연구동향 분석

Explainable radionuclide identification algorithm based on the convolutional neural network and class activation mapping

Wang Yu Yao Qingxu Zhang Quanhu Zhang He Lu Yunfeng Fan Qimeng Jiang Nan Yu Wangtao 2022년

논문상세정보
인용/피인용
' Explainable radionuclide identification algorithm based on the convolutional neural network and class activation mapping' 의 주제별 논문영향력
논문영향력 선정 방법
논문영향력 요약
주제
  • Class activation mapping
  • Convolutional neural network
  • Gamma- ray spectrum
  • Radionuclide identification
동일주제 총논문수 논문피인용 총횟수 주제별 논문영향력의 평균
206 0

0.0%

' Explainable radionuclide identification algorithm based on the convolutional neural network and class activation mapping' 의 참고문헌

  • Visualizing and Interpreting Deep Learning Models
    W. Samek [2017]
  • Visualizations of deep neural networks in computer vision: a survey
    C. Seifert [2017]
  • Use of artificial neural networks in measuring characteristics of shielded plutonium for arms control
  • Toward Interpretable Machine Learning: Transparent Deep Neural Networks and beyond, CoRR. Abs/2003
  • TensorFlow, Large-Scale Machine Learning on Heterogeneous Systems
  • Technical and Functional Specifications for Border Monitoring Equipment
  • Status of research and development of learning-based approaches in nuclear science and engineering: A review
  • Rapid nuclide identification algorithm based on convolutional neural network
  • Radionuclide identification method for NaI low-count gamma-ray spectra using artificial neural network
    Sheng Qi [2022]
  • Prototypical Networks for Few-Shot Learning
  • People's Republic of China national metrology specification, Calibration specification for hand-held radiation monitors for detection and identification of radionuclides (JJF 1687-2018)
  • Peak-searching method for low count rate <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e597" altimg="si14.svg"><mml:mi>γ</mml:mi></mml:math> spectrum under short-time measurement based on a generative adversarial network
    Sunci Wu [2021]
  • NMIS plus gamma spectroscopy for attributes of HEU, PU and HE detection
  • Meta-Learning for Semi-supervised Few-Shot Classification
  • Machine learning interpretability : a survey on methods and metrics
  • Machine behaviour
    I. Rahwan [2019]
  • Learning Multiple Layers of Features from Tiny Images
  • Learning Deep Features for Discriminative Localization
    B. Zhou [2016]
  • Largescale Video Classification with Convolutional Neural Networks
  • Isotope identification using deep learning: An explanation
  • ImageNet classification with deep convolutional neural networks
  • Identification of SNM based on low-resolution gamma-ray characteristics and neural network
  • Geant4—a simulation toolkit
  • Gated Bi-directional CNN for object detection
    X. Zeng [2016]
  • Development of neural network model with explainable AI for measuring uranium enrichment
    J. Ryu [2021]
  • Development of a radionuclide identification algorithm based on a convolutional neural network for radiation portal monitoring system
  • Automatic and realtime identification of radionuclides in gamma-ray spectra : a new method based on convolutional neural network trained with synthetic data set
    G. Daniel [2020]
  • A new radionuclide identification method for low-count energy spectra with multiple radionuclides
    Chunmiao Li [2022]
  • A comparison of machine learning methods for automated gamma-ray spectroscopy
    Mark Kamuda [2020]
  • A comparative study of machine learning methods for automated identifi cation of radioisotopes using NaI gamma-ray spectra
    S.M. Galib [2021]