A rotating tokamak plasma can interact resonantly with the external helical magnetic perturbation... more A rotating tokamak plasma can interact resonantly with the external helical magnetic perturbations, also known as error fields. This can lead to locking and then to disruptions. We leverage machine learning (ML) methods to predict the locking events. We use a coupled third-order nonlinear ordinary differential equation model to represent the interaction of the magnetic perturbation and the plasma rotation with the error field. This model is sufficient to describe qualitatively the locking and unlocking bifurcations. We explore using ML algorithms with the simulation data and experimental data, focusing on the methods that can be used with sparse datasets. These methods lead to the possibility of the avoidance of locking in real-time operations. We describe the operational space in terms of two control parameters: the magnitude of the error field and the rotation frequency associated with the momentum source that maintains the plasma rotation. The outcomes are quantified by order par...
Currently deployed passive gamma and neutron detectors screen for illicit nuclear material. Archi... more Currently deployed passive gamma and neutron detectors screen for illicit nuclear material. Archived data can help evaluate special nuclear material detection probabilities and investigate several related issues, including (1) nuisance gamma alarms arising from naturally occurring radiation, (2) the impact of drifting neutron and gamma background rates, and (3) radioisotope identification performance. This paper illustrates roles for data mining to investigate issue (1) and briefly reviews data mining to investigate issues (2) and (3).
Remote detection and identification of chemicals in a scene is a challenging problem. We introduc... more Remote detection and identification of chemicals in a scene is a challenging problem. We introduce an approach that uses some of the image's pixels to establish the background characteristics while other pixels represent the target for which we seek to identify all ...
In order to recycle the nuclear resource and reduce the environmental burden, a closed fuel cycle... more In order to recycle the nuclear resource and reduce the environmental burden, a closed fuel cycle has been pursued in Japan. The total Pu throughput in a large reprocessing plant for mixed oxide (MOX) spent fuels produced from light water reactors becomes a tremendous quantity over time. Development of safeguards technologies and proliferation-resistant technologies is important to respond to nonproliferation concerns. Solution monitoring (SM) is currently used as an additional safeguards measure to confirm declared operations and to complement near-real-time accounting (NRTA) and containment and surveillance (C/S). Recent quantitative evaluations of SM have shown high detection probability (DP) for abrupt loss and moderate DP for protracted loss. In these studies, DP evaluation with multivariate statistical analysis was proposed as a quantified C/S. Moreover, a bias estimation and subtraction method was proposed to reduce the systematic error components for nuclear material account...
Recent implementations of Bayesian approaches are one of the largest advances in phylogenetic tre... more Recent implementations of Bayesian approaches are one of the largest advances in phylogenetic tree estimation in the last 10 years. Markov chain Monte Carlo (MCMC) is used in these new approaches to estimate the Bayesian posterior probability for each tree topology of interest. Our goal is to assess the confidence in the estimated tree (particularly in whether prespecified groups are monophyletic) using MCMC and to compare the Bayesian estimate of confidence to a bootstrap-based estimate of confidence. We compare the Bayesian posterior probability to the bootstrap probability for specified groups in two real sets of influenza sequences and two sets of simulated sequences for our comparison. We conclude that the bootstrap estimate is adequate compared to the MCMC estimate except perhaps if the number of DNA sites is small.
Thomas L. Burr, CCS-6; Nicholas W. Hengartner, CCS-3; Steven C. Myers, N-2 The energy spectra of ... more Thomas L. Burr, CCS-6; Nicholas W. Hengartner, CCS-3; Steven C. Myers, N-2 The energy spectra of gamma-rays emitted by radioisotopes act as fingerprints that enable identification of the source. Such identification from low-resolution sodium iodide (NaI) detectors over short time periods is challenging for several reasons, including the Poisson fluctuations in the recorded counts. Smoothing the data over neighboring energy bins can reduce noise in the raw counts, at the cost of introducing a bias that de-emphasizes the peaks and valleys of the spectrum. This note describes a new two-stage smoothing procedure that uses a multiplicative bias correction for adjusting initial smoothed spectra. The benefits of this new method are illustrated on real spectra.
