基于Hilbert-Huang变换的ECG消噪

 提出一种基于Hilbert-Huang变换的ECG消噪方法,该方法对含噪ECG进行经验模态分解,对分解后的IMF进行Hilbert频谱分析,然后根据ECG信号噪声特点对三种主要噪声分别消噪.工频干扰和高频噪声主要存在于ECG的低阶IMF中,而基线漂移主要存在于ECG的高阶IMF中,对低阶IMF采用基于自适应阈值的形态学滤波方法进行消噪,对高阶IMF采用平滑滤波法进行基线漂移估计.仿真实验和实际应用结果表明该方法优于小波消噪法,不仅对三种主要噪声具有较好的抑制作用,还能很好的保留ECG波形特征.     

HHT在眼底图像边缘检测中的应用

在眼底图像处理中,边缘检测技术是许多研究领域的重要预处理手段,如眼底图像特征提取和基于图像特征的图像配准方法等等。将希尔波特-黄变换（HHT,Hilbert-Huang Transform）方法应用于眼底图像边缘检测,提出了基于二维经验模式分解的图像边缘检测算法。实验结果表明,利用HHT方法的自适应多分辨特性,可以有效提取出眼底图像的细节信息,又不会检测出过多的虚假边缘。该方法为眼底图像的其它处理方法提供了有效的预处理手段。     

GPS‐Based Orbit Determination for KOMPSAT‐5 Satellite

Korea Multi‐Purpose Satellite‐5 (KOMPSAT‐5) is the first satellite in Korea that provides 1 m resolution synthetic aperture radar (SAR) images. Precise orbit determination (POD) using a dual‐frequency IGOR receiver data is performed to conduct high‐resolution SAR images. We suggest orbit determination strategies based on a differential GPS technique. Double‐differenced phase observations are sampled every 30 seconds. A dynamic model approach using an estimation of general empirical acceleration every 6 minutes through a batch least‐squares estimator is applied. The orbit accuracy is validated using real data from GRACE and KOMPSAT‐2 as well as simulated KOMPSAT‐5 data. The POD results using GRACE satellite are adjusted through satellite laser ranging data and compared with publicly available reference orbit data. Operational orbit determination satisfies 5 m root sum square (RSS) in one sigma, and POD meets the orbit accuracy requirements of less than 20 cm and 0.003 cm/s RSS in position and velocity, respectively.     

基于互信息波段选择和经验模态分解的高精度高光谱数据分类

在遥感数据处理研究中,高维高光谱数据的冗余信息和噪声严重影响高光谱数据的分类精度,针对此问题提出基于互信息波段选择和经验模态分解的高精度高光谱数据分类算法(MI-EMD-SVM).分别采用基于互信息波段选择方法和经验模态分解实现对高光谱数据的冗余信息处理和特征提取,并获得处理后的高光谱数据X'.采用支持向量机分类算法...     

作用在柔性支护结构上的水及土压力的分析与探讨

大量的实测结果表明,经典理论计算出的水土压力值与工程实测存在着很大的差别,在软土地区尤为明显。本文通过对基坑工程的实测水土压力、孔隙水压力和地下水位的分析,阐述了基坑外侧水土压力、土压力、孔隙水压力随工况、时间的变化规律,给出了开挖、降水后基坑底面以上的土压力系数估算公式,基坑开挖前的孔隙水压力系数估算公式。通过分析认为有效应力原理同样适用于粘性土,从理论上来讲软土地区也宜采用水土分算,但考虑到孔隙水及土压力随时间、工况不断发生变化,精确计算十分困难,在综合考虑影响水土压力的各种因素后,给出了作用在支护结构上的水土压力合算经验公式。     

汶川地震基岩强震记录的Hilbert-Huang变换分析

详细解释了Hilbert-Huang变换（HHT）的思想和基本理论,利用HHT方法对汶川8.0级的地震记录进行了经验模态分解（EMD）,并对其Hilbert-Huang 三维时间-频率-幅值（能量）图谱、边际能量谱和瞬时能量谱进行了分析讨论。研究表明,基于HHT的Hilbert谱能够精准和清晰地揭示地震记录的时-频-能量分布。     

基于中值滤波-SVD和EMD的声发射信号特征提取

针对随机噪声和脉冲干扰对经验模态分解(EMD)质量的影响,提出中值滤波和奇异值分解(SVD)联合降噪方法,并将其与EMD分解相结合形成一种新的声发射(AE)信号特征提取方法.首先对原始AE信号进行中值滤波,去除幅值较大的异常值;其次对去除异常值的信号序列进行相空间重构和SVD分解,并针对难以确定重构阶数这一问题,提出奇异值能量差分谱概念,利用谱峰的较大值位置来确定重构阶数,以进一步降噪;最后对降噪信号进行EMD分解,以本征模态函数(IMF)的能量占比作为表征各损伤信号的特征向量.数值仿真和5层胶合板损伤的实测数据表明,该方法不仅能够滤除噪声干扰,提高EMD分解的时效性和准确性,而且能够有效地提取出胶合板AE信号特征,对其损伤类型进行有效地识别.     

Fault Diagnosis Model Based on Feature Compression with Orthogonal Locality Preserving Projection

Based on feature compression with orthogonal locality preserving projection(OLPP),a novel fault diagnosis model is proposed in this paper to achieve automation and high-precision of fault diagnosis of rotating machinery.With this model,the original vibration signals of training and test samples are first decomposed through the empirical mode decomposition(EMD),and Shannon entropy is constructed to achieve high-dimensional eigenvectors.In order to replace the traditional feature extraction way which does the selection manually,OLPP is introduced to automatically compress the high-dimensional eigenvectors of training and test samples into the low-dimensional eigenvectors which have better discrimination.After that,the low-dimensional eigenvectors of training samples are input into Morlet wavelet support vector machine(MWSVM) and a trained MWSVM is obtained.Finally,the low-dimensional eigenvectors of test samples are input into the trained MWSVM to carry out fault diagnosis.To evaluate our proposed model,the experiment of fault diagnosis of deep groove ball bearings is made,and the experiment results indicate that the recognition accuracy rate of the proposed diagnosis model for outer race crack、inner race crack and ball crack is more than 90%.Compared to the existing approaches,the proposed diagnosis model combines the strengths of EMD in fault feature extraction,OLPP in feature compression and MWSVM in pattern recognition,and realizes the automation and high-precision of fault diagnosis.     

振动模态固有频率和阻尼比的EMD识别方法

针对机械系统固有频率和阻尼比的识别问题,提出了基于经验模式分解(EMD)的模态参数识别方法.该方法首先对脉冲激励下机械系统的位移响应进行了EMD分解,确定与该系统的各阶模态对应的固有模式函数(IMF),分别对各阶IMF进行希尔伯特变换以得到各自的瞬时幅值和瞬时相位曲线,并对所得曲线进行线性拟合,最后根据拟合曲线的参数来...     

基于EMD与同态滤波解调的矿用齿轮箱故障诊断

针对矿用齿轮箱振动信号的特点,提出了一种基于经验模态分解(empirical mode decomposition,简称EMD)与同态滤波相结合的故障解调方法.利用EMD对某矿用皮带机齿轮箱故障信号进行分解,得到若干个本征模态函数(intrinsic mode functions,简称IMFs)分量,然后对其中较突出的IMFs进行同态滤波解调分析,提取出了频率为7.0Hz的调制故障信号.研究表明,EMD与同态滤波解调相结合是一种有效的齿轮箱故障诊断方法.