The communication system is essential for the devices used in IoT. Nevertheless, the consumption caused by transmitting and receiving data is a concern in many scenarios. That is due to these devices commonly having l...
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This paper addresses the problem of input-to-state stability (ISS) and stabilization of linear ordinary differential equations (ODEs) coupled with a system of homogeneous linear hyperbolic partial differential equatio...
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Accurate displacement calculation in the laminated iron cores of electric machines is important for the accurate prediction of noise generation in the electric machines. The authors proposed a modeling method of displ...
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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 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
A molecular imprinted polymer-based optical sensor is proposed to detect methanol. The sensor is tested with different concentrations of methanal vapour for sensitivity and ethanol for selectivity. The result shows a ...
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This paper addresses the problem of input-to-state stability (ISS) and stabilization of linear ordinary differential equations (ODEs) coupled with a system of homogeneous linear hyperbolic partial differential equatio...
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ISBN:
(数字)9798350316339
ISBN:
(纸本)9798350316346
This paper addresses the problem of input-to-state stability (ISS) and stabilization of linear ordinary differential equations (ODEs) coupled with a system of homogeneous linear hyperbolic partial differential equations (PDEs) through the boundaries. First, a Lyapunov result characterizing the ISS property for finite-dimensional systems is extended to deal with coupled ODE and PDE systems. The proposed ISS condition is then applied to derive stability and stabilization conditions in terms of linear matrix inequality constraints assuming magnitude bounded disturbances at the boundaries. Two convex optimization problems are also proposed in order to obtain either an optimized reachable set estimate or a boundary controller that minimizes the disturbance effects on the ${\mathcal{L}}_{2} \times \mathbb{R}$-norm of the system states. Numerical examples illustrate the potential of the proposed approach.
Modern industrial processes are hierarchically controlled in two levels. A continuous time control logic, such as Proportional integral derivative (PID) control, is implemented at the lower level to assure that the sy...
Modern industrial processes are hierarchically controlled in two levels. A continuous time control logic, such as Proportional integral derivative (PID) control, is implemented at the lower level to assure that the system behavior attains specifications of transient and permanent regimes, while at the higher level, a supervisory control ensures safety specifications for routine and non-routine operations. In general, both control strategies are projected without considering that the system components can suffer faults that can modify their expected behavior. Thus, a fault diagnosis strategy is fundamental to quickly indicate a fault event occurrence that can cause a deviation from the expected system behavior. In this paper, a discrete event diagnosis strategy is exploited for a two-level controlled tank liquid level system. To do so, we consider that a fault event can occur in the valve, which causes the valve to be stuck closed. We propose a modeling strategy for the valve that considers the fault consequences and its interaction with the remaining components. We show that this approach does not interfere with the control project, which allows the diagnoser synthesis to be achieved and implemented in an already-running controlled system.
The communication system is essential for the devices used in IoT. Nevertheless, the consumption caused by transmitting and receiving data is a concern in many scenarios. That is due to these devices commonly having l...
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ISBN:
(数字)9798350373011
ISBN:
(纸本)9798350373028
The communication system is essential for the devices used in IoT. Nevertheless, the consumption caused by transmitting and receiving data is a concern in many scenarios. That is due to these devices commonly having limited energy and computational capacities. Transmission of redundant or irrelevant samples frequently wastes the device’s resources. Furthermore, Storing a large amount of redundant data could consume storage space and not offer any benefit. Data Compression (DC) methods are a potential solution. DC could reduce communication usage and storage demand. This research proposes the Training Swing Door Trending (TSDT) for being implemented in IoT Devices. TSDT is a new algorithm that improves the classic Swing Door Trending (STD). They represent the data by trend lines and have a constant computational complexity. TSDT has a training step for the automatic configuration of its parameters. This article additionally presents the Compression Factor (C-Score), a new quality metric to analyze the compression results in lossy DC methods. C-Score takes as a basis the F-Score, a measure of predictive performance. The C-Score uses the Compression and Error metrics to evaluate the compression performance in Lossy Algorithms.
A factor that must be taken into account in the modern design of power electronics converters is the reliability of dc-link capacitors, but traditional condition monitoring methods require extra hardware that increase...
A factor that must be taken into account in the modern design of power electronics converters is the reliability of dc-link capacitors, but traditional condition monitoring methods require extra hardware that increase the overall cost. This work proposes and experimentally evaluates a condition monitoring method for electrolytic dc-link capacitors in three-phase front-end diode rectifier motor drives which does not require hardware modifications. The presented method uses an artificial neural network (ANN) to predict the capacitance value of the dc-link capacitor bank. Based on time-domain parameters, the ANN is trained and evaluated using an error analysis to determine the effectiveness of the proposed method with a printed circuit board capacitor jig and aged samples. The proposed method is evaluated in several operating conditions and the results show that the prediction errors are less than 2.4% and that the method is able to monitor the degradation level of the dc-link capacitor bank.
This article presents the mathematical model of a 2 KW DBI (Dual Buck Inverter) converter that can be used in renewable energy applications. The inverter operating modes are described with the equations that model the...
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