As a key axis of metropolitan public transport, exclusive median bus lanes (EMBLs) are facing operational limits owing to urban expansion and increased traffic demand, with queues at bus stops during peak hours causing severe delays. This study aims to empirically identify the phenomenon of queue-based delays at the stop level, that is difficult to explain using conventional capacity calculation methods, and to propose an operational strategy for its mitigation. By realistically assessing passenger inconvenience through a revised “additional passenger travel time” calculation based on bus travel, this study provides a balanced analysis of the tradeoff between system efficiency and passenger convenience, thereby contributing to the development of sustainable urban transit systems. This study compared current all-stop operations with two skip-stop scenarios in Songpa-daero, a major arterial corridor in Seoul. Using an actual bus management system and transit card data, key performance indicators including queue length, travel time, dwell time, and additional passenger travel time were analyzed. Scenario I applied an A/B service-style alternating stop operation, whereas Scenario II implemented a hybrid approach, designating hub stops at key locations. Simulation modeling was used to evaluate the system-wide impacts during peak hours. The analysis revealed that skip-stop operations had significant potential to improve EMBL performance; however, the benefits were subject to a trade-off with passenger inconvenience. Scenario I with alternating stops was most effective in reducing the queue length and overall travel time. However, it also resulted in the largest increase in additional passenger travel time calculated with the revised methodology. In contrast, Scenario II with hub stops, while showing slightly less improvement in operational efficiency, presented a more balanced outcome by mitigating the burden of additional travel time for passengers through hub stops, thereby enhancing service equity. Both scenarios showed reduced dwell times at most stops, indicating the alleviation of boarding and alighting congestion. This study confirmed that skip-stop strategies could effectively improve the operational efficiency of EMBLs by reducing queue lengths and travel times. However, the additional passenger travel time, including bus transfers, is a critical factor that must be considered. Scenario I was evaluated as superior for maximizing the operational efficiency, whereas Scenario II was a better alternative for securing a balance with passenger convenience. This study is significant because it presents an analytical framework for quantifying queue-based delays and realistically assessing passenger impact. Although limitations remain, such as not fully capturing the complex decision-making processes of actual passengers, the methodology and findings offer practical guidance for urban transport planners seeking data-driven solutions to EMBL congestion, emphasizing the importance of the passenger perspective in skip-stop strategy design.

In apartment buildings in Korea, irregular walls, such as T-, L-, and U-shaped walls, are commonly used. However, in practical design, the geometric irregularities of walls are often neglected when determining the length of the lateral confinement region. Further, although earthquake loads apply from various directions, the lateral confinement region is typically determined for the in-plane direction of the web. Thus, using finite element analysis, this study investigated the structural performance of irregular walls subjected to various loading directions. As the design parameters, wall shape, cross-sectional aspect ratio, and loading direction were addressed. According to the parametric analysis results, as the length of flange in tension increased, the lateral confinement region should be evaluated with consideration of the geometric irregularity. Further, for the L- and U-shaped walls, it is recommended to evaluate the lateral confinement region for various loading directions. Based on these results, a design method to determine the lateral confinement region of irregular walls was suggested.

This study proposes a real-time content design pipeline optimized for Unreal Engine, integrating generative AI-based image creation with AI-assisted 3D modeling tools. The pipeline aims to streamline the production of high-quality assets for real-time applications, including games and simulations. Two types of subjects were selected: a bust combining organic character features, and a stone slab characterized by planar and symmetrical structure. Multi-angle image data were first synthesized using advanced generative AI models to simulate diverse viewpoints. These were then processed using AI-enhanced photogrammetry and modeling tools to reconstruct detailed 3D meshes and extract base textures. Post-processing steps, including mesh decimation, UV unwrapping, and texture baking, were performed to ensure compatibility with Physically Based Rendering (PBR) workflows used in Unreal Engine. The final assets were successfully imported into Unreal Engine, demonstrating visual fidelity and performance suitability in a real-time environment. The study confirms the pipeline’s potential for accelerating asset development and suggests promising future directions in AI-driven digital content creation.

The number of significant issues on many welding processes are often connected to high productivity and manufacturability at low costs. The research on welding processes in the literature has reported several research activities, but there is still scope for improvement in most industrial settings. The primary goal of this research is to determine the best super-TIG welding settings to use for groove welding. First, in order to determine the quality characteristics and risks associated with them, concepts and frameworks of quality by design (QbD) which is a new standard in pharmaceutical area in order to improve drug qualities were integrated into this process optimization. Second, stepwise experimental design approaches including a factorial design as well as a response surface methodology (RSM) were customized and performed for this specific automated super-TIG welding process. Third, based on experimental design results, the optimal operating conditions with both design space (i.e., acceptable range of operating conditions) and safe operating space (i.e., safe range of operating conditions) were obtained. Finally, a case study including QbD steps, stepwise experimental design approaches, design and operating spaces, the optimal factor settings, and their association validation results was conducted for verification purposes.

