• 한국멀티미디어학회논문지
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• 제12권5호
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• pp.728-736
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• 2009

가변감지범위를 갖는 무선센서네트워크의 수명연장을 위한 센서 노드의 전원 관리에서 요구되는 최대집합 커버문제를 유전자알고리즘을 이용하여 해결하였다. 기존의 경험적 탐용법(greedy heuristic method)에서는 네트워크의 동작 중 스케줄링을 반복 수행하므로 센서노드의 통신량이 증가한다. 제안한 방법에는 센서 노드의 통신 트래픽을 감소시켜 노드의 에너지 소모를 절약하여 네트워크의 수명을 연장하였다. 컴퓨터 시뮬레이션을 통해 제안한 방법의 유효성을 확인했으며 통신동작의 에너지 소모를 고려할 때 네트워크의 수명 이 약 10% 증가하였다.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 2005년도 추계학술대회 및 정기총회
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• pp.321-333
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• 2005

With the limited frequency spectrum and an increasing demand for cellular communication services, the problem of channel assignment becomes increasingly important. However, finding a conflict free channel assignment with the minimum channel span is NP hard. As demand for services has expanded in the cellular segment, sever innovations have been made in order to increase the utilization of bandwidth. The innovations are cellular concept, dynamic channel assignment and hierarchical network design. Hierarchical network design holds the public eye because of increasing demand and quality of service to mobile users. We consider the frequency assignment problem and the base station placement simultaneously. Our model takes the candidate locations emanating from this process and the cost of assigning a frequency, operating and maintaining equipment as an input. In addition, we know the avenue and demand as an assumption. We propose the network about the profit maximization. This study can apply to GSM(Global System for Mobile Communication) which has 70% portion in the world. Hierarchical network design using GA(Genetic Algorithm) is the first three-tier (Macro, Micro, Pico) model, We increase the reality through applying to EMC (Electromagnetic Compatibility Constraints). Computational experiments on 72 problem instances which have 15${\sim}$40 candidate locations demonstrate the computational viability of our procedure. The result of experiments increases the reality and covers more than 90% of the demand.

• Journal of information and communication convergence engineering
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• 제6권2호
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• pp.222-227
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• 2008

The important problem in designing a Fuzzy Logic Controller(FLC) is generation of fuzzy control rules and it is usually the case that they are given by human experts of the problem domain. However, it is difficult to find an well-trained expert to any given problem. In this paper, I describes an application of genetic algorithm, a well-known global search algorithm to automatic generation of fuzzy control rules for FLC design. Fuzzy rules are automatically generated by evolving initially given fuzzy rules and membership functions associated fuzzy linguistic terms. Using genetic algorithm efficient fuzzy rules can be generated without any prior knowledge about the domain problem. In addition expert knowledge can be easily incorporated into rule generation for performance enhancement. We experimented genetic algorithm with a non-trivial vehicle controling problem. Our experimental results showed that genetic algorithm is efficient for designing any complex control system and the resulting system is robust.

• International journal of advanced smart convergence
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• 제8권2호
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• pp.140-146
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• 2019

Andrew Niccols's movie Gattaca (1997) inspired the formulation of the "Gattaca Argument" concerning the negative outcome of biotechnology, which has since been critiqued especially in the context of transhumanism and posthumanism. According this argument the development of genetic enhancement will produce a genetic discrimination and lead us to serious form of inequality. However, in particular transhumanists deny that here are reasons to worry and advocating instead the transformation of human condition in terms of genetic enhancement. Moreover, they question that genetic enhancement will necessarily lead to social inequality. In what follows, we will reexamine the Gattaca Argument and its critiques based on the movie in order to reassess the role the movie plays in the subsequent scholarly discussion. We will argue that existing critiques fall short of capturing the problem posed in the movie - the problem of the inhumane. Based on a hermeneutic approach to the movie we will both reconstruct the arguments and evaluate the transhuman counterarguments in terms of modern history of philosophical ideas.

• International Journal of Contents
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• 제7권1호
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• pp.8-13
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• 2011

For energy-efficiency in Wireless Sensor Networks (WSNs), when a sensor node detects events, the sensing period for collecting the detailed information is likely to be short. The lifetime of WSNs decreases because communication modules are used excessively on a specific sensor node. To solve this problem, the TARP decentralized network packets to neighbor nodes. It considered the average data transmission rate as well as the data distribution. However, since the existing scheme did not consider the energy consumption of a node in WSNs, its network lifetime is reduced. In this paper, we propose an energy awareness congestion control scheme based on genetic algorithms in WSNs. The proposed scheme considers the remaining amount of energy and the transmission rate on a single node in fitness evaluation. Since the proposed scheme performs an efficient congestion control, it extends the network lifetime. In order to show the superiority of the proposed scheme, we compare it with the existing scheme through performance evaluation. It is shown that the proposed scheme enhances the data fairness and improves the network lifetime by about 27% on average over the existing scheme.

