• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.69-71
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• 2022

The 4th industrial revolution is regarded as a paradigm that determines future national competitiveness through the convergence and intelligence of various ICT technologies. In order to ensure the realization of the Korean military's national tasks and the successful implementation of Defense Reform 2.0, the vision for the promotion of the 'Defense Innovation Plan in the Era of the 4th Industrial Revolution' was set as 'implementation of smart defense innovation based on the 4th industrial revolution'. However, it is time to review whether the data value chain, which is the core of the 4th industrial revolution, is considered before full-scale business promotion according to the vision. In this paper, we compared and analyzed the smart defense innovation promotion project and the military and pan-government data platform project in terms of the data value chain, and suggested policy alternatives to build a defense data ecosystem.

• Korean Journal of Remote Sensing
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• v.39 no.5_4
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• pp.1075-1084
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• 2023

Satellite-derived data is collected by observing the Earth and is used in various fields such as national defense, natural disasters, location-based services, infrastructure, environment, energy, marine, and insurance. This study aims to present the virtuous cycle structure of the satellite information data industry and the business ecosystem model of the industry. As a research method, cases were collected and categorized from the following areas: literature, online, application, and content. The results show that the ecosystem model of the satellite information data industry provides an approach to content services in public and commercial areas, and develops various algorithmic technologies to facilitate content production and services at the level of complex general-purpose technologies. Second, in terms of content typology, satellite information data can be subdivided into monitoring content, urban space monitoring content, and satellite information content. Third, the consumption value of satellite content could be subdivided into informational value, environmental, social and governance (ESG) value, educational value, and content value. In order to expand the global content market, Korea will need to focus on creating an ecosystem for the satellite information industry and discovering differentiated content. It will also need to increase the popularization and accessibility of data to the general public and promote the Korean K-Satellite Information Data Industry ecosystem through government support, policy efforts, and policies such as establishing legal systems, increasing investment, and training human resources.

• Korean Journal of Remote Sensing
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• v.38 no.3
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• pp.299-309
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• 2022

Recently, The sea water temperature around Korean Peninsula is steadily increasing. Water temperature changes not only affect the fishing ecosystem, but also are closely related to military operations in the sea. The purpose of this study is to suggest which model is more suitable for the field of water temperature prediction by attempting short-term water temperature prediction through various prediction models based on deep learning technology. The data used for prediction are water temperature data from the East Sea (Goseong, Yangyang, Gangneung, and Yeongdeok) from 2016 to 2020, which were observed through marine observation by the National Fisheries Research Institute. In addition, we use Long Short-Term Memory (LSTM), Bidirectional LSTM, and Gated Recurrent Unit (GRU) techniques that show excellent performance in predicting time series data as models for prediction. While the previous study used only LSTM, in this study, the prediction accuracy of each technique and the performance time were compared by applying various techniques in addition to LSTM. As a result of the study, it was confirmed that Bidirectional LSTM and GRU techniques had the least error between actual and predicted values at all observation points based on 1 hour prediction, and GRU was the fastest in learning time. Through this, it was confirmed that a method using Bidirectional LSTM was required for water temperature prediction to improve accuracy while reducing prediction errors. In areas that require real-time prediction in addition to accuracy, such as anti-submarine operations, it is judged that the method of using the GRU technique will be more appropriate.

• Journal of the Korean Society for information Management
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• v.38 no.3
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• pp.23-39
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• 2021

This study aims to explore research trends in Blockchain studies in South Korea using dynamic topic modeling and network analysis. To achieve this goal, we conducted the university & institute collaboration network analysis, the keyword co-occurrence network analysis, and times series topic analysis using dynamic topic modeling. Through the university & institute collaboration network analysis, we found major universities such as Soongsil University, Soonchunhyang University, Korea University, Korea Advanced Institute of Science and Technology (KAIST) and major institutes such as Ministry of National Defense, Korea Railroad Research Institute, Samil PricewaterhouseCoopers, Electronics and Telecommunications Research Institute that led collaborative research. Next, through the analysis of the keyword co-occurrence network, we found major research keywords including virtual assets (Cryptocurrency, Bitcoin, Ethereum, Virtual currency), blockchain technology (Distributed ledger, Distributed ledger technology), finance (Smart contract), and information security (Security, privacy, Personal information). Smart contracts showed the highest scores in all network centrality measures showing its importance in the field. Finally, through the time series topic analysis, we identified five major topics including blockchain technology, blockchain ecosystem, blockchain application 1 (trade, online voting, real estate), blockchain application 2 (food, tourism, distribution, media), and blockchain application 3 (economy, finance). Changes of topics were also investigated by exploring proportions of representative keywords for each topic. The study is the first of its kind to attempt to conduct university & institute collaboration networks analysis and dynamic topic modeling-based times series topic analysis for exploring research trends in Blockchain studies in South Korea. Our results can be used by government agencies, universities, and research institutes to develop effective strategies of promoting university & institutes collaboration and interdisciplinary research in the field.