About: Efficient Sequential Monte Carlo Sampling for Continuous Monitoring of a Radiation Situation

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An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

Attributes	Values
rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
rdfs:seeAlso	http://library.utia.cas.cz/separaty/2014/AS/smidl-0433631.pdf
Description	The monitoring of a radiation situation around a nuclear power plant is a demanding task due to the high uncertainty of all involved variables and limited availability of measurements from a sparse monitoring network. Assessment of the situation requires experienced specialists who may be unavailable during critical times. Our goal is to provide an automated method of instant radiation situation assessment that does not underestimate its uncertainty. We propose a state space model based on an atmospheric dispersion model, local correction of a numerical weather model, and a temporal model of the released activity. This state space model is highly nonlinear and evaluation of the likelihood function requires extensive numerical calculations. The sequential Monte Carlo method is one of the few options for estimating the state recursively. Since the simple bootstrap approach yields an extremely computationally demanding algorithm, we investigate the use of existing techniques for the design of a more efficient proposal density. The monitoring of a radiation situation around a nuclear power plant is a demanding task due to the high uncertainty of all involved variables and limited availability of measurements from a sparse monitoring network. Assessment of the situation requires experienced specialists who may be unavailable during critical times. Our goal is to provide an automated method of instant radiation situation assessment that does not underestimate its uncertainty. We propose a state space model based on an atmospheric dispersion model, local correction of a numerical weather model, and a temporal model of the released activity. This state space model is highly nonlinear and evaluation of the likelihood function requires extensive numerical calculations. The sequential Monte Carlo method is one of the few options for estimating the state recursively. Since the simple bootstrap approach yields an extremely computationally demanding algorithm, we investigate the use of existing techniques for the design of a more efficient proposal density. (en)
Title	Efficient Sequential Monte Carlo Sampling for Continuous Monitoring of a Radiation Situation Efficient Sequential Monte Carlo Sampling for Continuous Monitoring of a Radiation Situation (en)
skos:prefLabel	Efficient Sequential Monte Carlo Sampling for Continuous Monitoring of a Radiation Situation Efficient Sequential Monte Carlo Sampling for Continuous Monitoring of a Radiation Situation (en)
skos:notation	RIV/67985556:_____/14:00433631!RIV15-MV0-67985556
http://linked.open...avai/riv/aktivita	I P
http://linked.open...avai/riv/aktivity	I, P(VG20102013018)
http://linked.open...iv/cisloPeriodika	4
http://linked.open...vai/riv/dodaniDat	2015
http://linked.open...aciTvurceVysledku	Šmídl, Václav Hofman, Radek
http://linked.open.../riv/druhVysledku	J - Článek v odborném periodiku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	Ústav teorie informace a automatizace AV ČR, v. v. i.
http://linked.open...dnocenehoVysledku	13874
http://linked.open...ai/riv/idVysledku	RIV/67985556:_____/14:00433631
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	radiation protection; atmospheric dispersion model; importance sampling (en)
http://linked.open.../riv/klicoveSlovo	atmospheric dispersion model radiation protection importance sampling
http://linked.open...odStatuVydavatele	US - Spojené státy americké
http://linked.open...ontrolniKodProRIV	[2F1710857CFF]
http://linked.open...i/riv/nazevZdroje	Technometrics
http://linked.open...in/vavai/riv/obor	BD
http://linked.open...ichTvurcuVysledku	2 (xsd:int)
http://linked.open...cetTvurcuVysledku	2 (xsd:int)
http://linked.open...vavai/riv/projekt	Application of advanced statistical assimilation techniques of model predictions with observations from terrain in the form of modern software tool for emergency management support
http://linked.open...UplatneniVysledku	2014
http://linked.open...v/svazekPeriodika	56
http://linked.open...iv/tvurceVysledku	Hofman, Radek Šmídl, Václav
http://linked.open...ain/vavai/riv/wos	000346192000011
issn	0040-1706
number of pages	14 (xsd:int)
http://bibframe.org/vocab/doi	10.1080/00401706.2013.860917

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