时间异步无线传感器网络的分布式目标跟踪

为了降低无线传感器网络在目标跟踪过程中的网络能耗,提出了一种时间异步条件下的分布式目标跟踪方法.首先,依据节点到目标的距离进行动态成簇,以跟踪簇为时间的计算单元,由簇头完成簇内跟踪时间计算及簇间贯序传递,然后引入并行粒子滤波(PPF)算法将粒子集分为多个子集,在子节点处并行采样、计算权重和重采样,最后,簇头节点收集各子节点上传的结果并完成目标的局部状态估计.仿真结果表明,PPF算法具有较好的跟踪精度,且相比于集中式粒子滤波(CPF)算法,可降低约38%的通信量.     

基于数据压缩和异步通信策略的分布式图算法优化研究

图是一种非常重要的数据结构形式,被广泛用于社交网络、交通网络和搜索引擎等领域。随着图数据规模爆发式增长,存储容量受限,分布式图计算成为处理大规模图数据的焦点。宽度优先搜索(breadth first search,BFS)算法是图遍历和许多图分析算法的基础,而在分布式图计算过程中存在严重的通信开销。针对上述问题,本文提出了一种综合的数据压缩编码优化方案,结合位图和变长压缩数组,通过更高的压缩率来降低数据通信开销;此外,还提出了一种点对点异步环形通信策略,进一步降低分布式图计算中计算-通信的同步开销。通过这些优化手段,本文在8节点的分布式集群上对优化后BFS算法的性能进行了系统评估,结果表明,当图数据规模为28时,优化后的BFS算法平均性能为46.79亿条边每秒遍历(giga-traversed edges per second,GTEPS),性能比优化前提升了接近7.82%。     

分布式光纤传感器的周界安防入侵信号识别

在分布式光纤周界安防系统中,对距离较长、背景环境复杂的边境进行检测时,系统根据光缆沿线发生的事件进行识别.为了区分各种引起光缆振动的激励,本文根据入侵信号与环境引起的振动信号在小波尺度上方差幅值的分布特征,利用小波多尺度分析理论构造了由各尺度下的方差组成的特征向量,提出了根据方差特征向量的不同来识别各种振动信号的“尺度-方差”信号的方法.在实验系统中,光缆总长度为56 km,光源的功率为300 μW,工作波长为1 550 nm.实验结果表明,此方法可以有效区分入侵信号、环境噪声和人为活动引起的非入侵事件,提高了系统的检测概率和降低系统的虚警率.     

分布式教室智能控制系统的总体设计

本分布式教室智能网络模型采用现场总线技术,构成了“CAN＋RS485”的三层两级网络。其中上层是由带CAN总线端口的主控机和中间控制器构成的CAN网络层,下层为由带RS485端口的下位机和中间控制器构成了RS485网络层,从而实现智能教室网络的数据通信。     

分布式智能门禁监近代系统的研制

文章介绍的分布式智能门禁监控系统采用RS-485总线技术，以程控电话网为媒介来进行远程的数据采集和设备控制。文中对监控系统的结构和工作原理进行了分析，对监控主机的软件工作流程和各部分的功能进行了详细的介绍。     

基于分布式网络的液压系统仿真软件设计

主要研究的是液压系统仿真软件的整体规划,分析基于分布式网络的液压系统真软件的总体需求、功能需求,详细介绍该软件系统的结构和主要功能模块的划分。     

微波接力机无线分布式监测系统的研究与开发

本文论述了WJ100微波接力机远程分布式无线监测系统的研究与开发,该系统具有性价比高、受地理条件限制少、易于扩展、易于维护和操作、实时数据采集、实时声音动画报警、数据远距离无线传输等特点,该系统所使用的通信技术特别适用于在高山丛林、沼泽、沙漠等地区建立无人职守监测网络,具有很大的推广价值.     

