• Science of Emotion and Sensibility
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• v.26 no.3
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• pp.69-82
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• 2023

High-tech sensibility textile and fashion, in which consumers' emotions and various textile and fashion technologies are converged, is an important industrial group. It is important to develop the ability to apply in practice by gathering the creative by understanding other fields and exchanging ideas through interdisciplinary collaboration in the field of emotional engineering. Through interdisciplinary research and collaboration, talent must be nurtured of individuals who would lead the era of the 4^th Industrial Revolution with the ability to empathize with others as well as the creative convergence-type intellectual ability necessary for the rapidly changing society. To determine content-creation methods, basic research is conducted. Additionally, this study investigates on the current status and educational process of the emotional textile-fashion industry worldwide. To nurture talents in the textile and fashion sensibility science, the basic contents are created to manage the knowledge that delivers sensibility science and the ICT related to this field, as well as in the intensive, PB-style conceptual design based on sensibility. The process from derivation of consumer emotion analysis and product development can be experienced through smart kit practice. Moreover, various methods are developed to set up intellectual property rights generated while developing ICT convergence products as start-ups. The study also covers new knowledge rights to develop emotional textile fashion.

• Journal of the Korea institute for structural maintenance and inspection
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• v.28 no.3
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• pp.83-90
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• 2024

This study investigates the 3D printing characteristics of strain hardening cement composites (SHCC) reinforced by PVA fibers. Three SHCC mixtures with diverse fiber volume fractions (1.0% for F1.0 mixture, 1.5% for F1.5 mixture, and 1.8% for F1.8 mixture) were designed. Except for the F1.0 mixture, all mixtures met the necessary conditions for multiple micro-cracking, with higher fiber volume fractions more readily satisfying these conditions. The flow values of three SHCC mixtures were within the 3D printable range of 120~160 mm, exhibiting decreased flow values with increasing the fiber volume fractions. Observation of the printed SHCC surfaces indicated that the F1.0 mixture had a Level-3 (good) rating, while F1.5 and F1.8 were rated as Level-2 (average). Higher fiber volume fractions resulted in poorer surface quality, thus, further research needs to be performed for modulating SHCC mixture suitable for 3D printing. The uniaxial tension behavior showed that the F1.0 mixture failed at lower strain, whereas F1.5 and F1.8 exhibited higher strain performance with multiple micro-cracks occurring.

• Fashion & Textile Research Journal
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• v.20 no.5
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• pp.616-620
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• 2018

Recently, many researches have been conducted to improve the performance and wearability of smart wearables. In this study, we designed and fabricated the signal and power transmission textile cables for smart wearables which have excellent wearability, durability and reliability. For the signal transmission textile cables, conductive yarns for the signal line and the ground line were developed. Three types of signal transmission textile cables have been developed using the conductive yarns. Linear density, tensile properties, electrical resistance and RF characteristics were tested to characterize the physical and electrical properties of three signal transmission textile cables. The conductive yarns have the very low resistance of $0.05{\Omega}/cm$ and showed excellent uniformity of electric resistance. Therefore, the electrical resistance of the signal transmission fiber cable can be reduced by increasing the number of conductive yarns used in signal and ground lines. However, the radio frequency (RF) characteristics of the signal transmission textile cables were better as the number of strands of the conductive yarns used was smaller. This is because the smaller the number of strands of conductive yarn used in signal transmission textile cables, the narrower and more parallel the distance between the signal line and the ground line. It is expected that the signal and power transmission textile cable for signal and power transmission will be utilized in smart wearable products.

• Fashion & Textile Research Journal
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• v.18 no.3
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• pp.363-373
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• 2016

This study aims to develop an all-fabric integrated smart jacket in order to create a temperature-regulating system based on a user experience design. For this research, previous research technologies of a textile switch interface and a temperature-regulating system were utilized and a unifying technology for the all-fabric integrated smart jacket was developed which can provide the appropriate temperature environments to the human body. A self-heating textile was applied at the areas of the back and hood in the final tested jacket, and an embroidery circuit was developed in the form of a rectangle in the back and in both ears of the hood, taking into account the pattern of the jacket part where it was be applied and the embroidery production method. The textile switch interface was designed in a three-layer structure: an embroidery circuit line in a conductive yarn, an interval material, and a conductive sensing material, and it was made to work with the input and output sensors through the multiple input method. After the all-fabric integrated smart jacket was produced according to the pattern, all of the textile band lines for transmission were gathered and connected with a miniature module for controlling temperature and then integrated into the inside of the left chest pocket of the jacket. After the users put on this jacket, they were asked to assess the wearing satisfaction. Most of them reported a very low level of irritation and discomfort and said that the jacket was as comfortable as everyday clothing.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.2
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• pp.210-220
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• 2013

Recently, as we move toward a society with an increasingly aged population, wearable U-health devices in various shapes with smart wear have been developed in order to conveniently measure health variables without using hands in daily life or at home. However, the problem is that only supply of the wearable U-health devices is focused and its applicable devices are studied and developed, which has resulted in lack of awareness of importance of performance evaluation. In this study, two electrodes were fabricated using conductive fabric which can be used as electrode if attached to wearable U-health devices or smart wear in order to measure ECG signal. Two electrodes those were fabricated using conductive fabric were compared the correlation, impedance and CMRR with patch typed Ag-AgCl electrode-normally used for measurement of ECG signal, so that the study would find out if the fabricated electrode can be used with the wearable U-health devices by testing and evaluating performances.

