Dynamic mode decomposition (DMD) is a data-driven, modal decomposition technique that describes spatiotemporal features of high-dimensional dynamic data. The method is equation-free in the sense that it does not require knowledge of the underlying go...
Higher-order dynamic mode decomposition (HODMD) has proved to be an efficient tool for the analysis and prediction of complex dynamical systems described by data-driven models. In the present paper, we propose a realization of HODMD that is based on...
Dynamic mode decomposition with control (DMDc) is a widely used technique for analyzing dynamic systems influenced by external control inputs. It is a recent development and an extension of dynamic mode decomposition (DMD) tailored for input–ou...
Turbulent mixing layers are canonical flow in nature and engineering, and deserve comprehensive studies under various conditions using different methods. In this paper, turbulent mixing layers are investigated using large eddy simulation and dynamic...
Dynamic Mode Decomposition with Control is a powerful technique for analyzing and modeling complex dynamical systems under the influence of external control inputs. In this paper, we propose a novel approach to implement this technique that offers co...
Spatio-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. T...
It is challenging to detect a moving target in the reverberant environment for a long time. In recent years, a kind of method based on low-rank and sparse theory was developed to study this problem. The multiframe data containing the target echo and...
Decomposition-based multi-objective evolutionary algorithms provide a good framework for static multi-objective optimization. Nevertheless, there are few studies on their use in dynamic optimization. To solve dynamic multi-objective optimization prob...
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...
Understanding the spatiotemporal dynamics of atmospheric PM2.5 concentration is highly challenging due to its evolution processes have complex and nonlinear patterns. Traditional mode decomposition methods struggle to accurately capture the mode feat...
Dynamic mode decomposition (DMD) is a popular data-driven framework to extract linear dynamics from complex high-dimensional systems. In this work, we study the system identification properties of DMD. We first show that DMD is invariant under linear...
The proliferation of renewable energy sources and distributed generation systems interfaced to the grid by power electronics systems is forcing us to better understand the issues arising due to the quality of electrical signals generated through thes...
To address the issue of low accuracy in the dynamic modal decomposition (DMD) method used for operational modal analysis (OMA) under noise conditions of aerospace structures, an enhanced identification approach is proposed in this paper, which integr...
Dynamic mode decomposition (DMD) is a powerful data-driven tool for analyzing complex systems that has gained significant attention in various scientific and engineering disciplines. It is suitable for the analysis of flow structures in numerical and...
Dynamic Mode Decomposition (DMD) techniques have risen as prominent feature identification methods in the field of fluid dynamics. Any of the multiple variables of the DMD method allows to identify meaningful features from either experimental or nume...
Traditional centralized modeling and fault detection methods for large-scale industrial processes have limitations, including a significant computational load and reduced performance. To address these issues, this paper proposes a distributed dynamic...
A data-driven analysis method known as dynamic mode decomposition (DMD) approximates the linear Koopman operator on a projected space. In the spirit of Johnson–Lindenstrauss lemma, we will use a random projection to estimate the DMD modes in a reduce...
Traditional flow field modeling methods are limited by high computational complexity, making them difficult to apply in practical engineering. This study applies the Dynamic Mode Decomposition (DMD) method to perform reduced-order modeling of unstead...
To solve the intractable problems of optimal rank truncation threshold and dominant modes selection strategy of the standard dynamic mode decomposition (DMD), an improved DMD algorithm is introduced in this paper. Distinct from the conventional metho...
Blind Source Separation (BSS) is a significant field of study in signal processing, with many applications in various fields such as audio processing, speech recognition, biomedical signal analysis, image processing and communication systems. Traditi...
Transient analyses of liquid-fueled molten salt reactors involve strong coupling among neutronics, delayed neutron precursor transport, thermal–hydraulics, and solid heat transfer, leading to high computational costs for repeated high-fidelity...
The majority of observations and criteria related to brain injuries predominantly focus on acceleration and forces, leaving the understanding of the brain in the frequency domain relatively limited. The impact of an injury can be more profound when c...
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 DM...
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...
Sweeping jet actuators (SJAs) are promising for active flow control in aerospace systems, but integrating actuator-resolved unsteady CFD into full-configuration simulations is often impractical due to small geometric scales and
Dynamic mode decomposition (DMD) is essentially a hybrid algorithm based on mode decomposition and singular value decomposition, and it inevitably inherits the drawbacks of these two algorithms, including the selection strategy of truncated rank orde...
