• Korean Journal of Remote Sensing
• /
• v.38 no.6_2
• /
• pp.1691-1707
• /
• 2022

Cheonji Lake, the caldera of Baekdu Mountain, located on the border of the Korean Peninsula and China, alternates between melting and freezing seasonally. There is a magma chamber beneath Cheonji, and variations in the magma chamber cause volcanic antecedents such as changes in the temperature and water pressure of hot spring water. Consequently, there is an abnormal region in Cheonji where ice melts quicker than in other areas, freezes late even during the freezing period, and has a high-temperature water surface. The abnormal area is a discharge region for hot spring water, and its ice gradient may be used to monitor volcanic activity. However, due to geographical, political and spatial issues, periodic observation of abnormal regions of Cheonji is limited. In this study, the degree of ice change in the optimal region was quantified using a Landsat -5/-7/-8 optical satellite image and a Modified U-Net regression model. From January 22, 1985 to December 8, 2020, the Visible and Near Infrared (VNIR) band of 83 Landsat images including anomalous regions was utilized. Using the relative spectral reflectance of water and ice in the VNIR band, unique data were generated for quantitative ice variability monitoring. To preserve as much information as possible from the visible and near-infrared bands, ice gradient was noticed by applying it to U-Net with two encoders, achieving good prediction accuracy with a Root Mean Square Error (RMSE) of 140 and a correlation value of 0.9968. Since the ice change value can be seen with high precision from Landsat images using Modified U-Net in the future may be utilized as one of the methods to monitor Baekdu Mountain's volcanic activity, and a more specific volcano monitoring system can be built.

• Journal of Korean Ophthalmic Optics Society
• /
• v.12 no.2
• /
• pp.1-12
• /
• 2007

Object of this research is to estimate the effect of myopia correction and safety on reverse geometry contact lens fitting in school children. This research include 53(106eyes) schoolchildren among 7 to 18 years who has low to moderate myopia(-1.00D~-5.00D) and prescribed reverse geometry contact lens for purpose on orthokeratology between January to July 2004 and had 3months full follow up examination. They were tested for slit lamp examinations, BUT(Break up time), direct ophthalmoscopy, retinoscopy, uncorrected visual acuity, best corrected visual acuity, autorefraction, autokeratometry and corneal topography in each examination(1day, 1week, 2weeks, 1, 2, and 3months) of before-and-after lens wearing to find out the effect of myopic correction and side effect. The results came out as follow. The average of uncorrected visual acuity was $0.0938{\pm}0.378$ before lens wear and $0.3136{\pm}0.283$ after 1day lens wear, and there was fast improvement after 1week($0.7925{\pm}0.301$) and little improvement after 2weeks period but still they shows better uncorrected visual acuity(p<0.01). The result of this study, the reverse geometry lens is very useful to correct refractive error and control the progression of myopia temporally among low to moderate myopic patient. The side effects were relatively rare but further study should be necessary with long term lens wear effect on eye health. For the lens prescription, the clinical fitting process had higher rate of success with consideration of eccentricity and corneal topography.

• Journal of the Korean earth science society
• /
• v.39 no.3
• /
• pp.228-240
• /
• 2018

Solar energy is calculated using meteorological (14 station), ceilometer (2 station) and microwave radiometer (MWR, 7 station)) data observed from the Weather Information Service Engine (WISE) on the Seoul metropolitan area. The cloud optical thickness and the cloud fraction are calculated using the back-scattering coefficient (BSC) of the ceilometer and liquid water path of the MWR. The solar energy on the surface is calculated using solar radiation model with cloud fraction from the ceilometer and the MWR. The estimated solar energy is underestimated compared to observations both at Jungnang and Gwanghwamun stations. In linear regression analysis, the slope is less than 0.8 and the bias is negative which is less than $-20W/m^2$. The estimated solar energy using MWR is more improved (i.e., deterministic coefficient (average $R^2=0.8$) and Root Mean Square Error (average $RMSE=110W/m^2$)) than when using ceilometer. The monthly cloud fraction and solar energy calculated by ceilometer is greater than 0.09 and lower than $50W/m^2$ compared to MWR. While there is a difference depending on the locations, RMSE of estimated solar radiation is large over $50W/m^2$ in July and September compared to other months. As a result, the estimation of a daily accumulated solar radiation shows the highest correlation at Gwanghwamun ($R^2=0.80$, RMSE=2.87 MJ/day) station and the lowest correlation at Gooro ($R^2=0.63$, RMSE=4.77 MJ/day) station.

