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Variational Mode Decomposition
IEEE Transactions on Signal Processing, 2014During the late 1990s, Huang introduced the algorithm called Empirical Mode Decomposition, which is widely used today to recursively decompose a signal into different modes of unknown but separate spectral bands. EMD is known for limitations like sensitivity to noise and sampling. These limitations could only partially be addressed by more mathematical
Dominique Zosso
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Identification of electromechanical oscillatory modes based on variational mode decomposition
Electric Power Systems Research, 2019Abstract This paper introduces Variational Mode Decomposition (VMD) to identify electromechanical oscillatory modes in power systems. The identification process is based on the time-frequency analysis of nonlinear signals which arise after a large disturbance.
Mario R Arrieta Paternina +2 more
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Self-tuning variational mode decomposition
Journal of the Franklin Institute, 2021zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Qiming Chen +5 more
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Successive multivariate variational mode decomposition
Multidimensional Systems and Signal Processing, 2022zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Shuaishuai Liu, Kaiping Yu
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Diffraction separation by variational mode decomposition
Geophysical Prospecting, 2021ABSTRACTDiffracted wavefields with superior illumination encode key geologic information about small‐scale geologic discontinuities or inhomogeneities in the subsurface and thus possess great potential for high‐resolution imaging. However, the weak diffracted wavefield is easily masked by the dominant reflected data.
Peng Lin +3 more
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Generalized Variational Mode Decomposition: A Multiscale and Fixed-Frequency Decomposition Algorithm
IEEE Transactions on Instrumentation and Measurement, 2021To overcome the limitations of variational mode decomposition (VMD) algorithm that its frequency scales and spectrum positions cannot be flexibly adjusted to decompose signals as required, a generalized VMD (GVMD) was proposed. This article addresses the fundamental theory of GVMD. In order to highlight the local characteristics of the signal much more
Yanfei Guo, Zhousuo Zhang
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Hierarchical decomposition based on a variation of empirical mode decomposition
Signal, Image and Video Processing, 2016Adaptive methods of signal analysis have proved a very useful tool for analysis of non-stationary signals. This is due to the ability of these methods to adapt to the local structures of the signals being analysed, as these methods are not constrained by a fixed basis.
Muhammad Kaleem +2 more
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Bayesian Dynamic Mode Decomposition with Variational Matrix Factorization
Proceedings of the AAAI Conference on Artificial Intelligence, 2021Dynamic mode decomposition (DMD) and its extensions are data-driven methods that have substantially contributed to our understanding of dynamical systems. However, because DMD and most of its extensions are deterministic, it is difficult to treat probabilistic representations of parameters and predictions.
Takahiro Kawashima +2 more
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Enhancement of variational mode decomposition with missing values
Signal Processing, 2018A new variational mode decomposition that efficiently handles missing data is proposed.A practical algorithm that reflects the adjustment of the missing sample effects under the framework of VMD algorithm is developed.Proposed method can be applicable to analyze various kind of signals through wavelet transform.
Guebin Choi, Hee-Seok Oh, Donghoh Kim
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Radiometric identification using variational mode decomposition
Computers & Electrical Engineering, 2019Abstract Radiometric Identification (RAI) is the identification of wireless devices through their Radio Frequency (RF) emissions. In recent years, the research community has investigated it applying different methods and sets of statistical features extracted from the digitized RF emissions.
Gianmarco Baldini +3 more
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