Fabric Identification Based on Friction Wave

Chen Tao; Yan Chen

doi:10.4028/www.scientific.net/AMR.796.211

Paper Titles

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Preparation, Structure and Properties of ε–Polylysine Grafted Silk Fabric with Laccase
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R & D Status and Countermeasures of Natural Functional Kapok Fiber
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Cognitive Evaluation of Silk Fabrics Based on Physiological Signal
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Fabric Identification Based on Friction Wave
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The Research on the Development Strategy of Modern Industry Cluster for "Home Class Textile"
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Study on Conventional Method of Test and Evaluation of Woven Fabric's Elasticity on Wrinkle Resistance
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Research on Performance of MPIA/PTFE Union Fabric Used as Self-Lubricating Bearings Liner
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The Strategic Study on Shanghai Holding Group Leading the Development of Industrial Clusters
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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 796Fabric Identification Based on Friction Wave

Fabric Identification Based on Friction Wave

Abstract:

This paper presents an approach to identify the fabric of styles by wave of friction. First, the friction waves of typical cotton, wool, silk and hemp styled fabrics are collected under controlled conditions with a self-made device. In allusion to the non-stationary and attenuation of the signals, differences based on scopes are proposed to represent the features of wave signal in various aspects, which forms into a feature impulse. Pairwise comparisons are made among feature impulses of the four typical fabrics and it is revealed that their features are unfolded gradually from microcosmic to macrocosmic. Then, considerable amount of fabrics of each style are included to extract feature impulses, which constructed a feature library. Finally, the indeterminate fabrics are identified by the feature library, and the results are reflected in a ocular way into a discrimination chart.

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Periodical:

Advanced Materials Research (Volume 796)

Pages:

211-214

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.796.211

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Online since:

September 2013

Authors:

Chen Tao, Yan Chen

Keywords:

Differencing, Fabric Identification, Feature Extraction, Friction Wave

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