数据挖掘中的序列模式的研究

本文就数据挖掘中序列模式的概念和作用进行了探讨,并对序列模式的实现过程用程序流程的方式加以说明,并给出了序列模式实现算法的步骤。     

运动图像处理在汽车车型识别中的应用

本文介绍了“多品种混流机器人喷漆自动线的汽车车型识别系统”的一种算法,提出了基于运动序列图像的自适应阈值判别法和利用特征匹配法解决了同种车型的图像在结构上产生局部变化的识别问题。     

相位相序安排与交叉口设计之间的关系

交叉口信号控制中,相位相序的安排与交叉口设计之间存在着非常密切的关系,两者之间需要相互协调、相互适应,才能充分发挥交通信号的效益。目前,由于交叉口设计与相位相序安排不适应造成的交叉口时空资源损失和事故隐患越来越多。另外,多数交通控制系统中对相位相序的优化考虑较少,更少涉及与交叉口设计相适应的调节方法。随着交叉口设计在中国的普及,它的不适应性也会凸显。因此有必要进一步研究两者之间的关系,以更大限度地提高交叉口通行能力利用率,减少事故隐患,同时为智能交通信号控制系统提供优化根据。     

Vehicle classification from low-frequency GPS data with recurrent neural networks

The categorization of the type of vehicles on a road network is typically achieved using external sensors, like weight sensors, or from images captured by surveillance cameras. In this paper, we leverage the nowadays widespread adoption of Global Positioning System (GPS) trackers and investigate the use of sequences of GPS points to recognize the type of vehicle producing them (namely, small-duty, medium-duty and heavy-duty vehicles). The few works which already exploited GPS data for vehicle classification rely on hand-crafted features and traditional machine learning algorithms like Support Vector Machines. In this work, we study how performance can be improved by deploying deep learning methods, which are recently achieving state of the art results in the classification of signals from various domains. In particular, we propose an approach based on Long Short-Term Memory (LSTM) recurrent neural networks that are able to learn effective hierarchical and stateful representations for temporal sequences. We provide several insights on what the network learns when trained with GPS data and contextual information, and report experiments on a very large dataset of GPS tracks, where we show how the proposed model significantly improves upon state-of-the-art results.     

意大利玛连尼沥青拌和设备雾化燃烧器的正确使用和调整

简述了雾化燃烧器的工作原理及对不同燃料油的使用要求，并详述了系统启动运转前的检查调整要点和燃烧器的点火程序及故障检查和部件维修。     

多路直扩信号并行IMPA捕获算法仿真实现

针对长PN序列作扩频地址码的多路直扩信号的快速捕获问题,提出多路直扩信号并行IMPA(迭代信息传递算法)捕获方案,该方案能够根据接收信号的信道信息直接生成与各路接收信号序列粗同步的本地伪码序列,改变了传统捕获方法中本地伪码序列的生成方式,大大缩短了信号的捕获时间。详细说明了该方案的实现原理和实现方法,并对该方案进行了仿真和实际验证。结果表明该方案能够在低信噪比下实现多路直扩信号的快速捕获,而且实现的复杂度较低。     

三峡右岸地下电站主厂房系统爆破开挖程序及开挖方法研究

主厂房系统爆破开挖程序和开挖方法,最直接地影响着整个工程工期、质量、安全及成本。如何结合工程具体特点,确定最优的爆破开挖程序和开挖方法,这是主厂房系统爆破开挖的关键所在。三峡右岸地下电站主厂房系统爆破开挖程序及开挖方法的研究成果,可供有关单位参考。     

Sequence Patterns of Identity Authentication Protocols

From the viewpoint of protocol sequence, analyses are made of the sequence patterns of possible identity authentication protocol under two cases： with or without the trusted third party （TFP）. Ten feasible sequence patterns of authentication protocol with TIP and 5 sequence patterns without TFP are gained. These gained sequence patterns meet the requirements for identity authentication, and basically cover almost all the authentication protocols with TFP and without TFP at present. All of the sequence patterns gained are classified into unilateral or bilateral authentication. Then, according to the sequence symmetry, several good sequence patterns with TFP are evaluated. The accompolished results can provide a reference to design of new identity authentication protocols.     

ESD_FT综合分析法在船用堆失水事故分析中的应用

ESD（事件序列图）方法常用于对事故发展进程及其后果进行分析,但在模型控制及图形建模能力方面存在不足。对较复杂的系统而言,用纯粹的ESD方法所产生的模型往往过于庞大、复杂且不直观。相反,在ESD方法的基础上,对ESD方法进行拓展和完善,采用ESD_FT（故障树）综合分析法所产生的模型比较简洁直观,且可以充分利用故障树分析技术的优势。探讨ESD_FT综合分析法在船用反应堆事故分析中的应用,提出一种新的由内部故障树展开的ESD基本事件,建立船用堆失水事故的ESD_FT分析模型,得到了事故的发生概率。从分析结果可知,ESD_FT综合分析法是一种有效而实用的事故分析方法。     

形成教育合力重在"三个协同"

将协同论的思想运用于教育研究,研究和思考影响学校现代管理发展的序参量,并提出"三个协同",即理念协同、管理协同和文化协同,旨在形成教育合力,以期实现学生、学校和用人单位的完全协同。