A Recurrent Neural Network Classifier for Ultrasonic NDE Applications

@article{Marino2018ARN,
  title={A Recurrent Neural Network Classifier for Ultrasonic NDE Applications},
  author={Michael Marino and Kushal Virupakshappa and Erdal Oruklu},
  journal={2018 IEEE International Ultrasonics Symposium (IUS)},
  year={2018},
  pages={1-4}
}
This work presents a classifier architecture for N on-Destructive Evaluation (NDE)applications which can robustly detect the presence and location of flaws using Wavelet Transforms (WT)and Recurrent Neural Networks (RNN). A pre-processing WT decomposition is used as a feature extractor prior to the RNN. WT can analyze both time and frequency information simultaneously and can filter out higher frequency clutter via the selection of the appropriate wavelet and number of decomposition levels. Two… CONTINUE READING

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