基于ZigBee的矿用应力监测系统的研究

提出了一种新的煤矿巷道监测方法 ,即采用测力锚杆对巷道顶部深处的应力变化进行定时监测。系统主要分为三部分:传感器节点、通信主站和上位机。传感器节点负责采集和上传数据,数据主站负责管理各传感器节点并存储和上传数据,上位机负责汇总各传感器数据并对其进行分析处理。     

A two‐level detector of short‐term unique changes in time series based on a similarity method

In the paper, a novel two‐level algorithm of time‐series change detection is presented. In the first level, to identify non‐stationary sequences in a processed signal, preliminary detection of events is performed with a short‐term prediction comparison. In the second stage, to confirm the changes detected in the first level, a similarity method aimed at identification of unique changes is employed. The detection of changes in a non‐stationary time series is discussed, implemented algorithms are described and the results produced on a sample four financial time series are shown. General conditions for implementing the proposed algorithm as an immune‐like event detector are discussed.     

基于Hadoop的云端异常流量检测与分析平台

Hadoop系统作为一种开源的分布式云计算平台已获得广泛应用,但其云端易受到各种威胁和攻击,基于此,开发了一种基于Hadoop的云端异常流量检查与分析平台。首先,使用Mapper周期性地从所有存储流量信息的文件中提取流量的部分信息;然后,通过Reducer将异常流量提取并保存。通过对流量数据的存储、检测与分析可成功地检测出有威胁的攻击,从而保障云端的安全。由于本平台基于开源的Hadoop实现,因此成本较低;同时,基于Java语言实现,可成功移植于各种主流操作系统,具有广泛适用性。基于局域网进行监控试验,结果表明本平台可成功地检测出异常流量,并输出友好的用户界面。     

Improving classification-based diagnosis of batch processes through data selection and appropriate pretreatment

This work considers the application of classification algorithms for data-driven fault diagnosis of batch processes. A novel data selection methodology is proposed which enables online classification of detected disturbances without requiring the estimation of unknown (future) process behavior, as is the case in previously reported approaches.The proposed method is benchmarked in two case studies using the Pensim process model of Birol et al. (2002) implemented in RAYMOND. Both a simple k Nearest Neighbors (k-NN) and complex Least Squares Support Vector Machine (LS-SVM) are employed for classification to demonstrate the generic nature of the proposed approach. In addition, the influence of different data pretreatment methods on the classification performance is discussed, together with a motivation for selecting the correct pretreatment steps. Finally, the influence of the number of available training batches is studied.The results demonstrate that a good classification performance can be achieved with the proposed data selection method even with a low number of faulty training batches by exploiting knowledge on the nature of the to-be-diagnosed faults in the data pretreatment. This provides a proof of concept for classification-based batch diagnosis and demonstrates the importance of incorporating process insight in the construction of data-driven process monitoring and diagnosis tools.     

Mooring system diagnosis and structural reliability control for position moored vessels

Early diagnosis and fault-tolerant control are essential for safe operation of floating platforms where mooring systems maintain vessel position and must withstand environmental loads. This paper considers two critical faults, line breakage and loss of a buoyancy element and employs vector statistical change detection for timely diagnosis of faults. Diagnosis design is scrutinised and a procedure is proposed based on specified false alarm probability and estimation of the distribution of the test statistics on which change detection is based. A structural reliability index is applied for monitoring the safety level of each mooring line and a set-point chasing algorithm accommodates the effects of line failure, as an integral part of the reliability-based set-point chasing control algorithm. The feasibility of the diagnosis and of the fault-tolerant control strategy is verified in model basin tests.     

