Principal Component Analysis Techniques for Visualization of Volumetric Data

2017 ◽  
pp. 99-120
Author(s):  
Salaheddin Alakkari ◽  
John Dingliana
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Volumetric Data ◽  
Analysis Techniques

scholarly journals Diagnosis of Esophagitis Based on Face Recognition Techniques

2010 ◽  
Vol 4 (1) ◽  
pp. 58-62
Author(s):  
Santosh S Saraf ◽  
Gururaj R Udupi ◽  
Santosh D Hajare
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Analysis Techniques ◽  
The Face ◽  
Pca Method ◽  
Recognition Efficiency ◽  
Motivating Factor

Face recognition technology has evolved over years with the Principal Component Analysis (PCA) method being the benchmark for recognition efficiency. The face recognition techniques take care of variation of illumination, pose and other features of the face in the image. We envisage an application of these face recognition techniques for classification of medical images. The motivating factor being, given a condition of an organ it is represented by some typical features. In this paper we report the use of the face recognition techniques to classify the type of Esophagitis, a condition of inflammation of the esophagus. The image of the esophagus is captured in the process of endoscopy. We test PCA, Fisher Face method and Independent Component Analysis techniques to classify the images of the esophagus. Esophagitis is classified into four categories. The results of classification for each method are reported and the results are compared.

scholarly journals Actuator and Sensor Fault Classification for Wind Turbine Systems Based on Fast Fourier Transform and Uncorrelated Multi-Linear Principal Component Analysis Techniques

Processes ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1066 ◽  
Author(s):  
Yichuan Fu ◽  
Zhiwei Gao ◽  
Yuanhong Liu ◽  
Aihua Zhang ◽  
Xiuxia Yin
Keyword(s):  
Principal Component Analysis ◽  
Fourier Transform ◽  
Fault Diagnosis ◽  
Fast Fourier Transform ◽  
Wind Turbine ◽  
Principal Component ◽  
Component Analysis ◽  
Fault Classification ◽  
Analysis Techniques ◽  
Diagnosis And Classification

In response to the high demand of the operation reliability and predictive maintenance, health monitoring and fault diagnosis and classification have been paramount for complex industrial systems (e.g., wind turbine energy systems). In this study, data-driven fault diagnosis and fault classification strategies are addressed for wind turbine energy systems under various faulty scenarios. A novel algorithm is addressed by integrating fast Fourier transform and uncorrelated multi-linear principal component analysis techniques in order to achieve effective three-dimensional space visualization for fault diagnosis and classification under a variety of actuator and sensor faulty scenarios in 4.8 MW wind turbine benchmark systems. Moreover, comparison studies are implemented by using multi-linear principal component analysis with and without fast Fourier transform, and uncorrelated multi-linear principal component analysis with and without fast Fourier transformation data pre-processing, respectively. The effectiveness of the proposed algorithm is demonstrated and validated via the wind turbine benchmark.

Worst-case simulation using principal component analysis techniques: An investigation

1991 ◽  
Vol 15 (1-4) ◽  
pp. 213-216
Author(s):  
J.A. Power ◽  
D. Barry ◽  
A. Mathewson ◽  
W.A. Lane
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Worst Case ◽  
Analysis Techniques ◽  
Case Simulation
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