|
|||||
|
|
| 基于动载的路面不平度识别的小波特征提取 | |
| 引用本文： | 李忠国,张为公,匡军,刘广孚.基于动载的路面不平度识别的小波特征提取[J].江苏大学学报(自然科学版),2007,28(4):305-308. |
| 作者姓名： | 李忠国 张为公 匡军 刘广孚 |
| 作者单位： | 1. 东南大学,仪器科学与工程学院,江苏,南京,210096;江苏科技大学,机械与动力工程学院,江苏,镇江,212003 2. 东南大学,仪器科学与工程学院,江苏,南京,210096 3. 莱阳农学院机电学院,山东,青岛,265200 |
| 基金项目： | 江苏省交通厅科研项目 , 江苏省重点实验室基金 |
| 摘 要： | 提出了基于车轮垂直动载的路面不平度识别的小波特征提取方案.对垂直动载采用bior 1.5小波,做4层小波分解,求4层细节系数和第4层近似系数幅值的均值和方差作为特征参数.使用Fisher判据和自组织映射(SOM)神经网络对小波参数、快速傅立叶变换(FFT)分段系数参数、全部数值的均值和方差、全变差、过零率、共振峰幅值参数以及实倒谱系数参数的分类能力进行了判定和比较.试验结果表明在路面不平度识别方面小波参数组合优于其他参数组合. |
| 关 键 词： | 路谱 特征提取 小波变换 车轮力 动载 路面不平度 识别 小波特征提取 dynamic load based recognition road roughness feature extraction 参数组合 组合优 结果 试验 比较 分类能力 实倒谱系数 共振峰 过零率 全变差 数值 |
| 文章编号： | 1671-7775（2007）04-0305-04 |
| 修稿时间： | 2006-12-25 |
Wavelet feature extraction in road roughness recognition based on dynamic load |
|
| LI Zhong-guo,ZHANG Wei-gong,KUANG Jun,LIU Guang-fu.Wavelet feature extraction in road roughness recognition based on dynamic load[J].Journal of Jiangsu University:Natural Science Edition,2007,28(4):305-308. | |
| Authors: | LI Zhong-guo ZHANG Wei-gong KUANG Jun LIU Guang-fu |
| Institution: | 1. School of Instrument Science and Engineering, Southeast University, Nanjing, Jiangsu 210096, China; 2. School of Mechanical and Power Engineering, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu 212003, China; 3. School of Mechanical and Electronic Engineering, Laiyang Agricultural College, Qingdao, Shandong 265200, China |
| Abstract: | Wavelet feature extraction scheme is proposed for Road Roughness Recognition(RRR) based on Vehicle Vertical Dynamic Load(VVDL).VVDL is decomposed to 4 layers with wavelet named bior 1.5.Means and variances of amplitude of wavelet coefficient(4 layers detailed coefficients and the 4th layer approximate coefficient) are taken as feature parameters.Fisher criterion and Self-Organizing Feature Map net are comparatively employed to determine the classification abilities of wavelet parameters,Fast Fourier Transform(FFT) subsection coefficient parameters,mean and variance of whole data,total variation,zero-cross ratio and formant amplitudes.Experimental results indicate that the wavelet parameter combination is superior to other combinations in RRR.road spectrum;feature extraction;wavelet transform; wheel force |
| Keywords: | road spectrum feature extraction wavelet transform wheel force |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ | |