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Combining the over-sampling method, low-pass filter, and Empirical Mode Decomposition improves the signal-to-noise ratio.
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An alternative empirical mode decomposition (EMD) method improved by wavelet packet decomposition was developed to process the fault signals.
Both SSG and DACTAL-based decomposition improved MP-EST in all cases, sometimes substantially.
The DCM1 decomposition improved MP-EST but was less computationally efficient than the SSG-decomposition or the DACTAL-decomposition. Therefore, we focus the remainder of our discussion on the other two techniques.
An improved Empirical Mode Decomposition (EMD) algorithm is proposed to identify the modal parameters of transformer winding with obtained experiment data.
In this paper, a novel method that integrates the LS-SVM and Empirical Mode Decomposition (EMD) is proposed to improve the performance of conventional EMD.
In this paper, a hybrid method based on improved empirical mode decomposition enhanced with masking signals is presented to extract single-frequency harmonics from disturbed power signals accurately.
Improved ensemble empirical mode decomposition.
MEMD, which improved from empirical mode decomposition (EMD), is proposed by Rehman and Mandic in 2010 [ 11].
The forecast performance can be improved using ensemble empirical mode decomposition (EEMD) to produce cleaner signals as model inputs.
Hence, integrated ensemble noise-reconstructed empirical mode decomposition is proposed to overcome the drawbacks, improved by two noise estimation techniques for different SNRs as well as the noise estimation strategy.
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