• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.159-159
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• 2023

The Amazon River basin is one of the largest basins in the world, and its ecosystem is vital for biodiversity, hydrology, and climate regulation. Thus, understanding the hydrometeorological process is essential to the maintenance of the Amazon River basin. However, it is still tricky to monitor the Amazon River basin because of its size and the low density of the monitoring gauge network. To solve those issues, remote sensing products have been largely used. Yet, those products have some limitations. Therefore, this study aims to do bias corrections to improve the accuracy of Satellite Precipitation Products (SPPs) in the Amazon River basin. We use 331 rainfall stations for the observed data and two daily satellite precipitation gridded datasets (CHIRPS, TRMM). Due to the limitation of the observed data, the period of analysis was set from 1st January 1990 to 31st December 2010. The observed data were interpolated to have the same resolution as the SPPs data using the IDW method. For bias correction, we use convolution neural networks (CNN) combined with an autoencoder architecture (ConvAE). To evaluate the bias correction performance, we used some statistical indicators such as NSE, RMSE, and MAD. Hence, those results can increase the quality of precipitation data in the Amazon River basin, improving its monitoring and management.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.120-120
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• 2020

Spatial precipitation data is one of the essential components in modeling hydrological problems. The estimation of these data has achieved significant achievements own to the recent advances in remote sensing technology. However, there are still gaps between the satellite-derived rainfall data and observed data due to the significant dependence of rainfall on spatial and temporal characteristics. An effective approach based on the Convolutional Neural Network (CNN) model to correct the satellite-derived rainfall data is proposed in this study. The Mekong River basin, one of the largest river system in the world, was selected as a case study. The two gridded precipitation data sets with a spatial resolution of 0.25 degrees used in the CNN model are APHRODITE (Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation) and PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks). In particular, PERSIANN-CDR data is exploited as satellite-based precipitation data and APHRODITE data is considered as observed rainfall data. In addition to developing a CNN model to correct the satellite-based rain data, another statistical method based on standard deviations for precipitation bias correction was also mentioned in this study. Estimated results indicate that the CNN model illustrates better performance both in spatial and temporal correlation when compared to the standard deviation method. The finding of this study indicated that the CNN model could produce reliable estimates for the gridded precipitation bias correction problem.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.166-166
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• 2020

Recently, As the frequency of localized heavy rains increases, the use of high-resolution radar data is increasing. The produced radar rainfall has still gaps of spatial and temporal compared to gauge observation rainfall, and in many studies, various statistical techniques are performed for correct rainfall. In this study, the precipitation correction of the S-band Dual Polarization radar in use in the flood forecast was performed using the ConvAE algorithm, one of the Convolutional Neural Network. The ConvAE model was trained based on radar data sets having a 10-min temporal resolution: radar rainfall data, gauge rainfall data for 790minutes(July 2017 in Cheongju flood event). As a result of the validation of corrected radar rainfall were reduced gaps compared to gauge rainfall and the spatial correction was also performed. Therefore, it is judged that the corrected radar rainfall using ConvAE will increase the reliability of the gridded rainfall data used in various physically-based distributed hydrodynamic models.

• Journal of Korean Society of Forest Science
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• v.85 no.1
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• pp.56-65
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• 1996

Current volume tables might underestimate or overestimate the volumes of individual trees in a specific region because the tables were made using the data from broad region. This study provides a statistical method of local correction, which is the ratio-of-means estimator, when the table is applied to the data from a specific region. Data used in this study were 411 trees of Larix leptolepis from Hongchon region. Five statistical models for individual tree volume equation were evaluated based on 3 evalation criteria and the best equation fitted to the data from Hongchon region was selected. The volume estimated by the selected equation was then compared with the volume estimated by the current volume table. From the ratio-of-means estimate based on the volumes estimated by selected equation and by current volume table, the local correction was made. The correction equation was $V_{Hongchon}=1.078$ $V_{volume\;table}$. It is also proved that the correction equation can simply and precisely estimate tree volumes of Larix leptolepis in Hongchon region using the current volume table.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2017.11a
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• pp.71-71
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• 2017

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.415-423
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• 2021

Recently, an R-based point time series data validation system has been established for the statistical post processing and improvement of the National Center for AgroMeteorology-Land Atmosphere Modeling Package (NCAM-LAMP) medium-range prediction data. The time series verification system was used to compare the NCAM-LAMP with the AWS observations and GDAPS medium-range prediction model data operated by Korea Meteorological Administration. For this comparison, the model latitude and longitude data closest to the observation station were extracted and a total of nine points were selected. For each point, the characteristics of the model prediction error were obtained by comparing the daily average of the previous prediction data of air temperature, wind speed, and hourly precipitation, and then we tried to improve the next prediction data using Support Vector Machine( SVM) method. For three months from August to October 2017, the SVM method was used to calibrate the predicted time series data for each run. It was found that The SVM-based correction was promising and encouraging for wind speed and precipitation variables than for temperature variable. The correction effect was small in August but considerably increased in September and October. These results indicate that the SVM method can contribute to mitigate the gradual degradation of medium-range predictability as the model boundary data flows into the model interior.

