Empirical Mode Decomposition Algorithm Research Articles

Forecasting Building Energy Consumption Using Ensemble Empirical Mode Decomposition, Wavelet Transformation, and Long Short-Term Memory Algorithms

A building, a central location of human activities, is equipped with many devices that consume a lot of electricity. Therefore, predicting the energy consumption of a building is essential because it helps the building management to make better energy management policies. Thus, predicting energy consumption of a building is very important, and this study proposes a forecasting framework for energy consumption of a building. The proposed framework combines a decomposition method with a forecasting algorithm. This study applies two decomposition algorithms, namely the empirical mode decomposition and wavelet transformation. Furthermore, it applies the long short term memory algorithm to predict energy consumption. This study applies the proposed framework to predict the energy consumption of 20 buildings. The buildings are located in different time zones and have different functionalities. The experiment results reveal that the best forecasting algorithm applies the long short term memory algorithm with the empirical mode decomposition. In addition to the proposed framework, this research also provides the recommendation of the forecasting model for each building. The result of this study could enrich the study about the building energy forecasting approach. The proposed framework also can be applied to the real case of electricity consumption.

The adaptive LASSO regression and empirical mode decomposition algorithm for enhancing modelling accuracy

The first part of the Hilbert–Huang transformation is named the empirical mode decomposition (EMD). Which employed to decompose the non-stationary and non-linear time series dataset into a finite set of orthogonal decomposition components. These components have been used in several studies as the new predictor variables to predict the behavior of the response variables. Adaptive LASSO (AdLASSO) regression is a technical penalized regression method used to determine the most relevant predictors on the response variable with achieving the consistency in terms of variable selection and ensuring that they are asymptotically normal. Hence, the main objective of this study is to apply the proposed EMD-AdLASSO method involving two cases of initial weights to identify the decomposed components that exhibit the strongest effects to produce a consistent model and to improve the prediction accuracy. The simulation study and real dataset used the daily exchange rate dataset of three countries against the US dollar are applied. The results showed that the proposed method in the two cases of the initial weight outperformed other existing methods by effectively identifying the decomposition components, with high prediction accuracy. This is primarily observed in the case of using the ridge regression method based on the EMD as the initial weight in the proposed EMD-AdLASSO method.

Component-scaled signal reconstruction for enhanced noise filtration

This research introduces a procedure for signal denoising based on linear combinations of intrinsic mode functions (IMFs) extracted using empirical mode decomposition (EMD). The method, termed component-scaled signal reconstruction, employs the standard EMD algorithm, with no enhancements to decompose the signal into a set of IMFs. The problem of mode mixing is leveraged for noise removal by constructing an optimal linear combination of the potentially mixed IMFs. The optimal linear combination is determined using an optimization routine with an objective function that maximizes and minimizes the information and noise, respectively, in the denoised signal. The method is demonstrated by applying it to a computer-generated voice sample and the displacement response of a cantilever beam with local stiffness nonlinearity. In the first application, the noise is introduced into the sample manually by adding a Gaussian white-noise signal to the signal. In the second application, the response of the entire beam is filmed using two 1-megapixel cameras, and the three-dimensional displacement field is extracted using digital image correlation. The noise in this application arises entirely from the images captured. The proposed method is compared to existing EMD, ensemble EMD, and LMD based denoising approaches and is found to perform better.

