边缘电场传感器测量金属衬底绝缘层厚度

针对金属衬底上的绝缘层,研究基于边缘电场传感器的厚度测量方法。位于同一平面的传感电极形成边缘场电容,边缘电场将穿透绝缘层,在衬底表面发生突变。本文用三电容模型分析了传感器的测量原理,衬底作为一个浮动电极分别与两个传感器电极构成平行极板电容,与边缘场电容共同构成传感电容。通过二维仿真讨论了电容结构参数对传感电容的影响。实验结果表明,边缘场电容传感器应用于金属衬底上的绝缘层测量时,表现出边缘场电容与平行板电容复合的特性,传感器灵敏度高。当绝缘层厚度相比电极长度较小时,电极的间距对传感器电容输出影响小。     

用于地下电缆绝缘状态监测的边缘电场法研究

根据地下电力电缆绝缘状念移动监测对传感器的要求,提出运用边缘电场法进行水树老化检测的方案。针对利用传统叉指型FEF传感器进行水树检测干扰大的问题,设计了新型基于弧形基板的叉指型FEF传感器,采用有限无法建立其数学模型,通过MaXwe1l有限元分析软件仿真比较两种传感器在绝缘层中的电场分布情况,并分析电极叉指宽度以及问距宽度变化对传感器电场穿透效果的影响。仿真证明了该新型传感器对电缆绝缘层穿透能力强于传统FEF传感器,最终确定了新型传感器设计参数的最优值。     

多层传感器故障数据的挖掘模型仿真

多层传感器的故障准确定位对保证各自应用安全至关重要.多层传感器不同于传统的传感器网络,其不同层次的传感器故障的特征差异较大,不同层次传感器之间存在故障特征“断层”问题.传统的基于流数据异常特征识别的多层传感器故障数据的挖掘模型需要明确层次网络故障之间的关联特征,若传感器层次之间的故障特征关联性不强,故障挖掘的阀值就无法固定,产生故障特征无法定位问题,导致误警率较高.提出了一种基于贝叶斯信念网络的多层传感器故障数据的挖掘模型,针对多层传感器故障数据属性多样性的问题,分析了贝叶斯信念网络的结构,搜索一个最匹配待分类故障数据样本的贝叶斯信念网络,通过评估函数评估各个可能的网络结构与样本多层传感器故障数据间的契合度,采集一个最佳样本多层传感器故障数据解,通过“压缩侯选”的贝叶斯信念网络算法,计算样本多层传感器故障数据间的依赖关系,集中扫描最可能是待挖掘数据的变量集,实现故障数据的挖掘.实验结果表明,利用所提模型能够有效提高多层传感器故障数据的挖掘的准确性.     

基于边缘电场传感器的大范围位移测量方法

主要研究基于边缘电场传感器的位移测量方法,由于在测量位移时交叉指型传感器互导电容的变化具有周期性,因而可以实现大范围的位移测量.运用Ansoft Maxwell有限元仿真软件对边缘电场传感器的二维半波长模型进行了仿真分析,仿真结果表明当传感器工作区间内有物体移动时,会改变传感器的电场分布,使传感器互导电容发生周期变化.设计了一个简单的、低成本的调理电路,可以将电容变化转化为电压输出信号,得到边缘电场传感器输出电压与电容的关系.对电极尺寸为35 mm×30 mm×0.02 mm的边缘电场传感器进行了测量实验,实验结果与仿真结果吻合较好.     

基于小波包与SOM神经网络的传感器故障诊断

研究传感器实时故障诊断问题.首先采用MATLAB2015仿真得到传感器各种典型工作状态下的运行数据样本;其次将这些故障样本作3层小波包分解,分别求出第3层小波包基对应的各频率段的能量,利用这些能量值与正常工作时各频段的能量值之比构造出传感器故障诊断的特征向量;最后构建基于3×3的SOM神经网络的传感器故障诊断算法.测试证明了所提算法的有效性和准确性.     

图像边缘检测的多尺度Gap统计方法

基于Tibshirani等的“Gap Statistic”思想,利用样本灰度数据分布差分析了图像分布间隙的规律,并在讨论随机含义下屋脊边缘和阶跃边缘等概念的基础上,建立了图像边缘检测的多尺度随机模型,给出了图像边缘检测算法,最后通过实例验证了该模型的抗噪声与多尺度特性。     

基于统计学习预测技术提高传感器稳定度

针对传感器的输出随温度和电源的波动以及自身的某些特性的改变等因素而发生零点漂移现象,提出了一种对以上非目标量进行预测处理以消除它们对传感器输出影响的支持向量机(SVM)预测技术.这种方法不需要建立大量的观测样本,也不需要对观测样本进行特征提取就可以取得良好的改善效果,具有较好的实用价值.以2只半导体传感器为研究对象的测试结果表明:利用该方法使稳定性提高了6倍.同时,提出了2种优化算法:增量样本法和降一阶算法.     

