• Magazine of the Korean Society of Agricultural Engineers
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• v.26 no.2
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• pp.106-124
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• 1984

Monthly streanflow of watersheds is one of the most important elements for the planning, design, and management of water resources development projects, e.g., determination of storage requirement of reservoirs and control of release-water in lowflow rivers. Modeling of longterm runoff is theoretically based on water-balance analysis for a certain time interval. The effect of the casual factors of rainfall, evaporation, and soil-moisture storage on streamflow might be explained by multiple regression analysis. Using the basic concepts of water-balance and regression analysis, it was possible to develop a generalized model called the Regionalized Regression Model for Monthly Streamflow in Korean Watersheds. Based on model verification, it is felt that the model can be reliably applied to any proposed station in Korean watersheds to estimate monthly streamflow for the planning, design, and management of water resources development projects, especially those involving irrigation. Modeling processes and properties are summarized as follows; 1. From a simplified equation of water-balance on a watershed a regression model for monthly streamflow using the variables of rainfall, pan evaporation, and previous-month streamflow was formulated. 2. The hydrologic response of a watershed was represented lumpedly, qualitatively, and deductively using the regression coefficients of the water-balance regression model. 3. Regionalization was carried out to classify 33 watersheds on the basis of similarity through cluster analysis and resulted in 4 regional groups. 4. Prediction equations for the regional coefficients were derived from the stepwise regression analysis of watershed characteristics. It was also possible to explain geographic influences on streamflow through those prediction equations. 5. A model requiring the simple input of the data for rainfall, pan evaporation, and geographic factors was developed to estimate monthly streamflow at ungaged stations. The results of evaluating the performance of the model generally satisfactory.

• The Journal of Advanced Prosthodontics
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• v.16 no.3
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• pp.151-162
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• 2024

PURPOSE. This study aimed to analyze factors influencing the success and failure of implant prostheses and to estimate the lifespan of prostheses using standardized evaluation criteria. An online survey platform was utilized to efficiently gather large samples from multiple institutions. MATERIALS AND METHODS. During the one-year period, patients visiting 16 institutions were assessed using standardized evaluation criteria (KAP criteria). Data from these institutions were collected through an online platform, and various statistical analyses were conducted. Risk factors were assessed using both the Cox proportional hazard model and Cox regression analysis. Survival analysis was conducted using Kaplan-Meier analysis and nomogram, and lifespan prediction was performed using principal component analysis. RESULTS. The number of patients involved in this study was 485, with a total of 841 prostheses evaluated. The median survival was estimated to be 16 years with a 95% confidence interval. Factors found to be significantly associated with implant prosthesis failure, characterized by higher hazard ratios, included the 'type of clinic', 'type of antagonist', and 'plaque index'. The lifespan of implant prostheses that did not fail was estimated to exceed the projected lifespan by approximately 1.34 years. CONCLUSION. To ensure the success of implant prostheses, maintaining good oral hygiene is crucial. The estimated lifespan of implant prostheses is often underestimated by approximately 1.34 years. Furthermore, standardized form, online platform, and visualization tool, such as nomogram, can be effectively utilized in future follow-up studies.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.1
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• pp.73-80
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• 2018

In motorway traffic flow control, the conventional way based on real-time response has been changed into advanced way based on proactive response. Future traffic conditions over multiple time intervals are crucial input data for advanced motorway traffic flow control. It is necessary to overcome the uncertainty of the future state in order for forecasting multiple-period traffic volumes, as the number of uncertainty concurrently increase when the forecasting horizon expands. In this vein, multi-interval forecasting of traffic volumes requires a viable approach to conquer future uncertainties successfully. In this paper, a forecasting model is proposed which effectively addresses the uncertainties of future state based on the behaviors of temporal evolution of traffic volume states that intrinsically exits in the big past data. The model selects the past states from the big past data based on the state evolution of current traffic volumes, and then the selected past states are employed for estimating future states. The model was also designed to be suitable for data management systems in practice. Test results demonstrated that the model can effectively overcome the uncertainties over multiple time periods and can generate very reliable predictions in term of prediction accuracy. Hence, it is indicated that the model can be mounted and utilized on advanced data management systems.

