• The Transactions of the Korean Institute of Power Electronics
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• v.23 no.5
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• pp.359-365
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• 2018

This study introduces a model predictive control method for controlling a cascaded flying capacitor multilevel rectifier used as an AC-DC rectifier of a solid-state transformer for DC distribution systems. The proposed method reduces the number of states that need to be considered in model predictive control by separately controlling input current, output DC link voltage, and flying capacitor voltage. Thus, calculation time is shortened to facilitate the level expansion of the cascaded flying capacitor multilevel rectifier. The selection of weighting factors did not present difficulties because the weighting factors in the cost function of the conventional model predictive control are not used. The effectiveness of the proposed method is verified through computer simulation using powersim and experiment.

• Radiation Oncology Journal
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• v.30 no.3
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• pp.99-107
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• 2012

Purpose: The aim of this study was to identify clinical predictive factors for tumor response after preoperative chemoradiotherapy (CRT) in rectal cancer. Materials and Methods: The study involved 51 patients who underwent preoperative CRT followed by surgery between January 2005 and February 2012. Radiotherapy was delivered to the whole pelvis at a dose of 45 Gy in 25 fractions, followed by a boost of 5.4 Gy in 3 fractions to the primary tumor with 5 fractions per week. Three different chemotherapy regimens were used (5-fluorouracil and leucovorin, capecitabine, or tegafur/uracil). Tumor responses to preoperative CRT were assessed in terms of tumor downstaging and pathologic complete response (ypCR). Statistical analyses were performed to identify clinical factors associated with pathologic tumor response. Results: Tumor downstaging was observed in 28 patients (54.9%), whereas ypCR was observed in 6 patients (11.8%). Multivariate analysis found that predictors of downstaging was pretreatment relative lymphocyte count (p = 0.023) and that none of clinical factors was significantly associated with ypCR. Conclusion: Pretreatment relative lymphocyte count (%) has a significant impact on the pathologic tumor response (tumor downstaging) after preoperative CRT for locally advanced rectal cancer. Enhancement of lymphocyte-mediated immune reactions may improve the effect of preoperative CRT for rectal cancer.

• Journal of Home Health Care Nursing
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• v.15 no.1
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• pp.22-28
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• 2008

Purpose: The aim of this study was to analyze the effects of time management on the clinical nurse's organizational commitment and job satisfaction. Methods: Subjects were recruited in two general hospitals in Seoul and Incheon. Data collection was done using a self-report questionnaire. Time management was measured using the questionnaire developed by Han (1992). Organizational commitment and job satisfaction were measured using the questionnaire developed by Yoon (2000), based on Mowday et al. (1979) and Stamps et al. (1978). The data were analyzed using the SAS statistical package program, version 10.0. Specifically, descriptive statistics and stepwise multiple regression were performed. Results: The predictive time management factors for organizational commitment included deadline decision, simplification, and goal-setting. The predictive time management factors for job satisfaction included planning/making the priority order, deadline decision, simplification, asking for help, and responsibility reduction. Conclusion: Time management factors are highly correlated with organizational commitment and job satisfaction in clinical nurses. Deadline decision and simplification are common predictive factors for organizational commitment and job satisfaction. These results can be used to develop more effective time management strategies for increasing organizational effectiveness in clinical nurses.

• Childhood Kidney Diseases
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• v.10 no.2
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• pp.132-141
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• 2006

