基于小波变换去噪的ARMA堤坝水平位移预测模型

堤坝位移监测数据可以被视为非平稳时间序列,但是因为受到诸多因素的影响,位移的测值常含有随机误差。在传统时间序列预测方法的基础上,提出了基于小波变换去噪的时间序列预测方法。基本步骤是:采用小波分解与重构法去噪,将信号分解到不同的频带上,再直接提取有用信号的频带进行重构,减小测值中的随机误差;进一步对去噪后不平稳的位移时间序列差分建立预测模型。工程实例计算分析表明,基于小波去噪所建立的模型位移预测结果要明显优于传统的模型位移预测结果,可以用于短期内堤坝水平位移预测中。     

基于小波分析以及ε-SVR的隧道围岩位移时序预测

根据龙滩地下洞室围岩现场长期的观测数据,利用小波变换阈值去噪法对数据信号的高频系数进行量化处理,恢复原有的监测信号。实例结果表明,小波变换阈值去噪法能够有效地去除噪声的影响,获得围岩变形的真实信号。同时将去噪后的数据用于支持向量机(ε-SVR)建立时序分析模型中,并与GA-BP模型进行对比,结果表明ε-SVR模型的误差更小,预测效果更佳。更多还原     

GIS局部放电检测中的小波包变换提取信号

在介绍小波包变换算法的基础上，将小波包变换用于白噪声干扰下的气体绝缘组合电器（GIS）局部放电信号提取。对模拟的局部放电信号进行了小波包分解与重构的计算机仿真研究，分析了不同的局部放电波形、噪声水平及采样率对去噪结果的影响，最后对GIS内置传感器实测波形进行了小波包变换去噪。     

CEEMDAN与小波变换混合去噪方法在光纤陀螺监测系统信号去噪中的应用

为了有效降低噪声对光纤陀螺监测系统实测信号的影响,提出基于CEEMDAN与小波变换混合去噪的方法。先将信号进行CEEMDAN分解,得到一系列IMF分量,计算每一个IMF分量与原始信号的相关系数,利用相关系数的大小筛选出主要的IMF分量。结合小波变换,对筛选出的IMF分量进行降噪处理,最后进行信号重构。引入含噪信号与降噪误差比和均方误差两个指标来判断降噪效果,利用单一的小波变换、CEEMDAN方法、CEEMDAN与小波变换混合去噪三种方法对仿真信号和实测信号进行分析。结果表明,基于CEEMDAN与小波变换混合去噪方法的去噪效果最好,有效地降低了噪声对真实信号的影响,去噪后的信号能准确地表征真实信号的变化特征。该方法非常适合光纤陀螺监测系统的信号去噪,能进一步提高光纤陀螺监测系统的测量精度。     

小波去噪在变形监测数据处理中的应用

阐述了小波去噪的基本原理,通过实例证明了小波变换可实现对GPS变形监测数据的有效去噪,是一种可靠的分析方法。     

基于小波变换的风力发电机组故障检测方法

针对传统风力发电机组故障检测方法受到非平稳振动信号影响，导致检测结果不精准的问题，提出了基于小波变换的风力发电机组故障检测方法。根据风力发电机组轴承非平稳信号特征，使用小波变换降噪技术，分解非平稳信号，获取有限长度离散含噪信号。消除噪声项后，利用峭度对非平稳信号的敏感性，提取故障自旋频率特征，实现轴承的故障检测。利用卷积神经网络提取齿轮箱阶次信号时序特征，通过齿轮箱故障时序特征的小波变换平移，利用阶次跟踪分析方法推导不同转速级的故障特征，以此对非平稳工况下齿轮箱故障状态诊断。由实验结果可知，该方法内、外滚道加速度时域信号变化范围分别为-0.3～0.3、-0.06～0.05 m/s²，小、大齿轮断齿故障幅值为0.2、 0.4，轴承故障和齿轮箱故障变化范围均与实际范围一致。     

基于经验模态分解的局部放电去噪方法

在简单介绍经验模态分解（EMD）的基础之上，将经验模态分解用于局部放电的信号分析。根据含噪声信号分解后固有模态函数（IMF）的统计特征，提出了一种基于向量阈值的新去噪算法，相比于常规的小波去噪算法，该算法具有形式简单、应用方便灵活、不受傅里叶变换及小波函数选择的限制等特点。实际处理结果及与小波的对比表明，新算法可以有效地抑制白噪声，取得和小波变换几乎一致的效果。     

小波阈值与小波包对南水北调工程防渗墙监测数据去噪有效性研究

以南水北调北京段大宁水库防渗墙加高施工工程为背景,基于小波包变换去噪法和小波变换阀值去噪法,对现场监测数据进行了消噪处理,比较了两者在工程实际中的应用效果。研究表明,对防渗墙工程安全监测数据,小波包的处理结果更为理想,能够更有效地去除噪音突变信号,保留原始有用信号的突变点,使重构信号能够更光滑地重现原始信号,其去噪法性能比小波变换阀值去噪法更佳,具有更好的实际应用价值。研究结论为后期更加准确地评价防渗墙施工期间的变形和进行墙体长期稳定性分析提供重要依据,研究方法可为类似工程提供参考。     

