基于Voronoi图的无线传感网络覆盖盲区检测方法

传感网络的空洞暴露程度较少,在检测网络覆盖情况时更容易出现误差,影响盲区的检测效果。为此,提出基于Voronoi图的无线传感网络覆盖盲区检测方法。根据节点分布关系推测无线传感网络覆盖情况,在Voronoi图的指导下排除已覆盖区域,获取具备检测条件的未覆盖无线传感网络空洞。计算网络空洞的暴露程度和节点能量,作为特征样本输入粒子群分离器中,根据分类器的输出结果,实现无线传感网络覆盖盲区检测。仿真结果表明,所提方法不同覆盖盲区数量下的检测时长低于0.2 s、不同节点数量下的检测能耗低于20 J、不同空洞圆心距下的覆盖盲区差异度最高为0.24,证明所提方法具有较好的无线传感网络覆盖盲区检测效果。     

数控车床主轴系统误差测试及分析

机床主轴部件的动态和热特性是衡量机床性能水平的重要技术指标。主轴误差测试及误差分析一直是检测及提高机床性能的重要手段。本文在分析主轴动态和热态性能的基础上,针对具体的数控机床重点研究如何通过实验方法获得机床的回转误差和热漂移误差分析方法。     

地下电缆通道的无线温湿度传感网与BP评估

无线传感网技术线路较少,串绕简单等特点,使得短距离的无线传感技术能很好地解决地下电缆通道电缆线路繁多、运营维护监测困难等问题。通过建立基于无线温湿度传感网的地下电缆通道系统,用温湿度传感器对其进行监测,监测的实时数据用无线传感网技术传回,并通过BP神经网络模型对监测数据进行网格训练,建立优化模型,得到优化前后的权值和阈值训练误差曲线,优化前的训练样本和测试样本的仿真误差分别为0.19和0.30,优化后这两个数值分别为0.17和0.024。随机选取楚雄腰站变电站20组监测数据带入BP模型,得到结果与电缆通道温湿度安全等级对比,准确度达到93.3%。     

有限元分析在数控铣床热变形方面的研究

在多种热源的作用下,数控铣床产生热变形,影响工件与刀具间的相对位移,造成加工误差,从而影响零件的加工精度,因此减小热误差对提高机床的加工精度至关重要。控制机床热误差涉及到如何查找敏感点,然而找出机床敏感点是个非常棘手的问题。本文在对数控铣床热边界条件进行分析的基础上,应用有限元分析软件ANSYS,对ZK7640数控铣床进行整机热特性分析,为机床敏感点的查找提供依据,对数控机床热误差进行了定量计算,并通过实验检测验证其正确性。     

一种无干扰可配置的后向散射无线传感网络架构

低成本、低功耗是无线传感网络得到普遍应用的重要原因。基于软件无线电技术、LabVIEW和USRP（Universal Software Radio Peripheral）平台,本文提出并实现了一种面向多传感应用、无干扰可配置的后向散射无线传感网络架构原型,架构由可配置零中频接收的后向散射无线传感收发器和传感节点组成,通过无线射频能量获取和传感节点的射频唤醒,有效地降低了系统功耗。论述副载波可配置的频分多址接入方法,给出并证明了避免多传感器接入碰撞的传感数据脉冲周期约束条件。实验和仿真验证了提出架构的有效性和可行性,实验测试误差矢量幅度EVM值小于2.8%,且数值分析和Monte Carlo仿真验证间的中断率误差小于1.86%。     

移动无线物联网感知层传感节点定位算法研究

为了解决当前算法在锚节点密度较低时,传感节点定位精度不高的问题,提出了移动无线物联网感知层传感节点定位算法。建立了包含锚节点误差的移动无线物联网模型;根据共线度筛选候选锚节点。加权平均处理锚节点可信度,计算每个未知传感节点的平均跳距和未知传感节点定位误差,将计算误差低于设定阈值的未知传感节点转化为锚节点,并对初步定位结果进行循环位置修正,实现感知层传感节点定位。仿真结果表明,所提算法在锚节点密度相同时的定位误差低于2.5。     

基于MDP的无线传感网络Sybil攻击检测方法研究

为了提高无线传感网络中对Sybil攻击的检测精准度,提出基于MDP的无线传感网络Sybil攻击检测方法。采用近似梯度下降算法结合小波去噪法对无线传感网络信号展开去噪处理,进而利用马尔科夫决策过程(MDP)表示去噪后的无线传感网络信号,并对其展开跟踪,通过RSSI算法对Sybil攻击展开定位,实现无线传感网络中Sybil攻击检测。实验结果表明,所提方法的检测率为99.9%,误检率为0.06%,检测时间为0.4 s,该方法的检测率高、误检率低、检测效率高,可有效提高无线传感网络中对Sybil攻击的检测精准度。     

无线传感器网络技术的应用研究

无线传感网络通过在随机环境中植入大量的通信传感节点来实时检测当前环境的变化,并通过无线传感网络将当前环境的变化传输到嵌入式系统中进行计算分析.无线传感网络相比于其他检测手段,它能够更加准确和高效地识别环境的变化并做出实时预警,在智能电网的传输和军事等领域都有着非常广阔的应用.     

