• Progress in Medical Physics
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• v.19 no.4
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• pp.269-275
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• 2008

Although MR imaging is generally applicable to depict knee joint deterioration it, is sometimes occurred to mis-read and mis-diagnose the common knee joint diseases. In this study, we employed magnetization transfer ratio (MTR) method to improve the diagnosis of the various knee joint diseases. Spin-echo (SE) T2-weighted images (TR/TE 3,400-3,500/90-100 ms) were obtained in seven cases of knee joint deterioration, FSE T2-weighted images (TR/TE 4,500-5,000/100-108 ms) were obtained in seven cases of knee joint deterioration, gradient-echo (GRE) T2-weighted images (TR/TE 9/4.56/$50^{\circ}$ flip angle, NEX 1) were obtained in 3 cases of knee joint deterioration, In six cases of knee joint deterioration, fat suppression was performed using a T2-weighted short T1/tau inverse recovery (STIR) sequence (TR/TE =2,894-3,215 ms/70 ms, NEX 3, ETL 9). Calculation of MTR for individual pixels was performed on registration of unsaturated and saturated images. After processing to make MTR images, the images were displayed in gray color. For improving diagnosis, three-dimensional isotropic volume images, the MR tristimulus color mapping and the MTR map was employed. MTR images showed diagnostic images quality to assess the patients' pathologies. The intensity difference between MTR images and conventional MRI was seen on the color bar. The profile graph on MTR imaging effect showed a quantitative measure of the relative decrease in signal intensity due to the MT pulse. To diagnose the pathologies of the knee joint, the profile graph data was shown on the image as a small cross. The present study indicated that MTR images in the knee joint were feasible. Investigation of physical change on MTR imaging enables to provide us more insight in the physical and technical basis of MTR imaging. MTR images could be useful for rapid assessment of diseases that we examine unambiguous contrast in MT images of knee disorder patients.

• Korean Journal of Agricultural and Forest Meteorology
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• v.4 no.4
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• pp.224-236
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• 2002

A long-term growth simulation was performed at 496 land units in the western coastal plains (WCP) of North Korea to test the potential adaptability of each land unit for growing South Korean rice cultivars. The land units for rice cultivation (CZU), each of them represented by a geographically referenced 5 by 5 km grid tell, were identified by analyzing satellite remote sensing data. Surfaces of monthly climatic normals for daily maximum and minimum temperature, precipitation number of rain days and solar radiation were generated at a 1 by 1 km interval by spatial statistical methods using observed data at 51 synoptic weather stations in North and South Korea during 1981-2000. Grid cells felling within a same CZU and, at the same time, corresponding to the satellite data- identified rice growing pixels were extracted and aggregated to make a spatially explicit climatic normals relevant to the rice growing area of the CZU. Daily weather dataset for 30 years was randomly generated from the monthly climatic normals of each CZU. Growth and development parameters of CERES-rice model suitable for 11 major South Korean cultivars were derived from long-term field observations. Eight treatments comprised of 2 transplanting dates $\times$ 2 cropping systems $\times$ 2 irrigation methods were assigned to each cultivar. Each treatment was simulated with the randomly generated 30 years' daily weather data (from planting to physiological maturity) for 496 land units in WCP to simulate the growth and yield responses to the interannual climate variation. The same model was run with the input data from the 3 major crop experiment stations in South Korea to obtain a 30 year normal performance of each cultivar, which was used as a "reference" for comparison. Results were analyzed with respect to spatial and temporal variation in yield and maturity, and used to evaluate the suitability of each land unit for growing a specific South Korean cultivar. The results may be utilized as decision aids for agrotechnology transfer to North Korea, for example, germplasm evaluation, resource allocation and crop calendar preparation.

