49
Views
0
CrossRef citations to date
0
Altmetric
Research Articles

Surrogate Model of Solved Poisson Kriging Method for Radiation Field Reconstruction

, , , , , , & ORCID Icon show all
Received 22 Nov 2023, Accepted 27 Feb 2024, Published online: 19 Apr 2024

References

  • X. XIE, J. CAI, and Z. TANG, “The Reconstruction of 3D Radiation Field Based on Sparse Measurement Data,” Ann. Nucl. Energy, 179, 109391 (2022); http://dx.doi.org/10.1016/j.anucene.2022.109391.
  • S. ZHU et al., “3-D Gamma Radiation Field Reconstruction Method Using Limited Measurements for Multiple Radioactive Sources,” Ann. Nucl. Energy, 175, 109247 (2022); http://dx.doi.org/10.1016/j.anucene.2022.109247.
  • S. ZHU et al., “3-D Gamma Dose Rate Reconstruction for a Radioactive Waste Processing Facility Using Sparse and Arbitrarily-Positioned Measurements,” Prog. Nucl. Energy, 144, 104073 (2022); http://dx.doi.org/10.1016/j.pnucene.2021.104073.
  • V. C. LEITE et al., “Convolutional Neural Network–Aided Temperature Field Reconstruction: An Innovative Method for Advanced Reactor Monitoring,” Nucl. Technol., 209, 5, 645 (2023); http://dx.doi.org/10.1080/00295450.2022.2151822.
  • B. FUCHS, L. L. E. COQ, and M. D. MIGLIORE, “On the Interpolation of Electromagnetic Near Field Without Prior Knowledge of the Radiating Source,” IEEE Trans. Antennas Propag., 65, 7, 3568 (2017); http://dx.doi.org/10.1109/TAP.2017.2705163.
  • J. LI and A. D. HEAP, “Spatial Interpolation Methods Applied in the Environmental Sciences: A Review,” Environ. Modell. Software, 53, 173 (2014); http://dx.doi.org/10.1016/j.envsoft.2013.12.008.
  • J. RODENAS, “Developing a Virtual Reality Application for Training Nuclear Power Plant Operators: Setting Up a Database Containing Dose Rates in the Refuelling Plant,” Radiat. Prot. Dosim., 111, 2, 173 (2004); http://dx.doi.org/10.1093/rpd/nch043.
  • Z. WANG and J. CAI, “Reconstruction of the Neutron Radiation Field on Nuclear Facilities Near the Shield Using Bayesian Inference,” Prog. Nucl. Energy, 118, 103070 (2020); http://dx.doi.org/10.1016/j.pnucene.2019.103070.
  • Z. WANG and J. CAI, “Inversion of Radiation Field on Nuclear Facilities: A Method Based on Net Function Interpolation,” Radiat. Phys. Chem., 153, 27 (2018); http://dx.doi.org/10.1016/j.radphyschem.2018.09.003.
  • R. KIKAWA, K. OYAMA, and H. MING, “Landcover Based 3-Dimensional Inverse Distance Weighting for Visualization of Radiation Dose,” Proc. 2019 IEEE World Congress on Services (SERVICES), pp. 370–371, Milan, Italy, Institute of Electrical and Electronics Engineers (2019); http://dx.doi.org/10.1109/SERVICES.2019.00108.
  • D.-G. KIM et al., “An Estimation Method for Radiation Contrast via the Inverse Distance Weighting,” J. Mech. Sci. Technol., 29, 6, 2529 (2015); http://dx.doi.org/10.1007/s12206-015-0549-4.
  • A. M. GRIGORYEV et al., “Determination of Radiation Field Parameters for the Problems of Routing Optimization Based on Interpolation with Radial Basis Functions,” presented at the 2nd Int. Conf. on Physical Instrumentation and Advanced Materials 2019, p. 020007, Surabaya, Indonesia (2020); http://dx.doi.org/10.1063/5.0032248.
  • B. A. KHUWAILEH and W. A. METWALLY, “Gaussian Process Approach for Dose Mapping in Radiation Fields,” Nucl. Eng. Technol., 52, 8, 1807 (2020); http://dx.doi.org/10.1016/j.net.2020.01.013.
  • F. KÜLAHCI and Z. ŞEN, “Cumulative Ordinary Kriging Interpolation Model to Forecast Radioactive Fallout, and Its Application to Chernobyl and Fukushima Assessment: A New Method and Mini Review,” Environ. Sci. Pollut. Res., 29, 43, 64298 (2022); http://dx.doi.org/10.1007/s11356-022-21921-4.
