This study develops a design-data-based lifecycle greenhouse gas emission assessment framework for jointed concrete pavement highways in Korea. The framework considers road pavements as a long-life infrastructure system that includes material production, transport, construction, maintenance, end-of-life treatment, and recycling benefits beyond the system boundary. A functional unit comprising 1 km of jointed concrete pavement was defined, and 16 datasets were constructed from highway concrete pavement projects using bills of quantities, material summary sheets, and geometric information. A key feature of this framework is the incorporation of project-specific maintenance scenarios. The mainline and tunnel sections were separately evaluated and weighted based on their actual length ratios. The numbers of milling and overlay applications were estimated using the slab thickness and traffic volume from the design data. After each overlay, the cumulative ESALs and crack progression were recalculated from the overlay year to determine the subsequent overlay timing, instead of applying a fixed maintenance cycle. The application of the framework yielded an average lifecycle GHG emission of 1,294 t CO₂eq./km, with a standard deviation of 284 t CO₂eq./km. The proposed framework provides a basis for a consistent lifecycle GHG assessment and design-stage environmental evaluation of concrete pavement highways.

This study explores creative experiences and educational needs related to the use of generative artificial intelligence (AI) in fashion design and proposes educational strategies through an AI-based fashion design process framework. A qualitative research design was employed, involving semi-structured in-depth interviews with 10 fashion design educators who had experience using generative AI. Inductive content analysis was performed on the collected data using NVivo, comprising coding, categorization, and theme development. The findings were organized into four major themes: (a) perceptions of AI use in fashion design, (b) functional roles of AI in the creative process, (c) human–AI collaboration and creative agency, and (d) educational needs and ethical considerations. The results showed that generative AI was perceived not as a substitute for human designers but as a supportive tool that could enhance creative thinking, particularly in the ideation and visualization stages. Specifically, AI enabled rapid exploration of diverse design alternatives and reduced psychological pressure in early creative phases. Human–AI collaboration was characterized by a complementary structure in which AI generated visual suggestions but human designers retained the responsibility for aesthetic judgment and final decision-making. Finally, an AI-based fashion design process framework aligned with the Double Diamond model was derived from these findings, providing a conceptual basis for educational strategies in fashion design education.

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.

To achieve competitive design, it is essential to develop an optimization method that ensures both high customer satisfaction and robustness for products with multiple criteria. While several studies have proposed optimization methods that integrate TOPSIS with Taguchi method or desirability function, no single study has yet combined all three methods into a unified optimization framework. Therefore, this study proposes an integrated optimization method that combines TOPSIS, Taguchi method and desirability function. The overall process of proposed method is based on the TOPSIS framework. To incorporate Taguchi method and desirability function into TOPSIS, we propose using desirability function for normalization, replacing the traditional vector normalization used in standard TOPSIS. In addition, Signal-to-Noise(S/N) ratios are calculated to evaluate the degree of customer satisfaction. To demonstrate the effectiveness of the proposed method, a hypothetical example is generated under specific conditions, and the resulting rankings are compared with those derived using the original TOPSIS approach. The comparison revealed that the rankings of design alternatives differed between the original TOPSIS and the proposed method. This difference is attributed to the influence of the desirability function’s threshold points, the specific type of desirability function applied (from Kano’s perspective), and the Taguchi S/N ratio used to assess satisfaction levels. These factors enabled a more nuanced evaluation of customer satisfaction and robustness, thereby validating the effectiveness of the proposed optimization method.

최근 어린이 보호구역, 생활도로, 교차로 및 터널 구간 등에서 교통사고 저감을 위한 다양한 정책이 추진되고 있으나, 물리 적 시설 중심의 대책만으로는 사고 다발 구간의 문제를 근본적으로 해소하는 데 한계가 있다. 2020년 민식이법 시행 이후 무인단속카메라, 과속방지턱, 노면 색채 포장 등이 확대되었으나, 여전히 교통사고는 지속적으로 발생하고 있다. 이에 본 연 구에서는 강제나 규제가 아닌 환경 설계를 통해 사람의 행동을 자연스럽게 유도하는 개념인 넛지(Nudge) 이론을 활용하여 도로포장과 도로시설물의 특성을 심리적 자극 요소로 운전자의 안전한 행동을 유도할 수 있는 환경적 요소를 반영한 설계 요소 개발의 필요성과 타당성을 분석하였다. 이를 위해 시각 분석 프로그램(VAS)을 활용하여 블록 패턴, 색채, 노면표시, 시 설물 설치 조건 등을 변수로 각 조건에 따른 시선 유도 및 분산 효과를 분석하였다. 그 결과, 색채 대비가 명확한 포장은 전방 주시 집중도를 향상시키는 경향을 보였으며, 도로 포장과 시설물을 통합 적용한 경우 시선 유도 및 주의 환기 효과가 더욱 향상되는 것으로 분석되었다. 본 연구의 성과물은 사람의 심리적 요소까지 반영한 새로운 설계 요소의 개발 도입 가능 성을 확인할 수 있는 기초자료로 활용될 수 있으며, 향후 연구 수행 결과를 적극적으로 활용하기 위한 정밀한 시인성 분석 프로그램의 개발과 실제 주행 환경 기반의 정밀 검증 연구가 추가적으로 수행될 필요가 있을 것으로 판단된다.

