适用于人力车的摇杆驱动机构

适用于人力车的摇杆驱动机构用摇杆代替了传统人力车的曲柄,乘骑者踏动脚蹬时,摇杆绕中轴上下摆动,摆角小于100°,不存在驱动力臂为零的死点,而且可根据车辆载荷人工调整驱动力臂的长度,改变驱动力矩的大小和车辆的行走速度,具有很强的爬坡能力.如果爬坡不顺利时,车会停在坡道上而不后退,既能充分发挥人力的作用,又大大提高了乘骑的安全性.     

潜水污泥泵的研制

概述了潜水污泥泵研制的意义，介绍了此类泵的结构特点及设计中的一些问题。     

微机械装置的液体输送性能实验与分析

进行了微机构液体输送的实验研究。在分析了挤出各阶段的工作过程的基础上,分析了粘度对转矩、有效功和摩擦力等性能的影响,并实验测量了液体粘度对挤出效率和挤出时间的影响。结果表明:液体输送过程中,液体的粘度对挤出率和功耗等有显著影响,且液体粘度的影响存在一个最优点。在该点处,不仅可以实现较高的挤出效率和较少的时间,而且所用的功率、转矩较小,摩擦损耗也较少。     

基于双压驱动的恒流斩波系统的设计

恒流斩波电路中，步进电机的驱动电压高，绕组回路阻抗低，电流上升较快。单一的恒流驱动电路在低频时绕组电流上升过快，容易导致低频过冲振荡现象。为避免振荡，本文采用双压驱动与恒流斩波驱动相结合：低速运行时采用低电压供电，低频过冲振荡小，转动平稳；高速运行时采用高电压供电，高频输出扭矩大，不易堵转。     

新型液压打桩锤的系统仿真研究

对一种新型液压打桩锤的打桩过程进行了运动学及动力学分析,并在此基础上提出了桩锤在各个打击过程的数学模型.最后对各阶段进行了仿真及分析,仿真研究对改进和优化打桩锤的各个设计参数具有指导意义.     

五相步进电机在平流泵中的应用

较详细介绍了五相步进电机在平流泵中的应用,给出了一种基于斩波恒流方式的步进电机驱动电路和驱动方案,详细说明了其原理;对平流泵应用过程中的高频出力、低频振荡及平流泵的控制精度等给出了较详细的解决方法.     

热超导管式全玻璃真空管集热器的热性能分析

对热超导管作传热管的全玻璃真空管太阳能集热器的传热机理进行了分析,给出了其总热损失系数、效率因子、热迁移因子和瞬时效率的表达式.并通过实例将瞬时效率的理论计算值与实验测定值进行了比较,两者的相对误差小于6.74%.说明相关的计算表达式较好地反映了全玻璃真空管太阳能集热器的传热性能与特点,同时讨论了如何提高集热器的性能,其结论可用于该类真空管集热器的设计与性能预测.     

基于GASA的柔性作业车间动态调度研究

针对柔性作业车间调度在机器故障扰动情况下的动态性及工件交货期模糊的情况,研究采用基于事件与周期混合驱动的滚动窗口再调度策略,并运用线性加权和的方法,以最大完工时间最小、能耗最小、客户满意度最大为目标,建立多目标柔性作业车间动态调度模型,并设计了遗传算法与模拟退火算法结合的GASA算法。将算例仿真结果与遗传算法取得的结果进行对比,验证算法的有效性。     

Identification of Driving Intention Based on EEG Signals

The driver''s intention is recognized by electroencephalogram(EEG) signals under different driving conditions to provide theoretical and practical support for the applications of automated driving. An EEG signal acquisition system is established by designing a driving simulation experiment, in which data of the driver''s EEG signals before turning left, turning right, and going straight, are collected in a specified time window. The collected EEG signals are analyzed and processed by wavelet packet transform to extract characteristic parameters. A driving intention recognition model, based on neural network, is established, and particle swarm optimization (PSO) is adopted to optimize the model parameters. The extracted characteristic parameters are inputted into the recognition model to identify driving intention before turning left, turning right, and going straight. Matlab is used to simulate and verify the established model to obtain the results of the model.The maximum recognition rate of driving intention is 92.9%. Results show that the driver''s EEG signal can be used to analyze the law of EEG signals. Furthermore, the PSO-based neural network model can be adapted to recognize driving intention.     

Psychological constructs in driving automation: a consensus model and critical comment on construct proliferation

As automation in vehicles becomes more prevalent, the call for understanding the behaviour of the driver while driving an automated vehicle becomes more salient. Although a variety of driver behaviour models exist, and various psychological constructs have been said to be influenced by automation, an empirically testable psychological model of automated driving has yet to be developed. Building upon Stanton and Young's model of driving automation, this article presents an updated model of interrelated psychological constructs. The proposed model was created based upon a systematic literature search of driving automation papers and a subsequent quantification of the number of reported links between a selected set of psychological constructs. A secondary aim of this article is to reach consensus in the use of psychological constructs regarding driving automation. Henceforth special attention is paid to resolving the issue of construct proliferation.