Emd-based filtering using the Hausdorff distance

Komaty, Ali; Boudraa, Abdel-Ouahab; Dare, Delphine

doi:10.1109/isspit.2012.6621303

Cited by 34 publications

(27 citation statements)

References 8 publications

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“…a) Empirical Mode Decomposition: The EMD is a selfadaptive filter developed by Huang in 1998 for analysis of nonlinear and non-stationary signals [19]. This method has been applied in the study of gravitational waves [27], noise analysis [28], acoustic signals [30], image processing [31], etc. The use of EMD method is relatively new in seismology.…”

Section: B Feature Extractionmentioning

confidence: 99%

Automatic Multichannel Volcano-Seismic Classification Using Machine Learning and EMD

Lara

Fernandes

Inza

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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This article proposes the design of an automatic classifier using the empirical mode decomposition (EMD) along with machine learning techniques for identifying the five most important types of events of the Ubinas volcano, the most active volcano in Peru. The proposed method uses attributes from temporal, spectral and cepstral domains, extracted from the EMD of the signals, as well as a set of pre-processing and instrument correction techniques. Due to the fact that multichannel sensors are currently being installed in seismic networks worldwide, the proposed approach uses a multichannel sensor to perform the classification, contrary to the usual approach of the literature of using a single channel. The presented method is scalable to use data from multiple stations with one or more channels. The principal component analysis (PCA) method is applied to reduce the dimensionality of the feature vector and the supervised classification is carried out by means of several machine learning algorithms, the support vector machine (SVM) providing the best results. The presented investigation was tested with a large database that has a considerable number of explosion events, measured at the Ubinas volcano, located in Arequipa, Peru. The proposed classification system achieved a success rate of more than 90%.

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Section: B Feature Extractionmentioning

confidence: 99%

Automatic Multichannel Volcano-Seismic Classification Using Machine Learning and EMD

Lara

Fernandes

Inza

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Factory noise is caused by mechanical shock, rub impact, and air disturbance and includes numerous intermittent and impulse noises. For signals with different noises, we compare several selection criteria, including the Hausdorff distance [24], power amplitude [20], and correlation coefficient [25]. The evaluation steps are as follows.…”

Section: Performance Of the Rementioning

confidence: 99%

“…These figures showed that the SNRouts of the RE selection criteria are higher than other selection criteria, which indicates that the RE selection criteria outperforms the other selection criteria. compare several selection criteria, including the Hausdorff distance [24], power amplitude [20], and correlation coefficient [25]. where P and P correspond to the powers of the original and reconstructed signals, respectively.…”

Section: Performance Of the Rementioning

confidence: 99%

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Digital Image Stabilization Method Based on Variational Mode Decomposition and Relative Entropy

Hao

2017

Entropy

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Cameras mounted on vehicles frequently suffer from image shake due to the vehicles' motions. To remove jitter motions and preserve intentional motions, a hybrid digital image stabilization method is proposed that uses variational mode decomposition (VMD) and relative entropy (RE). In this paper, the global motion vector (GMV) is initially decomposed into several narrow-banded modes by VMD. REs, which exhibit the difference of probability distribution between two modes, are then calculated to identify the intentional and jitter motion modes. Finally, the summation of the jitter motion modes constitutes jitter motions, whereas the subtraction of the resulting sum from the GMV represents the intentional motions. The proposed stabilization method is compared with several known methods, namely, medium filter (MF), Kalman filter (KF), wavelet decomposition (MD) method, empirical mode decomposition (EMD)-based method, and enhanced EMD-based method, to evaluate stabilization performance. Experimental results show that the proposed method outperforms the other stabilization methods.

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“…Recently, based on the statistical characteristics analysis of white Gaussian noise and fractional Gaussian noise in EMD sifting process [9][10][11], Flandrin et al put forward an EMD denoising scheme with partial reconstruction (EMD-PR) of relevant IMFs in an adaptive way [12], and many attempts have been made to select relevant IMFs in an efficient way [13][14][15][16][17][18][19][20]. Boudraa and Cexus proposed a distortion measure method called consecutive mean square error (CMSE) to determine the relevant IMFs [13].…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Entropy EMD Thresholding for Periodic Fault Signal Enhancement in Rotating Machine

Dang

Jia

Wang

et al. 2017

Shock and Vibration

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Since the slight fault feature of incipient fault is usually polluted by heavy background noise, it is difficult to extract the weak feature signal in rotating machine. As an adaptive decomposing technique, empirical mode decomposition (EMD) based denoising methods have a good effect on the feature separation and noise elimination. However, for rotating machine with poor working environment, the components attributed to noise might have higher amplitudes, which restrict the efficiency of noise reduction in current EMD-based denoising methods. Therefore, a probabilistic entropy EMD thresholding algorithm for periodic fault signal enhancement in rotating machine is proposed in this paper. In this method, the entropy threshold of each IMF is constructed instead of the threshold applied to sampling points of each IMF directly, which overcomes the shortcoming of the denoising effect limited by larger amplitude noise reservation and smaller amplitude feature signal reduction in the current denoising methods. Meanwhile, in order to make the amplitudes of all the IMF reduce in a smooth way, a multiscale thresholding algorithm based on quantile statistics to provide probability indexes is presented. Engineering application demonstrates that the proposed method is effective in the noise reduction and fault feature enhancement in the rotating machine.

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Emd-based filtering using the Hausdorff distance

Cited by 34 publications

References 8 publications

Automatic Multichannel Volcano-Seismic Classification Using Machine Learning and EMD

Automatic Multichannel Volcano-Seismic Classification Using Machine Learning and EMD

Digital Image Stabilization Method Based on Variational Mode Decomposition and Relative Entropy

Probabilistic Entropy EMD Thresholding for Periodic Fault Signal Enhancement in Rotating Machine

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