基于小波分析理论的轨道不平顺分析

研究目的:利用非平稳信号处理方法——小波分析方法分析局部轨道不平顺特殊波型、轨道不平顺病害的识别以及轨道不平顺功率谱分析等,以深化对轨道不平顺特性的认识.研究结果:利用小波分析方法可以较好地辨识轨道随机性不平顺中隐含的有规律的波形,如正弦、三角形波等;利用小波分析方法可以较好地对轨道局部发生的高频病害进行检测;通过小波分解,可以识别不同波长范围内突出的不平顺(特征不平顺),为进而提取这些不平顺做深入研究提供了便利.由此可见,利用小波分析理论从时频角度分析轨道不平顺,是一条保障铁路安全运营的新技术途径.     

自动闭塞区段站间分割存在问题探讨

从现场调研结果分析,由于自动闭塞区段站间分割没有一个明确的规定,站间分割不合理加大了设备投入成本和维护难度、降低了设备可靠性。本文针对这一现象进行详细的分析比较,提出合理的分割方案和解决问题的措施。     

在Struts框架下构建MVC模式的Web应用

阐述了Struts框架基本体系结构,MVC模式。在重点分析研究Struts实现任务分发和任务处理的基础上,结合实例给出了MVC设计模式在J2EE Web层的一种有效的应用,并运用开源的Struts框架实现了MVC设计模式。在实践中体现出MVC设计模式与Struts框架的高效和结构清晰的优点。     

浅谈基于CAN现场总线的数据采集系统

本文以微机监测为例介绍了基于CAN总线的数据采集方案。     

自动化驼峰溜放数据自动分析的实现

把驼峰自动化控制系统产生的控制数据，经专用软件的分析和统计，再对日常维护作业进行指导，提出维修的重点。     

模式类及其上的知识表示和推理

指出知识之间的关系广泛存在，这些关系正是插值推理所需要的。然而还没有一种容纳知识之间的关系的知识表示的方法，模糊推理方法隐含了这些知识但没有明确地表示出来。文中还建立了一种表示不确定知识的框架－模式类，此框架用于表示知识之间的关系，并且建立了在此框架上进行知识推理的方法，该方法可以应用到传统模糊推理能够应用的地方，且应用范围更广。     

面向混合交通流的智能网联车鸣笛意图识别模型

为使混合交通流(Mixed Traffic Stream，MTS)下智能网联车(Intelligent Connected Vehicle，ICV)实现鸣笛意图(Horn’s Intention，HI)识别，更好地遵循常规车辆(Manual Vehicle， MV)的驾驶意图，提出ICV 对MV 鸣笛声的“ 感知(Perception) — 定位(Location) — 识别 (Recognition)”模型(简称HI-PLR)，采用深度卷积—循环神经网络(Deep Convolution Recurrent Neural Network, DCRNN)算法感知鸣笛车辆(Horning Vehicles, HV)的鸣笛声；采用到达时差 (Time Difference of Arrival, TDOA)算法定位HV；再基于运动时间窗(Motion Time Window, MTW)的支持向量机(Support Vector Machine, SVM)算法识别HI.实验结果表明，HI-PLR可使 ICV 对混流中车辆的鸣笛声感知准确率达90.4%，定位角度估计误差小于5°，HI 识别率达 82.5%，为ICV在MTS中的智能驾驶决策提供依据.     

Improving assimilation of SeaWiFS data by the application of bias correction with a local SEIK filter

Ocean-biogeochemical models show typically significant errors in the representation of chlorophyll concentrations. The model state can be improved by the assimilation of satellite chlorophyll data with algorithms based on the Kalman filter. However, these algorithms do usually not account for the possibility that the model prediction contains systematic errors in the form of model bias. Accounting explicitly for model biases can improve the assimilation performance. To study the effect of bias estimation on the estimation of surface chlorophyll concentrations, chlorophyll data from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) are assimilated on a daily basis into the NASA Ocean Biogeochemical Model (NOBM). The assimilation is performed by the ensemble-based SEIK filter combined with an online bias correction scheme. The SEIK filter is simplified here by the use of a static error covariance matrix. The performance of the filter algorithm is assessed by comparison with independent in situ data over the 7-year period 1998–2004. The bias correction results in significant improvements of the surface chlorophyll concentrations compared to the assimilation without bias estimation. With bias estimation, the daily surface chlorophyll estimates from the assimilation show about 3.3% lower error than SeaWiFS data. In contrast, the error in the global surface chlorophyll estimate without bias estimation is 10.9% larger than the error of SeaWiFS data.     

Evolving the subspace of the three-dimensional multiscale ocean variability: Massachusetts Bay

A data and dynamics driven approach to estimate, decompose, organize and analyze the evolving three-dimensional variability of ocean fields is outlined. Variability refers here to the statistics of the differences between ocean states and a reference state. In general, these statistics evolve in time and space. For a first endeavor, the variability subspace defined by the dominant eigendecomposition of a normalized form of the variability covariance is evolved. A multiscale methodology for its initialization and forecast is outlined. It combines data and primitive equation dynamics within a Monte-Carlo approach.The methodology is applied to part of a multidisciplinary experiment that occurred in Massachusetts Bay in late summer and early fall of 1998. For a 4-day time period, the three-dimensional and multivariate properties of the variability standard deviations and dominant eigenvectors are studied. Two variability patterns are discussed in detail. One relates to a displacement of the Gulf of Maine coastal current offshore from Cape Ann, with the creation of adjacent mesoscale recirculation cells. The other relates to a Bay-wide coastal upwelling mode from Barnstable Harbor to Gloucester in response to strong southerly winds. Snapshots and tendencies of physical fields and trajectories of simulated Lagrangian drifters are employed to diagnose and illustrate the use of the dominant variability covariance. The variability subspace is shown to guide the dynamical analysis of the physical fields. For the stratified conditions, it is found that strong wind events can alter the structures of the buoyancy flow and that circulation features are more variable than previously described, on multiple scales. In several locations, the factors estimated to be important include some or all of the atmospheric and surface pressure forcings, and associated Ekman transports and downwelling/upwelling processes, the Coriolis force, the pressure force, inertia and mixing.     

基于SPCE061A单片机的语音控制机器人的设计

利用凌阳16位单片机的特性,根据语音识别的基本原理,设计了一种语音控制机器人,利用C语言和汇编语言进行系统软件编程,最终完成了可以通过对语音的识别,自动完成向前走、倒退、左转、右转、跳舞、向左瞄准、向右瞄准、发射、连续发射等功能的智能机器人的设计。