• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.1
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• pp.51-66
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• 2022

SST (Sea Surface Temperature) is based on the atmosphere-ocean interaction, one of the most important mechanisms for the Earth system. Because it is a crucial oceanic and meteorological factor for understanding climate change, gap-free grid data at a specific spatial and temporal resolution is beneficial in SST studies. This paper examined the production of daily SST grid maps from 137 stations in 2020 through the ordinary kriging with variogram optimization and their accuracy assessment. The variogram optimization was achieved by WLS (Weighted Least Squares) method, and the blind tests for the interpolation accuracy assessment were conducted by an objective and spatially unbiased sampling scheme. The four-round blind tests showed a pretty high accuracy: a root mean square error between 0.995 and 1.035℃ and a correlation coefficient between 0.981 and 0.982. In terms of season, the accuracy in summer was a bit lower, presumably because of the abrupt change in SST affected by the typhoon. The accuracy was better in the far seas than in the near seas. West Sea showed better accuracy than East or South Sea. It is because the semi-enclosed sea in the near seas can have different physical characteristics. The seasonal and regional factors should be considered for accuracy improvement in future work, and the improved SST can be a member of the SST ensemble around South Korea.

• Progress in Medical Physics
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• v.23 no.2
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• pp.91-98
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• 2012

Verification of internal organ motion during treatment and its feedback is essential to accurate dose delivery to the moving target. We developed an offline based internal organ motion verification system (IMVS) using cine EPID images and evaluated its accuracy and availability through phantom study. For verification of organ motion using live cine EPID images, a pattern matching algorithm using an internal surrogate, which is very distinguishable and represents organ motion in the treatment field, like diaphragm, was employed in the self-developed analysis software. For the system performance test, we developed a linear motion phantom, which consists of a human body shaped phantom with a fake tumor in the lung, linear motion cart, and control software. The phantom was operated with a motion of 2 cm at 4 sec per cycle and cine EPID images were obtained at a rate of 3.3 and 6.6 frames per sec (2 MU/frame) with $1,024{\times}768$ pixel counts in a linear accelerator (10 MVX). Organ motion of the target was tracked using self-developed analysis software. Results were compared with planned data of the motion phantom and data from the video image based tracking system (RPM, Varian, USA) using an external surrogate in order to evaluate its accuracy. For quantitative analysis, we analyzed correlation between two data sets in terms of average cycle (peak to peak), amplitude, and pattern (RMS, root mean square) of motion. Averages for the cycle of motion from IMVS and RPM system were $3.98{\pm}0.11$ (IMVS 3.3 fps), $4.005{\pm}0.001$ (IMVS 6.6 fps), and $3.95{\pm}0.02$ (RPM), respectively, and showed good agreement on real value (4 sec/cycle). Average of the amplitude of motion tracked by our system showed $1.85{\pm}0.02$ cm (3.3 fps) and $1.94{\pm}0.02$ cm (6.6 fps) as showed a slightly different value, 0.15 (7.5% error) and 0.06 (3% error) cm, respectively, compared with the actual value (2 cm), due to time resolution for image acquisition. In analysis of pattern of motion, the value of the RMS from the cine EPID image in 3.3 fps (0.1044) grew slightly compared with data from 6.6 fps (0.0480). The organ motion verification system using sequential cine EPID images with an internal surrogate showed good representation of its motion within 3% error in a preliminary phantom study. The system can be implemented for clinical purposes, which include organ motion verification during treatment, compared with 4D treatment planning data, and its feedback for accurate dose delivery to the moving target.

• Journal of dental hygiene science
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• v.14 no.2
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• pp.158-166
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• 2014

