A NEW BIDIMENSIONAL EMPIRICAL MODE DECOMPOSITION BY USING RADON TRANSFORM

2011 ◽  
Vol 09 (03) ◽  
pp. 387-396 ◽  
Author(s):  
ZHIHUA YANG ◽  
LIHUA YANG
Keyword(s):  
Detailed Analysis ◽  
Empirical Mode Decomposition ◽  
Radon Transform ◽  
Intrinsic Mode Function ◽  
Two Dimensional ◽  
One Dimensional ◽  
Mode Function ◽  
Mode Decomposition ◽  
Bidimensional Empirical Mode Decomposition ◽  
The One

Based on a detailed analysis for the one-dimensional intrinsic mode function, a model of the bidimensional intrinsic mode function is found firstly. Then a new bidimensional empirical mode decomposition algorithm is developed by using the Radon transform. The existing methods have to locate the maxima and minima in a bidimensional function, and further, produce bidimensional envelopes. Those are usually hard for a real two-dimensional function. The proposed algorithm successfully avoids these difficulties. Experiments show encouraging results.

Over-Sifting Components Analysis in Bidimensional Empirical Mode Decomposition

2010 ◽  
Vol 159 ◽  
pp. 377-382
Author(s):  
Guang Tao Ge
Keyword(s):  
Empirical Mode Decomposition ◽  
Intrinsic Mode Function ◽  
Mode Function ◽  
Mode Decomposition ◽  
The Mean ◽  
Components Analysis ◽  
Bidimensional Empirical Mode Decomposition

Define the course of getting mean envelope as an operation (mean envelope operation) in Empirical mode decomposition (EMD), so as to express the Intrinsic Mode Function (IMF) with mean envelopes. Summarize several rules of the mean envelope operation. On this fundamental, the abnormal components exist in the over-sifting IMFs are extracted out, and the conclusion is testified with the infinite sifting experiment.

THE MULTI-DIMENSIONAL ENSEMBLE EMPIRICAL MODE DECOMPOSITION METHOD

2009 ◽  
Vol 01 (03) ◽  
pp. 339-372 ◽  
Author(s):  
ZHAOHUA WU ◽  
NORDEN E. HUANG ◽  
XIANYAO CHEN
Keyword(s):  
Time Series ◽  
Empirical Mode Decomposition ◽  
Spatial Data ◽  
Ensemble Empirical Mode Decomposition ◽  
Two Dimensional ◽  
Combination Strategy ◽  
One Dimensional ◽  
Mode Decomposition ◽  
The One ◽  
Dimensional Series

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 applications of ensemble empirical mode decomposition (EEMD) to slices of data in each and every dimension involved. The final reconstruction of the corresponding intrinsic mode function (IMF) is based on a comparable minimal scale combination principle. For two-dimensional spatial data or images, f(x,y), we consider the data (or image) as a collection of one-dimensional series in both x-direction and y-direction. Each of the one-dimensional slices is decomposed through EEMD with the slice of the similar scale reconstructed in resulting two-dimensional pseudo-IMF-like components. This new two-dimensional data is further decomposed, but the data is considered as a collection of one-dimensional series in y-direction along locations in x-direction. In this way, we obtain a collection of two-dimensional components. These directly resulted components are further combined into a reduced set of final components based on a minimal-scale combination strategy. The approach for two-dimensional spatial data can be extended to multi-dimensional data. EEMD is applied in the first dimension, then in the second direction, and then in the third direction, etc., using the almost identical procedure as for the two-dimensional spatial data. A similar comparable minimal-scale combination strategy can be applied to combine all the directly resulted components into a small set of multi-dimensional final components. For multi-dimensional temporal-spatial data, EEMD is applied to time series of each spatial location to obtain IMF-like components of different time scales. All the ith IMF-like components of all the time series of all spatial locations are arranged to obtain ith temporal-spatial multi-dimensional IMF-like component. The same approach to the one used in temporal-spatial data decomposition is used to obtain the resulting two-dimensional IMF-like components. This approach could be extended to any higher dimensional temporal-spatial data.

Empirical Mode Decomposition on Geometry Signal Based Spherical Parameterization

2014 ◽  
Vol 998-999 ◽  
pp. 860-863
Author(s):  
Jian Guo Wang ◽  
Qun E ◽  
Ke Ming Yao ◽  
Xin Long Wan
Keyword(s):  
Empirical Mode Decomposition ◽  
Main Idea ◽  
Intrinsic Mode Function ◽  
Mode Function ◽  
Mode Decomposition ◽  
Spherical Parameterization ◽  
Novel Method ◽  
Key Steps

A novel method based onEmpirical Mode Decomposition(EMD) is approached to process the geometry signal. The main idea is to decompose the signal into some different detail components called Intrinsic Mode Function (IMF). The key steps are as follows: First, the signal is spherical parameterization; Second it is transformed into the plane signal and sampled regularly; Third, the translated signal is processed as an image using Bid-Empirical Mode Decomposition, getting several image IMFs; Finally, invert mapping these IMFs to geometry signal and getting the geometry signal’s IMFs.We demonstrate the power of the algorithms through a number of application examples including de-noising and enhancement.

