• Proceedings of the KIEE Conference
• /
• 2000.07d
• /
• pp.2995-2997
• /
• 2000

원자력발전소의 열출력 계산 결과에 가장 큰 영향을 미치는 변수는 주급수 유량이며, 측정방식상의 특성(Venturi Fouling)으로 인해 계산시 과다하게 반영될 소지가 있다 본 연구에서는 이 측정 오차를 최소화하기 위하여 뉴로-퍼지 논리를 이용하여 주급수 유량을 예측한 후 그 결과를 통해 열출력을 재평가하고자 하였다. 즉, 뉴로-퍼지로의 입력 변수(증기발생기 압력 및 수위. 터빈 충동실 압력)들은 모의훈련으로 출력을 상승시키면서 취득한 후 Wavelet Denoising 기법을 이용하여 노이즈를 제거시키고. 뉴로-퍼지 추론 계통의 파라메타들을 최적화시키기 위하여 유전적 알고리듬 및 최소자승법에 의한 Hybrid Learning Rule을 이용하여 학습시켰다. 시뮬레이션을 수행한 결과, 주급수 유량이 양호하게 예측되어, 이 결과를 토대로 열출력을 평가하는데 본 알고리듬의 적용이 성공적임을 입증하였다.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
• /
• 2005.07a
• /
• pp.300-302
• /
• 2005

X7R 특성을 가지는 적층 칩 캐패시터 제작시 그린시트의 두께에 따라 유전율, 절연파괴 전압. C-V특성이 변화되며 특히, 그린시트의 두께는 C-V특성에 중요한 인자임을 확인할 수 있었다. 적층수 증가에 따른 특성 검토시 80층 이상부터 재료의 물성 변화로 예상되는 특성을 볼 수 있으며 특히 전류-전압특성에서의 층수 증가에 따른 영향을 몰 때 유전체 조성 및 공정조건을 최적화하여야 함을 확인하였다.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.38 no.1
• /
• pp.48-57
• /
• 2010

A comparison of two kinds of scheduling optimization algorithms is presented in this paper. As satellite control and operation techniques have been developed, satellite missions became more complicated and overall quantity of missions also increased. These changes require more specific consideration and a huge amount of computation for the satellite mission scheduling. Therefore, it is a good strategy to make a scheduling optimization algorithm for the efficient satellite mission scheduling operation. In this paper, two kinds of scheduling optimization algorithms are designed with tabu-search algorithm and genetic algorithm respectively. These algorithms are applied for the same mission scenario and the results of each algorithm are compared and analyzed.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.15 no.6
• /
• pp.661-667
• /
• 2005

In this paper, we propose a new hybrid genetic algorithm for sequential ordering problem (SOP). In the proposed genetic algorithm, the Voronoi quantized crossover (VQX) is used as a crossover operator and the path-preserving 3-Opt is used as a local search heuristic. VQX is a crossotver operator that uses the epistasis information of given problem instance. Since it is a crossover proposed originally for the traveling salesman problem (TSP), its application to SOP requires considerable modification. In this study, we appropriately modify VQX for SOP, and develop three algorithms, required in the modified VQX, named Feasible solution Generation Algorithm, Precedence Cycle Decomposition Algorithm, and Genic Distance Assignment Method. The results of the tests on SOP instances obtained from TSPLIB and ZIB-MP-Testdata show that the proposed genetic algorithm outperforms other genetic algorithms in stability and solution quality.

• Journal of the Institute of Electronics Engineers of Korea TC
• /
• v.45 no.2
• /
• pp.127-136
• /
• 2008

This paper proposes the efficient system for automatic design of FSS(Frequency Selective Surface) with periodic pattern and frequency characteristics specified by operator. The proposed system derives optimal design parameters through tool for analysis of FSS with arbitrary pattern, DB(Data Base) implemented from limited simulation and measurement data of FSS, and GA(Genetic Algorithm) for optimizing design parameters. FSS analysis tool consists of two analysis tools. One is the simulator for analysis of monolayer FSS that using moment method, another is the tool with approximated analysis method of FSS with dielectric layer. Given pattern configuration and characteristics specified by operators, the DB system searches the best matching FSS, and provides initial genes to GA from the searched parameters, which drastically reduces running time of GA for finding the FSS design parameters. In this paper, the proposed design system is verified through simulation and measurement about FSS with various patterns.

