Decentralized identification is an interesting topic for Internet-based systems. Although the use of centralized systems for identification is prevalent, there is still a need for decentralized identification systems ...
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This research paper introduces a graphical user interface (GUI)-based communication program designed for scalable service-oriented middleware over IP (SOME/IP) packet communication. The program establishes an environm...
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This paper proposes a framework designed to optimise energy consumption in vertical farming. It aims to maximise cost efficiency by balancing between minimising system operations during the electricity price peaks and...
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The traditional model predictive control (MPC) for power inverters is designed upon a certain operating point and is solved with quadratic programming (QP). The performance may deteriorate under variations of converte...
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This paper considers the problem of controlling distributed energy resources (DERs) in a distribution network (DN);the paper focuses on the voltage regulation task and on the concept of virtual power plant (VPP). For ...
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News Image Captioning aims to create captions from news articles and images, emphasizing the connection between textual context and visual elements. Recognizing the significance of human faces in news images and the f...
This paper presents a new and efficient tree data structure for sorting and collision detection of disks in 2D based on a new tree-based data structure, called hexatree, which is introduced for the first time in this ...
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Forecasting electricity demand is an essential part of the smart grid to ensure a stable and reliable power grid. With the increasing integration of renewable energy resources into the grid, forecasting the demand for...
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Forecasting electricity demand is an essential part of the smart grid to ensure a stable and reliable power grid. With the increasing integration of renewable energy resources into the grid, forecasting the demand for electricity is critical at all levels, from the distribution to the household. Most existing forecasting methods, however, can be considered black-box models as a result of deep digitalization enablers, such as deep neural networks, which remain difficult to interpret by humans. Moreover, capture of the inter-dependencies among variables presents a significant challenge for multivariate time series forecasting. In this paper we propose eXplainable Causal Graph Neural Network (X-CGNN) for multivariate electricity demand forecasting that overcomes these limitations. As part of this method, we have intrinsic and global explanations based on causal inferences as well as local explanations based on post-hoc analyses. We have performed extensive validation on two real-world electricity demand datasets from both the household and distribution levels to demonstrate that our proposed method achieves state-of-the-art performance.
In addressing labor-intensive process of manual plant disease detection, this article introduces an innovative solution—the lightweight parallel depthwise separable convolutional neural network (PDSCNN) coupled with ...
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Nowadays, power quality issues are more and more frequent due to disturbances such as harmonics, flicker, swells/sags, interruptions, unbalanced voltage, etc. This paper presents a solution developed by the authors fo...
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