• Korean Journal of Radiology
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• v.22 no.11
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• pp.1777-1785
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• 2021

Objective: To investigate the accuracy of the Agatston score obtained with the ultra-high-pitch (UHP) acquisition mode using tin-filter spectral shaping (Sn150 kVp) and a kVp-independent reconstruction algorithm to reduce the radiation dose. Materials and Methods: This prospective study included 114 patients (mean ± standard deviation, 60.3 ± 9.8 years; 74 male) who underwent a standard 120 kVp scan and an additional UHP Sn150 kVp scan for coronary artery calcification scoring (CACS). These two datasets were reconstructed using a standard reconstruction algorithm (120 kVp + Qr36d, protocol A; Sn150 kVp + Qr36d, protocol B). In addition, the Sn150 kVp dataset was reconstructed using a kVp-independent reconstruction algorithm (Sn150 kVp + Sa36d, protocol C). The Agatston scores for protocols A and B, as well as protocols A and C, were compared. The agreement between the scores was assessed using the intraclass correlation coefficient (ICC) and the Bland-Altman plot. The radiation doses for the 120 kVp and UHP Sn150 kVp acquisition modes were also compared. Results: No significant difference was observed in the Agatston score for protocols A (median, 63.05; interquartile range [IQR], 0-232.28) and C (median, 60.25; IQR, 0-195.20) (p = 0.060). The mean difference in the Agatston score for protocols A and C was relatively small (-7.82) and with the limits of agreement from -65.20 to 49.56 (ICC = 0.997). The Agatston score for protocol B (median, 34.85; IQR, 0-120.73) was significantly underestimated compared with that for protocol A (p < 0.001). The UHP Sn150 kVp mode facilitated an effective radiation dose reduction by approximately 30% (0.58 vs. 0.82 mSv, p < 0.001) from that associated with the standard 120 kVp mode. Conclusion: The Agatston scores for CACS with the UHP Sn150 kVp mode with a kVp-independent reconstruction algorithm and the standard 120 kVp demonstrated excellent agreement with a small mean difference and narrow agreement limits. The UHP Sn150 kVp mode allowed a significant reduction in the radiation dose.

• Korean Journal of Remote Sensing
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• v.30 no.2
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• pp.275-292
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• 2014

The objective of this study is to determine Tasseled Cap Transformation (TCT) coefficients for the Geostationary Ocean Color Imager (GOCI). TCT is traditional method of analyzing the characteristics of the land area from multi spectral sensor data. TCT coefficients for a new sensor must be estimated individually because of different sensor characteristics of each sensor. Although the primary objective of the GOCI is for ocean color study, one half of the scene covers land area with typical land observing channels in Visible-Near InfraRed (VNIR). The GOCI has a unique capability to acquire eight scenes per day. This advantage of high temporal resolution can be utilized for detecting daily variation of land surface. The GOCI TCT offers a great potential for application in near-real time analysis and interpretation of land cover characteristics. TCT generally represents information of "Brightness", "Greenness" and "Wetness". However, in the case of the GOCI is not able to provide "Wetness" due to lack of ShortWave InfraRed (SWIR) band. To maximize the utilization of high temporal resolution, "Wetness" should be provided. In order to obtain "Wetness", the linear regression method was used to align the GOCI Principal Component Analysis (PCA) space with the MODIS TCT space. The GOCI TCT coefficients obtained by this method have different values according to observation time due to the characteristics of geostationary earth orbit. To examine these differences, the correlation between the GOCI TCT and the MODIS TCT were compared. As a result, while the GOCI TCT coefficients of "Brightness" and "Greenness" were selected at 4h, the GOCI TCT coefficient of "Wetness" was selected at 2h. To assess the adequacy of the resulting GOCI TCT coefficients, the GOCI TCT data were compared to the MODIS TCT image and several land parameters. The land cover classification of the GOCI TCT image was expressed more precisely than the MODIS TCT image. The distribution of land cover classification of the GOCI TCT space showed meaningful results. Also, "Brightness", "Greenness", and "Wetness" of the GOCI TCT data showed a relatively high correlation with Albedo ($R^2$ = 0.75), Normalized Difference Vegetation Index (NDVI) ($R^2$ = 0.97), and Normalized Difference Moisture Index (NDMI) ($R^2$ = 0.77), respectively. These results indicate the suitability of the GOCI TCT coefficients.

