A Novel Linear Spectrum Frequency Feature Extraction Technique for Warship Radio Noise Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, Duffing Chaotic Oscillator, and Weighted-Permutation Entropy

Warships play an important role in the modern sea battlefield. Research on the line spectrum features of warship radio noise signals is helpful to realize the classification and recognition of different types of warships, and provides critical information for sea battlefield. In this paper, we proposed a novel linear spectrum frequency feature extraction technique for warship radio noise based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), duffing chaotic oscillator (DCO), and weighted-permutation entropy (W-PE). The proposed linear spectrum frequency feature extraction technique, named CEEMDAN-DCO-W-PE has the following advantages in comparison with other linear spectrum frequency feature extraction techniques; (i) as an adaptive data-driven algorithm, CEEMDAN has more accurate and more reliable decomposition performance than empirical mode decomposition (EMD) and ensemble EMD (EEMD), and there is no need for presetting parameters, such as decomposition level and basis function; (ii) DCO can detect the linear spectrum of narrow band periodical warship signals by way of utilizing its properties of sensitivity for weak periodical signals and the immunity for noise; and (iii) W-PE is used in underwater acoustic signal feature extraction for the first time, and compared with traditional permutation entropy (PE), W-PE increases amplitude information to some extent. Firstly, warship radio noise signals are decomposed into some intrinsic mode functions (IMFs) from high frequency to low frequency by CEEMDAN. Then, DCO is used to detect linear spectrum of low-frequency IMFs. Finally, we can determine the linear spectrum frequency of low-frequency IMFs using W-PE. The experimental results show that the proposed technique can accurately extract the line spectrum frequency of the simulation signals, and has a higher classification and recognition rate than the traditional techniques for real warship radio noise signals.

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Dynamic mode decomposition: A feature extraction technique based hidden Markov model for detection of Mysticetes' vocalisations

Ecological Informatics ◽

10.1016/j.ecoinf.2021.101306 ◽

2021 ◽

pp. 101306

Author(s):

O.O. Ogundile ◽

A.M. Usman ◽

O.P. Babalola ◽

D.J.J. Versfeld

Keyword(s):

Feature Extraction ◽

Markov Model ◽

Hidden Markov Model ◽

Hidden Markov ◽

Dynamic Mode Decomposition ◽

Extraction Technique ◽

Dynamic Mode ◽

Mode Decomposition ◽

Feature Extraction Technique

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

10.20944/preprints201611.0052.v1 ◽

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.

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Noise Reduction Method of Underwater Acoustic Signals Based on Uniform Phase Empirical Mode Decomposition, Amplitude-Aware Permutation Entropy, and Pearson Correlation Coefficient

Entropy ◽

10.3390/e20120918 ◽

2018 ◽

Vol 20 (12) ◽

pp. 918 ◽

Cited By ~ 7

Author(s):

Guohui Li ◽

Zhichao Yang ◽

Hong Yang

Keyword(s):

Noise Reduction ◽

Empirical Mode Decomposition ◽

High Frequency ◽

Reduction Method ◽

Pearson Correlation ◽

Low Frequency ◽

Acoustic Signals ◽

Permutation Entropy ◽

Underwater Acoustic ◽

Mode Decomposition

Noise reduction of underwater acoustic signals is of great significance in the fields of military and ocean exploration. Based on the adaptive decomposition characteristic of uniform phase empirical mode decomposition (UPEMD), a noise reduction method for underwater acoustic signals is proposed, which combines amplitude-aware permutation entropy (AAPE) and Pearson correlation coefficient (PCC). UPEMD is a recently proposed improved empirical mode decomposition (EMD) algorithm that alleviates the mode splitting and residual noise effects of EMD. AAPE is a tool to quantify the information content of nonlinear time series. Unlike permutation entropy (PE), AAPE can reflect the amplitude information on time series. Firstly, the original signal is decomposed into a series of intrinsic mode functions (IMFs) by UPEMD. The AAPE of each IMF is calculated. The modes are separated into high-frequency IMFs and low-frequency IMFs, and all low-frequency IMFs are determined as useful IMFs (UIMFs). Then, the PCC between the high-frequency IMF with the smallest AAPE and the original signal is calculated. If PCC is greater than the threshold, the IMF is also determined as a UIMF. Finally, all UIMFs are reconstructed and the denoised signal is obtained. Chaotic signals with different signal-to-noise ratios (SNRs) are used for denoising experiments. Compared with EMD and extreme-point symmetric mode decomposition (ESMD), the proposed method has higher SNR and smaller root mean square error (RMSE). The proposed method is applied to noise reduction of real underwater acoustic signals. The results show that the method can further eliminate noise and the chaotic attractors are smoother and clearer.

