Empirical Mode Decomposition Approach Research Articles

ELINA: Emotion low-resources INference algorithm based on 1D ConvNets

In this study, a thorough analysis of the proposed approach in the context of emotion classification using both single-modal (A-13sbj) and multi-modal (B-12sbj) sets from the YAAD dataset was conducted. This dataset encompassed 25 subjects exposed to audiovisual stimuli designed to induce seven distinct emotional states. Electrocardiogram (ECG) and galvanic skin response (GSR) biosignals were collected and classified using two deep learning models, BEC-1D and ELINA, along with two different preprocessing techniques, a classical fourier-based filtering and an Empirical Mode Decomposition (EMD) approach. For the single-modal set, this proposal achieved an accuracy of 84.43±30.03, precision of 85.16±28.91, and F1-score of 84.06±29.97. Moreover, in the extended configuration the model maintained strong performance, yielding scores of 80.95±22.55, 82.44±24.34, and 79.91±24.55, respectively. Notably, for the multi-modal set (B-12sbj), the best results were obtained with EMD preprocessing and the ELINA model. This proposal achieved an improved accuracy, precision, and F1-score scores of 98.02±3.78, 98.31±3.31, and 97.98±3.83, respectively, demonstrating the effectiveness of this approach in discerning emotional states from biosignals.

Significant Wave Height Forecasting Based on EMD-TimesNet Networks

Significant Wave Height (SWH) is a crucial parameter in ocean wave dynamics, impacting coastal safety, maritime transportation, and meteorological research. Building upon the TimesNet neural network, a recent advancement in the realm of time series prediction in deep learning, this study proposes an integrated approach combining Empirical Mode Decomposition (EMD) with TimesNet, introducing the EMD-TimesNet model for SWH forecasting. The TimesNet model’s multidimensional spatial mapping guarantees effective historical information extraction, while the EMD approach makes it easier to decompose subsequence characteristics inside the original SWH data. The predicted Root Mean Square Error (RMSE) and Correlation Coefficient (CC) values of the EMD-TimesNet model are 0.0494 m and 0.9936; 0.0982 m and 0.9747; and 0.1573 m and 0.9352 at 1 h, 3 h, and 6 h, respectively. The results indicate that the EMD-TimesNet model outperforms existing models, including the TimesNet, Autoformer, Transformer, and CNN-BiLSTM-Attention models, both in terms of overall evaluation metrics and prediction performance for diverse sea states. This integrated model represents a promising advancement in enhancing the accuracy of SWH predictions.

Data Mining Applied to the Electrochemical Noise Technique in the Time/Frequency Domain for Stress Corrosion Cracking Recognition

In this paper, time/frequency domain data processing was proposed to analyse the EN signal recorded during stress corrosion cracking on precipitation-hardening martensitic stainless steel in a chloride environment. Continuous Wavelet Transform, albeit with some limitations, showed a suitable support in the discriminatory capacity among transient signals related to the different stress corrosion cracking mechanisms. In particular, the aim is to propose the analysis of electrochemical noise signals under stress corrosion cracking conditions in the time–frequency domain by using the Hilbert–Huang approach. The Hilbert–Huang Transform (performed by the Empirical Mode Decomposition approach) was finally proposed to carry out an identification of the corrosion mechanisms in comparison to conventional data processing methods. By using this approach, a detailed simultaneous decomposition of the original electrochemical noise data in the time and frequency domain was carried out. The method gave useful information about transitions among different corrosion mechanisms, allowing us to (i) identify a specific characteristic response for each corrosion damaging phenomenon induced by stress corrosion cracking, (ii) time each corrosion of the damaging phenomenon, and (iii) provide a topological description of the advancing SCC damaging stages. This characteristic evidences that the Hilbert–Huang Transform is a very powerful technique to potentially recognize and distinguish the different corrosion mechanisms occurring during stress corrosion cracking.

