• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.14 no.1
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• pp.84-92
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• 2001

In this papers, we proposed the new method in order to diagnosis aging state of transformers. For wavelet transform, Daubechies filter is used, we can obtain wavelet coefficients which is used to extract feature of statistical parameters (maximum value, average value, dispersion skewness, kurtosis) about each acoustic emission signal. Also, these coefficients are used to identify normal and fault signal of internal partial discharge in transformer. As improved method for classification use neural network. Extracted statistical parameters are input into an back-propagation neural network. The number of neurons of hidden layer are obtained through Result of Cross-Validation. The network, after training, can decide whether the test signal is early aging state, alst aging state or normal state. In quantity analysis, capability of proposed method is superior to compared that of classical method.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.29A no.2
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• pp.1-7
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• 1992

A consideration for image improvement under the Born approximation in the microwave diffraction tomography is suggested by using a projection function. The limiting factors in the degrading reconstructed image due to Born approximation are identified in terms of projection function and its modification is suggested to improve the degraded image based upon the Born approximation. In order to verify the proposed method, the reconstructed images are shown by computer simulation from the back-scattered data of angular and frequency diversity for squared dielectric cylinder with a various relative dielectric constant. From simulation results, it was shown that the proposed method can lead to a fairly good improved image for a severe degraded one irrespective of homogeneous and inhomogeneous dielectric object. In the future, the analysis on the limitation of this method should be considered and performed by means of more quantitative method.

• Journal of Advanced Marine Engineering and Technology
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• v.27 no.2
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• pp.289-298
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• 2003

Meshfree approximations exhibit significant Potential to solve partial differential equations. Meshfree methods have been successfully applied to various problems which the traditional finite element methods have difficulties to handle including the quasi-static and dynamic fracture, large deformation problems, contact problems, and strain localization problems. Reproducing Kernel Particle Method (RKPM) is used in this research fur to its built-in feature of multi-resolution. the sound mathematical foundation and good numerical performance. A formulation of RKPM is reviewed and numerical examples are given to verify the accuracy of the proposed meshfree method for largely deformed elasto-plastic material.

• Journal of Advanced Marine Engineering and Technology
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• v.6 no.2
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• pp.42-50
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• 1982

The controller, generally, is to be designed to optimize the system in somesence subject to some constraints. In this paper the method to determine the optimal parameters of the PI controller in the unity feed back control system is proposed. Here the sence of the optimal is to minimize the integral of the squared error under the constraints that the maximum absolute value of control input, maximum overshoot, rise time, etc., should be smaller than the pre-given values. In the analysis the traditional method and the state variable method are combined in order to reduce the computational procedures required in the design of PI controller. Therefore, the procedure proposed in the paper is usefully applicable to the controller system with a general second-order plant. Finally, the indicial response curves generated at the optimal state are compared with those of non-optimal state and the effectiveness of this method is assertained.

• Transactions of the Korean Society of Automotive Engineers
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• v.5 no.6
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• pp.44-52
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• 1997

As part of the effort to reduce the weight of powertrain, a hollow crankshaft has been designed. The mass reduction of the crankshaft changes the dynamic properties of the crankshaft such as moment of inertia, and torsional, bending stiffness. The purpose of this paper is to compare the dynamic behavior of the hollow crankshaft with that of the original, solid crankshaft. Global dynamic behavior of the crankshaft is analyzed bgy the transfer matrix method(TMM). The crankshaft has been modeled by 38 lumped mass and stiffness elements. The dynamic patameters of each lumped element are provided by Finite Element Method(FEM). The responses of the crankshaft from TMM are fed back as loading conditions to the Finite Element model to obtain dynamic stresses for critical areas of the crankshaft.

• Geomechanics and Engineering
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• v.15 no.5
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• pp.1061-1070
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• 2018

The main purpose of retaining wall methods for deep excavation is to keep the construction site safe from the earth pressure acting on the backfill during the construction period. Currently used retaining wall methods include the common strut method, anchor method, slurry wall method, and raker method. However, these methods have drawbacks such as reduced workspace and intrusion into private property, and thus, efforts are being made to improve them. The most advanced retaining wall method is the prestressed wale system, so far, in which a load corresponding to the earth pressure is applied to the wale by using the tension of a prestressed (PS) strand wire. This system affords advantages such as providing sufficient workspace by lengthening the strut interval and minimizing intrusion into private properties adjacent to the site. However, this system cannot control the tension of the PS strand wire, and thus, it cannot actively cope with changes in the earth pressure due to excavation. This study conducts a preliminary numerical analysis of the field applicability of the controllable prestressed wale system (CPWS) which can adjust the tension of the PS strand wire. For the analysis, back analysis was conducted through two-dimensional (2D) and three-dimensional (3D) numerical analyses based on the field measurement data of the typical strut method, and then, the field applicability of CPWS was examined by comparing the lateral deflection of the wall and adjacent ground surface settlements under the same conditions. In addition, the displacement and settlement of the wall were predicted through numerical analysis while the prestress force of CPWS was varied, and the structural stability was analysed through load tests on model specimens.

