基于LabVIEW的列车测速定位平台研究

针对单一列车测速定位技术的不足,设计了多传感器融合列车测速定位软硬件平台。主要描述了硬件平台的组成,并介绍了基于虚拟仪器软件LabVIEW以及MATLAB的软件平台功能。最后通过铁路现场的实验获得了数据并进行了离线数据处理。     

基于多Agent信息融合的轨道交通信号设备故障诊断技术要点

针对我国城市轨道交通信号设备故障的特点,设计了一个基于多Agent信息融合技术的轨道交通信号设备故障诊断系统。介绍信息融合技术的相关要点,重点阐述多Agent信息融合技术的定义和理论。对多Agent轨道交通信号设备故障诊断的总体结构进行分析和设计。详细分析了信息采集Agent、管理Agent、诊断Agent等主要Agent的基本职能,以及各Agent间的通信协作方式。     

信息融合技术在地铁中央空调风机故障诊断中的应用

以神经网络信息融合故障诊断技术为基础,利用多传感器对风机的运行状态进行监测,建立了地铁中央空调风机的故障诊断系统模型.该系统结构能有效地为地铁车站设备的维护提供方案,实用性强.     

人B7.2(CD86)胞外区原核表达及其工程菌发酵培养的实验研究

目的　利用原核系统表达人B7.2 (IgV +C)并对工程菌发酵培养条件进行优化。方法　用聚合酶链反应 (PCR)技术从B7.2cDNA全长中克隆B7.2 (IgV +C) ,将PCR产物克隆入表达载体 pGEX 4T 3从而得到重组体 pGEX 4T 3 /hB7.2 (IgV +C) ,SDS PAGE及Westernblot用于检测目的蛋白的表达 ,同时对目的蛋白诱导表达时间及诱导剂浓度进行优化 ,对重组质粒的遗传稳定性进行鉴定。结果　Westernblot结果显示相应分子质量 5 5kD处有hB7.2 (IgV+C)与GST融合蛋白的高效表达 ,表达量占菌体总蛋白的 3 0 %左右。对工程菌进行发酵培养研究的结果表明 ,所构建的重组质粒在工程菌DH5α中传代稳定 ,未见质粒丢失 ,目的蛋白的表达不受影响 ,且在 3 7℃诱导 5h、IPTG终浓度为 4mmol·L- 1 时其表达量最多。结论　证明了利用原核系统表达人B7.2 (IgV +C)的可行性。     

Optimal Sensor Decision Based on Particle Filter

Introduction Multi-sensor tracking technique has receivedmuch attention in recent years. Many tracking sys-tems involve basically active (radar) and passive(infrared tracking) subsystems. The main disad-vantage of infrared search and tracking is the prob-lem of observability and filtering divergence. Actu-ally, quite a lot of research activities have beendone in this field and some researchers more thanonce simulated the feasibility of the approach[1,2].The main idea of the methods is as foll…     

基于小波变换的图像融合算法研究

提出了一种新的基于小波多尺度分解的分层图像融合方法；总结出基于小波分解子图像面积比和低频系数均方根误差2个标准，来确定最佳小波分解层数的方法。通过仿真实验，对分解层数、区域大小对该融合方法的性能影响进行了比较分析，并得出融合结果，将其与金字塔算法融合结果进行了比较。实验结果表明，该融合方法是十分有效的。     

数据融合综述

新兴的数据融合科学内容丰富且应用广泛,正确理解融合算法与层次分类,能帮助我们避免数据富有但信息贫乏的情况发生.     

面向ITS共用信息平台的数据融合研究

信息融合系统通过关联系统的信息整合,为ITS共用信息平台的各种功能提供信息支撑.基于对ITS共用信息平台中数据的特点,数据预处理的分析,结合数据融合的级别,提出一种新的面向ITS共用信息平台的数据融合方法和技术,来满足智能交通各子系统的不同的数据需求.这种方法在技术上是可行的,具有一定的实用性和广泛的应用价值.     

Improving rail network velocity: A machine learning approach to predictive maintenance

Rail network velocity is defined as system-wide average speed of line-haul movement between terminals. To accommodate increased service demand and load on rail networks, increase in network velocity, without compromising safety, is required. Among many determinants of overall network velocity, a key driver is service interruption, including lowered operating speed due to track/train condition and delays caused by derailments. Railroads have put significant infrastructure and inspection programs in place to avoid service interruptions. One of the key measures is an extensive network of wayside mechanical condition detectors (temperature, strain, vision, infrared, weight, impact, etc.) that monitor the rolling-stock as it passes by. The detectors are designed to alert for conditions that either violate regulations set by governmental rail safety agencies or deteriorating rolling-stock conditions as determined by the railroad.Using huge volumes of historical detector data, in combination with failure data, maintenance action data, inspection schedule data, train type data and weather data, we are exploring several analytical approaches including, correlation analysis, causal analysis, time series analysis and machine learning techniques to automatically learn rules and build failure prediction models. These models will be applied against both historical and real-time data to predict conditions leading to failure in the future, thus avoiding service interruptions and increasing network velocity. Additionally, the analytics and models can also be used for detecting root cause of several failure modes and wear rate of components, which, while do not directly address network velocity, can be proactively used by maintenance organizations to optimize trade-offs related to maintenance schedule, costs and shop capacity. As part of our effort, we explore several avenues to machine learning techniques including distributed learning and hierarchical analytical approaches.     

一种多通道信息融合的横向减振器性能退化阶段辨别方法

对于转向架这样复杂的系统,分布在系统不同位置的传感器可以实时检测大量数据。这些数据能够反映高速列车运行过程中转向架关键部件的性能退化状态,但单一通道的振动信号存在着信息缺失、信噪比低等缺陷,无法据此实现转向架关键部件性能退化阶段的精确辨识。因此,本文以横向减振器为研究对象,通过对转向架振动数据相关性分析,提出了车体和转向架上多个通道的振动信号共同用于横向减振器性能退化阶段辨识的方法。并通过构建基于CNN的多通道信息融合模型,将多个通道信息进行融合,实现了横向减振器性能退化特征的自适应提取与阶段辨识。将该方法与基于单个通道的方法进行比较,结果表明,该方法能够精确实现横向减振器间隔10%的性能退化阶段辨识。