There have been meaningful changes in column stirrup spacing by KDS 41 20 00 in 2022, which is to decrease one of the spacing limits from the minimum section dimension to half of the minimum section dimension. Decreased column stirrup spacing increases the seismic shear resistance of columns and the seismic performance of the entire building. Among the effects of the column stirrup spacing change, this study focused on deformation compatibility in the seismic design of building frame system buildings with ordinary shear walls for seismic design category D. The beams and columns in building frame systems shall satisfy moment and shear strength, or deformation capability induced by seismic design displacement for satisfaction of the deformation compatibility. The commentary of KDS 41 17 00 describes that the deformation compatibility check can be ignored if the members in moment frames are upgraded to intermediate section details. The study showed that the deformation compatibility of columns was satisfied without additional consideration if the building frame systems were designed by the decreased column spacing in KDS 41 20 00. However, beams adjacent to walls needed further consideration, such as the recommendation of commentary in the code.

In this paper, a water rescue mission system was developed for water safety management areas by utilizing unmanned mobility( drone systems) and AI-based visual recognition technology to enable automatic detection and localization of drowning persons, allowing timely response within the golden time. First, we detected suspected human subjects in daytime and nighttime videos, then estimated human skeleton-based poses to extract human features and patterns using LSTM models. After detecting the drowning person, we proposed an algorithm to obtain accurate GPS location information of the drowning person for rescue activities. In our experimental results, the accuracy of the Drown detection rate is 80.1% as F1-Score, and the average error of position estimation is about 0.29 meters.

Fault detection in electromechanical systems plays a significant role in product quality and manufacturing efficiency during the transition to smart manufacturing. Because collecting a sufficient number of datasets under faulty conditions of the system is challenging in practical industrial sites, unsupervised fault detection methods are mainly used. Although fault datasets accumulate during machine operation, it is not straightforward to utilize the information it contains for fault detection after the deep learning model has been trained in an unsupervised manner. However, the information in fault datasets is expected to significantly contribute to fault detection. In this regard, this study aims to validate the effectiveness of the transition from unsupervised to supervised learning as fault datasets gradually accumulate through continuous machine operation. We also focus on experimentally analyzing how changes in the learning paradigm of the deep learning model and the output representation affect fault detection performance. The results demonstrate that, with a small number of fault datasets, a supervised model with continuous outputs as a regression problem showed better fault detection performance than the original model with one-hot encoded outputs (as a classification problem).

To support the International Maritime Organization’s (IMO) 2050 greenhouse gas reduction targets, hybrid propulsion energy management systems (EMS)—which integrate multi-energy coordination and dynamic scheduling—have become a critical pathway for enabling low-carbon transitions and improving energy efficiency in the maritime sector. This paper conducts a comprehensive and structured analysis of EMS technologies applied to ship hybrid propulsion systems. It evaluates the functional roles of EMS under varying system architectures, synthesizes mainstream energy management strategies, and identifies current technological bottlenecks, thereby contributing theoretical foundations for the green transformation of the shipping industry. The study first examines representative hybrid propulsion architectures, detailing their technical characteristics to clarify the functional positioning and optimization priorities of EMS in each configuration. It then reviews prevailing energy management and control strategies, with a focus on their integration with artificial intelligence (AI) and the emergence of adaptive and data-driven approaches. Finally, the paper identifies key challenges in hybrid propulsion EMS, proposes future research directions, and offers practical recommendations to support the advancement and implementation of intelligent energy management technologies in maritime applications.

This study aims to explore the public perception of sports welfare by employing big data analysis techniques and to analyze it through a multi-layered lens grounded in Bronfenbrenner’s ecological systems theory. To this end, text mining software Textom and Ucinet 6 were utilized to examine online textual data related to “sports welfare” collected from May 2017 to February 2025. frequency analysis, TF-IDF analysis, degree centrality analysis, and CONCOR analysis were conducted. The results identified keywords such as “physical education.” “fitness.” “citizens.” “society.” “support.” “disability.” “voucher.” “university.” and “center.” as having high co-occurrence with sports welfare. CONCOR analysis revealed six major clusters: National Fitness 100 Service, Sports Voucher Program, Health Programs at Public Sports Centers, Community-Based Sports Welfare Environment, Training of Sports Welfare Professionals, and Support System Centered on the Korea Sports Promotion Foundation. This study suggests that the level of individual sports welfare can be enhanced through dynamic and interactive relationships between the individual and various environmental systems. Furthermore, to realize sustainable sports welfare, it is essential to develop long-term national strategies that holistically integrate all levels of the ecological systems from the micro system to the chrono system.

