基于深度学习的结合轨道角运动量干扰计用于平面内移位测量
概括
一个新的深度学习算法使用结合轨道角动量 (OAM) 干扰计准确测量平面内位移. 这种阶段解调混合神经网络 (PDHNN) 即使在杂的环境中也能提供强大而稳定的测量.
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
- 计量学 计量学 计量学
背景情况:
- 在各种科学和工程领域,准确测量飞机内位移至关重要.
- 干涉测量中的传统相模解调技术可能很复杂,对噪声敏感.
- 轨道角动量 (OAM) 干涉测量为移位传感提供了独特的优势.
研究的目的:
- 开发一个强大的,准确的相位调节算法,用于OAM干涉测量的平面内位移测量.
- 引入基于深度学习的方法,即阶段解调混合神经网络 (PDHNN),用于直接干扰图分析.
- 在噪音条件下提高位移测量的稳定性和可靠性.
主要方法:
- 为PDHNN设计了一个定制的ResNet转换器架构.
- 包括可变形卷曲和注意力机制,以提取旋转敏感的特征.
- 该算法经过训练和验证,使用模拟的和实验的花形干扰图.
- 干扰图的直接解调是在单个步骤中进行的.
主要成果:
- 在1°的误差范围内,PDHNN实现了91.60%的解调精度.
- 在0.1°的误差范围内记录了0.13nm的平均位移误差.
- 该算法在存在噪音的情况下表现出高强度和稳定性.
- 使用模拟和实验数据实现了成功的验证.
结论:
- 拟议的PDHNN算法为OAM干扰计中准确的平面内位移测量提供了有效的解决方案.
- 深度学习,特别是ResNet-变压器架构,显著提高了阶段解调的稳定性和准确性.
- 该方法显示了对于在具有挑战性的环境中需要精确的位移传感的应用具有很大的潜力.
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