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As a result, the surficial neotectonic fault pattern resembles the inherited wrenching fault pattern, although the faulting mode is normal-slip.
Finally, identify the fault pattern via the demodulation spectrum.
GLCM features are extracted for capturing fault pattern information underlying sound fields.
Finally, the features are fed into multi-class support vector machine for fault pattern identification.
Bearing condition and fault pattern can be revealed by the proposed method resulting TFR.
Finite state automata is used as fault pattern recognizer which estimates the direction of fault.
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The results indicate that the differential energy based protection scheme can reliably detect the fault patterns in TCSC line including wide variations in operating conditions of the faulted power network.
Masson, D. G. Fault patterns at outer trench walls.
Finally, support vector machine (SVM) is used to classify different fault patterns.
The fault patterns of t-Adder gate under cell deposition defects are investigated.
The fault patterns considered are: bias, random drift, systematic drift, lag, gain degradation, and floating.
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