Phase space reconstruction of chaotic dynamical system based on wavelet decomposition

In view of the disadvantages of the traditional phase space reconstruction method, this paper presents the method of phase space reconstruction based on the wavelet decomposition and indicates that the wavelet decomposition of chaotic dynamical system is essentially a projection of chaotic attractor on the axes of space opened by the wavelet filter vectors, which corresponds to the time-delayed embedding method of phase space reconstruction proposed by Packard and Takens. The experimental results show that, the structure of dynamical trajectory of chaotic system on the wavelet space is much similar to the original system, and the nonlinear invariants such as correlation dimension, Lyapunov exponent and Kolmogorov entropy are still reserved. It demonstrates that wavelet decomposition is effective for characterizing chaotic dynamical system.

Prediction of Tight Sand Reservoir with Multi-Wavelet Decomposition and Reconstructing Method

Special reservoir or fluid has an abnormal response to some certain frequencies, so that seismic decomposition and reconstruction are used to highlight the seismic reflection at certain frequencies useful to identify special geological bodies. Because seismic wavelets are time-varying and spatial-variable in the propagation, synthetic traces based on single wavelet make some weak but useful information lost, and make artifacts form. However, Morlet wavelet aggregation with mathematical analytical expression is able to fully and correctly reflect the variations of wavelet in the propagation of underground medium. The matching pursuit algorithm on the basis of Morlet wavelet improves the calculating efficiency in decomposition and reconstruction greatly. This method is applied to the actual study area to do conjoint analysis of single well and well-tie multi-wavelet decomposition. It is found that frequencies sensitive to interest reservoirs range from 8 to 34 Hz. Reconstructing the wavelets at those special frequencies and analyzing the reconstructed seismic data, it is pointed out that interest reservoirs have abnormal characteristics with respectively strong RMS amplitude in the reconstructed data. Crossplot of gamma value at wells and reconstructed RMS amplitude suggests that anomalies caused by interest reservoirs are well separated from the background anomalies when the reconstructed RMS amplitude is greater than 3650. Quantitative prediction results of interest reservoirs distribution in the study area reveal that interest reservoirs of western and northern study area are distributed annularly and bandedly, while most contiguous sandstone in eastern regions appears sporadically.

Enhancing photovoltaic energy forecasting:a progressive approach using wavelet packet decomposition

Accurate photovoltaic(PV)energy forecasting plays a crucial role in the efficient operation of PV power stations.This study presents a novel hybrid machine-learning(ML)model that combines Gaussian process regression with wavelet packet decomposition to forecast PV power half an hour ahead.The proposed technique was applied to the PV energy database of a station located in Algeria and its performance was compared to that of traditional forecasting models.Performance evaluations demonstrate the superiority of the proposed approach over conventional ML methods,including Gaussian process regression,extreme learning machines,artificial neural networks and support vector machines,across all seasons.The proposed model exhibits lower normalized root mean square error(nRMSE)(2.116%)and root mean square error(RMSE)(208.233 kW)values,along with a higher coefficient of determination(R^(2))of 99.881%.Furthermore,the exceptional performance of the model is maintained even when tested with various prediction horizons.However,as the forecast horizon extends from 1.5 to 5.5 hours,the prediction accuracy decreases,evident by the increase in the RMSE(710.839 kW)and nRMSE(7.276%),and a decrease in R2(98.462%).Comparative analysis with recent studies reveals that our approach consistently delivers competitive or superior results.This study provides empirical evidence supporting the effectiveness of the proposed hybrid ML model,suggesting its potential as a reliable tool for enhancing PV power forecasting accuracy,thereby contributing to more efficient grid management.

De-noising of radiation pressure signal generated by bubble oscillation based on ensemble empirical mode decomposition

The radiation pressure signals generated by the bubble oscillation are often utilized to recognize the characteristics of the target objects in many fields.However,these signals are easily contaminated by complex background noises.In order to accurately extract the effective components of the radiation pressure signal generated by the bubble oscillation,this paper proposes a de-noising procedure for the radiation pressure signal,based on the ensemble empirical mode decomposition(EEMD),the autocorrelation function and the modified wavelet soft-threshold de-noising method.In order to verify the effectiveness of the procedure,the typical radiation pressure signal generated based on the Keller-Miksis model under the acoustic excitation is employed for the subsequent de-noising analysis.The results of the qualitative analysis show that the amplitude and the period of the bubble oscillation can be clearly observed in the time-domain diagram of the de-noised signal based on the EEMD.In the quantitative analysis,the de-noised signal based on the EEMD has better performance with higher signal-to-noise ratio(SNR),smaller root-mean-square error,and larger correlation coefficient than that based on the wavelet transform(WT)and the empirical mode decomposition(EMD).Furthermore,with the increase of the complexity of the radiation pressure signal(e.g.,the increase of the dimensionless pressure amplitude of the acoustic wave and the decrease of the SNR of the input signal),the above three evaluation indexes of the de-noised signal based on the EEMD are all better than those based on the other two methods.When the signal is more complex,the de-noising capabilities of the WT,the EMD are greatly reduced,but the EEMD can still maintain the good de-noising capability,which shows the superiority of the signal de-noising procedure proposed in the present paper.