Purpose : This study was aimed to determine the predictive risk factors for the treatment response and relapse rate in children diagnosed with idiopathic nephrotic syndrome. Methods : We analyzed the medical records of children who were diagnosed and treated for childhood idiopathic nephrotic syndrome from November 1991 to May 2005. Variables selected in this study were age at onset, sex, laboratory data, concomitant bacterial infections, days to remission, and interval to first relapse. Results : There were 46 males and 11 females, giving a male:female ratio of 4.2:1. The age($mean{\pm}SD$) of patients was $5.8{\pm}4.1$ years old. Of all patients who were initially given corticosteroids, complete remission(CR) was observed in 54(94.7%). Of the 54 patients who showed CR with initial treatment, 40(70.2%) showed CR within 2 weeks and 14(24.6%) showed CR after 2 weeks. The levels of serum IgG were lower in the latter group who showed CR after 2 weeks(P=0.036). Of the 54 patients who showed CR with initial treatment, 47(82.5%) relapsed. Of these patients, 35.1% were frequent relapsers and 43.9% were infrequent relapsers. There was no significant correlation between the frequency of relapse and the following variables : sex, days to remission, and laboratory data. However, age at onset and interval to first relapse had a negative correlation with the frequency of relapse(Pearson's coefficient=-0.337, -0.433, P<0.012, P<0.01). Conclusion : The age at onset and the interval to first relapse were found to be predictive clinical parameters for the relapse rate, while the levels of serum IgG at initial presentation were a predictive laboratory factor for treatment response in childhood idiopathic nephrotic syndrome.

• Health Policy and Management
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• v.16 no.2
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• pp.21-48
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• 2006

As prior studies indicate that chronic diseases are mainly attributed to health behavior, preventive health care rather than treatment for illness needs to improve health status. Since chronic conditions require long-term therapy, health care expenditures to treat chronic diseases have been substantial burden at national level. In this point of view, this study suggests that the health promotion program should be based on Knowledge Based System Using Data Mining Technique, we developed a predictive model for preventive healthcare management on diabetes mellitus. Generally, in the outbreak of diabetes mellitus there is a difference in lifestyle and the risk factors according to gender. So we developed a predictive model in accordance with gender difference and applied the Logistic Regression Model based on Data Mining process. The result of the study were as follow. The lift of the last predictive model was an average 2.23 times(male model : 2.13, female model 2.33) more improved than in the random model in upper 10% group. The health risk factors of diabetes mellitus are gender, age, a place of residence, blood pressure, glucose, smoking, drinking, exercise rate. On the basis of these factors, we suggest the program of the health promotion.

• Clinical and Experimental Pediatrics
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• v.64 no.4
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• pp.180-187
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• 2021

Background: Severe perinatal asphyxia results in multiple organ involvement, neonate hospitalization, and eventual death. Purpose: This study aimed to investigate the predictive factors of death in newborns with hypoxic-ischemic encephalopathy (HIE) receiving selective head cooling. Methods: This cross-sectional descriptive-retrospective study was conducted from 2013 to 2018 in Fatemieh Hospital of Hamadan and included 51 newborns who were admitted to the neonatal intensive care unit with a diagnosis of HIE. Selective head cooling for patients with moderate to severe HIE began within 6 hours of birth and continued for 72 hours. The required data for the predictive factors of death were extracted from the patients' medical files, recorded on a premade form, and analyzed using SPSS ver. 16. Results: Of the 51 neonates with moderate to severe HIE who were treated with selective head cooling, 16 (31%) died. There were significant relationships between death and the need for advanced neonatal resuscitation (P=0.002), need for mechanical ventilation (P=0.016), 1-minute Apgar score (P=0.040), and severely abnormal amplitude-integrated electroencephalography (a-EEG) (P=0.047). Multiple regression of variables or data showed that the need for advanced neonatal resuscitation was an independent predictive factor of death (P=0.0075) and severely abnormal a-EEG was an independent predictive factor of asphyxia severity (P=0.0001). Conclusion: All cases of neonatal death in our study were severe HIE (stage 3). Advanced neonatal resuscitation was an independent predictor of death, while a severely abnormal a-EEG was an independent predictor of asphyxia severity in infants with HIE.

