An inverse shock response spectrum

The shock response spectrum (SRS) is a tool commonly used by application engineers that characterizes the severity of a transient acceleration. Due to the definition of the SRS, neither an analytical nor a unique inverse exists for an arbitrary function. An SRS presented without any temporal information makes creating a corresponding acceleration time history for an experimental or numerical study prohibitively difficult without a rigorous method to determine an inverse of the SRS (a corresponding time history). The present work develops a method to calculate an inverse of an arbitrary SRS using three sets of well characterized basis functions: an impulse function, a sine function/damped sine function, and a modified Morlet wavelet. These three basis functions are specifically chosen for the properties of their transformations: the impulse introduces a constant increase to the SRS above a given frequency, the sine wave introduces a narrow peak at a given frequency, and the Morlet wavelet introduces a plateau with an adjustable width and relative height. Using the definition of the SRS, the transformations of the basis functions are calculated and these expressions are used to derive a methodology for calculating an inverse SRS. The effectiveness of the method is demonstrated by several examples. The quality of an inverse SRS is evaluated by comparing the SRS of the inverse to the target SRS. This method is developed in order to provide a quick estimate of a corresponding time history; in applications where a higher fidelity representation of the SRS is needed than can be provided by the method developed, a genetic algorithm is used to optimize the coefficients of the basis functions. Given a sufficient number of basis functions for the optimization, the resulting SRS can almost exactly match a randomly generated target SRS that is nonzero over the frequency range considered. For applications in which the permissable basis functions are limited (such as for an experimental test apparatus), an extension of the genetic algorithm method is discussed.     

Experimental evaluation of fault diagnosis in a skew-configured UAV sensor system

This paper presents an experimental evaluation of a hybrid fault detection and isolation scheme against three successive faults in skew-configured inertial sensors of an unmanned aerial vehicle (UAV). An additional small and low-cost inertial measurement unit is installed with a skewed angle to a primary inertial measurement unit. A parity space method and an in-lane monitoring method are combined to increase system tolerance to the occurrence of multiple successive faults during flight. The first and second faults are detected and isolated by the parity space method. The third fault is detected by the parity space method and isolated by the in-lane monitoring method based on the discrete wavelet transform. Hardware in-the-loop tests and flight experiments with a fixed-wing UAV are performed to verify the performance of the proposed fault diagnosis scheme.     

基于感兴趣区的图象近无损压缩

无损感兴趣区（Lossless Region of Interest (ROI)图象压缩方法,即在感兴趣区采用无损压缩,而在其他区域采用有损压缩,从而保证了重要信息不丢失,并尽可能提高衅象的压缩比,在整形小波变换（IWT）和嵌入式零树编码的基础上,实现了无损感兴趣区（Lossless ROI)的图象近无损压缩,并提出了一种小波变换域形状编码算法－－树映射形状编码,同时给出了算法的原理及实现,并进行了相关实验,实验结果表明,该算法能够提高压缩效率,且压缩效率取决于感兴趣区的大小以及对不感兴趣区图象的质量要求。     

基于小波的彩色序列图象压缩编码

此文提出了一种基于小波变换的运动估计算法，时间复杂度比常规算法降低了一个数量级，同时在估值精度上可与常规算法媲美。利用更佳频率分辨率小波分解处理运动补偿余量误差善了复现图象质量。实验结果表明，该算法在高压缩比情况下图象复现质量显著优于ＭＰＥＧ－２，具有广阔的应用前景。     

Automated ECG diagnostic P-wave analysis using wavelets

P-wave characteristics in the human ECG are an important source of information in the diagnosis of atrial conduction pathology. However, diagnosis by visual inspection is a difficult task since the P-wave is relatively small and noise masking is often present. This paper introduces novel wavelet characteristics derived from the continuous wavelet transform (CWT) which are shown to be potentially effective discriminators in an automated diagnostic process. Characteristics of the 12-lead ECG P-wave were derived using CWT and statistical methods. A normal control group and an abnormal (atrial conduction pathology) group were compared. The wavelet characteristics captured frequency, magnitude and variance components of the P-wave. The best individual characteristics (i.e. ones that significantly discriminated the groups) were entered into a linear discriminant analysis (LDA) for four different models: two-lead ECG, three-lead ECG, a derived three-lead ECG and a factor analysis solution consisting of wavelet characteristic loadings on the factors. A comparison was also made between wavelet characteristics derived form individual P-waves verses wavelet characteristics derived from a signal-averaged P-wave for each participant. These wavelet models were also compared to standard cardiological measures of duration, terminal force and duration divided by the PR segment. Results for the individual P-wave approach generally outperformed the standard cardiological measures and the signal-averaged P-wave approach. The best wavelet model on the basis of both classification performance and simplicity was the two-lead model that uses leads II and V1. It was concluded that the wavelet approach of automating classification is worth pursuing with larger samples to validate and extend the present study.     

