• Korean Journal of Social Welfare Studies
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• v.40 no.1
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• pp.235-260
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• 2009

This study aims to analyze consumption inequality of Korean elderly households. The justification for analyzing consumption inequality during old age could be summarized as follows. First, due to the rapid growth of elderly population, the intra generational inequality of older people will bring greater consequences to the society in the coming years. Second, inequality is more actualized during old age when income stops playing a major role and the everyday lives are based mostly on consumption activities. For analysis, this study used the 2nd, 5th, 7th and 9th wave of 『Korea Labor and Income Panel Study』. The findings are as follows. First, total consumption inequality of elderly households is gradually decreasing after the economic crisis. Also, the gini coefficient of consumption items representing modern consumption culture, such as expenditures on eating out and car maintenance is decreasing. However, the inequality contribution rate of such items is continually rising, indicating that whereas the elderly households in general are being assimilated to the mainstream consumption culture, the disparity between classes is continually expanding. Second, gini coefficient and inequality contribution rate of the essentials such as food and housing has decreased indicating that basic livelihoods in general has risen. Third, the inequality of education expenditure is increasing after the year 2000 which implies that the problem of education inequality in general might have an effect on elderly households.

• Economic Analysis
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• v.25 no.4
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• pp.58-108
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• 2019

This paper analyzes the determining factors in the unemployment rate among young people in their 20s by studying data from 30 OECD countries between 2000 and 2017. It identifies reasons why Korea has a higher youth unemployment rate than Japan, and assesses what implications Japan's youth unemployment measures could have on Korea. The study highlights the variables that have meaningful impacts on youth unemployment. They include the unemployment rate among the working-age population, the percentage of each age bracket in the overall population, the GDP growth rate, the percentage of wage laborers in each age group, the percentage of elderly people, and the percentage of part-time workers. This paper also finds that a decline in the youth population, especially among people in their 20s, does not help to address the issue of youth unemployment. Secondly, this paper explains the additional factors behind Korea's higher youth unemployment rates. One is Korea's disadvantageous employment environment, compared to that in Japan, in terms of wage earnings. Other factors include the existence of fewer decent corporate jobs than in Japan, and wide disparities in wages between large and small corporate jobs. Therefore, while making efforts to resolve long-term and structural problems, it is necessary to actively promote policy measures to solve short-term mismatch problems of youth employment by referring to Japanese policy examples.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.391-393
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• 2022

In recent years, technologies forecasting demand based on deep learning and big data have accelerated the smartification of the field of e-commerce, logistics and distribution areas. In particular, ports, which are the center of global transportation networks and modern intelligent logistics, are rapidly responding to changes in the global economy and port environment caused by the 4th industrial revolution. Port traffic forecasting will have an important impact in various fields such as new port construction, port expansion, and terminal operation. Therefore, the purpose of this study is to compare the time series analysis and deep learning analysis, which are often used for port traffic prediction, and to derive a prediction model suitable for the future container prediction of Busan Port. In addition, external variables related to trade volume changes were selected as correlations and applied to the multivariate deep learning prediction model. As a result, it was found that the LSTM error was low in the single-variable prediction model using only Busan Port container freight volume, and the LSTM error was also low in the multivariate prediction model using external variables.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2023.05a
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• pp.100-103
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• 2023

Recently, development of maritime autonomoust surface ships and eco-friendly ships, production and evaluation research considering various marine environments is needed in the field of optimal routes as the demand for accurate and detailed real-time marine environment prediction information expands. An algorithm that can calculate the optimal route while reducing the risk of the marine environment and uncertainty in energy consumption in smart ships was developed in 2 stages. In the first stage, a profile was created by combining marine environmental information with ship location and status information within the Automatic Ship Identification System(AIS). In the second stage, a model was developed that could define the marine environment energy map using the configured profile results, A regression equation was generated by applying Random Forest among machine learning techniques to reflect about 600,000 data. The Random Forest coefficient of determination (R²) was 0.89, showing very high reliability. The Dijikstra shortest path algorithm was applied to the marine environment prediction at June 1 to 3, 2021, and to calculate the optimal safety route and express it on the map. The route calculated by the random forest regression model was streamlined, and the route was derived considering the state of the marine environment prediction information. The concept of route calculation based on real-time marine environment prediction information in this study is expected to be able to calculate a realistic and safe route that reflects the movement tendency of ships, and to be expanded to a range of economic, safety, and eco-friendliness evaluation models in the future.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.141-148
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• 2023

The purpose of this study is to investigate the impact of college education satisfaction on the employment preparation and social participation of young adults during the pandemic period. To accomplish this research purpose, the researcher used the panel data of Korea Youth Policy Institute and analyzed the eight variables related to college education satisfaction, employment preparation, and social participation. This study's sample consisted of a total of 2,941 individuals, and both structural equation modeling and multigroup structural equation modeling were applied for the analysis. The results revealed that college education satisfaction had a significantly positive influence on the employment preparation activities and social participation of young adults. In the multigroup structural equation modeling analysis, it was found that college education satisfaction had a significantly positive impact on employment preparation and social participation for women, while for men, it only significantly affected employment preparation. Furthermore, college education satisfaction did not show significant effects on the employment preparation and social participation of graduates from junior colleges, but it did have a significantly positive impact on the employment preparation activities of graduates from four-year colleges. Based on these research findings, policy and educational interventions to enhance the college education satisfaction of male students and graduates from junior colleges are required.

