Hybrid Model Based on Wavelet Decomposition for Electricity Consumption Prediction

The effective supply of electricity is the basis of ensuring economic development and people's normal life. It is difficult to store electricity, as leading to the production and consumption must be completed simultaneously. Therefore, it is of great significance to accurately predict the demand for electricity consumption for the production planning of electricity and the normal operation of the society. In this paper, a hybrid model is constructed to predict the electricity consumption in China. The structural breaks test of monthly electricity consumption in China from January 2010 to December 2016 is carried out by using the structural breaks unit root test. Based on the existence of structura breaks, the electricity consumption data are decomposed into low-frequency and high-frequency components by wavelet model, and the separated low frequency signal and high frequency signal are predicted by autoregressive integrated moving average(ARIMA) and nonlinear autoregressive neural network(NAR), respectively. Therefore the wavelet-ARIMA-NAR hybrid model is constructed. In order to compare the effect of the hybrid model, the structural time series(STS) model is applied to predicting the electricity consumption. The results of prediction error test show that the hybrid model is more accurate for electricity consumption prediction.

Crustal density structure of the southern segment of the Liaocheng-Lankao fault, China

The 1:200,000 middle-large scale Bouguer gravity anomaly data covering the southern segment of the Liaocheng-Lankao fault(SLLF)and its vicinity are analyzed with two methods.First,the Bouguer gravity anomaly data are decomposed by two-dimensional(2 D)wavelet to make the family of multi-scale modes correspond with density structure at different depths.Second,a two and half dimension(2.5 D)human-computer interaction inversion of the Bouguer gravity anomaly data are conducted with the constraints provided by two deep seismic sounding profiles(DSS1 and DSS2)crossing the study area to get the crustal density profiles.Based on the integrated study,we can draw the following conclusions:1)SLLF appears to be a deep fault with almost vertical dipping and rooted into the uppermost mantle;2)In the middle to upper crust SLLF shows an clear turning patterns and segmentation features;3)In the study area the epicentral distributions of the precisely re-located small earthquakes and the historical large earthquakes have a good correspondence with the turning patterns and segmentation features of SLLF;and 4)The results of the horizontal slices from 2 D wavelet decomposition show that there are significant differences in the density structure on the two sides of the fault.A well-defined concave structure with low density exists in the upper crust of the Dongming Depression on the west side of the fault,with the concave center being estimated at a depth of about 8 km.In contrast,the upper crust on the east side presents a relative thinner pattern in depth with a bit higher density.Meanwhile,the low-density structure in the middle crust underneath the fault is presumably caused by the uplift of the upper mantle materials and their intrusion along the deep rupture system.This paper clarified the inconsistency of fault system and epicenters of small earthquakes from upper to lower crust.The results indicated that the fault system plays an important governing role to the seismicity in this area.

Fused empirical mode decomposition and wavelets for locating combined damage in a truss-type structure through vibration analysis

Structural health monitoring (SHM) is a relevant topic for civil systems and involves the monitoring, data processing and interpretation to evaluate the condition of a structure, in order to detect damage. In real structures, two or more sites or types of damage can be present at the same time. It has been shown that one kind of damaged condition can interfere with the detection of another kind of damage, leading to an incorrect assessment about the structure condition. Identifying combined damage on structures still represents a challenge for condition monitoring, because the reliable identification of a combined damaged condition is a difficult task. Thus, this work presents a fusion of methodologies, where a single wavelet-packet and the empirical mode decomposition (EMD) method are combined with artificial neural networks (ANNs) for the automated and online identification-location of single or multiple-combined damage in a scaled model of a five-bay truss-type structure. Results showed that the proposed methodology is very efficient and reliable for identifying and locating the three kinds of damage, as well as their combinations. Therefore, this methodology could be applied to detection-location of damage in real truss-type structures, which would help to improve the characteristics and life span of real structures.

迭代奇异值方法在机械结构模态分离重构中的应用

通过时频分解技术,将复杂的多模态信号分解成单模态成分,从而可以采用比较简单可靠的单模态识别方法对机械结构复杂模态信号进行参数辨识。经验小波变换(EWT)算法能有效解决模态分离问题,一些改进型EWT算法能有效克服噪声干扰,但是在模态重构时,滤波器彼此重叠、临近模态互相干扰,会不可避免地出现重构模态失真。本文针对模态分离重构问题展开研究,分析了EWT算法在模态分离重构中面临的重构失真问题,提出了基于迭代截断奇异值分解(ITSVD)方法的改进算法,并在仿真信号和含结合面机械结构模型振动响应信号上进行了应用。结果表明,所提ITSVD⁃EWT算法能够更好地实现机械结构模态分离重构。

