Parameter-Adaptive TVF-EMD Feature Extraction Method Based on Improved GOA [PDF]
Sensors, 2022 In order to separate the sub-signals and extract the feature frequency in the signal accurately, we proposed a parameter-adaptive time-varying filtering empirical mode decomposition (TVF-EMD) feature extraction method based on the improved grasshopper ...
Chengjiang Zhou +2 more
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Study on displacement by integrating acceleration of vibration screening excitation platform based on EBKA-TVF-EMD [PDF]
HeliyonThe method of obtaining displacement through acceleration integration is widely used. To accurately obtain the vibration displacement of the vibration screening excitation platform, in this paper, the preprocessing method (EBKA-TVF-EMD) involving the use
Chenggang Deng
exaly +6 more sources
Noise Cancellation Method Based on TVF-EMD with Bayesian Parameter Optimization
Algorithms, 2023 To separate the noise and important signal features of the indoor carbon dioxide (CO2) concentration signal, we proposed a noise cancellation method, based on time-varying, filtering-based empirical mode decomposition (TVF-EMD) with Bayesian optimization
Kaiyuan Li, Lizheng Deng, HongYong Yuan
exaly +5 more sources
Frontiers in Marine ScienceIntroductionAtmospheric ducts represent the most frequently observed refractive phenomenon in maritime atmospheric environments. Prior research has shown that the occurrence of elevated ducts exceeds 60% with high-resolution radiosonde data, providing a ...
Chuanzhi Yao +4 more
exaly +4 more sources
Short-Term PV Power Forecasting Using a Hybrid TVF-EMD-ELM Strategy
International Transactions on Electrical Energy Systems, 2023 This paper discusses the efficient implementation of a new hybrid approach to forecasting short-term PV power production for four different PV plants in Algeria. The developed model incorporates a time-varying filter-empirical mode decomposition (TVF-EMD)
Reski Khelifi +8 more
doaj +3 more sources
Early Fault Detection of Rolling Bearings Based on Time-Varying Filtering Empirical Mode Decomposition and Adaptive Multipoint Optimal Minimum Entropy Deconvolution Adjusted [PDF]
Entropy, 2023 Due to the early formation of rolling bearing fault characteristics in an environment with strong background noise, the single use of the time-varying filtering empirical mode decomposition (TVFEMD) method is not effective for the extraction of fault ...
Shuo Song, Wenbo Wang
doaj +2 more sources
Single-channel blind source separation for vibration signals based on TVF-EMD and improved SCA [PDF]
IET Signal Processing, 2020 A novel single‐channel blind source separation method is studied. This method not only addresses the problem that the traditional blind source separation method depends on the number of sensors but also achieves the purpose of effectively identifying the modal parameters.
Baoze Ma
exaly +3 more sources
Scattered light noise characterisation at the Virgo interferometer with tvf-EMD adaptive algorithm
Classical and Quantum Gravity, 2020 Abstract A methodology of adaptive time series analysis, based on empirical mode decomposition (EMD) and on its time varying version tvf-EMD, has been applied to strain data from the gravitational wave interferometer (IFO) Virgo in order to characterise scattered light noise affecting the sensitivity of the IFO in the detection ...
Nicolas Arnaud +2 more
exaly +4 more sources
An Adaptive TVF-EMD Method for Wind Turbine Tower Vibration Signal Reduction
IEEE AccessOffshore wind turbine tower acceleration data are often affected by significant noise and exhibit non-linear, non-stationary characteristics. Traditional Empirical Mode Decomposition (EMD) methods are prone to modal aliasing and the introduction of ...
Lele Li +4 more
doaj +3 more sources
Research on optimal selection of runoff prediction models based on coupled machine learning methods [PDF]
Scientific ReportsRunoff fluctuations under the influence of climate change and human activities present a significant challenge and valuable application in constructing high-accuracy runoff prediction models.
Xing Wei +5 more
doaj +2 more sources