• Journal of the Korean association of regional geographers
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• v.11 no.6
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• pp.571-584
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• 2005

The population concentration in the Capital region of Korea has become an important issue for the pursuit of the balanced regional human capital development. Considering migration both as a geographic and a social movement, migration to the capital region could be examined in the push factors and the selective migrant characteristics from the out-migration region. Their relative importance reveals that age and education level are important in almost all years, but the importance of the percentage of manufacturing sector and rural/urban region moves to the years of education, the percentage of unskilled occupation and manufacturing sector and unemployment ratio recently. Since the brain drain has been occurring under the highly unbalanced regional development in Korea, the results suggest that regional human capital investment should be accompanied with enlarging quality employment opportunities to reap the benefits.

• Journal of Digital Convergence
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• v.14 no.5
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• pp.273-281
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• 2016

The issue about the safety management of gas related work has been studied toward a direction to utilize IoT system recently. For this purpose, the matters of user's demand has been deduced through the literature survey, field survey, and professional consultation, by studying the characteristics of worker, work, and work site. In summary, these are the demands for mobile App, 1)a clear arrangement of contents, 2)a design with high readability, 3)a design with low death, 4) securing of user's accessibility, 5)an effective information transmission plan in the work section where it is impossible to operate the mobile device, 6)an activation of alarm function at the section of high working error, 7)a fast two-way transmission and receipt of safety inspection matter needed at work, 8)a selection of images and contents that can guide the situation to the worker in case of accident, 9)an alarm function for the degree of danger in an area of worker's location. Based on these, a basic design of safety application for gas related work has been proposed, that can secure the user accessibility.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.419-437
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• 2023

As the number of thin filers in Korea surpasses 12 million, there is a growing interest in enhancing the accuracy of assessing their credit default risk to generate additional revenue. Specifically, researchers are actively pursuing the development of default prediction models using machine learning and deep learning algorithms, in contrast to traditional statistical default prediction methods, which struggle to capture nonlinearity. Among these efforts, Graph Neural Network (GNN) architecture is noteworthy for predicting default in situations with limited data on thin filers. This is due to their ability to incorporate network information between borrowers alongside conventional credit-related data. However, prior research employing graph neural networks has faced limitations in effectively handling diverse categorical variables present in credit information. In this study, we introduce the Transformer embedded Graph Convolutional Network (TeGCN), which aims to address these limitations and enable effective default prediction for thin filers. TeGCN combines the TabTransformer, capable of extracting contextual information from categorical variables, with the Graph Convolutional Network, which captures network information between borrowers. Our TeGCN model surpasses the baseline model's performance across both the general borrower dataset and the thin filer dataset. Specially, our model performs outstanding results in thin filer default prediction. This study achieves high default prediction accuracy by a model structure tailored to characteristics of credit information containing numerous categorical variables, especially in the context of thin filers with limited data. Our study can contribute to resolving the financial exclusion issues faced by thin filers and facilitate additional revenue within the financial industry.