• Journal of the Korean Society of Radiology
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• v.14 no.6
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• pp.841-848
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• 2020

In the field of radiation therapy using photon beams and electron beams, since each patient has a different sensitivity to radiation, skin side effects may occur even at the same dose. Therefore, if there is a risk of excessive dose to the skin, a dosimeter is attached to verify whether the correct dose is being investigated. However, since the skin dosimeter checks the attachment site visually by measuring a point dose, it is difficult to confirm an accurate dose distribution. As a result, the measurement and simulation errors of the material HgI₂ in the 6 MV photon beam were 3.73% and 5.24%, respectively, at the minimum thickness of 25 ㎛, and the material PbI₂ was 4.73% and 5.65%, respectively. On the other hand, as a result of the 6 MeV electron beam, the measurement and simulation errors of the material HgI₂ were 1.35% and 1.12%, respectively, at a minimum thickness of 25 ㎛, and the material PbI₂ showed relatively low attenuation error, 1.67% and 1.20%, respectively. Therefore, it was evaluated that the thickness of the photon beam within 25 ㎛ and the electron beam within 100 ㎛ is suitable to have a reduction rate error within 5%. This study presents a new research direction for a flexible dosimeter attached to the human body that is required in clinical practice and the construction conditions of a future skin dosimeter.

• Investigative Magnetic Resonance Imaging
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• v.18 no.4
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• pp.303-313
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• 2014

Purpose : In-vivo conductivity reconstruction using transmit field ($B_1{^+}$) information of MRI was proposed. We assessed the accuracy of conductivity reconstruction in the presence of statistical noise in complex $B_1{^+}$ map and provided a parametric model of the conductivity-to-noise ratio value. Materials and Methods: The $B_1{^+}$ distribution was simulated for a cylindrical phantom model. By adding complex Gaussian noise to the simulated $B_1{^+}$ map, quantitative conductivity estimation error was evaluated. The quantitative evaluation process was repeated over several different parameters such as Larmor frequency, object radius and SNR of $B_1{^+}$ map. A parametric model for the conductivity-to-noise ratio was developed according to these various parameters. Results: According to the simulation results, conductivity estimation is more sensitive to statistical noise in $B_1{^+}$ phase than to noise in $B_1{^+}$ magnitude. The conductivity estimate of the object of interest does not depend on the external object surrounding it. The conductivity-to-noise ratio is proportional to the signal-to-noise ratio of the $B_1{^+}$ map, Larmor frequency, the conductivity value itself and the number of averaged pixels. To estimate accurate conductivity value of the targeted tissue, SNR of $B_1{^+}$ map and adequate filtering size have to be taken into account for conductivity reconstruction process. In addition, the simulation result was verified at 3T conventional MRI scanner. Conclusion: Through all these relationships, quantitative conductivity estimation error due to statistical noise in $B_1{^+}$ map is modeled. By using this model, further issues regarding filtering and reconstruction algorithms can be investigated for MREPT.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.2
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• pp.63-77
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• 2022

Our aim was to reduce estimation errors of a wind velocity model used as an early warning system for weather risk management in the agricultural sector. The Rural Development Administration (RDA) agricultural weather observation network's wind velocity data and its corresponding estimated data from January to December 2020 were used to calculate linear regression equations (Y = aX + b). In each linear regression, the wind estimation error at 87 points and eight time slots per day (00:00, 03:00, 06:00, 09.00, 12.00, 15.00, 18.00, and 21:00) is the dependent variable (Y), while the estimated wind velocity is the independent variable (X). When the correlation coefficient exceeded 0.5, the regression equation was used as the wind velocity correction equation. In contrast, when the correlation coefficient was less than 0.5, the mean error (ME) at the corresponding points and time slots was substituted as the correction value instead of the regression equation. To enable the use of wind velocity model at a national scale, a distribution map with a grid resolution of 250 m was created. This objective was achieved b y performing a spatial interpolation with an inverse distance weighted (IDW) technique using the regression coefficients (a and b), the correlation coefficient (R), and the ME values for the 87 points and eight time slots. Interpolated grid values for 13 weather observation points in rural areas were then extracted. The wind velocity estimation errors for 13 points from January to December 2019 were corrected and compared with the system's values. After correction, the mean ME of the wind velocities reduced from 0.68 m/s to 0.45 m/s, while the mean RMSE reduced from 1.30 m/s to 1.05 m/s. In conclusion, the system's wind velocities were overestimated across all time slots; however, after the correction model was applied, the overestimation reduced in all time slots, except for 15:00. The ME and RMSE improved b y 33% and 19.2%, respectively. In our system, the warning for wind damage risk to crops is driven by the daily maximum wind speed derived from the daily mean wind speed obtained eight times per day. This approach is expected to reduce false alarms within the context of strong wind risk, by reducing the overestimation of wind velocities.