• Progress in Medical Physics
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• v.26 no.3
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• pp.160-167
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• 2015

Radiation exposure from medical diagnostic imaging procedures to patients is one of the most significant interests in diagnostic x-ray system. A miniature x-ray intraoral tube was developed for the first time in the world which can be inserted into the mouth for imaging. Dose evaluation should be carried out in order to utilize such an imaging device for clinical use. In this study, dose evaluation of the new x-ray unit was performed by 1) using a custom made in vivo Pig phantom, 2) determining exposure condition for the clinical use, and 3) measuring patient dose of the new system. On the basis of DRLs (Diagnostic Reference Level) recommended by KDFA (Korea Food & Drug Administration), the ESD (Entrance Skin Dose) and DAP (Dose Area Product) measurements for the new x-ray imaging device were designed and measured. The maximum voltage and current of the x-ray tubes used in this study were 55 kVp, and 300 mA. The active area of the detector was $72{\times}72mm$ with pixel size of $48{\mu}m$. To obtain the operating condition of the new system, pig jaw phantom images showing major tooth-associated tissues, such as clown, pulp cavity were acquired at 1 frame/sec. Changing the beam currents 20 to $80{\mu}A$, x-ray images of 50 frames were obtained for one beam current with optimum x-ray exposure setting. Pig jaw phantom images were acquired from two commercial x-ray imaging units and compared to the new x-ray device: CS 2100, Carestream Dental LLC and EXARO, HIOSSEN, Inc. Their exposure conditions were 60 kV, 7 mA, and 60 kV, 2 mA, respectively. Comparing the new x-ray device and conventional x-ray imaging units, images of the new x-ray device around teeth and their neighboring tissues turn out to be better in spite of its small x-ray field size. ESD of the new x-ray device was measured 1.369 mGy on the beam condition for the best image quality, 0.051 mAs, which is much less than DRLs recommended by IAEA (International Atomic Energy Agency) and KDFA, both. Its dose distribution in the x-ray field size was observed to be uniform with standard deviation of 5~10 %. DAP of the new x-ray device was $82.4mGy*cm^2$ less than DRL established by KDFA even though its x-ray field size was small. This study shows that the new x-ray imaging device offers better in image quality and lower radiation dose compared to the conventional intraoral units. In additions, methods and know-how for studies in x-ray features could be accumulated from this work.

• Korean Journal of Remote Sensing
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• v.35 no.6_3
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• pp.1173-1185
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• 2019

We, for the first time, retrieved sulfur dioxide (SO₂) vertical column density (VCD) in industrial and volcanic areas from TROPOspheric Monitoring Instrument (TROPOMI) using the Principle component analysis(PCA) algorithm. Furthermore, SO₂ VCDs retrieved by the PCA algorithm from TROPOMI raw data were compared with those retrieved by the Differential Optical Absorption Spectroscopy (DOAS) algorithm (TROPOMI Level 2 SO₂ product). In East Asia, where large amounts of SO₂ are released to the surface due to anthropogenic source such as fossil fuels, the mean value of SO₂ VCD retrieved by the PCA (DOAS) algorithm was shown to be 0.05 DU (-0.02 DU). The correlation between SO₂ VCD retrieved by the PCA algorithm and those retrieved by the DOAS algorithm were shown to be low (slope = 0.64; correlation coefficient (R) = 0.51) for cloudy condition. However, with cloud fraction of less than 0.5, the slope and correlation coefficient between the two outputs were increased to 0.68 and 0.61, respectively. It means that the SO₂ retrieval sensitivity to surface is reduced when the cloud fraction is high in both algorithms. Furthermore, the correlation between volcanic SO₂ VCD retrieved by the PCA algorithm and those retrieved by the DOAS algorithm is shown to be high (R = 0.90) for cloudy condition. This good agreement between both data sets for volcanic SO₂ is thought to be due to the higher accuracy of the satellite-based SO₂ VCD retrieval for SO₂ which is mainly distributed in the upper troposphere or lower stratosphere in volcanic region.

