An information processing method for acoustic emission signal inspired from musical staff

Zheng, Wei; Wu, Chunxian

doi:10.1016/j.ymssp.2015.06.015

Cited by 7 publications

(1 citation statement)

References 24 publications

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“…The pitch replaces amplitude of original signal while the phonetic denotes the transition rate. The entire process is depicted in Figure. Experiment showed that the musical-staff-inspired model can reach a fidelity value as high as 98.54% in reproducing the information of the original signal while achieving 16.86% of data compression ratio; these characteristics are convenient for storage and management [5].…”

Section: Introductionmentioning

confidence: 98%

Rock Moisture Recognition by Combining AE with Musical-staff-inspired Model

Jiang¹,

Zheng²,

Tao³

2017

Proceedings of the 2016 International Conference on Modern Management, Education Technology, and Social Science (MMETSS 2016)

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Abstract. Mechanical properties of rocks change under hydrous condition, which easily causes instability and failure of rock, and triggers a disaster. Questions about determining the effect of moisture on the acoustic emission characteristics of rocks, and establishing an automatic identification model loom large at present. In this study, a large number of acoustic emission data were collected during the rupture of rocks with different moisture contents, and the data were turned into data that are composed of musical parameters by a musical-staff-inspired model which reflect AE characteristics of water-filled rocks. Through training these data with a curves similarity determination method, the measured rock could be classified into the corresponding moisture group. Results of experiments indicate success of recognition of rock moisture.

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Section: Introductionmentioning

confidence: 98%