Application of Nonnegative Tensor Factorization for neutron-gamma discrimination of Monte Carlo simulated fission chamber’s output signals

Laassiri, Mounia; Hamzaoui, El-Mehdi; Moursli, R. Cherkaoui El

doi:10.1016/j.rinp.2017.04.009

Cited by 4 publications

(2 citation statements)

References 17 publications

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“…This interpretation can be automated and substantially simplified by applying unsupervised Machine Learning (ML) methods such as Nonnegative Matrix Factorization (NMF) and Non-negative Tensor Factorization (NTF) [30]. For example, NMF/NTF-based ML methods have been successfully used for analysis of Monte Carlo simulated fission chamber's output signals [31], for compression of scientific simulation data [32], and for a variety of other applications [30]. To avoid confusion, we should emphasize that in this paper the term tensor is used to define two different types of mathematical objects.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised machine learning based on non-negative tensor factorization for analyzing reactive-mixing

Vesselinov

Mudunuru

Karra

et al. 2019

Journal of Computational Physics

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Analysis of reactive-diffusion simulations representing complex mixing processes requires a large number of independent model runs. For each high-fidelity model simulation, the model inputs are varied and the predicted mixing behavior is represented by temporal and spatial changes in species concentration. It is then required to discern how the model inputs (such as diffusivity, dispersion, anisotropy, and velocity field properties) impact the mixing process. This task is challenging and typically involves interpretation of large model outputs representing temporal and spatial changes of species concentration within the model domain. However, the task can be automated and substantially simplified by applying Machine Learning (ML) methods. In this paper, we present an application of an unsupervised ML method (called NTFk) using Non-negative Tensor Factorization (NTF) coupled with a custom clustering procedure based on k-means to reveal the temporal and spatial features in product concentrations. An attractive and unique aspect of the proposed ML method is that it ensures the extracted features are non-negative, which are important to obtain a meaningful deconstruction of the mixing processes. The ML methodology is applied to a large set of high-resolution finite-element model simulations representing anisotropic reaction-diffusion processes in perturbed vortex-based velocity fields. The applied finite-element method ensures that spatial and temporal species concentration are always non-negative, even in the case of high anisotropic contrasts. The simulated reaction is a fast irreversible bimolecular reaction A + B → C, where species A and B react to form species C. The reactive-diffusion model input parameters that control mixing include properties of velocity field (such as vortex structures), anisotropic dispersion, and molecular diffusion. We demonstrate the applicability of the ML feature extraction method to produce a meaningful deconstruction of model outputs to discriminate between different physical processes impacting the reactants, their mixing, and the spatial distribution of the product C. The presented ML analysis allowed us to identify additive temporal and spatial features that characterize mixing behavior. The application of the proposed NTFk approach is not limited to reactive-mixing. NTFk can be readily applied to any observed or simulated datasets that can be 1 arXiv:1805.06454v2 [cs.CE] 21 Feb 2019 represented as tensors and have separable latent signatures or features.

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Section: Introductionmentioning

confidence: 99%

Unsupervised machine learning based on non-negative tensor factorization for analyzing reactive-mixing

Vesselinov

Mudunuru

Karra

et al. 2019

Journal of Computational Physics

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show abstract

“…We have used in our previous works, the Nonnegative Matrix Factorization (NMF) methods to analyze fission chamber's output signals, produced using simulation codes, for neutron flux monitoring purpose [7]. e Nonnegative Tensor Factorization (NTF) algorithms has been applied by Laassiri et al to recover the original sources from simulated signals recorded by fission chambers in order to achieve the neutron-gamma discrimination task [8].…”

Section: Introductionmentioning

confidence: 99%

Improving Neutron-Gamma Discrimination with Stilbene Organic Scintillation Detector Using Blind Nonnegative Matrix and Tensor Factorization Methods

Arahmane

Hamzaoui

Moursli

2019

Journal of Spectroscopy

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In order to perform highly qualified neutron-gamma discrimination in mixed radiation field, we investigate the application of blind source separation methods based on nonnegative matrix and tensor factorization algorithms as new and robust neutron-gamma discrimination software-based approaches. These signal processing tools have allowed to recover original source components from real-world mixture signals which have been recorded at the output of the stilbene scintillation detector. The computation of the performance index of separability of each tested nonnegative algorithm has allowed to select Second-Order NMF algorithm and NTF-2 model as the most efficient techniques for discriminating neutrons and gammas. Furthermore, the neutron-gamma discrimination is highlighted through the computation of the cross-correlation function. The performance of the blind source separation methods has been quantified through the obtained results that prove a good neutron-gamma separation.

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Nonnegative Tensor Factorization Approach Applied to Fission Chamber’s Output Signals Blind Source Separation

Laassiri

Hamzaoui

Moursli

2018

J. Phys.: Conf. Ser.

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Application of Nonnegative Tensor Factorization for neutron-gamma discrimination of Monte Carlo simulated fission chamber’s output signals

Cited by 4 publications

References 17 publications

Unsupervised machine learning based on non-negative tensor factorization for analyzing reactive-mixing

Unsupervised machine learning based on non-negative tensor factorization for analyzing reactive-mixing

Improving Neutron-Gamma Discrimination with Stilbene Organic Scintillation Detector Using Blind Nonnegative Matrix and Tensor Factorization Methods

Nonnegative Tensor Factorization Approach Applied to Fission Chamber’s Output Signals Blind Source Separation

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