Vibration Measurement of Pedestrian Bridge Using Double Magnetic Suspension Vibrator Based on Wavelet Analysis

Aiming at the problem of pedestrian bridge vibration measurement,a vibration measurement system of pedestrian bridge with dual magnetic suspension vibrator structure was designed according to absolute vibration measurement principle. The relationship between the magnetic repulsion force of vibrator and its displacement was obtained by the experimental method and the least square fitting method. The vibration equations of two magnetic suspension vibrators were deduced respectively,and the measurement sensitivity of the system was deduced. The amplitude-frequency characteristic of the system was studied. A simulation model of vibrator measurement system with double magnetic suspension vibrator was established. The analysis shows that the sensitivity of the vibration measurement system with double magnetic suspension vibrator is higher than that with single magnetic suspension vibrator. The four vibration waveforms were measured,that is,no one passes through a pedestrian bridge,there are cars running under the pedestrian bridge,single pedestrian passes through the pedestrian bridge and multiple pedestrians pass through the pedestrian bridge. The multi-scale one-dimensional wavelet decomposition function was used to analyze the vibration signals. The vibration characteristics were obtained using one dimension wavelet decomposition function under four different conditions. Finally,the vibration waveforms of four cases were reconstructed. The measured results show that the vibration measurement system of pedestrian bridge with double magnetic suspension vibrator structure has high measurement sensitivity. The design has a certain value to monitor a pedestrian bridge.

头部动态场景下非接触式血氧饱和度测量

针对现有非接触式血氧饱和度测量方法在头部动态场景下准确性低的问题,提出一种基于改进的自适应噪声完全集合经验模态分解与小波阈值相结合的去噪方法,用于提取高信噪比的脉搏波信号。首先,为解决自适应噪声完全经验模态分解在分解重构早期产生虚假分量和模态混叠的问题,在分解过程中加入高斯白噪声,使其成为改进的自适应噪声完全集合经验模态分解(ICEEMDAN),从而减少模态分量中的残余噪声。然后,使用ICEEMDAN对红蓝色通道的脉搏波信号进行模态分解,并使用db8小波基函数对符合血氧频谱范围的分量进行3级分解和重构,将重构后的信号用于后续血氧值的计算。最后,将不同头部动态场景下测量的血氧饱和度结果进行实验对比分析。结果表明:不同头部场景下得到的血氧饱和度平均误差为0.73%,相较于其他算法平均误差降低1.93%。本文提出的去噪方法在不同头部场景下具有较好的稳定性,可满足日常血氧饱和度测量的需求。

地震子波分解与重构技术在薄储层预测中的应用

T813K-TK715井区是塔河油田的重点含油气井区,多口井在石炭系油气显示活跃,但砂体展布不明。在地震资料上,T813K-TK715井区储层响应特征不明确,且石炭系卡拉沙依组不整合面强反射屏蔽下伏储层反射信息,使得剥蚀线附近地层圈闭难以刻画。文中结合工区地质特征,建立二维地震和楔状地层模型,应用正演模拟技术得到正演模拟结果,利用子波分解与重构技术对正演模拟结果进行处理。结果表明:子波分解与重构技术有效压制了石炭系卡拉沙依组不整合面强反射,削弱了其对下伏砂体反射的屏蔽作用,增强了地层底部反射的连续性,并且提高了地震剖面识别尖灭点的精度,为塔河油田T813K-TK715井区识别强反射屏蔽背景下的薄砂体及地层尖灭点提供了新思路及技术支撑。

EEMD-小波在高边坡变形信息提取中的应用研究

针对高边坡变形呈现非平稳性及数据“噪声”多源的问题,提出了一种定向滤波的变形信息提取方法。首先,利用集合经验模态分解方法分解变形时序数据,结合定量分析法判别模态分量信号频段;然后,对高频模态分量中的“噪声”利用小波函数进行“靶向”消噪处理,并对趋势项进行傅里叶级数拟合;最后,重构高边坡变形分析模型,实现真实变形量的提取。结果表明,对比分析各项检验指标,通过“靶向”消噪,各高频模态分量消噪效果明显,重构后的集合经验模态分解(Ensemble Empirical Mode Decomposition,EEMD)-小波高边坡变形分析模型较原始形变和其他模型在精度指标方面提升显著,该方法可用于高边坡的变形预测分析和真实变形量提取。

