嵌入式图形用户界面系统的e-GUI的设计与实现

对基于面向对象技术的嵌入式GUI（图形用户界面系统）——e-GUI的总体结构、模块划分、模块之间的互动关系进行了分析．借助类结构图和时序图等UML分析图例，对e—GUI的类结构、对象创建、消息处理等过程进行了讨论．给出了核心模块——窗口管理器的实现。     

基于模糊模式识别的城市道路安全状况综合评价

针对影响城市道路安全状况的因素众多且具有模糊性的特点,采用模糊模式识别与模糊综合评价相结合的评价模型来评价城市道路安全状况.建立了城市道路安全状况综合评价指标体系,应用层次分析法确定了指标权重,应用集值统计法对指标特征值进行了量化.通过对大连市黄河路的评价实例证明,该模型实用性较强,可用于完善道路安全设计及辅助道路安全管理.     

混合动力电动汽车及其驱动模式

介绍了混合动力电动汽车（HEV）的优点、主要技术总成、发动机和电动机的组合方式，分析了串联、并联、混联式混合动力汽车的驱动模式，指出混合动力汽车在遇到堵车时的燃油消耗量、尾气排放量等要远远低于仅靠汽、柴油内燃机驱动的车辆，在动力性能、续驶里程、使用方便性等方面大大优于仅靠电力驱动且需要反复充电的纯电动车。     

校企合作"订单式培养"学生管理模式的研究

校企合作订单式培养高素质技能人才,是高职教育改革、校企人力资源建设、行业发展的共同需要.这一模式有助于学生关键能力和良好职业素养的培养,提升就业率和就业质量.而学生管理工作是"订单式"培养过程控制的关键,文中通过校企双方在订单式培养过程中的长期实践,探讨校企合作中学生管理工作的创新点,以优质的实践效果推动订单式培养的良...     

沿江高速铁路引入合肥枢纽的关键问题

规划年度(2030年)合肥枢纽将形成衔接9个方向、13条线路的大型放射状枢纽.但随着沿江高速铁路建设的提前,沿江高速铁路与既有沪蓉铁路在枢纽内共通道能力是否满足需求、枢纽的客运系统布局和客运站分工是否合理、引入车站站型方案是否最优是面临的关键问题.本文根据研究年度(近期2035年、远期2045年)的枢纽客运量及车流特点,结合既有沪蓉铁路的功能定位及分工调整,对枢纽内主要客运站和区段能力进行分析,对引入车站站型进行研究.通过定量和定性分析,从契合城市总体规划、运输组织的便捷性、线路平面条件、工程实施难易度、工程建设条件、投资等多方面综合比选,优化了枢纽的客运系统布局和客运站分工,确定了合理的枢纽引入方案.     

西安地区成人不同型颌面部骨骼结构特征的X线头影测量

目的　探讨不同面型颌骨骨骼结构特征及其与牙合型的关系。方法　运用X线定位头侧位片 ,图形数字化仪计算机测量分析系统 ,对具有双侧远中磨牙关系 ,前牙深覆牙合深覆盖的凸面型 1 7～ 2 5岁男性 2 0名和具有双侧近中磨牙关系 ,前牙反牙合的凹面型 1 7～ 2 5岁男性 2 0名分别与双侧中性磨牙关系的浅覆牙合浅覆盖的直面型 1 7～ 2 5岁男性 2 0名的颌骨骨骼结构进行比较。结果　面型与牙合型之间存在一定的相关关系和规律性。结论　在对患者进行颌位记录和牙合重建时 ,应结合其颌面部骨骼结构特征 ,恢复其原有牙合型及面型 ,以利于患者咀嚼功能的恢复和口颌系统的健康     

高职高专《思想道德修养与法律基础》课教学模式创新研究

《思想道德修养与法律基础》是高等学校思想政治理论课程的重要组成部分。本文通过分析高职高专《思想道德修养与法律基础》课的现状，针对高职高专人才培养目标提出构建传统教学模式与多元教学方式兼顾，校内实践与社会实践兼顾，基本素养与职业素质兼顾——三位一体的《思想道德修养与法律基础》课教学模式，以期提高《思想道德修养与法律基础》课的教学实效性。     

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.     

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.