首页> 外文期刊>Optics Letters >Artificial neural network for the reduction of birefringence-induced errors in fiber shape sensors based on cladding waveguides gratings
【24h】

Artificial neural network for the reduction of birefringence-induced errors in fiber shape sensors based on cladding waveguides gratings

机译:基于包层覆盖物光栅的纤维形传感器中的双折射引起的双折射诱导误差的人工神经网络

获取原文
获取原文并翻译 | 示例
           

摘要

Cladding waveguide fiber Bragg gratings (FBGs) provide a compact and simple solution for fiber shape sensing. The shape sensing accuracy is limited by birefringence, which is induced by bending and the non-isotropic FBG structure (written by femtosecond laser point-by-point technique). An algorithm based on an artificial neural network for fiber shape sensing is demonstrated, which enables increased accuracy, better robustness, and less time latency. This algorithm shows great potential in the application of highaccuracy real-time fiber shape measurements. (C) 2020 Optical Society of America
机译:包层波导纤维布拉格光栅(FBGS)提供了一种紧凑而简单的纤维形状传感解决方案。 形状感测精度受双折射的限制,其通过弯曲和非各向同性FBG结构诱导(由飞秒激光点对点技术书写)。 对基于用于光纤形状感测的人工神经网络的算法进行了说明,这使得能够提高精度,更好的鲁棒性和更少的时间延迟。 该算法在应用高可加工实时光纤形状测量中显示出很大的潜力。 (c)2020美国光学学会

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号