• Tuberculosis and Respiratory Diseases
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• v.47 no.5
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• pp.669-680
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• 1999

Background: Pulmonary thromboembolism(PTE) is a life threatening disease that needs early diagnosis. Spiral CT angiography depict thromboemboli in the central pulmonary vessels with greater than 90% sensitivity and specificity, which approaches the results of pulmonary angiography in the Prospective Investigation of Pulmonary Embolism Diagnosis(PIOPED) study. This study was performed to evaluate the findings and the diagnostic value(clinical utility) of the spiral CT angiography with 2D image(multiplanar reformation) and 3D images(Shaded surface display, Minimal intensity projection) in the pulmonary thromboembolism. Methods: We retrospectively analysed spiral CT angiography and pulmonary angiography, lung scan and clinical recordings of 20 patients who had PTE diagnosed by spiral CT angiography(n=19 cases) or pulmonary angiography(n=l case) from September 1997 to August 1998. Among 20 patients who had underwent spiral CT angiography, 14 patients could be performed lung perfusion scan at the same time. We analyzed the vascular and parenchymal change in spiral CT angiogram. Results: Anatomical distribution of PTE was as follows: 1) left lung(n= 103)

• Radiation Oncology Journal
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• v.22 no.1
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• pp.64-68
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• 2004

Purpose : To study the feasibility of verifying real-time 2-D dose distribution measurement system with the scintillation screen for the quality assurance. Materials and Methods : The water phantom consisted of a scintillation screen (LANEX fast screen, Kodak, USA) that was axially located in the middle of an acrylic cylinder with a diameter of 25 cm. The charge-coupled device (CCD) camera was attached to the phantom In order to capture the visible light from the scintillation screen. To observe the dose distribution In real time, the intensity of the light from the scintillator was converted to a dosage. The isodose contours of the calculations from RTP and those of the measurements using the scintillation screen were compared for the arc therapy and the Intensity modulated radiation therapy (IMRT). Results : The kernel, expressed as a multiplication of two error functions, was obtained in order to correct the sensitivity of the CCD of the camera and the scintillation screen. When comparing the calculated isodose and measured isodose, a discrepancy of less than 8 mm in the high dose region was observed. Conclusion : Using the 2-D dosimetry system, the relationship between the light and the dosage could be found, and real-time verification of the dose distribution was feasible.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.64 no.4
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• pp.353-365
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• 2019

