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• W4295308146 abstract "To measure the moisture content of granules during the industrial fluidized bed drying (FBD) process, a semi-supervised calibration model is proposed for using the near-infrared (NIR) spectroscopy to conduct in-situ measurement. To solve the dilemma of lacking sufficiently labeled samples as often encountered in various batch FBD processes, a semi-supervised variational inference PLS method is proposed to use up all the labeled and unlabeled spectra measured for calibration model building. Moreover, an adaptive Gamma distribution based sparsing algorithm is established to select the spectral variables for modeling, in order to overcome the high-dimensional input collinearity. Owing to the use of a variational inference learning approach, the constructed model can ensure not only prediction accuracy but also credibility. A numerical example and experiments on batch FBD processes of silica gel granules are shown to demonstrate the effectiveness and merit of the proposed method." @default.
• W4295308146 created "2022-09-12" @default.
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• W4295308146 date "2022-01-01" @default.
• W4295308146 modified "2023-10-01" @default.
• W4295308146 title "Variational PLS-Based Calibration Model Building With Semi-Supervised Learning for Moisture Measurement During Fluidized Bed Drying by NIR Spectroscopy" @default.
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• W4295308146 doi "https://doi.org/10.1109/tim.2022.3205663" @default.
• W4295308146 hasPublicationYear "2022" @default.
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