• Journal of the Korean Society of Safety
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• v.32 no.1
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• pp.128-133
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• 2017

This study deals with the roundabout traffic accidents by traffic impeding factor. The purpose of this study is to comparatively analyze the characteristics of accidents and to develop the accident models. In pursuing the above, this study used a statistical program SPSS 20.0 to analyze 2,342 accidents occurred within 79 roundabouts in Korea. The main results are as follows. First, 4 accident models which were all statistically significant were developed. Second, the traffic volume and width of right-turn-only lane were analyzed to be common variable in the bus stop related models. The variables such as right-turn-only lane, street light, turning radius of entry lane were selected as specific variables. Especially street light and turning radius of entry lane were evaluated to have negative effects to the accidents. It is, therefore, essential to install the street light and place a sufficient turning radius in order to reduce the roundabout accidents. Finally, the traffic volume and number of entry lane were analyzed to be common variable in the on-street parking related models. Also, the width of right-turn-only lane and bus stop were evaluate to be specific variables in the model with on-street parking. This can be expected to give some implications to making the accident reduction guidelines.

• Geomechanics and Engineering
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• v.6 no.1
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• pp.1-15
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• 2014

In this study, artificial neural network (ANN) and multiple regression (MR) models were developed to predict the critical factor of safety ($F_s$) of the homogeneous finite slopes subjected to earthquake forces. To achieve this, the values of $F_s$ in 5184 nos. of homogeneous finite slopes having different slope, soil and earthquake parameters were calculated by using the Simplified Bishop method and the minimum (critical) $F_s$ for each of the case was determined and used in the development of the ANN and MR models. The results obtained from both the models were compared with those obtained from the calculations. It is found that the ANN model exhibits more reliable predictions than the MR model. Moreover, several performance indices such as the determination coefficient, variance account for, mean absolute error, root mean square error, and the scaled percent error were computed. Also, the receiver operating curves were drawn, and the areas under the curves (AUC) were calculated to assess the prediction capacity of the ANN and MR models developed. The performance level attained in the ANN model shows that the ANN model developed can be used for predicting the critical $F_s$ of the homogeneous finite slopes subjected to earthquake forces.

• Structural Engineering and Mechanics
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• v.72 no.1
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• pp.31-41
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• 2019

Structural integrity assessment of piping components is of paramount important for remaining life prediction, residual strength evaluation and for in-service inspection planning. For accurate prediction of these, a reliable fracture parameter is essential. One of the fracture parameters is stress intensity factor (SIF), which is generally preferred for high strength materials, can be evaluated by using linear elastic fracture mechanics principles. To employ available analytical and numerical procedures for fracture analysis of piping components, it takes considerable amount of time and effort. In view of this, an alternative approach to analytical and finite element analysis, a model based on relevance vector machine (RVM) is developed to predict SIF of part through crack of a piping component under fatigue loading. RVM is based on probabilistic approach and regression and it is established based on Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. Model for SIF prediction is developed by using MATLAB software wherein 70% of the data has been used for the development of RVM model and rest of the data is used for validation. The predicted SIF is found to be in good agreement with the corresponding analytical solution, and can be used for damage tolerant analysis of structural components.

• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.75-83
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• 2016

We propose methodology to analyze the dynamic mechanisms of financial market contagion under market integration using a biological contagion analytical approach. We employ U-statistic to measure market integration, and a dynamic model based on an error correction mechanism (single equation error correction model) and latent factor model to examine market contagion. We also use quantile regression and Wald-Wolfowitz runs test to test market contagion. This methodology is designed to effectively handle heteroscedasticity and correlated errors. Our simulation results show that the single equation error correction model fits well with the linear regression model with a stationary predictor and correlated errors.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.27 no.4
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• pp.105-109
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• 2004

The purpose of this paper is to examine the empirical analysis for charging factors and the evaluation models of internet site. Thus this is to analyze the revenue creation factors affecting the customer satisfaction of tourism information sites in the Internet. Based on the regression model applied to a customer survey, this study shows that among the characteristics of the site, technical factor and community factor have the most significant effect on charging factor and customer satisfaction.

