• Geophysics and Geophysical Exploration
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• v.12 no.1
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• pp.99-104
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• 2009

We propose a waveform inversion method for SH-wave data obtained in a shallow seismic refraction survey, to determine a 2D inhomogeneous S-wave profile of shallow soils. In this method, a 2.5D equation is used to simulate SH-wave propagation in 2D media. The equation is solved with the staggered grid finite-difference approximation to the 4th-order in space and 2nd-order in time, to compute a synthetic wave. The misfit, defined using differences between calculated and observed waveforms, is minimised with a hybrid heuristic search method. We parameterise a 2D subsurface structural model with blocks with different depth boundaries, and S-wave velocities in each block. Numerical experiments were conducted using synthetic SH-wave data with white noise for a model having a blind layer and irregular interfaces. We could reconstruct a structure including a blind layer with reasonable computation time from surface seismic refraction data.

• Korean Journal of Optics and Photonics
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• v.28 no.1
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• pp.1-8
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• 2017

A method for inspecting defects in display substrates utilizing Fourier optics is proposed in this paper. A cost-effective inspection system can be realized with the proposed method, because it does not require a high-magnification microscope. Also, the proposed method can avoid tight tolerance for variations in displacement between substrate and camera, which is stems from shallow depth of field of the high-magnification microscope. In addition, possible damage caused by collisions between substrate and the inspection equipment can be avoided. The decision algorithm can be simpler than for a conventional inspection system, because spatial shift of periodic substrate patterns does not affect the intensity distribution of the diffracted light, by the Fourier transform property. The proposed method is explained with numerical studies, and experiments are carried out to check its feasibility for color-filter substrates of a liquid-crystal display.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.25 no.1
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• pp.101-108
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• 2012

Initial shape analysis for cable-stayed bridges should be able to find optimizated initial cable forces and unstrained length that minimize deflection and bending moments of the deck and pylon. Comparison study of an improved initial force method and TCUD method for determination of initial cable forces in cable-stayed bridges is presented in this paper. For this purpose, an elastic catenary cable element and a nonlinear frame element are firstly described. And concepts and algorithm of two analysis methods are then presented. Finally to demonstrate the validity and the accuracy of two methods, numerical examples for initial state problems of cable-stayed bridges are given and compared based on these methods.

• Transactions of the Korean Society of Mechanical Engineers
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• v.12 no.1
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• pp.106-115
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• 1988

A numerical procedure for determining the airfoil shape that maximizes the lift is presented. The structure of the flow field is calculated by iteratively coupling potential flow and boundary analysis using the viscous-inviscid interaction method. The potential flow field is obtained by the vortex panel method and boundary layer flow is analyzed by means of integral approximation method which is capable of handling the laminar, transition and turbulent flow regimes. As the result of this study, it is found that the calculated flow regimes have good agreement with the existing experimented data. Davidon-Fletcher-Powell method and Augmented Lagrange Multiplier method are used for the optimal techniques. NACA 23012, NACA 65-3-21, NACA 64-2-415, NACA 64-2-A215 airfoils are used for determining the optimal airfoil shapes as a basic and compensate airfoils. Optimal design showed that the lift coefficients are increased by 17.4% at M$_{0}$=0.2 and 29% at M$_{0}$=0.3, compared with those of basic airfoil.oil.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.449-455
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• 2002

This paper considers that advanced planning and scheduling (APS) in manufacturing and the efficient purchasing where each customer order has its due date and multi-suppliers exit We present a Make-To­Order Supply Chan (MTOSC) model of efficient purchasing process from multi-suppliers and APS with outsourcing in a supply chain, which requires the absolute due date and minimized total cost. Our research has included two states. One is for efficient purchasing from suppliers: (a) selection of suppliers for required parts; (b) optimum part lead­time of selected suppliers. Supplier selection process has received considerable attention in the business­management literature. Determining suitable suppliers in the supply chain has become a key strategic consideration. However, the nature of these decisions usually is complex and unstructured. These influence factors can be divided into quantitative and qualitative factors. In the first level, linguistic values are used to assess the ratings for the qualitative factors such as profitability, relationship closeness and quality. In the second level a MTOSC model determines the solutions (supplier selection and order quantity) by considering quantitative factors such as part unit price, supplier's lead-time, and storage cost, etc. The other is for APS: (a) selection of the best machine for each operation; (b) deciding sequence of operations; (c) picking out the operations to be outsourcing; and (d) minimizing makespan under the due date of each customer's order. To solve the model, a genetic algorithm (GA)-based heuristic approach is developed. From the numerical experiments, GA­based approach could efficiently solve the proposed model, and show the best process plan and schedule for all customers' orders.