Empirical Mode Decomposition with Imf Using Ant Colony Optimization

The serial remote sensing images allow us to view how a certain area has changed and grown across period. Even though these raw images have fewer opportunities to generate information and additional insight, serial remote sensing images (SRSI) offer a significant opportunity to generate clusters and patterns. The implementation of SRSI aggregation is supported by the developments of patterns in a variety of circumstances, which include urbanization, the spread of native vegetation, and farming. We propose a novel outlook to mining sequence patterns that makes use of Ant Colony Optimization and Empirical Mode Decomposition. The newly created Empirical Mode Decomposition (EMD) and Ant Colony Optimization (ACO) approach is integrated to extract the most important characteristics features of significantly accurate performance from serial remote sensing images based on distinct criteria. The outcomes demonstrate that the accuracy of projected sequence patterns can be greatly improved by combining a new hybrid method with EMD and ACO feature selection. Because of this, the method is appealing for employing a farmland dataset based on serial remote sensing images to create ground-based stream-flow for strategically extracting spatially sequential patterns despite requesting less computational mining time and cost.

Investigating the Dynamic Relationship Between Greenhouse Gas Emissions and Gross Domestic Product in Türkiye

This study aims to investigate the causal relationship between Gross Domestic Product and greenhouse gas emissions in Türkiye from 1951 to 2018, using the Causal Decomposition Method that integrates Ensemble Empirical Mode Decomposition, Hilbert-Huang Transform, and Phase Coherence Methods. The primary focus is on identifying the key sectors contributing significantly to greenhouse gas emissions, particularly those connected to industrial production. The analysis reveals a one-way, short-term causal relationship from Gross Domestic Product to greenhouse gas emissions, spanning approximately 3 years. This finding suggests that changes in Gross Domestic Product have short-term effects on emissions, but not vice versa. Special emphasis is placed on the gases Cardon Dioxide, Methane and Nitrous Oxide, as they demonstrate a strong, consistent causal connection with Gross Domestic Product. The significance of this study lies in its utilization of the Ensemble Empirical Mode Decomposition approach to investigate this dynamic causality and address a notable gap in the existing literature. Empirical results indicate a complex yet observable association between Gross Domestic Product growth and greenhouse gas emissions in Türkiye, and that this relationship becomes more important, especially in the short and long term, with periodic fluctuations.

ELM-based stroke classification using wavelet and empirical mode decomposition techniques

ABSTRACT Biomedical signal processing is crucial in many sectors that save lives. Artificial intelligence improvement in signal collection and conditioning boosted this application’s adaptability to varied bodily circumstances. In this study, a novel method is put forth for predicting the type of stroke in the human brain based on the observation of the Electroencephalography (EEG) signal. The signal is the first condition for removing undesirable frequencies by passing through a lowpass filter. To accurately extract the signal features, the signal is first transformed into a 1-second frame format and then normalised. Certain statistical and frequency domain aspects are highlighted to increase taxonomic accuracy. Under the wavelet packet transform, the empirical mode decomposition approach is utilised to recover the most information feasible from the signal. After training on extracted characteristics, the extreme learning machine is regarded to conduct classification. These work achieves 94.95 of Sensitivity, 84.95 of Specificity, 93.74 of Precision, 96.96 of Accuracy, 96.12 of F1 Score. Compared to the standard procedures, the proposed techniques have a greater accuracy rate of about 98%.

On application of empirical mode decomposition for turbulence analysis in open-channel flows

Large-scale coherent structures are key elements of open-channel flow turbulence, quantification of which remains elusive. In this work, we use empirical mode decomposition (EMD) to break down a velocity time series into different modes, denoted as “intrinsic mode functions” (IMFs). Analysis of velocity auto- and co-spectra indicates that large-scale (LSMs) and very large-scale (VLSMs) fluid motions are sufficiently represented by particular groups of IMFs. A correlation between LSMs and VLSMs, identified by the EMD analysis, was found to generate 7% of the Reynolds shear stresses. However, the EMD analysis of surrogate velocity signals with randomized spectral phases demonstrated that the revealed correlation is actually an artefact of the EMD approach and should not be interpreted physically.

