Energy spectrum analysis method based on Hilbert curve transformation and depth learning

Abstract

The invention discloses an energy spectrum analysis method based on Hilbert curve transformation and depth learning, belonging to the field of radiation environment monitoring and image recognition, which has the characteristics of high recognition rate, good stability and strong adaptability. The invention comprises the following steps: (1) acquiring a detection energy spectrum and a simulation energy spectrum and performing pretreatment; (2) Transforming the traditional one-dimensional energy spectrum analysis into two-dimensional image recognition, training and testing the full spectrum input depth learning; (3) A depth learning algorithm for fast nuclide recognition is constructed, and the effect of the depth learning classifier is analyzed by determining the classification threshold and ROC curve.

Description

technical field [0001] The invention belongs to the fields of radiation environment monitoring and image recognition, and in particular relates to an energy spectrum analysis method based on Hilbert curve transformation and deep learning. Background technique [0002] With the continuous development of society, the application of nuclear technology and nuclear radiation detection technology play an increasingly important role in the fields of production and life, national defense construction, etc., and the resulting radioactive hazards have also attracted more and more attention. The energy spectrum analysis method can identify the radionuclides existing in the environment and measure the activity through the detected energy spectrum, and is widely used in astrophysics, environmental science and other fields. [0003] At present, in the energy spectrum analysis of long-time detection, there has been a high recognition accuracy rate. However, for rapid nuclide identificatio...

AI Technical Summary

Problems solved by technology

However, for fast nuclide identification, there are problems of low count, low spectral resolution and large interference, which cause great difficulties for subsequent spectral analysis

Existing methods such as peak-seeking algorithms have high requirements for energy spectrum data, and cannot accurately identify low-count, low-resolution energy spectra. Artificial neural network methods have poor recognition rates for coherent nuclide energy spectra, which can easily cause misidentification

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