scholarly journals An incremental approach to online dynamic mode decomposition for time-varying systems with applications to EEG data modeling

2020 ◽  
Vol 7 (2) ◽  
pp. 209-241
Author(s):  
Mustaffa Alfatlawi ◽  
◽  
Vaibhav Srivastava
Keyword(s):  
Data Modeling ◽  
Dynamic Mode Decomposition ◽  
Dynamic Mode ◽  
Time Varying ◽  
Incremental Approach ◽  
Mode Decomposition ◽  
Eeg Data ◽  
Time Varying Systems

scholarly journals Online Dynamic Mode Decomposition for Time-Varying Systems

2019 ◽  
Vol 18 (3) ◽  
pp. 1586-1609 ◽  
Author(s):  
Hao Zhang ◽  
Clarence W. Rowley ◽  
Eric A. Deem ◽  
Louis N. Cattafesta
Keyword(s):  
Dynamic Mode Decomposition ◽  
Dynamic Mode ◽  
Time Varying ◽  
Mode Decomposition ◽  
Time Varying Systems

scholarly journals Pattern Recognition in Epileptic EEG Signals via Dynamic Mode Decomposition

Mathematics ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 481 ◽  
Author(s):  
Jong-Hyeon Seo ◽  
Ichiro Tsuda ◽  
Young Ju Lee ◽  
Akio Ikeda ◽  
Masao Matsuhashi ◽  
...  
Keyword(s):  
Dynamic Mode Decomposition ◽  
High Frequency Oscillation ◽  
Dynamic Mode ◽  
Channel Correlation ◽  
Simultaneous Interpretation ◽  
Mode Decomposition ◽  
Eeg Data ◽  
Specific Frequency ◽  
Current Shift ◽  
Interictal State

In this paper, we propose a new method based on the dynamic mode decomposition (DMD) to find a distinctive contrast between the ictal and interictal patterns in epileptic electroencephalography (EEG) data. The features extracted from the method of DMD clearly capture the phase transition of a specific frequency among the channels corresponding to the ictal state and the channel corresponding to the interictal state, such as direct current shift (DC-shift or ictal slow shifts) and high-frequency oscillation (HFO). By performing classification tests with Electrocorticography (ECoG) recordings of one patient measured at different timings, it is shown that the captured phenomenon is the unique pattern that occurs in the ictal onset zone of the patient. We eventually explain how advantageously the DMD captures some specific characteristics to distinguish the ictal state and the interictal state. The method presented in this study allows simultaneous interpretation of changes in the channel correlation and particular information for activity related to an epileptic seizure so that it can be applied to identification and prediction of the ictal state and analysis of the mechanism on its dynamics.

Experimental Measurement of In-Plane Rolling Nonpneumatic Tire Vibrations Using High-Speed Imaging

2019 ◽  
Vol 47 (3) ◽  
pp. 196-210
Author(s):  
Meghashyam Panyam ◽  
Beshah Ayalew ◽  
Timothy Rhyne ◽  
Steve Cron ◽  
John Adcox
Keyword(s):  
High Speed ◽  
Image Correlation ◽  
Dynamic Mode Decomposition ◽  
Dynamic Mode ◽  
High Speed Imaging ◽  
Correlation Algorithm ◽  
Mode Decomposition ◽  
Series Of Experiments ◽  
Plane Deformations ◽  
Dominant Frequencies

ABSTRACT This article presents a novel experimental technique for measuring in-plane deformations and vibration modes of a rotating nonpneumatic tire subjected to obstacle impacts. The tire was mounted on a modified quarter-car test rig, which was built around one of the drums of a 500-horse power chassis dynamometer at Clemson University's International Center for Automotive Research. A series of experiments were conducted using a high-speed camera to capture the event of the rotating tire coming into contact with a cleat attached to the surface of the drum. The resulting video was processed using a two-dimensional digital image correlation algorithm to obtain in-plane radial and tangential deformation fields of the tire. The dynamic mode decomposition algorithm was implemented on the deformation fields to extract the dominant frequencies that were excited in the tire upon contact with the cleat. It was observed that the deformations and the modal frequencies estimated using this method were within a reasonable range of expected values. In general, the results indicate that the method used in this study can be a useful tool in measuring in-plane deformations of rolling tires without the need for additional sensors and wiring.

Coherency Identification in Multimachine Power Systems Using Dynamic Mode Decomposition

2020 ◽  
Author(s):  
A. Saija ◽  
K. Sonam ◽  
F. Kazi ◽  
N. M. Singh
Keyword(s):  
Power Systems ◽  
Dynamic Mode Decomposition ◽  
Dynamic Mode ◽  
Mode Decomposition

Action Recognition Using Dynamic Mode Decomposition for Temporal Representation

2020 ◽  
Author(s):  
Kyle Pawlowski ◽  
Sumit Chakravarty ◽  
Ying Xie ◽  
Arjun Kumar Joginipelly
Keyword(s):  
Action Recognition ◽  
Dynamic Mode Decomposition ◽  
Dynamic Mode ◽  
Temporal Representation ◽  
Mode Decomposition

