A Fractal Dimension and Empirical Mode Decomposition-Based Method for Protein Sequence Analysis

2019 ◽  
Vol 33 (11) ◽  
pp. 1940020 ◽  
Author(s):  
Lina Yang ◽  
Pu Wei ◽  
Cheng Zhong ◽  
Zuqiang Meng ◽  
Patrick Wang ◽  
...  
Keyword(s):  
Fractal Dimension ◽  
Empirical Mode Decomposition ◽  
Protein Sequence ◽  
Intrinsic Mode Functions ◽  
Protein Sequence Analysis ◽  
Mode Decomposition ◽  
Signal Characteristics ◽  
Protein Sequence Comparison ◽  
Sequence Characteristics ◽  
Emd Method

In bioinformatics, the biological functions of proteins and their interactions can often be analyzed by the similarity of their sequences. In this paper, the authors combine the fractal dimension, empirical mode decomposition (EMD), and sliding window for protein sequence comparison. First, the protein sequence is characterized and digitized into a signal, and then the signal characteristics are obtained by using EMD and fractal dimension. Each protein sequence can be decomposed into Intrinsic Mode Functions (IMFs). The fixed window’s fractal dimension is applied to each IMF and the original signal to extract the protein sequence characteristics. Experiments have shown that the feature extracted by this hybrid method is superior to the EMD method alone.

scholarly journals A VARIANT OF THE EMD METHOD FOR MULTI-SCALE DATA

2009 ◽  
Vol 01 (04) ◽  
pp. 483-516 ◽  
Author(s):  
THOMAS Y. HOU ◽  
MIKE P. YAN ◽  
ZHAOHUA WU
Keyword(s):  
Empirical Mode Decomposition ◽  
Decomposition Method ◽  
Separation Property ◽  
Intrinsic Mode Functions ◽  
Scale Separation ◽  
Multi Scale ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method ◽  
Emd Method ◽  
Scale Data

In this paper, we propose a variant of the Empirical Mode Decomposition method to decompose multiscale data into their intrinsic mode functions. Under the assumption that the multiscale data satisfy certain scale separation property, we show that the proposed method can extract the intrinsic mode functions accurately and uniquely.

CONSTRUCTING CROSSOVER-FRACTALS USING INTRINSIC MODE FUNCTIONS

2010 ◽  
Vol 02 (04) ◽  
pp. 509-520 ◽  
Author(s):  
SY-SANG LIAW ◽  
FENG-YUAN CHIU
Keyword(s):  
Fractal Dimension ◽  
Real Time ◽  
Empirical Mode Decomposition ◽  
Fractal Dimensions ◽  
The Other ◽  
Partial Sums ◽  
Intrinsic Mode Functions ◽  
Displacement Method ◽  
Mode Decomposition ◽  
Time Sequences

Real nonstationary time sequences are in general not monofractals. That is, they cannot be characterized by a single value of fractal dimension. It has been shown that many real-time sequences are crossover-fractals: sequences with two fractal dimensions — one for the short and the other for long ranges. Here, we use the empirical mode decomposition (EMD) to decompose monofractals into several intrinsic mode functions (IMFs) and then use partial sums of the IMFs decomposed from two monofractals to construct crossover-fractals. The scale-dependent fractal dimensions of these crossover-fractals are checked by the inverse random midpoint displacement method (IRMD).

Applications of Ceemdan in Dynamic Behavior of Defected Spur Gearbox Running Under Acyclism Regime

2020 ◽  
Vol 36 (6) ◽  
pp. 825-839
Author(s):  
A. Hammami ◽  
A. Hmida ◽  
M. T. Khabou ◽  
F. Chaari ◽  
M. Haddar ◽  
...  
Keyword(s):  
Dynamic Behavior ◽  
Empirical Mode Decomposition ◽  
Combustion Engine ◽  
Stationary Regime ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Adaptive Noise ◽  
Emd Method ◽  
Spur Gearbox

ABSTRACTEmpirical Mode Decomposition (EMD) and its approaches are powerful techniques in signal processing especially for the diagnosis of rotating machinery running in non-stationary regime. We are interested in this paper to the dynamic behavior of a defected one stage gearbox equipped with an elastic coupling and loaded under acyclism regime generated by a combustion engine. Actually, we adopt an approach to the EMD method called Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) as a technique to perform the diagnosis of the studied system. Since the obtained signals are modulated, all obtained Intrinsic Mode Functions (IMFs) are modulated and are processed and shown by the Wigner-Ville distributions (WVD) as well as the spectrum of their envelope in order to detect defects such as cracked tooth defect in the wheel of the spur gearbox and eccentricity defect in the gear.

