有效和抗干扰的塑料分类方法,适合基于激光诱导分解光谱的一次性学习

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Zhiying Xu¹, Xingyu Zhao², Xinying Peng³

¹State Key Laboratory of Nuclear Physics and Technology, and Key Laboratory of HEDP of the Ministry of Education, CAPT, Peking University, Beijing, 100871, China; Guangdong Institute of Laser Plasma Accelerator Technology, 510540, China.

|April 16, 2025

概括

本研究介绍了一种高效的,使用激光诱导分解光谱 (LIBS) 和一次性学习进行塑料分类的抗干扰方法. 新技术显著提高了回收应用的塑料识别精度.

科学领域:

材料科学材料科学材料科学
分析化学分析化学
计算机科学计算机科学

背景情况:

有效的塑料回收对于环境可持续性至关重要.
塑料的准确和快速分类是回收行业的一个关键挑战.
现有的方法经常与塑料添加剂的干扰作斗争.

研究的目的:

开发一种高效,防干扰的塑料分类方法.
为此目的,利用一次性学习和激光诱导分解光谱 (LIBS).
提高回收过程中塑料识别的准确性和速度.

主要方法:

开发一个带有全频培训的残余神经网络 (ResNet-FST),用于一次性学习分类.
实现多参数峰值搜索算法用于光谱特征提取.
创建一个具有峰值自动搜索 (LRC-PAS) 的线性残余分类模型,以提高效率和防干扰能力.
优化模型参数,包括残余块 (2) 和神经元 (80).

主要成果:

在一次性学习分类中,ResNet-FST模型实现了99.65%的准确性.
与ResNet-FST相比,LRC-PAS模型显著提高了分类效率.
从塑料添加剂的光谱干扰的机制被阐明.
在抗干扰分类中实现了高精度,有效处理添加剂诱导的光谱干扰.

结论:

拟议的方法为塑料分类提供了高效和抗干扰的解决方案.
开发的LRC-PAS模型显示了回收行业塑料实时分类的巨大潜力.
这种方法有助于通过改进材料识别来推进可持续的塑料回收实践.

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