• Nuclear Engineering and Technology
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• v.55 no.8
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• pp.3030-3038
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• 2023

Nowadays, mobile robots have been used to search for uncontrolled radioactive source in indoor environments to avoid radiation exposure for technicians. However, in the indoor environments, especially in the presence of obstacles, how to make the robots with limited sensing capabilities automatically search for the radioactive source remains a major challenge. Also, the source search efficiency of robots needs to be further improved to meet practical scenarios such as limited exploration time. This paper proposes an automatic source search strategy, abbreviated as ACA: the location of source is estimated by a convolutional neural network (CNN), and the path is planned by the A-star algorithm. First, the search area is represented as an occupancy grid map. Then, the radiation dose distribution of the radioactive source in the occupancy grid map is obtained by Monte Carlo (MC) method simulation, and multiple sets of radiation data are collected through the eight neighborhood self-avoiding random walk (ENSAW) algorithm as the radiation data set. Further, the radiation data set is fed into the designed CNN architecture to train the network model in advance. When the searcher enters the search area where the radioactive source exists, the location of source is estimated by the network model and the search path is planned by the A-star algorithm, and this process is iterated continuously until the searcher reaches the location of radioactive source. The experimental results show that the average number of radiometric measurements and the average number of moving steps of the ACA algorithm are only 2.1% and 33.2% of those of the gradient search (GS) algorithm in the indoor environment without obstacles. In the indoor environment shielded by concrete walls, the GS algorithm fails to search for the source, while the ACA algorithm successfully searches for the source with fewer moving steps and sparse radiometric data.

• Nuclear Engineering and Technology
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• v.56 no.4
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• pp.1153-1164
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• 2024

In recent years, unmanned ground vehicles (UGVs) have been used to search for lost or stolen radioactive sources to avoid radiation exposure for operators. To achieve autonomous localization of radioactive sources, the UGVs must have the ability to automatically determine the next radiation measurement location instead of following a predefined path. Also, the radiation field of radioactive sources has to be reconstructed or inverted utilizing discrete measurements to obtain the radiation intensity distribution in the area of interest. In this study, we propose an effective source localization framework and method, in which UGVs are able to autonomously explore in the radiation area to determine the location of radioactive sources through an iterative process: path planning, radiation field reconstruction and estimation of source location. In the search process, the next radiation measurement point of the UGVs is fully predicted by the design path planning algorithm. After obtaining the measurement points and their radiation measurements, the radiation field of radioactive sources is reconstructed by the Gaussian process regression (GPR) model based on machine learning method. Based on the reconstructed radiation field, the locations of radioactive sources can be determined by the peak analysis method. The proposed method is verified through extensive simulation experiments, and the real source localization experiment on a Cs-137 point source shows that the proposed method can accurately locate the radioactive source with an error of approximately 0.30 m. The experimental results reveal the important practicality of our proposed method for source autonomous localization tasks.

• Journal of Radiation Protection and Research
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• v.42 no.4
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• pp.212-221
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• 2017

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.

• Nuclear Engineering and Technology
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• v.55 no.5
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• pp.1587-1592
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• 2023

Time-encoded imagers (TEI) are important class of instruments to search for potential radioactive sources to prevent illicit transportation and trafficking of nuclear materials and other radioactive sources. The energy of the radiation cannot be known in advance due to the type and shielding of source is unknown in practice. However, the response function of the time-encoded imagers is related to the energy of neutrons or gamma-rays. An improved image reconstruction method based on MLEM was proposed to correct for the energy induced response difference. In this method, the count vector versus time was first smoothed. Then, the preset response function was adaptively corrected according to the measured counts. Finally, the smoothed count vector and corrected response were used in MLEM to reconstruct the source distribution. A one-dimensional dual-particle time-encode imager was developed and used to verify the improved method through imaging an Am-Be neutron source. The improvement of this method was demonstrated by the image reconstruction results. For gamma-ray and neutron images, the angular resolution improved by 17.2% and 7.0%; the contrast-to-noise ratio improved by 58.7% and 14.9%; the signal-to-noise ratio improved by 36.3% and 11.7%, respectively.

• Nuclear Engineering and Technology
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• v.52 no.7
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• pp.1524-1531
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• 2020

The aim of this work is to develop a Geiger-Muller (GM) detector system for robot to look for a radioactive source in case of a nuclear emergency or in a high radiation environment. In order to find a radiation source easily, a detector system, including 3 detectors, was designed to search γ-ray radiation sources autonomously. First, based on GEANT4 simulation, radiation dose rates in 3 Geiger-Muller (GM) counters were simulated at different source-detector distances, distances between detectors and angles. Various sensitivity analyses were performed experimentally to verify the simulated designed detector system. A mono-energetic ¹³⁷Cs γ-ray source with energy 662 keV and activity of 1.11 GBq was used for the observation. The simulated results were compared with the experimental dose rate values and good agreements were obtained for various cases. Only based on the dose rates in three detectors, the radiation source with a specific source activity and angle was localized in the different location. A method was adopted with the measured dose rates and differences of distances to find the actual location of the lost γ-ray source. The corresponding angles of deviation and detection limits were calculated to determine the sensitivity and abilities of our designed detector system. The proposed system can be used to locate radiation sources in low and high radiation environments.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.4
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• pp.71-77
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• 2008

This paper presents a methodology for recovering the industrial radiographic images from the effects of nonlinear distortion. Analytical approach based on the inverse square law and Beer's law is developed in order to improve a mathematic model of nonlinear type. The geometric effect due to dimensions of the radioactive source appeals on the digitized images. The relation that expresses parameters values(angle, position, absorption coefficient, length, width and pixel account) is defined in this model, matching with the sample image. To perform the search for image recovery most similar to the model, a correction procedure is designed. The application of this method on the radiographic images of steel tubes is shown and recovered results are discussed.