2 research outputs found
Evaluating uncertainties in electrochemical impedance spectra of solid oxide fuel cells
Electrochemical impedance spectroscopy (EIS) is a widely used tool for
characterization of fuel cells and other electrochemical conversion systems.
When applied to the on-line monitoring in the context of in-field applications,
the disturbances, drifts and sensor noise may cause severe distortions in the
evaluated spectra, especially in the low-frequency part. Failure to ignore the
random effects can result in misinterpreted spectra and, consequently, in
misleading diagnostic reasoning. This fact has not been often addressed in the
research so far. In this paper, we propose an approach to the quantification of
the spectral uncertainty, which relies on evaluating the uncertainty of the
equivalent circuit model (ECM). We apply the computationally efficient
variational Bayes (VB) method and compare the quality of the results with those
obtained with the Markov chain Monte Carlo (MCMC) algorithm. Namely, MCMC
algorithm returns accurate distributions of the estimated model parameters,
while VB approach provides the approximate distributions. By using simulated
and real data we show that approximate results provided by VB approach,
although slightly over-optimistic, are still close to the more realistic MCMC
estimates. A great advantage of the VB method for online monitoring is low
computational load, which is several orders of magnitude lower compared to
MCMC. The performance of VB algorithm is demonstrated on a case of ECM
parameters estimation in a 6 cell solid oxide fuel cell (SOFC) stack. The
complete numerical implementation for recreating the results can be found at
https://repo.ijs.si/lznidaric/variational-bayes-supplementary-material.Comment: 28 pages, 18 figures. Submitted to: Applied Energ
Variational Bayes survival analysis for unemployment modelling
Mathematical modelling of unemployment dynamics attempts to predict the
probability of a job seeker finding a job as a function of time. This is
typically achieved by using information in unemployment records. These records
are right censored, making survival analysis a suitable approach for parameter
estimation. The proposed model uses a deep artificial neural network (ANN) as a
non-linear hazard function. Through embedding, high-cardinality categorical
features are analysed efficiently. The posterior distribution of the ANN
parameters are estimated using a variational Bayes method. The model is
evaluated on a time-to-employment data set spanning from 2011 to 2020 provided
by the Slovenian public employment service. It is used to determine the
employment probability over time for each individual on the record. Similar
models could be applied to other questions with multi-dimensional,
high-cardinality categorical data including censored records. Such data is
often encountered in personal records, for example in medical records