This study is about the evaluation for shock-proof performance of the system, elastically support the low accumulator of the naval artillery against underwater explosion, using DDAM. For the evaluation, the shock analysis procedure using DDAM, supported by MSC/NASTRAN, was briefly described. In addition, in order to perform the shock analysis, the elastic support system was modeled as a finite element. The shock analysis of the elastic support system was performed by selecting the analysis frequency range so that reliable results can be obtained. Finally, the shock-proof performance of the system was evaluated by comparing the shock analysis results with the properties of the elastic support system.

PURPOSES : For autonomous vehicles, abnormal situations, such as sudden changes in driving speed and sudden stops, may occur when they leave the operational design domain. This may adversely affect the overall traffic flow by affecting not only autonomous vehicles but also the driving environment of manual vehicles. Therefore, to minimize the traffic problems and adverse effects that may occur in mixed traffic situations involving manual and autonomous vehicles, an autonomous vehicle driving support system based on traffic operation optimization is required. The main purpose of this study was to build a big-data-classification system by specifying data classification to support the self-driving of Lv.4 autonomous vehicles and matching it with spatio-temporal data. METHODS : The research methodology is explained through a review of related literature, and a traffic management index and big-dataclassification system were built. After collecting and mapping the ITS history traffic information data of an actual Living Lab city, the data were classified using the traffic management indexing method. An AI-based model was used to automatically classify traffic management indices for real-time driving support of Lv.4 autonomous vehicles. RESULTS : By evaluating the AI-based model performance using the test data from the Living Lab city, it was confirmed that the data indexing accuracy was more than 98% for the KNN, Random Forest, LightGBM, and CatBoost algorithms, but not for Logistics Regression. The data were severely unbalanced, and it was necessary to classify very low probability nonconformities; therefore, precision is also important. All four algorithms showed similarly good performances in terms of accuracy. CONCLUSIONS : This paper presents a method for efficient data classification by developing a traffic management index to easily fuse and analyze traffic data collected from various institutions and big data collected from autonomous vehicles. Additionally, EdgeRSU is presented to support the driving of Lv.4 autonomous vehicles in mixed autonomous and manual vehicles traffic situations. Finally, a database was established by classifying data automatically indexed through AI-based models to quickly collect and use data in real-time in large quantities.

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.

The 4th Industrial Revolution and the continuous development of Science and Technology have also required a speedy business promotion method in the defense industry. Advanced countries including the United States are already boldly innovating the existing high-cost and long-term acquisition system with the highest priority in weapons development to cope with the military rise of Russia and China. The Ministry of National Defense and the Defense Acquisition Program Administration have also recently introduced a quick acquisition system and are applying it to business promotion. In addition, some small-scale projects and weapons systems are being reorganized so that they can be managed by the units demanding them. After an organizational diagnosis of the Project Acquisition Group by the Ministry of National Defense in 2020, it has been reassigned as a subordinate unit of the Army Logistics Command from a direct unit managed by the Army HQ. As a result, problems such as work conflict or redundancy have been identified. In addition, a system has been implemented to shorten the acquisition period by applying a rapid acquisition program in the field of weapons systems by benchmarking the rapid acquisition program of advanced countries. The force support system project process will also need to introduce such a quick acquisition system. In addition, the Ministry of National Defense is considering ways to delegate some weapon systems to each military, which will then carry out tasks ranging from requirements determination to project management. Accordingly, it is now time to expand the organization for the management of the Army's weapons system acquisition project. Therefore, in this paper, the Army Project Acquisition Group was analyzed on its organization, acquisition procedures, and cooperation systems, with presentations of development plans for each field.

최근 4차산업과 급격한 디지털화, 세계 경제성장률의 하락 등으로 노 동시장은 급격하게 변화하고 있으며 이로 인해 청년 실업률은 매우 높은 상태이다. 또한, 인구구조와 산업구조의 재편에 따라 청년층의 취업여건 은 나아지지 않고, 기업은 신입보다 경험을 가진 인력을 채용하고 있다. 이에 따라 본 연구는 해외 주요국(독일, 덴마크, 영국, 프랑스, 호주)의 청년 일경험 지원정책에 대해 살펴보고 일경험 프로그램을 종합적으로 비교함으로써 우리나라의 청년 일경험 정책에 관한 시사점을 도출하였 다. 분석결과 주요국의 청년 일경험 프로그램은 목적을 달리하여 운영되 고 있고, 대상자의 근로능력 수준에 따라 적합한 프로그램을 연계하고 있다. 또한, 단계별 프로그램과 대상에 따른 프로그램으로 구성되어 있고 운영은 직접 운영과 민간위탁 방식으로 구분되었다. 이러한 비교분석 결 과를 바탕으로 정책적 시사점을 제시하였다.

