• Journal of Preventive Medicine and Public Health
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• v.39 no.2
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• pp.165-170
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• 2006

Objectives : The determination of oxalate in urine is required for the diagnosis and treatment of primary hyperoxaluria, idiopathic stone disease and various intestinal diseases. We examined the possibility of using Fourier transform near-infrared (FT-NIR) spectroscopy analysis to quantitate urinary oxalate. The practical advantages of this method include ease of the sample preparation and operation technique, the absence of sample pre-treatments, rapid determination and noninvasiveness. Methods : The range of oxalate concentration in standard urine solutions was $0-221mg/{\ell}$. These 80 different samples were scanned in the region of 780-1,300 nm with a 0.5 nm data interval by a Spectrum One NTS FT-NIR spectrometer. PCR, PLSR and MLR regression models were used to calculate and evaluate the calibration equation. Results : The PCR and PLSR calibration models were obtained from the spectral data and they are exactly same. The standard error of estimation (SEE) and the % variance were $10.34mg/{\ell}$ and 97.86%, respectively. After full cross validation of this model, the standard error of estimation was $5,287mg/{\ell}$, which was much smaller than that of the pre-validation. Furthermore, the MCC (multiple correlation coefficient) was 0.998, which was compatible with the 0.923 or 0.999 obtained from the previous enzymatic methods. Conclusions : These results showed that FT-NIR spectroscopy can be used for rapid determination of the concentration of oxalate in human urine samples.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.4
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• pp.238-249
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• 2019

The evapotranspiration is estimated based on weather factors such as temperature, wind speed and humidity, and the Hargreaves equation is a simple equation for calculating evapotranspiration using temperature data. However, the Hargreaves equation tends to be underestimated in areas with wind speeds above 3 m s^-1 and overestimated in areas with high relative humidity. The study was conducted to determine Hargreaves equation coefficient in 82 regions in Korea by comparing evapotranspiration determined by modified Hargreaves equation and the Penman-Monteith equation for the time period of 2008~2018. The modified Hargreaves coefficients for 50 inland areas were estimated to be 0.00173~0.00232(average 0.00196), which is similar to or lower than the default value 0.0023. On the other hand, there are 32 coastal areas, and the modified coefficients ranged from 0.00185 to 0.00303(average 0.00234). The east coastal area was estimated to be similar to or higher than the default value, while the west and south coastal areas showed large deviations by area. As results of estimating the evapotranspiration by the modified Hargreaves coefficient, root mean square error(RMSE) is reduced from 0.634~1.394(average 0.857) to 0.466~1.328(average 0.701), and Nash-Sutcliffe Coefficient(NSC) increased from -0.159~0.837(average 0.647) to -0.053~0.910(average 0.755) compared with original Hargreaves equation. Therefore, we confirmed that the Hargreaves equation can be overestimated or underestimated compared to the Penman-Monteith equation, and expected that it will be able to calculate the high accuracy evapotranspiration using the modified Hargreaves equation. This study will contribute to water resources planning, irrigation schedule, and environmental management.

• Journal of the Korean Association of Geographic Information Studies
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• v.12 no.2
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• pp.82-98
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• 2009

This study applied SWAT model to analyze suspended sediment load that is influence on the high density turbid water in Donghyang and Cheoncheon basin, which are located in the upstream of Yongdam Dam. GIS data such as DEM, land cover map and soil map, and meteorological data were used as the input data of SWAT model. And the rating curve equation and Q-SS equation of Donghyang and Cheoncheon gauge station were applied as the measured values of them. As the result of flowout, the coefficient of determination ($R^2$) and the Nash-Sutcliffe coefficient of efficiency (EI) of model calibration showed high as 0.87 and 0.87 at Donghyang gauge station, and the $R^2$ and EI of model validation were high as 0.95 at Cheoncheon gauge station. Also, as the result of suspended sediment load, the $R^2$ and EI of model calibration were high as 0.77 and 0.76 at Donghyang gauge station, and the $R^2$ and EI of model validation marked high as 0.867 and 0.80 at Cheoncheon gauge station. It is considered that the suspended sediment load of 2003 showed the highest due to rainfall amounts and rainfall intensity in using SWAT model. The results of suspended sediment modeled in this study can be applied to the decision-making support data for the evaluation of soil erosion possibility and turbid water potential in the management of reservoir.

