• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1161-1162
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• 2007

In year 2004 korea runs high speed train KTX for the 5th time in the world. And now the traction power system is quite stabilized. But still a lot of work to develop and abnormal voltage problem is one of them. In this paper, by actual measuring we have collected the abnormal data on the high speed train sub-staion for more than 15 days. The collected data has been evaluated. In the near future collected data will be used planing a countermeasure.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.5D
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• pp.443-451
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• 2010

This paper proposes the methodology about advisory safety speed based on real-time vehicular data collected from highway. The proposed model is useful information to drivers by appling seamless wireless communication and being collected from ECU(Engine Control Unit) equipment in every vehicle. Furthermore, this model also permits the use of realtime sensing data like as adverse weather and road-surface data. Here, the advisory safety speed is defined "the safety speed for drivers considering the time-dependent traffic condition and road-surface state parameter at uniform section", and the advisory safety speed model is developed by considering the parameters: inter-vehicles safe stopping distance, statistical vehicle speed, and real-time road-surface data. This model is evaluated by using the simulation technique for exploring the relationships between advisory safety speed and the dependent parameters like as traffic parameters(smooth condition and traffic jam), incident parameters(no-accident and accident) and road-surface parameters(dry, wet, snow). A simulation's results based on 12 scenarios show significant relationships and trends between 3 parameters and advisory safety speed. This model suggests that the advisory safety speed has more higher than average travel speed and is changeable by changing real-time incident states and road-surface states. The purpose of the research is to prove the new safety related services which are applicable in SMART Highway as traffic and IT convergence technology.

• Transactions of the Korean Society of Automotive Engineers
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• v.10 no.1
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• pp.179-186
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• 2002

The absolute longitudinal speed of a vehicle is estimated by using vehicle acceleration data from an accelerometer and wheel speed data from standard 50-tooth antiknock braking system wheel speed sensors. An intuitive solution to this problem is, "When wheel slip is low, calculate absolute velocities from the wheel speeds; when wheel slip is high, calculate absolute velocity by integrating the accelerometer." Fuzzy logic is introduced to implement the above idea and a new algorithm of "modified velocities with step integration" is proposed. This algorithm is verified experimentally to estimate speed of a vehicle, and is also shown to estimate absolute longitudinal vehicle speed with a 6% worst-case error during a hard braking maneuver lasting three seconds.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.05a
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• pp.1210-1216
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• 2001

The impinging tones by high-speed plane jets are investigated for the characteristics of edgetone generation based on experimental observations. Experiment has been performed for edgetones with a slit nozzle and a wedge system. The jet in the experiment is varied from low to high subsonic speed to obtain the effect of the speed on the frequency characteristics of impinging tones. The experimental data obtained previously for edgetones and platetones by various nozzles are compared with the present edgetone data for the condition of tone generation, the frequency ranges and the effective source point. It is found that the jet speed has no fundamental influence on the impinging tone characteristics. Regardless of the jet speed, the effective source point is about a quarter wavelength downstream from the edge tip. With increase in jet speed, the influence of the nozzle configuration is decreased and the operating frequencies show good coincidencies by normalized parameters based on the slit thickness.

• Proceedings of the KIEE Conference
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• 2003.04a
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• pp.331-334
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• 2003

In this paper, on the subject of a speed control wind turbine, the type of wind speed reference decision between conventional MPPT tracking speed control and MPPT with LPF(Low Pass Filter) speed control algorithm are introduced and its performances are compared using a model based on MATLAB Simulink, and to get more realistic output data, the stored wind data as its wind speed input from 30kW wind power system in Buan, Haechang is used.

• 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.