小波包分解重构的励磁变压器整流谐波附加损耗计算

谐波损耗是造成励磁变压器安全事故的主要原因之一,损耗值过大易影响正常变电和输电,为此提出基于小波包分解重构的励磁变压器整流谐波附加损耗计算方法。通过获取小波包分解和重构系数,分析励磁变压器两种谐波附加损耗;利用开路和短路实验得到励磁变压器等效电路参数,采用小波包分解重构算法,计算不同谐波次数下的电阻和电抗并与基准值对比,得到励磁变压器整流谐波附加损耗值。最后,选取某种型号的励磁变压器,利用所提方法计算其在不同谐波次数下的附加损耗值,结果表明,得到的损耗值计算结果与实际结果非常接近,验证了所提方法具有较高的实用价值。

自适应Laplace小波字典的矿山微震信号稀疏降噪方法

针对矿山微震信号降噪问题,提出了一种基于自适应Laplace小波字典的稀疏降噪方法。该方法从矿山微震信号特征波形的先验知识分析入手,首先通过相关滤波法选取与微震信号特征波形最匹配的Laplace小波参数,并以此构造Laplace小波参数字典。然后结合正交匹配追踪(OMP)算法,稀疏重构出微震信号的特征波形。考虑到相关滤波法计算量大、时间较长,使用鲸鱼优化算法(WOA)进行快速的全局参数寻优。仿真微震信号和实测信号分析结果表明,所提出方法可有效重构出微震信号的特征波形,实现微震信号的降噪,且对噪声具有一定的抗干扰能力。相比常用的集合经验模态分解(EEMD)方法,提出的基于自适应Laplace小波字典的稀疏降噪方法具有一定的优越性。

基于低频扰动的多分布式电源孤岛检测方法

针对被动法检测盲区较大和主动法易产生稀释效应的问题,提出基于主、被动结合的多分布式电源(distributed generation,DG)孤岛检测方法。首先,该方法利用有源电力滤波器(active power filter,APF)实现无功功率扰动,即通过扰动APF的q轴参数来控制其输出频率为2 Hz的无功功率,以扰动孤岛频率周期性波动。然后,利用小波分解和小波重构算法提取频率信号中的低频分量。最后,利用快速傅里叶变换(fast fourier transform,FFT)算法计算2 Hz低频分量的幅值并判断是否越限,以实现多DG孤岛检测。Matlab/Simulink建模仿真结果表明:该方法能够有效地避免稀释效应,实现多DG孤岛检测。

基于小波包的电缆混合线路接地故障诊断方法研究

发生接地故障时负载电压数值会出现明显增大的情况,根据负载电压数值变化行为,提出基于小波包的电缆混合线路接地故障诊断方法。根据小波包分解与重构原则,对故障数据实施预处理,通过求解功率预测指标的方式,完成对电缆混合线路的短期故障功率预测。在故障属性集合中,判定故障行为表现特征,联合三角模糊数的计算结果,完成基于小波包的电缆混合线路接地故障诊断。对比实验结果表明,在小波包理论作用下,发生接地故障时负载电压数值达到了485 V,与非故障电压之间的差值超过100 V,电缆主机可以根据负载电压变化行为实现对混合线路接地故障行为的准确诊断。

基于小波包分解与重构算法的谐波电能计量

为实现复杂工业现场的电能准确计量,提出了基于小波包分解与重构算法的谐波电能计量方法,介绍了小波包的分解与重构算法和基于该算法的谐波信号提取方法,给出了谐波功率计算式与谐波电能计量原理;利用db42小波函数进行谐波电能计量仿真分析,仿真信号的基波频率为50Hz,采用2层小波包变换,取40个基波周期内的仿真输入信号,采样频率fs为800Hz,稳态、非稳态谐波有功电能与谐波无功电能计量仿真实验结果证明了算法的可行性与准确性;在此基础上研制的基于虚拟仪器技术的三相谐波电能计量装置实现了各次谐波有功电能、无功电能计量,准确度达0.2S级,符合GB/T14549—1993的A类标准要求。

