Paper Info
Title
Image classifier for the TJ-II thomson scattering diagnostic: evaluation with a feed forward neural network
Abstract
There are two big stages to implement in a signal classification process: features extraction and signal classification. The present work shows up the development of an automated classifier based on the use of the Wavelet Transform to extract signal characteristics, and Neural Networks (Feed Forward type) to obtain decision rules. The classifier has been applied to the nuclear fusion environment (TJ-II stellarator), specifically to the Thomson Scattering diagnostic, which is devoted to measure density and temperature radial profiles. The aim of this work is to achieve an automated profile reconstruction from raw data without human intervention. Raw data processing depends on the image pattern obtained in the measurement and, therefore, an image classifier is required. The method reduces the 221.760 original features to only 900, being the success mean rate over 90%. This classifier has been programmed in MATLAB.
Year
DOI
Venue
2005
10.1007/11499305_62
IWINAC (2)
Keywords
Field
DocType
automated profile reconstruction,signal classification process,image classifier,neural network,tj-ii thomson,image pattern,present work,features extraction,signal classification,automated classifier,raw data,signal characteristic,data processing,feature extraction,wavelet transform,feed forward,decision rule,feed forward neural network
Computer science,Image processing,Artificial intelligence,Artificial neural network,Classifier (linguistics),Wavelet transform,Computer vision,Feedforward neural network,Pattern recognition,Feature extraction,Speech recognition,Margin classifier,Quadratic classifier
Conference
Volume
ISSN
ISBN
3562
0302-9743
3-540-26319-5
Citations 
PageRank 
References 
1
0.39
1
Authors
4
Name
Order
Citations
PageRank
G. Farias1837.69
R. Dormido29010.76
M. Santos3163.93
N. Duro49813.59