• Fashion & Textile Research Journal
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• v.15 no.6
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• pp.977-984
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• 2013

This study classified the type of sole for female high school students and analyzed the characteristics of each type by the shape of the sole (plantar view) using 2D scan data. The data were collected from a foot anthropometry of 310 female high school students in Gwangju and Jeollanam-do. Left feet and right feet were measured indirectly by using flatbed scanner. The sole anthropometric measurements consisted of 24 items. The results of the investigation into the differences between left and right feet soles by the 2D measurements data indicated that there was no significant statistical differences in the length of items. The left sole had higher values than right sole in the width items and angle items; however, the lateral side of the right feet projected to the outside more often than left feet. In analyzing foot sole of female high school students, the shapes of sole were classified into three types. Type 1(41.94%), Type 2(36.77%), Type 3(21.29%). The most characteristic sole type for female high school students was Type 1. Type 1 referred to a narrow foot width with little or no curvature of the toe. Type 2 represented the longest foot, with foot width shown as a spacious and distinctive feature in width at the medial area of the foot. Type 3 represented the shortest and widest of ball width, gathered inside toe 5, and lateral side as the most projected among the three types.

• Smart Media Journal
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• v.12 no.7
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• pp.43-51
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• 2023

As online transactions increase, the image of clothing has a great influence on consumer purchasing decisions. The importance of image information for clothing materials has been emphasized, and it is important for the fashion industry to analyze clothing images and grasp the materials used. Textile materials used for clothing are difficult to identify with the naked eye, and much time and cost are consumed in sorting. This study aims to classify the materials of textiles from clothing images based on deep learning algorithms. Classifying materials can help reduce clothing production costs, increase the efficiency of the manufacturing process, and contribute to the service of recommending products of specific materials to consumers. We used machine vision-based deep learning algorithms ResNet and Vision Transformer to classify clothing images. A total of 760,949 images were collected and preprocessed to detect abnormal images. Finally, a total of 167,299 clothing images, 19 textile labels and 20 fabric labels were used. We used ResNet and Vision Transformer to classify clothing materials and compared the performance of the algorithms with the Top-k Accuracy Score metric. As a result of comparing the performance, the Vision Transformer algorithm outperforms ResNet.

• Journal of the Korean Society of Clothing and Textiles
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• v.30 no.12 s.159
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• pp.1768-1777
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• 2006

As the fashion industry comes under the influence of globalization throughout all fields of industry, the globalization and the market entry strategies are required for Korean fashion firms. This study attempted to analyze the factors influencing foreign entry mode of Korean fashion business based on Eclectic Theory. Data collection has been carried out from November 25 until December 25, 2005. The questionnaires were sent through e-mail or fax to 622 trading companies. 67 questionnaires were returned for a response rate of 10.7%. Of these returns, 61 usable questionnaires were employed for data analyses. Descriptive analysis, factor analysis, discriminant analysis, and t-test were used for data analysis. First, the most important venture motivation was price competitiveness and many firms were engaged in both production and sales in their target countries, which were mainly in Southeast Asia. Second, the firm's ability and experience were found out as ownership advantage factor, investment stability and market potential as location advantage factor, and contract stability as internalization advantage factor. Third, the result of discriminant analysis showed that location advantage factor was a significant factor in predicting the entry of fashion firms into foreign countries.

• Journal of the Korean Society of Clothing and Textiles
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• v.41 no.2
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• pp.378-391
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• 2017

The application of Big Data has been introduced to the Korean fashion industry; however, the literature has not yet investigated how well high technologies are being perceived and adopted by the practitioners of fashion companies. Recognizing the lack of research, the current research explores how big data analysis has been adopted by fashion practitioners based on the Technology Acceptance Model (TAM) that considers the effect of organizational characteristics (i.e., innovation, slack, and IS infra maturity). First, all TAM relationships were accepted as significant; however, the effect of perceived ease of use on the attitude toward big data was greater than perceived usefulness. Regarding organizational characteristics, while organization innovation had positive impacts on perceived usefulness as well as perceived ease of use, organization slack did not show significant and positive influence on perceived ease of use only. On the other hand, IS infra maturity had a negative effect on perceived usefulness while it did not have any significant impact on perceived ease of use. Finally, the level of perceived usefulness is decreasing as the IS infra of the fashion organization becomes more mature. With the results, the study suggested that fashion industry needs more education on the usage of big data analysis systems and development in related analysis tools.

• Fashion & Textile Research Journal
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• v.22 no.5
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• pp.551-560
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• 2020

Due to the nature of fashion design that responds quickly and sensitively to changes, accurate forecasting for upcoming fashion trends is an important factor in the performance of fashion product planning. This study analyzed the major phenomena of fashion trends by introducing text mining and a big data analysis method. The research questions were as follows. What is the key term of the 2010SS~2019FW fashion trend? What are the terms that are highly relevant to the key trend term by year? Which terms relevant to the key trend term has shown high frequency in news articles during the same period? Data were collected through the 2010SS~2019FW Pre-Trend data from the leading trend information company in Korea and 45,038 articles searched by "fashion+material" from the News Big Data System. Frequency, correlation coefficient, coefficient of variation and mapping were performed using R-3.5.1. Results showed that the fashion trend information were reflected in the consumer market. The term with the highest frequency in 2010SS~2019FW fashion trend information was material. In trend information, the terms most relevant to material were comfort, compact, look, casual, blend, functional, cotton, processing, metal and functional by year. In the news article, functional, comfort, sports, leather, casual, eco-friendly, classic, padding, culture, and high-quality showed the high frequency. Functional was the only fashion material term derived every year for 10 years. This study helps expand the scope and methods of fashion design research as well as improves the information analysis and forecasting capabilities of the fashion industry.

