Ethereum is one of the most popular blockchain platforms with a high number of adoption in the blockchain world today. Ethereum token (ERC-20) can tokenize any real-world object while it is also possible to exchange t...
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Software process definition is a complex, time consuming and error prone activity. Such activity can be facilitated by a process reuse strategy. This strategy can be implemented through process line and components str...
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A semi-analytical finite element method(SAFEM),based on the two-scale asymptotic homogenization method(AHM)and the finite element method(FEM),is implemented to obtain the effective properties of two-phase fiber-reinfo...
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A semi-analytical finite element method(SAFEM),based on the two-scale asymptotic homogenization method(AHM)and the finite element method(FEM),is implemented to obtain the effective properties of two-phase fiber-reinforced composites(FRCs).The fibers are periodically distributed and unidirectionally aligned in a homogeneous *** framework addresses the static linear elastic micropolar problem through partial differential equations,subject to boundary conditions and perfect interface contact *** mathematical formulation of the local problems and the effective coefficients are presented by the *** local problems obtained from the AHM are solved by the FEM,which is denoted as the *** numerical results are provided,and the accuracy of the solutions is analyzed,indicating that the formulas and results obtained with the SAFEM may serve as the reference points for validating the outcomes of experimental and numerical computations.
In recent years, digital technologies have been used to support discussions about the city and also to involve citizens in participatory public processes. However, despite the widespread use of social media platforms,...
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We present three different approaches for multi-step prediction using the fuzzy Markov predictor (FMP). The FMP is a modification of the hidden Markov model in order to enable it to predict numerical values. In the fi...
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We present three different approaches for multi-step prediction using the fuzzy Markov predictor (FMP). The FMP is a modification of the hidden Markov model in order to enable it to predict numerical values. In the first approach, the one normally used in neural networks, past predictions are used as input for the next predictions. The second and third approaches follow the standard way of making multi-step prediction in a dynamic Bayesian network. FMP using these three approaches is applied to the task of monthly electric load multi-step forecasting and successfully compared with two Kalman filter models, BATS and STAMP, and two traditional forecasting methods, Box-Jenkins and Winters exponential smoothing.
The impacts incurred by floods regularly affect the planets population, inflicting social and economic problems. Optimal control strategies based on reservoir management may aid in controlling floods and mitigating th...
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The impacts incurred by floods regularly affect the planets population, inflicting social and economic problems. Optimal control strategies based on reservoir management may aid in controlling floods and mitigating the resulting damage. To this end, an accurate dynamic representation of water systems is needed. In practice, flood control strategies rely on hydrological forecasting models obtained fromconceptual or data-drivenmethods. Encouraged by recent works, this research proposes a novel surrogate model for water flow in a river channel based on physics-informed neural networks (PINNs). This approach achieved promising results regarding the assimilation of real-data measurements and the parameter identification of differential equations that govern the underlying dynamics. This article investigates PINN performance in a simulated environment built directly from a configuration of the Saint-Venant equations. The objective is to create a suitable model with high prediction accuracy and scientifically consistent behavior for use in real-Time applications. The experiments revealed promising results for hydrological modeling and presented alternatives to solve the main challenges found in conventional methods while assisting in synthesizing real-world representations. Impact Statement-The research seeks to contribute to the hydrological modeling area with a surrogate model based on physicsinformed neural networks (PINNs) to water flow in a watershed. In practice, thesemodels use conceptual or *** models to reach the precision provided by themethodology use large numbers of physical parameters. These parameters can demand deep knowledge about the environment and are possibly hard to identify in a complex basin. On the other hand, while data-driven methods do not require such knowledge about the dynamic system, they depend on a reliable and useful database to guarantee the accuracy of system *** introduce PINNs as a viable solution for
We present a new hybrid system that merges fuzzy logic with dynamic Bayesian networks (DBN's): the fuzzy hidden Markov predictor. It is a modification of the hidden Markov model, a particular case of DBN's, in...
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We present a new hybrid system that merges fuzzy logic with dynamic Bayesian networks (DBN's): the fuzzy hidden Markov predictor. It is a modification of the hidden Markov model, a particular case of DBN's, in order to enable it to predict continuous values of a time series. A DBN is a Bayesian network that represents a temporal probability model. This hybrid system is applied to the task of monthly electric load single-step forecasting and successfully compared with three regression-by-discretization systems, two fuzzy hybrid systems, two Kalman filter models, and Box-Jenkins and Winters exponential smoothing. The employed time series present a sudden significant changing behavior at their last years, as it occurs in an energy rationing.
This paper describes a co-evolutionary algorithm for generating simple spatially oriented tactics and considers whether students can learn better by playing against co-evolved opponents or by playing against an expert...
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Over the last years, the engine calibration task has mostly been conducted based on the engineers' knowledge. As a result, considering the complexity of modern engines, finding the most suitable configuration for ...
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Reservoir Computing (RC) is a computational model in which a trained readout layer interprets the dynamics of a component called a reservoir that is excited by external input stimuli. The reservoir is often constructe...
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