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《JOURNAL OF CHENGDU UNIVERSITY OF TECHNOLOGY》 1996-03
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NEURAL NETWORK COUPLED ACOUSTIC EMISSION SENSORS FOR ROCK GRINDING AND DRILLING

Wu Guanglin; S. J. Jung; K. Prisbrey(Chengdu Institute of Technology, China) (University of Idaho, USA )T. L. Nichols (United States Bureau of Mines)  
This study is to evaluate the on-line detection of rock properties through acoustic emission sensors. At first, the relation of acoustic emission with rock fracture mechanism is investigated. Then it is described that the acoustic emission sensor is attached to a drill for roof bolting and the rock fracture characteristics and rock type are “learned” by a back propagation neural network with 99% classification accuracy. Experiments indicate that a neural network′s calibration and computational abilities suggest better acoustic emission sensor capabilities.
【CateGory Index】: TD231
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【Co-references】
Chinese Journal Full-text Database 10 Hits
1 Liu Dongyan et al.;A Study on Acoustic Emission Characters of Failure of Fissured Rock on Compression[J];UNDERGROUND SPACE;1998-04
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6 Dai Guang(Dept of Mechanical Engineering, Daqing Petroleum Institute, Anda, Heilongjiang, China, 151400);Study on Reverse Source of AE Source Signal Intensity[J];JOURNAL OF DAQING PETROLEUM INSTITUTE;1998-03
7 JIANG HAIKUN 1 MA SHENGLI 2 ZHANG LIU 2 CAO WENHAI 1 HOU HAIFENG 11 Seismological Bureau of Shandong Province, Ji′nan 250014, China 2 Institute of Geology, Laboratory of Tectonophysics, China Seismological Bureau, Beijing 100029, China;SPATIAL-TEMPORAL CHARACTERISTICS OF ACOUSTIC EMISSION OF ROCKSAMPLE WITH REGULAR FAULT AND A COLUMNAR BARRIER DURINGDEFORMATION[J];Chinese Journal of Geophysics;2003-02
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【Secondary References】
Chinese Journal Full-text Database 3 Hits
1 ;Artificial Intelligence and Its Application in Coal Mine Safety Technique[J];Mining Safety & Environmental Protection;2005-05
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