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.

The optimal design of steel plate girders has traditionally relied on meta-heuristic techniques, such as Genetic Algorithms (GA), to handle discrete design variables and complex non-linear constraints, including shear buckling and section classification. However, these methods suffer from high computational costs as they require repetitive re-optimization for every new load condition. To address this limitation, this study proposes a highly efficient Sequential Multi-Agent Reinforcement Learning (MARL) framework based on the Agent-Environment Cycle (AEC) architecture. Unlike parallel one-shot approaches, the proposed model effectively learns the dependencies between design variables by determining them sequentially. Furthermore, to maximize cost efficiency during the inference phase, we introduce an Adaptive Inference Chain combined with a deterministic DCR-based Shrink-Refine algorithm. Experimental results on 100 diverse load cases demonstrate that the proposed method achieves an average cost reduction of 8.2% compared to the GA baseline while maintaining 100% feasibility. With an inference time reduced to approximately 76 ms, the model demonstrates significant potential for real-time automated design. Additionally, an in-depth analysis of cases where the Demand-Capacity Ratio (DCR) fell short of the target clarifies the exploration limits within the discrete design space and validates the robustness of the algorithm.

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.

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.

This study examines the historical background, types, and characteristics of apron-style clothing from the Joseon dynasty and reinterprets them through contemporary fashion design. Here, apron-style clothing refers to clothing worn over a first layer, secured with straps, and structured to cover the front of the body. During the Joseon dynasty, apron-style clothing was differentiated according to purpose as follows: daily use, performance use, and ceremonial use. Structurally, it can be classified into four types: square, square with multiple straps, three-pronged with waist gathers, and three-pronged with narrow pleats. Based on Joseon-period apron-style clothing’s historical significance, structural features, and layered visual effects a total of four contemporary fashion designs were developed using CLO 3D as a design tool: Design 1 is a mini dress in a tube-top style derived from a simple square apron, Design 2 is a mini dress that reinterprets the Yuso [流蘇] decoration of the Boro [甫老], Design 3 is a cape design divided into three sections that is inspired by the Suboro [繡甫老], and Design 4 is a layered skirt reflecting the pleated structure of the Jeonhaeng-utchima [前香上裳]. The design outcomes demonstrate that the structural characteristics of traditional apron-style clothing are effectively expressed through silhouette, while their layered qualities are rendered with depth using different materials and colors. This research are expected to contribute to a deeper understanding of Korean traditional costume and to expand its value and potential for contemporary application.

본 연구는 국내 준정부기관인 K기관의 사내코칭 제도를 대상으로 설계 원리, 운영 체계, 성과 및 한계를 분석하여 공공부문 HRD 혁신 전략으로서의 가능성을 탐색하였다. 혼합연구 설계를 적용하여 문서자료, 참여자 설문(n=72), 운영 실적 자료를 종합 분석하였다. 분석 결과, 참여자 만족도(M=4.94)와 추천 의향 (M=4.89)은 높게 나타났다. 서술형 응답 72건의 내용분석에서 도출된 90개 의미 단위는 경력개발, 자기성장, 직장 내 관계, 직무 어려움, 건강관리, 일·생활 균형 의 여섯 영역에 걸쳐 인지·정서·행동 차원의 변화를 보여주었다. 참여자의 서술 에서는 ‘구체적·계획·실행’ 등 목표 전환 표현, ‘자신감·용기·의지’ 등 자기효능감 표현, ‘안정·후련·해소’ 등 정서 완화 표현, ‘존중·안전·경청’ 등 소통 관련 표현이 여러 영역에 걸쳐 반복적으로 확인되었다. 본 연구는 공공조직 맥락에서 사내코 칭의 참여자 반응과 경험적 효과를 탐색하고 제도 설계의 성공 조건을 도출하였 다는 점에서 이론적 기여를 지닌다. 실천적으로는 정서적 소진 완화, 경력개발 지원, 조직몰입 제고를 통해 공공서비스 품질 향상에 기여할 가능성을 제시한다.

A needle-free automatic injection syringe is a device that delivers drugs into the skin and tissues using a high-speed fluid jet without using an injection needle. This technology is attracting attention as an efficient means of vaccine delivery in the veterinary and livestock fields that reduce the risk of cross-infection and require mass vaccination. In particular, animal vaccination provides various advantages over conventional needle injection methods in terms of worker safety, inoculation speed, and maintenance cost. Among these drivers, Jet Injector Nozzle's flow path design is very important in needleless automatic injection syringes. This paper was conducted to solve the problem of pressure loss at the nozzle discharge side of the existing Jet Injector in designing the flow path of the animal vaccine-free automatic injection syringe nozzle. To this end, CAE was performed and the optimum design of the flow path required by the company was performed, and a large flow rate was possible in the optimal shape design, but this focuses on the nozzle flow path, which requires a review of design additions of cylinders and motors on the rear side.

