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pro vyhledávání: '"Bennun, Leonardo"'
In this work we have studied the limitations of the TXRF spectroscopy in the upper limit of validity of the technique, when the analyzed specimen ceases to be a thin film. We have evaluated the non-linear effects in spectra obtained from samples made
Externí odkaz:
http://arxiv.org/abs/2208.03356
Autor:
Bennun, Leonardo
In this paper we propose an ad-hoc construction of the Likelihood Function, in order to develop a data analysis procedure, to be applied in atomic and nuclear spectral analysis. The classical Likelihood Function was modified taking into account the u
Externí odkaz:
http://arxiv.org/abs/1704.03380
Autor:
Bennun, Leonardo
A new smoothing method for the improvement on the identification and quantification of spectral functions based on the previous knowledge of the signals that are expected to be quantified, is presented. These signals are used as weighted coefficients
Externí odkaz:
http://arxiv.org/abs/1603.02061
Autor:
Bennun, Leonardo, Santibanez, Mauricio
Improvements in performance and approval obtained by first year engineering students from University of Concepcion, Chile, were studied, once a virtual didactic model of multiple-choice exam, was implemented. This virtual learning resource was implem
Externí odkaz:
http://arxiv.org/abs/1506.03719
This work presents a study on the fundamental aspects of a reliable gold determination in voluminous mineral samples by Neutron Activation Analysis, using a Linear Particle Accelerator as a source of irradiation and a single detector as a detection s
Externí odkaz:
http://arxiv.org/abs/1504.07684
The usual method for determining the sensitivity curve of a TXRF spectrometer relies on calibration using a set of vendor-certified concentration values of reference calibration standards. These samples, which are certified by the provider, are costl
Externí odkaz:
http://arxiv.org/abs/1503.09044
This work develops a cross correlation maximization technique, based on statistical concepts, for pattern matching purposes in time series. The technique analytically quantifies the extent of similitude between a known signal within a group of data,
Externí odkaz:
http://arxiv.org/abs/1503.03022
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Autor:
Navarro, Henry, Bennun, Leonardo
We show with a few descriptive examples the limitations of Artificial Neural Networks when they are applied to the analysis of independent stochastic data.
Externí odkaz:
http://arxiv.org/abs/1404.5598
Autor:
Aguero, Leonardo, Bennun, Leonardo
This work describes an error in the uncertainty assessment of uncertainties of the Total-Reflection X-ray Fluorescence technique Ref [1]. A confusion, between the precision and accuracy of a measurement produced an incomplete evaluation of the uncert
Externí odkaz:
http://arxiv.org/abs/1307.0552