利用机器学习对基于伽马散射光谱的材料进行分类

IF 1.1 4区工程技术 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEJ Transactions on Electrical and Electronic Engineering Pub Date : 2025-07-28 DOI:10.1002/tee.70120

Huynh Thanh Nhan, Nguyen Duy Thong, Le Hoang Minh, Tran Thien Thanh, Chau van Tao

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引用次数: 0

摘要

在本研究中，使用机器学习来确定基于伽马散射光谱的材料和材料厚度。本研究使用的材料有：Al， Si, Fe, Mn, Mg, Co, Cu, Zn, Ti，厚度从1mm到50mm不等。为了同时估计厚度和材料，采用了1-散射光谱和2-散射光谱。随机森林算法用于训练和评估机器学习模型。本研究结果的决定系数R2 = 0.990，均方误差MSE = 1.250。©2025日本电气工程师协会和Wiley期刊有限责任公司。

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Use Machine Learning to Classify Materials Based on Gamma Scattering Spectra

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Use Machine Learning to Classify Materials Based on Gamma Scattering Spectra

In this study, machine learning is used to determine materials and thickness of materials based on gamma scattering spectra. Materials used in this study are: Al, Si, Fe, Mn, Mg, Co, Cu, Zn, and Ti, which have thicknesses varying from 1 mm to 50 mm. In order to estimate thickness as well as material simultaneously, 1-scattering spectrum and 2-scattering spectrum are used. The Random Forest algorithm was used in training and evaluating the machine learning model. Results of this study provided a coefficient of determination R² = 0.990 and mean squared error MSE = 1.250. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

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来源期刊

IEEJ Transactions on Electrical and Electronic Engineering 工程技术-工程：电子与电气

CiteScore

2.70

自引率

10.00%

发文量

199

审稿时长

4.3 months

期刊介绍： IEEJ Transactions on Electrical and Electronic Engineering (hereinafter called TEEE ) publishes 6 times per year as an official journal of the Institute of Electrical Engineers of Japan (hereinafter "IEEJ"). This peer-reviewed journal contains original research papers and review articles on the most important and latest technological advances in core areas of Electrical and Electronic Engineering and in related disciplines. The journal also publishes short communications reporting on the results of the latest research activities TEEE ) aims to provide a new forum for IEEJ members in Japan as well as fellow researchers in Electrical and Electronic Engineering from around the world to exchange ideas and research findings.