• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.10
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• pp.2157-2163
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• 2011

In this paper, we designed 2D, 2.5D cloth design system and a 3D cloth animation system. They make the 3D cloth animation possible by using coordinate points extracted from 2D and 2.5D cloth design system in order to realize a system that allows customers to wear clothes in the virtual space. To make natural draping, it uses for description the mesh creation and transformation algorithms, path extraction algorithm, warp algorithm, and brightness extraction and application algorithms. The coordinate points extracted here are received as text format data and inputted as clothing information in the cloth file. Moreover, the cloth file has a 2D pattern and is realized to be used in the 3D cloth animation system. The 3D cloth animation system generated in this way builds a web-based fashion mall using ISB (Internet Space Builder) and lets customers view the clothing animation on the web by adding the animation process to the simulation result.

• Journal of the Korea Society of Computer and Information
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• v.25 no.5
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• pp.107-116
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• 2020

The purpose of this study was to compare the pattern of two men's pants for education and two for industry, and to analyze the patterning method that is most suitable for the body shape of middle-aged men. As a result of appearance evaluation for 3D simulation, J pattern was evaluated as the best in most items, and H pattern was analyzed to be the most inappropriate. As a result of measuring the airgap, all four patterns of the waist circumference were evaluated to have very small airgap, and the J pattern had the smallest airgap for the pants. J pattern was analyzed as the most suitable pattern for middle-aged men's body shape by synthesizing the appearance evaluation of virtual wear, color distribution, sectional view, airgap, etc. However, in the case of J pattern, it was analyzed that the pants length needs to be modified. Since this study compared and analyzed only two patterns for education and two for industry, it is thought that the development of pants for middle-aged men should be developed through actual wearing experiments with J patterns.

• Journal of the Korea Fashion and Costume Design Association
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• v.23 no.4
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• pp.149-166
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• 2021

This study tried to suggest a drafting method to draft tight fit torso patterns suitable for adult males with a sway back somatotype and a bend forward somatotype by setting and distributing the ease through the completion of the somatic surface pattern using the 3D body surface segment method. It was intended to be presented as a drafting method. As for the research method, the suitability of the somatic surface patterns and the tight fit torso patterns were confirmed by the evaluation of virtual wear, and the patterns were modified and supplemented. The research results are as follows. In the first evaluation of the tight fit torso patterns, the average, for 55 evaluation items, was 3.92 points for the sway back somatotype and 3.89 points for the bend forward somatotype. In the second evaluation, the bend forward somatotype was 4.51 points and the sway back somatotype was 4.62 points. The chest circumference ease amount for the bend forward somatotype and the sway back somatotype are 6.5% (6.8 cm) and 7% (6.8 cm) of the chest circumference, respectively, and the distribution of the front and back ease is the same at 4:6 (2.72 cm:4.08 cm). The waist circumference ease amount is 6.5% (5.8cm) and 6.5% (5.6 cm) of the waist circumference dimensions, and the distribution of the front and backease are 5:5 (1.45 cm: 1.45 cm) and 4:6 (2.24 cm: 3.36 cm), respectively. The completed tight fit torso patterns were converted into institutional formulas and presented as a drafting method.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.8
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• pp.171-180
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• 2020

Smart Factory consists of digital automation solutions throughout the production process, including design, development, manufacturing and distribution, and it is an intelligent factory that installs IoT in its internal facilities and machines to collect process data in real time and analyze them so that it can control itself. The smart factory's equipment works in a physical combination of numerous hardware, rather than a virtual character being driven by a single object, such as a game. In other words, for a specific common goal, multiple devices must perform individual actions simultaneously. By taking advantage of the smart factory, which can collect process data in real time, if reinforcement learning is used instead of general machine learning, behavior control can be performed without the required training data. However, in the real world, it is impossible to learn more than tens of millions of iterations due to physical wear and time. Thus, this paper uses simulators to develop grid sortation systems focusing on transport facilities, one of the complex environments in smart factory field, and design cooperative multi-agent-based reinforcement learning to demonstrate efficient behavior control.