• Journal of Korean Society of Disaster and Security
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• v.12 no.2
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• pp.65-72
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• 2019

More than 64% of Korea's land is occupied by mountain regions, which have terrain characteristics that make it vulnerable to mountain disasters. The trails of Taebaeksan Mountain National Park-the region considered in this study-are located in the vicinity of steep slopes, and therefore, the region is vulnerable to landslides and debris flow during heavy storms. In this study, a slope stability model, which is a deterministic analysis method, was used to examine the potential occurrence of landslides. According to the soil classification of the detailed soil map, the specific weight of soil, effective cohesion, internal friction angle of soil, effective soil depth, and ground slope were used as the parameters of the model, and slope stability was evaluated based on the DEM of a 1 m grid. The results of the slope stability analysis showed that the more hazardous the area was, the closer the ratio of groundwater/effective soil depth is to 1.0. Further, many of the private houses and commercial facilities in the lower part of the national park were shown to be exposed to danger.

• Journal of Korea Planning Association
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• v.54 no.2
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• pp.93-108
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• 2019

This study examines the relationship between three-dimensional urban built environment and urban surface temperature using LANDSAT 8 satellite image data in Seoul city. The image was divided into 600m×600m grid units as an unit of analysis. Due to the high level of spatial dependency in surface temperature, this study uses spatial statistics to take into account spatial auto-correlation. The spatial error model shows the best goodness of fit. The analysis results show that the three-dimensional built environment and transport environment as well as natural environment have statistically significant associations with surface temperature. First, natural environment variables such as green space, streams and river, and average elevation show statistically significant negative association with surface temperature. Second, the building area shows a positive association with surface temperature. In addition, while sky view factor (SVF) has a positive association with surface temperature, surface roughness (SR) shows a negative association with it. Third, transportation related variables such as road density, railway density, and traffic volume show positive associations with surface temperature. Moreover, this study finds that SVF and SR have different effects on surface temperature in regard to the levels of total floor areas in built environment. The results indicate that interactions between floor area ratio (FAR) and three-dimensional built environmental variables such as SVF and SR should be considered to reduce urban surface temperature.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.5
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• pp.841-852
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• 2022

In this paper, a 3-phase inverter output waveform generator based on SPWM control was developed to verify the performance of the LCL filter. In order to obtain a test signal for verifying the performance of the filter, first, a DSP-based 3-phase SPWM signal generation algorithm was developed, and then a three-phase voltage source inverter circuit was designed using three half-bridge gate drivers. Next, one LCL filter was experimentally fabricated to verify the effectiveness of the developed SPWM-based 3-phase inverter output waveform generator as a test signal generator, and a DSP-based performance verification system was experimentally constructed. Finally, by comparing the three-phase voltage waveform before and after the LCL filter obtained in the output control experiment with the given time ratio, the effectiveness of the SPWM-based 3-phase inverter output waveform generator was verified.

• Journal of the Korea Society of Computer and Information
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• v.28 no.1
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• pp.121-127
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• 2023

In this study, the design conditions and CFD analysis results are compared and reviewed in SCR that can optimally reduce nitrogen compounds. To this end, it was analyzed and compared using CFD to see if the design criteria were satisfied for the shell and tube areas of the boiler. In the SCR system, the analysis area is the gas/air heat exchanger on the shell side, and eight tubes of the gas/air heat exchanger on the tube side. Through CFD analysis, the gas velocity distribution on the primary catalyst side of the SCR system was designed to be 2.4%, and the NH3/NOx molar ratio distribution was 3.7%, which satisfied the design criteria. In addition, the uniformity of the temperature distribution was confirmed and the required condition of 260℃ or higher was satisfied. The angle of the gas entering the catalyst met the design conditions at 2.9 degrees, and the pressure loss that occurred also satisfied the design requirements. Through this CFD analysis, it was confirmed that it was designed and operated by satisfying the design conditions required for each area.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.136-136
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• 2021

In this study, a deep convolutional neural network (DCNN) model is proposed for short-term precipitation forecasting using weather radar-based images. The DCNN model is a combination of convolutional neural networks, autoencoder neural networks, and U-net architecture. The weather radar-based image data used here are retrieved from competition for rainfall forecasting in Korea (AI Contest for Rainfall Prediction of Hydroelectric Dam Using Public Data), organized by Dacon under the sponsorship of the Korean Water Resources Association in October 2020. This data is collected from rainy events during the rainy season (April - October) from 2010 to 2017. These images have undergone a preprocessing step to convert from weather radar data to grayscale image data before they are exploited for the competition. Accordingly, each of these gray images covers a spatial dimension of 120×120 pixels and has a corresponding temporal resolution of 10 minutes. Here, each pixel corresponds to a grid of size 4km×4km. The DCNN model is designed in this study to provide 10-minute predictive images in advance. Then, precipitation information can be obtained from these forecast images through empirical conversion formulas. Model performance is assessed by comparing the Score index, which is defined based on the ratio of MAE (mean absolute error) to CSI (critical success index) values. The competition results have demonstrated the impressive performance of the DCNN model, where the Score value is 0.530 compared to the best value from the competition of 0.500, ranking 16^th out of 463 participating teams. This study's findings exhibit the potential of applying the DCNN model to short-term rainfall prediction using weather radar-based images. As a result, this model can be applied to other areas with different spatiotemporal resolutions.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.4B
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• pp.375-381
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• 2008

In this research, a physics based grid-multi layer distributed flood runoff model was developed to analyze discharge for the Namgang Dam Watershed ($2,293km^2$) and applied for sensitivity analysis for estimation of parameters, mainly initial soil moisture condition and saturate infiltration coefficient, which have a strong influence on discharge. Capability of the model was evaluated using VER and QER from the results of rainfall-runoff analysis and showed enhanced results of 6% compared to parameters before calibration. As the result with the sensitivity analysis of parameters, the part of the most influence on the runoff was the infiltration coefficient and ratio of layer partition. The total discharge and peak time showed comparatively precise runoff results without the initial calibration of the parameters.

