An Independent Component Analysis Based Defect Detection for the OLED Display

2012 ◽  
Vol 605-607 ◽  
pp. 724-728 ◽  
Author(s):  
Zhi Liang Wang ◽  
Jian Gao ◽  
Chuan Xia Jian ◽  
Yao Cong Liang ◽  
Yu Cen
Keyword(s):  
Independent Component Analysis ◽  
Defect Detection ◽  
Detection System ◽  
Component Analysis ◽  
Rapid Identification ◽  
Independent Component ◽  
Display Device ◽  
Low Contrast ◽  
Light Emitting ◽  
Mixing Matrix

Organic Light Emitting Displays (OLED) is a new type of display device which has become increasingly attractive and popular. Due to the complex manufacturing process, various defects may exist on the OLED panel. These defects have the characteristics of fuzzy boundaries, irregular in shape, low contrast with background and they are mixed with the texture background increasing the difficulty of a rapid identification. In this paper, we proposed an approach to detect these defects based on the model of independent component analysis (ICA). The ICA model is applied to a perfect OLED image to determine the de-mixing matrix and its corresponding independent components (ICs). Through the choice of a proper ICi row vector, the new de-mixing matrix is generated which contains only uniform information and is used to reconstruct the OLED background image. The defect result can be obtained by the subtracting operation between the reconstructed background and the source images. The detection system is implemented in the Labview and the testing results show that the ICA based OLED defect detection method is feasible and effective.

scholarly journals A Novel Method for Surface Defect Detection of Photovoltaic Module Based on Independent Component Analysis

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Xuewu Zhang ◽  
Hao Sun ◽  
Yun Zhou ◽  
Ji Xi ◽  
Min Li
Keyword(s):  
Independent Component Analysis ◽  
Defect Detection ◽  
Surface Defect ◽  
Reconstruction Algorithm ◽  
Component Analysis ◽  
Training Image ◽  
Independent Component ◽  
Photovoltaic Module ◽  
Surface Defect Detection ◽  
Mixing Matrix

This paper proposed a new method for surface defect detection of photovoltaic module based on independent component analysis (ICA) reconstruction algorithm. Firstly, a faultless image is used as the training image. The demixing matrix and corresponding ICs are obtained by applying the ICA in the training image. Then we reorder the ICs according to the range values and reform the de-mixing matrix. Then the reformed de-mixing matrix is used to reconstruct the defect image. The resulting image can remove the background structures and enhance the local anomalies. Experimental results have shown that the proposed method can effectively detect the presence of defects in periodically patterned surfaces.

Statistical Independence and Independent Component Analysis

2014 ◽  
Vol 553 ◽  
pp. 564-569
Author(s):  
Yaseen Unnisa ◽  
Danh Tran ◽  
Fu Chun Huang
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Statistical Testing ◽  
P Value ◽  
Statistical Independence ◽  
Mixing Matrix ◽  
Recent Method ◽  
Novel Method ◽  
Source Signals

Independent Component Analysis (ICA) is a recent method of blind source separation, it has been employed in medical image processing and structural damge detection. It can extract source signals and the unmixing matrix of the system using mixture signals only. This novel method relies on the assumption that source signals are statistically independent. This paper looks at various measures of statistical independence (SI) employed in ICA, the measures proposed by Bakirov and his associates, and the effects of levels of SI of source signals on the output of ICA. Firstly, two statistical independent signals in the form of uniform random signals and a mixing matrix were used to simulate mixture signals to be anlysed byfastICApackage, secondly noise was added onto the signals to investigate effects of levels of SI on the output of ICA in the form of soure signals, the mixing and unmixing matrix. It was found that for p-value given by Bakirov’s SI statistical testing of the null hypothesis H0is a good indication of the SI between two variables and that for p-value larger than 0.05, fastICA performs satisfactorily.

Fast Independent Component Analysis (FastICA) in Separating Vocals and Instruments in the Art of Geguntangan

2020 ◽  
Vol 8 (3) ◽  
pp. 219
Author(s):  
Angga Pramana Putra ◽  
I Gede Arta Wibawa
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Blind Signal Separation ◽  
Sound Sources ◽  
Blind Signal ◽  
Fast Independent Component Analysis ◽  
Fastica Algorithm ◽  
Mixing Matrix ◽  
Sound Separation

Geguntangan is pesantian in religious ceremonies in Bali accompanied by gamelan music. The human sense of hearing tends to have limitations, which causes not all vocals mixed with gamelan to be heard clearly. Therefore we need a system that can be used to separate vocals with gamelan in the geguntangan. Separation of sound sources is categorized as Blind Source Separation (BSS) or also called Blind Signal Separation, which means an unknown source. The algorithm used to handle BSS is the Fast Independent Component Analysis (FastICA) algorithm with a focus on separating the sound signal in a wav-format sound file. FastICA algorithm is used for the sound separation process with the value parameter used is Mean Square Error (MSE). From the simulation results show the results of MSE calculations using the mixing matrix [0.3816, 0.8678], [0.8534, -0.5853] obtained the results for the FastICA method, the MSE value is 3.60 x 10-5 for the vocal and 1.71 x 10-6 for the instrument.

Robust Method via Independent Component Analysis with Additive Noise

2010 ◽  
Vol 113-116 ◽  
pp. 272-275
Author(s):  
Yong Jian Zhao ◽  
Bo Qiang Liu ◽  
Hong Run Wang
Keyword(s):  
Independent Component Analysis ◽  
High Stability ◽  
Additive Noise ◽  
Group Structure ◽  
Processing System ◽  
Component Analysis ◽  
Independent Component ◽  
Signal Processing System ◽  
Mixing Matrix ◽  
Simulation Results

Blind source separation via independent component analysis (ICA) has received increasing attention because of its potential application in signal processing system. The existing ICA methods can not give a consistent estimator of the mixing matrix because of additive noise. Based on interpretation and properties of the vectorial spaces of sources and mixtures, a new ICA method is presented in this paper that may constructively reject noise so as to estimate the mixing matrix consistently. This procedure may capture the underlying source dynamics effectively even if additive noise exists. The simulation results show that this method has high stability and reliability in the process of revealing the undering group structure of extracted ICA components.

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