Material irradiation experiment is dangerous and complex, thus it requires those with a vast advanced expertise to process the images and data manually. In this paper, we propose a generative adversarial model based on prior knowledge and attention mechanism to achieve the generation of irradiated material images (data-to-image model), and a prediction model for corresponding industrial performance (image-to-data model). With the proposed models, researchers can skip the dangerous and complex irradiation experiments and obtain the irradiation images and industrial performance parameters directly by inputing some experimental parameters only. We also introduce a new dataset ISMD which contains 22000 irradiated images with 22,143 sets of corresponding parameters. Our model achieved high quality results by compared with several baseline models. The evaluation and detailed analysis are also performed.

本文提出了基于生成对抗模型和注意力机制的辐照材料数据到图像模型以及图像到数据模型, 使研究人员能够跳过危险和复杂的辐照实验, 并直接通过输入实验参数获得辐照图像和工业性能参数, 并介绍了一个包含22000个辐照图像的新数据集(ISMD)。本模型与多个基线模型相比具有更高的质量。评估和详细分析也已进行。

基于先验知识和注意机制的材料实验生成模型