31,298 research outputs found
Sequential joint signal detection and signal-to-noise ratio estimation
The sequential analysis of the problem of joint signal detection and
signal-to-noise ratio (SNR) estimation for a linear Gaussian observation model
is considered. The problem is posed as an optimization setup where the goal is
to minimize the number of samples required to achieve the desired (i) type I
and type II error probabilities and (ii) mean squared error performance. This
optimization problem is reduced to a more tractable formulation by transforming
the observed signal and noise sequences to a single sequence of Bernoulli
random variables; joint detection and estimation is then performed on the
Bernoulli sequence. This transformation renders the problem easily solvable,
and results in a computationally simpler sufficient statistic compared to the
one based on the (untransformed) observation sequences. Experimental results
demonstrate the advantages of the proposed method, making it feasible for
applications having strict constraints on data storage and computation.Comment: 5 pages, Proceedings of IEEE International Conference on Acoustics,
Speech, and Signal Processing (ICASSP), 201
SHM strategy optimization and structural maintenance planning based on Bayesian joint modelling
In this contribution, an example is used to illustrate the application of
Bayesian joint modelling in optimizing the SHM strategy and structural maintenance
planning. The model parameters were evaluated first, using the Markov
Chain Monte Carlo (MCMC) method. Then different parameters including expected
SHM accuracy and risk acceptance criteria were investigated in order to
give an insight on how the maintenance planning and life-cycle benefit are influenced.
The optimal SHM strategy was then identified as the one that maximizes
the benefit
A multi-objective framework for the optimisation of life-cycle costs of wind turbines
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