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SRMD: Sparse Random Mode Decomposition
Communications on Applied Mathematics and Computation, 2023 Signal decomposition and multiscale signal analysis provide many useful tools for time-frequency analysis. We proposed a random feature method for analyzing time-series data by constructing a sparse approximation to the spectrogram. The randomization is both in the time window locations and the frequency sampling, which lowers the overall sampling and ...
Richardson, Nicholas +2 more
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Codage des signaux par EMD [PDF]
, 2012 In this letter a new signals coding framework based on the Empirical Mode Decomposition (EMD) is introduced. The EMD breaks down any signal into a reduced number of oscillating components called Intrinsic Modes Decomposition (IMFs).
BOUDRAA, Abdel-Ouahab, KHALDI, Kais
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Port-Hamiltonian Dynamic Mode Decomposition
SIAM Journal on Scientific Computing, 2023 We present a novel physics-informed system identification method to construct a passive linear time-invariant system. In more detail, for a given quadratic energy functional, measurements of the input, state, and output of a system in the time domain, we find a realization that approximates the data well while guaranteeing that the energy functional ...
Riccardo Morandin +2 more
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Dynamic Mode Decomposition for Compressive System Identification [PDF]
, 2020 Dynamic mode decomposition has emerged as a leading technique to identify spatiotemporal coherent structures from high-dimensional data, benefiting from a strong connection to nonlinear dynamical systems via the Koopman operator.
Bai, Zhe +4 more
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DYNAMIC BANDWIDTH VARIATIONAL MODE DECOMPOSITION
ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023 <p>Signal decomposition techniques aim to break down nonstationary signals into their oscillatory components, serving as a preliminary step in various practical signal processing applications. This has motivated researchers to explore different strategies, yielding several distinct approaches.
Andreas Angelou +2 more
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A characteristic dynamic mode decomposition [PDF]
Theoretical and Computational Fluid Dynamics, 2019 Temporal or spatial structures are readily extracted from complex data by modal decompositions like Proper Orthogonal Decomposition (POD) or Dynamic Mode Decomposition (DMD). Subspaces of such decompositions serve as reduced order models and define either spatial structures in time or temporal structures in space.
Sesterhenn, Jörn, Shahirpour, Amir
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2-D Prony-Huang Transform: A New Tool for 2-D Spectral Analysis [PDF]
, 2014 This work proposes an extension of the 1-D Hilbert Huang transform for the analysis of images. The proposed method consists in (i) adaptively decomposing an image into oscillating parts called intrinsic mode functions (IMFs) using a mode decomposition ...
Borgnat, Pierre +4 more
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Mode decomposition approach in robust control design for horizontal axis wind turbines
Wind Energy, 2020 A robust multivariable strategy for pitch and torque control design of variable‐speed variable‐pitch wind turbines in the full load region is introduced in this paper.
Ali Poureh, Amin Nobakhti
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IEEE Access, 2021 Numerous studies employ multi-scale decomposition to improve the prediction performance of neural networks, but the grounds for selecting the decomposition algorithm are not explained, and the effects of decomposition algorithms on other performance of ...
Haichao Huang +4 more
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Higher Order Dynamic Mode Decomposition [PDF]
SIAM Journal on Applied Dynamical Systems, 2017 This paper deals with an extension of dynamic mode decomposition (DMD), which is appropriate to treat general periodic and quasi-periodic dynamics, and transients decaying to periodic and quasiperiodic attractors, including cases (not accessible to standard DMD) that show limited spatial complexity but a very large number of involved frequencies.
Le Clainche Martínez, Soledad +1 more
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