• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.707-723
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• 2022

Although satellite-based sea surface temperature (SST) is advantageous for monitoring large areas, spatiotemporal data gaps frequently occur due to various environmental or mechanical causes. Thus, it is crucial to fill in the gaps to maximize its usability. In this study, daily SST composite fields with a resolution of 4 km were produced through a two-step machine learning approach using polar-orbiting and geostationary satellite SST data. The first step was SST reconstruction based on Data Interpolate Convolutional AutoEncoder (DINCAE) using multi-satellite-derived SST data. The second step improved the reconstructed SST targeting in situ measurements based on light gradient boosting machine (LGBM) to finally produce daily SST composite fields. The DINCAE model was validated using random masks for 50 days, whereas the LGBM model was evaluated using leave-one-year-out cross-validation (LOYOCV). The SST reconstruction accuracy was high, resulting in R² of 0.98, and a root-mean-square-error (RMSE) of 0.97℃. The accuracy increase by the second step was also high when compared to in situ measurements, resulting in an RMSE decrease of 0.21-0.29℃ and an MAE decrease of 0.17-0.24℃. The SST composite fields generated using all in situ data in this study were comparable with the existing data assimilated SST composite fields. In addition, the LGBM model in the second step greatly reduced the overfitting, which was reported as a limitation in the previous study that used random forest. The spatial distribution of the corrected SST was similar to those of existing high resolution SST composite fields, revealing that spatial details of oceanic phenomena such as fronts, eddies and SST gradients were well simulated. This research demonstrated the potential to produce high resolution seamless SST composite fields using multi-satellite data and artificial intelligence.

• 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.

• Korean Journal of Soil Science and Fertilizer
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• v.23 no.2
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• pp.87-93
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• 1990

A statistical model to predict soil temperature from the ambient meteorological factors including mean, maximum and minimum air temperatures, precipitation, wind speed and snow depth combined with Fourier time series expansion was developed with the data measured at the Suwon Meteorolical Service from 1979 to 1988. The stepwise elimination technique was used for statistical analysis. For the yearly oscillation model for soil temperature with 8 terms of Fourier expansion, the mean square error was decreased with soil depth showing 2.30 for the surface temperature, and 1.34-0.42 for 5 to 500-cm soil temperatures. The $r^2$ ranged from 0.913 to 0.988. The number of lag days of air temperature by remainder analysis was 0 day for the soil surface temperature, -1 day for 5 to 30-cm soil temperature, and -2 days for 50-cm soil temperature. The number of lag days for precipitaion, snow depth and wind speed was -1 day for the 0 to 10-cm soil temperatures, and -2 to -3 days for the 30 to 50-cm soil teperatures. For the statistical soil temperature prediction model combined with the yearly oscillation terms and meteorological factors as remainder terms considering the lag days obtained above, the mean square error was 1.64 for the soil surfac temperature, and ranged 1.34-0.42 for 5 to 500cm soil temperatures. The model test with 1978 data independent to model development resulted in good agreement with $r^2$ ranged 0.976 to 0.996. The magnitudes of coeffcicients implied that the soil depth where daily meteorological variables night affect soil temperature was 30 to 50 cm. In the models, solar radiation was not included as a independent variable ; however, in a seperated analysis on relationship between the difference(${\Delta}Tmxs$) of the maximum soil temperature and the maximum air temperature and solar radiation(Rs ; $J\;m^{-2}$) under a corn canopy showed linear relationship as $${\Delta}Tmxs=0.902+1.924{\times}10^{-3}$$ Rs for leaf area index lower than 2 $${\Delta}Tmxs=0.274+8.881{\times}10^{-4}$$ Rs for leaf area index higher than 2.