Three-Dimensional Density Distribution and Seismic Activity along the Guxiang–Tongmai Segment of the Jiali Fault,Tibet

The Guxiang-Tongmai segment of the Jiali fault is situated northeast of the Namche Barwa Syntaxis in northeastern Tibet.It is one of the most active strike-slip faults near the syntaxis and plays a pivotal role in the examination of seismic activity within the eastern Himalayan Syntaxis.New study in the research region has yielded a 1:200000 gravity dataset covering an area 1500 km^(2).Using wavelet transform multiscale decomposition,scratch analysis techniques,and 3D gravity inversion methods,gravity anomalies,fault distributions,and density structures were determined across various scales.Through the integration of our new gravity data with other geophysical and geological information,our findings demonstrate substantial variations in the overall crustal density within the region,with the fault distribution closely linked to these density fluctuations.Disparities in stratigraphic density are important causes of variations in the capacity of geological formations to endure regional tectonic stress.Earthquakes are predominantly concentrated within the density transition zone and are primarily situated in regions of elevated density.The hanging wall stress within the Guxiang-Tongmai segment of the Jiali fault exhibits a notable concentration,marked by pronounced anisotropy,and is positioned within the density differential zone,which is prone to earthquakes.

基于损伤指数矩阵对层状管道结构损伤识别的有限元模拟

为研究基于损伤指数对层状管道结构损伤程度识别的可行性,创造性地采用小波包分解方法,以损伤指数为参数,分别研究损伤径向深度与损伤指数的关系和多种轴向位置损伤对损伤指数的影响。使用有限元分析软件ABAQUS,建立在同一轴向位置且多种径向损伤深度的层状管道结构模型,对压电元件位置提取的压电传感信号进行5层小波包分解,计算损伤指数值,将其组成损伤指数矩阵。针对同一轴向损伤位置,结合多种损伤程度组成损伤指数矩阵,分析损伤径向深度与损伤指数的关系并建立拟合曲线。随后改变损伤轴向位置且径向损伤深度同样递增,建立有限元模型,并获得相应的损伤指数拟合曲线。结果表明:在同一损伤轴向位置处,损伤指数拟合曲线都随着损伤径向深度的增加而呈现出先减后增的变化趋势;当改变损伤轴向位置时,损伤与信号接收位置轴向距离的增加使损伤指数的变化幅度减缓,但不改变曲线的总体趋势。研究结果表明:针对传感信号采用小波包分解方法,获得的损伤径向深度与损伤指数的拟合曲线可用于层状管道结构损伤程度的识别,并且该损伤程度识别方法受与信号接收位置轴向距离的限制较小,为快速诊断层状管道结构损伤程度提供了有效的有限元分析方法。

基于尺度-频率的小波微幅构造识别方法与应用——以南美厄瓜多尔X区块为例

针对构造幅度小于10 m的微幅构造精细识别,受地震资料品质及解释精度限制,常规成图与分析方法面临着难以有效突出微幅构造细节与提高识别效率的问题。以南美厄瓜多尔X区块微幅构造研究为目标,探索以小波构造分解的方法,实现对目标层3 m左右微幅构造快速有效的精细识别。目标层受安第斯造山运动弱挤压作用影响,大部分微幅构造幅度小于5 m。采用小波构造分解方法,将等深或等T0构造数据进行等间距采样,建立尺度随频率变化的“尺度-频率”小波函数,通过该函数多尺度地分解微幅构造起伏特征。利用阈值函数控制优选低频、中频、高频构造起伏等信息,根据相应小波系数对构造低频、中频、高频成分进行多尺度重组,提高相应高频成分对微幅构造的识别权重,从而降低构造低频成分对微幅构造识别的遮蔽作用。基于小波变换的微幅构造识别方法,可以有效识别幅度小于3 m的微幅构造,极大提高了斜坡区小型微幅构造油藏预测精度与分析效率,应用该成果相继部署完钻一系列评价井,均获得成功及良好油气显示,取得了良好实钻效果。