A rotating tokamak plasma can interact resonantly with the external helical magnetic perturbation... more A rotating tokamak plasma can interact resonantly with the external helical magnetic perturbations, also known as error fields. This can lead to locking and then to disruptions. We leverage machine learning (ML) methods to predict the locking events. We use a coupled third-order nonlinear ordinary differential equation model to represent the interaction of the magnetic perturbation and the plasma rotation with the error field. This model is sufficient to describe qualitatively the locking and unlocking bifurcations. We explore using ML algorithms with the simulation data and experimental data, focusing on the methods that can be used with sparse datasets. These methods lead to the possibility of the avoidance of locking in real-time operations. We describe the operational space in terms of two control parameters: the magnitude of the error field and the rotation frequency associated with the momentum source that maintains the plasma rotation. The outcomes are quantified by order par...
Currently deployed passive gamma and neutron detectors screen for illicit nuclear material. Archi... more Currently deployed passive gamma and neutron detectors screen for illicit nuclear material. Archived data can help evaluate special nuclear material detection probabilities and investigate several related issues, including (1) nuisance gamma alarms arising from naturally occurring radiation, (2) the impact of drifting neutron and gamma background rates, and (3) radioisotope identification performance. This paper illustrates roles for data mining to investigate issue (1) and briefly reviews data mining to investigate issues (2) and (3).
Remote detection and identification of chemicals in a scene is a challenging problem. We introduc... more Remote detection and identification of chemicals in a scene is a challenging problem. We introduce an approach that uses some of the image's pixels to establish the background characteristics while other pixels represent the target for which we seek to identify all ...
In order to recycle the nuclear resource and reduce the environmental burden, a closed fuel cycle... more In order to recycle the nuclear resource and reduce the environmental burden, a closed fuel cycle has been pursued in Japan. The total Pu throughput in a large reprocessing plant for mixed oxide (MOX) spent fuels produced from light water reactors becomes a tremendous quantity over time. Development of safeguards technologies and proliferation-resistant technologies is important to respond to nonproliferation concerns. Solution monitoring (SM) is currently used as an additional safeguards measure to confirm declared operations and to complement near-real-time accounting (NRTA) and containment and surveillance (C/S). Recent quantitative evaluations of SM have shown high detection probability (DP) for abrupt loss and moderate DP for protracted loss. In these studies, DP evaluation with multivariate statistical analysis was proposed as a quantified C/S. Moreover, a bias estimation and subtraction method was proposed to reduce the systematic error components for nuclear material account...
Recent implementations of Bayesian approaches are one of the largest advances in phylogenetic tre... more Recent implementations of Bayesian approaches are one of the largest advances in phylogenetic tree estimation in the last 10 years. Markov chain Monte Carlo (MCMC) is used in these new approaches to estimate the Bayesian posterior probability for each tree topology of interest. Our goal is to assess the confidence in the estimated tree (particularly in whether prespecified groups are monophyletic) using MCMC and to compare the Bayesian estimate of confidence to a bootstrap-based estimate of confidence. We compare the Bayesian posterior probability to the bootstrap probability for specified groups in two real sets of influenza sequences and two sets of simulated sequences for our comparison. We conclude that the bootstrap estimate is adequate compared to the MCMC estimate except perhaps if the number of DNA sites is small.
Thomas L. Burr, CCS-6; Nicholas W. Hengartner, CCS-3; Steven C. Myers, N-2 The energy spectra of ... more Thomas L. Burr, CCS-6; Nicholas W. Hengartner, CCS-3; Steven C. Myers, N-2 The energy spectra of gamma-rays emitted by radioisotopes act as fingerprints that enable identification of the source. Such identification from low-resolution sodium iodide (NaI) detectors over short time periods is challenging for several reasons, including the Poisson fluctuations in the recorded counts. Smoothing the data over neighboring energy bins can reduce noise in the raw counts, at the cost of introducing a bias that de-emphasizes the peaks and valleys of the spectrum. This note describes a new two-stage smoothing procedure that uses a multiplicative bias correction for adjusting initial smoothed spectra. The benefits of this new method are illustrated on real spectra.
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