As renewable energy integration increases, ensuring stability of Inverter-Based Resources (IBRs) in weak grids is crucial, as grid-following (GFL) converters often become unstable under such conditions. Integrating vi...
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With the progress of power grid technology and intelligent technology, intelligent inspection robot (IR) came into being and are expected to become the main force of substation inspection in the future. Among them, mo...
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Deep learning-based representation getting to know for automated detection of lung cancer on chest CT scans has accomplished massive progress in recent years. This era uses convolution neural networks (CNNs) to derive...
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This paper investigates the performance of simultaneously transmitting and reflecting surface (STARS) assisted semi-grant-free non-orthogonal multiple access network with randomly distributed users. By deploying STARS...
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The accurate prediction of photovoltaic(PV)power generation is significant to ensure the economic and safe operation of power *** this end,the paper establishes a new digital twin(DT)empowered PV power prediction fram...
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The accurate prediction of photovoltaic(PV)power generation is significant to ensure the economic and safe operation of power *** this end,the paper establishes a new digital twin(DT)empowered PV power prediction framework that is capable of ensuring reliable data transmission and employing the DT to achieve high accuracy of power *** this framework,considering potential data contamination in the collected PV data,a generative adversarial network is employed to restore the historical dataset,which offers a prerequisite to ensure accurate mapping from the physical space to the digital ***,a new DT-empowered PV power prediction method is ***,we model a DT that encompasses a digital physical model for reflecting the physical operation mechanism and a neural network model(i.e.,a parallel network of convolution and bidirectional long short-term memory model)for capturing the hidden spatiotemporal *** proposed method enables the use of the DT to take advantages of the digital physical model and the neural network model,resulting in enhanced prediction ***,a real dataset is conducted to assess the effectiveness of the proposed method.
Sensory networks in environmental monitoring provide real-time data on critical parameters, but the costs of installation and maintenance limit high-resolution data acquisition. Researchers aim to estimate values at s...
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ISBN:
(数字)9798350376340
ISBN:
(纸本)9798350376357
Sensory networks in environmental monitoring provide real-time data on critical parameters, but the costs of installation and maintenance limit high-resolution data acquisition. Researchers aim to estimate values at specific locations without prior data samples, considering two approaches: virtual sensors and kriging. While virtual sensors face challenges in dynamic sensor networks where for every sensor added or disconnected the whole network should be retrained, kriging, especially spatio-temporal kriging using Graph Neural networks, overcomes traditional kriging drawbacks and allows adaptability in dynamic sensor networks without frequent retraining. Despite their success, existing spatio-temporal kriging methods face challenges, notably the over-smoothing problem, restricting their ability to utilize deeper graph structures for a more comprehensive latent representation. In this paper, we propose a two-part method based on neural differential equations. The first part estimates values using spatial adjacency, while the second part refines these estimates considering temporal dependencies. Our approach explicitly addresses the over-smoothing problem, leading to a 2-8% improvement over state-of-the-art baseline methods. The results hold promise for enhancing the accuracy and effectiveness of environmental monitoring applications.
Currently, the privacy protection technology of blockchain is not mature enough, such as user data leakage and lack of anonymity exist, and these problems are especially serious when conducting cross-chain transaction...
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this paper evaluates diverse time collection algorithms for extracting quantitative facts from hyperspectral imagery. Mainly, it compares four statistical strategies (seasonal autoregressive incorporated transferring ...
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This paper aims to leverage massive statistics algorithms and cloud computing to create centralized electronic fitness statistics (EHRs) that are quickly and easily handy for all authorized healthcare professionals. T...
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