基于关键度度量的决策树算法改进及其在铁路运输中的应用

采用决策树方法对客票数据及行车安全数据进行分析时,发现在训练数据集的主类类属分布不平衡的情况下,无法对数据量占相对少数的小类属数据进行描述,究其原因在于现有决策树方法的节点类标号标示采用的是"多数表决"的方式。为此,本文提出关键度度量的概念,用于改进决策树的叶节点竞争机制,旨在改进决策树方法在解决弱势类属在数据建模分析中公平获得"发言权"的问题。算例表明,这种改进较好地解决了偏类数据集的数据分析问题。     

高速动车组轮盘用紧固螺栓仿真分析

高速动车组轮装制动盘通过多个螺栓与车轮固定,制动过程中,由于制动盘和螺栓产生热变形,导致螺栓承受较大拉应力,存在失效可能。利用有限元软件ABAQUS,建立了与实际相符的高速动车组轮装制动盘、车轮、联接件、螺栓套的有限元模型,螺栓施加相应的预紧力。计算得到300km/h高速动车组制动盘和螺栓联接件的温度场和应力场结果,校核了螺栓强度,为不同时速的动车组轮装制动盘用螺栓提供了模拟计算方法。在1∶1台架试验上,验证了模型温度场计算的准确性。     

超声诊断SD大鼠非酒精性脂肪肝的价值

目的评价超声诊断非酒精性脂肪肝(non-alcoholic fatty liver,NAFL)的价值。方法48只SD大鼠随机分成正常对照组(6只)和高脂饮食组(42只),正常对照组饲养8周;高脂饮食组分为7组,每组6只,每隔2周处死1组,处死后观察肝组织学改变,处死前1 d行超声检查。结果超声对脂肪肝阳性诊断准确率是78.2%,其中轻度脂肪肝诊断准确率约为70%,中度脂肪肝诊断准确率为90%。结论超声是诊断脂肪肝的首选方法。     

慢性盐缺乏、盐负荷对血清内源性哇巴因水平的影响

目的观察慢性盐缺乏和盐负荷对内源性哇巴因(EO)分泌的影响,探讨在慢性盐缺乏或盐负荷状态下EO对SD大鼠钠代谢和血压的调节作用。方法以含0.02%(低盐)、0.5%(普食)及8%(高盐)的普通加碘盐饲料喂养SD大鼠8周,原子吸收分光光度法检测尿液中钠离子、钾离子浓度,酶联免疫吸附法(ELISA)检测EO水平。结果高盐组SD大鼠血压较同期普食组和低盐组显著性升高(P<0.01)。高盐组24 h尿钠排泄量较普食组和低盐组显著增加(P<0.01)。高盐组24 h尿钾排泄量显著低于对照组和低盐组(P<0.01)。高盐组与普食组比较血清EO水平无统计学差异,低盐组于第4周开始血清EO水平高于普食组和高盐组,有统计学差异(P<0.01)。结论慢性盐缺乏时SD大鼠EO水平升高,提示其为慢性盐缺乏状态下维持血压的调节因子;而慢性盐负荷时血压明显升高,但未发现EO显著升高。     

基于MTSP的机车周转图编制模型与算法

为了提高机车的工作效率，探讨了机车周转图编制模型与算法．对于给定的列车运行图，综合考虑机车使用台数最少和图形均衡性，提出了一种编制机车周转图的新算法．将机车周转图编制问题转化为多旅行商问题（MTSP）并建立数学模型，从而求得问题的最优解．最后，用列车运行图实际数据进行了验证，证明了该算法的有效性．     

基于IECP的CTCS-3列控车载TSM曲线完备性测试用例集生成方法研究

作为CTCS-3级列控系统的核心安全功能,速度-距离模式曲线具有参数输入域规模大、故障模式复杂等特点,如何完备地测试列控车载设备速度-距离监控曲线功能异常困难。提出了一种基于输入等价类划分测试理论的CTCS-3级列控车载目标速度监控曲线完备性测试用例生成方法。首先,结合CTCS-3级列控系统需求规范,建立了满足司机制动优先和设备制动优先两种不同制动优先级情况下的TSM有限状态机模型,并利用反应式状态迁移系统的形式化语义,描述了不同输入情况下模型的内部状态迁移过程。其次,利用I/O等价原理和等价类划分理论,得到了两种不同制动优先情况下的输入等价类划分。在此基础上,通过引入了被测系统的故障模型和故障域范围,采用W-method测试用例集生成方法,在满足模型故障域的条件下,最终得到两种不同制动优先的目标速度监控曲线模型完备测试集。设备制动优先情况生成的测试用例数量高于司机制动优先约33.3%,且所需测试时间和内存消耗更多。     