scholarly journals INFORMATION QUANTIFICATION OF EMPIRICAL MODE DECOMPOSITION AND APPLICATIONS TO FIELD POTENTIALS

2011 ◽  
Vol 21 (01) ◽  
pp. 49-63 ◽  
Author(s):  
ZAREEN MEHBOOB ◽  
HUJUN YIN
Keyword(s):  
Empirical Mode Decomposition ◽  
Field Potential ◽  
Stationary Time Series ◽  
Intrinsic Mode Functions ◽  
Information Analysis ◽  
Neural Recordings ◽  
Mode Decomposition ◽  
Non Stationary Signal ◽  
Instantaneous Frequencies ◽  
Emd Method

The empirical mode decomposition (EMD) method can adaptively decompose a non-stationary time series into a number of amplitude or frequency modulated functions known as intrinsic mode functions. This paper combines the EMD method with information analysis and presents a framework of information-preserving EMD. The enhanced EMD method has been exploited in the analysis of neural recordings. It decomposes a signal and extracts only the most informative oscillations contained in the non-stationary signal. Information analysis has shown that the extracted components retain the information content of the signal. More importantly, a limited number of components reveal the main oscillations presented in the signal and their instantaneous frequencies, which are not often obvious from the original signal. This information-coupled EMD method has been tested on several field potential datasets for the analysis of stimulus coding in visual cortex, from single and multiple channels, and for finding information connectivity among channels. The results demonstrate the usefulness of the method in extracting relevant responses from the recorded signals. An investigation is also conducted on utilizing the Hilbert phase for cases where phase information can further improve information analysis and stimulus discrimination. The components of the proposed method have been integrated into a toolbox and the initial implementation is also described.

Ensemble Noise-Reconstructed Empirical Mode Decomposition for Mechanical Fault Detection

2013 ◽  
Vol 135 (2) ◽  
Author(s):  
Jing Yuan ◽  
Zhengjia He ◽  
Jun Ni ◽  
Adam John Brzezinski ◽  
Yanyang Zi
Keyword(s):  
Fault Detection ◽  
Empirical Mode Decomposition ◽  
Measured Signal ◽  
Accelerometer Data ◽  
Intrinsic Mode Functions ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Mixing Problem ◽  
Emd Method ◽  
Mode Mixing

Various faults inevitably occur in mechanical systems and may result in unexpected failures. Hence, fault detection is critical to reduce unscheduled downtime and costly breakdowns. Empirical mode decomposition (EMD) is an adaptive time-frequency domain signal processing method, potentially suitable for nonstationary and/or nonlinear processes. However, the EMD method suffers from several problems such as mode mixing, defined as intrinsic mode functions (IMFs) with incorrect scales. In this paper, an ensemble noise-reconstructed EMD method is proposed to ameliorate the mode mixing problem and denoise IMFs for enhancing fault signatures. The proposed method defines the IMF components as an ensemble mean of EMD trials, where each trial is obtained by sifting signals that have been reconstructed using the estimated noise present in the measured signal. Unlike traditional denoising methods, the noise inherent in the input data is reconstructed and used to reduce the background noise. Furthermore, the reconstructed noise helps to project different scales of the signal onto their corresponding IMFs, instrumental in alleviating the mode mixing problem. Two critical issues concerned in the method, i.e., the noise estimation strategy and the number of EMD trials required for denoising are discussed. Furthermore, a comprehensive noise-assisted EMD method is proposed, which includes the proposed method and ensemble EMD (EEMD). Numerical simulations and experimental case studies on accelerometer data collected from an industrial shaving process are used to demonstrate and validate the proposed method. Results show that the proposed method can both detect impending faults and isolate multiple faults. Hence, the proposed method can act as a promising tool for mechanical fault detection.

