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서명원(Myung-Won Suh),문영준(Young-Jun Moon),윤경한(Kyoung-han Yoon),권성진(Seong-Jin Kwon) 한국자동차공학회 2003 한국자동차공학회 춘 추계 학술대회 논문집 Vol.- No.-
In this paper, driving characteristics of ASV (Advanced Safety Vehicle) equipped with ACC (Adaptive Cruise Control) system are investigated by vehicle dynamic simulation, which is embodied in VR (Virtual Reality) environment. The vehicle model contains the nonlinear subcomponents of engine, torque converter, automatic transmission and wheels. Longitudinal controller for ACC system is established by using sliding control theory and headway time policy. VR environment enables the visualization of vehicle motions and simulation reality on various conditions. Also, we research the real-time simulation algorithms that synchronized vehicle dynamic simulation with graphic rendering.
서명원(Myung-Won Suh),권성진(Seong-Jin Kwon),심문보(Mun-Bo Shim),조기용(Ki-Yong Cho),이은표(Eun-PyoLee) 한국자동차공학회 2003 한국자동차공학회 춘 추계 학술대회 논문집 Vol.- No.-
As the bus is the most public transportation system, the existing researches have focused on the transportation capacity and the carrying power. On the order hand, the research and development on the driving stability, safety and convenience of passengers has been increased actively in these days. in this paper, we have investigated the design of door mechanism in urban buses that are useful for passengers. The mathematical models for the kinematic and dynamic analysis of door mechanism are constructed for computer simulation, which may reduce time and cost of a vehicle test and an experiment. The simulation program enables designers to analyze the behavior characteristics of door mechanism according to the design variables and the various simulation conditions.
심문보,서명원,Shim, Mun-Bo,Suh, Myung-Won 대한기계학회 2002 大韓機械學會論文集A Vol.26 No.6
In an attempt to solve multiobjective optimization problems, many traditional methods scalarize the objective vector into a single objective. In those cases, the obtained solution is highly sensitive to the weight vector used in the scalarization process and demands the user to have knowledge about the underlying problem. Moreover, in solving multiobjective problems, designers may be interested in a set of Pareto-optimal points, instead of a single point. In this paper, pareto-based Continuous Evolutionary Algorithms for Multiobjective Optimization problems having continuous search space are introduced. This algorithm is based on Continuous Evolutionary Algorithms to solve single objective optimization problems with a continuous function and continuous search space efficiently. For multiobjective optimization, a progressive reproduction operator and a niche-formation method fur fitness sharing and a storing process for elitism are implemented in the algorithm. The operator and the niche formulation allow the solution set to be distributed widely over the Pareto-optimal tradeoff surface. Finally, the validity of this method has been demonstrated through a numerical example.
서진원(JIN WON SUH),서명원(MYUNG WON SUH) 한국자동차공학회 1993 한국자동차공학회 춘 추계 학술대회 논문집 Vol.- No.-
전체 강성을 만족하여 중량을 최소로 하는 차체단면들을 결정하기위한 연구이다. 두 단계의 최적화 과정을 거치는데 처음엔 차체중량을 최소로하며 강성을 만족하기위해 차체단면이 가져야할 단면계수를 구한다. 구한 계수갑들을 만족하여 면적을 최소로하는 단면형상이 다음의 최적화 작업을 통하여얻어진다.
박재실,석창성,서명원,홍경태,Park, Jae-Sil,Seok, Chang-Sung,Suh, Myung-Won,Hong, Kyung-Tae 대한기계학회 2000 大韓機械學會論文集A Vol.24 No.10
Steam turbine rotors are the most critical and highly stressed components of a steam power plant; therefore, the life expectancy of the turbine rotor is an important consideration for the safety of a steam power plant. The objective of this paper is to develop a life estimation program for turbine rotors for all possible operating conditions. For this purpose, finite element analysis was carried out for four normal operating modes (cold, warm, hot and very hot starts) using ABAQUS codes. The results are made into databases to evaluate the life expenditure for an actual operating condition. For any other possible abnormal operating condition, the operating data are transmitted to the server (workstation) through a network to carry out finite element analysis. Damage estimation is carried out by transmitting the finite element analysis results to the personal computer, and then the life expectancy is calculated.
