Neural Network Radionuclide Identification Algorithm Based on Exponential Smoothing
- Resource Type
- Conference
- Authors
- Wang, Tang; He, Xin; Ge, Liangquan; Li, Fei; Gu, Yi; Gu, Zhixing
- Source
- 2022 International Conference on Computation, Big-Data and Engineering (ICCBE) Computation, Big-Data and Engineering (ICCBE), 2022 International Conference on. :154-157 May, 2022
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Smoothing methods
Fluctuations
Neural networks
Interference
Detectors
Feature extraction
Real-time systems
γ spectrum
exponential smoothing
neural network
radionuclide identification
- Language
The development of nuclear energy and the application of nuclear technology is inseparable from the detection of radionuclides. Considering the incomplete utilization of γ-spectral information in traditional spectral analysis methods, the accuracy is not high. In this study, exponential smoothing is performed on the energy spectrum signal of length 1024 to eliminate the influence of the statistical fluctuation of the detector, and then it is used as the input of the neural network to realize the full spectrum analysis. The method avoids the complex feature extraction process and can identify a single radionuclide in real time.