DocumentCode :
522848
Title :
The Objective Evaluation Model on Wearing Touch and Pressure Sensation Based on GRNN
Author :
Meng, Xiangling ; Zhang, Weiyuan ; Cong, Shan
Author_Institution :
Fashion Inst., Donghua Univ., Shanghai, China
Volume :
2
fYear :
2010
fDate :
4-6 June 2010
Firstpage :
289
Lastpage :
292
Abstract :
Sixty fabrics with elasticity were made up into two hundred fifty eight samples to evaluate the touch and pressure sensation in this paper. Twelve female were selected as subjects for wear trials. Nineteen physical parameters of fabrics were measured by FAST system. Four principal factors were obtained through data reduction with factor analysis method. The objective evaluation model of wearing touch and pressure sensation was established with GRNN. Four principal factors, initial elastic modulus of fabrics and clothing allowance rate were entered into the model as input parameters. Evaluation value of tightness, softness, roughness, compression and comfort, as output parameters, were exported from the model. Seventy, one hundred and ten and one hundred and sixty sets of samples were selected to train the net model respectively. Twenty five random samples were then used to test the net mode. GRNN110_0.1 and GRNN160_0.1 were determined as the final predictive model for objective evaluation. Another twenty five randomly picked samples were used to examine prediction ability. Pearson coefficient and linear regression relation between predicted values and targets are both good. It´s concluded that objective evaluation model on wearing touch and pressure sensation based on GRNN has practical significance.
Keywords :
data analysis; elasticity; ergonomics; fabrics; production engineering computing; radial basis function networks; regression analysis; Pearson coefficient; comfort factor; compression factor; data reduction; factor analysis method; generalized regression neural network; linear regression relation; objective evaluation model; pressure sensation; roughness factor; softness factor; tightness factor; touch sensation; Artificial neural networks; Clothing; Educational institutions; Elasticity; Fabrics; Humans; Linear regression; Neural networks; Predictive models; Testing; GRNN; evaluation model; objective evaluation; regression; subjective evaluation; touch and pressure sensation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information and Computing (ICIC), 2010 Third International Conference on
Conference_Location :
Wuxi, Jiang Su
Print_ISBN :
978-1-4244-7081-5
Electronic_ISBN :
978-1-4244-7082-2
Type :
conf
DOI :
10.1109/ICIC.2010.168
Filename :
5513849
Link To Document :
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