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Sensitivity Analysis of Neural Networks and the Correction of Emission Parameters to One Reference Source Location
Chlada, Milan ; Převorovský, Zdeněk
In the report, detailed description of neural network sensitivity analysis - the method for insignificant input parameters reduction - is mentioned. The starting point of any AE signal parameterization is the signal onset detection. The results of currently used localization methods appear to be insufficient. Hence, the new signal-shape based algorithm, modeling an expert system of the elastic wave arrival detection, has been proposed and tested. It is described in the first part of the report. The last part is located to the correction of AE signal parameters to one reference location in source vicinity. To illustrate the new partial inverse problem solution method, the numerical models with artificial emission sources and both theoretical and practically recorded Green's functions are discussed.
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Threshold counting in wavelet domain
Chlada, Milan ; Převorovský, Zdeněk
New AE signal parameters (wavelet counts) are introduced using atwo-level threshold counting of wavelet coefficients. The application of wavelet counts is illustrated in three examples of both real and simulated AE data. The significance of various classicaland newly introduced AE signal parameters used to AE source identification is tested using theneural network sensitivity and factor analyses.
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