• 한국의상디자인학회지
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• 제11권1호
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• pp.87-97
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• 2009

A technology in development called CACD (Computer Aided Clothing Design) can reproduce fashion shows by utilizing computers, and is of particular interest. Considering the growth potential of this area, the purpose of this study is to present the development potentials that CACD technology will bring to the fashion area and to promote the diversity of the fashion industry. This will be realized by identifying the current status of CACD and its reach in the field of Fashion, followed by an in-depth analysis of its application. The methodologies employed in this study are as follows; in-depth study of related literature, field research of business firms, and investigation on Internet data. For the systematic advance of CACD, the development of user-friendly programs for 3D clothing design is of the utmost priority. The four technologies that should be intensively developed to enhance the development of the clothing industry through the utilization and commercialization of CACD are as follows; First, technology capable of performing accurate three-dimension measurement of the human body is needed. Second, technology which realizes automatic pattern formation is needed. Third, the nature physical properties of the material and textile design when applied to pre-formed patterns must be expressed similarly to the real thing. Last of all, an integrative technology which can conduct fast and accurate clothing simulations must be developed.

• Journal of Information Processing Systems
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• 제14권4호
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• pp.926-940
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• 2018

In clustering-based approaches, cluster heads closer to the sink are usually burdened with much more relay traffic and thus, tend to die early. To address this problem, distance-aware clustering approaches, such as energy-efficient unequal clustering (EEUC), that adjust the cluster size according to the distance between the sink and each cluster head have been proposed. However, the network lifetime of such approaches is highly dependent on the distribution of the sensor nodes, because, in randomly distributed sensor networks, the approaches do not guarantee that the cluster energy consumption will be proportional to the cluster size. To address this problem, we propose a novel approach called CACD (Clustering Algorithm Considering node Distribution), which is not only distance-aware but also node density-aware approach. In CACD, clusters are allowed to have limited member nodes, which are determined by the distance between the sink and the cluster head. Simulation results show that CACD is 20%-50% more energy-efficient than previous work under various operational conditions considering the network lifetime.