Flood simulation using parallel genetic algorithm integrated wavelet neural networks

The conventional means of flood simulation and prediction using conceptual hydrological model or artificial neural network (ANN) has provided promising results in recent years. However, it is usually difficult to obtain ideal flood reproducing due to the structure of hydrological model. Back propagation (BP) algorithm of ANN may also reach local optimum when training nodal weights. To improve the mapping capability of neural networks, wavelet function was adopted (WANN) to strengthen the non-linear simulation accuracy and generality. In addition, genetic algorithm is integrated with WANN (GAWANN) to avoid reaching local optimum. Meanwhile, Message Passing Interface (MPI) subroutines are introduced for distributed implement considering the time consumption during nodal weights training. The GAWANN was applied in the flood simulation and prediction in arid area. The test results of 4 independent cases were compared to reveal the relations between historical rainfall and runoff under different time lags. The simulation was also carried out with Xinanjiang model to demonstrate the capability of GAWANN. The numerical experiments in this paper indicated that the parallel GAWANN has strong capability of rain-runoff mapping as well as computational efficiency and is suitable for applications of flood simulation in arid areas.     

利用点弦距离递归的图像角点检测算法

目的 传统的基于边缘轮廓的角点检测算法需要计算每一个边缘像素点的曲率,对噪声和局部变化敏感,极易造成检测结果的不稳定。针对这一问题,提出一种利用点弦距离递归的角点检测算法。方法 首先,利用Canny边缘检测器提取边缘轮廓线。其次,用3个不同尺度的高斯核对边缘线进行平滑,对每一个高斯尺度平滑后的边缘线,连接首尾端点形成一条弦,计算边缘轮廓上每个边缘像素点到弦的距离,将点弦距离最长的像素点标为候选角点,该像素点将原边缘轮廓线分成两条边缘,然后将该像素点与首尾端点连接成两条弦,重新计算点弦距离,将所有距离大于设定阈值的点作为候选角点。最后,利用多尺度技术对候选角点进行判决并得到最终角点。结果 与现有的基于曲率计算的角点检测算法相比,本文算法不需要计算一、二阶导数,有效避免了局部变化带来的计算误差。通过计算得到4个角点检测器的平均排名依次为Harris （4.0）、He&Yung （2.67）、CPDA （1.83）、本文算法 （1.5）。与其他3种经典的角点检测算法相比,本文提出的检测算法排名第一,因此表现出了更好的检测性能。结论 提出了一种新的利用点弦距离递归的角点检测算法。从实验结果看,本文提出的角点检测器在图像仿射变换、JPEG质量压缩和高斯噪声条件下有更好的平均重复性和定位误差。     

一种基于分形小波变换的工业电视图象编码方法研究

通过分析工业电视图象的特点,提出了一种基于分形小波变换编码的工业电视图象压缩方法,从而较好地解决了分形编码只能压缩静止图象的不足,该方法不仅适合于煤矿工业电视图象压缩,而且还可用于其它背景区图象相对静止的工业电视图象的压缩编码,实验结果表明,该法可取得较高压缩比和峰值信噪比,因此具有实用价值。     

一种基于小波变换提取拐点的手写签名认证方法

为了对手写签名进行准确认证 ,介绍了一种基于小波变换的用于提取手写签名笔划坐标 -时间曲线拐点的手写签名认证方法 ,该方法首先采用以高斯函数的二阶导数作为小波基的小波变换技术来进行拐点提取 ,由于变换后的曲线可以近似认为是原函数的二次导数曲线 ,它不仅能够从签名笔划曲线中准确可靠地提取出具有特殊意义的拐点 ,并具有分辨尺度可调的优点 ,从而提高了认证的普适性 ;然后以该方法为基础 ,进行不同签名之间拐点序列的匹配 ;最后再利用提取的拐点来对签名进行分段和段 -段对应处理 .若由于利用拐点分段能体现出各分段局部相似性与稳定性不同的特点 ,因而拉开了真伪签名之间的差距 ,若进一步与动态时间弯曲算法相结合 ,即可取得比较满意的认证效果 .     

基于最优熵与EEMD的滚动轴承微故障诊断方法

滚动轴承是易损件,为了更好并及时检测出在信噪比低的情况下的轴承早期微故障振动信号,提出了小波包最优熵和EEMD相结合的方法。运用小波包最优熵对采集信号实现信噪分离,突出了小波包降噪效果明显;通过EEMD将信号分解成多个分量;最后以互相关、峭度准则提取故障信号分量以避免分量选择的盲目性。结果表明:该方法对轴承初期故障具有良好的降噪效果,可以准确快速地检测出轴承故障,从而表明该方法是有效且可行的。