An Adaptive Conjugate Gradient Neural Network–Wavelet Model for Traffic Incident Detection

Artificial neural networks are known to be effective in solving problems involving pattern recognition and classification. The traffic incident-detection problem can be viewed as recognizing incident patterns from incident-free patterns. A neural network classifier has to be trained first using incident and incident-free traffic data. The dimensionality of the training input data is high, and the embedded incident characteristics are not easily detectable. In this article we present a computational model for automatic traffic incident detection using discrete wavelet transform, linear discriminant analysis, and neural networks. Wavelet transform and linear discriminant analysis are used for feature extraction, denoising, and effective preprocessing of data before an adaptive neural network model is used to make the traffic incident detection. Simulated as well as actual traffic data are used to test the model. For incidents with a duration of more than 5 minutes, the incident-detection model yields a detection rate of nearly 100 percent and a false-alarm rate of about 1 percent for two- or three-lane freeways.     

基于线性预测和小波变换的语音基音周期检测新算法

将小波变换与语音信号短时线性预测（LPC）及自相关函数相结合，得出了清、浊音判决及基音周期检测的新算法，实验结果表明该算法性能良好．     

结构多分辨率分析法识别缺陷的小波选择影响研究

本文建议了一种基于小波多分辨率分析的拱结构损伤定位方法,并针对于小波的消失矩、对称性等特点,比较了常用小波对该方法损伤识别的影响特点,为该方法在小波母函数选择方面提供一定的参考。本文首先利用ANSYS通用有限元程序,获得了弹性模量损失的拱结构一阶模态曲线;其次,将该曲线作为空间域信号进行小波多分辨率分析,从第一层分解的高频信号重构系数上清晰地发现了结构中的损伤位置;最后,比较了sym5、db3、db2小波对识别小波的影响特点。研究发现,具有高阶消失矩的小波有利于提高信号重构时的光滑度,并且选用对称小波有利于识别结构中的损伤缺陷。     

提取绝缘子泄漏电流特征时母小波的选择

母小波的选择对提取绝缘子泄漏电流中的突变成分具有重要作用。根据对大量泄漏电流信号的分析,得出小波分解后的细节的能量越集中,所采用的母小波越适合提取泄漏电流的突变成分。据此采用熵作为评价母小波优劣的方法,并给出了泄漏电流熵的计算过程,进而给出了选择最佳母小波的步骤。以高压试验室采集到的泄漏电流为分析对象,以db(Daubechies)小波系为待选母小波集合,根据各小波分解泄漏电流之后的熵值,确定了db2小波为最佳母小波。采用不同母小波对泄漏电流进行了分析,结果显示db2小波优于其他小波。从而得出结论:给出的判断最佳母小波的方法是有效的。     

Crack Detection from the Slope of the Mode Shape Using Complex Continuous Wavelet Transform

Abstract: A new method for cracks detection in beams is proposed by using the slope of the mode shape to detect cracks, and by introducing the angle coefficients of complex continuous wavelet transform. This study is aimed at detecting the location of the nonpropagating transverse crack. A series of beams with cracks that are simulated by rotational springs with equivalent stiffness are analyzed. The mode shape and the slope of this lumped crack model are calculated. Through complex continuous wavelet transform of the slope of the mode shape using Complex Gaus1 wavelet (CGau1), the locations of cracks are detected from the modulus line and the angle line of wavelet coefficients. By comparison, the singularity is much more apparent from the angle line of complex continuous wavelet transform. This demonstrates that the proposed method outperforms the existing method of wavelet transform of the mode shape with real wavelets. Also, this method can detect cracks in beams with different boundary conditions. The influence of crack locations and crack depth on crack detection is discussed. Finally, the noise effect is studied. Through the multiscale analysis, the locations of cracks may be detected from the angle of wavelet coefficients.     

小波变换模极大值在遥感影像边缘特征提取中的应用

本文利用小波变换的"多分辨"特性,在小波变换的模极大值点对应于信号的突变点,而边界就是信号的突变部分的理论基础上,依据模糊数学理论,用二进制小波变换模极大值改进阈值算法对模极大值进一步取舍,对得到的模极大值点进行遥感影像边缘提取,采用分段三次样条插值算法进行小波系数重构,最终可以得到单像素级的边缘,同时结果证明了该算法的可行性。     

