多普勒计程仪的数据平滑方法

多普勒计程仪输出数据中有局外数据,可能会导致组合导航系统中的卡尔曼滤波器发散。文章介绍了4种数据平滑方法：动窗平均、动窗加权平均、动窗中值滤波以及动窗稳健平均。仿真结果表明：动窗稳健平均的输出不会被多个局外数据所影响,并且其处理增益接近理论极限。     

基于神经网络的图像混合滤波及融合算法研究

当图像中同时存在高斯噪声和椒盐噪声时,单一的均值滤波或中值滤波很难达到最佳滤波效果。 分析了噪声特点和各种滤波方法的优势,提出了一种基于神经网络的图像混合滤波及融合算法:首先建立概率神经网络,检测椒盐噪声和高斯噪声点,并分别利用中值滤波和均值滤波去除噪声点,然后建立径向基函数神经网络,利用训练好的径向基函数神经网络融合 2 种不同滤波的图像,输出理想的融合图像。 Matlab 仿真实验结果表明,该算法有效去除混合噪声的同时,能很好地保护图像的边缘与细节,是一种有效的方法。     

去除脉冲噪声的自适应开关中值滤波

为消除图像中的脉冲噪声,提出了自适应开关中值(ASM)滤波算法。该算法采用一种新的噪声检测方法将图像中的像素分为信号点和噪声点两类。对检测出的噪声点统计其个数并由此估算图像中的噪声密度,根据估计的噪声密度自适应确定滤波窗口尺寸,采用改进的中值滤波对检测出的噪声点进行处理;而信号点则保留其灰度值不予处理。对ASM滤波进行仿真实验,结果表明,它能在有效去除噪声的同时很好地保护图像细节,较传统中值滤波及其它改进中值滤波算法有更优的滤波性能。     

基于阈值判断的自适应中值滤波算法

针对标准的中值滤波算法在去除噪声与保留图像细节方面难以取舍的缺陷,在自适应中值滤波算法的基础上提出了一种改进的基于噪声点检测的自适应中值滤波算法.该算法在进行噪声点检测时采用了一种阈值判断法,充分利用了当前像素点与邻域像素点的灰度值之间的关系.结果表明,在噪声浓度较高时仍然可以区分噪声点与边缘点,滤波的同时有效地保护了图像的细节.     

一种椒盐噪声点的检测算法技术研究

针对中值滤波算法虽然能够在很大程度上改善噪声带来的影响,但是使图像边缘变得模糊这一问题,提出了一种检测椒盐噪声的新算法。使用该算法对图像进行噪声检测后再对噪声点进行中值滤波能够有效地去除椒盐噪声。实验证明该算法具有明显的去噪能力,且当椒盐噪声比较严重时,也能够取得较好的滤波效果。     

基于滑动中值滤波的多尺度主元分析方法

提出了一种基于滑动中值滤波的多尺度主元分析(MSPCA)方法,该方法利用中值滤波对主元分析(PCA)前的原始数据进行预处理,以去除异常点,并用多尺度主元分析方法把小波变换和主元分析有机结合起来,通过对过程数据的多尺度建模,来消除系统中的次要主元和小的小波系数,这样既提高了对数据中细微、重要变化的检测灵敏度,又解决了在测量数据中含有异常点的情况下,现有多尺度主元分析难以去除因异常点的存在而产生的虚警问题.仿真验证了该方法的有效性和可行性.     

集合经验模态分解的稳健滤波方法研究

为了消除野值和噪声信号对观测数据的影响,给出一种基于集合经验分解的具有稳健性的滤波算法：首先用滑动中值滤波算法剔除原始数据中的野值,然后采用集合经验模态分解算法,抑制数据中的噪声。数值仿真和实际工程应用表明,该方法不仅能剔除野值,抑制信号中的噪声,提高信噪比,还能够有效消除模态混叠问题,将被测信号中不同的频率成分独立分解在不同的固有模态函数中,从而得到更清晰的时频分布,有利于实际数据处理中的信号分析和故障诊断。     

噪声概率快速估计的自适应椒盐噪声消除算法

提出一种可识别噪声概率自动调节滤波窗口的自适应椒盐噪声消除算法。对非理想椒盐噪声污染图像随机区域进行变窗口中值滤波,将结果与滤波前比对获得噪声点数,滤波区域即按此点数排序。然后取每种滤波窗口下的中间三组数据,该数据平均加权获取图像噪声概率初估计,对初估计平均加权即得图像噪声概率。滤波前首先采用阈值法排除明显噪声点,剩余像素中再以离窗口中心像素距离平方的倒数为权值估计中心像素。最后由噪声概率按照T-S模糊规则对不同模型的输出估计值进行融合。实验证明,与传统中值滤波等算法相比,该算法具有噪声自动估计和自适应窗口调节能力,滤波后标准均方差可减少20%以上,速度可提高一倍多。     

基于噪声点多级检测的自适应中值滤波算法

针对传统中值滤波算法在图像去噪过程中造成较多图像细节信息丢失的问题,提出了一种基于噪声点多级检测的自适应中值滤波算法。该算法根据像素的空间相关性,逐级检测不同空间特征的噪声点。首先根据滤波窗口中相近像素点的数量来检测空间孤立的单个噪声点;然后通过扩展邻近窗口的方式检测空间连续的两个噪声点;接着进一步增加约束条件对空间连续的三个及以上的噪声点进行检测;最后对判断为噪声的像素用滤波窗口的中值替换。此外,该算法还能通过自适应地调整像素空间相关性判别阈值来处理不同分布特征的噪声。实验结果表明,与现有中值滤波算法相比,算法在有效滤除图像噪声的同时能更好地保护图像细节信息。     