CNN with Multiple Inputs for Automatic Glaucoma Assessment Using Fundus Images

In the area of ophthalmology, glaucoma affects an increasing number of people. It is a major cause of blindness. Early detection avoids severe ocular complications such as glaucoma, cystoid macular edema, or diabetic proliferative retinopathy. Intelligent artificial intelligence has been confirmed beneficial for glaucoma assessment. In this paper, we describe an approach to automate glaucoma diagnosis using funds images. The setup of the proposed framework is in order: The Bi-dimensional Empirical Mode Decomposition (BEMD) algorithm is applied to decompose the Regions of Interest (ROI) to components (BIMFs+residue). CNN architecture VGG19 is implemented to extract features from decomposed BEMD components. Then, we fuse the features of the same ROI in a bag of features. These last very long; therefore, Principal Component Analysis (PCA) are used to reduce features dimensions. The bags of features obtained are the input parameters of the implemented classifier based on the Support Vector Machine (SVM). To train the built models, we have used two public datasets, which are ACRIMA and REFUGE. For testing our models, we have used a part of ACRIMA and REFUGE plus four other public datasets, which are RIM-ONE, ORIGA-light, Drishti-GS1, and sjchoi86-HRF. The overall precision of 98.31%, 98.61%, 96.43%, 96.67%, 95.24%, and 98.60% is obtained on ACRIMA, REFUGE, RIM-ONE, ORIGA-light, Drishti-GS1, and sjchoi86-HRF datasets, respectively, by using the model trained on REFUGE. Again an accuracy of 98.92%, 99.06%, 98.27%, 97.10%, 96.97%, and 96.36% is obtained in the ACRIMA, REFUGE, RIM-ONE, ORIGA-light, Drishti-GS1, and sjchoi86-HRF datasets, respectively, using the model training on ACRIMA. The experimental results obtained from different datasets demonstrate the efficiency and robustness of the proposed approach. A comparison with some recent previous work in the literature has shown a significant advancement in our proposal.

NA-MEMD Is Actually What You Need for Computational Pulse Analysis

Time-frequency analysis is of necessity for wrist pulse signal due to its complexity, among which, empirical mode decomposition (EMD) algorithm and its improved noise-assisted versions (such as ensemble EMD, noise-assisted multivariate EMD (NA-MEMD) and very recently median EMD) are deemed to be the most representative ones. In this study, we provide an in-depth evaluation of these well-established noise-assisted EMD algorithms in computational pulse analysis for the first time. In particular, we compare the performance of the different algorithms systematically and quantitatively based on objective quantitative criteria: number and central frequency of intrinsic mode function (IMF) components, total orthogonality index and mode mixing. Rather than using synthetic signals with visual inspection in most existing literature, the wrist pulse signals used in the evaluation are real recorded samples acquired from both healthy and patient subjects. Through extensive experiments, we found that: 1) Advanced EMD algorithm that has the best performance in other areas may not be the most suitable method for pulse signal analysis, which indicates its high dependence on the type of signal to be analyzed; 2) Adding noise can significantly improve algorithm performance, but tends to produce physiologically not relevant components, which however are usually neglected throughout the intelligent pulse diagnosis literature. Therefore, excluding redundant components and then extracting features is expected to improve performance further. Together, currently NA-MEMD achieves a better performance consistently, potential to become a powerful tool for computational pulse analysis, which in itself have not been applied in wrist pulse analysis before. We believe our works can bring up a new perspective to application of EMD-like algorithms in computational pulse analysis/diagnosis with effective information and guidance. Additionally, considering the similarity between physiological signals, especially such as photoplethysmogram/electrocardiogram, our research can be extended to wearable health monitoring technologies, including smart watches and fitness trackers, and their potential future applications, such as in heart rate estimation and evaluate various cardiovascular-related diseases. The present study underscored the necessity of evaluation noise-assisted EMDs or other adaptive decomposition algorithms based on real recorded signals with more objective measures. Especially, caution the possible redundant components that are introduced.

Multistep-Ahead Prediction of Ocean SSTA Based on Hybrid Empirical Mode Decomposition and Gated Recurrent Unit Model

The prediction of sea surface temperature anomalies (SSTA) is vital to the study of marine ecosystems and global climate. The SSTA can be accurately forecasted one step ahead by numerical and statistical methods. However, multi-step-ahead forecasting for SSTA is greatly challenging since the nonlinearity and non-stationarity of SSTA and the lag problem of prediction. Therefore, in this paper, a multi-step-ahead SSTA forecasting method based on the hybrid empirical mode decomposition (EMD) and gated recurrent unit (GRU) model is proposed considering that EMD has the advantage of reducing data complexity and GRU is good at long-term prediction of data. First, the EMD algorithm is applied to obtain several intrinsic modal mode functions (IMFs) that are more stationary than the original data. Then GRU is used for multi-step forecasting, in which three multi-step forecasting strategies (recursive, direct, and multi-output) are compared. The proposed hybrid model is validated by multi-step forecasting for monthly average SSTA at the Niño3.4 region. The experimental results show that using reconstruction error as part of the prediction effectively improves the prediction accuracy of the EEMD-GRU model and outperforms other EMD algorithms combined with GRU. Compared with traditional models, the EEMD-GRU model can better predict future multi-month trends of SSTA and effectively alleviate the problem of prediction lag of the traditional model. Finally, this model is applied to the Niño3.4 regional SSTA prediction, and the results show that this model can provide a reference for ocean research.

