• Journal of KIISE:Software and Applications
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• v.32 no.12
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• pp.1217-1228
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• 2005

Recently, since DNA computing has been widely studied for various applications, DNA sequence design which is the most basic and important step for DNA computing has been highlighted. In previous works, DNA sequence design has been formulated as a multi-objective optimization task, and solved by elitist non-dominated sorting genetic algorithm (NSGA-II). However, NSGA-II needed lots of computational time. Therefore, we use an $\varepsilon$- multiobjective evolutionarv algorithm ($\varepsilon$-MOEA) to overcome the drawbacks of NSGA-II in this paper. To compare the performance of two algorithms in detail, we apply both algorithms to the DTLZ2 benchmark function. $\varepsilon$-MOEA outperformed NSGA-II in both convergence and diversity, $70\%$ and $73\%$ respectively. Especially, $\varepsilon$-MOEA finds optimal solutions using small computational time. Based on these results, we redesign the DNA sequences generated by the previous DNA sequence design tools and the DNA sequences for the 7-travelling salesman problem (TSP). The experimental results show that $\varepsilon$-MOEA outperforms the most cases. Especially, for 7-TSP, $\varepsilon$-MOEA achieves the comparative results two tines faster while finding $22\%$ improved diversity and $92\%$ improved convergence in final solutions using the same time.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.46 no.2
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• pp.27-37
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• 2009

Sensor nodes forming a sensor network have limited energy capacity such as small batteries and when these nodes are placed in a specific field, it is important to research minimizing sensor nodes' energy consumption because of difficulty in supplying additional energy for the sensor nodes. Clustering has been in the limelight as one of efficient techniques to reduce sensor nodes' energy consumption in sensor networks. However, energy saving results can vary greatly depending on election of cluster heads, the number and size of clusters and the distance among the sensor nodes. /This research has an aim to find the optimal set of clusters which can reduce sensor nodes' energy consumption. We use a Genetic Algorithm(GA), a stochastic search technique used in computing, to find optimal solutions. GA performs searching through evolution processes to find optimal clusters in terms of energy efficiency. Our results show that GA is more efficient than LEACH which is a clustering algorithm without evolution processes. The two-dimensional GA (2D-GA) proposed in this research can perform more efficient gene evolution than one-dimensional GA(1D-GA)by giving unique location information to each node existing in chromosomes. As a result, the 2D-GA can find rapidly and effectively optimal clusters to maximize lifetime of the sensor networks.

• Electronics and Telecommunications Trends
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• v.23 no.1 s.109
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• pp.99-108
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• 2008

3G LTE 이동통신 시스템은 패킷 데이터 전송에 기반을 둔 다양한 서비스 지원을 목표로 하는 기술로서 최대 20MHz 대역폭 기준 하향링크 최대 전송속도 100Mbps, 상향 링크 50Mbps의 전송속도를 지원한다. 그리고 데이터 전송 효율 향상, 효율적인 주파수 자원 이용, 이동성, 낮은 latency, 패킷 데이터 전송에 최적화된 기술과 서비스 품질보장 등을 제공한다. 3G LTE 시스템은 기존 시스템에 비해 주파수 및 고속의 멀티미디어 서비스를 효율적으로 사용하는 IP 네트워크로 진화되는 이동통신 시스템이다. 3G LTE 이동통신 단말은 대역폭 20MHz 기준으로 이동속도 120km/h에서 하향링크 30 Mbps, 상향링크 15Mbps의 데이터 전송속도를 지원한다. 또한, 고품질 및 고속의 멀티미디어 서비스를 제공하는 단말로서 3.5세대인 HSDPA에 반해 모바일 영상 서비스가 본격적으로 제공되는 3G LTE 이동통신 시스템의 단말이다. 본 고에서는 3G LTE 이동통신 시스템 단말 플랫폼 기술 동향과 전망에 대하여 논의한다. II장에서는 3G LTE 표준 규격을 기반으로 구현하는 3G LTE 이동통신 시스템 및 단말 플랫폼을 서술하며, III장에서는 국ㆍ내외 단말 플랫폼 기술 동향에 관한 내용을 기술한다. IV장에서는 향후 발전 전망에 대해 살펴보고, 마지막으로 결론을 맺고자 한다.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.6
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• pp.56-66
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• 1998

This paper investigates a neuro-controller combined in parallel with a conventional linear controller of PID type in order to control nonminimum phase systems more efficiently. The objective is to minimize overall position errors as well as to maintain small undershooting. A costfunction is proposed with two conflict objectives. The neuro-controller is trained off-line with evolutionary programming(EP) in such a way that it becomes optimal by minimizing the given cost function through global evaluation based on desired control performance during the whole training time interval. However, it is not easy to find an optimal solution which satisfies individual objective simultaneously. With the concept of Pareto optimality and EP, we train the proposed controller more effectively and obtain a valuable set of optimal solutions. Simulation results show the efficacy of the proposed controller in a viewpoint of improvement of performance of a step response like fast settling time and small undershoot or overshoot compared with that of a conventional linear controller.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.10
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• pp.6969-6976
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• 2015

