Real Time Speed Bump Detection Using Gaussian Filtering and Connected Component Approach

An Intelligent Transportation System (ITS) is a new system developed for the betterment of user in traffic and transport management domain area for smart and safe driving. ITS subsystems are Emergency vehicle notification systems, Automatic road enforcement, Collision avoidance systems, Automatic parking, Map database management, etc. Advance Driver Assists System (ADAS) belongs to ITS which provides alert or warning or information to the user during driving. The proposed method uses Gaussian filtering and Median filtering to remove noise in the image. Subsequently image subtraction is achieved by subtracting Median filtered image from Gaussian filtered image. The resultant image is converted to binary image and the regions are analyzed using connected component approach. The prior work on speed bump detection is achieved using sensors which are failed to detect speed bumps that are constructed with small height and the detection rate is affected due to erroneous identification. And the smartphone and accelerometer methodologies are not perfectly suitable for real time scenario due to GPS error, network overload, real-time delay, accuracy and battery running out. The proposed system goes very well for the roads which are constructed with proper painting irrespective of their dimension.

Lumped-equivalent circuit model for multi-stage cascaded magnetoelectric dual-tunable bandpass filter

A lumped-equivalent circuit model of a novel magnetoelectric tunable bandpass filter, which is realized in the form of multi-stage cascading between a plurality of magnetoelectric laminates, is established in this paper for convenient analysis.The multi-stage cascaded filter is degraded to the coupling microstrip filter with only one magnetoelectric laminate and then compared with the existing experiment results. The comparison reveals that the insertion loss curves predicted by the degraded circuit model are in good agreement with the experiment results and the predicted results of the electromagnetic field simulation, thus the validity of the model is verified. The model is then degraded to the two-stage cascaded magnetoelectric filter with two magnetoelectric laminates. It is revealed that if the applied external bias magnetic or electric fields on the two magnetoelectric laminates are identical, then the passband of the filter will drift under the changed external field; that is to say, the filter has the characteristics of external magnetic field tunability and electric field tunability. If the applied external bias magnetic or electric fields on two magnetoelectric laminates are different, then the passband will disappear so that the switching characteristic is achieved. When the same magnetic fields are applied to the laminates, the passband bandwidth of the two-stage cascaded magnetoelectric filter with two magnetoelectric laminates becomes nearly doubled in comparison with the passband filter which contains only one magnetoelectric laminate. The bandpass effect is also improved obviously. This research will provide a theoretical basis for the design, preparation, and application of a new high performance magnetoelectric tunable microwave device.

Gabor Order Tracking Filtering Technology in Rotary Machinery

Order analysis is one of the most important technique means of condition monitoring and fault diagnosis for rotary machinery.The traditional order analyses usually employ the Vold-Kalman filtering,however this method is confined to the expensive hardware equipments.This paper starts from Gabor transform and applies the Gabor time-frequency filtering to vibration signal.The order component＇s time-frequency coefficients are extracted by mask operation.The order component is reconstructed from the obtained coefficients.The following four key technologies,such as smoothing rotary speed curve,defining filtering band width,constructing the mask operation matrix and reconstructing signal component,are also deeply discussed.Moreover,the technique to smooth the rotary speed curve based on polynomial approximation,the method to determine filtering band width,the arithmetic to constitute mask array and the iterative algorithm to reconstruct signal based on minimum mean square error are specifically analyzed.The 4th order component is successfully gained by using the methods that Gabor time-frequency filter,and the validity and feasibility of this method are approved.This method can solve the problem of order tracking filter technologies which used to depend on hardware and efficiently improve the accuracy of order analysis.

Characterization of three-dimensional channel reservoirs using ensemble Kalman filter assisted by principal component analysis

Ensemble-based analyses are useful to compare equiprobable scenarios of the reservoir models.However,they require a large suite of reservoir models to cover high uncertainty in heterogeneous and complex reservoir models.For stable convergence in ensemble Kalman filter(EnKF),increasing ensemble size can be one of the solutions,but it causes high computational cost in large-scale reservoir systems.In this paper,we propose a preprocessing of good initial model selection to reduce the ensemble size,and then,EnKF is utilized to predict production performances stochastically.In the model selection scheme,representative models are chosen by using principal component analysis(PCA)and clustering analysis.The dimension of initial models is reduced using PCA,and the reduced models are grouped by clustering.Then,we choose and simulate representative models from the cluster groups to compare errors of production predictions with historical observation data.One representative model with the minimum error is considered as the best model,and we use the ensemble members near the best model in the cluster plane for applying EnKF.We demonstrate the proposed scheme for two 3D models that EnKF provides reliable assimilation results with much reduced computation time.

