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基于卷积神经网络的束的拓电荷识别
概括
本研究引入了一种增强的IResNet18模型,用于识别因偏差扭曲的束中的拓电荷. 该模型准确地预测了拓电荷和偏差系数,提高了光通信的可靠性.
科学领域:
- 光学物理和通信工程.
- 在光学系统中的机器学习应用.
背景情况:
- 束对于光通信至关重要,但由于大气流和系统偏差 (如球形和昏迷偏差) 而退化.
- 精确识别束属性对于保持通信完整性至关重要.
研究的目的:
- 开发一种先进的深度学习模型,用于对束的强大的拓电荷识别.
- 从扭曲的强度模式同时预测拓电荷,球形偏差和昏迷偏差系数.
主要方法:
- 提出了一个增强的IResNet18模型,并对扭曲的旋转束强度模式进行了训练.
- 实验配置包括带有球形偏差,昏迷偏差和组合偏差的束.
- 模型性能在不同的传播距离和大气流强度下进行了评估.
主要成果:
- 改进的IResNet18模型在预测拓电荷和偏差系数方面表现出卓越的准确性.
- 与现有方法相比,该模型表现出提高了培训效率.
- 在不同的偏差类型和流条件下观察到强大的性能.
结论:
- 拟议的模型提供了一个可靠的框架,用于对偏差有意识的旋转束识别.
- 这项研究为提高自由空间光通信系统的性能和可靠性提供了宝贵的见解.
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