基于FW-PSO的大区域无线传感网络流量异常检测算法

针对大区域无线传感网络流量特征维度较高，现有神经网络算法只能假设所有区域特征一致，导致一旦网络规模过大，会存在较大误差的问题。采用烟花算法优化粒子群算法的搜索能力，设计一种FW-PSO（FireworksAlgorithm-Particle Swarm Optimization ,烟花算法-粒子群算法）算法，提升了算法的全局搜索能力及收敛速度，提出基于FW-PSO的大区域无线传感网络流量异常检测算法。首先采用时间滑动窗口处理大区域无线传感网络数据流信息，通过正态Grubbs法则剔除数据中粗大误差值。然后引入主成分分析法对传感数据特征降维，分段提取有价值的特征。设计FW-PSO算法，提升粒子群算法的搜索能力，实现流量异常检测。实验结果表明，所提方法的无线传感网络流量异常检测率准确率平均为94.8%，训练及检测耗时平均值分别为3.75s及0.25s。     

NLOS误差模型下的无线传感网定位方法与仿真

为了有效提升在NLOS环境下的定位精度和探讨不同NLOS模型和不同定位方法之间的特性,对多种NLOS误差模型进行建模,并给出模型数学公式,挖掘出适合无线传感网的NLOS误差模型,利用多种不同的NLOS误差模型来表征无线传感网中的非视距传播(NLOS)环境;利用常用经典定位方法对未知节点进行定位.通过对不同参数的设置进行计算机仿真实验,仿真实验数据结果有效地反映出各种定位法在不同NLOS误差场景下的性能,为无线传感网的实际定位应用提供理论基础.     

数控铣齿机主轴系统温度场有限元分析

热误差是影响机床加工精度的主要因素之一。主轴的热变形是机床总的热变形的重要组成部分。因此本文在分析数控铣齿机主轴热源的基础上,计算发热量,确定边界条件,并利用有限元软件abaqus建立主轴系统的温度场模型并进行了数字模拟仿真,为主轴系统的进一步热变形控制提供了基础。     

基于LabVIEW的机床热误差检测系统设计

针对机床高速旋转主轴的安装偏心及各种外界干扰对热误差精确测量的影响,本文选用了精度较高的电容式位移传感器及NI数据采集卡,基于LabVIEW平台开发了一套机床温度和位移数据实时检测系统。该检测系统可实现在主轴高速旋转下位移数据的高速率采样,并采用软件数字滤波方法对位移数据处理。最后将该检测系统进行实验测量,实验结果表明该检测系统具有一定的实用性。     

The application of ANFIS prediction models for thermal error compensation on CNC machine tools

Thermal errors can have significant effects on CNC machine tool accuracy. The errors come from thermal deformations of the machine elements caused by heat sources within the machine structure or from ambient temperature change. The effect of temperature can be reduced by error avoidance or numerical compensation. The performance of a thermal error compensation system essentially depends upon the accuracy and robustness of the thermal error model and its input measurements. This paper first reviews different methods of designing thermal error models, before concentrating on employing an adaptive neuro fuzzy inference system (ANFIS) to design two thermal prediction models: ANFIS by dividing the data space into rectangular sub-spaces (ANFIS-Grid model) and ANFIS by using the fuzzy c-means clustering method (ANFIS-FCM model). Grey system theory is used to obtain the influence ranking of all possible temperature sensors on the thermal response of the machine structure. All the influence weightings of the thermal sensors are clustered into groups using the fuzzy c-means (FCM) clustering method, the groups then being further reduced by correlation analysis.A study of a small CNC milling machine is used to provide training data for the proposed models and then to provide independent testing data sets. The results of the study show that the ANFIS-FCM model is superior in terms of the accuracy of its predictive ability with the benefit of fewer rules. The residual value of the proposed model is smaller than ±4 μm. This combined methodology can provide improved accuracy and robustness of a thermal error compensation system.     