• Geophysics and Geophysical Exploration
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• v.2 no.4
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• pp.191-201
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• 1999

The interpretation of resistivity data has, so far, mainly been made under the assumption that the earth is of relatively simple structure and then using one or two-dimensional inversion scheme. Since real earth structure and topography are fully three-dimensional and very complicated In nature, however, such assumptions often lead to misinterpretation of the earth structures. In such situations, three-dimensional inversion is probably the only way to get correct image of the earth. In this study, we have developed a three-dimensional inversion code using the finite element solution for the forward problem. The forward modeling algorithm simulates the real field situation with irregular topography. The inverse problem is solved iteratively using the least-squares method with smoothness constraint. Our inversion scheme employs ACB (Active Constraint Balancing) to enhance the resolving power of the inversion. Including Irregular surface topography in the inversion, we can accurately define the earth structures without artifact in the numerical tests. We could get reasonable image of earth structure by Inverting the real field data sets taken over highway bridge construction site.

• Journal of the Korean Society for Railway
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• v.18 no.4
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• pp.363-373
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• 2015

The purpose of this study is to develop a nonlinear algorithm for the dynamic interaction analysis of KTX trains and bridge girders with consideration of separation and flange contact phenomena between wheel and rail. For this, three interaction models between wheel-rail are implemented and compared through numerical examples. That is, the spring model and the non-jump model are briefly explained, and a nonlinear contact model is then proposed to accurately simulate interaction forces of the train-bridge system. Dynamic interaction analysis of a simply supported girder and trains is performed and the analyzed results are presented and compared for the proposed contact model and the other model types. Particularly, flange contact phenomena in the nonlinear contact model are demonstrated under a specific condition.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.42 no.6
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• pp.9-14
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• 2005

In this paper, we consider the synthesis of non-fragile $H_{\infty}$ state feedback controllers for singular systems and static state feedback controller with multiplicative uncertainty. The sufficient condition of controller existence, the design method of non-fragile $H_{\infty}$ controller, and the measure of non-fragility in controller are presented via LMI(linear matrix inequality) technique. Also, the sufficient condition can be rewritten as LMI form in terms of transformed variables through singular value decomposition, some changes of variables, and Schur complements. Therefore, the obtained non-fragile $H_{\infty}$ controller guarantees the asymptotic stability and disturbance attenuation of the closed loop singular systems within a prescribed degree. Moreover, the controller design method can be extended to the problem of robust and non-fragile $H_{\infty}$ controller design method for singular systems with parameter uncertainties. Finally, a numerical example is given to illustrate the design method.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.42 no.6
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• pp.77-82
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• 2005

This paper is proposed fuzzy neural network(FNN) and artificial neural network(ANN) based on the vector controlled induction motor drive system. The hybrid combination of fuzzy control and neural network will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed control and estimation of speed of induction motor using fuzzy and neural network. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The back propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. This paper is proposed the experimental results to verify the effectiveness of the new method.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.2
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• pp.56-64
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• 2018

In recent years, imbalanced data is one of the most important and frequent issue for quality control in industrial field. As an example, defect rate has been drastically reduced thanks to highly developed technology and quality management, so that only few defective data can be obtained from production process. Therefore, quality classification should be performed under the condition that one class (defective dataset) is even smaller than the other class (good dataset). However, traditional multi-class classification methods are not appropriate to deal with such an imbalanced dataset, since they classify data from the difference between one class and the others that can hardly be found in imbalanced datasets. Thus, one-class classification that thoroughly learns patterns of target class is more suitable for imbalanced dataset since it only focuses on data in a target class. So far, several one-class classification methods such as one-class support vector machine, neural network and decision tree there have been suggested. One-class support vector machine and neural network can guarantee good classification rate, and decision tree can provide a set of rules that can be clearly interpreted. However, the classifiers obtained from the former two methods consist of complex mathematical functions and cannot be easily understood by users. In case of decision tree, the criterion for rule generation is ambiguous. Therefore, as an alternative, a new one-class classifier using hyper-rectangles was proposed, which performs precise classification compared to other methods and generates rules clearly understood by users as well. In this paper, we suggest an approach for improving the limitations of those previous one-class classification algorithms. Specifically, the suggested approach produces more improved one-class classifier using hyper-rectangles generated by using Gaussian function. The performance of the suggested algorithm is verified by a numerical experiment, which uses several datasets in UCI machine learning repository.