• The Journal of Korean Society for Radiation Therapy
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• v.22 no.1
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• pp.53-60
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• 2010

Purpose: We try to calculate EDW-factor easily with the formula applies essential data of EDW-factor and evaluate the validity through a measurement. Materials and Methods: We used the given value of GSTT (Golden Segmented Treatment Table) for the calculation of the EDW-factor. As to the experimental device, 0.6 cc farmer-type ion-chamber, an electrometer and water- phantom were used. A measurement was made at the maximum dose depth of the photon beam energy 6 MV and 15 MV under the condition that SSD (Source to Surface Distance) was 100 cm. The angle of the EDW (Enhanced Dynamic Wedge) which we use in an experiment was 60 degree, 30 degree, 20 degree in the Y1-OUT direction. We used Eclipse planning system (Varian, USA) as RTP system and the EDW-factor was calculated about all fields and EDW direction. In order to show the EDW-factor feature, a measurement was made at the selected field that verify the influence of the dependability about X, Y jaw and off-axis field. Results: When we change the Y1 field, it influence on the EDW-Factor and measured value. But the error between measured values and calculated values was less than 1%. The experimental result indicated the tendency that the error of the result of calculation and measured value becomes smaller as the EDW angle become smaller whether the calculation point (measurement point) and iso-center are same or not. The influence of the field size and energy did not show up. We simulated with the same condition using the RTP system. And we found that it makes no difference between the MU which is calculated manually by applying the EDW-Factor obtained from the commercial program and the value which is calculated by using RTP system. Conclusion: We excluded fitting value from well-known EDW-Factor formula and calculated EDW-factor with the formula applies essential data of EDW-factor only. As a result, there are no significant difference between the measured value and calculated value and it showed errors less than 1%. Also, we implemented the commercial program to calculate EDW-Factor conveniently without measure a factor on each field.

• Magazine of the Korean Society of Agricultural Engineers
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• v.21 no.3
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• pp.104-120
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• 1979

I. Title of the Study Studies on the Development of Improved Subsurface Drainage Methods. -Drainage Performance of Various Subsurface Drain Materials- II. Object of the Study Studies were carried out to select the drain material having the highest performance of drainage; And to develop the water budget model which is necessary for the planning of the drainage project and the establishment of water management standards in the water-logged paddy field. III. Content and Scope of the Study 1. The experiment was carried out in the laboratory by using a sand tank model. The drainage performance of various drain materials was compared evaluated. 2. A water budget model was established. Various parameters necessary for the model were investigated by analyzing existing data and measured data from the experimental field. The adaptability of the model was evaluated by comparing the estimated values to the field data. IV. Results and Recommendations 1. A corrugated tube enveloped with gravel or mat showed the highest drainage performance among the eight materials submmitted for the experiment. 2. The drainage performance of the long cement tile(50 cm long) was higher than that of the short cement tile(25 cm long). 3. Rice bran was superior to gravel in its' drain performance. 4. No difference was shown between a grave envelope and a P.V.C. wool mat in their performance of drainage. Continues investigation is needed to clarify the envelope performance. 5. All the results described above were obtained from the laboratory tests. A field test is recommended to confirm the results obtained. 6. As a water balance model of a given soil profile, the soil moisture depletion D, could be represented as follows; $$D=\Sigma\limit_{t=1}^{n}(Et-R_{\ell}-I+W_d)..........(17)$$ 7. Among the various empirical formulae for potential evapotranspiration, Penman's formular was best fit to the data observed with the evaporation pans in Jinju area. High degree of positive correlation between Penman;s predicted data and observed data was confirmed. The regression equation was Y=1.4X-22.86, where Y represents evaporation rate from small pan, in mm/100 days, and X represents potential evapotranspiration rate estimated by Penman's formular. The coefficient of correlation was r=0.94.** 8. To estimate evapotranspiration in the field, the consumptive use coefficient, Kc, was introduced. Kc was defined by the function of the characteristics of the crop soil as follows; $Kc=Kco{\cdot}Ka+Ks..........(20)$ where, Kco, Ka ans Ks represents the crop coefficient, the soil moisture coefficient, and the correction coefficient, respectively. The value of Kco and Ka was obtained from the Fig.16 and the Fig.17, respectively. And, if $Kco{\cdot}Ka{\geq}1.0,$ then Ks=0, otherwise, Ks value was estimated by using the relation; $Ks=1-Kco{\cdot}Ka$. 9. Into type formular, $r_t=\frac{R_{24}}{24}(\frac{b}{\sqrt{t}+a})$, was the best fit one to estimate the probable rainfall intensity when daily rainfall and rainfall durations are given as input data, The coefficient a and b are shown on the Table 16. 10. Japanese type formular, $I_t=\frac{b}{\sqrt{t}+a}$, was the best fit one to estimate the probable rainfall intensity when the rainfall duration only was given. The coefficient a and b are shown on the Table 17. 11. Effective rainfall, Re, was estimated by using following relationships; Re=D, if $R-D\geq}0$, otherwise, Re=R. 12. The difference of rainfall amount from soil moisture depletion was considered as the amount of drainage required. In this case, when Wd=O, Equation 24 was used, otherwise two to three days of lag time was considered and correction was made by use of storage coefficient. 13. To evaluate the model, measured data and estimated data was compared, and relative error was computed. 5.5 percent The relative error was 5.5 percent. 14. By considering the water budget in Jinju area, it was shown that the evaporation amount was greater than the rainfall during period of October to March in next year. This was the behind reasonning that the improvement of surface drainage system is needed in Jinju area.

