An adaptive Bayesian inference algorithm ...
Type de document :
Compte-rendu et recension critique d'ouvrage
Titre :
An adaptive Bayesian inference algorithm to estimate the parameters of a hazardous atmospheric release
Auteur(s) :
Rajaona, Harizo [Auteur]
DAM Île-de-France [DAM/DIF]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Septier, Francois [Auteur]
Institut TELECOM/TELECOM Lille1
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Armand, Patrick [Auteur]
DAM Île-de-France [DAM/DIF]
Delignon, Yves [Auteur]
Institut TELECOM/TELECOM Lille1
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Olry, Christophe [Auteur]
ARIA Technologies
Albergel, Armand [Auteur]
ARIA Technologies
Moussafir, Jacques [Auteur]
ARIA Technologies
DAM Île-de-France [DAM/DIF]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Septier, Francois [Auteur]
Institut TELECOM/TELECOM Lille1
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Armand, Patrick [Auteur]
DAM Île-de-France [DAM/DIF]
Delignon, Yves [Auteur]
Institut TELECOM/TELECOM Lille1
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Olry, Christophe [Auteur]
ARIA Technologies
Albergel, Armand [Auteur]
ARIA Technologies
Moussafir, Jacques [Auteur]
ARIA Technologies
Titre de la revue :
Atmospheric Environment
Pagination :
748–762
Éditeur :
Elsevier
Date de publication :
2015-12
ISSN :
1352-2310
Mot(s)-clé(s) en anglais :
Source term estimation
Bayesian inference
Monte-Carlo techniques
Adaptive multiple importance sampling
Bayesian inference
Monte-Carlo techniques
Adaptive multiple importance sampling
Discipline(s) HAL :
Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]
Statistiques [stat]/Applications [stat.AP]
Statistiques [stat]/Calcul [stat.CO]
Statistiques [stat]/Méthodologie [stat.ME]
Statistiques [stat]/Applications [stat.AP]
Statistiques [stat]/Calcul [stat.CO]
Statistiques [stat]/Méthodologie [stat.ME]
Résumé en anglais : [en]
In the eventuality of an accidental or intentional atmospheric release, the reconstruction of the source term using measurements from a set of sensors is an important and challenging inverse problem. A rapid and accurate ...
Lire la suite >In the eventuality of an accidental or intentional atmospheric release, the reconstruction of the source term using measurements from a set of sensors is an important and challenging inverse problem. A rapid and accurate estimation of the source allows faster and more efficient action for first-response teams, in addition to providing better damage assessment.This paper presents a Bayesian probabilistic approach to estimate the location and the temporal emission profile of a pointwise source. The release rate is evaluated analytically by using a Gaussian assumption on its prior distribution, and is enhanced with a positivity constraint to improve the estimation. The source location is obtained by the means of an advanced iterative Monte-Carlo technique called Adaptive Multiple Importance Sampling (AMIS), which uses a recycling process at each iteration to accelerate its convergence.The proposed methodology is tested using synthetic and real concentration data in the framework of the Fusion Field Trials 2007 (FFT-07) experiment. The quality of the obtained results is comparable to those coming from the Markov Chain Monte Carlo (MCMC) algorithm, a popular Bayesian method used for source estimation. Moreover, the adaptive processing of the AMIS provides a better sampling efficiency by reusing all the generated samples.Lire moins >
Lire la suite >In the eventuality of an accidental or intentional atmospheric release, the reconstruction of the source term using measurements from a set of sensors is an important and challenging inverse problem. A rapid and accurate estimation of the source allows faster and more efficient action for first-response teams, in addition to providing better damage assessment.This paper presents a Bayesian probabilistic approach to estimate the location and the temporal emission profile of a pointwise source. The release rate is evaluated analytically by using a Gaussian assumption on its prior distribution, and is enhanced with a positivity constraint to improve the estimation. The source location is obtained by the means of an advanced iterative Monte-Carlo technique called Adaptive Multiple Importance Sampling (AMIS), which uses a recycling process at each iteration to accelerate its convergence.The proposed methodology is tested using synthetic and real concentration data in the framework of the Fusion Field Trials 2007 (FFT-07) experiment. The quality of the obtained results is comparable to those coming from the Markov Chain Monte Carlo (MCMC) algorithm, a popular Bayesian method used for source estimation. Moreover, the adaptive processing of the AMIS provides a better sampling efficiency by reusing all the generated samples.Lire moins >
Langue :
Anglais
Vulgarisation :
Non
Collections :
Source :