分析测试学报2024，Vol.43Issue(4)：607-613,7.DOI:10.12452/j.fxcsxb.23112615
基于深度残差网络和近红外光谱的煤矸石智能识别
Intelligent Recognition of Coal Gangue Based on Residual Network and Near Infrared Spectroscopy Technology
摘要
Abstract
This study innovatively developed a rapid classification method for coal and gangue,inte-grating near infrared spectroscopy technology with a one-dimensional residual network(1D-ResNet).To ensure the diversity of experimental samples,430 samples of coal and gangue were collected from multiple coal mines in provinces such as Henan,Hebei,and Shandong.Abnormal samples were eliminated based on Euclidean distance to obtain a high-quality dataset for modeling.Building on this,a 1D-ResNet-based classification model was constructed to accurately capture the complex mapping relationships between coal,gangue,and their spectral characteristics.This approach effec-tively solved the problem of gradient vanishing and deeply mined the spectral features of coal and gangue,resulting in highly accurate analysis.After five-fold cross-validation,the model achieved an average accuracy of 96.26%,significantly outperforming traditional machine learning algorithms such as support vector machines and random forests.The model demonstrated high consistency in the trend of loss function changes across both the training and test datasets,indicating good generaliza-tion ability.Tests revealed that the model processes every hundred samples in just 16.230 millisec-onds,further highlighting its advantages and potential application value in the online sorting field for coal and gangue.关键词
煤矸石识别/近红外光谱/深度学习/残差网络Key words
coal gangue identification/near infrared spectroscopy/deep learning/residual net-work分类
化学化工引用本文复制引用
王亚栋,贾俊伟,谭韦君,雷萌..基于深度残差网络和近红外光谱的煤矸石智能识别[J].分析测试学报,2024,43(4):607-613,7.基金项目
国家自然科学基金(62373360,51904197) （62373360,51904197）
天地(常州)自动化股份有限公司科研项目(2022FY0009) （常州）
