• Proceedings of the Korean Society of Dyers and Finishers Conference
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• 2012.03a
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• pp.99-99
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• 2012

소비자의 기호가 다양화되면서 다품종 소량생산에 따른 대량 맞춤화에 대한 관심이 높아지고 있으며, 1990년대 후반부터 급격히 확산되고 있는 인터넷의 발달 및 디지털 기기의 확산에 따라 인터넷을 기반으로 한 전자상거래 환경에서의 대량 맞춤화 프로세스는 최근 의류 분야에서 주요한 경향으로 받아들여지고 있다. 대량 맞춤화 의류 제품의 수요는 인터넷 등 IT 기술의 발달과 맞물려 최근 개인의 특성과 취향을 고려한 인터넷 맞춤서비스를 기반으로 증가추세에 있으며, CAD 등의 테크놀러지의 활용을 통하여 디자인, 패턴제작, 재단 등의 자동 시스템을 도입함으로써 그 영역을 확장하고 있다. 이에 발맞추어 국내에서는 '디지털 패션 시스템'을 통해 3차원 바디 스캔, 3차원 컴퓨터 그래픽스, 3차원 Simulation, DID, DTP, 맞춤주문형 제품의 대량 생산기술 등의 단위 기술이 기업단위로 활용되는 등 가상현실을 이용한 3차원 가상쇼핑 등을 포함하는 기술로서 차세대 섬유패션 산업의 창출에 기여하고 있다. 그 중, 디지털 텍스타일 프린팅은 디자인에서부터 날염까지 전체 공정을 컴퓨터로 처리하여 기존의 복잡한 날염 공정을 단축하여 다품종 소량생산이 가능한 날염 방식으로, 대량 맞춤형 프로세스를 지원하며, 프로슈머(prosumer)로서의 소비자가 직접 참여하여 그들의 욕구를 최대화 할 수 있는 소비자 만족을 극대화 시킬 수 있는 대량 맞춤화 프로세스 중 하나의 단계로 소비자의 수요 반영하는 요소로 작용하고 있다. 즉, 디지털 패션 시스템에서의 디지털 텍스타일 프린팅은 대량 맞춤형 의류 제품에 있어 소비자 만족도와 직결되는 요인으로 분석된다. 따라서, 본 연구에서는 디지털 패션 시스템 기반의 대량맞춤형 섬유제품 및 디지털 텍스타일 프린팅 적용 섬유제품에 대한 기초 평가 문항을 제시하고, 소비자 태도를 분석하여 제시하였다. 또한, 분석된 기초 소비자 태도를 반영하여, 디지털 패션 시스템 기반의 디지털 텍스타일 프린팅(Digital Textile Printing) 의류 제품의 선호도, 인지도, 수요도 및 구입의향을 분석하여 최종적으로 디지털 패션 시스템에 적용된 디지털 텍스타일 프린팅 의류 제품의 수요 경향을 제시하였다.

• Smart Media Journal
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• v.10 no.1
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• pp.116-123
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• 2021

Due to the rapid growth of the online fashion market and the resulting expansion of online choices, there is a problem that the seller cannot directly respond to a large number of consumers individually, although consumers are increasingly demanding for more personalized recommendation services. Images are being tagged as a way to meet consumer's personalization needs, but when people tagging, tagging is very subjective for each person, and artificial intelligence tagging has very limited words and does not meet the needs of users. To solve this problem, we designed an algorithm that recognizes the shape, attribute, and emotional information of the product included in the image with AI, and codes this information to represent all the information that the image has with a combination of codes. Through this algorithm, it became possible by acquiring a variety of information possessed by the image in real time, such as the sensibility of the fashion image and the TPO information expressed by the fashion image, which was not possible until now. Based on this information, it is possible to go beyond the stage of analyzing the tastes of consumers and make hyper-personalized clothing recommendations that combine the tastes of consumers with information about trends and TPOs.

