This paper proposes a technique to select a wavelet function that shows good characteristics for the identification of power quality disturbances. It considers the low frequency disturbances such as flicker and harmonics as well as high frequency disturbances such as transient and voltage sags. Due to time-frequency localization properties, the Discrete Wavelet Transform permits signal decomposition in different energy levels, which are used to characterize disturbances that contain information on the frequency domain. Four wavelet families were studied in which Biorthogonal showed excellent performance.
Seismic records are characterized by a high level of complexity resulting from the interaction of different types of waves propagating in the subsurface. Interpretation of the different wave modes and features present in a seismic record generally is done by expert judgment, and its automatization is a problem that has not been resolved completely. We present a methodology that uses pattern recognition to select the best seismic attributes that should be chosen to detect and classify surface waves in a seismic record, based on the notion of similarity, and that is applied on the automatic interpretation of three different seismic-data record sets. The classification obtained for these different real data sets exhibits well-differentiated zones that improve and automatize the expert judgment interpretation.
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