Design of data feature-driven 1D/2D convolutional neural networks classifier for recycling black plastic wastes through laser spectroscopy. (August 2022)

• Record Type: Journal Article
• Authors: Zhou, Kun; Oh, Sung-Kwun; Pedrycz, Witold; Qiu, Jianlong; Fu, Zunwei; Ryu, Byung-Gun
• Is Part Of: Advanced engineering informatics. Volume 53(2022)
• Publication Date: 2022-08
• Subjects: Classification of Black Plastic Wastes -- ATR FT-IR -- Raman -- Laser Spectroscopy -- 1D/2D CNN -- 2D Data Feature Selection; CNN Convolutional neural networks -- NIR Near-infrared -- ATR Attenuated total reflectance -- FT-IR Fourier transform-infrared radiation -- 1/2D One/two dimensional -- PVC Polyvinyl chloride -- PET polyethylene terephthalate -- PP Polypropylene -- PE Polyethylene -- PS Polystyrene -- LIBS Laser-induced breakdown spectroscopy -- RBF Radial basis function -- FRBFNN Fuzzy radial basis function neural network -- MIR Mid-infrared -- AFS ATR FT-IR spectrometer -- RS Raman spectrometer -- SVM Support vector machines -- KNN K-nearest neighbor -- PCA Principal component analysis -- MLP Multilayer perception -- DNA Deoxyribo nucleic acid -- ReLU Rectified linear unit -- RGBA Red green blue alpha -- LSTM Long short-term memory -- RNN Recurrent neural networks; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2022.101695
• Languages: English
• ISSNs: 1474-0346

Abstract

Highlights: AFS and RS are used to effectively obtain the transmittance spectra of black plastic wastes not available in the existing NIR. A feature selection method of the peak points is considered based on the analysis of chemical information for low cost spectrometer by using a small amount of input variables. A method of 2D reconstruction through mapping from 1D signal data based on peak points is proposed to effectively deal with spatial structure information between data. 2DCNN design driven by 2D information features or chemical peak points data leads to high performance as well as anti-noise capability through effective data mapping. Abstract: In this study, two types of convolutional neural network (CNN) classifiers are designed to handle the problem of classifying black plastic wastes. In particular, the black plastic wastes have the property of absorbing laser light coming from spectrometer. Therefore, the classification of black plastic wastes remains still a challenging problem compared to classifying other colored plastic wastes using existing spectroscopy (i.e., NIR). When it comes the classification problem of black plastic wastes, effective classification techniques by the laser spectroscopy of Fourier Transform-Infrared Radiation (FT-IR) with Attenuated Total Reflectance (ATR) and Raman to analyze the classification problem of black plastic wastes are introduced. Due to the strong ability of extracting spatial features and remarkable performance in image classification, 1D and 2D CNN through data features are designed as classifiers. The technique of chemical peak points selection is considered to reduce data redundancy. Furthermore, through the selection of data features based on the extracted 1D data with peak points is introduced. Experimental results demonstrate that 2DCNN classifier designed with the help of 2D data feature selection as well as 1DCNN classifier shows the best performance compared with other reported methods for classifying black plastic wastes.