A near-infrared reflectance spectroscopy (NIRS) prediction model was set to establish a rapid analysis system of wheat germplasm and provide statistical information on the characteristics of protein contents. The variability index value (VIV) of calibration resources was 0.80, the average protein content was 13.2%, and the content range was from 7.0% to 13.2%. After measuring the near-infrared spectra of calibration resources, the NIRS prediction model was developed through a regression analysis between protein content and spectra data, and then optimized by excluding outliers. The standard error of calibration, R², and the slope of the optimized model were 0.132, 0.997, and 1.000 respectively, and those of external validation results were 0.994, 0.191, and 1.013, respectively. Based on these results, a developed NIRS model could be applied to the rapid analysis of protein in wheat. The distribution of NIRS protein content of 6,794 resources were analyzed using a normal distribution analysis. The VIV was 0.79, the average protein was 12.1%, and the content range of resources accounting for 42.1% and 68% of the total accessions were 10-13% and 9.5-14.6%, respectively. The composition of total resources was classified into breeding line (3,128), landrace (2,705), and variety (961). The VIV in breeding line was 0.80, the protein average was 11.8%, and the contents of 68% of total resources ranged from 9.2% to 14.5%. The VIV in landrace was 0.76, the protein average was 12.1%, and the content range of resources of 68% of total accessions was 9.8-14.4%. The VIV in variety was 0.80, the protein average was 12.8%, and the accessions representing 68% of total resources ranged from 10.2% to 15.4%. These results should be helpful to the related experts of wheat breeding.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1739-1756
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• 2021

Atmospheric nitrogen dioxide (NO₂) is mainly caused by anthropogenic emissions. It contributes to the formation of secondary pollutants and ozone through chemical reactions, and adversely affects human health. Although ground stations to monitor NO₂ concentrations in real time are operated in Korea, they have a limitation that it is difficult to analyze the spatial distribution of NO₂ concentrations, especially over the areas with no stations. Therefore, this study conducted a comparative experiment of spatial interpolation of NO₂ concentrations based on two linear-regression methods(i.e., multi linear regression (MLR), and regression kriging (RK)), and two machine learning approaches (i.e., random forest (RF), and support vector regression (SVR)) for the year of 2020. Four approaches were compared using leave-one-out-cross validation (LOOCV). The daily LOOCV results showed that MLR, RK, and SVR produced the average daily index of agreement (IOA) of 0.57, which was higher than that of RF (0.50). The average daily normalized root mean square error of RK was 0.9483%, which was slightly lower than those of the other models. MLR, RK and SVR showed similar seasonal distribution patterns, and the dynamic range of the resultant NO₂ concentrations from these three models was similar while that from RF was relatively small. The multivariate linear regression approaches are expected to be a promising method for spatial interpolation of ground-level NO₂ concentrations and other parameters in urban areas.

• Progress in Medical Physics
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• v.23 no.1
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• pp.15-25
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• 2012

The aim of this study is to evaluate plan quality and dose accuracy for Volumetric Modulated Arc Therapy (VMAT) on the TG-119 and is to investigate the effects on variation of the selectable optimization parameters of VMAT. VMAT treatment planning was implemented on a Varian iX linear accelerator with ARIA record and verify system (Varian Mecical System Palo Alto, CA) and Oncentra MasterPlan treatment planning system (Nucletron BV, Veenendaal, Netherlands). Plan quality and dosimetric accuracy were evaluated by effect of varying a number of arc, gantry spacing and delivery time for the test geometries provided in TG-119. Plan quality for the target and OAR was evaluated by the mean value and the standard deviation of the Dose Volume Histograms (DVHs). The ionization chamber and $Delta^{4PT}$ bi-planar diode array were used for the dose evaluation. For treatment planning evaluation, all structure sets closed to the goals in the case of single arc, except for the C-shape (hard), and all structure sets achieved the goals in the case of dual arc, except for C-shape (hard). For the variation of a number of arc, the simple structure such as a prostate did not have the difference between single arc and dual arc, whereas the complex structure such as a head and neck showed a superior result in the case of dual arc. The dose distribution with gantry spacing of $4^{\circ}$ was shown better plan quality than the gantry spacing of $6^{\circ}$, but was similar results compared with gantry spacing of $2^{\circ}$. For the verification of dose accuracy with single arc and dual arc, the mean value of a relative error between measured and calculated value were within 3% and 4% for point dose and confidence limit values, respectively. For the verification on dose accuracy with the gantry intervals of $2^{\circ}$, $4^{\circ}$ and $6^{\circ}$, the mean values of relative error were within 3% and 5% for point dose and confidence limit values, respectively. In the verification of dose distribution with $Delta^{4PT}$ bi-planar diode array, gamma passing rate was $98.72{\pm}1.52%$ and $98.3{\pm}1.5%$ for single arc and dual arc, respectively. The confidence limit values were within 4%. The smaller the gantry spacing, the more accuracy results were shown. In this study, we performed the VMAT QA based on TG-119 procedure, and demonstrated that all structure sets were satisfied with acceptance criteria. And also, the results for the selective optimization variables informed the importance of selection for the suitable variables according to the clinical cases.

