3 research outputs found
Gaussianization of LA-ICP-MS features to improve calibration in forensic glass comparison
The forensic comparison of glass aims to compare a glass sample of an unknown source with a control glass
sample of a known source. In this work, we use multi-elemental features from Laser Ablation Inductively
Coupled Plasma with Mass Spectrometry (LA-ICP-MS) to compute a likelihood ratio. This calculation is a
complex procedure that generally requires a probabilistic model including the within-source and betweensource variabilities of the features. Assuming the within-source variability to be normally distributed is a
practical premise with the available data. However, the between-source variability is generally assumed to
follow a much more complex distribution, typically described with a kernel density function. In this work,
instead of modeling distributions with complex densities, we propose the use of simpler models and the
introduction of a data pre-processing step consisting on the Gaussianization of the glass features. In this
context, to obtain a better fit of the features with the Gaussian model assumptions, we explore the use of
different normalization techniques of the LA-ICP-MS glass features, namely marginal Gaussianization based
on histogram matching, marginal Gaussianization based on Yeo-Johnson transformation and a more
complex joint Gaussianization using normalizing flows. We report an improvement in the performance of
the Likelihood Ratios computed with the previously Gaussianized feature vectors, particularly relevant in
their calibration, which implies a more reliable forensic glass comparisonThis work has been supported by the Spanish Ministerio de
Ciencia e Innovación through grant PID2021-125943OB-I0
Score-based Bayesian network structure learning algorithms for modeling radioisotope levels in nuclear power plant reactors
Radioactive corrosion products released into the primary coolant loop dominate the final shutdown radiation fields of pressurized water reactors. Thus, reducing the concentration of these corrosion products is a paramount duty in the optimization process of the reactor performance. However, the complexity and uncertainty present in this process make it difficult to predict their evolution in a theoretical way. We propose the application of structural learning of Bayesian networks to discover the complex relations between the corrosion products and the most relevant variables in the primary loop, giving rise to probabilistic models that obtain accurate and reliable predictions of the corrosion products. Our analysis of 5 power plants demonstrates that our approach results in simpler and more reliable models. Additionally, we conclude that the learned structures may represent an interpretable tool for power plant technicians since they reveal useful information that can be directly employed to improve the reactor operationThe authors from the UAM have been supported by the Spanish
Ministerio de Ciencia e Innovación, Agencia del Fondo Europeo de
Desarrollo Regional (grant reference PID2021-125943OB-I00, MCIN
/AEI /10.13039/501100011033/FEDER, UE). The work has been conducted in the context of a signed collaboration agreement between
AUDIAS-UAM and ENUSA Industrias Avanzadas S.
Gaussian Processes for radiation dose prediction in nuclear power plant reactors
In nuclear power plants, there are high-exposure jobs, like refuelling and maintenance, that require getting close
to the reactor between operation cycles. Therefore, reducing radiation dose during these periods is of paramount
importance regarding safety regulations. While there are some manipulable variables, like levels of certain
corrosion products, that can influence the final level of radiation dose, there is no way to determine it in a
principled way. In this work, we propose to use Machine Learning to predict the radiation dose in the reactor at
the cycle end based on information available during the cycle operation. In particular, we use a Gaussian Process
to model the relation between cobalt radioisotopes (a certain kind of corrosion product) and radiation dose
levels. Gaussian Processes acknowledge the uncertainty on their predictions, a desirable property considering the
high-risk nature of the present application. We report experiments on real data gathered from five different
power plants in Spain. Results show that these models can be used to estimate the future values of radiation dose
in a data-driven way. Moreover, there are tools based on these models currently in development for their
application in power plantsThe authors from the UAM are funded by the Spanish Ministerio de
Ciencia, Innovacion y Universidades (MCIU) and Agencia Estatal de
Investigacion (AEI), and also by the European Regional Development
Fund (FEDER in Spanish, ERDF in English), by project RTI2018-098091-
B-I00. The work has been conducted in the context of a signed collaboration agreement between AUDIAS-UAM and ENUSA Industrias
Avanzadas S. A