• Journal of Practical Engineering Education
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• v.12 no.1
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• pp.177-186
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• 2020

The purpose of this study is to view the current status of the smart manufacturing ecosystem and to use it as an objective basis for establishing a sustainable manpower training policy support plans for smart manufacturing suppliers. Therefore, questionnaires were constructed and distributed to 2,200 domestic smart manufacturing supply firms and 6,000 demand firms, which received responses from 259 supply firms and 155 demand firms. The results showed that both supply and demand firms needed digital converged workforce with comprehensive expertise and field-oriented digital transformation workforce. In the method of securing professional manpower, the demand for in-service training and post-management training was high. Based on the results of this study, this study suggests the manpower training policy support plans for smart manufacturing supply firms and contributes to strengthening the global market competitiveness of smart manufacturing supply firms in Korea.

• Journal of Korean Society of Water and Wastewater
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• v.34 no.1
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• pp.35-43
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• 2020

Turbidity has various effects on the water quality and ecosystem of a river. High turbidity during floods increases the operation cost of a drinking water supply system. Thus, the management of turbidity is essential for providing safe water to the public. There have been various efforts to estimate turbidity in river systems for proper management and early warning of high turbidity in the water supply process. Advanced data analysis technology using machine learning has been increasingly used in water quality management processes. Artificial neural networks(ANNs) is one of the first algorithms applied, where the overfitting of a model to observed data and vanishing gradient in the backpropagation process limit the wide application of ANNs in practice. In recent years, deep learning, which overcomes the limitations of ANNs, has been applied in water quality management. LSTM(Long-Short Term Memory) is one of novel deep learning algorithms that is widely used in the analysis of time series data. In this study, LSTM is used for the prediction of high turbidity(>30 NTU) in a river from the relationship of turbidity to discharge, which enables early warning of high turbidity in a drinking water supply system. The model showed 0.98, 0.99, 0.98 and 0.99 for precision, recall, F1-score and accuracy respectively, for the prediction of high turbidity in a river with 2 hour frequency data. The sensitivity of the model to the observation intervals of data is also compared with time periods of 2 hour, 8 hour, 1 day and 2 days. The model shows higher precision with shorter observation intervals, which underscores the importance of collecting high frequency data for better management of water resources in the future.

• Journal of Korean Society of Rural Planning
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• v.29 no.4
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• pp.137-152
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• 2023

Data is indispensable for digital transformation of agriculture with the development of innovative information and communication technology (ICT). In order to devise and prioritize strategies for enhancing data competitiveness in the agricultural sector, we employed an Analytic Hierarchy Process (AHP) analysis. Drawing from existing research on data competitiveness indicators, we developed a three-tier decision-making structure reflecting unique characteristics of the agricultural sector such as farmers'awareness of the data industry or awareness of agriculture among data workers. AHP survey was administered to experts from both agricultural and non-agricultural sectors with a high understanding of data. The overall composite importance, derived from the respondents, was rated in the following order: 'Employment Support', 'Data Standardization', 'R&D Support', 'Start-up Ecosystem Support', 'Relaxation of Regulations', 'Legislation', and 'Data Analytics and Utilization Technology'. In the case of experts in the agricultural sector, 'Employment Support' was ranked as the top priorities, and 'Legislation', 'Undergrad and Grad Education', and 'In-house Training' were also regarded as highly important. On the other hand, experts in the non-agricultural sector perceived 'Data Standardization' and 'Relaxation of Regulations' as the top two priorities, and 'Data Center' and 'Open Public Data' were also highly rated.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.4
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• pp.213-221
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• 2018

