基于降噪自编码器降维的汽车行驶工况分析  

Analysis of Vehicle Driving Condition Based on De-Noise Autoencoder

作　　者：顾珉 施华君[1] GU Min;SHI Hua-Jun(East China Institute of Computing Technology,Shanghai 201808,China)

机构地区：[1]华东计算技术研究所,上海201808

出　　处：《计算机系统应用》2021年第1期38-44,共7页Computer Systems & Applications

摘　　要：近年,随着环境治理要求的不断提升,汽车尾气作为污染的主要来源之一.汽车工业是我国如今重点关注的对象,而汽车行驶工况在是汽车工业界内被看做是反映汽车性能的主要标准,因此对于工况的研究就成了时下急需的研究项目之一.本文根据汽车行驶工况的各项行业参数,研究出一种通用的反映我国不同地区汽车行驶工况的方法.同时在对复杂数据降维时,利用了深度学习中的降噪自编码器降维方式,取得了较优且贴合实际意义的实验效果.本文数据来源于对上海市嘉定区汽车实验所得,在对数据处理后,经过本征模态分析、特征提取、动态时间规划、小波分解等不同手段和方式实现了对通用工况图的构建,为以后我国其余城市和总体大致的工况图构建提供参考.In recent years,with the continuous improvement of environmental governance requirements,automobile exhaust becomes one of the main sources of pollution.The automobile industry is the focus of attention in China nowadays,and the driving conditions of automobiles are considered as reflections in the automobile industry.Therefore,the study of working conditions has become one of the urgently needed research projects.Based on various industry parameters of automobile driving conditions,this study develops a general method for reflecting automobile driving conditions in different regions of China.At the same time,we use the dimensionality reduction method of the de-noise encoder used in deep learning when reducing dimensionality of complex data,and has achieved good and practical experimental results.The data in this paper is derived from the automobile experiment in Jiading District,Shanghai.After processing the data,the construction of the general working condition map has been realized through different means and methods such as EMD,feature extraction,dynamic time planning,wavelet decomposition,etc.,providing a reference for the construction of the general working conditions map of the city and the overall.

关 键 词：汽车行驶工况 降噪自编码器 动态时间规整 本征模态分析 小波分解 近似熵 

分 类 号：U471.2[机械工程—车辆工程]