• Journal of Animal Environmental Science
• /
• v.18 no.3
• /
• pp.257-266
• /
• 2012

Near infrared reflectance spectroscopy (NIRS) has become increasingly used as a rapid, accurate method of evaluating some chemical constituents in cereal and dired animal forages. Analysis of forage quality by NIRS usually involves dry grinding samples. Costs might be reduced if samples could be analyzed without drying or grinding. The objective of this study was to investigate effect of sample preparations on prediction ability of chemical composition and fermentation parameter for Italian ryegrass silages by NIRS. A population of 147 Italian ryegrass silages representing a wide range in chemical parameters were used in this investigation. Samples were scanned at 1nm intervals over the wavelength range 680-2500 nm and the optical data recorded as log 1/Reflectance (log 1/R) and scanned in oven-dried grinding and fresh ungrinding condition. The spectral data were regressed against a range of chemical parameters using partial least squares (PLS) multivariate analysis in conjunction with four spectral math treatments to reduced the effect of extraneous noise. The optimum calibrations were selected on the basis of minimizing the standard error of cross validation (SECV) and maximizing the correlation coefficient of cross validation (${R^2}_{CV}$). The results of this study show that NIRS predicted the chemical parameters with high degree of accuracy in oven-dried grinding treatment except for moisture contents. Prediction accuracy of the moisture contents was better for fresh ungrinding treatment (SECV 1.37%, $R^2$ 0.96) than for oven-dried grinding treatments (SECV 4.31%, $R^2$ 0.68). Although the statistical indexes for accuracy of the prediction were the lower in fresh ungrinding treatment, fresh treatment may be acceptable when processing is costly or when some changes in component due to the processing are expected. Results of this experiment showed the possibility of NIRS method to predict the chemical composition and fermentation parameter of Italian ryegrass silages as routine analysis method in feeding value evaluation and for farmer advice.

• Journal of Korean Ophthalmic Optics Society
• /
• v.10 no.1
• /
• pp.71-82
• /
• 2005

The aim of this study was to provide fundamental data for the factors influencing the asthenopia of emmetropia with phoria and alleviation of asthenopia. A total of 348 subjects, aged between 19 and 30 years old, who had no strabismus, an eye trouble or whole body disease, were examined using corrected visual acuity, corrected diopter, stereopsis and suppression tests from September of 2002 to September of 2004. We excluded 21 subjects for the following reasons: if they had an amblyopia affecting binocular vision or inaccurate data. After these exclusions, 327 subjects remained. We then individually measured the refractive error correction, pupillary distance, optical center distance, phoria, convergence, accommodation and the AC/A as well as the asthenopia during binocular vision using a questionnaire. After analysis of factors affecting asthenopia, we also examined the reductive effect of a prism on the asthenopia in subjects who had asthenopia. To determine the factors affecting asthenopia during binocular vision, statistic analyses were carried out using the Chi-square test and the multivariate Logistic regression model. The results of this study were as follow. For asthenopia during near binocular vision of emmetropia with phoria, in case of the lower the accommodation and convergence, a significantly higher rate of asthenopia was observed (p<0.001). When the AC/A is lower, the higher the rate of asthenopia was observed but not significantly and there was no association between phoria and asthenopia. When the multivariate logistic regression model was used to determine factors affecting binocular vision of emmetropia with phoria, in case of the lower accommodation and convergence, a significantly higher rate of asthenopia was observed. when the phoria is esophoria or higher exophoria, or when the AC/A is lower than normal, the higher the rate of asthenopia was observed but not significantly and there was no association between phoria. AC/A and asthenopia. Therefore accommodation and convergence could be predictive factors for asthenopia during near distance binocular vision. Prism was used among' subjects who had asthenopia during near distance binocular vision, the symptom of asthenopia was eased up to 74.2% in emmetropia with phoria.

