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在可回收容器中的污染物的分类和识别,基于使用电子鼻子的递归特征消除-光梯度增强机器算法

Fushuai Ba¹, Peng Peng¹, Yafei Zhang¹

¹School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, China.

|November 25, 2023

概括

本研究介绍了一种使用电子鼻子和递归特征消除-光梯度增强机 (RFE-LightGBM) 算法的自动容器分类方法. 采用RFE-LightGBM方法实现了95%的回收精度,大大改善了手工方法.

科学领域:

环境科学环境科学
分析化学分析化学
计算机科学计算机科学

背景情况:

有效的回收机制对于环境保护至关重要.
可回收容器的手动分类是主观的,缺乏准确性.
需要自动化方法来提高集装箱分类的效率和可靠性.

研究的目的:

使用电子鼻子开发可回收容器的自动识别方法.
为了比较递归特征消除-光梯度增强机 (RFE-LightGBM) 与主要组件分析 (PCA) 的特征维度降低的有效性.
为了提高可回收容器的分类准确性.

主要方法:

使用电子鼻子提取了气味特征.
特性数据集是从气体反应数据构建的.
使用主要组件分析 (PCA) 和递归特征消除-光梯度增强机 (RFE-LightGBM) 进行了尺寸缩小.
对原始,PCA缩小和RFE-LightGBM缩小数据集进行了分类准确性评估.

主要成果:

该RFE-LightGBM算法实现了最高的分类准确率95%.
PCA缩小维度数据集的分类准确率为92.02%.
最初的特征数据集显示分类准确率为88.38%.

结论:

RFE-LightGBM算法为可回收容器的自动分类提供了一种卓越的方法.
使用电子鼻子技术进行自动分类,大大提高了回收效率.
这种方法克服了手工分类主观性的局限性,并提高了准确性.

关键词:

污染物的分类污染物的分类.电子鼻子电子鼻子增强光梯度的机器增强光梯度的机器递归特征消除递归特征消除

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