scholarly journals Extracting Reproducible Time-Resolved Resting State Networks Using Dynamic Mode Decomposition

2019 ◽  
Vol 13 ◽  
Author(s):  
James M. Kunert-Graf ◽  
Kristian M. Eschenburg ◽  
David J. Galas ◽  
J. Nathan Kutz ◽  
Swati D. Rane ◽  
...  
Keyword(s):  
Resting State ◽  
Dynamic Mode Decomposition ◽  
Dynamic Mode ◽  
Resting State Networks ◽  
Time Resolved ◽  
Mode Decomposition

scholarly journals Extracting Reproducible Time-Resolved Resting State Networks using Dynamic Mode Decomposition

2018 ◽  
Author(s):  
James M. Kunert-Graf ◽  
Kristian M. Eschenburg ◽  
David J. Galas ◽  
J. Nathan Kutz ◽  
Swati D. Rane ◽  
...  
Keyword(s):  
Resting State ◽  
Dynamic Properties ◽  
Brain Regions ◽  
Dynamic Mode Decomposition ◽  
Single Subject ◽  
Dynamic Mode ◽  
Resting State Networks ◽  
Time Resolved ◽  
Mode Decomposition ◽  
Human Connectome Project

AbstractResting state networks (RSNs) extracted from functional magnetic resonance imaging (fMRI) scans are believed to reflect the intrinsic organization and network structure of brain regions. Most traditional methods for computing RSNs typically assume these functional networks are static throughout the duration of a scan lasting 5–15 minutes. However, they are known to vary on timescales ranging from seconds to years; in addition, the dynamic properties of RSNs are affected in a wide variety of neurological disorders. Recently, there has been a proliferation of methods for characterizing RSN dynamics, yet it remains a challenge to extract reproducible time-resolved networks. In this paper, we develop a novel method based on dynamic mode decomposition (DMD) to extract networks from short windows of noisy, high-dimensional fMRI data, allowing RSNs from single scans to be resolved robustly at a temporal resolution of seconds. We demonstrate this method on data from 120 individuals from the Human Connectome Project and show that unsupervised clustering of DMD modes discovers RSNs at both the group (gDMD) and the single subject (sDMD) levels. The gDMD modes closely resemble canonical RSNs. Compared to established methods, sDMD modes capture individualized RSN structure that both better resembles the population RSN and better captures subject-level variation. We further leverage this time-resolved sDMD analysis to infer occupancy and transitions among RSNs with high reproducibility. This automated DMD-based method is a powerful tool to characterize spatial and temporal structures of RSNs in individual subjects.

scholarly journals Time-resolved denoising using model order reduction, dynamic mode decomposition, and kalman filter and smoother

2020 ◽  
Vol 7 (2) ◽  
pp. 469-487
Author(s):  
Mojtaba F. Fathi ◽  
◽  
Ahmadreza Baghaie ◽  
Ali Bakhshinejad ◽  
Raphael H. Sacho ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Model Order Reduction ◽  
Order Reduction ◽  
Dynamic Mode Decomposition ◽  
Dynamic Mode ◽  
Model Order ◽  
Time Resolved ◽  
Mode Decomposition

scholarly journals Dynamic mode decomposition of resting-state and task fMRI

NeuroImage ◽  
2019 ◽  
Vol 194 ◽  
pp. 42-54 ◽  
Author(s):  
Jeremy Casorso ◽  
Xiaolu Kong ◽  
Wang Chi ◽  
Dimitri Van De Ville ◽  
B.T. Thomas Yeo ◽  
...  
Keyword(s):  
Resting State ◽  
Dynamic Mode Decomposition ◽  
Dynamic Mode ◽  
Mode Decomposition ◽  
And Task

Dynamic Mode Decomposition for Extracting Cycle-to-Cycle Variation of SIDI Engine In-Cylinder Flow Under Motoring Condition

2020 ◽  
Author(s):  
Mengqi Liu ◽  
Fengnian Zhao ◽  
Xuesong Li ◽  
Min Xu ◽  
David L. S. Hung
Keyword(s):  
Flow Field ◽  
Direct Injection ◽  
Dynamic Mode Decomposition ◽  
Dynamic Mode ◽  
Field Variation ◽  
Dynamic Features ◽  
Time Resolved ◽  
Cycle Variation ◽  
Mode Decomposition ◽  
Cylinder Flow

