Train driver protection under secondary impact

An EMU train with detailed cabin structural is established based on the finite element method.The secondary impact between train driver and control desk is fully analysed and two measures are proposed to reduce the driver injury severity,such as the multi-objective optimization of the driver seat position and equipping the train with three-point seat belt.Simulation results indicate that the driver seat position has a significant effect on the driver injury severity during a secondary impact.According to the multi-objective optimization,some Pareto solutions are suggested to design the driver seat position.Besides that,it is also indicated although the chest and leg are well protected when the driver wears a two-point seat belt,it increases the head injure during a secondary impact.On the other hand,the three-point seat belt can supply the train driver with an overall protection against the secondary impact.The injury criteria(HIC,VC,TI)of the driver with the three-point seat belt is significantly lower than those of the driver without seat belt.Moreover,according to the simulation analysis,the limited load of the three-point seat belt is suggested about 1.5 kN.

The Influence of 8 Weeks Plyometrics Training on Golfers Hitting Distance——Taking the Hitting of Driver as an Example

Plyometrics training,can improve the club speed,ball speed and the hitting distance in swinging and hitting.According to the experimental method,15 professional golfers were randomly selected as the reference object,and 24 golfers joined the 8 weeks of plyometrics training.The conclusion was that after 8 weeks of plyometrics training,the experimental group had an increase of the club speed,ball speed and smash factor more obviously than that of the control group.Affected by training years,there is still a gap between the two and professional players.With the increase of club speed and ball speed,both groups have a certain increase in the carry length and total length.Compared to the control group,the experimental group has further hitting distance,but there are still significant differences between them affected by training years.It is suggested that during the teaching and training of golf techniques,practice of the stability of the lower limbs and the instability of the upper limbs should be increased,to improve the hitting distance.

Serum Concentrations of Angiotensin, C-Reactive Protein, Interleukin-8, and Tumor Necrosis Factor-α in Train Driver Population

Train drivers are engaged in high-stress job. It may induce sleep, fatigue, and alertness loss at work, and endanger public safety. It’s unclear that cytokines of train driver would be influenced by their job. The research considers the hypothesis that stressful professions, such as train driver, influence the body’s immune system through the long-time and high-pressure working, and change production of neuro-immune factors. Using enzyme linked immunosorbent assay (ELISA), several neuro-immune factors were assayed among train drivers (N = 82) and health blood donors (N = 80) enrolled in the Yunnan Collaborative Innovation Center for Public Health and Disease Control. The concentrations of angiotensin, C-reactive protein (CRP), interleukin-8 (IL-8), and tumor necrosis factor-alpha (TNF-α) were determined. Kruskal-Wallis test and Dunn’s multiple comparisons test were performed for overall comparison between groups and for pairwise comparison, respectively. Statistical significance level was set at P < 0.05. The profession of train driving was not associated with significant increases or decreases in the systemic levels of inflammatory (CRP, IL-8, and TNF-α), but it was associated with the high expression of angiotensin in vivo. These findings suggest that the job of train driving may not be associated with significant alterations in systemic immune condition, but arouse the level of angiotensin.

How to Provide Effective Feedback to Young Learners:Lessons Learnt from Driver Training

In Australia,and around the world,there is a continuous debate about the ways of delivering a message,instruction,or feedback effectively to improve students’learning and performance.Contemporary cognitive skills training approaches have emerged as a result of further development of effective cognitive skills training in different contexts,such as education,aviation,and driving.One of the effective cognitive-based training is feedback.Feedback is an important component in learning,including the development of safe driving for novice drivers.Research shows that feedback can reduce the number of speeding occurrences,and the likelihood of speeding-related incidents and accidents,but it is not clear how to provide effective feedback to young learners.This paper reviews the literature and examines various aspects of feedback as a training intervention for young drivers and provides recommendation for effective use for young learners.The results explored the characteristics of feedback including multiple dimensions:content,source,medium of delivery,timing and frequency.Importantly,its effectiveness in improving an individual performance depends on effective utilization of these characteristics.The results showed that the most effective type of feedback(considering all feedback characteristics)in improving young novice drivers’performance in terms of speed compliance is feedback about performance,financial and safety implications(content),provided verbally and graphically(medium in which provided),by an instructor(researcher;source),immediately after the drive(time),once or twice(frequency).These results have important implications for the development of new training approaches to improving young drivers’speed management behaviour.