• The Research Journal of the Costume Culture
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• v.31 no.2
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• pp.193-212
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• 2023

The purpose of this study is to classify the body types of obese men in their 50-60s and compare them with those of obese middle-aged men in their 30-40s. The 3D anthropometric data of obese men aged 50 to 60 years from the 6th Size Korea. The data are analyzed using SPSS 25.0 for Windows, and descriptive statistics, χ² test, correlation analysis, and cluster analysis are used to classify obese body types. As a result of the study, five factors are extracted to determine body types, which are classified into three obese body types through cluster analysis. 1) a large physique and consequently large circumference and height; 2) A short upper body length, short height, and thick belly; 3) the lowest rate of obesity and relatively flat abdominal curve. For the 30-40s group, Type1 showed the highest rate at 55.6%, whereas for the 50s group, Type3 showed the highest rate at 49.3%, and for 60s group, Type2 showed the highest rate at 41.2%. The classification accuracy of the discriminant function for each type is 94.7%, indicating relatively high accuracy. Furthemore, the recently changed obese body type are analyzed by comparing it with the 3D anthropometric data of 8th Size Korea, which will contribute to the utilization of basic data for manufacturing apparel for obese men.

• Fashion & Textile Research Journal
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• v.21 no.6
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• pp.789-799
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• 2019

This study classified and analyzed the upper body types of 7-13 years old elementary school boys, using 3D data from the 6th Size Korea. The results of this study are as follows. Seven factors were extracted from the factorial analysis as an independent factor for a cluster analysis. The cluster analysis generated four body types. Type 1 has large ratio of front and back depth as well as circumference, with a front protrusion. In Type 2, the vertical value of upper torso is longer than average; in addition, its flatness is the largest and produces a thin body type. Type 3 has a smaller flatness in the bust, waist, abdomen and hip than other types, while also having the largest BMI. Type 4 is characterized by a greater shoulder angle than other types and its other factors are close to average. As a result of the logistic regression analysis, the prediction model used eight variables to generate and its accuracy is 88.679%. The classification of upper body types from this study can be used as basic data to improve patternmaking for each body type. The generated prediction model is also expected to be used as a method to help classify upper body types using the eight variables.

• Fashion & Textile Research Journal
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• v.15 no.4
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• pp.574-582
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• 2013

This study categorizes and analyzes hand types based on 2-Dimensional measurements of women in their 60-80's in order to establish initial data that can help develop a well-fitted glove and hand protector for elderly women. A total of 22 measurement items were selected to provide information about Size Korea (2010) 3D hand measurements. Participants in the study were 353 elderly women over the age of 60. Subjects were divided into two age groups (60's and over 70's). Statistical tests (such as Descriptive Analysis and T-test) analyzed the data and ascertained the age differences. A factor analysis and cluster analysis were conducted to classify elderly women hand types. The disparities between 20-30's and over 60's age groups were compared by T-test with the SPSS 20 program for Windows. The results in this study are follows: The hand shapes for elderly women were divided into 3 groups. Elderly women's Hand Type A is average length and the medium breadth hand type. Type B is the biggest length and breadth, Type C is the smallest length and breadth hand type. There were significant differences in the 20-30's and over 60's age groups for most hand length and breadth items. In addition, the mean measurement value of the length items decreased as the age increased; however, the diversity of items increased, so that it became shorter and wider. Further study should include the classification of hand shape dimensions for each figure type of sizing gloves for elderly women. We expect hand types to be applicable to the manufacture of gloves for elderly women.

• Journal of Korean Home Economics Education Association
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• v.34 no.3
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• pp.1-23
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• 2022

This study examined the perceptions of Myanmar university students and professors regarding the status and necessity of higher education programs in fashion. Data were collected from professors in textile engineering at Yangon Technological University and Myanmar university students. Closed- and open-ended questions were asked either through interviews or by email. The responses were analyzed using keyword extraction and categorization, and descriptive statistics(closed questions). Generally, the professors perceived higher education, as well as the cultural industries including art and fashion, as important for Myanmar's social and economic development. According to the students interests in pursuing a degree in textile were limited, despite the high interest in fashion. Low wages in the apparel industry and lack of fashion degrees that meet the demand of students were cited as reasons. The demand was high for educational programs in fashion product development, fashion design, pattern-making, fashion marketing, branding, management, costume history, and cultural studies. Students expected to find their future career in textiles and clothing factories. Many students wanted to be hired by global fashion brands for higher salaries and training for advanced knowledge and technical skills. They perceived advanced fashion education programs will have various positive effects on Myanmar's national economy.

• Journal of Technology Innovation
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• v.14 no.1
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• pp.31-66
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• 2006

The country with a relative abundance of human capital conducts relatively more R&D in the steady state than its partner. This country acquires the know-how to produce a relatively wider range of innovative goods. High technology comprises a large share of the national economy in the human-capital rich country and real output growth is faster. This prediction would seem to accord weakly with empirical observation of Korean economy.