• KIPS Transactions on Computer and Communication Systems
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• v.6 no.11
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• pp.445-452
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• 2017

Electric pole is a supporting beam used for power transmission/distribution which accelerometer are used for measuring a external force. The meteorological condition has various effects on the external forces of electric pole. One of them is the elasticity change of the aerial wire. It is very important to perform modelling. The acceleration sensor is converted into a pitch and a roll angle. The meteorological condition has a high correlation between variables, and selecting significant explanatory variables for modeling may result in the problem of over-fitting. We constructed high deviance explained model considering multicollinearity using the Generalized Additive Model which is one of the machine learning methods. As a result of the Variation Inflation Factor Test, we selected and fitted the significant variable as temperature, precipitation, wind speed, wind direction, air pressure, dewpoint, hours of daylight and cloud cover. It was noted that the Hours of daylight, cloud cover and air pressure has high explained value in explonatory variable. The average coefficient of determination (R-Squared) of the Generalized Additive Model was 0.69. The constructed model can help to predict the influence on the external forces of electric pole, and contribute to the purpose of securing safety on utility pole.

• The Korean Journal of Financial Management
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• v.20 no.2
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• pp.41-72
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• 2003

The purposes of this paper are to build theoretical and empirically testable model to identify determining factors of ownership structure, and to analyze this model empirically using th Korea Stock Exchange panel data, and to test the impact of opening the stock market on the determinants of ownership structure. The determining factors of ownership structure identified in this paper include debt ratio, dividend, asset characteristics, profitability, growth business risk, size, institutional investors and chaebol-non chaebol dummy variable. Empirical panel estimation test reveals that this model can explain about $9\sim11%$ of the cross sectional variance in the equity ratio of large shareholders. The reasons that this model has too explanatory power are that some variables were measured with errors, and that there were some omitted variables in tested model. The regression results on the model variables ar generally in line with predictions. But the coefficient estimates on size is never significant. And it appears that the exogenous variable which explains opening the stock market has positive effect on the determinants of ownership structure.

• The Korean Journal of Applied Statistics
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• v.21 no.1
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• pp.141-149
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• 2008

Recently we frequently analyze multivariate data with quite large number of variables. In such data sets, virtually duplicated variables may exist simultaneously even though they are conceptually distinguishable. Duplicate variables may cause problems such as the distortion of principal axes in principal component analysis and factor analysis and the distortion of the distances between observations, i.e. the input for cluster analysis. Also in supervised learning or regression analysis, duplicated explanatory variables often cause the instability of fitted models. Since real data analyses are aimed often at multiple purposes, it is necessary to reduce the number of variables to a parsimonious level. The aim of this paper is to propose a practical algorithm for selection of a subset of variables from a given set of p input variables, by the criterion of minimum trace of partial variances of unselected variables unexplained by selected variables. The usefulness of proposed method is demonstrated in visualizing the relationship between selected and unselected variables, in building a predictive model with very large number of independent variables, and in reducing the number of variables and purging/merging categories in categorical data.

• International Journal of Highway Engineering
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• v.16 no.5
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• pp.143-154
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• 2014

PURPOSES : This study drew factors affecting motorcycle accidents in Seoul by severity using an ordered probit model and aimed to analyze and verify the drawn influence factors. METHODS : As the severity of the accidents could be classified into three types (fatal injury, serious injury and minor injury), this study drew the factors affecting accidents by a comparative analysis employing an ordered probit model, removed the variables that would not secure significance sequentially to construct a model with high explanatory power regarding the factors affecting the severity of motorcycle accidents, and calculated the marginal effect of each factor to understand the degree of each factor's impact on the severity. First, Model 1 put in all variables; Model 2 was constructed by removing the variables of the road surface conditions that could not meet the level of significance (p=0.608); Model 3 was constructed by removing gender variable (p=0.423); and Model 4 was constructed finally by removing age variable (p=0.320). RESULTS : As a result of an analysis, statistically significant variables were time of occurrence, type of accident, road alignment and motorcycle displacement, and it turned out that the impacts on the severity were in the following order: a road alignment of left downhill, the type of motorcycle-to-vehicle accidents and a road alignment of a flatland on the left. The significance of the models was tested using the likelihood ratio, the level of significance and suitability statistics about them, and as a result of the test, the significance level and suitability of the constructed models were all excellent. In addition, the model accuracy indicating the accuracy of a predicted value compared to that of the value actually observed was 70.3% for minor injury; 70.1% for serious injury; and 68.6% for fatal injury, and the overall accuracy was 70.2%, which was very high. CONCLUSIONS : As a result of an analysis of motorcycle accidents in Seoul through the ordered probit model and the marginal effect, it turned out that their severity increased in nighttime accidents as compared to daytime ones and gradually increased in the order of motorcycle-to-vehicle accidents, motorcycle-to-person ones and the ones involving motorcycle only. As a result of an analysis, the severity of accidents in road alignments of left downhill, left flatland and straight downhill increased as compared to those in a road alignment of straight flatland and that the severity of accidents of motorcycles with a displacement larger than 50cc was higher than that of those with a displacement smaller than 50cc.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.12
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• pp.81-90
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• 2016

