EEMD Method Research Articles

Inverted EEMD: a robust method to identify narrow absorption features form spectral data and cubes

Abstract Extracting information from complex data is a challenge shared by multiple frontiers of modern astrophysical research. Among those, analyzing spectra cubes, where the emission is mapped in the position-position-velocity space is a difficult task given the vast amount of information contained within. The cubes often contain a superposition of emissions and absorptions, where extracting absorption signatures is often necessary. One example is the extraction of narrow absorption structures in HI 21 cm emission spectra. These HI self-absorption (HISA) clouds trace the cold HI gas in interstellar space. We introduce an automatic and robust method called the inverted EEMD method to extract narrow features from spectral cubes. Our method is based on the EEMD method, an established method to decompose 1d signals. The method is robust and parameter-free, making it useful in analyzing spectral cubes containing localized absorption signals of different types. The inverted-EEMD method is suitable for the analysis of spectral cubes where it can produce a cube containing the absorption signal and one containing the unabsorbed signal, where cold clouds can be identified as coherent regions in the absorption map. A Python implementation of the method is available at https://github.com/zhenzhen-research/inverted_eemd_map.

Risk Prediction Model for Tailings Ponds Based on EEMD-DA-LSTM Model

With the passage of time, the constant changes in relevant factors, and the daily maintenance of tailings ponds, the difficulty of tailings pond safety management is increasing day by day. In order to systematically improve the early warning ability for tailings pond dam break risk, the relationship between and influence of various related dam break risk factors of tailings ponds are utilised and the combination with dual attention is innovatively proposed. The risk prediction model for tailings ponds, EEMD-DA-LSTM, is improved. First, Pearson correlation coefficients are used to analyse the correlation between risk factors of tailings ponds. Then, the EEMD method is used to decompose the nonlinear displacement sequence, and the weights of input features are dynamically adjusted by double attention (DA). Finally, the LSTM network model is constructed to predict the displacement change. Taking valley-type tailings pond WKB-1 and mountainside tailings pond WKB-2 as examples, the dam break risk prediction models for tailings ponds are constructed based on three different models, the prediction results of different models are compared and analysed, and the prediction accuracy of the models is evaluated by three different evaluation criteria. The research results show that the integration of the EEMD-LSTM model with the DA model, that is, the EEMD-DA-LSTM model, has a better prediction effect for the dam break risk of tailings ponds WKB-1 and WKB-2 than other models through experimental verification. Therefore, the EEMD-DA-LSTM model is of great significance for preventing and resolving the safety risks of tailings ponds. It is valuable for practitioners in the mining industry and environmentally sustainable development.

Signal processing for miniature mass spectrometer based on LSTM-EEMD feature digging

Miniature mass spectrometers exhibit immense application potential in on-site detection due to their small size and low cost. However, their detection accuracy is severely affected by factors such as sample pre-processing and environmental conditions. In this study, we propose a data processing method based on long short-term memory-ensemble empirical mode decomposition (LSTM-EEMD) to improve the quality of on-site detection data from miniature mass spectrometers. The EEMD method can clearly decompose the different physical feature components in the small-scale spectrometer signals, while the LSTM method can adaptively learn the internal feature relationships of the signals. Thus, by combining the two, the parameters for the EEMD signal reconstruction can be optimized in an adaptive manner, obtaining the optimized coefficients. Compared to the previous EEMD feature enhancement approach, the LSTM-EEMD method not only significantly improves the coefficient of determination (R2) and relative standard deviation (RSD) of the data, enhancing the linear range, but also achieves fully adaptive processing throughout the workflow, greatly boosting the efficiency. By leveraging a miniature mass spectrometer, data for N-acetyl-l-aspartic acid (NAA), 2-Hydroxyglutarate (2-HG), and γ-Aminobutyric acid (GABA) in actual blood samples have been obtained. The experimental results demonstrate that the LSTM-EEMD method can markedly enhance the accuracy and usability of the biological sample data in practical testing, providing new perspectives and possibilities for research and applications in the relevant domain.

