Application of the Operational Modal Analysis Using the Independent Component Analysis for a Quarter Car Vehicle Model

2016 ◽  
pp. 125-133 ◽  
Author(s):  
Dorra Ben Hassen ◽  
Mariem Miladi ◽  
Mohamed Slim Abbes ◽  
S. Caglar Baslamisli ◽  
Fakher Chaari ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Modal Analysis ◽  
Component Analysis ◽  
Operational Modal Analysis ◽  
Independent Component ◽  
Vehicle Model ◽  
Quarter Car

VIBRATORY BEHAVIOR OF A DOUBLE PANEL SYSTEM BY THE OPERATIONAL MODAL ANALYSIS

2011 ◽  
Vol 02 (04) ◽  
pp. 459-479 ◽  
Author(s):  
MOHAMED SLIM ABBES ◽  
MARIEM MILADI CHAABANE ◽  
ALI AKROUT ◽  
TAHAR FAKHFAKH ◽  
MOHAMED HADDAR
Keyword(s):  
Independent Component Analysis ◽  
Modal Analysis ◽  
Blind Source Separation ◽  
Source Separation ◽  
Component Analysis ◽  
Operational Modal Analysis ◽  
Independent Component ◽  
Good Argument ◽  
Experimental Works

The present study tackles the vibratory behavior of a double panel system by operational modal analysis (OMA) using one of the major techniques of blind source separation (BSS), which is the independent component analysis (ICA). For this purpose, the OMA method and the ICA concept are presented and exploited in order to identify the eigenmodes of a double panel system. Then, results obtained by the OMA technique are presented and compared with those achieved by the modal recombination method. Since a good argument is observed, this approach can be used in conjunction with experimental works.

Operation modal analysis following fast independent component analysis

2016 ◽  
Vol 52 (1-2) ◽  
pp. 103-111 ◽  
Author(s):  
Cheng Wang ◽  
Jianying Wang ◽  
Xiongming Lai ◽  
Bineng Zhong ◽  
Xiangyu Luo ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Modal Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Fast Independent Component Analysis

scholarly journals Comparison of Different Independent Component Analysis Algorithms for Output-Only Modal Analysis

2016 ◽  
Vol 2016 ◽  
pp. 1-25 ◽  
Author(s):  
Jianying Wang ◽  
Cheng Wang ◽  
Tianshu Zhang ◽  
Bineng Zhong
Keyword(s):  
Independent Component Analysis ◽  
Parameter Identification ◽  
Modal Analysis ◽  
Optimization Methods ◽  
Component Analysis ◽  
Independent Component ◽  
Modal Parameter ◽  
Modal Parameter Identification ◽  
Modal Parameters Identification ◽  
Output Only

From the principle of independent component analysis (ICA) and the uncertainty of amplitude, order, and number of source signals, this paper expounds the root reasons for modal energy uncertainty, identified order uncertainty, and modal missing in output-only modal analysis based on ICA methods. Aiming at the problem of lack of comparison and evaluation of different ICA algorithms for output-only modal analysis, this paper studies the different objective functions and optimization methods of ICA for output-only modal parameter identification. Simulation results on simply supported beam verify the effectiveness, robustness, and convergence rate of five different ICA algorithms for output-only modal parameters identification and show that maximization negentropy with quasi-Newton iterative of ICA method is more suitable for modal parameter identification.

Blind Separation of Excavator Noise Signals in Frequency Domain

2011 ◽  
Vol 105-107 ◽  
pp. 723-728
Author(s):  
Li Da Liao ◽  
Qing Hua He ◽  
Zhong Lin Hu
Keyword(s):  
Independent Component Analysis ◽  
Modal Analysis ◽  
Frequency Domain ◽  
Time Domain ◽  
Component Analysis ◽  
Independent Component ◽  
Blind Separation ◽  
Noise Sources ◽  
Amplitude Spectra ◽  
Complex Valued

In order to identify noise sources of an excavator in non-library environment, a complex-valued algorithm in frequency domain was applied. Firstly, an acoustic camera was used to acquire excavator’s noise signals, which were convolutive mixtures in time domain interfered by echo. Secondly, signals in time domain transformed into frequency domain by FT, turned to be complex-valued mixtures. Then, independent components of noise signals were obtained through separation of complex-valued mixtures using complex-valued algorithm based on independent component analysis. Finally, according to noise of diesel with muffler was mainly consist of surface noise, the relationship between principal frequencies and structrual parts was founded by comparing frequency-amplitude spectra and modal analysis in Ansys. Research shows that complex-valued algorithm based on fast fixed-point independent component analysis can effectively separate noise signals from an excavator in time domain, and noise sources can be well ascertained by comparing the modal analysis with blind separation components.

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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