汽车传动系统机械故障诊断方法的研究

本文针对汽车传动系统主要零部件的特点，研究了基于振动信号的故障诊断方法，包括特征提取、模式分类和诊断决策全过程。     

基于线谱特征提取的被动声纳目标识技术研究

该文针对被动声纳目标识别,着重研究了线谱特征提取方法,提出了一种自适应遗传BP算法,并用该算法训练神经网络目标分类器。经对上海上实录三类目标噪声分类识别实验结果表明,所设计的被动声纳目标识别系统具有很好的分类效果。     

基于高阶谱的水下目标识别

利用高阶谱估值法,对具有很强非高斯性和非线性的舰船辐射噪声信号进行分析及特征提取,并通过结构自适应神经网络作为分类实验,表明基于高阶谱的特征提取具有较强的类别可分性,在无源声纳目标识别中特具潜力。     

航站楼内部通信系统在铁路客运站应用的初探

本文介绍了航站楼内部通信系统的概念及特点，对内部通信系统的性能、功能、终端和接口等多个方面进行了详细的阐述，并对内部通信系统在铁路客运站的应用进行了展望。     

运动图像处理在汽车车型识别中的应用

本文介绍了“多品种混流机器人喷漆自动线的汽车车型识别系统”的一种算法,提出了基于运动序列图像的自适应阈值判别法和利用特征匹配法解决了同种车型的图像在结构上产生局部变化的识别问题。     

支持向量机和基于IMF的特征能量法在汽车变速器轴承故障诊断中的应用

针对汽车变速器轴承振动信号的非平稳特征和现实中难以获得大量典型故障样本的实际情况,提出了一种基于内禀模态函数(IMF)的特征能量法和支持向量机的变速器轴承故障诊断方法。对变速器轴承内圈、外圈故障振动信号的分析结果表明,该方法在小样本情况下仍可有效提取变速器轴承的故障特征,并能成功地对其工作状态和故障类型进行分类。     

基于支持向量数据描述的驱动桥异响检测

针对驱动桥异响检测缺乏故障样本的问题,提出了异响检测的单值分类法———支持向量数据描述法(SVDD)。这种方法只需要正常运行状态的数据样本,就可以建立单值分类器,区分出正常和异常状态。试验中,提取驱动桥振动信号频谱的二维谱熵作为特征指标,输入到SVDD分类器。结果表明SVDD算法计算效率高,分类效果好,可以满足在线检测的要求。     

秦岭越岭长隧道地区构造应力场特征分析

利用微断层研究方法,由定性到半定量,由点到面地分析了西安—南京线越岭长隧道地区的区域构造应力场演化、性质、方向和构造应力作用方式,为越岭长隧道方案比选提供了应力场依据。根据微构造法计算结果,本区新构造及现代构造应力场经历了:松弛期应力场、NW向挤压NE向拉伸的构造应力期、NE向挤压NW向拉伸的构造应力期。自第三纪以来,区域应力状态从总体上讲,是以垂直方向抬升、水平方向拉伸为主,现代构造应力场的性质是NE—SW或NEE—SWW向的挤压。从晚近地质时期到现代主压应力方向为NE向,σ1优势方位为NNE—NE向。自第二期应力场以来,主应力作用方式都是水平挤压或水平拉伸。     

A context aware system for driving style evaluation by an ensemble learning on smartphone sensors data

There are many systems to evaluate driving style based on smartphone sensors without enough awareness from the context. To cover this gap, we propose a new system namely CADSE system to consider the effects of traffic levels and car types on driving evaluation. CADSE system includes three subsystems to calibrate smartphone, to classify the maneuvers, and to evaluate driving styles. For each maneuver, the smartphone sensors data are gathered in three successive time intervals referred as pre-maneuver, in-maneuver, and post-maneuver times. Then, we extract some important mathematical and experimental features from these data. Afterwards, we propose an ensemble learning method on these features to classify the maneuvers. This ensemble method includes decision tree, support vector machine, multi-layer perceptron, and k-nearest neighbors. Finally, we develop a rule-based fuzzy inference system to integrate the outputs of these algorithms and to recognize dangerous and safe maneuvers. CADSE saves this result in driver’s profile to consider more for dangerous driving recognition. The experimental results show that accuracy, precision, recall, and F-measure of CADSE system are greater than 94%, 92%, 92%, and 93%, respectively that prove the system efficiency.     

Travel mode estimation for multi-modal journey planner

For route planning and tracking, it is sometimes necessary to know if the user is walking or using some other mode of transport. In most cases, the GPS data can be acquired from the user device. It is possible to estimate user’s transportation mode based on a GPS trace at a sampling rate of once per minute. There has been little prior work on the selection of a set of features from a large number of proposed features, especially for sparse GPS data. This article considers characteristics of distribution, auto- and cross-correlations, and spectral features of speed and acceleration as possible features, and presents an approach to selecting the most significant, non-correlating features from among those. Both speed and acceleration are inferred from changes in location and time between data points. Using GPS traces of buses in the city of Tampere, and of walking, biking and driving from the OpenStreetMap and Microsoft GeoLife projects, spectral bins were found to be among the most significant non-correlating features for differentiating between walking, bicycle, bus and driving, and were used to train classifiers with a fair accuracy. Auto- and cross-correlations, kurtoses and skewnesses were found to be of no use in the classification task. Useful features were found to have a fairly large (>0.4) correlation with each other.