scholarly journals A Novel EMG-Based Hand Gesture Recognition Framework Based on Multivariate Variational Mode Decomposition

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7002
Author(s):  
Kun Yang ◽  
Manjin Xu ◽  
Xiaotong Yang ◽  
Runhuai Yang ◽  
Yueming Chen
Keyword(s):  
Low Frequency ◽  
Recognition Task ◽  
Decomposition Methods ◽  
Variational Mode Decomposition ◽  
Low Frequency Oscillation ◽  
Hand Gestures ◽  
Semg Signal ◽  
Lightweight Framework ◽  
Mode Decomposition ◽  
Average Accuracy

Surface electromyography (sEMG) is a kind of biological signal that records muscle activity noninvasively, which is of great significance in advanced human-computer interaction, prosthetic control, clinical therapy, and biomechanics. However, the number of hand gestures that can be recognized is limited and the recognition accuracy needs to be further improved. These factors lead to the fact that sEMG products are not widely used in practice. The main contributions of this paper are as follows. Firstly, considering the increasing number of gestures to be recognized and the complexity of gestures, an extensible two-stage machine learning lightweight framework was innovatively proposed for multi-gesture task recognition. Secondly, the multivariate variational mode decomposition (MVMD) is applied to extract the spatial–temporal features from the multiple channels to the EMG signals, and the separable convolutional neural network is used for modelling. In this work, the experimental results for 52 hand gestures recognition task show that the average accuracy on each stage is about 90%. The potential movement information is mainly contained in the low-frequency oscillator of the sEMG signal, and the model performs better with the low-frequency oscillation from the MVMD algorithm on the second stage classification than that of other decomposition methods.

scholarly journals Evaluating three different adaptive decomposition methods for EEG signal seizure detection and classification

2019 ◽  
Author(s):  
Vinícius R. Carvalho ◽  
Márcio F.D. Moraes ◽  
Antônio P. Braga ◽  
Eduardo M.A.M. Mendes
Keyword(s):  
Wavelet Transform ◽  
Real Time ◽  
Empirical Mode Decomposition ◽  
Decomposition Methods ◽  
Seizure Detection ◽  
Variational Mode Decomposition ◽  
Real Time Processing ◽  
Mode Decomposition ◽  
Empirical Wavelet Transform ◽  
Adaptive Decomposition

AbstractSignal processing and machine learning methods are valuable tools in epilepsy research, potentially assisting in diagnosis, seizure detection, prediction and real-time event detection during long term monitoring. Recent approaches involve the decomposition of these signals in different modes or functions in a data-dependent and adaptive way. These approaches may provide advantages over commonly used Fourier based methods due to their ability to work with nonlinear and non-stationary data. In this work, three adaptive decomposition methods (Empirical Mode Decomposition, Empirical Wavelet Transform and Variational Mode Decomposition) are evaluated for the classification of normal, ictal and inter-ictal EEG signals using a freely available database. We provide a previously unavailable common methodology for comparing the performance of these methods for EEG seizure detection, with the use of the same classifiers, parameters and spectral and time domain features. It is shown that the outcomes using the three methods are quite similar, with maximum accuracies of 97.5% for Empirical Mode Decomposition, 96.7% for Empirical Wavelet Transform and 98.2% for Variational Mode Decomposition. Features were also extracted from the original non-decomposed signals, yielding inferior, but still fairly accurate (95.3%) results. The evaluated decomposition methods are promising approaches for seizure detection, but their use should be judiciously analysed, especially in situations that require real-time processing and computational power is an issue. An additional methodological contribution of this work is the development of two python packages, already available at the PyPI repository: One for the Empirical Wavelet Transform (ewtpy) and another for Variational Mode Decomposition (vmdpy).

scholarly journals Desert seismic signal denoising by 2D compact variational mode decomposition

