Empirical mode decomposition (EMD) is a widely used adaptive signal processing method, which has shown some shortcomings in engineering practice, such as sifting stop criteria of intrinsic mode function (IMF), mode mixing and end effect. In this pape...
A novel noise filtering algorithm based on ensemble empirical mode decomposition (EEMD) is proposed to remove artifacts in electrocardiogram (ECG) traces. Three noise patterns with different power—50 Hz, EMG, and base line wander – were embedded into...
In this paper, ensemble empirical mode decomposition (EEMD) and empirical mode decomposition (EMD) methods are used for the effective identification of bridge natural frequencies from drive-by measurements. A vehicle bridge interaction (VBI) model is...
Aiming at non-stationary signals with complex components, the performance of a variational mode decomposition (VMD) algorithm is seriously affected by the key parameters such as the number of modes K, the quadratic penalty parameter α and the u...
The vibration based signal processing technique is one of the principal tools for diagnosing faults of rotating machinery. Empirical mode decomposition (EMD), as a time-frequency analysis technique, has been widely used to process vibration signals...
Sudden cardiac death (SCD) is a global health problem, which represents 15–20% of global deaths. This type of death can be due to different heart conditions, where ventricular fibrillation has been reported as the main one. These cardiac altera...
This paper aims to develop an activity recognition algorithm to allow parents to monitor their children at home after school. A common method used to analyze electroencephalograms is to use infinite impulse response filters to decompose the electroen...
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 fr...
Air quality has a significant influence on people’s health. Severe air pollution can cause respiratory diseases, while good air quality is beneficial to physical and mental health. Therefore, the prediction of air quality is very important. Sin...
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 diese...
The detection of flaw echoes in backscattered signals in ultrasonic nondestructive testing can be challenging due to the existence of backscattering noise and electronic noise. In this article, an empirical mode decomposition (EMD) methodology is pro...
The electrical energy supply relies on the satisfactory operation of insulators. The ultrasound recorded from insulators in different conditions has a time series output, which can be used to classify faulty insulators. The random convolutional kerne...
Analysis of non-stationary and nonlinear sound signals obtained from dynamical processes is one of the greatest challenges in signal processing. Turning machine operation is a highly dynamic process influenced by many events, such as dynamical respon...
Ventricular fibrillation (VF) is a critical ventricular arrhythmia with severe consequences. Due to the severity of VF, it urgently requires a rapid and accurate detection of abnormal patterns in ECG signals. Here, we present an efficient method to d...
A novel scheme to perform the fusion of multiple images using the multivariate empirical mode decomposition (MEMD) algorithm is proposed. Standard multi-scale fusion techniques make a priori assumptions regarding input data, whereas standard univaria...
Micromilling is an extremely important advanced manufacturing technology in the micromanufacturing industry. Compared with the traditional milling process, micromilling has stricter requirements on the surface roughness of the workpiece, and the roug...
Neural decoding is useful to explore the timing and source location in which the brain encodes information. Higher classification accuracy means that an analysis is more likely to succeed in extracting useful information from noises. In this paper, w...
Impedance plethysmography provides a way to measure respiratory activity by sensing the change of thoracic impedance caused by inspiration and expiration. This measurement imposes little pressure on the body and uses the human body as the sensor, th...
Sub-hourly load forecasting can provide accurate short-term load forecasts, which is important for ensuring a secure operation and minimizing operating costs. Decomposition algorithms are suitable for extracting sub-series and improving forecasts in...
In order to investigate the analysis and processing methods for nonstationary signals generated in bridge health monitoring systems, this study combines the advantages of complete ensemble empirical mode decomposition (CEEMD) and wavelet threshold de...
Data-driven modeling methods have been widely used in many applications or studies of traffic systems with complexity and chaos. The empirical mode decomposition (EMD) family provides a lightweight analytical method for non-stationary and non-linear...
Partial discharge detection is an important means of insulation diagnosis of electrical equipment. To effectively suppress the periodic narrowband and white noise interferences in the process of partial discharge detection, a partial discharge interf...
The crack-induced changes in the vertical transient response of a rotating shaft–disc system, Jeffcott rotor, are investigated for transverse crack detection. The crack is considered as a breathing crack. A novel breathing function is proposed,...
Cardiopulmonary monitoring is important and useful for diagnosing and managing multiple conditions, such as stress and sleep disorders. Wearable ambulatory systems can provide continuous, comfortable, and inexpensive means for monitoring; it always h...
In order to study the characteristics of pressure fluctuation during unstable combustion, experimental studies had been conducted on the mechanism model of the swirl combustor and the industrial swirl combustor. The signal of dynamic pressure, heat r...
