Fashion and Textiles最新文献

The Color Garments Encouragement Banner was designated a Korean Heritage in 2014 to recognize it as the most significant object of the color garments encouragement campaign. However, despite its significance, nothing is known about its manufacture. Therefore, this study attempted to analyze the materials of the banners to estimate when they were manufactured and repaired. The investigation of materials on the banner involved visual examination, literature review, microscopy, SEM–EDS, FT-IR, Py-GC–MS, ICP-MS, and LC–MS. The fabric, patch, and threads comprising the artifact were identified as cotton. FT-IR and Py-GC–MS confirmed that the repair patch was a woven blend of polyester and cotton yarns. EDS analysis indicated that the polyester was treated with titanium delustering. ICP-MS detected high concentrations of chromium that were not used in traditional dyeing techniques. The azo and sulfur compounds were identified by LC–MS analysis. The material layered on the grommet patch was thought to be a mixture of Pb, Ti with CaCO₃ and BaSO₄. Based on the overall results, the production date of the banner was narrowed down to the late 1920s, and the repair date to the mid-1950s. Although the materials used could not be identified owing to the limitations of the applicable analysis. Nonetheless, it is hoped that the analyses conducted in this study can serve as a scientific foundation for dating modern cultural heritage objects with limited handed-down record and historical documentation.

With the rise of online shopping due to the COVID-19 pandemic, Recommender Systems have become increasingly important in providing personalized product recommendations. Recommender Systems face the challenge of efficiently extracting relevant items from vast data. Numerous methods using deep learning approaches have been developed to classify fashion images. However, those models are based on a single model that may or may not be reliable. We proposed a deep ensemble classifier that takes the probabilities obtained from five pre-trained models such as MobileNet, DenseNet, Xception, and the two varieties of VGG. The probabilities obtained from the five pre-trained models are then passed as inputs to a deep ensemble classifier for the prediction of the given item. Several similarity measures have been studied in this work and the cosine similarity metric is used to recommend the products for a classified product given by a deep ensemble classifier. The proposed method is trained and validated using benchmark datasets such as Fashion product images dataset and Shoe dataset, demonstrating superior accuracy compared to existing models. The results highlight the potential of leveraging transfer learning and deep ensemble techniques to enhance fashion recommendation systems. The proposed model achieves 96% accuracy compared to the existing models.

This study forged an interdisciplinary design partnership between an apparel designer, biomechanical kinesiologist, and a professional female athlete to design and test a novel new upper body performance garment for improved fit, mobility, comfort, donning/doffing and throwing performance in female professional athletes. Researchers collaborated to address garment functionality and performance during the multi-phase prototyping design process. The final designed performance top was tested against a control for satisfaction differences with fit, mobility, comfort, donning, and doffing, along with throwing performance utilizing a mixed methods questionnaire and motion capture equipment. Open-ended questions were analyzed with content analysis, while quantitative measures utilized ANOVA and independent t-tests. Results indicated the designed performance top had higher fit satisfaction and more optimal positioning during the overhead throw when compared to the control garment, with no difference in comfort or mobility, and the designed top had lower donning and doffing satisfaction. The designed performance top illustrates the need for further research in increasing satisfaction in donning and doffing with bust/bra satisfaction in performance garments and future research to better understand the psychological sensory cues in posture cueing garments.

This study aimed to investigate the determinants of transaction satisfaction and intention to use local second-hand marketplace platforms, focusing on the impact of negotiation costs and the moderating influence of users’ sense of their neighborhood. Building on transaction cost theory, we conceptualized negotiation costs as a multidimensional construct that encompassed economic, performance, time, and psychological costs. Our findings revealed that economic and time costs had a significant negative effect on transaction satisfaction, whereas performance and psychological costs did not exhibit such an impact. Furthermore, transaction satisfaction positively influenced users’ intention to use the platform, thereby mediating the relationship between negotiation costs and intention to use. Additionally, we found that users’ sense of their neighborhood enhanced transaction satisfaction and acted as a moderator, attenuating the negative impact of negotiation costs on satisfaction. For users with a high sense of my neighborhood, the negative impact of economic, performance, and time costs on transaction satisfaction was attenuated. These findings contribute to a deeper understanding of consumer behavior on local second-hand trading platforms and emphasize the importance of social cues in shaping transaction satisfaction and intention to use. The implications of our study offer valuable insights for second-hand platform operators.

This paper presents research into the parameters affecting the real and virtual drapability of silk fabrics for traditional Hanbok. The KES-FB (Kawabata evaluation system of fabric) and the CLO (3D fashion design software) fabric kit system were used to evaluate the physical properties. The relationships between physical properties and the drape coefficient of silk fabrics were statistically analyzed. It was determined whether the objectified real fabric data could be used and applied as a database when implementing virtual clothing using a 3D virtual clothing program. The KES-FB properties and CLO fabric kit results indicated that the bending property and weight were significantly correlated with the drape coefficient. The bending rigidity and weight per unit area were influential parameters of the drape coefficient. The regression analysis results of both the KES-FB and the CLO fabric kit revealed that bending rigidity and thickness were statistically significantly correlated with the drape coefficient. The bending property was the most determining parameter for the drape coefficient of silk fabrics. A statistically significant difference between the real and virtual drape coefficients of stiffer silk fabrics was found. In the CLO 7.1 program, the accuracy of drape implementation of virtual fabrics was found to be useful when the bending stiffness of the actual fabric was less than 0.089 gf·cm²/cm and the elasticity was more than 1.03%. The results revealed that more research on the program modeling method considering the mechanical and physical properties of the real fabric and its structure is needed.

