@inproceedings{4bc1a76df6d34d748021ef5da86e8f02,
title = "Drift-Resistant Hydrogen Sensing via Domain Adaptation Graph Neural Networks",
abstract = "To mitigate the impact of sensor drift in hydrogen detection exposed to complex environmental interferences, this study proposes a Domain Adaptation Graph Neural Network (DAGNN). By leveraging Graph Attention Networks (GAT) to model nonlinear relationships among sensors and employing Representation Subspace Distance (RSD) for dynamic domain alignment, DAGNN effectively compensates for drift induced by environmental disturbances. Experiments were conducted using an array of five hydrogen sensors under 32 environmental conditions, yielding 960 samples. Results demonstrate that DAGNN achieves a MRE of 4.0\% and a R2 of 0.99, outperforming existing methods. These findings underscore the potential of DAGNN in enhancing the robustness and precision of hydrogen sensing, offering valuable contributions to hydrogen safety monitoring technologies.",
keywords = "Domain Alignment, GAT, RSD, Sensor Drift",
author = "Haifeng Se and Kai Song and Bo Wang and Lu Xia",
note = "Publisher Copyright: {\textcopyright} Beijing Paike Culture Commu. Co., Ltd. 2026.; 20th Annual Conference of China Electrotechnical Society, ACCES 2025 ; Conference date: 19-09-2025 Through 21-09-2025",
year = "2026",
doi = "10.1007/978-981-95-9043-8\_28",
language = "英语",
isbn = "9789819590421",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "252--259",
editor = "Qingxin Yang and Dianguo Xu and Xuerong Ye and Qiuyue Nie and Yueshi Guan",
booktitle = "The Proceedings of the 20th Annual Conference of China Electrotechnical Society - Volume I",
address = "德国",
}