A High-Precision Time-Frequency Entropy Based on Synchrosqueezing Generalized S-Transform Applied in Reservoir Detection

Chen, Hui; Chen, Yuanchun; Sun, Shaotong; Hu, Ying; Feng, Jun

doi:10.3390/e20060428

Cited by 5 publications

(2 citation statements)

References 35 publications

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“…TN is a powerful tool that originates from quantum many-body physics and quantum information sciences; it can be applied to efficiently deal with the states and operators defined in many-body Hilbert space whose dimension increases exponentially with the number of sites (or physical particles) [7][8][9][10][11][12][13]. As a novel extension, TN is considered as a universal model for supervised and unsupervised learning [14][15][16][17][18][19][20][21][22]. Its applications on, e.g., image recognition, already exhibit competitive performance to the conventional models such as neural networks.…”

Section: Introductionmentioning

confidence: 99%

Generative tensor network classification model for supervised machine learning

et al. 2020

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Tensor network (TN) has recently triggered extensive interests in developing machine-learning models in quantum many-body Hilbert space. Here we purpose a generative TN classification (GTNC) approach for supervised learning. The strategy is to train the generative TN for each class of the samples to construct the classifiers. The classification is implemented by comparing the distance in the many-body Hilbert space. The numerical experiments by GTNC show impressive performance on the MNIST and Fashion-MNIST dataset. The testing accuracy is competitive to the state-of-the-art convolutional neural network while higher than the naive Bayes classifier (a generative classifier) and support vector machine. Moreover, GTNC is more efficient than the existing TN models that are in general discriminative. By investigating the distances in the manybody Hilbert space, we find that (a) the samples are naturally clustering in such a space; and (b) bounding the bond dimensions of the TN's to finite values corresponds to removing redundant information in the image recognition. These two characters make GTNC an adaptive and universal model of excellent performance.

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Section: Introductionmentioning

confidence: 99%

Generative tensor network classification model for supervised machine learning

et al. 2020

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“…Consequently, false spectral energies would be observed on the TF plane at locations where no spectral energy should exist. The existence of the false spectral energies may very likely cause mistakes in interpreting the results of the spectral decomposition [12]. Moreover, the "false bandwidth" of the frequency axis has a great influence on the correlation analysis.…”

Section: Introductionmentioning

confidence: 99%

A Time–Frequency Correlation Analysis Method of Time Series Decomposition Derived from Synchrosqueezed S Transform

Pan

Zhu

2019

Applied Sciences

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Traditional correlation analysis is analyzed separately in the time domain or the frequency domain, which cannot reflect the time-varying and frequency-varying characteristics of non-stationary signals. Therefore, a time–frequency (TF) correlation analysis method of time series decomposition (TD) derived from synchrosqueezed S transform (SSST) is proposed in this paper. First, the two-dimensional time–frequency matrices of the signals is obtained by synchrosqueezed S transform. Second, time series decomposition is used to transform the matrices into the two-dimensional time–time matrices. Third, a correlation analysis of the local time characteristics is carried out, thus attaining the time–frequency correlation between the signals. Finally, the proposed method is validated by stationary and non-stationary signals simulation and is compared with the traditional correlation analysis method. The simulation results show that the traditional method can obtain the overall correlation between the signals but cannot reflect the local time and frequency correlations. In particular, the correlations of non-stationary signals cannot be accurately identified. The proposed method not only obtains the overall correlations between the signals, but can also accurately identifies the correlations between non-stationary signals, thus showing the time-varying and frequency-varying correlation characteristics. The proposed method is applied to the acoustic signal processing of an engine–gearbox test bench. The results show that the proposed method can effectively identify the time–frequency correlation between the signals.

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