Using a Multivariate Gaussian Mixture Model With Expectation Maximization to Identify Characteristic Bursting Strength in Woven Hemp Fabrics

2017 ◽  
Author(s):  
Leigh N. Gaither ◽  
C. Riedel
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Expectation Maximization ◽  
Gaussian Mixture ◽  
Strength Test ◽  
Bursting Strength ◽  
Elastic Range ◽  
Data Points ◽  
Data Point ◽  
Fabric Surface

This paper proposes to identify the strength characteristics for a particular woven hemp fabric from a collection of data representing the strengths derived from bursting strength testing based on moisture content. The Ball Bursting Strength Test, D3787 and ASTM 6797, define the size of puncture tool and the speed of force application for the bursting test procedure. The bursting strength test is a method of defining the strength of a woven fabric in two directions simultaneously given a single force perpendicular to the fabric surface. Plotting the resultant bursting force against the apparent modulus of elasticity for each sample, sets the variance in strength for the elastic range against the variance for the elastic range. The amount of variance of any particular data point from an overall group mean will help identify its association with a group of data points all belonging to a common family of test samples. Recognizing that a particular data point is likely to belong to a group of data points and less likely to belong to another group of data points given the parameters of variance and mean for any group of points, is the function of the Gaussian Mixture Model with Expectation Maximization (GMMEM).

Gaussian Mixture Model of Ground Filtering Based on Hierarchical Curvature Constraints for Airborne Lidar Point Clouds

2021 ◽  
Vol 87 (9) ◽  
pp. 615-630
Author(s):  
Longjie Ye ◽  
Ka Zhang ◽  
Wen Xiao ◽  
Yehua Sheng ◽  
Dong Su ◽  
...  
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Expectation Maximization ◽  
Latent Variables ◽  
Expectation Maximization Algorithm ◽  
Point Clouds ◽  
Gaussian Mixture ◽  
Airborne Lidar ◽  
Height Difference ◽  
Hierarchical Constraints

This paper proposes a Gaussian mixture model of a ground filtering method based on hierarchical curvature constraints. Firstly, the thin plate spline function is iteratively applied to interpolate the reference surface. Secondly, gradually changing grid size and curvature threshold are used to construct hierarchical constraints. Finally, an adaptive height difference classifier based on the Gaussian mixture model is proposed. Using the latent variables obtained by the expectation-maximization algorithm, the posterior probability of each point is computed. As a result, ground and objects can be marked separately according to the calculated possibility. 15 data samples provided by the International Society for Photogrammetry and Remote Sensing are used to verify the proposed method, which is also compared with eight classical filtering algorithms. Experimental results demonstrate that the average total errors and average Cohen's kappa coefficient of the proposed method are 6.91% and 80.9%, respectively. In general, it has better performance in areas with terrain discontinuities and bridges.

scholarly journals Detecting Clusters in Atom Probe Data with Gaussian Mixture Models

2017 ◽  
Vol 23 (2) ◽  
pp. 269-278 ◽  
Author(s):  
Jennifer Zelenty ◽  
Andrew Dahl ◽  
Jonathan Hyde ◽  
George D. W. Smith ◽  
Michael P. Moody
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Expectation Maximization ◽  
Clustering Algorithm ◽  
Simulated Data ◽  
Atom Probe ◽  
Gaussian Mixture ◽  
Separation Method ◽  
Detection Accuracy ◽  
Maximum Separation

AbstractAccurately identifying and extracting clusters from atom probe tomography (APT) reconstructions is extremely challenging, yet critical to many applications. Currently, the most prevalent approach to detect clusters is the maximum separation method, a heuristic that relies heavily upon parameters manually chosen by the user. In this work, a new clustering algorithm, Gaussian mixture model Expectation Maximization Algorithm (GEMA), was developed. GEMA utilizes a Gaussian mixture model to probabilistically distinguish clusters from random fluctuations in the matrix. This machine learning approach maximizes the data likelihood via expectation maximization: given atomic positions, the algorithm learns the position, size, and width of each cluster. A key advantage of GEMA is that atoms are probabilistically assigned to clusters, thus reflecting scientifically meaningful uncertainty regarding atoms located near precipitate/matrix interfaces. GEMA outperforms the maximum separation method in cluster detection accuracy when applied to several realistically simulated data sets. Lastly, GEMA was successfully applied to real APT data.

scholarly journals Expectation-Maximization Algorithm of Gaussian Mixture Model for Vehicle-Commodity Matching in Logistics Supply Chain

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Qi Sun ◽  
Liwen Jiang ◽  
Haitao Xu
Keyword(s):  
Supply Chain ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Expectation Maximization ◽  
Service Providers ◽  
Gaussian Mixture ◽  
Classification Model ◽  
Mixed Integer ◽  
Matching Problem ◽  
The Right

A vehicle-commodity matching problem (VCMP) is presented for service providers to reduce the cost of the logistics system. The vehicle classification model is built as a Gaussian mixture model (GMM), and the expectation-maximization (EM) algorithm is designed to solve the parameter estimation of GMM. A nonlinear mixed-integer programming model is constructed to minimize the total cost of VCMP. The matching process between vehicle and commodity is realized by GMM-EM, as a preprocessing of the solution. The design of the vehicle-commodity matching platform for VCMP is designed to reduce and eliminate the information asymmetry between supply and demand so that the order allocation can work at the right time and the right place and use the optimal solution of vehicle-commodity matching. Furthermore, the numerical experiment of an e-commerce supply chain proves that a hybrid evolutionary algorithm (HEA) is superior to the traditional method, which provides a decision-making reference for e-commerce VCMP.

Narrowband Emitter Identification by Exploiting Gaussian Mixture Model

2011 ◽  
Vol 65 ◽  
pp. 503-508
Author(s):  
Yu Yu Liao ◽  
Ke Xin Jia ◽  
Zi Shu He ◽  
Song Feng Deng
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Expectation Maximization ◽  
Execution Time ◽  
Minimum Description Length ◽  
Gaussian Mixture ◽  
Frequency Information ◽  
Second Stage ◽  
Length Variant ◽  
Mixture Of Gaussian

Narrowband emitter identification is used to correctly identify unknown narrowband emitters from the results of direction finding (DF). In this paper, we modeled the set of azimuth angles by a mixture of Gaussian densities, and divided narrowband emitter identification into two different stages. In the first stage, a competitive stop expectation-maximization (CSEM) algorithm was developed, which was based on Shapiro-Wilk test and minimum description length variant (MDL2) criterion. The CSEM only employed the estimated azimuth angles at all the signal-occupied frequency bins as feature parameters, while the frequency information implied in each cluster was not exploited sufficiently. So based on the implied frequency information, a postprocessing algorithm was introduced in the second stage. The experimental results show that the CSEM algorithm is more robust, and it has an increased capability to find the underlying model while maintaining a low execution time. By adopting CSEM and postprocessing algorithm in narrowband emitter identification, we are able to identify narrowband emitters with high correctness.

Sign in / Sign up

Export Citation Format

Share Document