Mid-Infrared Acetone Sensor for Exhaled Gas Using Fwa-Lssvm and Empirical Mode Decomposition Algorithm

Mid-Infrared Acetone Sensor for Exhaled Gas Using Fwa-Lssvm and Empirical Mode Decomposition Algorithm

Fault Analysis of Wind Power Rolling Bearing Based on EMD Feature Extraction

In a wind turbine, the rolling bearing is the critical component. However, it has a high failure rate. Therefore, the failure analysis and fault diagnosis of wind power rolling bearings are very important to ensure the high reliability and safety of wind power equipment. In this study, the failure form and the corresponding reason for the failure are discussed firstly. Then, the natural frequency and the characteristic frequency are analyzed. The Empirical Mode Decomposition (EMD) algorithm is used to extract the characteristics of the vibration signal of the rolling bearing. Moreover, the eigenmode function is obtained and then filtered by the kurtosis criterion. Consequently, the relationship between the actual fault frequency spectrum and the theoretical fault frequency can be obtained. Then the fault analysis is performed. To enhance the accuracy of fault diagnosis, based on the previous feature extraction and the time-frequency domain feature extraction of the data after EMD decomposition processing, four different classifiers are added to diagnose and classify the fault status of rolling bearings and compare them with four different classifiers.

Combined Prediction of Short-Term Travel Time of Expressway Based on CEEMDAN Decomposition

Travel time is the basis for emergency intelligent control and guidance in expressway networks. To realize its accurate prediction and improve the expressway service level during emergencies, this study uses the combined model to predict the short-term travel time of expressway sections based on the expressway gantry data of Sichuan Province. First, the travel time series is extracted using a data matching algorithm, and the double standard deviation-cyclic elimination (2SD-CE) algorithm is used to clean the data. Then, combined with the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm, the travel time subsequence was extracted, and the frequency of the subsequence was divided by Sample entropy (SampEn) algorithm. Based on this, bidirectional long short-term memory (BiLSTM), long short-term memory (LSTM), and vanilla recurrent neural network (vanilla RNN) models are used to construct prediction combination model 1 (CM1) under the condition of a single feature. Subsequently, the CEEMDAN and empirical mode decomposition (EMD) algorithms were combined with the LSTM algorithm to obtain the combination models (CM2 and CM3) without frequency division. The example calculation and analysis show that under different time granularities (5 min, 10 min, and 15 min) and different highway sections, the combined model can integrate the advantages of all prediction models and has higher prediction accuracy and stability, among which the prediction effect of CM1 can reduce the prediction value of root mean squared error (RMSE) by 18.8~26.4%, 0.8~41%, 4.1~13.3%.

Deep Neural Network Driven Automated Underwater Object Detection

Object recognition and computer vision techniques for automated object identification are attracting marine biologist's interest as a quicker and easier tool for estimating the fish abundance in marine environments. However, the biggest problem posed by unrestricted aquatic imaging is low luminance, turbidity, background ambiguity, and context camouflage, which make traditional approaches rely on their efficiency due to inaccurate detection or elevated false-positive rates. To address these challenges, we suggest a systemic approach to merge visual features and Gaussian mixture models with You Only Look Once (YOLOv3) deep network, a coherent strategy for recognizing fish in challenging underwater images. As an image restoration phase, pre-processing based on diffraction correction is primarily applied to frames. The YOLOv3 based object recognition system is used to identify fish occurrences. The objects in the background that are camouflaged are often overlooked by the YOLOv3 model. A proposed Bi-dimensional Empirical Mode Decomposition (BEMD) algorithm, adapted by Gaussian mixture models, and integrating the results of YOLOv3 improves detection efficiency of the proposed automated underwater object detection method. The proposed approach was tested on four challenging video datasets, the Life Cross Language Evaluation Forum (CLEF) benchmark from the F4K data repository, the University of Western Australia (UWA) dataset, the bubble vision dataset and the DeepFish dataset. The accuracy for fish identification is 98.5 percent, 96.77 percent, 97.99 percent and 95.3 percent respectively for the various datasets which demonstrate the feasibility of our proposed automated underwater object detection method.