列车司机手势识别方法研究

按照司乘标准执行规定的手势是列车司机驾驶操作的重要环节,通过对司机手势进行检测,能够有效评估列车司机的驾驶状态和操作质量,保证列车行车安全。传统人工检查方式效率低,难以满足实际需求,现有的手势识别算法存在模型参数量大、检测精度较低、检测速度慢等问题。随着智能铁路的发展,利用深度学习方法构建轻量化、高效、高精度的列车司机手势识别模型逐渐成为行业发展需求。针对上述需求,提出一种基于改进YOLOv5的列车司机手势识别模型。首先,引入轻量化卷积PConv改进YOLOv5中的C3模块,降低检测网络的参数量和计算量,提升模型检测效率,并在其后添加CBAM模块,加强重要特征信息,抑制无关信息的干扰,强化检测网络特征提取能力;其次,在颈部层引入BiFPN网络结构替换PANet网络结构,增强不同尺度特征的融合能力,同时通过新增小目标检测层,提高模型对小目标的检测能力;最后,选择Focal-EIoU作为边界框损失优化模型损失函数,加快模型的收敛速度,提高手势定位精度。实验结果表明,改进模型在测试集下mAP@0.5可达97.7%,平均检测时间为23.2 ms,相较于YOLOv5计算量降低了23.1%,mAP@0.5和平均检测时间分别提升了0.6个百分点和7.1 ms。所提模型可在降低参数量和计算量的同时有效提高检测精度和检测效率,可为列车司机手势识别提供新思路。

机车司机一次乘务作业领航式操纵指导系统研究

在列车运行监控装置(LKJ)参数输入、操作等关键环节,由于机车司机简化作业、机车乘务班组互控不到位等原因,可能因输入或错误操作LKJ而引发作业事故。以机车司机一次乘务作业标准为基础,通过梳理机车司机作业指导书、操纵提示卡、LKJ操作使用说明、安全风险提示卡等安全关键内容和要求,研究并形成结构化的机车司机一次乘务作业全过程领航式操纵指导项点及其行车指导内容;基于LKJ、车载无线传输装置(TSC2)等既有设备,通过配置车载行车安全数据终端(DT50)与车载数据集成装置(BX10),开发DT50设备应用软件,搭建领航式操纵指导系统。研究表明,将指导项点及其指导内容结合列车运行线路纵断面等数据,可建立满足领航式操纵指导的基础数据知识库;通过DT50设备采集LKJ的实时数据、运行记录文件等数据,远程下载针对特定线路、车次的基础数据知识,实现机车司机从始发站至终到站全过程的领航式操纵指导功能。该系统具有灵活配置模式的客户端配置平台,铁路部门业务人员可通过配置平台持续丰富基础数据知识库,实现客运列车针对车次、货运列车针对区段的订单式一次乘务作业全过程领航式操纵指导,提升列车运行安全保障能力,减少因机车司机人因失误引发的作业事故。

市域列车司机制动试验方案研究

[目的]制动系统作为市域列车核心系统之一,其性能决定着车辆的运行安全;司机制动试验是市域列车制动系统必备功能,其可有效保障列车投入运营前制动系统的安全、可靠,故有必要对其进行深入研究。[方法]通过对我国市域列车车辆的情况分析,明确了司机制动试验设计思路;制定了司机制动的试验方案,并进行验证。[结果及结论]结合市域列车运营特点,阐述了适用于市域列车的司机制动试验的技术方案,为市域列车制动系统自检功能提供专业支持。

基于铁路5G-R的自主化机车无线同步操控技术方案

为探讨基于5G-R的自主化机车无线同步操控技术方案,通过调研自主化机车无线同步操控业务需求,分析我国重载铁路既有通信系统的不足,充分考虑5G-R网络的高带宽、低延迟、IP化链路、分组域传输、QoS优先级保障等特性,提出总体技术方案,重点论述主从控机车数据传输、可控列尾数据传输、编组请求、多司机通话等功能的实现方案。该研究结果有利于提升自主化机车无线同步操控系统的先进性,确保其在复杂环境下的可靠运行,对提升重载铁路运输生产安全和效率具有指导意义,为重载铁路的现代化和智能化发展提供强有力的技术支持。

