• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.4
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• pp.83-100
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• 2020

This study evaluated the accuracy of soil moisture and evapotranspiration by calculating the hydrological parameters in Korean peninsula using Land Information System(LIS) developed by US NASA. We used Noah-MP surface model to calculate hydrological parameters, and used MERRA2(Modern-Era Retrospective analysis for Research and Applications, Version 2) for hydrological forcing data. And, International Geosphere-Biosphere Program(IGBP) and University of Maryland(UMD) land cover maps were applied to compare the output accuracy, and Automated Synoptic Observing System(ASOS) of KMA was used as ground observation data. In order to evaluate the accuracy of the output data, the correlation coefficient(CC), BIAS, and efficiency factor (NSE, Nash-Sutcliffe Efficiency) were analyzed with soil moisture and evapotranspiration by ASOS ground observation data. As a result, the correlation coefficient of soil moisture using IGBP was 0.56 on average, and evapotranspiration was about 0.71. On the other hand, soil moisture using UMD was 0.68 on average and evapotranspiration was about 0.72, and the correlation coefficient by UMD was evaluated as high accuracy compared to the results by using IGBP. The correlation coefficient of soil moisture was an average of 0.68 and evapotranspiration was an average of 0.72 when MERRA2 was used as hydrological forcing data. On the other hand, the soil moisture applied with ASOS was an average of 0.66, and evapotranspiration was an average of 0.72. It is judged that the ASOS point data was reanalyzed as 0.65°× 0.5°grids, which is the same spatial resolution with MERRA2, resulting in differences in accuracy depending on the region.

• Journal of Korea Water Resources Association
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• v.53 no.12
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• pp.1081-1095
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• 2020

This study is to evaluate stream flow and water quality changes of Yeongsan river basin (3,371.4 km2) by inter-basin water transfer (IBWT) from Juam dam of Seomjin river basin using SWAT (Soil and Water Assessment Tool). The SWAT was established using inlet function for IBWT between donor and receiving basins. The SWAT was calibrated and validated with 14 years (2005 ~ 2018) data of 1 stream (MR) and 2 multi-functional weir (SCW, JSW) water level gauging stations, and 3 water quality stations (GJ2, NJ, and HP) including data of IBWT and effluent from wastewater treatment plants of Yeongsan river basin. For streamflow and weir inflows (MR, SCW, and JSW), the coefficient of determination (R2), Nash-Sutcliffe efficiency (NSE), root mean square error (RMSE), and percent bias (PBIAS) were 0.69 ~ 0.81, 0.61 ~ 0.70, 1.34 ~ 2.60 mm/day, and -8.3% ~ +7.6% respectively. In case of water quality, the R2 of SS, T-N, and T-P were 0.69 ~ 0.81, 0.61 ~ 0.70, and 0.54 ~ 0.63 respectively. The Yeongsan river basin average streamflow was 12.0 m3/sec and the average SS, T-N, and T-P were 110.5 mg/L, 4.4 mg/L, 0.18 mg/L respectively. Under the 130% scenario of IBWT amount, the streamflow, SS increased to 12.94 m3/sec (+7.8%), 111.26 mg/L (+0.7%) and the T-N, T-P decreased to 4.17 mg/L (-5.2%), 0.165 mg/L (-8.3%) respectively. Under the 70% scenario of IBWT amount, the streamflow, SS decreased to 11.07 m3/sec (-7.8%), 109.74 mg/L (-0.7%) and the T-N, T-P increased to 4.68 mg/L (+6.4%), 0.199 mg/L (+10.6%) respectively.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.66 no.2
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• pp.105-111
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• 2021

