Estimating ECG intervals from a deep learning morphology model

Abstract

The statistical fitting of existing data is an efficient method for calculating the gamma-ray buildup factors. However, the existing Geometric Progression (GP) fitting formula requires the interpolation of five parameters: a, b, c, d, and Xk. GP fitting formula also considers three factors: photon energy, shielding material and shielding in the interpolation process, leading to the complex calculations of buildup factor. On the other hand, the large and bloated parameter library also brings inconvenience to the application of GP formula in radiation calculation code. This paper proposes a regression method for calculating gamma-ray buildup factors based on Extra-Trees (ET). The training set is obtained from the exposure buildup factors of the ANS standard database to establish the ET with superior performance for buildup factor. The ET model can reproduce the data of exposure buildup factor in ANS without deviation. For materials not considered in ANS, the buildup factors calculated by ET model are consistent with the results calculated by GP fitting formula. The performance in time consumption is also satisfactory. The method proposed in this paper has the potential to replace GP fitting formula and be used in situations where the buildup factor needs to be calculated, such as design, shielding calculation and analysis of gamma-rays.