• Journal of The Korean Society of Agricultural Engineers
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• v.46 no.7
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• pp.3-12
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• 2004

The need to predict the rate of soil erosion, both under existing conditions and those expected to occur following soil conservation practice, has been led to the development of various models. In this study Morgan model especially developed for field-sized areas on hill slopes was applied to assess the rate of soil erosion using RS/GIS environment in the Dukchun river basin, one of two tributaries flowing into Jinyang lake. In order to run the model, land cover mapping was made by the supervised classification method with Landsat TM satellite image data, the digital soil map was generated from scanning and screen digitizing from the hard copy of soil maps, digital elevation map (DEM) in order to generate the slope map was made by the digital map (DM) produced by National Geographic Information Institute (NGII). Almost all model parameters were generated to the multiple raster data layers, and the map calculation was made by the raster based GIS software, IL WIS which was developed by ITC, the Netherlands. Model results show that the annual soil loss rates are 5.2, 18.4, 30.3, 58.2 and 60.2 ton/ha/year in forest, paddy fields, built-up area, bare soil, and upland fields respectively. The estimated rates seemed to be high under the normal climatic conditions because of exaggerated land slopes due to DEM generation using 100 m contour interval. However, the results were worthwhile to estimate soil loss in hilly areas and the more precise result could be expected when the more accurate slope data is available.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.2
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• pp.261-284
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• 2017

This paper describes the results of a low-frequency oscillation analysis using data measured in PMU installed in the KEPCO system, and the comparison with eigenvalues computed from the linear model. The dominant oscillation modes are estimated by applying various algorithms. The algorithms are: the extended Prony method; multiple time interval parameter estimation method; subspace system identification method; and spectral analysis. From the measurement data, modes of frequency 0.68[Hz] and 0.92[Hz] were estimated, and modes of frequency 0.63[Hz] and 0.80[Hz] were computed from the eigenvalue calculation. There was a difference between the mode estimated from measurement data and that from the linear model. This is possibly because of an error in the dynamic data of the KEPCO system used in eigenvalue calculation. Because wide area modes exist in the KEPCO system, these modes should be monitored continuously for the reliable operation of the system. In order to prevent total blackouts caused by wide area oscillation, moreover, contingency analysis should be performed in relation to this mode and appropriate measures should be established.

• Journal of The Korean Society of Emergency Medicine
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• v.29 no.5
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• pp.430-436
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• 2018

Objective: The purpose of this study was to evaluate whether or not the d-dimer level indicating hyperfibrinolysis could be a predictor of early poor outcome (massive transfusion, death within 24 hours) associated with trauma-induced coagulopathy in blunt trauma without significant brain injury. Methods: This study was a retrospective observational study using 516 blunt trauma patients without significant brain injury. The poor outcome group, including patients receiving massive transfusion and those who died within 24 hours, consisted of 33 patients (6.4%). The variables were compared between the poor outcome group and good outcome group, and logistic regression analysis was performed using statistically significant variables. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the poor outcome prediction ability of the initial d-dimer level. Results: The poor outcome group showed more serious anatomical, physiological, and laboratory data than the good outcome group. In the ROC curve analysis for evaluation of the poor outcome prediction of the d-dimer level, the area under the curve value was 0.87 (95% confidence interval [CI], 0.84-0.90) while the cut-off value was 27.35 mg/L. In the logistic regression analysis, the high d-dimer level was shown to be an independent predictor of poor outcome (adjusted odds ratio, 14.87; 95% CI, 2.96-74.67). Conclusion: The high d-dimer level (>27.35 mg/L) can be used as a predictor for the poor outcome of patients with blunt trauma without significant brain injury.