• Communications for Statistical Applications and Methods
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• v.23 no.4
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• pp.343-353
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• 2016

In doubly-censored data, an originating event time and a terminating event time are interval-censored. In certain analyses of such data, a researcher might be interested in the elapsed time between the originating and terminating events as well as regression modeling with risk factors. Therefore, in this study, we introduce a model evaluation method to measure the predictive ability of a model based on negative predictive values. We use a semiparametric estimate of the predictive accuracy to provide a simple and flexible method for model evaluation of doubly-censored survival outcomes. Additionally, we used simulation studies and tested data from a prostate cancer trial to illustrate the practical advantages of our approach. We believe that this method could be widely used to build prediction models or nomograms.

• Journal of the Korean Geophysical Society
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• v.9 no.3
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• pp.171-179
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• 2006

Predictive equations of ground motions are one of the most important factors in the seismic hazard analysis. Unfortunately, studies on predictive equations of ground motions in Korea had been hampered due to the lack of seismic data. To overcome the lack of data, seismologists adopted the stochastic method based on the seismological model. Korean predictive equations developed by the stochastic method show large differences in their predictions. It was turned out through the analysis of the existing studies that the main sources of the differences are the uncertainties in the (Brune) stress drop and spectral decay rate . Therefore, it is necessary to focus the future research on the reduction of the uncertainties in the two parameters.

• The Journal of Information Systems
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• v.30 no.2
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• pp.29-56
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• 2021

Purpose Cyber deviance of adolescents has become a serious social problem. With a widespread use of smartphones, incidents of cyber deviance have increased in Korea and both quantitative and qualitative damages such as suicide and depression are increasing. Research has been conducted to understand diverse factors that explain adolescents' delinquency in cyber space. However, most previous studies have focused on a single theory or perspective. Therefore, this study aims to comprehensively analyze motivations of juvenile cyber deviance and to develop a predictive model for delinquent adolescents by integrating four different theories on cyber deviance. Design/methodology/approach By using data from Korean Children & Youth Panel Survey 2010, this study extracts 27 potential factors for cyber deivance based on four background theories including general strain, social learning, social bonding, and routine activity theories. Then this study employs econometric analysis to empirically assess the impact of potential factors and utilizes a machine learning approach to predict the likelihood of cyber deviance by adolescents. Findings This study found that general strain factors as well as social learning factors have positive effects on cyber deviance. Routine activity-related factors such as real-life delinquent behaviors and online activities also positively influence the likelihood of cyber diviance. On the other hand, social bonding factors such as community commitment and attachment to community lessen the likelihood of cyber deviance while social factors related to school activities are found to have positive impacts on cyber deviance. This study also found a predictive model using a deep learning algorithm indicates the highest prediction performance. This study contributes to the prevention of cyber deviance of teenagers in practice by understanding motivations for adolescents' delinquency and predicting potential cyber deviants.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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• pp.351-361
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• 2021

The seriousness of fire is rising because fire causes enormous damage to property and human life. Therefore, this study aims to predict various risk factors affecting fire by fire type. The predictive analysis of fire factors was carried out targeting Gyeonggi-do, which has the highest number of fires in the country. For the analysis, using machine learning methods SVM (Support Vector Machine), RF (Random Forest), GBRT (Gradient Boosted Regression Tree) the accuracy of each model was presented with a high fit model through MAE (Mean Absolute Error) and RMSE (Root Mean Squared Error), and based on this, predictive analysis of fire factors in Gyeonggi-do was conducted. In addition, using machine learning methods such as SVM (Support Vector Machine), RF (Random Forest), and GBRT (Gradient Boosted Regression Tree), the accuracy of each model was presented with a high-fit model through MAE and RMSE. Predictive analysis of occurrence factors was achieved. Based on this, as a result of comparative analysis of three machine learning methods, the RF method showed a MAE = 1.765 and RMSE = 1.876, as well as the MAE and RMSE verification and test data were very similar with a difference between MAE = 0.046 and RMSE = 0.04 showing the best predictive results. The results of this study are expected to be used as useful data for fire safety management allowing decision makers to identify the sequence of dangers related to the factors affecting the occurrence of fire.