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

被引：0
作者：
Huynh, Thanh Nhan [1 ,2 ]
Le, Hoang Minh [1 ,2 ]
Vo, Hoang Nguyen [1 ,2 ]
Nguyen, Duy Thong [1 ,2 ]
Tran, Thien Thanh [1 ,2 ]
Chau Van, Tao [1 ,2 ]
机构：
[1] Faculty of Physics and Engineering Physics, University of Science Ho Chi Minh City, Vietnam 227, Nguyen Van Cu Std., Ward 4, District 5, Ho Chi Minh City
[2] Vietnam National University Ho Chi Minh City, Vietnam Linh Trung Ward, Thu Duc City, Ho Chi Minh City
来源：
IEEJ Transactions on Sensors and Micromachines | 2024年 / 144卷 / 10期
关键词：
gamma scattering; machine learning; Monte Carlo; thickness estimation;
D O I：
10.1541/ieejsmas.144.303
中图分类号：
学科分类号：
摘要：
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. © 2024 The Institute of Electrical Engineers of Japan.
引用
收藏
页码：303 / 306
页数：3
相关论文
共 8 条
  • [1] Hussein E. M. A., Whynot T. M., A Compton scattering method for inspecting concrete structures, Nuclear Instruments and Methods in Physics Research A, 283, pp. 100-106, (1989)
  • [2] Anjos M. J., Lopes R. T., Borges J. C., Compton Scattering of gamma rays as surface inspection technique, Nuclear Instruments and Methods in Physics Research A, 280, pp. 535-538, (1989)
  • [3] Mullin S. K., Hussein E. M. A., A compton-scatter spectrometry technique for flaw detection, Nuclear Instruments and Methods in Physics Research A, 353, pp. 663-667, (1994)
  • [4] Priyada P., Ramar R., Shivaramu, Application of gamma ray scattering technique for non-destructive evaluation of voids in concrete, Applied Radiation and Isotopes, 74, pp. 13-22, (2013)
  • [5] Samir A. M., Ahmed B., Dheya A. O., Ahmed A., Hussein A. H., Corrosion imaging and thickness determination using micro-Curie radiation sources based on gamma-ray backscattering: experiments and MCNP simulation, Research in Nondestructive Evaluation, 26, pp. 43-59, (2015)
  • [6] Keller P. E., Kangas L. J., Troyer G. L., Hashem S., Kouzes R. T., Nuclear Spectral Analysis via Artificial Neural Networks for Waste Handling, IEEE Transactions on Nuclear Science, 42, 4, pp. 709-715, (1995)
  • [7] Kamuda M., Stinnett J., Sullivan C. J., Automated Isotope Identification Algorithm Using Artificial Neural Networks, IEEE Transactions on Nuclear Science, 64, 7, pp. 1858-1864, (2017)
  • [8] Kamuda M., Zhao J., Huff K., A comparison of machine learning methods for automated gamma-ray spectroscopy, Nuclear Instruments and Methods in Physics Research A, 954, (2020)
1