MACHINE LEARNING APPROACH TO DETECT ECG ABNORMALITIES USING COST-SENSITIVE DECISION TREE CLASSIFIER

Cardiac Arrhythmia is an abnormal heart rhythm that develops when the electrical impulses control the heart’s contraction which does not function properly. The heart can beat too fast (tachycardia), too slow (bradycardia), or in an irregular pattern. Observing ECG signal peaks and channels freehand is difficult due to their ingenious modification. Automated detection of cardiovascular abnormalities is preferred for the early diagnosis of cardiac disorders. This paper used machine learning approaches for detecting ECG abnormality utilizing a Support Vector Machine (SVM) and Cost-Sensitive Decision-Tree (CS-DT) classifier. The Empirical Mode Decomposition approach was utilized to examine the properties of R-peaks and QRS complexes in ECG signs. Various morphological characteristics are analyzed from the signal penetrated by the classifier to diagnose the irregular beats. A set of twenty-two clinically feasible features comprising temporal, morphological, and statistical were extracted from the processed ECG signals and applied to the classifier to categorize cardiovascular irregularities like Normal (N), Left Bundle Branch Block (LBBB), Right Bundle Branch Block (RBBB), Atrial Premature Beats (APB), and Premature Ventricular Contraction (PVC). The Beth Israel Hospital at Massachusetts Institute of Technology (MIT-BIH) dataset has been used for this work, where feature datasets are split into training and evaluation subsets. The training set is used to train machine learning models on the extracted features, while the evaluation set is used to assess the performance of the trained models. The evaluation metrics such as Accuracy (Acc), Sensitivity (Se), Specificity (Sp), and Positive Predictivity (Pp), are frequently used to evaluate the model’s performance in Arrhythmia detection along with classification. The simulation has been conducted using SVM and CS-DT classifier with performance for all individual class labels at a Confidence Factor (CF) of 0.5. The performance of the time and frequency domain features is merged resulting in higher classification of Sensitivity, Specificity, Positive Predictivity, and Accuracy of 89.5%, 98.11%, 87.76%, and 96.8% in SVM, 97.71%, 99.58%, 97.66%, 99.32% in CS-DT classifier in identifying the irregular heartbeats.

Speech Enhancement Using Sliding Window Empirical Mode Decomposition and Hurst-based Technique

The most challenging in speech enhancement technique is tracking non-stationary noises for long speech segments and low Signal-to-Noise Ratio (SNR). Different speech enhancement techniques have been proposed but, those techniques were inaccurate in tracking highly non-stationary noises. As a result, Empirical Mode Decomposition and Hurst-based (EMDH) approach is proposed to enhance the signals corrupted by non-stationary acoustic noises. Hurst exponent statistics was adopted for identifying and selecting the set of Intrinsic Mode Functions (IMF) that are most affected by the noise components. Moreover, the speech signal was reconstructed by considering the least corrupted IMF. Though it increases SNR, the time and resource consumption were high. Also, it requires a significant improvement under nonstationary noise scenario. Hence, in this article, EMDH approach is enhanced by using Sliding Window (SW) technique. In this SWEMDH approach, the computation of EMD is performed based on the small and sliding window along with the time axis. The sliding window depends on the signal frequency band. The possible discontinuities in IMF between windows are prevented by the total number of modes and the number of sifting iterations that should be set a priori. For each module, the number of sifting iterations is determined by decomposition of many signal windows by standard algorithm and calculating the average number of sifting steps for each module. Based on this approach, the time complexity is reduced significantly with suitable quality of decomposition. Finally, the experimental results show the considerable improvements in speech enhancement under non-stationary noise environments.

Hedge and safe-haven attributes of faith-based stocks vis-à-vis cryptocurrency environmental attention: a multi-scale quantile regression analysis

ABSTRACT The attractiveness of the equities of the Islamic faith-compliant companies as a hedge or possible diversifier has been underscored; however, there is a lack of empirical research on their safe-haven and hedge attributes against changes in the level of cryptocurrency environmental attention (ICEA). We examine whether various distributions of the ICEA possess a significant predictive power on various quantiles of Islamic sectoral stock returns by employing weekly data on the ICEA and Shariah-compliant stocks from 10 sectors of economic activity and base their multi-scale analysis on the complete ensemble empirical mode decomposition (CEEMDAN) approach. We present the asymmetric causality-in-means and quantile-on-quantile regression between the ICEA and Islamic stocks. The empirical results show a significant predictive power of the ICEA on various quantiles of Islamic sectoral stocks in the medium- and long term. We find that the safe-haven and hedging attributes of investments in Islamic stocks are sector-dependent across the medium- and long-term scales. Hence, our findings emphasize that based on market states, possible safe-haven attributes, diversification opportunities, and hedges for cross-sectoral investments with Islamic stocks are viable along various investment horizons for diverse levels of cryptocurrency environmental attention. These findings provide original valuable insights for portfolio management and improving financial stability.

