气体检测模式识别算法的进展,用于化学抵抗性气体传感器
Guangying Zhou1, Bingsheng Du1,2, Jie Zhong1
1Chongqing Key Laboratory of Optical Fiber Sensor and Photoelectric Detection, Chongqing University of Technology, Chongqing 400054, China.
Materials (Basel, Switzerland)
|November 9, 2024
概括
模式识别方法通过提高选择性和稳定性来增强化学阻力气体传感器. 本综述详细介绍了用于先进气体检测和监测的数据处理技术和算法.
科学领域:
- 材料科学 材料科学 材料科学
- 分析化学 分析化学
- 传感器技术 传感器技术
背景情况:
- 化学阻抗传感器对于气体检测至关重要,提供灵活性和快速响应等优势.
- 挑战包括复杂环境中的选择性和稳定性差.
- 工业化需要改进的气体监测解决方案.
研究的目的:
- 系统地审查化学阻力气体传感器数据处理方面的进展.
- 要突出模式识别对气体传感的影响.
- 分析特征提取方法和识别算法.
主要方法:
- 对特征提取技术的审查 (响应曲线特征,曲线拟合,转换域).
- 分析基于传统和神经网络的识别算法,用于气体歧视.
- 对现有研究进行了广泛的文献审查.
主要成果:
- 模式识别显著提高了选择性和抗干扰稳定性.
- 各种数据处理方法提供了有效的特征提取.
- 神经网络算法在复杂的气体歧视方面表现有前途.
结论:
- 模式识别对于克服化学阻力气体传感器的局限性至关重要.
- 进一步研究数据处理和算法将提高气体检测能力.
- 未来的前景在于集成的传感器系统和用于现实世界的先进人工智能应用.
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