• Journal of Korean Society of Transportation
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• v.8 no.1
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• pp.73-88
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• 1990

This study aims to develop a traffic accident forecasting model using the data, which are based on the past accidents in Korea. The regression analysis was used in conjuction with the variables of the traffic accidents and social behaviours. The objectives of this study are as follows; 1. The number of behicles has given a strong affect to increase the traffic accidents in Korea since a factor of vehicles has shown 86% over of total accidents. 2. The forecasting model regarding the traffic accidents, deaths and injuries, which was formulated for this study, proved to be useful in light of the results of the regression diagnostics. 3. It is expected that the traffic accidents in Korea in 1991 may take place as follows on condition that the traffic environment would worsen ; 274,000 cases of accidents with 13,600 deaths and 367,000 injuries, in 1994, 451,000 cases with 24,900 deaths and 71,500 injuries respectively.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.22 no.3
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• pp.8-14
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• 2014

Arrival management is a tool which provides efficient flow of traffic and reduces ATC workload by determining aircraft's sequence and schedules while they are in cruise phase. As a decision support tool, arrival management should advise on air traffic control service based on the understanding of human factor of its user, air traffic controller. This paper proposed a prediction model for air traffic controller sequencing strategy by analyzing the historical trajectory data. Statistical analysis is used to find how air traffic controller decides the sequence of aircraft based on the speed difference and the airspace entering time difference of aircraft. Logistic regression was applied for the proposed model and its performance was demonstrated through the comparison of the real operational data.

• Environmental Engineering Research
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• v.21 no.1
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• pp.74-83
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• 2016

The purpose of this study is to identify the driven factors affecting the changes in energy-related $CO_2$ emissions in Hebei Province of China from 1995 to 2013. This study confirmed that energy-related $CO_2$ emissions are correlated with the population, urbanization level, economic development degree, industry structure, foreign trade degree, technology level and energy proportion through an improved STIRPAT model. A reasonable and more reliable outcome of STIRPAT model can be obtained with the introducing of the Ridge Regression, which shows that population is the most important factor for $CO_2$ emissions in Hebei with the coefficient 2.4528. Rely on these discussions about affect abilities of each driven factors, we conclude several proposals to arrive targets for reductions in Hebei's energy-related $CO_2$ emissions. The method improved and relative policy advance improved pointing at empirical results also can be applied by other province to make study about driven factors of the growth of carbon emissions.

• International Journal of Naval Architecture and Ocean Engineering
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• v.7 no.4
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• pp.708-719
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• 2015

The two most important tasks of icebreakers are first to secure a sailing route by breaking the thick sea ice and second to sail efficiently herself for purposes of exploration and transportation in the polar seas. The resistance of icebreakers is a priority factor at the preliminary design stage; not only must their sailing efficiency be satisfied, but the design of the propulsion system will be directly affected. Therefore, the performance of icebreakers must be accurately calculated and evaluated through the use of model tests in an ice tank before construction starts. In this paper, a new procedure is developed, based on model tests, to estimate a ship's ice breaking resistance during continuous ice-breaking in ice. Some of the factors associated with crushing failures are systematically considered in order to correctly estimate her ice-breaking resistance. This study is intended to contribute to the improvement of the techniques for ice resistance prediction with ice breaking ships.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.10
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• pp.42-47
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• 2019

Helical machining is an efficient method for machining holes using an endmill. In this study, a surface roughness prediction model was constructed for improving the productivity of hole machining. Experiments were conducted to form holes by the helical machining of AL6061-T4 aluminum sheets and correlation analysis was performed to examine the relationships between the variables based on the measured results. Meanwhile, a regression analysis technique was used to construct and evaluate the prediction model. Through these analyses, the parameter which has the greatest influence on the surface roughness when the hole is formed by the helical machining is the feed, followed by the number of revolutions of the endmill. Moreover, for the axial feed of the endmill, it was concluded that the influence of the surface roughness is small compared to the other two parameters but it is a factor worth considering to improve the accuracy when constructing the predictive model.

• Journal of the Korean Data and Information Science Society
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• v.23 no.2
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• pp.235-245
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• 2012

Logistic discrimination is an useful statistical technique for quantitative analysis of financial service industry. Especially it is not only easy to be implemented, but also has good classification rate. Generalized additive model is useful for credit scoring since it has the same advantages of logistic discrimination as well as accounting ability for the nonlinear effects of the explanatory variables. It may, however, need too many additive terms in the model when the number of explanatory variables is very large and there may exist dependencies among the variables. Mixtures of factor analyzers can be used for dimension reduction of high-dimensional feature. This study proposes to use the low-dimensional factor scores of mixtures of factor analyzers as the new features in the generalized additive model. Its application is demonstrated in the classification of some real credit scoring data. The comparison of correct classification rates of competing techniques shows the superiority of the generalized additive model using factor scores.

