficient problem formulation method for the task-oriented design of robot kinematics. However, our previous research was limited in two ways. First, it gave only a local optimum due to its use of a local optimization technique. Second, it used constant weights for a cost function chosen by the manual weights tuning algorithm, thereby showing low efficiency in finding an optimal solution. To overcome these two limitations, therefore, this paper presents a global optimization technique and an adaptive weights tuning algorithm to solve a formulated problem using the Grid Method. The efficiencies of the proposed algorithms have been confirmed through the kinematic design examples of various robot manipulators.

• International Journal of Aeronautical and Space Sciences
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• 제15권1호
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• pp.63-73
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• 2014

For robust stability analysis of parameters uncertainty missiles, the traditional frequency domain method can only analyze each respective channel at several interval points within uncertain parameter space. Discontinuous calculation and couplings between channels will lead to inaccurate analysis results. A method based on the ${\nu}$-gap metric is proposed, which is able to comprehensively evaluate the robust stability of missiles with uncertain parameters; and then a genetic-simulated annealing hybrid optimization algorithm, which has global and local searching ability, is used to search for a parameters combination that leads to the worst stability within the space of uncertain parameters. Finally, the proposed method is used to analyze the robust stability of a re-entry missile with uncertain parameters; the results verify the feasibility and accuracy of the method.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 C
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• pp.1195-1198
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• 1999

This paper presents optimal placement of minimal set of phasor measurement units(PMU's) and observability of measurement system with PMU. By using the incidence matrix symbolic method which directly assigns measurement and pseudo-measurement to incidence matrix, it is much simpler and easier to analyze observability. The optimal PMU set is found through the simulated-annealing(SA) and the direct combinational method. The cooling schedule parameter which is suitable to the property of problem to solve is specified and optimal placement is proven by presented direct combinational method. Search spaces are limited within reasonable feasible solution region to reduce a unnecessary one in the SA implementation based on global search. The proposed method presents to save CPU time and estimate state vectors based on optimal PMU set.