Fiber-Optic Sensor Spectrum Noise Reduction Based on a Generative Adversarial Network
In the field of fiber-optic sensing, effectively reducing the noise of sensing spectra and achieving a high signal-to-noise ratio (SNR) has consistently been a focal point of research. This study proposes a deep-learning-based denoising method for fiber-optic sensors, which involves pre-processing t...
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2024-11-01
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| author | Yujie Lu Qingbin Du Ruijia Zhang Bo Wang Zigeng Liu Qizhe Tang Pan Dai Xiangxiang Fan Chun Huang |
| author_facet | Yujie Lu Qingbin Du Ruijia Zhang Bo Wang Zigeng Liu Qizhe Tang Pan Dai Xiangxiang Fan Chun Huang |
| author_sort | Yujie Lu |
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| description | In the field of fiber-optic sensing, effectively reducing the noise of sensing spectra and achieving a high signal-to-noise ratio (SNR) has consistently been a focal point of research. This study proposes a deep-learning-based denoising method for fiber-optic sensors, which involves pre-processing the sensor spectrum into a 2D image and training with a cycle-consistent generative adversarial network (Cycle-GAN) model. The pre-trained algorithm demonstrates the ability to effectively denoise various spectrum types and noise profiles. This study evaluates the denoising performance of simulated spectra obtained from four different types of fiber-optic sensors: fiber Fabry–Perot interferometer (FPI), regular fiber Bragg grating (FBG), chirped FBG, and FBG pair. Compared to traditional denoising algorithms such as wavelet transform (WT) and empirical mode decomposition (EMD), the proposed method achieves an SNR improvement of up to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>13.71</mn><mo> </mo><mi mathvariant="normal">dB</mi></mrow></semantics></math></inline-formula>, an RMSE that is up to three times smaller, and a minimum correlation coefficient (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></mrow></semantics></math></inline-formula>) of no less than 99.70% with the original high-SNR signals. Additionally, the proposed algorithm was tested for multimode noise reduction, demonstrating an excellent linearity in temperature response with a <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></mrow></semantics></math></inline-formula> of 99.95% for its linear fitting and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>99.74</mn><mo>%</mo></mrow></semantics></math></inline-formula> for the temperature response obtained from single-mode fiber sensors. The proposed denoising approach effectively reduces the impact of various noises from the sensing system, enhancing the practicality of fiber-optic sensing, especially for specialized fiber applications in research and industrial domains. |
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| spelling | doaj-art-c6ac7a70aa4342c5a0e4310d4bb5e0542025-08-20T02:04:40ZengMDPI AGSensors1424-82202024-11-012422712710.3390/s24227127Fiber-Optic Sensor Spectrum Noise Reduction Based on a Generative Adversarial NetworkYujie Lu0Qingbin Du1Ruijia Zhang2Bo Wang3Zigeng Liu4Qizhe Tang5Pan Dai6Xiangxiang Fan7Chun Huang8School of Information Engineering, Huzhou University, Huzhou 313000, ChinaSchool of Information Engineering, Huzhou University, Huzhou 313000, ChinaSchool of Information Engineering, Huzhou University, Huzhou 313000, ChinaAcoustic Science and Technology Laboratory, Harbin Engineering University, Harbin 150001, ChinaAcoustic Science and Technology Laboratory, Harbin Engineering University, Harbin 150001, ChinaSchool of Information Engineering, Huzhou University, Huzhou 313000, ChinaSchool of Information Engineering, Huzhou University, Huzhou 313000, ChinaSchool of Information Engineering, Huzhou University, Huzhou 313000, ChinaSchool of Information Engineering, Huzhou University, Huzhou 313000, ChinaIn the field of fiber-optic sensing, effectively reducing the noise of sensing spectra and achieving a high signal-to-noise ratio (SNR) has consistently been a focal point of research. This study proposes a deep-learning-based denoising method for fiber-optic sensors, which involves pre-processing the sensor spectrum into a 2D image and training with a cycle-consistent generative adversarial network (Cycle-GAN) model. The pre-trained algorithm demonstrates the ability to effectively denoise various spectrum types and noise profiles. This study evaluates the denoising performance of simulated spectra obtained from four different types of fiber-optic sensors: fiber Fabry–Perot interferometer (FPI), regular fiber Bragg grating (FBG), chirped FBG, and FBG pair. Compared to traditional denoising algorithms such as wavelet transform (WT) and empirical mode decomposition (EMD), the proposed method achieves an SNR improvement of up to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>13.71</mn><mo> </mo><mi mathvariant="normal">dB</mi></mrow></semantics></math></inline-formula>, an RMSE that is up to three times smaller, and a minimum correlation coefficient (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></mrow></semantics></math></inline-formula>) of no less than 99.70% with the original high-SNR signals. Additionally, the proposed algorithm was tested for multimode noise reduction, demonstrating an excellent linearity in temperature response with a <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></mrow></semantics></math></inline-formula> of 99.95% for its linear fitting and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>99.74</mn><mo>%</mo></mrow></semantics></math></inline-formula> for the temperature response obtained from single-mode fiber sensors. The proposed denoising approach effectively reduces the impact of various noises from the sensing system, enhancing the practicality of fiber-optic sensing, especially for specialized fiber applications in research and industrial domains.https://www.mdpi.com/1424-8220/24/22/7127fiber-optic sensinggenerative adversarial networknoise reductionsignal processing |
| spellingShingle | Yujie Lu Qingbin Du Ruijia Zhang Bo Wang Zigeng Liu Qizhe Tang Pan Dai Xiangxiang Fan Chun Huang Fiber-Optic Sensor Spectrum Noise Reduction Based on a Generative Adversarial Network Sensors fiber-optic sensing generative adversarial network noise reduction signal processing |
| title | Fiber-Optic Sensor Spectrum Noise Reduction Based on a Generative Adversarial Network |
| title_full | Fiber-Optic Sensor Spectrum Noise Reduction Based on a Generative Adversarial Network |
| title_fullStr | Fiber-Optic Sensor Spectrum Noise Reduction Based on a Generative Adversarial Network |
| title_full_unstemmed | Fiber-Optic Sensor Spectrum Noise Reduction Based on a Generative Adversarial Network |
| title_short | Fiber-Optic Sensor Spectrum Noise Reduction Based on a Generative Adversarial Network |
| title_sort | fiber optic sensor spectrum noise reduction based on a generative adversarial network |
| topic | fiber-optic sensing generative adversarial network noise reduction signal processing |
| url | https://www.mdpi.com/1424-8220/24/22/7127 |
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