Solution for mode mixing phenomenon of the empirical mode decomposition

Wu, Shuen-De; Chiou, Jin-Chern; Goldman, E. I.

doi:10.1109/icacte.2010.5579417

Cited by 7 publications

(9 citation statements)

References 9 publications

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“…Several researchers have described the subtle elements of the HH technique alongside a few varieties and different enhancements. [17][18][19] The intermittency signals and noises cause the problem of mode mixing 20,21 in EMD. Further, the technique of masking signals has also been advised in some literature, 22 and it also can help EMD from mode mixing.…”

Section: Modified Hilbert-huang Transform For Arc Detectionmentioning

confidence: 99%

Characterization and classification of high impedance arc fault in distribution system involving metallic and nonmetallic surfaces using empirical mode decomposition

Lala

Karmakar

2019

Int Trans Electr Energ Syst

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Summary The high impedance arc fault (HIAF) poses a significant threat to the living being as it involves arcing. The enormous amount of heat generation during arc is a major concern in this regard. There are different types of arc that may occur depending on the arcing conditions and involved surfaces. The severity of the arc is determined by the involved arcing surface. In this study, arc in metallic (sphere gap, rod gap) and nonmetallic (leaning tree, concrete) surfaces on a distribution system is considered for the analysis. An empirical mode decomposition (EMD)‐based approach is applied along with k‐nearest neighbor (KNN) for the characterization and classification of the real‐time arc of different arcing conditions. The results obtained using EMD and KNN algorithm on arc signals successfully characterize and classify different HIAF by their harmonic signature. Along with KNN, the cross‐validation data‐mining algorithm is also applied to check the robustness of the approach.

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Section: Modified Hilbert-huang Transform For Arc Detectionmentioning

confidence: 99%

Characterization and classification of high impedance arc fault in distribution system involving metallic and nonmetallic surfaces using empirical mode decomposition

Lala

Karmakar

2019

Int Trans Electr Energ Syst

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“…Harmonics with very close frequencies and small amplitude The IMF's obtained from EMD decomposition clearly depict the problem of mode mixing (overlapping of frequencies).The author [9] proposed the solutions for the mode mixing phenomenon but the solutions itself introduces some errors.…”

Section: Empirical Mode Decomposition and Problem Of Mode Mixingmentioning

confidence: 99%

“…In addition to successful applications of the EMD methods, a large number of publications have attempted to improve, or at least to modify, the original method. These include modifying definition of Intrinsic Mode Function [9,10], replacing cubic spline interpolation by some other higher order interpolation, choosing the stoppage criteria [11]. Concomitantly, studies devoted to analyze the important shortcomings of the EMD and its limitations in comparison with other decomposition methods began to appear.…”

Section: Introductionmentioning

confidence: 99%

“…Their work was further continued by Feldman [14] who proposed a theoretical limiting amplitude-frequency resolution in EMD method and thus proposing conditions for mode mixing problem for two harmonics. The solution of mode mixing in EMD was provided by Shuen-De, et al [9] .The author proposed two solutions, namely, to increase the number of EMD iterations and additional mathematical operations based on integration and differentiation both of them were computationally expensive and integration process introduces errors. However, the Ensemble Empirical Mode Decomposition (EEMD) [15] method largely overcomes the false mode mixing problem of the original EMD and provides physically unique decompositions but again is computationally expensive.…”

Section: Introductionmentioning

confidence: 99%

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Solution to the Mode Mixing Problem in Two Harmonic Signals: Empirical Wavelet Transform

Verma¹,

Singh²

2016

IJERT

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Empirical Wavelet Transform and Empirical Mode Decomposition are two powerful adaptive signal decomposition techniques. Although widely used, EMD method has some limitations in decomposing signals where amplitudefrequency ranges are too close to each other. The aim of present paper is to review existing knowledge base for mode mix problem of two harmonics decomposition and introduce its solution using EWT for this topic. In this paper, the EWT decomposition is compared with the conventional EMD by numerical examples and the results of EWT decomposition are compared to original signal parameters such as energy, phase and amplitude. Finally, it is concluded from the parameters that EWT efficiently detects the components of the signals where EMD suffers mode mixing.

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“…However, separating intrinsic EEG components and artifacts are not successfully completed by this approach because frequency characteristics of biological artifacts and EEG components could be overlapped. In addition, a presence of similar oscillations in different modes or a presence of disparate amplitude oscillations in the same mode, named "mode mixing" makes the performance of artifact rejection worse [60]. Signal distortion or attenuation typically occurs according to the above-mentioned methods by excessive interference.…”

Section: Electroencephalographymentioning

confidence: 99%

Review of Artifact Rejection Methods for Electroencephalographic Systems

Kanoga¹,

Mitsukura²

2017

Electroencephalography

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Technologies using electroencephalographic (EEG) signals have been penetrated into public by the development of EEG systems. During EEG system operation, recordings ought to be obtained under no restriction of movement for routine use in the real world. However, the lack of consideration of situational behavior constraints will cause technical/biological artifacts that often mixed with EEG signals and make the signal processing difficult in all respects by ingeniously disguising themselves as EEG components. EEG systems integrating gold standard or specialized device in their processing strategies would appear as daily tools in the future if they are unperturbed to such obstructions. In this chapter, we describe algorithms for artifact rejection in multi-/single-channel. In particular, some existing single-channel artifact rejection methods that will exhibit beneficial information to improve their performance in online EEG systems were summarized by focusing on the advantages and disadvantages of algorithms.

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Solution for mode mixing phenomenon of the empirical mode decomposition

Cited by 7 publications

References 9 publications

Characterization and classification of high impedance arc fault in distribution system involving metallic and nonmetallic surfaces using empirical mode decomposition

Characterization and classification of high impedance arc fault in distribution system involving metallic and nonmetallic surfaces using empirical mode decomposition

Solution to the Mode Mixing Problem in Two Harmonic Signals: Empirical Wavelet Transform

Review of Artifact Rejection Methods for Electroencephalographic Systems

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