A direct method of nuclear pulse shape discrimination based on principal component analysis and support vector machine
- Resource Type
- Authors
- Y.T. Li; J.J. Zhu; Z.H. Zhang; B. Liao; Liwei Zhang; C.Y. Hu; X.Y. Fan
- Source
- Journal of Instrumentation. 14:P06020-P06020
- Subject
- 010308 nuclear & particles physics
business.industry
Pulse (signal processing)
Dimensionality reduction
Direct method
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Pattern recognition
Information loss
Fault (power engineering)
01 natural sciences
030218 nuclear medicine & medical imaging
Support vector machine
03 medical and health sciences
symbols.namesake
ComputingMethodologies_PATTERNRECOGNITION
0302 clinical medicine
0103 physical sciences
Principal component analysis
Gaussian function
symbols
Artificial intelligence
business
Instrumentation
Mathematical Physics
Mathematics
- Language
- ISSN
- 1748-0221
Fault diagnosis and particle discrimination can be fundamentally solved as a case of pulse shape discrimination (PSD). The classical methods of PSD are inconvenient or not effective when more than two pulse shapes need to be discriminated or the pulse shapes have only small differences. A direct method to discriminate nuclear pulse shapes based on principal component analysis (PCA) and support vector machine (SVM) is reported in this paper. The training and testing accuracies of SVM classifiers with different kernels were not the same, and the algorithms were shown to have great noise immunity. Though the samples in the Group A and Group C cannot be discriminated with the naked eye, the accuracies are all above 94.7% if suitable SVM kernels are selected. There is no evidence showing that the Gaussian kernel is superior. The lower sampling frequency of the analog-to-digital converter and the information loss caused by dimension reduction were also considered.