加权均值滤波在机场跑道路面裂纹图像检测中的应用

图像去噪是图像处理中的一个重要环节。基于均值滤波和中值滤波的经典算法,结合数字图像处理技术,以拍摄的直升机机场跑道路面裂纹图像作为研究对象,提出了一种改进的加权均值滤波算法,并通过仿真给出了试验效果图及数据结果。结果表明：改进的加权均值滤波算法较传统均值滤波能更好地保护图像的细节,失真小,在去除噪声的同时较好地保留边缘等细节信息,降低了图像处理后的模糊化程度,优于经典的滤波算法。该研究为机场跑道路面裂纹图像检测提供了一种新方法。     

Noise Immunity of High-Precision Low-Temperature Experiments

In the high-precision low-temperature experiments, noise immunity has long been an important issue. We consider the problem of measuring the slowly changing data in the presence of both white noise and sudden, short noise spikes. The Kalman filter is applied to attenuate the white noise and a spike detecting algorithm is applied to remove spikes resulting from the charged particles. Experimental data show that, with the Kalman filter, the RMS of the measurement noise in a 0.5 Hz bandwidth can be attenuated from 4 nK to 0.2 nK in the best case. A simple spike detecting algorithm worked effectively to remove spikes without increasing the random noise level.     

Wavelet-based method for noise characterization and rejection in high-performance liquid chromatography coupled to mass spectrometry

We present a new method for rejecting noise from HPLC-MS data sets. The algorithm reveals peptides at low concentrations by minimizing both the chemical and the random noise. The goal is reached through a systematic approach to characterize and remove the background. The data are represented as two-dimensional maps, in order to optimally exploit the complementary dimensions of separation of the peptides offered by the LC-MS technique. The virtual chromatograms, reconstructed from the spectrographic data, have proved to be more suitable to characterize the noise than the raw mass spectra. By means of wavelet analysis, it was possible to access both the chemical and the random noise, at different scales of the decomposition. The novel approach has proved to efficiently distinguish signal from noise and to selectively reject the background while preserving low-abundance peptides.     

一种基于关联处理的声呐图像去噪技术

为了改善声呐图像中存在大量杂波而导致目标跟踪困难,设备虚警率高的缺点,提出一种图像关联降噪算法。首先利用目标特性对静止目标进行剔除,之后利用目标运动的持续性和目标回波在连续多帧图像中的空间位置关联性,对随机噪声进行进一步滤除。仿真和试验数据验证,所提出的算法对于背景比较稳定的图像,降噪效率不低于95%。     

Model-based process monitoring using robust generalized linear models

Model-based process-monitoring procedures are extremely useful in situations where an output variable of interest is impacted by one or more inputs to the process, and where there are multistage processes with multiple inputs and outputs. To build the model relating input and output variables, the procedure uses historical data, which often contain outliers. To accommodate the presence of these outliers, a robust fitting scheme is introduced for the Generalized Linear Model in process monitoring. Robust deviance residuals are defined and used as the basis of the monitoring procedure. An example and a simulation study for a gamma-distributed response are included. The average run length performance reveals that the procedure is effective for detecting small process shifts when outliers are present.     

Nondestructive detection of flaws in material using transducer arrays

In this paper, we describe a system which uses array filters for processing the recordings of an array of transducers for the purpose of locating possible flaws in the material being tested nondestructively. When applied, in order to focus the array outputs at a point inside the material, these filters are able to attenuate, very effectively, interfering residual energy scattered from adjacent flaws, as well as random noise. If there is a flaw at the point of focus, the summed output of the filters will have a large signal at a delay time corresponding to the total travel time for this point; otherwise the output will consist mainly of residual random noise. Thus, the location of flaws or even microflaws in material may be achieved by simple threshold detection.     

基于集成学习的强鲁棒性三维点云数据分类研究

目的通过三维扫描仪得到的点云数据往往存在很多异常值,例如噪点、遗失点和外部点等。在这些异常值存在的情况下,为了提高三维点云数据的分类精度,提出一种基于集成学习的强鲁棒性三维点云数据分类方法。方法提出一种基于最大投票法的集成学习思想,将2个深度神经网络的分类结果进行集成,从而提高网络的泛化性和准确性;采用全局特征增强和中心损失函数来优化神经网络结构,提高分类精度并增强鲁棒性。结果文中方法缩短模型训练时间至30个迭代次数,且在有噪点、丢失点和外部点的情况下分类精度均得到有效提升。结论提出的EL-3D算法在含有噪点、丢失点和外部点的情况下,鲁棒性效果要优于目前的点云分类方法。     

Monitoring dependent process steps using robust cause‐selecting control charts

Cause‐selecting control charts are believed to be invaluable for monitoring and diagnosing multistage processes where the output quality of some stages is significantly impacted by the output quality of preceding stages. To establish a relationship between input and output variables, a standard procedure uses historical data, which are often prone to hold outliers. The presence of outliers tends to decrease the effectiveness of monitoring procedures because the regression model is distorted and the control limits become stretched. To dampen the negative repercussions of outliers, robust fitting techniques based on M‐estimators are implemented instead of the ordinary least‐squares method and two robust monitoring approaches are presented. An example is given to illustrate the application and performance of the proposed control charts. Furthermore, a simulation‐based study is included to investigate and compare the average run length of robust and non‐robust schemes. The results reveal that the robust procedure far outperforms the non‐robust counterpart due to its prompt detection of out‐of‐control conditions when outliers exist. Copyright © 2009 John Wiley & Sons, Ltd.