• Information Systems Review
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• v.25 no.4
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• pp.113-130
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• 2023

Due to economic development and increasing gross national income, the number of automobiles continues to rise, leading to a serious issue of illegal parking due to limited road conditions and insufficient parking facilities. Illegal parking causes significant inconvenience and displeasure to people and can even result in accidents and loss of lives. The severity of accidents and their consequences, related to the growing number of vehicles and illegal parking, is escalating, particularly in the metropolitan areas. Consequently, efforts are being made to address this problem as a cause of social issues and come up with measures to reduce illegal parking. In particular, half of the public complaints in the metropolitan area are related to illegal parking, and the highest physical and human damage occurs in Gyeonggi. Thus, this study aims to use machine learning techniques based on data related to illegal parking in Suwon city, Gyeonggi, to categorize regional characteristics and propose effective measures to crack down on illegal parking. Additionally, practical, social, policy, and legal measures to decrease illegal parking in the metropolitan area are suggested. This study has academic significance in that it solved the problem of illegal parking, which is mentioned as one of the social problems that cause traffic congestion, by classifying regional characteristics using K-prototype, a machine learning algorithm. Furthermore, the results of this study contribute to practical and social aspects by providing measures to decrease illegal parking in the metropolitan area.

• Information Systems Review
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• v.25 no.4
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• pp.265-290
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• 2023

Even though crowdfunding has become popular as a novel means of raising capital for early-stage ventures and startups through an Internet-based platform, it is unclear how a funder's characteristics, such as motivation and ability, influence their information processing and pledging decision. This study aims to propose and test a research model for determining the relationships between a funder's personal attributes, information processing style, and funding intention. To test the research model, we collected data from 139 Amazon Mechanical Turk participants through an online questionnaire survey. The findings indicate that a funder's self-efficacy has a positive effect on heuristic processing but has no significant effect on systematic processing. By contrast, a funder's personal relevance positively influences both systematic and heuristic processing. Furthermore, heuristic processing, as well as perceived value and perceived risk, influence pledging intentions positively. Our findings potentially contribute to improving the design of crowdfunding platforms to better support a funder's information needs. Based on our findings, we discuss the implications of our study as well as the directions for future research.

• Korean Journal of Remote Sensing
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• v.39 no.5_2
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• pp.755-770
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• 2023

Due to recent severe climate change, abnormal weather phenomena, and other factors, the frequency and magnitude of natural disasters are increasing. The need for disaster management using artificial satellites is growing, especially during large-scale disasters due to time and economic constraints. In this study, we have summarized the current status of next-generation medium-sized satellites and microsatellites in operation and under development, as well as trends in satellite imagery analysis techniques using a large volume of satellite imagery driven by the advancement of the space industry. Furthermore, by utilizing satellite imagery, particularly focusing on recent major disasters such as floods, landslides, droughts, and wildfires, we have confirmed how satellite imagery can be employed for damage analysis, thereby establishing its potential for disaster management. Through this study, we have presented satellite development and operational statuses, recent trends in satellite imagery analysis technology, and proposed disaster response strategies that utilize various types of satellite imagery. It was observed that during the stages of disaster progression, the utilization of satellite imagery is more prominent in the response and recovery stages than in the prevention and preparedness stages. In the future, with the availability of diverse imagery, we plan to research the fusion of cutting-edge technologies like artificial intelligence and deep learning, and their applicability for effective disaster management.

• Journal of Korea Water Resources Association
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• v.56 no.10
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• pp.641-653
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• 2023

As climate change and urbanization are causing unprecedented natural disasters in urban areas, it is crucial to have urban flood predictions with high fidelity and accuracy. However, conventional physically- and deep learning-based urban flood modeling methods have limitations that require a lot of computer resources or data for high-resolution flooding analysis. In this study, we propose and implement a method for improving the spatial resolution of urban flood analysis using a deep learning based super-resolution technique. The proposed approach converts low-resolution flood maps by physically based modeling into the high-resolution using a super-resolution deep learning model trained by high-resolution modeling data. When applied to two cases of retrospective flood analysis at part of City of Portland, Oregon, U.S., the results of the 4-m resolution physical simulation were successfully converted into 1-m resolution flood maps through super-resolution. High structural similarity between the super-solution image and the high-resolution original was found. The results show promising image quality loss within an acceptable limit of 22.80 dB (PSNR) and 0.73 (SSIM). The proposed super-resolution method can provide efficient model training with a limited number of flood scenarios, significantly reducing data acquisition efforts and computational costs.

• Korea Trade Review
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• v.48 no.4
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• pp.23-45
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• 2023

This study aims to investigate the relationship between institutional distance and FDI and focuses on China's outward FDI. The institutional distance between China and the host country is measured using the institutional quality published by the World Bank. This study collects panel data from 50 countries in which China invested from 2008 to 2019 and use the panel GLS methodology to examine the factors affecting outward FDI through three models. First, this study examines the impact of the absolute value of institutional distance on China's OFDI across all countries in which China invests. Second, this study divides countries with positive and negative institutional distance to China into two groups and examine the relationship between institutional distance and OFDI in each group. Finally, this study examines the non-linear relationship between institutional distance and OFDI from China. To test this, this study adds the squared term of institutional distance to the model. The results of the analysis are as follows Institutional distance is positively related to China's OFDI. The relationship between institutional distance and OFDI is inverted U-shaped in the group of host countries with relatively higher institutional quality than China, but positive in the group of low-quality host countries. In addition, China's OFDI is affected by a combination of institutional and economic factors. The results of this study have implications not only for FDI host countries but also for MNCs' choice of FDI destinations.