• Journal of Korea Water Resources Association
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• v.38 no.8 s.157
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• pp.595-604
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• 2005

Precipitation simulation for making the data size larger is an important task for hydrologic analysis. The simulation can be divided into two major categories which are the parametric and nonparametric methods. Also, precipitation simulation depends on time intervals such as daily or hourly rainfall simulations. So far, Markov model is the most favored method for daily precipitation simulation. However, most models are consist of state transition probability by using the homogeneous Markov chain model. In order to make a state vector, the small size of data brings difficulties, and also the assumption of homogeneousness among the state vector in a month causes problems. In other words, the process of daily precipitation mechanism is nonstationary. In order to overcome these problems, this paper focused on the nonparametric method by using uni-variate and multi-variate when simulating a precipitation instead of currently used parametric method.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.7 no.4
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• pp.115-120
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• 2008

The fatalities of pedestrian accounted for about 40.0% of all fatalities in Korea (2005 year). In pedestrian involved accident, the most important data to inspect accident is throw distance of pedestrian. The throw distance of pedestrian can be influenced by many variables, such as vehicular frontal shape, vehicular impact speed, the offset of impact point, the height of pedestrian, and road condition. The trajectory of pedestrian after collision can be influenced by vehicular frontal shape classified into sedan type, box type, SUV type and van type. Many studies have been done about pedestrian accident with passenger car model and bus model for simple factors. But the study of pedestrian accident by van type vehicle was much insufficient, and even that the influence of multiple factors such as the offset of impact point was neglected. In this paper, a series of pedestrian kinetic simulation were conducted to inspect relationship between throw distance and multiple factors with using PC-CRASH s/w, a kinetic analysis program for a traffic accident for van type. By based on the simulation results, multi-variate regression was conducted, and regression equation was presented.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.8
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• pp.4881-4894
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• 2014

This study selected 83 manufacturing firms that had been delisted from the KOSDAQ market from 2009 to 2012 and the sample firms for the two-paired sampling method were compared with 83 normal firms running businesses with same items or in same industry. The 75 financial ratios for five years immediately before delisting were used for Mean Difference Analysis with those of normal firms. Fifteen variables assumed to be significant variables for five consecutive years out of the analysis were used to in the Dichotomous Classification Technique, Logistic Regression Analysis and Discriminant Analysis. As a result of those three analyses, the Logistic Regression Analysis model was found to show the greatest discrimination. This study is differentiated from previous studies as it assumed that the firm's failure proceeded slowly over long period of time and it tried to predict the firm's failure earlier using the five years' historical data immediately before failure, whereas previous studies predicted it using three years' data only. This study is also differentiated from the proceeding comparative studies by its statistically complex Multi-Variate Analysis and Dichotomous Classification Analysis, which general stakeholders can easily approach.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2003.09a
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• pp.13-19
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• 2003

Discriminant method using seismic signal was studied for discrimination of surface explosion. By means of the seismic spectral characteristics, multi-variate discriminant analysis was performed. Four single discriminant techniques - Pg/Lg, Lg1/Lg2, Pg1/Pg2, and Rg/Lg - based on seismic source theory were applied to explosion and earthquake training data sets. The Pg/Lg discriminant technique was most effective among the four techniques. Nevertheless, it could not perfectly discriminate the samples of the training data sets. In this study, a compound linear discriminant analysis was defined by using common characteristics of the training data sets for the single discriminants. The compound linear discriminant analysis was used for the single discriminant as an independent variable. From this analysis, all the samples of the training data sets were correctly discriminated, and the probability of misclassification was lowered to 0.7％.

• Journal of Environmental Science International
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• v.15 no.9
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• pp.887-895
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• 2006

Through this research of the analysis on the frequency flood discharges regarding basin property factors, a linear regression system was introduced, and as a result, the item with the highest correlation with the frequency flood discharges from Nakdong river basin is the basin area, and the second highest is the average width of basin and the river length. The following results were obtained after looking at the multi correlation between the flood discharge and the collected basin property factors using the data from the established river maintenance master plan of the one hundred twenty-five rivers in the Nakdong river basin. The result of analysis on multivariate correlation between the flood discharges and the most basic data in determining the flood discharges as basin area, river length, basin slope, river slope, average width of basin, shape factor and probability precipitation showed more than 0.9 of correlation in terms of the multi correlation coefficient and more than 0.85 for the determination coefficient. The model which induced a regression system through multi correlation analysis using basin property factors is concluded to be a good reference in estimating the design flood discharge of unmeasured basin.

