• Title/Summary/Keyword: Fassion

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Commercial Cluster Characteristics in Residential District Focusing on Garosu Street (주거지내 상업화 발생영역에서 군집형성현상과 영향요인 연구 - 가로수길을 대상으로 -)

  • Hong, Ha-Yeon;Koo, Ja-Hoon
    • Journal of Cadastre & Land InformatiX
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    • v.46 no.2
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    • pp.57-77
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    • 2016
  • This paper analysis spatial correlation applying commercial activating factor and categories clusters among have homogeneity in garosu street which are rising commercial issue in residential district. Based on this research we can draw several implications. Firstly, Garosu street are forming unique space around fassion feature like clothes and food and Beverage stores are supporting main functions. secondly, in terms of utilization of semi-public space in individual buildings, main Street are using display goods and put product.Also restaurants and cafes are using public space as terrace seats. These results mean principal road emphasizes displaying and passing but inner road emphasizes taking a break and staying. Third, repetitive action between high rising vacancy and new building cause negative effects city decline and lossing identity. So residents and merchants should cooperate and make communities for sustainable district.

Performance Comparisons of GAN-Based Generative Models for New Product Development (신제품 개발을 위한 GAN 기반 생성모델 성능 비교)

  • Lee, Dong-Hun;Lee, Se-Hun;Kang, Jae-Mo
    • The Journal of the Convergence on Culture Technology
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    • v.8 no.6
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    • pp.867-871
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    • 2022
  • Amid the recent rapid trend change, the change in design has a great impact on the sales of fashion companies, so it is inevitable to be careful in choosing new designs. With the recent development of the artificial intelligence field, various machine learning is being used a lot in the fashion market to increase consumers' preferences. To contribute to increasing reliability in the development of new products by quantifying abstract concepts such as preferences, we generate new images that do not exist through three adversarial generative neural networks (GANs) and numerically compare abstract concepts of preferences using pre-trained convolution neural networks (CNNs). Deep convolutional generative adversarial networks (DCGAN), Progressive growing adversarial networks (PGGAN), and Dual Discriminator generative adversarial networks (DANs), which were trained to produce comparative, high-level, and high-level images. The degree of similarity measured was considered as a preference, and the experimental results showed that D2GAN showed a relatively high similarity compared to DCGAN and PGGAN.