32
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found

      Photon dosimetry using selective data sampling with Particle Swarm optimization algorithm based on NaI(Tl) scintillation detector

      1 , 2 , 1
      Kerntechnik
      Walter de Gruyter GmbH

      Read this article at

      ScienceOpenPublisher
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Sodium Iodide doped with Thallium NaI(Tl) scintillation detectors have potential for the development of an active dosimeter for photon radiation. We aim to show that the photon dosimetry response for NaI(Tl) scintillation detector may be optimized by employing the Particle Swarm optimization algorithm, when the selective data sampling is applied for the detector readout. In this work, Sodium Iodide doped with Thallium NaI(Tl) scintillation detector is considered due to being highly sensitive to gamma radiation, and one of the affordable room temperature detectors. In this research, we intend to measure the dosimetry response of the NaI(Tl) detector for various gamma sources, as an example, by measuring the ambient dose equivalent H*(10) for different gamma radioactive sources. Furthermore, we demonstrate that the photon dosimetry response may be well optimized for various energies, especially at lower energies, by increasing the energy interval number in data sampling over the NaI(Tl) scintillation detector readout with the help of an optimization algorithm. The simulation software Geant4 has been used for determining the NaI(Tl) scintillation detector readout. To this end, experimental ambient dose equivalent measurements for gamma radiation sources are compared with the theoretical results. As three and six energy intervals are considered for the selective data sampling along with an optimization algorithm based on NaI(Tl) detector output, the error percentage will be less than 20 and 10%, respectively.

          Most cited references37

          • Record: found
          • Abstract: not found
          • Article: not found

          Geant4—a simulation toolkit

            Bookmark
            • Record: found
            • Abstract: not found
            • Conference Proceedings: not found

            A new optimizer using particle swarm theory

              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

              Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms.
                Bookmark

                Author and article information

                Journal
                Kerntechnik
                Walter de Gruyter GmbH
                0932-3902
                2195-8580
                March 11 2022
                March 11 2022
                : 0
                : 0
                Affiliations
                [1 ]Physics Department , Faculty of Science, Urmia University , Urmia , Iran
                [2 ]Faculty of Science, Imam Hossein Comprehensive University , Tehran , Iran
                Article
                10.1515/kern-2021-1035
                3dbac5bd-14c0-409e-a26d-42dc43fc67fe
                © 2022
                History

                Sustainable & Green chemistry,Materials for energy,Chemistry,Batteries & Fuel cells,Industrial chemistry,Materials science

                Comments

                Comment on this article