Fast nuclide identification based on a sequential Bayesian method

Machine learning in nuclear physics at low and intermediate energies

“…Using the event mode sequence information of radionuclides, a well trained sequential Bayesian method can achieve the rapid identification of radioactive substances [163]. The machine learning method, e.g.…”

Aging Time Prediction Model Analysis and Numerical Simulation of Random Degradation Equipment Based on Big Data Linkage Technology

“…e remaining life of the equipment is predicted based on the condition monitoring data. Typical parameter identification and updating methods include Kalman filtering [11,12], particle filtering [13,14], and Bayesian methods [15][16][17].…”

“…The remaining life of the equipment is predicted based on the condition monitoring data. Typical parameter identification and updating methods include Kalman filtering [ 11 , 12 ], particle filtering [ 13 , 14 ], and Bayesian methods [ 15 – 17 ]. The common mechanistic models used for the remaining life prediction include the Paris model, Forman model, and various improvements and extensions based on them, mainly to describe the crack expansion and laminar crack growth [ 18 ].…”

Detection of Isotopes in Urban Source Search Gamma Spectra Using Hopfield Neural Network

“…A genetic algorithm was proposed in [17] to deconvolve overlapping photopeaks in gamma-ray spectra aiming at identifying radionuclides. Other statistical-based methods developed for isotope identification include multiple linear regression [18], sequential deconvolution [19], Bayesian statistical inference [20], and physicsbased importance weighting [21]. In addition, several methods utilize tools from artificial intelligence such as particle swarm optimization [22], fireworks algorithm [23], expert systems [24], clustering [25], fuzzy logic [11][26], fuzzy support vector regression [27], wavelet processing [28], and fuzzy-genetic hybrid approaches [29].…”