变分模态分解和小波等值线法识别结构瞬时频率

针对土木工程结构多分量时变模态参数识别困难问题,提出一种基于变分模态分解(Variational Modal Decomposition,VMD)、小波分析和等值线法相结合的瞬时频率识别新方法。首先,通过连续小波变换确定模态分量个数,利用VMD分解多分量信号得到各模态分量;然后,采用小波分析和等值线法识别每个模态分量的瞬时频率;最后,叠加展示各模态分量的时频曲线。为解决长时段信号小波能量衰变问题,提出一种等间距拆分时段、逐段分析的方法。采用多分量时变数值算例验证该方法的有效性和准确性。设计一个具有时变刚度拉索结构试验,对线性和正弦两种时变工况下索的振动位移响应数据进行分析,从而识别出拉索的时频变化曲线。结果表明,提出的方法可以准确有效地识别出多模态时变结构的瞬时频率,其拆时段分析手段在组合的可视化展示中有很好的时频效果,该方法具有较强的抗噪性和时频特征提取的稳定性。

Interpretations of gravity and Songliao Basin with Wavelet magnetic anomalies in the Multi-scale Decomposition

In this paper, we introduce the method of Wavelet Multi-scale Decomposition （WMD） combined with Power Spectrum Analysis （PSA） for the separation of regional gravity and magnetic anomalies. The Songliao Basin is situated between the Siberian Plate and the North China Plate, and its main structural trend of gravity and magnetic anomaly fields is NNE. The study area shows a significant feature of deep collage-type construction. According to the feature of gravity field, the region was divided into five sub-regions. The gravity and magnetic fields of the Songliao Basin were separated using WMD with a 4th order separation. The apparent depth of anomalies in each order was determined by Logarithmic PSA. Then, the shallow high-frequency anomalies were removed and the 2nd-4th order wavelet detail anomalies were used to study the basin＇s major faults. Twenty-six faults within the basement were recognized. The 4th order wavelet approximate anomalies were used for the inversion of the Moho discontinuity and the Curie isothermal surface.

Homogeneous wavelets and framelets with the refinable structure

Homogeneous wavelets and framelets have been extensively investigated in the classical theory of wavelets and they are often constructed from refinable functions via the multiresolution analysis. On the other hand, nonhomogeneous wavelets and framelets enjoy many desirable theoretical properties and are often intrinsically linked to the refinable structure and multiresolution analysis. In this paper, we provide a comprehensive study on connecting homogeneous wavelets and framelets to nonhomogeneous ones with the refinable structure. This allows us to understand better the structure of homogeneous wavelets and framelets as well as their connections to the refinable structure and multiresolution analysis.

自适应分解层数的小波域中值滤波振动信号降噪法

为降低结构健康监测加速度信号中常见的白噪声以及脉冲噪声,提出了中值滤波与小波阈值降噪相结合的方法。分解层数对降噪效果有着重要影响,为取得更好的降噪效果,提出一种分解层数自适应确定法,并给出了各层阈值的取值方法。数值模拟以及国家游泳中心健康监测系统实测数据分析结果表明,所提出的方法克服了传统阈值降噪法对脉冲噪声拟制效果不理想的缺陷,能同时有效降低白噪声以及脉冲噪声,为模态参数识别等提供更高信噪比的分析数据。

面向结构损伤预警的小波包能量谱识别参数

对Benchmark钢框架结构试验数据和润扬大桥悬索桥监测数据进行了小波包能量谱损伤预警分析,在此基础上详细考察了不同小波函数和小波包分解层次的损伤预警效果.结果表明:Daubechies小波和Coiflets小波适用于结构损伤预警,并且实际工程中应采用较大的小波函数阶次以获取较好的时域分辨能力和损伤预警能力;小波包分解层次愈大,小波包能量谱的频域分辨能力和损伤预警能力愈强.润扬大桥悬索桥的实测分析表明,随着小波包分解层次的增加,特征频带能量比与温度保持明显的季节相关性,其年变化更为明显.因此,实际工程应用中应尽量选择较大的小波包分解层次.

基于小波变换和奇异值分解的模态参数识别方法

提出了一种新的基于小波变换和奇异值分解相结合的结构模态参数识别方法.该方法首先对环境激励下的结构加速度响应信号进行互协方差分析,得到时域互协方差响应,通过小波变换将互协方差响应转换到时频域中得到信号的时频系数并沿每一个尺度点提取协方差响应的小波系数阵,然后对提取的小波系数阵进行奇异值分解得到奇异值和奇异向量,最后从重组的奇异值和奇异向量中识别出结构的模态参数.文章对提出的方法进行了理论证明,通过三自由度系统的数值算例验证了该方法的可行性,表明与直接小波变换方法相比,其识别结果精度更高.

