Details
| Original language | English |
|---|---|
| Title of host publication | IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CIES 2014 |
| Subtitle of host publication | 2014 IEEE Symposium on Computational Intelligence for Engineering Solutions, Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 117-124 |
| Number of pages | 8 |
| ISBN (electronic) | 978-1-4799-4509-2 |
| ISBN (print) | 9781479945108 |
| Publication status | Published - 2014 |
| Externally published | Yes |
| Event | 2014 IEEE Symposium on Computational Intelligence for Engineering Solutions, CIES 2014 - Orlando, United States Duration: 9 Dec 2014 → 12 Dec 2014 |
Abstract
A compressive sensing (CS) based approach is developed in conjunction with an adaptive basis reweighting procedure for stochastic process power spectrum estimation. In particular, the problem of sampling gaps in stochastic process records, occurring for reasons such as sensor failures, data corruption, and bandwidth limitations, is addressed. Specifically, due to the fact that many stochastic process records such as wind, sea wave and earthquake excitations can be represented with relative sparsity in the frequency domain, a CS framework can be applied for power spectrum estimation. To this aim, an ensemble of stochastic process realizations is often assumed to be available. Relying on this attribute an adaptive data mining procedure is introduced to modify harmonic basis coefficients, vastly improving on standard CS reconstructions. The procedure is shown to perform well with stationary and non-stationary processes even with up to 75% missing data. Several numerical examples demonstrate the effectiveness of the approach when applied to noisy, gappy signals.
Keywords
- Compressive sensing, data mining, missing data, power spectrum, stochastic process
ASJC Scopus subject areas
- Computer Science(all)
- Computer Science Applications
- Computer Science(all)
- Artificial Intelligence
- Computer Science(all)
- Computational Theory and Mathematics
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IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CIES 2014: 2014 IEEE Symposium on Computational Intelligence for Engineering Solutions, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2014. p. 117-124 7011840.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Compressive sensing based power spectrum estimation from incomplete records by utilizing an adaptive basis
AU - Comerford, Liam A.
AU - Beer, Michael
AU - Kougioumtzoglou, Ioannis A.
PY - 2014
Y1 - 2014
N2 - A compressive sensing (CS) based approach is developed in conjunction with an adaptive basis reweighting procedure for stochastic process power spectrum estimation. In particular, the problem of sampling gaps in stochastic process records, occurring for reasons such as sensor failures, data corruption, and bandwidth limitations, is addressed. Specifically, due to the fact that many stochastic process records such as wind, sea wave and earthquake excitations can be represented with relative sparsity in the frequency domain, a CS framework can be applied for power spectrum estimation. To this aim, an ensemble of stochastic process realizations is often assumed to be available. Relying on this attribute an adaptive data mining procedure is introduced to modify harmonic basis coefficients, vastly improving on standard CS reconstructions. The procedure is shown to perform well with stationary and non-stationary processes even with up to 75% missing data. Several numerical examples demonstrate the effectiveness of the approach when applied to noisy, gappy signals.
AB - A compressive sensing (CS) based approach is developed in conjunction with an adaptive basis reweighting procedure for stochastic process power spectrum estimation. In particular, the problem of sampling gaps in stochastic process records, occurring for reasons such as sensor failures, data corruption, and bandwidth limitations, is addressed. Specifically, due to the fact that many stochastic process records such as wind, sea wave and earthquake excitations can be represented with relative sparsity in the frequency domain, a CS framework can be applied for power spectrum estimation. To this aim, an ensemble of stochastic process realizations is often assumed to be available. Relying on this attribute an adaptive data mining procedure is introduced to modify harmonic basis coefficients, vastly improving on standard CS reconstructions. The procedure is shown to perform well with stationary and non-stationary processes even with up to 75% missing data. Several numerical examples demonstrate the effectiveness of the approach when applied to noisy, gappy signals.
KW - Compressive sensing
KW - data mining
KW - missing data
KW - power spectrum
KW - stochastic process
UR - http://www.scopus.com/inward/record.url?scp=84922782396&partnerID=8YFLogxK
U2 - 10.1109/CIES.2014.7011840
DO - 10.1109/CIES.2014.7011840
M3 - Conference contribution
AN - SCOPUS:84922782396
SN - 9781479945108
SP - 117
EP - 124
BT - IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CIES 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 IEEE Symposium on Computational Intelligence for Engineering Solutions, CIES 2014
Y2 - 9 December 2014 through 12 December 2014
ER -