Deep Multiple Instance Learning for Automatic Breast Cancer Assessment Using Digital Mammography

Breast cancer causes serious public health problems; it is the most common cancer among women worldwide. Screening and early detection of signs of breast cancer increase the chance of survival. Early diagnosis is a crucial task for radiologists and physicians. Therefore, many Computer-Aided Detection and Diagnosis (CADx) systems are being developed to ensure the survival of radiologists’ decisions. In this paper, we describe a framework to automate assessment of suspicious regions, detected in screening mammography, without having carried out additional examinations, especially unnecessary biopsies in the case where the suspect regions are benign tumors. The setup of the proposed framework is ordered as follows: Regions of interest (ROIs) have been segmented using a modified K-means algorithm; the Bi-dimensional empirical mode decomposition (BEMD) algorithm is applied to derive many layers (Bi-dimensional Intrinsic Mode Function BIMF) from ROIs. Then, textural features are extracted from the obtained ROIs. First, directly from segmented ROI, second from the ROI and its sub-layers (BIMFs + Residue). The features extracted in the second time have been grouped into a bag descriptive of the ROI under consideration. This bag is the input parameter of the classification algorithm based on the support vector machine (which has been confirmed to be beneficial for the classification of breast cancer). The average obtained sensitivity, specificity, accuracy and AUC rates, are, respectively 98.60%, 98.65%, 98.62%, and 98.23%. Generally, the experimental results in: INbreast, DDSM, and MIAS datasets demonstrate the robustness and the efficiency of the developed framework compared to previous works in the literature and have shown a significant advance.

Empirical Mode Decomposition: Theory and Applications in Underwater Acoustics

Empirical mode decomposition (EMD) is a signal processing method that produces a data-driven time-frequency representation suited to characterize time-varying and nonlinear phenomena. In EMD, intrinsic mode functions (IMF) are sequentially estimated from the signal of interest to represent different intrinsic oscilation modes and produce an orthogonal representation of the original information. Different algorithms have been proposed for EMD estimation to deal with limitations such as mode-mixing and noise sensitivity. To obtain a frequency-domain representation, EMD is usually associated with the Hilbert transform, in this case, the method is referred to as the Hilbert-Huang transform (HHT). This paper presents a theoretical review of the fundamental aspects of both EMD and HHT, such as IMF estimation procedure and IMF orthogonality. Variations of the original EMD algorithm are also presented. Both simulated and experimental underwater acoustic signals are used to illustrate the efficiency of EMD/HHT in revealing relevant characteristics from time-varying and nonlinear information.

Wind speed prediction based on CEEMD-SE and multiple echo state network with Gauss-Markov fusion.

The purpose of this study is to improve the prediction accuracy of wind speed. The wind speed has the characteristics of unstable, non-stationary, and non-linear, so it is difficult to predict the wind speed. This study proposes a prediction model based on the complementary ensemble empirical mode decomposition-sample entropy and multiple echo state network (ESN) with Gauss-Markov fusion for wind speed. The proposed prediction model consists of the following steps: (a) using the complementary ensemble empirical mode decomposition algorithm, it decomposes the initial wind speed time series and obtains some components with different scales, and (b) using the sample entropy algorithm, it determines the complexity of each component. The components whose entropy is larger than the original wind speed remain unchanged, while the components whose entropy is smaller than the original wind speed are merged into one. The reconstructed component greatly reduces the number of prediction models. (c) After reconstruction, the ESN has good regression prediction ability, so it is chosen as the prediction model of each component. The gray wolf optimization algorithm is introduced to optimize the parameters of the ESN. (d) The Gauss-Markov algorithm is adopted to fuse the predicted values of multiple ESN models. The variance of the predicted value obtained using the Gauss-Markov fusion is less than that of the single ESN model, which significantly increases the prediction accuracy. In order to verify the prediction performance of the proposed model, the actual ultra-short-term and short-term wind speed sample data are compared. At the same time, seven prediction models are chosen as the comparison model. Finally, through the comparison of the prediction error and its histogram distribution, eight performance indicators, Pearson's correlation coefficient, and Diebold-Mariano test, all the results show that the proposed prediction model has high prediction accuracy.

