哈特芬:基于混合注意力机制的前神经网络深度学习模型,用于对光谱数据上的三阶段材料的塑料材料分类
Nazym Alimbekova1,2, Hari Mohan Rai3, Tursinbay Turymbetov4
1Department of Computer and Software Engineering, L.N. Gumilyov Eurasian National University, Astana, Kazakhstan.
PloS one
|December 2, 2025
概括
一个基于混合注意力机制的新Feedforward神经网络 (HAttFFNN) 准确地使用光谱数据对聚乙烯二甲 (PET) 材料进行分类. 这种先进的技术在精密回收和材料分析方面超越了现有的模型.
科学领域:
- 频谱学是一种光谱学.
- 材料科学 材料科学 材料科学
- 机器学习 机器学习
背景情况:
- 准确的塑料材料分类对于自动回收,环境监测和质量控制至关重要.
- 现有的方法难以区分视觉和光谱相似的塑料类型,影响可持续性和监管合规性.
- 聚乙烯二甲 (PET) 的分类由于其阶段和外观的变化而带来了独特的挑战.
研究的目的:
- 引入一种基于混合注意力机制的新型前神经网络 (HAttFFNN),用于精确的三阶段聚乙烯二甲 (PET) 材料检测.
- 评估HAttFFNN模型的性能与各种最先进的 (SOTA) 模型相比,使用大型光谱数据集.
- 证明模型在复杂的分类任务中的有效性,特别是对光谱相似材料的区分.
主要方法:
- 收集了295,327个样本的数据集,其中包括从900-1700nm的吸收和波长数据,使用数字光处理 (DLP) 近红外 (NIR) 扫描纳米评估模块 (EVM).
- 应用了预处理技术,包括Savitzky-Golay过器,干扰去除,标准正常变量 (SNV) 和多倍散射校正 (MSC) 对光谱数据.
- 开发和训练HAttFFNN模型,将其性能与多头神经网络 (MHNN),VGG16,1D-CNN,ResNet,LSTM和GRU模型进行比较.
主要成果:
- 与SOTA模型相比,HAttFFNN模型在所有指标 (准确性,精度,回忆,F1得分,特异性) 上取得了优异的性能.
- 第1阶段 (PET清晰与PET危险) 的准确率达到99.33%,第二阶段 (PET与其他) 的准确率达到99.32%,第三阶段 (PET有色与PET透明) 的准确率达到99.28%.
- 对于每个类别,观察到始终高的精度,回忆和特异性,根平均平方误差 (RMSE) 值较低 (分别为0.1408,0.1249,0.1403).
结论:
- 该HAttFFNN模型在PET材料的光谱分类中表现出极高的准确性和低的错误率.
- 拟议的模型有效地解决了复杂的差异化挑战,在光谱相似的材料上表现优于传统方法.
- HAttFFNN为先进的塑料材料识别提供了强大而有效的解决方案,增强了回收和质量控制中的应用.
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