Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 32nd European Safety and Reliability Conference, ESREL 2022 - Understanding and Managing Risk and Reliability for a Sustainable Future |
| Editors | Maria Chiara Leva, Edoardo Patelli, Luca Podofillini, Simon Wilson |
| Pages | 2553-2560 |
| Number of pages | 8 |
| Publication status | Published - 28 Aug 2022 |
| Event | 32nd European Safety and Reliability Conference (ESREL 2022) - Dublin, Ireland Duration: 28 Aug 2022 → 1 Sept 2022 Conference number: 32 |
Abstract
The interval discrete Fourier transform (DFT) algorithm can propagate in polynomial time signals carrying interval uncertainty. By computing the exact theoretical bounds on signal with missing data, the algorithm can be used to assess the worst-case scenario in terms of maximum or minimum power, and to provide insights into the amplitude spectrum bands of the transformed signal. The uncertainty width of the spectrum bands can also be interpreted as an indicator of the quality of the reconstructed signal. This strategy must however, assume upper and lower values for the missing data present in the signal. While this may seem arbitrary, there are a number of existing techniques that can be used to obtain reliable bounds in the time domain, for example Kriging regressor or interval predictor models. Alternative heuristic strategies based on variable (as opposed to fixed) bounds can also be explored, thanks to the flexibility and efficiency of the interval DFT algorithm. This is illustrated by means of numerical examples and sensitivity analyses.
Keywords
- Exact bounds, Interval discrete Fourier transform, Interval uncertainty, Missing data, Power spectral density estimation, Uncertainty quantification
ASJC Scopus subject areas
- Engineering(all)
- Safety, Risk, Reliability and Quality
- Social Sciences(all)
- Safety Research
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Proceedings of the 32nd European Safety and Reliability Conference, ESREL 2022 - Understanding and Managing Risk and Reliability for a Sustainable Future. ed. / Maria Chiara Leva; Edoardo Patelli; Luca Podofillini; Simon Wilson. 2022. p. 2553-2560.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
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TY - GEN
T1 - Assessing the severity of missing data problems with the interval discrete Fourier transform algorithm
AU - Behrendt, Marco
AU - de Angeli, Marco
AU - Comerford, Liam
AU - Beer, Michael
N1 - Conference code: 32
PY - 2022/8/28
Y1 - 2022/8/28
N2 - The interval discrete Fourier transform (DFT) algorithm can propagate in polynomial time signals carrying interval uncertainty. By computing the exact theoretical bounds on signal with missing data, the algorithm can be used to assess the worst-case scenario in terms of maximum or minimum power, and to provide insights into the amplitude spectrum bands of the transformed signal. The uncertainty width of the spectrum bands can also be interpreted as an indicator of the quality of the reconstructed signal. This strategy must however, assume upper and lower values for the missing data present in the signal. While this may seem arbitrary, there are a number of existing techniques that can be used to obtain reliable bounds in the time domain, for example Kriging regressor or interval predictor models. Alternative heuristic strategies based on variable (as opposed to fixed) bounds can also be explored, thanks to the flexibility and efficiency of the interval DFT algorithm. This is illustrated by means of numerical examples and sensitivity analyses.
AB - The interval discrete Fourier transform (DFT) algorithm can propagate in polynomial time signals carrying interval uncertainty. By computing the exact theoretical bounds on signal with missing data, the algorithm can be used to assess the worst-case scenario in terms of maximum or minimum power, and to provide insights into the amplitude spectrum bands of the transformed signal. The uncertainty width of the spectrum bands can also be interpreted as an indicator of the quality of the reconstructed signal. This strategy must however, assume upper and lower values for the missing data present in the signal. While this may seem arbitrary, there are a number of existing techniques that can be used to obtain reliable bounds in the time domain, for example Kriging regressor or interval predictor models. Alternative heuristic strategies based on variable (as opposed to fixed) bounds can also be explored, thanks to the flexibility and efficiency of the interval DFT algorithm. This is illustrated by means of numerical examples and sensitivity analyses.
KW - Exact bounds
KW - Interval discrete Fourier transform
KW - Interval uncertainty
KW - Missing data
KW - Power spectral density estimation
KW - Uncertainty quantification
UR - http://www.scopus.com/inward/record.url?scp=85208258866&partnerID=8YFLogxK
U2 - 10.3850/978-981-18-5183-4_S14-05-243-cd
DO - 10.3850/978-981-18-5183-4_S14-05-243-cd
M3 - Conference contribution
AN - SCOPUS:85208258866
SN - 9789811851834
SP - 2553
EP - 2560
BT - Proceedings of the 32nd European Safety and Reliability Conference, ESREL 2022 - Understanding and Managing Risk and Reliability for a Sustainable Future
A2 - Leva, Maria Chiara
A2 - Patelli, Edoardo
A2 - Podofillini, Luca
A2 - Wilson, Simon
T2 - 32nd European Safety and Reliability Conference (ESREL 2022)
Y2 - 28 August 2022 through 1 September 2022
ER -