优化机动车驾驶员培训管理的路径探索

改革开放以来,人们的生活水平与生活质量不断提高,在出行方式上面,人们会选择自驾车和公共交通+租车自驾的模式出行,汽车成为家家户户不可缺少的代步工具。因此,为取得驾驶资格,需进行规定项目的学习和考试,这也推动了社会上一些驾驶员培训机构的诞生。然而,当前我国的机动车驾驶员培训机构还存在着一些经营管理不够完善、驾驶员培训不严格等现状和问题,为此,本文立足于机动车驾驶员培训管理来具体阐述与分析当前我国机动车驾驶员培训管理存在的问题及原因,并结合当前的驾驶员考试情况提出优化机动车驾驶员培训管理的路径,以维护更加高效有序的驾驶员培训管理。

驾驶员培训机构中新能源车辆的应用与推广研究

随着全球能源危机的不断加剧和环境问题的愈发严峻,新能源车辆的推广和应用已成为社会各界关注的焦点。驾驶员培训机构作为交通运输体系的重要组成部分,如何有效地应用和推广新能源车辆,不仅关系到培训质量的提升,更直接影响到社会环保和节能减排的整体目标。本文旨在探讨驾驶员培训机构在新能源车辆应用与推广方面的现状、优势、挑战及未来发展策略,为相关领域的实践提供支持。通过系统研究和有效实践,期望为相关行业的从业者提供有价值的参考,助力中国新能源车辆的推广与应用,实现驾培行业的绿色、智能转型。

校政企合作背景下定制化人才培养模式创新机制研究——以山东省为例

基于山东省现代服务业转型升级对高技能专业人才的迫切要求,文章应用“新旧动能转换”理论,在分析山东省职业教育人才培养模式现状及存在的问题的基础上,重点阐述了校政企合作背景下定制化人才培养模式的内涵,并从多个层面提出了山东省职业教育定制化人才培养模式的创新机制和运行思路,形成系统化、理论化的定制化人才培养模式创新思路,旨在为山东省定制化人才培养模式的有效创建提供借鉴。

基于改进Faster RCNN的驾驶员手持通话检测

针对现有驾驶员通话行为识别误判率较高的问题,提出一种基于改进Faster RCNN的驾驶员行为检测方法,对驾驶员的违规手持通话进行检测.介绍了针对区域建议网络(RPN)及其损失函数的优化策略,并在原始Faster RCNN上运用多尺度训练、增加锚点数量以及引入残差扩张网络的方法增强网络检测不同尺寸目标的鲁棒性.基于车载平台上采集的驾驶员行为图像,对文中提出的方法进行仿真试验.结果表明:RPN和Faster RCNN通过交替优化共享特征提取网络部分,实现高效的目标检测,相较于原始Faster RCNN,检测精确度提高了3.8%,对环境的适应性更强.

基于改进YOLOv5s算法的列车驾驶员手势识别

随着智能铁路的发展,人工智能技术得到广泛应用。通过深度学习对列车驾驶员手势实时检测,可提高检查驾驶员手势执行情况的效率,保证列车运行安全。针对现有手势识别算法识别精度低、检测速度慢、难以部署等问题,提出一种改进的YOLOv5s列车驾驶员手势识别算法。首先在原始主干网络的卷积操作之后添加CBAM模块,加强重要特征信息,抑制次要特征信息;其次在颈部网络中引入BiFPN模块,在不过多增加计算量的情况下,更好地实现多尺度特征融合;最后通过改进的K-means聚类算法生成适合本文数据集的先验框,更好地提升预测框精准度,加强模型的识别性能。实验结果表明,本算法在测试集上平均精确率均值为0.955,检测速度为71 FPS,网络模型所占内存为15.9 MB,验证了该算法具有识别精度高、检测速度快、模型内存占比小等优势,对于实现工程部署有重要意义。

福建省儿科医师转岗培训项目的实践探索

目的:分析儿科医师转岗培训存在的问题,探讨应用“一动力三循环”管理模式的“精准施教”法提高儿科医师转岗培训质量。方法:应用“一动力三循环”医疗质量管理模式,引入“精准施教”方法,即夯实专业基础、优化教学模式、健全考核机制,对转岗培训过程中存在的问题进行改进,分析改进后学员的学习满意度。结果:学员对培训安排管理和实施效果的满意度较高,并建议调整培训方案中各亚专科轮转时长。结论:应用“一动力三循环”管理模式的“精准施教”法能有效提升培训效果,满足基层儿科医师医疗服务能力培养的需要。

