DocumentCode
2210464
Title
Notice of Retraction
Research of Variational Curve Model Base on Level Set and GVF
Author
ShangGuan Wei ; Wang Jian ; Cai Bai-gen ; Wang Jun
Author_Institution
State Key Lab. of Rail Traffic Control & Safety, Beijing Jiaotong Univ., Beijing, China
fYear
2009
fDate
26-28 Dec. 2009
Firstpage
2141
Lastpage
2144
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.
As we known, how to describe rail track with precise data is one important problem in train positioning research fields, research of variational curve model base on Level Set and GVF (Gradient Vector Flow) was studied systematically and the GPS data reduction model, method and algorithm base on variational curve model in limited restriction condition was studied, combine with real track data, analyzed the limited restriction condition in practical rail application environment, raised the limited restriction mathematical model, using the GPS data reduction algorithm Base on Level Set and GVF variational curve model adapt to different environment to realize data reduction and error point deletion, extract some key point or track data from track database to describe rail track in a certain satisfactory error region. The experiment proves that the algorithm model Base on Level Set and GVF could extract precise positioning information and improve rail track database automatic generation method, which is adapt to precise positioning of train control system base on GPS.
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.
As we known, how to describe rail track with precise data is one important problem in train positioning research fields, research of variational curve model base on Level Set and GVF (Gradient Vector Flow) was studied systematically and the GPS data reduction model, method and algorithm base on variational curve model in limited restriction condition was studied, combine with real track data, analyzed the limited restriction condition in practical rail application environment, raised the limited restriction mathematical model, using the GPS data reduction algorithm Base on Level Set and GVF variational curve model adapt to different environment to realize data reduction and error point deletion, extract some key point or track data from track database to describe rail track in a certain satisfactory error region. The experiment proves that the algorithm model Base on Level Set and GVF could extract precise positioning information and improve rail track database automatic generation method, which is adapt to precise positioning of train control system base on GPS.
Keywords
Global Positioning System; gradient methods; railways; set theory; vectors; GPS data reduction model; error point deletion; gradient vector flow; level set; limited restriction condition; mathematical model; rail track; train control system base; train positioning research fields; variational curve model base; Algorithm design and analysis; Automatic generation control; Control system synthesis; Data analysis; Data mining; Databases; Global Positioning System; Level set; Mathematical model; Rails;
fLanguage
English
Publisher
ieee
Conference_Titel
Information Science and Engineering (ICISE), 2009 1st International Conference on
Conference_Location
Nanjing
Print_ISBN
978-1-4244-4909-5
Type
conf
DOI
10.1109/ICISE.2009.927
Filename
5454635
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