DocumentCode
553985
Title
Notice of Retraction
A design of fault diagnose system for array infrared sensor based on data fusion technology
Author
Li Qi ; Qiu Cheng
Author_Institution
Sch. of Autom., Xian Univ. of Technol., Xi´an, China
Volume
1
fYear
2011
fDate
26-28 July 2011
Firstpage
5
Lastpage
8
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.
Based on data fusion technology, a design of fault diagnose of array infrared sensor is proposed. The design is using multi-neural network to track status of every single sensor of the detect sensor system. Abstracting characteristic vectors to analysis, the method uses Support Vector Machine (SVM) to classify different kinds of situations for sensor failure. The simulation result shows that multi-neural network could track sensors´ status well and SVM could distinguish sensor failure efficiently. It is reward for the application on multi-sensor data fusion technology used in fault diagnose area.
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.
Based on data fusion technology, a design of fault diagnose of array infrared sensor is proposed. The design is using multi-neural network to track status of every single sensor of the detect sensor system. Abstracting characteristic vectors to analysis, the method uses Support Vector Machine (SVM) to classify different kinds of situations for sensor failure. The simulation result shows that multi-neural network could track sensors´ status well and SVM could distinguish sensor failure efficiently. It is reward for the application on multi-sensor data fusion technology used in fault diagnose area.
Keywords
computerised instrumentation; fault diagnosis; infrared detectors; neural nets; sensor fusion; support vector machines; array infrared sensor; fault diagnose system; multineural network; multisensor data fusion technology; support vector machine; Arrays; Artificial neural networks; Feature extraction; Heating; Infrared sensors; Support vector machines; Transducers; Array Infrared Sensor; Data Fusion Technology; Fault Diagnose; Multi-neural Network; Support Vector Machine;
fLanguage
English
Publisher
ieee
Conference_Titel
Natural Computation (ICNC), 2011 Seventh International Conference on
Conference_Location
Shanghai
ISSN
2157-9555
Print_ISBN
978-1-4244-9950-2
Type
conf
DOI
10.1109/ICNC.2011.6022085
Filename
6022085
Link To Document