• KOREAN JOURNAL OF CROP SCIENCE
• /
• v.12
• /
• pp.1-5
• /
• 1972

In order to compare the analytical efficiency of the Kjeldahal, Dye binding and Biurett method for the determination of nitrogen content in the brown rice, correlation coefficients were calculate with the analytical data obtained by the above mentioned 3 different methods for the brown rice of 36 varieties or lines grown at 5 different nitrogen levels (0, 7.5, 15.0, 22.5 and 30.0kg/10a). Analysis of variance were made for the data of 6 varieties among those 36, and compared the precision of the data obtained by the 3 analytical methods. The expenditure (in terms of chemicals and labour) required for the 3 methods are also compared. The results are summarized as follows; 1. The correlation between D. B. C. and Kjeldahl value were generally more significant than the correlation between the value of Biurett and the value of Kjeldahl. But, the D. B. C. method generally overestimates than the Kjeldahl method at both extreme low and extreme high nitrogen contents, and the Biurett method includes more dispersed error than other two methods, though the optical values are parallel to the Kjeldahl nitrogen values at any levels of nitrogen applied. 2. The varietal difference in nitrogen value obtained by the 3 methods were different at the different nitrogen level applied. That is the interaction between variety and analytical method, and between the nitrogen level and analytical method were significant statistically. 3. The coefficient of variance (C, V.) was largest in the data analyzed by Biurett method and next in the data analayred by D. B. C. method. In the data analyzed by Biurett, the C. V. increased along onglong increase of nitrogen applied. But, in the data obtained by D. B. C. or Biurett the C. V. increased along the decrease of nitrogen applied. 4. From the comparison of the expenditure (in terms of chemicals and labour) required for the analysis of 100 samples by 3 methods, it was noticed that, the Biurett or D. B. C. method largely curtail the chemical expenditure and labour costs. Especially the Biurett method could curtail more labour costs.

• Journal of The Korean Society of Grassland and Forage Science
• /
• v.39 no.4
• /
• pp.258-263
• /
• 2019

Near infrared reflectance spectroscopy (NIRS) has become increasingly used as a rapid and accurate method of evaluating some chemical compositions in forages. The objective of this study was to evaluate the potential of NIRS, applied to imported forage, to estimate the moisture and chemical parameters for imported hays. A population of 392 imported hay representing a wide range in chemical parameters was used in this study. Samples of forage were scanned at 1 nm intervals over the wavelength range 680-2500nm and the optical data was recorded as log 1/Reflectance(log 1/R), which scanned in intact fresh condition. The spectral data were regressed against a range of chemical parameters using partial least squares(PLS) multivariate analysis in conjunction with spectral math treatments to reduced the effect of extraneous noise. The optimum calibrations were selected based on the highest coefficients of determination in cross validation(R²) and the lowest standard error of cross-validation(SECV). The results of this study showed that NIRS predicted the chemical parameters with very high degree of accuracy. The R² and SECV for imported hay calibration were 0.92(SECV 0.61%) for moisture, 0.98(SECV 0.65%) for acid detergent fiber, 0.97(SECV 0.40%) for neutral detergent fiber, 0.99(SECV 0.06%) for crude protein and 0.97(SECV 3.04%) for relative feed value on a dry matter(%), respectively. Results of this experiment showed the possibility of NIRS method to predict the moisture and chemical composition of imported hay in Korea for routine analysis method to evaluate the feed value.

• Journal of The Korean Society of Grassland and Forage Science
• /
• v.32 no.3
• /
• pp.301-308
• /
• 2012

