Analysis of EEG using variational mode decomposition method for diagnosis of epilepsy

2020 ◽  
Author(s):  
Arya Surendran ◽  
Jisu Elsa Jacob ◽  
K. Gopakumar
Keyword(s):  
Decomposition Method ◽  
Variational Mode Decomposition ◽  
Mode Decomposition

Improved variational mode decomposition method for vibration signal processing of flood discharge structure

2021 ◽  
pp. 107754632110161
Author(s):  
Huokun Li ◽  
Gang Wang ◽  
Bowen Wei ◽  
Hanyue Liu ◽  
Wei Huang
Keyword(s):  
High Frequency ◽  
Decomposition Method ◽  
Vibration Signal ◽  
Structural Vibration ◽  
Variational Mode Decomposition ◽  
Flood Discharge ◽  
Mode Decomposition ◽  
Discharge Structure ◽  
Frequency Components ◽  
Structure Vibration

It is crucial for flood discharge structure vibration safety evaluations to filter low-frequency noise, separate dense-frequency components and obtain high-frequency component accurately from vibration signals. Variational mode decomposition, a novel signal adaptive decomposition method, effectively processes flood discharge structures. However, the mode number and quadratic penalty item uncertainty in variational mode decomposition directly affects the vibration signal decomposition. Therefore, an improved variational mode decomposition method for vibration signal processing is proposed in this study. The proposed method adaptively determines the mode number based on singular entropy and frequency stability to completely separate the structural vibration components (including dense-frequency components and high-frequency components) and noise components from the vibration signal. Next, an objective quadratic penalty item function based on sample entropy and mutual information is proposed to quantify the mode mixing between the structural vibration components. Finally, a particle swarm optimisation algorithm based on beetle antenna search is proposed to optimise the quadratic penalty item, which overcomes the shortcomings of traditional algorithms and suppresses the mode mixing between the structural vibration components. The validity and feasibility of the proposed method was verified by the simulation signal and was applied to a sluice prototype project. The results showed that the method effectively filtered noise, greatly improved the vibration response signal-to-noise ratio and obtained the structural vibration component time history signal, which provides a foundation for flood discharge structure vibration safety evaluation and health monitoring.

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