Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in . Accor ding to news originating from Beijing, People’s Republic of China, by NewsRx cor respondents, research stated, “To solve the problems of high false alarm rate an d leakage rate of coal mine gas and coal dust explosion disaster alarm methods, and improve the accuracy of coal mine gas and coal dust explosion perception, so und identification method of coal mine gas and coal dust explosion based on wave let scattering transform was proposed: install mining sound pickup equipment in the critical monitoring area of coal mine underground, and collect equipment wor king sound and environmental sound in real time, the wavelet scattering coeffici ents were obtained from the collected sound by wavelet scattering transform, the wavelet scattering coefficients of the sound signal were constructed, the colle cted the 11-dimensional feature parameters consisting of small gradient dominanc e, large gradient dominance, energy, gray average, gradient average, gray mean s quare difference, gradient mean square difference, correlation, gray entropy, gr adient entropy, mixing entropy were obtained by calculating the image gray gradi ent co-generation matrix of the wavelet scattering coefficient map, which consti tuted the feature vector characterizing the sound signal, and were input to the support vector machine for training to obtain the coal mine the 11-dimensional f eature vectors were obtained by extracting the gray gradient covariance matrix o f the wavelet scattering coefficient map of the sound signal to be measured, and bring it into the trained coal mine gas and coal dust explosion sound recogniti on model for sound recognition classification, it verified by experiments.”
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