A review of ultrasonic sensing and machine learning methods to monitor industrial processes

Bowler, A.L. orcid.org/0000-0003-3209-2774, Pound, M.P. and Watson, N.J. orcid.org/0000-0001-5216-4873 (2022) A review of ultrasonic sensing and machine learning methods to monitor industrial processes. Ultrasonics, 124. 106776. ISSN 0041-624X

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Item Type: Article
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© 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Keywords: Ultrasonic measurements; Machine learning; Deep learning; Industrial digital technologies; Transfer learning; Domain adaptation
Dates:
  • Published: August 2022
  • Published (online): 28 May 2022
  • Accepted: 26 May 2022
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Environment (Leeds) > School of Food Science and Nutrition (Leeds) > FSN Nutrition and Public Health (Leeds)
The University of Leeds > Faculty of Environment (Leeds) > School of Food Science and Nutrition (Leeds) > FSN Colloids and Food Processing (Leeds)
Depositing User: Symplectic Publications
Date Deposited: 12 Jul 2024 09:44
Last Modified: 12 Jul 2024 09:44
Status: Published
Publisher: Elsevier
Identification Number: 10.1016/j.ultras.2022.106776
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