As new technologies are advancing and development paradigms are changing, software development process and development methods are evolving progressively. As a result, because the number of developing and managing processes and methodologies are increasing as a project in companies, effective management methods are needed. Especially, because companies should apply optimized methodology according to project's size and characteristics, customization technique of methodology is required urgently. In this paper, we propose a meta-model based methodology framework which can integrate and manage methodologies being developed progressively. Applying proposed methodology framework, a company is able to manage as well as develop optimized methodology easily as a project. Especially, because a proposed methodology framework is developed by meta-model, adding or extending new methodology elements can be realized simply as well as method elements are reused easily in case of customization of methodology as a project.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.9
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• pp.51-56
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• 2016

Corrugated bulkheads have many advantages compared to stiffened bulkheads, and they have thus been used for the cargo tank bulkheads of commercial vessels, such as bulk carriers, product oil carriers, and chemical tankers. Various studies have been carried out to find the optimum corrugation shape for bulk carriers, but optimum design studies for chemical tankers with bulkheads made of high-priced materials are scarce. The purpose of this study is to develop a minimum weight design method for horizontal corrugated bulkheads for a chemical tanker. An evolution strategy (ES) that searches for a reliable global optimum point was applied as an optimization technique, and the structural safety of the optimum design was verified through structural analysis using the finite element method (FEM). The results were compared with those of an existing ship, which showed a weight reduction of about 14% with equivalent structural strength.

• Journal of the Earthquake Engineering Society of Korea
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• v.15 no.2
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• pp.1-9
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• 2011

An optimal design method for a structural control system considering load variations due to their uncertain characteristics is studied in this paper. The conventional design problem for a control system generally deals with the optimization problem of a structural control system and interaction between the structure and the control device. This study deals with the optimization problem of a load-structure-control system and the more complicated interactions with each other. The problem of finding the load that maximizes the structural responses and the structural control system that minimizes the responses simultaneously is formulated as the min-max problem. In order to effectively obtain the optimal design variables, a co-evolutionary algorithm is adopted and, as a result, an optimal design procedure for the linear structural control system with uncertain dynamic characteristics is proposed. The example design and simulated results of an earthquake excited structure validates the proposed method.

• Journal of Korea Water Resources Association
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• v.45 no.1
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• pp.15-27
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• 2012

SWMM (Storm Water Management Model) has been widely used in the world as a typical model for flood runoff analysis of urban areas. However, the calibration of the model is difficult, which is an obstacle to easy application. The purpose of the study is to develop an automatic calibration module of the SWMM linked with SCE-UA (Shuffled Complex Evolution-University of Arizona) algorithm. Generally, various objective functions may produce different optimization results for an optimization problem. Thus, five single objective functions were applied and the most appropriate one was selected. In addition to the objective function, another objective function was used to reduce peak flow error in flood simulation. They form a multiple objective function, and the optimization problem was solved by determination of Pareto optima. The automatic calibration module was applied to the flood simulation on the catchment of the Guro 1 detention reservoir and pump station. The automatic calibration results by the multiple objective function were more excellent than the results by the single objective function for model assessment criteria including error of peak flow and ratio of volume between observed and calculated flow. Also, the verification results of the model calibrated by the multiple objective function were reliable. The program could be used in various flood runoff analysis in urban areas.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.5
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• pp.294-302
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• 2020

In a genetic algorithm, computer simulations are performed based on the natural evolution process of life, such as selection, crossover, and mutation. The genetic algorithm searches the approximate optimal solution by the parallel arrangement of Schema, which has a short definition length, low order, and high adaptability. This study examined the possibility of improving the efficiency of the optimal solution by considering the characteristics of the building block hypothesis, which are one of the key operating principles of a genetic algorithm. This study evaluated the efficiency of the optimization results according to the gene sequence for the implementation in solving problems. The optimal design problem of the water pipe was selected, and the genetic arrangement order reflected the engineering specificity by dividing into the existing, the network topology-based, and the flowrate-based arrangement. The optimization results with a flowrate-based arrangement were, on average, approximately 2-3% better than the other batches. This means that to increase the efficiency of the actual engineering optimization problem, a methodology that utilizes clear prior knowledge (such as hydraulic properties) to prevent such excellent solution characteristics from disappearing is essential. The proposed method will be considered as a tool to improve the efficiency of large-scale water supply network optimization in the future.

• Journal of Satellite, Information and Communications
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• v.6 no.1
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• pp.6-11
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• 2011

In this paper presents a new approach to evaluate reliability indices of electric distribution systems using genetic Algorithm (GA). The use of reliability evaluation is an important aspect of distribution system planning and operation to adjust the reliability level of each area. In this paper, the reliability model is based on the optimal load transferring problem to minimize load generated load point outage in each sub-section. This approach is one of the most difficult procedures and become combination problems. A new approach using GA was developed for this problem. GA is a general purpose optimization technique based on principles inspired from the biological evolution using metaphors of mechanisms such as natural selection, genetic recombination and survival of the fittest. Test results for the model system with 24 nodes 29 branches are reported in the paper.