Enhanced Classification of Focal and Generalized Epilepsy Using EEMD and CEEMDAN Methods

Enhanced Classification of Focal and Generalized Epilepsy Using EEMD and CEEMDAN Methods

Sudden cardiac death prediction based on the complete ensemble empirical mode decomposition method and a machine learning strategy by using ECG signals

Cardiovascular diseases are a significant global health problem, often culminating in sudden cardiac death (SCD). Approximately 4 million annual deaths worldwide are attributed to SCD. Although the causes are varied, the heart’s electrical activity is always affected prior to an SCD event. This work explores the capabilities of the complete ensemble empirical mode decomposition (CEEMD) method for extracting relevant time-domain features from electrocardiogram (ECG) signals. CEEMD method is compared with EMD and EEMD methods. Additionally, a machine learning approach incorporating statistical indices, the Kruskal-Wallis test, and a support vector machine (SVM) is utilized for automatic analysis and prediction. SVM results are benchmarked against k-nearest neighbors, decision trees, and neural networks. Results demonstrate that the proposed methodology can predict the SCD event 30 min before it occurs with an accuracy of 97.28%, positively impacting on the improvement of timely interventions and, consequently, the survival rates and quality of life of people.

Early warning of systemic risk in stock market based on EEMD-LSTM.

With the increasing importance of the stock market, it is of great practical significance to accurately describe the systemic risk of the stock market and conduct more accurate early warning research on it. However, the existing research on the systemic risk of the stock market lacks multi-dimensional factors, and there is still room for improvement in the forecasting model. Therefore, to further measure the systemic risk profile of the Chinese stock market, establish a risk early warning system suitable for the Chinese stock market, and improve the risk management awareness of investors and regulators. This paper proposes a combination model of EEMD-LSTM, which can describe the complex nonlinear interaction. Firstly, 35 stock market systemic risk indicators are selected from the perspectives of macroeconomic operation, market cross-contagion and the stock market itself to build a comprehensive indicator system that conforms to the reality of China. Furthermore, based on TEI@I complex system methodology, an EEMD-LSTM model is proposed. The EEMD method is adopted to decompose the composite index sequence into intrinsic mode function components (IMF) of different scales and one trend term. Then the LSTM algorithm is used to predicted and model the decomposed sub-sequences. Finally, the forecast result of the composite index is obtained through integration. The empirical results show that the stock market systemic risk index constructed in this paper can effectively identify important risk events within the sample period. In addition, compared with the benchmark model, the EEMD-LSTM model constructed in this paper shows a stronger early warning ability for systemic financial risks in the stock market.

Analysis of cardiovascular function in diabetic patients using EEMD-ICA fusion multi-scale percussion entropy.

Diabetes is a chronic disease that can lead to a variety of complications and even cause death. The signal characteristics of the photoplethysmography signals (PPG) and electrocardiogram signals (ECG) can reflect the autonomic and vascular aspects of the effects of diabetes on the body. Based on the complex mechanism of interaction between PPG and ECG, a set of ensemble empirical mode decomposition-independent component analysis (EEMD-ICA) fusion multi-scale percussion entropy index (MSPEI) method was proposed to analyze cardiovascular function in diabetic patients. Firstly, the original signal was decomposed into multiple Intrinsic Mode Function (IMFs) by ensemble empirical mode decomposition EEMD, principal components of IMF were extracted by independent component analysis (ICA), then the extracted principal components were reconstructed to eliminate the complex high and low frequency noise of physiological signals. In addition, the MSPEI was calculated for the ECG R-R interval and PPG amplitude sequence.(RRI and Amp) The results showed that, compared with EEMD method, the SNR of EEMD-ICA method increases from 2.1551 to 11.3642, and the root mean square error (RMSE) decreases from 0.0556 to 0.0067. This algorithm can improve the performance of denoising and retain more feature information. The large and small scale entropy of MSPEI (RRI,Amp) was significantly different between healthy and diabetic patients (p< 0.01). Compared with arteriosclerosis index (AI) and multi-scale cross-approximate entropy (MCAE): MSPEISS (RRI,Amp) indicated that diabetes can affect the activity of human autonomic nervous system, while MSPEILS (RRI,Amp) indicated that diabetes can cause or worsen arteriosclerosis. Multi-scale Percussion Entropy algorithm has more advantages in analyzing the influence of diabetes on human cardiovascular and autonomic nervous function.

