scholarly journals Gesture Recognition Based on Multiscale Singular Value Entropy and Deep Belief Network

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 119
Author(s):  
Wenguo Li ◽  
Zhizeng Luo ◽  
Yan Jin ◽  
Xugang Xi
Keyword(s):  
Gesture Recognition ◽  
Language Translation ◽  
Singular Value ◽  
Deep Belief Network ◽  
Permutation Entropy ◽  
Multiple Time ◽  
Belief Network ◽  
Time Frequency ◽  
S Transform ◽  
Semg Signals

As an important research direction of human–computer interaction technology, gesture recognition is the key to realizing sign language translation. To improve the accuracy of gesture recognition, a new gesture recognition method based on four channel surface electromyography (sEMG) signals is proposed. First, the S-transform is applied to four channel sEMG signals to enhance the time-frequency detail characteristics of the signals. Then, multiscale singular value decomposition is applied to the multiple time-frequency matrix output of S-transform to obtain the time-frequency joint features with better robustness. The corresponding singular value permutation entropy is calculated as the eigenvalue to effectively reduce the dimension of multiple eigenvectors. The gesture features are used as input into the deep belief network for classification, and nine kinds of gestures are recognized with an average accuracy of 93.33%. Experimental results show that the multiscale singular value permutation entropy feature is especially suitable for the pattern classification of the deep belief network.

Dynamic contraction and fatigue analysis in biceps brachii muscles using synchrosqueezed wavelet transform and singular value features

2021 ◽  
pp. 095441192110480
Author(s):  
Lakshmi M Hari ◽  
Gopinath Venugopal ◽  
Swaminathan Ramakrishnan
Keyword(s):  
Wavelet Transform ◽  
Biceps Brachii ◽  
Singular Value ◽  
Time Frequency ◽  
Singular Value Decomposition Method ◽  
Dynamic Contractions ◽  
Synchrosqueezed Wavelet Transform ◽  
Value Decomposition ◽  
Neuromuscular Conditions ◽  
Semg Signals

In this study, the dynamic contractions and the associated fatigue condition in biceps brachii muscle are analysed using Synchrosqueezed Wavelet Transform (SST) and singular value features of surface Electromyography (sEMG) signals. For this, the recorded signals are decomposed into time-frequency matrix using SST. Two analytic functions namely Morlet and Bump wavelets are utilised for the analysis. Singular Value Decomposition method is applied to this time-frequency matrix to derive the features such as Maximum Singular Value (MSV), Singular Value Entropy (SVEn) and Singular Value Energy (SVEr). The results show that both these wavelets are able to characterise nonstationary variations in sEMG signals during dynamic fatiguing contractions. Increase in values of MSV and SVEr with the progression of fatigue denotes the presence of nonstationarity in the sEMG signals. The lower values of SVEn with the progression of fatigue indicate the randomness in the signal. Thus, it appears that the proposed approach could be used to characterise dynamic muscle contractions under varied neuromuscular conditions.

scholarly journals Radar Emitter Recognition Based on the Energy Cumulant of Short Time Fourier Transform and Reinforced Deep Belief Network

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3103 ◽  
Author(s):  
Xuebao Wang ◽  
Gaoming Huang ◽  
Zhiwen Zhou ◽  
Wei Tian ◽  
Jialun Yao ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Signal To Noise Ratio ◽  
Recognition Rate ◽  
Deep Belief Network ◽  
Short Time Fourier Transform ◽  
Belief Network ◽  
Time Frequency ◽  
Low Snr ◽  
Emitter Recognition ◽  
Short Time

To cope with the complex electromagnetic environment and varied signal styles, a novel method based on the energy cumulant of short time Fourier transform and reinforced deep belief network is proposed to gain a higher correct recognition rate for radar emitter intra-pulse signals at a low signal-to-noise ratio. The energy cumulant of short time Fourier transform is attained by calculating the accumulations of each frequency sample value with the different time samples. Before this procedure, the time frequency distribution via short time Fourier transform is processed by base noise reduction. The reinforced deep belief network is proposed to employ the input feature vectors for training to achieve the radar emitter recognition and classification. Simulation results manifest that the proposed method is feasible and robust in radar emitter recognition even at a low SNR.

scholarly journals Study on the Thick-Walled Pipe Ultrasonic Signal Enhancement of Modified S-Transform and Singular Value Decomposition

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Haichao Cai ◽  
Chunguang Xu ◽  
Shiyuan Zhou ◽  
Hongjuan Yan ◽  
Liu Yang
Keyword(s):  
Singular Value Decomposition ◽  
Singular Value ◽  
Frequency Resolution ◽  
Scanning System ◽  
Frequency Noise ◽  
Echo Signal ◽  
Time Frequency ◽  
S Transform ◽  
Ultrasonic Flaw ◽  
Value Decomposition

When detecting the ultrasonic flaw of thick-walled pipe, the flaw echo signals are often interrupted by scanning system frequency and background noise. In particular when the thick-walled pipe defect is small, echo signal amplitude is often drowned in noise signal and affects the extraction of defect signal and the position determination accuracy. This paper presents the modified S-transform domain singular value decomposition method for the analysis of ultrasonic flaw echo signals. By changing the scale rule of Gaussian window functions with S-transform to improve the time-frequency resolution. And the paper tries to decompose the singular value decomposition of time-frequency matrix after the S-transform to determine the singular entropy of effective echo signal and realize the adaptive filter. Experiments show that, using this method can not only remove high frequency noise but also remove the low frequency noise and improve the signal-to-noise ratio of echo signal.