• Geophysics and Geophysical Exploration
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• v.19 no.1
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• pp.1-10
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• 2016

The hypocenter distribution of microseismic events generated by hydraulic fracturing for shale gas development provides essential information for understanding characteristics of fracture network. In this study, we evaluate how inaccurate velocity models influence the inversion results of two widely used location programs, hypoellipse and hypoDD, which are developed based on an iterative linear inversion. We assume that 98 stations are densely located inside the circle with a radius of 4 km and 5 artificial hypocenter sets (S0 ~ S4) are located from the center of the network to the south with 1 km interval. Each hypocenter set contains 25 events placed on the plane. To quantify accuracies of the inversion results, we defined 6 parameters: difference between average hypocenters of assumed and inverted locations, $d_1$; ratio of assumed and inverted areas estimated by hypocenters, r; difference between dip of the reference plane and the best fitting plane for determined hypocenters, ${\theta}$; difference between strike of the reference plane and the best fitting plane for determined hypocenters, ${\phi}$; root-mean-square distance between hypocenters and the best fitting plane, $d_2$; root-mean-square error in horizontal direction on the best fitting plane, $d_3$. Synthetic travel times are calculated for the reference model having 1D layered structure and the inaccurate velocity model for the inversion is constructed by using normal distribution with standard deviations of 0.1, 0.2, and 0.3 km/s, respectively, with respect to the reference model. The parameters $d_1$, r, ${\theta}$, and $d_2$ show positive correlation with the level of velocity perturbations, but the others are not sensitive to the perturbations except S4, which is located at the outer boundary of the network. In cases of S0, S1, S2, and S3, hypoellipse and hypoDD provide similar results for $d_1$. However, for other parameters, hypoDD shows much better results and errors of locations can be reduced by about several meters regardless of the level of perturbations. In light of the purpose to understand the characteristics of hydraulic fracturing, $1{\sigma}$ error of velocity structure should be under 0.2 km/s in hypoellipse and 0.3 km/s in hypoDD.