• Korean Journal of Radiology
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• v.22 no.6
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• pp.994-1004
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• 2021

Objective: To extract pulmonary and cardiovascular metrics from chest CTs of patients with coronavirus disease 2019 (COVID-19) using a fully automated deep learning-based approach and assess their potential to predict patient management. Materials and Methods: All initial chest CTs of patients who tested positive for severe acute respiratory syndrome coronavirus 2 at our emergency department between March 25 and April 25, 2020, were identified (n = 120). Three patient management groups were defined: group 1 (outpatient), group 2 (general ward), and group 3 (intensive care unit [ICU]). Multiple pulmonary and cardiovascular metrics were extracted from the chest CT images using deep learning. Additionally, six laboratory findings indicating inflammation and cellular damage were considered. Differences in CT metrics, laboratory findings, and demographics between the patient management groups were assessed. The potential of these parameters to predict patients' needs for intensive care (yes/no) was analyzed using logistic regression and receiver operating characteristic curves. Internal and external validity were assessed using 109 independent chest CT scans. Results: While demographic parameters alone (sex and age) were not sufficient to predict ICU management status, both CT metrics alone (including both pulmonary and cardiovascular metrics; area under the curve [AUC] = 0.88; 95% confidence interval [CI] = 0.79-0.97) and laboratory findings alone (C-reactive protein, lactate dehydrogenase, white blood cell count, and albumin; AUC = 0.86; 95% CI = 0.77-0.94) were good classifiers. Excellent performance was achieved by a combination of demographic parameters, CT metrics, and laboratory findings (AUC = 0.91; 95% CI = 0.85-0.98). Application of a model that combined both pulmonary CT metrics and demographic parameters on a dataset from another hospital indicated its external validity (AUC = 0.77; 95% CI = 0.66-0.88). Conclusion: Chest CT of patients with COVID-19 contains valuable information that can be accessed using automated image analysis. These metrics are useful for the prediction of patient management.

• Clinical and Experimental Pediatrics
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• v.51 no.11
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• pp.1158-1164
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• 2008

Purpose : To investigate the discriminative ability of pediatric index of mortality 2 (PIM2) and pediatric risk of mortality III (PRISM III) in predicting mortality in children admitted into the intensive care unit (ICU). Methods : We retrospectively analyzed variables of PIM2 and PRISM III based on medical records with children cared for in a single hospital ICU from January 2003 to December 2007. Exclusions were children who died within 2 h of admission into ICU or hopeless discharge. We used Students t test and ANOVA for general characteristics and for correlation between survivors and non-survivors for variables of PIM2 and PRISM III. In addition, we performed multiple logistic regression analysis for Hosmer-Lemeshow goodness-of-fit, receiver operating characteristic curve (ROC) for discrimination, and calculated standardized mortality ratio (SMR) for estimation of prediction. Results : We collected 193 medical records but analyzed 190 events because three children died within 2 h of ICU admission. The variables of PIM2 correlated with survival, except for the presence of post-procedure and low risk. In PRISM III, there was a significant correlation for cardiovascular/neurologic signs, arterial blood gas analysis but not for biochemical and hematologic data. Discriminatory performance by ROC showed an area under the curve 0.858 (95% confidence interval; 0.779-0.938) for PIM2, 0.798 (95% CI; 0.686-0.891) for PRISM III, respectively. Further, SMR was calculated approximately as 1 for the 2 systems, and multiple logistic regression analysis showed ${\chi}^2(13)=14.986$, P=0.308 for PIM2, ${\chi}^2(13)=12.899$, P=0.456 for PRISM III in Hosmer-Lemeshow goodness-of-fit. However, PIM2 was significant for PRISM III in the likelihood ratio test (${\chi}^2(4)=55.3$, P<0.01). Conclusion : We identified two acceptable scoring systems (PRISM III, PIM2) for the prediction of mortality in children admitted into the ICU. PIM2 was more accurate and had a better fit than PRISM III on the model tested.