强污染单历元GPS形变信号的提取和粗差识别

阐述了小波分析技术用于强污染GPS单历元形变信号处理的基本原理及其实现方法。以含少量粗差的低信噪比形变信号为例 ,研究了基于Mallat分解和合成算法 ,分离信号趋势项并进行粗差识别的技术。采用小波软阈值降噪的方法去除强污染数据中的随机噪声 ,降噪效果远好于中值滤波。最后对识别的粗差信息进行线性修复 。

经验模态分解、数值仿真与形变信号滤波试验

首先介绍了经验模态分解的方法与原理,给出了由经验模态分解产生的固有模态函数重构特定滤波器的算法,然后通过对已知数据的仿真检验,认为该方法在分解与重构滤波器方面具有较高的精度,最后通过对形变数据中几类典型干扰信号的初步试验与对比分析,可看出获得了满意的处理效果。综合分析认为,经验模态分解及其组合滤波器在干扰消除或某些频率成分滤波与研究中具有重要意义,可作为形变数据预处理中一种行之有效的数据分解方法。

基于经验模态分解的形变信号滤波处理与异常提取

本文介绍了经验模态分解的方法与原理,给出了由经验模态分解产生的固有模态函数重构组合滤波器的原理与详细算法。通过对形变观测中典型干扰信号的综合处理、异常提取试验及其所取得的效果,综合分析认为,经验模态分解组合滤波器方法可作为形变观测数据处理中一种新的行之有效的分析方法。

基于小波技术的GPS单历元形变信号降噪研究

基于小波变换多分率分析原理,本文阐述了阈值降噪、中值滤波小波降噪原理,在中值滤波基础上通过改进,提出加权中值滤波小波降噪方法。通过仿真实验分别与软硬阈值降噪,中值滤波降噪方法作比较。实验表明,对小波高频系数采用加权中值滤波,然后重构信号,不仅可以最大限度地保留有效信号、有效降噪,保持信号的光滑性,而且具有良好的抗粗差性。

经验模态分解法在高频形变干扰分析中的应用

通过经验模态分解法(EMD)对辽宁省典型形变干扰异常进行分析。通过对形变数据的分解,发现该方法能提取不同尺度的干扰异常,效果较好。然后对不同尺度的形变信号进行分析,剔除高频噪声信号,对形变观测曲线进行重构滤波,发现该方法可以去除信号干扰噪声,提高信噪比,可作为形变观测数据处理中的一种行之有效的数据分解方法。

基于声发射技术的PDC复合片性能的分析

PDC钻头在钻进过程中出现崩齿、掉片以及聚晶金刚石(PDC)层脱落是影响钻头工作的关键因素。使用声发射仪采集了PDC复合片在加载过程中的声发射信号,并且采用小波分析方法对PDC的声发射信号进行了时频分解。结果表明,通过时频分解可以得到PDC复合片的AE特征信号;PDC复合片中弹性变形的信号频率集中出现在低频信号中,且AE信号的幅度与载荷大小呈正相关;高频信号中的突发型信号可以反映PDC层在载荷作用下的损伤情况。

蠕变试验装置的微机巡检系统

研制了微机巡检系统，巡回监测蠕变试验的温度和形变信号。介绍系统组成及其软件功能。

配电网络连接线一致性的不断电检测技术

针对配电网络中的10k V线路、配电变压器、分支箱、表箱、计量装置及实际用户之间存在着对应关系不一致的情况,传统方法采用电力载波信号通讯识别由于存在共高压串线、共地串线、共电缆沟串线等情况,经常会造成误判。该文介绍一种不需停电即可完成低压配电网络连接线一致性快速检测的方案和装置。该装置分核查主机和核查副机2部分,检测时,核查副机在被检测电路的潮流末端发出一个形变脉动电流信号,通过低频过零载波通讯技术沿电缆传输,如果主机收到副机的形变脉动信号说明接线正确;否则,被检测的电缆接线不正确。该方案对排查现场串户具有参考价值。

