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      <div class="TdmEntry">New Results<ul><li><a href="uid26.html&#10;&#9;&#9;  ">A new characterization of the jump rate for piecewise-deterministic Markov processes with discrete transitions</a></li><li><a href="uid27.html&#10;&#9;&#9;  ">Estimation of the average number of continuous crossings for non-stationary non-diffusion processes</a></li><li><a href="uid28.html&#10;&#9;&#9;  ">ClustGeo: an R package for hierarchical clustering with spatial constraints</a></li><li><a href="uid29.html&#10;&#9;&#9;  ">Change-point detection for Piecewise Deterministic Markov Processes</a></li><li><a href="uid30.html&#10;&#9;&#9;  ">A sharp first order analysis of Feynman–Kac particle models, Part I: Propagation of chaos</a></li><li><a href="uid31.html&#10;&#9;&#9;  ">A sharp first order analysis of Feynman–Kac particle models, Part II: Particle Gibbs samplers</a></li><li><a href="uid32.html&#10;&#9;&#9;  ">Exponential mixing properties for time inhomogeneous diffusion processes with killing</a></li><li><a href="uid33.html&#10;&#9;&#9;  ">Investigation of asymmetry in E. coli growth rate</a></li><li><a href="uid34.html&#10;&#9;&#9;  ">Design of estimators for restoration of images degraded by haze using genetic programming</a></li><li><a href="uid35.html&#10;&#9;&#9;  ">Controlling IL-7 injections in HIV-infected patients</a></li><li><a href="uid36.html&#10;&#9;&#9;  ">Stochastic Control of Observer Trajectories in Passive Tracking with Acoustic Signal Propagation Optimization</a></li><li><a href="uid37.html&#10;&#9;&#9;  ">Computable approximations for average Markov decision processes in continuous time</a></li><li><a href="uid38.html&#10;&#9;&#9;  "> Zero-Sum Discounted Reward Criterion Games for Piecewise Deterministic Markov Processes</a></li><li><a href="uid39.html&#10;&#9;&#9;  ">Approximation of discounted minimax Markov control problems and zero-sum Markov games using Hausdorff and Wasserstein distances</a></li><li><a href="uid40.html&#10;&#9;&#9;  ">On the expected total cost with unbounded returns for Markov decision processes</a></li><li><a href="uid41.html&#10;&#9;&#9;  ">Applying Genetic Improvement to a Genetic Programming library in C++</a></li></ul></div>
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	    Raweb 
	    2018</a> | <a href="http://www.inria.fr/en/teams/cqfd">Presentation of the Project-Team CQFD</a></small>
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        <h2>Section: 
      Application Domains</h2>
        <h3 class="titre3">Dependability and safety</h3>
        <p>Our abilities in probability and statistics apply naturally to industry, in particular in studies of dependability and safety.
An illustrative example is the collaboration that started in September 2014 with with THALES Optronique.
The goal of this project is the optimization of the maintenance of an onboard system equipped with a HUMS (Health Unit Monitoring Systems).
The physical system under consideration is modeled by a piecewise deterministic Markov process.
In the context of impulse control, we propose a dynamic maintenance policy, adapted to the state of the system and taking into account both random failures and those related to the degradation phenomenon.</p>
        <p>The spectrum of applications of the topics that the team can address is large and can concern many other fields. Indeed non parametric and semi-parametric regression methods can be used in biometry, econometrics or engineering for instance. Gene selection from microarray data and text categorization are two typical application domains of dimension reduction among others. We had for instance the opportunity via the scientific program PRIMEQUAL to work on air quality data and to use dimension reduction techniques as principal component analysis (PCA) or positive matrix factorization (PMF) for pollution sources identification and quantization.</p>
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