This study aims to develop an AI-based analysis system that aligns with the international trend of AI legislation, including the EU's AI Act, while also addressing the analytical needs of the public sector. The focus is on providing timely and objective information to policymakers and specialized researchers by exploring advanced analytical methodologies. As the complexity and volume of data rapidly increase in the modern policy environment, these methods have become essential for governments to obtain the objective information needed for critical decision-making. To achieve this, the study integrates machine learning, natural language processing (NLP), and Large Language Models (LLM) to create a system capable of meeting the analytical demands of government entities. The target dataset consists of “quantum” field data collected from South Korea's National R&D Information System (NTIS). Machine learning was applied to this data to assess the validity of the analysis, while BERTopic, a natural language analysis package, was used for text analysis. With the introduction of LLMs, the extracted information from machine learning and natural language analysis was not merely listed but also connected in meaningful ways to provide policy insights. This approach enhanced the transparency and reliability of AI analysis, minimizing potential errors or distortions in the data analysis process. In conclusion, this study emphasizes the development of a system that enables rapid and accurate information provision while maintaining compatibility with international AI regulations such as the AI Act. The use of LLMs, in particular, contributed to enhancing the system’s capabilities for deeper and more multifaceted analysis.

As the number of enlistees decreases due to social changes like declining birth rates, it is necessary to conduct research on the appropriate recalculation of the force that considers the future defense sufficiency and sustainability of the Army. However, existing research has primarily focused on qualitative studies based on comprehensive evaluations and expert opinions, lacking consideration of sustained support activities. Due to these limitations, there is a high possibility of differing opinions depending on perspectives and changes over time. In this study, we propose a quantitative method to calculate the proper personnel by applying system dynamics. For this purpose, we consider a standing army that can ensure the sufficiency of defense between battles over time as an adequate force and use battle damage calculated by wargame simulation as input data. The output data is the number of troops required to support activities, taking into account maintenance time, complexity, and difficulty. This study is the first quantitative attempt to calculate the appropriate standing army to keep the defense sufficiency of the ROK Army in 2040, and it is expected to serve as a cornerstone for adding logical and rational diversity to the qualitative force calculation studies that have been conducted so far.

계명대학교 인텔리전트건설시스템핵심지원센터(이하 INTEL센터)는 2020년 교육부 주관 핵심연구지 원센터 조성지원과제로 선정되어 현재 2단계 2차년도(5년차)를 진행하고 있습니다. INTEL센터는 사용 률이 낮고 노후화된 기존의 유휴 연구장비를 집적하여 성능을 보완하고, 다양한 분야 및 연구에 장비 및 전담인력을 지원하는 역할을 하고 있습니다. 또한 계명대학교 성서캠퍼스 내 첨단건설실험센터 및 사용성평가연구센터의 건설 및 IoT 분야의 첨단장비를 접목하여 대내외 연구원 및 산업계에 공동연구 및 장비활용을 할 수 있도록 서비스를 지원하고 있습니다. INTEL센터는 4차 산업혁명의 핵심기술인 IoT 분야를 구축 시스템에 융합하여 지능형 평가 시스템 구축을 목표로 하고 있습니다. 원격실험제어시스템 도입 및 인프라 구축을 통하여 실험의 실시간 협업 이 가능한 서비스를 제공하며, 디지털트윈 기반 3D 공간건설시스템을 구축하여 웹을 통한 시험시설환 경과 보유 장비들의 탐색이 가능한 서비스를 제공하고 있어 사용자 요구에 맞는 장비를 선택, 예약까 지 진행할 수 있는 One-Stop 서비스 제공이 가능합니다. INTEL센터가 갖춘 첨단화된 인프라와 IoT, 건설, 기계, 의용공학 등의 다양한 분야의 전문가들을 바탕으로 차세대 융복합 연구 및 연구지원을 이어가고 있으며, 혁신적인 연구 및 전문인력 양성을 통 해 연구장비 공동활용 및 산학연 공동연구에 기여하도록 노력하고 있습니다.

Agrophotovoltaic (APV) system is an integrated system producing crops as well as solar energy. Because crop production underneath Photovoltaic (PV) modules requires delicate management of crops, smart farming equipment such as real-time remote monitoring sensors (e.g., soil moisture sensors) and micro-climate monitoring sensors (e.g., thermometers and irradiance sensors) is installed in the APV system. This study aims at introducing a decision support system (DSS) for smart farming in an APV system. The proposed DSS is devised to provide a mobile application service, satellite image processing, real-time data monitoring, and performance estimation. Particularly, the real-time monitoring data is used as an input of the DSS system for performance estimation of an APV system in terms of production yields of crops and monetary benefit so that a data-driven function is implemented in the proposed system. The proposed DSS is validated with field data collected from an actual APV system at the Jeollanamdo Agricultural Research and Extension Services in South Korea. As a result, farmers and engineers enable to efficiently produce solar energy without causing harmful impact on regular crop production underneath PV modules. In addition, the proposed system will contribute to enhancement of the smart farming technology in the field of agriculture.

Agrophotovoltaic (APV) system is an integrated system producing crops as well as solar energy. Because crop production underneath Photovoltaic (PV) modules requires delicate management of crops, smart farming equipment such as real-time remote monitoring sensors (e.g., thermometers, irradiance sensors, and soil moisture sensors) is installed in the APV system. This study aims at introducing a simulation-based decision support system (DSS) for smart farming in an APV system. The proposed DSS is devised to provide a mobile application service, satellite image processing, real-time data monitoring, and simulation-based performance estimation. Particularly, an agent-based simulation (ABS) is used to mimic functions of an APV system so that a data-driven function and digital twin environment are implemented in the proposed system. The ABS model is validated with field data collected from an actual APV system at the Jeollanamdo Agricultural Research and Extension Services in South Korea. As a result, farmers and engineers enable to efficiently produce solar energy without causing harmful impact on regular crop production underneath PV modules. In addition, the proposed system will contribute to enhancement of the digital twin technology in the field of agriculture.