해상운송에서 충돌사고는 인명과 재산에 막대한 피해를 끼치는 중요한 안전 문제로써, 국제해사기구(International Maritime Organization, IMO)가 제정한 국제해상충돌예방 규칙(International Regulations for Preventing Collisions at Sea, COLREGs)의 철저한 준수가 권장 된다. 그러나 복잡한 해상 환경과 인간의 인지적 한계로 인해, 항해사가 실시간으로 최적의 충돌회피 의사결정을 내리기란 쉽지 않다. 본 연구는 대형 언어 모델(Large Language Model, LLM)인 GPT를 활용하여, 유인선 항해사가 COLREGs 규칙에 부합하는 충돌회피 판단을 신속 하고 정확하게 내릴 수 있도록 지원하는 단계별 프롬프트 설계안을 제시한다. 특히 4단계 충돌회피 과정을 확장하여, 항해사가 GPT와 자 연어로 상호작용할 때 사용할 표준화된 프롬프트를 구체화하였다. 가상의 시나리오 적용 결과, 항해사는 GPT의 조언을 통해 주변 상황 인식부터 회피경로 선정, 실행 단계까지 일관적으로 보고받을 수 있었으며, COLREGs의 준수와 충돌위험지수(Collision Risk Index, CRI) 계 산 등의 복잡한 작업을 AI가 보완함으로써 인적 오류를 줄일 가능성을 보였다. 이러한 결과는 자율운항선 뿐만 아니라 현행 유인선 운항 에서도 AI-항해사 협업을 통한 안전성 향상을 도모할 수 있을 것으로 기대한다.

본 연구는 쇄빙연구선 아라온호의 국부 빙하중을 정확하게 추정하기 위해, 국제선급협회(IACS) Polar Class에 따른 실제 설계 하중(Design Ice Load)과 빙하중 면적(Load Patch)을 활용하여 영향계수행렬을 산정하였다. 기존 연구에서는 단위하중 또는 집중하중 형태 로 구조해석을 수행하여 영향계수행렬을 구했으나, 본 연구에서는 아라온호의 설계하중을 기반으로 실제 빙 충돌 상황과 유사한 면적 에 하중을 부여함으로써 보다 현실적인 해석 결과를 얻고자 하였다. 해석에는 선급 규칙으로부터 도출한 직사각형 형태의 빙하중 패치 와 평균 압력을 적용하였으며, 아라온호 선수부에 총 21개의 스트레인 게이지 위치에 대해 각각 해석을 진행하였다. 이후, 각 하중 조건 에서의 변형량을 토대로 21×21 행렬 형태의 영향계수행렬을 도출하였다. 본 연구의 결과로 도출된 영향계수행렬을 사용하면 실제 계측 된 스트레인 값을 기반으로 빙하중을 보다 정밀하게 추정할 수 있을 것으로 기대된다. 이는 기존 방식 대비 얼음 충돌 시 발생하는 면 적 효과를 고려했다는 점에서 의미가 있으며, 향후 쇄빙선 설계 및 운항 과정에서의 빙하중 평가 정확도를 높이는 데 기여할 것으로 판단된다.

Reinforcement learning (RL) is successfully applied to various engineering fields. RL is generally used for structural control cases to develop the control algorithms. On the other hand, a machine learning (ML) is adopted in various research to make automated structural design model for reinforced concrete (RC) beam members. In this case, ML models are developed to produce results that are as similar to those of training data as possible. The ML model developed in this way is difficult to produce better results than the training data. However, in reinforcement learning, an agent learns to make decisions by interacting with an environment. Therefore, the RL agent can find better design solution than the training data. In the structural design process (environment), the action of RL agent represent design variables of RC beam. Because the number of design variables of RC beam section is many, multi-agent DQN (Deep Q-Network) was used in this study to effectively find the optimal design solution. Among various versions of DQN, Double Q-Learning (DDQN) that not only improves accuracy in estimating the action-values but also improves the policy learned was used in this study. American Concrete Institute (318) was selected as the design codes for optimal structural design of RC beam and it was used to train the RL model without any hand-labeled dataset. Six agents of DDQN provides actions for beam with, beam depth, bottom rebar size, number of bottom rebar, top rebar size, and shear stirrup size, respectively. Six agents of DDQN were trained for 5,000 episodes and the performance of the multi-agent of DDQN was evaluated with 100 test design cases that is not used for training. Based on this study, it can be seen that the multi-agent RL algorithm can provide successfully structural design results of doubly reinforced beam.

