scholarly journals Multi-Dimensional Uniform Initialization Gaussian Mixture Model for Spar Crack Quantification under Uncertainty

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1283
Author(s):  
Qiuhui Xu ◽  
Shenfang Yuan ◽  
Tianxiang Huang
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Expectation Maximization Algorithm ◽  
Gaussian Mixture ◽  
Fatigue Tests ◽  
Guided Wave ◽  
Monitoring Method ◽  
Notched Specimen ◽  
Small Cracks ◽  
The Relationship

Guided Wave (GW)-based crack monitoring method as a promising method has been widely studied, as this method is sensitive to small cracks and can cover a wide monitoring range. Online crack quantification is difficult as the initiation and growth of crack are affected by various uncertainties. In addition, crack-sensitive GW features are influenced by time-varying conditions which further increase the difficulty in crack quantification. Considering these uncertainties, the Gaussian mixture model (GMM) is studied to model the probability distribution of GW features. To further improve the accuracy and stability of crack quantification under uncertainties, this paper proposes a multi-dimensional uniform initialization GMM. First, the multi-channel GW features are integrated to increase the accuracy of crack quantification, as GW features from different channels have different sensitivity to cracks. Then, the uniform initialization method is adopted to provide more stable initial parameters in the expectation-maximization algorithm. In addition, the relationship between the probability migration index of GMMs and crack length is calibrated with fatigue tests on prior specimens. Finally, the proposed method is applied for online crack quantification on the notched specimen of an aircraft spar with complex fan-shaped cracks under uncertainty.

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.

A contour-guided pose alignment method based on Gaussian mixture model for precision assembly

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Pengyue Guo ◽  
Zhijing Zhang ◽  
Lingling Shi ◽  
Yujun Liu
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Expectation Maximization Algorithm ◽  
Gaussian Mixture ◽  
Assembly System ◽  
Alignment Method ◽  
Content Type ◽  
Point Set Registration ◽  
Contour Point ◽  
Point Set

Purpose The purpose of this study was to solve the problem of pose measurement of various parts for a precision assembly system. Design/methodology/approach A novel alignment method which can achieve high-precision pose measurement of microparts based on monocular microvision system was developed. To obtain the precise pose of parts, an area-based contour point set extraction algorithm and a point set registration algorithm were developed. First, the part positioning problem was transformed into a probability-based two-dimensional point set rigid registration problem. Then, a Gaussian mixture model was fitted to the template point set, and the contour point set is represented by hierarchical data. The maximum likelihood estimate and expectation-maximization algorithm were used to estimate the transformation parameters of the two point sets. Findings The method has been validated for accelerometer assembly on a customized assembly platform through experiments. The results reveal that the proposed method can complete letter-pedestal assembly and the swing piece-basal part assembly with a minimum gap of 10 µm. In addition, the experiments reveal that the proposed method has better robustness to noise and disturbance. Originality/value Owing to its good accuracy and robustness for the pose measurement of complex parts, this method can be easily deployed to assembly system.

An enhanced dynamic Gaussian mixture model–based damage monitoring method of aircraft structures under environmental and operational conditions

2018 ◽  
Vol 18 (2) ◽  
pp. 524-545 ◽  
Author(s):  
Lei Qiu ◽  
Fang Fang ◽  
Shenfang Yuan ◽  
Christian Boller ◽  
Yuanqiang Ren
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Health Monitoring ◽  
Gaussian Mixture ◽  
Monitoring Method ◽  
Structural Health ◽  
Model Based ◽  
Damage Monitoring

Gaussian mixture model–based structural health monitoring methods have been studied in recent years to improve the reliability of damage monitoring under environmental and operational conditions. However, most of these methods only use the ordinary expectation maximization algorithm to construct the Gaussian mixture model but the expectation maximization algorithm can easily lead to a local optimal solution and a singular solution, which also results in unreliable and unstable damage monitoring especially for complex structures. This article proposes an enhanced dynamic Gaussian mixture model–based damage monitoring method. First, an enhanced Gaussian mixture model constructing algorithm based on a Gaussian mixture model merge-split operation and a singularity inhibition mechanism is developed to keep the stability of the Gaussian mixture model and to obtain a unique optimal solution. Then, a probability similarity–based damage detection index is proposed to realize a normalized and general damage detection. The method combined with guided wave structural health monitoring technique is validated by the hole-edge cracks monitoring of an aluminum plate and a real aircraft wing spar. The results indicate that the method is efficient to improve the reliability and the stability of damage detection under fatigue load and varying structural boundary conditions. The method is simple and reliable regarding aviation application. It is a data-driven statistical method which is model-independent and less experience-dependent. It can be used by combining with different kinds of structural health monitoring techniques.

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 Automatic Enhancement of Low Light Level Image on the Basis of a Gaussian Mixture Model

2019 ◽  
Vol 7 (3) ◽  
Author(s):  
Liju Yin ◽  
Tingdong Kou ◽  
Xuan Wang ◽  
Guofeng Zou ◽  
Jinfeng Pan ◽  
...  
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Expectation Maximization Algorithm ◽  
Objective Evaluation ◽  
Gaussian Mixture ◽  
Light Level ◽  
Optimal Number ◽  
Low Light ◽  
The Laplace Operator ◽  
Relationship Of

As the first medium to transmit information under a low light level environment. The low light level image is needed in hot-light image imaging technology. The quality of the image will be reduced given the influence of external factors. For example, a sampled image may become blurry. This paper proposes a method for automatic enhancement of low light level image on the basis of a Gaussian mixture model. First, the histogram of the image is modeled with a Gaussian mixture model that is solved by the expectation maximization algorithm of accelerated convergence. The histogram is then partitioned according to the intersection of each cluster. Finally, the mapping relationship of the cluster to which the output image belongs is ascertained and the final enhancement image is obtained. This algorithm can be used to identify the optimal number of clusters and accelerate the convergence speed of the algorithm. Objective evaluation of the Laplace operator value, as well as the grayscale average gradient and contrast (Tab. 1), indicates that the algorithm effectively improves image contrast while maintaining the details of the image.

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