Estimation of Thickness Samples Using Gamma Scattering Techniques Based on Machine Learning Approach

カテゴリ: 論文誌(論文単位)

グループ名: 【E】センサ・マイクロマシン部門

発行日: 2024/10/01

タイトル(英語): Estimation of Thickness Samples Using Gamma Scattering Techniques Based on Machine Learning Approach

著者名: Huynh Thanh Nhan (Faculty of Physics and Engineering Physics, University of Science /Vietnam National University ), Le Hoang Minh (Faculty of Physics and Engineering Physics, University of Science /Vietnam National University ), Vo Hoang Nguyen (Faculty o

著者名(英語): Huynh Thanh Nhan (Faculty of Physics and Engineering Physics, University of Science /Vietnam National University ), Le Hoang Minh (Faculty of Physics and Engineering Physics, University of Science /Vietnam National University ), Vo Hoang Nguyen (Faculty of Physics and Engineering Physics, University of Science /Vietnam National University ), Nguyen Duy Thong (Faculty of Physics and Engineering Physics, University of Science /Vietnam National University ), Tran Thien Thanh (Faculty of Physics and Engineering Physics, University of Science /Vietnam National University ), Chau Van Tao (Faculty of Physics and Engineering Physics, University of Science /Vietnam National University )

キーワード: thickness estimation，machine learning，gamma scattering，Monte Carlo

要約(英語): Gamma-ray scattering is a powerful method in the non-destructive testing field. Many researches related to gamma-ray scattering is being used in the world. Gamma-ray scattering can be used to determine thickness, structure as well as components in a material. Along with computer science, application of computer science in many scientific fields may constitute good achievements such as precision and speed of data analysis. In this paper, Machine learning is being used in gamma-ray scattering to determine thickness of material based on gamma-ray spectrum. To provide a dataset for machine learning, Monte Carlo was used for Ti, Mn, Fe, Co, Cu, Zn samples from 1mm to 50mm. In Machine learning, 8th-degree polynomial regression method is used.

本誌: 電気学会論文誌E（センサ・マイクロマシン部門誌） Vol.144 No.10 （2024） 特集：The 4th International Conference on Engineering Physics, MEMS-Biosensors and Applications (4ICEBA2023)

本誌掲載ページ: 303-306 p

原稿種別: 論文／英語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejsmas/144/10/144_303/_article/-char/ja/