• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2004년도 춘계학술대회논문집
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• pp.410-415
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• 2004

The oscillation of the fluid caused by external forces is call ed sloshing, which occurs in moving vehicles with contained liquid masses, such as trucks, railroad cars, aircraft, and liquid rocket. This sloshing effect could be a severe problem in vehicle stability and control. In this study, the optimization design technique for reduction of the sloshing using evolutionary method is suggested. Two evolutionary methods are employed, respectively the artificial neural network(ANN) and genetic algorithm. An artificial neural network is used for the analysis of sloshing and genetic algorithm is adopted as optimization algorithm. As a result of optimization design, the optimized size and location of the baffle is presented

• 한국지능시스템학회논문지
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• 제10권4호
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• pp.338-342
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• 2000

In order to approximate a nonlinear function, wacelet neural networks combining wacelet theory and neural networks have been proposed as an alternative to conventional multi-layered neural networks. wacelet neural networks provide better approximating performance than conventional neural networks. In this paper, an effective method to construct an optimal wavelet neural network is proposed using genetic alogorithm. Genetic Algorithm is used to determine dilationa and translations of wavelet basic functions of wavelet neural networks. Then, these determined dilations dilations and translations, wavelet neural networks are funther trained by back propagation learning algorithm. The effectiveness of the final network is verified thrifigh the approximation result of a nonlinear function and comparison with conventional neural networks.

• 대한전기학회논문지:시스템및제어부문D
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• 제53권7호
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• pp.531-535
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• 2004

A new method is presented to construct a classifier. This was accomplished by combining a generalized regression neural network (GRNN) and a genetic algorithm (GA). The classifier constructed in this way is referred to as a GA-GRNN. The GA played a role of controlling training factors simultaneously. The GA-GRNN was applied to classify 4 different Promoter sequences. The training and test data were composed of 115 and 58 sequence patterns, respectively. The classifier performance was investigated in terms of the classification sensitivity and prediction accuracy. Compared to conventional GRNN, GA-GRNN significantly improved the total classification sensitivity as well as the total prediction accuracy. As a result, the proposed GA-GRNN demonstrated improved classification sensitivity and prediction accuracy over the convention GRNN.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2004년도 ICEIC The International Conference on Electronics Informations and Communications
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• pp.10-14
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• 2004

When providing flexible data transmission for future CDMA(Code Division Multiple Access) cellular networks, problems arise in two aspects: transmission rate. This paper has proposed an approach to maximize the cellular network capacity by combining the genetic transmission rate allocation and a rapid power control algorithm. We present a genetic chromosome representation to express call drop numbers and transmission rate to control mobile's transmission power levels while handling their flexible transmission rates. We suggest a rapid power control algorithm, which is based on optimal control theory and Steffenson acceleration technique comparing with the existing algorithms. Computer simulation results showed effectiveness and efficiency of the proposed algorithm Conclusively, our proposed scheme showed high potential for increasing the cellular network capacity and it can be the fundamental basis of future research.

• Industrial Engineering and Management Systems
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• 제8권1호
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• pp.14-21
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• 2009

We formulate a mathematical model of remanufacturing system as multi-stage reverse Logistics Network Problem (mrLNP) with minimizing the total costs for reverse logistics shipping cost and inventory holding cost at disassembly centers and processing centers over finite planning horizons. For solving this problem, in the 1st and the 2nd stages, we propose a Genetic Algorithm (GA) with priority-based encoding method combined with a new crossover operator called as Weight Mapping Crossover (WMX). A heuristic approach is applied in the 3rd stage where parts are transported from some processing centers to one manufacturer. Computer simulations show the effectiveness and efficiency of our approach. In numerical experiments, the results of the proposed method are better than pnGA (Prufer number-based GA).

