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增强的神经架构用于实时深度学习波面传感.

Jianyi Li1,2, Qingfeng Liu1,2, Liying Tan1,2

  • 1Free-Space Optical Communication Technology Research Center, Harbin Institute of Technology, Harbin 150001, China.

Sensors (Basel, Switzerland)
|January 25, 2025
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概括

这项研究引入了一种增强的深度学习波浪前感应神经网络 (WFSNet),用于实时对大气流进行校正. 这种新的方法显著降低了计算成本,并提高了激光通信系统的波浪前线传感精度.

关键词:
在美国,CNN是CNN.大气流是大气中的流.深度学习是一种深度学习.多目标神经架构搜索多目标神经架构实时波浪前线传感感应.

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科学领域:

  • 光学和光子学 在光学和光子学.
  • 人工智能的人工智能
  • 航空航天工程 航空航天工程

背景情况:

  • 大气中的流会导致动态波面扭曲,从而挑战光学系统的实时传感.
  • 精确的波面传感对于自适应光学至关重要,特别是在卫星到地面激光通信 (SGLC) 中.
  • 当前的方法经常与动态扭曲的速度和计算需求作斗争.

研究的目的:

  • 开发一个实时深度学习波传感 (DLWFS) 方法,用于动态大气流.
  • 使用卷积神经网络 (CNN) 增强一个波浪感应神经网络 (WFSNet).
  • 为了优化WFSNet的准确性和计算效率.

主要方法:

  • 为WFSNet优化提出了一种新的多目标神经架构搜索 (MNAS).
  • 实现了帕雷托最佳度平衡误差和浮点运算 (FLOP).
  • 利用EfficientNet-B0原型进行增强的神经架构设计.

主要成果:

  • 开发了一个增强的WFSNet,将计算成本降低了80%.
  • 在波面传感准确度方面取得了22%的改进.
  • 室内实验验证了拟议的WFSNet的有效性.

结论:

  • 该研究提出了实时DLWFS的新方法.
  • 增强的WFSNet为高速,具有成本效益的波浪前线传感提供了潜在的解决方案.
  • 这项技术适用于SGLC终端中的自适应光学.