通过转移网络重新设计,在非破坏性木材测试中有效地检测近红外频谱
Dapeng Jiang1, Keqi Wang1, Hongbo Li2
1College of Computer and Control Engineering, Northeast Forestry University, 26 Hexing Rd., Harbin 150040, China.
Sensors (Basel, Switzerland)
|February 24, 2024
概括
开发了一种新的深度传输网络,用于近红外 (NIR) 频谱检测,增强非破坏性木材测试. 条件域对抗网络和全球化损失优化转移网络在光谱分析中表现出卓越的性能.
科学领域:
- 频谱学是一种光谱学.
- 机器学习 机器学习
- 材料科学 材料科学 材料科学
背景情况:
- 近红外 (NIR) 光谱对于非破坏性材料分析至关重要.
- 为各种样本集开发强大的检测模型仍然是一个挑战.
- 深度学习为高级光谱数据分析提供了潜力.
研究的目的:
- 系统地开发和评估用于NIR频谱检测的深度传输网络.
- 为了确定NIR光谱分析的最佳网络组件和结构.
- 评估无监督域适应在非破坏性木材测试中的有效性.
主要方法:
- 开发使用卷积神经网络模块的深度传输网络.
- 探索三个不受监督的领域适应结构.
- 为了提高性能,重新设计和评估五个传输网络.
- 对直接标准化,零碎直接标准化和光谱空间转换进行比较分析.
主要成果:
- 条件域对抗网络 (CDAN) 和全球化损失优化转移 (GLOT) 网络显示出卓越的性能.
- CDAN实现了82.11%的确定系数 (R2) 和12.237.237的预测平方平均误差 (RMSEP).
- GLOT实现了83.59%的R2和11.582的RMSEP,优于其他车型.
结论:
- 深度传输网络,特别是CDAN和GLOT,对于NIR频谱检测非常有效.
- 这些网络为实用,可靠和高效的非破坏性木材测试提供了重大进展.
- 这项研究强调了深度学习在光谱分析中用于材料表征的潜力.
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