• 한국전자파학회논문지
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• 제26권8호
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• pp.758-764
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• 2015

본 논문에서는 2차 함수 곡선의 회전체형 도체 곡면 위에 있는 컨포멀 배열 안테나의 패턴을 EAGA(Enhanced Adaptive Genetic Algorithm)를 이용하여 합성한 내용을 보이고 있다. 다양한 곡면의 회전체 도체를 고찰하기 위해 2차 함수의 계수를 바꿔 세 가지 유형의 회전체형 곡면을 형성시켰고, 각 유형의 컨포멀 배열 안테나 패턴을 합성하였다. 패턴 합성에 소요되는 시간의 단축을 위해 3차원 컨포멀 배열 안테나의 능동 소자 패턴 대신에 2차원 평면 배열 안테나의 능동 소자 패턴을 구한 후, 이를 오일러 변환(Euler transform)시켜 이용하였다. EAGA를 이용하여 합성된 패턴의 검증을 위해 MWS(Microwave Studio)를 통해 구한 패턴과 비교하였으며, 두 패턴은 전반적으로 유사하였다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제1권1호
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• pp.87-94
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• 2001

Silicon nitride films grown by plasma-enhanced chemical vapor deposition (PECVD) are useful for a variety of applications, including anti-reflecting coatings in solar cells, passivation layers, dielectric layers in metal/insulator structures, and diffusion masks. PECVD systems are controlled by many operating variables, including RF power, pressure, gas flow rate, reactant composition, and substrate temperature. The wide variety of processing conditions, as well as the complex nature of particle dynamics within a plasma, makes tailoring SiN film properties very challenging, since it is difficult to determine the exact relationship between desired film properties and controllable deposition conditions. In this study, SiN PECVD modeling using optimized neural networks has been investigated. The deposition of SiN was characterized via a central composite experimental design, and data from this experiment was used to train and optimize feed-forward neural networks using the back-propagation algorithm. From these neural process models, the effect of deposition conditions on film properties has been studied. A recipe synthesis (optimization) procedure was then performed using the optimized neural network models to generate the necessary deposition conditions to obtain several novel film qualities including high charge density and long lifetime. This optimization procedure utilized genetic algorithms, hybrid combinations of genetic algorithm and Powells algorithm, and hybrid combinations of genetic algorithm and simplex algorithm. Recipes predicted by these techniques were verified by experiment, and the performance of each optimization method are compared. It was found that the hybrid combinations of genetic algorithm and simplex algorithm generated recipes produced films of superior quality.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2003년도 춘계학술대회논문집
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• pp.514-519
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• 2003

The aircraft engine is usually supported by rolling element bearings and has a small damping rate, which is vol y sensitive to external force. The high-performance requirement of the rotors leads to complex assembly designs and are more flexible. Squeeze film dampers (SFDs) are introduced to provide damping while crossing the critical speeds and stability to the rotor s :stem. Hence, the focus of the present investigation is on the decision of an optimal size of the flexible rotor system supported by the squeeze film dampers to minimize the maximum transmitted load and unbalance response over a range operating speeds. The enhanced genetic algorithm (EGA), which was developed by authors, is used in the optimization process. This algorithm is based on the synthesis of a modified genetic algorithm and simplex method. The results show significant benefits in using EGA when compared with nonlinear programming (NLP).

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 C
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• pp.1123-1125
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• 1999

This paper proposes an optimization technique using Genetic Algorithm(GA) for service restoration in the distribution system. Restoration planning problem can be treated as a combinatorial optimization problem. So GA is appropriate to solve the service restoration problem in the distribution network. But searching capabilities of the GA can be enhanced by developing relevant repairing operation and modifying GA operations. In this paper, we aimed at finding appropriate open sectionalizing switch position for the restoration of distribution networks after disturbances using enhanced GA with repairing operation and modified mutation. Simulation results show that proposed method found the open sectionalizing switches with less out of service area and minimize transmission line losses and voltage drop.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 심포지엄 논문집 정보 및 제어부문
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• pp.108-110
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• 2006

A prediction model of charge concentration of silicon nitride (SiN) thin films was constructed by using neural network and genetic algorithm. SIN films were deposited by plasma enhanced chemical vapor deposition and the deposition process was characterized by means of $2^{6-1}$ fractional factorial experiment. Effect of five training factors on the model prediction performance was optimized by using genetic algorithm. This was examined as a function of the learring rate. The root mean squared error of optimized model was 0.975, which is much smaller than statistical regression model by about 45%. The constructed model can facilitate a Qualitative analysis of parameter effects on the charge concentration.