Fading noise suppression method of Ф-OTDR system based on non-local means filtering

Distributed optical fiber acoustic sensing (DAS) is a kind of optical fiber sensing system based on phase sensitive optical time domain reflection technology (Ф-OTDR). It has the characteristics of high sensitivity, long distance and real-time detection, and has been widely used in the positioning and monitoring of various engineering events. However, the high coherent detection light source used in the Ф-OTDR technology will produce coherent fading phenomenon in the fiber, which causes fading noise. This will seriously affecting the monitoring effect of the DAS system. Therefore, in order to address the fading noise problem in DAS system, a system fading noise suppression method based on non-local means filtering is proposed, which can effectively suppress the system fading noise in the signals and improve signal-to-noise ratio while preserving the characteristics of the target event signals. The experimental results show that the proposed method has better DAS system noise suppression effect in the face of different intensities and types of noise interference. Further, the proposed method is compared with EEMD, wavelet and outlier noise suppression methods. The results show that the noise suppression effect is significantly better than other methods, and the signal-to-noise ratio is improved by more than 30%, which verifies the effectiveness and superiority of the proposed method.

Spatial variations and mechanisms for the stability of water use efficiency in China.

A clearer understanding of the stability of water use efficiency (WUE) and its driving factors contributes to improving water use efficiency and strengthening water resource management. However, the stability of WUE is unclear. Based on the EEMD method, this study analyses the spatial variations and mechanisms for the stability of WUE in China, especially in the National Forest Protection Project (NFPP) areas. It is found that the stable WUE was dominated by non-significant trends and increasing trends in China, accounting for 33.59% and 34.19%, respectively. The non-significant trend of stable WUE was mainly located in the Three-North shelterbelt program area, and the increasing trend of stable WUE was in Huaihe and Taihu, Taihang Mountains, and Pearl River shelterbelt program areas. Precipitation and soil moisture promoted the stable WUE in these project areas. The unstable WUE was dominated by positive reversals or negative reversals of WUE trends. The positive reversals of unstable WUE were mainly located in the Yellow River shelterbelt program areas, which was promoted by temperature and radiation, while the negative reversals of unstable WUE were mainly distributed in the Yangtze River and Liaohe shelterbelt program areas, which were mainly induced by saturation water vapor pressure difference (VPD). Our results highlight that some ecological restoration programs need to be improved to cope with the negative climate impact on the stability of WUE.

Exploration of Vegetation Change Trend in the Greater Khingan Mountains Area of China Based on EEMD Method

Vegetation, especially forest ecosystems, plays an important role in the global energy flow and material cycle. The vegetation index (VI) is an important index reflecting the dynamic change in vegetation and directly reflects the response of ecosystem to global climate change. The Greater Khingan Mountains Forest region is located in the northeast of China. It is the largest primeval forest region in China, which is well preserved and less affected by human activities. It is of great significance to study the driving mechanism of forest vegetation change for future ecological prediction and management. In this study, GIMMS NDVI data were used to explore the characteristics of nonlinear temporal and spatial variation of NDVI in the Greater Khingan Mountains and its relationship with climatic factors. Firstly, the EEMD method was used to analyze the characteristics of vegetation change in the study area from 1982 to 2015. Secondly, the relationship between vegetation change and climate was discussed by using precipitation and temperature data. The results showed that the following: (1) from 1982 to 2015, the interannual change in vegetation in the Greater Khingan Mountains presented a trend of slow fluctuation and gradual decrease (SLOPE = −0.1645/10,000, p &lt; 0.01). (2) The spatial distribution of vegetation change had obvious geographical differences, and in the central region, the overall distribution characteristics had an obvious browning trend, and in the northwest and southeast, the distribution characteristics had a green trend. (3) The correlation analysis results of vegetation change and climate factors showed that NDVI change was significantly positively correlated with temperature and precipitation; additionally, NDVI change was more correlated with temperature with a range of 0.8–1 than precipitation. (4) The results of vegetation attribution analysis in four typical areas of the study area showed that the following: the coniferous forest area has good cold tolerance and drought tolerance, the correlation between vegetation change and climate factors (temperature, precipitation) was not the strongest, which was 0.537 and 0.828, respectively. The ecological transition area and the broad-leaved forest area, which was located at the edge of the study area, have relatively fragile ecosystems, showed a strong correlation with precipitation, and the correlation coefficients reached 0.670 and 0.632, respectively. The surface water resources provide favorable conditions for the growth of vegetation, it showed a weak correlation with precipitation, and the correlation coefficient was 0.5349.