微型折叠式三维电场传感器

提出一种基于柔性衬底和三维组装方法的新型三维电场传感器.利用柔性衬底的折叠,构建一种互相垂直的新型三维电场传感器,研究了测量空间电场时的耦合,提出解耦算法,标定灵敏度矩阵,实现了空间电场三个方向分量的准确测量;通过一系列的温湿度实验研究了三维结构的形变,证明了三维电场传感器结构稳定可靠.实验结果表明:所研制的三维电场传感器不仅能消除耦合干扰,实现空间电场分量的准确测量,测量误差在3.61％以内;而且通过新的三维组装方法,大大减少了三维电场传感器的质量、体积,折叠工艺简化了三维传感器的组装,有利于批量化生产.     

改进型RBF神经网络的多标签算法研究

针对已有的RBF神经网络多标签算法未充分考虑多个样本标签之间的关联性,从而导致泛化性能受到一定影响的问题,研究分析了一种改进型RBF神经网络的多标签算法.该算法首先优化隐含层RBF神经网络基函数中心求取算法——k-均值聚类.采用AP聚类自动寻找k值以获得隐含层节点数目,并构造Huff man树来选取初始聚类中心以防k-均值聚类结果陷入局部最优.然后构造体现标签类之间信息的标签计数向量C,并将其与由优化k-均值聚类得到的聚类中心进行线性叠乘,进而改进RBF神经网络基函数中心,建立RBF神经网络.在公共多标签数据集emotion上的实验表明了该算法能够有效地进行多标签分类.     

WSN中的一种多传感器数据融合算法

为了适应无线传感器网络资源受限的特点,提出一种多传感器数据融合算法,算法在传感层和网络层之间增加数据融合层,将采集的数据分为实时数据和非实时数据,对数据进行约筒处理,提高传感器节点传输被采集信息的效率.利用TinyOS进行仿真测试,结果表明:数据融合算法可以有效地减少网络中数据通讯流量,节省传感器节点能量,延长网络生存时间.     

基于抗差估计方法的WSN节点定位算法研究

针对无线传感器网络在实际应用中,节点间测距结果往往含有粗差,并会严重影响未知节点坐标估计值的准确性和可靠性这一问题,引入抗差估计理论,采用IGGⅢ权因子函数,设计了一种基于抗差估计的节点定位算法。该算法能对含有不同幅度的测量误差分别采取保权、降权和淘汰等相应处理,明显提高定位精度。仿真实验表明:在无粗差的情况下,本文算法与经典最小二乘定位算法的定位效果保持了良好的等效性;在含有粗差的情况下,本文算法借助于选择的阈值,对不同的粗差采取剔除以及降权等适当处理,比经典最小二乘定位取得了更高的定位精度,保证了估计结果的无偏性,体现出良好的抗差性能。     

多传感器测量中的方差估计

在多传感器加权融合算法中，各传感器的权值仅由传感器的测量方差决定。假设各传感器噪声为平稳过程且相互独立，本文提出了待测状态未知且时变情况下．多传感器测量方差的估计算法，并讨论了估计方差的统计特性，证明了估计的无偏性。针对工程中环境噪声的变化，进一步提出了加窗方差估计方法，并给出了窗口宽度和估计精度的关系．使算法能有效地跟踪环境噪声的变化。该算法无需设置依赖于环境的初始值，并给出了递推公式．使其可用于对多传感器测量方差的实时自适应估计。仿真结果直观地说明了估计方法的有效性。     

Two-dimensional angle estimation for monostatic MIMO arbitrary array with velocity receive sensors and unknown locations

In this paper, the problem of two-dimensional angle estimation for monostatic multi-input multi-output (MIMO) array is studied, and an algorithm based on the usage of velocity receive sensors is proposed. The algorithm applies the estimation method of signal parameters via rotational invariance technique (ESPRIT) algorithm to obtain automatically paired two-dimensional angle estimation. By utilizing the relationship within the outputs of velocity sensors, the rotational invariance property of ESPRIT does not depend on the array geometry any more. Hence, the proposed algorithm can provide two-dimensional DOA estimation for the MIMO array without the knowledge of sensor locations in the array. The algorithm requires no peak searches, so it has low complexity. Furthermore, it has better angle estimation performance than propagator method using the same sensor configuration. Error analysis and Cramér–Rao bound (CRB) of angle estimation in MIMO radar are derived. Simulation results verify the usefulness of the algorithm.     