• Journal of Korean Society on Water Environment
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• v.26 no.5
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• pp.841-849
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• 2010

Soil erosion is an natural phenomenon. However accelerated soil erosion has caused many environmental problems. To reduce soil loss from a watershed, many management practices have been proposed worldwide. To develop proper and efficient soil erosion best management practices, soil erosion rates should be estimated spatially and temporarily. The Universal Soil Loss Equation (USLE) and USLE-based soil erosion and sediment modelling systems have been developed and tested in many countries. The Sediment Assessment Tool for Effective Erosion Control (SATEEC) system has been developed and enhanced to provide ease-of-use interface to the USLE users. However many researchers and decision makers have requested to enhance the SATEEC system for simulation of soil erosion and sediment reflecting effects of single storm event. Thus, the SATEEC R factors were estimated based on 5 day antecedent rainfall data. The SATEEC 2.1 daily R factor was applied to the study watershed and it was found that the R2 and EI values (0.776 and 0.776 for calibration and 0.927 and 0.911 for validation) with the daily R were greater than those (0.721 and 0.720 for calibration and 0.906 and 0.881 for validation) with monthly R, which was available in the SATEEC 2.0 system. As shown in this study, the SATEEC with daily R can be used to estimate soil erosion and sediment yield at a watershed scale with higher accuracy. Thus the SATEEC with daily R can be efficiently used to develop site-specific soil erosion best management practices based on spatial and temporal analysis of soil erosion and sediment yield at a daily-time step, which was not possible with USLE-based soil erosion modeling system.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.41 no.1
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• pp.13-19
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• 1996

This study was conducted to establish the rapid determination method for major components of maturing covered barley grains, and to improve the efficiency of selection in barley breeding. Near Infrared Reflectance Spectroscopy (NIRS) is an established, economical and nondestructive technique applied widely to the food and feed industry. 34 barley lines were sampled at 5 day-interval from 25 to 35 days after heading. A standard regression analysis for the data obtained by analytical laboratory methods and NIRS method was carried out to get a useful calibration equation. The simple significant correlation between these two methods at 25 days after heading was recognized in starch and $\beta$-glucan contents. At 30 days after heading the data obtained by two methods showed significant correlation in starch, $\beta$-glucan and protein contents. Analyzed data and that from NIRS method at 35 days after heading was significantly correlated in starch and protein contents. It was concluded that the applicability of NIRS method for the components analysis in maturing barley grains was different depending on maturing stages and components.

• Journal of The Korean Society of Grassland and Forage Science
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• v.37 no.2
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• pp.154-160
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• 2017

This study was conducted to find out an alternative way of rapid and accurate analysis of chemical composition of permanent pastures in hilly grazing area. Near reflectance infrared spectroscopy (NIRS) was used to evaluate the potential for predicting proximate analysis of permanent pastures in a vegetative stage. 386 pasture samples obtained from hilly grazing area in 2015 and 2016 were scanned for their visible-NIR spectra from 400~2,400nm. 163 samples with different spectral characteristics were selected and analysed for moisture, crude protein (CP), crude ash (CA), acid detergent fiber (ADF) and neutral detergent fiber (NDF). Multiple linear regression was used with wet analysis data and spectra for developing the calibration and validation mode1. Wavelength of 400 to 2500nm and near infrared range with different critical T outlier value 2.5 and 1.5 were used for developing the most suitable equation. The important index in this experiment was SEC and SEP. The $R^2$ value for moisture, CP, CA, CF, Ash, ADF, NDF in calibration set was 0.86, 0.94, 0.91, 0.88, 0.48 and 0.93, respectively. The value in validation set was 0.66, 0.86, 0.83, 0.71, 0.35 and 0.88, respectively. The results of this experiment indicate that NIRS is a reliable analytical method to assess forage quality for CP, CF, NDF except ADF and moisture in permanent pastures when proper samples incorporated into the equation development.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.4
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• pp.23-32
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• 2019