With the rapid development of technology and media, people’s experiences of other cultures have shifted from direct only to a combination of direct and virtual experiences. This study investigated how global consumers directly and indirectly experience other cultures, mediated by YouTube. It focused on the consumer experience of the Korean traditional costume, the Hanbok. YouTube content focusing on Hanbok experiences was collected to understand the direct Hanbok experiences, while viewers’ reactions to YouTube content (i.e., views, likes, and comments) were collected to understand indirect Hanbok experiences. The analysis involved two steps. First, the latent Dirichlet allocation topic modeling algorithm in Python was used to extract topics from direct and indirect Hanbok experiences. Second, simple regression analysis was conducted using numerical data derived from textual data through linguistic inquiry and word count to examine the relationship between direct Hanbok experiences via YouTube and viewer engagement as an indicator of indirect Hanbok experiences. The results showed that YouTubers’ Hanbok experiences were categorized into seven topics, namely novelty, sensory, daily, cultural, trial, pleasing, and intellectual experiences. Indirect Hanbok experiences were identified based on dimensions of consumer engagement, such as social, cognitive, learning, behavioral, and affective engagement. The regression analysis results revealed that cultural and affective Hanbok experiences significantly impacted consumer engagement. This study expanded the scope of research by exploring indirect cultural experiences mediated by a media platform.

As the fashion industry increasingly adopts digital platforms, fashion designers face challenges in digitally exploring and selecting fabrics. This transition underscores the complexities in digitally capturing fabrics. Extending our previous research, this study specifically examined the relationship between designers' certainty and their perceptual accuracy in digital fabric evaluations because uncertainty can diminish confidence in digital assessments, leading to an over-reliance on physical samples and slowing digital progress. The research employed a comparative analysis, engaging designers in both digital and physical fabric evaluations. The objectives of this study were: to evaluate the precision of digital fabric representations against physical assessments; to assess designers' psychological certainty and its correlation with assessment accuracy; and to identify fabric attributes that are most prone to misrepresentation digitally. The findings indicated that there is no direct correlation between evaluation accuracy and psychological certainty. Notably, for drapability, digital evaluations exhibited both high levels of certainty and accuracy, implying potential enhancements in digital representations of other textile attributes. The results of this study will contribute to the development of advanced digital fabric evaluation systems that can accurately replicate the physical fabric experience, thus ensuring a high level of certainty. This, in turn, will enhance the reliability and effectiveness of fabric selection on digital platforms.

This study developed a wearable device designed to aid visually impaired people (VIP) who are unfamiliar with Braille and/or struggle to learn and use Braille employing the design thinking process. VIP often face challenges in learning and using Braille, and even with existing equipment, including smart technologies, they frequently experience aesthetic and emotional discomfort beyond usability and accessibility. The interdisciplinary research team conducted mixed-method research following the double diamond module of the design thinking process. Initially, qualitative interviews to analyze VIP’s Braille utilization were conducted to derive design factors. Subsequently, identified design factors were applied to conduct experiments with appropriate embroidery methods and connection structures using MXene-coated yarn. The strategies effectively enhanced pressure sensing performance by two times and realized highly localized and independent sensing to enable Braille recognition. Finally, a specific design for the wearable Braille recognition glove was proposed that incorporated these findings. The proposed wearable glove, equipped with a high-performance Braille recognition sensor, is designed to enhance the psychological and aesthetic satisfaction of VIP. The glove’s universal design and accompanying technical support allow VIP to maintain their existing Braille reading methods while enjoying enhanced performance and avoiding social stigmas.

Owing to global warming and pollution concerns, reducing the environmental footprint of the textile and fashion industry has received considerable attention. Within this industry, the dyeing and finishing processes contribute significantly to greenhouse gas emissions and water pollution. This study introduces an innovative approach to address these challenges by leveraging digital twin technology to optimize the textile dyeing process. A smart analysis module was developed to continuously monitor and analyze the dyeing parameters in real time to implement control actions to automatically reduce the process duration. Integrated with this module, a digital twin of the dyeing machine enabled the real-time monitoring of energy consumption and process parameters. A case study comparing the traditional dyeing process with the optimized process was conducted. The results showed that dyeing time was reduced by ~ 17.5% without compromising dyeing quality. Energy consumption and greenhouse gas emissions were also reduced by ~ 12.1% when using the optimized process. This study offers a practical and sustainable option for textile dyeing, particularly for small and medium-sized enterprises.

Recently, there has been a lot of interest in industry and academia in virtual influencers (VIs). This study aimed to augment the understanding of VI marketing by exploring the relationship between VI’s characteristics (i.e., their humanlikeness level and operator type), their social media post characteristics (i.e., emotional expression, use of pronouns, use of punctuation marks), and consumer engagement. The analysis of three popular VIs’ Instagram profiles and posts revealed that VIs with less humanlike appearances tend to exhibit stronger positive emotional expressions. Interestingly, the level of humanlikeness in appearance was not associated with VI's other post characteristics, suggesting other intrinsic factors (e.g., the VI’s character and virtual universe) may play a pivotal role. Whether a brand operates the VI or not made a statistically significant difference in the VI’s post characteristics. Assessment of user engagement metrics (i.e., number of likes and comments) showed that users prefer hyper-realistic VIs and VIs operated by non-brand agencies, underscoring the importance of perceived authenticity in the digital realm. The emotions expressed by VIs, along with their use of punctuation, were positively related to user engagement.