• The Korean Journal of Applied Statistics
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• v.37 no.3
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• pp.323-338
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• 2024

This study analyzes the Korean professional volleyball league and predict match outcomes using popular machine learning classification methods. Match data from the 2012/2013 to 2022/2023 seasons for both male and female leagues were collected, including match details. Two different data structures were applied to the models: Separating matches results into two teams and performance differentials between the home and away teams. These two data structures were applied to construct a total of four predictive models, encompassing both male and female leagues. As specific variable values used in the models are unavailable before the end of matches, the results of the most recent 3 to 4 matches, up until just before today's match, were preprocessed and utilized as variables. Logistc Regrssion, Decision Tree, Bagging, Random Forest, Xgboost, Adaboost, and Light GBM, were employed for classification, and the model employing Random Forest showed the highest predictive performance. The results indicated that while significant variables varied by gender and data structure, set success rate, blocking points scored, and the number of faults were consistently crucial. Notably, our win-loss prediction model's distinctiveness lies in its ability to provide pre-match forecasts rather than post-event predictions.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.243-264
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• 2018

Although the worldwide battery market is recently spurring the development of lithium secondary battery, lead acid batteries (rechargeable batteries) which have good-performance and can be reused are consumed in a wide range of industry fields. However, lead-acid batteries have a serious problem in that deterioration of a battery makes progress quickly in the presence of that degradation of only one cell among several cells which is packed in a battery begins. To overcome this problem, previous researches have attempted to identify the mechanism of deterioration of a battery in many ways. However, most of previous researches have used data obtained in a laboratory to analyze the mechanism of deterioration of a battery but not used data obtained in a real world. The usage of real data can increase the feasibility and the applicability of the findings of a research. Therefore, this study aims to develop a model which predicts the battery deterioration using data obtained in real world. To this end, we collected data which presents change of battery state by attaching sensors enabling to monitor the battery condition in real time to dozens of golf carts operated in the real golf field. As a result, total 16,883 samples were obtained. And then, we developed a model which predicts a precursor phenomenon representing deterioration of a battery by analyzing the data collected from the sensors using machine learning techniques. As initial independent variables, we used 1) inbound time of a cart, 2) outbound time of a cart, 3) duration(from outbound time to charge time), 4) charge amount, 5) used amount, 6) charge efficiency, 7) lowest temperature of battery cell 1 to 6, 8) lowest voltage of battery cell 1 to 6, 9) highest voltage of battery cell 1 to 6, 10) voltage of battery cell 1 to 6 at the beginning of operation, 11) voltage of battery cell 1 to 6 at the end of charge, 12) used amount of battery cell 1 to 6 during operation, 13) used amount of battery during operation(Max-Min), 14) duration of battery use, and 15) highest current during operation. Since the values of the independent variables, lowest temperature of battery cell 1 to 6, lowest voltage of battery cell 1 to 6, highest voltage of battery cell 1 to 6, voltage of battery cell 1 to 6 at the beginning of operation, voltage of battery cell 1 to 6 at the end of charge, and used amount of battery cell 1 to 6 during operation are similar to that of each battery cell, we conducted principal component analysis using verimax orthogonal rotation in order to mitigate the multiple collinearity problem. According to the results, we made new variables by averaging the values of independent variables clustered together, and used them as final independent variables instead of origin variables, thereby reducing the dimension. We used decision tree, logistic regression, Bayesian network as algorithms for building prediction models. And also, we built prediction models using the bagging of each of them, the boosting of each of them, and RandomForest. Experimental results show that the prediction model using the bagging of decision tree yields the best accuracy of 89.3923%. This study has some limitations in that the additional variables which affect the deterioration of battery such as weather (temperature, humidity) and driving habits, did not considered, therefore, we would like to consider the them in the future research. However, the battery deterioration prediction model proposed in the present study is expected to enable effective and efficient management of battery used in the real filed by dramatically and to reduce the cost caused by not detecting battery deterioration accordingly.

