ReviewSensors (Basel, Switzerland)2018
A Comparative Study of Four Kinds of Adaptive Decomposition Algorithms and Their Applications.
Review in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
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- A genetic algorithm optimized hybrid model for agricultural price forecasting based on VMD and LSTM network.Scientific reports · 2025Article
- Laser Linewidth Analysis and Filtering/Fitting Algorithms for Improved TDLAS-Based Optical Gas Sensor.Sensors (Basel, Switzerland) · 2023Article
- Circulant Singular Spectrum Analysis and Discrete Wavelet Transform for Automated Removal of EOG Artifacts from EEG Signals.Sensors (Basel, Switzerland) · 2023Article
- Application of EEG in migraine.Frontiers in human neuroscience · 2023Review
- A two-step pre-processing tool to remove Gaussian and ectopic noise for heart rate variability analysis.Scientific reports · 2022Article
- The evaluation of seismocardiogram signal pre-processing using hybridized variational mode decomposition method.Biomedical engineering letters · 2022Article
- A comparative study of single-channel signal processing methods in fetal phonocardiography.PloS one · 2022Article
- Matrix Pencil Method for Vital Sign Detection from Signals Acquired by Microwave Sensors.Sensors (Basel, Switzerland) · 2021Article
- A Hydraulic Pump Fault Diagnosis Method Based on the Modified Ensemble Empirical Mode Decomposition and Wavelet Kernel Extreme Learning Machine Methods.Sensors (Basel, Switzerland) · 2021Article
- An Adaptive Filter for Nonlinear Multi-Sensor Systems with Heavy-Tailed Noise.Sensors (Basel, Switzerland) · 2020Article
- Feature Extraction and Reconstruction by Using 2D-VMD Based on Carrier-Free UWB Radar Application in Human Motion Recognition.Sensors (Basel, Switzerland) · 2019Article
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4 authors.
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Abstract
The adaptive decomposition algorithm is a powerful tool for signal analysis, because it can decompose signals into several narrow-band components, which is advantageous to quantitatively evaluate signal characteristics. In this paper, we present a comparative study of four kinds of adaptive decomposition algorithms, including some algorithms deriving from empirical mode decomposition (EMD), empirical wavelet transform (EWT), variational mode decomposition (VMD) and Vold⁻Kalman filter order tracking (VKF_OT). Their principles, advantages and disadvantages, and improvements and applications to signal analyses in dynamic analysis of mechanical system and machinery fault diagnosis are showed. Examples are provided to illustrate important influence performance factors and improvements of these algorithms. Finally, we summarize applicable scopes, inapplicable scopes and some further works of these methods in respect of precise filters and rough filters. It is hoped that the paper can provide a valuable reference for application and improvement of these methods in signal processing.
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