Significant wave height forecasts integrating ensemble empirical mode decomposition with sequence-to-sequence model

As wave height is an important parameter in marine climate measurement,its accurate prediction is crucial in ocean engineering.It also plays an important role in marine disaster early warning and ship design,etc.However,challenges in the large demand for computing resources and the improvement of accuracy are currently encountered.To resolve the above mentioned problems,sequence-to-sequence deep learning model(Seq-to-Seq)is applied to intelligently explore the internal law between the continuous wave height data output by the model,so as to realize fast and accurate predictions on wave height data.Simultaneously,ensemble empirical mode decomposition(EEMD)is adopted to reduce the non-stationarity of wave height data and solve the problem of modal aliasing caused by empirical mode decomposition(EMD),and then improves the prediction accuracy.A significant wave height forecast method integrating EEMD with the Seq-to-Seq model(EEMD-Seq-to-Seq)is proposed in this paper,and the prediction models under different time spans are established.Compared with the long short-term memory model,the novel method demonstrates increased continuity for long-term prediction and reduces prediction errors.The experiments of wave height prediction on four buoys show that the EEMD-Seq-to-Seq algorithm effectively improves the prediction accuracy in short-term(3-h,6-h,12-h and 24-h forecast horizon)and long-term(48-h and 72-h forecast horizon)predictions.

A novel noise reduction technique for underwater acoustic signals based on complete ensemble empirical mode decomposition with adaptive noise,minimum mean square variance criterion and least mean square adaptive filter

Underwater acoustic signal processing is one of the research hotspots in underwater acoustics.Noise reduction of underwater acoustic signals is the key to underwater acoustic signal processing.Owing to the complexity of marine environment and the particularity of underwater acoustic channel,noise reduction of underwater acoustic signals has always been a difficult challenge in the field of underwater acoustic signal processing.In order to solve the dilemma,we proposed a novel noise reduction technique for underwater acoustic signals based on complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN),minimum mean square variance criterion(MMSVC) and least mean square adaptive filter(LMSAF).This noise reduction technique,named CEEMDAN-MMSVC-LMSAF,has three main advantages:(i) as an improved algorithm of empirical mode decomposition(EMD) and ensemble EMD(EEMD),CEEMDAN can better suppress mode mixing,and can avoid selecting the number of decomposition in variational mode decomposition(VMD);(ii) MMSVC can identify noisy intrinsic mode function(IMF),and can avoid selecting thresholds of different permutation entropies;(iii) for noise reduction of noisy IMFs,LMSAF overcomes the selection of deco mposition number and basis function for wavelet noise reduction.Firstly,CEEMDAN decomposes the original signal into IMFs,which can be divided into noisy IMFs and real IMFs.Then,MMSVC and LMSAF are used to detect identify noisy IMFs and remove noise components from noisy IMFs.Finally,both denoised noisy IMFs and real IMFs are reconstructed and the final denoised signal is obtained.Compared with other noise reduction techniques,the validity of CEEMDAN-MMSVC-LMSAF can be proved by the analysis of simulation signals and real underwater acoustic signals,which has the better noise reduction effect and has practical application value.CEEMDAN-MMSVC-LMSAF also provides a reliable basis for the detection,feature extraction,classification and recognition of underwater acoustic signals.

样本熵改进EEMD算法在继电器参数异常值处理中的应用

针对继电器参数中存在的异常值问题,文章提出了一种模态异常值处理模型。首先,依据继电器特点对集合经验模态分解(EEMD)算法中的参数进行灵敏性分析,确定优化参数;其次,针对EEMD分解中存在的模态混叠现象,采用样本熵和哈里斯鹰优化算法得到有效的模态分量;最后,分别采用拉依达准则及三次样条插值法对各模态异常数据进行识别及替换,将处理后的所有分量进行重构异常值,得到处理后的数据序列。继电器接触压降参数的实例分析结果表明,该模型具有良好的泛化能力,且能够有效地识别出潜在异常值。

