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Properties of EMD BasisPowerPoint Presentation

Properties of EMD Basis

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### The combination of Hilbert Spectral Analysis and Empirical Mode Decomposition has been designated by NASA as

### Surrogate Signal:Non-uniqueness signal vs. Spectrum

### To quantify nonlinearity we also need instantaneous frequency.

### How to define Nonlinearity?

### The term, ‘Nonlinearity,’ has been loosely used, most of the time, simply as a fig leaf to cover our ignorance.

### How is nonlinearity defined?

### How is nonlinearity defined?

### How should nonlinearity be defined?

### Global Temperature Anomaly

### Do mathematical results make physical sense?

Properties of EMD Basis

The Adaptive Basis based on and derived from the data by the empirical method satisfy nearly all the traditional requirements for basis empirically and a posteriori:

Complete

Convergent

Orthogonal

Unique

HHT

(HHT vs. FFT)

Comparisons: Fourier, Hilbert & Wavelet

I. Hello

Additionally

How to quantify it through data alone (model independent)?

Can we be more precise?

Based on Linear Algebra: nonlinearity is defined based on input vs. output.

But in reality, such an approach is not practical: natural system are not clearly defined; inputs and out puts are hard to ascertain and quantify. Furthermore, without the governing equations, the order of nonlinearity is not known.

In the autonomous systems the results could depend on initial conditions rather than the magnitude of the ‘inputs.’

The small parameter criteria could be misleading: sometimes, the smaller the parameter, the more nonlinear.

Linear Systems

Linear systems satisfy the properties of superpositionand scaling. Given two valid inputs to a system H,

as well as their respective outputs

then a linear system, H, must satisfy

for any scalar values α and β.

Based on Linear Algebra: nonlinearity is defined based on input vs. output.

But in reality, such an approach is not practical: natural system are not clearly defined; inputs and out puts are hard to ascertain and quantify. Furthermore, without the governing equations, the order of nonlinearity is not known.

In the autonomous systems the results could depend on initial conditions rather than the magnitude of the ‘inputs.’

The small parameter criteria could be misleading: sometimes, the smaller the parameter, the more nonlinear.

The alternative is to define nonlinearity based on data characteristics: Intra-wave frequency modulation.

Intra-wave frequency modulation is known as the harmonic distortion of the wave forms. But it could be better measured through the deviation of the instantaneous frequency from the mean frequency (based on the zero crossing period).

Characteristics of Data from Nonlinear Processes

Duffing Equation : Data

Hilbert’s View on Nonlinear DataIntra-wave Frequency Modulation

Duffing Type WaveData:x = cos(wt+0.3 sin2wt)

Duffing Type WavePerturbation Expansion

Duffing Type WaveWavelet Spectrum

Duffing Type WaveHilbert Spectrum

Degree of Nonlinearity

DNis determined by the combination of δηprecisely with Hilbert Spectral Analysis. Either of them equals zero means linearity.

We can determine δand ηseparately:

ηcan bedetermined from the instantaneous frequency modulations relative to the mean frequency.

δ can be determined from DN with known η.

NB: from any IMF, the value of δη cannot be greater than 1.

The combination of δ and η gives us not only the Degree of Nonlinearity, but also some indications of the basic properties of the controlling Differential Equation, the Order of Nonlinearity.

Lorenz Model

Lorenz is highly nonlinear; it is the model equation that initiated chaotic studies.

Again it has three parameters. We decided to fix two and varying only one.

There is no small perturbation parameter.

We will present the results for ρ=28, the classic chaotic case.

The State-of-the arts: Trend“One economist’s trend is another economist’s cycle”Watson : Engle, R. F. and Granger, C. W. J. 1991 Long-run Economic Relationships. Cambridge University Press.

Definition of the TrendProceeding Royal Society of London, 1998Proceedings of National Academy of Science, 2007

Within the given data span, the trend is an intrinsically fitted monotonic function, or a function in which there can be at most one extremum.

The trend should be an intrinsic and local property of the data; it is determined by the same mechanisms that generate the data.