探地雷达信号小波变换去噪

本文简述了小波变换去噪的方法原理,并用该方法对含有随机干扰的合成探地雷达记录及野外实测数据进行了处理。对理论模型的处理结果表明,相对于常规去噪方法来说,小波变换去噪法能更彻底去除噪声、完整保留信号细节。而地基强夯加固探地雷达剖面图像经小波去噪处理后,不同介质界面变得明显清晰,墩长及墩形较处理前得到突出,尤其是土石墩剖面,墩形及墩长较清晰可辨。理论模型及实测数据研究结果均表明,小波变换去噪法能有效改善探地雷达剖面质量,提高解释的准确性和精确性。     

Detection of Traffic Jams Using ALISA

Based on collective learning systems theory, ALISA (adaptive learning image and signal analysis) is an adaptive image classification engine that has been designed and tested at the Research Institute for Applied Knowledge Processing (FAW) in Ulm, Germany, at Robert Bosch GmbH in Stuttgart, Germany, and at The George Washington University in Washington, D.C., over the last 5 years. Based on an appropriate set of features, during training, ALISA accumulates an n-dimensional histogram that estimates the probability density function of the feature space, which becomes the basis for classification during testing. The results of the research reported in this paper suggest that ALISA can be used successfully to detect traffic jams on highways. Based on images captured by a video camera observing different highway traffic conditions, ALISA was trained to recognize and differentiate between steadily flowing traffic and stalled traffic. Several standard features were extracted from preprocessed images based on the differences between successive video frames that were integrated over fairly large receptive fields to reduce differential noise. During testing, ALISA displays a picture of the highway with areas of flowing traffic shown in white and areas of stalled traffic shown as images of the stalled vehicles themselves. Because ALISA classifies every segment of the image independently, even directly adjacent lanes and/or clusters of stalled and flowing traffic are correctly classified. Thus either a summary result (i.e., traffic jam or not) or a more detailed spatial distribution of traffic conditions on the highway can be obtained.     

Wavelet Packet-Autocorrelation Function Method for Traffic Flow Pattern Analysis

Abstract:   Accurate and timely forecasting of traffic flow is of paramount importance for effective management of traffic congestion in intelligent transportation systems. A detailed understanding of the properties of traffic flow is essential for building a reliable forecasting model. The discrete wavelet packet transform (DWPT) provides more coefficients than the conventional discrete wavelet transform (DWT), representing additional subtle details of a signal. In wavelet multiresolution analysis, an important decision is the selection of the decomposition level. In this research, the statistical autocorrelation function (ACF) is proposed for the selection of the decomposition level in wavelet multiresolution analysis of traffic flow time series. A hybrid wavelet packet-ACF method is proposed for analysis of traffic flow time series and determining its self-similar, singular, and fractal properties. A DWPT-based approach combined with a wavelet coefficients penalization scheme and soft thresholding is presented for denoising the traffic flow. The proposed methodology provides a powerful tool in removing the noise and identifying singularities in the traffic flow. The methods created in this research are of value in developing accurate traffic-forecasting models .     

利用小波去噪方法处理建筑物变形监测数据

本文基于小波分析理论 ,利用小波去噪技术 ,对一组建筑物变形监测数据进行了去噪处理。实际计算结果表明 ,小波去噪合理有效 ,能够敏感识别观测噪声和有用信息 ,不需要待处理数据的先验知识 ,特别适合于建筑物变形监测的数据处理。     

基于形态学及边缘检测的雷达图像特征提取

介绍了雷达技术在混凝土结构无损检测中的应用原理,制作了模拟空洞、裂缝、不密实缺陷的混凝土试件,得到了缺陷的雷达图像典型特征.将数字图像处理技术应用于雷达图像的后处理中,形态学腐蚀、膨胀处理后图像的主要特征得到体现,次要因素得以忽略,为雷达图像的判读、解释起到良好的区分作用.边缘检测结合腐蚀、膨胀能快速而准确地得到图像的...     

四川正河金多金属矿地球化学特征及找矿方向

通过对正河金多金属矿大地构造位置、地层、构造、蚀变及地球化学特征的综合分析研究,与区域上典型金矿床的对比分析,认为矿床具有热液成因,未来找矿重点区域为Hs-1异常区,其次为Hs-2异常区,以探寻石英脉型和热液型Au为主,兼顾Cu、Pb、Zn。     

遥感线性构造提取方法及意义——以高燕锰矿区为例

线性构造在遥感影像上主要表现为连续或不连续的线性体,能大致反映一个地区的基本构造格局[1]。以重庆高燕锰矿区为例,介绍了一种基于Landsat 8等多光谱遥感数据、ASTER GDEM(30m)数字高程模型数据以及Google Earth 3D影像数据并结合GIS系统进行人机交互的遥感线性构造信息提取的方法,同时结合野外地质事实分析其构造意义。     

