scholarly journals Analysis Strategy Based on Wavelet Decomposition for Classification of Voltage Sags

2004 ◽  
Vol 1 (02) ◽  
pp. 161-167
Author(s):  
J. Xargayó ◽  
◽  
J. Meléndez ◽  
J. Colomer
Keyword(s):  
Wavelet Decomposition ◽  
Voltage Sags ◽  
Analysis Strategy

WAVELET FEATURES BASED SLEEP STAGES DETECTION USING SINGLE CHANNEL EEG

2017 ◽  
Vol 5 (4) ◽  
pp. 99-102
Author(s):  
Vandana Roy ◽  
Anand Prakash ◽  
Shailja Shukla
Keyword(s):  
Feature Extraction ◽  
Wavelet Decomposition ◽  
Single Channel ◽  
Kappa Coefficient ◽  
Sleep Stages ◽  
Chi Square ◽  
Wavelet Features ◽  
Stage Classification ◽  
Selection Of

The sleep stages determination is important for the identification and diagnosis of different diseases. An efficient algorithm of wavelet decomposition is used for feature extraction of single channel EEG. The Chi-Square method is applied for the selection of the best attributes from the extracted features. The classification of different staged techniques is applied with the help AdaBoost.M1 algorithm. The accuracy of 89.82% achieved in the six stage classification. The weighted sensitivity of all stages is 89.8% and kappa coefficient of 77.93% is obtained in the six stage classification.

scholarly journals A Five-Level Wavelet Decomposition and Dimensional Reduction Approach for Feature Extraction and Classification of MR and CT Scan Images

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Varun Srivastava ◽  
Ravindra Kumar Purwar
Keyword(s):  
Neural Network ◽  
Ct Scan ◽  
Wavelet Decomposition ◽  
Nearest Neighbor ◽  
Principal Component ◽  
Two Dimensional ◽  
Mr Images ◽  
Probabilistic Principal Component Analysis ◽  
Different Sources

This paper presents a two-dimensional wavelet based decomposition algorithm for classification of biomedical images. The two-dimensional wavelet decomposition is done up to five levels for the input images. Histograms of decomposed images are then used to form the feature set. This feature set is further reduced using probabilistic principal component analysis. The reduced set of features is then fed into either K nearest neighbor algorithm or feed-forward artificial neural network, to classify images. The algorithm is compared with three other techniques in terms of accuracy. The proposed algorithm has been found better up to 3.3%, 12.75%, and 13.75% on average over the first, second, and third algorithm, respectively, using KNN and up to 6.22%, 13.9%, and 14.1% on average using ANN. The dataset used for comparison consisted of CT Scan images of lungs and MR images of heart as obtained from different sources.

scholarly journals Classification of voltage sags according to the severity of the effects on the induction motor

DYNA ◽  
2015 ◽  
Vol 82 (190) ◽  
pp. 96-104 ◽  
Author(s):  
Adolfo Andres Jaramillo Matta ◽  
Luis Guasch Pesquer ◽  
Cesar Leonardo Trujillo Rodriguez
Keyword(s):  
Induction Motor ◽  
Induction Motors ◽  
Initial Point ◽  
Steady States ◽  
Voltage Sag ◽  
Voltage Sags ◽  
Cage Rotor ◽  
Rotor Model

In this paper, symmetrical and unsymmetrical voltage sags are classified according to the severity of the effects produced on the behavior of induction motors, using the double-cage rotor model. The analyzed variables are: current and torque peaks, and speed loss in transient and steady states. The severity of these effects is analyzed with 14640 voltage sags, both at the beginning of the voltage sag and when the voltage is recovered, by changing the type, magnitude, duration, and initial-point-of-wave. Four mechanical torque loads were used for the analysis, three with constant behavior and one with quadratic behavior. The results show the durations and initial-point-of-wave with more severe effects for each type of voltage sag. Finally, a classification that depends on the type of voltage sag and the variable of interest is obtained.

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