scholarly journals A Method of Barkhausen Noise Feature Extraction Based on an Adaptive Threshold

2019 ◽  
Vol 9 (15) ◽  
pp. 2964 ◽  
Author(s):  
Hang ◽  
Liu ◽  
Wang
Keyword(s):  
Feature Extraction ◽  
Maximum Amplitude ◽  
Adaptive Threshold ◽  
Barkhausen Noise ◽  
New Approach ◽  
Feature Extraction Method ◽  
Modelling Techniques ◽  
New Feature ◽  
Low Dispersion ◽  
Pulse Count

This paper reports on a new feature extraction method for detection of applied stress using magnetic Barkhausen noise (MBN). Some previous methods for extracting MBN features need to choose a suitable threshold so that these features can have good linearity and low dispersion, such as pulse count and full width at 25, 50 and 75% of the maximum amplitude. A new approach has been proposed for selecting the appropriate threshold for MBN features adaptively using a genetic algorithm (GA). The criterion for selecting the threshold is the lowest standard deviation of features and new proposed ‘overlap’ of features. In order to verify the effectiveness of the adaptive pulse count feature for stress detection, different modelling techniques are compared, including multivariable linear regression (MLR) and multilayer perceptron (MLP). The results obtained have proven that adaptive threshold features can effectively distinguish between different stress conditions compared with traditional MBN features.

scholarly journals A new feature extraction method for signal classification applied to cord dorsum potential detection

2012 ◽  
Vol 9 (5) ◽  
pp. 056009 ◽  
Author(s):  
D Vidaurre ◽  
E E Rodríguez ◽  
C Bielza ◽  
P Larrañaga ◽  
P Rudomin
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Signal Classification ◽  
Feature Extraction Method ◽  
New Feature

Feature Extraction of Vibration of Centrifugal Fan Based on LLE

2014 ◽  
Vol 1070-1072 ◽  
pp. 1941-1944
Author(s):  
Yong Hao Liao ◽  
Bo Liu
Keyword(s):  
Feature Extraction ◽  
Diagnostic Accuracy ◽  
Wavelet Packet ◽  
Centrifugal Fan ◽  
Analysis Method ◽  
Feature Extraction Method ◽  
Different Types ◽  
Feature Information ◽  
New Feature ◽  
Better Than

In order to improve classification ability and diagnostic accuracy of centrifugal fan signals, a new feature extraction method from fault signals of centrifugal fan vibration based on manifold learning method (MLM) that is a kind of reduction method of data dimension is proposed in this paper.The MLM is able to remain nonlinear information of original signal, to improve the classification and diagnostic ability of fault better than traditional reducing dimension methods. The results in this paper show that, fault feature information of centrifugal fan vibration is extracted effectively by the MLM and the fault feature information of different types are separated effectively in themselves areas. The diagnostic accuracy by feature extracted by the MLM is significantly higher than by the wavelet packet analysis method.

A new feature extraction method for odour classification

2011 ◽  
Vol 158 (1) ◽  
pp. 75-88 ◽  
Author(s):  
Bernd Ehret ◽  
Konstantin Safenreiter ◽  
Frank Lorenz ◽  
Joachim Biermann
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Feature Extraction Method ◽  
New Feature

scholarly journals Action Intention Understanding EEG Signal Classification Based on Improved Discriminative Spatial Patterns

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Xingliang Xiong ◽  
Hua Yu ◽  
Haixian Wang ◽  
Jiuchuan Jiang
Keyword(s):  
Feature Extraction ◽  
Spatial Patterns ◽  
Extraction Method ◽  
Signal Classification ◽  
Eeg Signal ◽  
Feature Extraction Method ◽  
Intention Understanding ◽  
Eeg Signal Classification ◽  
New Feature ◽  
Action Intention

Objective. Action intention understanding EEG signal classification is indispensable for investigating human-computer interactions and intention understanding mechanisms. Numerous investigations on classification tasks extract classification features by using graph theory metrics; however, the classification results are usually not good. Method. To effectively implement the task of action intention understanding EEG signal classification, we proposed a new feature extraction method by improving discriminative spatial patterns. Results. The whole frequency band and fusion band achieved satisfactory classification accuracies. Compared with other authors’ methods for action intention understanding EEG signal classification, the new method performs more satisfactorily in some aspects. Conclusions. The new feature extraction method not only effectively avoids complex values when solving the generalized eigenvalue problem but also perfectly realizes appreciable classification accuracies. Fusing the classification features of different frequency bands is a useful strategy for the classification task.

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