• Corpus ID: 54575480

Method for Mode Mixing Separation in Empirical Mode Decomposition

  title={Method for Mode Mixing Separation in Empirical Mode Decomposition},
  author={Olav Bjarte Fosso and Marta Molinas},
  journal={arXiv: Methodology},
The Empirical Mode Decomposition (EMD) is a signal analysis method that separates multi-component signals into single oscillatory modes called intrinsic mode functions (IMFs), each of which can generally be associated to a physical meaning of the process from which the signal is obtained. When the phenomena of mode mixing occur, as a result of the EMD sifting process, the IMFs can lose their physical meaning hindering the interpretation of the results of the analysis. In the paper, "One or Two… 

EMD Mode Mixing Separation of Signals with Close Spectral Proximity in Smart Grids

  • O. FossoM. Molinas
  • Physics
    2018 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe)
  • 2018
A method to separate spectral components that reside within the same octave, is presented based on reversing the conditions by which mode mixing occurs, which has potential application for identifying the cause of different oscillatory modes with spectral proximity present in the smart grid.

A method for detection of Mode-Mixing problem

The suggested method, which is a measurement of the similarity of stationary signals and based on Fourier spectrums, is modified by applying Kaiser filter onto short-time signals, and the results show successful detection of the mode-mixing if it exists in time series.

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The results of a performance comparison between a well known strategy, the Ensemble EMD (EEMD), and a new strategy proposed by the authors for mitigating the mode mixing problem are reported.

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The dynamic response of a vehicle–bridge interaction (VBI) system is a noisy, nonstationary, and multicomponent response with closely spaced spectral components. Considering the ultimate goal as

Within-cycle instantaneous frequency profiles report oscillatory waveform dynamics

An analytical framework is developed that isolates oscillatory signals from time-series using masked Empirical Mode Decomposition to quantify dynamical changes in the shape of individual cycles (along with amplitude, frequency and phase) using instantaneous frequency and finds that the instantaneous frequency tracks non-sinusoidal shapes in both simulated and real data.

A signal analysis toolbox for power system identification in Smart Grids

This thesis delves into the two fields of signal analysis and small-signal stability of power systems, using Prony’s method, Robust Recursive Least Squares (RRLS) and the FFT based Welch's method, as well as the EMD and Clustering technique.

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Analytical and empirical signal analysis methods for analyzing EEG signals, motivated by the search for features directly related to the color perception in the human brain are presented.



The use of a masking signal to improve empirical mode decomposition

  • Ryan DeeringJ. Kaiser
  • Computer Science
    Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005.
  • 2005
It is shown that EMD yields its own interpretation of combinations of pure tones, and the problem of mode mixing is presented and a solution involving a masking signal is given.

Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method

The effect of the added white noise is to provide a uniform reference frame in the time–frequency space; therefore, the added noise collates the portion of the signal of comparable scale in one IMF.

One or Two Frequencies? The Empirical Mode Decomposition Answers

This paper investigates how the empirical mode decomposition (EMD), a fully data-driven technique recently introduced for decomposing any oscillatory waveform into zero-mean components, behaves in

The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis

  • N. HuangZheng Shen Henry H. Liu
  • Mathematics
    Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences
  • 1998
A new method for analysing nonlinear and non-stationary data has been developed. The key part of the method is the ‘empirical mode decomposition’ method with which any complicated data set can be

The Multi-Dimensional Ensemble Empirical Mode Decomposition Method

A multi-dimensional ensemble empirical mode decomposition (MEEMD) for multi-dimensional data (such as images or solid with variable density) is proposed here. The decomposition is based on the

System and Method of Conjugate Adaptive Conjugate Masking Empirical Mode Decomposition, U.S

  • Patent 2017/0116155
  • 2017