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Based on noise-assisted approach, ensemble local mean decomposition (ELMD) method alleviates the mode mixing issue of LMD to some degree.
Using the ensemble EMD (EEMD) process is the solution to a mode mixing issue.
In the Chicago data analysis we encountered the mode mixing issue and the EEMD process was employed to generate IMFs.
To calculate the excess mortality associated with the July 1995 Chicago heatwave, the HHT algorithm needed to handle the mode mixing issue.
The analysis of the Chicago data encountered the mode mixing issue, as shown in the left column plots of Figure 3.
Comparing the results obtained from EMD and EEMD (i.e., top-left plot versus middle-left plot), we concluded that the mode mixing issue could not be ignored and the EEMD process was necessary.
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Mode mixing is often a consequence of signal intermittency [ 16].
There are two reasons of the generation of mode mixing.
EEMD removes the mode mixing problem in traditional EMD.
If some mode mixing is present, d1[n] is not stationary.
A modified version of empirical mode decomposition (EMD) is presented to solve the mode mixing problem.
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