基于EEMD分解的阶次跟踪方法研究

摆线针轮减速器组成零部件繁多、构成复杂,工作时噪声干扰大且多在变转速、往复的复杂工况下工作,因此,难以准确提取其内部的故障特征。针对这一问题,提出了一种基于集合经验模态分解(EEMD)与阶次跟踪分析的方法,对摆线针轮减速器进行了故障诊断。首先,对采集到的时域振动信号和转速信号进行了等角度域差值采样,得到了振动信号的等角域平稳信号;然后,对等角域信号进行了集合经验模态分解,得到了若干个固有模态分量(IMFs),计算了各个固有模态分量的峭度值,选取目标模态分量进行了信号重构;接着,采用快速傅里叶变换得到了故障信号的阶次图;最后,根据减速器的传动方式、各零部件的模数,计算出了各主要部件的故障阶次,对比减速器在故障前后阶次图的能量峰值进行了故障诊断。研究结果表明:该方法能够准确提取包含故障信息的固有模态分量,实现从等时域信号到等角域信号的转换,并提取摆线针轮减速器的滚针故障阶次(8.37阶),故障准确率达到99.6%,可实现摆线针轮减速器在非平稳工况下的故障特征识别,并验证该方法的可行性和有效性。

基于EEMD-CNN-LSTM的新型综合模型在滑坡位移预测中的应用

滑坡位移预测是滑坡稳定性评价的重要环节。尽管基于深度学习范式的时间序列方法预测滑坡位移取得了一定的成果,但由于滑坡位移数据的非平稳性、周期性和趋势性变化特征,导致当前时间序列模型的滑坡位移的多变量预测容易过拟合。为解决这一问题,针对滑坡位移数据的波动性和由周期项与趋势项位移叠加组成的特性,提出一种基于孤立森林(Isolation Forest,IF)异常检测、集成经验模态分解(Ensemble Empirical Mode Decomposition,EEMD)、卷积神经网络(Convolutional Neural Networks,CNN)和长短期记忆神经网络(Long Short-Term Memory,LSTM)相结合的滑坡位移预测模型。选择三峡库区以降雨为影响因子的阶跃型白家包滑坡为研究对象,引入IF算法对滑坡原始位移数据进行异常检测,使用EEMD方法提取滑坡趋势项和周期项位移,通过CNN捕捉局部周期项和趋势模式,并基于LSTM模型预测总体位移。结果表明,EEMD-CNN-LSTM在预测降雨情况时滑坡总体位移的均方根误差(RMSE)、平均绝对误差(MAE)、评价绝对百分比误差(MAPE)和决定系数(R2)4种指标分别为0.4190、0.3139、0.2379和0.9997,前3种精度评价指标较现有模型分别提升32.3%、25.1%、7.3%。相较于传统的LSTM模型、随机森林方法和EEMD-LSTM方法,EEMD-CNN-LSTM模型在有、无降雨这一外部影响因素下具有显著优势,能够较大地降低过拟合,提高预测的准确性。

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.

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。

A hybrid approach based on complete ensemble empirical mode decomposition with adaptive noise for multi-step-ahead solar radiation forecasting

Accurate measurements of solar radiation are required to ensure that power and energy systems continue to function effectively and securely.On the other hand,estimating it is extremely challenging due to the non-stationary behaviour and randomness of its components.In this research,a novel hybrid forecasting model,namely complete ensemble empirical mode decomposition with adaptive noise-Gaussian process regression(CEEMDAN-GPR),has been developed for daily global solar radiation prediction.The non-stationary global solar radiation series is transformed by CEEMDAN into regular subsets.After that,the GPR model uses these subsets as inputs to perform its prediction.According to the results of this research,the performance of the developed hybrid model is superior to two widely used hybrid models for solar radiation forecasting,namely wavelet-GPR and wavelet packet-GPR,in terms of mean square error,root mean square error,coefficient of determination and relative root mean square error values,which reached 3.23 MJ/m^(2)/day,1.80 MJ/m^(2)/day,95.56%,and 8.80%,respectively(for one-step forward forecasting).The proposed hybrid model can be used to ensure the safe and reliable operation of the electricity system.

