• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.7
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• pp.1248-1256
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• 2015

It is necessary to apply an exact data and a precise prediction model for a rain attenuation to design the link margin for a data link using Ku-band with the serious effect by rain. In this paper, we investigate the regional rainfall-rate distribution of Korea proposed in TTAK.KO-06.0122/R1 and compare it with the distribution provided by Rec. ITU-R PN.837-1 and Crane. And, the rain rate climate regions similar with the rainfall-rate distribution of Korea in Rec. ITU-R PN.837-1 and Crane model are selected. Finally, using Rec. ITU-R P.618-8 and Crane rain attenuation prediction model, we derive and analyze the rain attenuation for Ku-band frequency according to the time percentage of an average year and the distance of wireless communication link between unmanned aerial vehicle (UAV) and ground data terminal (GDT).

• Journal of Korea Water Resources Association
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• v.30 no.2
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• pp.131-141
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• 1997

In this study, the Storage Function Method and Loopnet Model (Unsteady flow analysis model) were used to construct the flood prediction system which can predict the effects of the water release in the downstream region of Teachong Dam. The regional frequency analysis (L-moment) was applied to compute frequency-based precipitation, and the flood prediction system was also used for flood routing of the down stream region of Teachong Dam in the Kum River Basin to calculate frequency based flood. The magnitude of flood, water level, discharge, and travel time to the major points of the downstream region of Teachong Dam, which can be used as an imdex of flood control management of Teachong Dam, were calculated.

• Atmosphere
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• v.26 no.1
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• pp.47-58
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• 2016

On 10 July 2014, tornado outbreak occurred over Goyang province in Korea. This was the first supercell tornado ever reported or documented in Korea. The characteristics of the supercell tornado were investigated using an X-band polarimetric radar, surface meteorological observation, wind profiler, and operational numerical weather prediction (Regional Data Assimilation and Prediction System, RDAPS). The supercell tornado developed along a preexisting dryline that was contributed to surface wind shear. The radar analyses examined here show that the supercell tornado indicated a hook echo with mesocyclone. The decending reflectivity core as well was detected before tornadogenesis and prior to intensification of supercell. The supercell tornado exhibited characteristics similar to typical supercell tornado over the Great Plains of the United States, such as hook echo, bounded weak echo region, and slower movement speed relative to the mean wind. Compared to the typical supercell tornado over U.S., this tornado showed horizontal scale of the mesocyclone was relatively smaller and left-mover.

• Journal of the Korean Solar Energy Society
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• v.31 no.2
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• pp.37-46
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• 2011

The GWNU (Gangnung-Wonju national university) solar radiation model was developed with radiative transfer theory by Iqbal and it is applied the NREL (National Research Energy Laboratory). Input data were collected and accomplished from the model prediction data from RDAPS (Regional Data Assimilated Prediction Model), satellite data and ground observations. And GWNU solar model calculates not only horizontal surface but also complicated terrain surface. Also, We collected the statistical data related on photovoltaic power generation of the Korean Peninsula and analyzed about photovoltaic power efficiency of the Gangwon region. Finally, the solar energy resource and photovoltaic generation possibility map established up with 4 km, 1 km and 180 m resolution on Gangwon region based on actual equipment from Shinan solar plant,statistical data for photovoltaic and complicated topographical effect.

• Transactions of Materials Processing
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• v.31 no.5
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• pp.296-301
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• 2022

In this study, influence of the process parameters of the roller leveling process on the straightness of the steel wire was analyzed using numerical analysis. To construct the numerical analysis model, cross-sectional and longitudinal element sizes, which affect the prediction accuracy of longitudinal stress caused by bending deformation of the steel wire, were optimized, and mass scaling that satisfies prediction accuracy while reducing computational time was confirmed. By using the constructed numerical analysis model, the influence of various process parameters such as input direction of the steel wire, initial diameter of the steel wire, back tension and intermesh on the straightness was confirmed. The simulation result shows that the 3^rd and 4^th roller of vertical straightener had a significant influence on vertical shape of the steel wire.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.26-26
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• 2023

General circulation models (GCMs) are widely used in hydrological prediction, however their coarse grids make them unsuitable for regional analysis, therefore a downscaling method is required to utilize them in hydrological assessment. As one of the downscaling methods, convolutional neural network (CNN)-based downscaling has been proposed in recent years. The aim of this study is to generate the process of dynamic downscaling using CNNs by applying GCM output as input and RCM output as label data output. Prediction accuracy is compared between different input datasets, and model structures. Several input datasets with key atmospheric variables such as precipitation, temperature, and humidity were tested with two different formats; one is two-dimensional data and the other one is three-dimensional data. And in the model structure, the hyperparameters were tested to check the effect on model accuracy. The results of the experiments on the input dataset showed that the accuracy was higher for the input dataset without precipitation than with precipitation. The results of the experiments on the model structure showed that substantially increasing the number of convolutions resulted in higher accuracy, however increasing the size of the receptive field did not necessarily lead to higher accuracy. Though further investigation is required for the application, this paper can contribute to the development of efficient downscaling method with CNNs.

