• Proceedings of the KSME Conference
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• 2000.04b
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• pp.605-610
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• 2000

A series of wind tunnel test were conducted on Korean high speed train model to understand the flow physics around the vehicle related to the aerodynamic drag. For the wind tunnel test on high-speed ground vehicle, a moving ground simulation is necessary to predict the aerodynamic drag accurately. So, the models were tested in three wind tunnels with various ground simulation facility including moving belt ground plane system and tangential blowing system. The test results including measured aerodynamic drag and flow visualization showed that a tangential blowing method can be an alternative ground simulation method in short time using conventional wind tunnel.

• Journal of Korea Water Resources Association
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• v.35 no.5
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• pp.563-573
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• 2002

In this study, reflection of water waves over a train of rectangular submerged breakwaters is experimentally investigated. Measured reflection coefficients of regular waves are compared with predicted coefficients obtained from the eigenfunction expansion method. Although measured coefficients are slightly smaller than predicted ones, the overall agreement is very good.

• Proceedings of the KIPE Conference
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• 1998.10a
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• pp.944-949
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• 1998

This paper presents a new high-performance intelligent controlled current-source (CCS) system which can compensate for modelling errors of armature resistance, leakage and magnetizing inductances and for space harmonic components of speed EMF's. A shuttle-motion simulation is presented in the mass-reduced-mode in which an equivalent vehicle weight is reduced, by 40kg to 7kg. This study provides one of the most important key-tech-nologies in driving practical linear synchronous motor (LSM) Maglev vehicle.

• Journal of Electrical Engineering and Technology
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• v.9 no.1
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• pp.214-230
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• 2014

This paper presents a high performance sensor less control four motorized wheels for electric vehicle. Firstly, we applied a sensor less master-slave DTC based control to both the two in wheel motors by using sliding mode observer for its quick response and its high reliability in electric vehicle application. Secondly, to overcome the possible loss of adherence of one of the four wheels which is likely to destabilize the vehicle a solution is proposed in this paper. Thirdly, a Fuzzy logic anti-skid control structure well adapted to the non-linear system is used to overcome the main problem of power train system in the wheel road adhesion characteristic. Various Simulation results have been include in this paper to show that the proposed control strategy can prevent vehicle sliding and show good vehicle stability on a curved path.

• The KSFM Journal of Fluid Machinery
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• v.11 no.2
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• pp.46-54
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• 2008

This study presents a numerical optimization to optimize an axial flow fan blade to increase the efficiency. The radial basis neural network is used as an optimization method with the numerical analysis by Reynolds-averaged Navier-Stokes equations using SST model as turbulence closure. Four design variables related to airfoil maximum camber, maximum camber location, leading edge radius and trailing edge radius, respectively, are selected, and efficiency is considered as objective function which is to be maximized. Thirty designs are evaluated to get the objective function values of each design used to train the neural network. Optimum shape shows the efficiency increased by 1.0%.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.3
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• pp.174-178
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• 2002

This paper discusses a blind equalization technique for FIR channel system, that might be minimum phase or not, in digital communication. The proposed techniques consist of two parts. One is to estimate the original channel coefficients based on fourth-order cumulants of the channel output, the other is to employ RBF neural network to model an inverse system fur the original channel. Here, the estimated channel is used as a reference system to train the RBF. The proposed RBF equalizer provides fast and easy teaming, due to the structural efficiency and excellent recognition-capability of R3F neural network. Throughout the simulation studies, it was found that the proposed blind RBF equalizer performed favorably better than the blind MLP equalizer, while requiring the relatively smaller computation steps in tranining.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.3
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• pp.65-71
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• 1997

With the emergence of neural network, there is a revived interest in identification of nonlinear systems. So in this paper, to identify unknown nonlinear systems dynamically we propose DPNN(Dynamic Polynomial Neural Network) using GMDH (Group Method of Data Handling) algorithm. The dynamic system identification using GMDH consists of applying a set of inputloutput data to train the network by dynamically computing the necessary coeffici1:nt sets. Then, MRAC(Mode1 Reference Adaptive Control) is designed to control nonlinear systems using DPNN. In the result, we can see that the modelling and control using DPNN work well by computer simulation.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.435-435
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• 2000

Light Rail Transit (LRT) system is a complex and large system in which there are many subsystems, interfaces, functions and demanding performance requirements. Because many contractors participate in the development, it is necessary to apply methods of sharing common objectives and communicating effectively among all of the stakeholders. This paper shows not only the methodology and the results of computer-aided systems engineering including requirement management, functional analysis and architecting LRT system, but also propose a tool to help manage a project by linking WBS (Work Breakdown Structure), work organization and PBS (Product Breakdown Structure). The application of computer-aided tool RDD-100 provides the capability to model product design knowledge and decisions about important issues such as architecting the top-level system. The product design knowledge will be essential in integrating the following life-cycle phase activities over the life of the LRT system. Additionally, when a new generation train system is required, the reuse of the database can increase the system design productivity and effectiveness significantly.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.414-419
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• 1994

In recent years, there has been an increasing intest in control of active automotive suspension systems with a goal of improving the ride comfort and safety. Many approaches for these purposes have used linearized models of the suspension's dynamics, allowing the use of linear control theory. However, the linearized model does not well descriibe the actual system behavior which is inherently nonlinear. The object of this study is to develop a neuro controlled active suspension for the ride quality improvement. After obtaining active control law using optimal control theory, we use the artificial neural network to train the neuro controller to learn the relation of road input and control force. Form the numerical results, we found that back propagation learning does show good pattern matching and vertical acceleration of the driver's seat and sprung mass.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.16 no.2 s.107
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• pp.156-162
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• 2006

For the reduction and efficient management of railway noise, first of all prediction of railway noise is necessarily requisited. Many studies for prediction of railway nearby noise have been accomplished. But it is impossible to predict easily and exactly for the Korean Railway, because the acoustic powers for each rolling stock operated in Korea have not been built yet. So in this study, Prediction model equation for environmental noise for Korean rolling stock Saemaeul was suggested using SEL of engine and rolling noise component separately. Finally for the validation of prediction equation, the predicted result was compared to the measured.