scholarly journals A Cluster-Based Method for Hydrometeor Classification Using Polarimetric Variables. Part I: Interpretation and Analysis

2015 ◽  
Vol 32 (7) ◽  
pp. 1320-1340 ◽  
Author(s):  
Guang Wen ◽  
Alain Protat ◽  
Peter T. May ◽  
Xuezhi Wang ◽  
William Moran
Keyword(s):  
Electromagnetic Scattering ◽  
Expectation Maximization ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Radar Data ◽  
Probability Density Functions ◽  
Classification Algorithms ◽  
Classification Methods ◽  
Membership Functions ◽  
Polarimetric Radar

AbstractHydrometeor classification methods using polarimetric radar variables rely on probability density functions (PDFs) or membership functions derived empirically or by using electromagnetic scattering calculations. This paper describes an objective approach based on cluster analysis to deriving the PDFs. An iterative procedure with K-means clustering and expectation–maximization clustering based on Gaussian mixture models is developed to generate a series of prototypes for each hydrometeor type from several radar scans. The prototypes are then grouped together to produce a PDF for each hydrometeor type, which is modeled as a Gaussian mixture. The cluster-based method is applied to polarimetric radar data collected with the CP-2 S-band radar near Brisbane, Queensland, Australia. The results are illustrated and compared with theoretical classification boundaries in the literature. Some notable differences are found. Automated hydrometeor classification algorithms can be built using the PDFs of polarimetric variables associated with each hydrometeor type presented in this paper.

Fuzzy Logic Classification of S-Band Polarimetric Radar Echoes to Identify Three-Body Scattering and Improve Data Quality

2014 ◽  
Vol 53 (8) ◽  
pp. 2017-2033 ◽  
Author(s):  
Vivek N. Mahale ◽  
Guifu Zhang ◽  
Ming Xue
Keyword(s):  
Fuzzy Logic ◽  
Standard Deviation ◽  
Radar Data ◽  
Classification Algorithm ◽  
Radar Reflectivity ◽  
Membership Functions ◽  
Polarimetric Radar ◽  
Radar Echo ◽  
Three Body ◽  
Reflectivity Factor

AbstractThe three-body scatter signature (TBSS) is a radar artifact that appears downrange from a high-radar-reflectivity core in a thunderstorm as a result of the presence of hailstones. It is useful to identify the TBSS artifact for quality control of radar data used in numerical weather prediction and quantitative precipitation estimation. Therefore, it is advantageous to develop a method to automatically identify TBSS in radar data for the above applications and to help identify hailstones within thunderstorms. In this study, a fuzzy logic classification algorithm for TBSS identification is developed. Polarimetric radar data collected by the experimental S-band Weather Surveillance Radar-1988 Doppler (WSR-88D) in Norman, Oklahoma (KOUN), are used to develop trapezoidal membership functions for the TBSS class of radar echo within a hydrometeor classification algorithm (HCA). Nearly 3000 radar gates are removed from 50 TBSSs to develop the membership functions from the data statistics. Five variables are investigated for the discrimination of the radar echo: 1) horizontal radar reflectivity factor ZH, 2) differential reflectivity ZDR, 3) copolar cross-correlation coefficient ρhv, 4) along-beam standard deviation of horizontal radar reflectivity factor SD(ZH), and 5) along-beam standard deviation of differential phase SD(ΦDP). These membership functions are added to an HCA to identify TBSSs. Testing is conducted on radar data collected by dual-polarization-upgraded operational WSR-88Ds from multiple severe-weather events, and results show that automatic identification of the TBSS through the enhanced HCA is feasible for operational use.

PROBABILISTIC HARMONIC ANALYSIS OF WIND GENERATORS BASED ON GENERALIZED GAMMA MIXTURE MODELS

2012 ◽  
Vol 27 (03) ◽  
pp. 1350020 ◽  
Author(s):  
GUANGLONG XIE ◽  
BUHAN ZHANG
Keyword(s):  
Harmonic Analysis ◽  
Mixture Models ◽  
Power Quality ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Probability Density Functions ◽  
Density Functions ◽  
Generalized Gamma ◽  
Wind Generators ◽  
Harmonic Currents

Grid-connected wind generators pose the power quality problems such as harmonic propagation and summation, and these problems are hard to solve by deterministic harmonic analysis due to the random harmonic current emissions. In this paper, probabilistic harmonic analysis is utilized to approximate harmonic currents of wind generators. Generalized gamma mixture models based on Gaussian mixture models, phasor clustering and generalized gamma models, are proposed to approximate the probability density functions of harmonic propagation and summation. And the simulation network built on PSCAD/EMTDC is utilized to verify the proposed models and method.

