Sentence examples for modal estimation using from inspiring English sources

Sahnoun et al. [8] address multi-dimensional modal estimation using sparse estimation techniques in combination with an efficient multigrid approach.

We address the problem of multidimensional modal estimation using sparse estimation techniques coupled with an efficient multigrid approach.

One of the most important issues in modal estimation of power systems using PMUs data is the negative effect of outliers.

In particular, we can envisage to used multiple 1-D modal estimation to get a low dimension initial dictionary for R-D modal estimation.

Therefore, the modal estimation based on the signal portion only would remain the same by using either the measured or the noisy-noisy signals.

(iii) We show how to extend the sparse 1-D modal estimation problem to R-D modal problems.

In this section, we present some experimental results for the multigrid sparse modal estimation.

In Section 5, we extend the sparse multigrid approach to the R-D modal estimation problem.

Then the modal estimation problem can be formulated as a penalized ℓ2 - ℓ0 sparse signal estimation problem (2).

The decaying amplitude of the extracted modal responses is then used to identify the modal damping ratios using a linear fitting function on modal response data.

For instance, [19] presents a sparse estimation example of 1-D NMR (modal) data by using Lasso [20], LARS [21] and OMP [22].