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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| Veröffentlicht in: | IEEE transactions on instrumentation and measurement Jg. 71; S. 1 - 8 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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IEEE
2022
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| Abstract | 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 virtual spectra such that the augmentation training set is employed to develop the calibration model. To use the generated unlabeled samples for modeling with online measurement purposes, a semi-supervised ladder network (S2-LN)-based regression learning model is developed. The proposed method incorporates all generated virtual unlabeled samples with real labeled samples. An important advantage of this approach is that it ensures that the generated virtual spectra and the real labeled spectra are the same distribution, which in turn ensures the effectiveness of semi-supervised learning. A numerical simulation example and an experimental example of the glucose fermentation process illustrate the effectiveness of the approach. |
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| AbstractList | 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 virtual spectra such that the augmentation training set is employed to develop the calibration model. To use the generated unlabeled samples for modeling with online measurement purposes, a semi-supervised ladder network (S2-LN)-based regression learning model is developed. The proposed method incorporates all generated virtual unlabeled samples with real labeled samples. An important advantage of this approach is that it ensures that the generated virtual spectra and the real labeled spectra are the same distribution, which in turn ensures the effectiveness of semi-supervised learning. A numerical simulation example and an experimental example of the glucose fermentation process illustrate the effectiveness of the approach. |
| Author | Chen, Junghui Mu, Guoqing |
| Author_xml | – sequence: 1 givenname: Guoqing orcidid: 0000-0002-4593-1007 surname: Mu fullname: Mu, Guoqing email: guoqingmu@foxmail.com organization: School of Information and Control Engineering, Qingdao University of Technology, Qingdao, China – sequence: 2 givenname: Junghui orcidid: 0000-0002-9994-839X surname: Chen fullname: Chen, Junghui email: jason@wavenet.cycu.edu.tw organization: Department of Chemical Engineering, Chung Yuan Christian University, Taoyuan, Taiwan |
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| SubjectTerms | 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 |
| Title | Developing a Conditional Variational Autoencoder to Guide Spectral Data Augmentation for Calibration Modeling |
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