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Short-term prediction of UT1-UTC and LOD via Dynamic Mode Decomposition and combination of least-squares and vector autoregressive model

Michalczak Maciej, Ligas Marcin

Reports on Geodesy and Geoinformatics

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2024

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Vol. 117

45--54

EN

This study presents a short-term forecast of UT1-UTC and LOD using two methods, i.e. Dynamic Mode Decomposition (DMD) and combination of Least-Squares and Vector Autoregression (LS+VAR). The prediction experiments were performed separately for yearly time spans, 2018-2022. The prediction procedure started on January 1 and ended on December 31, with 7-day shifts between subsequent 30-day forecasts. Atmospheric Angular Momentum data (AAM) were used as an auxiliary time series to potentially improve the prediction accuracy of UT1-UTC and LOD in LS+VAR procedure. An experiment was also conducted with and without elimination of effect of zonal tides from UT1-UTC and LOD time series. Two approaches to using the best steering parameters for the methods were applied:. First, an adaptive approach, which observes the rule that before every single forecast, a preliminary one must be performed on the pre-selected sets of parameters, and the one with the smallest prediction error is then used for the final prediction; and second, an averaged approach, whereby several forecasts are made with different sets of parameters (the same parameters as in adaptive approach) and the final values are calculated as the averages of these predictions. Depending on the method and data combination mean absolute prediction errors (MAPE) for UT1-UTC vary from 0.63 ms to 1.43 ms for the 10th day and from 3.07 ms to 8.05 ms for the 30th day of the forecast. Corresponding values for LOD vary from 0.110 ms to 0.245 ms for the 10th day and from 0.148 ms to 0.325 ms for the 30th day.

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Research on chaotic features of a flow field over a plunging airfoil based on dynamic mode decomposition

Qi Yuhang, Yang Wenqing, Wang Yue, Song Bifeng

Journal of Theoretical and Applied Mechanics

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2021

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Vol. 59 nr 2

293--305

EN

Periodic motion of a plunging airfoil causes continuous changes in the surrounding flow field. The time-dependent thrust coefficient depends entirely on unsteady characteristics of the flow field. On the contrary, the time-dependent thrust coefficient may also reflect the unsteady characteristics of the corresponding flow field. With the fast Fourier transform (FFT) and dynamic mode decomposition (DMD), unsteady aerodynamic forces can be correlated with the flow field characteristics in the frequency domain. In the present paper, DMD is performed to analyze the unsteady characteristics of the flow field around a plunging NACA0012 airfoil at the Reynolds number of 20 000.

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Analysis of epileptic EEG signals by using dynamic mode decomposition and spectrum

Karabiber Cura Ozlem, Akan Aydin

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 1

28--44

EN

Dynamic mode decomposition (DMD) is a new matrix decomposition method proposed as an iterative solution to problems in fluid flow analysis. Recently, DMD algorithm has successfully been applied to the analysis of non-stationary signals such as neural recordings. In this study, we propose single-channel, and multi-channel EEG based DMD approaches for the analysis of epileptic EEG signals. We investigate the possibility of utilizing the ‘‘DMD Spectrum’’ for the classification of pre-seizure and seizure EEG segments. We introduce higher-order DMD spectral moments and DMD sub-band powers, and extract them as features for the classification of epileptic EEG signals. Experiments are conducted on multi-channel EEG signals collected from 16 epilepsy patients. Single-channel, and multi-channel EEG based DMD approaches have been tested on epileptic EEG data recorded from only right, only left, and both brain hemisphere channels. Performance of various classifiers using the proposed DMD-Spectral based features are compared with that of traditional spectral features. Experimental results reveal that the higher order DMD spectral moments and DMD sub-band power features introduced in this study, outperform the analogous spectral features calculated from traditional power spectrum.