• Journal of the Korean earth science society
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• v.35 no.1
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• pp.1-18
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• 2014

The purpose of this study was to analyze the sensitivity of meteorological fields and the variation of concentration of particulate matters (PMs) due to aerosol schemes and dust options within the WRF-Chem model to estimate Asian dusts affected on 29 May 2008 in the Korean peninsula. The anthropogenic emissions within the model were adopted by the $0.5^{\circ}{\pm}0.5^{\circ}$ RETRO of the global emissions, and the photolysis option was by Fast-J photolysis. Also, three scenarios such as the RADM2 chemical mechanism and MADE/SORGAM aerosol, the MOSAIC 8 section aerosol, and the GOCART dust erosion were simulated for calculating Asian dust emissions. As a result, the scenario of the RADM2 chemical mechanism & MADE/SORGAM aerosol depicted higher concentration than the others' in both Asian dusts and the background concentration of PMs. By comparing of the daily mean of PM10 measured at each air quality monitoring site in Seoul with the scenario results, the correlation coefficient was 0.67, and the root mean square error was $44{\mu}gm^{-3}$. In addition, the air temperature, the wind speed, the planetary boundary layer height, and the outgoing long-wave radiation were simulated under conditions of no chemical option with these three scenarios within the WRF or WRF-Chem model. Both the spatial distributions of the PBL height and the wind speed of u component among the meteorological factors were similar to those of the Asia dusts in range of 1,800-3,000 m and $2-16ms^{-1}$, respectively. And, it was shown that both scenarios of the RADM2 chemical mechanism and MADE/SORGAM aerosol and the GOCART dust erosion were interacted on-line between meteorological factors and Asian dusts or aerosols within the model because the outgoing long-wave radiation was changed to lower than the others.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.24 no.3
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• pp.375-388
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• 2019

Accurate evaluation of sea-to-air $CO_2$ flux and its variability is crucial information to the understanding of global carbon cycle and the prediction of atmospheric $CO_2$ concentration. $fCO_2$ observations are sparse in space and time in the East Sea. In this study, we derived high resolution time series of surface $fCO_2$ values in the southwest East Sea, by feeding sea surface temperature (SST), salinity (SSS), chlorophyll-a (CHL), and mixed layer depth (MLD) values, from either satellite-observations or numerical model outputs, to three machine learning models. The root mean square error of the best performing model, a Random Forest (RF) model, was $7.1{\mu}atm$. Important parameters in predicting $fCO_2$ in the RF model were SST and SSS along with time information; CHL and MLD were much less important than the other parameters. The net $CO_2$ flux in the southwest East Sea, calculated from the $fCO_2$ predicted by the RF model, was $-0.76{\pm}1.15mol\;m^{-2}yr^{-1}$, close to the lower bound of the previous estimates in the range of $-0.66{\sim}-2.47mol\;m^{-2}yr^{-1}$. The time series of $fCO_2$ predicted by the RF model showed a significant variation even in a short time interval of a week. For accurate evaluation of the $CO_2$ flux in the Ulleung Basin, it is necessary to conduct high resolution in situ observations in spring when $fCO_2$ changes rapidly.

• Clean Technology
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• v.25 no.4
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• pp.324-330
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• 2019

The selective catalytic reduction system is a highly effective technique for the denitrification of the flue gases emitted from the industrial facilities. The distribution of mixing ratio between ammonia and nitrogen oxide at the inlet of the catalyst layers is important to the efficiency of the de-NOx process. In this study, computational analysis tools have been applied to improve the uniformity of NH₃/NO molar ratio by controlling the flow rate of the ammonia injection nozzles according to the distribution pattern of the nitrogen oxide in the inlet flue gas. The root mean square of NH₃/NO molar ratio was chosen as the optimization parameter while the design of experiment was used as the base of the optimization algorithm. As the inlet conditions, four (4) types of flow pattern were simulated; i.e. uniform, parabolic, upper-skewed, and random. The flow rate of the eight nozzles installed in the ammonia injection grid was adjusted to the inlet conditions. In order to solve the two-dimensional, steady, incompressible, and viscous flow fields, the commercial software ANSYS-FLUENT was used with the k-𝜖 turbulence model. The results showed that the improvement of the uniformity ranged between 9.58% and 80.0% according to the inlet flow pattern of the flue gas.