Analysis and application of deep space exploration satellite telemetry data combined with empirical mode decomposition and ARIMA model

With China’s deep space exploration program constantly expanding, the number of satellites on-orbit is increasing year by year. The stable operation of satellites in orbit hinges on long-term monitoring and analysis of the telemetry data downloaded from the satellite on the ground, which enables quick detection of any irregularities. If we can grasp the changing trend and operation law of satellite telemetry data, we can monitor and control satellites and reduce losses better. Aiming at a large amount of telemetry data of deep space exploration satellites, the data presents non-stationary and nonlinear characteristics, The time series to be processed is constructed sequentially using the acquired telemetry data, and it is decomposed using the empirical mode decomposition approach. The non-stationary signal is divided into a stationary sequence and the sum of a series of stationary intrinsic mode functions. Then the decomposed sets of results are used as the input of the ARIMA model, and the ARIMA model is constructed to predict the predicted value of each decomposed sequence, and the weighted summation of the predicted values is performed to reconstruct the predicted results. Lastly, actual satellite telemetry data is used to confirm the method’s viability. The experimental findings demonstrate that the model can more successfully anticipate the short-term trend of satellite telemetry data.

Filtering GRACE temporal gravity field solutions using ensemble empirical mode decomposition approach

Due to the strong noise that exists in GRACE (Gravity Recovery and Climate Experiment) temporal gravity field solutions, geophysical signals are normally drowned which need many effective filtering approaches. Considering the advantage of the ensemble empirical mode decomposition (EEMD) approach, we used the EEMD to filter the noise in this study together with the empirical mode decomposition (EMD) for comparisons. EMD method is a spectrum analysis method, which is very effective for non-stationary signals. EMD process is essentially a means to process non-stationary signals. It has been applied in many fields in recent years. Considering the characteristics of the spherical harmonic coefficient model that the noise level higher with the increasing degree, we divided the gravity field solutions into two parts (degrees 2–28 and degrees 29–60) based on the ratios of the latitude-weighted root mean square (RMS) over the land and ocean signals when adopting different truncated degrees. For the real GRACE solution experiments, the results show that the fitting errors of EEMD approach are always smaller than those of EMD approach, and the mean RMS ratio of EEMD is 3.45, larger than 3.40 of EMD. The simulation results show that the latitude weighted root mean square errors for EEMD approach are smaller than those of EMD, indicating that EEMD can extract the geophysical signals more accurately. Therefore, it is reasonable to conclude that EEMD performs better than EMD for filtering GRACE solutions.

Interannual modulations in sea level rise over tropical Northwest Pacific in satellite altimeter measurements

The sea level rise (SLR) estimates in tropical Pacific are dominantly influenced by interannual and interdecadal variations in limited data sets such as satellite altimeter measurements. The Sea Level Anomaly (SLA) trends during 1993–2012 (extensively reported) and 1993–2020 significantly differ with substantial weakening during 1993–2020 over tropical Northwest Pacific (NWP; 0–15°N; 120°E −170°E). An ensemble empirical mode decomposition (EEMD) approach is employed to derive residual SLA by removing the high-frequency SLA variations including the interannual and interdecadal variations. The linear trends estimated from residual SLA provides a better estimate for the trends in SLA. The residual trends have greatly reduced the observed differences in SLA trends between 1993–2012 and 1993–2020. Our analysis suggests that it is the interdecadal variations in SLA that caused the weakening of overall trend during 1993–2020. In particular, the interdecadal trend in SLA over NWP is ≈ −1.73 mm year−1, whereas the trends in total and the residual SLA are estimated as 4.26 mm year−1 and 6.32 mm year−1, respectively. Furthermore, it is observed that there is an in-phase connection between the interannual and the interdecadal variations of SLA associated with La Niña compared with El Niño. Therefore, the non-occurrence of La Niña after 2012 to 2020 possibly has contributed for the decreasing trend in interdecadal variations due to its in-phase connection. In short, the complexity of variations in SLA at different timescales over NWP and the Pacific significantly influences the SLA trend estimates.