• Korean Journal of Soil Science and Fertilizer
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• v.42 no.2
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• pp.79-87
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• 2009

Pot experiments using sand culture were conducted in 2004 under greenhouse conditions to evaluate the effect of nitrogen deficiency on red pepper biomass. Nitrogen stress was imposed by implementing 6 levels (40% to 140%) of N in Hoagland's nutrient solution for red pepper. Canopy reflectance measurements were made with hand held spectral sensors including $GreenSeeker^{TM}$, $Crop\;Circle^{TM}$, and $Field\;Scout^{TM}$ Chlorophyll meter, and a spectroradiometer as well as Minolta SPAD-502 chlorophyll meter. Canopy reflectance and dry weight of red pepper were measured at five growth stages, the 30th, 40th, 50th, 80th and 120th day after planting(DAT). Dry weight of red pepper affected by nitrogen stress showed large differences between maximum and minimum values at the 120th DAT ranged from 48.2 to $196.6g\;plant^{-1}$, respectively. Several reflectance indices obtained from $GreenSeeker^{TM}$, $Crop\;Circle^{TM}$ and Spectroradiometer including chlorophyll readings were compared for evaluation of red pepper biomass. The reflectance indices such as rNDVI, aNDVI and gNDVI by the $Crop\;Circle^{TM}$ sensor showed the highest correlation coefficient with dry weight of red pepper at the 40th, 50th, and 80th DAT, respectively. Also these reflectance indices at the same growth station was closely correlated with dry weight, yield, and nitrogen uptake of red pepper at the 120th DAT, especially showing the best correlation coefficient at the 80th DAT. From these result, the aNDVI at the 80th DAT can significantly explain for dry weight of red pepper at the 120th DAT as well as for application level of nitrogen fertilizer. Consequently ground remote sensing as a non-destructive real-time assessment of plant nitrogen status was thought to be a useful tool for in season nitrogen management for red pepper providing both spatial and temporal information.

• Korean Journal of Remote Sensing
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• v.14 no.1
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• pp.53-68
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• 1998

Observation of hydrometeors' behavior in the atmosphere is important to understand weather and climate. By conventional observations, we can get the distribution of water vapor at limited number of points on the earth. In this study, the precipitable water has been estimated from the split window channel data on GMS-5 based upon the technique developed by Chesters et al.(1983). To retrieve the precipitable water, water vapor absorption parameter depending on filter function of sensor has been derived using the regression analysis between the split window channel data and the radiosonde data observed at Osan, Pohang, Kwangiu and Cheju staions for 4 months. The air temperature of 700 hPa from the Global Spectral Model of Korea Meteorological Administration (GSM/KMA) has been used as mean air temperature for single layer radiation model. The retrieved precipitable water for the period from August 1996 through December 1996 are compared to radiosonde data. It is shown that the root mean square differences between radiosonde observations and the GMS-5 retrievals range from 0.65 g/$cm^2$ to 1.09 g/$cm^2$ with correlation coefficient of 0.46 on hourly basis. The monthly distribution of precipitable water from GMS-5 shows almost good representation in large scale. Precipitable water is produced 4 times a day at Korea Meteorological Administration in the form of grid point data with 0.5 degree lat./lon. resolution. The data can be used in the objective analysis for numerical weather prediction and to increase the accuracy of humidity analysis especially under clear sky condition. And also, the data is a useful complement to existing data set for climatological research. But it is necessary to get higher correlation between radiosonde observations and the GMS-5 retrievals for operational applications.