Fault diagnosis of rolling bearing based on multimodal data fusion and deep belief network

2021 ◽  
pp. 095440622110084
Author(s):  
Defeng Lv ◽  
Huawei Wang ◽  
Changchang Che
Keyword(s):  
Fault Diagnosis ◽  
Data Fusion ◽  
Rolling Bearing ◽  
Typical Feature ◽  
Deep Belief Network ◽  
Fine Tuning ◽  
Multimodal Data ◽  
Belief Network ◽  
Time Frequency ◽  
Multimodal Data Fusion

Aiming at raw vibration signal of rolling bearing with long time series, a fault diagnosis model based on multimodal data fusion and deep belief network is proposed in this paper. First, multimodal data composed of artificial features and model features can be obtained by time-frequency domain analysis and unsupervised learning based on restricted Boltzmann machine (RBM). Second, canonical correlation analysis method is used to extract the typical feature pairs from the multimodal data to realize the feature-level multimodal data fusion. Third, deep belief network is applied to extract deep feature mapping between typical feature pairs and fault types. After greedy layer-wise pre-training and fine-tuning, it is available to achieve the trained model for fault diagnosis of rolling bearing. Typical rolling bearing datasets are used to testify the effectiveness of the proposed method. It is verified that the robustness and accuracy of the proposed method are superior to common methods.

Bearing fault diagnosis method based on the generalized S transform time–frequency spectrum de-noised by singular value decomposition

2018 ◽  
Vol 233 (7) ◽  
pp. 2467-2477 ◽  
Author(s):  
Jianhua Cai ◽  
Yongliang Xiao
Keyword(s):  
Fault Diagnosis ◽  
Frequency Spectrum ◽  
Singular Values ◽  
Rolling Bearing ◽  
Singular Value ◽  
Time Frequency ◽  
Bearing Fault ◽  
S Transform ◽  
Generalized S Transform ◽  
Value Decomposition

In view of the fact that the random noise interferes with the characteristic extraction of a rolling bearing fault signal, a new method of fault feature extraction is proposed based on the combination of the generalized S transform and singular value decomposition (SVD). Firstly, the 2D time–frequency spectrum bearing fault signal is obtained by applying the generalized S transform, and the time–frequency spectrum matrix is used as the objective matrix of SVD to solve the singular values. Then the K-means clustering algorithm is used to classify the singular value sequence, and the singular values for reconstruction are determined. Finally, the de-noised matrix is carried out the generalized S inversion transform to get the de-noised fault signal, and the power spectrum is calculated to finish the fault diagnosis. By analyzing the simulated signal and the actual bearing fault data, results show that the proposed method can effectively identify typical faults of rolling bearings and improve the diagnosis effect of rolling bearing faults. And it provides a new way to realize the fault diagnosis of rolling bearings under noise.

scholarly journals Analysis of Weak Fault in Hydraulic System Based on Multi-scale Permutation Entropy of Fault-Sensitive Intrinsic Mode Function and Deep Belief Network

Entropy ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. 425 ◽  
Author(s):  
Jie Huang ◽  
Xinqing Wang ◽  
Dong Wang ◽  
Zhiwei Wang ◽  
Xia Hua
Keyword(s):  
Intrinsic Mode Function ◽  
Deep Belief Network ◽  
Permutation Entropy ◽  
Hydraulic Systems ◽  
Belief Network ◽  
Multi Scale ◽  
Identification Method ◽  
Mode Function ◽  
Correlation Analysis Method ◽  
Weak Fault

With the aim of automatic recognition of weak faults in hydraulic systems, this paper proposes an identification method based on multi-scale permutation entropy feature extraction of fault-sensitive intrinsic mode function (IMF) and deep belief network (DBN). In this method, the leakage fault signal is first decomposed by empirical mode decomposition (EMD), and fault-sensitive IMF components are screened by adopting the correlation analysis method. The multi-scale entropy feature of each screened IMF is then extracted and features closely related to the weak fault information are then obtained. Finally, DBN is used for identification of fault diagnosis. Experimental results prove that this identification method has an ideal recognition effect. It can accurately judge whether there is a leakage fault, determine the degree of severity of the fault, and can diagnose and analyze hydraulic weak faults in general.

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