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The long-term safe and stable operation of oil-impregnated paper (OIP) bushings is of great significance to the operation of power systems. With the growth of OIP bushing, its internal insulation will gradually decay. Aramid insulation paper has excellent thermal aging characteristics and its insulation performance can be improved by using nano-modification technology. In this paper, the nano-SiO2 particles were used as the modified additives, and the modified aramid insulation paper was prepared through four steps: ultrasonic stirring, fiber dissociation, paper sample copying and superheated calendering. The microscopic physical morphology and chemical components of the insulation specimens before and after modification were analyzed by atomic force microscopy (AFM), scanning electron microscopy (SEM) and X-ray photoelectron spectroscopy (XPS), and an OIP bushing model based on the modified aramid insulation paper was constructed and its electric field distribution was analyzed. The simulation results show that the use of SiO2-modified aramid insulation paper can improve the electric field distribution of OIP bushings and increase the operating life of power transformers.

Unsupervised learning-based medical image registration approaches have witnessed rapid development in recent years. We propose to revisit a commonly ignored while simple and well-established principle: recursive refinement of deformation vector fields across scales. We introduce a recursive refinement network (RRN) for unsupervised medical image registration, to extract multi-scale features, construct normalized local cost correlation volume and recursively refine volumetric deformation vector fields. RRN achieves state of the art performance for 3D registration of expiratory-inspiratory pairs of CT lung scans. On DirLab COPDGene dataset, RRN returns an average Target Registration Error (TRE) of 0.83 mm, which corresponds to a 13% error reduction from the best result presented in the leaderboard 4 . In addition to comparison with conventional methods, RRN leads to 89% error reduction compared to deep-learning-based peer approaches. Code: https://github.com/Novestars/Recursive Refinement Network.

Objective. Effective learning and modelling of spatial and semantic relations between image regions in various ranges are critical yet challenging in image segmentation tasks. Approach. We propose a novel deep graph reasoning model to learn from multi-order neighborhood topologies for volumetric image segmentation. A graph is first constructed with nodes representing image regions and graph topology to derive spatial dependencies and semantic connections across image regions. We propose a new node attribute embedding mechanism to formulate topological attributes for each image region node by performing multi-order random walks (RW) on the graph and updating neighboring topologies at different neighborhood ranges. Afterwards, multi-scale graph convolutional autoencoders are developed to extract deep multi-scale topological representations of nodes and propagate learnt knowledge along graph edges during the convolutional and optimization process. We also propose a scale-level attention module to learn the adaptive weights of topological representations at multiple scales for enhanced fusion. Finally, the enhanced topological representation and knowledge from graph reasoning are integrated with content features before feeding into the segmentation decoder. Main results. The evaluation results over public kidney and tumor CT segmentation dataset show that our model outperforms other state-of-the-art segmentation methods. Ablation studies and experiments using different convolutional neural networks backbones show the contributions of major technical innovations and generalization ability. Significance. We propose for the first time an RW-driven MCG with scale-level attention to extract semantic connections and spatial dependencies between a diverse range of regions for accurate kidney and tumor segmentation in CT volumes.