• Investigative Magnetic Resonance Imaging
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• v.12 no.1
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• pp.8-19
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• 2008

Purpose : To investigate the 3-bond and spatial connectivity of human brain metabolites by scalar coupling and dipolar nuclear Overhauser effect/enhancement (NOE) interaction through 2D- correlation spectroscopy (COSY) and 2D- NOE spectroscopy (NOESY) techniques. Materials and Methods : All 2D experiments were performed on Bruker Avance 500 (11.8 T) with the zshield gradient triple resonance cryoprobe at 298 K. Human brain metabolites were prepared with 10% $D_2O$. Two-dimensional spectra with 2048 data points contains 320 free induction decay (FID) averaging. Repetition delay was 2 sec. The Top Spin 2.0 software was used for post-processing. Total 7 metabolites such as N-acetyl aspartate (NAA), creatine (Cr), choline (Cho), lutamine (Gln), glutamate (Glu), myo-inositol (Ins), and lactate (Lac) were included for major target metabolites. Results : Symmetrical 2D-COSY and 2D-NOESY pectra were successfully acquired: COSY cross peaks were observed in the only 1.0-4.5 ppm, however, NOESY cross peaks were observed in the 1.0-4.5 ppm and 7.9 ppm. From the result of the 2-D COSY data, cross peaks between the methyl protons ($CH_3$(3)) at 1.33 ppm and methine proton (CH(2)) at 4.11 ppm were observed in Lac. Cross peaks between the methylene protons (CH2(3,$H{\alpha}$)) at 2.50ppm and methylene protons ($CH_2$,(3,$H_B$)) at 2.70 ppm were observed in NAA. Cross peaks between the methine proton (CH(5)) at 3.27 ppm and the methine proton (CH(4,6)) at 3.59 ppm, between the methine proton (CH(1,3)) at 3.53 ppm and methine proton (CH(4,6)) at 3.59 ppm, and between the methine proton (CH(1,3)) at 3.53 ppm and methine proton (CH(2)) at 4.05 ppm were observed in Ins. From the result of 2-D NOESY data, cross peaks between the NH proton at 8.00 ppm and methyl protons ($CH_3$) were observed in NAA. Cross peaks between the methyl protons ($CH_3$(3)) at 1.33 ppm and methine proton (CH(2)) at 4.11 ppm were observed in Lac. Cross peaks between the methyl protons (CH3) at 3.03 ppm and methylene protons (CH2) at 3.93 ppm were observed in Cr. Cross peaks between the methylene protons ($CH_2$(3)) at 2.11 ppm and methylene protons ($CH_2$(4)) at 2.35 ppm, and between the methylene protons($CH_2$ (3)) at 2.11 ppm and methine proton (CH(2)) at 3.76 ppm were observed in Glu. Cross peaks between the methylene protons (CH2 (3)) at 2.14 ppm and methine proton (CH(2)) at 3.79 ppm were observed in Gln. Cross peaks between the methine proton (CH(5)) at 3.27 ppm and the methine proton (CH(4,6)) at 3.59 ppm, and between the methine proton (CH(1,3)) at 3.53 ppm and methine proton (CH(2)) at 4.05 ppm were observed in Ins. Conclusion : The present study demonstrated that in vitro 2D-COSY and NOESY represented the 3-bond and spatial connectivity of human brain metabolites by scalar coupling and dipolar NOE interaction. This study could aid in better understanding the interactions between human brain metabolites in vivo 2DCOSY study.

