A power system blackout is a high-impact but low-probable event. Power system restoration, after a complete blackout, creates substantial challenges for power system operators. To restore the system within a minimum t...
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Recent research has focused on exploring uncrewed aerial vehicle (UAV)-to-ground communication channels, leading to the development of new fading channel models that incorporate both fading and shadowing phenomena. It...
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Given the large number of existing metaheuristic optimisation algorithms, there has been an increasing focus on improving benchmarking practices, to gain an improved understanding empirical performance, as well as mat...
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Coronary artery disease (CAD) is a major cause of mortality, with diagnosis largely dependent on quantitative coronary analysis (QCA) using coronary angiogram (CAG) images. Advances in deep learning have significantly...
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In this paper, cascaded shadowed uncrewed aerial vehicles (UAVs)-to-ground fading channels are introduced and mathematically characterized. This is done in order to provide a comprehensive statistical analysis by deri...
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Understanding complex events from different modalities, associating to external knowledge and generating response in a clear point of view are still unexplored in today’s multi-modal dialogue research. The great chal...
Uncrewed aerial vehicle (UAV)-based communications have been suggested as an essential enabling technology for beyond fifth-generation (5G) cellular networks. This has resulted in the proposal of novel channel models ...
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We introduce data-driven, scalable digital twins (DTs) and real-time data imputation to improve inverter synchronization in low-inertia microgrids. The DTs act as cyber-physical replicas, enabling real-time monitoring...
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The transformation of age-old farming practices through the integration of digitization and automation has sparked a revolution in agriculture that is driven by cutting-edge computer vision and artificial intelligence...
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The transformation of age-old farming practices through the integration of digitization and automation has sparked a revolution in agriculture that is driven by cutting-edge computer vision and artificial intelligence(AI)*** transformation not only promises increased productivity and economic growth,but also has the potential to address important global issues such as food security and *** survey paper aims to provide a holistic understanding of the integration of vision-based intelligent systems in various aspects of precision *** providing a detailed discussion on key areas of digital life cycle of crops,this survey contributes to a deeper understanding of the complexities associated with the implementation of vision-guided intelligent systems in challenging agricultural *** focus of this survey is to explore widely used imaging and image analysis techniques being utilized for precision farming *** paper first discusses various salient crop metrics used in digital *** this paper illustrates the usage of imaging and computer vision techniques in various phases of digital life cycle of crops in precision agriculture,such as image acquisition,image stitching and photogrammetry,image analysis,decision making,treatment,and *** establishing a thorough understanding of related terms and techniques involved in the implementation of vision-based intelligent systems for precision agriculture,the survey concludes by outlining the challenges associated with implementing generalized computer vision models for real-time deployment of fully autonomous farms.
Accurately predicting the Remaining Useful Life(RUL)of lithium-ion batteries is crucial for battery management *** learning-based methods have been shown to be effective in predicting RUL by leveraging battery capacit...
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Accurately predicting the Remaining Useful Life(RUL)of lithium-ion batteries is crucial for battery management *** learning-based methods have been shown to be effective in predicting RUL by leveraging battery capacity time series ***,the representation learning of features such as long-distance sequence dependencies and mutations in capacity time series still needs to be *** address this challenge,this paper proposes a novel deep learning model,the MLP-Mixer and Mixture of Expert(MMMe)model,for RUL *** MMMe model leverages the Gated Recurrent Unit and Multi-Head Attention mechanism to encode the sequential data of battery capacity to capture the temporal features and a re-zero MLP-Mixer model to capture the high-level ***,we devise an ensemble predictor based on a Mixture-of-Experts(MoE)architecture to generate reliable RUL *** experimental results on public datasets demonstrate that our proposed model significantly outperforms other existing methods,providing more reliable and precise RUL predictions while also accurately tracking the capacity degradation *** code and dataset are available at the website of github.
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