Image-Based Flow Regime Recognition in Aerated Stirred Tanks Using Deep Transfer Learning
Research output: Contribution to journal › Research article › Contributed › peer-review
Contributors
Abstract
Monitoring of flow regimes in aerated stirred tanks is important to ensure energy efficiency and product quality. The use of deep learning models for the recognition of flow regimes shows promising results. However, such models require a large amount of data for training. The aim of this paper is to apply the deep transfer learning approach to address this challenge. We compare various pre-trained models with the differential learning rate and 2-step transfer learning approaches to analyse the resultant model performance. We also investigate the effect of the dataset size on the classification accuracy.
Details
Original language | English |
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Pages (from-to) | 1172-1179 |
Number of pages | 8 |
Journal | Chemie Ingenieur Technik |
Volume | 95 |
Issue number | 7 |
Publication status | Published - 10 May 2023 |
Peer-reviewed | Yes |
External IDs
Scopus | 85152554073 |
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WOS | 000984990500001 |
ORCID | /0000-0001-5165-4459/work/142248296 |
Keywords
Research priority areas of TU Dresden
DFG Classification of Subject Areas according to Review Boards
Subject groups, research areas, subject areas according to Destatis
Sustainable Development Goals
ASJC Scopus subject areas
Keywords
- Deep learning, Deep transfer learning, Flow regime recognition, Machine learning