• 한국산학경영학회:학술대회논문집
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• 2004.11a
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• pp.55-72
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• 2004

Before 2003 the companies In Korea should capitalize the interest expenses that are attributable to the acquisition, construction or production of a qualifying assets. However, according to the revised standard which should be applied from 2003, the companies can either capitalize the interest expenses or recognize as an expense when they are incurred. Therefore almost all the companies confronted with the decision making of accounting choices on the interest capitalization. This paper empirically examines which financial characteristics of the companies affect the accounting choice by using logistic regression model and reviews the sufficiency of the foot notes disclosures regarding the capitalized interest. The variables of the financial characteristics are change of debt-equity ratio, borrowing ratio, qualifying assets ratio, firm sire and income smoothing. The results of this study are summarized as follows. First, among the financial characteristics, only qualifying asset ratio has the significant difference between capitalized companies and expensing companies. Second, the results of logistic regression indicate that qualifying asset ratio and firm size have the significant influence on the accounting choices. Therefore, I cannot find the evidence supporting that the companies use the accounting choice to manage the financial ratios.

• Korean Journal of Social Welfare
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• v.58 no.4
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• pp.141-168
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• 2006

The present study explored and compared the risk factors of two subtypes of maritally violent men with those of a nonviolent comparison group. One type of batterers consisted of a community sample, and the other was sought from the criminal justice system. The identities of the male community batterers were not exposed to the society since their victims did not contact any of the social service agents related to domestic violence. To identify the different characteristics associated with two subtypes of woman abusers, a total of 152 nonviolent men, 82 male community batterers, and 336 offenders in a criminal justice system were considered. The results of the descriptive analysis showed that the level of physical violence of the community batterers was two times lower than that of the batterers who received legal punishments. The results of the multinominal logistic regression were as follows: (1) The variables that distinguished the male community batterers from the nonviolent men were the use of physical violence towards children, marital decision power, and income. (2) Four factors had been found to distinguish batterers in the criminal justice system from nonbatterers, namely: attitudes towards woman battering, education, violence towards children, and level of jealousy. (3) The community batterers showed a higher level of education and of stress as well as a longer period of marital relationship compared to the batterers in the criminal justice system. On the other hand, the batterers who received legal punishments had more severe alcohol problems and had an accepting attitude towards the use of violence. This study also investigated psychopathology among batterers using MCMI-III, based on 333 subjects. In terms of the mean scores, there were no subscales associated with personality pathology in all the male groups. Based on the logit model, the community batterers showed a stronger tendency towards having a passive-aggressive personality than did their counterparts, and they recorded a higher level of narcissism compared to the court-referred battering men. Post-traumatic stress was the only symptom that distinguished the batterers who received legal punishments from the other groups. The theoretical and practical implications of these results were pointed out and discussed in the paper.

• Journal of Korean Society of Forest Science
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• v.106 no.4
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• pp.397-407
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• 2017

This research aims to analyze and compare the drought resistance of 7 species of landscape trees commonly grown in Korea. The 7 species are: Camellia japonica, Rhaphiolepis indica, Quercus glauca, Machilus thunbergii, Daphniphyllum macropodum, Dendropanax morbifera and Cinnamomum camphora. In order to analyze their drought resistance, the samples were left without irrigation for 30 days (05/09/2016 ~ 05/10/2016), during which period their respective drought resistor, relative water content, electrolyte elution figures and proline content were measured. As the non-irrigation proceeded, C. camphora was the first to wither, followed by D. morbifera, then D. macropodum, then M. thunbergii, then Q. glauca, then R. indica then finally C. japonica. Of the 7 species, Q. glauca, C. japonica and R. indica can be considered highly drought resistant, since they survived for longer than 3 weeks without irrigation. Relative water content (RWC) plummeted dramatically after the first 15 days of non-irrigation. Whereas RWC readings of C. camphora, D. morbifera, D. macropodum and M. tunbergii dropped by 40% or more, the other 4 species reported a relatively low rate of decrease at 20% or lower. The Camellia japonica, the R. indica and Q. glauca, which were the species with relatively high drought resistance, showed low proline content and electrolyte elution figures, whereas those of C. camphora, D. macropodum, D. morbifera and M. tunbergii were higher. Analysis through the nonlinear regression analysis logistic model showed that non-irrigation proved fatal for the 7 sample species in a range of 22.7 to 37.6 days. The C. japonica, R. indica, Q. glauca and M. tunbergii demonstrated a high drought resistance of 30 days or longer, whereas C. camphora, D. morbifera and D. macropodum had a low resistance of 25 days or less to drought from lack of water. In conclusion, out of the 7 species of broad-leaved evergreen trees tested, C. japonica, R. indica and Q. glauca seem to be suitable for use as landscape trees, owing to their high drought resistance.