• 대한전기학회논문지:시스템및제어부문D
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• 제49권3호
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• pp.157-163
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• 2000

In this paper, an Evolving Neural Network Controller(ENNC) which its structure and its connection weights are optimized simultaneously by Real Variable Elitist Genetic Algoithm(RVEGA) was presented for stabilization of an Inverter Pendulum(IP) system with nonlinearity. This proposed ENNC was described by a simple genetic chromosome. And the deletion of neuron, the determinations of input or output neuron, the deleted neuron and the activation functions types are given according to the various flag types. Therefore, the connection weights, its structure and the neuron types in the given ENNC can be optimized by the proposed evolution strategy. Through the simulations, we showed that the finally acquired optimal ENNC was successfully applied to the stabilization control of an IP system.

• 대한산업공학회지
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• 제27권2호
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• pp.140-149
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• 2001

The paper discusses the problem of finding the Origin-Destination(O-D) shortest paths in internodal transportation networks with time-schedule constraints. The shortest path problem on the internodal transportation network is concerned with finding a path with minimum distance, time, or cost from an origin to a destination using all possible transportation modalities. The time-schedule constraint requires that the departure time to travel from a transfer station to another node takes place only at one of pre-specified departure times. The scheduled departure times at the transfer station are the times when the passengers are allowed to leave the station to another node using the relative transportation modality. Therefore, the total time of a path in an internodal transportation network subject to time-schedule constraints includes traveling time and transfer waiting time. In this paper, a genetic algorithm (GA) approach is developed to deal with this problem. The effectiveness of the GA approach is evaluated using several test problems.

• 대한안전경영과학회지
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• 제22권4호
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• pp.45-52
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• 2020

Recently, a multi facility, multi product and multi period industrial problem has been widely investigated in Supply Chain Network(SCN). One of keys issues in the current SCN research area involves minimizing both production and distribution costs. This study deals with finding an optimal solution for minimizing the total cost of production and distribution problems in supply chain network. First, we presented an integrated mathematical model that satisfies the minimum cost in the supply chain. To solve the presented mathematical model, we used a genetic algorithm with an excellent searching ability for complicated solution space. To represent the given model effectively, the matrix based real-number coding schema is used. The difference rate of the objective function value for the termination condition is applied. Computational experimental results show that the real size problems we encountered can be solved within a reasonable time.

• 한국콘텐츠학회논문지
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• 제18권2호
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• pp.47-56
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• 2018

본 연구는 국내 외의 종양유전간호 관련 연구를 분석하여 연구동향을 파악하고자 하였다. 종양유전간호 관련 주제로 학술지에 게재된 논문들의 초록에서 제시한 핵심어들을 중심으로 한 텍스트 네트워크 분석을 실시하였다. 핵심어이자 중심성이 높은 주제어로 Nurse, Cancer, Genetic, Patient, Knowledge, Care, Genetic Test 등이 확인되었으며, 시기별 연구동향을 확인한 결과, 2003년 이후 Information, Care, Knowledge 등의 주제어를 포함한 연구들이 증가하였다. 간호학의 메타 패러다임으로 주제어를 분류한 결과, 건강, 간호, 인간, 환경 순으로 중심성이 높게 나타났다. 건강 영역 중 건강 위험 범주에서 Genetics, Risk, 건강 증진 범주에서 Genetic Test, Prevention 등이 가장 높은 빈도로 나타났다. 본 연구를 통해 종양유전간호 연구의 동향을 파악할 수 있으며, 유전성 암 환자들을 위한 간호 중재에 주축이 되는 간호사의 역할 및 중재프로그램 개발의 방향 설정에 활용될 수 있다는 점에서 의미가 있다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 학술대회 논문집 정보 및 제어부문
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• pp.72-75
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• 2004

In this paper, the task assignment strategy considering communication delay and the priority of distributed tasks is proposed for the real-time network system in order to maximize the utilization of the system. For the task assignment strategy, the relationship among priority of tasks in network nodes, the calculation time of each task, and the end-to-end response time including the network delay is formulated firstly. Then, the task assignment strategy using the genetic algorithm is proposed to optimize the utilization of the system considering the LCM(Least Common Multiple) period. The effectiveness of proposed strategy is proven by the simulation for estimating the performance such as the utilization and the response time of the system in case of changing the number of tasks and the number of network nodes.