分布式光纤传感器周界安防入侵信号的多目标识别

针对分布式光纤在周界安防系统中信号种类,即不同的环境下产生的噪声信号干扰和常见的入侵产生的信号。本文基于全光纤马赫—泽德干涉仪的分布式光纤传感模型,提出了一种识别常见的越境信号和消除环境噪声干扰信号的方法,实现了在去除环境干扰的情况下,用BP神经网络对多种入侵信号识别。实验结果证明,该方法能够有效的区分越境信号和不同环境状态产生的噪声信号,极大的提高了整个系统的识别率,降低了其虚警率。     

旋转叶片异步振动的频率识别技术

基于非接触式叶尖定时测振原理,针对叶片异步振动的欠采样问题,采用“5＋2”双采样速率方案进行采样,通过频率辨识技术来获得叶片的真实振动。将实验测得的振动频率与应变片法测量结果进行比对表明,所测频率与叶片振动的坎贝尔曲线一致,是叶片振动的真实频率。同时,比对结果验证了测量方案的正确性,也说明了该辨识技术具有很高的实际应用价值。     

基于分布式部署的高校贫困生评定系统的设计与实现

通过基于分布式部署的贫困生评定系统的设计与实现,解决高校中学生和学校对贫困生评定难题。学校可以根据国家政策数据因子、学生在学校的表现因子等重要参数通过系统自动评定出真正的贫困生。系统采用分布式技术和模糊数学技术,对系统的数据显示、数据采集、数据存储、数据智能分析进行了有效部署,增加了系统的健壮性、效率和准确性。     

时间异步无线传感器网络的目标跟踪动态成簇算法

为了解决时间异步无线传感器网络在目标跟踪时的节点协作管理和跟踪时间配准问题,提出了一种适用于时间异步条件下目标跟踪的动态成簇算法。该方法通过分析目标的无线信号强度和各节点至目标的距离来动态组建跟踪簇,然后依据目标及簇头的通信距离对簇头射频信号的覆盖区域进行功能划分,实现节点对目标的协作跟踪,同时以簇为跟踪时间的计算单元,通过簇内计时和簇间贯序传递的方法实现跟踪时间的配准。仿真实验表明,该算法进行目标跟踪时能有效均衡网络能耗,且具有较好的跟踪精度和系统鲁棒性。     

An adaptive algorithm for nonlinear system identification

There are several methods — fixed, adaptive, recursive — for the identification of linear and bilinear systems from input-output measurements that are noisy. However, literature is rather scarce as far as such techniques are concerned for the identification of nonlinear systems. The objective of this paper, therefore, is to suggest an iterative technique for the identification of nonlinear system parameters from measurements that are noisy. This technique requires the transformation of a nonlinear system in the state variable form into an input-output autoregressive moving average exogenous (armax) model. The pseudo linear regression algorithm, which has been extensively used for the identification of linear systems, can then be used to identify the nonlinear system parameters. Using this technique simulation studies were carried out which, indeed, confirm the efficacy of the method.     

Design and implementation of an identification system in construction site safety for proactive accident prevention

Identifying accident precursors using real-time identity information has great potential to improve safety performance in construction industry, which is still suffering from day to day records of accident fatality and injury. Based on the requirements analysis for identifying precursor and the discussion of enabling technology solutions for acquiring and sharing real-time automatic identification information on construction site, this paper proposes an identification system design for proactive accident prevention to improve construction site safety. Firstly, a case study is conducted to analyze the automatic identification requirements for identifying accident precursors in construction site. Results show that it mainly consists of three aspects, namely access control, training and inspection information and operation authority. The system is then designed to fulfill these requirements based on ZigBee enabled wireless sensor network (WSN), radio frequency identification (RFID) technology and an integrated ZigBee RFID sensor network structure. At the same time, an information database is also designed and implemented, which includes 15 tables, 54 queries and several reports and forms. In the end, a demonstration system based on the proposed system design is developed as a proof of concept prototype. The contributions of this study include the requirement analysis and technical design of a real-time identity information tracking solution for proactive accident prevention on construction sites. The technical solution proposed in this paper has a significant importance in improving safety performance on construction sites. Moreover, this study can serve as a reference design for future system integrations where more functions, such as environment monitoring and location tracking, can be added.     

