• Journal of Korean Society of Rural Planning
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• v.11 no.4 s.29
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• pp.67-74
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• 2005

The Doam watershed is located at alpine areas and the annual average precipitation, including snow accumulation, is significant higher than other areas. Thus, pollutant laden runoff and sediment discharge from the alpine agricultural fields are causing water quality degradation at the Doam watershed. To estimate soil erosion from the agricultural fields, the Universal Soil Loss Equation (USLE) has been widely used because of its simplicity to use. In the early spring at the Doam watershed, the stream flow increases because of snow melt, which results in erosion of loosened soil experiencing freezing and thaw during the winter. Also, extremely torrential rainfall, such as the typhoons 'RUSA' in 2002 and 'MAEMI' in 2003, caused significant amounts of soil erosion and sediment at the Doam watershed. However, the USLE model cannot simulate impacts on soil erosion of freezing and thaw of the soil. It cannot estimate sediment yield from a single torrential rainfall event. Also, it cannot simulate temporal changes in USLE input parameters. Thus, the Soil and Water Assessment Tool (SWAT) model was investigated for its applicability to estimate soil erosion at the Doam watershed, instead of the widely used USLE model. The SWAT hydrology and erosion/sediment components were validated after calibration of the hydrologic component. The R$^2$ and Nash-Sutcliffe coefficient values are higher enough, thus it is found the SWAT model can be efficiently used to simulate hydrology and sediment yield at the Doam watershed. The effects of snow melt on SWAT estimated stream flow and sediment were investigated using long-term precipitation and temperature data at the Doam watershed. It was found significant amount of flow and sediment in the spring are contributed by melting snow accumulated during the winter. Two typhoons in 2002 and 2003, MAEMI and RUSA, caused 33% and 22% of total sediment yields at the Doam watershed, respectively. Thus, it is recommended that the SWAT model, capable of simulating snow melt, sediment yield from a single storm event, and long-term weather data, needs to be used in estimating soil erosion at alpine agricultural areas to develop successful soil erosion management instead of the USLE.

• Journal of Bio-Environment Control
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• v.29 no.1
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• pp.9-19
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• 2020

Convective heat transfer is the main component of greenhouse energy loss because the energy loss by this mechanism is greater than those of the other two components (radiative and conductive). Previous studies have examined the convective heat transfer coefficients under natural conditions, but they are not applicable to symmetric thermal screens with zero porosity, and such screens are largely produced and used in Korea. However, the properties of these materials have not been reported in the literature, which causes selectivity issues for users. Therefore, in this study, three screens having similar color and zero porosity were selected, and a mathematical procedure based on radiation balance equations was developed to determine their convective heat transfer coefficients. To conduct the experiment, a hollow wooden structure was built and the thermal screen was tacked over this frame; the theoretical model was applied underneath and over the screen. Input parameters included three components: 1) solar and thermal fluxes; 2) temperature of the screen, black cloth, and ambient air; and 3) wind velocity. The convective heat transfer coefficients were determined as functions of the air-screen temperature difference under open-air environmental conditions. It was observed from the outcomes that the heat transfer coefficients decreased with the increase of the air-screen temperature difference provided that the wind velocity was nearly zero.

• The Journal of Engineering Geology
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• v.15 no.2 s.42
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• pp.145-154
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• 2005

This study conducted to develop an assessment method of runout distance of debris flow that is a major type of landslides in Korea. In order to accomplish the objectives, this study performed detailed field survey of runout distance and laboratory soil tests using 24 landslides over three pilot sites. Based on the data of the field survey and the laboratory tests, an assessment method of runout distance was suggested using the artificial neural network. The input data for the analysis of artificial neural network are change rate of slope angle, Permeability coefficient of in-situ soil, dry density, void ratio, volume of debris and the measured runout distance. The analyzed results using the artificial neural network show low error rate of inference distributing lower than $10\%$. Some cases have $5\%$ and $2\%$ of error rates of inferences. The results can be thought as excellent teaming rates. However, it is difficult to be accepted as excellent results if it is considered with the results derived using only 24 landslide data. Therefore, more landslide data should be surveyed and analyzed to increase the confidence in the assessment results.

