Fast detection of wool content in wool/polyester blended fabric by Near-Infrared Spectroscopy technology
To speed up the testing and analysis accuracy of textile fibers,80 pure wool samples,80 pure polyester samples and 150 wool / polyester blended fabrics were taken as research objects for analysis in this paper. Collecting the near infrared spectra of this samples,and selecting the mean value centralization and reciprocal and multiplicative scatter correction as the preprocess method,calibration models for wool content were established with Partial Least Square( PLS). To verify the models,wool content prediction of 50 samples was conducted. The experimental results showed that: the absolute average value of wool content was 1. 036 4%,and the standard deviation was 0. 695 4%. This study provided powerful evidence of Near- Infrared Spectroscopy to detect the content of textiles.
In this paper a new method of neural filtering using artificial neural network systems is presented for the filtering problems of linear and nonlinear, stationary and nonstationary stochastic signals. The neural filter (denoted neurofilter) developed in this paper has either finite impulse response (FIR) structure or infinite impulse response (IIR) structure. The neurofilter differs from the conventional linear digital FIR and IIR filters because the artificial neural network system used in the neurofilter has a nonlinear structure due to the sigmoid function. Numerical studies for the estimation of a second-order Butterworth process are performed by changing the structures of the neurofilter in order to evaluate the performance indices under changes of the output noises or disturbances. The results obtained from these studies verified the capabilities which are essentially necessary for on-line filtering of various stochastic signals.