순차적 실험계획법과 마이크로 유전알고리즘을 이용한 최적화 알고리즘 개발
이정환(Jung Hwan Lee),서명원(Myung Won Suh) 대한기계학회 2014 大韓機械學會論文集A Vol.38 No.5
마이크로 유전알고리즘은 적은 수의 개체 사용 및 무작위 개체 구성을 통한 돌연변이 기능 대체의 특징을 갖는 진화연산을 수행하여 일반적인 유전알고리즘이 갖는 각 세대당 많은 계산 량이 요구되는 단점을 극복하고자 하였다. 이러한 마이크로 알고리즘은 특히 설계변수가 3~5 개를 갖는 문제에 효율적이라는 것이 많은 연구자들에 의하여 알려졌다. 따라서 본 연구의 목적은 순차적 실험계획법과 마이크로 유전알고리즘을 이용한 최적화 알고리즘을 개발하는 것이며, 이를 수학예제와 구조물 문제에 적용하여 실용성을 확인하고자 한다. 순차적 실험계획법은 저자들의 선행연구에서 제안되었으며, 실험계획법과 반응표면법을 이용하는 근사최적화 기법에 의한 시행착오적인 반복과정을 최소화하고자 하는 방법으로써, 행렬실험과 평균분석을 반복 적용하는 개념이다. A micro-genetic algorithm (MGA) is one of the improved forms of a genetic algorithm. It is used to reduce the number of iterations and the computing resources required by using small populations. The efficiency of MGAs has been proved through many problems, especially problems with 3?5 design variables. This study proposes an optimization algorithm based on the sequential design of experiments (SDOE) and an MGA. In a previous study, the authors used the SDOE technique to reduce trial-and-error in the conventional approximate optimization method by using the statistical design of experiments (DOE) and response surface method (RSM) systematically. The proposed algorithm has been applied to various mathematical examples and a structural problem.
센터 필라트림의 FMH 충격성능 향상을 위한 순차적 실험계획법과 인공신경망 기반의 최적설계
이정환(Jung Hwan Lee),서명원(Myung Won Suh) 대한기계학회 2013 大韓機械學會論文集A Vol.37 No.11
본 연구에서 탑승자 머리 보호를 위한 센터 필라 트림의 리브 패턴 최적설계는 두 가지 방법에 의해 수행된다. 첫째는 실험계획법과 반응표면법을 이용한 근사최적화 기법으로써, 상대적으로 큰 비중을 차지하는 해석비용 저감을 위하여 근사모델 구성에 필요한 최소한의 해석만을 수행하고 실제 최적화 과정에는 구성된 모델을 이용함으로써 근사적으로 최적 점을 찾아가는 방법이다. 하지만 이러한 방법은 시행착오적인 반복과정을 거쳐야 하는 단점이 있다. 따라서 저자들의 선행연구에서 제안한 순차적 실험계획법과 인공신경망을 이용하여 인자의 상한 또는 하한에 걸리지 않는 근사최적 해를 체계적인 반복과정을 통해 도출하고자 하며, 이를 수학적인 예제와 구조물 문제에 적용함으로써 실용성을 확인하고자 한다. The optimal rib pattern design of B-pillar trim considering occupant head protection can be determined by two methods. One is the conventional approximate optimization method that uses the statistical design of experiments (DOE) and response surface method (RSM). Generally, approximated optimum results are obtained through the iterative process by trial-and-error. The quality of results strongly depends on the factors and levels assigned by a designer. The other is a methodology derived from previous work by the authors, called the sequential design of experiments (SDOE), to reduce the trial-and-error procedure and to find an appropriate condition for using artificial neural network (ANN) systematically. An appropriate condition is determined from the iterative process based on the analysis of means. With this new technique and ANN, it is possible to find an optimum design accurately and efficiently.