Identification of Stochastic Nature of Yarn Hairiness

Yam hairiness is a complex concept, which generally cannot be completely defined by a single figure. Hairiness can be considered as the fiber ends and loops standing out from the main compact yarn body. Uster hairiness system characterizes the hairiness by H value, i.e. the mean value of the total length of all hairs within one centimeter of yarn. The raw data HI are in fact realization of spatial process （hairiness spatial process -- HSP） and can be used for more complex evaluation of hairiness characteristics in the space and frequency domain. The main aim of this contribution is description of some tools for spatial characterization of yarn hairiness. The simple methods for complex characterization of lISP statistical behavior （stationarity, independence, linearity etc. ） are presented. The techniques based on the embedding dimension and correlation integral or long-range dependences evaluation are discussed. The selected methods are core of HYARN program in MATLAB. Application of this program for deeper characterization of artificial data and cotton type yam are shown.

Yarn hairiness is a complex concept, which generally cannot be completely defined by a single figure. Hairiness can be considered as the fiber ends and loops standing out from the main compact yarn body. Uster hairiness system characterizes the hairiness by H value, i.e. the mean value of the total length of all hairs within one centimeter of yarn. The raw data H_i are in fact realization of spatial process (hairiness spatial process — HSP) and can be used for more complex evaluation of hairiness characteristics in the space and frequency domain. The main aim of this contribution is description of some tools for spatial characterization of yarn hairiness. The simple methods for complex characterization of HSP statistical behavior (stationarity, independence, linearity etc.) are presented. The techniques based on the embedding dimension and correlation integral or long-range dependences evaluation are discussed. The selected methods are core of HYARN program in MATLAB. Application of this program for deeper characterization of artificial data and cotton type yarn are shown.