Higham et al 2016.pdf (5.88 MB)
A rapid non-iterative proper orthogonal decomposition based outlier detection and correction for PIV data
journal contributionposted on 2018-06-19, 12:56 authored by J.E. Higham, W. Brevis, Chris KeylockChris Keylock
The present work proposes a novel method of detection and estimation of outliers in particle image velocimetry measurements by the modification of the temporal coefficients associated with a proper orthogonal decomposition of an experimental time series. Using synthetic outliers applied to two sequences of vector fields, the method is benchmarked against state-of-the-art approaches recently proposed to remove the influence of outliers. Compared with these methods, the proposed approach offers an increase in accuracy and robustness for the detection of outliers and comparable accuracy for their estimation.
The UK Natural Environment Research Council for the PhD studentship of the first author and Engineering and Physical Sciences Research Council for the second author.
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Published inMeasurement Science and Technology
CitationHIGHAM, J.E., BREVIS, W. and KEYLOCK, C.J., 2016. A rapid non-iterative proper orthogonal decomposition based outlier detection and correction for PIV data. Measurement Science and Technology, 27 (12), 125303.
Publisher© IOP Publishing
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Publisher statementThis work is made available according to the conditions of the Creative Commons Attribution 3.0 Unported (CC BY 3.0) licence. Full details of this licence are available at: http://creativecommons.org/licenses/by/3.0/
NotesThis is an Open Access Article. It is published by IOP Publishing under the Creative Commons Attribution 3.0 Unported Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/3.0/