This study explores the use of Midjourney (V6) by fashion design undergraduates for AI-supported ideation, focusing on how outcomes differ based on fashion-domain competence and prompt/parameter instruction. A focused ethnographic, comparative case-study design was used to observe a short collection-development module. Data included Discord prompt and parameter logs, generated image outputs (mood boards, look proposals, and pattern drafts), one-on-one interviews, classroom observation notes, and expert co-coding and qualitative evaluation. Participants were organized into four groups by crossing Basic vs. Advanced Fashion competence (BF/AF) with Basic vs. Advanced Prompt training (BP/AP): BF-BP, AF-BP, BF-AP, and AF-AP. BF-BP depended on repetitive/imaginary use and generic descriptors, resulting in visually appealing yet conceptually fragmented and low-feasibility results. AF-BP leveraged a richer domain vocabulary to improve item-level adequacy but struggled to maintain collection-level consistency, leading to the use of external editing tools such as Photoshop and Illustrator for portfolio-level refinement. BF-AP quickly mastered commands and parameters (e.g., /describe, --chaos, --stylize, --ar, --tile, --no, --sref, --cref), generating appealing concept imagery while failing to convert outputs into wearable garments and cohesive collections. AF-AP combined advanced fashion knowledge with strategic parameter sequencing— broad exploration, followed by consistency control and selective refinement—achieving the most coherent, feasible outcomes and positioning AI as an early-stage accelerator rather than a substitute for core design and making skills. Overall, this study proposes “parameter literacy” as a domain-specific extension of GenAI literacy and offers a parameter–process mapping (divergent generation, consistency control, and editing/refinement) to enhance fashion curricula.

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.

본 연구에서는 고속선 선형설계의 효율성과 재현성을 향상시키기 위하여 선형 최적설계 자동화 기법을 제안하였다. 고속선에 서는 조파저항의 영향이 크고, 선형의 미세한 형상 변화가 저항 성능에 비선형적으로 작용해 경험 기반 설계에 한계가 있으므로, 이를 개 선하고자 다중단계 최적화와 ADAMS 알고리즘을 적용하여 복잡한 설계공간에서도 안정적인 탐색과 수렴성을 확보하고자 하였다. 선형 변경은 가우시안 구적법으로 국부 변형을 매끄럽게 제어하고자 하였으며, 목적함수인 조파저항을 구하기 위하여 포텐셜 기반 패널법을 적용하였다. 또한 민감도 분석을 통해 설계변수를 체계적으로 선정하고 변수 범위를 합리적으로 설정함으로써 비현실적 선형 생성과 최 적해 발산을 방지했다. R/V Athena 선형(Fn=0.45) 적용 결과, 배수량 변화는 거의 없으면서 조파저항은 유의미하게 감소했고, 최적화 전 과 정에서 선형의 기하학적 안정성도 확인되었다.

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.

본 연구는 AI 컴패니언을 활용한 관계 중심 게이미피케이션 기반 소아 디지털 치료 시스 템의 UX 설계 사례를 탐색적으로 제시한다. 기존 소아 디지털 치료제가 보상 중심의 행동 강화에 주로 초점을 맞춰 온 것과 달리, 본 연구는 게이미피케이션을 아동과 AI 컴패니언 간의 관계적 상호작용을 지원하는 정서적 경험 프레임으로 재정의하였다. 이를 위해 Norman의 정서디자인 이론을 기반으로 단계적 정서 UX 구조를 적용한 감정 중심 AI 워크북 을 설계하였다. 파일럿 사용자 관찰을 포함한 탐색적 사용성 연구 결과, 캐릭터 기반 AI 컴패니언과 음성 중심 인터랙션은 아동의 정서 표현 부담을 완화하고 자발적 참여를 유도 하는 데 긍정적인 역할을 하는 것으로 나타났다. 본 연구는 임상적 효과 검증을 목적으로 하지 않으며, 관계 중심 UX 접근이 소아 디지털 치료 설계에서 가질 수 있는 가능성을 제 시하는 탐색적 설계 사례로서, 향후 임상 및 장기 연구를 위한 기초적 논의를 제공한다.