基于EEMD-CBAM-BiLSTM的牵引负荷超短期预测

针对电气化铁路牵引负荷难以预测的问题,构建了一种由集合经验模态分解(EEMD)、改进型卷积块注意力模块(CBAM)和双向长短期神经网络(BiLSTM)组合成的EEMD-CBAM-BILSTM预测方法,有效地降低了牵引负荷超短期预测误差与计算成本。首先,通过EEMD将牵引负荷数据分解为多个稳定、有规律的时序模态函数,突出负荷数据的时序特征;其次,将分解后的各分量整体通入由卷积神经网络(CNN)和改进型CBAM组成的特征提取模块提取全局时序特征;最后,利用贝叶斯优化(BO)搜寻BiLSTM最优参数,并将全局特征通入优化后的神经网络进行超短期时序预测。仿真算例表明,该文所提预测框架在各预测步长下均能很好地把握牵引负荷变化趋势,显著提升了牵引负荷预测的精度。

基于CEEMDAN-GMDH-ARIMA的大坝变形预测模型研究

为提高大坝变形预测精度,针对大坝变形数据的复杂性和非线性等特征,基于自适应噪声完备集成经验模态分解(CEEMDAN)、数据处理群集法(GMDH)和差分自回归移动平均模型算法(ARIMA)进行大坝变形预测研究。采用CEEMDAN将大坝变形原始数据分解为高频随机分量、中频周期分量和低频趋势分量,再分别采用GMDH模型、ARIMA模型对高中频分量、低频分量进行预测,建立基于CEEMDAN-GMDH-ARIMA的大坝变形预测模型。以江西上犹江水电站为例,将该模型预测结果与反向传播(BP)、径向基函数(RBF)、GMDH和CEEMDAN-GMDH模型的预测结果进行对比分析。结果表明:CEEMDAN-GMDH-ARIMA模型的均方根误差(E_(RMS))、平均绝对误差(E_(MA))、相关系数(r)分别为0.048 mm、0.035 mm、0.994,均优于BP、RBF、GMDH、CEEMDAN-GMDH模型,模型预测效果最好,能够很好地体现监测点水平位移变化趋势。

基于PCA和EEMD的柔性直流配电网故障选线算法

柔性直流故障选线技术的发展对直流配电网有着至关重要的作用。本文针对现有柔性直流故障存在的可利用的故障信息较少等问题,提出了一种新算法,该算法有效利用了集合经验模态分解EEMD(ensemble empirical mode decomposition)算法、主成分分析PCA(principal component analysis)和相关系数各自的优势。首先,提取暂态电流样本信号,采用EEMD得到以正交基函数表示的数据矩阵;接着,基于PCA进行该矩阵元素特征向量到主成分的转换,将样本信号投影到主元空间实现坐标变换,从而得到对样本数据的聚类和识别结果;最后,基于相关系数进行故障线路判别。本文算法的EEMD揭露了原始历史数据的内在变化规律,PCA能够有效选择故障有效特征。大量实验表明,该新算法准确有效,与现有其他方法相比,在故障信息不明显、不同过渡电阻方面具有优势。

联合加权小波和EEMD的GNSS坐标时间序列降噪分析

针对GNSS坐标时间序列中有用信号与噪声难以准确分离这一问题,本文提出加权小波Z变换(weighted wavelet Z-transform,WWZ)和集合经验模态分解(ensemble empirical mode decomposition,EEMD)的降噪方法.通过对西北地区70个陆态网络连续站垂向坐标时间序列的降噪处理,分别采用均方根误差(root mean squared error,RMSE)、信噪比(signal to noise ratio,SNR)、闪烁噪声(flicker noise,FN)振幅及速度不确定度为评价指标,验证了本文方法的降噪效果在一定程度上优于小波降噪和EEMD降噪.结果显示:WWZ-EEMD相比小波降噪和EEMD降噪,降噪后信号序列RMSE分别降低了0.331 mm、0.757 mm,SNR分别提高了1.911 dB、3.635 dB;FN振幅及速度不确定度均有明显改善,验证了本文降噪方法的有效性.