GAUSSIAN PRINCIPLE COMPONENTS FOR NONLOCAL MEANS IMAGE DENOISING

NonLocal Means(NLM),taking fully advantage of image redundancy,has been proved to be very effective in noise removal.However,high computational load limits its wide application.Based on Principle Component Analysis(PCA),Principle Neighborhood Dictionary(PND) was proposed to reduce the computational load of NLM.Nevertheless,as the principle components in PND method are computed directly from noisy image neighborhoods,they are prone to be inaccurate due to the presence of noise.In this paper,an improved scheme for image denoising is proposed.This scheme is based on PND and uses preprocessing via Gaussian filter to eliminate the influence of noise.PCA is then used to project those filtered image neighborhood vectors onto a lower-dimensional space.With the preproc-essing process,the principle components computed are more accurate resulting in an improved de-noising performance.A comparison with some NLM based and state-of-art denoising methods shows that the proposed method performs well in terms of Peak Signal to Noise Ratio(PSNR) as well as image visual fidelity.The experimental results demonstrate that our method outperforms existing methods both subjectively and objectively.

Multiple Tracking of Moving Objects with Kalman Filtering and PCA-GMM Method

In this article we propose to combine an integrated method, the PCA-GMM method that generates a relatively improved segmentation outcome as compared to conventional GMM with Kalman Filtering (KF). The combined new method the PCA-GMM-KF attempts tracking multiple moving objects;the size and position of the objects along the sequence of their images in dynamic scenes. The obtained experimental results successfully illustrate the tracking of multiple moving objects based on this robust

Polarization Filtering Method for Suppressing Surface Wave in Time-Frequency Domain

In order to suppress the surface wave in three-component seismic exploration, according to the polarization characteristics of body wave and surface wave, a time-frequency domain polarization filtering method based on wavelet transform was studied. A covariance matrix was constructed in the time-frequency domain for the three-component seismic data, measured the polarization parameters of seismic waves. Combining the corresponding eigenvalues and eigenvectors of the matrix, the elliptic rate and elevation angle were used as constraints, and the polarization filter function was built to separate the surface waves. The separated surface waves were inversely transformed and then were adaptively subtracted from the original records. After the polarization filtering suppressed the surface wave, the signal-to-noise ratio of the converted wave was effectively improved. It laid a good foundation for the next seismic data processing and seismic exploration development. The actual data processing results show that the method can effectively extract surface waves from three-component seismic records and avoid the interference of surface waves on seismic signals.

一种基于寿命预测的飞机部件预防性维修计划编制优化方法

针对现有飞机系统维修计划可行性不高、成本高昂的问题,提出了一种基于寿命预测的飞机部件预防性维修计划编制优化方法。使用滚动地平线方法确定维修计划时间窗口。基于多传感器数据及粒子滤波模型建立飞机部件剩余使用寿命预测模型。利用线性整数规划对多飞机系统的预防性维修策略进行规划,建立以维护总成本为目标函数,飞机部件的剩余使用寿命预测、可用备件和可执行维护的可用时间为约束条件的预防性维修计划模型。实验结果表明,实际部件的剩余使用寿命在预测的概率分布内。同时,与CM和PM策略相比,所提预防性维修计划具有最低的预期维护成本和最低的AOG次数。

基于SVD的复数UKF及电力系统对称分量估计

电力系统对称分量的检测对于电力系统安全稳定的运行具有很重要的意义。利用复数域无迹卡尔曼滤波算法,对三相电压系统的正负序分量及频率进行了估计。为了提高复数无迹卡尔曼滤波的参数估计精度及算法稳定性,引入最优自适应因子并对预测协方差矩阵进行SVD分解,提出了基于SVD的自适应CUKF算法。为消除零序分量,对三相电压分量进行αβ变换,定义了复数形式的状态变量,建立了非线性状态方程及观测方程,实现了正序、负序对称分量估计。通过与普通复数域无迹卡尔曼滤波算法对比,所提研究方法在估计精度及收敛速度等方面优于传统无迹卡尔曼滤波方法。

