This study investigated the visualization accuracy and educational applicability of generative artificial intelligence (AI) tools in fashion design education by comparing images generated from the same blouse sketch using GPT-based tools, LOOK AI, and Stable Diffusion under identical prompt conditions. Thirty-two professional fashion designers evaluated the generated outputs using a structured 10-item assessment scale, focusing on silhouette accuracy, detail representation, structural clarity, and overall visual completeness. Statistical differences among the tools were analyzed using one-way analysis of variance followed by post-hoc comparisons. The results revealed significant differences (p<.05) in key evaluation criteria: silhouette accuracy, detail implementation, structural interpretability, and overall completeness. LOOK AI excelled in representing structural elements such as seams, pleats, and pattern logic, indicating its strength in design-oriented applications and technical visualization tasks. In contrast, Stable Diffusion received higher ratings for overall visual balance and aesthetic coherence, despite showing relatively lower structural fidelity. GPT-based outputs received lower ratings for structural accuracy but were seen as valuable for promoting critical AI literacy via prompt-based exploration, iterative refinement, and reflective evaluation. These findings suggest that differences among AI tools should not be interpreted in terms of absolute superiority but as distinct educational affordances. Accordingly, this study proposes a three-axis instructional framework that integrates structure-oriented learning, creative visualization, and critical inquiry-based learning.

The National Highway Traffic Safety Administration (NHTSA) and the California Department of Motor Vehicles (CA DMV) collect and utilize data from traffic accidents caused by Automated Driving Systems (ADS) driving on real roads, as a policy. Leading autonomous driving technology companies such as Tesla and Waymo collect their own driving and accident data and use them for technology advancement. ADS traffic accident data that occur when driving on real roads are valuable for identifying problems in unexpected situations. This study analyzes the risk of traffic accidents by Operational Design Domain (ODD) on ADS traffic accident data that occurred while driving on an actual road and aims to present a road traffic law-based driving ability evaluation scenario in a complex ODD configuration in high-risk situations, wherein an ADS can be particularly vulnerable in mixed traffic situations. The actual road traffic accident data of ADS from 2,289 accidents as provided by the NHTSA were analyzed. Analysis of the characteristics of ADS traffic accidents revealed that accidents occurred mainly on ordinary ODDs with high traffic demand during actual road driving, that is, on dry roads during clear days and daylight. In traffic situations including ADS and Human Driving Vehicle(HDV), approximately 40% of traffic accidents were confirmed to have occurred because of HDV colliding with stationary ADS and occurred in unexpected situations, such as changing the HDV when driving straight ahead of the ADS. Results of analyzing the risk of traffic accidents on the driving status of ADS by ODD, showed that the risk of traffic accidents that occurred while the ADS was driving straight ahead was 2.27, with dry road conditions, sunny weather, and a road speed limit of 21 to 30 mph at night when streetlights were turned on. Thus, the ADS road traffic law-based driving ability evaluation scenario can be used to evaluate whether to recognize and respond to accident risk situations by developing ADS road traffic law-based driving ability evaluation scenarios for situations vulnerable to accidents due to HDV cut-in in traffic situations that include ADS and HDV. In future, this can be used as basic data for preparing related regulations and institutional devices, such as traffic accident investigations and driving ability evaluations by ADS.

This study examines the renovation of the Jiaxing Sports Center as a case to explore how spatial aesthetics intervene in organizational identity construction prior to building completion and facilitate its continuous reproduction through institutionalized spatial scripts. Drawing on theories of organizational identity, organizational aesthetics, and the spatial turn, the study adopts an interpretive case study approach combined with visual narrative analysis. Design statements, renderings, and public documents are systematically coded and thematically analyzed using Braun and Clarke’s reflexive thematic analysis.The findings indicate that spatial aesthetics reproduces organizational identity through four interrelated dimensions. First, stable visual orders are constructed through form, materiality, color, and lighting. Second, functional zoning and circulation systems are reorganized around competition standards and spectator units, embedding identity in operational practices. Third, implicit normative support is provided through equipment and supporting facilities, reinforcing legitimacy and reliability. Fourth, post-event operations, green strategies, and multi-functional conversion mechanisms extend identity across temporal contexts.Furthermore, the study proposes a tripartite mechanism of symbolization, structuration, and anticipation, illustrating how spatial aesthetics contributes to symbolic construction, institutional embedding, and future-oriented governance. By extending organizational identity analysis to aesthetic–spatial–institutional mechanisms, this study highlights design texts as cognitive artifacts with institutional significance in public architecture governance. It also acknowledges limitations related to single-case generalizability and the lack of post-occupancy investigation.

