• Journal of applied mathematics & informatics
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• v.26 no.1_2
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• pp.223-233
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• 2008

In this paper we give sufficient conditions for a point to be a solution of an optimization problem in complex space.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.16 no.6 s.111
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• pp.619-626
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• 2006

Body-on-frame type vehicle has a set of frame bushes between body and frame for vibration isolation. Such frame bushes are important vibration transmission paths to passenger space for excitations during driving. In order to reduce the vibration level of passenger space, therefore, change of complex stiffness of the frame bushes is more efficient than modification of other parts of the vehicle such as body, frame and suspension. The purpose of this study is to reduce the vibration level for ride comfort by optimization of complex stiffness of frame bushes. In order to do this, a simple finite element vehicle model was constructed and complex stiffness of the frame bushes was set to be design variables. The objective function was defined to reflect frequency dependence of passenger ride comfort. Genetic algorithm and sub-structure synthesis were applied for minimization of the objective function. After optimization level at a position of interest on the car body was reduced by about 43.7 % in RMS value. Causes for optimization results are discussed.

• Journal of Korean Association for Spatial Structures
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• v.10 no.4
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• pp.113-122
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• 2010

Space frame structures are included in the large spatial structures and can adopt various structure types. But, it is not easy to choose the optimal member size and shape because it depends on the structural engineer's experience and the repeated trial and error. Therefore, in this study, the final goal is to help the designer with the selection of the optimum shape. First, various space frame structures with ellipse dome and vault complex types are chosen and the shape generation method is considered to generate the nodes, coordinates and members. In optimal design process of space frame structure, each node coordinate changes according to height variation or the number of rings. Therefore, the auto generation technique of nodes and members is required in order to consider this phenomenon in optimal design process. Next, the shape generation module is created, base on the shape generation method. This module is connected with the analysis module and the optimization algorithm. Finally, the example model is presented for the evaluation of the efficiency of optimization algorithms.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.10
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• pp.1558-1565
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• 2004

It is important to determine supporting locations for structural stability when a structure is loaded with non-uniform load or supporting locations as well as the number of the supporting structures are restricted by the problem of space. Moreover, the supporting location optimization of complex structure in real world is frequently faced with discontinuous design space. Therefore, the traditional optimization methods based on derivative are not suitable Whereas, Genetic Algorithm (CA) based on stochastic search technique is a very robust and general method. The KSTAR in-vessel control coil installed in vacuum vessel is loaded with non- uniform electro-magnetic load and supporting locations are restricted by the problem of space. This paper shows the supporting location optimization for structural stability of the in-vessel control coil. Optimization has been performed by means of a developed program. It consists of a Finite Element Analysis interfaced with a Genetic Algorithm. In addition, this paper presents an algorithm to find an optimum solution in discontinuous space using continuous design variables.

• Korean Institute of Interior Design Journal
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• v.20 no.5
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• pp.68-77
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• 2011

In this research it is concentrated first of all on the attempts to reconstruct the historical context of the idea for the space design based on the natural construction and to re-appropriate il critically to the present context. Sequentially in the areas of philosophy, biology, neuroscience, and architecture it has been variously discussed on the problems about the synthesis of biology and techniques. In the context of the consilience of biology and technique Werner Nachtigall, who has intended to shed light on the morphological principles in the natural construction, founded the 'Bionik', which is different from the bionics or the biomechanics that are oriented to the imitation of natural forms. The space design that is on the basis of the Bionik treats organisms as a functional whole. Therefore the Bionik space design follows two kinds of principle such as the principle of analogy and the principle of optimization. After all the understanding of the consilience of nature and technique for Nachtigall and Bionik designers tends toward the explication of the complex process in which the human perceptions, the environment, and the phenomenal techniques are united together, and this complex process is associated with the space design based on the Bionik.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.35 no.12
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• pp.1121-1128
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• 2007

MDO(Multidisciplinary Design and Optimization) can be applied for design of complex systems such as aircraft and SLV(Space Launch Vehicle). MDO System can be an integrated environment or a system, which is for synthetic and instantaneous analysis and design optimization in various design fields. MDO System has to efficiently use and integrate distributed resources such as various analysis codes, optimization codes, CAD, DBMS, GUI, and etc. in heterogeneous environments. In this paper, we present Web Services-based MDO System that integrates resources for MDO using Globus Toolkit and provides organic autonomous execution using automation technique such as Workflow system and agent. And also, it provides collaborative design environment through web user interfaces.

