• 대한기계학회:학술대회논문집
• /
• 대한기계학회 2001년도 춘계학술대회논문집C
• /
• 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.

• 한국시뮬레이션학회논문지
• /
• 제13권1호
• /
• pp.11-23
• /
• 2004

The decomposition of a structuring element for a morphological operation reduces the amount of the computation required for executing the operation. In this paper, we present a new technique for the decomposition of convex structuring elements for morphological operations. We formulated the linear constraints for the decomposition of a convex polygon in discrete space, then the constraints are applied to the decomposition of a convex structuring element. Also, a cost function is introduced to represent the optimal criteria for decomposition. We use linear integer programming technique to find the combination of basis structuring elements which minimizes the amount of the computation required for executing the morphological operation. Formulating different cost functions for different implementation methods and computer architectures, we can determine the optimal decompositions which guarantee the minimal amounts of computation on different computing environment.

• 한국전산구조공학회:학술대회논문집
• /
• 한국전산구조공학회 2006년도 정기 학술대회 논문집
• /
• pp.291-298
• /
• 2006

A parallelized topology design optimization method is developed on a distributed memory system. The parallelization is based on a domain decomposition method and a boundary communication scheme. For the finite element analysis of structural responses and design sensitivities, the PCG method based on a Krylov iterative scheme is employed. Also a parallelized optimization method of optimality criteria is used to solve large-scale topology optimization problems. Through several numerical examples, the developed method shows efficient and acceptable topology optimization results for the large-scale problems.

• 한국전산유체공학회:학술대회논문집
• /
• 한국전산유체공학회 2009년 춘계학술대회논문집
• /
• pp.57-65
• /
• 2009

An efficient and high-fidelity design approach for wing-body shape optimization is presented. Depending on the size of design space and the number of design of variable, aerodynamic shape optimization process is carried out via different optimization strategies at each design stage. In the first stage, global optimization techniques are applied to planform design with a few geometric design variables. In the second stage, local optimization techniques are used for wing surface design with a lot of design variables to maintain a sufficient design space with a high DOF (Degree of Freedom) geometric change. For global optimization, Kriging method in conjunction with Genetic Algorithm (GA) is used. Asearching algorithm of EI (Expected Improvement) points is introduced to enhance the quality of global optimization for the wing-planform design. For local optimization, a discrete adjoint method is adopted. By the successive combination of global and local optimization techniques, drag minimization is performed for a multi-body aircraft configuration while maintaining the baseline lift and the wing weight at the same time. Through the design process, performances of the test models are remarkably improved in comparison with the single stage design approach. The performance of the proposed design framework including wing planform design variables can be efficiently evaluated by the drag decomposition method, which can examine the improvement of various drag components, such as induced drag, wave drag, viscous drag and profile drag.

• 한국항공우주학회지
• /
• 제31권5호
• /
• pp.18-24
• /
• 2003

많은 공학 시스템은 여러 개의 해석모듈들이 다양한 데이터의 입출력 관걔로 연관된 형태로 모델링 된다. 이와 같은 복잡한 하나의 시스템을 몇 개의 시스템으로 나누어 해석 및 다분야통합최적설계를 수행하면 계산소요시간 및 병렬처리 측면에서 효율적인 것으로 알려져 있다. 따라서 전체 시스템을 몇 개의 하부시스템으로 분해하는 방법에 대한 연구가 진행되어 왔으나 하부시스템 간의 계산소요시간 분배에 대한 고려가 없이 설계자가 임의로 하부시스템의 크기를 자동으로 결정하도록 하였다. 이를 위하여 적응분해기법은 유전알고리듬을 사용하였고, 기존의 병렬분해기법에서 사용된 염색체에 시스템분해 위치를 나타내는 정보를 추가한 확장염색체를 제안하여 병렬처리에 적합한 시스템분해기법을 구현하였다. 그리고, 항공기 설계 문제와 헬기 설계 문제에 적응분해기법을 적용하여 개발된 알고리듬의 효율성을 보였다.

