• Journal of Korean Society of Archives and Records Management
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• v.24 no.2
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• pp.89-112
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• 2024

Metadata is a crucial component of record management, playing a vital role in properly managing and understanding the record. In cases where automatic metadata assignment is not feasible, manual input by records professionals becomes necessary. This study aims to alleviate the challenges associated with manual entry by proposing a method that harnesses ChatGPT technology for extracting records management metadata elements. To employ ChatGPT technology, a Python program utilizing the LangChain library was developed. This program was designed to analyze PDF documents and extract metadata from records through questions, both with a locally installed instance of ChatGPT and the ChatGPT online service. Multiple PDF documents were subjected to this process to test the effectiveness of metadata extraction. The results revealed that while using LangChain with ChatGPT-3.5 turbo provided a secure environment, it exhibited some limitations in accurately retrieving metadata elements. Conversely, the ChatGPT-4 online service yielded relatively accurate results despite being unable to handle sensitive documents for security reasons. This exploration underscores the potential of utilizing ChatGPT technology to extract metadata in records management. With advancements in ChatGPT-related technologies, safer and more accurate results are expected to be achieved. Leveraging these advantages can significantly enhance the efficiency and productivity of tasks associated with managing records and metadata in archives.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.183-192
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• 2024

The purpose of this study is to empirically analyze the determining factors (economy, security, domestic politics, administration, and international politics) that affect the ROK-US defense cost sharing decision. Through this, we will gain a deeper understanding of the defense cost sharing decision process and improve the efficiency of defense cost sharing calculation and execution. The scope of the study is ROK-US defense cost sharing from 1991 to 2021. The data used in the empirical analysis were various secondary data such as Ministry of National Defense, government statistical data, SIPRI, and media reports. As an empirical analysis method, multiple regression analysis using time series was used and the data was analyzed using an autoregressive model. As a result of empirical research through multiple regression analysis, we derived the following results. It was analyzed that the size of Korea's economy, that is, GDP, the previous year's defense cost share, and the number of U.S. troops stationed in Korea had a positive influence on the decision on defense cost sharing. This indicates that Korea's economic growth is a major factor influencing the increase in defense cost sharing, and that the gradual increase in the budget and the negotiation method of the Special Agreement (SMA) for cost sharing of stationing US troops in Korea play an important role. On the other hand, the political tendencies of the ruling party, North Korea's military threats, and China's defense budget were found to have no statistically significant influence on the decision to share defense costs.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.19-24
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• 2024

The future military combat environment is rapidly expanding the role and importance of artificial intelligence (AI) in defense, aligning with the current trends of declining military populations and evolving dynamics. Particularly, in the civilian sector, AI development has surged into new domains based on foundation models, such as OpenAI's Chat-GPT, categorized as Super-Giant AI or Hyperscale AI. The U.S. Department of Defense has organized Task Force Lima under the Chief Digital and AI Office (CDAO) to conduct research on the application of Large Language Models (LLM) and generative AI. Advanced military nations like China and Israel are also actively researching the integration of Super-Giant AI into their military capabilities. Consequently, there is a growing need for research within our military regarding the potential applications and fields of application for Super-Giant AI in weapon systems. In this paper, we compare the characteristics and pros and cons of specialized AI and Super-Giant AI (Foundation Models) and explore new application areas for Super-Giant AI in weapon systems. Anticipating future application areas and potential challenges, this research aims to provide insights into effectively integrating Super-Giant Artificial Intelligence into defense operations. It is expected to contribute to the development of military capabilities, policy formulation, and international security strategies in the era of advanced artificial intelligence.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.891-899
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• 2024

The Fourth Industrial Revolution is centered on a personalized demand fulfillment economy and is all about transformation and flexible processing that can deliver what customers want in real time across space and time. This paper implements the construction and operation of a packaging platform that can instantly procure the required packaging products based on real-time orders and evaluates its performance. The components of customer satisfaction are flexible and dependent on the situation which requires efficient management of enterprise operational processes based on an e-SCM platform. An OMS optimized for these conditions plays an important role in maximizing and differentiating the efficiency of a company's operations and improving its cost advantage. OMS is a system of mass customization that provides efficient MOT(Moment of Truth) logistics services to meet the eco-friendly issues of many individual customers and achieve optimized logistics operation goals to enhance repurchase intentions and sustainable business. OMS precisely analyzes the collected data to support information and decision-making related to efficiency, productivity, cost and provide accurate reports. It uses data visualization tools to express data visually and suggests directions for improvement of the operational process through statistics and prediction analysis.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.209-217
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• 2024

The purpose of this study is to explore development plans by analyzing social interests and perceptions of jiu-jitsu using big data analysis. Network analysis, centrality analysis, and CONCOR analysis were conducted by collecting data for the last 10 years of major domestic portal sites. First, 'judo' was found to be the most important related word in network analysis, and 'judo' was also an important word in the analysis of dgree centrality. In the closeness centrality analysis, "defender" was the most important word, and "sports" was the most important word in betweenness centrality. Finally, as a result of CONCOR analysis, four clusters (related sports and marketing, jiu-jitsu competitions, belt test, supplies and expenses) were formed. As a conclusion of the study, first, words such as 'judo', 'exercise', 'competition', 'dobok', 'gym', and 'graduation' should be actively used to promote jiu-jitsu.As a conclusion of the study, first, words such as 'judo', 'exercise', 'contest', 'dobok', 'gym', and 'graduation' should be actively used to promote jiu-jitsu. Second, it is necessary to share information on training costs through various routes, to make awareness of the graduation process or method common, and to develop safety products and create a safe training culture. Third, it is necessary to find ways to continuously increase the influx of new trainees by attracting steady competitions.