• The Korean Journal of Community Living Science
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• v.27 no.1
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• pp.95-108
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• 2016

The objectives of this study are to analyze the characteristics of multi-cultural families in rural areas and to classify their types by applying multi-variate techniques and cluster analyses. Data for the study were obtained by a surveying 120 married migrant females in rural areas of Chungchengnam-do, South Korea. By utilizing the factor analysis to analyze the characteristics of multi-cultural families, 16 basic variables related to these female subjects were categorized into 6 factors: 'marriage length and age', 'language skill of migrant female', 'language skill of husband', 'family satisfaction', 'income and education', and 'general living satisfaction in Korea with remittance'. By appling the cluster analysis, multi-cultural families in rural areas were divided into the following 5 types: 'stable settlement', 'average but stagnant', 'below average yet positive', 'high- income with little communication', and 'young low-income'. In all types, it is strongly recommended to develop various programs regarding vocational education for the migrant females in order to increase their economic opportunities as well as social status.

• Journal of the Korean Society of School Health
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• v.22 no.2
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• pp.85-101
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• 2009

Purpose: We interviewed 500 students attending to universities in Gwangju and Jeonnam using a questionnaire. The questionnaire was composed of some questions on general characteristics of the subjects, academic characteristics, health-related behaviors, family function, and drinking problems. Methods: The data collected were analysed with uses of t-test, dispersion analysis, correlations analysis and multi-variate regression analysis. Results: As a result of the simple analysis we found that variables related to drinking problems of college students were religion, family, residence, parents' job, living standard, major, academic year, exercise, parents' drinking, parents' attitude to drinking, drinking quantity, intimacy, conflicts, and upbringing tendency. As a result of the multi-variate regression analysis, we found that the higher intimacy between family members, deterioration in behaviors, family and personal relations, and social functions was statistically significantly low. Conclusion: This study demonstrated that drinking problems of college students had significant relations with intimacy, conflicts and upbringing tendency and suggests that an approach in an aspect of family functions is important to overcome drinking problems of college students.

• The Journal of Korean Society for School & Community Health Education
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• v.15 no.1
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• pp.31-43
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• 2014

Objectives: The purpose of this study is to identify the relationship of personal hygiene education and hand-washing practices among adolescents. Then the impact of such factors on the hand-washing practices was analyzed. Methods: The data of the 2012 Youth Health Risk Behavior web-based Survey collected by Korean Center for Disease Control was analyzed using SPSS. Total 74,186 of middle and hish school students were included. Uni-variate analysis was done by complex sample crosstabs and multi-variate analysis was done by complex sample logistic regression. Results: The 26.8% of students experienced personal hygiene education. The students of boys, low school grade, coeducation, metropolitan, high school record and high economic status experienced more hygiene education. The hand-washing practices were high in the students with the experience of personal hygiene education. In the factors affecting the hand-washing practice, the experience of personal hygiene education was consistently significant. If students experienced the personal hygiene education, they showed 20~30% more rates of hand-washing practices. Conclusions: Hand-washing practice was high when experiencing personal hygiene education. The personal hygiene education was necessary to improve the rate of hand-washing practices.

• Health Policy and Management
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• v.27 no.4
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• pp.315-323
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• 2017

Background: This study was purposed to analyze the effect of spatial accessibility to the psychiatry department in general hospital on the outpatient visit of mental patients. Methods: Data was provided from the Statistics Korea and Statistical Geographic Information Service, National Health Insurance Service, Health Insurance Review and Assessment Service, and Korea Transport Institute in 2015. The study regions were 103 administrative regions such as Si and Gu. The 103 regions had at least one general hospitals with a psychiatry department. The number of outpatient visit of mental patients in regions was used as the dependent variable. Spatial accessibility to mental general hospital was used as the independent variable. Control variables included such as demographic, economic, and health medical factors. This study used network analysis and multi-variate regression analysis. Network analysis by ArcGIS ver. 10.0 (ESRI, Redlands, CA, USA) was used to evaluate the average travel time and travel distance in Korea. Multi-variate regression analysis was conducted by SAS ver. 9.4 (SAS Institute Inc., Cary, NC, USA). Results: Travel distance and time had significant effects on the number of outpatient visits in mental patients in general hospital. Average travel time and travel distance had negative effects on the number of visits. Variables such as (number of total population, percentage of aged population over 65, and number of mental general hospital) had significant effects on the number of visit in mental patients. Conclusion: Health policy makers will need to consider the spatial accessibility to the mental healthcare organization in conducting regional health planning.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.2
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• pp.81-86
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• 2022

Given that time series are used in various fields, such as finance, IoT, and manufacturing, data analytical methods for accurate time-series forecasting can serve to increase operational efficiency. Among time-series analysis methods, multi-horizon forecasting provides a better understanding of data because it can extract meaningful statistics and other characteristics of the entire time-series. Furthermore, time-series data with exogenous information can be accurately predicted by using multi-horizon forecasting methods. However, traditional deep learning-based models for time-series do not account for the heterogeneity of inputs. We proposed an improved time-series predicting method, called the temporal fusion transformer method, which combines multi-horizon forecasting with interpretable insights into temporal dynamics. Various real-world data such as stock prices, fine dust concentrates and electricity consumption were considered in experiments. Experimental results showed that our temporal fusion transformer method has better time-series forecasting performance than existing models.