基于小波变换的多尺度SSIM算法

提出一种基于小波变换的多尺度结构相似度的图像质量评价(WWMS-SSIM)算法。首先对原始图像和失真图像分别进行小波变换,在对应的低频子带图像中比较亮度和相关度差异,在对应高频子带图像中比较对比度、相关度和结构差异,然后赋予不同子带图像不同的视觉加权值,最后得到归一化的图像质量评价值。通过对不同失真类型图像以及Live数据库图像的评价,证明了提出的算法(WWMS-SSIM)相对均方误差、峰值信噪比、结构相似度以及改进的结构相似度评价算法更符合人类的主观视觉感受。

应用VC理论结构风险最小原则的局部放电信号消噪方法

为提高局部放电信号的消噪效果,根据统计学习理论中的结构风险最小化思想,基于小波的多分辨分析技术,给出一种针对局部放电的消噪方法。该方法不同于以往的对噪声进行估计并加以阈值处理的消噪方法,而从估计真实放电信号的角度出发,根据小波分解的系数能量大小选择能够表征真实局放信号的小波系数,并通过结构风险最小化原则确定模型的VC维,从而获得最优的真实信号估计。仿真结果表明,该方法在信噪比和均方误差指标上均要高于传统的史坦无偏似然估计阈值方法(SURE)、固定阈值法(VISU)及最小最大阈值方法(Min-iMax)。实验室实验和现场实测数据的处理结果表明,该方法能够还原真实的放电信号,提高检测信号的信噪比,是一种对局部放电信号消噪的较优算法。

运用统计小波的光纤光栅结构健康监测技术

针对振动型结构健康监测方法的特点,搭建了基于非平衡M-Z干涉仪和相位载波解调技术的光纤光栅损伤识别系统。运用小波包分解振动信号,建立了基于小波包节点能量相对变化率之和的结构损伤识别指标;介绍了统计过程控制原理,推导了使用均值-极差控制图分析损伤识别指标,识别结构连续损伤的过程。实验测试了铝制简支梁结构处于健康状态和3种损伤状态下的各40次振动信号。信号时域图显示各状态振动信号持续时间均约为0.05ms,幅值基本相同。依据结构健康状态下的统计过程控制限(12.85,41.35)进行了均值-极差控制图损伤识别分析,结果表明,搭建的损伤识别系统能连续地对结构进行健康监测。

基于小波多通道特征级融合的彩色纹理图像分析

在不完全树型小波分解基础上将纹理和颜色特征进行融合 ,提出了适合彩色纹理图像分析的新的特征 ,它比单纯的灰度纹理特征或颜色特征具有更强的分类能力 同时还利用 2 0类真实彩色自然纹理图像对塔式小波分解、不完全树型小波分解和小波包分解进行了多特征融合的分类比较 ,实验结果表明

一种新阈值函数的小波包语音增强算法

针对传统软、硬阈值函数去噪方法增强的语音存在失真的问题,提出一种新阈值函数的小波包语音增强算法,同时给出了新阈值函数和新的Bark尺度小波包分解结构。新阈值函数在小波包系数绝对值大于给定阈值的区间内,灵活地结合了软、硬阈值函数;在小波包系数绝对值小于给定阈值的区间内,用一种非线性函数代替传统阈值函数中的简单置零,实现了阈值函数的平缓过渡;新的60个频带Bark尺度小波包分解结构能更好地模拟人耳的听觉感知特性。仿真实验结果表明,在高斯白噪声和有色噪声背景下,与传统软、硬阈值函数去噪方法相比,新算法有效提高了增强语音信噪比和分段信噪比,减少了语音失真,具有更好的去噪效果。

基于支持向量机的钢筋混凝土桥梁损伤识别

为了克服现有方法存在的一些不足,提出基于小波包和支持向量机的混凝土桥梁损伤识别方法。采用小波包对环境振动下的信号进行分解,获得各个频带上的能量,该向量对损伤敏感,可以作为模型识别的输入向量。利用支持向量机强大的分类功能,提出根据频带能量建立支持向量机并进行损伤模式识别的方法。应用该方法对一座三跨连续梁桥进行了损伤识别分析。结果表明经过训练的支持向量机可以较准确地识别出损伤位置和程度。对小波频带能量进行主成分分析后建立的支持向量机会获得更好的识别效果。获得更精确的实际信号特征将进一步提高有限元模型精度和实际应用效果。

基于小波空间的图像分解变分模型

本文从不同的角度考虑OSV模型,提出一种基于全变差和H-1范数的图像分解变分模型.通过分析OSV模型的性质,给出该模型基于小波空间的非线性偏微分方程和迭代算法.同时,从理论上分析了该模型的极小值存在性.实验表明该方法具有可行性.