This study examines group differences in AI dependence, self-efficacy, and design fixation based on learners’ AI utilization experience, and further explores the relationships between AI dependence and perception-related variables within an AI-based design education context. To this end, I surveyed 42 learners who participated in an AI-based design class. I then performed data analysis in IBM SPSS Statistics 26, using one-way analysis of variance (ANOVA) and Pearson correlation. The ANOVA revealed statistically significant group differences in AI dependence by AI usage level, but no significant differences in self-efficacy or design fixation. Furthermore, age and professional experience showed no significant influence on most variables. Meanwhile, the correlation analysis revealed that AI dependence was significantly positively associated with design fixation, but not with self-efficacy. These results suggest that, in AI-based design education, learners’ perceptions and attitudes are not strongly differentiated by personal background factors such as age or professional experience, but are instead associated with their experience with AI use and perceived dependence on AI. By distinguishing and analyzing AI utilization and AI dependence, this study provides empirical evidence that contributes to a more nuanced understanding of learners’ cognitive perceptions in AI-based design education in practice.

This study investigated the root causes of wheel hub assembly looseness and abnormal brake pad wear observed during a 32,000 km durability driving test of a military medium truck. Structural analysis and component-level durability tests were conducted to identify the failure mechanisms and evaluate improvement measures. Test conditions were established based on relevant regulations and previous studies, and braking force and braking torque equations were derived through mathematical modeling. The results showed that the spacer used to fix the brake disc in the original design did not satisfy the required safety factor greater than 1.0. Furthemore, a wheel hub assembly torque durability test under more severe conditions than the original test was performed, and the results demonstrated a significant improvement in durability perfomance.

미세먼지는 먼지 입자의 크기에 따라 일반 미세먼지(PM10)와 초미세먼지(PM2.5)로 나뉘어 분류하고 있다. 미세먼지는 심혈관 질환(Cardiovascular disease) 등을 증가시킬 수 있는 위험성을 보유하고 있기 때문에 저감하기 위한 노력이 필요하다. 본 논문에서는 소형선 박용 디젤엔진에 설치될 미세먼지 저감 장치의 콘 형상에 따른 유동 균일도 특성과 압력강하에 미치는 영향에 대해서 수치해석을 통하여 조사하였다. 사례별로 유동 균일도 및 압력강하는 상용해석 코드인 AVL社의 FIRE-M을 이용하여 분석하였으며, 콘 형상에 따른 두 가지 모드에 대하여 비교분석 하였다. 콘 형상 변경에 의해 압력분포가 개선되었으며, 특히 소형선박에서는 유의미한 결과라고 분석되어 진다.

자율운항선박은 인적 개입 없이 운항되는 특성상, 추진력 전달계통과 같은 핵심 기계장치의 상태를 실시간으로 감시하고 이상 징후를 조기에 식별할 수 있는 고장진단 및 예지 기술이 필수적이다. 특히 추진축계는 주기관과 프로펠러를 연결하는 구조로서 자율운항 성능과 안전성에 직접적인 영향을 미치는 핵심 계통이므로, 고장의 전개 경로와 주요 취약 지점을 체계적으로 규명하는 구조 기반 위험 성 평가가 요구된다. 본 연구에서는 선행연구를 통해 수행된 추진축계에 대한 FMEA(failure modes and effects analysis) 결과를 기반으로, 정 상 사상(top event)으로 ‘프로펠러 파손’과 ‘추진력 상실’을 정의하고, 이를 중심으로 FTA(fault tree analysis)를 구성하였다. 각 고장 경로는 베어링 열화, 윤활 상태 저하, 체결력 약화, 반복 및 충격 하중과 같은 주요 인자를 중심으로 계층적으로 구조화되었으며, 복합적인 기계· 환경적 요인의 상호작용에 의해 고장이 전개됨을 구조적으로 확인하였다. 또한 FTA 분석 결과를 바탕으로 실제 자율운항 환경에 적용 가 능한 네 가지 고장진단 실험 시나리오(베어링 마모, 윤활유 부족, 반복 충격, 체결 불량)를 도출하였다. 각 시나리오는 무선 축계시스템의 센서 데이터를 활용하여 동역학적 징후를 실험적으로 관찰할 수 있도록 설계되었다. 본 연구는 FMEA–FTA 연계 분석을 기반으로 고장 구조 해석과 진단 시나리오 설계 간의 연계성을 제시한 것으로, 자율운항 추진계의 상태 기반 유지보수 및 예지정비 기술 개발에 실질적 인 기초자료로 활용될 수 있다.

철근 콘크리트의 부식으로 인해 비부식성 대체재의 채택이 촉진되었으며, GFRP 철근은 가장 널리 채택된 경제적이고 균형 잡힌 성능 옵션 중 하나이다. GFRP 철근의 탄성 계수는 강철보다 낮아 완전한 대체가 어렵다. 하지만 최근 GFRP의 탄성 계수는 국제 기준 30∼40 GPa에서 약 60 GPa(국외 생산 기준), 50 GPa(국내 생산 기준)로 증가했습니다. 그러나 대부분의 설계 방정식은 기존의 저탄성계수 GFRP를 기준으로 보정되었다. 본 연구에서는 고탄성계수 GFRP 보강근으로 보강된 두 개의 콘크리트 보의 휨 거동을 분석하며, 실험 하중-변위 응답을 Adam, ACI 440.1R-06, CSA S806-12 및 최신 ACI 440.11-22의 예측값과 비교한다. 이 모델들은 모두 비균열 영역의 초기 경사와 일치하지만, 저탄성계수 GFRP를 사용한 보정으로 인해 실제 강성을 과소평가하고 처짐 을 과대 예측하는 경향이 있습니다. 균열 발생 후 편차가 증가하며, 이때, Adam 방정식이 가장 큰 편차를 보인다. 이는 기존 모델의 한계를 보여주며, 변위 제어가 매우 중요한 경우 기존 모델의 사용에 신중해야 함을 보여준다.