• Journal of the Korean earth science society
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• v.44 no.4
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• pp.255-263
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• 2023

Western coastal area of Chungnam, including Cheonsu Bay and Garorim Bay, has suffered from hot and cold extremes. In this study, the extreme sea surface temperature on the western coast of Chungnam was analyzed using the quantile regression method, which extracts the linear regression values in all quantiles. The regional MOHID (MOdelo HIDrodinâmico) model, with a high resolution on a 1/60° grid, was constructed to reproduce the extreme sea surface temperature. For future prediction, the SSP5-8.5 scenario data of the CMIP6 model were used to simulate sea surface temperature variability. Results showed that the extreme sea surface temperature of Cheonsu Bay in August 2017 was successfully simulated, and this extreme sea surface temperature had a significant negative correlation with the Pacific decadal variability index. As a result of future climate prediction, it was found that an average of 2.9℃ increased during the simulation period of 86 years in the Chungnam west coast and there was a seasonal difference (3.2℃ in summer, 2.4℃ in winter). These seasonal differences indicate an increase in the annual temperature range, suggesting that extreme events may occur more frequently in the future.

• Journal of The Korean Society of Agricultural Engineers
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• v.65 no.2
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• pp.1-11
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• 2023

This study analyzed the impact of greenhouse cultivation area and groundwater level changes due to the water curtain cultivation in the greenhouse complexes. The groundwater observation data in the Miryang study area were used and classified into greenhouse and field cultivation areas to compare the groundwater impact of water curtain cultivation in the greenhouse complex. We identified the characteristics of the groundwater time series data by the terrain of the study area and selected the optimal model through time series analysis. We analyzed the time series data for each terrain's two representative groundwater observation wells. The Seasonal ARIMA model was chosen as the optimal model for riverside well, and for plain and mountain well, the ARIMA model and Seasonal ARIMA model were selected as the optimal model. A suitable prediction model is not limited to one model due to a change in a groundwater level fluctuation pattern caused by a surrounding environment change but may change over time. Therefore, it is necessary to periodically check and revise the optimal model rather than continuously applying one selected ARIMA model. Groundwater forecasting results through time series analysis can be used for sustainable groundwater resource management.

• Journal of Powder Materials
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• v.30 no.3
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• pp.210-216
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• 2023

In this study, machine learning models are proposed to predict the Vickers hardness of AlSi10Mg alloys fabricated by laser powder bed fusion (LPBF). A total of 113 utilizable datasets were collected from the literature. The hyperparameters of the machine-learning models were adjusted to select an accurate predictive model. The random forest regression (RFR) model showed the best performance compared to support vector regression, artificial neural networks, and k-nearest neighbors. The variable importance and prediction mechanisms of the RFR were discussed by Shapley additive explanation (SHAP). Aging time had the greatest influence on the Vickers hardness, followed by solution time, solution temperature, layer thickness, scan speed, power, aging temperature, average particle size, and hatching distance. Detailed prediction mechanisms for RFR are analyzed using SHAP dependence plots.

• Journal of Korean Society on Water Environment
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• v.28 no.3
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• pp.347-358
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• 2012

The SWAT (Soil and Water Assessment Tool) should be calibrated and validated with observed data to secure accuracy of model prediction. Recently, the SWAT-CUP (Calibration and Uncertainty Program for SWAT) software, which can calibrate SWAT using various algorithms, were developed to help SWAT users calibrate model efficiently. In this study, three algorithms (GLUE: Generalized Likelihood Uncertainty Estimation, PARASOL: Parameter solution, SUFI-2: Sequential Uncertainty Fitting ver. 2) in the SWAT-CUP were applied for the Soyang-gang dam watershed to evaluate these algorithms. Simulated total streamflow and 0~75% percentile streamflow were compared with observed data, respectively. The NSE (Nash-Sutcliffe Efficiency) and $R^2$ (Coefficient of Determination) values were the same from three algorithms but the P-factor for confidence of calibration ranged from 0.27 to 0.81 . the PARASOL shows the lowest p-factor (0.27), SUFI-2 gives the greatest P-factor (0.81) among these three algorithms. Based on calibration results, the SUFI-2 was found to be suitable for calibration in Soyang-gang dam watershed. Although the NSE and $R^2$ values were satisfactory for total streamflow estimation, the SWAT simulated values for low flow regime were not satisfactory (negative NSE values) in this study. This is because of limitations in semi-distributed SWAT modeling structure, which cannot simulated effects of spatial locations of HRUs (Hydrologic Response Unit) within subwatersheds in SWAT. To solve this problem, a module capable of simulating groundwater/baseflow should be developed and added to the SWAT system. With this enhancement in SWAT/SWAT-CUP, the SWAT estimated streamflow values could be used in determining standard flow rate in TMDLs (Total Maximum Daily Load) application at a watershed.