Electric load forecasting refers to forecasting the electricity demand at aggregated levels. Utilities use the predictions of this technique to keep a balance between electricity generation and consumption at each tim...
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Microgrids are building blocks of smart grids. With increasing penetration of renewable energy-based distributed generation (DG) in distribution networks, microgrid formation is an effective way to improve resiliency ...
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In this paper, a system for reducing the file size of an audio signal, and then performing super-resolution on the resultant signal to estimate the original, is proposed and designed. This design takes influence from ...
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Time-Sensitive Networking (TSN) has emerged as a promising communication technology for automotive applications. The TSN standards provide a flexible toolkit, enabling network designers to select the features and mech...
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Recently, drones have been applied and used in various tasks in many fields. Due to the development of camera technology and hardware, it is possible to detect objects using deep learning technology in real time. Most...
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For effective and sustainable energy solutions, this paper investigates at a hybrid energy system that uses Internet of Things technologies. Three components make up the system: PV system, an AC turbine voltage source...
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This paper proposes a kit for the detection of unintentional islanding, neutral conductor loss and meter tampering (i.e., electricity or power theft) events, applicable to low voltage electrical installations, in the ...
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The creation of new approaches to the design and configuration of smart buildings relies heavily on AI tools and Machine Learning (ML) algorithms, particularly optimization techniques. The widespread use of electronic...
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Due to the recent advancements in computer vision and scene understanding, detection & recognition of text within scene images have attracted significant interest from both academia and industry. To develop a reli...
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We introduce a general method for achieving robust group-invariance in group-equivariant convolutional neural networks (G-CNNs), which we call the G-triple-correlation (G-TC) layer. The approach leverages the theory o...
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