Advances in digital tools and building structure technologies have enabled more flexible architectural design, with AI-based performance design gaining considerable attention as a new design methodology. Stadium design must consider the two primary elements of sports events: athletes and spectators. Given that the facade of a stadium directly impacts solar energy efficiency, it is essential to incorporate environmental performance considerations from the initial design phase. This study employs an AI-based Generative Design process to generate a facade form that efficiently manages solar radiation and daylight, satisfying two conflicting performance objectives: max- imizing sunlight for turf growth in the pitch zone and minimizing direct sunlight exposure in the stadium seating zone. The optimal solution derived ranks 331st for pitch zone sunlight and 408th for stadium seating sunlight out of a dataset of 1,000 models. While this solution does not represent the absolute best for either individual objective, it is evaluated as the most balanced alternative, achieving the goal of maximizing sunlight in the pitch zone and minimizing it in the seating zone

최근 팬데믹으로 인해 혼술 및 홈술 문화가 확산되고, 고급 주류에 대한 관심이 증가하면서 온라인 주류 구매 및 픽업 서비스 이용이 활성화되고 있다. 이에 따라, 온라인 주류 앱과 같은 디지털마케팅 채널에서 차별화된 사용자 경험(UX)이 핵심 경쟁 요소로 부각되고 있다. 이러한 상황에서, 본 연구는 디자인씽킹 기반 디지털마케팅 교육과정을 통한 UX 디자인 개선 프로젝트를 진행하여, 주류 앱 ‘데일리샷’의 온라인 주류 구매 경험을 향상시키기 위한 방안 도출 과정을 분석하는 것을 목표로 한다. 본 프로젝트는 디자인씽킹 기반 디지털마케팅 프로세스의 단계적 접 근을 활용하여 진행되었으며, 교육과정의 실습 활동으로 운영되었다. ‘탐색(Discover)’ 및 ‘정의(Define)’ 단계에서는 데스크 리서치를 통해 주류 시장과 소비자 트렌드를 분석하고, 사용자 리서치(사용성 평가, 앱 리뷰 분석, 심층 인터 뷰)를 수행하여 사용자의 니즈를 도출하였다. 연구 결과, 팬데믹 이후 증가한 주류 입문자들을 위한 ‘주류 입문 가이 드’의 필요성이 높아졌으며, 주류 소비가 단순한 음용을 넘어 취향을 공유하고 사회적 상호작용의 수단으로 활용되 고 있음이 확인되었다. 이러한 발견점을 바탕으로 ‘개발(Develop)’ 단계에서 개인화된 주류 추천 기능 강화, 네비게 이션 간소화, 커뮤니티 상호작용 기능 활성화 등의 UX 디자인 개선 방안을 제안하였다. 이를 통해, 데일리샷이 단순 한 주류 픽업 서비스를 넘어, 개인의 주류 취향을 탐색하고 차별화된 구매 경험을 제공할 수 있는 플랫폼으로 발전할 기반을 마련하고자 하였다. 본 연구는 디자인씽킹 기반의 디지털마케팅 프로세스가 주류 앱 UX 디자인 개선에 효과 적인 접근법임을 강조하고, 디자인씽킹을 적용한 디지털마케팅 교육과정이 학생들의 UX 실무 역량 향상에 기여할 수 있음을 보여주었다.

한국형 포장설계법(KPRP)은 한국의 기후, 교통, 재료 조건을 반영하여 개발된 포장설계법으로, 성능 기반 분석과 역학적-경험적 원 리를 결합하여 국내 도로포장의 내구성과 효율성 향상에 기여해왔다. KPRP는 지역별 환경 데이터, 교통 하중, 재료 특성을 고려하 여 최적의 포장 구조를 설계하며, 2011년 개발 이후 도로포장의 수명 연장과 경제성 향상을 이루어냈다. 그러나 KPRP에 적용되는 기후 및 교통 데이터는 2000년대 초반의 자료를 기반으로 하고 있어, 현재 기준으로 약 10년 이상의 차이가 존재한다. 이에 따라 최 신 데이터를 반영하여 포장설계를 개선할 필요성이 제기되고 있다. 본 연구에서는 최근 10년간의 최신 기후 데이터를 활용하여 줄눈 콘크리트 포장(JCP)의 콘크리트 슬래브 컬링 시간을 계산하고, 이를 기반으로 온도응력 및 교통응력의 산정 방식을 현 시점에 맞게 개선하고자 한다. 또한, 2023년 도로포장관리시스템(PMS) 데이 터를 이용하여 한국도로공사가 관리하는 모든 고속국도 중 JCP가 적용된 구간을 대상으로 표면 균열(SD), 설계 차로별 AADT, 관 리구간별 도로 연장, 차로 폭 등의 데이터를 분석하였다. 이를 통해 각 도로의 피로균열율을 산정하고, 고속국도를 대상으로 줄눈 콘 크리트 포장의 전이함수를 개선하여 보다 정밀한 설계를 가능하게 하고자 한다. 본 연구는 최신 기후 및 교통 데이터를 반영한 KPRP 기반 줄눈 콘크리트 포장설계의 실현에 기여할 것으로 기대된다.

