Smyl, D.J. orcid.org/0000-0002-6730-5277 and Liu, D. (2020) Self-filtering electrical area sensors emerging from deep learning. Measurement Science and Technology, 31 (6). 065107. ISSN 0957-0233
Abstract
In this work, we introduce the concept of self-filtering electrically-conductive area sensors for use in nondestructive evaluation, state estimation, and other contemporary engineering applications. For this, a deep learning-based approach is used to optimize sensors' conductivity distributions in order to minimize the effect of noise on Electrical Impedance Tomography (EIT) reconstructions. From sensor design examples, it is shown that (a) the underlying physical self-filtering mechanism arising from the deep learning-based approach functions by reducing the sensitivity of the internal electric potential field to noise perturbations and (b) the use of optimized self-filtering sensors can improve the robustness to noise by approximately 25% compared to standard homogeneous sensors. Lastly, using an alternative interpretation of the self-sensing optimization problem, it is shown that fundamental connections between modeling errors, measurement noise, and physics of self-filtering sensors can be linked and unlocked using deep learning.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 IOP Publishing Ltd. This Accepted Manuscript will be available for reuse under a CC BY-NC-ND 3.0 licence after a 12 month embargo period. (https://creativecommons.org/licenses/by-nc-nd/3.0/) |
Keywords: | Electrical Impedance Tomography; inverse problems; nondestructive evaluation; structural health monitoring; tomography |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Civil and Structural Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 13 Feb 2020 16:44 |
Last Modified: | 08 Dec 2021 11:54 |
Status: | Published |
Publisher: | IOP Publishing |
Refereed: | Yes |
Identification Number: | 10.1088/1361-6501/ab7314 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:157015 |
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