A Dimension Reduction Method and its Application in GIS Partial Discharge Pattern Recognition Research

2014 ◽  
Vol 602-605 ◽  
pp. 2105-2109
Author(s):  
Fang Cheng Lv ◽  
Hu Jin ◽  
Zi Jian Wang ◽  
Bo Zhang
Keyword(s):  
Principal Component Analysis ◽  
Pattern Recognition ◽  
Dimension Reduction ◽  
Partial Discharge ◽  
Principal Component ◽  
Original Data ◽  
Component Analysis ◽  
Discharge Pattern ◽  
Insulation Defect ◽  
Mean Vector

GIS partial discharge pattern recognition is an important part of its state evaluation, authors have set up 252kV GIS partial discharge detection simulation experiment platform based on UHF detection method, and designed four kinds of typical partial discharge models in laboratory, then established corresponding UHF signal mapping database through the experimental method, and also extracted the original feature parameters; because the original characteristic dimension is high, which is bad for pattern recognition, based on this, the article uses a species mean kernel principal component analysis method, it mapped the partial discharge original data samples to high-dimensional feature space, at first, it calculate all kinds of class mean vector data, and then do principal component analysis based on class mean vector space, build the class average kernel matrix, at last, the class kernel mean principal component analysis algorithm is established. Results show that characteristic of this method contained all the information of the original data, and dimension is less than GIS insulation defect category numbers, and it can realize data dimension reduction without information loss, which improve the pattern recognition rate.

scholarly journals Initial Dataset Dimension Reduction Using Principal Component Analysis

2020 ◽  
Vol 23 ◽  
pp. 41-44
Author(s):  
Oļegs Užga-Rebrovs ◽  
Gaļina Kuļešova
Keyword(s):  
Principal Component Analysis ◽  
Data Analysis ◽  
Initial Data ◽  
Dimension Reduction ◽  
Principal Component ◽  
Original Data ◽  
Component Analysis ◽  
Preliminary Processing ◽  
Implicit Form ◽  
Initial Dataset

Any data in an implicit form contain information of interest to the researcher. The purpose of data analysis is to extract this information. The original data may contain redundant elements and noise, distorting these data to one degree or another. Therefore, it seems necessary to subject the data to preliminary processing. Reducing the dimension of the initial data makes it possible to remove interfering factors and present the data in a form suitable for further analysis. The paper considers an approach to reducing the dimensionality of the original data based on principal component analysis.

High-Dimensional Data Dimension Reduction Based on KECA

2013 ◽  
Vol 303-306 ◽  
pp. 1101-1104 ◽  
Author(s):  
Yong De Hu ◽  
Jing Chang Pan ◽  
Xin Tan
Keyword(s):  
Principal Component Analysis ◽  
Dimension Reduction ◽  
High Dimensional Data ◽  
Principal Component ◽  
Good Method ◽  
Component Analysis ◽  
Renyi Entropy ◽  
Rényi Entropy ◽  
Kernel Principal Component Analysis ◽  
High Dimensional

Kernel entropy component analysis (KECA) reveals the original data’s structure by kernel matrix. This structure is related to the Renyi entropy of the data. KECA maintains the invariance of the original data’s structure by keeping the data’s Renyi entropy unchanged. This paper described the original data by several components on the purpose of dimension reduction. Then the KECA was applied in celestial spectra reduction and was compared with Principal Component Analysis (PCA) and Kernel Principal Component Analysis (KPCA) by experiments. Experimental results show that the KECA is a good method in high-dimensional data reduction.

scholarly journals Multi-Seam Coal Deposit Modeling via Principal Component Analysis & GIS

Minerals ◽  
2019 ◽  
Vol 9 (9) ◽  
pp. 532
Author(s):  
Georgios Louloudis ◽  
Christos Roumpos ◽  
Konstantinos Theofilogiannakos ◽  
Nikolaos Stathopoulos
Keyword(s):  
Principal Component Analysis ◽  
Surface Mining ◽  
Principal Component ◽  
Original Data ◽  
Drill Hole ◽  
Component Analysis ◽  
Coal Deposit ◽  
Mining Operations ◽  
Maximum Variance ◽  
Mine Development

Spatial modeling and evaluation is a critical step for planning the exploitation of mineral deposits. In this work, a methodology for the investigation of a multi-seam coal deposit spatial variability is proposed. The study area includes the Klidi (Florina, Greece) multi-seam lignite deposit which is suitable for surface mining. The analysis is based on the original data of 76 exploratory drill-holes in an area of 10 km2, in conjunction with the geological and geomorphological data of the deposit. The analytical methods include drill-hole data analysis and evaluation based on an appropriate algorithm, principal component analysis and geographic information techniques. The results proved to be very satisfactory for the explanation of the maximum variance of the initial data values as well as the identification of the deposit structure and the optimum planning of mine development. The proposed analysis can be also helpful for minimizing cost and optimizing efficiency of surface mining operations. Furthermore, the provided methods could be applied in other areas of geosciences, indicating the theoretical value as well as the important practical implications of the analysis.

Damage detection in nonlinear civil structures using kernel principal component analysis

2020 ◽  
Vol 23 (11) ◽  
pp. 2414-2430
Author(s):  
Khaoula Ghoulem ◽  
Tarek Kormi ◽  
Nizar Bel Hadj Ali
Keyword(s):  
Principal Component Analysis ◽  
Damage Detection ◽  
Structural Model ◽  
Principal Component ◽  
Original Data ◽  
Component Analysis ◽  
Correlated Data ◽  
Data Driven ◽  
Kernel Principal Component Analysis ◽  
Structural Systems

In the general framework of data-driven structural health monitoring, principal component analysis has been applied successfully in continuous monitoring of complex civil infrastructures. In the case of linear or polynomial relationship between monitored variables, principal component analysis allows generation of structured residuals from measurement outputs without a priori structural model. The principal component analysis has been widely used for system monitoring based on its ability to handle high-dimensional, noisy, and highly correlated data by projecting the data onto a lower dimensional subspace that contains most of the variance of the original data. However, for nonlinear systems, it could be easily demonstrated that linear principal component analysis is unable to disclose nonlinear relationships between variables. This has naturally motivated various developments of nonlinear principal component analysis to tackle damage diagnosis of complex structural systems, especially those characterized by a nonlinear behavior. In this article, a data-driven technique for damage detection in nonlinear structural systems is presented. The proposed method is based on kernel principal component analysis. Two case studies involving nonlinear cable structures are presented to show the effectiveness of the proposed methodology. The validity of the kernel principal component analysis–based monitoring technique is shown in terms of the ability to damage detection. Robustness to environmental effects and disturbances are also studied.

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