• The KIPS Transactions:PartA
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• v.10A no.1
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• pp.15-24
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• 2003

In this paper, an optimization model for the clustered node allocation systems in the distributed computing environment is presented. In the presented model with a distributed file system framework, the dynamics of system behavior over times is carefully thought over the nodes and hence the functionality of the cluster monitor node to check the feasibility of the current set of clustered node allocation is given. The cluster monitor node of the node allocation system capable of distributing the parallel modules to clustered nodes provides a good allocation solution using Genetic Algorithms (GA). As a part of the experimental studies, the solution quality and computation time effects of varying GA experimental parameters, such as the encoding scheme, the genetic operators (crossover, mutations), the population size, and the number of node modules, and the comparative findings are presented.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.26 no.4
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• pp.275-281
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• 2013

In this paper, determination of the scalability of the cluster composed common personal computer was performed when optimization of reinforced concrete structure using genetic algorithm. The goal of this research is watching the potential of multi-core-PC cluster for optimization of seismic design of reinforced-concrete structures. By increasing the number of core-processer of cluster, decreasing of computation time per each generation of genetic algorithm was observed. After classifying the components in singular personal computer, the estimation of the expected bottle-neck phenomenon and comparison with wall-clock time and Amdahl's law equation was performed. So we could obseved the scalability of the cluster appear complex tendency. For separating the bottle-neck phenomenon of physical and algorithm, the different size of population was selected for genetic algorithm cases. When using 64 core-processor, the efficiency of cluster is low as 31.2% compared with Amdahl's law efficiency.

• Korean Journal of Microbiology
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• v.51 no.4
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• pp.329-337
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• 2015

All known genomes (N=10) in the order Nitrosomonadales were analyzed to contain 9,808 and 908 gene clusters in their pan-genome and core genome, respectively. Analyses with reference genomes belonging to other orders in Betaproteobacteria revealed that sizes of pan-genome and core genome were dependent on the number of genomes compared and the differences of genomes within a group. The sizes of pan-genomes of the genera Nitrosomonas and Nitrosospira were 7,180 and 4,586 and core genomes, 1,092 and 1,600, respectively, which implied that similarity of genomes in Nitrosospira were higher than Nitrosomonas. The genomes of Nitrosomonas contributed mostly to the size of the pan-genome and core genomes of Nitrosomonadales. COG analysis of gene clusters showed that the J (translation, ribosomal structure and biogenesis) category occupied the biggest proportions (9.7-21.0%) among COG categories in core genomes and its proportion increased in the group which genetic distances among members were high. The unclassified category (-) occupied very high proportions (34-51%) in pan-genomes. Ninety seven gene clusters existed only in Nitrosomonadales and not in reference genomes. The gene clusters contained ammonia monooxygenase (amoA and amoB) and -related genes (amoE and amoD) which were typical genes characterizing the order Nitrosomonadales while they contained significant amount (16-45%) of unclassified genes. Thus, these exclusively-conserved gene clusters might play an important role to reveal genetic specificity of the order Nitrosomonadales.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.5
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• pp.1661-1669
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• 2010

In this paper, I propose an Energy efficient Clustering based on Genetic Algorithm(ECGA) which reduces energy consumption by distributing energy overload to cluster group head and cluster head in order to lengthen the lifetime of sensor network. ECGA algorithm calculates the values like estimated energy cost summary, average and standard deviation of residual quantity of sensor node and applies them to fitness function. By using the fitness function, we can obtain the optimum condition of cluster group and cluster. I demonstrated that ECGA algorithm reduces the energy consumption and lengthens the lifetime of network compared with the previous clustering method by stimulation.

• Proceedings of the Korean Information Science Society Conference
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• 1998.10c
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• pp.54-56
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• 1998

본 논문에서는 병렬 정보검색 시스템에 있어 클러스터 문서할당을 위한 두 가지 휴리스틱 기법을 제시한다. 효율적 문서할당에 관한 매핑 문제를 정의하고 유전알고리즘과 모의냉각기법에 기반을 두는 휴리스틱 매핑 알고리즘을 기술한다. 알고리즘 성능실험과 관련하여 시뮬레이션을 통한 다른 할당 알고리즘과 비교평가한 결과 개선된 성능을 얻을 수 있었다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.12
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• pp.2687-2696
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• 2011

