Sentence examples for mode decompositions are from inspiring English sources

Elementary mode decompositions are not unique.

Since elementary mode decompositions are not unique [ 23], the principal advantage of these previous approaches in comparison to ours is that they are capable of selecting a particular unique decomposition based on some criterion (typically the minimization of the Euclidean norm of the weight vector), while our approach simply generates a valid decomposition among all possibilities.

In literature, wavelet transform and empirical mode decomposition are two popular feature extraction methods used to analyze a signal having non-linear and non-stationary property.

EMD (Empirical Mode Decomposition) is an adaptive signal decomposition method and is exclusive of base functions.

As a comparison, Hilbert-Huang transformation, based on empirical mode decomposition, is also applied to analyze the multi-faults data.

Empirical mode decomposition is employed to decouple global degradation, local regeneration and various fluctuations in battery capacity time series.

Then, Hough mode decomposition is performed to extract the contributions of different tidal modes for each decomposed tidal component.

First, variational mode decomposition is employed to decompose the original wind speed time series into a set of modes and into one bias series.

A multivariate feature selection method based on wavelet transform and empirical mode decomposition is proposed in [11] for mental task classification.

Variational mode decomposition was developed by Dragomiretskiy and Zosso [96] to remove noise sensitivity and sampling problems that accompany standard EMD.

The effectiveness of the proposed technique for the Lamb wave mode decomposition is investigated through numerical simulation and experimental tests performed on an aluminum plate.