• Journal of the Korean Institute of Landscape Architecture
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• v.30 no.2
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• pp.39-49
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• 2002

The purpose of this study is to propose an objective and rational methodology for the selection of proposed sites far the National Trust(NT), which is the new alterative proposal far the conservation of natural environments destroyed by injudicious land development and economic growth. That is to enforce many analysis for the effective estimation of rare ecological and landscape resources and to propose a model based on estimation and united indicators. Using the estimative model, we apply it to the selection of the proposed site in micro scale and simultaneously offer the basic methodology of effective and systematic land conservation in macro scale. The results of this study are as follows: 1) The results of analysis for the reliability of estimative items and indicators, presented no problem in that the coefficient of reliability was over 0.7. 2) The correlation measure of the estimative indicator indicated that 'succession'and 'regenerating restorability' were highly correlative in the item of plants. Another three items showed a tendency to be alike. 3) The results of factor analysis on the characteristics of indicators, classified plants into four categories including a stable factor. The item of animals was classified as a stable and rare factor. The item of landscape was classified as a physical and mental factor and the environment as a pollutional and conditional factor. 4) The model of estimation created through factor analysis was valid for the approval of the regression model because significant probability was 0.00. When we consider the NT proposed site as a complex body that is composed of diverse natural and manmade resources, certainly the synthetic methodology of estimation is needed. If these studies are carried out, NT sites will be selected more rationally and effectively than at present. Consequently, they have the potential to play a core role of natural ecosystem conservation in Korea.

• The Korean Journal of Applied Statistics
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• v.31 no.4
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• pp.463-473
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• 2018

Variable selection algorithms for linear regression models of large data are considered. Many algorithms are proposed focusing on the speed and the robustness of algorithms. Among them variance inflation factor (VIF) regression is fast and accurate due to the use of a streamwise regression approach. But a VIF regression is susceptible to outliers because it estimates a model by a least-square method. A robust criterion using a weighted estimator has been proposed for the robustness of algorithm; in addition, a robust VIF regression has also been proposed for the same purpose. In this article a fast and robust variable selection method is suggested via a VIF regression with detecting and removing potential outliers. A simulation study and an analysis of a dataset are conducted to compare the suggested method with other methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.5
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• pp.1414-1430
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• 2022

Due to the Internet of Things popularity, many agricultural data are collected by sensors automatically. The abundance of agricultural data makes precise prediction of rice yield possible. Because the climate factors have an essential effect on the rice yield, we considered the climate factors in the prediction model. Accordingly, this paper proposes a machine learning model for rice yield prediction in Taiwan, including the genetic algorithm and support vector regression model. The dataset of this study includes the meteorological data from the Central Weather Bureau and rice yield of Taiwan from 2003 to 2019. The experimental results show the performance of the proposed model is nearly 30% better than MARS, RF, ANN, and SVR models. The most important climate factors affecting the rice yield are the total sunshine hours, the number of rainfall days, and the temperature.The proposed model also offers three advantages: (a) the proposed model can be used in different geographical regions with high prediction accuracies; (b) the proposed model has a high explanatory ability because it could select the important climate factors which affect rice yield; (c) the proposed model is more suitable for predicting rice yield because it provides higher reliability and stability for predicting. The proposed model can assist the government in making sustainable agricultural policies.

• Culinary science and hospitality research
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• v.13 no.2
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• pp.22-34
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• 2007

We researched the previous study about the restaurant's physical environment and had made up questionnaires. The purpose of this study is to analyze the effect of physical facilities of family restaurants on customers' satisfaction. The result was as follows: First, customers visited with friends or family irrespective of days $2{\sim}3$ times a month. Second, the physical environment factors of family restaurants were interior design, interior, making atmosphere and exterior. Third, it was the interior factor(0.268), making atmosphere factor(0.353) and exterior factor(0.244) that affected customers' satisfaction in family restaurants(p<0.001). $R^2$ change was 0.659 and the regression model was suited to our study(F=56.475). To increase customers' satisfaction, the physical environment of family restaurants needs remodeling in proper time.