Nanocomposite magnets are of importance because they are expected to provide a very high maximum‐energy product. Here is presented a route to α‐Fe/Sm2(Fe,Si)17Cx nanocomposite magnets with fine grain size below 10 nm. Annealing amorphous Sm8Fe85‐Si2C5 alloy under high pressure gives nanocomposite magnets (see Figure) with significantly increased coercivity and remnant magnetization.
Abstract. Although deep learning has greatly improved the semantic segmentation accuracy of point clouds, the segmentation of rare classes in large-scale urban scenes has not been targeted in available methods. This paper proposes a two-stage segmentation framework with automated workflows for imbalanced rare classes based on general semantic segmentation. The proposed approach includes two stages: general semantic segmentation and object-based refined semantic segmentation. Firstly, general segmentation networks are utilized to segment general large objects. Secondly, refined semantic segmentation is conducted by an automated workflow: 3D clustering and bounding box (BBox) generation are applied to the point cloud of rare fine-grained objects during the training, followed by object detection to extract fine-grained objects. Afterwards, as the constraints, the extracted BBoxes further refine the segmentation results. Our approach is evaluated on the Hessigheim High-Resolution 3D Point Cloud (H3D) Benchmark and obtains state-of-the-art 89.35% overall accuracy and outstanding 75.70% mean F1-Score. Furthermore, rare classes Vehicle and Chimney achieve breakthroughs from zero to 63.63% and 52.00% in F1-score, respectively.
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