This study examines the structural characteristics of interface design in experiential content environments, where users must process complex information in real time. Despite the growing importance of such environments, interface design has been primarily studied in terms of usability and efficiency, with limited attention to structural and cognitive aspects. To address this gap, this study analyzes aviation simulation content as a representative case of experiential media. Three cases—Microsoft Flight Simulator, X-Plane, and VR-based aviation simulation content—were comparatively examined based on visual interface structure, information visualization methods, and user interaction characteristics. The results reveal that experiential content interfaces share common structural patterns, including hierarchical information organization, multi-layered visualization, cognitive–immersion balance, and real-time interaction loops. Based on these findings, this study proposes a cognitive-based interface design model that explains how users prioritize, interpret, and respond to information within experiential environments. This study contributes to the field by reframing interface design as a structural and cognitive system, rather than merely a usability-driven process.

This study proposes an automated torch angle and position adjustment mechanism for three-dimensional curved-surface welding in shipbuilding. Designed to replace manual operations, the mechanism actively responds to the relative angle between the base plate and stiffener, enabling simultaneous control of torch orientation and positioning using a single power source. The system is integrated into a tracker consisting of a pinion-sector gear assembly for angle adjustment and a cam mechanism for position control. Dynamic simulations confirmed that the torch stably follows the stiffener angle across varying welding speeds, with smooth compensatory motion between the carriage and tracker. Furthermore, a motor-torque-based PID control was implemented, maintaining the torch angle error within 0.23° and the wire tip position error within 0.02 mm. These results verify that the proposed mechanism is highly effective for the automated welding of complex curved structures.

Locally resonant metamaterials (LRMs) are artificial periodic structures that effectively suppress elastic wave propagation within specific frequency bands, known as bandgaps, by utilizing local resonance phenomena of embedded mass-spring resonators. Conventional LRMs, however, are limited by fixed bandgap characteristics once fabricated, necessitating re-fabrication or complex processing for any frequency adjustment. This study proposes a novel, tunable bandgap LRM architecture constructed from readily available, off-the-shelf mechanical components: a plastic bolt serving as the stiffness element and a changeable steel square nut as the mass element. Numerical analyses, employing Bloch-Wave theory for dispersion curve calculations and finite element methods for frequency response function (FRF) simulations, validate the systematic tunability of the bandgap. Specifically, by simply adjusting the nut's position along the bolt, the bandgap's central frequency and bandwidth can be effectively modulated without re-machining. Experimental validation on an 8x8 finite array structure confirms the formation and adjustable nature of these bandgaps, demonstrating a consistent shift in the bandgap frequency range in response to nut position changes, which aligns well with numerical predictions. This approach offers a practical, low-cost, and easily manufacturable solution for vibration mitigation, enabling on-site adaptable designs for targeted frequency ranges.

A needleless automatic injection syringe (Jet Injector) is a device that delivers drugs into the skin and tissues using high-speed fluid jets without the use of injection needles. Jet Injector's core technology is to penetrate the stratum corneum of the skin by converting pressure energy generated in the driving unit into fluid kinetic energy, and structural loads and dynamic responses generated in this process directly affect the performance and durability of the device. Therefore, the structural mechanical design of the drive unit can be said to be a key factor in securing the reliability and injection accuracy of the Jet Injector. This paper is intended to provide the basis data for the basic design of the drive unit of the "Animal Vaccine-free Automatic Injection Syringe (hereinafter referred to as the "Jet Injector"), and this calculation data provides basic data for the design. Based on this, it is possible to design the drive unit according to various assumptions and given conditions, and the expected performance can be reasonably inferred according to the design.

Hybrid transmissions are a component being researched and developed by major automakers to reduce fuel consumption and emissions. Power-split hybrid transmissions utilize two or more motors to enable continuously variable gear shifting and control the operating points of the engine and motor, thereby reducing engine fuel consumption and emissions. This study proposes a systematic design method for a two-mode hybrid system that minimizes power circulation. To achieve this, we design a two-mode hybrid system that combines three-axis and four-axis systems using two planetary gears and two clutches. We also propose a structure that operates only in modes where power circulation does not occur, thereby improving transmission efficiency.