基于小波变换的传感器故障诊断研究

主元分析法可以应用于空调系统的传感器故障诊断,但是测量数据中隐含的噪声及系统的动态性影响了这一方法在故障诊断时的效果。本文提出采用小波变换的方法对测量数据进行分解,利用不含噪声及动态性的低频信号进行传感器的故障诊断,即基于小波变换的主元分析故障诊断法。该方法采用一大型离心式制冷机的实测运行数据进行验证,且同时与常规的主元分析法进行比较,结果说明基于小波变换的主元分析法可以提高故障诊断水平。     

基于小波变换对GPS信号去噪的分析

小波分解能够精细地把信号划分到不同的频带范围内,因此可对含噪信号在不同频带范围内的特征进行信噪分离.本文从GPS精密测量、导航领域的信号去噪角度,探讨了基于小波分析的信噪分离方法,并结合具体实例,说明小波分析对GPS信号消噪处理的实用性及有效性.     

Short-Term Traffic Volume Forecasting Using Kalman Filter with Discrete Wavelet Decomposition

Abstract:   This article investigates the application of Kalman filter with discrete wavelet analysis in short-term traffic volume forecasting. Short-term traffic volume data are often corrupted by local noises, which may significantly affect the prediction accuracy of short-term traffic volumes. Discrete wavelet decomposition analysis is used to divide the original data into several approximate and detailed data such that the Kalman filter model can then be applied to the denoised data and the prediction accuracy can be improved. Two types of wavelet Kalman filter models based on Daubechies 4 and Haar mother wavelets are investigated. Traffic volume data collected from four different locations are used for comparison in this study. The test results show that both proposed wavelet Kalman filter models outperform the direct Kalman filter model in terms of mean absolute percentage error and root mean square error.     

Time Dependent Gaussian Equivalent Linearization of Duffing Oscillator Using Continuous Wavelet Transform

Evolutionary response analysis of Duffing oscillator using Gaussian equivalent linearization in wavelet based time-frequency frame work is presented here. Cubic （i.e., odd type） non-linearity associated with stiffness and damping is modeled. The goal of this research is to develop the mathematical model of an equivalent linear system which is applicable for different non-stationary input processes （i.e., either summation of amplitude modulated stationary orthogonal processes or digitally simulated non-stationary processes）. The instantaneous parameters of the ELTVS （equivalent linear time varying system） are evaluated by minimizing the error between the displacements of non-linear and equivalent linear systems in wavelet domain. For this purpose, three different basis functions （i.e., Mexican Hat, Morlet and a modified form of Littlewood-Paley） are used. The unknown parameters （i.e., natural frequency and damping） of the ELTVS are optimized in stochastic least square sense. Numerical results are presented for different types of input to show the applicability and accuracy of the proposed wavelet based linearization technique.     

Wavelet-Clustering-Neural Network Model for Freeway Incident Detection

Abstract:   An improved freeway incident-detection model is presented based on speed, volume, and occupancy data from a single detector station using a combination of wavelet-based signal processing, statistical cluster analysis, and neural network pattern recognition. A comparative study of different wavelets (Haar, second-order Daubechies, and second- and fourth-order Coifman wavelets) and filtering schemes is conducted in terms of efficacy and accuracy of smoothing. It is concluded that the fourth-order Coifman wavelet is more effective than other types of wavelets for the traffic incident detection problem. A statistical multivariate analysis based on the Mahalanobis distance is employed to perform data clustering and parameter reduction to reduce the size of the input space for the subsequent step of classification by the Levenberg–Marquardt backpropagation (BP) neural network. For a straight two-lane freeway using real data, the model yields an incident detection rate of 100%, false alarm rate of 0.3%, and detection time of 35.6 seconds.     

某二级热网水力工况实测与节能改造方案

对某小区二级管网的水力工况进行了实测。该二级管网的水力平衡度、管网输送效率不符合相关标准要求。提出了改善水力平衡、提高管网输送效率的方法。     

基于小波变换的高压输电线路故障分析

针对高压输电线路故障检测困难的问题,采用单端行波测距法和小波变换的有机结合,获得故障点发生在线路前后两个半段的精确距离,再由双端法判断具体故障点位置.该方法降低了因线路长度、故障时间、输电线路现场噪音带来的误差影响,系统仿真模型验证了该方法的有效性和实用性.