• Korean Journal of Remote Sensing
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• v.30 no.4
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• pp.481-491
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• 2014

Blue-ice area is a glacial ice field in ice sheet, ice shelf and glaciers where snow ablation and sublimation is larger than snowfall. As the blue-ice area has large influences on the meteorite concentration mechanism and ice mass balance, it is required to quantify the concentration of blue-ice. We analyzed spectral reflectance characteristics of blue-ice, snow and cloud by using MODIS images obtained over blue-ice areas in McMurdo Dry Valleys, East Antarctica, from 2007 to 2012. We then developed Normalized Difference Blue-ice Index (NDBI) algorithm which quantifies the concentration of blue-ice. Snow and cloud have a high reflectance in visible and near-infrared (NIR) bands. Reflectance of blue-ice is high in blue band, while that lowers in the NIR band. NDBI is calculated by dividing the difference of reflectance in the blue and NIR bands by the sum of reflectances in the two bands so that NDBI = (Blue-NIR)/(Blue + NIR). NDBI calculated from the MODIS images showed that the blue-ice areas have values ranging from 0.2 to 0.5, depending on the exposure and concentration of blue-ice. It is obviously different from that of snow and cloud that has values less than 0.2 or rocks with negative values. The change of NDBI values in the blue-ice area has higher correlation with snow depth ($R^2=0.699$) than wind speed ($R^2=0.012$) or air temperature ($R^2=0.278$), all measured at a meteorological station installed in McMurdo Dry Valleys. As the snow depth increased, the NDBI value decreased, which suggests that snow depth can be estimated from NDBI values over blue-ice areas. The NDBI algorithm developed in this study will be useful for various polar research fields such as meteorite exploration, analysis of ice mass balance as well as the snow depth estimation.

• Journal of Korean Society of Environmental Engineers
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• v.35 no.12
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• pp.897-905
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• 2013

In this study, sorption coefficients (${\log}K_{OC}$, n) for the binding of phenanthrene (PHE) to soil humins, insoluble fraction of soil humc substances (HS), were determined and relationship between the sorption coefficients and structural characteristics of the soil humins were investigated. The soil humins used in the present study were isolated from 7 different soils including 5 domestic soils, an IHSS standard and a peat soil, and characterized by elemental analysis and CPMAS $^{13}C$ NMR method. $^{13}C$ NMR spectral features indicate that the soil humins are mainly made up of aliphatic carbons (57.1~72.3% in total carbon) with high alkyl-C moiety, and the alkyl-C contents ($C_{Al-H,C}$, %) was in order of granite soil Hu (26~42%) > volcanic ash soil, HL Hu (23.9%) > Peat Hu (14.0%). The results of correlation study show that a positive relationship ($r^2$ = 0.77, p < 0.05) between organic carbon normalized-sorption coefficients ($K_{OC}$, mL/g) and alkyl-C contents($C_{Al-H,C}$, %), while negative relationship ($r^2$ = (-)0.74, p < 0.05) between Freundlich sorption parameter (n) and H,C-substituted aromatic carbon contents ($C_{Ar-H,C}$, %). The magnitude of $K_{OC}$ values are also negatively well correlated with polarity index (e.g., PI, N + O)/C) ($r^2$ = (-)0.74, p < 0.1). These results suggest that the binding capacity (e.g., $K_{OC}$) for PHE is increased in soil humin molecules having high contents of alkyl-C or lower polarity, and nonlinear sorption for PHE increased as the H,C-substituted aromatic carbon contents ($C_{Ar-H,C}$, %) in the soil humins increased. The PHE sorption characteristics on soil humins are discussed based on the dual reactive mode of sorption model.

• Journal of Plant Biotechnology
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• v.42 no.1
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• pp.60-70
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• 2015