Exploring the Dynamic Impact between the Industries in China: New Perspective Based on Pattern Causality and Time-Varying Effect

Real economy has always been a crucial component of China’s economic development, while fictitious economy has experienced rapid growth in past decades. As a result, the connection between the real and fictitious economy has become increasingly complex. This study utilized a hierarchical framework for classifying real economy and conducted a hidden causality test and EEMD method to explore a causal relationship between markets. Monthly data from July 2001 to September 2022 were analyzed using a TVP-SV-VAR model to investigate dynamic relationships among the manufacturing, construction, real estate, and financial industries as well as the mechanisms between the real and fictitious economies. The study outcomes demonstrated that the financial and real estate industries have only short-term positive effects on the manufacturing and construction industries, and in the later period of sample intervals, both industries had negative effects on the construction industry. The construction industry in the real economy has already shown a trend of moving “from Real to Virtual”, while the core manufacturing industry in the real economy has not yet exhibited this trend. To prevent the spread of this trend in the real economy, it is necessary to guide the fictitious economy to serve the real economy by regulating its development appropriately. This study offers a novel perspective for examining the real economy and the fictitious economy in China.

Investigating price fluctuations in copper futures: Based on EEMD and Markov-switching VAR model

Predictable global copper prices are crucial to the transition to a green economy. This paper examines the nonlinear characteristics between the international copper futures prices and their drivers, using combined Empirical Mode Decomposition (EEMD) and the Markov-switching VAR (MSVAR) models. We decompose the monthly international copper futures prices from January 2015 to October 2021 into its low-frequency and high-frequency components by using the EEMD method. In the next step, we employ the MSVAR to examine the nonlinear fluctuation of the international copper futures prices under different regimes. The results indicate that fluctuations of the international copper futures prices exhibit a dynamic regime switching patterns that is characterized as “expansion”, “plateau” and “contraction”. The long-term stability of the international copper futures prices is determined by factors associated with demand, especially demand for strategic metals. Both the London Metal Exchange (LME) copper stocks and the LME copper stock futures have a significant impact on the international copper futures prices in each regime. In several regimes, the global refined copper consumption has no significant effect on the international copper futures prices. The increase in copper turnover is the primary driver of the international copper futures prices during the contraction regime. Regarding the supply factor, an increase in global refined copper capacity would result in a rise in the international copper futures prices during the expansion regime. While a slump would occur during a plateau or contraction regime. The financial factor, reflected by non-commercial traders affects the international copper futures prices volatility differently under different regimes. The increase in speculation reduces the market volatility during regimes of expansion and contraction. In contrast, in plateau regime, speculation increases market volatility and activates the market. The broad dollar index has little impact on the international copper futures prices during each regime. The above conclusions indicate that we should focus on the fluctuations in the international copper futures prices that are caused by the demand for strategic metals and financial factors.

Prediction of river inflow of the major tributaries of Indus river basin using hybrids of EEMD and LMD methods

Prediction of river inflow of the major tributaries of Indus river basin using hybrids of EEMD and LMD methods

Millennial-scale climate oscillations over the last two climatic cycles revealed by a loess–paleosol sequence from central Asia

• Multiscale climate variability is decomposed by using EEMD method in central Asia. • Northern high-latitude temperature regulated the millennial moisture variability. • Higher insolation during the MIS6.5 promoted the moisture oscillations. Characteristics of millennial-scale climate variability over central Asia during the last two climatic cycles remain unclear. Here, multi-time scale climate variability during the previous two climatic cycles over central Asia is demonstrated using a 50-m thick loess–paleosol sequence and the ensemble empirical mode decomposition method. Decomposed orbital-scale components show that climate oscillation across central Asia is particularly influenced by northern high-latitude ice volume and solar insolation, while the millennial-scale moisture variability shows similar periodicities to the rhythms of North Atlantic cooling. Millennial-scale moisture variability exhibits a pattern of long duration/high amplitude during interglacial periods and short duration/low amplitude during glacial periods, a pattern also reflected in loess sections from the Chinese Loess Plateau (CLP) and CH 4 records from Antarctic ice -cores. These characteristics differ from the high-latitude temperature variability recorded in NGRIP δ 18 O data, which indicates that millennial-scale moisture oscillation in central Asia is possibly regulated by the northern high-latitude temperature, with lower temperatures limiting moisture fluctuations. We also find that only four millennial-scale moisture oscillations can be clearly detected during the MIS6.5 period, during which increased summer insolation provided favorable conditions to trigger abrupt climate events. However, high-resolution climate proxies that are more sensitive to environmental change are required to detect moisture variability during extreme cold and dry periods.

A Comparative Study of DWT and EEMD Methods for Validation and Correction of Pyroshock Data

A Comparative Study of DWT and EEMD Methods for Validation and Correction of Pyroshock Data

The Feature Recognition of Motor Noise Based on the Improved EEMD Model.

