Research on Leak Location Method of Water Supply Pipeline Based on MVMD

At present,the leakage rate of the water distribution network in China is still high,and the waste of water resources caused by water distribution network leakage is quite serious every year.Therefore,the location of pipeline leakage is of great significance for saving water resources and reducing economic losses.Acoustic emission technology is the most widely used pipeline leak location technology.The traditional non-stationary random signal de-noising method mainly relies on the estimation of noise parameters,ignoring periodic noise and components unrelated to pipeline leakage.Aiming at the above problems,this paper proposes a leak location method for water supply pipelines based on a multivariate variational mode decomposition algorithm.This method combines the two parameters of the energy loss coefficient and the correlation coefficient between adjacent modes,and adaptively determines the decomposition mode number K according to the characteristics of the signal itself.According to the correlation coefficient,the effective component is selected to reconstruct the signal and the cross-correlation time delay is estimated to determine the location of the pipeline leakage point.The experimental results show that this method has higher accuracy than the cross-correlation method based on VMD and the cross-correlation method based on EMD,and the average relative positioning error is less than 2.2%.

Noise-assisted MEMD based relevant IMFs identification and EEG classification

Noise-assisted multivariate empirical mode decomposition(NA-MEMD) is suitable to analyze multichannel electroencephalography(EEG) signals of non-stationarity and non-linearity natures due to the fact that it can provide a highly localized time-frequency representation.For a finite set of multivariate intrinsic mode functions(IMFs) decomposed by NA-MEMD,it still raises the question on how to identify IMFs that contain the information of inertest in an efficient way,and conventional approaches address it by use of prior knowledge.In this work,a novel identification method of relevant IMFs without prior information was proposed based on NA-MEMD and Jensen-Shannon distance(JSD) measure.A criterion of effective factor based on JSD was applied to select significant IMF scales.At each decomposition scale,three kinds of JSDs associated with the effective factor were evaluated:between IMF components from data and themselves,between IMF components from noise and themselves,and between IMF components from data and noise.The efficacy of the proposed method has been demonstrated by both computer simulations and motor imagery EEG data from BCI competition IV datasets.

A Multi-Scale Method for PM2.5 Forecasting with Multi-Source Big Data

In the age of big data,the Internet big data can finely reflect public attention to air pollution,which greatly impact ambient PM2.5 concentrations;however,it has not been applied to PM2.5 prediction yet.Therefore,this study introduces such informative Internet big data as an effective predictor for PM2.5,in addition to other big data.To capture the multi-scale relationship between PM2.5 concentrations and multi-source big data,a novel multi-source big data and multi-scale forecasting methodology is proposed for PM2.5.Three major steps are taken:1)Multi-source big data process,to collect big data from different sources(e.g.,devices and Internet)and extract the hidden predictive features;2)Multi-scale analysis,to address the non-uniformity and nonalignment of timescales by withdrawing the scale-aligned modes hidden in multi-source data;3)PM2.5 prediction,entailing individual prediction at each timescale and ensemble prediction for the final results.The empirical study focuses on the top highly-polluted cities and shows that the proposed multi-source big data and multi-scale forecasting method outperforms its original forms(with neither big data nor multi-scale analysis),semi-extended variants(with big data and without multi-scale analysis)and similar counterparts(with big data but from a single source and multi-scale analysis)in accuracy.