2019 ◽  
Vol 16 (6) ◽  
pp. 1048-1060 ◽  
Author(s):  
Yue Li ◽  
Linlin Li ◽  
Chao Zhang
Keyword(s):  
Seismic Data ◽  
Random Noise ◽  
Low Frequency ◽  
Seismic Signal ◽  
Frequency Noise ◽  
Variational Mode Decomposition ◽  
Support Functions ◽  
Noise Characteristics ◽  
Mode Decomposition ◽  
Effective Signal

Abstract Noise suppression and effective signal recovery are very important for seismic signal processing. The random noise in desert areas has complex characteristics due to the complex geographical environment; noise characteristics such as non-stationary, non-linear and low frequency. These make it difficult for conventional denoising methods to remove random noise in desert seismic records. To address the problem, this paper proposes a two-dimensional compact variational mode decomposition (2D-CVMD) algorithm for desert seismic noise attenuation. This model decomposes the complex desert seismic data into an finite number of intrinsic mode functions with specific directions and vibration characteristics. The algorithm introduces binary support functions, which can detect the edge region of the signal in each mode by penalizing the support function through the L1 and total variation (TV) norm. Finally, the signal can be reconstructed by the support functions and the decomposed modes. We apply the 2D-CVMD algorithm to synthetic and real seismic data. The results show that the 2D-CVMD algorithm can not only suppress desert low-frequency noise, but also recover the weak effective signal.

scholarly journals A Short-Term Prediction Model of PM2.5 Concentration Based on Deep Learning and Mode Decomposition Methods

2021 ◽  
Vol 11 (15) ◽  
pp. 6915
Author(s):  
Jun Wei ◽  
Fan Yang ◽  
Xiao-Chen Ren ◽  
Silin Zou
Keyword(s):  
Neural Networks ◽  
Low Frequency ◽  
Decomposition Methods ◽  
Prediction Errors ◽  
Beijing City ◽  
Short Term ◽  
Mode Decomposition ◽  
Short Term Prediction ◽  
Bp Model ◽  
Pm2.5 Concentration

Based on a set of deep learning and mode decomposition methods, a short-term prediction model for PM2.5 concentration for Beijing city is established in this paper. An ensemble empirical mode decomposition (EEMD) algorithm is first used to decompose the original PM2.5 timeseries to several high- to low-frequency intrinsic mode functions (IMFs). Each IMF component is then trained and predicted by a combination of three neural networks: back propagation network (BP), long short-term memory network (LSTM), and a hybrid network of a convolutional neural network (CNN) + LSTM. The results showed that both BP and LSTM are able to fit the low-frequency IMFs very well, and the total prediction errors of the summation of all IMFs are remarkably reduced from 21 g/m3 in the single BP model to 4.8 g/m3 in the EEMD + BP model. Spatial information from 143 stations surrounding Beijing city is extracted by CNN, which is then used to train the CNN+LSTM. It is found that, under extreme weather conditions of PM2.5 <35 g/m3 and PM2.5 >150 g/m3, the prediction errors of the CNN + LSTM model are improved by ~30% compared to the single LSTM model. However, the prediction of the very high-frequency IMF mode (IMF-1) remains a challenge for all neural networks, which might be due to microphysical turbulences and chaotic processes that cannot be resolved by the above-mentioned neural networks based on variable–variable relationship.

Vibration feature extraction based on the improved variational mode decomposition and singular spectrum analysis combination algorithm

2018 ◽  
Vol 22 (7) ◽  
pp. 1519-1530 ◽  
Author(s):  
Hui Li ◽  
Tengfei Bao ◽  
Chongshi Gu ◽  
Bo Chen
Keyword(s):  
Spectrum Analysis ◽  
Damage Identification ◽  
Singular Spectrum Analysis ◽  
Low Frequency ◽  
Frequency Noise ◽  
Variational Mode Decomposition ◽  
Low Frequency Noise ◽  
Flood Discharge ◽  
Mode Decomposition ◽  
Discharge Structure

