SM-GMVAE: an intelligent model for defect quantification evaluation based on few ultrasonic signals

The conventional defect quantification evaluation approaches based on machine learning requires massive amounts of labelled defect signals, which is expensive and time-consuming works. This paper proposed a novel Similarity Metric Gaussian Mixture Variational Auto-Encoder (SM-GMVAE) model, which ena...

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Veröffentlicht in:	Engineering Research Express 2024-09, Vol.6 (3), p.35234
Hauptverfasser:	Li, Zhifeng, Liu, Xiaojian, Li, Runchen, Song, Shaoheng, Liu, Weihua, Song, Yaqin
Format:	Artikel
Sprache:	eng
Schlagworte:	defect quantification evaluation few-shot learning Gaussian mixture model variational auto-encoder
Online-Zugang:	Volltext
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