Retrieving Rain Drop Size Distribution Moments from GPM Dual-Frequency Precipitation Radar

A novel method for retrieving the moments of rain drop size distribution (DSD) from the dual-frequency precipitation radar (DPR) onboard the global precipitation mission satellite (GPM) is presented. The method involves the estimation of two chosen reference moments from two specific DPR products, namely the attenuation-corrected Ku-band radar reflectivity and (if made available) the specific attenuation at Ka-band. The reference moments are then combined with a function representing the underlying shape of the DSD based on the generalized gamma model. Simulations are performed to quantify the algorithm errors. The performance of methodology is assessed with two GPM-DPR overpass cases over disdrometer sites, one in Huntsville, Alabama and one in Delmarva peninsula, Virginia, both in the US. Results are promising and indicate that it is feasible to estimate DSD moments directly from DPR-based quantities.

Order Statistics of Additive Uniform Exponential Distribution

In this paper we investigated the order statistics by using Additive Uniform Exponential Distribution (AUED) proposed by Venkata Subbarao Uppu (2010).The probability density functions of rth order Statistics, lth moment of the rth order Statistic, minimum, maximum order statistics, mean of the maximum and minimum order statistics, the joint density function of two order statistics were calculated and discussed in detailed . Applications and several aspects were discussed Keywords: Additive Uniform Exponential Distribution, Moments, Minimum order statistic, Maximum order statistic, Joint density of the order Statistics, complete length of service.

Pneumonia detection using butterfly optimization and hybrid butterfly optimization algorithm

Pneumonia affects so many people around the world, which leads to many of them being killed. In order to identify and diagnose the disease, the patient must first undergo an x-ray scan of the chest (CXR). Disease will be identified according to the CXR images. Software diagnostic tools are used to help decision-making and to promote the pneumonia diagnosis process. From these tools is the gray level distribution moments (GLDM) algorithm which is used for CXR image features extraction, and we used the meta-heuristic algorithm representing the basic butterfly optimization algorithm using lèvy flight (BOALF) and the modified butterfly optimization algorithm (BOARN) to detect pneumonia on the basis of these extracted features. And then we've also been making hybrid between the BOA with fuzzy membership function to get as novel method called it fuzzy butterfly optimization algorithm (FBOA). These methods were based on various x-ray images of the chest. In testing phase, the proposed method obtained the highest diagnostic rate of the disease compared to the other two methods in this work.

Parameterization of the collision-coalescence process using series of basis functions: COLNETv1.0.0 model development using a machine learning approach

A parameterization for the collision-coalescence process is presented, based on the methodology of basis functions. The whole drop spectra is depicted as a linear combination of two lognormal distribution functions, in which all distribution parameters are formulated by means of six distribution moments included in a system of equations, thus eliminating the need of fixing any parameters. This basis functions parameterization avoids the classification of drops in artificial categories such as cloud water (cloud droplets) or rain water (raindrops). The total moment tendencies are calculated using a machine learning approach, in which one deep neural network was trained for each of the total moment orders involved. The neural networks were trained using randomly generated data following a uniform distribution, over a wide range of parameters employed by the parameterization. An analysis of the predicted total moment errors was performed, aimed to stablish the accuracy of the parameterization at reproducing the integrated distribution moments representative of physical variables. The applied machine learning approach shows a good accuracy level when compared to the output of an explicit collision-coalescence model.

Domain Structure and Reversal Mechanisms through Diffracted Magneto-optics in Fe80B20 Microsquare Arrays

In this paper, the predictive power of diffracxtive magneto-optics concerning domain structure and reversal mechanisms in ordered arrays of magnetic elements is demonstrated. A simple theoretical model based on Fraunhoffer diffraction theory is used to predict the magnetisation reversal mechanisms in an array of magnetic elements. Different domain structures and simplified models (or educated guesses) of the associated reversal mechanisms produce marked differences in the spatial distributions of the magnetisation. These differences and the associated magnetisation distribution moments are experimentally accessible through conventional and diffractive magneto-optical Kerr effect measurements. The domain and magnetisation reversal predictions are corroborated with Magnetic Force Microscopy (MFM) measurements.

The Effect of Instrumentation on Sports Balls During Flight, Exemplified by A Smart AFL Ball: Model and Experiment

Sensors incorporated in a sports ball for data collection can affect the properties of a ball, specifically the spin rate of a ball if the mass distribution (moments of inertia, MOI) is altered. This paper provides a method for assessing the MOIs of a smart ball by means of spin rate data, collected from a gyroscopic sensor. The critical elevation angle of the angular velocity vector defines the separatrix condition, which decides whether the angular velocity vector precesses about the axis with the greatest MOI or with the smallest MOI. The critical elevation angle can be directly determined from the experimental of the angular velocity data, and, together with the ratio of precession speed to angular velocity, applied to calculating the three MOIs. In the smart AFL ball used for the experiments, the critical angle was 13.5°, and the ratio of the two small MOIs was 1.014.

Characterization of symmetric distributions based on concomitants of ordered variables from FGMs family of bivariate distributions

Several characterization results of a symmetric distribution based on concomitants of order statistics as well as k-records from Farlie-Gumbel-Morgenstern (FGM) family of bivariate distributions are established. These include characterizations of a symmetric distribution on the basis of equality in distribution, moments, R?nyi and Tsallis entropies of concomitants of upper and lower order statistics, also in terms of the same properties of concomitants of upper and lower k-records.