This study is trying to grasp the stress of the male high school students and the correlation between the stress according to the oral health important cognitive and self-rated oral health status and number of brushing, emphasizing the need for the education of oral health important, providing the basic data in order to accomplish correctly until the enhance of oral health-related quality of the oral health correct behavior. From May to July 2013, a self administered survey was conducted by the selected by convenience sampling from subjects of two high school located in Chungcheongnam-do 1, 2 grade. The SPSS PASW Statistics 18.0 and Amos 5.0 program had been used for the statistical data analysis. The study results were as follow: 1) Among five areas of stress, the stress of school life was the highest as 2.11 points and the stress of home problem was the lowest as 1.51 points; 2) The significance analysis results between the five areas of stress according to the stress of latent variable and the oral health-related quality of life all showed the significant difference (p<0.001). 3) Oral health-related quality of life was higher as oral health important and self-rated oral health status positive. Furthermore oral health-related quality of life was higher as number of brushing increased; 4) Fit Measures test result of stress, academic level, and family economic level model all showed more than 0.9 in goodness of fit index (GFI), adjusted GFI, normed fit index and root mean square residual and root mean square error of approximation values is all estimated less than 0.1, so it showed good model. From this study, it can be concluded that there is the correlation between stress and oral health-related quality of life.

• Journal of dental hygiene science
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• v.13 no.4
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• pp.471-479
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• 2013

This study is trying to grasp the stress of the male high school students and the correlation between the stress according to the academic and economic level and oral parafunctional habits, emphasizing the need for the education of oral parafunctional habits, providing the basic data in order to accomplish correctly until the oral health of the oral maxillofacial region. From May 2013 till July 2013, a self administered survey was conducted by the selected by convenience sampling from subjects of 1, 2 grade of two high school located in Chungnam, Korea. The study results were as follow: 1) Among five areas of stress, the stress of school life was the highest as 2.11 points and the stress of home problem was the lowest as 1.51 points; 2) the stress by class showed that grade 2 was higher than grade 1 in all areas. The stress of the school life (2.21) (p<0.01), interpersonal relationship (p<0.01), and own problem (p<0.05) showed the significant difference; 3) The significance analysis results between the five areas of stress according to the stress of latent variable and the oral parafunctional habits all showed the significant difference (p<0.001). The correlation between the stress and the oral parafunctional habits showed a weak negative correlation as -0.30, and the stress of the school life, own problem, environment problem, and interpersonal relationship showed very strong correlations more than 0.7; 4) Fit measures test result of stress, academic level, and family economic level model all showed more than 0.9 in good of fit index, adjusted goodness of fit index, normed fit index and root mean square residual and root mean square error of approximation values is all estimated less than 0.1, so it showed good model. From this study, it can be concluded that there is the correlation between stress and oral parafunctional habits.

• Investigative Magnetic Resonance Imaging
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• v.13 no.1
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• pp.31-39
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• 2009

Purpose : This study proposes the keyhole method in order to improve the time resolution of the proton resonance frequency(PRF) MR temperature monitoring technique. The values of Root Mean Square (RMS) error of measured temperature value and Signal-to-Noise Ratio(SNR) obtained from the keyhole and full phase encoded temperature images were compared. Materials and Methods : The PRF method combined with GRE sequence was used to get MR temperature images using a clinical 1.5T MR scanner. It was conducted on the tissue-mimic 2% agarose gel phantom and swine's hock tissue. A MR compatible coaxial slot antenna driven by microwave power generator at 2.45GHz was used to heat the object in the magnetic bore for 5 minutes followed by a sequential acquisition of MR raw data during 10 minutes of cooling period. The acquired raw data were transferred to PC after then the keyhole images were reconstructed by taking the central part of K-space data with 128, 64, 32 and 16 phase encoding lines while the remaining peripheral parts were taken from the 1st reference raw data. The RMS errors were compared with the 256 full encoded self-reference temperature image while the SNR values were compared with the zero filling images. Results : As phase encoding number at the center part on the keyhole temperature images decreased to 128, 64, 32 and 16, the RMS errors of the measured temperature increased to 0.538, 0.712, 0.768 and 0.845$^{\circ}C$, meanwhile SNR values were maintained as the phase encoding number of keyhole part is reduced. Conclusion : This study shows that the keyhole technique is successfully applied to temperature monitoring procedure to increases the temporal resolution by standardizing the matrix size, thus maintained the SNR values. In future, it is expected to implement the MR real time thermal imaging using keyhole method which is able to reduce the scan time with minimal thermal variations.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.207-221
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• 2023

