DocumentCode
3317891
Title
Notice of Violation of IEEE Publication Principles
Dynamic compensation for infrared thermometer based on Wiener model and particle swarm optimization algorithm
Author
Tian, WenJie ; Tian, Yue ; Ai, Lan ; Liu, JiCheng
Author_Institution
Autom. Inst., Beijing Union Univ., Beijing, China
fYear
2009
fDate
8-11 Aug. 2009
Firstpage
585
Lastpage
589
Abstract
Notice of Violation of IEEE Publication Principles
"Dynamic Compensation for Infrared Thermometer Based on Wiener Model and Particle Swarm Optimization Algorithm"
by WenJie Tian, Yue Tian, Lan Ai, JiCheng Liu
in the 2nd IEEE International Conference on Computer Science and Information Technology (ICCSIT 2009), 2009, pp. 585 – 589
After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE\´s Publication Principles.
This paper contains significant portions of original text from the paper cited below. The original text was copied with insufficient attribution (including appropriate references to the original author(s) and/or paper title) and without permission.
Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article:
"Infrared thermometer sensor dynamic error compensation using Hammerstein neural network"
by Dehui Wu, Songling Huang, Wei Zhao, Junjun Xin
in Sensors and Actuators A: Physical, 2009, Pages 152 – 158
A novel structure of dynamic model is proposed in this paper and applied to construct a dynamic model to correct the dynamic errors of the infrared thermometer, because of which the dynamic performance of the thermometer is effectively improved. The dynamic compensator is established and the compensation is described and explicated by the Wiener model. According to Wiener model, the novel structure is devised. The identification of thermometer non-linear dynamic compensator is achieved by particle swarm optimization algorithm. The results show that the stabilizing time of the thermometer is reduced less than 7 ms from 26 ms and the dynamic performance is obviously improved after compensation.
"Dynamic Compensation for Infrared Thermometer Based on Wiener Model and Particle Swarm Optimization Algorithm"
by WenJie Tian, Yue Tian, Lan Ai, JiCheng Liu
in the 2nd IEEE International Conference on Computer Science and Information Technology (ICCSIT 2009), 2009, pp. 585 – 589
After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE\´s Publication Principles.
This paper contains significant portions of original text from the paper cited below. The original text was copied with insufficient attribution (including appropriate references to the original author(s) and/or paper title) and without permission.
Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article:
"Infrared thermometer sensor dynamic error compensation using Hammerstein neural network"
by Dehui Wu, Songling Huang, Wei Zhao, Junjun Xin
in Sensors and Actuators A: Physical, 2009, Pages 152 – 158
A novel structure of dynamic model is proposed in this paper and applied to construct a dynamic model to correct the dynamic errors of the infrared thermometer, because of which the dynamic performance of the thermometer is effectively improved. The dynamic compensator is established and the compensation is described and explicated by the Wiener model. According to Wiener model, the novel structure is devised. The identification of thermometer non-linear dynamic compensator is achieved by particle swarm optimization algorithm. The results show that the stabilizing time of the thermometer is reduced less than 7 ms from 26 ms and the dynamic performance is obviously improved after compensation.
Keywords
compensation; particle swarm optimisation; thermometers; Wiener model; dynamic compensation; dynamic errors; infrared thermometer; nonlinear dynamic compensator; particle swarm optimization algorithm; Automation; Frequency; Nonlinear distortion; Particle swarm optimization; Sensor phenomena and characterization; Temperature distribution; Temperature measurement; Temperature sensors; Thermal conductivity; Thermal resistance; Wiener model; compensation; identification; infrared thermometer; particle swarm optimization algorithm;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Science and Information Technology, 2009. ICCSIT 2009. 2nd IEEE International Conference on
Conference_Location
Beijing
Print_ISBN
978-1-4244-4519-6
Electronic_ISBN
978-1-4244-4520-2
Type
conf
DOI
10.1109/ICCSIT.2009.5234882
Filename
5234882
Link To Document