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An Integrated Fault Diagnosis Method for Rotating Machinery Based on Improved Multivariate Multiscale Amplitude-Aware Permutation Entropy and Uniform Phase Empirical Mode Decomposition

Shock and Vibration ◽

10.1155/2021/2098892 ◽

2021 ◽

Vol 2021 ◽

pp. 1-22

Author(s):

Jiancheng Gong ◽

Xiaoqiang Yang ◽

Fan Pan ◽

Wuqiang Liu ◽

Fuming Zhou

Keyword(s):

Feature Extraction ◽

Empirical Mode Decomposition ◽

Rotating Machinery ◽

Coarse Grained ◽

Normal Operation ◽

Permutation Entropy ◽

High Dimensional ◽

Feature Vectors ◽

Mode Decomposition ◽

Fault Feature Extraction

Rotating machinery refers to machinery that executes specific functions mainly relying on their rotation. They are widely used in engineering applications. Bearings and gearboxes play a key role in rotating machinery, and their states can directly affect the operation status of the whole rotating machinery. Accurate fault detection and judgment of bearing, gearbox, and other key parts are of great significance to the rotating machinery’s normal operation. A new fault feature extraction algorithm for rotating machinery called Improved Multivariate Multiscale Amplitude-Aware Permutation Entropy (ImvMAAPE) is proposed in this paper, and the application of an improved coarse-grained method in fault feature extraction of multichannel signals is realized in this method. This algorithm is combined with the Uniform Phase Empirical Mode Decomposition (UPEMD) method and the t-distributed Stochastic Neighbor Embedding (t-SNE) method, forming a new time-frequency multiscale feature extraction method. Firstly, the multichannel vibration signals are decomposed adaptively into sets of Intrinsic Mode Functions (IMFs) using UPEMD; then, the IMF components containing the main fault information are screened by correlation analysis to get the reconstructed signals. The ImvMAAPE values of the reconstructed signals are calculated to generate the initial high-dimensional fault features, and the t-SNE method with excellent nonlinear dimensionality reduction performance is then used to reduce the dimensionality of the initial high-dimensional fault feature vectors. Finally, the low dimensional feature vectors with high quality are input to the random forest (RF) classifier to identify and judge the fault types. Experiments were conducted to verify whether this method has higher accuracy and robustness than other methods.

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Superiorities of variational mode decomposition over empirical mode decomposition particularly in time–frequency feature extraction and wind turbine condition monitoring

IET Renewable Power Generation ◽

10.1049/iet-rpg.2016.0088 ◽

2016 ◽

Vol 11 (4) ◽

pp. 443-452 ◽

Cited By ~ 35

Author(s):

Wenxian Yang ◽

Zhike Peng ◽

Kexiang Wei ◽

Pu Shi ◽

Wenye Tian

Keyword(s):

Feature Extraction ◽

Wind Turbine ◽

Empirical Mode Decomposition ◽

Condition Monitoring ◽

Variational Mode Decomposition ◽

Time Frequency ◽

Mode Decomposition ◽

Frequency Feature

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Transform Domain Analysis Of EMG Signal For Efficient And Useful Feature Extraction Technique

i-manager s Journal on Digital Signal Processing ◽

10.26634/jdp.3.2.3385 ◽

2015 ◽

Vol 3 (2) ◽

pp. 25-34

Author(s):

Pradeep Kumar Jaisal ◽

◽

R.N. Patel ◽

Keyword(s):

Feature Extraction ◽

Domain Analysis ◽

Extraction Technique ◽

Transform Domain ◽

Emg Signal ◽

Feature Extraction Technique

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

Brazilian Journal of Geophysics ◽

10.22564/rbgf.v31i4.342 ◽

2013 ◽

Vol 31 (4) ◽

pp. 619 ◽

Cited By ~ 1

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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