基于小波去噪的高斯过程回归模型在面板堆石坝沉降预测中的应用研究

引起面板堆石坝沉降变形的环境因素复杂,观测数据呈现出明显的噪声干扰特性,限制了数学模型拟合及预测的精度。对原始信号进行小波变换可有效分解其中的有用信号和噪声,因此,引入小波变换理论建立了基于小波阈值去噪的数学模型,并对面板堆石坝(CFRD)的沉降变形实测数据实施去噪,再对去噪后的数据进行高斯过程回归(GPR),建立了预测堆石坝沉降变形的模型。依托CFRD的实测沉降变形资料,采用Wavelet-GPR模型对大坝沉降进行了拟合与预测,并与未进行去噪的GPR模型计算结果进行对比。结果表明：Wavelet-GPR模型观测值与预测值的残差符合正态分布,去噪后学习段的均方根误差(RMSE)由0.928 7 mm减小至0.457 7 mm,平均绝对误差(MAE)由0.485 0 mm减小至0.330 6 mm;预测段的RMSE由1.308 9 mm减小至0.917 6 mm,MAE由0.926 3 mm减小至0.730 3 mm;且去噪后模型的样本观测值个数在其预测值95%置信范围内的占比有明显提升。因此,利用小波阈值去噪对实测沉降数据进行降噪处理能够降低噪声导致的数据观测值与真实值之间的误差,Wa...     

基于小波变换的高光谱遥感影像光谱匹配技术研究

以中国的高光谱影像OMIS为例,研究了基于小波变换的高光谱遥感影像光谱匹配算法,然后将该方法用于遥感影像智能化解译。实验结果表明,该算法先对光谱曲线进行小波去噪,再对不同频率信号进行小波多分辨率分析,能够在匹配过程中拉大异类间的特征值距离,提高分类精度。     

超稠原油的流变学特性及流动特征研究

该文针对三种不同类型的超稠原油的触变性、屈服应力和黏弹性等开展了研究工作。实验首先采用Haake RS6000旋转流变仪进行了流变学测试,结果表明随温度的上升,超稠原油的黏度、触变性和屈服应力均呈现出指数衰减的趋势;超稠原油表现出明显的黏弹性性质,且随频率的增大,样品从黏性主导转变成弹性主导。室内管道流动实验中,分别考虑温度、流速等因素研究了原油管道输运时的启动应力和流动规律,并将管流的测试结果与流变仪测量数据进行了对比分析,采用流变仪得到的在低剪切速率区间的流变学参数可以很好地应用于高剪切速率区的管道流动中。     

A Rigorous Wavelet-Packet Transform to Retrieve Snow Depth from SSMIS Data and Evaluation of its Reliability by Uncertainty Parameters

Water Resources Management - This study demonstrates the application of wavelet transform comprising discrete wavelet transform, maximum overlap discrete wavelet transform (MODWT), and...     

Effect of Utilization of Discrete Wavelet Components on Flood Forecasting Performance of Wavelet Based ANFIS Models

Wavelet based flood forecasting models are known to perform better than conventional models, yet the effect of the way wavelet components are combined to develop a model on the forecasting performance, is inadequately investigated. To demonstrate this, two types of wavelet- adaptive neuro-fuzzy inference system (WANFIS), i.e. WANFIS-split data model (WANFIS-SD) and WANFIS-modified time series model (WANFIS-MS) are developed to forecast river water levels with 1-day lead time. To develop these models, first the original level time series (OLTS) is decomposed into discrete wavelet components (DWCs) by discrete wavelet transform (DWT) upto three resolution levels. In WANFIS-SD, all wavelet components are used as inputs while WANFIS-MS ignores the noise wavelet components and utilizes only the effective wavelet components. The effectiveness of the developed models are evaluated through application to two Indian rivers, Kamla and Kosi, which vary significantly in their catchment area and flow patterns. The proposed models are found to forecast river water levels accurately. On comparison, the WANFIS-SD is found to perform better than WANFIS-MS for high flood levels.     

Wavelet transform‐based analysis of periodicities and trends of Sakarya basin (Turkey) streamflow data

In this study, the wavelet transform and the Mann–Kendall test are used to determine possible trends in annual streamflow series. The wavelet analysis provides detailed information about the time‐frequency contents of the data. Using wavelet components of the original data, it was aimed to find which periodicities are mainly responsible for a trend in the original data. Also, the global wavelet spectra and the continuous wavelet transform (CWT) were used for the analysis of the streamflow data, in order to explain its time‐frequency characteristics. Annual streamflow series across Turkey were used for the detection of trends for the original data and the periodic wavelet components (obtained by discrete wavelet transform). It was found that some periodic events clearly affect the trend in the streamflow series. The DW4 component (16‐yearly periodic component) at the stations of the Sakarya basin is the effective periodic component and is responsible for producing a real trend in the data. The effects of regional differences on the wavelet‐trend analysis are studied using records of the stations located in different climate areas. DW2 (4‐yearly component) and DW3 (8‐yearly component) are the dominant periodic components of this data. This study aims to explain the trend structure in the data. Copyright © 2009 John Wiley & Sons, Ltd.     

