高速列车万向轴动不平衡检测的EEMD-Hankel-SVD方法

针对聚合经验模式分解(Ensemble empirical model decomposition,EEMD)的等效滤波特性依然存在模式分量间频带重叠较大的根本缺陷,提出一种高速列车万向轴动不平衡动态检测的新方法。该方法的核心是对万向节安装机座的振动信号进行EEMD分解得到基本模式分量,应用基本模式分量信号来构造Hankel矩阵,对该矩阵进行正交化奇异值(Singular value decomposition,SVD)分解,以奇异值关键叠层作为奇异值的选择准则对信号进行重构,应用重构信号的傅里叶谱来检测高速列车万向轴的动不平衡,消除EEMD分解模式频带重叠对故障特征的淹没和混淆效应,提高了谱的清晰度,凸显了故障特征。应用万向轴动不平衡试验数据对该方法进行试验验证,结果表明,该方法能够有效检测万向轴动不平衡引起的故障特征和万向轴的固有振动特征,与纯EEMD方法相比,该方法在谱的清晰度和故障表征力上得到了显著提高。

Runoff of arid and semi-arid regions simulated and projected by CLM-DTVGM and its multi-scale fluctuations as revealed by EEMD analysis

Runoff is a major component of the water cycle, and its multi-scale fluctuations are important to water resources management across arid and semi-arid regions. This paper coupled the Distributed Time Variant Gain Model （DTVGM） into the Community Land Model （CLM 3.5）, replacing the TOPMODEL-based method to simulate runoff in the arid and semi-arid regions of China. The coupled model was calibrated at five gauging stations for the period 1980-2005 and validated for the period 2006-2010. Then, future runoff （2010-2100） was simulated for different Representative Concentration Pathways （RCP） emission scenarios. After that, the spatial distributions of the future runoff for these scenarios were discussed, and the multi-scale fluctuation characteristics of the future annual runoff for the RCP scenarios were explored using the Ensemble Empirical Mode Decomposition （EEMD） analysis method. Finally, the decadal variabilities of the future annual runoff for the entire study area and the five catchments in it were investigated. The results showed that the future annual runoff had slowly decreasing trends for scenarios RCP 2.6 and RCP 8.5 during the period 2010-2100, whereas it had a non-monotonic trend for the RCP 4.5 scenario, with a slow increase after the 2050s. Additionally, the future annual runoff clearly varied over a decadal time scale, indicating that it had clear divisions between dry and wet periods. The longest dry period was approximately 15 years （2040-2055） for the RCP 2.6 scenario and 25 years （2045-2070） for the RCP 4.5 scenario. However, the RCP 8.5 scenario was predicted to have a long dry period starting from 2045. Under these scenarios, the water resources situation of the study area will be extremely severe. Therefore, adaptive water management measures addressing climate change should be adopted to proactively confront the risks of water resources.

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.

Long-term ocean temperature trend and marine heatwaves

Marine heatwaves(MHWs)can cause irreversible damage to marine ecosystems and livelihoods.Appropriate MHW characterization remains difficult,because the choice of a sea surface temperature(SST)temporal baseline strongly influences MHW identification.Following a recent work suggesting that there should be a communicating baseline for long-term ocean temperature trends(LTT)and MHWs,we provided an effective and quantitative solution to calculate LTT and MHWs simultaneously by using the ensemble empirical mode decomposition(EEMD)method.The long-term nonlinear trend of SST obtained by EEMD shows superiority over the traditional linear trend in that the data extension does not alter prior results.The MHWs identified from the detrended SST data exhibited low sensitivity to the baseline choice,demonstrating the robustness of our method.We also derived the total heat exposure(THE)by combining LTT and MHWs.The THE was sensitive to the fixed-period baseline choice,with a response to increasing SST that depended on the onset time of a perpetual MHW state(identified MHW days equal to the year length).Subtropical areas,the Indian Ocean,and part of the Southern Ocean were most sensitive to the long-term global warming trend.

Projection of the Zhujiang(Pearl) River Delta's potential submerged area due to sea level rise during the 21st century based on CMIP5 simulations

