• Journal of Korean Port Research
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• v.14 no.1
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• pp.66-75
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• 2000

From the traffic analysis, and model constructions and verifications for speed prediction on the freeway ramp junctions with 70mph speed limit, the following results were obtained : ⅰ) The traffic flow distribution showed a big difference depending on the time periods. Especially, more traffic flows were concentrated on the freeway junctions in the morning peak period when compared with the afternoon peak period. ⅱ) The occupancy distribution was also shown to be varied by a big difference depending on the time periods. Especially, the occupancy in the morning peak period showed over 100% increase when compared with the 24hours average occupancy, and the occupancy in the afternoon peak period over 25% increase when compared with the same occupancy. ⅲ) The speed distribution was not shown to have a big difference depending on the time periods. Especially, the speed in the morning peak period showed 10mph decrease when compared with the 24hours'average speed, but the speed did not show a big difference in the afternoon peak period. ⅳ) The analyses of variance showed a high explanatory power between the speed predictive models(SPM) constructed and the variables used, especially the upstream speed. ⅴ) The analysis of correlation for verifying the speed predictive models(SPM) constructed on the ramp junctions were shown to have a high correlation between observed data and predicted data. Especially, the correlation coefficients showed over 0.95 excluding the unstable condition on the diverge section. ⅵ) Speed predictive models constructed were shown to have the better results than the HCM models, even if the speed limits on the freeway were different between the HCM models and speed predictive models constructed.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 1999.10a
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• pp.111-121
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• 1999

From the traffic analyses, and model constructions and verifications for speed prediction on the freeway ramp junctions with 70mph speed limit, the following results obtained: ⅰ) The traffic flow distribution showed a big difference depending on the time periods. Especially, more traffic flows were concentrated on the freeway junctions in the morning peak period when compared with the afternoon peak period. ⅱ) The occupancy distribution was also shown to be varied by a big difference depending on the time periods. Especially, the occupancy in the morning peak period showed over 100% increase when compared with the 24hours average occupancy, and the occupancy in the afternoon peak period over 25% increase when compared with the same occupancy.ⅲ) The speed distribution was not shown to have a big difference depending on the time periods. Especially, the speed in the morning peak period shown 10mph decrease when compared with the 24hours' average speed, but the speed did not show a big difference in the afternoon peak period.ⅳ) The analyses of variance showed a high explanatory power between the speed predictive models(SPM) constructed and the variables used, especially the upstream speed. ⅴ) The analysis of correlation for verifying the speed predictive models(SPM) constructed on the ramp junctions were shown to have a high correlation between observed data and predicted data. Especially, the correlation coefficients showed over 0.95 excluding the unstable condition on the diverge sectionⅵ) Speed predictive models constructed were shown to have the better results than the HCM models, even if the speed limits on the freeway were different between the HCM models and speed predictive models constructed.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 1999.10a
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• pp.131-140
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• 1999

Today freeway is experiencing a severe congestion with incoming or outgoing traffic through freeway ramps during the peak periods. Thus, the objectives of this study is to identify the traffic characteristics, analyze the relationships between the traffic characteristics and finally construct the delay predictive models on the ramp junctions of freeway with 70mph speed limit. From the traffic analyses, and model constructions and verifications for delay prediction on the ramp junctions of freeway, the following results were obtained: ⅰ) Traffic flow showed a big difference depending on the time periods. Especially, more traffic flows were concentrated on the freeway junctions in the morning peak period when compared with the afternoon peak period. ⅱ) The occupancy also showed a big difference depending on the time periods, and the downstream occupancy(Od) was especially shown to have a higher explanatory power for the delay predictive model construction on the ramp junction of freeway. ⅲ) The speed-occupancy curve showed a remarkable shift based on the occupancies observed ; Od < 9% and Od$\geq$9%. Especially, volume and occupancy were shown to be highly explanatory for delay prediction on the ramp junctions of freeway under Od$\geq$9%, but lowly for delay predicion on the ramp junctions of freeway under Od<9%. Rather, the driver characteristics or transportation conditions around the freeway were through to be a little higher explanatory for the delay perdiction under Od<9%. ⅳ) Integrated delay predictive models showed a higher explanatory power in the morning peak period, but a lower explanatory power in the non-peak periods.

• Journal of Digital Convergence
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• v.13 no.3
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• pp.75-84
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• 2015

As the service industry has been getting increased attention as the core for the national economic growth, in order to promote growth and development of the industry, in-depth research on the workers in the industry has been timely called for. Therefore current study aims to explore the effect of leadership style on organizational citizenship behavior(ie OCB) for increased productivity of workers in the service industry, and the role of multi-dimensional justice in the relationship between them. The findings suggest that transformational and transactional leadership both have positive effect on OCB, implying that application of leadership style that is appropriate to the context may be more important, Also, mediation test of multi-dimensional justice resulted partial mediation effect except for the procedural justice in the relationship between transformational leadership and OCB.

