• Journal of Digital Convergence
• /
• v.17 no.9
• /
• pp.135-145
• /
• 2019

Prolonged recession has caused the youth unemployment rate in Korea to remain at a high level of approximately 10% for years. Recently, the number of unemployed Koreans in their 30s and 40s has shown an upward trend. To expand the government's employment promotion and unemployment benefits from youth-centered policies to diverse age groups, including people in their 30s and 40s, prediction models for different age groups are required. Thus, we aimed to develop unemployment prediction models for specific age groups (30s and 40s) using available unemployment rates provided by Statistics Korea and Google search queries related to them. We first estimated multiple linear regressions (Model 1) using seasonal autoregressive integrated moving average approach with relevant unemployment rates. Then, we introduced Google search queries to obtain improved models (Model 2). For both groups, consequently, Model 2 additionally using web queries outperformed Model 1 during training and predictive periods. This result indicates that a web search query is still significant to improve the unemployment predictive models for Koreans. For practical application, this study needs to be furthered but will contribute to obtaining age-wise unemployment predictions.

• Journal of Internet Computing and Services
• /
• v.16 no.2
• /
• pp.99-107
• /
• 2015

In this era of smart convergent environment wherein all industries are converged on ICT infrastructure and industries and cultures come together, the information and communication construction business is becoming more important. For the information and communication construction business to continue growing, it is very important to ensure that technical manpower is stably supplied. To date, however, there has been no theoretically methodical analysis of manpower supply and demand in the information and communications construction business. The need for the analysis of manpower supply and demand has become even more important after the government announced the road map for the development of construction business in December 2014 to seek measures to strengthen the human resources capacity based on the mid- to long-term manpower supply and demand analysis. As such, this study developed the manpower supply and demand forecast model for the information and communications construction business and presented the result of manpower supply and demand analysis. The analysis suggested that an overdemand situation would arise since the number of graduates of technical colleges decreased beginning 2007 because of fewer students entering technical colleges and due to the restructuring and reform of departments. In conclusion, it cited the need for the reeducation of existing manpower, continuous upgrading of professional development in the information and communications construction business, and provision of various policy incentives.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.21 no.7
• /
• pp.625-637
• /
• 2020

In order to decide the naval ship weapon system acquisition for national policy/market economy activities, the decision makers can determine policy based on current technology level/concentration/utilization. For this, the decision makers apply the major common technology field analysis using patents data. As a method for collecting patent data, we can collect patent data of domestic mobile carriers through the Korea Intellectual Property Rights Information System of Korean Intellectual Property Office. As a result, we collected 14,964 patents/352 International Patent Classification(IPC) types. Based on these data, we performed three analysis processes (SNA, PCA, ARIMA, Text Mining) and got each result from extracting 58 IPC types of SNA and 7 IPC types of PCA. Based on the analysis results, we have confirmed that 7 IPC(B63B, H01M, F03D, B01D, H02K, B23K, H01H) types are the Major Common Technology Distribution of domestic Defense Companies.

• Journal of Korea Water Resources Association
• /
• v.48 no.12
• /
• pp.981-993
• /
• 2015

The analysis of hydrologic time series data is crucial for the effective management of water resources. Therefore, it has been widely used for the long-term forecasting of hydrologic variables. In tradition, time series analysis has been used to predict a time series without considering exogenous variables. However, many studies using decomposition have been widely carried out with the assumption that one data series could be mixed with several frequent factors. In this study, the empirical mode decomposition method was performed for decomposing a hydrologic time series data into several components, and each component was applied to the time series models, autoregressive moving average (ARMA). After constructing the time series models, the forecasting values are added to compare the results with traditional time series model. Finally, the forecasted estimates from ARMA model with empirical mode decomposition method showed better performance than sole traditional ARMA model indicated from comparing the root mean square errors of the two methods.