• Korean Journal of Remote Sensing
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• v.33 no.5_2
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• pp.631-640
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• 2017

The estimation of rice yield affects the income of farmers as well as the fields related to agriculture. Moreover, it has an important effect on the government's policy making including the control of supply demand and the price estimation. Thus, it is necessary to build the crop yield estimation model and from the past, many studies utilizing empirical statistical models or artificial neural network algorithms have been conducted through climatic and satellite data. Presently, scientists have achieved successful results with deep learning algorithms in the field of pattern recognition, computer vision, speech recognition, etc. Among deep learning algorithms, the SSAE (Stacked Sparse AutoEncoder) algorithm has been confirmed to be applicable in the field of forecasting through time series data and in this study, SSAE was utilized to estimate the rice yield in South Korea. The climatic and satellite data were used as the input variables and different types of input data were constructed according to the period of rice growth in South Korea. As a result, the combination of the satellite data from May to September and the climatic data using the 16 day average value showed the best performance with showing average annual %RMSE (percent Root Mean Square Error) and region %RMSE of 7.43% and 7.16% that the applicability of the SSAE algorithm could be proved in the field of rice yield estimation.

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.4
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• pp.67-74
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• 2016

Drone images with high spatial resolution are emerging as an alternative to previous studies with extraction limits in high density forests. Individual tree in the dense forests were extracted from drone images. To detect the individual tree extracted through the image segmentation process, the image segmentation results were compared between the combination of DSM and all R,G,B band and the combination of DSM and R,G,B band separately. The changes in the tree density of a deciduous forest was experimented by time and image. Especially the image of May when the forests are dense, among the images of March, April, May, the individual tree extraction rate based on the trees surveyed on the site was 50%. The analysis results of the width of crown showed that the RMSE was less than 1.5m, which was the best result. For extraction of the experimental area, the two sizes of medium and small trees were extracted, and the extraction accuracy of the small trees was higher. The forest tree volume and forest biomass could be estimated if the tree height is extracted based on the above data and the DBH(diameter at breast height) is estimated using the relational expression between crown width and DBH.

• Journal of the Korean GEO-environmental Society
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• v.14 no.11
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• pp.5-12
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• 2013

The purpose of this study is to estimate the behavior and the mechanism of debris flow at the end of mountain side when a berm was set on the inclined plane. The numerical model was performed by using the Finite Difference Method(FDM) based on the equation for the mass conservation and momentum conservation. In order to measure the behavior of the debris flow, the debris flow of a straight channel slope and the debris flow of channel slope with 3 types of berms were compared. First, the flow discharge and the sediment volume concentration at the downstream of the channel slope, depending on the various berm width and the different inflow discharges at the upstream of the channel were analyzed. The longer the berm width, the flow discharge at the downstream of the channel was decreased and the high flow fluctuation was reduced by a berm. And it means that a berm can effect for the delay of the debris flow. Through Root Mean Square ratio(RMS) comparison, the flow discharge of the channel slope with a berm was lower than that of a straight channel slope. The longer the berm width, for the sediment volume concentration, an inflection point did not show but mild curve. Because the low sediment concentration with water mixture by a berm continuously flow at the downstream end, it will be effect for reducing the disaster caused by debris flow. The results of this study will provide useful information in predicting and preventing disaster caused by the debris flow.

