Nonstationary harmonic signal extraction from strong chaotic interference based on synchrosqueezed wavelet transform

Wang, Xiang-Li; Li, Chunlin; Yan, Xiaogang

doi:10.1007/s11760-018-1368-8

Cited by 3 publications

(3 citation statements)

References 19 publications

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“…However, it should be emphasized the frequency of bowhead whale whistles does not change much and low-order SWT can be used to obtain better results; we find it is limited to signals with slower instantaneous frequency changes. This is consistent with the research conclusions of J. Shi [52] and Xiang-Li Wang et al [53]. For fast time-varying signals, the first-order frequency rate of change cannot be ignored; otherwise, there is a gradual increase in the frequency estimation error and the time-frequency aggregation will be reduced when dealing with signals with fast frequency changes.…”

Section: Discussionsupporting

confidence: 91%

An Automatic Deep Learning Bowhead Whale Whistle Recognizing Method Based on Adaptive SWT: Applying to the Beaufort Sea

Feng,

Xu,

Jin

et al. 2023

Remote Sensing

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The bowhead whale is a vital component of the maritime environment. Using deep learning techniques to recognize bowhead whales accurately and efficiently is crucial for their protection. Marine acoustic remote sensing technology is currently an important method to recognize bowhead whales. Adaptive SWT is used to extract the acoustic features of bowhead whales. The CNN-LSTM deep learning model was constructed to recognize bowhead whale voices. Compared to STFT, the adaptive SWT used in this study raises the SCR for the stationary and nonstationary bowhead whale whistles by 88.20% and 92.05%, respectively. Ten-fold cross-validation yields an average recognition accuracy of 92.85%. The method efficiency of this work was further confirmed by the consistency found in the Beaufort Sea recognition results and the fisheries ecological study. The research results in this paper help promote the application of marine acoustic remote sensing technology and the conservation of bowhead whales.

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Section: Discussionsupporting

confidence: 91%

An Automatic Deep Learning Bowhead Whale Whistle Recognizing Method Based on Adaptive SWT: Applying to the Beaufort Sea

Feng,

Xu,

Jin

et al. 2023

Remote Sensing

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“…The research of detection has become a problem that needs to be solved urgently in the current power system. Prior to this, a large number of scholars have carried out a lot of research on the detection and elimination of harmonics in power systems, and have proposed many advanced research theories and solutions, including Fast Fourier Transform [3][4][5] (FFT), wavelet transform [6][7] (WT), instantaneous reactive power [8] and other methods and related improved methods. At present, most of the commonly used algorithms are improved algorithms based on fast Fourier transform or wavelet transform combined with other methods.…”

Section: Introductionmentioning

confidence: 99%

Adaptive harmonic separation and detection algorithm based on deep neural network

Wang¹,

Zhang²

2022

5th International Conference on Computer Information Science and Application Technology (CISAT 2022)

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“…Its principle is to calculate time frequency distribution of the signal, then squeeze the frequency of the signal in time frequency domain, and rearrange its time frequency energy, so as to improve time frequency resolution greatly. Synchrosqueezing transform mainly includes continuous wavelet transform-based synchrosqueezing transform [1], short time Fourier transform-based synchrosqueezing transform [2], and S transform-based synchrosqueezing transform [3]. Synchrosqueezing transform methods have been widely applied in seismic signal analysis [4], biomedical signal processing [5,6], radar imaging [7], mechanical fault diagnosis, and other fields [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Applications of Fractional Lower Order Synchrosqueezing Transform Time Frequency Technology to Machine Fault Diagnosis

Wang

Long

2020

Mathematical Problems in Engineering

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Synchrosqueezing transform (SST) is a high resolution time frequency representation technology for nonstationary signal analysis. The short time Fourier transform-based synchrosqueezing transform (FSST) and the S transform-based synchrosqueezing transform (SSST) time frequency methods are effective tools for bearing fault signal analysis. The fault signals belong to a non-Gaussian and nonstationary alpha (α) stable distribution with 1<α<2 and even the noises being also α stable distribution. The conventional FSST and SSST methods degenerate and even fail under α stable distribution noisy environment. Motivated by the fact that fractional low order STFT and fractional low order S-transform work better than the traditional STFT and S-transform methods under α stable distribution noise environment, we propose in this paper the fractional lower order FSST (FLOFSST) and the fractional lower order SSST (FLOSSST). In addition, we derive the corresponding inverse FLOSST and inverse FLOSSST. The simulation results show that both FLOFSST and FLOSSST perform better than the conventional FSSST and SSST under α stable distribution noise in instantaneous frequency estimation and signal reconstruction. Finally, FLOFSST and FLOSSST are applied to analyze the time frequency distribution of the outer race fault signal. Our results show that FLOFSST and FLOSSST extract the fault features well under symmetric stable (SαS) distribution noise.

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Nonstationary harmonic signal extraction from strong chaotic interference based on synchrosqueezed wavelet transform

Cited by 3 publications

References 19 publications

An Automatic Deep Learning Bowhead Whale Whistle Recognizing Method Based on Adaptive SWT: Applying to the Beaufort Sea

An Automatic Deep Learning Bowhead Whale Whistle Recognizing Method Based on Adaptive SWT: Applying to the Beaufort Sea

Adaptive harmonic separation and detection algorithm based on deep neural network

Applications of Fractional Lower Order Synchrosqueezing Transform Time Frequency Technology to Machine Fault Diagnosis

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