Double precision nonlinear cell for fast independent component analysis algorithm

2006 ◽  
Author(s):  
V. K. Jain
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Double Precision ◽  
Analysis Algorithm ◽  
Fast Independent Component Analysis ◽  
Independent Component Analysis Algorithm

Research of improved fast independent component analysis algorithm in rectal diagnosis signal preprocessing

2019 ◽  
Vol 42 (11) ◽  
pp. 636-644
Author(s):  
Peng Zan ◽  
Yingjie Xue ◽  
Meihan Chang
Keyword(s):  
Independent Component Analysis ◽  
Signal Sequence ◽  
Component Analysis ◽  
Independent Component ◽  
Artificial Organ ◽  
Anal Sphincters ◽  
Analysis Algorithm ◽  
Fast Independent Component Analysis ◽  
Reconstructed Signal ◽  
Independent Component Analysis Algorithm

With the maturity of artificial organ technology, the use of artificial anal sphincters was proposed to help patients who suffered anal incontinence for various causes reconstruct rectal perception, monitor rectal pressure and diagnose rectal lesion. Aimed at the lack of signal pretreatment in the artificial anal sphincter system, we find a way to solve it, that is, the multi-dimensional reconstruction of the intestinal one-dimensional pressure signal sequence by using phase space reconstruction, and the separation of the reconstructed signal by using the improved fast independent component analysis algorithm. We did some relevant experiments, further extracted the features of the isolated rectal signal, and used back propagation neural network to diagnose the rectal lesions. Experiments show that the method can pretreat the rectal signal, and further analyze the separated signal to diagnose of rectal function. The improved fast independent component analysis algorithm has few iterations, fast convergence, short run time, low requirements on initial weights and good diagnosis. This study lays a foundation for the diagnosis of rectal function by using artificial anal sphincters.

scholarly journals A Fast Independent Component Analysis Algorithm for Geochemical Anomaly Detection and Its Application to Soil Geochemistry Data Processing

2014 ◽  
Vol 2014 ◽  
pp. 1-12
Author(s):  
Bin Liu ◽  
Si Guo ◽  
Youhua Wei ◽  
Zedong Zhan
Keyword(s):  
Independent Component Analysis ◽  
Anomaly Detection ◽  
Data Processing ◽  
Component Analysis ◽  
Independent Component ◽  
Geochemical Data ◽  
Frequency Method ◽  
Analysis Algorithm ◽  
Fast Independent Component Analysis ◽  
Independent Component Analysis Algorithm

A fast independent component analysis algorithm (FICAA) is introduced to process geochemical data for anomaly detection. In geochemical data processing, the geological significance of separated geochemical elements must be explicit. This requires that correlation coefficients be used to overcome the limitation of indeterminacy for the sequences of decomposed signals by the FICAA, so that the sequences of the decomposed signals can be correctly reflected. Meanwhile, the problem of indeterminacy in the scaling of the decomposed signals by the FICAA can be solved by the cumulative frequency method (CFM). To classify surface geochemical samples into true anomalies and false anomalies, assays of the 1 : 10 000 soil geochemical data in the area of Dachaidan in the Qinghai province of China are processed. The CFM and FICAA are used to detect the anomalies of Cu and Au. The results of this research demonstrate that the FICAA can demultiplex the mixed signals and achieve results similar to actual mineralization when 85%, 95%, and 98% are chosen as three levels of anomaly delineation. However, the traditional CFM failed to produce realistic results and has no significant use for prospecting indication. It is shown that application of the FICAA to geochemical data processing is effective.

A Periodic Noise Separation Method Research of well Field Three-Dimensional Drill String Vibration Signal

2013 ◽  
Vol 318 ◽  
pp. 27-32
Author(s):  
Hao Cheng Wu ◽  
Yong Shou Dai ◽  
Wei Feng Sun ◽  
Li Gang Li ◽  
Ya Nan Zhang
Keyword(s):  
Independent Component Analysis ◽  
Vibration Signal ◽  
Component Analysis ◽  
Drill String ◽  
Independent Component ◽  
Analysis Algorithm ◽  
String Vibration ◽  
Periodic Noise ◽  
Well Field ◽  
Independent Component Analysis Algorithm

Periodic noise is an important manifestation of the drill string vibration signal noise. In order to extract the characteristics of the signals which reflect the situation of the tools in drilling, the periodic components which influence the original drill string vibration signal in the well field were researched and the independent component analysis algorithm which is on the basis of negative entropy for periodic vibration noise separation was adopted. At the same time, the effect of algorithm demixing was improved where periodic noise components which existed in three directions of drill string vibration signals were used, combining with the improved particle swarm optimization algorithm to seek the optimal mixed matrix by which the multi-channel mixed-signal of independent component analysis algorithm could be structured. This method in operation was fast. And after separation each signal was of high similarity. Through the experimental simulation, the method was proven effective in the drill string vibration periodic noise signal separation.

Sign in / Sign up

Export Citation Format

Share Document