Fan Xiu-juan

The research on graphic outline capturing algorithm based on coloured embroidery pattern of HIS space

Computerized embroidery needs outline capturing of color images and stitch filling. Embroidery machines can make stitch filling according to color blocks in different areas. Considering the character of stitching of embroidery machines and referring to color image morphology, this thesis puts forward multi-scale graphic outline capturing algorithm based on color embroidery pattern of HIS space. And a variety of edge detection algorithms were compared. Experiment results show that this method can effectively control impact of noise on images. And the effect of image capturing can meet requirements of embroidery patterns.

Study of a New Cotton Assorting Algorithm

This paper analyzes the cotton assorting by computer, and studies the theory of strategies such as group sorting, elitist preservation, local optimal and self-adaptive mutation which are based upon the basic inheritance algorithm, and designs the hybrid genetic algorithm based computer automatic cotton assorting mathematical model. The computer automatic cotton-blending issue is experimented respectively using the basic genetic algorithms and the improved hybrid genetic algorithm, the results show that the cotton assorting scheme obtained by the improved hybrid genetic algorithm is more reasonable and the accuracy of mixed cotton quality is high. Effectiveness of this model has been verified through example analysis.

Research and Application of Algorithm for Comprehensive Quality Evaluation of College Students

Analytic Hierarchy Process (AHP) is adopted to determine the weight of index for the comprehensive quality evaluation of the college students. The fuzzy theory is adopted for the level-four fuzzy judgment on the comprehensive quality. Based on results of the analysis and judgment, an improved algorithm for the comprehensive evaluation of the fuzzy neural network is produced. The BP algorithm is used to adjust the parameters of the membership functions of the fuzzy system, strengthen the self-adapting ability of the system and raise the accuracy of the system. The applied examples show that the model of comprehensive quality evaluation for the college students, which are based on the fuzzy neural network algorithm, integrates with the merits of the fuzzy operation and neural network operation, and the judgment result is reasonable and the method is practical.

The Research in Yarn Quality Prediction Model Based on an Improved BP Algorithm

This paper analyzes the defects and reasons for using standard BP neural network algorithm in building quality prediction model of yarns and explores an improved BP neural network algorithm. By increasing the back-propagation error-feedback signals and applying sell-adaptive and adjusting learning rate, the research has reinforced the adjustment of network weights and prevented network entering saturated region too early. These methods can increase the convergent speed of network and improve system stability. The experiment has proved that the forecast result is of high accuracy which comes from the improved BP neural network algorithm, and the design of quality prediction model is reasonable.