  • N. S. V. RAO et al., “Identification of Low-Level Point Radiation Sources Using a Sensor Network,” Proc. 2008 Int. Conf. on Information Processing in Sensor Networks (IPSN 2008), pp. 493–504 (2008); http://dx.doi.org/10.1109/IPSN.2008.19.
  • J. ZHAO, Z. ZHANG, and C. J. SULLIVAN, “Identifying Anomalous Nuclear Radioactive Sources Using Poisson Kriging and Mobile Sensor Networks,” PLoS One, 14, 5, e0216131 (2019); http://dx.doi.org/10.1371/journal.pone.0216131.
  • H. BAI et al., “Multi-Source Term Estimation Based on Parallel Particle Filtering and Dynamic State Space in Unknown Radiation Environments,” Build. Environ., 236, 110281 (2023); http://dx.doi.org/10.1016/j.buildenv.2023.110281.
  • P. MONESTIEZ et al., “Geostatistical Modelling of Spatial Distribution of Balaenoptera Physalus in the Northwestern Mediterranean Sea from Sparse Count Data and Heterogeneous Observation Efforts,” Ecol. Modell., 193, 3–4, 615 (2006); http://dx.doi.org/10.1016/j.ecolmodel.2005.08.042.
  • Z. WANG, W. HUANG, and J. SUN, “A Comparative Study of Poisson Kriging’s Matrix Solving Method and Surrogate Model Solving Method,” Proc. 2021 2nd Int. Conf. on Computer Engineering and Intelligent Control (ICCEIC 2021), p 107 (2021); http://dx.doi.org/10.1109/icceic54227.2021.00029.
  • M. OLIVER and R. WEBSTER, “A Tutorial Guide to Geostatistics: Computing and Modelling Variograms and Kriging,” CATENA, 113, 56 (2014); http://dx.doi.org/10.1016/j.catena.2013.09.006.
  • Y. ZHANG, Y. ZHONG, and K. LIAO, “A Coordinated Hazardous Source Search Algorithm Based on Gaussian Process Regression,” Proc. 2020 Chinese Automation Congress (CAC), pp. 5345–5350, Shanghai, China, Institute of Electrical and Electronics Engineers (2020); http://dx.doi.org/10.1109/CAC51589.2020.9326943.
  • A. WEST et al., “Use of Gaussian Process Regression for Radiation Mapping of a Nuclear Reactor with a Mobile Robot,” Sci. Rep., 11, 1, 13975 (2021); http://dx.doi.org/10.1038/s41598-021-93474-4.
  • W. HE et al., “A Novel Improvement of Kriging Surrogate Model,” AEAT, 91, 7, 994 (2019); http://dx.doi.org/10.1108/AEAT-06-2018-0157.
  • I. MOSER, “Hooke-Jeeves Revisited,” Proc. 2009 IEEE Congress on Evolutionary Computation, pp. 2670–2576, Trondheim, Norway, Institute of Electrical and Electronics Engineers (2009); http://dx.doi.org/10.1109/CEC.2009.4983277.
  • M. BALABAN, “Review of Dace-Kriging Metamodel,” Interdiscip. Descr. Complex Syst., 21, 3, 316 (2023); http://dx.doi.org/10.7906/indecs.21.3.8.
  • Y. WU et al., “CAD-Based Monte Carlo Program for Integrated Simulation of Nuclear System SuperMC,” Ann. Nucl. Energy, 82, 161 (2015); http://dx.doi.org/10.1016/j.anucene.2014.08.058.
  • Y.-L. ZOU et al., “Analysis of Radial Basis Function Interpolation Approach,” Appl. Geophys., 10, 4, 397 (2013); http://dx.doi.org/10.1007/s11770-013-0407-z.
  • Q. LIANG et al., “Real‐Time Inverse Distance Weighting Interpolation for Streaming Sensor Data,” Trans. GIS, 22, 5, 1179 (2018); http://dx.doi.org/10.1111/tgis.12458.
  • “Determination of Space Dose Rate according to the Distribution of Radioactive Materials (H23-H28),” Japan Atomic Energy Agency (2016); https://emdb.jaea.go.jp/emdb_old/portals/b1010116/ (current as of Nov. 1, 2022).
  • “Distribution of Radioactive Substances Monitored and Investigated by aircraft(H23-H29),” Japan Atomic Energy Agency (2017); https://emdb.jaea.go.jp/emdb_old/portals/b1010301/ (current as of Nov. 1, 2022).
  • D. EDELMANN, T. F. MÓRI, and G. J. SZÉKELY, “On Relationships Between the Pearson and the Distance Correlation Coefficients,” Stat. Prob. Lett., 169, 108960 (2021); http://dx.doi.org/10.1016/j.spl.2020.108960.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.