The so-called aircraft stealth refers to coating or pasting absorbing materials on the relevant parts of the aircraft, reasonably designing the aircraft shape and layout, so that the enemy detection system (such as radio radar, infrared radar, lidar, etc.) can only receive the greatly weakened aircraft reflection signal. In this way, the possibility of aircraft being found or tracked is reduced. The basic research of stealth technology includes designing the absorbing material into a reasonable shape to maximize its efficiency. Its achievements can be applied not only to military equipment such as aircraft, ships and tanks, but also to other scientific and technological fields. Although the mathematical and physical problems composed of Maxwell equations and corresponding boundary conditions can be analyzed and calculated strictly in theory, the model is complex and the calculation is complicated. Focusing on the problem of radiation absorption, the microwave absorption performance of wedge shaped absorber is studied in this paper. Simulation results show the effectiveness of the research.
This paper focuses on the intelligent recognition of images in the Tiangong remote sensing image dataset and its interpretability analysis. In this paper, we classified the aforementioned dataset, retrained the Resnet-18 model on the training set, and then verified the results on the validation set with an accuracy of 97.9%. Furthermore, this paper presented an interpretability analysis of deep learning for intelligent recognition of the Tiangong remote sensing image dataset.
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