气体传感器漂移补偿使用半监督集体分类器,具有多层特征和中心损失
Kai Jiang1,2, Min Zeng1, Tao Wang3
1National Key Laboratory of Advanced Micro and Nano Manufacture Technology, Shanghai Jiao Tong University, Shanghai 200240, China.
ACS sensors
|April 8, 2025
概括
这项研究引入了一种新的半监督域自适应卷积神经网络 (CNN),以解决电子鼻子中的气体传感器漂移问题. 该方法通过有效补偿传感器漂移而提高性能,而不需要广泛的标记数据.
科学领域:
- 传感器技术 传感器技术
- 人工智能的人工智能是人工智能.
- 机器学习是机器学习.
背景情况:
- 气体传感器的漂移显著降低了电子鼻子 (E-nose) 系统的性能.
- 传统的漂移补偿方法在复杂的数据关系中扎,并且通常需要不切实际的标记数据,无论是漂移还是非漂移状态.
研究的目的:
- 开发一种有效可靠的算法级解决方案,用于E-nose系统中的气体传感器漂移补偿.
- 通过使用半监督域自适应方法,通过准确补偿传感器漂移来提高E-nose性能.
主要方法:
- 提出了一个半监督域自适应卷积神经网络 (CNN),包含集体分类器,多层特征提取,预训练和中心损失.
- 在希尔伯特空间中利用最大平均差异 (MMD) 来评估跨特征级别和加权集团预测的域相似性.
- 采用MMD作为强大的特征学习的预训损失和集中类内特征表示的中心损失.
主要成果:
- 在两个数据集上实现了76.06% (长漂移) 和82.07% (短漂移) 的平均分类准确度.
- 在回归任务中获得0.804的平均R平方得分,比传统方法显著改进.
- 验证了拟议方法在解决气体传感器漂移补偿方面的有效性和可靠性.
结论:
- 拟议的半监督域适应CNN有效地弥补了气体传感器漂移,显著提高了E-nose系统的性能.
- 该方法能够利用多层次功能和域调整技术,为现实世界E-nose应用提供了强大的解决方案.
- 这项工作为解决气体传感技术中传感器漂移的持续挑战提供了宝贵的算法进步.
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