BDSER-InceptionNet:一种基于深度学习和平衡分布适应的近红外光谱模型转移的新方法
Jianghai Chen1, Jie Ling1, Nana Lei1
1School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China.
Sensors (Basel, Switzerland)
|July 12, 2025
概括
转移学习通过适应新仪器和样本来增强近红外光谱学 (NIRS) 模型. 这种方法提高了预测准确性和数据利用率,克服了工业应用中的挑战.
科学领域:
- 分析化学 分析化学
- 化学测量 化学测量 化学测量
- 机器学习 机器学习
背景情况:
- 近红外光谱 (NIRS) 模型由于仪器差异,环境因素和样本多样性而难以概括.
- 数据分布的转移和特征不匹配限制了模型在不同NIRS仪器和作物品种的可转移性.
- 现有的NIRS数据和模型在新应用中往往未得到充分利用,阻碍了效率和准确性.
研究的目的:
- 开发一种新的转移学习框架,以提高NIRS分析中的跨仪器兼容性.
- 提高工业环境中NIRS模型的概括能力和预测准确度.
- 为了使以前积累的NIRS数据能够有效地用于新的仪器和品种.
主要方法:
- 提出了一个转移学习框架,将多尺度网络架构 (RX-Inception) 与平衡分布适应 (BDA) 集成在一起.
- 嵌入RX-Inception结构,结合深度可分离的卷积和剩余连接,以增强功能合.
- 集成的挤压和刺激 (SE) 关注动态重新校准光谱带重量,以改善特征表示.
- 系统地评估了六种模型适应性能转移策略.
主要成果:
- 拟议的BDSER-InceptionNet在NIRS分析的主要仪器上实现了最先进的性能.
- 方法6证明了从初级到二级仪器的NIRS模型共享成功,减轻了光谱差异.
- 转移效率的显著改善被观察到,使得模型能够更好地适应不同的仪器.
结论:
- 开发的转移学习框架有效地解决了NIRS中的工具异质性.
- 拟议的方法增强了模型共享和预测准确性,最大限度地提高了现有的NIRS数据的实用性.
- 这种方法为各种工业应用中强大的和可适应的NIRS建模提供了有希望的解决方案.
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