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Generative data augmentation and automated optimization of convolutional neural networks for process monitoring.

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第一作者： Robin,Schiemer

第一单位： Institute of Process Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany.

作者： Robin,Schiemer ^[1] ; Matthias,Rüdt ^[2] ; Jürgen,Hubbuch ^[1]

作者单位： Institute of Process Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany. ^[1] Institute of Life Technologies, HES-SO Valais-Wallis, Sion, Switzerland. ^[2]

关键词 chemometrics convolutional neural networks data augmentation feature importance hyperparameter optimization process analytical technology

DOI 10.3389/fbioe.2024.1228846

PMID 38357704

发布时间 2024-02-16

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Frontiers in bioengineering and biotechnology

2024年12卷

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Frontiers in bioengineering and biotechnology

2024年12卷

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Generative data augmentation and automated optimization of convolutional neural networks for process monitoring.

Frontiers in bioengineering and biotechnology

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Generative data augmentation and automated optimization of convolutional neural networks for process monitoring.

Frontiers in bioengineering and biotechnology

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Frontiers in bioengineering and biotechnology

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