China
Africa
Explore chapters and articles related to this topic
Monte Carlo Modeling of Solid State X-Ray Detectors for Medical Imaging
Published in Salim Reza, Krzysztof Iniewski, Semiconductor Radiation Detectors, 2017
Compared to optical detectors, the charge generation models for radiation detectors are more complex due to generation of many EHPs by a single incident photon. Photoelectric absorption is the dominant x-ray interaction mechanism in the energy range of interest. It creates a secondary photoelectron, with most of the energy of the initial x-ray, capable of further ionizing the material and producing a significant number of EHPs. X-ray photons that are Compton-scattered can also produce energetic electrons capable of creating many EHPs; however, the particle’s kinetic energy is lower compared to the photoelectron. As the high energy ionizing electron travels through the detector material, it gradually loses its energy through inelastic scattering, and the energy lost, Ed, is deposited in the semiconductor material, leading to the generation of EHPs. The mean number of EHPs generated, N¯EHP, can be estimated via Poisson sampling from the energy deposited and the material ionization energy, Wo:
*
Published in Kai Hormann, N. Sukumar, Generalized Barycentric Coordinates in Computer Graphics and Computational Mechanics, 2017
A common method for generating the RCP points is based on a variant of a Poisson process and is known as random sequential adsorption, or maximal Poisson sampling (MPS). In this approach, points are randomly and sequentially placed in a domain with a constraint on minimum distance between points [60]. The constraint on distance between points is enforced by merely discarding those new points that violate the constraint. The seeding process stops when the maximum packing threshold is reached within tolerance. This approach to generating the RCP seeding can be inefficient, and does not have a quantifiable stopping time. Recently, a very efficient MPS method has been proposed with a finite stopping time [135].
Discrete Graphical Models and Their Parameterization
Published in Marloes Maathuis, Mathias Drton, Steffen Lauritzen, Martin Wainwright, Handbook of Graphical Models, 2018
Luca La Rocca, Alberto Roverato
We conclude the chapter with a discussion of the inferential issues raised by a random sample from a distribution in a discrete quasi-graphical model of UG, BG or RG type. We take a likelihood based approach, and we consider as data a table of multinomial cross-classified counts, which is a sufficient statistic in this setting. The reader is referred to Lauritzen [36, Sect. 4.2.1] for an illustration of the link to Poisson sampling. We focus for simplicity on the binary case, but we note that this restriction is not at all essential.
An adaptive sampling method for STL free-form surfaces based on the quasi-Gauss curvature grid
Published in International Journal of Computer Integrated Manufacturing, 2023
Gaocai Fu, Buyun Sheng, Yuzhe Huang, Ruiping Luo, Geng Chen, Ganlin Sheng
Further analysis, the maximum deviations of the curved surface under the method in this article, ‘surface-curves-points’ sampling method, CVT sampling method, and ‘patches-Poisson’ sampling method are 0.0234 mm, 0.0217 mm, 0.0229 mm, 0.0241 mm, which are 0.86%, 6.47%, 1.29%, 3.88% away from the estimated the E_real of 0.0232 mm. Overall this ratio of the method in this article is closer to the estimated true value compared to other methods. Therefore, this method in this paper can achieve almost the same measurement effect or even slightly better, compared with ‘surface-curves-points’ sampling, CVT sampling, and ‘patches-Poisson’ sampling methods. Moreover, the calculation time of these latter four sampling methods is close to about 10s, which is within an acceptable range. In terms of calculation time, the proposed method is slightly better than the ‘surface-curves-points’ sampling method and the ‘patches-Poisson’ sampling method. Although the time consuming of CVT sampling is the shortest among the last four methods, when the method is executed again, the position of the measuring points will change and cannot be repeated again due to the randomness of the occurrence element, there is also the ‘patches-Poisson’ sampling method. Thus, the proposed method is slightly superior to the CVT sampling method and the ‘patches-Poisson’ sampling method in terms of measuring points’ repeatability.
Counting efficiency evaluation of optical particle counters in micrometer range by using an inkjet aerosol generator
Published in Aerosol Science and Technology, 2018
Table 3 shows the summary of the uncertainty analysis of the reported counting efficiency, . At = 10 μm the largest source of the uncertainty is the systematic error, . That is, the delivery pattern affects the counting efficiency at = 10 μm. The table also gives the RSU of the Poisson sampling process, . The values are calculated by where is the average particle counts among twelve repeated measurements ( = 12). The underlined value shows that the uncertainty due to the repeatability, which occurs when the counting efficiency based on particle count rate is essentially 100%, is significantly smaller than those of the Poisson sampling process. As mentioned in the Introduction section, this is one important difference of the IAG-based method from the parallel comparison method. Sampling the entire population of the particles generated by the IAG is not a Poisson process; therefore, the uncertainty of the results does not have this particular random component.
A method to deposit a known number of polystyrene latex particles on a flat surface
Published in Aerosol Science and Technology, 2019
Naoko Tajima, Kenjiro Iida, Kensei Ehara, Sommawan Khumpuang, Shiro Hara, Hiromu Sakurai
Poisson uncertainty is present when the particle number on a flat surface is predicted from the number concentration measured in parallel with a deposition device, because the counted and deposited droplets are different. The gray lines in Figure 8 indicate 95% confidence intervals in a scenario in which the Poisson sampling process is the only source of uncertainty, with the values estimated by 1 ± 2()−0.5. The value of of Wafer No. 2 is close to the confidence intervals of the Poisson sampling process.