• Journal of Mechanical Science and Technology
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• v.20 no.4
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• pp.482-493
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• 2006

The carbody structure of a high speed train passing through a tunnel is subjected to pressure fluctuation. Fatigue strength of the carbody structure against the fluctuating pressure loading should be proved in the design stage for safety. In this study, to get the useful information on the pressure fluctuation in the tunnel, measurement has been conducted during test running of KHST on the high speed line for two years. The measured results were analyzed and arranged to be used for carbody design. A prediction method for the magnitude and frequency of pressure change was proposed and the propagating characteristics of pressure wave was investigated. By statistical analysis for the measured results, a pressure loading spectrum for the high speed train was given. The proposed method can also be used to estimate the pressure loading spectrum for new high speed line at design stage combined with the results of train performance simulation.

• Korean Journal of Computational Design and Engineering
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• v.8 no.4
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• pp.222-230
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• 2003

Artificial intelligence has been used in the configuration design stage of high-speed train. The traction system of a high-speed train is composed of transformers, motor blocks, and traction motors of which locations and number in the trainset should be determined in the early stage of the train conceptual design. Components of the traction system are heavy parts in the train, so it gives strong influence to the top speeds and overall train configuration of high-speed trains. Top speeds have been predicted using the neural network with the associated data of the traction system. The neural networks have been learned with data sets of many commercially operated high-speed trains, and the predicted results have been compared with the actual values. The configuration design of the train set of a high-speed train determines the basic specification of the train and layout of the traction system. The neural networks is a useful design tool when there is not sufficient data for the configuration design and we need to use the existing data of other train for the prediction of trainset in development.

• International Journal of Advanced Culture Technology
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• v.7 no.4
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• pp.327-333
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• 2019

Wind speed data constitute important weather information for aircrafts flying at low altitudes, such as drones. Currently, the accuracy of low altitude wind predictions is much lower than that of high-altitude wind predictions. Deep neural networks are proposed in this study as a method to improve wind speed forecast information. Deep neural networks mimic the learning process of the interactions among neurons in the brain, and it is used in various fields, such as recognition of image, sound, and texts, image and natural language processing, and pattern recognition in time-series. In this study, the deep neural network model is constructed using the wind prediction values generated by the numerical model as an input to improve the wind speed forecasts. Using the ground wind speed forecast data collected at the Boseong Meteorological Observation Tower, wind speed forecast values obtained by the numerical model are compared with those obtained by the model proposed in this study for the verification of the validity and compatibility of the proposed model.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.11b
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• pp.1207-1214
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• 2001

The paper deals with the computational approach in analysis and design of pantograph panhead strips of high-speed railway in aerodynamic and aeroacoustic concerns. Pantograph is an equipment such that the electric power is supplied from catenary system to train. Due to the nature of complexity in high-speed fluid flow, turbulence and downstream vortices result in the instability in the aerodynamic contact between panhead strips and catenary system, and consequently generate the considerable levels of flow-induced sound. In this paper, based on the preceding low speed wind-tunnel test and simulations, the aerodynamic and aeroacoustic characteristics in low speed are analyzed.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.1
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• pp.139-147
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• 2003

Dynamic neural networks have been applied to diverse fields requiring temporal signal processing such as signal prediction. This paper proposes the hybrid network, composed of locally(LRNN) and globally recurrent neural networks(GRNN), to improve dynamics of multilayered recurrent networks(RNN) and then describes nonlinear adaptive prediction using the proposed network as an adaptive filter. The hybrid network consists of IIR-MLP and Elman RNN as LRNN and GRNN, respectively. The proposed network is evaluated in nonlinear signal prediction and compared with Elman RNN and IIR-MLP networks for the relative comparison of prediction performance. Experimental results show that the hybrid network performs better with respect to convergence speed and accuracy, indicating that the proposed network can be a more effective prediction model than conventional multilayered recurrent networks in nonlinear prediction for nonstationary signals.