• Korean journal of applied entomology
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• v.48 no.2
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• pp.237-243
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• 2009

This study was conducted to develop economic injury level (ElL) and economic threshold (ET) of Cabbage armyworm, Mamestra brassicae L. on cabbage (Brassica oleracea L. var). The changes of cabbage biomass and M. brassicae density were investigated after introduction of larval M. brassicae (2nd instar) at different densities: 0, 1, 2, 4, 8, and 16 larvae per plant at 40 d after planting for an open field experiment, and 0, 2, 5, 8 and 12 larvae per plant at 25 d after planting for a glass house experiment. In the field experiment, the yield loss of cabbage was not significantly different among treated-plots at 30 d after the larval introduction, showing an over-compensatory response of cabbage plants to M. brassicae attack. In the glasshouse experiment, however, the biomass of cabbage at 15 d after the larval introduction significantly decreased with increasing the initial introduced number of M. brassicae, resulting in 38.3, 36.7, 21.7, 23.3 and 16.7g in above treated-plots, respectively. The relationship between cumulative insect days (CID) and yield loss (%) of cabbage was well described by a nonlinear logistic equation. Using the estimated equation, ElL of M. brassicae on cabbage was estimated at 44 CID per plant based on the yield loss 14%, which take into account of an empirical gain threshold 5% and marketable rate 91% of cabbage. Also, ET was calculated at 80% of the EIL: 35 CID per plant. Until a more elaborate EIL-model is developed, the present result may be useful for M. brassicae management at early growth stage of cabbage.

• Investigative Magnetic Resonance Imaging
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• v.18 no.2
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• pp.120-132
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• 2014

Purpose : To compare dynamic susceptibility contrast imaging, diffusion-weighted imaging, and susceptibility-weighted imaging (SWI) for the differentiation of tumor recurrence and delayed radiation therapy (RT)-related changes in patients treated with RT for primary brain tumors. Materials and Methods: We enrolled 24 patients treated with RT for various primary brain tumors, who showed newly appearing enhancing lesions more than one year after completion of RT on follow-up MRI. The enhancing-lesions were confirmed as recurrences (n=14) or RT-changes (n=10). We calculated the mean values of normalized cerebral blood volume (nCBV), apparent diffusion coefficient (ADC), and proportion of dark signal intensity on SWI (proSWI) for the enhancing-lesions. All the values between the two groups were compared using t-test. A multivariable logistic regression model was used to determine the best predictor of differential diagnosis. The cutoff value of the best predictor obtained from receiver-operating characteristic curve analysis was applied to calculate the sensitivity, specificity, and accuracy for the diagnosis. Results: The mean nCBV value was significantly higher in the recurrence group than in the RT-change group (P=.004), and the mean proSWI was significantly lower in the recurrence group (P<.001). However, no significant difference was observed in the mean ADC values between the two groups. A multivariable logistic regression analysis showed that proSWI was the only independent variable for the differentiation; the sensitivity, specificity, and accuracy were 78.6% (11 of 14), 100% (10 of 10), and 87.5% (21 of 24), respectively. Conclusion: The proSWI was the most promising parameter for the differentiation of newly developed enhancing-lesions more than one year after RT completion in brain tumor patients.

• Journal of agricultural medicine and community health
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• v.28 no.2
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• pp.87-105
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• 2003

