Influences of some electrolyte impurities within starch and starch cationization on the adhesion of quaternary ammonium cornstarch to cotton and polyester fibers were investigated. The electrolytes considered incl...Influences of some electrolyte impurities within starch and starch cationization on the adhesion of quaternary ammonium cornstarch to cotton and polyester fibers were investigated. The electrolytes considered included NaCI, Na2SO4, NaH2PO4 and Na2HPO4. The adhesion to fibers was evaluated in terms of maximum strength and work-to-break of the roving sized with the starch pastes containing electrolytes. It was found that the cationization showed a positive effect on the adhesion to both fibers whereas the electrolytes gave an adverse effect and reduced the adhesion. The adverse effect depends on the type and amount of electrolytes. The influence of electrolytes on the adhesion can be ranked in a series of NaH2PO4〉 Na2 HPO4〉 Na2SO4 〉 NaCl. The adhesion enhances as the modification extent increases and the electrolyte content decreases. Furthermore, the adverse effect can be compensated by the positive effect of the starch modification even at a low modification extent. If the electrolytes are fully eliminated, the cationic starch can increase the adhesion strength by more than 10% for both fibers.展开更多
This paper introduces the identification of the defects on the fabric by using two-double neural network and wavelet analysis. The purpose is to fit for the automatic cloth inspection system and to avoid the disadvant...This paper introduces the identification of the defects on the fabric by using two-double neural network and wavelet analysis. The purpose is to fit for the automatic cloth inspection system and to avoid the disadvantages of traditional human inspection. Firstly, training the normal fabric to acquire its characteristics and then using the BP neural network to tell the normal fabric apart from the one with defects. Secondly, doing the two-dimeusional discrete wavelet transformation based on the image of the defects, then wiping off the proper characteristics of the fabric, and identifying the defects utilizing the trained BP neural network. It is proved that this method is of high speed and accuracy. It comes up to the requirement of automatic cloth inspection.展开更多
基金The Foundation for the Talents by Anhui Province,China(No.2002Z036)
文摘Influences of some electrolyte impurities within starch and starch cationization on the adhesion of quaternary ammonium cornstarch to cotton and polyester fibers were investigated. The electrolytes considered included NaCI, Na2SO4, NaH2PO4 and Na2HPO4. The adhesion to fibers was evaluated in terms of maximum strength and work-to-break of the roving sized with the starch pastes containing electrolytes. It was found that the cationization showed a positive effect on the adhesion to both fibers whereas the electrolytes gave an adverse effect and reduced the adhesion. The adverse effect depends on the type and amount of electrolytes. The influence of electrolytes on the adhesion can be ranked in a series of NaH2PO4〉 Na2 HPO4〉 Na2SO4 〉 NaCl. The adhesion enhances as the modification extent increases and the electrolyte content decreases. Furthermore, the adverse effect can be compensated by the positive effect of the starch modification even at a low modification extent. If the electrolytes are fully eliminated, the cationic starch can increase the adhesion strength by more than 10% for both fibers.
文摘This paper introduces the identification of the defects on the fabric by using two-double neural network and wavelet analysis. The purpose is to fit for the automatic cloth inspection system and to avoid the disadvantages of traditional human inspection. Firstly, training the normal fabric to acquire its characteristics and then using the BP neural network to tell the normal fabric apart from the one with defects. Secondly, doing the two-dimeusional discrete wavelet transformation based on the image of the defects, then wiping off the proper characteristics of the fabric, and identifying the defects utilizing the trained BP neural network. It is proved that this method is of high speed and accuracy. It comes up to the requirement of automatic cloth inspection.