Detection of delamination in laminated composites with limited measurements combining PCA and dynamic QPSO

This paper presents an output only damage diagnostic algorithm based on frequency response functions and the principal components for health monitoring of laminated composite structures. The principal components evaluated from frequency response data, are employed as dynamical invariants to handle the effects of operational/environmental variability on the dynamic response of the structure. Finite element models of a laminated composite beam and plate are used to generate vibration data for healthy and damaged structures. Three numerical examples include a laminated composite beam, cantilever plate made of carbon–epoxy and a laminated composite simply supported plate. Varied levels of delamination of laminated composite plies and matrix cracking at varied locations in the plies are simulated at different spatial locations of the structure. Numerical investigations have been carried out to identify the spatial location of damage using the proposed principal component analysis (PCA) based algorithm. In order to limit the number of sensors on the structure, an optimal sensor placement algorithm based on PCA is employed in the present work and the effectiveness of the proposed algorithm with a limited number of sensors is also investigated. Finally, the inverse problem associated with the detection of delamination and matrix cracking is formulated as an optimization problem and is solved using the newly developed dynamic quantum particle swarm optimization (DQPSO) algorithm. Studies carried out and presented in this paper clearly indicate that the proposed SHM scheme can robustly identify the instant of damage, spatial location, the extent of delamination and matrix cracking even with limited sensor measurements and also with noisy data.     

高速铁路沿线土地利用/覆盖变化检测方法研究

为开展高速铁路沿线土地利用/覆盖的快速变化检测工作,本文首先对两期高分辨率遥感影像进行主成分分析得到两期影像的第一主成分量,再用GIS矢量数据分割两期第一主成分量得到影像对象层,构造对象的灰度均值向量特征,最后利用最大类间方差阈值分割算法(Otsu法),自动提取变化类与未变化类。为验证该方法在高速铁路沿线土地利用/覆盖变化检测应用中的有效性,本文对2009年与2012年武广高铁武江特大桥沿线的GeoEye影像(分辨率1.6m)进行了变化检测验证性实验,得到变化检测结果的精度能够达到84.7579%,仅耗时5s。在此基础上将该方法应用于2009年与2010年石武客专郑州东站段WorldView-2影像(分辨率2.0m)的变化检测中,得到的变化检测结果精度为88.6953%,耗时86s。可见,该方法在检测高速铁路沿线土地利用/覆盖变化中具有较高的可靠性和效率,对更新高速铁路数据库具有一定的使用价值。     

一种基于视频的公交客流自动统计方法

为提高公交系统智能化管理水平,提出一种公交客流自动统计方法。采用直方图统计与多帧平均的方法提取视频背景,使用背景边缘去除算法得到乘客目标边缘轮廓信息。在此基础上,根据乘客头部轮廓的类圆特性,利用基于梯度信息的Hough变换圆检测算法完成乘客头部轮廓的识别,通过基于Kalman滤波预测的CamShift目标跟踪算法实现乘客的检测与计数。实验结果表明,该方法能有效消除背景图像中的噪声以及背景边缘,准确识别乘客目标并对其跟踪计数,从而提高城市公共交通运输效率。     

基于日志分析的增量数据捕获方法研究

通过扫描数据库日志文件可以捕获变化数据,但已有研究仅适应某种特定类型的数据库管理系统并且缺乏对冗余信息的消除。针对上述不足,提出一个基于日志分析的增量数据检测及其净效应处理通用模型,描述增量检测过程中的通用处理步骤,即日志抽取、日志分析和净效应处理3个模块。通过实验对净效应处理速度、冗余数据压缩率、网络传输速度等因素进行分析,结果表明,净效应处理可以有效地减少数据的网络传输时间和更新时间,提高运行效率。     

无线传感器网络中基于ELM的混合入侵检测方案

在研究机器学习算法的基础上,提出一种基于极限学习机( ELM)的混合入侵检测方案。将无线传感器网络分为感知层、数据汇聚层和核心控制层,在每层分别设置与其相适应的入侵检测方案,并在能量充足的核心控制层布置信任管理模块和ELM模块。信任模块可以及时筛去异常节点,相比于支持向量机算法训练速度更快,可提高入侵检测效率。实验结果表明,该方案在保证较高检测率的基础上,降低了能耗,延长网络运行时间,更适合于资源受限的无线传感器网络。