• Journal of Korea Water Resources Association
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• v.40 no.1 s.174
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• pp.73-88
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• 2007

The goal of this research is to develop and apply the generalized regression neural networks model(GRNNM) embedding genetic algorithm(GA) for the estimation and calculation of the pan evaporation(PE), which is missed or ungaged and of the alfalfa reference evapotranspiration ($ET_r$), which is not measured in South Korea. Since the observed data of the alfalfa 37. using Iysimeter have not been measured for a long time in South Korea, the Penman-Monteith(PM) method is used to estimate the observed alfalfa $ET_r$. In this research, we develop the COMBINE-GRNNM-GA(Type-1) model for the calculation of the optimal PE and the alfalfa $ET_r$. The suggested COMBINE-GRNNM-GA(Type-1) model is evaluated through training, testing, and reproduction processes. The COMBINE-GRNNM-GA(Type-1) model can evaluate the suggested climatic variables and also construct the reliable data for the PE and the alfalfa $ET_r$. We think that the constructive data could be used as the reference data for irrigation and drainage networks system in South Korea.

• Journal of Veterinary Clinics
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• v.32 no.4
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• pp.380-384
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• 2015

The relationships between end-tidal partial pressure of carbon dioxide ($P_{ET}CO_2$) and arterial partial pressure of carbon dioxide ($P_aCO_2$), and between $P_{ET}CO_2$ and blood pH in isoflurane-anesthetized cinereous vultures were evaluated. Nine cinereous vultures (weighing 7.3-9.5 kg) were anesthetized via mask with isoflurane, intubated, and connected to a mechanical ventilator. By mechanical ventilator, respiratory rate was altered to achieve a $P_{ET}CO_2$ randomly selected among 30 to 35 mmHg. $P_aCO_2$ and blood pH were determined in serial arterial samples and compared with concurrent $P_{ET}CO_2$ measured with a sidestream capnography. Thirty six samples, nine cinereous vultures were tested 4 times each, were used to determine correlations coefficients between $P_aCO_2$ and $P_{ET}CO_2$, and between $P_{ET}CO_2$ and blood pH. Strong correlations were observed between $P_aCO_2$ and $P_{ET}CO_2$ (r = 0.951, P = 0.000, y = 1.679x - 30.236, $R^2=0.905$) as well as between $P_{ET}CO_2$ and blood pH (r = -0.728, P = 0.000, y = -0.024x + 8.389, $R^2=0.530$) with Pearson's correlation coefficient and simple linear regression. These results suggest that the capnography used provided a sufficiently accurate estimation of $P_aCO_2$ and blood pH. Thus, the monitoring of $P_{ET}CO_2$ can be useful method to prevent hypoventilation for cinereous vultures receiving mechanical ventilation.

• 한국해양학회지
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• v.24 no.4
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• pp.194-205
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• 1989

In order to study the biomass of primary producer, phytoplankton is collected monthly September 1985 to August 1986 in Ch$\check{o}$nsu Bay. Phytoplankton carbon contents which are calculated from phytoplankton volume were ranged from $26.7{\mu}gC/l$ to $960.7{\mu}gC/l$, and average carbon contents of each month lie in the range of $58.6-684.7{\mu}gC/l$(annual mean $208.5{\mu}gC/l$). For net plankton analysis with the carbon contents, cell numbers, and chlorophyll concentrations show a close correlation, while for nanoplankton the correlation was low, indicating that nano-fraction includes a significant portion of picoplankton. Also, the multiple regression analysis with carbon content, cell number, and chlorophyll concentration to size fraction well illustrate the prime importance of the net-fraction in phytoplankton group. C/Chl-a ratios ranged from 9.1 to 100.5, average rations of net- and nanoplankton are 111 and 6.4, respectively. The greater net plankton faction is, the higher C/Chl-a ratio is, however in case of high nanoplankton portion C/Chl-a ratio show low level. These results indicate that the difference of C/Chl-a ratio per phytoplankton cell size be main factor for the variation of C/Chl-a ratio in Ch$\check{o}$nsu Bay. As C/Chl-a ratio fluctuates greatly in coastal ecosystem, that use of a direct conversion of convert chlorophyll to organic carbon may lead erronous estimation.

