DocumentCode
1655871
Title
Notice of Retraction
Fatalness evaluation of flight safety hidden danger based on support vector machine
Author
Gan Xusheng ; Duanmu Jingshun ; Cong Wei
Author_Institution
XiJing Coll., Xi´an, China
Volume
3
fYear
2010
Firstpage
567
Lastpage
571
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
A method for fatalness evaluation of flight safety hidden danger, based on support vector machine (SVM), is proposed. And the corresponding model, which makes the basic evaluation factors of flight safety hidden danger fatalness as input node and evaluation results as output node, is established. Then the safety situation of a regiment of China Air Force is evaluated. The application result shows that, for fatalness evaluation of flight safety hidden danger, SVM has better performance on precision, rapidity and realization in comparison with traditional neural network.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
A method for fatalness evaluation of flight safety hidden danger, based on support vector machine (SVM), is proposed. And the corresponding model, which makes the basic evaluation factors of flight safety hidden danger fatalness as input node and evaluation results as output node, is established. Then the safety situation of a regiment of China Air Force is evaluated. The application result shows that, for fatalness evaluation of flight safety hidden danger, SVM has better performance on precision, rapidity and realization in comparison with traditional neural network.
Keywords
aerospace computing; aerospace safety; decision making; support vector machines; China Air Force; flight safety hidden danger fatalness evaluation; neural network; safety management decision making; support vector machine; Artificial neural networks; Gallium nitride; Presses; Fatalness evaluation; Flight safety; Neural network; Support vector machine;
fLanguage
English
Publisher
ieee
Conference_Titel
Advanced Management Science (ICAMS), 2010 IEEE International Conference on
Conference_Location
Chengdu
Print_ISBN
978-1-4244-6931-4
Type
conf
DOI
10.1109/ICAMS.2010.5553176
Filename
5553176
Link To Document