Raw-signal neural networks are the most energy-efficient option for embedded acoustic emission classification, beating feature-based models by 52-71x on energy because feature extraction dominates the total time.
Micro- cracking Monitoring and Fracture Evaluation for Crumb Rubber Con- crete based on Acoustic Emission Techniques,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
eess.SP 1years
2024 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Comparison of Tiny Machine Learning Techniques for Embedded Acoustic Emission Analysis
Raw-signal neural networks are the most energy-efficient option for embedded acoustic emission classification, beating feature-based models by 52-71x on energy because feature extraction dominates the total time.