Jeans, emblematic of enduring fashion appeal, serve as a barometer of societal trends. This study explores the evolving landscape of meta trend analysis in the fashion industry, acknowledging the need for methodologies tailored to the vast amounts of data available through social media. By focusing on jeans, a quintessential fashion staple, the research applies text mining techniques, specifically TF-IDF analysis, to examine design style changes over a decade. Methodologically rigorous, the study meticulously curates and analyzes Naver blog posts spanning from 2013 to 2022, filtering out content unrelated to design. Morpheme analysis isolates pertinent nouns, facilitating comprehensive TF-IDF examinations. Design elements—fit, color, material, detail, and rise length—are methodically dissected, revealing notable shifts over time. The skinny fit, once dominant, diminished in prevalence by 2022, contrasting with the ascendant popularity of the wide fit. Noteworthy trends emerge in color preferences, with black and white prevailing alongside a burgeoning interest in light blue. Elasticity appears as a key material characteristic that remains consistent throughout the study period. Moreover, temporal fluctuations in detailing, such as tears and decorative stitching, underscore the dynamic nature of fashion. This research makes a unique contribution to the literature on the intersection of fashion and big data, emphasizing design perspectives amid the prevalence of consumer-focused analyses. Its practical implications extend to informing online fashion product development and prioritizing design elements that resonate with contemporary consumer preferences.

In this paper, the structural optimization and experimental validation of lightweight, high stiffness rollers for roll-to-roll(R2R) processing of lithium metal electrodes are presented. Precise dimensional control of electrode thickness below 50㎛ is essential for next-generation high energy density batteries, yet elastic recovery during rolling hinders the achievement of target specifications. To address this challenge, finite element(FE) analysis was employed to determine the optimal rolling gap and roller geometry, and the results were verified through R2R experiments. Simulations indicated that a rolling gap of 153㎛ yielded a final sheet thickness of about 49.6㎛, meeting the design requirement. Experimental results confirmed the validity of the numerical model, with thickness measurements deviating less than ±10% from FE analysis predictions. These findings demonstrate that the proposed roller design not only ensures thickness precision but also improves system efficiency, offering practical guidelines for scalable lithium metal electrode manufacturing.

본 연구는 네트워크 분석 기법을 적용하여 색상(Color), 소재(Material), 가공기술(Technique) 간 조합을 체계적으 로 탐색하고 분석하는 것을 목적으로 한다. 한국디자인진흥원의 CMF HOW’S 아카이브 데이터를 기반으로 C–M–T 통합 네트워크를 구축하고, 이분 네트워크 분할과 투영(Projection) 분석을 통해 구조적 특성과 조합 양상을 정량적으 로 도출하였다. 중심성, 밀도, 군집 계수, 모듈러리티 지표를 활용한 결과, 색상은 다양한 소재⋅기술과 폭넓게 결합 되는 유연성을 보였고, 소재는 가공기술 선택을 제약하거나 반복적 조합을 형성하는 핵심 요소로 확인되었다. 일부 소재는 높은 중심성을 보여 다수의 색상⋅기술과 연결된 반면, 다른 소재는 제한적 적용성을 나타냈다. 또한 모듈러 리티 분석을 통해 유사한 가공 전략을 공유하는 조합군이 식별되어, 제품군별 설계 전략이나 공정 최적화로 확장될 수 있음을 시사한다. 전문가 인터뷰에서는 본 분석틀이 CMF 기획 및 실무 의사결정에서 활용 가능한 참조 지표로 평가되었으며, 향후 친환경 규제 대응, 산업군 비교, 제품군 사례 분석 등으로 확장 가능성이 제시되었다. 본 연구는 CMF 데이터를 구조 화하여 조합 경향을 객관적으로 이해하고, 디자인 실무에 적용 가능한 분석 도구를 제시한다는 점에서 학술적⋅실무 적 의의를 갖는다.

In this study, structural analysis was performed to select the optimal design shape through failure identification and design changes in turbine housing. Damage in the inlet flange is considered to be high cycle fatigue due to the vibration excitation in the engine full load test. Therefore, the FE analyses were performed natural vibration analysis and frequency response analysis for the initial shape and design change models. The stress magnitudes were obtained as a function of frequency through frequency response analysis according to engine vibration excitation. As a result, the dynamic stiffness of Case (1) increased by approximately 3.6% compared to the initial model, and Case (2) increased by 4.6%. In addition, the stress magnitude was greatly reduced in the design improvement. Therefore, the model with only the flange thickness increased is thought to be optimal design for securing the durability of the turbine housing.