A Comparative Study of Physicochemical, Dielectric and Thermal Properties of Pressboard Insulation Impregnated with Natural Ester and Mineral Oil

Natural ester is considered to be a substitute of mineral oil in the future. To apply natural ester in large transformers safely, natural ester impregnated solid insulation should be proved to have comparable dielectric strength and thermal stability to mineral oil impregnated solid insulation. This paper mainly focuses on a comparative study of physicochemical, ac breakdown strength and thermal stability behavior of BIOTEMP natural ester/pressboard insulation and Karamay 25# naphthenic mineral oil/pressboard insulation after long term thermal ageing. The physicochemical and dielectric parameters including moisture, acids and the ac breakdown strength of these two oil/pressboard insulation systems at different ageing status were compared. The permittivity and ac breakdown strength of these two oil/pressboard insulation systems at different temperatures were also investigated. And a comparative result of the thermal stability behavior of these two oil/pressboard insulation systems with different ageing status was provided at last. Results show that though natural ester has higher absolute humidity and acidity during the long ageing period, the lower relative humidity of natural ester helps to keep its ac breakdown strength higher than mineral oil. The pressboard aged in natural ester also has higher ac breakdown strength than that aged in mineral oil. The lower relative permittivity ratio of natural ester impregnated paper to natural ester is beneficial to its dielectric strength. Using natural ester in transformer, the resistance to thermal decomposition of the oil/pressboard insulation system could be also effectively improved

ShaDDR: Real-Time Example-Based Geometry and Texture Generation via 3D Shape Detailization and Differentiable Rendering

We present ShaDDR, an example-based deep generative neural network which produces a high-resolution textured 3D shape through geometry detailization and conditional texture generation applied to an input coarse voxel shape. Trained on a small set of detailed and textured exemplar shapes, our method learns to detailize the geometry via multi-resolution voxel upsampling and generate textures on voxel surfaces via differentiable rendering against exemplar texture images from a few views. The generation is real-time, taking less than 1 second to produce a 3D model with voxel resolutions up to 512^3. The generated shape preserves the overall structure of the input coarse voxel model, while the style of the generated geometric details and textures can be manipulated through learned latent codes. In the experiments, we show that our method can generate higher-resolution shapes with plausible and improved geometric details and clean textures compared to prior works. Furthermore, we showcase the ability of our method to learn geometric details and textures from shapes reconstructed from real-world photos. In addition, we have developed an interactive modeling application to demonstrate the generalizability of our method to various user inputs and the controllability it offers, allowing users to interactively sculpt a coarse voxel shape to define the overall structure of the detailized 3D shape

MobileNeRF: Exploiting the Polygon Rasterization Pipeline for Efficient Neural Field Rendering on Mobile Architectures

Neural Radiance Fields (NeRFs) have demonstrated amazing ability to synthesize images of 3D scenes from novel views. However, they rely upon specialized volumetric rendering algorithms based on ray marching that are mismatched to the capabilities of widely deployed graphics hardware. This paper introduces a new NeRF representation based on textured polygons that can synthesize novel images efficiently with standard rendering pipelines. The NeRF is represented as a set of polygons with textures representing binary opacities and feature vectors. Traditional rendering of the polygons with a z-buffer yields an image with features at every pixel, which are interpreted by a small, view-dependent MLP running in a fragment shader to produce a final pixel color. This approach enables NeRFs to be rendered with the traditional polygon rasterization pipeline, which provides massive pixel-level parallelism, achieving interactive frame rates on a wide range of compute platforms, including mobile phones.Comment: CVPR 2023. Project page: https://mobile-nerf.github.io, code: https://github.com/google-research/jax3d/tree/main/jax3d/projects/mobilener

Normal scalar curvature conjecture and its applications

In this paper, we proved the Normal Scalar Curvature Conjecture and the Bottcher-Wenzel Conjecture. We also established some new pinching theorems for minimal submanifolds in spheres.Comment: minor and final typo correction