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3D프린팅을 이용한 편성물의 역학적 특성 연구 -PLA, TPU 필라멘트를 중심으로-
한유정 ( Yoojung Han ),김종준 ( Jongjun Kim ) 한국패션비즈니스학회 2018 패션 비즈니스 Vol.22 No.4
Using FDM 3D printing, yarn shape and composition were modeled and 3D printed with PLA and TPU filaments currently used for apparel. Based on this, mechanical characteristics were measured to determine 3D printing yarn according to type of filaments in the 3D printed output and deformation and recovery characteristics due to differences in structure type. As a result of examining tensile and shear characteristics of PLA and TPU 3D printing compiles, TPU overall was measured with significantly lower stress than PLA. This is due to high elasticity of TPU's character, revealing that it has better flexibility than PLA. In addition, during deformation due to external forces, the more freedom between the head and foot parts of the loop, and the lower the force associated with each other, the more flexible it is. TPU revealed that it was easier to tension and recovery from tensile deformation than PLA, indicating potential for clothing materials using 3D printing. If high-molecular materials, such as PLA flexibility, it is likely to provide some flexibility through development of styles, including degree of freedom in modeling. Based on this, we provide basic data for developing 3D printing textures that can be satisfied with textile for apparel.
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챗봇 에이전트 정체성(identity)에 따른 사용자의 인식 및 행동 차이에 대한 연구
김유정(Yoojung Kim),한상규(Sang Kyu Han),윤종묵(Zongmuk Yoon),허은영(Eunyoung Heo),김정훈(Jeong-Whun Kim),이중식(Joongseek Lee) 한국HCI학회 2017 한국HCI학회 논문지 Vol.12 No.4
최근 헬스케어 분야에서 지속적인 건강 관리 서비스를 제공하기 위해 챗봇 에이전트를 도입하는 시도가 늘어나고 있다. 하지만 아직까지 챗봇 에이전트 설계에 대한 연구는 인간과 기계라는 구분에 머물러 있어, 에이전트에 대한 신뢰, 친밀감, 행동 변화를 모두 고려해야 하는 헬스케어 영역에서의 챗봇 설계에 활용하기에는 한계가 있다. 따라서 본 연구에서는 인간과 기계라는 구분에서 나아가, 보다 다양한 에이전트 정체성에 따라 사용자의 인식과 행동이 달라지는지 조사하고자 했다. 이를 위해 정체성을 다르게 설정한 3개의 챗봇 에이전트인 의사(개인), 병원(기관), 가상 에이전트(기계)를 제작한 후, 집단 간 설계(between-group design) 방식으로 36명의 사용자를 세 그룹으로 나누어 6일간 챗봇을 사용하며 건강 습관을 기록하도록 하였다. 6일간의 챗봇 사용을 마친 후, 사후 설문 및 인터뷰를 통하여 사용자들의 신뢰, 친밀감, 행동 개선에 어떤 영향이 있었는지 살펴보았다. 그 결과 의사와 병원 에이전트는 건강지키미 에이전트보다 높은 신뢰를 보였으며, 친밀감에 있어서도 더욱 높은 평가를 받았다. 또한, 많은 참가자가 병원 에이전트를 선호하는 것으로 드러났다. 하지만 세 개 에이전트 모두 사용자들의 행동 변화를 성공적으로 유도하지는 못했던 것으로 나타났다. 이 같은 결과를 바탕으로 본 연구는 정체성이 다른 챗봇 에이전트가 사용자에게 신뢰, 친밀감의 차원에서 다른 영향을 미칠 수 있음을 밝혀냄으로써, 헬스케어 분야에서 챗봇 에이전트 설계에 대한 실용적인 가이드라인을 제공한다는 점에서 의의가 있다. In recent years, some service providers have introduced chatbot agents to provide engagement in the healthcare field. However, current research on chatbot agents is still limited to designing various chatbot identities for healthcare services. By contrast, this study aims to investigate how various agent identities affect users’ perceptions and behaviors differently. We developed three chatbot agents with different identities: a doctor (an individual), a hospital (an institution), and a virtual agent (a machine). Then, we recruited 36 users and divided them into three groups, each using a different chatbot agent. They were asked to track their behaviors and review advice from the chatbot agent for six days. Post-hoc surveys and interviews were conducted in order to investigate users’ perceptions. The findings are as follows: participants felt more trusting and intimate with the doctor and hospital agents than with the virtual agent. Many of the participants preferred the hospital agent due to its higher reliability. However, all three agents did not lead the participants to change their behaviors. This study contributes to providing practical guidelines for designing chatbots in the healthcare field by studying users’ perceptions and behaviors depending on chatbot identities.
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Development of a fabric classification system using drapability and tactile characteristics
이소민,Yoojung Han,윤창상 한국의류학회 2024 Fashion and Textiles Vol.11 No.1
When producing clothing using virtual fitting technology or purchasing textile and clothing products online, it is challenging to make judgments or communicate information about sensory characteristics, such as drapability and tactile sensations, as there are no clear objective indicators for these factors. Therefore, the study aims to develop a classification system for the sensory properties of fabrics using drapability and tactile characteristics as quantitative indicators. The developed system was verified through subjective evaluations by an expert group, and it was found to be meaningful in reflecting classification levels in practice. The drapability and tactile sensation (softness; TS7) of the fabric were classified using fuzzy c-means cluster analysis, and the results were confirmed through a subjective evaluation by experts. The classification system was then used to predict the classification group, constituted by drapability and tactile characteristics, from mechanical properties using an artificial neural network. The network was trained on 534 fabric samples for drapability and tactile sensation (softness), and it correctly predicted 202 samples out of 243 validation data, with a forecasting accuracy of 83.5%. The developed classification system enables predictions and judgments about subjective characteristics like fabric drapability and tactile sensation based on the mechanical property values of various samples.
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