• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.27 no.4
• /
• pp.499-506
• /
• 2003

The design cycle associated with large engineering systems requires an initial decomposition of the complex system into design processes which are coupled through the transference of output data. Some of these design processes may be grouped into iterative subcycles. In analyzing or optimizing such a coupled system, it is essential to determine the best order of the processes within these subcycles to reduce design cycle time and cost. This is accomplished by decomposing large multidisciplinary problems into several sub design structure matrices (DSMs) and processing them in parallel This paper proposes a new method for parallel decomposition of multidisciplinary problems to improve design efficiency by using the multi-objective genetic algorithm and two sample test cases are presented to show the effect of the suggested decomposition method.

• Proceedings of the KSME Conference
• /
• 2001.06c
• /
• pp.170-175
• /
• 2001

The design cycle associated with large engineering systems requires an initial decomposition of the complex system into design processes which are coupled through the transference of output data. Some of these design processes may be grouped into iterative subcycles. In analyzing or optimizing such a coupled system, it is essential to determine the best order of the processes within these subcycles to reduce design cycle time and cost. This is accomplished by decomposing large multidisciplinary problems into several multidisciplinary analysis subsystems (MDASS) and processing it in parallel. This paper proposes new strategy for parallel decomposition of multidisciplinary problems to improve design efficiency by using the multiple objective genetic algorithm (MOGA), and a sample test case is presented to show the effects of optimizing the sequence with MOGA.

• Proceedings of the KSME Conference
• /
• 2000.11a
• /
• pp.818-823
• /
• 2000

In practical design studies, most of designers solve multidisciplinary problems with complex design structure. These multidisciplinary problems have hundreds of analysis and thousands of variables. The sequence of process to solve these problems affects the speed of total design cycle. Thus it is very important for designer to reorder original design processes to minimize total cost and time. This is accomplished by decomposing large multidisciplinary problem into several multidisciplinary analysis subsystem (MDASS) and processing it in parallel. This paper proposes new strategy for parallel decomposition of multidisciplinary problem to raise design efficiency by using genetic algorithm and shows the relationship between decomposition and multidisciplinary design optimization (MDO) methodology.

• Proceedings of the KSRS Conference
• /
• 2003.11a
• /
• pp.1272-1274
• /
• 2003

In this paper, we present a new algorithm for high dimensional data reduction based on wavelet decomposition and Genetic Algorithm (GA). Comparative results show the superiority of our algorithm for dimensionality reduction and accuracy improvement.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.12 no.4
• /
• pp.619-627
• /
• 1999

다분야 통합 시스템의 설계문제는 다량의 설계변수와 구속조건으로 구성되며 다수의 공학적 현상으로 연관되어 있다. 다분야 통합 최적설계 문제를 효과적으로 다루기 위해서는 다양한 해석분야의 공학적 설계원리를 동시에 고려하여 균형 있고 유기적인 방법으로 최적의 설계를 결정하는 체계적인 설계자동화기술이 요구된다. 다분야 통합 설계문제를 위한 효율적인 설계방법론으로 분리기반 최적화 기법이 적용되는데 이 방법은 한 단위의 대규모 설계문제를 여러 개의 하부시스템으로 분리하여 독립적으로 최적화를 수행하고 각 하부 시스템으로부터의 설계해 사이의 중재 및 통합화를 거쳐 최종적으로 수렴된 최적설계를 찾는 방법이다. 본 논문에서는 분리기반 최적화기법을 다분야 통합최적 설계문제에 적용하는데 필요한 시스템분리기법을 유전알고리즘 및 다층 역전 파 신경회로망을 이용하여 정립하였다. 시스템분리기법을 검증하기 위해 최근 미국 Boeing사에서 개발중인 고속 민간항공기인 HSCT의 시뮬레이션기반 설계문제를 이용하였다. 대규모 설계시스템의 분리결과는 전체 설계문제의 특성을 파악하기 위한 자료로 활용되며 향후, 분리기반 최적화과정에서 최종적으로 통합된 최적설계를 탐색하는데 필요한 기반구조를 제공한다.

