Mainlobe interference suppression method based on blind source separation with support vector machine

2021 ◽  
Author(s):  
Z. Yu ◽  
Z. Wang ◽  
S. Gao ◽  
X. Yang
Keyword(s):  
Support Vector Machine ◽  
Blind Source Separation ◽  
Interference Suppression ◽  
Source Separation ◽  
Support Vector ◽  
Suppression Method

Intelligent Fault Diagnosis of Gear Box Based on BSS and SVM

2013 ◽  
Vol 818 ◽  
pp. 218-223 ◽  
Author(s):  
Wei Jin Ma ◽  
Rui Xiang Yu ◽  
Jun Yuan Wang ◽  
Xiao Fei Wan
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Blind Source Separation ◽  
Source Separation ◽  
Vibration Signal ◽  
Support Vector ◽  
Intelligent Fault Diagnosis ◽  
Basic Model ◽  
The Common ◽  
Gear Box

The vibration signal of gear box shows the information of its running state. The thesis explains the basic model and its algorithm of blind source separation, simulates the common fault of gear box in the condition of laboratory, disposing the fault signals of gear box by blind source separation and intelligently identifying the faulty condition of gear box by the method of support vector machine (SVM) after extracting eigenvector, which achieves success.

scholarly journals Online Artifact Removal for Brain-Computer Interfaces Using Support Vector Machines and Blind Source Separation

2007 ◽  
Vol 2007 ◽  
pp. 1-10 ◽  
Author(s):  
Sebastian Halder ◽  
Michael Bensch ◽  
Jürgen Mellinger ◽  
Martin Bogdan ◽  
Andrea Kübler ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Blind Source Separation ◽  
Source Separation ◽  
Signal Decomposition ◽  
Support Vector ◽  
Proof Of Concept ◽  
Artifact Removal ◽  
Computer Interfaces ◽  
Vector Machines ◽  
Online Feedback

We propose a combination of blind source separation (BSS) and independent component analysis (ICA) (signal decomposition into artifacts and nonartifacts) with support vector machines (SVMs) (automatic classification) that are designed for online usage. In order to select a suitable BSS/ICA method, three ICA algorithms (JADE, Infomax, and FastICA) and one BSS algorithm (AMUSE) are evaluated to determine their ability to isolate electromyographic (EMG) and electrooculographic (EOG) artifacts into individual components. An implementation of the selected BSS/ICA method with SVMs trained to classify EMG and EOG artifacts, which enables the usage of the method as a filter in measurements with online feedback, is described. This filter is evaluated on three BCI datasets as a proof-of-concept of the method.

Blind Source Separation and Equalization Based on Support Vector Regression for MIMO Systems

2018 ◽  
Vol E101.B (3) ◽  
pp. 698-708 ◽  
Author(s):  
Chao SUN ◽  
Ling YANG ◽  
Juan DU ◽  
Fenggang SUN ◽  
Li CHEN ◽  
...  
Keyword(s):  
Support Vector Regression ◽  
Blind Source Separation ◽  
Mimo Systems ◽  
Source Separation ◽  
Support Vector

scholarly journals Blind source separation based interference suppression schemes for OFDM and DS-CDMA

2015 ◽  
Vol 61 (2) ◽  
pp. 349-358 ◽  
Author(s):  
M. G. S. Sriyananda ◽  
J. Joutsensalo ◽  
T. Hämäläinen
Keyword(s):  
Blind Source Separation ◽  
Interference Suppression ◽  
Source Separation ◽  
Suppression Schemes
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