Implications of path tolerance and path characteristics on critical vehicle manoeuvres

Path planning and path following are core components in safe autonomous driving. Typically, a path planner provides a path with some tolerance on how tightly the path should be followed. Based on that, and other path characteristics, for example, sharpness of curves, a speed profile needs to be assigned so that the vehicle can stay within the given tolerance without going unnecessarily slow. Here, such trajectory planning is based on optimal control formulations where critical cases arise as on-the-limit solutions. The study focuses on heavy commercial vehicles, causing rollover to be of a major concern, due to the relatively high centre of gravity. Several results are obtained on required model complexity depending on path characteristics, for example, quantification of required path tolerance for a simple model to be sufficient, quantification of when yaw inertia needs to be considered in more detail, and how the curvature rate of change interplays with available friction. Overall, in situations where the vehicle is subject to a wide range of driving conditions, from good transport roads to more tricky avoidance manoeuvres, the requirements on the path following will vary. For this, the provided results form a basis for real-time path following.     

Distributed and self-adaptive vehicle speed estimation in the composite braking case for four-wheel drive hybrid electric car

Considering the controllability and observability of the braking torques of the hub motor, Integrated Starter Generator (ISG), and hydraulic brake for four-wheel drive (4WD) hybrid electric cars, a distributed and self-adaptive vehicle speed estimation algorithm for different braking situations has been proposed by fully utilising the Electronic Stability Program (ESP) sensor signals and multiple powersource signals. Firstly, the simulation platform of a 4WD hybrid electric car was established, which integrates an electronic-hydraulic composited braking system model and its control strategy, a nonlinear seven degrees-of-freedom vehicle dynamics model, and the Burckhardt tyre model. Secondly, combining the braking torque signals with the ESP signals, self-adaptive unscented Kalman sub-filter and main-filter adaptable to the observation noise were, respectively, designed. Thirdly, the fusion rules for the sub-filters and master filter were proposed herein, and the estimation results were compared with the simulated value of a real vehicle speed. Finally, based on the hardware in-the-loop platform and by picking up the regenerative motor torque signals and wheel cylinder pressure signals, the proposed speed estimation algorithm was tested under the case of moderate braking on the highly adhesive road, and the case of Antilock Braking System (ABS) action on the slippery road, as well as the case of ABS action on the icy road. Test results show that the presented vehicle speed estimation algorithm has not only a high precision but also a strong adaptability in the composite braking case.     

Gain scheduled linear quadratic tracking system tuned optimally by covariance matrix adaption evolutionary strategy for automotive engine coldstart control

In this paper, a gain scheduled linear quadratic tracking system (LQTS) tuned optimally by an evolutionary strategy (ES) is devised to reduce the total tailpipe hydrocarbon (HC) emissions of an automotive engine over the coldstart period. As the engine’s behavior during coldstart operations is nonlinear, the system dynamics is clearly analyzed and represented by a number of separate linear models generated based on a coldstart model verified by experimental data. An independent LQTS is then implemented for each of these linear models. In this way, several control laws are created, and the corresponding gains are calculated for each of the independent control laws. ES is then used to tune the adjustable parameters of LQTSs to calculate the control inputs, namely air/fuel ratio (AFR) and spark timing (Δ), such that the resulting exhaust gas temperature (T _exh) and engine-out HC emissions (HC _raw) be close to a set of optimum profiles. This enables the controller reduce the cumulative tailpipe hydrocarbon emissions (HC _cum) to the highest possible extent. To demonstrate the acceptable performance of the proposed controller, an optimal controller derived from the Pontryagin’s minimum principle (PMP) is also taken into account. Based on the results of the conducted comparative study, it is shown that the proposed control technique has a very good performance, and also, can be easily used for real-time applications, as it consumes a remarkably trivial computational time for calculating the controlling commands.     

Experimental and 3D,embedded modeling for diesel engine SCR deposit

Nowadays, the selective catalytic reduction (SCR) is a promising solution to fulfill stringent nitrogen oxide (NO_X) emission standards enforced by worldwide regulation bodies for lean burning engines. However, in the practical operation, urea deposits occur under unfavorable conditions, which bring about a failure of urea dosing strategy, affect the SCR system de-NOx performance and lowering the fuel economy by increasing the engine back pressure. This paper will present 3D deposit model, which can be used to predict the deposit position. Furthermore, a model-based controls strategy and calibration are designed. The comparison test results of both engine emission tests and vehicle field tests shows there is significant deposits improvement with the embedded model.