GT-KANet:对于以伊特里亚稳定基混合潜能类型传感器的强大的酸盐预测
Qi Pu1, Menglin Zhou1, Daping Chen1
1State Key Laboratory of Integrated Optoelectronics, Key Laboratory of Advanced Gas Sensors, Jilin Province, College of Electronic Science and Engineering, Jilin University, 2699 Qianjin Street, Changchun130012, China.
ACS sensors
|February 3, 2026
概括
我们开发了GT-KANet,这是一种混合深度学习算法,用于使用yttria稳定 (YSZ) 传感器准确检测乙. 这种方法有效地弥补了温度和湿度的变化,改进了便携式气体传感系统.
科学领域:
- 材料科学与工程 材料科学与工程
- 化学传感器 化学传感器
- 人工智能的人工智能
背景情况:
- 基于伊特里亚稳定 (YSZ) 的混合电位气体传感器为乙检测提供了高灵敏度和选择性.
- 实际应用受到传感器输出漂移,噪音和温度和湿度变化导致的波动的限制.
研究的目的:
- 开发一个强大的深度学习算法,用于准确预测乙度和YSZ气体传感器中的温度-湿度补偿.
- 为了提高便携式乙检测系统的可行性,用于现实世界的应用.
主要方法:
- 开发了GT-KANet,这是一个混合深度学习算法,集成Gated Recurrent Unit (GRU) 网络,变压器层与ContraNorm,以及一个Kolmogorov-Arnold网络 (KAN) 模块.
- 在受控的环境条件下,通过自定义的YSZ传感器在综合数据集上训练模型.
- 利用知识蒸来减少模型参数大小.
主要成果:
- 在不同的工作温度,湿度水平和酸盐度中,GT-KANet实现了高预测精度 (RMSE = 0.0920 ppm,MAE = 0.0542 ppm).
- 证明了出色的稳定性和适应性,优于现有的补偿方法.
- 通过知识蒸将模型参数大小减少了55%,同时保持了预测准确性.
结论:
- GT-KANet提供了一种具有竞争力和有效的方法,用于基于YSZ的乙传感器中的温度和湿度补偿.
- 该算法显著提高了在具有挑战性的环境中部署准确和稳定的便携式气体传感系统的潜力.
- 优化的模型适用于资源受限的嵌入式平台,扩大了应用范围.
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