32,013 research outputs found
Reasoning about the Reliability of Diverse Two-Channel Systems in which One Channel is "Possibly Perfect"
This paper considers the problem of reasoning about the reliability of fault-tolerant systems with two "channels" (i.e., components) of which one, A, supports only a claim of reliability, while the other, B, by virtue of extreme simplicity and extensive analysis, supports a plausible claim of "perfection." We begin with the case where either channel can bring the system to a safe state. We show that, conditional upon knowing pA (the probability that A fails on a randomly selected demand) and pB (the probability that channel B is imperfect), a conservative bound on the probability that the system fails on a randomly selected demand is simply pA.pB. That is, there is conditional independence between the events "A fails" and "B is imperfect." The second step of the reasoning involves epistemic uncertainty about (pA, pB) and we show that under quite plausible assumptions, a conservative bound on system pfd can be constructed from point estimates for just three parameters. We discuss the feasibility of establishing credible estimates for these parameters. We extend our analysis from faults of omission to those of commission, and then combine these to yield an analysis for monitored architectures of a kind proposed for aircraft
The International Workshop on Wave Hindcasting and Forecasting and the Coastal Hazards Symposium
Following the 13th International Workshop on Wave Hindcasting and Forecasting
and 4th Coastal Hazards Symposium in October 2013 in Banff, Canada, a topical
collection has appeared in recent issues of Ocean Dynamics. Here we give a
brief overview of the history of the conference since its inception in 1986 and
of the progress made in the fields of wind-generated ocean waves and the
modelling of coastal hazards before we summarize the main results of the papers
that have appeared in the topical collection
A radial basis function neural network based approach for the electrical characteristics estimation of a photovoltaic module
The design process of photovoltaic (PV) modules can be greatly enhanced by
using advanced and accurate models in order to predict accurately their
electrical output behavior. The main aim of this paper is to investigate the
application of an advanced neural network based model of a module to improve
the accuracy of the predicted output I--V and P--V curves and to keep in
account the change of all the parameters at different operating conditions.
Radial basis function neural networks (RBFNN) are here utilized to predict the
output characteristic of a commercial PV module, by reading only the data of
solar irradiation and temperature. A lot of available experimental data were
used for the training of the RBFNN, and a backpropagation algorithm was
employed. Simulation and experimental validation is reported
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