概括
我们为多核纤维布拉格格 (MCFBG) 传感器开发了一个深度学习模型. 这种方法使用较少的训练数据准确地预测形状空间坐标,从而实现高精度的形状传感.
科学领域:
- 光电子产品
- 光纤传感器
- 机器学习用于传感器数据分析
背景情况:
- 多核纤维布拉格格 (MCFBG) 是用于形状感应的先进传感器.
- 从MCFBG数据准确重建形状是具有挑战性的,通常需要广泛的实验数据.
- 目前的方法可能缺乏要求高的应用所需的精度.
研究的目的:
- 为MCFBG传感器提出一个新的形状空间坐标预测模型.
- 利用深度学习,预训练和转移学习来提高准确性和数据效率.
- 建立从MCFBG波长数据到形状空间坐标的端到端映射.
主要方法:
- 开发用于MCFBG数据分析的深度学习架构.
- 整合预培训和转移学习策略以提高模型的性能.
- 从中心波长数据建立直接映射到形状空间坐标.
主要成果:
- 该模型显示了基于MCFBG的形状传感的改进准确性.
- 实验培训数据需求显著减少.
- 最好的模型实现了中位终点误差,相对误差低至0.76%.
结论:
- 拟议的模型提供了一个高度准确的MCFBG形状传感方法.
- 这种方法可以减少数据采集的成本和时间.
- 该方法适用于要求高精度形状重建的应用.
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