A Practical Guide to Discrete Wavelet Decomposition of Hydrologic Time Series

Discrete wavelet transform (DWT) is commonly used for wavelet threshold de-noising, wavelet decomposition, wavelet aided hydrologic series simulation and prediction, as well as many other hydrologic time series analyses. However, its effectiveness in practice is influenced by many key factors. In this paper the ??reference energy function?? was firstly established by operating Monte-Carlo simulation to diverse noise types; then, energy function of hydrologic series was compared with the reference energy function, and four key issues on discrete wavelet decomposition were studied and the methods for solving them were proposed, namely wavelet choice, decomposition level choice, wavelet threshold de-noising and significance testing of DWT, based on which a step-by-step guide to discrete wavelet decomposition of hydrologic series was provided finally. The specific guide is described as: choose appropriate wavelet from the recommended wavelets and according to the statistical characters relations among original series, de-noised series and removed noise; choose proper decomposition levels by analyzing the difference between energy function of the analyzed series and reference energy function; then, use the chosen wavelet and decomposition level, estimate threshold according to series?? complexity and set the same threshold under each level, and use the mid-thresholding rule to remove noise; finally, conduct significance testing of DWT by comparing energy function of the de-noised series with the reference energy function. Analyses of both synthetic and observed series indicated the better performance and easier operability of the proposed guide compared with those methods used presently. Following the guide step by step, noise and different deterministic components in hydrologic series can be accurately separated, and uncertainty can also be quantitatively estimated, thus the discrete wavelet decomposition result of series can be improved.     

二维离散小波变换在电能质量检测数据压缩中的应用

提出了用二维离散小波变换和能量阈值相结合的方法来解决电能质量扰动信号的压缩问题。利用二维db小波变换对矩阵数据分别进行行卷积和列卷积，把检测数据的高频信号和噪声信号分解在3个不同的方向上，且信号的能量集中在很少的小波系数上。再通过改进的能量阈值法，利用能量均值修正系数设置阈值使得压缩后的能量保留在99%以上，从而保证了重构信号的失真度很小且自适应地消除了加在扰动信号上的噪声。对6种扰动信号进行仿真并与小波包的压缩结果进行比较，结果表明该方法极大地提高了压缩率，并对噪声干扰有很好的去噪能力。     

Reservoir Inflow Prediction by Ensembling Wavelet and Bootstrap Techniques to Multiple Linear Regression Model

In this study, a new hybrid model, bootstrap multiple linear regression (BMLR) is suggested to investigate the potential of bootstrap resampling technique for daily reservoir inflow prediction. The proposed model compares with three other models: Multiple linear regression (MLR), wavelet multiple linear regression (WMLR) and wavelet bootstrap multiple linear regression (WBMLR). River stage data of monsoon season (1st July 2010 to 30 September 2010) from three gauging stations of Chenab river basin are used. In wavelet transformation, input vectors are decomposed using discrete wavelet transformation (DWT) into discrete wavelet components (DWCs). Then suitable DWCs are used to provide input to MLR model to develop WMLR model. Bootstrap technique coupled with MLR model to build up BMLR model. While WBMLR model is the conjunction of suitable DWCs and bootstrap technique to MLR model. Performance indices namely root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe coefficient of efficiency (NSC), and persistence index (CP) are used in study to evaluate the performance of model. Results showed that hybrid model BMLR produce significantly better results on performance indices than other models MLR, WMLR and WBMLR.

     

正常工况下石油类污染物运移及对地下水的污染分析

以兰州市某原油储备库为例,建立符合实际情况的三维地下水流数值模型,应用危害最大化原则模拟该油库营运后未来50 a内地下水中污染物迁移情况。结果表明:在正常工况下,油类污染物质量浓度为0.3 mg/L的等值线临近黄河最快需40 a;建立原油储备库不会对三滩水源地、黄河水质带来影响。     

嘉陵江流域北碚站年输沙量的变化规律及预测研究

首先利用小波变换对北碚站年输沙量的变化规律进行了研究,结果表明:年输沙量呈现出明显的减少趋势;其次,将小波变换结合BP神经网络建立小波网络模型,并利用该模型对北碚站的年输沙量进行预测,同时将预测结果与BP神经网络模型的预测结果进行了比较。认为在缺乏其它相关资料的情况下,单从输沙量和径流量资料出发,小波网络模型的预测效果明显优于BP神经网络模型。由此表明,小波网络模型不仅能对年输沙量的趋势进行预测,还能对年输沙量的大小进行较为准确的预测,从而为在资料较少的情况下进行输沙量的定量分析提供了一种新的方法。