• Korean Journal of Agricultural and Forest Meteorology
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• v.11 no.1
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• pp.13-18
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• 2009

Accurate solar radiation data are critical to evaluate major physiological responses of plants. For most upland crops and orchard plants growing in complex terrain, however, it is not easy for farmers or agronomists to access solar irradiance data. Here we suggest a simple method using a sun-slope geometry based topographical coefficient to estimate daily solar irradiance on any sloping surfaces from global solar radiation measured at a nearby weather station. An hourly solar irradiance ratio ($W_i$) between sloping and horizontal surface is defined as multiplication of the relative solar intensity($k_i$) and the slope irradiance ratio($r_i$) at an hourly interval. The $k_i$ is the ratio of hourly solar radiation to the 24 hour cumulative radiation on a horizontal surface under clear sky conditions. The $r_i$ is the ratio of clear sky radiation on a given slope to that on a horizontal reference. Daily coefficient for slope correction is simply the sum of $W_i$ on each date. We calculated daily solar irradiance at 8 side slope locations circumventing a cone-shaped parasitic volcano(c.a., 570m diameter for the bottom circle and 90m bottom-to-top height) by multiplying these coefficients to the global solar radiation measured horizontally. Comparison with the measured slope irradiance from April 2007 to March 2008 resulted in the root mean square error(RMSE) of $1.61MJ\;m^{-2}$ for the whole period but the RMSE for April to October(i.e., major cropping season in Korea) was much lower and satisfied the 5% error tolerance for radiation measurement. The RMSE was smallest in October regardless of slope aspect, and the aspect dependent variation of RMSE was greatest in November. Annual variation in RMSE was greatest on north and south facing slopes, followed by southwest, southeast, and northwest slopes in decreasing order. Once the coefficients are prepared, global solar radiation data from nearby stations can be easily converted to the solar irradiance map at landscape scales with the operational reliability in cropping season.

• Journal of the Korean earth science society
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• v.40 no.6
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• pp.584-598
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• 2019

In this study, the effects of air-sea interactions on precipitation over the Seoul-Gyeonggi region of the Korean Peninsula from 28 to 30 August 2018, were analyzed using a Regional atmosphere-ocean Coupled Model (RCM). In the RCM, a WRF (Weather Research Forecasts) was used as the atmosphere model whereas ROMS (Regional Oceanic Modeling System) was used as the ocean model. In a Regional Single atmosphere Model (RSM), only the WRF model was used. In addition, the sea surface temperature data of ECMWF Reanalysis Interim was used as low boundary data. Compared with the observational data, the RCM considering the effect of air-sea interaction represented that the spatial correlations were 0.6 and 0.84, respectively, for the precipitation and the Yellow Sea surface temperature in the Seoul-Gyeonggi area, which was higher than the RSM. whereas the mean bias error (MBE) was -2.32 and -0.62, respectively, which was lower than the RSM. The air-sea interaction effect, analyzed by equivalent potential temperature, SST, dynamic convergence fields, induced the change of SST in the Yellow Sea. In addition, the changed SST caused the difference in thermal instability and kinematic convergence in the lower atmosphere. The thermal instability and convergence over the Seoul-Gyeonggi region induced upward motion, and consequently, the precipitation in the RCM was similar to the spatial distribution of the observed data compared to the precipitation in the RSM. Although various case studies and climatic analyses are needed to clearly understand the effects of complex air-sea interaction, this study results provide evidence for the importance of the air-sea interaction in predicting precipitation in the Seoul-Gyeonggi region.