• Advanced Industrial SCIence
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• v.3 no.1
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• pp.23-29
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• 2024

In this scholarly investigation, the focus is placed on the transformative potential of edge computing in enhancing Intelligent Transportation Systems (ITS) for the facilitation of autonomous driving. The intrinsic capability of edge computing to process voluminous datasets locally and in a real-time manner is identified as paramount in meeting the exigent requirements of autonomous vehicles, encompassing expedited decision-making processes and the bolstering of safety protocols. This inquiry delves into the synergy between edge computing and extant ITS infrastructures, elucidating the manner in which localized data processing can substantially diminish latency, thereby augmenting the responsiveness of autonomous vehicles. Further, the study scrutinizes the deployment of edge servers, an array of sensors, and Vehicle-to-Everything (V2X) communication technologies, positing these elements as constituents of a robust framework designed to support instantaneous traffic management, collision avoidance mechanisms, and the dynamic optimization of vehicular routes. Moreover, this research addresses the principal challenges encountered in the incorporation of edge computing within ITS, including issues related to security, the integration of data, and the scalability of systems. It proffers insights into viable solutions and delineates directions for future scholarly inquiry.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.68 no.4
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• pp.343-370
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• 2023

Developing improved wheat varieties is vital for global food security to meet the rising demand for food. Therefore, assessing the genetic diversity across wheat genotypes is crucial. This study examined the diversity of 168 durum wheat and 47 common wheat collections from 54 different countries using twelve agro-morphological parameters. Geumgang, a prominent Korean common wheat variety, was used as a control. Both qualitative and quantitative agronomical characteristics showed wide variations. Most durum wheats were shown to possess dense spikes (90%), while common wheats showed dense (40%) or loose (38%) spikes, with yellowish-white being the dominant spike color. The majority of the accessions were awned regardless of wheat type, yellowish-white being the main awn color. White or red kernels were produced, with white kernels dominating in both common (74%) and durum (79%) wheats. Days to heading (DH) and days to maturity (DM) were in the ranges of 166-215 and 208-250 days, respectively, while the culm length (CL), spike length (SL), and awn length (AL) were in the ranges of 53.67-163, 5.33-18.67, and 0.50-19.00 cm, respectively. Durum wheats possessed the shortest average DH, DM, and SL, while common wheat had the longest CL and AL (p < 0.05). Common wheats also exhibited the highest average one-thousand-kernel weight. Hierarchical cluster analysis, aided by principal component analysis, grouped the population into seven clusters with significant differences in their quantitative variables (p < 0.05). In conclusion, this research revealed diversity among common and durum wheat genotypes. Notably, 26 durum wheat and 17 common wheat accessions outperformed the control, offering the potential for developing early-maturing, high-yielding, and lodging-resistant wheat varieties.

• Smart Media Journal
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• v.13 no.4
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• pp.33-48
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• 2024

With the advent of 5G, characterized by Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communications (URLLC), and Massive Machine Type Communications (mMTC), efficient network management and service provision are becoming increasingly critical. This paper proposes a novel approach to address key challenges of 5G networks, namely ultra-high speed, ultra-low latency, and ultra-reliability, while dynamically optimizing network slicing and resource allocation using machine learning (ML) and deep learning (DL) techniques. The proposed methodology utilizes prediction models for network traffic and resource allocation, and employs Federated Learning (FL) techniques to simultaneously optimize network bandwidth, latency, and enhance privacy and security. Specifically, this paper extensively covers the implementation methods of various algorithms and models such as Random Forest and LSTM, thereby presenting methodologies for the automation and intelligence of 5G network operations. Finally, the performance enhancement effects achievable by applying ML and DL to 5G networks are validated through performance evaluation and analysis, and solutions for network slicing and resource management optimization are proposed for various industrial applications.

• Information Systems Review
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• v.19 no.3
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• pp.155-177
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• 2017

The purpose of this study is to investigate those factors that are influenced when a user recognizes his/her private information value as an economic asset. The relationship among these factors will also be discussed. This research targets Internet users, and the value of their private information will be converted into economic figures. How economic value changes in relation with individual attributes, dealer's traits, and circumstantial properties will also be studied. The changes in the factors of private information value under different situations will be analyzed from an economic perspective. By using the cost-benefit analysis framework, this work hypothesizes that the user's private information value can be influenced by individual attributes and situational properties. in the business aspect, this study can help users recognize the true value of their personal information and minimize the cost resulting from private information security incidents. This work also highlights the necessity of estimating the scale of investments for protecting private information. Overall, this research will proceed under the hypothesis that the users' recognition of their private information value is influenced by the attributes of the individual, dealers, or situations.

• Information Systems Review
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• v.20 no.4
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• pp.43-57
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• 2018

We propose network effects upon the investment decision of cloud-based ERP. Using the survey data collected from 82 companies in 2015, we examine whether IT managers have an intention to adopt real options in order to manage the risk of cloud-based ERP investments and how the network effects influence upon the intention to adopt real options. Based on prior literature, we propose a research model with 4 hypotheses. We find partial support of the hypotheses from the empirical analysis: technological risks has a positive impact upon the adoption of real options such as defer, contract, and abandon. In contrast, we find no significant impact of security risks upon real options. We validate positive network effects upon the adoption of real options such as defer, contract, and abandon. This work empirically find that IT managers in Korean middle and small sized firms have an intention to adopt real options when the managers realize economic, technological, and relationship risks and when they expect network effects.