Development of Diagnosis System for Machine Tool Shaft Inspection

2010 ◽  
Vol 156-157 ◽  
pp. 1717-1724
Author(s):  
Nan Kai Hsieh ◽  
Wei Yen Lin ◽  
Hong Tsu Young
Keyword(s):  
Machine Tool ◽  
Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Zero Crossing ◽  
Vibration Signals ◽  
Diagnosis System ◽  
Mode Decomposition ◽  
Faults Diagnosis ◽  
Zero Crossing Rate ◽  
Emd Method

Aiming at reducing cost and time of repair, condition-based shaft faults diagnosis is considered an efficient strategy for machine tool community. While the shaft with faults is operating, its vibration signals normally indicate nonlinear and non-stationary characteristics but Fourier-based approaches have shown limitations for handling this kind of signals. The methodology proposed in this research is to extract the features from shaft faults related vibration signals, from which the corresponding fault condition is then effectively identified. With an incorporation of empirical mode decomposition (EMD) method, the model applied in this research embraces some characteristics, like zero-crossing rate and energy, of intrinsic mode functions (IMFs) to represent the feature of the shaft condition.

NOISE-MODULATED EMPIRICAL MODE DECOMPOSITION

2010 ◽  
Vol 02 (01) ◽  
pp. 25-37 ◽  
Author(s):  
PO-HSIANG TSUI ◽  
CHIEN-CHENG CHANG ◽  
NORDEN E. HUANG
Keyword(s):  
Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Hilbert Spectrum ◽  
Hilbert Huang Transform ◽  
The Core ◽  
Mode Decomposition ◽  
Threshold Amplitude ◽  
The Mean ◽  
Mixing Problem ◽  
Emd Method

The empirical mode decomposition (EMD) is the core of the Hilbert–Huang transform (HHT). In HHT, the EMD is responsible for decomposing a signal into intrinsic mode functions (IMFs) for calculating the instantaneous frequency and eventually the Hilbert spectrum. The EMD method as originally proposed, however, has an annoying mode mixing problem caused by the signal intermittency, making the physical interpretation of each IMF component unclear. To resolve this problem, the ensemble EMD (EEMD) was subsequently developed. Unlike the conventional EMD, the EEMD defines the true IMF components as the mean of an ensemble of trials, each consisting of the signal with added white noise of finite, not infinitesimal, amplitude. In this study, we further proposed an extension and alternative to EEMD designated as the noise-modulated EMD (NEMD). NEMD does not eliminate mode but intensify and amplify mixing by suppressing the small amplitude signal but the larger signals would be preserved without waveform deformation. Thus, NEMD may serve as a new adaptive threshold amplitude filtering. The principle, algorithm, simulations, and applications are presented in this paper. Some limitations and additional considerations of using the NEMD are also discussed.

Experimental Research on Flow Induced Oscillation of Circle Cavity With Free Surface

2006 ◽  
Author(s):  
Xiong-Liang Yao ◽  
Zhuang Kang ◽  
Wei-Jun Xu ◽  
Wei Dai ◽  
Fu-Zheng Pang
Keyword(s):  
Flow Velocity ◽  
Empirical Mode Decomposition ◽  
Comparative Method ◽  
Resonance Phenomenon ◽  
Intrinsic Mode Functions ◽  
Data Set ◽  
Acoustic Data ◽  
Flow Noise ◽  
Mode Decomposition ◽  
Emd Method

With the help of empirical mode decomposition method this flow noise experiment was investigated in the towing tank. The key part of the EMD method is that with which and complicated data set can be decomposed into a finite and often small number of ‘intrinsic mode functions’. By the comparative method, the acoustic data have been collected with and without the cavity in all conditions respectively. After the proceeding of empirical mode decomposition, the intrinsic mode functions of the acoustic data have been gotten, and then do the PSD spectrum of each IMF. Compare the difference of each PSD spectrum between the result of with and without cavity, the flow noise which result from the cavity can be got. In this experiment, when the flow velocity is from 0.4m/s to 0.6m/s, the resonance phenomenon is observed. It appears the water in the cavity is up and down in the deep direction. This is so-called “Piston” phenomenon. The fluid fluctuating force in the cavity is a harmonic force. The frequency is the same with the resonance frequency and the value level is greater. When the flow velocity is more than 0.8m/s, there is no resonance phenomenon any more, under the help of EMD method, the fluctuating force which is consist of the shear layer oscillation and the water oscillation can be gotten.