• Journal of the Korea Safety Management & Science
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• v.6 no.4
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• pp.123-137
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• 2004

It is an important and hot issue how to improve the competitiveness concerned on product, company and industry. It is necessary to develop the strategy of competitiveness for an efficient operation as well as improving the competitiveness in view of product, system, industry, price, quality and so on. This paper aims at proposing a model to choose dominating factors of competitiveness including a method o( resources allocation which can be applied to all products. And we show its empirical application on tile-industry.

• The Korean Journal of Applied Statistics
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• v.21 no.4
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• pp.639-647
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• 2008

This study used the characteristics of the knowledge discovery and data mining algorithms to develop tailored hypertension follow up management model - hypertension care predictive model and hypertension care compliance segmentation model - for hypertension management using the Korea National Health Insurance Corporation database(the insureds’ screening and health care benefit data). This study validated the predictive power of data mining algorithms by comparing the performance of logistic regression, decision tree, and ensemble technique. On the basis of internal and external validation, it was found that the model performance of logistic regression method was the best among the above three techniques on hypertension care predictive model and hypertension care compliance segmentation model was developed by Decision tree analysis. This study produced several factors affecting the outbreak of hypertension using screening. It is considered to be a contributing factor towards the nation’s building of a Hypertension follow up Management System in the near future by bringing forth representative results on the rise and care of hypertension.

• Journal of the Korean Society of Hazard Mitigation
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• v.9 no.1
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• pp.57-64
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• 2009

This study intends to evaluate of the characteristics of pavement deformation and develop the model for prediction model in the asphalt layer using a regression analysis. In test, there are two different asphalt binders and 5 different aggregate types. The air voids of hot mix asphalt are 6% and 10% for target value. Repeated triaxial compression test with 3 different confining pressures was used for test at 3 different test temperatures. It is going to verify the main parameters for permanent deformation of HMA and to develop the distress model. This paper is to figure out the factor affecting the pavement deformation, and then to develop model the pavement deformation for asphalt mixture. Also, the reliability of prediction model has been studied. The permanent deformation prediction model for asphalt mixtures with temperature, loading time, and air voids has been developed and the proposed permanent deformation prediction model has been validated by using the multiple regression approach which is called Statistical Package for the Social Sciences(SPSS).

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.22 no.6
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• pp.44-49
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• 2008

Contract power conversion factor which is applied to estimate contract power of industrial customers is an important standard to caculate transformer capacity. This paper shows a reasonable contract power conversion factor, that was made by the systematic and statistical way considering actual conditions, such as investigated contact power and peak power for the last 5 years of each customer for 349 industrial customers as to AMR. In this dissertation, it is necessary to analyze the key features and general trend from the investigated data. It made an analysis of the feature parameters, such as average, standard deviation, median, maximum, minimun and thus it was carried by the linear and nonlinear regression analysis. Therefore, this paper compared characteristics for a contract power conversion factor which is a lied to calculate contract power with characteristics for a regression model for customers which maximum utilization factor of transformer is more than 60(%).

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.22 no.5
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• pp.80-85
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• 2008

Contract power conversion factor which is applied to estimate contract power of general customers is an important standard to caculate transformer capacity. This paper shows a reasonable contract power conversion factor, that was made by the systematic and statistical way considering actual conditions, such as investigated contract power and peak power for the last 5 years of each customer for 461 general customers as to AMR. In this dissertation, it is necessary to analyze the key features and general trend from the investigated data It made an analysis of the feature parameters, such as average, standard deviation, median, maximum, minimun and thus it was carried by the linear and nonlinear regression analysis. Therefore, this paper compared characteristics for a contract power conversion factor which is applied to estimate contract power with characteristics for a regression model for customers which maximum utilization factor of transformer is more than 60[%].