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Personalized prediction of gait freezing using dynamic mode decomposition [PDF]
Scientific ReportsFreezing of gait (FoG) is a common severe gait disorder in patients with advanced Parkinson’s disease. The ability to predict the onset of FoG episodes early on allows for timely intervention, which is essential for improving the life quality of patients.
Zhiwen Fu, Congping Lin, Yiwei Zhang
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Bayesian Dynamic Mode Decomposition [PDF]
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017 Dynamic mode decomposition (DMD) is a data-driven method for calculating a modal representation of a nonlinear dynamical system, and it has been utilized in various fields of science and engineering. In this paper, we propose Bayesian DMD, which provides a principled way to transfer the advantages of the Bayesian formulation into DMD.
Naoya Takeishi +3 more
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Dynamic mode decomposition of extreme events [PDF]
Nonlinear Processes in GeophysicsMost data-driven methods, among them Dynamic Mode Decomposition (DMD), focus on analysing and reconstructing the average behaviour of a system. However, the primary interest often lies in the anomalous behaviour, known as extreme events.
M. Ann, J. Behrens, J. Sillmann
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Delay-Embedding Spatio-Temporal Dynamic Mode Decomposition
MathematicsSpatio-temporal dynamic mode decomposition (STDMD) is an extension of dynamic mode decomposition (DMD) designed to handle spatio-temporal datasets. It extends the framework so that it can analyze data that have both spatial and temporal variations.
Gyurhan Nedzhibov
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AIP Advances, 2017 In this letter, we propose a simple and efficient framework of dynamic mode decomposition (DMD) and mode selection for large datasets. The proposed framework explicitly introduces a preconditioning step using an incremental proper orthogonal ...
Yuya Ohmichi
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Camera-Based Dynamic Vibration Analysis Using Transformer-Based Model CoTracker and Dynamic Mode Decomposition [PDF]
SensorsAccelerometers are commonly used to measure vibrations for condition monitoring in mechanical and civil structures; however, their high cost and point-based measurement approach present practical limitations.
Liangliang Cheng +4 more
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Latent Diffeomorphic Dynamic Mode Decomposition
Applied Mathematics LettersWe present Latent Diffeomorphic Dynamic Mode Decomposition (LDDMD), a new data reduction approach for the analysis of non-linear systems that combines the interpretability of Dynamic Mode Decomposition (DMD) with the predictive power of Recurrent Neural Networks (RNNs).
Willem Diepeveen +2 more
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Dynamic Mode Decomposition of Fast Pressure Sensitive Paint Data [PDF]
Sensors, 2016 Fast-response pressure sensitive paint (PSP) is used in this work to measure and analyze the acoustic pressure field in a rectangular cavity. The high spatial resolution and fast frequency response of PSP effectively captures the spatial and temporal ...
Mohd Y. Ali +2 more
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Correction of blink artifacts using independent component analysis and empirical mode decomposition. [PDF]
Blink-related ocular activity is a major source of artifacts in electroencephalogram (EEG) data. Independent component analysis (ICA) is a well-known technique for the correction of such ocular artifacts, but one of the limitations of ICA is that the ICs
Bhattacharya, Joydeep, Lindsen, Job P.
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Dynamic-mode decomposition and optimal prediction [PDF]
Physical Review E, 2021 The Dynamic-Mode Decomposition (DMD) is a well established data-driven method of finding temporally evolving linear-mode decompositions of nonlinear time series. Traditionally, this method presumes that all relevant dimensions are sampled through measurement.
Christopher W. Curtis +1 more
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