Yao, Houpu, Wen, Jingjing, Ren, Yi, Wu, Bin and Ji, Ze ![]() |
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Abstract
Special high-end sensors with expensive hardware are usually needed to measure shock signals with high accuracy. In this paper, we show that cheap low-end sensors calibrated by deep neural networks are also capable to measure high-g shocks accurately. Firstly we perform drop shock tests to collect a dataset of shock signals measured by sensors of different fidelity. Secondly, we propose a novel network to effectively learn both the signal peak and overall shape. The results show that the proposed network is capable to map low-end shock signals to its high-end counterparts with satisfactory accuracy. To the best of our knowledge, this is the first work to apply deep learning techniques to calibrate shock sensors.
Item Type: | Conference or Workshop Item (Paper) |
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Date Type: | Published Online |
Status: | Published |
Schools: | Engineering |
Subjects: | T Technology > TJ Mechanical engineering and machinery T Technology > TK Electrical engineering. Electronics Nuclear engineering |
Publisher: | IEEE |
ISBN: | 9781479981311 |
Date of Acceptance: | 1 February 2019 |
Last Modified: | 25 Oct 2022 13:34 |
URI: | https://orca.cardiff.ac.uk/id/eprint/120156 |
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