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http://dx.doi.org/10.6109/jkiice.2015.19.2.279

Data-Driven Signal Decomposition using Improved Ensemble EMD Method  

Lee, Geum-Boon (Department of Computer Security, Chosun College of Science & Technology)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.19, no.2, 2015 , pp. 279-286 More about this Journal
Abstract
EMD is a fully data-driven signal processing method without using any predetermined basis function and requiring any user parameters setting. However EMD experiences a problem of mode mixing which interferes with decomposing the signal into similar oscillations within a mode. To overcome the problem, EEMD method was introduced. The algorithm performs the EMD method over an ensemble of the signal added independent identically distributed white noise of the same standard deviation. Even so EEMD created problems when the decomposition is complete. The ensemble of different signal with added noise may produce different number of modes and the reconstructed signal includes residual noise. This paper propose an modified EEMD method to overcome mode mixing of EMD, to provide an exact reconstruction of the original signal, and to separate modes with lower cost than EEMD's. The experimental results show that the proposed method provides a better separation of the modes with less number of sifting iterations, costs 20.87% for a complete decomposition of the signal and demonstrates superior performance in the signal reconstruction, compared with EEMD.
Keywords
Empirical Mode Decomposition(EMD); Ensemble EMD; Intrinsic Mode Function(IMF); Mode Mixing; Data-driven Decomposition;
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Times Cited By KSCI : 1  (Citation Analysis)
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