DocumentCode
526480
Title
Notice of Retraction
Prediction of neck deceleration characteristic of Hybrid III 5th Female Dummy based on BP neural network method
Author
Zhixin Liu ; Xiaolong Zhang ; Yongwan Shi ; Chen Hong
Author_Institution
Automotive Testing & Res. Inst., China Automotive Technol. & Res. Center, Tianjin, China
Volume
1
fYear
2010
fDate
9-11 July 2010
Firstpage
626
Lastpage
629
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.
It was found that many of factors would evidently affect the calibration results in Hybrid III 5th Female Dummy neck calibration tests. This paper developed a BP neural network model for analysis and prediction of the relationship between calibration parameters and neck deceleration characteristics. The input parameters of the BP model are calibration temperature, impact velocity of pendulum, cells of honeycomb and its cut style, the output parameters of the model are three neck velocities at the moment of 10, 20 and 30 ms, respectively. The model consists of a three-layered feed-forward back-propagation neural network. The network is trained with pairs of inputs/outputs datasets generated from 30 calibration tests. A very good performance of the neural network, in terms of agreement with additional experimental data was achieved.
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.
It was found that many of factors would evidently affect the calibration results in Hybrid III 5th Female Dummy neck calibration tests. This paper developed a BP neural network model for analysis and prediction of the relationship between calibration parameters and neck deceleration characteristics. The input parameters of the BP model are calibration temperature, impact velocity of pendulum, cells of honeycomb and its cut style, the output parameters of the model are three neck velocities at the moment of 10, 20 and 30 ms, respectively. The model consists of a three-layered feed-forward back-propagation neural network. The network is trained with pairs of inputs/outputs datasets generated from 30 calibration tests. A very good performance of the neural network, in terms of agreement with additional experimental data was achieved.
Keywords
automotive engineering; backpropagation; calibration; feedforward neural nets; honeycomb structures; impact testing; pendulums; vehicle dynamics; Hybrid III 5th female dummy neck calibration tests; calibration parameters; honeycomb; neck deceleration characteristic prediction; pendulum; three-layered feed-forward back-propagation neural network; Calibration; Force; Force measurement; MATLAB; Mathematical model; Neck; Predictive models; BP neural network; Central composite design; Female dummy; Neck calibration;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on
Conference_Location
Chengdu
Print_ISBN
978-1-4244-5537-9
Type
conf
DOI
10.1109/ICCSIT.2010.5564123
Filename
5564123
Link To Document