Near infrared reflectance spectroscopy (NIRS) has become increasingly used as a rapid and accurate method of evaluating some chemical compositions in forages. This study was carried out to explore the accuracy of near infrared spectroscopy (NIRS) for the prediction of chemical parameters of Italian ryegrass silages. A population of 267 Italian ryegrass silages representing a wide range in chemical parameters and fermentative characteristics was used in this investigation. Samples of silage were scanned at 2 nm intervals over the wavelength range 680~2,500 nm and the optical data recorded as log 1/Reflectance (log 1/R) and scanned in intact fresh condition. The spectral data were regressed against a range of chemical parameters using partial least squares (PLS) multivariate analysis in conjunction with spectral math treatments to reduced the effect of extraneous noise. The optimum calibrations were selected on the basis of the highest coefficients of determination in cross validation ($R^2$) and the lowest standard error of cross validation (SECV). The results of this study showed that NIRS predicted the chemical parameters with very high degree of accuracy. The $R^2$ and SECV were 0.98 (SECV 1.27%) for moisture, 0.88 (SECV 1.26%) for ADF, 0.84 (SECV 2.0%), 0.93 (SECV 0.96%) for CP and 0.78 (SECV 0.56), 0.81 (SECV 0.31%), 0.88 (SECV 1.26%) and 0.82 (SECV 4.46) for pH, lactic acid, TDN and RFV on a dry matter (%), respectively. Results of this experiment showed the possibility of NIRS method to predict the chemical composition and fermentation quality of Italian ryegrass silages as routine analysis method in feeding value evaluation and for farmer advice.

• Journal of The Korean Society of Grassland and Forage Science
• /
• v.36 no.1
• /
• pp.50-57
• /
• 2016

This study was conducted to determine the effect of mathematical transformation on near infrared spectroscopy (NIRS) calibrations for the prediction of chemical composition and fermentation parameters in corn silage. Corn silage samples (n=407) were collected from cattle farms and feed companies in Korea between 2014 and 2015. Samples of silage were scanned at 1 nm intervals over the wavelength range of 680~2,500 nm. The optical data were recorded as log 1/Reflectance (log 1/R) and scanned in intact fresh condition. The spectral data were regressed against a range of chemical parameters using partial least squares (PLS) multivariate analysis in conjunction with several spectral math treatments to reduce the effect of extraneous noise. The optimum calibrations were selected based on the highest coefficients of determination in cross validation ($R^2{_{cv}}$) and the lowest standard error of cross validation (SECV). Results of this study revealed that the NIRS method could be used to predict chemical constituents accurately (correlation coefficient of cross validation, $R^2{_{cv}}$, ranging from 0.77 to 0.91). The best mathematical treatment for moisture and crude protein (CP) was first-order derivatives (1, 16, 16, and 1, 4, 4), whereas the best mathematical treatment for neutral detergent fiber (NDF) and acid detergent fiber (ADF) was 2, 16, 16. The calibration models for fermentation parameters had lower predictive accuracy than chemical constituents. However, pH and lactic acids were predicted with considerable accuracy ($R^2{_{cv}}$ 0.74 to 0.77). The best mathematical treatment for them was 1, 8, 8 and 2, 16, 16, respectively. Results of this experiment demonstrate that it is possible to use NIRS method to predict the chemical composition and fermentation quality of fresh corn silages as a routine analysis method for feeding value evaluation to give advice to farmers.

• Journal of The Korean Society of Grassland and Forage Science
• /
• v.39 no.2
• /
• pp.114-120
• /
• 2019

This study was carried out to explore the accuracy of near infrared spectroscopy(NIRS) for the prediction of moisture content and chemical parameters on winter annual forage crops. A population of 2454 winter annual forages representing a wide range in chemical parameters was used in this study. Samples of forage were scanned at 1nm intervals over the wavelength range 680-2500nm and the optical data was recorded as log 1/Reflectance(log 1/R), which scanned in intact fresh condition. The spectral data were regressed against a range of chemical parameters using partial least squares(PLS) multivariate analysis in conjunction with spectral math treatments to reduced the effect of extraneous noise. The optimum calibrations were selected based on the highest coefficients of determination in cross validation($R^2$) and the lowest standard error of cross-validation(SECV). The results of this study showed that NIRS calibration model to predict the moisture contents and chemical parameters had very high degree of accuracy except for barely. The $R^2$ and SECV for integrated winter annual forages calibration were 0.99(SECV 1.59%) for moisture, 0.89(SECV 1.15%) for acid detergent fiber, 0.86(SECV 1.43%) for neutral detergent fiber, 0.93(SECV 0.61%) for crude protein, 0.90(SECV 0.45%) for crude ash, and 0.82(SECV 3.76%) for relative feed value on a dry matter(%), respectively. Results of this experiment showed the possibility of NIRS method to predict the moisture and chemical composition of winter annual forage for routine analysis method to evaluate the feed value.