Empirical Mode Decomposition — Window Fractal (EMDWF) Algorithm in Classification of Fingerprint of Medicinal Herbs

2017 ◽  
Vol 31 (12) ◽  
pp. 1756017 ◽  
Author(s):  
Jianwei Du ◽  
Zhengguang Xu ◽  
Zhichun Mu ◽  
Patrick Shen-Pei Wang ◽  
Yuan Yan Tang ◽  
...  
Keyword(s):  
Fractal Dimension ◽  
Empirical Mode Decomposition ◽  
Signal Sequence ◽  
Medicinal Herbs ◽  
Medicinal Herb ◽  
Intrinsic Mode Functions ◽  
New Approach ◽  
Mode Decomposition ◽  
Training Samples

This paper presents a new approach called the empirical mode decomposition — window fractal (EMDWF) algorithm in classification of fingerprint of medicinal herbs. In this way, we consider a glycyrrhiza fingerprint of medicinal herb as a signal sequence, and apply empirical mode decomposition (EMD) and Hiaguchis fractal dimension to construct a feature vector. By using EMD, the glycyrrhiza fingerprint of medicinal herb can be decomposed into some intrinsic mode functions (IMFs). As window fractal dimension (WFD) is applied to each IMF and original signal, the features of the glycyrrhiza fingerprint of medicinal herb can be obtained. Thereafter, SVM is applied as a classifier. The results of the experiments state clearly that the feature extracted by EMDWF is better than that of the existing methods including the pure EMD. With the increase of the number of training samples and the increase of the number of layers in EMD, the classification result achieves more stability.

APPLICATION OF THE EMPIRICAL MODE DECOMPOSITION METHOD TO GROUND-ROLL NOISE ATTENUATION IN SEISMIC DATA

2013 ◽  
Vol 31 (4) ◽  
pp. 619 ◽  
Author(s):  
Luiz Eduardo Soares Ferreira ◽  
Milton José Porsani ◽  
Michelângelo G. Da Silva ◽  
Giovani Lopes Vasconcelos
Keyword(s):  
Empirical Mode Decomposition ◽  
Seismic Data ◽  
Low Frequency ◽  
High Amplitude ◽  
Seismic Line ◽  
Seismic Processing ◽  
Mode Decomposition ◽  
Ground Roll ◽  
Fortran 90 ◽  
Emd Method

ABSTRACT. Seismic processing aims to provide an adequate image of the subsurface geology. During seismic processing, the filtering of signals considered noise is of utmost importance. Among these signals is the surface rolling noise, better known as ground-roll. Ground-roll occurs mainly in land seismic data, masking reflections, and this roll has the following main features: high amplitude, low frequency and low speed. The attenuation of this noise is generally performed through so-called conventional methods using 1-D or 2-D frequency filters in the fk domain. This study uses the empirical mode decomposition (EMD) method for ground-roll attenuation. The EMD method was implemented in the programming language FORTRAN 90 and applied in the time and frequency domains. The application of this method to the processing of land seismic line 204-RL-247 in Tacutu Basin resulted in stacked seismic sections that were of similar or sometimes better quality compared with those obtained using the fk and high-pass filtering methods.Keywords: seismic processing, empirical mode decomposition, seismic data filtering, ground-roll. RESUMO. O processamento sísmico tem como principal objetivo fornecer uma imagem adequada da geologia da subsuperfície. Nas etapas do processamento sísmico a filtragem de sinais considerados como ruídos é de fundamental importância. Dentre esses ruídos encontramos o ruído de rolamento superficial, mais conhecido como ground-roll . O ground-roll ocorre principalmente em dados sísmicos terrestres, mascarando as reflexões e possui como principais características: alta amplitude, baixa frequência e baixa velocidade. A atenuação desse ruído é geralmente realizada através de métodos de filtragem ditos convencionais, que utilizam filtros de frequência 1D ou filtro 2D no domínio fk. Este trabalho utiliza o método de Decomposição em Modos Empíricos (DME) para a atenuação do ground-roll. O método DME foi implementado em linguagem de programação FORTRAN 90, e foi aplicado no domínio do tempo e da frequência. Sua aplicação no processamento da linha sísmica terrestre 204-RL-247 da Bacia do Tacutu gerou como resultados, seções sísmicas empilhadas de qualidade semelhante e por vezes melhor, quando comparadas as obtidas com os métodos de filtragem fk e passa-alta.Palavras-chave: processamento sísmico, decomposição em modos empíricos, filtragem dados sísmicos, atenuação do ground-roll.

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