• Journal of Nutrition and Health
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• v.9 no.3
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• pp.8-17
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• 1976

This study was carried out to estimate the minimum protein requirement for the Korean college girls, and to see the effect of carbohydrate intake on the minimum protein requirement in the rat. In the first experiment, three students attending Ewha Womans University were given protein free diet for 7 days while caloric intake were adjusted to maintain constant body weight. Endogenous nitrogen excretion was measured to estimate their minimum protein requirement. Second experiment was carried out in 3 parts to find out the effect of carbohydrate intake on the minimum protein requirement using 15 female, 50 days old rats weighing 120g. Inexperiment 2-1, the rats were fed protein free diet for 9 days and endogenous nitrogen excretion was measured to determine the minimum protein requirement of the rats. In experiment 2-2, the minimum casein requirement of the rats needed to maintain nitrogen equilibrium was estimated by the least squares regression method feeding 3 different levels of casein determined from the result of experiment 2-1. Finally, in experiment 2-3, the rats were given the minimum casein requirement for 7 days with 3 different levels of carbohydrate to find out the effect of carbohydrate intake on the nitrogen balance. The results of this study were as follows. Experiment I. Daily endogenous nitrogen losses of 3 college girls were 1.89g in urine, 1.10g in feces and 0.0108g from skin since the total daily endogenous nitrogen loss was 3.0g and the total daily nitrogen intake was 0.52g, the minimum nitrogen requirement of these subjects was about 2.48g/day (15.5g protein/day). Experiment II. Experiment 2-1: daily endogenous nitrogen losses of the rats fed protein free diet were $22.63{\pm}0.48\;mg$ through urine and $13.12{\pm}0.47\;mg$ in feces. The total daily endogenous nitrogen ouput was $35.71{\pm}1.27\;mg$ and the minimum protein requirement of the rats was 225mg/day in this study. Experiment 2-2: the minimum casein requirement needed to maintain the rats in nitrogen equilibrium was determined to be 340mg/day. Experiment 2-3: the rats were given 350mg/day casein with 3 different levels of carbohydrate (12,26 or 46 Cal by sugar-cornstarch mixture). The nitrogen balances were measured to be +0.0021, +0.0073 and +0.0143 respectively and there were significant differences among 3 groups.

• Korean Journal of Remote Sensing
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• v.24 no.4
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• pp.309-324
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• 2008

In this study the retrieval algorithms have been developed to retrieve total precipitable water (TPW) from Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) infrared measurements using a physical iterative retrieval method and a split-window technique over East Asia. Retrieved results from these algorithms were validated against Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I) over ocean and radiosonde observation over land and were analyzed for investigating the key factors affecting the accuracy of results and physical processes of retrieval methods. Atmospheric profiles from Regional Data Assimilation and Prediction System (RDAPS), which produces analysis and prediction field of atmospheric variables over East Asia, were used as first-guess profiles for the physical retrieval algorithm. We used RTTOV-7 radiative transfer model to calculate the upwelling radiance at the top of the atmosphere. For the split-window technique, regression coefficients were obtained by relating the calculated brightness temperature to the paired radiosonde-estimated TPW. Physically retrieved TPWs were validated against SSM/I and radiosonde observations for 14 cases in August and December 2004 and results showed that the physical method improves the accuracy of TPW with smaller bias in comparison to TPWs of RDAPS data, MODIS products, and TPWs from split-window technique. Although physical iterative retrieval can reduce the bias of first-guess profiles and bring in more accurate TPWs, the retrieved results show the dependency upon initial guess fields. It is thought that the dependency is due to the fact that the water vapor absorption channels used in this study may not reflect moisture features in particular near surface.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.260-260
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• 2016

Soil erosion is a very serious problem from agricultural as well as environmental point of view. Various computer models have been used to estimate soil erosion and assess erosion control practice. Universal Soil loss equation (USLE) is a popular model which has been used in many countries around the world. Erosivity (USLE R-factor) is one of the USLE input parameters to reflect impacts of rainfall in computing soil loss. Value of R factor depends upon Energy (E) and maximum rainfall intensity of specific period ($I30_{max}$) of that rainfall event and thus can be calculated using higher temporal resolution rainfall data such as 10 minute interval. But 10 minute interval rainfall data may not be available in every part of the world. In that case we can use hourly rainfall data to compute this R factor. Maximum 60 minute rainfall ($I60_{max}$) can be used instead of maximum 30 minute rainfall ($I30_{max}$) as suggested by USLE manual. But the value of Average annual R factor computed using hourly rainfall data needs some correction factor so that it can be used in USLE model. The objective of our study are to derive relation between averages annual R factor values using 10 minute interval and hourly rainfall data and to determine correction coefficient for R factor using hourly Rainfall data.75 weather stations of Korea were selected for our study. Ten minute interval rainfall data for these stations were obtained from Korea Meteorological Administration (KMA) and these data were changed to hourly rainfall data. R factor and $I60_{max}$ obtained from hourly rainfall data were compared with R factor and $I30_{max}$ obtained from 10 minute interval data. Linear relation between Average annual R factor obtained from 10 minute interval rainfall and from hourly data was derived with $R^2=0.69$. Correction coefficient was developed for the R factor calculated using hourly rainfall data.. Similarly, the relation was obtained between event wise $I30_{max}$ and $I60_{max}$ with higher $R^2$ value of 0.91. Thus $I30_{max}$ can be estimated from I60max with higher accuracy and thus the hourly rainfall data can be used to determine R factor more precisely by multiplying Energy of each rainfall event with this corrected $I60_{max}$.

