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The capability of the proposed filter to significantly reduce data noise is demonstrated by numerical tests including the application to experimental data.
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A noise synthesis technique for aircraft noise is demonstrated by predicting the noise at a noise monitoring location near an airport.
To perturb the data, noise is randomly added to gene expression data with certain signal-to-noise ratio (SNR).
Influence of data noise was quantitatively evaluated.
Approaches that work robustly against data noise are needed.
The ability to simultaneously and accurately predict the EIFS distribution and crack growth rate noise from a small, highly-scattered data set is demonstrated in a numerical example.
Experimental data, as it is demonstrated in this work, has noise- enriched BEs accumulated mainly below the specificity threshold values.
Detection of damage sites in data affected by noise spikes caused by environmental disturbances is demonstrated.
Finally, the robustness of the TOF P-UPS inversion method is demonstrated for noisy data by evaluating the results for a range of signal-to-noise ratios.
Performance is demonstrated on real cetacean recordings and ocean noise.
It is demonstrated how this process is effected by the influence of noise and the data-acquision process using numerical simulations of the experimental data.
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