An Improved Empirical Mode Decomposition Algorithm Based on Frequency Rotation Factor and Vector Operation in Complex Plane

In order to overcome the phenomenon of aliasing mode in empirical mode decomposition (EMD), an improved EMD method based on frequency rotation factor and vector operation is proposed. First, fast Fourier transform (FFT) is used to determine the frequency components contained in the original signal. Second, Hilbert transform is applied to transform the real plane analysis into complex plane analysis, then the amplitude frequency product of the signal is satisfied with adaptive full decomposed by frequency inversion and vector operation. Third, the traditional EMD algorithm is used to extract the lowest frequency mode in the complex frequency domain while it is the highest frequency in the time domain (because the frequency inversion operation is performed in the previous step), and finally the original mode of the real plane is restored by forward frequency rotation and inverse vector operation, so as to complete all modal decomposition through this loop algorithm. The simulation results show that the method can effectively separate the aliasing problem and extract the useful signals with low time cost.

Complex image background segmentation for cable force estimation of urban bridges with drone‐captured video and deep learning

Drone-assisted structural health monitoring has aroused extensive attention due to its high mobility and low cost. However, drone motion and complex image backgrounds severely impede its application in the cable force measurement of urban bridges. To fill this research gap, this paper proposed a deep learning-based complex background segmentation approach for cable force estimation of urban bridges from the drone-captured video. The main contribution of this article includes two aspects: (1) A pre-trained fully convolutional network (FCN) model was first adopted to identify bridge cables from drone-captured video and further to extract sub-pixel-level displacement using line segment detection (LSD) algorithm, and (2) an empirical mode decomposition algorithm was employed for extracting the vibration signal of bridge cables by eliminating the effect of drone motion on measured dynamic displacement. Finally, natural frequencies of bridge cables were obtained by performing Fourier analysis on extracted cable vibration and further adopted for cable force estimation. The effectiveness and robustness of the proposed method have been successfully verified by field testing of an urban cable-stayed footbridge. Estimated cable forces using the proposed method are consistent with the traditional contact-type measurements and design values, demonstrating the potential of this method for applying into rapid cable force estimation of numerous urban bridges.

Analysis of differences in vibroacoustic signals between healthy and osteoarthritic knees using EMD algorithm and statistical analysis

The knee joint is the largest and one of the most vulnerable and most frequently damaged joints in the human body. It is characterized by a complex structure. All articular surfaces are covered with hyaline cartilage. This cartilage has minimal regenerative capacity. Under the influence of cyclical micro-injuries, inflammatory mediators, prolonged excessive pressure or immobility, and thus disturbance of tissue nutrition, the cartilage becomes susceptible to damage and is easily covered with villi, cracks and abrasion. As a result, this translates into changes in the friction and lubrication processes within the joint and may affect the generated vibroacoustic processes. In this study, the signals recorded in a group of 28 volunteers were analysed, 15 of them were healthy people (HC) and 13 were people diagnosed with osteoarthritis (OA) qualified for surgery. The study aims to check the usefulness of the EMD (Empirical Mode Decomposition) algorithm in the filtration procedures of vibroacoustic signals. This algorithm is most often used in the analysis of signals that are most often nonlinear and non-stationary. Selected statistical indicators, such as RMS, VMS, variance and energy, were determined for the signals constituting the sum of the IMFs (Intrinsic Mode Functions) 1-8, having a normal distribution in the assessment of damage to the articular cartilage of the knee joint. Statistical analysis was performed for the values of individual indicators obtained. The vibroacoustic signals were recorded using CM-01B contact microphones placed in the central part of the medial and lateral joint fissure for movement in the range of 90°–0°–90° in closed kinetic chains (CKC) in the control group (HC) and the group of patients diagnosed with osteoarthritis (OA).