This study suggests deep neural network models for estimating air temperature with Level 1B (L1B) datasets of GEO-KOMPSAT-2A (GK-2A). The temperature at 1.5 m above the ground impact not only daily life but also weather warnings such as cold and heat waves. There are many studies to assume the air temperature from the land surface temperature (LST) retrieved from satellites because the air temperature has a strong relationship with the LST. However, an algorithm of the LST, Level 2 output of GK-2A, works only clear sky pixels. To overcome the cloud effects, we apply a deep neural network (DNN) model to assume the air temperature with L1B calibrated for radiometric and geometrics from raw satellite data and compare the model with a linear regression model between LST and air temperature. The root mean square errors (RMSE) of the air temperature for model outputs are used to evaluate the model. The number of 95 in-situ air temperature data was 2,496,634 and the ratio of datasets paired with LST and L1B show 42.1% and 98.4%. The training years are 2020 and 2021 and 2022 is used to validate. The DNN model is designed with an input layer taking 16 channels and four hidden fully connected layers to assume an air temperature. As a result of the model using 16 bands of L1B, the DNN with RMSE 2.22℃ showed great performance than the baseline model with RMSE 3.55℃ on clear sky conditions and the total RMSE including overcast samples was 3.33℃. It is suggested that the DNN is able to overcome cloud effects. However, it showed different characteristics in seasonal and hourly analysis and needed to append solar information as inputs to make a general DNN model because the summer and winter seasons showed a low coefficient of determinations with high standard deviations.

• The Journal of Korean Society for Radiation Therapy
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• v.26 no.2
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• pp.305-312
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• 2014

Purpose : The purpose of this study is to analyze the mechanical and leaf speed accuracy of the dynamic multileaf collimator (DMLC) and determine the appropriate period of quality assurance (QA). Materials and Methods : The quality assurance of the DMLC equipped with Millennium 120 leaves has been performed total 92 times from January 2012 to June 2014. The the accuracy of leaf position and isocenter coincidence for MLC were checked using the graph paper and Gafchromic EBT film, respectively. The stability of leaf speed was verified using a test file requiring the leaves to reach maximum leaf speed during the gantry rotation. At the end of every leaf speed QA, dynamic dynalog files created by MLC controller were analyzed using dynalog file viewer software. This file concludes the information about the planned versus actual position for all leaves and provides error RMS (root-mean square) for individual leaf deviations and error histogram for all leaf deviations. In this study, the data obtained from the leaf speed QA were used to screen the performance degradation of leaf speed and determine the need for motor replacement. Results : The leaf position accuracy and isocenteric coincidence of MLC was observed within a tolerance range recommanded from TG-142 reports. Total number of motor replacement were 56 motors over whole QA period. For all motors replaced from QA, gradually increased patterns of error RMS values were much more than suddenly increased patterns of error RMS values. Average error RMS values of gradually and suddenly increased patterns were 0.298 cm and 0.273 cm, respectively. However, The average error RMS values were within 0.35 cm recommended by the vendor, motors were replaced according to the criteria of no counts with misplacement > 1 cm. On average, motor replacement for gradually increased patterns of error RMS values 22 days. 28 motors were replaced regardless of the leaf speed QA. Conclusion : This study performed the periodic MLC QA for analyzing the mechanical and leaf speed accuracy of the dynamic multileaf collimator (DMLC). The leaf position accuracy and isocenteric coincidence showed whthin of MLC evaluation is observed within the tolerance value recommanded by TG-142 report. Based on the result obtained from leaf speed QA, we have concluded that QA protocol of leaf speed for DMLC was performed at least bimonthly in order to screen the performance of leaf speed. The periodic QA protocol can help to ensure for delivering accurate IMRT treatment to patients maintaining the performance of leaf speed.

• Korean Journal of Agricultural and Forest Meteorology
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• v.15 no.2
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• pp.76-84
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• 2013

