Spectrogram Analysis of Fission Chamber's Outputs Signals Using Nonnegative Matrix and Tensor Factorization Algorithms

In order to achieve best neutron flux-mapping within nuclear research reactors, we investigate in this paper, the application of blind source separation algorithms to extract different independent components that form the fission chamber output signals. More specifically, we simulated the fission ch...

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Bibliographic Details
Main Authors: Arahmane, Hanane, El Moursli, Rajaa Cherkaoui, El-Mehdi, Hamzaoui
Format: Conference Proceeding
Language:English
Subjects:
Classification algorithms
Detectors
Fission chamber
Neutrons
nonnegative matrix and tensor factorization
Performance analysis
pyFC
Spectrogram
Time-frequency analysis
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Summary:In order to achieve best neutron flux-mapping within nuclear research reactors, we investigate in this paper, the application of blind source separation algorithms to extract different independent components that form the fission chamber output signals. More specifically, we simulated the fission chamber using python-based of Fission Chambers (pyFC) code suite. The resulting simulated signals are considered observation vectors. They are then, processed via nonnegative matrix and tensor factorization (NMF&NTF) methods to isolate the independent components that form a neutron signal. Thus, in order to select the most efficient one to analyze our set of data, various algorithms have been tested and classified according to their performance index of separability (PI). The extracted independent components have been used to characterize neutron signal through spectrogram time-frequency representation. Obtained results will be illustrated and discussed.
ISSN:2474-0446
DOI:10.1109/SSD.2018.8570624