Grinding chatter reduces the long-term reliability of grinding machines. Detecting the negative effects of chatter requires improved chatter detection techniques. The vibration signals collected from grinders are mainly nonstationary, nonlinear and m...
Hilbert–Huang transform (HHT) is a popular method to analyze nonlinear and non-stationary data. It has been widely used in geophysical prospecting. This paper analyzes the mode mixing problems of empirical mode decomposition (EMD) and introduces the...
As a multichannel signal processing method based on data-driven, multivariate empirical mode decomposition (MEMD) has attracted much attention due to its potential ability in self-adaption and multi-scale decomposition for multivariate data. Commonly...
In this paper, a human electrocardiogram (ECG) identification system based on ensemble empirical mode decomposition (EEMD) is designed. A robust preprocessing method comprising noise elimination, heartbeat normalization and quality measurement is pro...
Existing methods to eliminate the laser speckle noise in quantitative phase imaging always suffer from the loss of detailed phase information and the resolution reduction in the reproduced image. To overcome these problems, this paper proposes a spec...
In the fault diagnosis system using empirical mode decomposition (EMD), it is important to select the intrinsic mode functions (IMFs) which contain as much fault information as possible and to alleviate the problems of mode mixing and spurious modes....
In monitoring the depth of anesthesia (DOA), the electroencephalography (EEG) signals of patients have been utilized during surgeries to diagnose their level of consciousness. Different entropy methods were applied to analyze the EEG signal and measu...
As vital comments on landslide early warning systems, accurate and reliable displacement prediction is essential and of significant importance for landslide mitigation. However, obtaining the desired prediction accuracy remains highly difficult and c...
The development of metro systems can be a good solution to many problems in urban transport and promote sustainable urban development. A metro system plays an important role in urban public transit, and the passenger-flow forecasting is fundamental t...
The present study was aimed at evaluating the Empirical Mode Decomposition (EMD) method to estimate the 3D orientation of the lower trunk during walking using the angular velocity signals generated by a wearable inertial measurement unit (IMU) and no...
The purpose of this paper is to determine whether gamma-band activity detection is improved when a filter, based on empirical mode decomposition (EMD), is added to the pre-processing block of single-channel electroencephalography (EEG) signals. EMD d...
Empirical mode decomposition (EMD) is good at analyzing nonstationary and nonlinear signals while support vector machines (SVMs) are widely used for classification. In this paper, a combination of EMD and SVM is proposed as an improved method for fus...
With the increasing proportion of various unbalanced loads in the power grid, power quality is seriously challenged. It is of great significance to effectively detect, analyze, and evaluate the power quality problems. First, this paper introduces the...
Operation of wind power generation in a large farm is quite challenging in a smart grid owing to uncertain weather conditions. Consequently, operators must accurately forecast wind speed/power in the dispatch center to carry out unit commitment, real...
To suppress the random drift error of a gyroscope signal, this paper proposes a novel denoising method, which is based on processing the intrinsic mode functions (IMFs) obtained by empirical mode decomposition (EMD). Considering that a gyroscope sign...
The carbon dioxide (CO2) differential absorption lidar echo signal is susceptible to noise and must satisfy the high demand for signal-retrieval precision. Thus, a proper de-noising method should be selected to improve the inversion result. In this p...
The root mean square (RMS) value of a vibration signal is an important indicator used to represent the amplitude of vibrations in evaluating the quality of high-speed spindles. However, RMS is unable to detect a number of common fault characteristics...
In the case of strong non-Gaussian noise in the measurement information of the distribution network, the strong non-Gaussian noise significantly interferes with the filtering accuracy of the state estimation model based on deep learning. To address t...
This study extracts the energy characteristic distributions of the intrinsic mode functions (IMFs) and residue functions (RF) for a blue whale sound signal, with empirical mode decomposition (EMD) as the basic theoretical framework. A high-resolution...
In environment sound classification, log Mel band energies (MBEs) are considered as the most successful and commonly used features for classification. The underlying algorithm, fast Fourier transform (FFT), is valid under certain restrictions. In thi...
We introduce a multistep-ahead forecasting methodology that combines empirical mode decomposition (EMD) and support vector regression (SVR). This methodology is based on the idea that the forecasting task is simplified by using as input for SVR the t...
Three-dimensional elliptical vibration cutting (3D-EVC) is one of the machining methods with the most potential in ultra-precision machining; its unique characteristics of intermittent cutting, friction reversal, and ease of chip removal can improve...
Data scarcity is a common problem in hydrological calculations that often makes water resources planning and engineering design challenging. Combining ensemble empirical mode decomposition (EEMD), a radial basis function (RBF) neural network, and an...
Lightning parameters are needed in different engineering applications. For the prediction of the severity of transient voltages in power systems, an accurate knowledge of the parameters of lightning currents is essential. All relevant standards and t...
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 energ...
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