• Journal of Korean Society of Transportation
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• v.22 no.2 s.73
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• pp.121-130
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• 2004

In these days, multiple-path generation method is highly demanded in practice and research areas, which can represents realistically travelers behavior in choosing possible alternative paths. The multiple-path generation algorithm is one of the key components for policy analysis related to ATIS, DRGS and ATMS in ITS. This study suggested a method to generate multiple Possible paths from an origin to a destination. The approach of the suggested method is different from an other existing methods(K-shortest path algorithm) such as link elimination approach, link penalty approach and simulation approach. The result of the multi-label vine-building shortest path algorithm(MVA) by Kim (1998) and Kim(2001) was used to generate multiple reasonable possible paths with the concept of the rational upper boundary. Because the MVA algorithm records the cost, back-node and back-back node of the minimum path from the origin to the concerned node(intersection) for each direction to the node, many potential possible paths can be generated by tracing back. Among such large number of the potential possible paths, the algorithm distinguishes reasonable alternative paths from the unrealistic potential possible paths by using the concept of the rational upper boundary. The study also shows the very simple network examples to help the concept of the suggested path generation algorithm.

• International Journal of Aeronautical and Space Sciences
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• v.9 no.1
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• pp.100-110
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• 2008

It is not easy to monitor and identify all engine faults and conditions using conventional fault detection approaches like the GPA (Gas Path Analysis) method due to the nature and complexity of the faults. This study therefore focuses on a model based diagnostic method using Neural Network algorithms proposed for fault detection on a turbo shaft engine (PW 206C) selected as the power plant for a tilt rotor type unmanned aerial vehicle (Smart UAV). The model based diagnosis should be performed by a precise performance model. However component maps for the performance model were not provided by the engine manufacturer. Therefore they were generated by a new component map generation method, namely hybrid method using system identification and genetic algorithms that identifies inversely component characteristics from limited performance deck data provided by the engine manufacturer. Performance simulations at different operating conditions were performed on the PW206C turbo shaft engine using SIMULINK. In order to train the proposed BPNN (Back Propagation Neural Network), performance data sets obtained from performance analysis results using various implanted component degradations were used. The trained NN system could reasonably detect the faulted components including the fault pattern and quantity of the study engine at various operating conditions.

• Journal of the Korean Magnetics Society
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• v.25 no.6
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• pp.203-207
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• 2015

This paper introduces a probabilistic analysis method in order to analyze the effect of manufacturing tolerance on induction motor performance occurring in massive production. The univariate dimension reduction method is adapted to predict probabilistic characteristics of a performance function due to certain probabilistic distributions of design variables. Moreover, the sensitivity information on mean and variance of the performance function is estimated, and then the effect of randomness of individual design variables on the probability performance function is analyzed. The effectiveness and accuracy of the method is investigated with a mathematical model and an induction motor.

• Fibers and Polymers
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• v.7 no.4
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• pp.404-413
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• 2006

This study examines multiple quality optimization of the injection molding for Polyether Ether Ketone (PEEK). It also looks into the dimensional deviation and strength of screws that are reduced and improved for the molding quality, respectively. This study applies the Taguchi method to cut down on the number of experiments and combines grey relational analysis to determine the optimal processing parameters for multiple quality characteristics. The quality characteristics of this experiment are the screws' outer diameter, tensile strength and twisting strength. First, one should determine the processing parameters that may affect the injection molding with the $L_{18}(2^1{\times}3^7)$ orthogonal, including mold temperature, pre-plasticity amount, injection pressure, injection speed, screw speed, packing pressure, packing time and cooling time. Then, the grey relational analysis, whose response table and response graph indicate the optimum processing parameters for multiple quality characteristics, is applied to resolve this drawback. The Taguchi method only takes a single quality characteristic into consideration. Finally, a processing parameter prediction system is established by using the back-propagation neural network. The percentage errors all fall within 2%, between the predicted values and the target values. This reveals that the prediction system established in this study produces excellent results.