• Journal of Information Processing Systems
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• v.4 no.4
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• pp.153-158
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• 2008

This paper deals with the geometric fitting algorithms for parametric curves and surfaces in 2-D/3-D space, which estimate the curve/surface parameters by minimizing the square sum of the shortest distances between the curve/surface and the given points. We identify three algorithmic approaches for solving the nonlinear problem of geometric fitting. As their general implementation we describe a new algorithm for geometric fitting of parametric curves and surfaces. The curve/surface parameters are estimated in terms of form, position, and rotation parameters. We test and evaluate the performances of the algorithms with fitting examples.

• Proceedings of the KIEE Conference
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• 2005.07b
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• pp.1095-1097
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• 2005

This paper deals with an automatic design standard computation based on a rated watt for a synchronous reluctance motor(SynRM). The focus of this paper is making the design relative to the output power on the basis of rotor shape of a SynRM in each rated watt using a coupled FEM & sequential unconstrained minimization technique(SUMT). The coupled finite elements analysis(FEA) & Preisach model have been used to evaluate nonlinear solutions. The proposed procedure allows to define the rotor geometric dimensions according to the rotor diameter and rated watt starting from an existing motor or a preliminary design.

• Proceedings of the Korea Water Resources Association Conference
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• 2002.05b
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• pp.829-836
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• 2002

The paper presents a multi-objective genetic algorithm approach to the design of a water distribution network. The objectives considered are minimization of network cost and maximization of a reliability measure. In this study, a new reliability measure, called network resilience, is introduced. This measure mimics a designer's desire of providing excess power at nodes and designing reliable loops with practicable pipe diameters. The proposed method produces a set of Pareto-optimal solutions in the search space of cost and network resilience. Genetic algorithms are observed to be poor in handling constraints. To handle constraints in a better way, a constraint handling technique that does not require a penalty coefficient and applicable to water distribution systems is presented. The present model is applied to two example problems, which were widely reported. Pipe failure analysis carried out on some of the solutions obtained revealed that the network resilience based approach gave better results in terms of network reliability.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.11a
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• pp.623-628
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• 2000

This paper deals with an analysis and reduction of escalator vibration by using the response surface model. Optimization of the escalator vibration is performed by minimization of the vibration responses which are measured at steps. The response surface models of the factors are constructed by using the experimental data based on the D optimal design method. The multi-objective optimization is also performed by applying desirability function and overlaid contour plot techniques. The optimal solution, which is obtained for a typical escalator system, is applied to reduce the escalator vibration.

• Structural Engineering and Mechanics
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• v.60 no.6
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• pp.923-937
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• 2016

The common prospect in diminishing mine-blast vibration is decreasing vibration with increasing distance. This paper indicates that, contrary to the general expectancy, vibration waves change their forms when they are travelling through the low velocity layer like coal and so-called guided waves moving the vibration waves to longer distances without decreasing their amplitudes. The reason for this unexpected vibration increase is the formation of guided waves in the coal bed which has low density and low seismic velocity with respect to the neighboring layers. The amplitudes of these guided waves, that are capable of traveling long distances depending on the seam thickness, are several times higher than that of the usual vibration waves. This phenomenon can many complaints from the residential areas very far away from the blasting sites. Thus, this unexpected behavior of the coal beds in the surface coal mines should also be considered in vibration minimization studies. This study developed a model to predict the effects of guided waves on the propagation ways of blast-induced vibrations. Therefore, vibration mitigation studies considering the nearby buildings can be focused on these target places.

• Structural Engineering and Mechanics
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• v.70 no.6
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• pp.649-659
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• 2019

Vibration-based structural damage detection through optimization algorithms and minimization of objective function has recently become an interesting research topic. Application of various objective functions as well as optimization algorithms may affect damage diagnosis quality. This paper proposes a new damage identification method using Moth-Flame Optimization (MFO). MFO is a nature-inspired algorithm based on moth's ability to navigate in dark. Objective function consists of a term with modal assurance criterion flexibility and natural frequency. To show the performance of the said method, two numerical examples including truss and shear frame have been studied. Furthermore, Los Alamos National Laboratory test structure was used for validation purposes. Finite element model for both experimental and numerical examples was created by MATLAB software to extract modal properties of the structure. Mode shapes and natural frequencies were contaminated with noise in above mentioned numerical examples. In the meantime, one of the classical optimization algorithms called particle swarm optimization was compared with MFO. In short, results obtained from numerical and experimental examples showed that the presented method is efficient in damage identification.

• East Asian mathematical journal
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• v.37 no.3
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• pp.333-354
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• 2021

A support vector machine (SVM) is a state-of-the-art machine learning model rooted in structural risk minimization. SVM is underestimated with regards to its application to real world problems because of the difficulties associated with its use. We aim at showing that the performance of SVM highly depends on which kernel function to use. To achieve these, after providing a summary of support vector machines and kernel function, we constructed experiments with various benchmark datasets to compare the performance of various kernel functions. For evaluating the performance of SVM, the F1-score and its Standard Deviation with 10-cross validation was used. Furthermore, we used taylor diagrams to reveal the difference between kernels. Finally, we provided Python codes for all our experiments to enable re-implementation of the experiments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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• pp.3479-3492
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• 2022

Longitudinal quantification of brain changes due to development, aging or disease plays an important role in the filed of personalized-medicine applications. However, due to the temporal variability in shape and different imaging equipment and parameters, estimating anatomical changes in longitudinal studies is significantly challenging. In this paper, a longitudinal Magnetic Resonance(MR) brain image segmentation algorithm proposed by combining intensity information and anisotropic smoothness term which contain a spatial smoothness constraint and longitudinal consistent constraint into a variational framework. The minimization of the proposed energy functional is strictly and effectively derived from a fast optimization algorithm. A large number of experimental results show that the proposed method can guarantee segmentation accuracy and longitudinal consistency in both simulated and real longitudinal MR brain images for analysis of anatomical changes over time.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.3
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• pp.34-41
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• 1996

This work deals with finding a suitable position correction algorithm of industrial robot based on measuring particular points, which calculates two dimensional correction quantities and the must allow visually acceptable door-chassis assembly task. Three optimizing algorithms corresponding to three differ- ent error based performance indices are compared and selected to the best one, in terms of the predefined total uniformity, line uniformity and computational time. The selected algorithm(Total Error Minimization) is implemented for a simple door-chassis model to show its effectiveness.

• Progress in Medical Physics
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• v.5 no.1
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• pp.41-53
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• 1994

The accurate localization of electrical sources in the brain is one of the most important questions in EEG, especially in the analysis of evoked responses and of epileptiform spike activity. A detailed simulation study of single dipole source estimation based on EEG is given in this paper. The effects of dipole model parameters on single dipole source tracing in EEG are examined in some detail using the Monte Carlo simulation. The error of source localization is found to be greatly influenced by how the electrodes are distributed over the head and the number of them.