Extraction of the vibration characteristics of a flood discharge structure under the influence of intensive background noise is one of the main challenges in vibration-based damage identification. A novel algorithm called normalized central frequency difference spectrum is proposed to improve the variational mode decomposition algorithm for high-frequency noise filtering. To eliminate the errors caused by end effect, the waveform matching extension algorithm is used to further improve the variational mode decomposition. However, the vibration signal is still coupled in low-frequency noise. Thereupon, the singular spectrum analysis algorithm is applied to filter the low-frequency noise. In this article, a simulated signal and the measured signals from a dam model are analyzed by the proposed algorithm. The results indicate that the proposed algorithm is robust to noise and has high denoising precision. In addition, this algorithm can offer clues for damage identification and localization of a flood discharge structure.

scholarly journals Robust Estimation of Arrival Time of Complex Noisy Partial Discharge Pulse in Power Cables Based on Adaptive Variational Mode Decomposition

2020 ◽  
Vol 10 (5) ◽  
pp. 1641
Author(s):  
Kang Sun ◽  
Tong Wu ◽  
Xinwei Li ◽  
Jing Zhang
Keyword(s):  
White Noise ◽  
Arrival Time ◽  
Partial Discharge ◽  
Low Frequency ◽  
Discrete Fourier Transformation ◽  
Accurate Estimation ◽  
Narrowband Interference ◽  
Variational Mode Decomposition ◽  
Adaptive Wavelet ◽  
Mode Decomposition

Periodic narrowband signals and white noise are the main interferences in online detection and localization of cable partial discharge (PD), however, existing research has always focused on the white noise suppression only, which is not in line with the actual scene. A novel de-noising method for effectively extracting random PD pulse from complex and strong interferences is proposed in this paper and applied to PD localization. Firstly, an improved adaptive variational mode decomposition (AVMD) is used to decompose periodic narrowband interference, white noise, and PD signal into different intrinsic mode. According to the characteristic that the power of intrinsic mode component of periodic narrowband interference in the discrete Fourier transformation (DFT) power spectrum is much larger than that of PD and white noise, the periodic narrowband is removed out. In order to effectively filter out white noise, a scale adaptive wavelet packet decomposition method based on correlation coefficient is proposed, which decomposes the signal into high, middle, and low-frequency components. The components with low frequency, small amplitude are removed out as the white noise interference according to the threshold method, and the residual is the de-noising PD signal. Experimental results show that the proposed method can robustly suppress the interference of periodic narrowband signal and white noise, and effectively preserve the essential characteristics of the real PD signal. In the multi-sensor travelling wave based localization system of cable PD source using time-varying kurtosis, accurate estimation of first arrival time of PD pulse can be achieved by the de-noising results.

Study on Low Frequency Oscillation of Wind Power System Based on HHT

2014 ◽  
Vol 981 ◽  
pp. 663-667
Author(s):  
Hong Ling Xie ◽  
Ting Yue
Keyword(s):  
Power System ◽  
Wind Power ◽  
Empirical Mode Decomposition ◽  
Low Frequency ◽  
Support Vector ◽  
Frequency Oscillation ◽  
Low Frequency Oscillation ◽  
B Spline ◽  
Mode Decomposition ◽  
Wind Power System

For the output of wind power system has the characteristics of randomness, volatility and intermittence, the voltage of wind power system low frequency oscillation is one of the most common fluctuations in the system. For the problem of low frequency oscillation, the limitations of the detection methods such as the Lyapunov linearization method, the Prony method, wavelet transform method are summed up, and a new detecting method named Hilbert-huang Transform (HHT) is put forward in this paper, which can detect the oscillation accurately and timely. To solve the problem of end effect in the process of empirical mode decomposition (EMD), B-spline empirical mode decomposition based on support vector machine is applied in dealing with the end issue. an extension of the original signal is applied. Then, calculating the average curve of the signal by B-spline interpolation method. Finally getting the intrinsic mode function (IMF) by empirical mode decomposition (EMD). The practicality of the method is verified by Matlab simulation.

