Integration of human knowledge and measured data for optimization of fabric hand

Zeng, Xiaoyang; Koehl, Ludovic; Sanoun, M.; Bueno, Marie-Ange; Renner, Marc

doi:10.1080/03081070310001633572

Cited by 32 publications

(15 citation statements)

References 8 publications

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“…They had issued 15 terms. The aim of this study was to correlate process parameters to expert evaluation by the use of fuzzy techniques (Zeng, Koehl, Sahnoun, Bueno, & Renner, 2004).…”

Section: Sample Handling and Termsmentioning

confidence: 99%

Sensory evaluation of the touch of a great number of fabrics

Pensé‐Lhéritier

Guilabert

Bueno

et al. 2006

Food Quality and Preference

Self Cite

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“…They had issued 15 terms. The aim of this study was to correlate process parameters to expert evaluation by the use of fuzzy techniques (Zeng, Koehl, Sahnoun, Bueno, & Renner, 2004).…”

Section: Sample Handling and Termsmentioning

confidence: 99%

Sensory evaluation of the touch of a great number of fabrics

Pensé‐Lhéritier

Guilabert

Bueno

et al. 2006

Food Quality and Preference

Self Cite

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“…The first part ( VA k ) of the ranking criterion is determined with the aid of the human knowledge 20. As shown in Fig.…”

Section: Input Variable Selectionmentioning

confidence: 99%

An Input Variable Selection Method for the Artificial Neural Network of Shear Stiffness of Worsted Fabrics

ChenTing

ZhangChong

Chen-xia

et al. 2009

Statistical Analysis

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Abstract:The relationship between yarn properties, fabric parameters, and shear stiffness of worsted fabrics is modeled using the soft computing technique. Because of the small number of samples, the artificial neural network model to be established must be a small-scale one. Therefore, this soft computing approach includes two stages. First, the yarn properties and fabric parameters are selected by utilizing an input variable selection method, so as to find the most relevant yarn properties and fabric parameters as the input variables to fit the small-scale artificial neural network model. The first part of this method takes the human knowledge on the shear stiffness of fabrics into account. The second part utilizes a data sensitivity criterion based on a distance method. Second, the artificial neural network model of the relationship between yarn properties, fabric parameters, and shear stiffness of fabrics is established. The results show that the artificial neural network model yields accurate prediction and a reasonably good artificial neural network model can be achieved with relatively few data points by integrating with the input variable selecting method developed in this research. The results also show that there is great potential for this research in the field of computer-assisted design in textile technology. 

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“…A method for modelling the relationship between objective and subjective fabric hand evaluations and between adjusting parameters of fabric production and objective fabric hand features has been further proposed [18]. The present authors have also suggested an intelligent system for supporting the design of fashion oriented personalised garments.…”

Section: Introductionmentioning

confidence: 96%

Interaction of textile parameters, wash-ageing and fabric conditioner with mechanical properties and correlation with textile-hand. II. Relationship between mechanical properties and textile-hand

et al. 2011

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In first part of this study, influences of wash-ageing and the use of fabric softener on the different mechanical parameters of textiles were examined. This paper deals with correlation of those mechanical parameters with sensory attributes. We defined the tracking criterion to investigate the relevancy of mechanical parameters with different sensory attributes. An intelligent system based on Fuzzy-Logic was developed in order to predict the sensory attribute score using two most relevant mechanical parameters.

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Integration of human knowledge and measured data for optimization of fabric hand

Cited by 32 publications

References 8 publications

Sensory evaluation of the touch of a great number of fabrics

Sensory evaluation of the touch of a great number of fabrics

An Input Variable Selection Method for the Artificial Neural Network of Shear Stiffness of Worsted Fabrics

Interaction of textile parameters, wash-ageing and fabric conditioner with mechanical properties and correlation with textile-hand. II. Relationship between mechanical properties and textile-hand

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