The objective of this study was to investigate the effect of an education method applying the flipped learning technique for college students. Both self-directed learning readiness and educational performance before and after applying the flipped learning were examined. After applying the flipped learning technique, teacher-student interaction, learning satisfaction, and learning motivation were identified. The correlation of each variable was examined after applying the flipped learning technique to investigate its influence on learning motivation. A total of 68 second-year nursing students enrolled in E University were analyzed. A difference between before and after applying the flipped learning was analyzed by the paired t-test; a correlation between the variables was analyzed via Pearson's correlation coefficient; and an influence on the dependent variable learning motivation was analyzed using the stepwise multiple regression analysis. The results showed that self-directed learning readiness increased before and after applying the flipped learning technique with statistical significance, and the difference of educational performance was not significant. After an education session applying the flipped learning technique, a learning motivation demonstrated a significantly positive correlation with self-directed learning readiness (r=0.33, p=.006), college student educational performance (r=0.51, p<.001), teacher-student interaction (r=0.72, p<.001), and learning satisfaction (r=0.79, p<.001). A significantly positive correlation was also observed between the other variables. Factors influencing learning motivation were learning satisfaction and teacher-student interaction. The explanatory power for learning motivation in the regression model considering these two variables was 71.3% (F=80.66, p<.001). Therefore, to enhance learning motivation in applying the flipped learning technique, it is necessary to increase learning satisfaction and to establish a strategy that further vitalizes the teacher-student interaction.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.4
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• pp.446-453
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• 2018

The study examines corporate research & development (R&D) expenditure in modern finance. Firms may face one of the essential issues to maintain their optimal levels of R&D expenditures in order to increase corporate profit. Accordingly, financial determinants that may influence R&D spending are statistically tested for firms listed on the KOSPI stock market during the period from 2010 to 2015. Financial determinants which may discriminate between firms in high-growth and low-growth industries are examined on a relative basis. Explanatory variables including one-period lagged R&D expenses (Lag_RD), cross-product term between the Lag_RD and type of industry (as a dummy variable), and advertising expenses (ADVERTISE) significantly influenced corporate R&D intensity. Moreover, high-growth firms in domestic capital markets showed higher Lag_RD, profitability (PROF) and foreign equity ownership (FOS) than their counterparts in low-growth sectors, whereas low-growth firms had higher market-value based leverage (MLEVER) and ADVERTISE. Overall, these results are expected to influence decision-making of firms concerning the optimal level of R&D expenditure, which may in turn enhance shareholder wealth.

• The Korean Journal of Applied Statistics
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• v.19 no.1
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• pp.1-12
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• 2006

The aim of this study is to analyse a survey data on the number of charitable donations using a mixture of two Poisson regression models. The survey was conducted in 2002 by Volunteer 21, an nonprofit organization, based on Koreans, who were older than 20. The mixture of two Poisson distributions is used to model the number of donations based on the empirical distribution of the data. The mixture of two Poisson distributions implies the whole population is subdivided into two groups, one with lesser number of donations and the other with larger number of donations. We fit the mixture of Poisson regression models on the number of donations to identify significant covariates. The expectation-maximization algorithm is employed to estimate the parameters. We computed 95% bootstrap confidence interval based on bias-corrected and accelerated method and used then for selecting significant explanatory variables. As a result, the income variable with four categories and the volunteering variable (1: experience of volunteering, 0: otherwise) turned out to be significant with the positive regression coefficients both in the lesser and the larger donation groups. However, the regression coefficients in the lesser donation group were larger than those in larger donation group.

