• Journal of Korean Society of Forest Science
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• v.97 no.6
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• pp.565-569
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• 2008

Geographic variation of characteristics of cone, seed and bract morphology were examined in 8 populations of rare endemic Abies nephrolepis (Trautv.) Maxim and A. koreana Wilson. Additionally we studied classification index to distinguish between the species by the method of discriminant analysis. Nested ANOVA showed that there were statistically significant differences among populations as well as among individuals within populations in all 13 morphological traits. In the seed length, seed index, bract width, and bract index of A. nephrolepis and the bract width and index of A. koreana, variance components among populations were larger than those among individuals within populations. In discriminant analysis, three traits (cone width, length of seed wing, and bract length) were found to be useful in discriminating A. nephrolepis from A. koreana. The optimal classification results of stepwise selection were discriminated length of seed wing and bract length.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.341-342
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• 2008

GA(Genetic Algorithm)는 NP-Complete 도메인이나 NP-Hard 도메인 내의 문제들에 대해서 최적의 해를 찾기 위해서 많이 사용되어 지는 진화 컴퓨팅 방법 중 하나이다. 모바일 로봇 기술 중 경로계획은 NP-Complete 도메인 영역의 문제 중 하나로 이를 해결하기 위해서 Dijkstra 등의 그래프 이론을 이용한 연구가 많이 연구되었고 최근에는 GA등 진화 컴퓨팅 기법을 이용하여 최적의 경로를 찾는 연구가 많이 수행되고 있다. 그러나 모바일 로봇이 처리해야 될 공간 정보 크기가 증가함에 따라 기존 GA의 개체의 크기가 증가되어 게산 복잡도가 높아져 시간 지연등의 문제가 발생할 수 있다. 이는 모바일 로봇의 잠재적 오류로 발생될 수 있다. 공간 정보에는 동적이 장애물들이 예측 불허하게 나타 날 수 있는데 이것은 전역 경로 계획을 수립할 때 또한 반영되어야 된다. 본 논문에서는 k-means 클러스터링 기법을 이용하여 장애물 밀집도 및 거리 정보를 기반으로 공간정보를 k개의 군집 공간으로 재분류하여 이를 기반으로 N*M개의 그리드 개체 집단을 생성하여 최적 경로계획을 수립하는 GA를 제시한다.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06a
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• pp.22-24
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• 2006

마이크로어레이는 전체 유전체 수준의 mRNA 발현 여부에 대한 측정이 가능하다는 점에서 분자생물학의 실험 도구로서 가장 강력한 도구 중에 하나로 부각되어 있다. 현재까지 마이크로어래이의 결과로부터 유사한 발현 패턴을 찾기 위한 여러 가지 바이클러스터링 알고리즘들이 개발되어 왔다. 하지만 대다수의 알고리즘들이 최적의 바이클러스터들을 찾기보다는 일정 수준의 가능한 바이클러스터의 결과만을 제시하고 있다. 본 논문에서는 다른 개체집단들과 상호 진화하는 공진화적 학습에 의한 진화연산 기법을 통하여 유전자-조건의 매트릭스로부터 열과 행을 동시에 클러스터링하는 공진화적 바이클러스터링 알고리즘(co-evolutionary biclustering algorithm: CBA)을 제안하고자 한다. CBA는 유전자발현 데이터에서 유전자-조건의 상호의존적인 부성분들로 구성된 최적화 문제에 적합한 계산방식이라고 할 수 있다. 인간 유전자 발현 데이터에 대한 실험 결과. 제시한 알고리즘은 이전의 알고리즘에 비해 발견한 바이클러스터의 패턴 유사도에 있어서 우수한 성능을 보이고 있다.

• Journal of the Society of Naval Architects of Korea
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• v.45 no.6
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• pp.590-600
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• 2008

This paper concerns the development of a designer friendly hull form parameterization and its coupling with advanced global optimization algorithms. As optimization algorithms, we choose the Partial Swarm Optimization(PSO) recently introduced to solve global optimization problems. Most general-purpose optimization softwares used in industrial applications use gradient-based algorithms, mainly due to their convergence properties and computational efficiency when a relatively few number of variables are considered. However, local optimizers have difficulties with local minima and non-connected feasible regions. Because of the increase of computer power and of the development of efficient Global Optimization (GO) methods, in recent years nongradient-based algorithms have attracted much attention. Furthermore, GO methods provide several advantages over local approaches. In the paper, the derivative-based SQP and the GO approach PSO are compared with their relative performances in solving some typical ship design optimization problem focusing on their effectiveness and efficiency.