• 한국생산제조학회지
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• 제20권2호
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• pp.174-179
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• 2011

This paper focuses on the optimal step-over value for magnetic tool path. Since magnetic flux density is changed according to distance from center of magnetic tool. Enhanced surface roughness is also different according to change of radius. Therefore, to get a identical surface roughness on workpiece, it is necessary to find optimal tool path including step-over. In this study, response surface models for surface roughness according to change of radiuses were developed, and then optimal enhanced surface roughness for each radius was selected using genetic algorithm and simulated annealing to investigate relation between radius and surface roughness. As a result, it found that step-over value of 6.6mm is suitable for MAP of magnesium alloy.

• 산업경영시스템학회지
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• 제39권2호
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• pp.88-102
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• 2016

This paper considers the allocation and engagement scheduling problem of interceptor missiles, and the problem was formulated by using MIP (mixed integer programming) in the previous research. The objective of the model is the maximization of total intercept altitude instead of the more conventional objective such as the minimization of surviving target value. The concept of the time window was used to model the engagement situation and a continuous time is assumed for flying times of the both missiles. The MIP formulation of the problem is very complex due to the complexity of the real problem itself. Hence, the finding of an efficient optimal solution procedure seems to be difficult. In this paper, an efficient genetic algorithm is developed by improving a general genetic algorithm. The improvement is achieved by carefully analyzing the structure of the formulation. Specifically, the new algorithm includes an enhanced repair process and a crossover operation which utilizes the idea of the PSO (particle swarm optimization). Then, the algorithm is throughly tested on 50 randomly generated engagement scenarios, and its performance is compared with that of a commercial package and a more general genetic algorithm, respectively. The results indicate that the new algorithm consistently performs better than a general genetic algorithm. Also, the new algorithm generates much better results than those by the commercial package on several test cases when the execution time of the commercial package is limited to 8,000 seconds, which is about two hours and 13 minutes. Moreover, it obtains a solution within 0.13~33.34 seconds depending on the size of scenarios.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2012년도 추계학술대회 논문집
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• pp.265-266
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• 2012

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2003년도 추계학술대회논문집
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• pp.109-115
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• 2003

This study intends to provide an optimum design of flexible matrix composite driveshafts using a genetic algorithm. An objective function is defined as a combination of shaft flexibility, whirling stability and torsional buckling and the design variables are selected as ply angles and the shaft thickness. Results show that the genetic algorithm can successfully find an optimum solution at which the overall performance of the FMC shafts is significantly enhanced

• 한국전자파학회논문지
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• 제26권12호
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• pp.1031-1043
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• 2015

본 논문에서는 Enhanced Adaptive Genetic Algorithm(EAGA)를 이용하여 두 가지 종류의 컨포멀 배열 안테나의 패턴 합성 과정을 기술하였다. 한 종류는 2차 함수 형태의 실린더 도체 위에 배열된 $1{\times}16$ 배열 안테나이고, 다른 종류는 2차 함수 곡선의 회전체 곡면 도체 위에 배열된 18개 소자 배열 안테나이다. 패턴 합성시 각 소자의 능동 소자 패턴을 이용하였고, 특히 회전체형 배열 안테나의 경우, 합성 시간을 크게 줄이기 위해 동심원형 평면 배열 안테나의 능동 소자 패턴을 구한 뒤 이를 변환하여 사용하였다. 제안한 합성 기법의 타당성을 검증하기 위해 MATLAB 내에서 합성된 배열 안테나의 패턴과 MWS(Microwave Studio) 내에서 구현된 배열 안테나의 패턴을 비교하였으며, 또한, $1{\times}16$ 배열 안테나의 경우는 측정 패턴과 합성 패턴도 비교하였다.