汽车驾驶员“一站式”培训模式研究

为有效提升汽车驾驶员培训质效,明确当前训练基地培训中存在的主要问题,详细分析实施“一站式”培训模式需关注3个方面重点和主要优势,从使命教育激发从业热情、穿插配合做好课目关联、模拟训练补齐实操短板、进阶考核检验培训质效和综合演练对接战场需求5个方面提出对策。

基于面部多信息融合的列车司机疲劳检测

为了提高列车司机行车疲劳检测的准确性,提出一种面部多信息融合的列车司机疲劳检测方法。首先,采用低光增强进行图像预处理,以基于局部二值模式(LBP)特征检测人脸。其次,利用回归树集合(ERT)算法获取司机人脸特征点,并通过人脸模型匹配得到司机头部姿态角。最后,根据列车司机的特殊驾驶环境,对最能表现疲劳的眼睛特征量进行自适应阈值修正和眼睛凝视修正,以模糊推理系统为融合工具,以眼睛、嘴巴、头部姿态特征量作为模糊推理系统输入,得到司机的疲劳值作为检测结果。实验结果表明,该检测方法可以区分司机疲劳等级,在正常环境下准确率为95%,在低光照环境下的准确率为86.8%。

C1电动教练车动力系统设计与控制策略

为了减少教练车的燃油消耗以及尾气排放,该文设计了符合驾校使用要求的C1电动教练车动力系统。基于软件开发的V模式,研究了电机模拟发动机点火、怠速、起步、行驶的控制策略及软件实现;将设计的动力系统与控制策略软件以及改进的基于滑模控制的直接转矩控制算法装载到奇瑞凯翼车上,进行了仿真与标定。结果表明:怠速时电机最高转速比发动机低100~200r/min,节省了能耗;起步转速电机比发动机快0.5 s,提高了动力性能;直角、S弯、侧方停车、倒车入库以及科三项目时,与燃油教练车的操控体验基本一致。在石家庄育华驾校进行的与燃油教练车的对比试验,验证了设计的该动力系统及其控制策略的技术可行性。

Braking distance prediction for vehicle consist in low-speed on-sight operation:a Monte Carlo approach

The first and last mile of a railway journey, in both freight and transit applications, constitutes a high effort and is either non-productive(e.g. in the case of depot operations) or highly inefficient(e.g. in industrial railways). These parts are typically managed on-sight, i.e. with no signalling and train protection systems ensuring the freedom of movement. This is possible due to the rather short braking distances of individual vehicles and shunting consists. The present article analyses the braking behaviour of such shunting units. For this purpose, a dedicated model is developed. It is calibrated on published results of brake tests and validated against a high-definition model for lowspeed applications. Based on this model, multiple simulations are executed to obtain a Monte Carlo simulation of the resulting braking distances. Based on the distribution properties and established safety levels, the risk of exceeding certain braking distances is evaluated and maximum braking distances are derived. Together with certain parameters of the system, these can serve in the design and safety assessment of driver assistance systems and automation of these processes.

基于改进YOLOV7与StrongSORT算法的列车司机手比行为检测

列车司机驾驶行为的规范性直接影响到列车行车安全与状态,但当前对列车司机手比行为的检测仍存在不足。为了对列车司机手比行为进行有效检测,本文利用动车组模拟驾驶系统所采集的司机乘务作业影像,结合采用融合注意力机制的You Only Look Once Version 7 (YOLOV7)神经网络模型与Strong Simple Online and Realtime Tracking (StrongSORT)算法,对动车组司机驾驶过程中的5种手比行为进行检测。实验结果表明:本文算法可以有效提升对列车司机乘务作业时不同类型手比行为的检测效果,其中检测精确率平均提升1.2%,检测召回率平均提升1.9%。本文提出的算法将有助于改进铁路院校、机务部门对列车司机日常训练、工作考评的效果,提高列车行车过程中的安全性。

基于PERCLOS的列车司机疲劳检测设计与实现

列车司机长期处于精神高度集中、工作强度较大的工作状态,容易产生生理和心理上的疲劳。本文通过Raspberry PI摄像头对列车司机疲劳状态进行采集,对采集的视频流进行人脸定位和面部特征点的提取,对该列车司机眼睛、嘴巴数据进行分析,结合PERCLOS标准判定列车司机是否疲劳,在GUI界面显示检测结果。