Mixture Model Approach for Acoustic Emission Control of Cylindrical Pressure Equipment

2005 ◽  
Vol 128 (3) ◽  
pp. 479-483
Author(s):  
Hani Hamdan ◽  
Gérard Govaert
Keyword(s):  
Acoustic Emission ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Expectation Maximization ◽  
Gaussian Mixture Models ◽  
Expectation Maximization Algorithm ◽  
Real Data ◽  
Gaussian Mixture ◽  
Mixture Model Approach ◽  
Model Approach

In this paper, we present a new and original mixture model approach for acoustic emission (AE) data clustering. AE techniques have been used in a variety of applications in industrial plants. These techniques can provide the most sophisticated monitoring test and can generally be done with the plant/pressure equipment operating at several conditions. Since the AE clusters may present several constraints (different proportions, volumes, orientations, and shapes), we propose to base the AE cluster analysis on Gaussian mixture models, which will be, in such situations, a powerful approach. Furthermore, the diagonal Gaussian mixture model seems to be well adapted to the detection and monitoring of defect classes since the weldings of cylindrical pressure equipment are lengthened horizontally and vertically (cluster shapes lengthened along the axes). The EM (Expectation-Maximization) algorithm applied to a diagonal Gaussian mixture model provides a satisfactory solution but the real time constraints imposed in our problem make the application of this algorithm impossible if the number of points becomes too big. The solution that we propose is to use the CEM (Classification Expectation-Maximization) algorithm, which converges faster and generates comparable solutions in terms of resulting partition. The practical results on real data are very satisfactory from the experts point of view.

scholarly journals Efficient Greedy Learning of Gaussian Mixture Models

2003 ◽  
Vol 15 (2) ◽  
pp. 469-485 ◽  
Author(s):  
J. J. Verbeek ◽  
N. Vlassis ◽  
B. Kröse
Keyword(s):  
Mixture Models ◽  
Density Estimation ◽  
Expectation Maximization ◽  
State Of The Art ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Optimal Number ◽  
Gaussian Mixtures ◽  
Data Points ◽  
Time Linear

This article concerns the greedy learning of gaussian mixtures. In the greedy approach, mixture components are inserted into the mixture one aftertheother.We propose a heuristic for searching for the optimal component to insert. In a randomized manner, a set of candidate new components is generated. For each of these candidates, we find the locally optimal new component and insert it into the existing mixture. The resulting algorithm resolves the sensitivity to initialization of state-of-the-art methods, like expectation maximization, and has running time linear in the number of data points and quadratic in the (final) number of mixture components. Due to its greedy nature, the algorithm can be particularly useful when the optimal number of mixture components is unknown. Experimental results comparing the proposed algorithm to other methods on density estimation and texture segmentation are provided.

Evolving Gaussian Mixture Models with Splitting and Merging Mutation Operators

2016 ◽  
Vol 24 (2) ◽  
pp. 293-317 ◽  
Author(s):  
Thiago Ferreira Covões ◽  
Eduardo Raul Hruschka ◽  
Joydeep Ghosh
Keyword(s):  
Em Algorithm ◽  
Mixture Models ◽  
Expectation Maximization ◽  
Complexity Analysis ◽  
A Priori ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Critical Parameters ◽  
Mutation Operators ◽  
Split And Merge

This paper describes the evolutionary split and merge for expectation maximization (ESM-EM) algorithm and eight of its variants, which are based on the use of split and merge operations to evolve Gaussian mixture models. Asymptotic time complexity analysis shows that the proposed algorithms are competitive with the state-of-the-art genetic-based expectation maximization (GA-EM) algorithm. Experiments performed in 35 data sets showed that ESM-EM can be computationally more efficient than the widely used multiple runs of EM (for different numbers of components and initializations). Moreover, a variant of ESM-EM free from critical parameters was shown to be able to provide competitive results with GA-EM, even when GA-EM parameters were fine-tuned a priori.

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