The most used self-assessment method for assessing driving style is the Multidimensional Driving Style Inventory (MDSI). This study aims to adapt the MDSI to Korean drivers (MDSI-K) and confirm the eight-factor driving style structure of the original version. Six hundred forty drivers aged 20-70 agreed to participate in this study. All participants had at least one year of driving experience. Confirmatory factor analysis was performed to determine whether the original MDSI fit the Korean driving style structure, and the goodness-of-fit values were not acceptable. Through correlation analysis, 11 survey questions were removed, and MDSI-K, which has the same structure as the original MDSI, was derived. Subsequent exploratory factor analysis yielded an 8-factor structure with several cross-loadings between factors. For more rigorous validation, Exploratory Structural Equation Modeling was employed, resulting in a refined model comprising 33 items across 8 factors, which incorporated these cross-loadings in the MDSI-K construction. We propose that these cross-loadings represent cultural differences in driving styles. In conclusion, the results of this study show that there is a common concept of driving style that has been previously investigated in various countries. This can be seen as a similarity in the driving culture commonly used across countries. On the other hand, the results of the different structures or survey questions for the same questionnaire by country and culture show differences between cultures.

The most used self-assessment method for assessing driving style is the Multidimensional Driving Style Inventory (MDSI). This study aims to adapt the MDSI to Korean drivers (MDSI-K) and confirm the eight-factor driving style structure of the original version. Six hundred forty drivers aged 20-70 agreed to participate in this study. All participants had at least one year of driving experience. Confirmatory factor analysis was performed to determine whether the original MDSI fit the Korean driving style structure, and the goodness-of-fit values were not acceptable. Through correlation analysis, 11 survey questions were removed, and MDSI-K, which has the same structure as the original MDSI, was derived. Subsequent exploratory factor analysis yielded an 8-factor structure with several cross-loadings between factors. For more rigorous validation, Exploratory Structural Equation Modeling was employed, resulting in a refined model comprising 33 items across 8 factors, which incorporated these cross-loadings in the MDSI-K construction. We propose that these cross-loadings represent cultural differences in driving styles. In conclusion, the results of this study show that there is a common concept of driving style that has been previously investigated in various countries. This can be seen as a similarity in the driving culture commonly used across countries. On the other hand, the results of the different structures or survey questions for the same questionnaire by country and culture show differences between cultures.

The paper commemorates five decades of ergonomics in South Korea, examining its historical development, key accomplishments, and future directions. By analyzing 1,504 abstracts from the Journal of the Ergonomics Society of Korea (JESK) published between 1982 and 2024, the study applies BERTopic modeling to identify significant research trends. Key focus areas include physical and cognitive ergonomics, work-related musculoskeletal disorders, safety management, and human-computer interaction (HCI). The paper also reflects on major milestones, such as the 2024 international Ergonomics Association Congress Organized by ESK in Jeju, underscoring the growing global prominence of South Korea in ergonomics and human factors. The study concludes by discussing the evolving role of ergonomics in addressing emerging technological challenges, including artificial intelligence and human-centered design, providing a comprehensive reflection on the field's past and its future prospects.

Over the last several years, in parallel with the general global advancement in mobile technology and a rise in social media network content consumption, multimedia content production and reproduction has increased exponentially. Therefore, enabled by the rapid recent advancements in deep learning technology, research on scene graph generation is being actively conducted to more efficiently search for and classify images desired by users within a large amount of content. This approach lets users accurately find images they are searching for by expressing meaningful information on image content as nodes and edges of a graph. In this study, we propose a scene graph generation method based on using the Resource Description Framework (RDF) model to clarify semantic relations. Furthermore, we also use convolutional neural network (CNN) and recurrent neural network (RNN) deep learning models to generate a scene graph expressed in a controlled vocabulary of the RDF model to understand the relations between image object tags. Finally, we experimentally demonstrate through testing that our proposed technique can express semantic content more effectively than existing approaches.