The Empirical Mode Decomposition and the Hilbert-Huang Transform

Attoh-Okine, Nii; Barner, Kenneth E.; Bentil, Daniel E.; Zhang, Ray

doi:10.1155/2008/251518

Cited by 25 publications

(16 citation statements)

References 18 publications

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“…Similarly, all local minima are interpolated to form the lower envelope of the data. To extract IMFs, an iterative process is described below [36,37].…”

Section: Methodsmentioning

confidence: 99%

Weak Knock Characteristic Extraction of a Two-Stroke Spark Ignition UAV Engine Burning RP-3 Kerosene Fuel Based on Intrinsic Modal Functions Energy Method

Sheng

Liu

2020

Sensors

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To solve the problem of the weak knock characteristic extraction for a port-injected two-stoke spark ignition (SI) unmanned aerial vehicle (UAV) engine burning aviation kerosene fuel, which is also known as the Rocket Propellant 3 (RP-3), the Intrinsic modal Functions Energy (IMFE) method is proposed according to the orthogonality of the intrinsic modal functions (IMFs). In this method, engine block vibration signals of the two-stroke SI UAV engine are decomposed into a finite number of intrinsic modal function (IMF) components. Then, the energy weight value of each IMF component is calculated, and the IMF component with the largest energy weight value is selected as the dominant characteristic component. The knock characteristic frequency of the two-stroke SI UAV engine is obtained by analyzing the frequency spectrum of the dominant characteristic component. A simulation experiment is designed and the feasibility of the algorithm is verified. The engine block vibration signals of the two-stroke SI UAV engine at 5100 rpm and 5200 rpm were extracted by this method. The results showed that the knock characteristic frequencies of engine block vibration signals at 5100 rpm and 5200 rpm were 3.320 kHz and 3.125 kHz, respectively. The Wavelet Packet Energy method was used to extract the characteristics of the same engine block vibration signal at 5200 rpm, and the same result as the IMFE method is obtained, which verifies the effectiveness of the IMFE method.

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“…Similarly, all local minima are interpolated to form the lower envelope of the data. To extract IMFs, an iterative process is described below [36,37].…”

Section: Methodsmentioning

confidence: 99%

Weak Knock Characteristic Extraction of a Two-Stroke Spark Ignition UAV Engine Burning RP-3 Kerosene Fuel Based on Intrinsic Modal Functions Energy Method

Sheng

Liu

2020

Sensors

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“…To understand the relationship between the first IMF and baseline drift noise, we propose the correlation coefficient to estimate the relationship. Assuming that the baseline drift noise model is u 1 (t) and the correlation coefficient ρ are calculated by Equation (13):…”

Section: Of 14mentioning

confidence: 99%

“…The main disadvantage of this method is that the selection of wavelet basis seriously affects the denoising results. Empirical Mode Decomposition (EMD) [12,13] is one of the decomposition methods of signal denoising, and is widely used to decompose a signal into different modes recursively. This method is, however, prone to mode mixing, and limited by sensitivity to noise and sampling [14].…”

Section: Introductionmentioning

confidence: 99%

Denoising of Magnetocardiography Based on Improved Variational Mode Decomposition and Interval Thresholding Method

Liao

Guo

2018

Symmetry

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Recently, magnetocardiography (MCG) has attracted increasing attention as a non-invasive and non-contact technique for detecting electrocardioelectric functions. However, the severe background noise makes it difficult to extract information. Variational Mode Decomposition (VMD), which is an entirely non-recursive model, is used to decompose the non-stationary signal into the intrinsic mode functions (IMFs). Traditional VMD algorithms cannot control the bandwidth of each IMF, whose quadratic penalty lacks adaptivity. As a result, baseline drift noise is still present or medical information is lost. In this paper, to overcome the unadaptable quadratic penalty problem, an improved VMD model via correlation coefficient and new update formulas are proposed to decompose MCG signals. To improve the denoising precision, this algorithm is combined with the interval threshold algorithm. First, the correlation coefficient is calculated, to determine quadratic penalty, in order to extract the first IMF made up of baseline drift. Then, the new update formulas derived from the variance that describes the noise level are used, to perform decomposition on the rest signal. Finally, the Interval thresholding algorithm is performed on each IMF. Theoretical analysis and experimental results show that this algorithm can effectively improve the output signal-to-noise ratio and has superior performance.

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“…Then, morphological operator is used to detect regional extrema. After that, an important aspect to be considered is the construction of envelopes, which involves interpolation of scattered data formed by the extrema of the image [10]. In this paper, the selected method of scattered data interpolation is the popular method using constructed smooth cubic splines.…”

Section: A Bidimensional Sifting Processmentioning

confidence: 99%

A Novel Multi-Focus Images Fusion Method Based on Bidimensional Empirical Mode Decomposition

Chen

Jiang

Chao

et al. 2009

2009 2nd International Congress on Image and Signal Processing

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A novel images fusion method based on bidimensional empirical mode decomposition (BEMD) is proposed, aiming at solving the fusion problem of multi-focus images. BEMD is a new form of fully two-dimensional multi-scale decomposition and has superior quality in extracting salient features in multi-focus images. This paper uses BEMD to decompose the source images into their components adaptively without using a priori basis so that the trouble in selecting some parameters such as priori basis can be leaved out. Here, Different fusion rules are applied to different image components obtained from the BEMD. Finally, all fused components are combined to get the reconstructed image. Experimental results demonstrate that the proposed method is successful in multi-focus images fusion and significantly outperforms the traditional wavelet transform-based method.

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The Empirical Mode Decomposition and the Hilbert-Huang Transform

Cited by 25 publications

References 18 publications

Weak Knock Characteristic Extraction of a Two-Stroke Spark Ignition UAV Engine Burning RP-3 Kerosene Fuel Based on Intrinsic Modal Functions Energy Method

Weak Knock Characteristic Extraction of a Two-Stroke Spark Ignition UAV Engine Burning RP-3 Kerosene Fuel Based on Intrinsic Modal Functions Energy Method

Denoising of Magnetocardiography Based on Improved Variational Mode Decomposition and Interval Thresholding Method

A Novel Multi-Focus Images Fusion Method Based on Bidimensional Empirical Mode Decomposition

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