• Journal of information and communication convergence engineering
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• 제8권1호
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• pp.13-18
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• 2010

The composite stock cutting problem is defined as allocating rectangular and irregular patterns onto a large composite stock sheet of finite dimensions in such a way that the resulting scrap will be minimized. In this paper, we introduce a novel approach to hybrid optimization algorithm called MGA in MPI (Message Passing Interface) environments. The proposed MGA combines the benefit of rapid convergence property of Mean Field Annealing and the effective genetic operations. This paper also proposes the efficient data structures for pattern related information.

• Journal of information and communication convergence engineering
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• 제8권4호
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• pp.370-376
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• 2010

Ensemble learning is a method for improving the performance of classification and prediction algorithms. However, its performance can be degraded due to multicollinearity problem where multiple classifiers of an ensemble are highly correlated with. This paper proposes genetic algorithm-based optimization techniques of SVM ensemble to solve multicollinearity problem. Empirical results with bankruptcy prediction on Korea firms indicate that the proposed optimization techniques can improve the performance of SVM ensemble.

• Journal of Genetic Medicine
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• 제8권2호
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• pp.105-112
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• 2011

목적: 유전성 유방암 가계에서 BRCA 유전자 돌연변이 결과 공유의 중요성은 가족검사를 통해 돌연변이 보인자를 확인하고 적극적인 암 발생 감시와 예방적 치료를 제공하는데 있다. 본 연구를 통하여 유전성 유방암 가계에서 돌연변이 사실에 대한 공유 정도, 등친 별 의사소통 차이와 이에 영향을 미치는 요인을 확인하고자 한다. 대상 및 방법: 한국인 유전성유방암 연구에 등록되어 BRCA1 또는 BRCA2 돌연변이가 확인된 발단자 106명을 대상으로 검사 후 유전상담, 유전성 유방암 지식도 평가, 돌연변이 사실에 대한 가족간의 의사소통 과정, 가족 검사 현황에 대해 설문조사를 시행하였다. 결과: 최종 응답자 106명 중 99명은 적어도 한 명 이상의 친족에게 자신의 유전자 검사결과를 알렸으며, 일등친 가족에게만 알린 경우는 68.7%, 일등친과 이등친 이상의 가족에게 돌연변이 사실을 알린 경우는 31.3%였다. 단변량 분석결과 일등친 가족에게만 검사결과를 알린 군이 이등친 또는 삼등친 가족에게 돌연변이 사실을 알린 군에 비해 기혼자의 비율이 더 높았으며, 검사 후 유전상담일로부터 설문조사 시점까지 기간이 유의하게 짧은 것으로 나타났다. 가족에게 돌연변이 사실을 알린 이유에 대해서는 가족들에게 BRCA 유전자 돌연변이 가능성과 유방암 발병위험성을 알리기 위함에 가장 큰 비중을 차지하였다. 결론: 유전성 유방암 가계에서 BRCA 돌연변이 사실에 대한 정보를 보다 많은 가족과 공유하기 위해서는 유전상담 시 환자 개개인의 가계 구조를 파악하여 차별화된 의사소통 방법을 제시해 주어야 할 것이다.

• 한국전자통신학회논문지
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• 제10권9호
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• pp.1039-1048
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• 2015

본 논문에서는 곤충형 다리 구조를 갖는 4족 로봇의 효율적인 계단 보행을 위해 유전 알고리즘(Genetic Algorithm: GA)에 기반한 계단 보행 방법을 제안한다. 제안한 방법에서는 우선 계단 보행을 위한 요소와 도달 영역을 정의한다. 또한 GA 수행을 위한 유전자와 적합도 함수를 설정하고, GA를 이용하여 최소 이동 거리와 최적 에너지 안정도 여유(Energy Stability Margin: ESM)을 갖는 4족 로봇의 착지 지점을 탐색하여 걸음새 궤적을 생성한다. 마지막으로, 컴퓨터 시뮬레이션을 통해 본 논문에서 제안한 계단 보행 방법의 효용성 및 우수성을 검증한다.

• Journal of Communications and Networks
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• 제8권1호
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• pp.116-122
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• 2006

Most of the multimedia applications require strict quality of service (QoS) guarantee during the communication between a single source and multiple destinations. This gives rise to the need for an efficient QoS multicast routing strategy. Determination of such QoS-based optimal multicast routes basically leads to a multi-objective optimization problem, which is computationally intractable in polynomial time due to the uncertainty of resources in Internet. This paper describes a network model for researching the routing problem and proposes a new multicast tree selection algorithm based on genetic algorithms to simultaneously optimize multiple QoS parameters. The paper mainly presents a QoS multicast routing algorithm based on genetic algorithm (QMRGA). The QMRGA can also optimize the network resources such as bandwidth and delay, and can converge to the optimal or near-optimal solution within few iterations, even for the networks environment with uncertain parameters. The incremental rate of computational cost can close to polynomial and is less than exponential rate. The performance measures of the QMRGA are evaluated using simulations. The simulation results show that this approach has fast convergence speed and high reliability. It can meet the real-time requirement in multimedia communication networks.