241 research outputs found
Global sensitivity analysis for the boundary control of an open channel
The goal of this paper is to solve the global sensitivity analysis for a
particular control problem. More precisely, the boundary control problem of an
open-water channel is considered, where the boundary conditions are defined by
the position of a down stream overflow gate and an upper stream underflow gate.
The dynamics of the water depth and of the water velocity are described by the
Shallow Water equations, taking into account the bottom and friction slopes.
Since some physical parameters are unknown, a stabilizing boundary control is
first computed for their nominal values, and then a sensitivity anal-ysis is
performed to measure the impact of the uncertainty in the parameters on a given
to-be-controlled output. The unknown physical parameters are de-scribed by some
probability distribution functions. Numerical simulations are performed to
measure the first-order and total sensitivity indices
Adaptive estimation of linear functionals by model selection
We propose an estimation procedure for linear functionals based on Gaussian
model selection techniques. We show that the procedure is adaptive, and we give
a non asymptotic oracle inequality for the risk of the selected estimator with
respect to the loss. An application to the problem of estimating
a signal or its derivative at a given point is developed and minimax
rates are proved to hold uniformly over Besov balls. We also apply our non
asymptotic oracle inequality to the estimation of the mean of the signal on an
interval with length depending on the noise level. Simulations are included to
illustrate the performances of the procedure for the estimation of a function
at a given point. Our method provides a pointwise adaptive estimator.Comment: Published in at http://dx.doi.org/10.1214/07-EJS127 the Electronic
Journal of Statistics (http://www.i-journals.org/ejs/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Generalized Hoeffding-Sobol Decomposition for Dependent Variables -Application to Sensitivity Analysis
In this paper, we consider a regression model built on dependent variables.
This regression modelizes an input output relationship. Under boundedness
assumptions on the joint distribution function of the input variables, we show
that a generalized Hoeffding-Sobol decomposition is available. This leads to
new indices measuring the sensitivity of the output with respect to the input
variables. We also study and discuss the estimation of these new indices
L2 Boosting on generalized Hoeffding decomposition for dependent variables. Application to Sensitivity Analysis
This paper is dedicated to the study of an estimator of the generalized
Hoeffding decomposition. We build such an estimator using an empirical
Gram-Schmidt approach and derive a consistency rate in a large dimensional
settings.
Then, we apply a greedy algorithm with these previous estimators to
Sensitivity Analysis. We also establish the consistency of this -boosting up to sparsity assumptions on the signal to analyse. We end the
paper with numerical experiments, which demonstrates the low computational cost
of our method as well as its efficiency on standard benchmark of Sensitivity
Analysis.Comment: 48 pages, 7 Figure
Bayesian Multiple Hypothesis Tracking of Merging and Splitting Targets
International audienceThis paper presents a Bayesian model for the multiple target tracking problem that handles a varying number of splitting and merging targets applied to convective cloud tracking. The model decomposes the tracking solution into events and targets state. The events include target births, deaths, splits, and merges. The target state contains both the target positions and attributes. By updating the target attributes and conditioning the events on their updated values we can include high level domain knowledge into the system. This strategy improves the tracking accuracy and the computational efficiency since we focus only on likely events for each situation. A two-step multiple hypothesis tracking algorithm has been developed to estimate the model state. The proposed approach is tested by both simulation and real data for mesoscale convective systems tracking
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