• 한국정보과학회:학술대회논문집
• /
• 한국정보과학회 2005년도 한국컴퓨터종합학술대회 논문집 Vol.32 No.1 (A)
• /
• pp.892-894
• /
• 2005

Graph partitioning provides an important tool for data clustering, but is an NP-hard combinatorial optimization problem. Spectral clustering where the clustering is performed by the eigen-decomposition of an affinity matrix [1,2]. This is a popular way of solving the graph partitioning problem. On the other hand, semidefinite relaxation, is an alternative way of relaxing combinatorial optimization. issuing to a convex optimization[4]. In this paper we present a semidefinite programming (SDP) approach to graph equi-partitioning for clustering and then we use eigen-decomposition to obtain an optimal partition set. Therefore, the method is referred to as semidefinite spectral clustering (SSC). Numerical experiments with several artificial and real data sets, demonstrate the useful behavior of our SSC. compared to existing spectral clustering methods.

• 한국전산유체공학회지
• /
• 제20권2호
• /
• pp.7-15
• /
• 2015

This paper attempts to evaluate the accuracy and efficiency of a design optimization procedure by combining wavelets-based multi resolution analysis method and proper orthogonal decomposition (POD) technique. Aerodynamic design procedure calls for high fidelity computational fluid dynamic (CFD) simulations and the consideration of large number of flow conditions and design constraints. Thus, even with significant computing power advancement, current level of integrated design process requires substantial computing time and resources. POD reduces the degree of freedom of full system by conducting singular value decomposition for various field simulations. In this research, POD combined Design Optimization model is proposed and its efficiency and accuracy are to be evaluated. For additional efficiency improvement of the procedure, multi resolution analysis method is also being employed during snapshot constructions (POD training period). The proposed design procedure was applied to the optimization of wing aerodynamic performance. Throughout the research, it was confirmed that the POD/MRA design procedure could significantly reduce the total design turnaround time and also capture all detailed complex flow features as in full order analysis.

• 한국생산제조학회지
• /
• 제22권1호
• /
• pp.100-106
• /
• 2013

This paper describes an integrated optimization design using multilevel decomposition technique on the base of the parametric distribution and independent axiom at the stages of lower level. Based on Pareto optimum solution, the detailed parameters at the lower level can be defined into the independent axiom. The suspension design is used as the simulation example.

• 한국경영과학회지
• /
• 제11권2호
• /
• pp.37-50
• /
• 1986

In combining dispersed optimization models, either primal or dual(or both) decomposition method widely used as an organizing device. Interpreting the methods economically, the concepts of price and resource-directive coordination are generally well accepted. Most of deomposition/ integration methods utilize either primal information of dual information, not both, from subsystems, while some authors have developed mixed decomposition approaches employing two master problems dealing primal and dual proposals separately. In this paper a hybrid decomposition method is introduced, where one hybrid master problem utilizes the underlying relationships between primal and dual information from each subsystem. The suggested method is well justified with respect to the flexibility in information flow pattern choice (some prices and other quantities) and to the compatibility of subdivision's optimum to the systemwide optimum, that is often lacking in conventional decomposition methods such as Dantzig-Wolfe's. A numerical example is also presented to illustrate the suggested approach.

• 대한원격탐사학회지
• /
• 제26권6호
• /
• pp.681-691
• /
• 2010

LiDAR waveform decomposition plays an important role in LiDAR data processing since the resulting decomposed components are assumed to represent reflection surfaces within waveform footprints and the decomposition results ultimately affect the interpretation of LiDAR waveform data. Decomposing the waveform into a mixture of Gaussians involves two related problems; 1) determining the number of Gaussian components in the waveform, and 2) estimating the parameters of each Gaussian component of the mixture. Previous studies estimated the number of components in the mixture before the parameter optimization step, and it tended to suggest a larger number of components than is required due to the inherent noise embedded in the waveform data. In order to tackle these issues, a new LiDAR waveform decomposition algorithm based on the sequential approach has been proposed in this study and applied to the ICESat waveform data. Experimental results indicated that the proposed algorithm utilized a smaller number of components to decompose waveforms, while resulting IMP value is higher than the GLA14 products.