Deep neural network training method based on vectorgraphs for designing of metamaterial broadband polarization converters.

In this work, we proposed a method of extracting feature parameters for deep neural network prediction based on the vectorgraph storage format, which can be applied to the design of electromagnetic metamaterials with sandwich structures. Compared to current methods of manually extracting feature parameters, this method can automatically and precisely extract the feature parameters of arbitrary two-dimensional surface patterns of the sandwich structure. The position and size of surface patterns can be freely defined, and the surface patterns can be easily scaled, rotated, translated, or transformed in other ways.

A New Long-Term Downward Surface Solar Radiation Dataset over China from 1958 to 2015.

Downward surface solar radiation () plays a dominant role in determining the climate and environment on the Earth. However, the densely distributed ground observations of are usually insufficient to meet the increasing demand of the climate diagnosis and analysis well, so it is essential to build a long-term accurate dataset. The extremely randomized trees (ERT) algorithm was used to generate using routine meteorological observations (2000-2015) from the Climate Data Center of the Chinese Meteorological Administration (CDC/CMA).