短波信道上基于PSAM的OFDM系统信道估计技术

正交频分复用(OFDM)是已知的频谱利用率最高的一种通信系统,在短波通信中有很广泛的应用前景.通过研究短波信道上基于PSAM的OFDM系统信道估计的算法,进行了几种典型算法的仿真与比较.     

A comparison of various algorithms to extract Magic Formula tyre model coefficients for vehicle dynamics simulations

Tyre models are a prerequisite for any vehicle dynamics simulation. Tyre models range from the simplest mathematical models that consider only the cornering stiffness to a complex set of formulae. Among all the steady-state tyre models that are in use today, the Magic Formula tyre model is unique and most popular. Though the Magic Formula tyre model is widely used, obtaining the model coefficients from either the experimental or the simulation data is not straightforward due to its nonlinear nature and the presence of a large number of coefficients. A common procedure used for this extraction is the least-squares minimisation that requires considerable experience for initial guesses. Various researchers have tried different algorithms, namely, gradient and Newton-based methods, differential evolution, artificial neural networks, etc. The issues involved in all these algorithms are setting bounds or constraints, sensitivity of the parameters, the features of the input data such as the number of points, noisy data, experimental procedure used such as slip angle sweep or tyre measurement (TIME) procedure, etc. The extracted Magic Formula coefficients are affected by these variants. This paper highlights the issues that are commonly encountered in obtaining these coefficients with different algorithms, namely, least-squares minimisation using trust region algorithms, Nelder–Mead simplex, pattern search, differential evolution, particle swarm optimisation, cuckoo search, etc. A key observation is that not all the algorithms give the same Magic Formula coefficients for a given data. The nature of the input data and the type of the algorithm decide the set of the Magic Formula tyre model coefficients.     

基于自适应尺度LE的轮廓编组算法

在图嵌入框架下，以编组线索的聚类来实现轮廓编组的方法，不仅可以有效地将局部特征和全局特征结合起来，而且更加符合人类视觉感知以流形存在的特点．本文在分析相似度矩阵对样本结构表示意义的基础上，提出了一种基于自适应尺度LaplacianEigenmap的轮廓编组算法．该算法能够根据编组线索多维特征的不同局部统计特性，自适应地改变相似度计算模型中的尺度因子，使相似度矩阵更准确地反映编组线索数据集的结构特性．在此基础上通过降维实现编组元的聚类，从而得到轮廓编组的结果．实验证明，该算法对于局部统计特性差异较大的编组线索有着良好的适应性，尤其对于有遮挡的感知目标，表现出比图分割方法更为优越的性能．     

The role of alternative fuel vehicles: Using behavioral and sensor data to model hierarchies in travel

Greater adoption and use of alternative fuel vehicles (AFVs) can be environmentally beneficial and reduce dependence on gasoline. The use of AFVs vis-à-vis conventional gasoline vehicles is not well understood, especially when it comes to travel choices and short-term driving decisions. Using data that contains a sufficiently large number of early AFV adopters (who have overcome obstacles to adoption), this study explores differences in use of AFVs and conventional gasoline vehicles (and hybrid vehicles). The study analyzes large-scale behavioral data integrated with sensor data from global positioning system devices, representing advances in large-scale data analytics. Specifically, it makes sense of data containing 54,043,889 s of speed observations, and 65,652 trips made by 2908 drivers in 5 regions of California. The study answers important research questions about AFV use patterns (e.g., trip frequency and daily vehicle miles traveled) and driving practices. Driving volatility, as one measure of driving practice, is used as a key metric in this study to capture acceleration, and vehicular jerk decisions that exceed certain thresholds during a trip. The results show that AFVs cannot be viewed as monolithic; there are important differences within AFV use, i.e., between plug-in hybrids, battery electric, or compressed natural gas vehicles. Multi-level models are particularly appropriate for analysis, given that the data are nested, i.e., multiple trips are made by different drivers who reside in various regions. Using such models, the study also found that driving volatility varies significantly between trips, driver groups, and regions in California. Some alternative fuel vehicles are associated with calmer driving compared with conventional vehicles. The implications of the results for safety, informed consumer choices and large-scale data analytics are discussed.     