匹配追踪子波分解重构技术在气层检测中的应用

川中地区震旦系岩溶风化壳储层缝洞系统复杂、非均质性强、上覆泥岩对储层地震反射的影响较大,使储层地震响应及AVO特征均不明显,给储层预测和气层检测带来了较大困难。运用基于匹配追踪算法的子波分解重构技术有效剔除了泥岩的影响,并在此基础上进行了储层预测和气层检测,均取得了良好的效果。子波分解重构技术可将以往只能从宏观上整体描述的地震数据分解为不同形状、不同频率和不同能量的子波,然后根据需要对分解得到的子波进行合理的重构,其时间分辨率和频率分辨率均较高,为含有强反射干扰的复杂储层预测和气层检测提供了新的思路和方法。

基于反对称双正交小波重构的图像增强方法

详细给出了基于反对称双正交小波重构的多尺度边缘检测方法的相关理论基础,即推导了反对称双正交小波变换所具有的卷积运算性质;分析了反对称双正交小波变换的微分算子功能;提出了一种针对图像多尺度边缘提取的小波重构算法.在此基础上,提出了基于反对称双正交小波重构的图像锐化增强方法.首先对图像进行多尺度小波分解;然后在小波重构中,计算模值图和相角图,提取各尺度边缘图像,并根据边缘图像,增强半重构图像的对应边缘点;最后继续逐级重构,实现图像增强.该方法在小波塔式分解数据的重构过程中有针对性地实现对图像边缘的锐化增强,对图像增强和图像滤噪增强提供了一种新的解决问题的思路.实验结果验证了该方法的有效性.

基于峰值信噪比和小波方向特性的图像奇异值去噪技术

提出一种利用小波变换子图像不同的方向特性和峰值信噪比进行奇异值分解的图像去噪算法。由于图像经过小波变换后,低频子图像集中了原图像的大部分能量噪声,故仅作简单维纳滤波;而噪声则主要集中在小波域中的三个不同方向的高频子图中,且系数较小,因此可以利用奇异值分解进行去噪处理,即用较大的奇异值和对应的特征向量重构出去噪图像,然而由于奇异值分解固有的行列方向性,对于高频对角线子图重构出的图像去噪效果不理想,故采取旋转至行列方向后再进行常用的奇异值滤波;最后将去噪后的低频和高频子图进行小波反变换重构出最终的去噪图像,其中重构所需的奇异值个数由图像的峰值信噪比确定。实验结果表明,该方法在有效去噪的同时较好的保留了原有的高频细节信息。

基于像素级的图像融合方法研究

图像融合是信息(数据)融合的重要分支和当前研究热点。其任务是对多幅源图像信息进行提取与综合,以获得对同一场景、目标的更准确、更全面、更可靠的图像描述,从而实现图像的进一步分析、理解及目标的检测、识别或跟踪。首先对基于像素级图像融合的基本方法、塔形分解法及小波变换法进行了详细介绍,然后针对各种方法的特点及应用场合进行了对比、分析与评价。

多子波分解与重构技术应用研究

探讨地震信号的多子波分解与重构方法,为地质解释与油气藏描述提供技术支撑。该方法在对地震信号进行分解时,利用匹配追踪算法,将原始地震信号分解成由一系列频率成分不同的Morlet子波函数组成的原子函数库;在重构地震信号时,根据油气响应特征、地质现象描述的需求,先对原子函数库进行筛选、优化,再按照频率属性对Morlet子波函数进行线性求和。在对地震信号进行分解与重构的过程中,实现了将地震信号在时间域内、时间-频率域内进行联合解释,挖掘出了单域内难以发现的地质现象、油气响应特征。将该技术应用到四川新场地区沙溪庙组的储层预测与含气性识别中,获得了与钻井实测结果相吻合的良好效果。

基于B样条小波预处理的短窗算法

提出了可滤除偶次和分次谐波的B样条小波滤波算法 ,将经过滤波预处理的短窗傅氏算法和传统傅氏算法进行了比较。从分析结果可看出 ,经过 2次分解与重构的信号接近于基波 ,减少了因傅氏算法不能滤除偶次谐波或非整次谐波而带来的误差。在同传统方法作比较后 ,提出了用基于B样条预处理的 2种短窗算法分别代替半波傅氏算法和全波傅氏算法的方案 。

小波分析及其在日流量过程随机模拟中的应用

小波分析能将交织在一起的不同频率成分组成的复杂时间序列分解成频率不相同的子序列 .基于小波分解和重构思想 ,本文以屏山站为例尝试将日流量过程分解成不同尺度下的小波系数 (细节 )和尺度系数 (背景 ) ,对分解所得的系数按实测资料显示的主周期 (年 )进行随机重构 ,从而获得各种各样的日流量过程线 .当小波函数和尺度函数或滤波器确定后 ,分解和重构过程不需估算参数 ,也不必进行前期分析和任何假定 ,因而 ,这种随机模拟方法具有非参数化特征 .实例分析表明是可行的 .