Increasing crop production with the same amount of resources is essential for enhancing the economy in agriculture. The first prerequisite is to understand relationships between the resources. The concept of WEF (Water-Energy-Food) nexus analysis was first introduced in 2011, which helps to interpret inter-linkages among the resources and stakeholders. The objective of this study was to analyze energy-water nexus in greenhouse cultivation by estimating reference evapotranspiration and heating load. For the estimation, this study used the physical model to simulate the inside temperature of the agricultural greenhouse using heating, solar radiation, ventilated and transferred heat losses as input variables. For estimating reference evapotranspiration and heating load, Penman-Monteith equation and seasonal heating load equation with HDH (Heating Degree-Hour) was applied. For calibration and validation of simulated inside temperature, used were hourly data observed from 2011 to 2012 in multi-span greenhouse. Results of the simulation were evaluated using $R^2$, MAE and RMSE, which showed 0.75, 2.22, 3.08 for calibration and 0.71, 2.39, 3.35 for validation respectively. When minimum setting temperature was $12^{\circ}C$ from 2013 to 2017, mean values of evapotranspiration and heating load were 687 mm/year and 2,147 GJ/year. For $18^{\circ}C$, Mean values of evapotranspiration and heating load were 707 mm/year and 5,616 GJ/year. From the estimation, the relationship between water and heat energy was estimated as 1.0~2.6 GJ/ton. Though additional calibrations with different types of greenhouses are necessary, the results of this study imply that they are applicable when evaluating resource relationship in the greenhouse cultivation complex.

• Journal of The Korean Society of Grassland and Forage Science
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• v.41 no.4
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• pp.295-301
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• 2021

In this study we developed the ground-based multispectral camera system to determine proper period to build and apply the calibration equation for dry matter of winter forage corps monitoring by unmanned aerial vehicle (UAV). Normalized difference vegetation index (NDVI) of rye, whole barley and Italian ryegrass (IRG) were measured and the growth period was divide by NDVI increasing period and decreasing period. Day of the maximum NDVI value of rye, whole barley and IRG were 8th, 9th and 5th April 2020. Regression analysis showed that the correlation coefficients (R²) between dry matter and NDVI were 0.84, 0.84, 0.78 during NDVI increasing period and 0.00, 0.02, 0.27 during NDVI decreasing period. Therefore, detailed NDVI monitoring is required to determine the proper period to build and apply the calibration equation and the ground-based multispectral camera system was effective tool for detailed NDVI monitoring.

• Journal of the Korean Geotechnical Society
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• v.26 no.12
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• pp.41-50
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• 2010

The behavior of laterally cyclic loaded piles is different from that of piles under monotonic loading and depends on soil and load characteristics. In this study, model pile load tests were performed using a calibration chamber to investigate the effects of load characteristics on the behavior of laterally cyclic loaded piles in sand. Results of the model tests show that the ultimate lateral load capacity of laterally cyclic loaded piles decreases linearly with increasing the number of cycles and increases slightly with increasing the magnitude of cyclic lateral loads. When the piles reach the ultimate state, the maximum bending moment developed in the piles decreases linearly with increasing the number of cycles and it occurs at a depth of 0.36 times pile embedded length for all the number of cycles. However, both the magnitude and depth of the maximum bending moment of piles in the ultimate state increase slightly as the magnitude of cyclic lateral loads increases. It is also observed that the cyclic lateral loading generates a decrease in the ultimate lateral load capacity and maximum bending moment for piles in the ultimate state. In addition, based on the model test results, a new empirical equation for the ultimate lateral load capacity of laterally cyclic loaded piles in dense sand is also proposed. A comparison between predicted and measured load capacities shows that the proposed equation reflects satisfactorily the model test results.

• Journal of Korea Water Resources Association
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• v.33 no.2
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• pp.247-262
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• 2000

In this paper, the WSANN(Water Stage Analysis with Neural Network) model was presented so as to predict flood water stage at Jindong which has been the major stream gauging station in Nakdong river basin. The WSANN model used the improved backpropagation training algorithm which was complemented by the momentum method, improvement of initial condition and adaptive-learning rate and the data which were used for this study were classified into training and testing data sets. An empirical equation was derived to determine optimal hidden layer node between the hidden layer node and threshold iteration number. And, the calibration of the WSANN model was performed by the four training data sets. As a result of calibration, the WSANN22 and WSANN32 model were selected for the optimal models which would be used for model verification. The model verification was carried out so as to evaluate model fitness with the two-untrained testing data sets. And, flood water stages were reasonably predicted through the results of statistical analysis. As results of this study, further research activities are needed for the construction of a real-time warning of the impending flood and for the control of flood water stage with neural network method in river basin. basin.