Adaptive decentralized AI scheme for signal recognition of distributed sensor systems
Artificial intelligence (AI) plays a critical role in signal recognition of distributed sensor systems (DSS), boosting its applications in multiple monitoring fields. Due to the domain differences between massive sensors in signal acquisition conditions, such as manufacturing process, deployment, an...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Institue of Optics and Electronics, Chinese Academy of Sciences
2024-12-01
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| Series: | Opto-Electronic Advances |
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| Online Access: | https://www.oejournal.org/article/doi/10.29026/oea.2024.240119 |
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| _version_ | 1832589859815948288 |
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| author | Shixiong Zhang Hao Li Cunzheng Fan Zhichao Zeng Chao Xiong Jie Wu Zhijun Yan Deming Liu Qizhen Sun |
| author_facet | Shixiong Zhang Hao Li Cunzheng Fan Zhichao Zeng Chao Xiong Jie Wu Zhijun Yan Deming Liu Qizhen Sun |
| author_sort | Shixiong Zhang |
| collection | DOAJ |
| description | Artificial intelligence (AI) plays a critical role in signal recognition of distributed sensor systems (DSS), boosting its applications in multiple monitoring fields. Due to the domain differences between massive sensors in signal acquisition conditions, such as manufacturing process, deployment, and environments, current AI schemes for signal recognition of DSS frequently encounter poor generalization performance. In this paper, an adaptive decentralized artificial intelligence (ADAI) method for signal recognition of DSS is proposed, to improve the entire generalization performance. By fine-tuning pre-trained model with the unlabeled data in each domain, the ADAI scheme can train a series of adaptive AI models for all target domains, significantly reducing the false alarm rate (FAR) and missing alarm rate (MAR) induced by domain differences. The field tests about intrusion signal recognition with distributed optical fiber sensors system demonstrate the efficacy of the ADAI scheme, showcasing a FAR of merely 4.3% and 0%, along with a MAR of only 1.4% and 2.7% within two specific target domains. The ADAI scheme is expected to offer a practical paradigm for signal recognition of DSS in multiple application fields. |
| format | Article |
| id | doaj-art-9b4eeafb02b54ad984a0da8595189392 |
| institution | Kabale University |
| issn | 2096-4579 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Institue of Optics and Electronics, Chinese Academy of Sciences |
| record_format | Article |
| series | Opto-Electronic Advances |
| spelling | doaj-art-9b4eeafb02b54ad984a0da85951893922025-01-24T06:20:28ZengInstitue of Optics and Electronics, Chinese Academy of SciencesOpto-Electronic Advances2096-45792024-12-0171211310.29026/oea.2024.240119OEA-2024-0119SunqizhenAdaptive decentralized AI scheme for signal recognition of distributed sensor systemsShixiong Zhang0Hao Li1Cunzheng Fan2Zhichao Zeng3Chao Xiong4Jie Wu5Zhijun Yan6Deming Liu7Qizhen Sun8School of Optical and Electronic Information, National Engineering Research Center of Next Generation Internet Access-system, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Optical and Electronic Information, National Engineering Research Center of Next Generation Internet Access-system, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Optical and Electronic Information, National Engineering Research Center of Next Generation Internet Access-system, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Optical and Electronic Information, National Engineering Research Center of Next Generation Internet Access-system, Huazhong University of Science and Technology, Wuhan 430074, ChinaNanjing Research Institute of Electronic Equipment, Nanjing 210007, ChinaWenzhou Quality and Technology Testing Research Institute, Wenzhou 325000, ChinaSchool of Optical and Electronic Information, National Engineering Research Center of Next Generation Internet Access-system, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Optical and Electronic Information, National Engineering Research Center of Next Generation Internet Access-system, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Optical and Electronic Information, National Engineering Research Center of Next Generation Internet Access-system, Huazhong University of Science and Technology, Wuhan 430074, ChinaArtificial intelligence (AI) plays a critical role in signal recognition of distributed sensor systems (DSS), boosting its applications in multiple monitoring fields. Due to the domain differences between massive sensors in signal acquisition conditions, such as manufacturing process, deployment, and environments, current AI schemes for signal recognition of DSS frequently encounter poor generalization performance. In this paper, an adaptive decentralized artificial intelligence (ADAI) method for signal recognition of DSS is proposed, to improve the entire generalization performance. By fine-tuning pre-trained model with the unlabeled data in each domain, the ADAI scheme can train a series of adaptive AI models for all target domains, significantly reducing the false alarm rate (FAR) and missing alarm rate (MAR) induced by domain differences. The field tests about intrusion signal recognition with distributed optical fiber sensors system demonstrate the efficacy of the ADAI scheme, showcasing a FAR of merely 4.3% and 0%, along with a MAR of only 1.4% and 2.7% within two specific target domains. The ADAI scheme is expected to offer a practical paradigm for signal recognition of DSS in multiple application fields.https://www.oejournal.org/article/doi/10.29026/oea.2024.240119artificial intelligence (ai)signal recognitiondistributed sensor systems (dss)distributed optical fiber sensors (dofs) |
| spellingShingle | Shixiong Zhang Hao Li Cunzheng Fan Zhichao Zeng Chao Xiong Jie Wu Zhijun Yan Deming Liu Qizhen Sun Adaptive decentralized AI scheme for signal recognition of distributed sensor systems Opto-Electronic Advances artificial intelligence (ai) signal recognition distributed sensor systems (dss) distributed optical fiber sensors (dofs) |
| title | Adaptive decentralized AI scheme for signal recognition of distributed sensor systems |
| title_full | Adaptive decentralized AI scheme for signal recognition of distributed sensor systems |
| title_fullStr | Adaptive decentralized AI scheme for signal recognition of distributed sensor systems |
| title_full_unstemmed | Adaptive decentralized AI scheme for signal recognition of distributed sensor systems |
| title_short | Adaptive decentralized AI scheme for signal recognition of distributed sensor systems |
| title_sort | adaptive decentralized ai scheme for signal recognition of distributed sensor systems |
| topic | artificial intelligence (ai) signal recognition distributed sensor systems (dss) distributed optical fiber sensors (dofs) |
| url | https://www.oejournal.org/article/doi/10.29026/oea.2024.240119 |
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