客车主要噪声源识别的试验研究

基于偏奇异值分析法识别噪声源的基本原理，采用了近场声压测量法，同步采集15个传感器的振动与声压信号，识别出客车车外噪声的主要噪声源，分析出各噪声源对车外噪声的贡献并做出降低车外噪声的预测分析。所采用的试验与分析方法具有计算量小、声源定位准确的特点，并且可以准确预测零部件的改进对车外噪声的影响。试验研究和实车应用表明，这种方法可以适用于复杂的工程实际。     

公路交通可持续发展评价指标及评价方法研究

根据公路交通可持续发展评价的内容和特点,从公路交通可持续发展水平、发展能力、发展协调性3个方面建立了评价指标体系;提出了指标属性值和无量纲化的计算方法;应用模糊识别原理,结合AHP权重确定方法,依据公路交通可持续发展目标(级别划分)的实现程度,提出公路交通可持续发展的多级模糊识别综合评价模型;其评价方法和模型,反映了公路交通可持续发展的内涵,对区域公路交通可持续发展评价有借鉴作用。最后,进行了实证研究。     

Detection of nonlinearity effects in structural integrity monitoring methods for offshore jacket-type structures based on principal component analysis

The detection of changes in the dynamic behavior of structures is an important issue in structural safety assessment. The development of detection methods assumes greater significance in the case of offshore platforms because the inherent problems are compounded by the harsh environment. Here, we describe an instrumented physical model for the structural health monitoring of an offshore jacket-type structure and the results of tests in several different damage scenarios. In a comparative investigation of two different methods, we discuss the difficulties of implementing damage detection techniques for complex structures, such as offshore platforms. The combined algorithm of a fuzzy logic system and a model updating method are briefly discussed, and a method based on stochastic autoregressive moving average with exogenous input is adopted for the structure. The consideration of uncertainties and the effects of nonlinearity were major objectives. So, the methods were also investigated based on the test scenarios consisting of the physical model with a geometric nonlinearity. The principal component analysis method was utilized for the detection of nonlinearity in the recorded data. The results show that the developed methods are suitable for damage classification, but the quality of the acquired signals must be considered an important factor influencing successful classification. The development of these methods may be extremely useful, as such technologies could be applied for offshore platforms in service, enabling damage detection with fewer false alarms.     

Plate scanning tools to obtain travel times in traffic networks

The management of vehicle travel times has been shown to be fundamental to traffic network analysis. To collect travel time measurement, some methods focus solely on isolated links or highway segments, and where two measurement points, at the beginning and at the end of a section, are deemed sufficient to evaluate users' travel time. However, in many cases, transport studies involve networks in which the problem is more complex. This article takes advantage of the plate scanning technique to propose an algorithm that minimizes the required number of registering devices and their location in order to identify vehicles candidates to compute the travel times of a given set of routes (or subroutes). The merits of the proposed method are explained using simple examples and are illustrated by its application to the real network of Ciudad Real.     

基于灰色理论的雪山梁隧道施工过程渗漏水水源识别研究

为了解决黄龙景区雪山梁隧道开挖过程中的涌水水源识别问题,结合黄龙景区雪山梁隧道的相关施工资料,依据已有的地质资料和涌突水预测,通过水分析化学实验,采用灰色理论计算方法研究不同水系之间的关联度;而后针对单一水源和混合水源分别进行试验和分析,得出雪山梁隧道出口段内渗漏水来源最可能为大气降水,关联度较高的还有淘金沟上游水系和隧道出口处水系;最后进行比对,得出隧道渗漏水主要来源是大气降水和淘金沟上游水体的结论,并依据现场资料及实地踏勘,验证了灰色理论分析结果的可靠性。     

基于小波和粒子群算法的HEV行驶状况辨识方法研究

针对混合动力汽车(HEV)行驶状况(道路坡度和整车载荷)变化难以有效识别,导致驱动系统控制策略不能有效满足驾驶员意图问题,以混联式HEV为研究对象,提出了基于小波滤波和粒子群算法的HEV行驶状况辨识方法。首先建立了汽车行驶状况辨识模型,采用最小二乘法确立了优化目标函数,其次研究了基于小波滤波和粒子群算法的HEV行驶状况辨识原理,最后进行了行驶状况粒子群智能算法辨识试验。在采集实车数据的基础上,对实车数据进行小波滤波,并运用行驶状况辨识方法对道路坡度和整车载荷进行了辨识,并对辨识结果进行小波滤波,结果表明,试验工况下整车载荷辨识的相对误差绝对平均值为2.71%,道路坡度辨识的相对误差绝对平均值为3.85%,验证了所提出方法的有效性。     

射频识别技术在物流工程中的应用研究

简要介绍了射频识别技术的工作原理和优点,综述了射频识别技术在国内各个领域的主要应用,最后指出了当前射频识别技术所存在的主要问题。     

自行车流视频识别判断方法研究

考虑算法的实时性,交通流个体识别往往采用轮廓特征来描述。由于自行车与行人的很多轮廓特征值相近,在复杂城市交通环境中,从慢行交通流中准确地识别出自行车是目前采用视频检测交通流的难点之一。通过现场视频检测获取足够的样本外轮廓数据,分析检测目标轮廓的高、宽、高宽比、面积等典型轮廓特征,发现目标轮廓高宽比特征能较好地识别出行人、自行车等慢行交通,并给出合适的取值范围。研究表明自行车高宽比能够较好地成为视频识别的依据,为自行车视频识别提供数据。     

基于神经网络的感应电动机特性辨识新方法

提出并论证了一种基于神经网络的感应电动机特性辨识新方法,只需测得电机两相电流数值便可以辨识出电动机转矩和转速,用改进的Levenberg-Marquardt算法对神经网络进行学习和训练,构建了适合电动机转矩转速观测的BP神经网络。由于RBF神经网络无论是在逼近能力、函数拟合和学习速度方面都优于BP网络,也利用RBF网络进行了辨识。该方法较已经提出的方法相比,需要的检测量少,辨识方法简单。仿真研究表明,RBF神经网络辨识效果优于BP神经网络。     

高架城市轨道交通的噪声特性分析

研究了上海轨道交通3号线的噪声特性,包括噪声的A声级时间历程、A声级频谱分析及时频分析,桥面、轨道振动加速度的频谱分析,主要声源的辨识,各声源对高架桥附近总噪声的贡献度分析.可为上海轨道交通3号线采取减振降噪措施方案提供参考数据.