Projections of potential submerged area due to sea level rise are helpful for improving understanding of the influence of ongoing global warming on coastal areas. The Ensemble Empirical Mode Decomposition method is used to adaptively decompose the sea level time series in order to extract the secular trend component. Then the linear relationship between the global mean sea level （GMSL） change and the Zhujiang （Pearl） River Delta （PRD） sea level change is calculated： an increase of 1.0 m in the GMSL corresponds to a 1.3 m （uncertainty interval from 1.25 to 1.46 m） increase in the PRD. Based on this relationship and the GMSL rise projected by the Coupled Model Intercomparison Project Phase 5 under three greenhouse gas emission scenarios （representative concentration pathways, or RCPs, from low to high emission scenarios RCP2.6, RCP4.5, and RCP8.5）, the PRD sea level is calculated and projected for the period 2006-2100. By around the year 2050, the PRD sea level will rise 0.29 （0.21 to 0.40） m under RCP2.6, 0.31 （0.22 to 0.42） m under RCP4.5, and 0.34 （0.25 to 0.46） m under RCP8.5, respectively. By 2100, it will rise 0.59 （0.36 to 0.88） m, 0.71 （0.47 to 1.02） m, and 1.0 （0.68 to 1.41） m, respectively. In addition, considering the extreme value of relative sea level due to land subsidence （i.e., 0.20 m） and that obtained from intermonthly variability （i.e., 0.33 m）, the PRD sea level will rise 1.94 m by the year 2100 under the RCP8.5 scenario with the upper uncertainty level （i.e., 1.41 m）. Accordingly, the potential submerged area is 8.57x103 km2 for the PRD, about 1.3 times its present area.

Spatial-temporal Analysis and Prediction of Precipitation Extremes: A Case Study in the Weihe River Basin, China

Extreme precipitation events bring considerable risks to the natural ecosystem and human life.Investigating the spatial-temporal characteristics of extreme precipitation and predicting it quantitatively are critical for the flood prevention and water resources planning and management.In this study,daily precipitation data(1957–2019)were collected from 24 meteorological stations in the Weihe River Basin(WRB),Northwest China and its surrounding areas.We first analyzed the spatial-temporal change of precipitation extremes in the WRB based on space-time cube(STC),and then predicted precipitation extremes using long short-term memory(LSTM)network,auto-regressive integrated moving average(ARIMA),and hybrid ensemble empirical mode decomposition(EEMD)-LSTM-ARIMA models.The precipitation extremes increased as the spatial variation from northwest to southeast of the WRB.There were two clusters for each extreme precipitation index,which were distributed in the northwestern and southeastern or northern and southern of the WRB.The precipitation extremes in the WRB present a strong clustering pattern.Spatially,the pattern of only high-high cluster and only low-low cluster were primarily located in lower reaches and upper reaches of the WRB,respectively.Hot spots(25.00%–50.00%)were more than cold spots(4.17%–25.00%)in the WRB.Cold spots were mainly concentrated in the northwestern part,while hot spots were mostly located in the eastern and southern parts.For different extreme precipitation indices,the performances of the different models were different.The accuracy ranking was EEMD-LSTM-ARIMA>LSTM>ARIMA in predicting simple daily intensity index(SDII)and consecutive wet days(CWD),while the accuracy ranking was LSTM>EEMD-LSTM-ARIMA>ARIMA in predicting very wet days(R95 P).The hybrid EEMD-LSTM-ARIMA model proposed was generally superior to single models in the prediction of precipitation extremes.

Characteristics of the dynamic changes in active accumulated temperature in Sichuan,China in the last 51 years against the background of climate change

It is of utmost necessity to understand the dynamics of regional active accumulated temperature(AAT)to cope with the negative impacts of global warming on agroforestry development and food security and to provide a real-time and effective reference basis for regional agroforestry planning.The daily temperature data from 30 meteorological stations in Sichuan Province from 1970 to 2020,and sea surface temperature(SST)index data from the Atlantic Multiphase Oscillation(AMO)and Pacific Decadal Oscillation(PDO)were used for the study.Sichuan Province was divided into the western region(WS)and the eastern region(ES),considering 1000 m above sea level as the boundary.The spatiotemporal characteristics of≥0℃ and≥10℃ active accumulated temperature(AAT0,AAT10)in WS and ES were analyzed comprehensively using 5-day average sliding,empirical orthogonal function(EOF),ensemble empirical mode decomposition(EEMD),and multiple mutation tests.The results show that(1)AAT0 and AAT10 of WS ranged from 3034℃ to 3586℃ and 1971℃ to 2636℃,respectively,while the AAT0 and AAT10 of ES ranged from 5863℃ to 6513℃ and 4847℃ to 5875℃,respectively.The period around 1997 was a significant abrupt change,and the AAT in the province generally increased during the subsequent time period(2)AAT in the study area is mainly driven by the fluctuations of AMO,as reflected by the low-to-high variation of AAT coinciding with the jump of the cold-to-warm phase of AMO.Considering different time scale fluctuations in the past 51 years,the major cycle for both AAT0 and AAT10 in WS is 3.40 a,while the major cycles in ES are 3.64 a and 3.19 a,respectively with a sub-cycle of 7.29 a.AAT fluctuation has an insignificant periodic characteristic of 25.50 a on the interdecadal scale(3)The spatial heterogeneity of AAT in WS is prominent and is mainly reflected by the significantly warm conditions in the south of the WS region and relatively slight warm conditions in the north,as well as by the isolated cooling area in the form of"freezing point",i.e.,Xiaojin county.In contrast,the spatial variability of AAT in ES is more or less consistent,with the warming areas concentrated in the foothills of the western edge of the basin and a slight increase in AAT observed in the central part of the basin.

