An Improved Blind Source Extraction Algorithm

2014 ◽  
Vol 926-930 ◽  
pp. 2964-2967
Author(s):  
Shou Cheng Zhang
Keyword(s):  
Computational Complexity ◽  
Independent Component Analysis ◽  
Reference Signal ◽  
Component Analysis ◽  
Independent Component ◽  
Blind Source Extraction ◽  
Extraction Algorithm ◽  
Source Signal ◽  
Constrained Independent Component Analysis ◽  
Source Extraction

One-unit independent component analysis with reference (ICA-R) is an efficient method capable of extracting a desired source signal by using reference signal. In this paper, a new fast one-unit ICA-R algorithm is derived by using kurtosis contrast function based on new constrained independent component analysis (cICA) theory. The proposed algorithm has lower computational complexity and accurate extraction. Experiments with synthetic signals demonstrate the efficacy and accuracy of the proposed algorithm.

scholarly journals A Method for Utility Harmonic Impedance Estimation Based on Constrained Complex Independent Component Analysis

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2247 ◽  
Author(s):  
Xianyong Xiao ◽  
Xian Zheng ◽  
Ying Wang ◽  
Shuangting Xu ◽  
Zixuan Zheng
Keyword(s):  
Independent Component Analysis ◽  
Prior Information ◽  
Measurement Data ◽  
Estimation Method ◽  
Component Analysis ◽  
Independent Component ◽  
Local Optimum ◽  
Source Signal ◽  
Harmonic Source ◽  
Constrained Independent Component Analysis

Utility harmonic impedance is an important parameter for harmonic mitigation. In this paper, a method for utility harmonic impedance estimation method based on constrained independent component analysis is proposed. The conventional impedance estimation method based on ComplexICA has two major problems: the algorithm is not suitable for separating weak and strong source mixed signals, and lots of sample data should be provided to avoid converging on a local optimum. To solve the two problems, the prior information of the utility harmonic source is added to the objective function of ComplexICA; in this paper, the measurement data at PCC when the load is shutdown are chosen as the prior information. Then the utility harmonic source signal can be recovered and the separated matrix can be obtained effectively. The connection between the utility harmonic source, utility harmonic impedance and the data at PCC are established using Norton equivalent circuit, and then the separation matrix is used to calculate utility harmonic impedance. The performance and feasibility of the proposed method are verified by the computer simulation and field test. Compared with the current ComplexICA method, the proposed method is more adaptive to changes in the background harmonic and the calculation result is more stable.

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.

An ICA Algorithm Based on Symmetric and Asymmetric Generalized Gaussian Model

2011 ◽  
Vol 204-210 ◽  
pp. 470-475
Author(s):  
Feng Zhao ◽  
Yun Jie Zhang ◽  
Min Cai
Keyword(s):  
Independent Component Analysis ◽  
Gradient Method ◽  
Gaussian Model ◽  
Component Analysis ◽  
Independent Component ◽  
Blind Signal Separation ◽  
Natural Gradient ◽  
Analysis Model ◽  
Practical Applications ◽  
Source Signal

Maximum likelihood estimation is a very popular method to estimate the independent component analysis model because of good performance. Independent component analysis algorithm (the natural gradient method) based on this method is widely used in the field of blind signal separation. It potentially assumes that the source signal was symmetrical distribution, in fact in practical applications, source signals may be asymmetric. This article by distinguishing that the source signal is symmetrical or asymmetrical, proposes an improved natural gradient method based on symmetric generalized Gaussian model (People usually call generalized Gaussian model) and asymmetric generalized Gaussian model. The random mixed-signal simulation results show that the improved algorithm is better than the natural gradient separation method.

Intelligent Fault Diagnosis of Planetary Wheel Bearing Using Constrained Independent Component Analysis and Stacked Sparse Autoencoder

2020 ◽  
Author(s):  
Hongkun Li ◽  
Gangjin Huang ◽  
Jiayu Ou ◽  
Yuanliang Zhang
Keyword(s):  
Independent Component Analysis ◽  
Time Domain ◽  
Component Analysis ◽  
Independent Component ◽  
Mechanical Equipment ◽  
Training Samples ◽  
Sparse Autoencoder ◽  
Stacked Sparse Autoencoder ◽  
Constrained Independent Component Analysis ◽  
Wheel Bearing

Abstract Industrial machinery is developing in the direction of large-scale, automation, and high precision, which brings novel troubles to mechanical equipment management and maintenance. Intelligent diagnosis of mechanical running state based on vibration signals is becoming increasingly important, and it is still a great challenge at pattern recognition. As one of the indispensable components in mechanical equipment, planetary gearboxes are widely used in wind power, aerospace, and heavy industry. However, the problem of automatically maximizing the accuracy of planetary gearbox under different working conditions has not been solved. Therefore, an intelligent diagnosis method for planetary wheel bearing based on constrained independent component analysis (CICA) and stacked sparse autoencoder (SSAE) is presented in this research. Firstly, the fault signal with obvious time-domain characteristics is extracted by constrained independent component analysis (CICA), and the fault signals and noise is separated. Then, calculating the correlation kurtosis value of the time domain signals at different iteration periods as the eigenvalue to obtain the training samples and the test samples. The parameters of the network layer, the number of hidden nodes and learning rate are determined to build the model of SSAE. In the end, the training samples are input into the model for training and the whole network is fine-tuned. The advantages and disadvantages of the model are verified by the test samples. The intelligent classification and diagnosis of the mechanical running state are completed. Experiments analysis with real datasets of planetary wheel bearing show that the proposed method can achieve higher accuracy and robustness for fault classification compared with other data-driven methods. The application of this method in other major machinery industry also has bright prospects.

An Improved Independent Component Analysis with Reference

2014 ◽  
Vol 667 ◽  
pp. 64-67
Author(s):  
Yan Fei Jia ◽  
Xiao Dong Yang ◽  
Li Yue Xu ◽  
Li Quan Zhao
Keyword(s):  
Independent Component Analysis ◽  
Cost Function ◽  
Newton Method ◽  
Lagrange Function ◽  
Component Analysis ◽  
Convergence Speed ◽  
Independent Component ◽  
Source Signal ◽  
The Cost ◽  
New Algorithms

Independent component analysis with reference is a general framework to incorporate a priori information of interesting source signal into the cost function as constrained terms to form an augmented Lagrange function, and utilizes Newton method to optimize the cost function. It can extract any interesting source signal without extracting all source signals comparing with the traditional Independent component analysis method. In this paper, to accelerate the convergence speed of the Independent component analysis with reference, two improved algorithms are presented. The new algorithms, firstly whiten the observed signals to avoid matrix inverse operation to reduce algorithm complexity, secondly use improved Newton method with fast convergence speed to optimize cost function,in the end deduce the improved Independent component analysis with reference algorithms. Simulation result demonstrates the new algorithms have faster convergence speed with smaller error compared with the original method.

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