Oscillatory flow around a vertical wall-mounted cylinder: Dynamic mode decomposition

2021 ◽  
Vol 33 (2) ◽  
pp. 025113
Author(s):  
H. K. Jang ◽  
C. E. Ozdemir ◽  
J.-H. Liang ◽  
M. Tyagi
Keyword(s):  
Oscillatory Flow ◽  
Vertical Wall ◽  
Dynamic Mode Decomposition ◽  
Dynamic Mode ◽  
Mode Decomposition

Modeling the Turbulent Wake Behind a Wall-Mounted Square Cylinder

2020 ◽  
Author(s):  
Christian Amor ◽  
José M Pérez ◽  
Philipp Schlatter ◽  
Ricardo Vinuesa ◽  
Soledad Le Clainche
Keyword(s):  
Proper Orthogonal Decomposition ◽  
Turbulent Wake ◽  
Orthogonal Decomposition ◽  
Dynamic Mode Decomposition ◽  
Dynamic Mode ◽  
Square Cylinder ◽  
Complex Flow ◽  
Reduced Order ◽  
Mode Decomposition ◽  
Proper Orthogonal

Abstract This article introduces some soft computing methods generally used for data analysis and flow pattern detection in fluid dynamics. These techniques decompose the original flow field as an expansion of modes, which can be either orthogonal in time (variants of dynamic mode decomposition), or in space (variants of proper orthogonal decomposition) or in time and space (spectral proper orthogonal decomposition), or they can simply be selected using some sophisticated statistical techniques (empirical mode decomposition). The performance of these methods is tested in the turbulent wake of a wall-mounted square cylinder. This highly complex flow is suitable to show the ability of the aforementioned methods to reduce the degrees of freedom of the original data by only retaining the large scales in the flow. The main result is a reduced-order model of the original flow case, based on a low number of modes. A deep discussion is carried out about how to choose the most computationally efficient method to obtain suitable reduced-order models of the flow. The techniques introduced in this article are data-driven methods that could be applied to model any type of non-linear dynamical system, including numerical and experimental databases.

scholarly journals Analysis of transonic buffet using dynamic mode decomposition

2021 ◽  
Vol 62 (4) ◽  
Author(s):  
Antje Feldhusen-Hoffmann ◽  
Christian Lagemann ◽  
Simon Loosen ◽  
Pascal Meysonnat ◽  
Michael Klaas ◽  
...  
Keyword(s):  
Shock Wave ◽  
Flow Field ◽  
Acoustic Waves ◽  
Feedback Loop ◽  
Measurement Data ◽  
Dynamic Mode Decomposition ◽  
Recirculation Region ◽  
Dynamic Mode ◽  
Mode Decomposition ◽  
Transonic Buffet

AbstractThe buffet flow field around supercritical airfoils is dominated by self-sustained shock wave oscillations on the suction side of the wing. Theories assume that this unsteadiness is driven by a feedback loop of disturbances in the flow field downstream of the shock wave whose upstream propagating part is generated by acoustic waves. High-speed particle-image velocimetry measurements are performed to investigate this feedback loop in transonic buffet flow over a supercritical DRA 2303 airfoil. The freestream Mach number is $$M_{\infty } = 0.73$$ M ∞ = 0.73 , the angle of attack is $$\alpha = 3.5^{\circ }$$ α = 3 . 5 ∘ , and the chord-based Reynolds number is $${\mathrm{Re}}_{c} = 1.9\times 10^6$$ Re c = 1.9 × 10 6 . The obtained velocity fields are processed by sparsity-promoting dynamic mode decomposition to identify the dominant dynamic features contributing strongest to the buffet flow field. Two pronounced dynamic modes are found which confirm the presence of two main features of the proposed feedback loop. One mode is related to the shock wave oscillation frequency and its shape includes the movement of the shock wave and the coupled pulsation of the recirculation region downstream of the shock wave. The other pronounced mode represents the disturbances which form the downstream propagating part of the proposed feedback loop. The frequency of this mode corresponds to the frequency of the acoustic waves which are generated by these downstream traveling disturbances and which form the upstream propagating part of the proposed feedback loop. In this study, the post-processing, i.e., the DMD, is highlighted to substantiate the existence of this vortex mode. It is this vortex mode that via the Lamb vector excites the shock oscillations. The measurement data based DMD results confirm numerical findings, i.e., the dominant buffet and vortex modes are in good agreement with the feedback loop suggested by Lee. Graphic abstract

Investigation of the near wake of a horizontal-axis wind turbine model by dynamic mode decomposition

Energy ◽  
2021 ◽  
pp. 120418
Author(s):  
Chong Sun ◽  
Tian Tian ◽  
Xiaocheng Zhu ◽  
Ouyang Hua ◽  
Zhaohui Du
Keyword(s):  
Wind Turbine ◽  
Horizontal Axis ◽  
Dynamic Mode Decomposition ◽  
Dynamic Mode ◽  
Horizontal Axis Wind Turbine ◽  
Near Wake ◽  
Mode Decomposition ◽  
Turbine Model
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