• Journal of Korean Tunnelling and Underground Space Association
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• v.15 no.3
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• pp.357-373
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• 2013

In this paper, a back analysis program for analyzing the behavior of tunnel-ground system and evaluating the material properties and tunnel design parameters was developed. This program was designed to be able to implement the back analysis of underground structure by combination of using FLAC and optimized algorithm as direct method. In particular, Rosenbrock method which is able to do direct search without obtaining differential coefficient was adopted for the back analysis algorithm among optimization methods. This back analysis program was applied to the site to evaluate the design parameters. The back analysis was carried out using field measurement results from 5 sites. In the course of back analysis, nonlinear regression analysis was carried out to identify the optimum function of the measured ground displacement. Exponential function and fractional function were used for the regression analysis and total displacement calculated by optimum function was used as the back analysis input data. As a result, displacement recalculated through the back analysis using measured displacement of the structure showed 4.5% of error factor comparing to the measured data. Hence, the program developed in this study proved to be effectively applicable to tunnel analysis.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.8
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• pp.41-47
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• 2017

With the introduction of new technologies, ground weapons have led to the development of artificial intelligence and the attention of major developed countries. In this study, CFD was performed through the BLU-103 model to obtain aerodynamic data for aircraft that are subjected to rotational motion. To simulate the steady-state of a rotating body, the body was fixed and the principle of rotating the body by rotating the surrounding air was used. In order to examine the aerodynamic feasibility of the rotating aircraft, the analysis was carried out at intervals of $30^{\circ}$ angle from $0^{\circ}$ to $90^{\circ}$ for the simple shape and the side slip angle. It was confirmed that the drag coefficient for the simple model satisfies the quantitative results of 1.0 ~ 1.2 through CD presented in "Drag Book". The aerodynamic data was constructed by applying the valid input verified through the simple type analysis conditions to the actual shape, and the tendency was analyzed. The analysis confirmed that CX, CZ and CY increase not only in the simple model but also in the rotation of the actual model. Especially, the influence of CZ was judged to have contributed to the flight.

• Journal of Korea Water Resources Association
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• v.53 no.4
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• pp.273-283
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• 2020

Quantitative forecasting of groundwater levels for the assessment of groundwater variation and vulnerability is very important. To achieve this purpose, various time series analysis and machine learning techniques have been used. In this study, we developed a prediction model based on LSTM (Long short term memory), one of the artificial neural network (ANN) algorithms, for predicting the daily groundwater level of 11 groundwater wells in Hankyung-myeon, Jeju Island. In general, the groundwater level in Jeju Island is highly autocorrelated with tides and reflected the effects of precipitation. In order to construct an input and output variables based on the characteristics of addressing data, the precipitation data of the corresponding period was added to the groundwater level data. The LSTM neural network was trained using the initial 365-day data showing the four seasons and the remaining data were used for verification to evaluate the fitness of the predictive model. The model was developed using Keras, a Python-based deep learning framework, and the NVIDIA CUDA architecture was implemented to enhance the learning speed. As a result of learning and verifying the groundwater level variation using the LSTM neural network, the coefficient of determination (R²) was 0.98 on average, indicating that the predictive model developed was very accurate.

• Journal of Environmental Science International
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• v.17 no.7
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• pp.755-765
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• 2008

To investigate correlation between the distribution of marine bacteria and environmental characteristics in the surface sediments of Kamak Bay, chemical oxygen demand(COD), acid volatile sulfide(AVS), ignition loss(IL), total organic carbon(TOC), and total organic nitrogen(TON) were measured and analyzed at 7 stations in winter and summer. In winter, COD and AVS ranged from 13.45 mg/g to 30.06 mg/g(average: 23.58 mg/g) and from 0.03 mg/g to 1.04 mg/g(average: 0.63 mg/g), respectively. IL, TOC, and TON ranged from 8.03% to 11.41%(average: 9.41%), from 1.17% to 2.10%(average: 1.62%), and from 0.09% to 0.18%(average 0.15%), respectively. In summer, COD, AVS, IL, TOC, and TON ranged from 14.06 mg/g to 32.19 mg/g(average: 24.71 mg/g), from 0.03 mg/g to 1.11 mg/g(average: 0.66 mg/g), from 9.00% to 12.15%(average: 10.96%), from 1.27% to 2.12%(average 1.77%), and from 0.12% to 0.19%(average: 0.16%), respectively. These values were relatively higher than those in winter. Kamak Bay had high C/N ratio that might be resulted from the input of terrestrial sewage and industrial wastewater. The number of marine viable bacteria was $8.9{\times}10^4\;cfu/g$ in winter and $9.7{\times}10^5\;cfu/g$ in summer. The most abundant species were Pseudomonas spp., Flavobacterium spp., and Vibrio spp, in the surface sediments of Kamak Bay. It was found that the concentration of organic matters and viable bacterial cells in the inner part were relatively higher than those in the outer of Kamak Bay. The distribution of viable bacterial cells was closely influenced by environmental factors.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.25 no.4
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• pp.476-482
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• 2014