基于激光超声技术的运动损伤组织自动检测方法

当前运动损伤组织检测存在误差大等问题,为了提高运动损伤组织检测效果,提出基于激光超声技术的运动损伤组织自动检测方法。首先分析运动损伤组织检测的研究现状,找到当前运动损伤组织检测效果差的原因,然后采集运动损伤组织的激光超声图像,采用主成分分析确认目标位置,双边滤波方法去除超声图像的噪声,最后运动损伤组织进行自动检测,并进行了运动损伤组织检测的仿真实验,结果表明,本方法的运动损伤组织图像的信噪比高,可以消除了噪声,运动损伤组织区域检测平均准确度为98.63%,运动损伤组织检测均方误差的平均值约为0.044。

固有成分滤波器的旋转机械故障诊断方法

针对噪声环境下旋转机械微弱复合故障诊断问题,提出了一种强噪声干扰下基于固有成分滤波器(intrinsic component filtering,简称ICF)的旋转机械故障检测和分离方法。ICF通过最小化样本间特征的L1/2范数和样本内特征的L3/2范数来实现样本之间特征的一致性和样本内部特征的稀疏性,并训练出最优滤波器组,是一种无监督多维盲解卷积算法。首先,构建输入信号的Hankel训练矩阵,通过权值矩阵与Hankel矩阵的乘积模拟卷积过程,再利用固有属性滤波器实现特征学习;其次,通过峭度信息选择最优滤波器;最后,根据滤波后的时域波形和包络谱实现故障诊断。仿真和试验信号验证了提出方法的故障诊断性能,研究结果表明,提出的方法无需任何先验经验,可以实现强噪声环境下的微弱故障的分离,同时具备很好的鲁棒性。

基于EEMD和特征降维的非侵入式负荷分解方法研究

针对现有非侵入式居民用电负荷监测缺乏对独立负荷完整、全面的分解方法,导致用电信息的完整性得不到保证的不足,提出一种基于集合经验模态分解(ensemble empirical mode decomposition,EEMD)和Pearson-PCA改进的盲源分离算法。利用EEMD对总功率信号分解,以消除经验模态在分解过程中易出现模态混叠的现象,并得到一系列固有模式函数(intrinsic mode functions,IMF)。结合Pearson相关系数和主成分分析法(principal component analysis,PCA),提出Pearson-PCA改进算法对IMF进行降维,剔除相关性较弱的IMF分量,以及估计源信号数目。运用快速独立分量分析(fast independent component analysis,FastICA)对降维后的IMF进行分解,计算得出源功率信号。将提出的改进算法应用于非侵入式居民用电负荷分解问题,采用能量分解数据集(reference energy disaggregation data,REDD)进行实验仿真。实验结果表明:在不同用电场景下,提出的改进算法均具有较好的分解效果。

基于自适应维纳滤波和2D-VMD的声呐图像去噪算法

声呐图像易产生对比度低、分辨率低、边缘失真等问题,所以在去除声呐图像噪声时难以将有效信号与噪声准确分离,从而导致去噪后图像对比度降低、边缘轮廓不清晰、细节丢失严重等问题.本文提出一种基于自适应维纳滤波和2D-VMD(二维变分模态分解)的声呐图像去噪算法.首先通过二维变分模态分解对含噪图像进行分解,得到一系列不同中心频率的模态分量,利用相关系数和结构相似度筛选出有效的模态分量,并使用自适应维纳滤波处理有效的模态分量,最后将滤波后的模态分量进行重构,从而去除图像中的噪声.实验结果表明:所提图像去噪算法在相关系数(CC)、结构相似度(SSIM)这两项客观数据上表现最优,峰值信噪比(PSNR)略低于NSST域去噪,综合客观数据与视觉效果,本文所提算法去除噪声后的图像细节和边缘保持能力效果最佳.