Rice lodging is an annual occurrence caused by typhoons accompanied by strong winds and strong rainfall, resulting in damage relating to pre-harvest sprouting during the ripening period. Thus, rapid estimations of the area of lodged rice are necessary to enable timely responses to damage. To this end, we obtained images related to rice lodging using a drone in Gimje, Buan, and Gunsan, which were converted to 128 × 128 pixels images. A convolutional neural network (CNN) model, a deep learning model based on these images, was used to predict rice lodging, which was classified into two types (lodging and non-lodging), and the images were divided in a 8:2 ratio into a training set and a validation set. The CNN model was layered and trained using three optimizers (Adam, Rmsprop, and SGD). The area of rice lodging was evaluated for the three fields using the obtained data, with the exception of the training set and validation set. The images were combined to give composites images of the entire fields using Metashape, and these images were divided into 128 × 128 pixels. Lodging in the divided images was predicted using the trained CNN model, and the extent of lodging was calculated by multiplying the ratio of the total number of field images by the number of lodging images by the area of the entire field. The results for the training and validation sets showed that accuracy increased with a progression in learning and eventually reached a level greater than 0.919. The results obtained for each of the three fields showed high accuracy with respect to all optimizers, among which, Adam showed the highest accuracy (normalized root mean square error: 2.73%). On the basis of the findings of this study, it is anticipated that the area of lodged rice can be rapidly predicted using deep learning.

• Journal of Korea Water Resources Association
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• v.54 no.11
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• pp.903-913
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• 2021

In the flood season, the measurement of the river discharge has many restrictions due to reasons such as budget, manpower, safety, convenience in measurement and so on. In particular, when heavy rain events occur due to typhoons, etc., it is difficult to measure the amount of flood due to the above problems. In order to improve this problem, in this study, a method was developed that can measure the river discharge in a flood season simply and safely in a short time with minimal manpower by combining the functions of a drone and a surface velocity doppler radar. To overcome the mechanical limitations of drones caused by weather issues such as wind and rainfall derived from the measurement of the river discharge using the conventional drone, we developed a drone with P56 grade dustproof and waterproof performance, stable flight capability at a wind speed of up to 36 km/h, and a payload weight of up to 10 kg. Further, to eliminate vibration which is the most important constraint factor in the measurement with a surface velocity doppler radar, a damper plate was developed as a device that combines a drone and a surface velocity Doppler radar. The velocity meter DSVM (Dron and Surface Veloctity Meter using doppler radar) that combines the flight equipment with the velocity meter was produced. The error of ±3.5% occurred as a result of measuring the river discharge using DSVM at the point of Geumsan-gun (Hwangpunggyo) located at Bonghwang stream (the first tributary stream of the Geum River). In addition, when calculating the mean velocity from the measured surface velocity, the measurement was performed using ADCP simultaneously to improve accuracy, and the mean velocity conversion factor (0.92) was calculated by comparing the mean velocity. In this study, the discharge measured by combining a drone and a surface velocity meter was compared with the discharge measured using ADCP and floats, so that the application and utility of DSVM was confirmed.

• The Journal of Korean Society for Radiation Therapy
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• v.33
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• pp.25-33
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• 2021

Purpose : The purpose of this study is to evaluate the accuracy and usefulness of the Trigger mode for the Respiratory Gated Radiation Therapy (RGRT) Materials and methods : A QUASAR respiratory phantom that inserted a 3 mm fiducial marker (a gold marker) was used to estimate the accuracy of the Trigger mode. And the 20 bpm was used as reference respiration rate in this study. The marker that placed at the center of the phantom was contoured, and the lower threshold of a gating window was fixed at 2.0 mm using an OBI with Truebeam STx^TM. The upper threshold was measured every 0.5 mm from 1.0 mm to 3.0 mm. The respiration rates were changed every 10 bpm from 10 bpm to 60 bpm. We repeatedly measured five times to check the error rate of the trigger mode in the same condition. Result : The differences of a distance from a peak phase to upper threshold, 1.0 to 3.0 mm at a 20 bpm as a reference for 3 days in a row were 0.68±0.05 mm, 0.91±0.03 mm, 1.23±0.03 mm, 1.42±0.04 mm, and 1.66±0.06 mm, respectively. Measurement result of changes in respiratory rate compared to baseline respiratory rate in maximum absolute difference. The coefficient of determination (R2) to estimate the correlation between the respiration velocity and variation of absolute difference was on average 0.838, 0.887, 0.770, 0.850, and 0.906. The p-values of all the variables were below 0.05. Conclusion : Using Trigger mode during respiratory gated radiation therapy (RGRT), accuracy and usefulness of trigger mode at reference breathing rate were confirmed. However, inaccuracies depending on the rate of breathing it could be uncertain in case of respiration rate is faster than 20 bpm as a standard respiration rate compared to slower than 20 bpm. Consequently, when conducting a RGRT using the trigger mode, real time monitoring is required with well educated respiration.

