Spectral Regression dimension reduction for multiple features facial image retrieval

2012 ◽  
Vol 4 (1) ◽  
pp. 77 ◽  
Author(s):  
Bailing Zhang ◽  
Yongsheng Gao
Keyword(s):  
Image Retrieval ◽  
Dimension Reduction ◽  
Facial Image ◽  
Multiple Features ◽  
Spectral Regression

scholarly journals Clothing Image Retrieval Based On Multiple Features for Smarter Shopping

2018 ◽  
Vol 125 ◽  
pp. 143-148 ◽  
Author(s):  
Megha Gupta ◽  
Charul Bhatnagar ◽  
A.S. Jalal
Keyword(s):  
Image Retrieval ◽  
Multiple Features

scholarly journals Roller Bearing Performance Degradation Assessment Based on Fusion of Multiple Features of Electrostatic Sensors

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 824 ◽  
Author(s):  
Ying Zhang ◽  
Anchen Wang ◽  
Hongfu Zuo
Keyword(s):  
Monitoring System ◽  
Mixed Model ◽  
Roller Bearing ◽  
Degradation Process ◽  
Performance Degradation ◽  
Vibration Monitoring ◽  
Multiple Features ◽  
Roller Bearings ◽  
The Time Domain ◽  
Spectral Regression

This paper presents a new method to assess the performance degradation of roller bearings based on the fusion of multiple features, with the aim of improving the early degradation detection ability of the electrostatic monitoring system. At first, a set of feature parameters of the electrostatic monitoring system indicating the normal state of the bearings are extracted from the perspective of the time domain, frequency domain and complexity. Then, the parameter set is processed to reduce the dimensions and eliminate the redundancy using spectral regression. With the processed features, a Gaussian mixed model is established to gauge the health of the bearing, providing the distance value obtained using Bayesian inference as a quantitative indicator for assessing the performance degradation. The method is applied to access the life of a bearing in which the mechanic fatigue is artificially accelerated. The test results show that the proposed method can better reflect the degradation process of the bearing compared to other evaluation methods. This enables the electrostatic monitoring technique to detect the degradation of the bearing earlier than the vibration monitoring, providing a powerful tool for the condition monitoring of roller bearings.

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