Investigating Empirical Mode Decomposition in the Parameter Estimation of a Three-Section Winding Model

Parameter estimation represents an important aspect of modeling electromagnetic systems, and a wide range of parameter estimation strategies has been explored in literature. Most parameter estimation methodologies make use of either time-domain or frequency-domain responses as measured from the terminals of the device under test. Very limited research has, however, been conducted into exploring the use of modal decomposition strategies on the time-domain waveforms for parameter estimation applications. In this paper, the use of Empirical Mode Decomposition for estimating the parameters of a three-section lumped parameter transformer model is explored. A novel approach is proposed to define the optimization cost function in terms of the intrinsic modes of simulated time-domain waveforms. The results are compared with results obtained using classical time-domain and frequency-domain approaches. It is shown through an impulse response test that weighting the modes obtained from the Inferred Empirical Mode Decomposition approach presented in this work offers advantages in terms of accurately representing the target model transfer function dynamics and can assist in interpreting the various dynamic modes associated with the target model.

Empirical mode decomposition approach to simplify the fracture roughness for numerical models

The shear strength of natural, unfilled rock fractures is influenced by surface roughness. The surface curve of a fracture can be viewed as a waveform graph, and in general, it is of the characteristic that high-frequency represents the low amplitude (local variation) and low-frequency represents the high amplitude (general trend). In this work, the signal processing method, Empirical Mode Decomposition (EMD) was employed to decompose the original fracture surface scanned by photogrammetry to several frequency-dependent curves. Low-frequency curves were selected and composed as the element geometry while high-frequency curves were ignored and replaced by parameters related to the roughness in each surface element in Abaqus. The process of push-shear test is simulated using the simplified fracture curve, showing the geometry simplification by EMD can help model the shear failure of rock fractures.

Empirical mode decomposition for post-processing the GRACE monthly gravity field models

Considering the advantage of Empirical Mode Decomposition (EMD) for extracting the geophysical signals and filtering out the noise, this paper will first apply the EMD approach to post-process the Gravity Recovery and Climate Experiment (GRACE) monthly gravity field models. A 14-year time-series of Release 06 (RL06) monthly gravity field models from the Center for Space Research (CSR) truncated to degree and order 60 from the period April 2002 to August 2016 are analyzed using the EMD approach compared with traditional Gaussian smoothing filtering. Almost all fitting errors of GRACE spherical harmonic coefficients by the EMD approach are smaller than those by Gaussian smoothing, indicating that EMD can retain more information of the original spherical harmonic coefficients. The ratios of latitude-weighted RMS over the land and ocean signals are adopted to evaluate the efficiency of eliminating noise. The results show that almost all ratios of RMS for the EMD approach are higher than those of Gaussian smoothing, with the mean ratio of RMS of 3.61 for EMD and 3.41 for Gaussian smoothing, respectively. Therefore, we can conclude that the EMD method can filter noise more effectively than Gaussian smoothing, especially for the high-degree coefficients, and retain more geophysical signals with less leakage effects.

Interval forecasting of carbon price: A novel multiscale ensemble forecasting approach

Aiming at the limitations of carbon price point forecasting, we propose a novel integrated approach of binary empirical mode decomposition (BEMD), differential evolution (DE) algorithm, and extreme gradient boosting (XGB) for carbon price interval forecasting. Firstly, BEMD, which is suitable for interval time series, is introduced into decomposing complex carbon data into simple components. Secondly, XGB is used to forecast the obtained components, and DE is used to synchronously optimize all parameters of XGB. Thirdly, the individual component forecasting values are aggregated into carbon price forecasting values. Taking Guangdong and Hubei carbon markets as samples, in comparison with other popular prediction models, the proposed approach has a higher coverage rate and lower prediction error. The sensitivity analysis verifies that the proposed approach is robust.

