• Journal of Korea Technical Association of The Pulp and Paper Industry
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• v.43 no.1
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• pp.47-56
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• 2011

This study was carried out to investigate the characteristics of the Hanji which had been used for movable types printing volumes of the Joseon Dynasty. A total 29 volumes which had printed with metal and wooden movable types, were examined on the physical properties, fiber morphology, and color reactions. The grammage of all of the Hanji which had been used for movable types printing volumes were $13.10-51.65g/m^2$ and on the average was $25g/m^2$. The apparent density of the Hanji was $0.23-1.65g/cm^3$ and on the average was $0.4g/cm^3$. In almost all movable types printing volumes, the Hanji had undergone Dochim which is a processing of printability improvement. Through the examination on fiber morphology and color reactions, Hanji was investigated into that all of those were made from paper mulberry bast fiber.

• Journal of Korea Technical Association of The Pulp and Paper Industry
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• v.33 no.4
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• pp.15-20
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• 2001

The function of the moisture-proof paper is to prevent moisture from adsorbing into the packed goods. Water-vapor transmission rate of the moisture-proof paper should be less than 100g/$m^2$.24hr and the optimum rate would be less than 50g/$m^2$.24hr. In general the moisture-proof paper has been made by laminating polyethylene or polypropylene on top of the base paper. However this kind of moisture-proof paper has a problem in recycling so that it brings about environmental pollution. In general the moisture-proof paper has been made by laminating polyethylene or polypropylene on top of the base paper. However this kind of moisture-proof paper has a problem in recycling so that it brings about environmental pollution. The purpose of this paper was to make moisture-proof paper using the mixture of SB latex and wax emulsion which was recyclable and environmentally friendly. Water vapor transmission rate showed less than 50g/$m^2$.24hr in mixture ratio of 85:15, 87:13, 90:10. Especially the mixture ratio of 87:13 showed the most favorable water-vapor transmission rate. However, the moisture-proof layer was destroyed slightly by folding in packing. It has been observed that there was no close relationship between water-vapor transmission rate of the moisture-proof paper and grammage of the base paper, but the density of base paper had influenced on water vapor transmission rate. It was also observed that the moisture-proof paper could be recycled. The moisture-proof paper was similar to base paper in degree of the pulping, and there was no significant difference in dispersion between moisture-proof paper and base paper. Most of wax particles which caused the spots during drying process could be removed by flotation process. Tensile strength and tear strength of both moisture-proof paper and base paper after pulping were measured to examine the fiber bonding, and no significant difference in physical properties was observed.

• Journal of the Korean Society of Clothing and Textiles
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• v.45 no.6
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• pp.978-985
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• 2021

This study aims to propose a prediction model for the drape coefficient using artificial neural networks and to analyze the nonlinear relationship between the drape properties and physical properties of fabrics. The study validates the significance of each factor affecting the fabric drape through multiple linear regression analysis with a sample size of 573. The analysis constructs a model with an adjusted R² of 77.6%. Seven main factors affect the drape coefficient: Grammage, extruded length values for warp and weft (m_warp, m_weft), coefficients of quadratic terms in the tensile-force quadratic graph in the warp, weft, and bias directions (c_warp, c_weft, c_bias), and force required for 1% tension in the warp direction (f_warp). Finally, an artificial neural network was created using seven selected factors. The performance was examined by increasing the number of hidden neurons, and the most suitable number of hidden neurons was found to be 8. The mean squared error was .052, and the correlation coefficient was .863, confirming a satisfactory model. The developed artificial neural network model can be used for engineering and high-quality clothing design. It is expected to provide essential data for clothing appearance, such as the fabric drape.