The Modified Ensemble Empirical Mode Decomposition Method and Extraction of Oceanic Internal Wave from Synthetic Aperture Radar Image

In this paper a modified ensemble empirical mode decomposition(EEMD) method is presented, which is named winning-EEMD(W-EEMD). Two aspects of the EEMD, the amplitude of added white noise and the number of intrinsic mode functions(IMFs), are discussed in this method. The signal-to-noise ratio(SNR) is used to measure the amplitude of added noise and the winning number of IMFs(which results most frequency) is used to unify the number of IMFs. By this method, the calculation speed of decomposition is improved, and the relative error between original data and sum of decompositions is reduced. In addition, the feasibility and effectiveness of this method are proved by the example of the oceanic internal solitary wave.

基于气象因子的EEMD-BP方法在电网用电量预测中的应用

[目的]随着风能、太阳能等清洁能源快速发展,电力系统的能源结构发生了重大变化,这使得电网安全运行的不确定性增大,也给精准用电量预测带来了新的挑战。电网用电量受众多因子的影响,而气象因子的影响显著,因此,分析气象因子对用电量精细化预测的影响显得尤为重要。[方法]利用2017年逐日用电量以及最高气温、平均气温、最低气温、气压、相对湿度、风速等气象数据,采用集合模态经验分解(EEMD)和BP神经网络组合预测方法,探讨气象因子对集合模态经验分解回归模型(EEMD-BP)方法预测用电量的影响。[结果]研究发现,平均气温、最高气温、最低气温、气压和相对湿度与用电量序列经EEMD分解后的低频分量存在较好的相关关系,而与高频分量和周期分量的相关性较弱。[结论]利用BP回归模型预测的用电量与实况误差较大,引进气象因子后,EEMD-BP得出的预测准确率有了明显的提高。研究表明,基于气象因子的EEMD-BP组合预测方法可有效提高用电量预测的准确率,可为完善短期用电量预测方法提供有效的技术支撑。

基于EEMD-NGO-LSTM神经网络耦合的月径流预测模型及应用

为了提高径流序列的稳定度和精度,减小参数优化不当导致的非线性误差,研究将长短期记忆神经网络(LSTM)、集成经验模态分解(EEMD)和北方苍鹰优化算法(NGO)相结合,构建了EEMD-NGO-LSTM耦合预测模型。将此预测模型应用于模拟东辽河中下游的控制总站——王奔水文站2012年~2021年逐月径流过程,并与鲸鱼算法(WOA)以及灰狼算法(GWO)优化的长短期记忆神经网络进行模型比较。结果表明,EEMD-NGO-LSTM耦合预测模型的超参数迭代速度最快,精度最高,预测结果最接近实测值,其决定系数R^(2)为0.8643。而后采用CMIP6气候模式(SSP126情景)下的2030年的降水、气温数据输入模型进行预测,在气温上升1℃,降水不变的情景下,年径流量将增加6.61%;在降水升高5%,气温不变的情景下,年径流量将增加6.95%;在气温上升1℃、降水升高5%的情境下,年径流量将增加22.16%。

