scholarly journals Automatic classification of field-collected dinoflagellates by artificial neural network

1996 ◽  
Vol 139 ◽  
pp. 281-287 ◽  
Author(s):  
PF Culverhouse ◽  
RG Simpson ◽  
R Ellis ◽  
JA Lindley ◽  
R Williams ◽  
...  
2009 ◽  
Vol 36 (8) ◽  
pp. 10914-10918 ◽  
Author(s):  
K. Rajan ◽  
V. Ramalingam ◽  
M. Ganesan ◽  
S. Palanivel ◽  
B. Palaniappan

2014 ◽  
Vol 59 (7) ◽  
pp. 1789-1800 ◽  
Author(s):  
J W Wright ◽  
R Duguid ◽  
F Mckiddie ◽  
R T Staff

2020 ◽  
pp. 61-64
Author(s):  
Yu.G. Kabaldin ◽  
A.A. Khlybov ◽  
M.S. Anosov ◽  
D.A. Shatagin

The study of metals in impact bending and indentation is considered. A bench is developed for assessing the character of failure on the example of 45 steel at low temperatures using the classification of acoustic emission signal pulses and a trained artificial neural network. The results of fractographic studies of samples on impact bending correlate well with the results of pulse recognition in the acoustic emission signal. Keywords acoustic emission, classification, artificial neural network, low temperature, character of failure, hardness. [email protected]


Sign in / Sign up

Export Citation Format

Share Document