An adequate downscaling of the official forecasts of Korea Meteorological Administration (KMA) is a prerequisite to improving the value and utility of agrometeorological information in rural areas, where complex terrain and small farms constitute major features of the landscape. In this study, we suggest a simple correction scheme for scaling down the KMA temperature forecasts from mesoscale (5 km by 5 km) to the local scale (30 m by 30 m) across a rural catchment, especially under temperature inversion conditions. The study area is a rural catchment of $50km^2$ area with complex terrain and located on a southern slope of Mountain Jiri National Park. Temperature forecasts for 0600 LST on 62 days with temperature inversion were selected from the fall 2011-spring 2012 KMA data archive. A geospatial correction scheme which can simulate both cold air drainage and the so-called 'thermal belt' was used to derive the site-specific temperature deviation across the study area at a 30 m by 30 m resolution from the original 5 km by 5 km forecast grids. The observed temperature data at 12 validation sites within the study area showed a substantial reduction in forecast error: from ${\pm}2^{\circ}C$ to ${\pm}1^{\circ}C$ in the mean error range and from $1.9^{\circ}C$ to $1.6^{\circ}C$ in the root mean square error. Improvement was most remarkable at low lying locations showing frequent cold pooling events. Temperature prediction error was less than $2^{\circ}C$ for more than 80% of the observed inversion cases and less than $1^{\circ}C$ for half of the cases. Temperature forecasts corrected by this scheme may accelerate implementation of the freeze and frost early warning service for major fruits growing regions in Korea.

• Korean Journal of Agricultural and Forest Meteorology
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• v.19 no.3
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• pp.75-85
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• 2017

A method to estimate hourly temperature profiles on calm and clear nights was developed based on temporal changes of inversion height and strength. A meteorological temperature profiler (Model MTP5H, Kipp and Zonen) was installed on the rooftop of the Highland Agriculture Research Institute, located in Daegwallyeong-myeon, Pyeongchang-gun, Gangwon-do. The hourly vertical distribution of air temperature was measured up to 600 m at intervals of 50 m from May 2007 to March 2008. Temperature and relative humidity data loggers (HOBO U23 Pro v2, Onset Computer Corporation, USA) were installed in the Jungdae-ri Valley, located between Gurye-gun, Jeollanam-do and Gwangyang-si, Jeollanam-do. These loggers were used to archive measurements of weather data 1.5 m above the surface from October 3, 2014, to November 23, 2015. The inversion strength was determined using the difference between the temperature at the inversion height, which is the highest temperature in the profile, and the temperature at 100 m from the surface. Empirical equations for the changes of inversion height and strength were derived to express the development of temperature inversion on calm and clear nights. To estimate air temperature near the ground on a slope exposed to crops, the equation's parameters were modified using temperature distribution of the mountain slope obtained from the data loggers. Estimated hourly temperatures using the method were compared with observed temperatures at 19 weather sites located within three watersheds in the southern Jiri-mountain in 2015. The mean error (ME) and root mean square error (RMSE) of the hourly temperatures were $-0.69^{\circ}C$ and $1.61^{\circ}C$, respectively. Hourly temperatures were often underestimated from 2000 to 0100 LST the next day. When temperatures were estimated at 0600 LST using the existing model, ME and RMSE were $-0.86^{\circ}C$ and $1.72^{\circ}C$, respectively. The method proposed in this study resulted in a smaller error, e.g., ME of $-0.12^{\circ}C$ and RMSE of $1.34^{\circ}C$. The method could be improved further taking into account various weather conditions, which could reduce the estimation error.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1053-1066
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• 2020

Sulfur dioxide (SO₂) is primarily released through industrial, residential, and transportation activities, and creates secondary air pollutants through chemical reactions in the atmosphere. Long-term exposure to SO₂ can result in a negative effect on the human body causing respiratory or cardiovascular disease, which makes the effective and continuous monitoring of SO₂ crucial. In South Korea, SO₂ monitoring at ground stations has been performed, but this does not provide spatially continuous information of SO₂ concentrations. Thus, this research estimated spatially continuous ground-level SO₂ concentrations at 1 km resolution over South Korea through the synergistic use of satellite data and numerical models. A stacking ensemble approach, fusing multiple machine learning algorithms at two levels (i.e., base and meta), was adopted for ground-level SO₂ estimation using data from January 2015 to April 2019. Random forest and extreme gradient boosting were used as based models and multiple linear regression was adopted for the meta-model. The cross-validation results showed that the meta-model produced the improved performance by 25% compared to the base models, resulting in the correlation coefficient of 0.48 and root-mean-square-error of 0.0032 ppm. In addition, the temporal transferability of the approach was evaluated for one-year data which were not used in the model development. The spatial distribution of ground-level SO₂ concentrations based on the proposed model agreed with the general seasonality of SO₂ and the temporal patterns of emission sources.