Injection molds, composed of components such as upper and lower cores, mold bases, pins, and cooling channels, serve as the primary tooling for manufacturing plastic products. Despite the often simple geometry of molded products, the configuration and design of mold components remain highly complex, making the technical expertise and accumulated know-how of mold designers essential. However, the mold industry is facing increasing difficulties due to the discontinuation of academic programs dedicated to mold design, the aging of experienced designers, and the lack of incoming skilled personnel. To address these challenges, research on automating mold design has continued, and recent advancements in artificial intelligence (AI) have accelerated efforts to internalize expert knowledge through a variety of computational approaches. In this study, we conducted foundational research aimed at constructing a DT-AX platform capable of handling multiple domains by implementing and modularizing diverse processes within a digital-twin (DT) environment and integrating AI modules specialized for each process. Given the input dimensions of a bottle-cap model (diameter and height), the simplified outer dimensions of a core mold were predicted and subsequently used to generate a 3D model. The resulting STEP file was verified to be compatible with commercial CAD and simulation software. Overall, the results demonstrate the feasibility of implementing an automated mold-design module within a digital-twin environment. Future work will focus on diversifying design variables and increasing geometric complexity to develop modules that more closely approximate real-world mold design.

본 연구는 고령중국어 학습자의 신체적, 인지적, 사회·환경적 및 고령자 외국어 학 습의 방면에서 학습자의 특성과 심리요인을 분석하고, 설문과 인터뷰를 통한 중국어 학습자의 학습현황을 통하여 수업설계의 기초가 되도록 하였다. 또한 수업설계의 활 용도를 검증하기 위하여 실제 수업을 진행하고 분석을 실시하였다. 다만 설문과 인 터뷰인원의 제한성으로 인해 연구의 한계성이 존재하지만 수업에서 학습자의 심리를 고려한 수업은 학습의 지속성과 학습자의 소속감을 향상시킨다는 긍정적인 결론을 도출하였다. 중국어 학습이 언어기능향상의 목적 외에도 고령학습자에게 노년기를 함께 보낼 수 있는 상호 보완적인 기능을 담당할 수 있도록 하는 것에 본 연구의 의 의를 두고자 한다.

This study presents the design and FPGA implementation of a low-power, high-throughput digital modem for Medical Implant Communication System (MICS) applications. The proposed system applies a π /4-D8PSSK modulation technique to achieve high data efficiency while maintaining low power consumption. Implemented on a Xilinx Spartan-7 FPGA, the modem achieves a data rate of 16.4 ± 0.3 Mbps, with a power consumption of 0.6 ± 0.02 W/h, demonstrating a 40% improvement in energy efficiency compared to conventional 4FSK systems. The system satisfies IEEE 802.15.6 and ITU-R RS.1346 standards, with verified waveforms through MATLAB–Simulink and Chipscope. This work contributes to localization of medical implant communication technologies and provides a foundation for ASIC-based integration for next-generation biomedical and industrial wireless systems.

The purpose of this study is to develop and implement a customized AI-based speaking diagnosis, learning, and assessment system, SpeakMaster, in order to overcome the lack of systematic evaluation and practice opportunities in school English speaking class. This system integrates automated speaking scoring to provide students with feedback on their speaking abilities across pronunciation, conversation, and presentation. This study adopts a design-based research methodology, demonstrating the development and implementation process. 1,451 students and eight teachers in elementary, middle, and high schools participated in the experiment. Data were collected through learning logs, teacher journals, interviews, and post-surveys. The findings indicate that the system design is appropriate for English class, promoting students’ flow in engaging speaking practice. Students showed motivation and satisfaction while teachers found the system valuable for monitoring student progress and facilitating speaking assessments. Despite the challenges of improving chatbot performance and enhancing scoring reliability, the results suggest that SpeakMaster shows potential to enhance English speaking education.