离散时间间接型模型参考自适应控制的一种系统化的分析方法

This paper presents the design and analysis of indirect model reference adaptive control(MRAC)with normalized adaptive law for a class of discrete-time systems.The main work includes three parts.Firstly,the constructed plant parameter estimation algorithm not only possesses the same properties as those of traditional estimation algorithms but also avoids the possibility of division by zero.Secondly,by finding the relationship between the plant parameter estimate and controller parameter estimate and using the properties of plant parameter estimate,the similar properties of controller parameter estimate are also established. Thirdly,based on the relationship properties between the normalizing signal and all the signals in the closed-loop system and on some important mathematical tools on discrete-time systems,as in the continuous-time case,a systematic stability and convergence analysis approach to the discrete indirect MRAC scheme is developed rigorously.     

Robustness analysis of discrete-time indirect model reference adaptive control with normalized adaptive laws

For a class of discrete-time systems with unmodeled dynamics and bounded disturbance, the design and analysis of robust indirect model reference adaptive control (MRAC) with normalized adaptive law are investigated. The main work includes three parts. Firstly, it is shown that the constructed parameter estimation algorithm not only possesses the same properties as those of traditional estimation algorithms, but also avoids the possibility of division by zero. Secondly, by establishing a relationship between the plant parameter estimate and the controller parameter estimate, some similar properties of the latter axe also established. Thirdly, by using the relationship between the normalizing signal and all the signals of the closed-loop system, and some important mathematical tools on discrete-time systems, as in the continuous-time case, a systematic stability and robustness analysis approach to the discrete indirect robust MRAC scheme is developed rigorously.     

Hierarchical Newton and least squares iterative estimation algorithm for dynamic systems by transfer functions based on the impulse responses

This paper develops a parameter estimation algorithm for linear continuous-time systems based on the hierarchical principle and the parameter decomposition strategy. Although the linear continuous-time system is a linear system, its output response is a highly nonlinear function with respect to the system parameters. In order to propose a direct estimation algorithm, a criterion function is constructed between the response output and the observation output by means of the discrete sampled data. Then a scheme by combining the Newton iteration and the least squares iteration is builded to minimise the criterion function and derive the parameter estimation algorithm. In light of the different features between the system parameters and the output function, two sub-algorithms are derived by using the parameter decomposition. In order to remove the associate terms between the two sub-algorithms, a Newton and least squares iterative algorithm is deduced to identify system parameters. Compared with the Newton iterative estimation algorithm without the parameter decomposition, the complexity of the hierarchical Newton and least squares iterative estimation algorithm is reduced because the dimension of the Hessian matrix is lessened after the parameter decomposition. The experimental results show that the proposed algorithm has good performance.     

Modified adaptive robust control system design

The robust control design problem is studied for a class of uncertain dynamical systems. The uncertainty of the system is time varying. The only assumption on the uncertainty is that it is bounded. No statistical property of the uncertainty is ever assumed and utilized. The robust control does not need the a priori estimation on the bound of uncertainty. An adaptive algorithm for the on-line estimation of the bound is constructed and the robust control is only dependent on this estimation. The adaptive algorithm is a modification of previous work by Corless and Leitmann (1983). The advantages of this new algorithm include a constant control design parameter (which should have been time varying previously) and the applicability to linear systems with mismatched uncertainty and measurement noise.     

一种基于多层神经网络的盲信号分离算法

基于多层神经网络,提出一种盲信号分离算法.该算法不对信号的密度模型做任何假设,通过多层神经网络估计任意信号的概率密度函数,并由此估计信号的评价函数.同其他方法相比,该方法不仅具有更好的分离性能,而且收敛速度较快.该方法可直接应用于所有以非线性函数代替评价函数的盲信号分离算法.实验验证了方法的有效性.     

改进BP算法在热流传感器温度补偿中的应用

针对帕尔贴( Peltier)热流传感器存在温度漂移问题,提出了一种基于改进BP神经网络的温度补偿模型.采用BP算法的多层前馈网络建立起热流传感器输出电压、实验温度与输入热流间的映射关系,又通过增加动量因子和自适应调节学习率来提高网络的收敛速度与增强网络平稳性.研究结果表明:动量因子-自适应学习率算法温度补偿模型效果明显.     

微粒群优化算法在传感器非线性自校正中的应用

微粒群优化(PSO)算法是一种进化算法,包含的概念简单。介绍了不同于传统的传感器非线性校正方法,将PSO算法应用于传感器非线性校正的参数估计,并通过电涡流微位移传感器非线性校正进行PSO算法效果测试。实验研究表明:PSO算法简单、得到的传感器非线性校正曲线精度高。PSO算法为传感器的非线性校正提供了一种新方法。