• Korean Journal of Agricultural and Forest Meteorology
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• v.8 no.4
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• pp.229-241
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• 2006

Global air temperature has risen by $0.6^{\circ}C$ over the last one hundred years due to increased atmospheric greenhouse gases. Moreover, this global warming trend is projected to continue in the future. This study was carried out to evaluate spatial variations in rice production areas by simulating rice-growth and development with projected high resolution climate data in Korea far 2011-2100, which was geospatially interpolated from the 25 km gridded data based on the IPCC SRES A2 emission scenario. Satellite remote sensing data were used to pinpoint the rice-growing areas, and corresponding climate data were aggregated to represent the official 'crop reporting county'. For the simulation experiment, we used a CERES-Rice model modified by introducing two equations to calculate the leaf appearance rate based on the effective temperature and existing leaf number and the final number of leaves based on day-length in the photoperiod sensitive phase of rice. We tested the performance of this model using data-sets obtained from transplanting dates and nitrogen fertilization rates experiments over three years (2002 to 2004). The simulation results showed a good performance of this model in heading date prediction [$R^2$=0.9586 for early (Odaebyeo), $R^2$=0.9681 for medium (Hwasungbyeo), and $R^2$=0.9477 for late (Dongjinbyeo) maturity cultivars]. A modified version of CERES-Rice was used to simulate the growth and development of three Japonica varieties, representing early, medium, and late maturity classes, to project crop status for climatological normal years between 2011 and 2100. In order to compare the temporal changes, three sets of data representing 3 climatological years (2011-2040, 2041-2070, and 2071-2100) were successively used to run the model. Simulated growth and yield data of the three Japonica cultivars under the observed climate for 1971-2000 was set as a reference. Compared with the current normal, heading date was accelerated by 7 days for 2011-2040 and 20 days for 2071-2100. Physiological maturity was accelerated by 15 days for 2011-2040 and 30 days for 2071-2100. Rice yield was in general reduced by 6-25%, 3-26%, and 3-25% per 10a in early, medium, and late maturity classes, respectively. However, mid to late maturing varieties showed an increased yield in northern Gyeonggi Province and in most of Kwangwon Province in 2071-2100.

• Journal of Korea Water Resources Association
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• v.49 no.7
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• pp.589-597
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• 2016

In this study, we investigated the use of satellite-derived flow (SDF) signals and a data-based model for the estimation of outflow for the river reach where in situ measurements are either completely unavailable or are difficult to access for hydraulic and hydrology analysis such as the upper basin of Imjin River. It has been demonstrated by many studies that the SDF signals can be used as the river width estimates and the correlation between SDF signals and river width is related to the shape of cross sections. To extract the nonlinear relationship between SDF signals and river outflow, Artificial Neural Network (ANN) model with SDF signals as its inputs were applied for the computation of flow discharge at Imjin Bridge located in Imjin River. 15 pixels were considered to extract SDF signals and Partial Mutual Information (PMI) algorithm was applied to identify the most relevant input variables among 150 candidate SDF signals (including 0~10 day lagged observations). The estimated discharges by ANN model were compared with the measured ones at Imjin Bridge gauging station and correlation coefficients of the training and validation were 0.86 and 0.72, respectively. It was found that if the 1 day previous discharge at Imjin bridge is considered as an input variable for ANN model, the correlation coefficients were improved to 0.90 and 0.83, respectively. Based on the results in this study, SDF signals along with some local measured data can play an useful role in river flow estimation and especially in flood forecasting for data-scarce regions as it can simulate the peak discharge and peak time of flood events with satisfactory accuracy.