Combined filter method for weakening GNSS multipath error

A filter method that combines ensemble empirical modal decomposition(EEMD)and wavelet analysis methods was proposed to separate and correct the global navigation satellite system(GNSS)multipath error more effectively.In this method,the GNSS signal is first decomposed into several intrinsic mode functions(IMFs)and a residual through EEMD.Then,the IMFs and residual are classified into noise terms,mixed terms,and useful terms according to a combined classification criterion.Finally,the mixed term denoised by wavelet and the useful term are reconstructed to obtain the multipath error and thus enable an error correction model to be built.The measurement data provided by the Curtin GNSS Research Center were used for processing and analysis.Results show that the proposed method can separate multipath error from GNSS data to a great extent,thereby effectively addressing the defects of EEMD and wavelet methods on multipath error weakening.The error correction model established with the separated multipath error has a higher accuracy and provides a certain reference value for research on related signal processing.

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.

A distributed EEMDN-SABiGRU model on Spark for passenger hotspot prediction

To address the imbalance problem between supply and demand for taxis and passengers,this paper proposes a distributed ensemble empirical mode decomposition with normalization of spatial attention mechanism based bi-directional gated recurrent unit(EEMDN-SABiGRU)model on Spark for accurate passenger hotspot prediction.It focuses on reducing blind cruising costs,improving carrying efficiency,and maximizing incomes.Specifically,the EEMDN method is put forward to process the passenger hotspot data in the grid to solve the problems of non-smooth sequences and the degradation of prediction accuracy caused by excessive numerical differences,while dealing with the eigenmodal EMD.Next,a spatial attention mechanism is constructed to capture the characteristics of passenger hotspots in each grid,taking passenger boarding and alighting hotspots as weights and emphasizing the spatial regularity of passengers in the grid.Furthermore,the bi-directional GRU algorithm is merged to deal with the problem that GRU can obtain only the forward information but ignores the backward information,to improve the accuracy of feature extraction.Finally,the accurate prediction of passenger hotspots is achieved based on the EEMDN-SABiGRU model using real-world taxi GPS trajectory data in the Spark parallel computing framework.The experimental results demonstrate that based on the four datasets in the 00-grid,compared with LSTM,EMDLSTM,EEMD-LSTM,GRU,EMD-GRU,EEMD-GRU,EMDN-GRU,CNN,and BP,the mean absolute percentage error,mean absolute error,root mean square error,and maximum error values of EEMDN-SABiGRU decrease by at least 43.18%,44.91%,55.04%,and 39.33%,respectively.

Instantaneous Attributes Analysis of Seismic Signals Using Improved HHT

As the key technique of improved Hilbert-Huang transform （HHT）, ensemble empiri- cal mode decomposition （EEMD） has a good performance of eliminating mode mixing phenomenon, which has a strong impact on the observation of seismic information. However, the intrinsic mode functions （IMF） obtained from EEMD contain noises, so that it is required to find a more robust frequency estimation method to calculate the instantaneous frequency （IF） of IMF. For this reason, the improved HHT algorithm based on the damped instantaneous frequency （DIF） is proposed to overcome the shortage of EEMD. Compared with other IF estimation methods, the DIF has strong antinoise ability and high estimation accuracy. The test results of synthetic and real seismic data show that the proposed algorithm is feasible and effective for extracting seismic instantaneous at- tributes.

Near-Term Projections of Global and Regional Land Mean Temperature Changes Considering Both the Secular Trend and Multidecadal Variability

Near-term climate projections are needed by policymakers; however, these projections are difficult because internally generated climate variations need to be considered. In this study, temperature change scenarios in the near-term period 2017-35 are projected at global and regional scales based on a refined multi-model ensemble approach that considers both the secular trend(ST) and multidecadal variability(MDV) in the Coupled Model Intercomparison Project Phase 5(CMIP5) simulations. The ST and MDV components are adaptively extracted from each model simulation by using the ensemble empirical mode decomposition(EEMD) filter, reconstructed via the Bayesian model averaging(BMA) method for the historical period 1901-2005, and validated for 2006-16. In the simulations of the "medium" representative concentration pathways scenario during 2017-35, the MDV-modulated temperature change projected via the refined approach displays an increase of 0.44℃(90% uncertainty range from 0.30 to 0.58℃) for global land, 0.48℃(90% uncertainty range from 0.29 to 0.67℃) for the Northern Hemispheric land(NL), and 0.29℃(90% uncertainty range from 0.23 to 0.35℃) for the Southern Hemispheric land(SL). These increases are smaller than those projected by the conventional arithmetic mean approach. The MDV enhances the ST in 13 of 21 regions across the world. The largest MDV-modulated warming effect(46%) exists in central America. In contrast,the MDV counteracts the ST in NL, SL, and eight other regions, with the largest cooling effect(220%) in Alaska.