Developing a Conditional Variational Autoencoder to Guide Spectral Data Augmentation for Calibration Modeling

To deal with the typically insufficiently labeled samples involved in practical spectroscopy measurements, a conditional variational autoencoder (CVAE) is proposed to guide the spectral data augmentation calibration modeling method for in situ measurement. First, the CVAE is designed to generate the...

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Vydané v:	IEEE transactions on instrumentation and measurement Ročník 71; s. 1 - 8
Hlavní autori:	Mu, Guoqing, Chen, Junghui
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Calibration Calibration model building conditional variational autoencoder (CVAE) Data augmentation Data models Fermentation In situ measurement Numerical models Regression models semi-supervised Semi-supervised learning Semisupervised learning Spectra Spectroscopy Spectrum analysis Task analysis Training virtual sample
ISSN:	0018-9456, 1557-9662
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