English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Material irradiation experiment is dangerous and complex, thus it requiresthose with a vast advanced expertise to process the images and data manually.In this paper, we propose a generative adversarial model based on priorknowledge and attention mechanism to achieve the generation of irradiatedmaterial images (data-to-image model), and a prediction model for correspondingindustrial performance (image-to-data model). With the proposed models,researchers can skip the dangerous and complex irradiation experiments andobtain the irradiation images and industrial performance parameters directly byinputing some experimental parameters only. We also introduce a new datasetISMD which contains 22000 irradiated images with 22,143 sets of correspondingparameters. Our model achieved high quality results by compared with severalbaseline models. The evaluation and detailed analysis are also performed.

Generative Model for Material Experiments Based on Prior Knowledge and Attention Mechanism