The noise generated by the machine is closely related to the running state of the machine, so the product can be effectively detected by analyzing the noise signal. The noise identification and control methods based on the EEMD model are widely used in motor noise control. However, the EEMD only considers the influence of noise amplitude on the decomposition results, and the added white noise cannot be completely neutralized. In this paper, an improved EEMD method is proposed by analyzing the influence of the maximum frequency on the decomposition results, in which the noise with different maximum frequency and amplitude is added to decompose the signal, and the decomposition effect is judged by the orthogonality coefficient of the decomposition result. Finally, the simulation signal and the measured signal are compared and analyzed, and the results show that the improved EEMD method has some advantages over the original method in suppressing mode confusion and fault diagnosis.

Partial Discharge (PD) Signal Detection and Isolation on High Voltage Equipment Using Improved Complete EEMD Method

Electricity has a crucial function in contemporary civilization. The power grid must be stable to ensure the efficiency and dependability of electrical equipment. This implies that the high-voltage equipment at the substation must be reliably operated. As a result, the appropriate and dependable use of systems to monitor the operating status of high-voltage electrical equipment has recently gained attention. Partial discharge (PD) analysis is one of the most promising solutions for monitoring and diagnosing potential problems in insulation systems. Noise is a major challenge in diagnosing and detecting defects when using this measurement. This study aims to denoise PD signals using a data decomposition method, improved complete ensemble empirical mode decomposition with adaptive noise algorithm, combined with statistical significance test to increase noise reduction efficiency and to derive and visualize the Hilbert spectrum of the input signal in time-frequency domain after filtering the noise. In the PD signal analysis, both artificial and experimental signals were used as input signals in the decomposition method. For these signals, this study has yielded significant improvement in the denoising and the PD detecting process indicated by statistical measures. Thus, the signal decomposition by using the proposed method is proven to be a useful tool for diagnosing the PD on high voltage equipment.

A Recognition Method Based on Speech Feature Parameters-English Teaching Practice

In order to improve the effect of English teaching practice, this paper constructs an intelligent English phonetic teaching system combined with the method of phonetic feature parameter recognition. Moreover, this paper simulates the self-mixing interference signal containing noise by establishing a simulation, analyzes the size of the noise and its various possibilities, and selects the EEMD method as the English speech denoising algorithm. In addition, with the support of an intelligent denoising algorithm, this paper implements an English intelligent teaching system based on the recognition algorithm of English speech feature parameters. Finally, this paper evaluates the teaching effect of the intelligent English speech feature recognition algorithm proposed in this paper and the intelligent teaching system of this paper by means of simulation teaching. The research shows that the English teaching system based on the intelligent speech feature recognition algorithm proposed in this paper has a good effect.

A Non-Parametric Empirical Method for Nonlinear and Non-Stationary Signal Analysis

A Non-parametric Ensemble Empirical Mode Decomposition (NCEEMD) method is a novel technique for nonlinear and non-stationary signal analysis to detect a gearbox fault. The NCEEMD method was based on the CEEMD, but the Gaussian white noise was replaced by the fractional Gaussian noise. The NCEEMD method does not need to choose the appropriate SNR and the number of ensemble trials before signal processing, which makes it a non-parametric method. This new approach was evaluated using a simulated malfunction signal representing two typical faults in gearbox systems: modulation and rub-impact. Its performance was evaluated in terms of MSE and computation time. A comparative study between the EMD, EEMD, CEEMD, and NCEEMD methods showed that the latter performed better by improving the computation time and accuracy of CEEMD. The proposed method is a non-parametric method that provides a powerful tool in extracting the modulation and the rub-impact features from a vibration signal. The NCEEMD method helps to track down the gearbox faults and resolve this crucial problem in mechanical machines.

Study on fault feature extraction of ECS turbine bearing by combination of EEMD and HMM

According to nonstationary characteristics of aircraft environment control system turbine bearings, the objective in the paper is to present EEMD and HMM method to extract characteristic quantities. Firstly, vibration signals under different operating conditions, such as normal bearing and cage fracture under partial load condition, are decomposited by EEMD to filter noise signal and advance signal-to-noise. Then HMM of those signals is calculated, contrasted and analyzed after space reconstruction. The experimental result show that the HMM can be used as the characteristic quantity of ECS turbine bearing on running state. Moreover, this method can accurately and effectively identify the running state of the bearing.