A Deep-Learning-Based Method for Optical Transmission Link Assessment Applied to Optical Clock Comparisons
We apply the Empirical Mode Decomposition (EMD) algorithm and the Time Convolutional Network (TCN) structure, predicated on Convolutional Neural Networks, to successfully enable feature extraction within high-precision optical time-frequency signals, and provide effective identification and alerts f...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-08-01
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| Series: | Photonics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2304-6732/10/8/920 |
| _version_ | 1797583508785332224 |
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| author | Sibo Gui Meng Shi Zhaolong Li Haitao Wu Quansheng Ren Jianye Zhao |
| author_facet | Sibo Gui Meng Shi Zhaolong Li Haitao Wu Quansheng Ren Jianye Zhao |
| author_sort | Sibo Gui |
| collection | DOAJ |
| description | We apply the Empirical Mode Decomposition (EMD) algorithm and the Time Convolutional Network (TCN) structure, predicated on Convolutional Neural Networks, to successfully enable feature extraction within high-precision optical time-frequency signals, and provide effective identification and alerts for abnormal link states. Experimental validation confirms that the proposed method not only delivers an efficacy on par with traditional manual techniques, but also excels in swiftly identifying anomalies that typically elude conventional approaches. This investigation furnishes novel theoretical backing and forecasting tools for high-precision optical transmission. |
| first_indexed | 2024-03-10T23:38:56Z |
| format | Article |
| id | doaj.art-c433ca726e354a9bacdf880e70db3727 |
| institution | Directory Open Access Journal |
| issn | 2304-6732 |
| language | English |
| last_indexed | 2024-03-10T23:38:56Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Photonics |
| spelling | doaj.art-c433ca726e354a9bacdf880e70db37272023-11-19T02:39:47ZengMDPI AGPhotonics2304-67322023-08-0110892010.3390/photonics10080920A Deep-Learning-Based Method for Optical Transmission Link Assessment Applied to Optical Clock ComparisonsSibo Gui0Meng Shi1Zhaolong Li2Haitao Wu3Quansheng Ren4Jianye Zhao5School of Electronics, Peking University, Beijing 100871, ChinaSchool of Electronics, Peking University, Beijing 100871, ChinaSchool of Electronics, Peking University, Beijing 100871, ChinaSchool of Electronics, Peking University, Beijing 100871, ChinaSchool of Electronics, Peking University, Beijing 100871, ChinaSchool of Electronics, Peking University, Beijing 100871, ChinaWe apply the Empirical Mode Decomposition (EMD) algorithm and the Time Convolutional Network (TCN) structure, predicated on Convolutional Neural Networks, to successfully enable feature extraction within high-precision optical time-frequency signals, and provide effective identification and alerts for abnormal link states. Experimental validation confirms that the proposed method not only delivers an efficacy on par with traditional manual techniques, but also excels in swiftly identifying anomalies that typically elude conventional approaches. This investigation furnishes novel theoretical backing and forecasting tools for high-precision optical transmission.https://www.mdpi.com/2304-6732/10/8/920optical clocktime-frequency transferartificial neural network |
| spellingShingle | Sibo Gui Meng Shi Zhaolong Li Haitao Wu Quansheng Ren Jianye Zhao A Deep-Learning-Based Method for Optical Transmission Link Assessment Applied to Optical Clock Comparisons Photonics optical clock time-frequency transfer artificial neural network |
| title | A Deep-Learning-Based Method for Optical Transmission Link Assessment Applied to Optical Clock Comparisons |
| title_full | A Deep-Learning-Based Method for Optical Transmission Link Assessment Applied to Optical Clock Comparisons |
| title_fullStr | A Deep-Learning-Based Method for Optical Transmission Link Assessment Applied to Optical Clock Comparisons |
| title_full_unstemmed | A Deep-Learning-Based Method for Optical Transmission Link Assessment Applied to Optical Clock Comparisons |
| title_short | A Deep-Learning-Based Method for Optical Transmission Link Assessment Applied to Optical Clock Comparisons |
| title_sort | deep learning based method for optical transmission link assessment applied to optical clock comparisons |
| topic | optical clock time-frequency transfer artificial neural network |
| url | https://www.mdpi.com/2304-6732/10/8/920 |
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