In this paper, the design feasibility of the high-temperature rotation test jig for the operating state of gas turbine blades was confirmed through thermal structural analysis and modal analysis. The structural analysis model was composed of assembled blade, disc, cover, and shaft. Here, the disc was designed to be assembled with two types of blade. First, thermal analysis was performed by applying the blade surface temperature of 800°C. Next, structural analysis was performed at 3600 RPM, the normal operating condition, and 4320 RPM, the overspeed operation condition. Lastly, modal analysis was performed to examine the natural frequency and deformation of the jig. The FE analysis showed that the temperature decreased from the blade to disc dovetail. Additionally, both the blade and disc showed structural stability as the maximum stress was below the yield strength. Also, the first natural frequency was 636.35Hz and 639.43Hz at 3600RPM and 4320RPM, respectively, satisfying gas turbine design standards and guidelines. Ultimately, the designed test jig was confirmed to be capable of high temperature and rotation testing of various blades.

The demand for secondary batteries is increasing rapidly with the popularization of electric vehicles and the expansion of wireless electronic devices. However, the most widely used lithium-ion batteries are subject to frequent fire incidents, limiting market growth. To avoid flammability, solid electrolyte-based systems are gaining attention for next-generation lithium-ion batteries. However, challenges such as limitations in ionic conductivity and high manufacturing costs require further research and development. In this study, we aim to identify a new nitrogen-based solid electrolyte material that has not yet been widely explored. We propose a methodology for selecting the final material through high-throughput screening (HTS), detailing the methods used for material selection and performance evaluation. In addition, we present ab initio molecular dynamics (AIMD) calculations and results for nitrogen-substituted materials with carbon and oxygen replacements, including Arrhenius plots, activation energy, and the predicted conductivity at 300K for the material with the highest Li-ion conductivity. While the performance does not yet surpass the ionic conductivity and activity of conventional solid-state electrolytes, our results provide a systematic framework for exploring and screening new solid electrolyte materials. This methodology can also be applied to the exploration of different battery materials and is expected to contribute significantly to the innovation of next-generation energy storage technologies.

해상 운송 시스템에 사이버 위협이 증가함에 따라, 안전한 운항을 보장하기 위한 사이버 복원력의 필요성이 부각되고 있다. 특 히, 자율운항선박과 같은 고도의 기술 융합이 요구되는 스마트선박은 기존보다 더 광범위한 사이버 공격 표면을 가지게 되어 이에 대한 리스크 관리가 필수적이다. 본 연구에서는 스마트선박의 사이버 복원력을 평가하기 위해 국제 표준인 IACS UR E26, E27, IEC 62443, NIST SP 800-160을 분석하고, 이를 통해 스마트선박의 선종과 자율화 수준에 따른 사이버 리스크 평가 및 각각의 리스크에 맞는 복원력 모델 개념을 설계하였다. 특히, 선박의 자율화 수준이 높아질수록 사이버 리스크가 커지므로 이를 반영한 맞춤형 대응 전략을 도출하고 스마트 선박의 사이버 복원력 향상을 위한 성숙도 모델을 제안했다.

Performance-Based Seismic Design (PBSD) is an approach that evaluates how structures will perform under different levels of seismic activity. It focuses on ensuring that buildings not only withstand earthquakes but also meet specific performance objectives, such as minimizing damage or maintaining functionality after the event. Unlike traditional methods, PBSD allows for more tailored, cost-effective designs by considering varying degrees of acceptable damage based on the structure's importance and use. PBSD was introduced in Korea in 2016 to replace elastic design, which is inevitable to over-design to cope with all variables such as earthquakes and winds. When PBSD is applied to the structural design new building, One of the challenges of PBSD is the complexity involved in creating accurate inelastic analysis models. The process requires significant time and effort to analyze the results, as it involves detailed simulations of how structures will behave under seismic stress. Additionally, organizing and interpreting the analysis data to meet performance objectives can be labor-intensive and technically demanding. In order to solve this problem, a post-processor program was developed in this study. A post-processor was developed based on Excel program using Visual Basic for Applications(VBA). Because analysis outputs of Perform-3D, that is a commercial software for structural analysis and design, are very complicated, generation of tables and graphs for report is significant time and effort consuming task. When the developed post-processor is used to make the seismic design report, the required task time is significantly reduced.