• The Journal of the Korea Contents Association
• /
• v.20 no.12
• /
• pp.278-286
• /
• 2020

The prisoner's dilemma game which is a representative example of game theory is being studied with interest by many economists, social scientists, and computer scientists. In recent years, many researches on computational approaches that apply evolutionary computation techniques such as genetic algorithms and particle swarm optimization have been actively conducted to analyze prisoner dilemma games. In this study, we intend to evolve a strategy for a iterated prisoner dilemma game participating two or more players using three different binary particle swarm optimization techniques. As a result of experimenting by applying three kinds of binary particle swarm optimization to the iterated prisoner's dilemma game, it was confirmed that mutual cooperation can be established even among selfish participants to maximize their own gains. However, it was also confirmed that the more participants, the more difficult to establish a mutual cooperation relationship.

• Korea Real Estate Review
• /
• v.28 no.1
• /
• pp.23-37
• /
• 2018

The genetic algorithm (GA) and branch and bound (B&B) methods are the useful methods of searching the optimal project combination (combinatorial optimization) to maximize the project effect considering the budget constraint and the balance of regional development with regard to the Urban Regeneration New Deal policy, the core real estate policy of the Moon Jae-in government. The Ministry of Land, Infrastructure, and Transport (MOLIT) will choose 13 central-city-area-type projects, 2 economic-base-type projects, and 10 public-company-proposal-type projects among the numerous projects from 16 local governments while each government can apply only 4 projects, respectively, for the 2017 Urban Regeneration New Deal project. If MOLIT selects only those projects with a project effect maximization purpose, there will be unselected regions, which will harm the balance of regional development. For this reason, an optimization model is proposed herein, and a combinatorial optimization method using the GA and B&B methods should be sought to satisfy the various constraints with the object function. Going forward, it is expected that both these methods will present rational decision-making criteria if the central government allocates a special-purpose-limited budget to many local governments.

• The Transactions of the Korea Information Processing Society
• /
• v.6 no.4
• /
• pp.1120-1128
• /
• 1999

The model-based object recognition is defined as a graph matching process between model images and an input image. In this paper, a graph matching problem is modeled as a n optimization problems and a genetic algorithm is proposed to solve the problems. For this work, fitness function, data structured and genetic operators are developed The simulation results are shown that the proposed genetic algorithm can match feature points between model image and input image for recognition of partially occluded two-dimensional objects. The performance fo the proposed technique is compare with that of a neural network technique.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 2019.11a
• /
• pp.185-186
• /
• 2019

카캐리어선에 화물을 선적하기 위해서는 각 화물의 선적 순서와 위치를 결정해야 하며, 이를 선적 계획이라 한다. 선적 계획은 선박의 무게 중심과 공간 손실률, 화물 재취급(re-handling) 횟수를 최소화하도록 수립된다. 최적의 선적 계획을 수립하기 위해서는 여러 후보 선적 계획들을 평가하여 적합도(fitness)가 가장 높은 것을 탐색하여야 한다. 하지만 화물 종류의 수와 도착지의 수가 증가할수록 후보 선적 계획의 수가 증가하게 되어, 탐색 시간과 계산 비용이 커지는 문제가 발생한다. 본 논문에서는 탐색 공간이 매우 큰 환경에서 최적의 선적 계획을 효율적으로 탐색하기 위해 유전 알고리즘(genetic algorithm)을 사용한다. 또한, 선박의 무게 중심과 공간 손실률, 화물 재취급 횟수로 목적 함수(objective function)를 구성하여 최적 선적 계획을 탐색한다. 실험 결과, 휴리스틱(heuristic) 방식보다 공간 손실률과 재취급 횟수가 향상되었으며, 특히 재취급 횟수는 70% 감소하였다.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.8 no.6
• /
• pp.78-84
• /
• 1998

In this paper, a fuzzy traffic controller using genetic algorithms is presented. Conventional fuzzy traffic controllers use membership functions generated by humans. However, this approach does not guarantee the optimal solution to design the fuzzy controller. Genetic algorithm is a good problem solving method requiring domain-specific knowledge that is often heuristic. To find fuzzy membership functions showing good performance, a fitness function must be defined. However it's not easy in traffic control to define such a function as a numeric expression. Thus, we use simulation approach, namely, the fitness value of a solution is determined by use of a performance measure that is obtained by traffic simulator. The proposed method outperforms the conventional fuzzy controllers.