• Transactions of The Korea Fluid Power Systems Society
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• v.3 no.2
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• pp.21-26
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• 2006

With the increasing concerns on noise and vibration in hydraulic fluid power systems, it is important to find better way to reduce noise and vibration. In this study, the authors survey former researches on hose(viscoelastic tube) modeling in advance. And a summary of several existing methods for measuring the speed of sound in the fluid in pipes is presented. Their basic principles, advantages and limitations are compared. And The authors suggest a far simple identification procedure to obtain wave speed in hose by just using an experimental pressure data for the object tube with hose. In the new procedure, flow in hose is basically modeled by transfer matrix method, and wave speed in hose is obtained as data in frequency series. The wave speed in hose as data in frequency series will be used to compute the pressure pulsation attenuation in hydraulic pipe systems. The computed results are compared with the experimental ones, and the validity of the new procedure to obtain wave speed in hose is confirmed

• Journal of Electrical Engineering and Technology
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• v.9 no.6
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• pp.1812-1821
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• 2014

Wind has been a rapidly growing renewable power source for the last twenty years. Since wind behavior is chaotic in nature, its forecasting is not easy. At the same time, developing an accurate forecasting method is essential when wind farms are integrated into the power grid. In fact, wind speed forecasting tools can solve issues related to grid stability and reserve allocation. In this paper 30 hours ahead wind speed profile forecast is proposed using Adaptive Wavelet Neural Network (AWNN). The implemented AWNN uses a Mexican hat mother Wavelet, and Morlet Mother Wavelet for seven, eight and nine levels decompositions. For wind speed forecasting, the time series data on wind speed has been gathered from the National Renewable Energy Laboratory (NREL) website. In this work, hourly averaged 10-min wind speed data sets for the year 2004 in the Midwest ISO region (site number 7263) is taken for analysis. Data sets are normalized in the range of [-1, 1] to improve the training performance of forecasting models. Total 8760 samples were taken for this forecasting analysis. After the forecasting phase, statistical parameters are calculated to evaluate system accuracy, comparing different configurations.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.27 no.2
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• pp.37-43
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• 2004

High speed machining is a machining process which cuts materials with the fast movement and rotation of a spindle in a machine tool. It reduces machining time because of the high feed and the high speed of a spindle. In addition it gets rid of post processes for high precision machining. When the high speed machining is applied to especially hardened steel, operators should select the proper parameters of machining. This can produce machining surfaces which is qualified with good surface roughness. This paper presents a method for selecting machining parameters to minimize surface roughness with high speed machining in cutting the hardened steels. Experimental data for surface roughness are collected in a machining shop based on the cutting feed and the spindle rotation. The data fits in hi-cubic polynomial surface of mathematical form. From the model this research minimize the surface roughness to find the optimal values of the feed and the spindle speed. This paper presents a program which automatically generates optimal solutions from the raw data of experiments.

• Smart Structures and Systems
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• v.30 no.3
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• pp.303-315
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• 2022

The degradation of wheel tread may result in serious hazards in the railway operation system. Therefore, timely wheel defect diagnosis of in-service trains to avoid tragic events is of particular importance. The focus of this study is to develop a novel wheel defect detection approach based on the relevance vector machine (RVM) which enables online detection of potentially defective wheels with trackside monitoring data acquired under different running-speed conditions. With the dynamic strain responses collected by a trackside monitoring system, the cumulative Fourier amplitudes (CFA) characterizing the effect of individual wheels are extracted to formulate multiple probabilistic regression models (MPRMs) in terms of multi-kernel RVM, which accommodate both variables of vibration frequency and running speed. Compared with the general single-kernel RVM-based model, the proposed multi-kernel MPRM approach bears better local and global representation ability and generalization performance, which are prerequisite for reliable wheel defect detection by means of data acquired under different running-speed conditions. After formulating the MPRMs, we adopt a Bayesian null hypothesis indicator for wheel defect identification and quantification, and the proposed method is demonstrated by utilizing real-world monitoring data acquired by an FBG-based trackside monitoring system deployed on a high-speed trial railway. The results testify the validity of the proposed method for wheel defect detection under different running-speed conditions.