机床主轴热误差建模算法*

针对机床主轴热误差补偿过程中现有建模方法的不足,提出一种新的热误差建模算法。首先应用FCM算法将众多温度测点予以分类,减少测点数量,提高测量精度。其次应用GCA算法对同类测点的热敏感度进行排序,选出该类中的关键测点。最后以优选出的测点为输入变量,以热位移为输出变量,利用ANFIS进行热误差模型设计,并与BP算法建立的模型进行了比较。实验数据表明：该方法降低了机床热误差,具有预测精度高的优点,能较好的实现机床主轴热误差的补偿。     

Virtual machining considering dimensional,geometrical and tool deflection errors in three-axis CNC milling machines

Virtual manufacturing systems can provide useful means for products to be manufactured without the need of physical testing on the shop floor. As a result, the time and cost of part production can be decreased. There are different error sources in machine tools such as tool deflection, geometrical deviations of moving axis and thermal distortions of machine tool structures. Some of these errors can be decreased by controlling the machining process and environmental parameters. However other errors like tool deflection and geometrical errors which have a big portion of the total error, need more attention. This paper presents a virtual machining system in order to enforce dimensional, geometrical and tool deflection errors in three-axis milling operations. The system receives 21 dimensional and geometrical errors of a machine tool and machining codes of a specific part as input. The output of the system is the modified codes which will produce actual machined part in the virtual environment.     

A PNN self-learning tool breakage detection system in end milling operations

With the advance of technology over the years, computer numerical control (CNC) has been utilized in end milling operations in many industries such as the automotive and aerospace industry. As a result, the need for end milling operations has increased, and the enhancement of CNC end milling technology has also become an issue for automation industry. There have been a considerable number of researches on the capability of CNC machines to detect the tool condition. A traditional tool detection system lacks the ability of self-learning. Once the decision-making system has been built, it cannot be modified. If error detection occurs during the detection process, the system cannot be adjusted.To overcome these shortcomings, a probabilistic neural network (PNN) approach for decision-making analysis of a tool breakage detection system is proposed in this study. The fast learning characteristic of a PNN is utilized to develop a real-time high accurate self-learning tool breakage detection system. Once an error occurs during the machining process, the new error data set is sent back to the PNN decision-making model to re-train the network structure, and a new self-learning tool breakage detection system is reconstructed. Through a self-learning process, the result shows the system can 100% monitor the tool condition. The detection capability of this adjustable tool detection system is enhanced as sampling data increases and eventually the goal of a smart CNC machine is achieved.     

Iterative learning control for integrated system of robot and machine tool

This study is concerned with the integrated system of a robot and a machine tool. The major task of robot is loading the workpiece to the machine tool for contour cutting. An iterative learning control (ILC) algorithm is proposed to improve the accuracy of the finished product. The proposed ILC is to modify the input command of the next machining cycle for both robot and machine tool to iteratively enhance the output accuracy of the robot and machine tool. The modified command is computed based on the current tracking/contour error. For the ILC of the robot, tracking error is considered as the control objective to reduce the tracking error of motion path, in particular, the error at the endpoint. Meanwhile, for the ILC of the machine tool, contour error is considered as the control objective to improve the contouring accuracy, which determines the quality of machining. In view of the complicated contour error model, the equivalent contour error instead of the actual contour error is taken as the control objective in this study. One challenge for the integrated system is that there exists an initial state error for the machine tool dynamics, violating the basic assumption of ILC. It will be shown in this study that the effects of initial state error can be significantly reduced by the ILC of the robot. The proposed ILC algorithm is verified experimentally on an integrated system of commercial robot and machine tool. The experimental results show that the proposed ILC can achieve more than 90% of reduction on both the RMS tracking error of the robot and the RMS contour error of the machine tool within six learning iterations. The results clearly validate the effectiveness of the proposed ILC for the integrated system.     

多传感器融合的数控机床热误差回归建模新方法

热误差对机床的加工精度影响很大,高性能的补偿系统依赖于多传感器融合建立的三维模型的精度、鲁棒性和合适的温度进行反馈输入。本文使用温度与位移传感器的模糊聚类进行温度分类,基于评价模型比对分析最优的温度分类,从每个分类中选择具有代表性温度作为候选温度。归纳试验数据,使用分段逆回归SIR模型进行热误差建模,SIR模型将高维前移回归问题转化为多个一维的回归问题,并且进一步消除了候选温度之间的耦合。热误差试验表明,SIR模型具有泛化能力强、预测精度高及鲁棒性好的特点,能够准确地描述多种典型工况条件下的实际热误差特性。     

基于LabVIEW数控机床几何误差补偿的研究方法

介绍一种由软件、硬件控制的数控机床几何误差补偿。误差补偿的原理主要是运用多体系统运动学理论建立机床几何误差模型,用硬件控制固化在程序存储器内的误差补偿程序完成补偿任务,并通过RS-232C实现数控机床与Windows平台通信与数据交换。     

基于Web的数控曲面磨床几何误差补偿技术

以误差补偿技术的网络化应用为目的展开研究,选取SMART-CNC数控曲面磨床为研究对象,运用多体理论,建立了磨床的运动模型,获得了与精密刀具轨迹相对应的逆变刀具路线或逆变数控指令的求解方法,在此基础开发了基于网络的误差补偿软件,构建了网络化误差补偿服务系统。仿真试验结果从理论上证明了本文建模方法的正确性和网络化应用方案的可行性。