Expanding exports of small and medium-sized companies is crucial for the continuous growth of the Korean economy. Therefore, the government operates various support systems to enhance the export capabilities of these companies. This study aims to analyze the impact of the Korean government's flagship export support system, known as the export initiation support system, on the performance of participating domestic companies. A fixed effect model using panel data was applied to examine the characteristics of 11,099 companies that participated in the export initiation support system from 2016 to 2019. The analysis revealed that the number of exporting countries, employees, and previous export volume had a significant impact on the export amount of participating companies. However, contrary to expectations, the number of overseas marketing participation and the GCL (global competence level) test did not show a significant impact. This study is significant as it provides implications for the development of support projects tailored to the specific needs of small and medium-sized companies, with the goal of improving the export support system.

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.

For countering nuclear proliferation, satellite imagery is being used to monitor suspicious nuclear activities in inaccessible countries or regions. Monitoring such activities involves detecting changes over time in nuclear facilities and their surroundings, and interpreting them based on prior knowledge in terms of nuclear proliferation or weaponisation. Therefore, analysts need to acquire and analyze satellite images periodically and have an understanding of nuclear fuel cycle as well as expertise in remote sensing. Meanwhile, as accessibility of satellite information has been increasing and accordingly a large amount of high-resolution satellite images is available, a lack of experts with expertise in both fields to perform satellite imagery analysis is being concerned. In this regard, the Institute of Korea Nonproliferation and Control (KINAC) has developed a prototype of semi-automatic satellite imagery analysis system that can support monitoring of potential nuclear activities to overcome the limitations of professionals and increase analysis efficiency. The system provides a satellite imagery database that can manage acquired images, and the users can load images from the database and analyze them in stages. The system includes a preprocessing module capable of resizing, correcting and matching images, a change detection module equipped with a pixel-object-based change detection algorithm for multi-temporal images, and a module that automatically generates reports with relevant information. In particular, this system continuously updates open-source information database related to potential nuclear activities and provides users with an integrated analytics platform that can support their interpretation by linking related images and textual information together. As such, the system could save time and cost in processing and interpreting satellite images by providing semi-automated analytic workflows for monitoring potential nuclear activities.

본 연구는 학점은행제 체육학전공 학습자의 사회적지지가 전공만족 및 학습지속의향에 미치는 영향을 규명하여 학점은행제의 체육학 전공자들의 중도탈락을 방지하고 효과적인 운영 방안을 모색하고자 하는데 연구의 목적이 있다. 연구방법은 학점은행제 체육학전공에 학습자를 대상으로 서울의 학점은행제 3곳을 선정하여 총 118부(90.8%)를 유효 표본으로 사용하였다. 조사도구의 타당도와 신뢰도 검증을 위해 확인적 요인분석과 집중타당도, 판별타당도, 평균분산추출(AVE), 개념신뢰도, Cronbach’s α 계수를 검증하였다. 자료처리방법은 IBM SPSS statistics 21과 IBM AMOS 21을 이용하여 빈도분석, 확인적 요인분석, 집중타당도, 판별타당도, Cronbach’s α 계수 산출을 통한 신뢰도분석(reliability analysis), 상관관계분석 (correlation analysis), 구조방정식모형(SEM) 검증을 실시하였다. 연구결과는 다음과 같다. 첫째, 학점은행제 체육학전공 학습자의 교수의 사회적지지와 전공만족 및 학습지속의향의 관계를 분석하기 연구모형의 적합도 검증한 결과 기준을 충족하였다. 둘째, 가설 1의 검증 결과, 학점은행제 체육학전공 학습자의 교수 의 사회적지지는 전공만족에 유의한 영향을 미치는 것으로 나타났다. 가설 2의 검증 결과, 학점은행제 체육 학전공 학습자의 교수의 사회적지지는 학습지속의향에 영향을 미치는 것으로 나타났다. 가설 3의 검증 결과, 전공만족은 학습지속의향에 유의한 영향을 미치는 것으로 나타났다.