A Clustering problem is one of the organizational problems to improve network lifetime and scalability in underwater acoustic sensor networks. This paper propose an algorithm to obtain an optimal clustering solution to be able to minimize a total transmission power for all deployed nodes to transmit data to the sink node through its clusterhead. In general, as the number of nodes increases, the amount of calculation for finding the solution would be too much increased. To obtain the optimal solution within a reasonable computation time, we propose a genetic algorithm to obtain the optimal solution of the cluster configuration. In order to make a search more efficient, we propose some efficient neighborhood generating operations of the genetic algorithm. We evaluate those performances through some experiments in terms of the total transmission power of nodes and the execution time of the proposed algorithm. The evaluation results show that the proposed algorithm is efficient for the cluster configuration in underwater acoustic sensor networks.

• Proceedings of the KIEE Conference
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• 2005.07a
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• pp.158-161
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• 2005

본 논문에서는 해집단을 다음세대로 진화시킬 때, 유전알고리즘과 진화전략을 동시에 사용하고, 적합도에 따라 복제하는 과정에서 유전알고리즘과 진화전략이 적용될 해집단의 비율이 적응적으로 변경되는 적응진화 알고리즘을 제안하였다. 또한 제안한 알고리즘을 실시간 적용하기 위해 PC 클러스터 시스템으로 병렬처리하여 최적해 탐색 성능 및 탐색속도를 개선하였다. 제안한 알고리즘을 실 배전계통 고장복구 문제에 적용해 본 결과, 유전 알고리즘 또는 진화전략을 단독으로 사용한 경우보다 제안한 방법이 더 빠른 시간내에 우수한 최적해를 탐색하였고, 병렬 연산의 수행 노드수 증가에 따라 최적해 탐색성능은 유지하면서 최적해 탐색시간을 상당히 단축시킴을 확인하였다.

• Proceedings of the KIEE Conference
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• 2005.07a
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• pp.162-165
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• 2005

본 논문에서는 해집단을 다음세대로 진화시킬 때, 유전알고리즘과 진화전략을 동시에 사용하고, 적합도에 따라 복제하는 과정에서 유전알고리즘과 진화전략이 적용될 해집단의 비율이 적응적으로 변경되는 적응진화 알고리즘을 제안하였다. 또한 제안한 알고리즘을 실시간 적용하기 위해 PC 클러스터 시스템으로 병렬처리하여 최적해 탐색 성능 및 탐색속도를 개선하였다. 제안한 알고리즘을 참고문헌의 배전계통 재구성 문제에 적용해본 결과, 유전 알고리즘 또는 진화전략을 단독으로 사용한 경우보다 제안한 방법이 더 빠른 시간내에 우수한 최적해를 탐색하였고, 병렬 연산의 수행 노드수 증가에 따라 최적해 탐색성능은 유지하면서 최적해 탐색 시간을 상당히 단축시킴을 확인하였다.

• Annual Conference of KIPS
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• 2018.10a
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• pp.50-53
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• 2018

차세대 염기서열 분석법이 생성한 유전체 원시 데이터를 기존의 방식대로 하나의 서버에서 분석하기 위해서는 수십 시간이 필요할 수 있고 이러한 시간을 최대한 줄여야 하는 응급 상황도 존재한다. 따라서 본 연구에서는 고속의 네트워크로 연결되고 병렬 파일 시스템을 공유하는 서버 클러스터를 활용하여 분석 시간을 크게 단축 시킬 수 있는 유전체 데이터 분석의 전처리 프로세스의 병렬화 방법을 제안한다. 기존의 검증된 분석도구를 기반으로 프로세스의 병렬화, 데이터의 분배 및 병렬 병합 기법을 개발하였고 실험을 통해 성능을 향상 시킬 수 있음을 증명하였다.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.4
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• pp.334-340
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• 2002

This paper proposes a self-organizing fuzzy modeling which can create a new hyperplane-shaped cluster by applying multiple regression to input/output data with relatively large fuzzy entropy, add the new cluster to fuzzy rule base and adjust parameters of the fuzzy model in repetition. Tn the coarse tuning, weighted recursive least squared algorithm and fuzzy C-regression model clustering are used and in the fine tuning, gradient descent algorithm is used to adjust parameters of the fuzzy model precisely And learning rates are optimized by utilizing meiosis-genetic algorithm. To check the effectiveness and feasibility of the suggested algorithm, four representative examples for system identification are examined and the performance of the identified fuzzy model is demonstrated in comparison with that of the conventional fuzzy models.