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Addressing End-Point Problems: DMP vs. EEMD Approaches

DESCRIPTION

This document explores the challenges of end-point problems in data extraction methods, specifically the Dual-mode Predictive (DMP) and Ensemble Empirical Mode Decomposition (EEMD) techniques. It highlights that DMP experiences more severe end-point issues than EEMD. The document outlines essential steps for parameter adjustments and specific tests for ensuring optimal behavior of both methods. It provides an intuitive example with a noisy time series, clarifying how to effectively resolve end-point problems and enhance data extraction accuracy through careful parameter management.

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Addressing End-Point Problems: DMP vs. EEMD Approaches

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Presentation Transcript

  1. Solving End-point problem

  2. Method • DMP have more serious end-point problem then EEMD • Test extraction should be performed to ensure the behavior of DMP and EEMD modes • Observe the range of EEMD that may combine with DMP • Adjust COMB(6) and COMB(7) to the desired range • Observe whether the end-point problem is serious

  3. Cont. • In case it is serious, we need to adjust 3 set of EJME parameters: • Critical Amplitude(COMB(5)) and • Critical correlation(COMB(4)) • The plateau function(COMB(2)) (COMB(3))

  4. How to set those parameters • Open ejme.m • Increase COMB(2) and decrease COMB(3) • compress the domain of plateau function • Decrease COMB(4) and COMB(5) to enhance combination • Alter COMB(6) and COMB(7) according to trial eemd • Note that COMB(2)-(5) should not be varied too much

  5. Demonstration(L:original,R:Modified)

  6. An Intuitive example

  7. Full Example • Noised time series that has • a trend term, • two sinusoidal components • a white noise term

  8. FFT Spectrum • 2 peaks • We extract f=1.5 first

  9. End-Point Problem • End point problem is a nuisance at the right end • DMP has 1 less peak than expected

  10. 2nd Extraction • Pick f=3.5

  11. End point problem • For this mode end-point problem is even more rampant. • Phase is slightly distorted

  12. EEMD modes

  13. p Target EEMD modes

  14. An Intuitive example(Revisited)

  15. EJME Result

  16. Comparison • IMF3 looks a lot better • slight improvement on IMF2 • This demonstrates how to save DMP from end-point problem

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