Objectives: The purpose of this study was to examine the health care utilization pattern and its related factors of low-income population with abnormal results through health examination. Methods: Analysed data were collected through a questionnaire survey, which was given to 263 persons who 30 years or over with abnormal results through health examination at Health Center. This survey was conducted in March, 2003. This study employed Andersen's prediction model as most well known medical demand mode and data were analysed through 2-test, and multiple logistic regression analysis. Results: The proportion of medical utilization for thorough examination or treatment among study subjects was 51.0%. In multiple logistic regression analysis as dependent variable with medical utilization, the variables affecting the medical utilization were 'feeling about abnormal result(anxiety versus no anxiety: odds ratio 2.25, 95% confidence intervals 1.07-4.75)', 'type of health security(medicaid type I versus health insurance: odds ratio 2.82, 95% confidence intervals 1.04-7.66; medicaid type II versus health insurance: odds ratio 3.22, 95% confidence intervals 1.37-7.53)', 'experience of health examination during past 2 years(odds ratio 2.39, 95% confidence intervals 1.09-5.21)' and 'family member's response for abnormal result(recommendation for medical utilization versus no response: odds ratio 4.90, 95% confidence intervals 1.75-13.75; family member recommended to utilize medical facilities with him/her versus no response: odds ratio 19.47, 95% confidence intervals 5.01-75.73)'. The time of medical utilization was 8-15 days after they received the result(29.9%), 16-30 days after they receive the result(27.6%), 2-7 days after they received the result(20.9%) in order. The most important reason why they didn't take a medical utilization was that it seemed insignificant to them(32.4%). Conclusions: In order to promote medical utilization of low-income population, health education for abnormal result and its management would be necessary to family member as well as person with abnormal result. And follow-up management program for person with abnormal result through health examination such as home-visit health care would be necessary.

• Journal of Korean Society for Geospatial Information Science
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• v.21 no.2
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• pp.53-62
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• 2013

Land-use/cover changes not only amplify or alleviate influence of climate changes but also they are representative factors to affect environmental change along with climate changes. Thus, the use of land-use/cover changes scenario, consistent climate change scenario is very important to evaluate reliable influences by climate change. The purpose for this study is to predict and analyze the future urban growth considering social and economic scenario from RCP scenario suggested by the 5th evaluation report of IPCC. This study sets land-use/cover changes scenario based on storyline from RCP 4.5 and 8.5 scenario. Urban growth rate for each scenario is calculated by urban area per person and GDP for the last 25 years and regression formula based on double logarithmic model. In addition, the urban demand is predicted by the future population and GDP suggested by the government. This predicted demand is spatially distributed by the urban growth probability map made by logistic regression. As a result, the accuracy of urban growth probability map is appeared to be 89.3~90.3% high and the prediction accuracy for RCP 4.5 showed higher value than that of RCP 8.5. Urban areas from 2020 to 2050 showed consistent growth while the rate of increasing urban areas for RCP 8.5 scenario showed higher value than that of RCP 4.5 scenario. Increase of urban areas is predicted by the fact that famlands are damaged. Especially RCP 8.5 scenario indicated more increase not only farmland but also forest than RCP 4.5 scenario. In addition, the decrease of farmland and forest showed higher level from metropolitan cities than province cities. The results of this study is believed to be used for basic data to clarify complex two-way effects quantitatively for future climate change, land-use/cover changes.

• Korean Journal of Psychosomatic Medicine
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• v.27 no.2
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• pp.101-110
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• 2019

Objectives : It is not clear which clinical variables are most closely associated with delirium in the Intensive Care Unit (ICU). By comparing clinical data of ICU delirium and non-delirium patients, we sought to identify variables that most effectively differentiate delirium from non-delirium. Methods : Medical records of 6,386 ICU patients were reviewed. Random Subset Feature Selection and Principal Component Analysis were utilized to select a set of clinical variables with the highest discriminatory capacity. Statistical analyses were employed to determine the separation capacity of two models-one using just the selected few clinical variables and the other using all clinical variables associated with delirium. Results : There was a significant difference between delirium and non-delirium individuals across 32 clinical variables. Richmond Agitation Sedation Scale (RASS), urinary catheterization, vascular catheterization, Hamilton Anxiety Rating Scale (HAM-A), Blood urea nitrogen, and Acute Physiology and Chronic Health Examination II most effectively differentiated delirium from non-delirium. Multivariable logistic regression analysis showed that, with the exception of vascular catheterization, these clinical variables were independent risk factors associated with delirium. Separation capacity of the logistic regression model using just 6 clinical variables was measured with Receiver Operating Characteristic curve, with Area Under the Curve (AUC) of 0.818. Same analyses were performed using all 32 clinical variables;the AUC was 0.881, denoting a very high separation capacity. Conclusions : The six aforementioned variables most effectively separate delirium from non-delirium. This highlights the importance of close monitoring of patients who received invasive medical procedures and were rated with very low RASS and HAM-A scores.