An improved method for particle measurement based on diffraction and pattern recognition

To improve the precision of particle measurement, an improved method for particle measurement based on diffraction and pattern recognition is presented. A three-parameter was brought forward and the eigenvectors of 360 patterns are worked out. Three template bases of B, C and D were established according to diffraction field distribution classification using geometry characteristic of different particles distribution in advance. During measurement, one of three template databases was selected and pattern recognition was performed within the selected template database. Therefore, the particles distribution was obtained by template matching. Simulation indicates the minimum recognition time is reduced to 0.05 times of that before. Thereupon, transitional patterns were supplemented and the precision increased markedly. But sometimes there was gross error. Therefore the pattern amendment function was introduced and the eigenvectors of amendment patterns were calculated. The normalized eigenvectors of amendment patterns ranked were stored in advance. During measurement the optimal patterns were recognized in the whole and amended in the local area according to the principle of the minimum of variance sum. Experiments proved the error of total particle and respiring particle declined from 6% to 2% and from 9% to 3%, respectively. It is concluded that the novel algorithm has improved the precision and real-time performance of particle sensor remarkably.