In this paper, we develop a distributionally robust model predictive control framework for the control of wind farms with the goal of power tracking and mechanical stress reduction of the individual wind turbines. We ...
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As one of the critical infrastructures, the safety and reliability of the smart grid are directly associated with the development and stability of society. However, studies have shown that the power grid is at risk wh...
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Metamaterial Antenna is a subclass of antennas that makes use of metamaterial to improve *** antennas can overcome the bandwidth constraint associated with tiny *** learning is receiving a lot of interest in optimizin...
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Metamaterial Antenna is a subclass of antennas that makes use of metamaterial to improve *** antennas can overcome the bandwidth constraint associated with tiny *** learning is receiving a lot of interest in optimizing solutions in a variety of *** learning methods are already a significant component of ongoing research and are anticipated to play a critical role in today’s *** accuracy of the forecast is mostly determined by the model *** purpose of this article is to provide an optimal ensemble model for predicting the bandwidth and gain of the Metamaterial *** Vector Machines(SVM),Random Forest,K-Neighbors Regressor,and Decision Tree Regressor were utilized as the basic *** Adaptive Dynamic Polar Rose Guided Whale Optimization method,named AD-PRS-Guided WOA,was used to pick the optimal features from the *** suggested model is compared to models based on five variables and to the average ensemble *** findings indicate that the presented model using Random Forest results in a Root Mean Squared Error(RMSE)of(0.0102)for bandwidth and RMSE of(0.0891)for *** is superior to other models and can accurately predict antenna bandwidth and gain.
In this paper, we present a novel distributed algorithm (herein called MaxCUCL) designed to guarantee that max−consensus is reached in networks characterized by unreliable communication links (i.e., links suffering fr...
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Output voltage regulation in a boost converter with Constant Power Load (CPL) can be obtained by means of sliding-mode control with a linear estimation loop of the output power. The estimation procedure is a simple in...
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Output voltage regulation in a boost converter with Constant Power Load (CPL) can be obtained by means of sliding-mode control with a linear estimation loop of the output power. The estimation procedure is a simple integrator of the output voltage error and confers an adaptive nature on the switching regulator. Two switching surfaces of quadratic type are considered to induce the sliding motions, and the corresponding conditions for the existence of both sliding mode and stability of the equilibrium point are derived. The resulting controller can be implemented using simple analog electronics requiring operational amplifier-based circuits in both cases plus a multiplier in one case and a multiplier and a divider in the other case. PSIM and MATLAB simulations show a fast recovery and zero steady-state output voltage error in response to large-signal disturbances in the output voltage and the load power.
Changes in coal seam hardness cause fluctuations in the feed resistance at the drill bit during the drilling process, leading to unstable feeding speed. This paper proposes a robust dynamic output feedback controller ...
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This paper considers the energy optimization problem with multiple time window constraints, which is motivated by the eco-driving of connected and automated vehicles (CAVs) in urban traffic. Timely crossing of signali...
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In recent years, there has been a lot of research in the fields of computer vision and multimedia to analyze human behavior and activities through images. A particular area of focus has been on estimating human pose, ...
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In recent years, there has been a lot of research in the fields of computer vision and multimedia to analyze human behavior and activities through images. A particular area of focus has been on estimating human pose, also known as skeleton estimation. Deep learning methods have been commonly used for this task, which primarily rely on the keypoint features of the human body. However, this approach can be limiting when there are occluded or incomplete poses, especially when multiple humans are present in a single frame. Other features like visibility conditions and body boundaries can also contribute to pose estimation in such cases. This paper outlines a method for multi-person pose estimation leveraging Sparse-RCNN with mixture models, aiming to reduce computational complexity while enhancing accuracy. The approach integrates innovative techniques of Sparse-RCNN such as learnable proposal boxes, dynamic heads, and an iteration structure to efficiently extract features with mixture models and human body masks. This approach has resulted in significant improvements, as indicated by an increase in average precision and faster processing compared to other state-of-the-art methods on the COCO and CIHP datasets.
Cobalt titanate, CoTiO3, is a honeycomb antiferromagnet recently confirmed experimentally to host Dirac magnons, topological spin-orbit excitons, and chiral phonons. Here, we investigate a magnon gap at the zone cente...
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Cobalt titanate, CoTiO3, is a honeycomb antiferromagnet recently confirmed experimentally to host Dirac magnons, topological spin-orbit excitons, and chiral phonons. Here, we investigate a magnon gap at the zone center which calls for a refined spin Hamiltonian. We propose a microscopic model for the magnon gap and attribute it to a lattice-distortion (phonon)-induced higher-order spin interaction. Strong magnetoelastic coupling in CoTiO3 is also evident in Raman spectra, in which the magnetic order exerts a stronger influence on phonons corresponding to in-plane ionic motions than those with out-of-plane motions. We further examine the evolution of the zone-center magnons in a high magnetic field up to 18.5 T via THz absorption spectroscopy measurements. Based on this field dependence, we propose a spin Hamiltonian that not only agrees with magnon dispersion measured by inelastic neutron scattering but also includes fewer exchange constants and a realistic anisotropy term. Our work highlights the broad implications of magnetoelastic coupling in the study of topologically protected bosonic excitations.
As renewable energy sources are increasingly incorporated into power grids, genuine forecasting of generation is necessary in order to avoid instability of grids and energy loss, among other things. In this research w...
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ISBN:
(数字)9798331502768
ISBN:
(纸本)9798331502775
As renewable energy sources are increasingly incorporated into power grids, genuine forecasting of generation is necessary in order to avoid instability of grids and energy loss, among other things. In this research we are exploring the Deep Learning Transformer Network with Google Cloud AutoML for AI optimized renewable energy forecasting approach. Transformer model, which can capture long range dependencies in time series data, helps in improving predictive performance but also Google Cloud AutoML automates model selection and hyperparameter guessing removing the need of during extensive human involvement. Experimental results yield improved precision of forecasting as compared to the traditional machine learning methods. Through this AI driven methodology, energy providers, grid operators, policymakers aim for a more sustainable energy future have a scalable and reliable solution.
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