scholarly journals Skin dose contamination conversion coefficients. Benchmark with three simulation codes

2021 ◽  
Author(s):  
Thomas Frosio ◽  
Philippe Bertreix ◽  
Nabil Menaa ◽  
Samuel Thomas ◽  
Holger EBERHARDT ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Radiation Protection ◽  
State Of The Art ◽  
Radioactive Material ◽  
Skin Dose ◽  
Radionuclide Contamination ◽  
Skin Contamination ◽  
Conversion Coefficients ◽  
Dose Coefficients ◽  
Increasing Demand

Abstract Handling of radioactive material by operators can lead to contamination at the surface of the skin in case of an accident. The quantification of the dose received by the skin due to a contamination scenario is performed by means of dedicated dose coefficients as it is the case for other radiation protection dose quantities described in the literature. However, most available coefficients do not match realistic scenarios according to state-of-the-art of science and technology. Therefore, this work deals with dedicated dose conversion factors for skin contamination. Since there is an increasing demand on dose coefficients in general, these specific coefficients can be used for various calculations in radiation protection. In this work a method to evaluate such coefficients for the skin contamination dose related to photons, electrons, positrons, alpha and neutron particles is proposed. The coefficients are generated using Monte-Carlo simulations with three well established calculation codes (FLUKA, MCNP, and GEANT4). The results of the various codes are compared against each other for benchmarking purposes. The new dose coefficients allow the computation of the skin received dose, in the case of skin contamination scenario of an individual, taking into account the decay radiation of the radionuclides of interest. To benchmark the quantity derived here, comparisons of radionuclide contamination doses to the skin using the VARSKIN code available in the literature are performed with the results of this work.

Dose Conversion Coefficients Based on Taiwanese Reference Phantoms and Monte Carlo Simulations for Use in External Radiation Protection

2017 ◽  
Vol 112 (5) ◽  
pp. 470-477 ◽  
Author(s):  
Shu-Jun Chang ◽  
Jui-Ting Hsu ◽  
Shih-Yen Hung ◽  
Yan-Lin Liu ◽  
Shiang-Huei Jiang ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Radiation Protection ◽  
External Radiation ◽  
Conversion Coefficients

scholarly journals Multiple particle filtering for tracking wireless agents via Monte Carlo likelihood approximation

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Stephan Schlupkothen ◽  
Gerd Ascheid
Keyword(s):  
Monte Carlo ◽  
Computational Complexity ◽  
Particle Filtering ◽  
Gaussian Approximation ◽  
State Of The Art ◽  
Approximation Techniques ◽  
Accurate Localization ◽  
Likelihood Approximation ◽  
Current State ◽  
Multiple Particle

Abstract The localization of multiple wireless agents via, for example, distance and/or bearing measurements is challenging, particularly if relying on beacon-to-agent measurements alone is insufficient to guarantee accurate localization. In these cases, agent-to-agent measurements also need to be considered to improve the localization quality. In the context of particle filtering, the computational complexity of tracking many wireless agents is high when relying on conventional schemes. This is because in such schemes, all agents’ states are estimated simultaneously using a single filter. To overcome this problem, the concept of multiple particle filtering (MPF), in which an individual filter is used for each agent, has been proposed in the literature. However, due to the necessity of considering agent-to-agent measurements, additional effort is required to derive information on each individual filter from the available likelihoods. This is necessary because the distance and bearing measurements naturally depend on the states of two agents, which, in MPF, are estimated by two separate filters. Because the required likelihood cannot be analytically derived in general, an approximation is needed. To this end, this work extends current state-of-the-art likelihood approximation techniques based on Gaussian approximation under the assumption that the number of agents to be tracked is fixed and known. Moreover, a novel likelihood approximation method is proposed that enables efficient and accurate tracking. The simulations show that the proposed method achieves up to 22% higher accuracy with the same computational complexity as that of existing methods. Thus, efficient and accurate tracking of wireless agents is achieved.

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