| bibtype |
J -
Journal Article
|
| ARLID |
0433631 |
| utime |
20240103204850.9 |
| mtime |
20141106235959.9 |
| SCOPUS |
84918553939 |
| WOS |
000346192000011 |
| DOI |
10.1080/00401706.2013.860917 |
| title
(primary) (eng) |
Efficient Sequential Monte Carlo Sampling for Continuous Monitoring of a Radiation Situation |
| specification |
| page_count |
14 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0255201 |
| ISSN |
0040-1706 |
| title
|
Technometrics |
| volume_id |
56 |
| volume |
4 (2014) |
| page_num |
514-527 |
|
| keyword |
radiation protection |
| keyword |
atmospheric dispersion model |
| keyword |
importance sampling |
| author
(primary) |
| ARLID |
cav_un_auth*0101207 |
| full_dept (cz) |
Adaptivní systémy |
| full_dept (eng) |
Department of Adaptive Systems |
| department (cz) |
AS |
| department (eng) |
AS |
| full_dept |
Department of Adaptive Systems |
| name1 |
Šmídl |
| name2 |
Václav |
| institution |
UTIA-B |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0228606 |
| full_dept (cz) |
Adaptivní systémy |
| full_dept |
Department of Adaptive Systems |
| department (cz) |
AS |
| department |
AS |
| full_dept |
Department of Adaptive Systems |
| name1 |
Hofman |
| name2 |
Radek |
| institution |
UTIA-B |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| project |
| ARLID |
cav_un_auth*0265869 |
| project_id |
VG20102013018 |
| agency |
GA MV |
|
| abstract
(eng) |
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. |
| RIV |
BD |
| reportyear |
2015 |
| num_of_auth |
2 |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0238355 |
| confidential |
S |
| mrcbT16-e |
STATISTICSPROBABILITY |
| mrcbT16-j |
1.706 |
| mrcbT16-s |
1.555 |
| mrcbT16-4 |
Q1 |
| mrcbT16-B |
81.433 |
| mrcbT16-C |
86.475 |
| mrcbT16-D |
Q1 |
| mrcbT16-E |
Q1 |
| arlyear |
2014 |
| mrcbU14 |
84918553939 SCOPUS |
| mrcbU34 |
000346192000011 WOS |
| mrcbU63 |
cav_un_epca*0255201 Technometrics 0040-1706 1537-2723 Roč. 56 č. 4 2014 514 527 |
|