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BackgroundAviation γ spectrometer, measuring at high-altitude with low background counting rate, is prone to spectral line drift caused by factors such as long stable spectral periods and significant temperature effects due to its low background counting rate, hence seriously affects the accuracy of measurement results.PurposeThis study aims to propose an adaptive derivative-Gaussian joint peak search algorithm so as to quickly find and stabilize the peak value of airborne γ-ray spectrometer in low background environment.MethodsFirstly, the process of derivative peak finding algorithm was optimized so that it automatically determined the peak position, channel address, and left and right boundaries based on limited conditions, achieving adaptive transformation background window width deduction of Gaussian peak finding algorithm. Then, a self-developed miniaturized aviation γ spectrometer was used for flight measurement experiments by mounting it on unmanned aerial vehicle, and temperature changing measurement experiments by placing it in a variable temperature oven. Finally, the adaptive derivative gaussian peak finding results were compared and verified with that of traditional derivative peak-seeking algorithm and traditional gaussian peak-seeking algorithm, to ensure the accuracy of the proposed peak finding results and the efficiency of γ spectral stabilization.ResultsThe actual measurement results show that the adaptive derivative Gaussian joint peak finding algorithm has fast calculation speed and high accuracy. With 1 024 aviation tracks γ, the maximum spectral drift of the 40K peak measured by the energy spectrometer within the range of -20 ℃ to 50 ℃, does not exceed ±3 channels.ConclusionsThis study provides a new spectral stabilization method for the accurate measurement of airborne γ-ray spectrum in low background environment.

High-level Automated Vehicles (HAVs) are expected to improve traffic safety significantly. However, verifying and evaluating HAVs remains an open problem. Scenario-based testing is a promising method for HAV testing. Boundary scenarios exist around the performance boundary between critical and non-critical scenarios. Testing HAVs in these boundary scenarios is crucial to investigate why collisions cannot be avoided due to small changes in scenario parameters. This study proposes a methodology to generate diverse boundary scenarios to test HAVs. First, an approach is proposed to obtain at least one High-Performance Classifier (HPC) based on two classification algorithms that iteratively guide each other to find uncertain scenarios to improve their performance. Then, the HPC is exploited to find candidate scenarios highly likely to be boundary scenarios. To increase the efficiency of candidate scenario generation, a strategy based on local sampling is presented to find more diverse candidate scenarios based on a small number of them. Numerical experiments show that the HPCs acquired by the method proposed in this study can achieve a classification accuracy of 98% and 99% for random car-following and cut-in scenarios, respectively. Moreover, more than 86% of 271,744 candidate cut-in scenarios derived by local sampling are near the performance boundary. [ABSTRACT FROM AUTHOR]