通过混合转移学习提高激光诱导分解光谱煤炭分析的精度
Ji Chen1, Wenhao Yan1, Lizhu Kang1
1Wuhan National Laboratory for Optoelectronics (WNLO), Huazhong University of Science and Technology (HUST), Wuhan, Hubei, 430074, P. R. China. xyli@mail.hust.edu.cn.
Analytical methods : advancing methods and applications
|September 27, 2023
概括
一种新的混合转移学习方法 (HTr-LIBS) 改进了使用激光诱导分解光谱 (LIBS) 的煤炭分析. 这种技术结合了微调和样本重量调整,以提高准确性,特别是在有限的数据.
科学领域:
- 分析化学 分析化学
- 频谱学是一种光谱学.
- 机器学习 机器学习
背景情况:
- 激光诱导分解光谱 (LIBS) 为煤炭提供实时在线分析.
- 转移学习,特别是微调,通过利用预训练模型,在有限的培训数据下提高LIBS准确性.
研究的目的:
- 提出和评估一种混合转移学习方法 (HTr-LIBS),用于增强LIBS煤炭分析.
- 提高使用LIBS测定煤炭成分的分析准确性,特别是当训练数据集很小时.
主要方法:
- 开发了一种混合转移学习方法 (HTr-LIBS),结合了微调和样本重量调整.
- 预先对源域和目标域数据进行神经网络训练,根据预测错误反复调整源域样本权重,并使用目标训练集对模型进行微调.
主要成果:
- 与直接建模相比,HTr-LIBS显著提高了灰和挥发物质含量的分析准确性.
- 在19个训练集大小下,HTr-LIBS实现了0.9029 (灰) 和0.9627 (挥发物质) 的R2P值,超过了直接建模 (0.8105和0.9440).
- 该方法表现出比未经样本重量调整的微调更显著和更稳定的改进.
结论:
- 拟议的HTr-LIBS方法有效地提高了LIBS煤炭分析的准确性.
- 在预训练期间整合目标域数据和代样本重量调整都有助于提高性能.
- 在有限的培训数据的情况下,HTr-LIBS提供了一个可靠的解决方案,用于精确的煤炭分析.
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