Practical and Accurate Evaluation of Numerical Aperture and Beam Quality Factor in Photonic Crystal Fibers by Mechanical Learning
This paper presents a convolutional neural network (CNN) model, enhanced with the convolutional block attention module (CBAM), designed to accurately predict the beam quality factor M<sup>2</sup>, and numerical aperture (NA) of photonic crystal fibers. The integration of CBAM significant...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Photonics Journal |
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| Online Access: | https://ieeexplore.ieee.org/document/10767412/ |
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| author | Mengda Wei Meisong Liao Liang Chen Yinpeng Liu Wen Hu Lidong Wang Dongyu He Tianxing Wang Shizi Yu Weiqing Gao |
| author_facet | Mengda Wei Meisong Liao Liang Chen Yinpeng Liu Wen Hu Lidong Wang Dongyu He Tianxing Wang Shizi Yu Weiqing Gao |
| author_sort | Mengda Wei |
| collection | DOAJ |
| description | This paper presents a convolutional neural network (CNN) model, enhanced with the convolutional block attention module (CBAM), designed to accurately predict the beam quality factor M<sup>2</sup>, and numerical aperture (NA) of photonic crystal fibers. The integration of CBAM significantly improves the model's feature extraction capability by enabling it to focus on key features and filter out irrelevant information. Simulation results demonstrate that the model achieves a mean relative error of only 0.381% for M<sup>2</sup> and 2.293% for NA, outperforming convolutional models without attention mechanisms. With a prediction time of approximately 7 ms, the model allows for rapid and efficient predictions of M<sup>2</sup> and NA. Moreover, when the noise factor remains below 0.32, the model's prediction error shows minimal fluctuation, highlighting its robustness. Comparative experimental analysis further validates the model's effectiveness. This approach offers a reliable and efficient solution for fast, accurate measurement of M² and NA, with significant implications for the prediction and analysis of beam performance in various applications. |
| format | Article |
| id | doaj-art-72591f76a7f34b2aaac2249ca3bba9ea |
| institution | DOAJ |
| issn | 1943-0655 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Photonics Journal |
| spelling | doaj-art-72591f76a7f34b2aaac2249ca3bba9ea2025-08-20T02:52:34ZengIEEEIEEE Photonics Journal1943-06552025-01-011711810.1109/JPHOT.2024.350662210767412Practical and Accurate Evaluation of Numerical Aperture and Beam Quality Factor in Photonic Crystal Fibers by Mechanical LearningMengda Wei0https://orcid.org/0009-0002-7675-5592Meisong Liao1https://orcid.org/0009-0000-5713-6716Liang Chen2https://orcid.org/0000-0001-5941-3174Yinpeng Liu3https://orcid.org/0009-0006-5490-6125Wen Hu4Lidong Wang5https://orcid.org/0009-0007-2106-1952Dongyu He6https://orcid.org/0009-0008-3106-4345Tianxing Wang7Shizi Yu8https://orcid.org/0000-0002-2923-1393Weiqing Gao9https://orcid.org/0000-0001-6181-5965Advanced Laser and Optoelectronic Functional Materials Department, Special Glasses and Fibers Research Center, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai, ChinaAdvanced Laser and Optoelectronic Functional Materials Department, Special Glasses and Fibers Research Center, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai, ChinaYangtze Delta Region Academy of Beijing Institute of Technology, Jiaxing, ChinaAdvanced Laser and Optoelectronic Functional Materials Department, Special Glasses and Fibers Research Center, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai, ChinaAdvanced Laser and Optoelectronic Functional Materials Department, Special Glasses and Fibers Research Center, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai, ChinaAdvanced Laser and Optoelectronic Functional Materials Department, Special Glasses and Fibers Research Center, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai, ChinaAdvanced Laser and Optoelectronic Functional Materials Department, Special Glasses and Fibers Research Center, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai, ChinaAdvanced Laser and Optoelectronic Functional Materials Department, Special Glasses and Fibers Research Center, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai, ChinaAdvanced Laser and Optoelectronic Functional Materials Department, Special Glasses and Fibers Research Center, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai, ChinaSchool of Electronic Science and Applied Physics, Hefei University of Technology, Hefei, ChinaThis paper presents a convolutional neural network (CNN) model, enhanced with the convolutional block attention module (CBAM), designed to accurately predict the beam quality factor M<sup>2</sup>, and numerical aperture (NA) of photonic crystal fibers. The integration of CBAM significantly improves the model's feature extraction capability by enabling it to focus on key features and filter out irrelevant information. Simulation results demonstrate that the model achieves a mean relative error of only 0.381% for M<sup>2</sup> and 2.293% for NA, outperforming convolutional models without attention mechanisms. With a prediction time of approximately 7 ms, the model allows for rapid and efficient predictions of M<sup>2</sup> and NA. Moreover, when the noise factor remains below 0.32, the model's prediction error shows minimal fluctuation, highlighting its robustness. Comparative experimental analysis further validates the model's effectiveness. This approach offers a reliable and efficient solution for fast, accurate measurement of M² and NA, with significant implications for the prediction and analysis of beam performance in various applications.https://ieeexplore.ieee.org/document/10767412/Attention mechanismbeam quality factordeep learningnumerical aperture |
| spellingShingle | Mengda Wei Meisong Liao Liang Chen Yinpeng Liu Wen Hu Lidong Wang Dongyu He Tianxing Wang Shizi Yu Weiqing Gao Practical and Accurate Evaluation of Numerical Aperture and Beam Quality Factor in Photonic Crystal Fibers by Mechanical Learning IEEE Photonics Journal Attention mechanism beam quality factor deep learning numerical aperture |
| title | Practical and Accurate Evaluation of Numerical Aperture and Beam Quality Factor in Photonic Crystal Fibers by Mechanical Learning |
| title_full | Practical and Accurate Evaluation of Numerical Aperture and Beam Quality Factor in Photonic Crystal Fibers by Mechanical Learning |
| title_fullStr | Practical and Accurate Evaluation of Numerical Aperture and Beam Quality Factor in Photonic Crystal Fibers by Mechanical Learning |
| title_full_unstemmed | Practical and Accurate Evaluation of Numerical Aperture and Beam Quality Factor in Photonic Crystal Fibers by Mechanical Learning |
| title_short | Practical and Accurate Evaluation of Numerical Aperture and Beam Quality Factor in Photonic Crystal Fibers by Mechanical Learning |
| title_sort | practical and accurate evaluation of numerical aperture and beam quality factor in photonic crystal fibers by mechanical learning |
| topic | Attention mechanism beam quality factor deep learning numerical aperture |
| url | https://ieeexplore.ieee.org/document/10767412/ |
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