• Fashion & Textile Research Journal
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• v.22 no.1
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• pp.55-65
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• 2020

Smart photonic clothing integrates light emitting technology inside and outside of the garment and integrates it as a fashion product. It expresses digital color that radiates light outside the body that expands the functionality of the clothing as well as makes new and various attempts visually. It is also is gradually expanding into a new area of fashion. LED, one of the digital color output devices, is a light emitting device that is suitable for presenting consumer customized designs in that the patterns and colors of clothes can be modified as desired by utilizing computer technology such as program coding. LED technology that can realize various digital colors is actively applied in various industrial design fields, but there are few previous studies on smart clothes using LED color in Korean fashion fields. Therefore, this study develops a prototype of a customized LED smart photonic garment that allows the user to directly participate in the color implementation of clothing and select a digital color suitable for the desired function. The LED module was designed to be detachable from clothing and made using a 256-pixel LED matrix. Various coding patterns of the LED were designed using the coding change of Arduino program.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.2-5
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• 2022

With the advent of deep learning and the rapid development of ICT (Information and Communication Technology), research using artificial intelligence is being actively conducted in various fields of society such as politics, economy, and culture and so on. Deep learning-based artificial intelligence technology is subdivided into various domains such as natural language processing, image processing, speech processing, and recommendation system. In particular, as the industry is advanced, the need for a recommendation system that analyzes market trends and individual characteristics and recommends them to consumers is increasingly required. In line with these technological developments, this paper extracts and classifies attribute information from structured or unstructured text and image big data through deep learning-based technology development of 'language processing intelligence' and 'image processing intelligence', and We propose an artificial intelligence-based 'customized fashion advisor' service integration system that analyzes trends and new materials, discovers 'market-consumer' insights through consumer taste analysis, and can recommend style, virtual fitting, and design support.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.2
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• pp.373-378
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• 2017

While on-line shopping markets are growing, consumer's procurement processes are being confused regardless of on or off line market and, smart consumers who want intelligent tailored services have emerged. Depending on the changeable pattern of consumer, most of related companies provide various Omni channel and O2O service. However, reactions of the fashion companies are tend to be late. Recently, the IoT environment has changed to standards-based open platform and it requires a variety of intelligent services depending on the type of environment and objects. This thesis proposes fashion O2O system using smart fitting display that is adaptable to fashion companies. This proposed system provides fitting information which is performed on off-line by users after constructing the database, it also support the works as on-line status, thus, it makes users' procurements to maintain continuously. For the more, customer oriented intelligent fitting service would be expected by the information connection with the shop and delivery systems.

• Journal of Service Research and Studies
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• v.14 no.1
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• pp.77-90
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• 2024

This research aims to investigate methods of leveraging generative artificial intelligence in service sectors where consumer sentiment and experience are paramount, focusing on minimizing hallucination phenomena during usage and developing strategic services tailored to consumer sentiment and experiences. To this end, the study examined both mechanical approaches and user-generated prompts, experimenting with factors such as business item definition, provision of persona characteristics, examples and context-specific imperative verbs, and the specification of output formats and tone concepts. The research explores how generative AI can contribute to enhancing the accuracy of personalized content and user satisfaction. Moreover, these approaches play a crucial role in addressing issues related to hallucination phenomena that may arise when applying generative AI in real services, contributing to consumer service innovation through generative AI. The findings demonstrate the significant role generative AI can play in richly interpreting consumer sentiment and experiences, broadening the potential for application across various industry sectors and suggesting new directions for consumer sentiment and experience strategies beyond technological advancements. However, as this research is based on the relatively novel field of generative AI technology, there are many areas where it falls short. Future studies need to explore the generalizability of research factors and the conditional effects in more diverse industrial settings. Additionally, with the rapid advancement of AI technology, continuous research into new forms of hallucination symptoms and the development of new strategies to address them will be necessary.