Artificial neural network for the reduction of birefringence-induced errors in fiber shape sensors based on cladding waveguides gratings
| Autor: | Günter Flachenecker, Zheng Hanrong, Wolfgang Schade, Martin Angelmahr, Yi Jiang, Haiwen Cai |
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| Rok vydání: | 2020 |
| Předmět: |
Materials science
Birefringence business.industry Physics::Optics 02 engineering and technology 021001 nanoscience & nanotechnology Laser Cladding (fiber optics) 01 natural sciences Atomic and Molecular Physics and Optics law.invention 010309 optics Optics Fiber Bragg grating Fiber optic sensor law Robustness (computer science) 0103 physical sciences Femtosecond 0210 nano-technology business Waveguide |
| Zdroj: | Optics letters. 45(7) |
| ISSN: | 1539-4794 |
| Popis: | 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 high-accuracy real-time fiber shape measurements. |
| Databáze: | OpenAIRE |
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