桥梁结构动力特性的模糊有限元分析

在研究模糊有限元分析基本理论以及借鉴前人研究成果的基础上,采用模糊变量表达结构参量的不确定性;并基于区间运算和区间有限元理论,提出了用于求解模糊有限元自由振动方程的1阶泰勒级数展开算法。该算法避免了由于2阶泰勒展开所造成的区间扩张问题,在保证精度的前提下大大减小了计算量;随后又基于APDL语言用大型有限元软件ANSYS进行了二次开发,通过参数化语言的方式实现了该算法。最后以某大跨拱桥为例,在对其刚度和质量进行模糊化的条件下,基于ANSYS软件的二次开发功能采用该算法对算例中拱桥的动力特性进行了模糊有限元计算,从而验证了该算法在实际结构动力设计中的可行性和有效性。     

提高GPS定位精度的新方法

提出了一种提高ＧＰＳ定位精度的新方法。用该方法可以准确地对ＧＰＳ定位数据进行建模和一步预测，在此基础上通过自适应滤能够提高ＧＰＳ定位精度３－５倍。定点实时数据处理证明了此方法的有效性。     

多发串联超高射频武器系统研究综述

对国内多发串联预装填超高射频武器系统研究现状进行综述。介绍国外超高射频武器研究历程,阐述超高射频武器研究建立的各种模型,总结超高射频武器计算和实验研究成果,并对超高射频武器技术领域的研究方向进行了展望。     

新奥法施工隧道掘进作业接尘量调查

新奥法施工隧道掘进作业粉尘时间加权平均浓度为１４．４９ｍｇ／ｍ■＾３。各工班中以炮后时间加权平均浓度最高（４１．８８ｍｇ／ｍ■＾３），装碴和打眼分别为２２．８４ｍｇ／ｍ■＾３和６．４４ｍｇ／ｍ■＾３。掘进作业全过程时间加权接尘量为１２２５９．５７ｍｇ／（ｍ■＾３．ｍｉｎ）。其中装碴接尘量最高７１７．７６ｍｇ／（ｍ■＾３．ｍｉｎ）占掘进全过程总接尘量的５８．５０％。各产尘工序产尘量构成比也以装碴最高     

HIERARCHICAL CONTENT BASED IMAGE RETRIEVAL

ＩｎｔｒｏｄｕｃｔｉｏｎＡｔｒａｄｅｍａｒｋｉｍａｇｅｄａｔａｂａｓｅｉｓａｓｅｔｏｆｔｒａｄｅ－ｍａｒｋｐｉｃｔｕｒｅｓ．Ｕｓｕａｌｙｉｔｉｓｔｈｅｄｕｔｙｏｆｐａｔｅｎｔａｎｄｔｒａｄｅｍａｒｋｏｆｉｃｅｔｏｅｓｔａｂｌｉｓｈａｎｄｍａｎａｇｅｔ...     

A latent segmentation based multiple discrete continuous extreme value model

We examine an alternative method to incorporate potential presence of population heterogeneity within the Multiple Discrete Continuous Extreme Value (MDCEV) model structure. Towards this end, an endogenous segmentation approach is proposed that allocates decision makers probabilistically to various segments as a function of exogenous variables. Within each endogenously determined segment, a segment specific MDCEV model is estimated. This approach provides insights on the various population segments present while evaluating distinct choice regimes for each of these segments. The segmentation approach addresses two concerns: (1) ensures that the parameters are estimated employing the full sample for each segment while using all the population records for model estimation, and (2) provides valuable insights on how the exogenous variables affect segmentation. An Expectation–Maximization algorithm is proposed to address the challenges of estimating the resulting endogenous segmentation based econometric model. A prediction procedure to employ the estimated latent MDCEV models for forecasting is also developed. The proposed model is estimated using data from 2009 National Household Travel Survey (NHTS) for the New York region. The results of the model estimates and prediction exercises illustrate the benefits of employing an endogenous segmentation based MDCEV model. The challenges associated with the estimation of latent MDCEV models are also documented.     

Local online kernel ridge regression for forecasting of urban travel times

Accurate and reliable forecasting of traffic variables is one of the primary functions of Intelligent Transportation Systems. Reliable systems that are able to forecast traffic conditions accurately, multiple time steps into the future, are required for advanced traveller information systems. However, traffic forecasting is a difficult task because of the nonlinear and nonstationary properties of traffic series. Traditional linear models are incapable of modelling such properties, and typically perform poorly, particularly when conditions differ from the norm. Machine learning approaches such as artificial neural networks, nonparametric regression and kernel methods (KMs) have often been shown to outperform linear models in the literature. A bottleneck of the latter approach is that the information pertaining to all previous traffic states must be contained within the kernel, but the computational complexity of KMs usually scales cubically with the number of data points in the kernel. In this paper, a novel kernel-based machine learning (ML) algorithm is developed, namely the local online kernel ridge regression (LOKRR) model. Exploiting the observation that traffic data exhibits strong cyclic patterns characterised by rush hour traffic, LOKRR makes use of local kernels with varying parameters that are defined around each time point. This approach has 3 advantages over the standard single kernel approach: (1) It allows parameters to vary by time of day, capturing the time varying distribution of traffic data; (2) It allows smaller kernels to be defined that contain only the relevant traffic patterns, and; (3) It is online, allowing new traffic data to be incorporated as it arrives. The model is applied to the forecasting of travel times on London’s road network, and is found to outperform three benchmark models in forecasting up to 1 h ahead.     

Dynamics of connected vehicle systems with delayed acceleration feedback

In this paper, acceleration-based connected cruise control (CCC) is proposed to increase roadway traffic mobility. CCC is designed to be able to use acceleration signals received from multiple vehicles ahead through wireless vehicle-to-vehicle (V2V) communication. We consider various connectivity structures in heterogeneous platoons comprised of human-driven and CCC vehicles. We show that inserting a few CCC vehicles with appropriately designed gains and delays into the flow, one can stabilize otherwise string unstable vehicle platoons. Exploiting the flexibility of ad-hoc connectivity, CCC can be applied in a large variety of traffic scenarios. Moreover, using acceleration feedback in a selective manner, CCC provides robust performance and remains scalable for large systems of connected vehicles. Our conclusions are verified by simulations at the nonlinear level.     

北美铁路时间标准化的考察

北美铁路时间标准化是19世纪全球标准时间改革中最为重要的环节,它开创了公共记时的新纪元,为国际子午线会议的召开奠定了基础。铁路网络和通讯手段的快速发展为铁路标准时间在北美的诞生提供了契机,同时,纷繁芜杂的地方时增加了铁路时间标准化的必要性和紧迫性。北美铁路标准时间的实施,体现的是社会各个层面共同发展所形成的社会合力的作用。