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Based on the fabric image mosaics problem is researched. In the aspect of image edge extraction using cellular automaton model, carries on the research on the characteristics of fabric image mosaic of images, for the rough match of two images,to determine the overlapping area, in the range of the image mosaic, afterwards the image mosaic, the seam is eliminated by rapid and simple weighted smoothing algorithm between two images. Through numerical examples indicate that the method in fabric image mosaicing aspects have better effect.

The article adopt cellular automata theory to avoid the unpredictable in process of pattern generated with the constraint of cellular automata rule, it makes the cellular automata iterate into ideal patterns. With the computer simulation we can get the three basic fabric weave patterns which are plain weave, twill, satin. Compared with the traditional fabric weave design, the fabric weave generation method is a new concept and algorithm.

The quasi-regular patterns generated from hamiltonian has unique balanced symmetry. The patterns are quite suitable for the textile fabric. However the number of patterns just generated from hamiltonian are limited. Therefore in this paper a 110 of functions built by the methods of hamiltonian of superposition, nesting, combination and parameter variations, and it can increase the type and quantity. All of functions can generate fine patterns suited for textile fabric after computer graphics.

It is difficult that the detection of printing fabric, because the more complex color and pictorial. This article opted for the fabric color difference detection problem. This article is on the basis of analyzing of the fuzzy color, the fuzzy method can be used to describe it, proposed based on fuzzy membership function method size of the fabric color identification methods, and gave the L * a * b * color in the color model identification steps. Through simulation results show that the method proposed by the color of the fabric printing identification is feasible and effective manner.

A lot of function database were built by researching evolution law of uniform stochastic web and quasi-regular patterns mathematical model. It related quasi-regular pattern evolution mechanism with textile pattern design to translate formerly invisible information in mathematic area into visible textile pattern of art design. So the visual field on design could be effectively expanded, and formed the new design concept and thinking.

The detection of fabric color difference and classification issues are studied. First, to the question of color differences and analysis of the characteristics of different color space, we chose L * a * b * color space model. In this method color difference is converted into geometric distance, so we can detect the color difference with the corresponding calculation. For the complexity of color difference classification, we consider that the classification of color difference is fuzzy, so we use fuzzy method to describe it and fuzzy neural network to complete the classification. We also explain the algorithm and structure of fuzzy neural network. Finally, for the detection and classification of color difference of the fabric samples, characteristic and comparative analysis of the detection and classification concludes that the methods chosen are effective and feasible.

Health monitoring of the hydropower turbine and timely identification of potential failure areas can prevent failure of the entire vertical axis hydropower turbine. A health monitoring system integrated within the turbine could locate blade failures, reducing hydropower turbine life-cycle costs and the costs of energy. System health management is a concerted effort with applicability to machinery maintenance operations and support. Condition based maintenance (CBM) seeks to generate a design for a machinery CMB system that performs diagnoses and failure prediction on hydropower turbine. Eventually, a variety of monitoring systems will be instrumented with embedded high-performance processors to monitor equipment performance, diagnosis failures, and predict anticipated failures. The purpose of this paper is to discuss an approach to integrate data collection and analysis of hydropower turbine for the purpose of assessing equipment condition and maintaining their operational performance to support a system wide CBM concept in a cost effective way.