• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.7
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• pp.3039-3056
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• 2020

Beaches have many risk factors that cause various accidents, such as drifting and drowning, these accidents have many risk factors. To analyze them, in this paper, we identify beach risk factors, and define the criteria and correlation for each risk factor. Then, we generate new risk factors based on Fuzzy theory, and define Situation Awareness for each time. Finally, we propose a beach risk assessment and prediction model based on linear regression using the calculated risk result and pre-defined risk factors. We use national public data of the Korea Meteorological Administration (KMA), and the Korea Hydrographic and Oceanographic Agency (KHOA). The results of the experiment showed the prediction accuracy of beach risk to be 0.90%, and the prediction accuracy of drifting and drowning accidents to be 0.89% and 0.86%, respectively. Also, through factor correlation analysis and risk factor assessment, the influence of each of the factors on beach risk can be confirmed. In conclusion, we confirmed that our proposed model can assess and predict beach risks.

• Journal of Environmental Science International
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• v.33 no.3
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• pp.193-203
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• 2024

This study aims to determine the factors affecting the level of satisfaction with the Cell Broadcast Service (CBS) among citizens in Incheon. Partial least squares (PLS) regression, instead of multiple regression, was used for the analysis because it can solve multicollinearity and sample size issues. The analysis results are as follows: The factor with the greatest effect on satisfaction with CBS among Incheon citizens, was the elimination of redundancies (VIP=1.185). Therefore, local governments, government agencies, and public organizations must coordinate their ideas and collectively create guidelines to eliminate redundancies. The second most influential factor was the expansion in the broadcast medium from legal, institutional, and policy aspects (VIP=1.087). This is because differences in generation, age, gender, and personal characteristics were not considered. Therefore, it is necessary to devise a customized messaging tool through the expansion of broadcast media. The broadcast criteria of the legal, institutional, and policy perspectives comprised the third most influential factor, with a high VIP value of 1.053. Consequently, it is essential to devise a plan to avoid distributing unnecessary cell broadcast services, by establishing criteria for areas and sections, time, and the direct and indirect impact zones of a disaster. In the future, this study could be used as base data to develop policies, guidelines, and response measures for Incheon CBS. Given the lack of research on the diverse characteristics of each social class and the city traits of each region, and a lack of concrete empirical research on each factor, continuous and in-depth studies are required in the future.

• Journal of Industrial Convergence
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• v.1 no.2
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• pp.107-124
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• 2003

With rapid growth and competition of electronic commerce through internet, various buying types and business models are being appeared. In this paper, we studied group buying which is new business model to consumer and factors affecting on trust and participation of group buying. The followings are the regression result of this study. First, familiarity factor, customer service factor, seal of security and product value factor made a significant effect on trust. Second, familiarity factor, perceived reputation factor, customer service factor, seal of security and product value factor made a significant effect on participation of group buying. Third, trust of group buying made a significant effect on participation of group buying. In this study, modeling and empirical test were implemented about structure of trust and participation of group buying. We can know where our group buying strategies should focus and which factor we should improve.

• Journal of the Korean Data and Information Science Society
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• v.19 no.3
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• pp.761-774
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• 2008

Agility is vital to real-time enterprises in comtemporary dynamic business environment. This study aims to investigate the relationships between traditional shipping and port logistics firms' customer agility obtained by adopting Internet business, and their corporate image and customer satisfaction. Using questionnaire data, factor analyses were used to figure out five major agility factors, corporate image factor, and customer satisfaction factor. The agility factors were then used to investigate how they improve the firms' corporate image and customer satisfaction. The results of the regression analyses show that agility factors significantly influence the firms' corporate image and customer satisfaction factors.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.7 no.4
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• pp.65-75
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• 2012

The purpose of this study was to examine the relationship between the food-related lifestyle of coffee consumer and their coffee satisfaction level in an attempt to lay the foundation for successful coffee marketing strategy setting. Self-reported questionnaires were completed by 300 adults who have visited coffee shop recently in the Seoul metropolitan area. The SPSS 18.0 program was used to analyze the samples. Data was analyzed by frequency, descriptive factor, reliability, ANOVA, and regression. A factor analysis extracted five factors comprising food related lifestyle, which we named health-seeking (factor 1), eating-out-seeking (factor 2), taste-seeking (factor 3), economy-seeking (factor 4) and convenience-seeking (factor 5). The results of the regression analysis suggested that health-seeking, eating-out-seeking, taste-seeking lifestyle had a statistically significantly positive influences on the degree of the satisfaction. health-seeking, eating-out-seeking, taste-seeking, convenience-seeking in food-related lifestyle had statistically significantly positive influences on purchase intention. These results provide an understanding for lifestyles of coffee consumers and give an insight into differentiated marketing plans for coffee industry.