Being local, it has to associate with a local length scale, and be valid only within that length span, and be part of a full wave length.

The method determining the trend should be intrinsic. Being intrinsic, the method for defining the trend has to be adaptive.

All traditional trend determination methods are extrinsic.

Algorithm for Trend

- Trend should be defined neither parametrically nor non-parametrically.
- It should be the residual obtained by removing cycles of all time scales from the data intrinsically.
- Through EMD.

Annual Data from 1856 to 2003

Comparison between non-linear rate with multi-rate of IPCC

Blue shadow and blue line are the warming rate of non-linear trend.

Magenta shadow and magenta line are the rate of combination of non-linear trend and AMO-like components.

Dashed lines are IPCC rates.

Conclusion

With HHT, we can define the true instantaneous frequency and extract trend from any data.

We can quantify the degree of nonlinearity. Among the applications, the degree of nonlinearity could be used to set an objective criterion in biomedical and structural health monitoring, and to quantify the degree of nonlinearity in natural phenomena.

We can also determine the trend, which could be used in financial as well as natural sciences.

These are all possible because of adaptive data analysis method.

1998: The Empirical Mode Decomposition Method and the Hilbert Spectrum for Non-stationary Time Series Analysis, Proc. Roy. Soc. London, A454, 903-995. The invention of the basic method of EMD, and Hilbert transform for determining the Instantaneous Frequency and energy.

1999: A New View of Nonlinear Water Waves – The Hilbert Spectrum, Ann. Rev. Fluid Mech. 31, 417-457.

Introduction of the intermittence in decomposition.

2003: A confidence Limit for the Empirical mode decomposition and the Hilbert spectral analysis, Proc. of Roy. Soc. London, A459, 2317-2345.

Establishment of a confidence limit without the ergodic assumption.

2004: A Study of the Characteristics of White Noise Using the Empirical Mode Decomposition Method, Proc. Roy. Soc. London, A460, 1179-1611.

Defined statistical significance and predictability.

2007: On the trend, detrending, and variability of nonlinear and nonstationary time series. Proc. Natl. Acad. Sci., 104, 14,889-14,894.

2009: On Ensemble Empirical Mode Decomposition. Adv. Adaptive data Anal., 1, 1-41

2009: On instantaneous Frequency. Adv. Adaptive Data Anal., 2, 177-229.

2010: The Time-Dependent Intrinsic Correlation Based on the Empirical Mode Decomposition. Adv. Adaptive Data Anal., 2. 233-266.

2010: Multi-Dimensional Ensemble Empirical Mode Decomposition. Adv. Adaptive Data Anal., 3, 339-372.

2011: Degree of Nonlinearity. (Patent and Paper)

2012: Data-Driven Time-Frequency Analysis (Applied and Computational Harmonic Analysis, T. Hou and Z. Shi)

Current Efforts and Applications

- Non-destructive Evaluation for Structural Health Monitoring
- (DOT, NSWC, DFRC/NASA, KSC/NASA Shuttle, THSR)

- Vibration, speech, and acoustic signal analyses
- (FBI, and DARPA)

- Earthquake Engineering
- (DOT)

- Bio-medical applications
- (Harvard, Johns Hopkins, UCSD, NIH, NTU, VHT, AS)

- Climate changes
- (NASA Goddard, NOAA, CCSP)

- Cosmological Gravity Wave
- (NASA Goddard)

- Financial market data analysis
- (NCU)

- Theoretical foundations
- (Princeton University and Caltech)

Outstanding Mathematical Problems

- Mathematic rigor on everything we do. (tighten the definitions of IMF,….)
- Adaptive data analysis (no a priori basis methodology in general)
- 3. Prediction problem for nonstationary processes
- (end effects)

- 4. Optimization problem (the best stoppage criterion and the unique decomposition ….)
- 5. Convergence problem (finite step of sifting, best splineimplement, …)

Good math, yes; Bad math ,no.

The job of a scientist is to listen carefully to nature, not to tell nature how to behave.

Richard Feynman

To listen is to use adaptive method and let the data sing, and not to force the data to fit preconceived modes.

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