基于交通大数据的移动模式分析综述

交通大数据可为揭示交通主体显性出行行为背后的深层规律（即移动模式）提供重要基础。精确掌握大数据驱动下交通主体的移动模式，可为需求预测、客流组织、土地利用、事件管理等应用提供理论依据。交通大数据主体繁多、时空多态、关联性复杂的特性迫使小数据时代下的移动模式分析方法转型和升级，但仍可能遇到移动模式一致性表达难、异常类型检测难、复杂关联性表达难、时空多态性建模难、一体可视化分析难等普遍问题。针对这些问题，利用科学知识图谱，对2010~2020年期间3 747篇文献的热点关键词分布、发表趋势分布、出版刊物分布等特点进行归纳总结，结合常见的移动模式分析数据集，系统综述现有研究在移动模式常态分析、非常态分析、关联分析、预测分析和可视化分析方向上的阶段性进展。其中，移动模式常态分析综述了个体活动特性分析、出行类别划分、特定群体分析等应用的研究进展。移动模式非常态分析综述了基于模板匹配和数据驱动的非常态事件检测方法的发展脉络。移动模式关联分析综述了面向不同数据源的关联特性检测方法的发展近况。移动模式预测分析综述了基于数理统计和数据驱动的交通属性预测方法的发展状况。移动模式可视化分析综述了用户交互设计、移动模式宏观可视化、微观可视化和整体可视化的发展近况。最后，系统总结各个分支方向面临的主要问题与挑战，并从数据整合、模型创新、机制变革等角度提炼移动模式分析研究的未来发展趋势，期望为后续研究采用新理论技术开展移动模式分析提供参考。     

吉昌公路改造工程施工组织设计编制探析

目前我国公路建设正处于迅速发展阶段,公路工程施工组织设计也越来越受到重视,结合工程实例,阐述了在施工组织设计编制的内容和注意的一系列问题.     

潜艇双层舷侧结构水下接触爆炸特性数值分析

为研究潜艇双层舷侧在水下接触爆炸作用下的破坏特性和抗爆性能,对潜艇舱段进行等比例实体建模,利用非线性有限元软件LS-DYNA作为工具进行仿真。归纳总结了舷侧主要结构的破坏模式及响应特点,并从吸能的角度探讨了各结构对抗爆性能的贡献度,结果可为结构的强度优化设计提供参考。     

基于图像熵的火灾烟雾识别

提出了一种基于图像熵的烟雾运动模式识别方法,用来实现对火灾烟雾的探测.通过对烟雾自由扩散模型的理论分析,得出了烟雾扩散熵增运动的特征.通过引入信号学中"图像熵"的理论,用来捕捉烟雾运动造成的熵变特征,进而实现火灾烟雾的检测.经过实验的验证和分析,并对图像熵值的变化进行傅立叶变换,得出了干扰运动和烟雾运动具有不同特征信号的结论,从而实现了烟雾的识别.与以往研究者通过研究视频烟雾的图像特征出发不同,文中从烟雾的热力学的性质和它在流体力学方面的运动特征,得出它与一般的刚体具有截然不同的运动模式的理论,以图像熵作为核心参量,得出一种实用高效的火灾烟雾检测算法.实验表明:这种检测算法受到环境的干扰较小,特别适合复杂环境的烟雾检测,并可以用于对火灾环境的评估.     

呼和浩特火车站交叉口交通组织优化分析研究

针对呼和浩特火车站交叉口的交通组织方式和信号配时进行实地调查,并对当前组织方式下的交通运行状况进行分析,以车辆安全畅通运行及延误时间短为目标,提出适合该路口交通条件的提前放行左转的保护加许可型信号配时优化方案,并应用synchro进行仿真,仿真结果表明该优化方案有效提高了交叉口通行能力且降低了延误时间。     

天津综合交通枢纽交通规划设计特色分析——以天津西站为例

该文从交通组织、立体交通衔接、安全、景观、高新技术等方面分析了天津西站交通枢纽的规划设计特色;结合我国国情,提出了更进一步的综合交通枢纽规划建议和策略,为我国的综合交通枢纽规划,提供参考.