• The Transactions of the Korea Information Processing Society
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• v.5 no.11
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• pp.3010-3023
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• 1998

GA has a fast convergence speed in searching the one point around optimal value. But it's convergence time increase in searching the region around optimal value because it has no regional searching mechanism. BP has the tendency to converge the local minimum because it has global searching mechanism. To overcome these problems, a method in which a genetic algorithm and a back propagation are applied in turn is proposed in this paper. By using a genetic algorithm, we compute optimal synaptic strength and offset value. And then, these values are fed to the input of the back propagation. This proposed method is superior to each above method in improving the convergence speed.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.1
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• pp.12-17
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• 2003

Optimal determination of cluster size has an effect on the result of clustering. In K-means algorithm, the difference of clustering performance is large by initial K. But the initial cluster size is determined by prior knowledge or subjectivity in most clustering process. This subjective determination may not be optimal. In this Paper, the genetic algorithm based optimal determination approach of cluster size is proposed for automatic determination of cluster size and performance upgrading of its result. The initial population based on attribution is generated for searching optimal cluster size. The fitness value is defined the inverse of dissimilarity summation. So this is converged to upgraded total performance. The mutation operation is used for local minima problem. Finally, the re-sampling of bootstrapping is used for computational time cost.

• Journal of Korean Society of Forest Science
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• v.96 no.4
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• pp.387-392
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• 2007

Characteristics of needle morphology and anatomy were examined in 14 populations of Abies nephrolepis (Trautv.) Maxim. and A. koreana Wilson. Additionally we studied the classification index to distinguish between the species by the method of discriminant analysis. Characteristics of needle for A. nephrolepis could be distinguished from those for A. koreana by flatten arrangement, thin and long length for needle form, many stomata row, and marginal position of resin duct Nested ANOVA showed that there were statistically significant differences among populations as well as among individuals within populations in all 9 needle traits. For the needle indices such as needle thickness, number of stomata row, and the distance between resin duct and vascular for both species, variance components among populations were larger than those among individuals within populations. The characteristics that contributed most to the separation of A. nephrolepis and A. koreana according to the discriminant analysis using stepdisc procedures were needle index and thickness of needle, needle arrangement index, distance between resin duct and vascular, and number of stomata row.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.7
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• pp.1732-1739
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• 2013

The identification of haplotypes, which encode SNPs in a single chromosome, makes it possible to perform a haplotype-based association test with diseases. Given a set of genotypes from a population, the process of recovering the haplotypes that explain the genotypes is called haplotype inference. We propose a new preprocessing algorithm for the haplotype inference by pure parsimony (HIPP). The proposed algorithm excludes a large amount of redundant candidate haplotypes by detecting some groups of haplotypes that are dispensable for optimal solutions. For the well-known synthetic and biological data, the experimental results of our method show that our method run much faster than other preprocessing methods. After applying our preprocessing results, the numbers of haplotypes of HIPP solvers are equal to or slightly larger than that of optimal solutions.

• Current Research on Agriculture and Life Sciences
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• v.12
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• pp.29-34
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• 1994

A leading sweet pepper cultivar, Keystone Resistant Giant #3, was crossed with a line with resistance to Phytophthora capsici, PI201232, for incorporation of the resistance and to study the inheritance of resistance to the disease. Seedlings of parents, $F_1$, $F_2$ and backcross populations of a cross between Keystone Resistant Giant #3 and PI201232 were inoculated with zoospore suspension of P. capsici at 36 days after seeding. Most of the $F_1$ seedlings survied the inoculation and this suggested that resistance is dominant over susceptibility. The number of survived plants in $F_2$ population was, however, much less than the killed. All the plants in a backcross to Keystone Resistant Giant #3 were killed. Therefore, the observed numbers did not fit the expected ratio for segregation of one or two dominant alleles as previously reported. The resistance to P. capsici appeared to be inherited in a quantitative mode in evaluation of root rot. Resistant individuals in $F_2$ population were selected and a breeding program for incorporation of the resistance to P. capsici by backcross method is continued.

• Proceedings of the Korean Information Science Society Conference
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• 2003.04c
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• pp.389-391
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• 2003

컴퓨터 학습의 군집화는 주어진 데이터를 서로 유사한 몇 개의 집단으로 묶는 작업을 수행한다. 군집화를 위한 유사도 결정을 위한 측도는 많은 기법들에서 매우 다양한 측도들이 사용되고 또한 연구되어 왔다. 하지만 군집화의 결과에 대한 성능측정에 대한 객관적인 기준 설정이 어렵기 때문에 군집화 결과에 대한 해석은 매우 주관적이고 애매한 경우가 많다. 퍼지 군집화는 이러한 애매한 군집화 문제에 있어서 융통성 있는 군집 결정 방안을 제시해 준다. 각 개체들이 특정 군집에 속하게 될 퍼지 멤버 함수값을 원소로 하는 유사도 행렬을 통하여 군집화를 수행한다. 본 논문에서는 베이지안 학습을 통하여 군집화를 위한 퍼지 멤버 함수값을 구하였다. 본 연구에서는 최적의 퍼지 군집화 수행을 위하여 베이지안 학습 기반의 퍼지 규칙을 추출하였다. 인공적으로 만든 데이터와 기존의 기계 학습 데이터를 이용한 실험을 통하여 제안 방법의 성능을 확인하였다.