• The Journal of Korean Society for Radiation Therapy
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• v.15 no.1
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• pp.19-27
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• 2003

I. Purpose In special cases of Total Body Irradiation(TBI), Half Body Irradiation(HBI), Non-Hodgkin's lymphoma, E-Wing's sarcoma, lymphosarcoma and neuroblastoma a large field can be used clinically. The dose distribution of a large field can use the measurement result which gets from dose distribution of a small field (standard SSD 100cm, size of field under $40{\times}40cm2$) in the substitution which always measures in practice and it will be able to calibrate. With only the method of simple calculation, it is difficult to know the dose and its uniformity of actual body region by various factor of scatter radiation. II. Method & Materials In this study, using Multidata Water Phantom from standard SSD 100cm according to the size change of field, it measures the basic parameter (PDD,TMR,Output,Sc,Sp) From SSD 180cm (phantom is to the bottom vertically) according to increasing of a field, it measures a basic parameter. From SSD 350cm (phantom is to the surface of a wall, using small water phantom. which includes mylar capable of horizontal beam's measurement) it measured with the same method and compared with each other. III. Results & Conclusion In comparison with the standard dose data, parameter which measures between SSD 180cm and 350cm, it turned out there was little difference. The error range is not up to extent of the experimental error. In order to get the accurate data, it dose measures from anthropomorphous phantom or for this objective the dose measurement which is the possibility of getting the absolute value which uses the unlimited phantom that is devised especially is demanded. Additionally, it needs to consider ionization chamber use of small volume and stem effect of cable by a large field.

• Journal of radiological science and technology
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• v.34 no.4
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• pp.277-285
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• 2011

To determine the quality control of UGIS, we acquired 105 patients sampling image at 21 general screening centers. The results of image quality evaluation table containing two countries's UGIS showed that the mean of image qualified education table of our country was 73.3 and the standard error was 4.49; In addition, 19 organizations of 21 general screening centers were given appropriate judgement. The average of image qualified education table of Japan was 58 and the standard error was 4.45. Only 8 organizations were given appropriate judgement. Although we made the image quality evaluation tables with same images, there were many differences in the result of two tables. We figured out the problem about the description of whole stomach and photograph skills. Furthermore, we analysed the situation of the UGIS at each general screening center with the acquired images. The biggest problem of the UGIS of our country was that the procedures were performed without clear medical methods. Methods of UGIS were different at every 21 general screening centers, and most of them did not take exam of anterior surface of stomach of the UGIS. In addition, some general screening centers did not include mucosal relief method or esophagography which is required to include in the image qualified education table of our country. Because polisography is used in the same body position, the problem occured about indiscreet exposure dose of patients. Therefore we have to make an effort to get X-ray images which have enough diagnosis information by the quality control of UGIS.

• Nuclear Medicine and Molecular Imaging
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• v.41 no.4
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• pp.299-308
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• 2007

Purpose: The aim of this study was to develop a bioinformatics software and to test it in serum samples of papillary thyroid cancer using mass spectrometry (SELDI-TOF-MS). Materials and Methods: Development of 'Protein analysis' software performing decision tree analysis was done by customizing C4.5. Sixty-one serum samples from 27 papillary thyroid cancer, 17 autoimmune thyroiditis, 17 controls were applied to 2 types of protein chips, CM10 (weak cation exchange) and IMAC3 (metal binding - Cu). Mass spectrometry was performed to reveal the protein expression profiles. Decision trees were generated using 'Protein analysis' software, and automatically detected biomarker candidates. Validation analysis was performed for CM10 chip by random sampling. Results: Decision tree software, which can perform training and validation from profiling data, was developed. For CM10 and IMAC3 chips, 23 of 113 and 8 of 41 protein peaks were significantly different among 3 groups (p<0.05), respectively. Decision tree correctly classified 3 groups with an error rate of 3.3% for CM10 and 2.0% for IMAC3, and 4 and 7 biomarker candidates were detected respectively. In 2 group comparisons, all cancer samples were correctly discriminated from non-cancer samples (error rate = 0%) for CM10 by single node and for IMAC3 by multiple nodes. Validation results from 5 test sets revealed SELDI-TOF-MS and decision tree correctly differentiated cancers from non-cancers (54/55, 98%), while predictability was moderate in 3 group classification (36/55, 65%). Conclusion: Our in-house software was able to successfully build decision trees and detect biomarker candidates, therefore it could be useful for biomarker discovery and clinical follow up of papillary thyroid cancer.

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