본 연구는 2016년 SM엔터테인먼트가 론칭한 다국적 보이그룹 NCT(Neo Culture Technology)의 유닛 시스템이 가진 차별화된 특성이 K-Pop 산 업에 미친 영향과 확산 과정을 분석하였다. 연구 방법은 질적 내용분석을 선택하였고, 로저스의 혁신 확산 이론의 네 가지 핵심 요소(혁신, 커뮤니케 이션 채널, 시간, 사회 시스템)를 분석 프레임워크로 활용하였다. 분석 결 과, NCT 유닛 시스템은 콘텐츠 다양화, 시장 확장성, 리스크 분산, 아티스 트 개발 측면에서 상대적 이점을 가진 혁신으로, SM의 전략적 커뮤니케이 션과 팬 커뮤니티의 정보 공유가 확산에 중요한 역할을 했음을 발견하였 다. 시간적 측면에서는 2016년부터 현재까지 초기 도입기, 확산 성장기, 급속 확산기, 안정화 단계로 이어지는 S자형 확산 곡선이 관찰되었다. 또 한 NCT 유닛 시스템은 K-Pop 산업의 기존 규범에 도전하며 아이돌 그룹 의 정체성 형성, 경력 관리, 글로벌 확장 전략, 인재 개발 방식에 변화를 가져왔다. 본 연구는 NCT 유닛 시스템이 K-Pop 그룹의 지속 가능한 성장 모델, 글로벌 시장 접근 전략, 유연한 인재 관리, 다층적 팬덤 참여, 미디 어 기술 적응, 비즈니스 모델 다각화 측면에서 K-Pop 산업의 미래 발전 방향에 중요한 시사점을 제공함을 확인하였다.

본 연구는 인공지능(AI) 기술의 발전이 음원 제작 시스템에 미치는 영향을 중심으로, 작곡, 편 곡, 믹싱, 마스터링의 핵심 제작 단계에서 인공지능 기술이 어떻게 적용되고 있는지를 체계적으로 분석하였다. 특히 뮤즈넷(MuseNet), 마젠타(Magenta), 수노(SUNO), 아이바(AIVA) 등 대표적인 인 공지능 작곡 도구의 기술 구조와 기능을 시대별로 비교함으로써, 음악 창작의 자동화 수준과 기술 적 한계를 실증적으로 조명하였다. 또한 하이브(HYBE), 에스엠(SM), 와이지(YG) 등 국내 주요 엔 터테인먼트 기업의 인공지능 기술 수용 사례를 통해, 산업 현장에서의 실제 활용 방식과 그로 인 한 제작 및 유통 시스템의 변화 양상을 분석하였다. 연구 결과, 인공지능은 음원 제작의 효율성과 확장성을 획기적으로 높이는 동시에, 콘텐츠 생산 방식과 산업 구조의 재편을 촉진하는 주요 요인 으로 작용하고 있음을 확인하였다. 본 연구는 이러한 변화를 바탕으로 향후 음악 산업이 나아가야 할 기술 통합 전략과 대응 방향에 대해 제언하고자 한다.

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.

Truss structures, widely used in engineering, consist of straight members transferring axial forces. Traditional analysis methods like FEM and the Force Method become computationally expensive for large-scale and nonlinear problems. Surrogate models using Artificial Neural Networks (ANNs), particularly Physics-Informed Neural Networks (PINNs), offer alternatives but require extensive training data and computational resources. Variational Quantum Algorithms (VQAs) address these challenges by leveraging quantum circuits for optimization with fewer parameters. Variational Quantum Circuits (VQCs) based on Quantum Neural Networks (QNNs) utilize quantum entanglement and superposition to approximate high-dimensional data efficiently, making them suitable for computationally intensive tasks like surrogate modeling in structural analysis. This study applies QNNs to truss analysis using 6-bar and 10-bar planar trusses, assessing their feasibility. Results indicate that residual-based loss functions enable QNNs to make reliable predictions, with increased layers improving accuracy and a higher Q-bit count contributing to performance, albeit marginally.