• Food Science of Animal Resources
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• v.38 no.5
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• pp.1109-1119
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• 2018

In this paper, we report the development of a nondestructive prediction model for lean meat percentage (LMP) in Korean pig carcasses and in the major cuts using a machine vision technique. A popular vision system in the meat industry, the VCS2000 was installed in a modern Korean slaughterhouse, and the images of half carcasses were captured using three cameras from 175 selected pork carcasses (86 castrated males and 89 females). The imaged carcasses were divided into calibration (n=135) and validation (n=39) sets and a multilinear regression (MLR) analysis was utilized to develop the prediction equation from the calibration set. The efficiency of the prediction equation was then evaluated by an independent validation set. We found that the prediction equation - developed to estimate LMP in whole carcasses based on six variables - was characterized by a coefficient of determination ($R^2_v$) value of 0.77 (root-mean square error [RMSEV] of 2.12%). In addition, the predicted LMP values for the major cuts: ham, belly, and shoulder exhibited $R^2_v$ values${\geq}0.8$ (0.73 for loin parts) with low RMSEV values. However, lower accuracy ($R^2_v=0.67$) was achieved for tenderloin cuts. These results indicate that the LMP in Korean pig carcasses and major cuts can be predicted successfully using the VCS2000-based prediction equation developed here. The ultimate advantages of this technique are compatibility and speed, as the VCS2000 imaging system can be installed in any slaughterhouse with minor modifications to facilitate the on-line and real-time prediction of LMP in pig carcasses.

• 한국신재생에너지학회:학술대회논문집
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• 2005.06a
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• pp.638-642
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• 2005

For gas hydrate exploration, long offset multichannel seismic data acquired using by the 4km streamer length in Ulleung basin of the East Sea. The dataset was processed to define the BSRs (Bottom Simulating Reflectors) and to estimate the amount of gas hydrates. Confirmation of the presence of Bottom Simulating reflectors (BSR) and investigation of its physical properties from seismic section are important for gas hydrate detection. Specially, faster interval velocity overlying slower interval velocity indicates the likely presences of gas hydrate above BSR and free gas underneath BSR. In consequence, estimation of correct interval velocities and analysis of their spatial variations are critical processes for gas hydrate detection using seismic reflection data. Using Dix's equation, Root Mean Square (RMS) velocities can be converted into interval velocities. However, it is not a proper way to investigate interval velocities above and below BSR considering the fact that RMS velocities have poor resolution and correctness and the assumption that interval velocities increase along the depth. Therefore, we incorporated Migration Velocity Analysis (MVA) software produced by Landmark CO. to estimate correct interval velocities in detail. MVA is a process to yield velocities of sediments between layers using Common Mid Point (CMP) gathered seismic data. The CMP gathered data for MVA should be produced after basic processing steps to enhance the signal to noise ratio of the first reflections. Prestack depth migrated section is produced using interval velocities and interval velocities are key parameters governing qualities of prestack depth migration section. Correctness of interval velocities can be examined by the presence of Residual Move Out (RMO) on CMP gathered data. If there is no RMO, peaks of primary reflection events are flat in horizontal direction for all offsets of Common Reflection Point (CRP) gathers and it proves that prestack depth migration is done with correct velocity field. Used method in this study, Tomographic inversion needs two initial input data. One is the dataset obtained from the results of preprocessing by removing multiples and noise and stacked partially. The other is the depth domain velocity model build by smoothing and editing the interval velocity converted from RMS velocity. After the three times iteration of tomography inversion, Optimum interval velocity field can be fixed. The conclusion of this study as follow, the final Interval velocity around the BSR decreased to 1400 m/s from 2500 m/s abruptly. BSR is showed about 200m depth under the seabottom

• Korean Journal of Remote Sensing
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• v.36 no.5_4
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• pp.1179-1194
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• 2020

This study investigates the feasibility of three algorithms, K-Nearest Neighbors (K-NN), Random Forest (RF) and Neural Network (NN), for estimating the air temperature of an unobserved area where the weather station is not installed. The satellite image were obtained from Landsat-8 and MODIS Aqua/Terra acquired in 2019, and the meteorological ground weather data were from AWS/ASOS data of Korea Meteorological Administration and Korea Forest Service. In addition, in order to improve the estimation accuracy, a digital surface model, solar radiation, aspect and slope were used. The accuracy assessment of machine learning methods was performed by calculating the statistics of R² (determination coefficient) and Root Mean Square Error (RMSE) through 10-fold cross-validation and the estimated values were compared for each target area. As a result, the neural network algorithm showed the most stable result among the three algorithms with R² = 0.805 and RMSE = 0.508. The neural network algorithm was applied to each data set on Landsat imagery scene. It was possible to generate an mean air temperature map from June to September 2019 and confirmed that detailed air temperature information could be estimated. The result is expected to be utilized for national disaster safety management such as heat wave response policies and heat island mitigation research.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.1698-1702
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• 2009

