• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.601-605
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• 1991

In this paper an approximate solution method for the large-scale Traveling Salesman Problem(TSP) is presented. The method start with the subdivision of the problem domain into a number of clusters by considering their geometries. The clusters have limited number of nodes so as to get local solutions. They are linked to give the least path which covers the whole domain and become TSPs with start- and end-node. The approximate local solutions in each cluster are obtained by using geometrical property of the cluster, and combined to give an overall-approximate solution for the large-scale TSP.

• Annual Conference of KIPS
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• 2005.11a
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• pp.477-480
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• 2005

메모리 기반 추론 기법에서 기억공간의 효율적 사용과 분류 시간을 줄이기 위한 다양한 방법들이 연구되고 있으며, NGE(Nested Generalized Exemplar) 이론을 예로 들 수 있다. 본 논문에서는 학습 패턴 집합으로부터 대표패턴을 생성하는 RPA(Recursive Partition Averaging) 기법과 점진적으로 대표패턴을 추출하는 IRPA(Incremental RPA) 기법을 제안한다.

• Journal of the Korean Statistical Society
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• v.33 no.4
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• pp.401-410
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• 2004

We address the problem of parameter estimation in multivariate distributions under ignorable non-monotone missing data. The factoring likelihood method for monotone missing data, termed by Rubin (1974), is extended to a more general case of non-monotone missing data. The proposed method is algebraically equivalent to the Newton-Raphson method for the observed likelihood, but avoids the burden of computing the first and the second partial derivatives of the observed likelihood. Instead, the maximum likelihood estimates and their information matrices for each partition of the data set are computed separately and combined naturally using the generalized least squares method.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11b
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• pp.721-723
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• 2005

패턴 분류에 많이 사용되는 MBR(Memory Based Reasoning) 기법은 메모리에 저장된 학습패턴과 테스트 패턴간의 거리를 계산하여 가장 가까운 학습패턴의 클래스로 분류하기 때문에 테스트 패턴을 분류하는 기준을 설명할 수 없다는 문제점을 가지고 있다. 본 논문에서는 RPA(Recursive Partition Averaging) 기법을 이용하여 분류 기준을 설명할 수 있는 IF-THIN 형태의 규칙을 생성하고 생성된 규칙의 일반화 성능을 향상시키기 위하여 불필요한 조건을 제거하는 규칙 pruning 알고리즘과 생성되는 규칙의 개수를 줄일 수 있는 점진적 규칙 추출 알고리즘을 제안한다.

• Proceedings of the Korea Society for Simulation Conference
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• 1999.04a
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• pp.122-126
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• 1999

시스템 레벨 PCB(Printed Circuit Board) 디자인은 최종적인 시스템 특성에 정확한 정보를 갖지 못하는 디자인 결정을 하기 위해 여러 가지 정보가 필요하다. 또한 분할 할 때 분할 시간과 방법은 매우 중요하고 합성 결과의 특성은 교환(tradeoffs)과 디자인 결정에 매우 민감하다. 그러므로 만일 디자인이 합성되고 단일 보드로 디자인된다 할지라도 후에 다중 보드로 분할 될 수 있다. 따라서 본 논문에서는 PCB회로 디자인의 제약구동 방법중 off-critical-path 분할기법을 사용한 휴리스틱(heuristic) 방법을 제안했고 교환 그래프 착색 알고리즘을 제안했다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1262-1265
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• 1993

The paper discusses the problem of controlling systems with a very high number of input variables effectively by fuzzy If . . . then rules. The basic idea is the partition of the state space into domains, which step can be done even iteratively several times, and every domain has its own sub rule base referring to a considerably lower number of variables than the original space. In this manner the number of necessary rules is drastically reduced and time complexity of the control algorithm remains acceptable.

• Bulletin of the Korean Mathematical Society
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• v.43 no.1
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• pp.119-123
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• 2006

Let G be a symmetric group. In this paper we describe a method that for a certain irreducible character X of G it finds a subgroup H such that the restriction of X on H has a linear constituent with multiplicity one. Then using a well known algorithm we can construct a representation of G affording X.

• Journal of the Korean Mathematical Society
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• v.59 no.5
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• pp.945-962
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• 2022

For a positive integer ℓ, $\bar{A}_{\ell}(n)$ denotes the number of over-partitions of n into parts not divisible by ℓ. In this article, we find certain Ramanujan-type congruences for $\bar{A}_{r{\ell}}(n)$, when r ∈ {8, 9} and we deduce infinite families of congruences for them. Furthermore, we also obtain Ramanujan-type congruences for $\bar{A}_{13}(n)$ by using an algorithm developed by Radu and Sellers [15].

• Asian-Australasian Journal of Animal Sciences
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• v.24 no.11
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• pp.1525-1528
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• 2011

The purpose of this study was to detect SNPs that were responsible for a carcass trait in Hanwoo populations. A non-parametric model applying a restricted partition method (RPM) was used, which exploited a partitioning algorithm considering statistical criteria for multiple comparison testing. Phenotypic and genotypic data were obtained from the Hanwoo Improvement Center, National Agricultural Cooperation Federation, Korea, in which the pedigree structure comprised 229 steers from 16 paternal half-sib proven sires that were born in Namwon or Daegwanryong livestock testing station between spring of 2002 and fall of 2003. A carcass trait, longissimus dorsi muscle area for each steer was measured after slaughter at approximately 722 days. Three SNPs (19_1, 18_4 and 28_2) near the microsatellite marker ILSTS035 on BTA6, around which the quantitative trait loci (QTL) for meat quality were previously detected, were used in this study. The RPM analyses resulted in two significant interaction effects between SNPs (19_1 and 18_4) and (19_1 and 28_2) at ${\alpha}$ = 0.05 level. However, under a general linear (parametric) model no interaction effect between any pair of the three SNPs was detected, while only one main effect for SNP19_1 was found for the trait. Also, under another non-parametric model using a multifactor dimensionality reduction (MDR) method, only one interaction effect of the two SNPs (19_1 and 28_2) explained the trait significantly better than the parametric model with the main effect of SNP19_1. Our results suggest that RPM is a good alternative to model choices that can find associations of the interaction effects of multiple SNPs for quantitative traits in livestock species.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.39 no.3
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• pp.97-104
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• 2002

In this paper we describe the method which optimizes the partition of the input space by means of measure of fuzziness for fuzzy neural network. It covers its generation of fuzzy rules for input sub space. It verifies the performance of the system depended on the various time interval of the input. This method divides the input space into several fuzzy regions and assigns a degree of each of the generated rules for the partitioned subspaces from the given data using the Shannon function and fuzzy entropy function generating the optimal knowledge base without the irrelevant rules. In this scheme the basic idea of the fuzzy neural network is to realize the fuzzy rule base and the process of reasoning by neural network and to make the corresponding parameters of the fuzzy control rules be adapted by the steepest descent algorithm. According to the input interval the proposed inference procedure proves that the fast convergence of root mean square error (RMSE) owes to the optimal partition of the input space