• International Journal of Fluid Machinery and Systems
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• v.3 no.1
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• pp.29-38
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• 2010

This paper presents a procedure for the design optimization of a centrifugal compressor. The centrifugal compressor consists of a centrifugal impeller, vaneless diffuser and volute. And, optimization techniques based on the radial basis neural network method are used to optimize the impeller of a centrifugal compressor. The Latin-hypercube sampling of design-of-experiments is used to generate the thirty design points within design spaces. Three-dimensional Reynolds-averaged Navier-Stokes equations with the shear stress transport turbulence model are discretized by using finite volume approximations and solved on hexahedral grids to evaluate the objective function of the total-to-total pressure ratio. Four variables defining the impeller hub and shroud contours are selected as design variables in this optimization. The results of optimization show that the total-to-total pressure ratio of the optimized shape at the design flow coefficient is enhanced by 2.46% and the total-to-total pressure ratios at the off-design points are also improved significantly by the design optimization.

• Journal of Korean Society of Steel Construction
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• v.16 no.6 s.73
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• pp.819-832
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• 2004

This study describes an efficient and facile method for configuration optimum design of structures. One of the ways to achieve numerical shape representation and the selection of design variables is using the design element concept. Using this technique, the number of design variables could be drastically reduced. Isoparametric mapping was utilized to automatically generate the finite element mesh during the optimization process, and this made it possible to easily calculate the derivatives of the coordinates of generated finite element nodes w.r.t. the design variables. For the structural analysis, finite element analysis was adopted in the optimization procedure, and two different techniques(the deterministic method, a modified method of feasible direction; and the stochastic method, a genetic algorithms) were applied to obtain the minimum volumes and section areas for an efficient configuration optimization procedure. Futhermore, spline interpolation was introduced to present a realistic optimum configuration that meet the manufacturing requirements. According to the results of several numerical examples(steel structures), the two techniques suggested in this study simplified the process of configuration optimum design of structures, and yielded improved objective function values with a robust convergence rate. This study's applicability and capability have therefore been demonstrated.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.1
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• pp.184-191
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• 2010

Many researchers have been considering genetic algorithms to system optimization problems. Especially, real-coded genetic algorithms are very effective techniques because they are simpler in coding procedures than binary-coded genetic algorithms and can reduce extra works that increase the length of chromosome for wide search space. Thus, this paper presents a fuzzy system design technique to improve the performance of the fuzzy system. The proposed system consists of two procedures. The primary tuning procedure coarsely tunes fuzzy sets of the system using the k-means clustering algorithm of which the structure is very simple, and then the secondary tuning procedure finely tunes the fuzzy sets using enhanced real-coded genetic algorithms based on the primary procedure. In addition, this paper constructs multiple fuzzy systems using a data preprocessing procedure which is contrived for reflecting various characteristics of nonlinear data. Finally, the proposed fuzzy system is applied to the field of time series prediction and the effectiveness of the proposed techniques are verified by simulations of typical time series examples.

• International Journal of Korean Welding Society
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• v.1 no.2
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• pp.12-17
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• 2001

With the advance of the robotic welding process, procedure optimization that selects the welding procedure and predicts bead width that will be deposited is increased. A major concern involving procedure optimization should define a welding procedure that can be shown to be the best with respect to some standard and chosen combination of process parameters, which give an acceptable balance between production rate and the scope of defects for a given situation. This paper presents a new algorithm to establish a mathematical model f3r predicting bead width through a neural network and multiple regression methods, to understand relationships between process parameters and bead width, and to predict process parameters on bead width for GMA welding process. Using a series of robotic arc welding, additional multi-pass butt welds were carried out in order to verify the performance of the neural network estimator and multiple regression methods as well as to select the most suitable model. The results show that not only the proposed models can predict the bead width with reasonable accuracy and guarantee the uniform weld quality, but also a neural network model could be better than the empirical models.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2008.10a
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• pp.308-311
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• 2008

The ant colony optimization (ACO) is a metaheuristic inspired by the behavior of real ants. Recently, ACO has been widely used to solve the difficult combinatorial optimization problems. In this paper, we propose an ACO algorithm to solve the two disjoint paths problem with dual link cost structure (TDPDCP). We propose a dual pheromone structure and a procedure for solution construction which is appropriate for the TDPDCP. Computational comparisons with the state-of-the-arts algorithms are also provided.

• Communications for Statistical Applications and Methods
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• v.6 no.1
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• pp.143-150
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• 1999

Ko(1998) proposed a procedure to enhance the efficiency of double sampling plans by allowing second-stage sample size and critical region to depend on first-stage evidence using constraint optimization approaches. In this study further developments of such plans by incorporating several practically possible researcher's aims into the optimization are considered. Comparisons are made with the optimal ordinary double sampling plan and also among them It is observed that it is to some extent possible to match the details of the optimization to certain qualitative methodological aims.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1996.10a
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• pp.113-120
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• 1996

This study presents the optimization technique to determine the cable areas of the cable-stayed bridge. The optimization method presented in this paper is based on an evolutionary procedure, in which the area of high stressed cable is increased step-by-step until an optimal area of the cable is obtained. A comparison between the maximum values of the present method and those of the cable-stayed bridge that has the same cable area shows the advantages of the present method.

• 한국전산유체공학회:학술대회논문집
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• 1995.10a
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• pp.154-159
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• 1995

Numerical optimization technique with Navier-Stokes code has been used to reduce the drag of conventional ogival nose. Forebody optimizations are performed for supersonic laminar and turbulent flow conditions. To alleviate the computing time of aerodynamic drag calculation, axisymmetric boundary condition is implemented in the 3-dimensional Navier-Stokes code. The automated optimization procedure with gradient based method results in a drag reduction of $4\;\%$.

• Journal of the Korean Operations Research and Management Science Society
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• v.11 no.2
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• pp.88-100
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• 1986

The focus of this paper is to develop the optimization procedures and analyze the complexities of the nonserial diverging branch systems in Dynamic Programming. The optimization procedure of the system is developed such that it helps to reduce the computational demands of the system. The complexity of the network is analyzed with the increasing number of nodes, branches and their connectedness to the main serial system. Determination of the optimal set of nodes for the main serial chain is also investigated.

• Journal of the Korean Data and Information Science Society
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• v.17 no.3
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• pp.905-912
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• 2006

Support vector classification(SVC) provides more complete description of the linear and nonlinear relationships between input vectors and classifiers. In this paper we propose to solve the optimization problem of SVC with a modified hinge loss function, which enables to use an iterative reweighted least squares(IRWLS) procedure. We also introduce the approximate cross validation function to select the hyperparameters which affect the performance of SVC. Experimental results are then presented which illustrate the performance of the proposed procedure for classification.