Chiari network diagnosis intraoperative based on echocardiography

Author(s):  
E.L. Rojas-Díaz ◽  
O.I. Vásquez-Gómez ◽  
W.F. Amaya-Zuñiga
2012 ◽  
Vol 142 (5) ◽  
pp. S-1004 ◽  
Author(s):  
Costin T. Streba ◽  
Dan Ionut Gheonea ◽  
Larisa D. Sandulescu ◽  
Liliana Streba ◽  
Tudorel Ciurea ◽  
...  

2012 ◽  
Vol 03 (04) ◽  
pp. 281-294 ◽  
Author(s):  
Thomas Djotio Ndié ◽  
Claude Tangha ◽  
Guy Bertrand Fopak

2016 ◽  
Vol 39 (6) ◽  
pp. 620-622 ◽  
Author(s):  
HTIN AUNG ◽  
RAUL E. ESPINOSA ◽  
BRIAN D. POWELL ◽  
CHRISTOPHER J. MCLEOD

2014 ◽  
Vol 571-572 ◽  
pp. 201-204
Author(s):  
Jian Li Yu ◽  
Zhe Zhang

According to the characteristics of fault types of the transformer ,RBF neural network is used to diagnose transformer fault. The paper regards six gases as inputs of the neural network and establishes RBF neural network model which can diagnose six transformer faults: low temperature overheat, medium temperature overheat, high temperature overheat, low energy discharge, high energy discharge and partial discharge . The Matlab simulation studies show that transformer fault diagnosis model based on RBF neural network diagnosis for failure beyond the traditional three-ratio method. The rate of the transformer fault diagnosis accuracy reaches 91.67% which is also much higher than the traditional three ratio method.


2020 ◽  
Vol 41 (7) ◽  
pp. 1529-1531
Author(s):  
Saif Aljemmali ◽  
John Bokowski ◽  
Raymond Morales ◽  
Ra-id Abdulla

Author(s):  
Ebenezer O. Olaniyi ◽  
Oyebade K. Oyedotun ◽  
Abdulkader Helwan ◽  
Khashman Adnan

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