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SMTNet: Hierarchical cavitation intensity recognition based on sub-main transfer network [article]

Yu Sha, Johannes Faber, Shuiping Gou, Bo Liu, Wei Li, Stefan Schramm, Horst Stoecker, Thomas Steckenreiter, Domagoj Vnucec, Nadine Wetzstein, Andreas Widl, Kai Zhou
2022 arXiv   pre-print
In this study, a novel hierarchical cavitation intensity recognition framework using Sub-Main Transfer Network, termed SMTNet, is proposed to classify acoustic signals of valve cavitation.  ...  The prediction accurcies of SMTNet for cavitation intensity recognition are as high as 95.32%, 97.16% and 100%, respectively.  ...  Eisenberg Lau-reatus Chair at Goethe Universität Frankfurt am Main (H. S.), by the NVIDIA GPU grant through NVIDIA Corporation (K. Z.).  ... 
arXiv:2203.01429v3 fatcat:huhpsck65fd77cwsusynx2xpfe