• Nuclear Engineering and Technology
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• v.56 no.2
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• pp.715-727
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• 2024

During fast neutron imaging, besides the dark current noise and readout noise of the CCD camera, the main noise in fast neutron imaging comes from high-energy gamma rays generated by neutron nuclear reactions in and around the experimental setup. These high-energy gamma rays result in the presence of high-density gamma white spots (GWS) in the fast neutron image. Due to the microscopic quantum characteristics of the neutron beam itself and environmental scattering effects, fast neutron images typically exhibit a mixture of Gaussian noise. Existing denoising methods in neutron images are difficult to handle when dealing with a mixture of GWS and Gaussian noise. Herein we put forward a deep learning approach based on the Swin Transformer UNet (SUNet) model to remove high-density GWS-Gaussian mixture noise from fast neutron images. The improved denoising model utilizes a customized loss function for training, which combines perceptual loss and mean squared error loss to avoid grid-like artifacts caused by using a single perceptual loss. To address the high cost of acquiring real fast neutron images, this study introduces Monte Carlo method to simulate noise data with GWS characteristics by computing the interaction between gamma rays and sensors based on the principle of GWS generation. Ultimately, the experimental scenarios involving simulated neutron noise images and real fast neutron images demonstrate that the proposed method not only improves the quality and signal-to-noise ratio of fast neutron images but also preserves the details of the original images during denoising.

• Journal of the Korean earth science society
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• v.28 no.7
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• pp.857-870
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• 2007

The structure and evolution of a thunderstorm outflow in two dimensions with no environmental wind are investigated using a cloud-resolving model with explicit liquid-ice phase microphysical processes (ARPS: Advanced Regional Prediction System). The turbulence structure of the outflow is explicitly resolved with a high-resolution grid size of 50m. The simulated single-cell storm and its associated Kelvin-Helmholtz (KH) billows are found to have the lift stages of development maturity, and decay. The secondary pulsation and splitting of convective cells resulted from interactions between cloud dynamics and microphysics are observed. The cooled downdrafts caused by the evaporation of rain and hail in the relatively dry lower atmosphere result in thunderstorm cold-air outflow. The outflow head propagates with almost constant speed. The KH billows formed by the KH instability cause turbulence mixing from the top of the outflow and control the structure of the outflow. Ihe KH billows are initiated at the outflow head, and pow and decay as moving rearward relative to the gust front. The numerical simulation results of the ratio of the horizontal wavelength of the fastest growing perturbation to the critical shear-layer depth and the ratio of the horizontal wavelength of the billow to its maximum amplitude are matched well with the results of other studies.

• Membrane Journal
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• v.30 no.3
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• pp.190-199
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• 2020

The utilization of multiple water sources becomes important due to the master plan for development of water supply released by Ministry of Environment, Korea in 2018. In this study, therefore, the analysis of comprehensive effect in blending applicable water sources in Daesan where 100,000 ㎥/d seawater desalination plant will be constructed for industrial use was performed. The increase in mixing ratio of other water sources with seawater reduced salinity up to 50%, but negatively impacted the turbid and organic matter. Lab-scale reverse osmosis performance test also found that membrane fouling was exacerbated in blended water condition. The simulation results of reverse osmosis indicated 39% energy saving on average is expected at the one-to-one blending ratio, however, long-term performance test at the pilot-scale plant is highly required to evaluate the inclusive impact of mixing seawater and other water sources.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.35 no.5
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• pp.299-308
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• 2022

This study was conducted to predict the tendency for heat exchange and boil-off gas (BOG) in a liquefied hydrogen tank under sloshing excitation. First, athe fluid domain excited by sloshing was modeled using a multiphase-thermal flow domain in which liquid hydrogen and hydrogen gas are in the saturated state. Both the the volume of fluid (VOF) and Eulerian-based multi-phase flow methods were applied to validate the accuracy of the pressure prediction. Second, it was indirectly shown that the fluid velocity prediction could be accurate by comparing the free surface and impact pressure from the computational fluid dynamics with those from the experimental results. Thereafter, the heat ingress from the external convective heat flux was reflected on the outer surfaces of the hydrogen tank. Eulerian-based multiphase-heat flow analysis was performed for a two-dimensional Type-C cylindrical hydrogen tank under rotational sloshing motion, and an inflation technique was applied to transform the fluid domain into a computational grid model. The heat exchange and heat flux in the hydrogen liquid-gas mixture were calculated throughout the analysis,, whereas the mass transfer and vaporization models were excluded to account for the pure heat exchange between the liquid and gas in the saturated state. In addition, forced convective heat transfer by sloshing on the inner wall of the tank was not reflected so that the heat exchange in the multiphase flow of liquid and gas could only be considered. Finally, the effect of sloshing on the amount of heat exchange between liquid and gas hydrogen was discussed. Considering the heat ingress into liquid hydrogen according to the presence/absence of a sloshing excitation, the amount of heat flux and BOG were discussed for each filling ratio.