• Fashion & Textile Research Journal
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• v.22 no.2
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• pp.233-239
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• 2020

We proposed a simple process of fabricating electroconductive textiles by coating conductive polymer PEDOT:PSS (Poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate)) on biocompatible PLA (Poly Lactic Acid) nanofiber web for application to smart healthcare. Electroconductive textiles were obtained by a drop-coating process using different amounts of PEDOT:PSS solutions., DMSO (dimethyl sulfoxide) was then used as an additive in the post-treatment process to improve conductivity. The surface morphology of the specimens was observed by FE-SEM. The chemical structures of the specimens were characterized using FTIR. The electrical properties (linear and sheet resistance) of the specimens were measured. The effect of the bending angles on the electrical properties was also investigated to confirm their applicability as wearable smart textiles. FE-SEM and FTIR analysis confirmed that the deposition of PEDOT:PSS on the PLA nanofiber web surface was successful. The conductivity of the PEDOT:PSS/PLA nanofiber web was enhanced up to 1.5 ml with an increasing amount of PEDOT:PSS solutions, but there was no significant difference at 2.0 ml. The optimum condition of PEDOT:PSS deposition was established to 1.5 ml. Even when the specimen coated with 1.5 ml was bent every 30°, the change in the electrical resistance values was still low within 3.7 Ω. It confirmed that stable electrical performance was maintained and proved the applicability as a flexible textile sensor.

• Journal of Powder Materials
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• v.26 no.3
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• pp.231-236
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• 2019

Ni hydroxides ($Ni(OH)_2$) are synthesized on Ni foam by varying the hexamethylenetetramine (HMT) concentration using an electrodeposition process for pseudocapacitor (PC) applications. In addition, the effects of HMT concentration on the $Ni(OH)_2$ structure and the electrochemical properties of the PCs are investigated. HMT is the source of amine-based $OH^-$ in the solution; thus, the growth rate and morphological structure of $Ni(OH)_2$ are influenced by HMT concentration. When $Ni(OH)_2$ is electrodeposited at a constant voltage mode of -0.85 V vs. Ag/AgCl, the cathodic current and the number of nucleations are significantly reduced with increasing concentration of HMT from 0 to 10 mM. Therefore, $Ni(OH)_2$ is sparsely formed on the Ni foam with increasing HMT concentration, showing a layered double-hydroxide structure. However, loosely packed $Ni(OH)_2$ grains that are spread on Ni foam maintain a much greater surface area for reaction and result in the effective utilization of the electrode material due to the steric hindrance effect. It is suggested that the $Ni(OH)_2$ electrodes with HMT concentration of 7.5 mM have the maximum specific capacitance (1023 F/g), which is attributed to the facile electrolyte penetration and fast proton exchange via optimized surface areas.

• Smart Media Journal
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• v.9 no.4
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• pp.162-169
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• 2020

Design is a key factor that determines the competitiveness of products in the textile and fashion industry. It is very important to measure the similarity of the proposed design in order to prevent unauthorized copying and to confirm the originality. In this study, a deep learning technique was used to quantify features from images of textile designs, and similarity was measured using Spearman correlation coefficients. To verify that similar samples were actually detected, 300 images were randomly rotated and color changed. The results of Top-3 and Top-5 in the order of similarity value were measured to see if samples that rotated or changed color were detected. As a result, the VGG-16 model recorded significantly higher performance than did AlexNet. The performance of the VGG-16 model was the highest at 64% and 73.67% in the Top-3 and Top-5, where similarity results were high in the case of the rotated image. appear. In the case of color change, the highest in Top-3 and Top-5 at 86.33% and 90%, respectively.

• Smart Media Journal
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• v.10 no.4
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• pp.28-34
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• 2021

Drape is one of the factors that determine the shape of clothes and is one of the very important factors in the textile and fashion industry. At a time when non-face-to-face transactions are being activated due to the impact of the coronavirus, more and more companies are asking for drape value. However, in the case of small and medium-sized enterprises (SMEs), it is difficult to measure the drape, because they feel the burden of time and money for measuring the drape. Therefore, this study aimed to generate a drape image for the material property value input using a conditional adversarial neural network through 3D simulation images generated by measuring digital properties. A drape image was created through the existing 736 digital property values, and this was used for model training. Then, the drape value was calculated for the image samples obtained through the generative model. As a result of comparing the actual drape experimental value and the generated drape value, it was confirmed that the error of the peak number was 0.75, and the average error of the drape value was 7.875

• Smart Media Journal
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• v.11 no.11
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• pp.92-98
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• 2022

Identifying defects in textiles is a key procedure for quality control. This study attempted to create a model that detects defects by analyzing the images of the fabrics. The models used in the study were deep learning-based VGGNet and ResNet, and the defect detection performance of the two models was compared and evaluated. The accuracy of the VGGNet and the ResNet model was 0.859 and 0.893, respectively, which showed the higher accuracy of the ResNet. In addition, the region of attention of the model was derived by using the Grad-CAM algorithm, an eXplainable Artificial Intelligence (XAI) technique, to find out the location of the region that the deep learning model recognized as a defect in the fabric image. As a result, it was confirmed that the region recognized by the deep learning model as a defect in the fabric was actually defective even with the naked eyes. The results of this study are expected to reduce the time and cost incurred in the fabric production process by utilizing deep learning-based artificial intelligence in the defect detection of the textile industry.