The intensive application of radioactivesources in various fields has led to a rise in incidents involving their loss. Focusing on the rapid and safe detection of radiation environments and the localization of radioa...
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ISBN:
(纸本)9798350352634;9798350352627
The intensive application of radioactivesources in various fields has led to a rise in incidents involving their loss. Focusing on the rapid and safe detection of radiation environments and the localization of radioactivesources, this paper integrates the design of detection robots, radiation localization techniques, and path tracking to create a radioactive source detection system based on autonomous mobile robots. The design of the detection robot considers the unique challenges posed by radiation environments. In terms of localization, an adaptive radioactivesource localization algorithm based on the Bayesian model, incorporating angle of arrival (AOA), is proposed to estimate the location of radioactivesources. Path planning is optimized using the Pure Pursuit algorithm to enhance tracking accuracy. Finally, on a mobile robot platform, an unknown radioactivesource search experiment is conducted, validating the effectiveness of the system and vigorously advancing the field of radioactive source detection.
Considering the difficulties of the low signal-to-noise ratio in weak radioactive source detections, this study proposes an abandon Gaussian tails method based on the analysis of the characteristic information denoted...
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Considering the difficulties of the low signal-to-noise ratio in weak radioactive source detections, this study proposes an abandon Gaussian tails method based on the analysis of the characteristic information denoted by the full-energy peak of the gamma spectrum of a gamma-emitting radioactivesource. Based on the study of the signal-to-background ratio and the statistical fluctuations in the signal of the weak radioactivesource, a factor zeta, incorporating the statistical fluctuations of signal and background and the signal-to-background ratio, is suggested to characterize the sensitivity of a radioactive source detection. When zeta reaches its maximum value, the optimal counting window around the centroid of the full-energy peak can be obtained. To evaluate the effectiveness of the proposed approach, comparisons between the proposed abandon Gaussian tails, the conventional full-energy counting, and other experiential methods were performed. The results show that the sensitivity can be significantly improved. Further, experiments with different intensity of radiation sources and duplicated experiments were conducted to examine the stability of the proposed method.
This work describes a study carried out to assess the feasibility of using radiation sensors for real-time detection of radioactivesources within a facility. The sourcedetection problem is formulated as a nonparamet...
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This work describes a study carried out to assess the feasibility of using radiation sensors for real-time detection of radioactivesources within a facility. The sourcedetection problem is formulated as a nonparametric hypothesis test using statistics computed from the radiation sensor signals. The purpose of this study is the development of tools that enhance the security and safety of nuclear material processing facilities by introducing new sensor modalities and developing remote monitoring capabilities. The results indicate that a source can be detected quite well using non parametric statistical tests in spite of poor signal to noise ratios, unknown background signals, and high measurement noise levels.
An ALISA Vector Module (AVM) is trained on the discrete gamma-ray emission spectra of 61 commonly occurring radioisotopes generated by an analytical model. The trained AVM is then used to decompose the spectra capture...
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ISBN:
(纸本)0819454796
An ALISA Vector Module (AVM) is trained on the discrete gamma-ray emission spectra of 61 commonly occurring radioisotopes generated by an analytical model. The trained AVM is then used to decompose the spectra captured from actual sources in the field using low-resolution thallium-activated sodium-iodide (NaI) detectors and/or high-resolution high-purity germanium (HPGe) detectors using QR Factorization to find the optimal least-squares solution for an overspecified system of equations, even if inconsistent. For low-resolution NaI detectors, formal experiments conducted under carefully controlled laboratory conditions yield average classification (spectral decomposition) errors less than 6% in mixtures with up to 10 components in test samples consisting of 1,000 photonic events, which requires just a few seconds to obtain in typical situations. Preliminary experiments with the high-resolution HPGe detector yield dramatically smaller errors than with the NaI detector. Further improvements in the accuracy and precision of the training data, as well as fusion with other powerful classification methods, are expected to reduce the error without prohibitively increasing the computation time.
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