• Journal of Korean Society for Quality Management
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• v.29 no.3
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• pp.86-96
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• 2001

The objective of this research is to develop a robust design methodology for plastic injection molded parts wherein warpage will be minimized by a complex method which is a kind of a simple direct search method. The design space considered for optimization is divided Into two sub-design space : mold and process conditions. Warpage is quantified using the Moldflow injection molding simulation software. The design methodology was applied to an actual part of a fax machine, the Guide-ASF model, through two different design policies. The significance of this study is the synthesis of a computer simulation of injection molding process and optimization technique to determine the optimal robust design solution.

• Steel and Composite Structures
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• v.25 no.6
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• pp.663-670
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• 2017

The problem of layup optimization of the composite laminates involves a very complex multidimensional solution space which is usually non-exhaustively explored using different heuristic computational methods such as genetic algorithms (GA). To ensure the convergence to the global optimum of the applied heuristic during the optimization process it is necessary to evaluate a lot of layup configurations. As a consequence the analysis of an individual layup configuration should be fast enough to maintain the convergence time range to an acceptable level. On the other hand the mechanical behavior analysis of composite laminates for any geometry and boundary condition is very convoluted and is performed by computational expensive numerical tools such as finite element analysis (FEA). In this respect some studies propose very fast FEA models used in layup optimization. However, the lower bound of the execution time of FEA models is determined by the global linear system solving which in some complex applications can be unacceptable. Moreover, in some situation it may be highly preferred to decrease the optimization time with the cost of a small reduction in the analysis accuracy. In this paper we explore some machine learning techniques in order to estimate the failure of a layup configuration. The estimated response can be qualitative (the configuration fails or not) or quantitative (the value of the failure factor). The procedure consists of generating a population of random observations (configurations) spread across solution space and evaluating using a FEA model. The machine learning method is then trained using this population and the trained model is then used to estimate failure in the optimization process. The results obtained are very promising as illustrated with an example where the misclassification rate of the qualitative response is smaller than 2%.

• Advances in Computational Design
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• v.2 no.3
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• pp.195-210
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• 2017

Simultaneous optimization of trusses which concurrently takes into account design variables related to the size, shape and topology of the structure is recognized as highly complex optimization problems. In this class of optimization problems, it is possible to encounter several unstable mechanisms throughout the solution process. However, to obtain a feasible solution, these unstable mechanisms somehow should be rejected from the set of candidate solutions. This study proposes triangular unit based method (TUBM) instead of ground structure method, which is conventionally used in the topology optimization, to decrease the complexity of search space of simultaneous optimization of the planar truss structures. TUBM considers stability of the triangular units for 2 dimensional truss systems. In addition, integrated particle swarm optimizer (iPSO) strengthened with robust technique so called improved fly-back mechanism is employed as the optimizer tool to obtain the solution for these class of problems. The results obtained in this study show the applicability and efficiency of the TUBM combined with iPSO for the simultaneous optimization of planar truss structures.

• Journal of Electrical Engineering and Technology
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• v.13 no.6
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• pp.2511-2520
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• 2018

In this study, Multi-Radial Basis Function Support Vector Machine (Multi-RBF SVM) classifier is introduced based on a composite kernel function. In the proposed multi-RBF support vector machine classifier, the input space is divided into several local subsets considered for extremely nonlinear classification tasks. Each local subset is expressed as nonlinear classification subspace and mapped into feature space by using kernel function. The composite kernel function employs the dual RBF structure. By capturing the nonlinear distribution knowledge of local subsets, the training data is mapped into higher feature space, then Multi-SVM classifier is realized by using the composite kernel function through optimization procedure similar to conventional SVM classifier. The original training data set is partitioned by using some unsupervised learning methods such as clustering methods. In this study, three types of clustering method are considered such as Affinity propagation (AP), Hard C-Mean (HCM) and Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA). Experimental results on benchmark machine learning datasets show that the proposed method improves the classification performance efficiently.