• Atmosphere
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• v.31 no.1
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• pp.1-15
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• 2021

High-resolution wind resources maps (maps, here after) with spatial and temporal resolutions of 100 m and 3-hours, respectively, over South Korea have been produced and evaluated for the period from July 2016 to June 2017 using Korea Meteorological Administration (KMA) Post Processing (KMAPP). Evaluation of the 10 m- and 80 m-level wind speed in the new maps (KMAPP-Wind) and the 1.5 km-resolution KMA NWP model, Local Data Assimilation and Prediction System (LDAPS), shows that the new high-resolution maps improves of the LDAPS winds in estimating the 10m wind speed as the new data reduces the mean bias (MBE) and root-mean-square error (RMSE) by 33.3% and 14.3%, respectively. In particular, the result of evaluation of the wind at 80 m which is directly related with power turbine shows that the new maps has significantly smaller error compared to the LDAPS wind. Analyses of the new maps for the seasonal average, maximum wind speed, and the prevailing wind direction shows that the wind resources over South Korea are most abundant during winter, and that the prevailing wind direction is strongly affected by synoptic weather systems except over mountainous regions. Wind speed generally increases with altitude and the proximity to the coast. In conclusion, the evaluation results show that the new maps provides significantly more accurate wind speeds than the lower resolution NWP model output, especially over complex terrains, coastal areas, and the Jeju island where wind-energy resources are most abundant.

• Journal of Korean Society of Transportation
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• v.13 no.2
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• pp.43-58
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• 1995

This paper discusses some of the results of investigation of railroad-highway grade crossing accidents and accident-related inventory information that was collected from the Pusan District Office of the Korean National Railroads. Established statistical techniques were applied to tabulated data to obtain an accident prediction equation that estimates the expected probability of accidents at each crossing under various grade crossing situations. It was found that the most significant factor that influences the railroad crossing accidents was flagger. The other factors were train and traffic volumes, number of tracks. crossing angle, maximum timetable train speed, algebraic grade difference, and lighting facility. No significant effects was identified with railroad crossing gates. The results of the analysis and the uses of the prediction equation for the development of warrants for safety improvements are also discussed.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.1281-1284
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• 1998

This paper proposes an efficient time series prediction of the nonlinear dynamical discrete-time systems using multilayer neural networks of a hybrid learning algorithm. The proposed learning algorithm is a hybrid backpropagation algorithm based on the steepest descent for high-speed optimization and the dynamic tunneling for global optimization. The proposed algorithm has been applied to the y00 samples of 700 sequences to predict the next 100 samples. The simulation results shows that the proposed algorithm has better performances of the convergence and the prediction, in comparision with that using backpropagation algorithm based on the gradient descent for multilayer neural network.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.1 no.1
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• pp.133-140
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• 2002

The object of this paper is to predict the surface roughness using the experiment equation of surface roughness, which is developed with a full factorial design in turning. $3^3$ full factorial design has been used to study main and interaction effects of main cutting parameters such as cutting speed, feed rate, and depth of cut, on surface roughness. For prediction of surface roughness, the arithmetic average (Ra) is used, and stepwise regression has been used to check the significance of all effects of cutting parameters. Using the result of these, the experimental equation of surface roughness, which consists of significant effects of cutting parameters, has been developed. The coefficient of determination of this equation is 0.9908. And the prediction ability of this equation was verified by additional experiments. The result of that, the coefficient of determination is 0.9718.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.04a
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• pp.317-322
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• 2004

This paper deals with a prediction method of tool life in view of the reliability assessment. In this study, the flank wear was studied among multi-factors deciding the tool wear state. Firstly, tool lift was predicted by correlation between flank wear and cutting time, based on the extended Taylor tool life equation of turning data, including parameters of cutting speed, feed rate, and cutting depth. Secondly, each of cutting conditions of endmilling was equivalently converted to apply ball endmill data to the extended Taylor equation. The web-based reliability prediction program for tool lift is being developed as one of reliability assessment programs to for the machine tools.

• Transactions of Materials Processing
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• v.23 no.1
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• pp.29-34
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• 2014

The damage level of the die surface was predicted by estimating the surface roughness with a finite element analysis and the wear characteristics. Wear and friction tests were conducted to compare the wear characteristics for three kinds of surface treatments - CrN, TiAlN and AlCrN coatings. A prediction model was derived from the surface roughness results with respect to contact pressure and sliding speed which were obtained from the wear test. Surface roughness values for the damage regions of the die surface were compared between the experiments and the prediction model, which shows fairly good agreement with each other.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.1
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• pp.39-42
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• 2002

For the purpose of modeling EEG signal which has nonstationary and nonlinear dynamic characteristics, this paper propose a state feedback real time recurrent neural network model. The state feedback real time recurrent neural network is structured to have memory structure in the state of hidden layers so that it has arbitrary dynamics and ability to deal with time-varying input through its own temporal operation. For the model test, Mackey-Glass time series is used as a nonlinear dynamic system and the model is applied to the prediction of three types of EEG, alpha wave, beta wave and epileptic EEG. Experimental results show that the performance of the proposed model is better than that of other neural network models which are compared in this paper in some view points of the converging speed in learning stage and normalized mean square error for the test data set.

• The KSFM Journal of Fluid Machinery
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• v.5 no.1 s.14
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• pp.33-41
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• 2002

Low NPSH and high pressure pumps we widely used for turbopump systems, which have an inducer and operate at high rotating speeds. In this paper, a meanline method has been established for the preliminary design and performance prediction of pumps having an inducer for cavitating or non-cavitating conditions at design or off-design points. The method was applied for the performance prediction of a fuel pump. Predicted performances by the method are shown to be in good agreement with experimental results for cavitating and non-cavitating conditions. The established meanline method can be used for the performance prediction and preliminary design of high speed pumps which have a inducer, impeller and volute.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.05a
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• pp.265-270
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• 2001

For the reduction and efficient management of railway noise, first of all prediction of railway noise is necessarily requested, At home and abroad, many studies for prediction of railway nearby noise have been accomplished, But it is impossible to predict exactly for the Korean Railroad, because the acoustic power DB for each rolling stock used in Korea has not been builded yet. So in this study, acoustic power DB for each Korean rolling stock such as Samaeul, Mugungwha was builded according to the speed and rail support systems. Predicted results using accumulated acoustic power DB are compared with measured results and it is known that accumulated acoustic power DB can be used for more precise prediction of railway nearby noise.

• Transactions of Materials Processing
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• v.25 no.2
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• pp.124-129
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• 2016

Recently, global railway car makers are competing desperately in developing high-speed railway vehicles. Ensuring passenger safety during a crash is essential. The design and the manufacturing of energy absorbing components are becoming more and more important. A deformation tube is a typical passive energy absorbing component for railway cars. In the current study the slab method was used to predict the energy absorbing capability of a deformation tube during the early design stage. The usefulness of the prediction method is verified through the comparisons between the results of FE simulations and those of the prediction method.