• Atmosphere
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• v.24 no.4
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• pp.491-502
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• 2014

As a part of the KIAPS (Korea Institute of Atmospheric Prediction Systems) Package for Observation Processing (KPOP), we have developed the modules for Advanced Microwave Sounding Unit-A (AMSU-A) pre-processing and its bias correction. The KPOP system calculates the airmass bias correction coefficients via the method of multiple linear regression in which the scan-corrected innovation and the thicknesses of 850~300, 200~50, 50~5, and 10~1 hPa are respectively used for dependent and independent variables. Among the four airmass predictors, the multicollinearity has been shown by the Variance Inflation Factor (VIF) that quantifies the severity of multicollinearity in a least square regression. To resolve the multicollinearity, we adopted simple linear regression and Principal Component Regression (PCR) to calculate the airmass bias correction coefficients and compared the results with those from the multiple linear regression. The analysis shows that the order of performances is multiple linear, principal component, and simple linear regressions. For bias correction for the AMSU-A channel 4 which is the most sensitive to the lower troposphere, the multiple linear regression with all four airmass predictors is superior to the simple linear regression with one airmass predictor of 850~300 hPa. The results of PCR with 95% accumulated variances accounted for eigenvalues showed the similar results of the multiple linear regression.

• The Journal of Advanced Prosthodontics
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• v.6 no.1
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• pp.39-45
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• 2014

PURPOSE. The aim of the present study was to evaluate the accuracy of three techniques for correction of cast implant bars. MATERIALS AND METHODS. Thirty cast implant bars were fabricated on a metal master model. All cast implant bars were sectioned at 5 mm from the left gold cylinder using a disk of 0.3 mm thickness, and then each group of ten specimens was corrected by gas-air torch soldering, laser welding, and additional casting technique. Three dimensional evaluation including horizontal, vertical, and twisting measurements was based on measurement and comparison of (1) gap distances of the right abutment replica-gold cylinder interface at buccal, distal, lingual side, (2) changes of bar length, and (3) axis angle changes of the right gold cylinders at the step of the post-correction measurements on the three groups with a contact and non-contact coordinate measuring machine. One-way analysis of variance (ANOVA) and paired t-test were performed at the significance level of 5%. RESULTS. Gap distances of the cast implant bars after correction procedure showed no statistically significant difference among groups. Changes in bar length between pre-casting and post-correction measurement were statistically significance among groups. Axis angle changes of the right gold cylinders were not statistically significance among groups. CONCLUSION. There was no statistical significance among three techniques in horizontal, vertical and axial errors. But, gas-air torch soldering technique showed the most consistent and accurate trend in the correction of implant bar error. However, Laser welding technique, showed a large mean and standard deviation in vertical and twisting measurement and might be technique-sensitive method.

• Asian Spine Journal
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• v.12 no.6
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• pp.1053-1059
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• 2018

Study Design: Retrospective review. Purpose: To detect the effect of cannulated (poly-axial head) and solid (mono-axial head) screws on the local kyphotic angle, vertebral body height, and superior and inferior angles between the screw and the rod in the surgical management of thoracolumbar fractures. Overview of Literature: Biomechanics studies showed that the ultimate load, yield strength, and cycles to failure were significantly lower with cannulated (poly-axial head) pedicle comparing to solid core (mono-axial head). Methods: The medical charts of patients with thoracolumbar fractures who underwent pedicle screw fixation with cannulated or solid pedicle screws were retrospectively reviewed; the subjects were followed up from January 2011 to December 2015. Results: Total 178 patients (average age, $36.1{\pm}12.4years$; men, 142 [84.3%]; women, 28 [15.7%]) with thoracolumbar fractures who underwent surgery and were followed up at Hamad Medical Corporation were classified, based on the screw type as those with cannulated screws and those with solid screws. The most commonly affected level was L1, followed by L2 and D12. Surgical correction of the local kyphotic angle was significantly different in the groups; however, there was no significant difference in the loss of correction of the local kyphotic angle of the groups. Surgical correction of the reduction in the vertebral body height showed statistical significance, while the average loss of correction in the reduction of the vertebral body height was not significantly different. The measurement of the angles made by the screws on the rods was not significantly different between the cannulated (poly-axial head) and solid (mono-axial head) screw groups. Conclusions: Solid screws were superior in terms of providing increased correction of the kyphotic angle and height of the fractured vertebra than the cannulated screws; however, no difference was noted between the screws in the maintenance of the superior and inferior angles of the screw with the rod.

• Structural Engineering and Mechanics
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• v.12 no.3
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• pp.249-266
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• 2001

Current seismic design provisions allow structures to deform into inelastic range during design level earthquakes since the chance to meet such event is quite rare. For this purpose, design base shear is defined in current seismic design provisions as the value of elastic seismic shear force divided by strength reduction factor, R (${\geq}1$). Strength reduction factor generally consists of four different factors, which can account for ductility capacity, overstrength, damping, and redundancy inherent in structures respectively. In this study, R factor is assumed to account for only the ductility rather than overstrength, damping, and redundancy. The R factor considering ductility is called "ductility factor" ($R_{\mu}$). This study proposes ductility factor with correction factor, C, which can account for dynamic P-${\Delta}$ effect. Correction factor, C is established as the functional form since it requires computational efforts and time for calculating this factor. From the statistical study using the results of nonlinear dynamic analysis for 40 earthquake ground motions (EQGM) it is shown that the dependence of C factor on structural period is weak, whereas C factor is strongly dependant on the change of ductility ratio and stability coefficient. To propose the functional form of C factor statistical study is carried out using 79,920 nonlinear dynamic analysis results for different combination of parameters and 40 EQGM.