Remote measurement of heart rate from facial video in different scenarios

Vital signs, such as heart rate (HR), can be remotely measured from facial video recorded by a consumer-level digital camera. However, the accuracy of heart rate estimation is typically affected by ambient light and body motions. Hence, we propose an anti-disturbance method that integrates Eulerian video magnification (EVM), signal quality assessment (QA), and adaptive chirp model decomposition (ACMD) to measure HR in the distance. Next, we evaluate and validate the performance of the proposed method in five different scenarios, including low-illumination, normal-illumination, high-illumination, unbalanced-illumination, and head-motion. The experimental results demonstrated that the estimations of HR using the proposed method in different scenarios have a high consistency with the corresponding ground truths. Moreover, comparing with the methods based on empirical mode decomposition (EMD) or variable mode decomposition (VMD) algorithms, the proposed method can provide a robust solution to eliminate disturbance and obtain a precise HR measurement from the facial videos in different scenarios.

Influence of Sampling Frequency Ratio on Mode Mixing Alleviation Performance: A Comparative Study of Four Noise-Assisted Empirical Mode Decomposition Algorithms

Four noise-assisted empirical mode decomposition (EMD) algorithms, i.e., ensemble EMD (EEMD), complementary ensemble EMD (CEEMD), complete ensemble EMD with adaptive noise (CEEMDAN), and improved complete ensemble EMD with adaptive noise (ICEEMDAN), are noticeable improvements to EMD, aimed at alleviating mode mixing. However, the sampling frequency ratio (SFR), i.e., the ratio between the sampling frequency and the maximum signal frequency, may significantly impact their mode mixing alleviation performance. Aimed at this issue, we investigated and compared the influence of the SFR on the mode mixing alleviation performance of these four noise-assisted EMD algorithms. The results show that for a given signal, (1) SFR has an aperiodic influence on the mode mixing alleviation performance of four noise-assisted EMD algorithms, (2) a careful selection of SFRs can significantly improve the mode mixing alleviation performance and avoid decomposition instability, and (3) ICEEMDAN has the best mode mixing alleviation performance at the optimal SFR among the four noise-assisted EMD algorithms. The applications include, for instance, tool wear monitoring in machining as well as fault diagnosis and prognosis of complex systems that rely on signal decomposition to extract the components corresponding to specific behaviors.

Rotating Machinery Diagnosing in Non-Stationary Conditions with Empirical Mode Decomposition-Based Wavelet Leaders Multifractal Spectra.

Diagnosing the condition of rotating machines by non-invasive methods is based on the analysis of dynamic signals from sensors mounted on the machine—such as vibration, velocity, or acceleration sensors; torque meters; force sensors; pressure sensors; etc. The article presents a new method combining the empirical mode decomposition algorithm with wavelet leader multifractal formalism applied to diagnosing damages of rotating machines in non-stationary conditions. The development of damage causes an increase in the level of multifractality of the signal. The multifractal spectrum obtained as a result of the algorithm changes its shape. Diagnosis is based on the classification of the features of this spectrum. The method is effective in relation to faults causing impulse responses in the dynamic signal registered by the sensors. The method has been illustrated with examples of vibration signals of rotating machines recorded on a laboratory stand, as well as on real objects.

Application of Empirical Mode Decomposition and Extreme Learning Machine Algorithms on Prediction of the Surface Vibration Signal

Accurately predicting surface vibration signals of diesel engines is the key to evaluating the operation quality of diesel engines. Based on an improved empirical mode decomposition and extreme learning machine algorithm, the characteristics of diesel engine surface vibration signal were detected, predicted, and analyzed. First, the surface vibration signal was decomposed into a series of signal components by an improved empirical mode decomposition algorithm. Then, the extreme learning machine algorithm was applied to each signal component to obtain the predicted value of the corresponding signal component and determine the characteristics of the ground vibration signal. Compared with the empirical mode decomposition–extremum learning machine algorithm and the extremum learning machine algorithm, the results show that the improved empirical mode decomposition–extremum learning machine algorithm is feasible and effective.