賀！周鴻儒、蘇韋翰 論文"Transformation of GPS Coordinates Using SR&GP"獲IEEE CIMSA 2005會議接受!
on 2005/5/25 17:10:00
(1893 reads)
Cconference: 2005 IEEE INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE FOR MEASUREMENT SYSTEMS AND APPLICATIONS (CIMSA 2005) to be held at TAORMINA, SICILY, ITALY on June 20-22, 2005. Paper title: Transformation of GPS Coordinates Using Symbolic Regression and Genetic Programming Authors: Hung-Ju Chou（周鴻儒） Chih-Hung Wu（吳志宏） Wei-Han Su（蘇韋翰） Congratulations!!
SEE http://www.ewh.ieee.org/soc/im/cimsa/cimsa2005/acceptedpapers.html Abstract –The coordinates signal received from GPS have to be transformed according to the geographic position of the user for locating positions precisely. Traditional coordinate transformation is a level-wised process consisting of several sets of equations obtained by geographical survey. Each level takes care of a step of geometric or geographic transformation of coordinates, which makes the whole process complicated. Also, the equations for coordinate transformation are unique with respect to different geographic regions. In this paper, we try to eliminate the steps of coordinate transformation using symbolic regression and genetic programming so that the receiver’s position can be obtained much faster with less computation complexity. We test this concept on different areas in Taiwan and find several new equations that can effectively and better produce accurate coordinates. Keywords –GPS, coordinate transformation, symbolic regression, machine learning, genetic programming
SEE http://www.ewh.ieee.org/soc/im/cimsa/cimsa2005/acceptedpapers.html Abstract –The coordinates signal received from GPS have to be transformed according to the geographic position of the user for locating positions precisely. Traditional coordinate transformation is a level-wised process consisting of several sets of equations obtained by geographical survey. Each level takes care of a step of geometric or geographic transformation of coordinates, which makes the whole process complicated. Also, the equations for coordinate transformation are unique with respect to different geographic regions. In this paper, we try to eliminate the steps of coordinate transformation using symbolic regression and genetic programming so that the receiver’s position can be obtained much faster with less computation complexity. We test this concept on different areas in Taiwan and find several new equations that can effectively and better produce accurate coordinates. Keywords –GPS, coordinate transformation, symbolic regression, machine learning, genetic programming
