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Transmission calculation by empirical numerical model and Monte Carlo simulation in high energy proton radiography of thick objects

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Chinese Physics C 2015年第10期39卷 127-130页

作者：郑娜许海波 Institute of Applied Physics and Computational Mathematics

An empirical numerical model that includes nuclear absorption, multiple Coulomb scattering and energy loss is presented for the calculation of transmission through thick objects in high energy proton radiography. In this numerical model the angular distributions are treated as gaussians in the laboratory frame. A Monte Carlo program based on the Geant4 toolkit was developed and used for high energy proton radiography experiment simulations and verification of the empirical numerical model. The two models are used to calculate the transmission fraction of carbon and lead step-wedges in proton radiography at 24 GeV/c, and to calculate radial transmission of the French Test Object in proton radiography at 24 GeV/c with different angular cuts. It is shown that the results of the two models agree with each other, and an analysis of the slight differences is given.

关键词： proton radiography angular distributions scattering gaussians Geant4 transmission

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Splitting of Gaussian Models via Adapted BML Method Pertaining to Cry-Based Diagnostic System

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Engineering（科研） 2013年第10期5卷 277-283页

作者： Hesam Farsaie Alaie Chakib Tadj Department of Electrical Engineering écolede Technology Supérieure Montréal Canada

In this paper,we make use of the boosting method to introduce a new learning algorithm for Gaussian Mixture Models (GMMs) called adapted Boosted Mixture Learning (BML). The method possesses the ability to rectify the existing problems in other conventional techniques for estimating the GMM parameters, due in part to a new mixing-up strategy to increase the number of Gaussian components. The discriminative splitting idea is employed for Gaussian mixture densities followed by learning via the introduced method. Then, the GMM classifier was applied to distinguish between healthy infants and those that present a selected set of medical conditions. Each group includes both full-term and premature infants. Cry-pattern for each pathological condition is created by using the adapted BML method and 13-dimensional Mel-Frequency Cepstral Coefficients (MFCCs) feature vector. The test results demonstrate that the introduced method for training GMMs has a better performance than the traditional method based upon random splitting and EM-based re-estimation as a reference system in multi-pathological classification task.

关键词： Adapted Boosted Mixture Learning Gaussian Mixture Model Splitting of gaussians Expected-Maximization Algorithm Cry Signals

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