Nucleation and propagation of excitation fronts in self-excited systems

Shiroky, I.B.; Papangelo, Antonio; Hoffmann, Norbert; Gendelman, Oleg

doi:10.1016/j.physd.2019.132176

Cited by 8 publications

(8 citation statements)

References 33 publications

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“…Sequence learning, recurrent neural networks (RNNs) is one of the dominant sources to find out temporal correlations and performs well to decide the present state-based historical cues in time-series prediction/data [49]. The RNNs units consist of hidden state cells, which contribute to future events by using temporal cues with high outcomes as compared to traditional techniques [50].…”

Section: B Sequence Learningmentioning

confidence: 99%

Short-Term Energy Forecasting Framework Using an Ensemble Deep Learning Approach

2021

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Industrial and building sectors demand efficient smart energy strategies, techniques of optimization, and efficient management for reducing global energy consumption due to the increasing world population. Nowadays, various artificial intelligence (AI) based methods are utilized to perform optimal energy forecasting, different simulation tools, and engineering methods to predict future demand based on historical data. Nevertheless, nonlinear energy demand modeling is still unfledged for a better solution to handle short-term and long-term dependencies and avoid static nature because it is purely on historical datadriven. In this paper, we propose an ensemble deep learning-based approach to predict and forecast energy demand and consumption by using chronological dependencies. Our system initially processes the data, cleaning, normalization, and transformation to ensure the model performance. Furthermore, the preprocess data is fed to propose the ensemble model to extract hybrid discriminative features by using convolution neural network (CNN), stacked, and bi-directional long-short term memory (LSTM) architectures. We trained our proposed system on the historical data to forecast the energy demand and consumption with a different time interval. In the proposed technique, we utilized the concept of active learning based on moving windows to ensure and improve the prediction performance of the system. The proposed system could be applicable to employ energy consumption in industrial and building sectors to demonstrate their significance and effectiveness. We evaluated the proposed system by using benchmark, residential UCI, and local Korean commercial building datasets. We conducted different extensive experimentation to show the error rate and used various kinds of evaluation matrices, which indicate the lower error rate of the proposed system.

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Section: B Sequence Learningmentioning

confidence: 99%

Short-Term Energy Forecasting Framework Using an Ensemble Deep Learning Approach

2021

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“…We consider a model of two linearly coupled identical bi-stable VDP oscillators similar to models previously discussed in [11,12,15,16,22]:…”

Section: Analysis Of the Model In Physical Coordinatesmentioning

confidence: 99%

“…The fast oscillations are averaged out from (4) [23,12], and one obtains the following set of equations for the slow flow:…”

Section:  =mentioning

confidence: 99%

“…In particular, it is easy to formulate a bi-stable variation of the VDP oscillator with two attractors: a stable fixed point and a stable limit cycle separated by an unstable limit cycle [11]. Discrete chains of bi-stable van der Pol oscillators were studied in [12,11] where basic regimes of localization and front propagation were demonstrated. .…”

Section: Introductionmentioning

confidence: 99%

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Modal synchronization of coupled bistable van der Pol oscillators

Shiroky

Gendelman

2021

Chaos, Solitons & Fractals

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“…For the chosen parameters, the investigated system becomes linearly unstable under small oscillations above a critical friction coefficient, but stick-slip limit cycle oscillations continue to exist (when l s /l d [ 1) even for smaller velocities. In multiple degrees of freedom system, vibration localization may occur in certain regions of the governing parameters, where multiple stable co-existing solutions exist [34,37]. Although several authors have attempted to study experimentally the nonlinearity of FIV, most of the literature results focus on lumped systems (typically a concentrated mass connected to springs and dampers and generally sliding on a substrate).…”

Section: Introductionmentioning

confidence: 99%

Numerical and experimental analysis of the bi-stable state for frictional continuous system

et al. 2020

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Unstable friction-induced vibrations are considered an annoying problem in several fields of engineering. Although several theoretical analyses have suggested that friction-excited dynamical systems may experience sub-critical bifurcations, and show multiple coexisting stable solutions, these phenomena need to be proved experimentally and on continuous systems. The present work aims to partially fill this gap. The dynamical response of a continuous system subjected to frictional excitation is investigated. The frictional system is constituted of a 3D printed oscillator, obtained by additive manufacturing that slides against a disc rotating at a prescribed velocity. Both a finite element model and an experimental setup has been developed. It is shown both numerically and experimentally that in a certain range of the imposed sliding velocity the oscillator has two stable states, i.e. steady sliding and stick–slip oscillations. Furthermore, it is possible to jump from one state to the other by introducing an external perturbation. A parametric analysis is also presented, with respect to the main parameters influencing the nonlinear dynamic response, to determine the interval of sliding velocity where the oscillator presents the two stable solutions, i.e. steady sliding and stick–slip limit cycle.

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Nucleation and propagation of excitation fronts in self-excited systems

Cited by 8 publications

References 33 publications

Short-Term Energy Forecasting Framework Using an Ensemble Deep Learning Approach

Short-Term Energy Forecasting Framework Using an Ensemble Deep Learning Approach

Modal synchronization of coupled bistable van der Pol oscillators

Numerical and experimental analysis of the bi-stable state for frictional continuous system

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