Uncertainty Quantification of Power Spectrum and Spectral Moments Estimates Subject to Missing Data

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems Part A Civil Engineering ◽

10.1061/ajrua6.0000925 ◽

2017 ◽

Vol 3 (4) ◽

pp. 04017020 ◽

Author(s):

Yuanjin Zhang ◽

Liam Comerford ◽

Ioannis A. Kougioumtzoglou ◽

Edoardo Patelli ◽

Michael Beer

Keyword(s):

Missing Data ◽

Power Spectrum ◽

Uncertainty Quantification ◽

Spectral Moments ◽

Moments Estimates

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Uncertainty Quantification in Power Spectrum Estimation of Stochastic Processes Subject to Missing Data

Vulnerability, Uncertainty, and Risk ◽

10.1061/9780784413609.038 ◽

2014 ◽

Author(s):

L. Comerford ◽

I. A. Kougioumtzoglou ◽

M. Beer

Keyword(s):

Missing Data ◽

Power Spectrum ◽

Stochastic Processes ◽

Uncertainty Quantification ◽

Spectrum Estimation ◽

Power Spectrum Estimation

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Compressive sensing for power spectrum estimation of multi-dimensional processes under missing data

2015 International Conference on Systems, Signals and Image Processing (IWSSIP) ◽

10.1109/iwssip.2015.7314202 ◽

2015 ◽

Author(s):

Yuanjin Zhang ◽

Liam Comerford ◽

Michael Beer ◽

Ioannis Kougioumtzoglou

Keyword(s):

Missing Data ◽

Power Spectrum ◽

Compressive Sensing ◽

Spectrum Estimation ◽

Power Spectrum Estimation

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Uncertainty Quantification and Missing Data for Turbomachinery With Probabilistic Equivalence and Arbitrary Polynomial Chaos, Applied to Scroll Compressors

10.1115/1.0002828v ◽

2021 ◽

Author(s):

Nick Pepper ◽

Francesco Montomoli ◽

Sanjiv Sharma ◽

Francesco Giacomel ◽

Michele Pinelli ◽

...

Keyword(s):

Missing Data ◽

Uncertainty Quantification ◽

Polynomial Chaos ◽

Arbitrary Polynomial

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Compressive sensing based stochastic process power spectrum estimation subject to missing data

Probabilistic Engineering Mechanics ◽

10.1016/j.probengmech.2015.09.015 ◽

2016 ◽

Vol 44 ◽

pp. 66-76 ◽

Author(s):

Liam Comerford ◽

Ioannis A. Kougioumtzoglou ◽

Michael Beer

Keyword(s):

Stochastic Process ◽

Missing Data ◽

Power Spectrum ◽

Compressive Sensing ◽

Spectrum Estimation ◽

Power Spectrum Estimation

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A Damage Estimation Method for Packaging Systems Based on Power Spectrum Densities Using Spectral Moments

Packaging Technology and Science ◽

10.1002/pts.2211 ◽

2016 ◽

Vol 29 (6) ◽

pp. 303-321 ◽

Author(s):

Victor Huart ◽

Jean-baptiste Nolot ◽

Jean-Charles Candore ◽

Jérôme Pellot ◽

Nicolas Krajka ◽

...

Keyword(s):

Power Spectrum ◽

Estimation Method ◽

Damage Estimation ◽

Spectral Moments

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An artificial neural network based approach for power spectrum estimation subject to limited and/or missing data

Safety, Reliability, Risk and Life-Cycle Performance of Structures and Infrastructures ◽

10.1201/b16387-159 ◽

2014 ◽

pp. 1083-1090

Author(s):

L Comerford ◽

I Kougioumtzoglou ◽

M Beer

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Missing Data ◽

Power Spectrum ◽

Spectrum Estimation ◽

Power Spectrum Estimation ◽

Artificial Neural

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An artificial neural network based approach for power spectrum estimation and simulation of stochastic processes subject to missing data

2013 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES) ◽

10.1109/cies.2013.6611738 ◽

2013 ◽

Author(s):

Liam A Comerford ◽

Ioannis A. Kougioumtzoglou ◽

Michael Beer

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Missing Data ◽

Power Spectrum ◽

Stochastic Processes ◽

Spectrum Estimation ◽

Power Spectrum Estimation ◽

Artificial Neural

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On quantifying the uncertainty of stochastic process power spectrum estimates subject to missing data

International Journal of Sustainable Materials and Structural Systems ◽

10.1504/ijsmss.2015.078358 ◽

2015 ◽

Vol 2 (1/2) ◽

pp. 185

Author(s):

Liam Comerford ◽

Ioannis A. Kougioumtzoglou ◽

Michael Beer

Keyword(s):

Stochastic Process ◽

Missing Data ◽

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An artificial neural network approach for stochastic process power spectrum estimation subject to missing data

Structural Safety ◽

10.1016/j.strusafe.2014.10.001 ◽

2015 ◽

Vol 52 ◽

pp. 150-160 ◽

Author(s):

Liam Comerford ◽

Ioannis A. Kougioumtzoglou ◽

Michael Beer

Keyword(s):

Neural Network ◽

Stochastic Process ◽

Artificial Neural Network ◽

Missing Data ◽

Power Spectrum ◽

Spectrum Estimation ◽

Network Approach ◽

Neural Network Approach ◽

Power Spectrum Estimation ◽

Artificial Neural Network Approach

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Characterization of the formation of base sheet paper using spectral moments determined from time-series models

Journal of Fluid Mechanics ◽

10.1017/s0022112077000652 ◽

1977 ◽

Vol 82 (2) ◽

pp. 261-272

Author(s):

Warren R. Devries ◽

S. M. Wu

Keyword(s):

Stochastic Process ◽

Time Series ◽

Continuous Time ◽

Light Transmission ◽

Time Series Models ◽

Spectral Moments ◽

Fibre Distribution ◽

Moments Estimates

The fibre distribution in a sheet of paper, referred to as the formation, is largely the result of turbulence, a stochastic process. Continuous time-series models developed from discrete light-transmission profiles are used to characterize formation. The models are used to obtain explicit expressions for the spectral moments of the profiles. From the moments, estimates of two characteristic lengths of the fibre distribution can be obtained and are interpreted as the average and largest flock size. These lengths are used to develop an index for evaluating the formation of four samples of base sheet paper. The results of this characterization agree with other methods, but this technique has the advantage of providing a physical interpretation of the index.

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