Exploring the effectiveness of using internal CNC system signals for chatter detection in milling process

Chatter is a harmful self-excited vibration that commonly occurs during milling processes. Data-driven chatter detection and prediction is critical to achieve high surface quality and process efficiency. Most existing chatter detection approaches are based on external sensors, such as accelerometers and microphones, which require installation of extra devices. Some recent studies have proved the feasibility of online chatter detection using internal signals such as drive motor current. This study aims to investigate the effectiveness of different internal signals extracted from CNC system for chatter detection and compare them with external acceleration signals. The external and internal signals are first compared with time–frequency analysis using Discrete Fourier Transform and Ensemble Empirical Mode Decomposition approaches. Two chatter detection methods are then presented based on manually and automatically extracted features respectively. The first method uses two nonlinear dimensionless indicators, C0 complexity and Power Spectral Entropy, of filtered signals. The second approach uses autoencoder for automatic feature extraction and Support Vector Machine as classifier for chatter identification. A series of milling experiments are conducted and chatters are intentionally created by changing the milling process parameters. Multiple internal signals are collected using software provided by the machine manufacturer. Results show that several internal CNC signals, such as the nominal current signal and the actual torque signal, can achieve comparable performance to external signals for chatter detection.

Islanding power quality detection using lighting search optimization with deep learning model on distributed generation systems

Recently, the significant penetration of distributed generations (DGs) in distribution networks has made new challenging issues in network protection. A major challenge exists in the distribution networks in the identification of islanding conditions, where a portion of the distribution network with DGs is accidentally detached from the central grid due to protection relay and circuit breaker operation. Since it results in damaging the DGs and their equipment, effective islanding condition detection model becomes essential. Besides, non-detected islanding conditions can lead to voltage and frequency deviances from the normal ranges, inappropriate operation of protection, and personnel hazards. Therefore, the recent developments in deep learning (DL) can be applied to perform island power quality detection process. In this view, this article introduces an Islanding Power Quality Detection using Lighting Search Optimization with Deep Learning (IPQD-LSODL) model. The proposed IPQD-LSODL model mainly aims to find the events in islanding power quality (IPQ) and non-islanding power quality (NIPQ). The proposed model initially applies down-sampling empirical mode decomposition (DEMD) approach which effectively filters out the basic signal from the polluted ones. In addition, deep belief network (DBN) model is used to classify the events into IPQ and NIPQ. Moreover, the hyperparameters of the DBN method were optimally chosen by the use of LSO algorithm with an intention of accomplishing maximum classification accuracy. The performance validation of the IPQD-LSODL model is carried out and validated through comparison study with existing models. The IPQD-LSODL method achieved an accuracy of 99.91 percent in the class 5. The results implied the promising performances of the IPQD-LSODL method over recent approaches.

An optimal deep learning based Islanding power quality detection technique for distributed generation systems

Power quality disturbance (PQD) defines the presence of inconsistencies that occur in the usual wave shapes of voltage and current signals. Power quality is considered the main challenge for power industry with the increase in dynamic load and highly subtle electronic devices. Besides, the islanding events, particularly unintended islanding, grasp significant challenges and it needs to be identified at the early stage. Islanding is an anomalousstate in the power system, where the distributed generators (DGs) are placed on supplying electrical energy to the local load even after the shortage of the major grid. Therefore, it is essential to identify and differentiate the PQ events and islanding events in ensuring pollution-free power, equipment, and labor safety. With this motivation, this paper presents an automated optimal deep learning based islanding detection (AODL-ID) technique. The proposed AODL-ID technique involves three major stages namely decomposition, classification, and hyperparameter tuning. Firstly, an empirical mode decomposition (EMD) approach is utilized to decompose the basic signals from the polluted signals. In addition, bidirectional gated recurrent neural network (BiGRNN) technique is employed for the classification of islanding and non-islanding PQ events in the wind energy penetrated DG systems by means of features (Voltage and current (RMS, half-cycle, peak and fundamental) Frequency. Power Factor / Cos Phi. Power and energy (active, reactive, harmonic, apparent)). Since the hyperparameters play a significant role in overall classification performance, the hyperparameter tuning of the BiGRNN model takes place using chaotic crow search algorithm (CCSA). To examine the enhanced classification outcome of the AODL-ID technique, a set of experimental analyses is carried out and the outcomes are investigated interms of various evaluation metrics. The simulation outcomes highlighted the supremacy of the AODL-ID technique over the compared techniques.