Higher order dynamic mode decomposition: From fluid dynamics to heart disease analysis

Groun, Nourelhouda; Villalba-Orero, María; Lara‐Pezzi, Enrique; Valero, Eusebio; Garicano‐Mena, Jesús; Clainche, Soledad Le

doi:10.1016/j.compbiomed.2022.105384

Cited by 16 publications

(7 citation statements)

References 42 publications

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“…These images are then processed to calculate the intrinsic and extrinsic parameters of the camera used. More details about the estimation of geometric parameters for a single camera can be found in various publications [ 22 , 23 ]. As shown in Figure 2 , the recorded image is presented before and after calibration, which was performed using the MATLAB ® Camera Calibrator App (version 2022b).…”

Section: Methodsmentioning

confidence: 99%

Camera-Based Dynamic Vibration Analysis Using Transformer-Based Model CoTracker and Dynamic Mode Decomposition

Cheng,

de Groot,

Xie

et al. 2024

Sensors

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Accelerometers 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. With rapid advancements in computer vision and deep learning, research into tracking the motion of individual pixels with vision cameras has increased. The recently developed CoTracker, a transformer-based model, has demonstrated excellence in motion tracking, yet its performance in measuring structural vibrations has not been fully explored. This paper investigates the efficacy of the CoTracker model in extracting full-field structural vibrations using cameras. It is initially applied to capture the dense point movements in video sequences of a cantilever beam recorded using a high-speed camera. Subsequently, modal analysis using delay-embedding dynamic mode decomposition (DMD) is conducted to extract modal parameters including natural frequencies, damping ratios, and mode shapes. The results, benchmarked against those from a reference accelerometer and the Finite Element Method (FEM) result, demonstrate CoTracker’s high potential for general applicability in structural vibration measurements.

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Section: Methodsmentioning

confidence: 99%

Camera-Based Dynamic Vibration Analysis Using Transformer-Based Model CoTracker and Dynamic Mode Decomposition

Cheng,

de Groot,

Xie

et al. 2024

Sensors

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“…Originally proposed by the fluid mechanics community to decompose complex flow fields, the DMD is a relatively new data-driven method for modal analysis of time-series acquisitions. 17 More broadly, it has been successfully demonstrated in various fields ranging from power systems analysis 15 to medical imaging, 18,19 to identify dominant spatiotemporal coherent structures. DMD is a data-driven method, which relies on measurements (also called snapshots) collected from a dynamic system.…”

Section: Dmdmentioning

confidence: 99%

Dynamic mode decomposition of dynamic MRI for assessment of pulmonary ventilation and perfusion

Ilicak

Ozdemir

Zapp

et al. 2023

Magnetic Resonance in Med

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To introduce dynamic mode decomposition (DMD) as a robust alternative for the assessment of pulmonary functional information from dynamic non-contrast-enhanced acquisitions.Methods: Pulmonary fractional ventilation and normalized perfusion maps were obtained using DMD from simulated phantoms as well as in vivo dynamic acquisitions of healthy volunteers at 1.5T. The performance of DMD was compared with conventional Fourier decomposition (FD) and matrix pencil (MP) methods in estimating functional map values. The proposed method was evaluated based on estimated signal amplitude in functional maps across varying number of measurements. Results: Quantitative assessments performed on phantoms and in vivo measurements indicate that DMD is capable of successfully obtaining pulmonary functional maps. Specifically, compared to FD and MP methods, DMD is able to reduce variations in estimated amplitudes across different number of measurements. This improvement is evident in the fractional ventilation and normalized perfusion maps obtain from phantom simulations with frequency variations and noise, as well as in the maps obtained from in vivo measurements. Conclusions:A robust method for accurately estimating pulmonary ventilation and perfusion related signal changes in dynamic acquisitions is presented. The proposed method uses DMD to obtain functional maps reliably, while reducing amplitude variations caused by differences in number of measurements.

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“…HODMD is a widely used algorithm in the fluid mechanics community, and its robustness and accuracy has been tested in a wide range of applications, including the analysis of complex flows (i.e., noisy experiments [47,50], transitional flows [57], turbulent flows [48,56]) to create reduced order models (ROMs) [55,58,59], to extract patterns and study flow physics [43,[60][61][62], or to calculate areas of structural sensitivity for applications in flow control [63]. The limits of applicability for this algorithm have also been successfully tested in other fields such as electrocardiograms for disease detection [64]. However, in all the studies carried out in the literature, the analysis was limited to databases including the three components of the velocity and, in some cases, the pressure field.…”

Section: Hodmd In Combustion Flowsmentioning

confidence: 99%

Higher Order Dynamic Mode Decomposition to Model Reacting Flows

Corrochano

D’Alessio

Parente

et al. 2023

New Achievements in Unmanned Systems

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This work presents a new application of higher order dynamic mode decomposition (HODMD) for the analysis of reactive flows. Due to the high complexity of the data analysed, consisting of more than 80 variables (i.e., temperature and chemically reacting species) a new extension of HODMD has been developed combining the multi-dimensional HODMD algorithm with classical preprocessing techniques generally used in machine learning analyses, such as principal component analysis (PCA). This new methodology has proved to be suitable to identify the main patterns driving the main dynamics of the flow, as well as to develop reduced order models grounded in physical principles. The new algorithm was tested by means of a database obtained from a Computational Fluid Dynamics simulation of an axy-symmetric time varying non-premixed co-flow nitrogen-diluted methane flame, carried out by means of a detailed kinetic mechanism. Different dynamics were identified, and they were associated to the different variables of the reactive flow analysed. Also, this algorithm has been coupled with an additional feature selection step carried out via PCA and varimax rotation. The results that were obtained by coupling PCA with varimax rotation show the outstanding capabilities of this novel methodology to compress original databases as function of the different preprocessing techniques that are used.

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Higher order dynamic mode decomposition: From fluid dynamics to heart disease analysis

Cited by 16 publications

References 42 publications

Camera-Based Dynamic Vibration Analysis Using Transformer-Based Model CoTracker and Dynamic Mode Decomposition

Camera-Based Dynamic Vibration Analysis Using Transformer-Based Model CoTracker and Dynamic Mode Decomposition

Dynamic mode decomposition of dynamic MRI for assessment of pulmonary ventilation and perfusion

Higher Order Dynamic Mode Decomposition to Model Reacting Flows

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