DOI QR코드

DOI QR Code

Calculation of Detector Positions for a Source Localizing Radiation Portal Monitor System Using a Modified Iterative Genetic Algorithm

  • Jeon, Byoungil (Neutron Science Research Center, Korea Atomic Energy Research Institute) ;
  • Kim, Jongyul (Neutron Science Research Center, Korea Atomic Energy Research Institute) ;
  • Lim, Kiseo (Department of Physics, Myongji University) ;
  • Choi, Younghyun (Neutron Science Research Center, Korea Atomic Energy Research Institute) ;
  • Moon, Myungkook (Neutron Science Research Center, Korea Atomic Energy Research Institute)
  • Received : 2017.08.22
  • Accepted : 2017.10.16
  • Published : 2017.12.31

Abstract

Background: This study aims to calculate detector positions as a design of a radioactive source localizing radiation portal monitor (RPM) system using an improved genetic algorithm. Materials and Methods: To calculate of detector positions for a source localizing RPM system optimization problem is defined. To solve the problem, a modified iterative genetic algorithm (MIGA) is developed. In general, a genetic algorithm (GA) finds a globally optimal solution with a high probability, but it is not perfect at all times. To increase the probability to find globally optimal solution rather, a MIGA is designed by supplementing the iteration, competition, and verification with GA. For an optimization problem that is defined to find detector positions that maximizes differences of detector signals, a localization method is derived by modifying the inverse radiation transport model, and realistic parameter information is suggested. Results and Discussion: To compare the MIGA and GA, both algorithms are implemented in a MATLAB environment. The performance of the GA and MIGA and that of the procedures supplemented in the MIGA are analyzed by computer simulations. The results show that the iteration, competition, and verification procedures help to search for globally optimal solutions. Further, the MIGA is more robust against falling into local minima and finds a more reliably optimal result than the GA. Conclusion: The positions of the detectors on an RPM for radioactive source localization are optimized using the MIGA. To increase the contrast of the measurements from each detector, a relationship between the source and the detectors is derived by modifying the inverse transport model. Realistic parameters are utilized for accurate simulations. Furthermore, the MIGA is developed to achieve a reliable solution. By utilizing results of this study, an RPM for radioactive source localization has been designed and will be fabricated soon.

Acknowledgement

Grant : Research on fundamental core technology for ubiquitous shipping and logistics

Supported by : Ministry of Oceans and Fisheries

References

  1. Coulon R, Vladimir K, Bouderguri K, Normand S. Moving sources detection algorithm for radiation portal monitors used in a linear network. IEEE Trans. Nucl. Sci. 2014 ;61(4):2189-2194. https://doi.org/10.1109/TNS.2014.2299872
  2. Rao NSV, et al. Network algorithms for detection of radiation sources. Nucl. Instrum. Methods Phys. Res. Sect. A. 2015;784: 326-331. https://doi.org/10.1016/j.nima.2015.01.037
  3. Miller KA, Charlton WS. An inverse transport approach to radiation source location for border security. Annual Meeting on the European Safeguards Research and Development Association. Aix-en-Provence France. May 22, 2007.
  4. Vilim R, Klann R. RadTrac: a system for detecting, localizing, and tracking radioactive sources in real time. Nucl. Technol. 2009;168:61-73. https://doi.org/10.13182/NT168-61
  5. Jarman KD, Miller EA, Wittman RS, Gesh CJ. Bayesian radiation source localization. Nucl. Technol. 2010;175:326-334.
  6. Miller EA, White TA, Jarman KD, Kouzes RT, Kulisek JA, Robinson SM, Wittman RA. Combining radiography and passive measurements for radiological threat localization in cargo. IEEE Trans. Nucl. Sci. 2015;62(5):2234-2244. https://doi.org/10.1109/TNS.2015.2474146
  7. Robinson SM, Kaye WR, Schweppe JE, Siciliano ER. Optimal background attenuation for fielded radiation detection system. IEEE Trans. Nucl. Sci. 2007;54(4):1279-1284. https://doi.org/10.1109/TNS.2007.901196
  8. Gilbert MR, Ghani Z, McMillan JE, Packer LW. Optimising the neutron environment of radiation portal monitors: a computational study. Nucl. Instrum. Methods Phys. Res. Sect. A. 2015; 795:174-185. https://doi.org/10.1016/j.nima.2015.05.060
  9. Tomanin A, Peerani P, Maenhout GJ. On the Optimisation of the use of 3He in radiation portal monitors. Nucl. Instrum. Methods Phys. Res. Sect. A. 2013;700:81-85. https://doi.org/10.1016/j.nima.2012.10.002
  10. Wacholder E, Elias E, Merlis Y. artificial neutral networks optimization method for radioactive source localization. Nucl. Technol. 1995;110:228-237. https://doi.org/10.13182/NT95-A35120
  11. Knoll GF. Radiation detection and measurements. 3rd edition. Hoboken NJ. John Wiley & Sons, Inc. 2000;116-118.
  12. Ajdler T, Kozintsev I, Lienhart R, Vetterli M. Acoustic source localization in distributed sensor networks. The 38th Asilomar Conference on Signal, Systems and Computers. Pacific Grove CA. November 7-10, 2004.
  13. Yang Y, Ho KC. Alleviating sensor position error in source localization using calibration emitters at inaccurate locations. IEEE Trans. Signal Process. 2010;58(1):67-83. https://doi.org/10.1109/TSP.2009.2028947
  14. Patwari N, Ash JN, Kyperountas S, Hero AO, Moses RL, Correal NS. Locating the nodes: cooperative locaization in wireless sensor networks. IEEE Signal Process Mag. 2005;22(4):54-69. https://doi.org/10.1109/MSP.2005.1458287
  15. Rao SS. Engineering optimization theory and practice. 4th edition. Hoboken NJ. John Wiley & Sons Inc. 2009;694-702.
  16. Michalewicz Z, Janikow CZ, Krawczyk JB. A modified genetic algorithm for optimal control problems. Comput. Math. Appl. 1992;23(12):83-94. https://doi.org/10.1016/0898-1221(92)90094-X
  17. Mutlu O, Polat O, Supciller AA. An iterative genetic algorithm for the assembly line worker assignment and balancing problem of type-2. Comput. Oper. Res. 2013;40(1):418-426. https://doi.org/10.1016/j.cor.2012.07.010
  18. Khatib T, Mohamed A, Sopian K. Optimization of a PV/wind micro-grid for rural housing electrification using a hybrid iterative/ genetic algorithm: case study of Kuala Terengganu Malaysia. Energy Build. 2012;47:321-331. https://doi.org/10.1016/j.enbuild.2011.12.006
  19. Firmo HT, Legey LFL. Generation expansion planning: an iterative genetic algorithm approach. IEEE Trans. Power Syst. 2002; 17(3):901-906.