基于拉曼光谱和区域适应卷积神经网络的轻燃料分类
Bin Tang1, Yongjiao Yuan1, Hua Yang1
1Chongqing Key Laboratory of Fiber Optic Sensing and Photoelectric Detection, Chongqing University of Technology, Chongqing, 400054, China. tangbin@cqut.edu.cn.
Analytical methods : advancing methods and applications
|November 3, 2025
概括
一个新的深度学习模型准确地使用拉曼光谱学对汽油和柴油等轻型燃料进行分类. 这种快速,适应现场的方法提高了战场燃料安全和战斗效率.
科学领域:
- 分析化学 分析化学
- 频谱学是一种光谱学.
- 化学中的人工智能.
背景情况:
- 现代战争需要强大的能源安全,以实现战斗效率和战略成功.
- 轻型燃料的现场适应性,快速和准确的检测对于供应链完整性至关重要.
- 便携式拉曼光谱对轻燃料分析具有前景,但面临的挑战是光谱相似性和低信号噪声比.
研究的目的:
- 开发一种使用拉曼光谱学分类光燃料的新,高度准确和高效的方法.
- 为了克服复杂燃料混合物的拉曼光谱中传统特征提取的局限性.
- 为在苛刻的环境中快速地在现场识别燃料提供实用解决方案.
主要方法:
- 从16种不同等级和轻型燃料 (汽油,柴油,喷气式飞机燃料) 的来源收集了拉曼光谱.
- 提出了用于燃料分类的新型一维卷积神经网络 (1D-CNN) 模型.
- 实施了特定区域的特征提取与适应加权,以增强辨别能力,专注于光谱峰值的重要性.
主要成果:
- 1D-CNN模型实现了高分类性能:96.16%的平均准确率,96.23%的精度,96.08%的回忆率,96.12%的F1得分.
- 与其他方法相比,该模型表现出优越的性能,参数复杂性低 (0.7M),训练时间短 (226.32秒).
- 灵敏度分析证实了选择的模型参数对于强大的燃料识别的最佳性.
结论:
- 拟议的区域认知1D-CNN为快速战场识别轻型燃料提供了一种新而实用的解决方案.
- 该方法有效地解决了雷曼光谱学中光谱相似性和低信号噪声比的挑战.
- 这项工作为应用深度学习模型用于细粒度光谱分析和分类提供了宝贵的见解.
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