• Journal of The Korean Society of Grassland and Forage Science
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• v.41 no.4
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• pp.223-233
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• 2021

The objective of this study was to investigate the effect of gypsum on the dry matter yield and the chemical composition of alfalfa in reclaimed tideland with soil dressing. The experimental site was Sukmoon reclaimed tideland. The tideland was reclaimed approximately 17 to 33 years ago and the 70 cm of soil was top-dressed. The soil that covers the reclaimed tideland brought from the island did not treat di-salinized. Treatments were consisted of three groups; control group where no gypsum (G0) was applied and two experimental groups where 2 ton/ha (G2) and 4 ton/ha (G4) of gypsum were applied, respectively. The first harvest was conducted when the alfalfa reached early flowering (open the flower 10%), and after that subsequent harvest was conducted at approximately 35 days intervals. The dry matter yield of the alfalfa showed that G2 was significantly higher in the first year than G0 and G4, and G2 tended to be higher in the second year than G0 and G4, although there were no significant differences between treatments. The reason for the high dry matter yield in G2 was that the soil pH and EC of the soil were at marginal and ideal levels and the coverage and alfalfa botanical composition were also high. In both years, there were no differences in the crude protein, neutral detergent fiber and acid detergent fiber contents and relative feed value between gypsum treatments. Meanwhile, the results in the first and second years showed that the alfalfa dry matter yield were negatively affected by droughts stress in spring and concentrated precipitation in summer. Therefore, this study suggests gypsum treatment in reclaimed tidal land could increase the dry matter yield of alfalfa, and 2 ton/ha of gypsum was the optimum rate.

• Journal of The Korean Society of Grassland and Forage Science
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• v.41 no.4
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• pp.287-294
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• 2021

The objective of this study was conducted to calculate the damage of whole crop maize in accordance with abnormal climate using the forage yield prediction model through machine learning. The forage yield prediction model was developed through 8 machine learning by processing after collecting whole crop maize and climate data, and the experimental area was selected as Gyeonggi-do. The forage yield prediction model was developed using the DeepCrossing (R²=0.5442, RMSE=0.1769) technique of the highest accuracy among machine learning techniques. The damage was calculated as the difference between the predicted dry matter yield of normal and abnormal climate. In normal climate, the predicted dry matter yield varies depending on the region, it was found in the range of 15,003~17,517 kg/ha. In abnormal temperature, precipitation, and wind speed, the predicted dry matter yield differed according to region and abnormal climate level, and ranged from 14,947 to 17,571, 14,986 to 17,525, and 14,920 to 17,557 kg/ha, respectively. In abnormal temperature, precipitation, and wind speed, the damage was in the range of -68 to 89 kg/ha, -17 to 17 kg/ha, and -112 to 121 kg/ha, respectively, which could not be judged as damage. In order to accurately calculate the damage of whole crop maize need to increase the number of abnormal climate data used in the forage yield prediction model.

• Journal of The Korean Society of Grassland and Forage Science
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• v.42 no.2
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• pp.127-136
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• 2022

This study was conducted to estimate the damage of Whole Crop Maize (WCM) according to abnormal climate using machine learning and present the damage through mapping. The collected WCM data was 3,232. The climate data was collected from the Korea Meteorological Administration's meteorological data open portal. Deep Crossing is used for the machine learning model. The damage was calculated using climate data from the Automated Synoptic Observing System (95 sites) by machine learning. The damage was calculated by difference between the Dry matter yield (DMY)_normal and DMY_abnormal. The normal climate was set as the 40-year of climate data according to the year of WCM data (1978~2017). The level of abnormal climate was set as a multiple of the standard deviation applying the World Meteorological Organization(WMO) standard. The DMY_normal was ranged from 13,845~19,347 kg/ha. The damage of WCM was differed according to region and level of abnormal climate and ranged from -305 to 310, -54 to 89, and -610 to 813 kg/ha bnormal temperature, precipitation, and wind speed, respectively. The maximum damage was 310 kg/ha when the abnormal temperature was +2 level (+1.42 ℃), 89 kg/ha when the abnormal precipitation was -2 level (-0.12 mm) and 813 kg/ha when the abnormal wind speed was -2 level (-1.60 m/s). The damage calculated through the WMO method was presented as an mapping using QGIS. When calculating the damage of WCM due to abnormal climate, there was some blank area because there was no data. In order to calculate the damage of blank area, it would be possible to use the automatic weather system (AWS), which provides data from more sites than the automated synoptic observing system (ASOS).

