Fabric Handle Clusters Based on Fuzzy Clustering Algorithm

Abstract

Fabric handle depends on physical and mechanical properties, which are measured by the two well-know systems, i.e., Kawabata's Evaluation System for Fabrics (KES-F) by Japan and Fabric Assurance by Simple Testing (FAST) by Austria. However, the two systems are too expensive and time-costing. A new comprehensive handle evaluation system for fabrics and yarns (CHES-FY) developed based on the single-test multiple indicator measuring principle can reflect the weight, bending, friction and tension basic mechanical behavior as well as handle through only one pulling-out test.The present paper is to select characteristic indexes from the pulling-out force and distance curve acquired by the CHES-FY system, and the characteristics make a vector which expresses the handle of the fabric based on Karhunen-Loève (K-L) transformation; the other is to make clustering on handle of measured fabrics based on K-means fuzzy clustering. The experiments of thirty fabrics and comparisons between experimental and theoretical results were conducted, which shows that K-means fuzzy clustering algorithm is effective and accurate in sorting fabric handle based on the features selected from the pulling-out force and distance curve of the CHES-FY system.

Deep learning for tactile understanding from visual and haptic data

“…The tactile properties of consumer products have been explored to better understand the quality of items such as fabric [31] and skin cream [25]. Progress in tactile understanding has been driven by both the creation of better mechanical measurement systems ( [11], [35]) as well as learning better algorithms for haptic understanding ( [11], [12]). A variety of work ( [12], [29]) concentrates on classifying specific textures (e.g., paper).…”

Experiments on Hypothesis "Fuzzy K-Means is Better than K-Means for Clustering"

“…To improve the quality of clustering and time performance Yu et al [5] applied Fuzzy K-Means to form a new sampling technique based on clonal optimization which provided quality results with huge databases. Du et al [6] used Fuzzy K-Means algorithm for fabric handle clusters that relay on mechanical and physical properties. The results proved that the algorithm is reliable.…”

“…We made an empirical study besides review of literature to prove that the Fuzzy K-Means exhibits better clustering performance than K-Means. The literature on these two and their comparison besides other derivatives of them [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23] and [24] can be found in section IV.…”