基于SOA优化网络控制系统故障检测模型仿真

研究网络控制系统故障准确检测。网络控制系统中的故障信息多为非线性。在不平衡外部干扰作用下,导致控制系统的故障信号出现不平衡和挠曲变形的现象,给故障识别量带来干扰,传统的故障迹象诊断方法在故障迹象发生明显改变的情况下,很难做出正确判断。提出了一种SOA优化网络控制系统的故障检测模型。引入一种非线性状态估计方法,能够对网络控制状态进行估计,最大化地排除信号的非线性形变干扰。利用排除干扰后的数据建立SOA优化网络控制系统故障检测模型,从而实现网络控制系统的故障检测。实验结果表明,利用改进算法进行网络控制系统的故障检测,能够实现网络控制系统的实时、准确检测。

Classification of forearm action surface EMG signals based on fractal dimension

Surface electromyogram （EMG） signals were identified by fractal dimension.Two patterns of surface EMG signals were acquired from 30 healthy volunteers＇ right forearm flexor respectively in the process of forearm supination （FS） and forearm pronation （FP）.After the raw action surface EMG （ASEMG） signal was decomposed into several sub-signals with wavelet packet transform （WPT）,five fractal dimensions were respectively calculated from the raw signal and four sub-signals by the method based on fuzzy self-similarity.The results show that calculated from the sub-signal in the band 0 to 125 Hz,the fractal dimensions of FS ASEMG signals and FP ASEMG signals distributed in two different regions,and its error rate based on Bayes decision was no more than 2.26%.Therefore,the fractal dimension is an appropriate feature by which an FS ASEMG signal is distinguished from an FP ASEMG signal.

Chaotic characteristics of electromagnetic emission signals during deformation and fracture of coal

Electromagnetic emission(EME) is a kind of physical phenomenon accompanying the process of deformation and fracture of loaded coal and rock and it is of importance in quantitatively analyzing its characteristics.This will reveal the process of deformation and fracture of coal and predicting dynamic disasters in coal mines.In this study,the G-P(Grassberger and Procaccia) algorithm,calculation steps of the(if only 1 dimension) correlation dimension of time series and the identification standards of chaotic signals are introduced.Furthermore,the correlation dimensions of EME and the acoustic emission(AE) signals of time series during deformation and fracture of coal bodies are calculated and analyzed.The results show that the time series of pulses number of EME and the time series of AE count rate are chaotic and that the saturation embedding dimensions of a K3 coal sample are,respectively,5 and 6.The results can be used to provide basic parameters for predicting of EME and AE time series.

Research on the detecting methods of singularity in deformation signal based on two kinds of wavelet entropy

There are various influencing factors that affect the deformation observation, and deformation signals show differ- ent characteristics under different scales. Wavelet analysis possesses multi-scale property, and the information entropy has great representational capability to the complexity of information. By hamming window to the wavelet coefficients and windowed wavelet energy obtained by multi-resolution analysis （MRA）, it can be achieved to measure the wavelet time entropy （WTE） and wavelet energy entropy （WEE）. The paper established deformation signals, selected the parameters, and compared the sin- gularity detection ability and anti-noise ability of two kinds of wavelet entropy and applied them to the singularity detection at the GPS continuously operating reference stations. It is shown that the WTE performs well in weak singularity information de- tection in finite frequency components signals and the WEE is more suitable for detecting the singularity in the signals with complex, strong background noise.

Convex Variational Formulation with Smooth Coupling for Multicomponent Signal Decomposition and Recovery

A convex variational formulation is proposed to solve multicomponent signal processing problems in Hilbert spaces.The cost function consists of a separable term, in which each component is modeled through its own potential,and of a coupling term, in which constraints on linear transformations of the components are penalized with smooth functionals.An algorithm with guaranteed weak convergence to a solution to the problem is provided.Various multicomponent signal decomposition and recovery applications are discussed.

多通道奇异谱分析提取的CORS站大地高形变信息

针对GNSS测站噪声与局部效应对坐标时间序列精度的影响,提出有效提取CORS站网大地高时序形变信息的多通道奇异谱分析方法。MSSA方法提取的研究区各测站共有垂直形变信号,总体趋势上表现较为一致的起伏波动,其振幅并非恒定,而是随着时间变化增大或减小。MSSA方法重构的时间序列精度普遍高于FFT多周期重构法,38座CORS站中92%的测站采用MSSA方法重构之后其精度均有不同程度地改善,精度提升了2.01%~16.89%。实验结果表明:MSSA方法相比FFT方法更合适区域CORS站网大地高时序共有形变信息的提取,研究结果有助于发掘水负荷迁移与形变之间的作用机理,可为陆地水储量变化精确监测提供重要技术参考。