효과적인 저수지 수질관리를 위해서는 신뢰도 높은 수리 및 수질모델이 필요하며, 이러한 모델의 성능은 다양한 수문사상에 대하여 적용함으로써 검증할 수 있다. CE-QUAL-W2 모델(이후 W2)은 횡방향 평균 2차원 수리 수질 해석 모델로써 수체의 길이에 비해 폭이 상대적으로 좁고 수심이 깊은 우리나라 대부분의 저수지 지형에 적합한 모델이다. 본 연구의 목적은 기존 연구에서 가뭄년인 2001년과 평수년인 2004년 수문사상에 대하여 보정한 대청호 W2 부영양화모델을 최근 평수년인 2006년과 가뭄년인 2008년을 대상으로 검증하는데 있다. 모델의 검증은 물수지, 수온성층 구조 변화, 부영양화 해석에 중점을 두었으며, 실측자료와 모의결과의 적합성 비교 평가는 결정계수값$(R^2)$, AME(absolute mean error)와 RMSE(root mean square error)를 이용하였다. 저수지 물수지의 적합성을 검증하기 위하여 모의수위와 실측수위를 비교한 결과, $R^2$값이 2006년과 2008년에 각각 0.9945, 0.9972로 나타나 신뢰도가 높은 것으로 확인되었다. 계절별 성층구조 변화 모의 성능을 검증하기 위해 회남수역과 댐 앞 지점에서 수심별 수온의 모의값과 실측값을 비교하였다. 2006년의 경우 모델은 홍수기 동안 안정적으로 수온 성층현상을 모의하였으나, 댐 앞 지점에서 수온 약층이 형성된 구간에서 실측값과 다소 편차를 보였으며, 오차크기는 AME가 $0.561\sim2.088^{\circ}C$, RMSE는 $0.797\sim2.762^{\circ}C$범위였다. 반면, 가뭄년인 2008년에는 전 기간에 걸쳐 모두 안정적으로 저수지 수온 성층현상을 모의하였으며, 오차크기는 AME $0.413\sim1.162^{\circ}C$, RMSE $0.546\sim1.415^{\circ}C$ 범위였다. 조류의 생산성이 높은 표층에서 T-N, T-P 및 Chl-a 농도 모의결과를 장계교, 대정리, 회남대교, 댐 앞, 추동취수탑 및 문의취수탑에서 시계열로 실측값과 비교 검증한 결과, T-N과 T-P는 2006년과 2008년 모두 모든 비교 지점에서 모의값과 실측값의 시계열 변동이 매우 잘 일치하였으며, 홍수기 이외 기간에는 큰 변동 폭을 보이지 않았다. 그러나 기존 연구에서 확인된 바와 같이 7월 이후부터 T-P 모의값이 실측값을 과대 산정하는 경향을 보였는데, 그 원인은 산소 결핍상태에서 저니층에서 용출되는 철(Fe) 또는 망간(Mn)과 같은 이온 성분이 인과 흡착하여 침전되는 기작이 모의과정에 적절히 반영되지 않은 것이 원인으로 판단된다. 조류(Chl-a)농도의 경우, 2006년과 2008년에 모든 지점에서 모델은 조류의 발생과 시계열 변화를 적절히 모의하였으나, 2008년 1월부터 8월까지 댐 앞과 추동 및 문의취수탑에서는 모의값이 실측값을 과대 산정하는 경향을 보였다. 이는 해마다 그리고 계절별로 우점하는 조류 종이 상이한 반면, 모델에서는 이에 대한 매개변수를 적절히 고려하지 못한 것이 원인으로 판단된다.