Independent component analysis based source number estimation and its comparison for mechanical systems

2012 ◽  
Vol 331 (23) ◽  
pp. 5153-5167 ◽  
Author(s):  
Wei Cheng ◽  
Seungchul Lee ◽  
Zhousuo Zhang ◽  
Zhengjia He
Keyword(s):  
Independent Component Analysis ◽  
Mechanical Systems ◽  
Component Analysis ◽  
Independent Component ◽  
Source Number Estimation ◽  
Source Number

A Comprehensive Study of Vibration Signals for a Thin Shell Structure Using Enhanced Independent Component Analysis and Experimental Validation

2014 ◽  
Vol 136 (4) ◽  
Author(s):  
Wei Cheng ◽  
Zhengjia He ◽  
Zhousuo Zhang
Keyword(s):  
Independent Component Analysis ◽  
Thin Shell ◽  
Component Analysis ◽  
Independent Component ◽  
Vibration Monitoring ◽  
Monitoring And Control ◽  
Source Information ◽  
Vibration Signals ◽  
Machinery Condition Monitoring ◽  
Source Number

Vibration source information (source number, source waveforms, and source contributions) of gears, bearings, motors, and shafts is very important for machinery condition monitoring, fault diagnosis, and especially vibration monitoring and control. However, it has been a challenging to effectively extract the source information from the measured mixed vibration signals without a priori knowledge of the mixing mode and sources. In this paper, we propose source number estimation, source separation, and source contribution evaluation methods based on an enhanced independent component analysis (EICA). The effects of nonlinear mixing mode and different source number on source separation are studied with typical vibration signals, and the effectiveness of the proposed methods is validated by numerical case studies and experimental studies on a thin shell test bed. The conclusions show that the proposed methods have a high accuracy for thin shell structures. This research benefits for application of independent component analysis (ICA) to solve the vibration monitoring and control problems for thin shell structures and provides important references for machinery condition monitoring and fault diagnosis.

Skin Chromophore Estimation from Mobile Selfie Images using Constrained Independent Component Analysis

2020 ◽  
Vol 2020 (14) ◽  
pp. 357-1-357-6
Author(s):  
Luisa F. Polanía ◽  
Raja Bala ◽  
Ankur Purwar ◽  
Paul Matts ◽  
Martin Maltz
Keyword(s):  
Independent Component Analysis ◽  
Spectral Reflectance ◽  
Source Separation ◽  
Principal Component ◽  
Component Analysis ◽  
Previous Method ◽  
Independent Component ◽  
Light Transport ◽  
Camera Model ◽  
Constrained Independent Component Analysis

Human skin is made up of two primary chromophores: melanin, the pigment in the epidermis giving skin its color; and hemoglobin, the pigment in the red blood cells of the vascular network within the dermis. The relative concentrations of these chromophores provide a vital indicator for skin health and appearance. We present a technique to automatically estimate chromophore maps from RGB images of human faces captured with mobile devices such as smartphones. The ultimate goal is to provide a diagnostic aid for individuals to monitor and improve the quality of their facial skin. A previous method approaches the problem as one of blind source separation, and applies Independent Component Analysis (ICA) in camera RGB space to estimate the chromophores. We extend this technique in two important ways. First we observe that models for light transport in skin call for source separation to be performed in log spectral reflectance coordinates rather than in RGB. Thus we transform camera RGB to a spectral reflectance space prior to applying ICA. This process involves the use of a linear camera model and Principal Component Analysis to represent skin spectral reflectance as a lowdimensional manifold. The camera model requires knowledge of the incident illuminant, which we obtain via a novel technique that uses the human lip as a calibration object. Second, we address an inherent limitation with ICA that the ordering of the separated signals is random and ambiguous. We incorporate a domain-specific prior model for human chromophore spectra as a constraint in solving ICA. Results on a dataset of mobile camera images show high quality and unambiguous recovery of chromophores.

Exploring Independent Component Analysis for GPR Signal Processing

PIERS Online ◽  
2005 ◽  
Vol 1 (6) ◽  
pp. 750-753 ◽  
Author(s):  
Anxing Zhao ◽  
Yansheng Jiang ◽  
Wenbing Wang
Keyword(s):  
Signal Processing ◽  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component
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