This study explored multidimensional value of the Moroccan Figuig Oasis in designated Globally Important Agricultural Heritage Systems (GIAHS) and explored strategies for sustainable management and dynamic conservation. The Figuig Oasis successfully preserves a unique agro-ecosystem in an arid desert environment, utilizing traditional irrigation systems and multi-layered agricultural systems. The local economy relies heavily on the date palm industry and livestock farming. The ethnic and cultural diversity of the region contributes to a strong community-based social fabric. However, this oasis faces serious challenges, including water resource depletion due to climate change, spread of Bayoud disease, population decline, youth migration away from agricultural activities, and economic vulnerability. In addition, the region’s ability to be economically self-sufficient is being undermined by a growing reliance on migrant remittances. Despite a growing number of women engaging in agriculture, they continue to be marginalized in land ownership and decision-making processes. In light of these challenges, this study assessed the status and characteristics of the Figuig Oasis inscribed on the FAO’s GIAHS. This study emphasizes the role of GIAHS as a catalyst for expressing and strengthening pluralistic values and public goods functions of agriculture. Through this analysis, this study seeks to reaffirm that the FAO GIAHS framework is a traditional knowledge system that contains concerns and wisdom of past generations, which, if effectively harnessed, can help overcome ecological challenges and realize the potential for future agricultural and rural development.

도심지에서는 증가하는 교통량으로 인해 지상에서 지하로 교통 시설을 확대하고 있다. 지하에 교통 시설물을 시공할 경 우 기존 도로를 굴착한 후에 지하 시설물을 시공하는 동안에 임시통행판을 사용하여 기존 도로의 역할을 대체하도록 하 고 있다. 이러한 임시통행판은 대부분 철재를 사용하고 있으며 표면에 아스팔트, 콘크리트 등 다양한 재료를 적용하여 사용하기도 한다. 본 연구에서는 콘크리트 슬래브를 임시통행판으로 적용하고 있는 사례를 조사하기 위해 미국 로스앤젤 레스 지역의 콘크리트 임시통행판이 설치된 구간에 대한 현장 조사를 실시하였으며 구성 요소와 손상 유형을 분석하였 다. 콘크리트 임시통행판의 주요 구성 요소로는 각각의 임시통행판을 연결해주는 연결부와 프리캐스트 콘크리트 임시통 행판을 인양할 수 있는 인양장치 체결부 등을 들 수 있다. 조사 구간은 하부의 보 구조 위에 프리캐스트 콘크리트 임시 통행판을 배치하였으며 연결부와 인양 장치 체결부를 그라우트로 채우는 방식으로 시공된 것으로 분석되었다. 손상 유형 을 분석한 결과, 차량 통행으로 인해 연결부와 인양장치 체결부의 그라우트 재료가 탈락되어 빈 공간이 보이는 부분이 많았으며 이러한 부분을 아스팔트 혼합물로 충진하여 사용하고 있었다. 또한, 콘크리트 임시통행판에 균열이 발생한 경 우도 조사되었다.