A necessity in the design of a path planning algorithm is to account for the environment. If the movement of the mobile robot is through a dynamic environment, the algorithm needs to include the main constraint: real-time collision avoidance. This ki...
By aiming at the common distributed random dynamic loads in engineering practice, an equivalent identification method that is based on K–L decomposition and sparse representation is proposed. Considering that the establishment of a probability...
This study investigates the connection between coherent structures in the flow around a vehicle and the aerodynamic forces acting on its body. Dynamic Mode Decomposition (DMD) was applied to analyze the flow field of a squareback Ahmed body at
As there are more and more available Web services with the same or similar functionalities but different Quality of Service (QoS), the challenge of QoS-aware service composition is to efficiently select appropriate component services to achieve maxim...
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 visio...
Nitrogen compression requires centrifugal compressors to operate under relatively high ambient pressure. However, the internal instability characteristics of compressors handling high-density working fluids remain unclear. Therefore, this study emplo...
Ecosystem respiration (Reco) represents a major component of the global carbon cycle. An accurate estimation of Reco dynamics is necessary for a better understanding of ecosystem–climate interactions and the impact of climate extremes on ecosys...
The dynamic mode decomposition serves as a useful tool for the coherent structure extraction of the complex flow fields with characteristic frequency identification, but the phase information of the flow modes is paid less attention to. In this study...
Cell-Free Massive Multiple-Input–Multiple-Output (CF-MIMO) systems have transformed the landscape of wireless communication, offering unparalleled enhancements in Spectral Efficiency and interference mitigation. Nevertheless, the large-scale de...
The high dimensions and governing non-linear dynamics in wind farm systems make the design of numerical optimal controllers computationally expensive. A possible pathway to circumvent this challenge lies in finding reduced order models which can accu...
This paper provides the theoretical foundation for the approximation of the regions of attraction in hyperbolic and polynomial systems based on the eigenfunctions deduced from the data-driven approximation of the Koopman operator. In addition, it sho...
Background: In the field of precision nutrition, predicting high-dimensional metabolic response to diet and identifying groups of differential responders are two highly desirable steps towards developing tailored dietary strategies. However, proper d...
As carbon intensity (CI) can better reflect the coordinated relationship between carbon emissions and economic growth, the related research has gradually increased in recent years. To better explore the influence of production technology and spatial...
Generating high-quality high-dynamic-range (HDR) images in dynamic scenes remains a challenging task. Recently, Transformers have been introduced into HDR imaging and have demonstrated superior performance over traditional convolutional neural networ...
An oscillating flow past a structure represents a complex, high-dimensional, and nonlinear flow phenomenon, which can lead to the failure of structures due to material fatigue or constraint relaxation. In order to better understand flow-induced vibra...
Accurate forecasting of sediment accumulation under extreme hydrodynamic forcing is essential for coastal engineering design and harbor management. This study evaluates the performance of Dynamic Mode Decomposition (DMD), optimized DMD (optDMD), and...
Accurate and reliable runoff forecasting is of great significance for hydropower station operation and watershed water resource allocation. However, various complex factors, such as climate conditions and human activities, constantly affect the forma...
Infrared-visible image fusion quality is critical for nighttime perception in autonomous driving and surveillance but suffers severe degradation under extreme low-light conditions, including irreversible texture loss in visible images, thermal bounda...
Intelligent surveillance systems enable secured visibility features in the smart city era. One of the major models for pre-processing in intelligent surveillance systems is known as saliency detection, which provides facilities for multiple tasks suc...
In this paper, a novel data-driven approach for the development of soft sensors (SSs) for multi-step-ahead prediction of industrial process variables is proposed. This method is based on the recent developments in Koopman operator theory and dynamic...
I propose a data-driven surrogate model for the In-Medium Similarity Renormalization Group (IMSRG) method using Dynamic Mode Decomposition (DMD). First, the Magnus formulation of the IMSRG is leveraged to represent the unitary transformation of many-...
With the development of digital power systems, the establishment of digital twin models for transformers is of great significance in enhancing power system stability. Consequently, greater demands are placed on the real-time performance and accuracy...
The purpose of this paper is the identification of high-fidelity digital twin data models from numerical code outputs by non-intrusive techniques (i.e., not requiring Galerkin projection of the governing equations onto the reduced modes basis). In th...
The quest for the physical mechanisms underlying turbulent high-speed jet flows is underpinned by the extraction of spatio-temporal coherent structures from their flow fields. Experimental measurements to enable data decomposition need to comprise ti...
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