Structural identification for electromagnetic system linear dynamic models and the relation to total power resolution

A structural identification method is proposed for linear dynamic models for single-phase alternating-current electromagnetic systems of source-load type. Matrix equations are derived that relate the Gram matrices of the Krylov bases to the power matrix, from which one can obtain the resolution of the total power. __________ Translated from Izmeritel’naya Tekhnika, No. 6, pp. 26–29, June, 2006.     

Distributed dynamic load identification based on shape function method and polynomial selection technique

A new approach based on shape function method of moving least square fitting (SFM_MLSF) and polynomial selection technique is proposed in this paper for distributed dynamic load identification. The distributed dynamic load is represented as a product of spatial distribution function and time history, and the two parts are assumed to be independent. The modal transformation of structural dynamic equation throws light on the fact that all modal loads share the same form with the distributed dynamic load in time domain. As the structural modal parameters are known, the time history of dynamic load can be precisely identified through SFM_MLSF method which approximates the local dynamic load with shape functions in the moving supporting time domain. Then, the spatial distribution function of the load is substituted as a series of basic functions, and the identification of distribution function is transformed into a linear fitting problem. Through polynomial selection technique based on error reduction ratio, the significant components are picked out from the basis function responses, which greatly improves stability and precision of the load distribution function. During the inverse analyses of both the time history and distribution function, appropriate regularization methods are still applied to overcome the unavoidable ill-conditioned problem. Numerical examples demonstrate the validity and accuracy of the proposed method.     

基于前向神经网络的时变非线性结构系统辨识快速递推最小二乘法

针对基于前向神经网络的普通递推最小二乘估计存在着自适应跟踪慢和精度低的问题，提出了一种可对非线性时变系统进行快速辨识的新方法，因该方法有类似递推最小二乘算法的形式，称其为基于前向神经网络的快速递推最小二乘算法。该算法对传统的递推最小二乘算法的递推方式进行了改变，以更好的跟踪非线性时变系统的动态特性。针对典型的系统辨识仿真算例，通过与现有常用方法的比较研究显示了这种算法具有计算简单、收敛速度快和辨识精度高的良好性能。最后将方法用于一个三自由度时变非线性振动系统，结果同样验证了方法的良好特性。     

Structural damage identification: influence of model incompleteness and errors

This paper presents a study of the influence of model incompleteness and errors in a structural damage identification technique based on modal data, leading to a set of linear equations. To deal with the incompleteness three methods are used: (1) dynamic and (2) static expansions of measured degrees of freedom and (3) replacement of unmeasured degrees of freedom by their undamaged counterparts. These methods are applied on damage identification of a laminated rectangular plate. The numerical tests show that the best accuracy is obtained with the dynamic expansion, followed by the static expansion and finally, the replacement of unmeasured degrees of freedom. It is also demonstrated that for small damage the errors are the main influence, whereas for large damage the incompleteness becomes the most important factor in the results. The identification of small and large damages is performed by deleting non-reliable equations, i.e., equations that contain large errors.     

微通道电泳芯片系统中信号识别算法的设计与实现

介绍适用于微通道电泳芯片系统的DNA测序软件的总体流程,以及其中的信号处理与识别算法。以激光诱导荧光检测系统所采集到的原始数据为源信号,以小波平滑和小波去噪为理论基础,将滤波算法和峰值识别算法综合在一起进行设计,从而使其适用于检测速度更快、样品量更少的微通道电泳芯片系统。将本算法应用于DNA片段的分离实验中,可以有效地达到滤波以及信号识别的目的。