• Atmosphere
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• v.31 no.1
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• pp.85-100
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• 2021

The necessity of accurate high-resolution meteorological forecasts becomes increasing in socio-economical applications and disaster risk management. The Korea Meteorological Administration Post-Processing (KMAPP) system has been operated to provide high-resolution meteorological forecasts of 100 m over the South Korea region. This study evaluates and improves the KMAPP performance in simulating wind speeds over complex terrain areas using the ICE-POP 2018 field campaign measurements. The mountainous measurements give a unique opportunity to evaluate the operational wind speed forecasts over the complex terrain area. The one-month wintertime forecasts revealed that the operational Local Data Assimilation and Prediction System (LDAPS) has systematic errors over the complex mountainous area, especially in deep valley areas, due to the orographic smoothing effect. The KMAPP reproduced the orographic height variation over the complex terrain area but failed to reduce the wind speed forecast errors of the LDAPS model. It even showed unreasonable values (~0.1 m s-1) for deep valley sites due to topographic overcorrection. The model's static parameters have been revised and applied to the KMAPP-Wind system, developed newly in this study, to represent the local topographic characteristics better over the region. Besides, sensitivity tests were conducted to investigate the effects of the model's physical correction methods. The KMAPP-Wind system showed better performance in predicting near-surface wind speed during the ICE-POP period than the original KMAPP version, reducing the forecast error by 21.2%. It suggests that a realistic representation of the topographic parameters is a prerequisite for the physical downscaling of near-ground wind speed over complex terrain areas.

• International Journal of Precision Engineering and Manufacturing
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• v.8 no.1
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• pp.27-31
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• 2007

A rolling speed dependent spread model is proposed for predicting the exit cross sectional shape in oval-round (or round-oval) pass rod rolling process when the rolling speed is very high. The effect of rolling speed on the exit cross sectional shape is measured by performing a four-pass continuous high speed (${\sim}80m/s$) rod rolling test and is described in terms of the spread correction parameter. The validity of the model is examined by applying it to rod rolling process at POSCO No.3 Rod Mill. The cross sectional shapes of workpiece predicted by the proposed model coupled with the surface profile prediction $method^{6}$ are in good agreement with those obtained experimentally.

• Journal of KSNVE
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• v.11 no.2
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• pp.354-364
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• 2001

The vibration induced by high speed train running on rail is dealt with as an environmental problem. The train induced vibration is characterized by moving loads at specific frequencies and soil conditions. In fact, it is predicted that the vibration sources are involved the wheel distance, number of cars, speed of operation, drift of rails, structural born vibration, etc. In this paper the characteristics of transferring vibration induced by the high-speed train in operation is discussed. Field measurements was conducted at region from Chungnam Yungj So-jung-myan to Chungbuk Chungwon hyun-do-myun. In the near future. these data will be used as the fundamental data for establishment of the countermeasure for vibrational reduction of high speed train using the results of the field measurements and quantitative prediction of the vibration level

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.47-51
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• 1997

The high speed machining which can improve the production and quality in machining has been adopted remarkably in dietmold industry. As the speed of machine tool spindle increases, the machinability evaluation and monitoring of high speed machining is necessary. In this study, the machinability of 30, 000rpm class spindle was evaluated by using the developed tool dynamometer and the machining properties of high hardened and toughness materials in high speed were examined. Finally, the in-process monitoring technologies of tool wear were presented through the prediction by the experimental formula and pattern recognition by the neural network.

• Proceedings of the KIEE Conference
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• 2006.07b
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• pp.901-902
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• 2006

In high-speed PM machines, rotor losses form a larger proportion of the total losses than usual in conventional low speed machines. In order to maintain the mechanical integrity of a high-speed PM rotor intended for high-speed operation, the rotor assembly is often retained within a sleeve or can. The sleeve is exposed to field produced by the stator from either the slotting or the mmf harmonics that are not synchronous with the rotor. These non-synchronous fields cause the significant rotor losses. An optimum design of high-speed PM machines requires the accurate prediction for these rotor losses. On the basis of analytical field analysis and 2D finite element analysis (FEA), this paper deals with the rotor losses.