Abstract Cycle-to-cycle variation (CCV) of in-cylinder flow strongly affects the performance and efficiency of spark ignition direct injection (SIDI) engines. In order to achieve a precise flow control inside the engine, the underlying dynamic features of flow field CCV must be thoroughly investigated. In this work, large-eddy simulations (LES) with 50 consecutive cycles are employed for high fidelity numerical realizations of engine flow under motoring condition. To supplement the numerical analysis, time-resolved particle image velocimetry (PIV) measurements are also conducted in several cutting planes. Although the velocity root mean square (RMS) is calculated to quantify the cyclic variation intensity of simulation and experiment results, some important dynamic characteristics cannot be observed directly from velocity data. Therefore, dynamic mode decompositions (DMD), which is a widely used modal decomposition algorithm on fluid study, is used to decompose flow fields into modes with specific frequencies and provide growth rates of corresponding flow structures. This spectral information of in-cylinder flow field is ponderable for uncovering dynamic features of engine CCV. In this study, DMD algorithm is applied on both LES and PIV datasets. The frequency and growth rate differences are employed to elucidate the CCV feature deviations captured by LES and PIV. This research provides a guideline for extracting engine flow field cyclic variability feature using DMD algorithm. Based on the discussion for spectral features and potential sources of flow field variation, the capability of LES to capture CCV features is evaluated. The DMD spectrum differences between PIV and LES can guide the boundary condition perturbations used for simulation fidelity improvements.

Analysis of synthetic jet PIV experiment based on dynamic mode decomposition

2020 ◽  
Vol 34 (14n16) ◽  
pp. 2040103
Author(s):  
Zhi-Xian Ye ◽  
Yi-Yang Jiang ◽  
Ze-Nan Tian ◽  
Shao-Chang Mo ◽  
Yuan-Qi Fang ◽  
...  
Keyword(s):  
Flow Field ◽  
Field Research ◽  
Input Voltage ◽  
Synthetic Jet ◽  
Dynamic Mode Decomposition ◽  
Dynamic Mode ◽  
Vibration Displacement ◽  
Time Resolved ◽  
Mode Decomposition ◽  
Flow Field Characteristics

Synthetic Jet Actuator (SJA) works cyclically with the directionally transportation of fluid near the exit, and the paper presents the performance of a loudspeaker driven SJA in static flow field. Time-Resolved Particle Image Velocimetry (TR-PIV) system is used to measure the flow field characteristics near the SJA slot exit with input voltage changing, and the flow field snapshots obtained by TR-PIV are modality analyzed by Dynamic Mode Decomposition (DMD) method. The PIV experiments show that by varying input voltage at fixed oscillating frequency, the loudspeaker diaphragm vibration displacement is the parameter that affects the jet velocity and the performance of the SJA. The traveling vortex vanishes at high voltage due to the interaction between vortex structures and the synthetic main jet. In DMD method, the first three-order modes can characterize the main information of the original flow field with reverting the flow field snapshot sequence. It indicates that the DMD method is applicable in the SJA flow field research and the reduced order model can effectively simplify the analysis of flow field.

scholarly journals Dynamic Denoising and Gappy Data Reconstruction Based on Dynamic Mode Decomposition and Discrete Cosine Transform

2018 ◽  
Vol 8 (9) ◽  
pp. 1515 ◽  
Author(s):  
Mojtaba Fathi ◽  
Ali Bakhshinejad ◽  
Ahmadreza Baghaie ◽  
Roshan D’Souza
Keyword(s):  
Discrete Cosine Transform ◽  
Duffing Oscillator ◽  
Reconstruction Error ◽  
Decay Rates ◽  
Dynamic Mode Decomposition ◽  
Dynamic Mode ◽  
Time Resolved ◽  
Cosine Transform ◽  
Mode Decomposition ◽  
Double Gyre

Dynamic Mode Decomposition (DMD) is a data-driven method to analyze the dynamics, first applied to fluid dynamics. It extracts modes and their corresponding eigenvalues, where the modes are spatial fields that identify coherent structures in the flow and the eigenvalues describe the temporal growth/decay rates and oscillation frequencies for each mode. The recently introduced compressed sensing DMD (csDMD) reduces computation times and also has the ability to deal with sub-sampled datasets. In this paper, we present a similar technique based on discrete cosine transform to reconstruct the fully-sampled dataset (as opposed to DMD modes as in csDMD) from sub-sampled noisy and gappy data using l 1 minimization. The proposed method was benchmarked against csDMD in terms of denoising and gap-filling using three datasets. The first was the 2-D time-resolved plot of a double gyre oscillator which has about nine oscillatory modes. The second dataset was derived from a Duffing oscillator. This dataset has several modes associated with complex eigenvalues which makes them oscillatory. The third dataset was taken from the 2-D simulation of a wake behind a cylinder at Re = 100 and was used for investigating the effect of changing various parameters on reconstruction error. The Duffing and 2-D wake datasets were tested in presence of noise and rectangular gaps. While the performance for the double-gyre dataset is comparable to csDMD, the proposed method performs substantially better (lower reconstruction error) for the dataset derived from the Duffing equation and also, the 2-D wake dataset according to the defined reconstruction error metrics.

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.

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