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Artificial Intelligence

To verify the proposed protection scheme, the simulation model of a six-terminal ring flexible DC distribution system is built in PSCAD/EMTDC , where the rated voltage of the DC line is ±10 kV . The fault inception is set at 0.6 s, the sampling frequency is 10 kHz and the protection data window length is 1 ms. The data set reflects the current and voltage values of each line after standardization

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Buildings are essential components of urban areas. While research on the extraction and 3D reconstruction of buildings is widely conducted, information on fine-grained roof types of buildings is usually ignored. This limits the potential of further analysis, e.g., in the context of urban planning applications. The fine-grained classification of building roof type from satellite images is a highly challenging task due to ambiguous visual features within the satellite imagery.

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https://www.microsoft.com/en-us/research/publication/msr-vtt-a-large-video-description-dataset-for-bridging-video-and-language/

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To enable intelligent vehicles and transportation systems, the vehicles and relevant systems need to have the ability to sense environment and recognize objects. In order to benefit from the robustness of radar for sensing, knowing how to use the radar system for effective object recognition is critical. Observing this, we in this paper propose a novel deep learning-aided object recognition system for radar systems by combining the You only look once (YOLO) system with a proposed object recheck system.

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Water leakage problems increased over the last few years, and innovative tools and techniques appeared to solve this widespread problem. The still unresolved problem concerns the identification of water leaks at the nearest point; at the household level, the most common and inexpensive devices are still mechanical meters, which cannot detect leaks.

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Deep video representation learning has recently attained state-of-the-art performance in video action recognition. However, when used with video clips from varied perspectives, the performance of these models degrades significantly. Existing VAR models frequently simultaneously contain both view information and action attributes, making it difficult to learn a view-invariant representation.

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