• Journal of the Korean Orthopaedic Association
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• v.55 no.3
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• pp.244-252
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• 2020

Purpose: Total femoral replacement (TFR) is an extreme form of limb salvage. Considering the rarity of this procedure, reports have focused on the complications and a proper indication is unclear. This study analyzed 36 patients with TFR who were asked the following: 1) prognostic factors related to survival in patients who underwent TFR with a tumoral cause; 2) overall implant and limb survival; 3) complications, functional outcome, and limb status for patients surviving for more than 3 years. Materials and Methods: According to the causes for TFR, 36 patients were categorized into three groups: extensive primary tumoral involvement (group 1, 15 cases), tumoral contamination by an inadvertent procedure or local recurrence (group 2, 16 cases), and salvage of a failed reconstruction (group 3, 5 cases). The factors that may affect the survival of patients included age, sex, cause of TFR, and tumor volume change after chemotherapy. Results: The overall five-year survival of the 36 patients was 31.5%±16.2%. The five-year survival of 31 patients with tumoral causes was 21.1%±15.6%. The five-year survival of 50.0%±31.0% in patients with a decreased tumor volume after chemotherapy was higher than that of increased tumor volume (p=0.02). The five-year survival of 12 cases with a wide margin was 41.7%±27.9%, whereas that of the marginal margin was 0.0%±0.0% (p=0.03). The ten-year overall implant survival of 36 patients was 85.9%±14.1%. The five-year revision-free survival was 16.6%±18.2%. At the final follow-up, 12 maintained tumor prosthesis, three underwent amputation (rotationplasty, 2; above knee amputation, 1), and the remaining one had knee fusion. Among 16 patients with a follow-up of more than three years, 14 patients underwent surgical intervention and two patients had conservative management. Complications included infection in 10 cases, local recurrences in two cases, and one case each of hip dislocation, bushing fracture, and femoral artery occlusion. Conclusion: Patients showing an increased tumor volume after chemotherapy and having an inadequate surgical margin showed a high chance of early death. In the long-term follow-up, TFR showed a high infection rate and the functional outcome was unsatisfactory. Nevertheless, this procedure is an inevitable option of limb preservation in selected patients.

• Journal of the Korean Orthopaedic Association
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• v.55 no.6
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• pp.511-519
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• 2020

Purpose: Schwannoma is a benign tumor that occurs mainly in the peripheral nerve. If the tumor is large or is in areas other than the nerves, it is likely to be mistaken for malignant soft tissue tumors. The authors reviewed 50 cases of giant schwannomas and assessed the distribution of the primary locations, clinical symptoms, radiological and pathological diagnosis, and diagnostic accuracy. Materials and Methods: Of the 214 pathologically confirmed schwannomas, 50 cases with a maximum diameter of 5 cm or more were extracted. The entire cohort was classified into three subgroups (major peripheral nerve, intramuscular, bone) according to the primary location, and the anatomical locations were specified. Results: When the entire cohort was classified according to the primary location, 14 tumors occurred in the major peripheral nerve, 31 cases in the muscle, and 5 cases in the bone. The mean size of the tumor in the entire cohort was 7.0 cm, and the intramuscular subgroup had the largest size with 8.0 cm. The radiological diagnosis revealed 33 out of 50 cases to be benign schwannoma (66.0%), 15 cases as low-grade malignancy (30.0%), and the remaining two cases (4.0%) as a suspicious tuberculosis abscess and tenosynovial giant cell tumor, respectively. On the clinical symptoms, Tinel sign was the most common in the peripheral nerve group with 78.6% (11/14), while 93.5% of the intramuscular group had palpation of the mass with a mean duration of 66.6 months. In the bone group, one out of five cases was reported as a low-grade malignancy. Two cases of postoperative complications were encountered; one was bleeding after tumor excision, which required hemostasis, and the other was peroneal nerve palsy after surgery. Conclusion: When assessing the large-sized soft tissue tumors in the muscles, the possibility of a benign schwannoma should be considered if 1) there is a long period of mass palpation and 2) non-specific findings in MRI. Preoperative pathology confirmation with a biopsy can help reduce the risk of overtreatment.