机器学习辅助的通向曲传感器基于一个级联不对称的双核PCF传感器
Optics letters
|September 29, 2023
概括
一个新型的全向曲传感器使用级联不对称的双核光子晶体纤维 (ADCPCF) 和机器学习. 该系统可以在没有复杂的制造过程中准确预测曲方向和曲率,从而实现多功能应用.
科学领域:
- 光子学 是一个光子学.
- 光纤光学是指光纤的使用.
- 机器学习 机器学习
背景情况:
- 曲传感器对于监测结构健康和机器人技术至关重要.
- 传统的光纤曲传感器通常需要复杂的制造和后处理.
- 准确的全向曲测量仍然是一个挑战.
研究的目的:
- 设计和演示使用级联非对称的双核光子晶体纤维 (ADCPCFs) 的全向曲传感器.
- 整合机器学习 (ML) 来预测曲曲率和方向.
- 通过利用全光谱特征来克服传统方法的局限性.
主要方法:
- 连接两个具有侧向旋转角度的ADCPCF,以创建易曲的结构.
- 采用机器学习算法来分析传输光谱和预测曲参数.
- 对传感器在测量曲率和360度曲方向方面的性能进行实验验证.
主要成果:
- 传感器在预测曲方向 (99.85%准确率,2.7°MAE) 和曲率 (98.08%准确率,0.03m-1MAE) 中表现出高准确度.
- ML集成系统成功地预测了在360°内无需后处理的全向曲.
- 该方法有效地利用了全球光谱特征,克服了对特定光谱深度的依赖.
结论:
- 连锁式ADCPCF传感器与ML相结合,为通向曲传感提供了强大而准确的解决方案.
- 这种方法消除了专门制造步骤的需要,简化了传感器的开发.
- 该传感器显示了结构健康监测,机器人,医疗设备和可穿戴设备等应用的巨大潜力.
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