최근 자율주행 기술의 급속한 발전으로 자율주행 기술이 탑재된 차량이 눈에 띄고 있다. 자율주행 기술로 인해 교통사 고 감소와 효율적인 교통운영을 유도할 수 있는데, 주행 환경뿐만 아니라 주차 환경에서도 큰 이점을 보이고 있다. 이러 한 자율주행 기술을 기반으로 한 로봇 파킹 시스템은 주차 소요 시간을 단축하고 주차 공간을 더욱 효율적으로 활용할 수 있는데, 이는 특히 교통약자들의 이동 편의성을 크게 향상시킬 수 있다. 따라서 본 연구에서는 차량의 진출입이 빈번 하고 보행자의 이동이 많은 고속도로 휴게시설을 대상으로 교통약자를 고려한 로봇 파킹 시스템을 도입하여 안정성과 효율성을 평가하고자 한다. 이를 위해 2010년부터 2022년까지의 고속도로 휴게시설 사고 데이터를 분석하여, 사고 빈도 와 사고 심각도를 고려한 EPDO(Equivalent Property Damage Only) 값이 높은 중부내륙선 충주휴게소(창원방향)를 분석 대상지로 선정하였다. 미시교통 시뮬레이션 VISSIM을 활용하여 대상 휴게소의 도로 및 주차장 네트워크를 구축하고 시 뮬레이션하였다. 안전성 평가를 위해 DRAC(Deceleration Rate to Avoid Crash) 및 PET(Post Encroachment Time) 지 표 등을 활용하였으며, 효율성 평가로는 주차 회전율(Parking Turning Rate) 및 정지횟수(Number of Stops) 지표 등을 사용하여 비교하였다. 본 연구는 기존 연구들과 달리 교통약자의 관점에서 로봇 파킹 시스템의 효과를 분석했다는 점에 서 차별성을 가진다.

Motorcycles are becoming a major means of transportation in the delivery industry because of their mobility and economic feasibility, and their use is increasing with the spread of non-face-to-face culture. However, owing to the absence of a systematic maintenance and inspection system, illegal modifications, and a lack of safety education, the possibility of accidents is increasing, and social problems are intensifying. To address this issue, we aim to find ways to improve motorcycle safety. Problems were identified by registering motorcycles, driver crashes, and surveys of the current status of laws and systems. Subsequently, a questionnaire was administered to assess the actual conditions and perceptions regarding motorcycles. Finally, to analyze the driving characteristics of delivery motorcycles, traffic safety education was conducted for new delivery riders, and the driving characteristics were analyzed by collecting driving record data. In this study, a plan to enhance the license system, education, insurance, and educational programs is proposed to strengthen motorcycle safety. The licensing system needs to be elevated by age and classified by displacement, and delivery riders can improve their driving skills through mandatory traffic safety education. The insurance sector should introduce a system that discounts insurance premiums upon completion of training. Additionally, it is essential to prepare a systematic education program, including obstacle avoidance and simulation-based learning, by reflecting on the analysis results of road environments and driving data. In this study, insensitivity to safety, insufficient management systems, and lack of education and publicity were identified as causes of motorcycle driver crashes. It was confirmed that most types of dangerous driving were improved through traffic safety education. However, some limitations were observed, such as an increase in the right-hand rotation over time during sudden turns. Future research is needed to enhance laws, systems, and driver safety by analyzing driving characteristics in a broader context based on actual driving records and images.

정년제도는 근로자가 일정 연령에 도달하면 고용을 종료하도록 규정하 는 제도로, 한국에서는 2016년 법적 정년 60세 의무화 이후 제도와 기 업 실무 간 디커플링 현상이 지속되고 있다. 다수의 한국 기업에서는 사 내 갈등과 법적 분쟁이 나타났지만, 일본 기업은 장기적인 준비, 단계적 입법, 기업 지원을 통해 제도를 추진하여 이러한 문제가 거의 없었다. 또 한, 일본 기업들은 임금체계 개편, 숙련과 건강 상태에 맞게 고령 근로자 직무 재배치 방식을 도입하여 효율성을 제고했다. 이러한 노력으로 일본 은 디커플링 현상을 최소화하며, 제도와 기업 실무 간의 실질적 조화를 이루었다. 한국의 정년제도 디커플링 현상 문제를 해결하려면, 고령 근로 자의 지속적 활용을 위한 제도적 기반을 마련하고, 숙련 전수와 멘토링 과 같은 고령 친화적 인사관리 방안을 확대해야 한다. 마지막으로, 정부 는 기업과 근로자 간 갈등을 완화하고 제도 이행을 지원하기 위해 지속 적인 모니터링과 맞춤형 컨설팅을 제공할 필요가 있다.