In this study, we analyzed the effect of corn growth on the radar backscattering coefficient. At first, we measured the backscattering coefficients of various corn fields using a polarimetric scatterometer system. The backscattering coefficients of the corn fields were also computed using the 1st-order VRT(Vector Radiative Transfer) model with field-measured input parameters. Then, we analyzed the experimental and numerical backscattering coefficients of corn fields. As a result, we found that the backscatter from an underlying soil layer is dominant for early growing stage. On the other hand, for vegetative stage with a higher LAI(Leaf-Area-Index), the backscatter from vegetation canopy becomes dominant, and its backscattering coefficients increase as incidence angle increases because of the effect of leaf angle distribution. It was also found that the estimated backscattering coefficients agree quite well with the field-measured radar backscattering coefficients with an RMSE(Root Mean Square Error) of 1.32 dB for VV-polarization and 0.99 dB for HH-polarization. Finally, we compared the backscattering characteristics of vegetation and soil layers with various LAI values.

• Journal of the Korean Geographical Society
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• v.49 no.3
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• pp.405-422
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• 2014

Two important issues in hedonic model are to specify accurate model and delineate submarkets. While the former has experienced much improvement over recent decades, the latter has received relatively little attention. However, the accuracy of estimates from hedonic model will be necessarily reduced when the analysis does not adequately address market segmentation which can capture the spatial scale of price formation process in real estate. Placing emphasis on improvement of performance in hedonic model, this paper tried to segment real estate markets in Gangnam-gu and Jungrang-gu, which correspond to most heterogeneous and homogeneous ones respectively in 25 autonomous districts of Seoul. First, we calculated variable coefficients from mixed geographically weighted regression model (mixed GWR model) as input for clustering, since the coefficient from hedonic model can be interpreted as shadow price of attributes constituting real estate. After that, we developed a spatially constrained data-driven methodology to preserve spatial contiguity by utilizing the SKATER algorithm based on a minimum spanning tree. Finally, the performance of this method was verified by applying a multi-level model. We concluded that submarket does not exist in Jungrang-gu and five submarkets centered on arterial roads would be reasonable in Gangnam-gu. Urban infrastructure such as arterial roads has not been considered an important factor for delineating submarkets until now, but it was found empirically that they play a key role in market segmentation.

• Journal of the Korean Geotechnical Society
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• v.19 no.1
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• pp.209-220
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• 2003

Modification of general Hoek and Brown criterion is carried out to estimate the shaft resistance of drilled shaft socketed into rock mass. Since the general Hoek-Brown criterion can consider the in-situ state of the rock mass, the proposed method, estimating the unit shaft resistance of drilled shafts based on the Hoek-Brown criterion, has increased flexibility compared to other methods exclusively considering uniaxial compressive strength of intact rocks. The proposed method can form the upper and lower bounds, and most culled data (from 21 pile load tests) from the literature can be found between these two bounds. A comparison between the estimated and observed unit shaft resistances shows quite a good correlation even with crude assumptions for the input parameters. The best-fit line drawn from this analysis shows that at the lower strength of intact rocks (up to 10MPa), Horvath and Kenney's equation shows a good correlation with the measured values, and fur strong rocks Rosenberg and Journeaux's equation provides a close estimation with colleted data. The results of parametric studies for GSI and confining stress show that the normalized unit shaft resistance increases with these two factors. In addition, coefficient of the equational form of the estimation can vary with GSI and confining stresses.