Novel Pattern Recognition Algorithm for real-time Measuring Coal Dust with Bimodal Peak Distribution
Real-time measurement of coal dust concentration is vital for colliery safety in production. To improve the precision, a novel inversion algorithm for dust distribution with bimodal peak is presented. A three-parameter was brought forward and the eigenvectors of 360 patterns are worked out. The pattern classification was performed according to diffraction angular with dust information. 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 dust and respiring dust 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 dust sensor remarkably.
Coal dust sensor Pattern classification Respiring coal dust Pattern recognition Pattern amendment
MA Fengying
Institute of Electrical Engineering and Automation, Shandong Polytechnic University, Shandong County, Jinan 250353, China
国际会议
The 31st Chinese Control Conference(第三十一届中国控制会议)
合肥
英文
3947-3951
2012-07-01（万方平台首次上网日期，不代表论文的发表时间）