標題: Semi-parametric linear mixed effects model for vehicles identification
作者: Jou, Yow-Jen
Yang, Chai-Tzu
Huang, Chien-Chia
Wu, Jennifer Yuh-Jen
資訊管理與財務金融系 註:原資管所+財金所
Department of Information Management and Finance
關鍵字: MANOVA;semi-parametric linear mixed effect model;fast Fourier transform (FFT);smoothing spline analysis of variance decompositions;radar
公開日期: 2007
摘要: In order to make the detecting of the lanes and the types of the vehicles traveling on various roadways affordable, radio-frequency (RF) system-on-chip is designed and will be mounted on the roadside to collect vehicle information. We use the Fast Fourier Transform (FFT) to transform the signal of the reflecting wave radar into the numerical data, and utilize it by the statistical approach to discriminate the size of cars and the lanes. In order to classify the types of the vehicles, two models are proposed to model the data. One is multivariate analysis of variance model to account for the main effect and the interaction effect between type and lane, the other is the semi-parametric linear mixed effect model to emphasize the functional characteristic of the data. Both models work well when the number of groups is small but deteriorate when the number of groups increases.
URI: http://hdl.handle.net/11536/8690
ISBN: 978-0-7354-0476-2
ISSN: 0094-243X
期刊: COMPUTATION IN MODERN SCIENCE AND ENGINEERING VOL 2, PTS A AND B
Volume: 2
起始頁: 984
結束頁: 988
顯示於類別:會議論文