• Journal of the Korean Society of Clothing and Textiles
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• v.30 no.12 s.159
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• pp.1723-1729
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• 2006

Drapability is an important factor determining the end-use of fabrics. In this research, characteristics of drapability of men's and women's suit fabrics were analyzed. The hand and the preference for suit fabrics were measured by the subjective and objective evaluations. To find out the details of the drapability characteristics of suit fabrics, the drape was measured by using Cusick drape tester and was processed by image analysis software. Seasonal difference was obvious both in men's and women's fabrics. The average drape ratio of women's S/S suit fabrics showed the highest value, 0.724, then decreased in the order of men's F/W> men's S/S> women's FW. Wave amplitude showed the same order to the drape ratio. Men's fabrics were more drapable in spring and summer season rather than in fall and winter season. Women's fabrics showed the opposite trend, in other words, S/S suit fabrics were less drapable than F/W fabrics. There was also a significant difference in drape ratio between men's and women's fabrics regardless of season. For S/S, men's fabrics were more drapable than women's ones, whereas, for F/W, women's fabrics were more drapable.

• Smart Media Journal
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• v.10 no.4
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• pp.28-34
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• 2021

Drape is one of the factors that determine the shape of clothes and is one of the very important factors in the textile and fashion industry. At a time when non-face-to-face transactions are being activated due to the impact of the coronavirus, more and more companies are asking for drape value. However, in the case of small and medium-sized enterprises (SMEs), it is difficult to measure the drape, because they feel the burden of time and money for measuring the drape. Therefore, this study aimed to generate a drape image for the material property value input using a conditional adversarial neural network through 3D simulation images generated by measuring digital properties. A drape image was created through the existing 736 digital property values, and this was used for model training. Then, the drape value was calculated for the image samples obtained through the generative model. As a result of comparing the actual drape experimental value and the generated drape value, it was confirmed that the error of the peak number was 0.75, and the average error of the drape value was 7.875

• Proceedings of the Korean Fiber Society Conference
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• 2003.10b
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• pp.142-144
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• 2003

3차원 apparel CAD system개발에 있어서 직물의 드레이프 모사가 걸림돌이 되어왔다. 최근 컴퓨터의 속도 개선으로 인해 정적 드레이프의 경우 데이블 보 문제와 같이 적절한 시간내에 드레이프 모사가 이루어지고 있다. 그러나 드레이프는 cloth와 body간의 충돌 검사에 많은 시간이 소요되므로 virtual fashion show와 같이 인체가 운동하는 동적 드레이프의 경우는 아직도 어려운 문제이다. (중략)

• Journal of the Korea Computer Graphics Society
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• v.26 no.5
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• pp.15-24
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• 2020

We present a supervised learning method that estimates the simulation parameters required to simulate the fabric from the static drape shape of a given fabric sample. The static drape shape was inspired by Cusick's drape, which is used in the apparel industry to classify fabrics according to their mechanical properties. The input vector of the training model consists of the feature vector extracted from the static drape and the density value of a fabric specimen. The output vector consists of six simulation parameters that have a significant influence on deriving the corresponding drape result. To generate a plausible and unbiased training data set, we first collect simulation parameters for 400 knit fabrics and generate a Gaussian Mixed Model (GMM) generation model from them. Next, a large number of simulation parameters are randomly sampled from the GMM model, and cloth simulation is performed for each sampled simulation parameter to create a virtual static drape. The generated training data is fitted with a log-linear regression model. To evaluate our method, we check the accuracy of the training results with a test data set and compare the visual similarity of the simulated drapes.

• Proceedings of the Korean Fiber Society Conference
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• 1998.04a
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• pp.262-265
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• 1998

직물의 드레이프 모사를 위해 쉘이론을 이용하여 개발된 정적변형(static deformation) 해석 프로그램은 직물의 크기가 비교적 작은 경우에는 다양한 상황에서의 직물의 드레이프 거동 예측에 만족할 만한 결과를 제시하였다[1]. 그러나 직물의 크기가 큰 경우에는 수치해석시에 나쁜 수렴성을 보여준다.(중략)

• Proceedings of the Korean Fiber Society Conference
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• 1999.10a
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• pp.203-206
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• 1999

• Proceedings of the Korean Fiber Society Conference
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• 1998.04a
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• pp.258-261
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• 1998

2차원 시스템과는 달리 3차원 의복 CAD 시스템을 이용하면, 직접 재단 봉제 과정을 거치지 않고도, 만들어진 의복의 형상과 착장 상태를 확인해볼 수 있다는 장점이 있다. 이러한 3차원 시스템에서는 의복의 자연스런 처짐, 이른바 드레이프(drape)를 잘 표현하여야만 실제에 최대한 가까운 의복의 형상을 보여줄 수 있다.(중략)

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.1
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• pp.185-191
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• 2004

This article is concerned with cloth wearing system issues arising in the computer graphics. In particular, we study the issues of fabric drape properties for representing cloth wearing system. The convex points based on distance function are calculated to represent useful fabric drape properties. The information such as perimeter area, max and min point among convex point, the average distance between convex points are extracted. A strategy of a circularity based on the perimeter and area is considered for fabric drape property measurement. By experimental result, the circularity is most powerful factor to represent the drape property among the several characteristics. The measured drape properties will contribute to cloth wearing system development.

• Journal of the Korea Computer Graphics Society
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• v.29 no.3
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• pp.117-125
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• 2023

In the development of clothing design through virtual simulation, it is essential to minimize the differences between the virtual and the real world as much as possible. The most critical task to enhance the similarity between virtual and real garments is to find simulation parameters that can closely emulate the physical properties of the actual fabric in use. The simulation parameter optimization process requires manual tuning by experts, demanding high expertise and a significant amount of time. Especially, considerable time is consumed in repeatedly running simulations to check the results of applying the tuned simulation parameters. Recently, to tackle this issue, artificial neural network learning models have been proposed that swiftly estimate the results of drape test simulations, which are predominantly used for parameter tuning. In these earlier studies, relatively simple linear stiffness models were used, and instead of estimating the entirety of the drape mesh, they estimated only a portion of the mesh and interpolated the rest. However, there is still a scarcity of research on non-linear stiffness models, which are commonly used in actual garment design. In this paper, we propose a learning model for estimating the results of drape simulations for non-linear stiffness models. Our learning model estimates the full high-resolution mesh model of drape. To validate the performance of the proposed method, experiments were conducted using three different drape test methods, demonstrating high accuracy in estimation.

• Proceedings of the Korean Fiber Society Conference
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• 1998.10a
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• pp.365-368
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• 1998

2차원으로 제도, 재단 과정을 거친 의복 패턴이 3차원의 형상을 갖는 의복으로 만들어져 인체와의 접촉의 결과 생기는 착장 상태를 확인하는 작업에 3차원 의복 CAD 시스템을 이용하면, 직접 재단 봉제 시착(찰볼) 등 일련의 과정을 거치지 않고도, 만들어진 의복의 형상과 착장 상태를 확인해볼 수 있다는 장점이 있다. 이러한 3차원 시스템에서는 의복의 자연스런 처짐, 이른바 드레이프(4.ape)를 잘 표현하여야만 실제에 최대한 가까운 의복의 형상을 보여줄 수 있다. (중략)