• Journal of the Korean GEO-environmental Society
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• v.10 no.7
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• pp.7-14
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• 2009

The correlation equation empirically proposed to obtain compression indexes has been proposed to conveniently obtain the value using the soil parameter that can be obtained through simple tests when the number of time of consolidation testing is low or the distribution is large but most of the analyzed regions are limited to certain regions abroad or in the country and multiple data were integrated for use in many cases, thus it is not very reasonable to apply it. Therefore, to establish a new design method considering the uncertainty of the ground, it was selected the Kwangyang port area of which the data have been collected recently thus are relatively more reliable as the subject region of the study in order to maximally reduce the uncertainty of test data. After performing the verification of the normality of the consolidation test data obtained from the selected region and the transformation of variables, a prediction formula was proposed through the regression model with the transformed variables and the proposed regression model with transformed variables was compared with existing empirical equations to verify the suitability of the proposed model formula. After analyzing, it was confirmed that the coefficient of determination was increased after the Box-Cox variable transformation, thus the explanatory power was being enhanced and through the root-mean-square-error method, it was confirmed that the proposed model formula showed the most closed value to the test value.

• Journal of Distribution Science
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• v.14 no.10
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• pp.93-107
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• 2016

Purpose - This study aims to derive directions and implications for improving performance in operating agrifood export organizations by identifying significant performance impact factors. Research design, data, and methodology - A seemingly unrelated regression (SUR) model was estimated using data from a survey conducted among 120 exporters including 16 leading export organizations. In the SUR estimation, the export volume and price are used as dependent variables and securing the quantity of products ordered and exported, quality management, and marketing activities are considered as explanatory variables for the operation performance. Results - The amount of farmer education, the manpower in charge of marketing, and the interaction terms between whether or not they belong to a leading export organization and the item dummy for mushrooms have a significant impact on the export volume where the export volume is specified as a dependent variable. The export volume is greater with a greater amount of farmer education and greater manpower in charge of marketing from the perspective of quality management. When the export price is estimated as a dependent variable, the manpower in charge of marketing is shown to have a significant impact on the export price. Conclusions - The government needs to strengthen its support of the performance of agrifood export organizations. The analysis indicates that the education of and consulting with farmers, and the manpower number in charge of marketing are key factors in the operation performance of export organizations. Therefore, supporting the export organizations in expanding their human resources in charge of marketing can increase the export volumes for agrifoods. Given, however, that the export volume associated with joint payments, human resources specialized in quality management, and the amount of participation in export exhibitions are not significant factors, it is essential to improve the supporting policies for those areas. The manpower in charge of marketing from the perspective of marketing has a significant impact on both the export volume and export price. Thus, we identify this as the most important category that should be supported to enhance performance in export organizations.

• Journal of Bio-Environment Control
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• v.30 no.4
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• pp.393-400
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• 2021

Body weight of livestock is a crucial indicator for assessing feed requirements and nutritional status. This study was performed to estimate the body weight of Korean cattle (Hanwoo) using body volume determined from three-dimensional (3-D) image. A TOF camera with a resolution of 640×480 pixels, a frame rate of 44 fps and a field of view of 47°(H)×37°(V) was used to capture the 3-D images for Hanwoo. A grid image of the body was obtained through preprocessing such as separating the body from background and removing outliers from the obtained 3-D image. The body volume was determined by numerical integration using depth information to individual grid. The coefficient of determination for a linear regression model of body weight and body volume for calibration dataset was 0.8725. On the other hand, the coefficient of determination was 0.9083 in a multiple regression model for estimating body weight, in which the age of Hanwoo was added to the body volume as an explanatory variable. Mean absolute percentage error and root mean square error in the multiple regression model to estimate the body weight for validation dataset were 8.2% and 24.5kg, respectively. The performance of the regression model for weight estimation was improved and the effort required for estimating body weight could be reduced as the body volume of Hanwoo was used. From these results obtained, it was concluded that the body volume determined from 3-D of Hanwoo could be used as an effective variable for estimating body weight.