• Geomechanics and Engineering
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• v.28 no.4
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• pp.397-404
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• 2022

Accurate prediction of the undrained shaft resistance is essential for robust design of bored piles in undrained condition. The undrained shaft resistance is calculated using the undrained adhesion factor multiplied by the undrained cohesion of the soil. However, the available correlations to predict the undrained adhesion factor have been developed using simple regression techniques and the accuracy of these correlations has not been thoroughly assessed in previous studies. The lack of the assessment of these correlations made it difficult for geotechnical engineers to select the most accurate correlation in routine designs. Furthermore, limited attempts have been made in previous studies to use advanced data mining techniques to develop simple and accurate correlation to predict the undrained adhesion factor. This research, therefore, has been conducted to fill these gaps in knowledge by developing novel and robust correlation to predict the undrained adhesion factor. The development of the new correlation has been conducted using the multi-objective evolutionary polynomial regression analysis. The new correlation outperformed the available empirical correlations, where the new correlation scored lower mean absolute error, mean square error, root mean square error and standard deviation of measured to predicted adhesion factor, and higher mean, a20-index and coefficient of correlation. The correlation also successfully showed the influence of the undrained cohesion and the effective stress on the adhesion factor. Hence, the new correlation enhances the design accuracy and can be used by practitioner geotechnical engineers to ensure optimized designs of bored piles in undrained conditions.

• Corrosion Science and Technology
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• v.19 no.6
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• pp.302-309
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• 2020

This study aimed to predict the pitting corrosion characteristics of AL-6XN super-austenitic steel using multiple linear regression. The variables used in the model are degree of sensitization, temperature, and pH. Experiments were designed and cyclic polarization curve tests were conducted accordingly. The data obtained from the cyclic polarization curve tests were used as training data for the multiple linear regression model. The significance of each factor in the response (critical pitting potential, repassivation potential) was analyzed. The multiple linear regression model was validated using experimental conditions that were not included in the training data. As a result, the degree of sensitization showed a greater effect than the other variables. Multiple linear regression showed poor performance for prediction of repassivation potential. On the other hand, the model showed a considerable degree of predictive performance for critical pitting potential. The coefficient of determination (R2) was 0.7745. The possibility for pitting potential prediction was confirmed using multiple linear regression.

• 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.

• The Journal of Engineering Geology
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• v.17 no.4
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• pp.555-566
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• 2007

In general, RMR classification system is used for the support design of a tunnel. Face mapping during excavation and RMR-based rock classifications are conducted in order to provide information for complementary changes to preliminary survey plans and for continuous geological estimations in direction of tunnel route. Although they are ever so important, there are not enough time for survey in general and sometimes even face mapping is not available. Linear regression analysis for the estimation of mediating RQD and condition of discontinuities, which require longer time and more detailed observation in RMR, was performed and optimum regression equations are suggest as the result. The geological data collected from tunnels were analyzed in accordance with three rock types as sedimentary rock, phyllite and granite to see geological effects, generally not been considered in previous researches. Parameters for the regression analysis were set another RMR factor.

• Journal of Environmental Science International
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• v.24 no.6
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• pp.743-753
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• 2015

In this study, it is an object to develop a regression model for the estimation of TOC (total organic carbon) concentration using investigated data for three years from 2010 to 2012 in the Gam Stream unit watershed, and applied in 2009 to verify the applicability of the regression model. TOC and $COD_{Mn}$ (chemical oxygen demand) were appeared to be derived the highest correlation. TOC was significantly correlated with 5 variables including BOD (biological oxygen demand), discharge, SS (suspended solids), Chl-a (chlorophyll a) and TP (total phosphorus) of p<0.01. As a result of PCA (principal component analysis) and FA (factor analysis), COD, TOC, SS, discharge, BOD and TP have been classified as a first factor. TOCe concentration was estimated using the model developed as an independent variable $BOD_5$ and $COD_{Mn}$. R squared value between TOC and measurement TOC is 0.745 and 0.822, respectively. The independent variable were added step by step while removing lower importance variable. Based on the developed optimal model, R squared value between measurement value and estimation value for TOC was 0.852. It was found that multiple independent variables might be a better the estimation of TOC concentration using the regression model equation(in a given sites).