• 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 Remote Sensing
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• v.37 no.5_3
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• pp.1475-1490
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• 2021

Detecting oil spill area using statistical characteristics of SAR images has limitations in that classification algorithm is complicated and is greatly affected by outliers. To overcome these limitations, studies using neural networks to classify oil spills are recently investigated. However, the studies to evaluate whether the performance of model shows a consistent detection performance for various oil spill cases were insufficient. Therefore, in this study, two CNNs (Convolutional Neural Networks) with basic structures(Simple CNN and U-net) were used to discover whether there is a difference in detection performance according to the structure of CNN and distribution characteristics of oil spill. As a result, through the method proposed in this study, the Simple CNN with contracting path only detected oil spill with an F1 score of 86.24% and U-net, which has both contracting and expansive path showed an F1 score of 91.44%. Both models successfully detected oil spills, but detection performance of the U-net was higher than Simple CNN. Additionally, in order to compare the accuracy of models according to various oil spill cases, the cases were classified into four different categories according to the spatial distribution characteristics of the oil spill (presence of land near the oil spill area) and the clarity of border between oil and seawater. The Simple CNN had F1 score values of 85.71%, 87.43%, 86.50%, and 85.86% for each category, showing the maximum difference of 1.71%. In the case of U-net, the values for each category were 89.77%, 92.27%, 92.59%, and 92.66%, with the maximum difference of 2.90%. Such results indicate that neither model showed significant differences in detection performance by the characteristics of oil spill distribution. However, the difference in detection tendency was caused by the difference in the model structure and the oil spill distribution characteristics. In all four oil spill categories, the Simple CNN showed a tendency to overestimate the oil spill area and the U-net showed a tendency to underestimate it. These tendencies were emphasized when the border between oil and seawater was unclear.

• Korean Journal of Remote Sensing
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• v.37 no.6_2
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• pp.1829-1836
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• 2021

Cloud detection means determining the presence or absence of clouds in a pixel in a satellite image, and acts as an important factor affecting the utility and accuracy of the satellite image. In this study, among the satellites of various advanced organizations that provide cloud detection data, we intend to perform quantitative and qualitative comparative analysis on the difference between the cloud detection data of GK-2A/AMI, Terra/MODIS, and Suomi-NPP/VIIRS. As a result of quantitative comparison, the Proportion Correct (PC) index values in January were 74.16% for GK-2A & MODIS, 75.39% for GK-2A & VIIRS, and 87.35% for GK-2A & MODIS in April, and GK-2A & VIIRS showed that 87.71% of clouds were detected in April compared to January without much difference by satellite. As for the qualitative comparison results, when compared with RGB images, it was confirmed that the results corresponding to April rather than January detected clouds better than the previous quantitative results. However, if thin clouds or snow cover exist, each satellite were some differences in the cloud detection results.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1413-1425
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• 2023

The increasing frequency of wildfires due to climate change is causing extreme loss of life and property. They cause loss of vegetation and affect ecosystem changes depending on their intensity and occurrence. Ecosystem changes, in turn, affect wildfire occurrence, causing secondary damage. Thus, accurate estimation of the areas affected by wildfires is fundamental. Satellite remote sensing is used for forest fire detection because it can rapidly acquire topographic and meteorological information about the affected area after forest fires. In addition, deep learning algorithms such as convolutional neural networks (CNN) and transformer models show high performance for more accurate monitoring of fire-burnt regions. To date, the application of deep learning models has been limited, and there is a scarcity of reports providing quantitative performance evaluations for practical field utilization. Hence, this study emphasizes a comparative analysis, exploring performance enhancements achieved through both model selection and data design. This study examined deep learning models for detecting wildfire-damaged areas using Landsat 8 satellite images in California. Also, we conducted a comprehensive comparison and analysis of the detection performance of multiple models, such as U-Net and High-Resolution Network-Object Contextual Representation (HRNet-OCR). Wildfire-related spectral indices such as normalized difference vegetation index (NDVI) and normalized burn ratio (NBR) were used as input channels for the deep learning models to reflect the degree of vegetation cover and surface moisture content. As a result, the mean intersection over union (mIoU) was 0.831 for U-Net and 0.848 for HRNet-OCR, showing high segmentation performance. The inclusion of spectral indices alongside the base wavelength bands resulted in increased metric values for all combinations, affirming that the augmentation of input data with spectral indices contributes to the refinement of pixels. This study can be applied to other satellite images to build a recovery strategy for fire-burnt areas.