基于RS-232C串口的直流电机调速系统的研究

介绍了RS-232C串行通信接口的功能，针对微机RS-232C串口在直流电机速度控制中的实际应用进行了探讨。     

智能型快速脉冲充电技术研究

结合蓄电池充电的极化现象和脉冲电镀原理,设计一种新的充电方式——智能型快速脉冲充电技术,适合各种类型的蓄电池。利用该充电技术的充电机可以用低于电池端电压的电压对蓄电池充电,提高蓄电池的使用和维护效率。     

股道占用智能判别系统

鉴于目前广泛使用的轨道电路出现的工作不可靠问题，设计了一种新的股道占用智能判别系统。系统由电感式接近传感器、可编程控制器（PLC）和显示控制中心组成，可以对股道是否被占用进行有效判别，可作为轨道电路的辅助判别。     

两种滑脉患者与健康妇女指端血管容积脉搏波的谱分析

本文对部分具有滑脉脉象的患者和健康青年女子的指端血管容积脉搏波进行了功率谱分析比较。见到健康青年女子脉搏波功率谱上的能量基本分布于0～20 Hz频段内。提示血流相对平稳。甲亢患者则在0～200 Hz频段内均有能量分布,其中较高的能量峰位于0～20 Hz段内,此段内能量分布呈早衰现象。提示甲亢患者血管内湍流明显增加,血流平稳度下降。早孕妇的谱分析显示主要能量分布于0～20 Hz段内,此段内能量随频率升高而缓慢下降,不似甲亢患者的早衰。在100 Hz附近多见一个较高的峰。提示早孕妇血流平稳度有所下降,但介介于前两者之间。为用现代科学技术探索脉象成因和研究中医脉学理论提供了依据。     

基于小波变换的补偿电容故障自动识别方法

针对当前高速综合检测车中,信号检测系统自动化水平较低、需要凭借人工经验检查故障等问题,提出一种基于小波变换的补偿电容故障自动识别方法。该方法首先对补偿电容检测数据进行降噪处理,然后对降噪处理后的数据进行脉冲识别,最后自动识别出补偿电容失效位置,从而达到补偿电容故障自动识别的目的。实验结果表明,该方法能满足现场使用需求。     

乘用车废气旁通式涡轮增压器匹配技术

文章旨在解决使用传统方法进行匹配乘用车废气旁通式涡轮增压器涡轮匹配过程存在的不足。使用传统定压增压系统的涡轮增压器匹配方法,即将算术平均值作为计算边界,不适用于乘用车发动机增压器的匹配,特别是小排量(<1.6L)3缸发动机,低速脉冲能量大,匹配结果与实际试验结论相差较大。文章引用脉冲修正系数对涡轮等熵效率进行修正,从而使匹配结果与实际试验结果相接近,实现更准确的匹配工作。     

城市道路网络动态OD估计模型

针对以动态交通管理与控制为目标的动态交通需求计算问题,分析了OD量与交叉口转向交通量的动态关系,将其作为新的系统测量量引入,以此建立了同时考虑路段断面交通量和交叉口转向交通量的状态空间模型,得到了基于城市道路网络的动态OD估计模型,给出了考虑不等式约束的卡尔曼滤波递推方程及相应算法过程。利用微观仿真软件Paramics所建立的实验平台对该模型进行了仿真验证。结果表明：应用该模型进行交通量计算,与传统的仅考虑路段断面交通量模型相比,绝对误差平均减少9％,相对误差平均减少20％,而且其能更好地反映交通量真实值随时间变化的情况,计算结果明显优于传统模型。     

基于微控制器的脉宽调制编码器在轻轨车辆上的应用

脉宽调制编码器(PWM encoder)在铁路动车组和轻轨、地铁车辆上担负着牵引、制动指令传输的重要任务。文章主要阐述了基于微控制器的脉宽调制编码器的设计理念、硬件组成、软件实现及其内部控制逻辑关系。     

Experimental Research on Atmospheric Electric Field and Extensive Air Shower Particle Increasing During Thunderstorms with ARGO-YBJ

From April lst to August 14th, 2006, thunderstorms had been recorded at Yangbajing Cosmic Ray Observatory by ARGO-YBJ experiment. This paper analyzed the correlation between atmospheric electric field (AEF) and "scaler mode" counting rate during thunderstorm. Counting rates of multiplicities n = 1,2 were found to have a large increase ( from 1.02% to 9.03% ), while there was few or no changes in those of multiplicities n =3 and n≥4 during the thunderstorms. The counts of different multiplicities had different feedbacks on the violent change of AEF, which showed that their energy and most components were distinguishing.     

Rationalizing Reliable Imputation Durations of Genetically Designed Time Delay Neural Network and Locally Weighted Regression Models

Abstract

Estimating missing values is known as data imputation. Previous research has shown that genetic algorithms (GAs) designed locally weighted regression (LWR) and time delay neural network (TDNN) models can generate more accurate hourly volume imputations for a period of 12 successive hours than traditional methods used by highway agencies. It would be interesting and important to further refine the models for imputing larger missing intervals. Therefore, a large number of genetically designed LWR and TDNN models are developed in this study and used to impute up to a week-long missing interval (168 hours) for sample traffic counts obtained from various groups of roads in Alberta, Canada. It is found that road type and functional class have considerable influences on reliable imputations. The reliable imputation durations range from 4–5 days for traffic counts with most unstable patterns to over 10 days for those with most stable patterns. The study results clearly show that calibrated GA-designed models can provide reliable imputations for missing data with ‘block patterns’, and demonstrate their further potentials in traffic data programs.