An adaptive and efficient variational mode decomposition and its application for bearing fault diagnosis

2020 ◽  
pp. 147592172097085
Author(s):  
Xingxing Jiang ◽  
Jun Wang ◽  
Changqing Shen ◽  
Juanjuan Shi ◽  
Weiguo Huang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Decomposition Method ◽  
Decomposition Methods ◽  
Variational Mode Decomposition ◽  
Bearing Fault ◽  
Spectral Kurtosis ◽  
Bearing Fault Diagnosis ◽  
Mode Decomposition ◽  
Efficient Extraction ◽  
Low Efficiency

Variational mode decomposition has been widely applied to machinery fault diagnosis during these years. However, it remains difficult to set proper hyperparameters for the variational mode decomposition, including number of decomposed modes, initial center frequencies, and balance parameter. Moreover, the low efficiency of the existing variational mode decomposition methods hinders their applications to practical diagnostic task. This article proposes an adaptive and efficient variational mode decomposition method after thoroughly investigating its convergence property characteristic. A convergent tendency phenomenon is discovered and is explained mathematically for the first time. Motivated by the convergent tendency phenomenon, the proposed method rapidly and adaptively determines the number and the optimal initial center frequencies of signal latent modes with the guidance of the convergent tendencies of the initial center frequencies changing from small to large. In the proposed method, the number of decomposed modes and initial center frequencies are not hyperparameters that require to be set in advance any more, but are parameters learned from the analyzed signals. The determined parameters enable efficient extraction of the main latent modes contained in the analyzed signals. Therefore, the proposed variational mode decomposition method represents a major improvement in parameter adaption and decomposition efficiency over the existing variational mode decomposition methods. In the application for bearing fault diagnosis, the faulty modes are selected adaptively and the corresponding balance parameters are further optimized efficiently. Two experimental cases validate the proposed method and its superiority over the existing variational mode decomposition methods and the classical fast spectral kurtosis in bearing fault diagnosis.

ANALYSIS OF STAND RESEARCH RESULTS OF AXIAL DYNAMIC FORCE IN DRILLING BY THREE ROLLER CONE BIT

2019 ◽  
pp. 81-93
Author(s):  
В. М. Мойсишин ◽  
M. V. Lyskanych ◽  
R. A. Zhovniruk ◽  
Ye. P. Majkovych
Keyword(s):  
Low Frequency ◽  
Maximum Energy ◽  
Dynamic Force ◽  
Low Frequency Oscillation ◽  
Drilling Tool ◽  
Rock Destruction ◽  
Frequency Components ◽  
Harmonic Components ◽  
Experimental Values ◽  
Harmful Effects

The purpose of the proposed article is to establish the causes of oscillations of drilling tool and the basic laws of the distribution of the total energy of the process of changing the axial dynamic force over frequencies of spectrum. Variable factors during experiments on the classical plan were the rigidity of drilling tool and the hardness of the rock. According to the results of research, the main power of the process of change of axial dynamic force during drilling of three roller cone bits is in the frequency range 0-32 Hz in which three harmonic frequency components are allocated which correspond to the theoretical values of low-frequency and gear oscillations of the chisel and proper oscillations of the bit. The experimental values of frequencies of harmonic components of energy and normalized spectrum as well as the magnitude of the dispersion of the axial dynamic force and its normalized values at these frequencies are presented. It has been found that with decreasing rigidity of the drilling tool maximum energy of axial dynamic force moves from the low-frequency oscillation region to the tooth oscillation area, intensifying the process of rock destruction and, at the same time, protecting the tool from the harmful effects of the vibrations of the bit. Reducing the rigidity of the drilling tool protects the bit from the harmful effects of the vibrations generated by the stand. The energy reductions in these fluctuations range from 47 to 77%.

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