• BMB Reports
• /
• v.42 no.9
• /
• pp.617-622
• /
• 2009

We investigated the genetic associations of ischemic stroke by identifying epistasis of its heterogeneous subtypes such as small vessel occlusion (SVO) and large artery atherosclerosis (LAA). Epistasis was analyzed with 24 genes in 207 controls and 271 patients (SVO = 110, LAA = 95) using multifactor dimensionality reduction and entropy decomposition. The multifactor dimensionality reduction analysis with any of 1- to 4-locus models showed no significant association with LAA (P > 0.05). The analysis of SVO, however, revealed a significant association in the best 3-locus model with P10L of TGF-$\beta{1}$, C1013T of SPP1, and R485K of F5 (testing balanced accuracy = 63.17%, P < 0.05). Subsequent entropy analysis also revealed that such heterogeneity was present and quite a large entropy was estimated among the 3 loci for SVO (5.43%), but only a relatively small entropy was estimated for LAA (1.81%). This suggests that the synergistic epistasis model might contribute specifically to the pathogenetsis of SVO, which implies a different etiopathogenesis of the ischemic stroke subtypes.

• The Mathematical Education
• /
• v.44 no.4 s.111
• /
• pp.547-563
• /
• 2005

There have been many papers reporting that the axiomatic approach in Abstract Algebra is a big obstacle to overcome for the students who are not trained to think in an abstract way. Therefore an instructor must seek for ways to help students grasp mathematical concepts in Abstract Algebra and select the ones suitable for students. Mathematics faculty and students generally consider Abstract Algebra in general and quotient groups in particular to be one of the most troublesome undergraduate subjects. For, an individual's knowledge of the concept of group should include an understanding of various mathematical properties and constructions including groups consisting of undefined elements and a binary operation satisfying the axioms. Even if one begins with a very concrete group, the transition from the group to one of its quotient changes the nature of the elements and forces a student to deal with elements that are undefined. In fact, we also have found through running abstract algebra courses for several years that students have considerable difficulty in understanding the concept of quotient groups. Based on the above observation, we explore and analyze the nature of students' knowledge about $Z_n$ that is the set of congruence classes modulo n. Applying the genetic decomposition method, we propose a model to lead students to achieve the correct concept of $Z_n$.

• Korean Journal of Fisheries and Aquatic Sciences
• /
• v.29 no.3
• /
• pp.386-392
• /
• 1996

A numerical method based on genetic algorithms was introduced to characterize the grain-size distribution more effectively. This technique was proved significant particularly for multimodal size distributions, as was verified for samples from Korea Strait continental shelf. Sediment samples collected from the Korea Strait continental shelf revealed that $96\%$ of the grain-size distributions were multimodal. Therefore, the use of grain-size parameters was not the ideal method. As an alternative method, the decomposition of sue curves into elementary normal component curves was used. Means and standard deviations of 593 decomposed normal components were calculated by a numerical method from 268 size curves of Korea Strait sediments. The mean values of decomposed normal components showed peaks at $1\~3\phi\;and\;7\~9\phi$ size classes. The plot of mean and standard deviation values of the coarse fraction normal components on the map showed a characteristic areal distribution. The characteristic distribution was found to derive from underlying Pleistocene sediment on the basis of sea bottom geologic distribution of the area. The method of decomposition into normal components was found to be more effective than the analysis using traditional grain-size parameters in investigation of multimodal size distribution of Korea Strait shelf sediment.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.31 no.5
• /
• pp.18-24
• /
• 2003

The design cycle associated with large engineering systems requires an initial decomposition of the complex system into design processes which are coupled through the transference of output data. Some of these design processes may be grouped into iterative sybcycles. Previous researches predifined the numbers of design processes in groups, but these group sizes should be determined optimally to balance the computing time of each groups. This paper proposes adaptive decomposition method, which determines the group sizes and the order of processes simultaneously to raise design efficiency by expanding the chromosome of the genetic algorithm. Finally, two sample cases are presented to show the effects of optimizing the sequence of processes with the adaptive decomposition method.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.25 no.5
• /
• pp.883-890
• /
• 2001

In practical design studies, most of designers solve multidisciplinary problems with large size and complex design system. These multidisciplinary problems have hundreds of analysis and thousands of variables. The sequence of process to solve these problems affects the speed of total design cycle. Thus it is very important for designer to reorder the original design processes to minimize total computational cost. This is accomplished by decomposing large multidisciplinary problem into several multidisciplinary analysis subsystem (MDASS) and processing it in parallel. This paper proposes new strategy for parallel decomposition of multidisciplinary problem to raise design efficiency by using genetic algorithm and shows the relationship between decomposition and multidisciplinary design optimization (MDO) methodology.