• Journal of the Korean Society of Costume
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• v.60 no.2
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• pp.1-19
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• 2010

The purpose of this research is to examine the rise of hip-hop culture and its characteristics which have been established as a part of popular culture amid other subcultures and to examine the overall concept of hip-hop fashion which has been developing since its rise in the 1970s and particularly to examine the hip-hop after the turn of the new century when it has gone through several iterations and changes while simultaneously accommodating different trends and reflecting a highly diverse larger culture. The research methodology is a literature-based research that uses classification and analysis based on the preceding research including related books and dissertations and, considering that this research focuses on the characteristics of hip-hop from the 2000s to the present, mainstream news media such as newspapers and magazines around New York images of famous hip-hop culture & stores websites were used in this research. The most characteristic hip-hop fashions since the 2000s examined by this research comprise prep-hop, slim formal suits, skurban, hip-hop Goth, and hip-hop Tatto. First, prep-hop is a mixed match of a preppy look with latest hip-hop sentiments and hip-hop styles. Second, there are slim formal suits that reflect a change of trends that strongly show changes of hip-hop fashion consumers who have been following sports stars' fashions. Third, skurban is a hip-hop fashion combined with the look of skaters. Fourth, hip-hop Goth is what emphasizes strong Goth images such as grunge, skull, and devil in hip-hop fashions based on vintage fashion. Fifth, hip-hop Tatto style designs have appeared in diverse items such as trendy clothes and accessories that were most popular since the 2000s. Hip-hop fashion since the 2000s up to the present is beginning to be integrated with diverse other cultures and its most prominent characteristics is a change into slimmer silhouette.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.27-35
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• 2023

In this paper, we propose a process of increasing productivity by applying a deep learning-based defect detection and classification system to the prepreg fiber manufacturing process, which is in high demand in the field of producing composite materials. In order to apply it to toe prepreg manufacturing equipment that requires a solution due to the occurrence of a large amount of defects in various conditions, the optimal environment was first established by selecting cameras and lights necessary for defect detection and classification model production. In addition, data necessary for the production of multiple classification models were collected and labeled according to normal and defective conditions. The multi-classification model is made based on CNN and applies pre-learning models such as VGGNet, MobileNet, ResNet, etc. to compare performance and identify improvement directions with accuracy and loss graphs. Data augmentation and dropout techniques were applied to identify and improve overfitting problems as major problems. In order to evaluate the performance of the model, a performance evaluation was conducted using the confusion matrix as a performance indicator, and the performance of more than 99% was confirmed. In addition, it checks the classification results for images acquired in real time by applying them to the actual process to check whether the discrimination values are accurately derived.