Process monitoring using a Gaussian mixture model via principal component analysis and discriminant analysis

2004 ◽  
Vol 28 (8) ◽  
pp. 1377-1387 ◽  
Author(s):  
Sang Wook Choi ◽  
Jin Hyun Park ◽  
In-Beum Lee
Keyword(s):  
Principal Component Analysis ◽  
Discriminant Analysis ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Process Monitoring ◽  
Principal Component ◽  
Component Analysis ◽  
Gaussian Mixture

scholarly journals Resident user load classification method based on improved Gaussian mixture model clustering

2022 ◽  
Vol 355 ◽  
pp. 02024
Author(s):  
Haojing Wang ◽  
Yingjie Tian ◽  
An Li ◽  
Jihai Wu ◽  
Gaiping Sun
Keyword(s):  
Principal Component Analysis ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Clustering Algorithm ◽  
Computational Cost ◽  
Principal Component ◽  
Component Analysis ◽  
Gaussian Mixture ◽  
Classification Method ◽  
Mixture Model Clustering

In view of the limitation of “hard assignment” of clusters in traditional clustering methods and the difficulty of meeting the requirements of clustering efficiency and clustering accuracy simultaneously in regard to massive data sets, a load classification method based on a Gaussian mixture model combining clustering and principal component analysis is proposed. The load data are fed into a Gaussian mixture model clustering algorithm after principal component analysis and dimensionality reduction to achieve classification of large-scale load datasets. The method in this paper is used to classify loads in the Canadian AMPds2 public dataset and is compared with K-Means, Gaussian mixed model clustering and other methods. The results show that the proposed method can not only achieve load classification more effectively and finely, but also save computational cost and improve computational efficiency.

scholarly journals Individual Tree Extraction from Terrestrial LiDAR Point Clouds Based on Transfer Learning and Gaussian Mixture Model Separation

2021 ◽  
Vol 13 (2) ◽  
pp. 223
Author(s):  
Zhenyang Hui ◽  
Shuanggen Jin ◽  
Dajun Li ◽  
Yao Yevenyo Ziggah ◽  
Bo Liu
Keyword(s):  
Transfer Learning ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Principal Component ◽  
Point Clouds ◽  
Gaussian Mixture ◽  
Individual Tree ◽  
Terrestrial Lidar ◽  
Initial Segmentation ◽  
Tree Extraction

Individual tree extraction is an important process for forest resource surveying and monitoring. To obtain more accurate individual tree extraction results, this paper proposed an individual tree extraction method based on transfer learning and Gaussian mixture model separation. In this study, transfer learning is first adopted in classifying trunk points, which can be used as clustering centers for tree initial segmentation. Subsequently, principal component analysis (PCA) transformation and kernel density estimation are proposed to determine the number of mixed components in the initial segmentation. Based on the number of mixed components, the Gaussian mixture model separation is proposed to separate canopies for each individual tree. Finally, the trunk stems corresponding to each canopy are extracted based on the vertical continuity principle. Six tree plots with different forest environments were used to test the performance of the proposed method. Experimental results show that the proposed method can achieve 87.68% average correctness, which is much higher than that of other two classical methods. In terms of completeness and mean accuracy, the proposed method also outperforms the other two methods.

Online reduced kernel principal component analysis for process monitoring

2018 ◽  
Vol 61 ◽  
pp. 1-11 ◽  
Author(s):  
Radhia Fezai ◽  
Majdi Mansouri ◽  
Okba Taouali ◽  
Mohamed Faouzi Harkat ◽  
Nasreddine Bouguila
Keyword(s):  
Principal Component Analysis ◽  
Process Monitoring ◽  
Principal Component ◽  
Component Analysis ◽  
Kernel Principal Component Analysis

Uncertain Nonlinear Process Monitoring Using Interval Ensemble Kernel Principal Component Analysis

2021 ◽  
Vol 25 (1) ◽  
pp. 101-109
Author(s):  
Xianrui Wang ◽  
Guoxin Zhao ◽  
Yu Liu ◽  
Shujie Yang ◽  
◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Process Monitoring ◽  
Principal Component ◽  
Nonlinear Process ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Gaussian Kernel ◽  
Bayesian Decision ◽  
Symbolic Data ◽  
Nonlinear Process Monitoring

To solve uncertainties in industrial processes, interval kernel principal component analysis (IKPCA) has been proposed based on symbolic data analysis. However, it is experimentally discovered that the performance of IKPCA is worse than that of other algorithms. To improve the IKPCA algorithm, interval ensemble kernel principal component analysis (IEKPCA) is proposed. By optimizing the width parameters of the Gaussian kernel function, IEKPCA yields better performances. Ensemble learning is incorporated in the IEKPCA algorithm to build submodels with different width parameters. However, the multiple submodels will yield a large number of results, which will complicate the algorithm. To simplify the algorithm, a Bayesian decision is used to convert the result into fault probability. The final result is obtained via a weighting strategy. To verify the method, IEKPCA is applied to the Tennessee Eastman (TE) process. The false alarm rate, fault detection rate, accuracy, and other indicators used in the IEKPCA are compared with those of other algorithms. The results show that the IEKPCA improves the accuracy of uncertain nonlinear process monitoring.

scholarly journals A Bayesian Framework Based on Gaussian Mixture Model and Radial Basis Function Fisher Discriminant Analysis for Flood Spatial Prediction (BayGmmKda V1.1)

2017 ◽  
Author(s):  
Dieu Tien Bui ◽  
Nhat-Duc Hoang
Keyword(s):  
Discriminant Analysis ◽  
Radial Basis Function ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Basis Function ◽  
Gaussian Mixture ◽  
Bayesian Framework ◽  
Fisher Discriminant Analysis ◽  
Fisher Discriminant ◽  
Radial Basis

Abstract. In this study, a probabilistic model, named as BayGmmKda, is proposed for flood assessment with a study area in Central Vietnam. The new model is essentially a Bayesian framework constructed a combination of Gaussian Mixture Model, Radial Basis Function Fisher Discriminant Analysis, and a Geographic Information System database. Experiments used for measuring the model performance point out that the hybrid framework is superior to other benchmark models including the adaptive neuro fuzzy inference system and the support vector machine. To facility the model implementation, a software program of BayGmmKda has been developed in Matlab environment. The newly proposed model is shown to be a very promising alternative for assisting decision-makers in flood assessment.

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