South Korea has been reluctant to open its geospatial information overseas to ensure security as a divided country. However, this cannot continue as the domestic and international environments related to geospatial information and the industrial ecosystem of information and communication technologies have been changing dramatically. Within this context, this study aims to analyze the causal relations among relevant variables and how they change and interact with time using a systems thinking process. First, causal maps were created for the domains of national security, map-based convergence service, and corporate competition. Then, the causal maps for each domain were integrated, based on which the points for policy intervention and dominant feedback loops were identified. The analysis results showed that securing the self-sufficiency of domestic geospatial businesses is a key element to determine the whole causal map, and the variable that changes the dominant feedback loop from a vicious circle to a virtuous one is the decision to open geospatial information overseas. In this study, I found the policy leverage that is a policy intervention point that can produce a great effect with little input by building a causal map of the interactions between major variables. This study is significant in that it identified and analyzed the dominant feedback loop as to which causal structure would dominate the system in the long term. The results of this study can be used to discuss not only the impacts of map data overseas opening on the national security and geospatial information industry, but also the interactions in the future when Google or other global companies request to release the geospatial information.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.2
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• pp.215-235
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• 2024

As artificial intelligence (AI) technology is recognized as a key technology that will determine future national competitiveness, competition for AI technology and industry promotion policies in major countries is intensifying. This study aims to present implications for domestic policy making by analyzing the policies of major countries on the start-up of AI companies, which are the basis of the AI industry ecosystem. The top four countries and the EU for the number of new investment attraction companies in the 2023 AI Index announced by the HAI Research Institute at Stanford University in the United States were selected, The United States enacted the National AI Initiative Act (NAIIA) in 2021. Through this law, The US Government is promoting continued leadership in the United States in AI R&D, developing reliable AI systems in the public and private sectors, building an AI system ecosystem across society, and strengthening DB management and access to AI policies conducted by all federal agencies. In the 14th Five-Year (2021-2025) Plan and 2035 Long-term Goals held in 2021, China has specified AI as the first of the seven strategic high-tech technologies, and is developing policies aimed at becoming the No. 1 AI global powerhouse by 2030. The UK is investing in innovative R&D companies through the 'Future Fund Breakthrough' in 2021, and is expanding related investments by preparing national strategies to leap forward as AI leaders, such as the implementation plan of the national AI strategy in 2022. Israel is supporting technology investment in start-up companies centered on the Innovation Agency, and the Innovation Agency is leading mid- to long-term investments of 2 to 15 years and regulatory reforms for new technologies. The EU is strengthening its digital innovation hub network and creating the InvestEU (European Strategic Investment Fund) and AI investment fund to support the use of AI by SMEs. This study aims to contribute to analyzing the policies of major foreign countries in making AI company start-up policies and providing a basis for Korea's strategy search. The limitations of the study are the limitations of the countries to be analyzed and the failure to attempt comparative analysis of the policy environments of the countries under the same conditions.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1043-1059
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• 2021

Forest and agricultural land are very important factors in the environmental ecosystem and securing food resources. Forest and agricultural land should be monitored regularly. CAS500-4 data are expected to be effectively used as a supplement of monitoring forest and agricultural land. Prior to the launch of the CAS500-4, the relative canopy height error analysis of the digital elevation model on South Korea was performed to determine the vertical target accuracy. Especially, by considering area of interest of the CAS500-4 (mountainous or agricultural area), it is conducted that vertical error analysis according to the slope and canopy. For Gongju, Jeju, and Samcheok, the average root mean squared differences were calculated compared to the drone LiDAR digitalsurface models, which were filmed in autumn and winter and the 5 m digital elevation model from the National Geographic Information Institute. As a result, the Shuttle radar topography mission digital elevation model showed a root mean squared differences of about 8.35, 8.19, and 7.49 m, respectively, while the Copernicus digital elevation model showed a root mean squared differences of about 5.65, 6.73, and 7.39 m, respectively. In addition, the root mean squared difference of shuttle radar topography mission digital elevation model and the Copernicus digital elevation model according to the slope angle were estimated on South Korea compared to the 5 m digital elevation model from the National Geographic Information Institute. At the slope angle of between 0° to 5°, root mean squared differences of the Shuttle radar topography mission digital elevation model and the Copernicus digital elevation model showed 3.62 and 2.52 m, respectively. On the other hands root mean squared differences of the Shuttle radar topography mission digital elevation model and the Copernicus digital elevation model respectively showed about 10.16 and 11.62 m at the slope angle of 35° or higher.