• Proceedings of the Korean Society of Precision Engineering Conference
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• 2013.05a
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• pp.927-928
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• 2013

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.10
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• pp.1621-1626
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• 2001

The structural optimization have been carried out in the continuous design space or in the discrete design space. Methods fur discrete variables such as genetic algorithms , are extremely expensive in computational cost. In this research, an iterative optimization algorithm using orthogonal arrays is developed for design in discrete space. An orthogonal array is selected on a discrete des inn space and levels are selected from candidate values. Matrix experiments with the orthogonal array are conducted. New results of matrix experiments are obtained with penalty functions leer constraints. A new design is determined from analysis of means(ANOM). An orthogonal array is defined around the new values and matrix experiments are conducted. The final optimum design is found from iterative process. The suggested algorithm has been applied to various problems such as truss and frame type structures. The results are compared with those from a genetic algorithm and discussed.

• Journal of Information Processing Systems
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• v.18 no.6
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• pp.719-728
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• 2022

The prediction of short-term traffic flow is the theoretical basis of intelligent transportation as well as the key technology in traffic flow induction systems. The research on short-term traffic flow prediction has showed the considerable social value. At present, the support vector regression (SVR) intelligent prediction model that is suitable for small samples has been applied in this domain. Aiming at parameter selection difficulty and prediction accuracy improvement, the artificial bee colony (ABC) is adopted in optimizing SVR parameters, which is referred to as the ABC-SVR algorithm in the paper. The simulation experiments are carried out by comparing the ABC-SVR algorithm with SVR algorithm, and the feasibility of the proposed ABC-SVR algorithm is verified by result analysis. Continuously, the simulation experiments are carried out by comparing the ABC-SVR algorithm with particle swarm optimization SVR (PSO-SVR) algorithm and genetic optimization SVR (GA-SVR) algorithm, and a better optimization effect has been attained by simulation experiments and verified by statistical test. Simultaneously, the simulation experiments are carried out by comparing the ABC-SVR algorithm and wavelet neural network time series (WNN-TS) algorithm, and the prediction accuracy of the proposed ABC-SVR algorithm is improved and satisfactory prediction effects have been obtained.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.14 no.1
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• pp.36-42
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• 2006

Kriging has been effectively used to approximate for optimization. This study has been devised to improve efficiency and accuracy of approximate optimal design using Kriging. The design of experiments (DOE), the classical design and space-filling design, are used to provide maximum information using minimum number of design of experiments. The proposed methodology is applied to the designs of 3-bar truss and Sandgren's pressure vessel.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.05a
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• pp.145-146
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• 2010

• Journal of the Microelectronics and Packaging Society
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• v.17 no.4
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• pp.67-72
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• 2010

In this paper, a design optimization of ball grid array packaging geometry is studied based on the Taguchi method, which allowed robust design by considering the variance of the input parameters during the optimization process. Molding compound and substrate were modeled as viscoelastic, and finite element analyses were performed to calculate the strain energy densities of the eutectic solder balls. Six quality factors of the dimensions of the packaging geometry were chosen as control factors. After performing noise experiments to determine the dominant factors, main experiments were conducted to find the optimum packaging geometry. Then the strain energy densities between the original and optimized geometries were compared. It was found that the effects of the packaging geometry on the solder ball reliability were significant, and more than 40% of the strain energy density was reduced by the geometry optimization.

• Transactions of the Korean hydrogen and new energy society
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• v.20 no.6
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• pp.492-498
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• 2009

Six sigma methode was applied for optimization of flow field design of a proton exchange membrane fuel cell (PEMFC). The optimization between number of channel and channel/rib width was suggested in this paper with six sigma method. With the help of six sigma design of experiment (DOE) the number of experiments may be reduced dramatically. The fuel cell channel design optimization with results of these experiments with a 100 $cm^2$ serpentine flow field indicates a optimization data for a given constant operating conditions.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.6 s.177
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• pp.1408-1418
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• 2000

Gradient-based optimization methods are inefficient in applications which require expensive function evaluations, and useless in applications where objective and/or constraint functions are 'noisy' due to modeling and cumulative numerical inaccuracy since gradient evaluation results cannot be reliable. Moreover, it is difficult to be integrated with commercial analysis software, and they cannot be employed when only experimental analysis results are available. In this research an optimization program based on a response surface method has been developed to overcome the aforementioned difficulties. Various methods for design of experiments and new proposed approximation models are implemented in the program. The effectiveness of the optimization program is tested on several test problems and results are discussed.

• Journal of IKEEE
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• v.24 no.4
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• pp.969-974
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• 2020

Research into hyper parameter optimization (HPO) has recently revived with interest in models containing many hyper parameters, such as deep neural networks. In this paper, we introduce the most widely used HPO methods, such as grid search, random search, and Bayesian optimization, and investigate their characteristics through experiments. The MNIST data set is used to compare results in experiments to find the best method that can be used to achieve higher accuracy in a relatively short time simulation. The learning rate and weight decay have been chosen for this experiment because these are the commonly used parameters in this kind of experiment.

• Transactions of the Korean Society of Automotive Engineers
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• v.10 no.1
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• pp.125-132
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• 2002

Recently, ULSAB(Ultra Light Steel Auto Body) concept is getting more attention due to various benefits in automotive body design. One of the ULSAB efforts is making a door with TWB(Tailor Welded Blanks). In TWB, two or more patches of steel panels are welded together before stamping process. In this research, domains and thicknesses of the patches in a front door structure are determined by a series of optimization schemes composed of topology, size and shape optimization and DOE(Design of Experiments) scheme. A door is designed to have better performances compared to exiting structure considering static stiffness and natural frequency. The final design is discussed and compared to the existing design.