Fault diagnosis of rolling bearing based on empirical mode decomposition and higher order statistics

2014 ◽  
Vol 229 (9) ◽  
pp. 1630-1638 ◽  
Author(s):  
Jian-hua Cai
Keyword(s):  
Power Spectrum ◽  
Order Statistics ◽  
Empirical Mode Decomposition ◽  
Gaussian Noise ◽  
Rolling Bearing ◽  
Higher Order ◽  
Intrinsic Mode Function ◽  
Order Spectrum ◽  
Mode Function ◽  
Mode Decomposition

In order to solve the problem of the faulted rolling bearing signal getting easily affected by Gaussian noise, a new fault diagnosis method was proposed based on empirical mode decomposition and high-order statistics. Firstly, the vibration signal was decomposed by empirical mode decomposition and the correlation coefficient of each intrinsic mode function was calculated. These intrinsic mode function components, which have a big correlation coefficient, were selected to estimate its higher order spectrum. Then based on the higher order statistics theory, this method uses higher order spectrum of each intrinsic mode function to reconstruct its power spectrum. And these power spectrums were summed to obtain the primary power spectrum of bearing signal. Finally, fault feature information was extracted from the reconstructed power spectrum. A model, using higher order spectrum to reconstruct power spectrum, was established. Meanwhile, analysis was conducted by using the simulated data and the recorded vibration signals which include inner race, out race, and bearing ball fault signal. Results show that the presented method is superior to traditional power spectrum method in suppressing Gaussian noise and its resolution is higher. New method can extract more useful information compared to the traditional method.

scholarly journals Feature Extraction of Ship Radiated Noise Based on Permutation Entropy of the Intrinsic Mode Function with the Highest Energy

2016 ◽  
Author(s):  
Yu-Xing Li ◽  
Ya-An Li ◽  
Zhe Chen ◽  
Xiao Chen
Keyword(s):  
Feature Extraction ◽  
Empirical Mode Decomposition ◽  
Characteristic Parameter ◽  
Intrinsic Mode Function ◽  
Permutation Entropy ◽  
Intrinsic Mode Functions ◽  
Characteristic Parameters ◽  
Radiated Noise ◽  
Mode Function ◽  
Mode Decomposition

In order to solve the problem of feature extraction of underwater acoustic signals in complex ocean environment, a new method for feature extraction from ship radiated noise is presented based on empirical mode decomposition theory and permutation entropy. It analyzes the separability for permutation entropies of the intrinsic mode functions of three types of ship radiated noise signals, and discusses the permutation entropy of the intrinsic mode function with the highest energy. In this study, ship radiated noise signals measured from three types of ships are decomposed into a set of intrinsic mode functions with empirical mode decomposition method. Then, the permutation entropies of all intrinsic mode functions are calculated with appropriate parameters. The permutation entropies are obviously different in the intrinsic mode functions with the highest energy, thus, the permutation entropy of the intrinsic mode function with the highest energy is regarded as a new characteristic parameter to extract the feature of ship radiated noise. After that, the characteristic parameters, namely, the energy difference between high and low frequency, permutation entropy, and multi-scale permutation entropy, are compared with the permutation entropy of the intrinsic mode function with the highest energy. It is discovered that the four characteristic parameters are at the same level for similar ships, however, there are differences in the parameters for different types of ships. The results demonstrate that the permutation entropy of the intrinsic mode function with the highest energy is better in separability as the characteristic parameter than the other three parameters by comparing their fluctuation ranges and the average values of the four characteristic parameters. Hence, the feature of ship radiated noise can be extracted efficiently with the method.

An Improved Empirical Mode Decomposition Based on Time Scale Allocation Method and the 2D Mode Mixing Phenomenon Judgement

2017 ◽  
Vol 09 (02) ◽  
pp. 1750002 ◽  
Author(s):  
Shu-Mei Guo ◽  
Jason Sheng-Hong Tsai ◽  
Chin-Yu Chen ◽  
Tzu-Cheng Yang
Keyword(s):  
Empirical Mode Decomposition ◽  
Time Scale ◽  
Computation Time ◽  
Image Data ◽  
Two Dimensional ◽  
Mode Function ◽  
Allocation Method ◽  
Mode Decomposition ◽  
Great Performance ◽  
Mode Mixing

In the sifting process of the traditional empirical mode decomposition (EMD), intermittence causes mode mixing phenomenon. The intrinsic mode function (IMF) with the mode mixing phenomenon loses its original real physical meaning. An improved EMD based on time scale allocation method and the two-dimensional (2D) version of our method has been extended to improve the decomposition of the mode mixing phenomenon in 2D image data. Experimental results show that the method not only improves the phenomenon correctly both for 1D signal and 2D image, but also exhibits great performance in quality and computation time.

VARIABLE SAMPLING OF THE EMPIRICAL MODE DECOMPOSITION OF TWO-DIMENSIONAL SIGNALS

2005 ◽  
Vol 03 (03) ◽  
pp. 435-452 ◽  
Author(s):  
ANNA LINDERHED
Keyword(s):  
Empirical Mode Decomposition ◽  
Sampling Rate ◽  
Two Dimensions ◽  
Two Dimensional ◽  
Intrinsic Mode Functions ◽  
Blocking Artifacts ◽  
Mode Function ◽  
Mode Decomposition ◽  
Variable Sampling ◽  
Minimum Number

Previous work on empirical mode decomposition in two dimensions typically generates a residue with many extrema points. In this paper we propose an improved method to decompose an image into a number of intrinsic mode functions and a residue image with a minimum number of extrema points. We further propose a method for the variable sampling of the two-dimensional empirical mode decomposition. Since traditional frequency concept is not applicable in this work, we introduce the concept of empiquency, shortform for empirical mode frequency, to describe the signal oscillations. The very special properties of the intrinsic mode functions are used for variable sampling in order to reduce the number of parameters to represent the image. This is done blockwise using the occurrence of extrema points of the intrinsic mode function to steer the sampling rate of the block. A method of using overlapping 7 × 7 blocks is introduced to overcome blocking artifacts and to further reduce the number of parameters required to represent the image. The results presented here shows that an image can be successfully decomposed into a number of intrinsic mode functions and a residue image with a minimum number of extrema points. The results also show that subsampling offers a way to keep the total number of samples generated by empirical mode decomposition approximately equal to the number of pixels of the original image.

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