噪声干扰下基于PCA-SF的轴承故障诊断方法

机械故障诊断对降低维修成本和预防事故至关重要。振动信号监测是机械故障诊断中一种有效可行的方法。然而,所采集故障信号往往容易受到其他设备噪声的干扰。因此,从受噪声干扰的监测信号中提取与故障相关的周期脉冲是故障诊断的基础,也是难点。为解决此问题,提出一种基于主成分分析(Principal Component Analysis,PCA)和稀疏滤波(Sparse Filtering,SF)的机械故障特征提取方法。具体来说,首先利用PCA提取噪声干扰信号段的主成分,然后利用SF从主成分中提取有效特征。为减小SF模型的过拟合问题,采用L1/2范数对其目标函数进行正则化约束。最后,将提取的特征输入到Softmax分类器中进行故障识别。分别通过一组仿真和实验案例对所提PCA-SF方法的有效性进行验证。实验结果表明,该方法不仅能准确实现故障分类,而且优于其他传统方法。

卷烟降焦减害滤棒构件的制备及应用

以添加石墨烯纳米分子的热可塑性弹体材料为基材,利用注塑成型机制备卷烟新型滤棒构件,将滤棒构件与醋纤滤棒进行三元复合后卷制复合滤棒卷烟样品,研究新型滤棒构件对卷烟主要物理指标、主流烟气中常规成分及7种有害成分的影响。结果表明:石墨烯对苯酚和苯并[a]芘具有明显的吸附性作用,对其他成分影响较小;与同规格醋纤滤棒卷烟相比,复合滤棒卷烟样品吸阻升高幅度为12.33%,滤嘴通风率和总通风率升高幅度分别为10.17%和10.22%;复合滤棒卷烟样品主流烟气中常规成分和7种有害成分释放量均有所降低,其中烟碱降低约36%,焦油降低约34%,有害成分一氧化碳、氰化氢、巴豆醛、亚硝胺的降低效果较为明显,分别降低约53%、50%、62%和78%,且复合滤棒卷烟样品危害性指数由10.28降低至平均4.95,说明该石墨烯异型滤棒构件具有明显的降焦减害作用。

基于多特征融合的地铁车辆制动组件异常检测

地铁车辆驾驶环境多变,导致制动组件异常检测存在精度误差,为此提出基于多特征融合的地铁车辆制动组件异常检测方法。通过无人机与云台搭载相机,采集地铁车辆制动组件运行图像。通过Gabor特征提取方法提取组件图像空间方向与尺度上的多种纹理特征。采用信息熵实现地铁车辆制动组件图像多个提取特征的融合。基于CNN设计BD-YOLO地铁车辆制动组件异常检测模型,实施制动组件异常检测。测试结果表明,在实验地铁车辆静止时,该方法的组件异常检测精确率达到了100%。在车辆正常运行的情况下,其组件异常检测精确率较高。在正常运行中列车管不充风的情况下,其组件异常检测宏平均召回率整体高于95%。

基于IFilter的非文本文件中抽取文本的关键技术

文本抽取是信息检索的一个重要问题。设计并实现了一个利用IFilter接口的过滤器组件,抽取非文本文件的文本信息的程序。对这一设计过程论述了其主要的关键技术。

基于XML与FILTER的WEB页面控制组件

WEB应用程序访问方式不仅可以使用页面之间的超级链接实现,也可以使用WEB浏览器的url输入框实现。后者可能使WEB应用程序的功能完整性受到破坏。通过使用java servlet过滤器技术,xml文档,设计并实现对WEB应用程序的控制,从而解决上述问题。

SAR image de-noising via grouping-based PCA and guided filter

A novel synthetic aperture radar(SAR)image de-noising method based on the local pixel grouping(LPG)principal component analysis(PCA)and guided filter is proposed.This method contains two steps.In the first step,we process the noisy image by coarse filters,which can suppress the speckle effectively.The original SAR image is transformed into the additive noise model by logarithmic transform with deviation correction.Then,we use the pixel and its nearest neighbors as a vector to select training samples from the local window by LPG based on the block similar matching.The LPG method ensures that only the similar sample patches are used in the local statistical calculation of PCA transform estimation,so that the local features of the image can be well preserved after coefficients shrinkage in the PCA domain.In the second step,we do the guided filtering which can effectively eliminate small artifacts left over from the coarse filtering.Experimental results of simulated and real SAR images show that the proposed method outstrips the state-of-the-art image de-noising methods in the peak signalto-noise ratio(PSNR),the structural similarity(SSIM)index and the equivalent number of looks(ENLs),and is of perceived image quality.