A BP neural network based information fusion method for urban traffic speed estimation

Obtaining comprehensive and accurate information is very important in intelligent traffic system (ITS). In ITS, the GPS floating car system is an very important approach for traffic data acquisition. However, in this system, the GPS blind areas caused by tall buildings and tunnels could affect the acquisition of traffic information and depress the system performance. Aiming at this problem, we developed a novel method employing a back propagation (BP) neural network to estimate the traffic speed in the GPS blind areas. When the speed of one road section is lost, we can use the speed of its related road sections to estimate its speed. The complete historical data of these road sections are used to train the neural network, using Levenberg-Marquardt learning algorithm. Then, the current speed of the related roads is used by the trained neural network to get the speed of the road section without GPS signal. We compare the speed of the road section estimated by our method with the real speed of this road section, and the experimental results show that the speed of this road section estimated by our method is better.     

Data acquisition, analysis and applications of multi-sensor integration

This paper presents some key techniques for multisensor integration system, which is applied to the intelligent transportation system industry and surveying and mapping industry, e.g. road surface condition detection, digital map making. The techniques are synchronization control of multisensor, spacetime benchmark for sensor data, and multisensor data fusion and mining. Firstly, synchronization control of multisensor is achieved through a synchronization control system which is composed of a time synchronization controller and some synchronization subcontrollers. The time synchronization controller can receive GPS time information from GPS satellites, relative distance information from distance measuring instrument and send spacetime information to the synchronization subcontroller. The latter can work at three types of synchronization mode, i.e. active synchronization, passive synchronization and time service synchronization. Secondly, spacetime benchmark can be established based on GPS time and global reference coordinate system, and can be obtained through position and azimuth determining system and synchronization control system. Thirdly, there are many types of data fusion and mining, e.g. GPS/Gyro/DMI data fusion, data fusion between stereophotogrammetry and PADS, data fusion between laser scanner and PADS, and data fusion between CCD camera and laser scanner. Finally, all these solutions presented in paper have been applied to two areas, i.e. landborne intelligent road detection and measurement system and 3D measurement system based on unmanned helicopter. The former has equipped some highway engineering Co., Ltd. and has been successfully put into use. The latter is an ongoing research.     

西堠门大桥结构监测系统的设计与实现（II）：系统实现

采用“西堠门大桥结构监测系统的设计与实现（I）：系统设计”中的设计方法,为西堠门大桥设计并实现桥梁结构监测系统。研究西堠门大桥结构监测系统的总体设计方案、子系统的设计方案及其软硬件的实现。分析西堠门大桥结构监测系统在运营中监测的桥梁荷载和结构响应。研究结果表明,西堠门大桥结构监测系统正常运行,实现了预期设计功能,基于工业以太网分布式数据采集与传输系统在桥梁结构监测系统中得以成功应用,可在其他工程中加以推广。     

基于特征融合的步态识别研究

提取了膝关节的速度距和路径距,膝关节到脚踝关节的距离作为步态特征,这些特征分别描述了步态的动态信息和静态信息.将这些特征融合得到了较全面的步态信息,利用融合特征进行步态识别,提高了步态的识别率.     

Two Optimal Aggregation Approaches of Fuzzy Opinions in Group Decision Analysis

The aggregation of fuzzy opinions is an important component of group decision analysis with fuzzy information. This paper proposes two new approaches for the assessment of the weights to be associated with fuzzy opinions. These approaches involve,respectively, the minimization of the sum of squared differences between the individual weighted fuzzy opinion and the weighted mean value of all fuzzy opinions, which is called the weighted minimum variance method (WMVM), and the minimax difference of any two adjacent individual weighted fuzzy opinions, which is called the mean value minimax differences method (MVMDM). The two approaches are developed and numerical examples are presented to illustrate their simplicity and effectiveness in aggregating fuzzy -opinions.     

电气信息类专业信号处理课程群的建设

针对电气信息类专业信号处理类课程内容衔接紧密,知识点覆盖存在重复现象,对电气信息类专业的信号处理类课程进行课程群建设.首先对课程群的教学内容进行统筹分析,然后进行课程内容的融合和分解,再进行教学内容的再设计,形成一套适合电气信息类专业学生学习的现代信号处理教学体系.论文还阐述课程群建设中理论与实践教学、研究性教学和传统性教学、微观教学与宏观教学、基础理论与创新的关系.通过信号处理课程群的建设,不仅优化课程体系,使学生易于掌握课程群的基本理论和方法,而且还提高此类课程的教学效果.     

飞机防滑刹车系统的智能故障诊断与重构

针对目前飞机在起飞着陆过程中故障率高的问题,设计一种交叉冗余飞机防滑刹车系统的智能故障诊断与重构系统。该系统基于BP神经网络专家系统,利用专家先验知识、神经网络的数值推理和自学习能力对飞机刹车过程出现的故障进行分析和建模,继而准确推断和定位故障;通过对系统信号流向优先级建立对应加权有向图模型,采用寻找最短路径的算法对系统进行优化重构,从而使飞机刹车系统达到更高的安全水平。研究结果证明:在刹车过程中,给定随机故障后,系统能迅速、准确地判断和定位故障并实时重构系统,达到1次和部分2次故障时能正常工作且保证工作性能的设计目标。     

应用个性化推荐的Web日志关联规则挖掘算法研究

对传统的关联规则挖掘算法FP-Growth方法进行改进,提出FP-Mine算法,并应用该算法对Web日志进行挖掘,探寻用户访问站点页面之间的关联规则,来帮助管理员改善站点的设计和企业改进市场商务决策.实验结果证明FP-Mine算法在生成频繁项集及关联规则的过程中,只需存储i-size和(i+1)-size频繁项集的节点的Freq-Set-Tree,且立即在其之上生成规则,所以缩短规则生成的时间,提高规则生成效率,同时释放i-size项集的节点,有效地节省内存空间.     

标准加权类分数傅里叶变换加权系数的多样性问题

基于标准加权类分数傅里叶变换[Optics comm.118(1995)495],在求解加权系数时,使用一个新的变量代换得到了一组新的加权系数,确定了一类新的标准加权类分数傅里叶变换算子,然后具体分析了这两组加权系数相应的性质以及它们之间的联系.     

网格化工商业市场监管系统设计与实现

介绍网格化工商市场监管系统的设计,采用分布式数据库结构,C/S与B/S相结合,实现了对企业监管的网格化、分级式管理,包括企业注册登记、企业信用等级监管、用户投诉与审核、企业荣誉与处罚等功能.