The aim of this study was to investigate whether fourier transform infrared (FT-IR) spectroscopy can be applied to simultaneous determination of fatty acids contents in different soybean cultivars. Total 153 lines of soybean (Glycine max Merrill) were examined by FT-IR spectroscopy. Quantification of fatty acids from the soybean lines was confirmed by quantitative gas chromatography (GC) analysis. The quantitative spectral variation among different soybean lines was observed in the amide bond region ($1,700{\sim}1,500cm^{-1}$), phosphodiester groups ($1,500{\sim}1,300cm^{-1}$) and sugar region ($1,200{\sim}1,000cm^{-1}$) of FT-IR spectra. The quantitative prediction modeling of 5 individual fatty acids contents (palmitic acid, stearic acid, oleic acid, linoleic acid, linolenic acid) from soybean lines were established using partial least square regression algorithm from FT-IR spectra. In cross validation, there were high correlations ($R^2{\geq}0.97$) between predicted content of 5 individual fatty acids by PLS regression modeling from FT-IR spectra and measured content by GC. In external validation, palmitic acid ($R^2=0.8002$), oleic acid ($R^2=0.8909$) and linoleic acid ($R^2=0.815$) were predicted with good accuracy, while prediction for stearic acid ($R^2=0.4598$), linolenic acid ($R^2=0.6868$) had relatively lower accuracy. These results clearly show that FT-IR spectra combined with multivariate analysis can be used to accurately predict fatty acids contents in soybean lines. Therefore, we suggest that the PLS prediction system for fatty acid contents using FT-IR analysis could be applied as a rapid and high throughput screening tool for the breeding for modified Fatty acid composition in soybean and contribute to accelerating the conventional breeding.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.53 no.2
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• pp.217-223
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• 2008

Amylose and protein contents are important traits determining the edible quality of rice, especially in East Asian countries. Near-Infrared Reflectance Spectroscopy (NIRS) has become a powerful tool for rapid and nondestructive quantification of natural compounds in agricultural products. To test the practically of using NIRS for estimation of brown rice amylose and protein contents, the spectral reflectances ($400{\sim}2500\;nm$) of total 9,483 accessions of rice germplasm in Rural development Administration (RDA) Genebank ere obtained and compared to chemically determined amylose and protein content. The protein content of tested 119 accessions ranged from 6.5 to 8.0% and 25 accessions exhibited protein contents between 8.5 to 9.5%. In case of amylose content, all tested accessions ranged from 18.1 to 21.7% and the grade from 18.1 to 19.9% includes most number of accessions as 152 and 4 accessions exhibited amylose content between 20.5 to 21.7%. The optimal performance calibration model could be obtained from original spectra of brown rice using MPLS (Modified Partial Least Squares) with the correlation coefficients ($r_2$) for amylose and protein content were 0.865 and 0.786, respectively. The standard errors of calibration (SEC) exhibited good statistic values: 2.078 and 0.442 for amylose and protein contents, respectively. All these results suggest that NIR spectroscopy may serve as reputable and rapid method for quantification of brown rice protein and amylose contents in large numbers of rice germplasm.

• Journal of Biomedical Engineering Research
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• v.23 no.1
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• pp.49-60
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• 2002

Receive dynamic focusing with an array transducer can provide near optimum resolution only in the vicinity of transmit focal depth. A customary method to increase the depth of field is to combine several beams with different focal depths, with an accompanying decrease in the frame rate. In this Paper. we Present a simultaneous multiple transmit focusing method in which chirp signals focused at different depths are transmitted at the same time. These chirp signals are mutually orthogonal in a sense that the autocorrelation function of each signal has a narrow mainlobe width and low sidelobe levels. and the crossorelation function of any Pair of the signals has values smaller than the sidelobe levels of each autocorrelation function. This means that each chirp signal can be separated from the combined received signals and compressed into a short pulse. which is then individually focused on a separate receive beamformer. Next. the individually focused beams are combined to form a frame of image. Theoretically, any two chirp signals defined over two nonoverlapped frequency bands are mutually orthogonal In the present work. however, a tractional overlap of adjacent frequency bands is permitted to design more chirp signals within a given transducer bandwidth. The elevation of the rosscorrelation values due to the frequency overlap could be reduced by alternating the direction of frequency sweep of the adjacent chirp signals We also observe that the Proposed method provides better images when the low frequency chirp is focused at a near Point and the high frequency chirp at a far point along the depth. better lateral resolution is obtained at the far field with reasonable SNR due to the SNR gain in Pulse compression Imaging .