Development of analysis method for airborne radiation monitoring using the inverse problem solutions

Sasaki, Miyuki; Ishizaki, Azusa; Sanada, Yukihisa

doi:10.15669/pnst.6.63

Cited by 11 publications

(9 citation statements)

References 5 publications

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“…The detailed procedure of the ML-EM method is given in the paper of Sasaki et al (2019) 10 , 11 . The ML-EM method is a type of inverse estimation method that employs radiation information from various directions.…”

Section: Methodsmentioning

confidence: 99%

“…A conversion method using the maximum-likelihood expectation-maximization (ML-EM) method was recently proposed to convert the value measured from the sky into an air dose rate of 1 m above the ground level (agl.) 10 , 11 . The conversion using the ML-EM method requires the creation of many types of attenuation parameters to improve the conversion accuracy.…”

Section: Introductionmentioning

confidence: 99%

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New method for visualizing the dose rate distribution around the Fukushima Daiichi Nuclear Power Plant using artificial neural networks

Sasaki

Sanada

Katengeza

et al. 2021

Sci Rep

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This study proposes a new method of visualizing the ambient dose rate distribution using artificial neural networks (ANNs) from airborne radiation monitoring results. The method was applied to the results of the airborne radiation monitoring which was conducted around the Fukushima Daiichi Nuclear Power Plant by an unmanned aerial vehicle. Much of the survey data obtained in the past were used as the training data for building a network. The number of training cases was related to the error between the ground and converted values by the ANN. The quantitative evaluation index (the root-mean-square error) between the ANN-converted value and the ground-based survey result converged at 200 training cases. This number of training case was considered a rough criterion of the required number of training cases. The reliability of the ANN method was evaluated by comparison with the ground-based survey data. The dose rate map created by the ANNs method reproduced ground-based survey results better than traditional methods.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

New method for visualizing the dose rate distribution around the Fukushima Daiichi Nuclear Power Plant using artificial neural networks

Sasaki

Sanada

Katengeza

et al. 2021

Sci Rep

Self Cite

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“…The ARS can quickly perform measurements in a wide range, but the distribution resolution of the ambient dose rate (air dose rate) by the ARS is rough compared with a ground-based survey because the detector position is far from the radiation source (ground). The inverse problem analysis and machine learning methods have been proposed to realize a more accurate visualization of the ARS data [2][3] [4]. In the Fukushima environmental field, providing an improved position resolution of the dose rate distribution map is helpful in estimating the dose exposure of residents and in making decisions as regards lifting the evacuation zone.…”

Section: Introductionmentioning

confidence: 99%

Improvement of training data for dose rate distribution using an artificial neural network

Sasaki

Sanada

2022

JASSE

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This study presents the evaluation results of the validity of the visualization map of the ambient dose rate at 1 m above the ground level using an artificial neural network. The dose rate map created using the artificial neural network-based method is found to reproduce ground-based survey results better than conventional methods. Suggested to improve the validity of the airborne radiation survey visualization, applying the color data obtained using a photogrammetry system is a new experience.

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“…Recently, unmanned aerial vehicle (UAV) technology has been used widely in different fields from the army to security [1][2][3][4][5][6][7][8] . The use of drones in these areas makes it possible to reduce the risk to human life, to control the situation very quickly and accurately 1 .…”

Section: Introductionmentioning

confidence: 99%

Gamma ray detection performance of newly developed MAPD-3NM-II photosensor with LaBr3(Ce) crystal

Holík

Ahmadov

Sadigov

et al. 2022

Preprint

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This paper is devoted to gamma-ray detection performance of the newly developed MAPD-3NM-II type SiPM sensor array (4x4) with LaBr3(Ce) scintillator. The gamma-ray spectra of various sources have been measured in the energy range from 26 keV up to 1332 keV. The newly developed array based on MAPD-3NM-II sensors proved ∼22 % enhancement in energy resolution in comparison to the former MAPD-3NM-I based array. The energy resolution of 662 keV gamma-rays measured by MAPD-3NM-II was 3.3 % while clearly surpassing 4.25 % resolution of MAPD-3NM-I predecessor. The enhancement is related to the high PDE of the new MAPD-3NM-II. Obtained results show that the new MAPD-3NM-II demonstrated good energy resolution and linearity in the studied energy region. The energy resolution of the new detector developed based on MAPD-3NM-II was better than all previous products of MAPD.

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Development of analysis method for airborne radiation monitoring using the inverse problem solutions

Cited by 11 publications

References 5 publications

New method for visualizing the dose rate distribution around the Fukushima Daiichi Nuclear Power Plant using artificial neural networks

New method for visualizing the dose rate distribution around the Fukushima Daiichi Nuclear Power Plant using artificial neural networks

Improvement of training data for dose rate distribution using an artificial neural network

Gamma ray detection performance of newly developed MAPD-3NM-II photosensor with LaBr3(Ce) crystal

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