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• W4385486717 abstract "Extracting information from the rich multi-source data of the power system is one of the keys to playing the role of the core production factors of the power grid. On the one hand, multi-source data fusion will facilitate an accurate and comprehensive analysis of various scenarios in all source, network, and load storage aspects. The fusion of multi-source information can complement and enhance each other, thereby effectively improving the accuracy of target perception. To this end, we propose a method based on the power Internet of Things and deep learning for feature extraction of multi-source information. Since traditional methods reveal hidden correlations between different morphologies of learned features through linear connections, it is difficult to obtain desirable results. Therefore, this paper proposes a deep learning computing model of DCCM, which further improves the feature extraction and fusion capabilities of rich multi-source data." @default.
• W4385486717 created "2023-08-03" @default.
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• W4385486717 date "2023-01-01" @default.
• W4385486717 modified "2023-09-23" @default.
• W4385486717 title "A Novel IoT Based Multi-modal Edge Computing Optimization Method" @default.
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• W4385486717 doi "https://doi.org/10.1007/978-981-99-4334-0_92" @default.
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