Liam Comerford
University of Liverpool
30 Papers
117 Citations
Liam Comerford is an academic researcher from University of Liverpool. The author has contributed to research in topics: Spectral density & Stochastic process. The author has an hindex of 7, co-authored 27 publications. Previous affiliations of Liam Comerford include Leibniz University of Hanover.
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Papers
Compressive sensing based stochastic process power spectrum estimation subject to missing data
TL;DR: In this paper, a compressive sensing based approach for stationary and non-stationary stochastic process power spectrum estimation subject to missing data is developed, where Fourier and harmonic wavelet bases are utilized for expanding the signal recorded in the time domain.
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An artificial neural network approach for stochastic process power spectrum estimation subject to missing data
TL;DR: In this article, an artificial neural network (ANN) based approach is developed for estimating the power spectrum of stochastic processes subject to missing/limited data, which is applicable for treating nonstationary processes not only with separable but non-separable in time and frequency evolutionary power spectra as well.
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Compressive sensing with an adaptive wavelet basis for structural system response and reliability analysis under missing data
TL;DR: In this paper, a compressive sensing based framework in conjunction with an adaptive wavelet basis is presented for reconstructing the samples with missing data and estimating the underlying process EPS, where the source load data records are incomplete.
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Lp-norm minimization for stochastic process power spectrum estimation subject to incomplete data
Yuanjin Zhang,Liam Comerford,Ioannis A. Kougioumtzoglou,Michael Beer,Michael Beer,Michael Beer +5 more
TL;DR: It is shown that the general L p norm minimization approach can satisfactorily estimate the spectral content of the underlying process by utilizing appropriate Fourier and wavelet bases and focusing on the L 1 and L 1 / 2 norms.
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Uncertainty Quantification of Power Spectrum and Spectral Moments Estimates Subject to Missing Data
TL;DR: The challenge of quantifying the uncertainty in stochastic process spectral estimates based on realizations with missing data is addressed and relying on relative LaSalle's inequality is addressed.