• Korean Journal of Soil Science and Fertilizer
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• v.44 no.3
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• pp.448-451
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• 2011

To establish the optimum nitrogen level for rice planting after Italian ryegrass cultivation, one experiment was conducted on a normal paddy soil (Jeonbug series soil taxonomy) with six different nitrogen treatments for two years from 2009 to 2010. The treatments were including no nitrogen fertilization (Free N), 50%-Basal N, 75%-Basal N, 100%-Basal N, 150%-Basal N and 100% of basal fertilization with $30kg\;N\;ha^{-1}$ (100%+N30-Basal N) for decomposing of Italian ryegrass stubble. The highest rice yields were 8,420 obtained by 100%-Basal N. 150%-Basal N and 100%+N30-Basal N produced a rice yield of $8,190kg\;ha^{-1}$. Those of 50%-Basal N and Free N were produced 8,020 and $7,370kg\;ha^{-1}$, respectively. The correlation between rice yield and nitrogen treatment showed a quadratic relationship in high significant. According to this regression, the highest level of nitrogen treatment was $73kg\;ha^{-1}$ and the highest rice yield was $8,405kg\;ha^{-1}$. Nitrogen uptake rates were relatively higher with lower amounts of nitrogen fertilizer treated.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.4
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• pp.69-75
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• 2019

The frequency of natural disasters and the scale of damage are increasing due to the abnormal weather phenomenon that occurs worldwide. Especially, damage caused by natural disasters in coastal areas around the world such as Earthquake in Japan, Hurricane Katrina in the United States, and Typhoon Maemi in Korea are huge. If we can predict the damage scale in response to disasters, we can respond quickly and reduce damage. In this study, we developed damage prediction functions for Wind waves caused by sea breezes and waves during various natural disasters. The disaster report (1991 ~ 2017) has collected the history of storm and typhoon damage in coastal areas in Korea, and the amount of damage has been converted as of 2017 to reflect inflation. In addition, data on marine weather factors were collected in the event of storm and typhoon damage. Regression analysis was performed through collected data, Finally, predictive function of the sea turbulent damage by the sea area in 74 regions of the country were developed. It is deemed that preliminary damage prediction can be possible through the wind damage prediction function developed and is expected to be utilized to improve laws and systems related to disaster statistics.

• Journal of Korea Water Resources Association
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• v.52 no.6
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• pp.381-395
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• 2019

The purpose of this study is to predict agricultural reservoir storage rate (RSR) in a month. This algorithm was developed by multiple linear regression model (MLRM) which included the past 3 months RSRs data and the future climate change scenarios. In order to improve use of predicted RSR, this study need the severe criteria in terms of drought. So, the predicted RSR was indexed as the 3 months reservoir drought index (RDI3) and then it was disaggregated into drought duration, severity, and intensity. For the future RSR estimation by climate change scenarios, the 6 RCP 8.5 scenarios of HadGEM2-ES, CESM1-BGC, MPI-ESM-MR, INM-CM4, FGOALS-s2, and HadGEM3-RA were used in three future evaluation periods (S1: 2011~2040, S2: 2041~2070, S3: 2071~2099). The future S3 period of HadGEM2-ES scenario which has the biggest increase in precipitation and temperature showed the largest decrease to 60.2% among the 6 scenarios compared to the historical RSR (1976~2005) 77.3%. In contrast, INM-CM4 scenario which has smallest changes in precipitation and temperature in S3 period showed the smallest decrease to 72.8%. For the CESM1-BGC and MPI-ESM-MR, FGOALS-s2, and HadGEM3-RA, the S3 period RSR showed 72.6%, 72.6%, 67.4%, and 64.5% decrease respectively. The future severe drought condition of RDI3 below -0.25 showed the increase trend for the number and severity up to -2.0 during S3 period.

• Journal of the Korean Geotechnical Society
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• v.35 no.6
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• pp.17-26
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• 2019

In this study, the statistical characteristics of the resistance bias factors were analyzed using a high-quality field load test database, and the total resistance bias factors were estimated considering the soil uncertainty and construction errors for the application of the limit state design of aggregate pier foundation. The MLR model by Bong and Kim (2017), which has a higher prediction performance than the previous models was used for estimating the resistance bias factors, and its suitability was evaluated. The chi-square goodness of fit test was performed to estimate the probability distribution of the resistance bias factors, and the normal distribution was found to be most suitable. The total variability in the nominal resistance was estimated including the uncertainty of undrained shear strength and construction errors that can occur during the aggregate pier construction. Finally, the probability distribution of the total resistance bias factors is shown to follow a log-normal distribution. The parameters of the probability distribution according to the coefficient of variation of total resistance bias factors were estimated by Monte Carlo simulation, and their regression equations were proposed for simple application.