• Journal of the Korea Convergence Society
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• v.9 no.2
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• pp.331-340
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• 2018

The purpose of this study is to verify resilience's mediation effects in the effects of adolescents' experience of abuse damage on aggressiveness in the convergence era. Towards this end, this study used the 7th year data (2016) (4th grade to the first year of high school) of Korea Children & Youth Panel Survey (KCYPS). The findings of this study are as follows: First, the correlations between experience of abuse damage, aggressiveness, and resilience variables were confirmed using multicollinearity VIF values. Second, adolescent's abuse experiences significantly affected their aggressiveness. Third, the partial mediation effects of resilience were shown in the effects of adolescents' experience of abuse damage on their aggressiveness. For the verification of mediation effects' significance, the Sobel test was carried out. The findings of this study are meaningful, as this study provides empirical data. Based on the findings, this study proposed the need of resilience consolidation programs for adolescents in the practice of social welfare.

• Journal of the Korean Applied Science and Technology
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• v.37 no.2
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• pp.192-205
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• 2020

The purpose of this study is to verify the structural relationship empirically among self-esteem, self-efficacy and life satisfaction of college students who answered that they had high academic stress. SPSS 23.0 and AMOS 21.0 were used to achieve the objectives of this study. In SPSS 23.0, frequency analysis to analyze demographic characteristics, correlation analysis to verify multi-collinearity among the questionnaire scales, and reliability verification to determine the reliability of questionnaire questions were conducted. In AMOS 21.0, confirmatory factor analysis was conducted to verify the construct validity of factors and to verify the causal relationship between variables. To determine the goodness of fit of the model, the 𝑥² test and the goodness-of-fit index were used. Judging. First, the self-efficacy of college students with high academic stress group through swimming class was positively influenced on self-esteem. Second, the self-efficacy of college students with high academic stress group through swimming class was positively influenced on life satisfaction. Third, the self-esteem of college students with high academic stress group through swimming class was found to affect life satisfaction. This study has significance in demonstrating the problem of academic stress of Korean university students and in proposing expansion of the physical education class as a solution.

• Journal of Korea Technology Innovation Society
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• v.19 no.4
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• pp.687-710
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• 2016

This study aims to empirically estimate distributed lag effects of expenditure by type of R&D on scientific publication in the national R&D program. To analyze the lag structure between them, we used a dataset comprised of panel data from 104 technologies categorized by 6T (IT, BT, NT, ST, ET, CT) from 2007 to 2014, and employed multiple regression analysis based on the polynomial distributed lag model. This is because it is highly likely to emerge multicollinearity, if a distributed lag model without special restrictions is applied to multiple regression analysis. The main results are as follows. In the case of basic research, its lag effects are relatively evenly distributed during four years. On the other hand, the applied research and experimental development have distributed lag effects for three years and two years respectively. Therefore, when it comes to analyzing performance of scientific publication, it is necessary to be performed with characteristics of the time lag by type of R&D.

• Journal of Korean Tunnelling and Underground Space Association
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• v.26 no.1
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• pp.39-58
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• 2024

Tunnel Boring Machines (TBM) use multiple disc cutters to excavate tunnels through rock. These cutters wear out due to continuous contact and friction with the rock, leading to decreased cutting efficiency and reduced excavation performance. The rock's abrasivity significantly affects cutter wear, with highly abrasive rocks causing more wear and reducing the cutter's lifespan. The Cerchar Abrasivity Index (CAI) is a key indicator for assessing rock abrasivity, essential for predicting disc cutter life and performance. This study aims to develop a new method for effectively estimating CAI using rock strength, petrological characteristics, linear regression, and machine learning. A database including CAI, uniaxial compressive strength, Brazilian tensile strength, and equivalent quartz content was created, with additional derived variables. Variables for multiple linear regression were selected considering statistical significance and multicollinearity, while machine learning model inputs were chosen based on variable importance. Among the machine learning prediction models, the Gradient Boosting model showed the highest predictive performance. Finally, the predictive performance of the multiple linear regression analysis and the Gradient Boosting model derived in this study were compared with the CAI prediction models of previous studies to validate the results of this research.

• Journal of the Korean Data and Information Science Society
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• v.26 no.2
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• pp.289-299
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• 2015

The objective of the present study is to develop statistical quantitative forecast model for PM10 concentration over Seoul. We used three types of data (weather observation data in Korea, the China's weather observation data collected by GTS, and air quality numerical model forecasts). To apply the daily forecast system, hourly data are converted to daily data and then lagging was performed. The potential predictors were selected based on correlation analysis and multicollinearity check. Model validation has been performed for checking model stability. We applied two models (multiple regression model and threshold regression model) separately. The two models were compared based on the scatter plot of forecasts and observations, time series plots, RMSE, skill scores. As a result, a threshold regression model performs better than multiple regression model in high PM10 concentration cases.

• The Journal of Bigdata
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• v.5 no.2
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• pp.135-143
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• 2020

This paper proposes a pre-processing method and a dimensional reduction method in the analysis of shopping carts where there are many correlations between variables when dividing the types of consumers in the agri-food consumer panel data. Cluster analysis is a widely used method for dividing observational objects into several clusters in multivariate data. However, cluster analysis through dimensional reduction may be more effective when several variables are related. In this paper, the food consumption data surveyed of 1,987 households was clustered using the K-means method, and 17 variables were re-selected to divide it into the clusters. Principal component analysis and factor analysis were compared as the solution for multicollinearity problems and as the way to reduce dimensions for clustering. In this study, both principal component analysis and factor analysis reduced the dataset into two dimensions. Although the principal component analysis divided the dataset into three clusters, it did not seem that the difference among the characteristics of the cluster appeared well. However, the characteristics of the clusters in the consumption pattern were well distinguished under the factor analysis method.