论文标题
基于强化学习的可重复使用的框架，以设计弯曲表面的天线
A Reusable Framework Based on Reinforcement Learning to Design Antennas for Curved Surfaces
论文作者
论文摘要
从过去的几十年中，从不同的角度分析了低调天线的设计和实施，而目的是在设备中尺寸较小，并且具有足够的电磁行为。这项工作追求一种方法来识别小天线，因此提出了一些相似之处。同时，考虑到尺寸降低的各种天线，考虑了弯曲表面。所谓的深钢筋学习技术被用作针对形态变化的帮助，这些变化在这项工作中被特异性考虑。目的是识别可以有效安装在金属管表面的天线，例如公共基础设施中经常存在的天线（例如，交通信号灯和照明器）。动机是减少视觉影响并优化天线的辐射模式。可以分析的是，如果出现诸如曲率半径或材料的电磁特性等变量的变化，则可以自动识别问题的基本特征（通过机器学习技术）可以有效地调整设计。根据通常用于表征天线的变量，例如其阻抗和辐射模式，对这项工作中获得的结果进行了分析。
The design and implementation of low-profile antennas has been analyzed in past decades from different perspectives while the purpose is to have a small size in the device, and an adequate electromagnetic behavior. This work pursues a methodology to identify small antennas and consequently presents some similarities. Meanwhile, curved surfaces are considered for a certain variety of antennas with reduced size. The so-called deep reinforcement learning technique is used as an assistance against morphological variations that are specifically taken into account in this work. The objective is to identify antennas that can be efficiently mounted on the surface of metal tubes such as those frequently present in public infrastructure (e.g. traffic lights and luminaries). The motivation is to reduce the visual impact and optimize the radiation pattern of the antenna. It is analyzed that if changes in variables such as the radius of curvature, or the electromagnetic properties of the materials appear, an automatic identification of the underlying characteristics of the problem (by means of machine learning techniques) can readjust the design efficiently. The results obtained in this work are analyzed based on variables that are typically used to characterize antennas, such as their impedance and radiation pattern.