CEEMDAN-CNN-BiLSTM混合模型矿区地表沉降预测

为了进一步发挥全球卫星导航系统(GNSS)实时监测优势,对时序数据中的潜藏特征与隐藏信息进行深度挖掘,提高地表沉降预测精度,提出基于自适应噪声完备集合经验模态分解(CEEMDAN)、卷积神经网络(CNN)和双向长短期记忆网络(BiLSTM)的CEEMDAN-CNN-BiLSTM混合地表沉降预测方法:以皖北某大型煤矿开采工作面与工业广场区域为验证对象,对比分析稳定区域和重点监测区域数据形态;然后基于CEEMDAN重构监测站高程数据分量,输入CNN模型提取分量隐含信息;最后构建BiLSTM模型,实现对沉降监测点位数据的短期预测。实验结果表明,相较于传统的CNN和长短期记忆模型,CEEMDAN-CNN-BiLSTM混合模型可有效降低预测误差,其中平均绝对百分比误差(MAPE)的降低范围为40%~90%,而均方根(RMS)误差的降低范围为52%~87%;该模型在时空特征捕捉和泛化能力方面表现性能较好,可为GNSS时间序列短期预测提供更为精准和可靠的解决方案。

基于CEEMDAN-BO-SVM的选煤设备故障诊断方法

针对选煤设备故障诊断准确率低且所需特征信号难以采集和提取等问题,提出一种基于自适应噪声完备经验模态分解(CEEMDAN)及黑猩猩算法(BO)优化支持向量机(SVM)的音频信号故障诊断方法.对设备的原始音频信号进行CEEMDAN分解后得到一系列本征模态分量(IMF),计算各IMF的峭度值-相关系数,依据筛选准则优选有效IMF分量.提取有效分量的能量系数及波形系数,组成故障诊断特征集,使用BO-SVM进行故障诊断.实验结果表明,本文方法的故障诊断平均准确率为96.8%,在音频信号特征提取及故障诊断领域有一定的优势,具有一定的应用价值.

基于双EEMD与重构的局部放电时延估计方法

对室内电气设备的局部放电检测与定位是保障设备长期稳定运行的有效手段,而时延估计精度是影响局部放电检测与定位准确度的重要因素。为解决局部放电信号在噪声及多径效应影响下的时延估计精度问题,本文提出了一种基于双集合经验模态分解(ensemble empirical mode decomposition,EEMD)与重构的局部放电信号预处理方法。模拟仿真与实验测试结果表明,本文所提出的方法与广义互相关算法相比有效提高了时延估计准确度,且稳定性与鲁棒性更好。本文所提方法有效提高了局部放电信号的信噪比(signal-to-noise ratio,SNR)以及时延估计算法的精度,可用于低SNR及多径效应明显的室内环境中局部放电信号的时延精确估计。

逆向云灰色关联相似日的EEMD-RL-GWO-LSTM区域风光功率短期预测

针对现有方法在风光预测时气象因素考虑不全面且未考虑风光功率关联性的问题,提出一种风光功率短期预测方法。首先,以云模型表征风光出力不确定性,逆向云结合灰色关联度分析不同气象特征对输出功率的影响程度,并设立选取标准及综合评分指标;其次,采用集合经验模态分解(EEMD)将选取相似日的功率数据分解为子序列;最后,将子序列和气象数据作为基于折射学习策略(RL)的灰狼算法(GWO)优化的改进长短期记忆网络(LSTM)模型的预测输入进行训练,对待预测日的子序列分别预测,并叠加得到短期区域风光发电功率的预测。以中国西北某风光联合电场数据为例,对该模型进行验证,结果表明,相比于现有预测模型,该文所提方法考虑了天气因素,具有较高的预测精度,能够较好地为区域风光联合电场的功率预测提供参考。

基于EEMD和小波阈值的局部放电去噪方法

局部放电是开关柜运行状态的重要表征。而现场采集得到的局部放电往往被周期窄带和高斯白噪声所掩盖,为了能准确对局部放电进行分析,保证开关柜安全性和可靠性,提出了基于集合经验模态分解(Ensemble Empirical Mode Decomposition,EEMD)和小波阈值的去噪方法。首先对含噪局部放电进行EEMD分解,使用相关系数对模态分量(Intrinsic Mode Functions,IMFs)进行阈值判断,以去除虚假分量,随后对保留的IMFs进行小波阈值处理,最后将IMFs重构即可得到有用的局部放电信号。测试结果证明,该方法不仅可以有效地去除噪声信号,还能较好地保留局部放电的特征。