Intrinsic mode functions (IMFs) provide an intuitive representation of the oscillatory modes and are mainly calculated using Hilbert–Huang transform (HHT) methods. Those methods, however, suffer from the end effects, mode-mixing and Gibbs pheno...
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...
Ground Penetrating Radar (GPR) has become a widely used technology in geophysical prospecting. The Variational Mode Decomposition (VMD) method is a fully non-recursive signal decomposition method with noise robustness for GPR data processing. The VMD...
Aiming at the problem of mutual interference between millimeter-wave frequency-modulation continuous-wave (FMCW) radars, an interference mitigation method based on outlier detection and variational mode decomposition (VMD) is proposed in this paper....
9 August 2020
To analyze the time–frequency characteristics of the particulate matter (PM10) concentration, data series measured at dry bulk ports were used to determine the contribution of various factors during different periods to the PM10 concentration l...
The fetal electrocardiogram (FECG) is critical for assessing fetal cardiac electrophysiology and detecting fetal distress and arrhythmias. Single-channel abdominal electrocardiogram (AECG) enables home-based monitoring but faces challenges posed by w...
In the context of the nationwide call for “energy savings” in China, it is desirable to establish a more accurate forecasting model to manage the electricity consumption from the university dormitory, to provide a suitable management appr...
In medical image processing, magnetic resonance imaging (MRI) and computed tomography (CT) modalities are widely used to extract soft and hard tissue information, respectively. However, with the help of a single modality, it is very challenging to ex...
Radiometric calibration for imaging sensors is a crucial procedure to ensure imagery quality. One of the challenges in relative radiometric calibration is to correct detector-level artifacts due to the fluctuation in discrepant responses (spatial) an...
Falls are a significant health concern leading to increased morbidity and healthcare costs, especially for the elderly. Early and accurate detection of fall events is critical for timely intervention and preventing severe complications. This study pr...
The real-time assessment of structural damage in concrete structures using the acoustic emission (AE) technique presents substantial challenges. Traditional AE parameters often fail to effectively quantify the extent of concrete damage in real time....
Pumped storage units serve as a crucial support for power systems to adapt to large-scale and high-proportion renewable energy sources by providing a stable and flexible energy supply. However, due to the coupling effects of electric power load deman...
Radio-echo sounding (RES) is widely used for polar ice sheet detection due to its wide coverage and high efficiency. The multivariate variational mode decomposition (MVMD) algorithm for the processing of RES data is an improvement to the variational...
For operational management of power plants, it is desirable to possess more precise short-term load forecasting results to guarantee the power supply and load dispatch. The empirical mode decomposition (EMD) method and the particle swarm optimization...
To address the issue of weak geoelectric current field signals that are severely affected by noise and cannot be directly used for geological structure analysis in injected geoelectric current field detection technology, this study proposes a complet...
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...
Deterministic–stochastic empirical mode decomposition (EMD) is used to obtain low-frequency (non-diffusive; i.e., background velocity) and high-frequency (diffusive; i.e., eddies) components from a Lagrangian drifter‘s trajectory. Eddy ch...
Tidal power generation technology has advanced quickly in recent years. In this study, the Hilbert-Huang transform (HHT) was used to examine the electrical energy distribution law of tidal power generation according to the time periods of days, month...
A novel noise filtering algorithm based on averaging Intrinsic Mode Function (aIMF), which is a derivation of Empirical Mode Decomposition (EMD), is proposed to remove white-Gaussian noise of foreign currency exchange rates that are nonlinear nonstat...
Traffic accidents occur frequently every year. Skid-resistance performance is an important indicator in evaluating road safety. In this paper, the road surface texture information is obtained by a line laser scanner. The original two-dimensional (2D)...
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 off-axis quantitative phase imaging, existing methods of suppressing the zero-frequency component (ZFC) always cause the loss of high-frequency phase information, thus degrading the accuracy of phase reconstruction. To overcome these problems, thi...
Arc fault diagnosis is necessary for the safety and efficiency of PV stations. This study proposed an arc fault diagnosis algorithm formed by combining variational mode decomposition (VMD), improved multi-scale fuzzy entropy (IMFE), and support vecto...
Accurate wind speed forecasting is a fundamental element of wind power prediction. Thus, a new hybrid wind speed forecasting model, using variational mode decomposition (VMD), the partial autocorrelation function (PACF), and weighted regularized extr...
Owing to the complexity of the ocean background noise, underwater acoustic signal denoising is one of the hotspot problems in the field of underwater acoustic signal processing. In this paper, we propose a new technique for underwater acoustic signal...
A novel electrocardiogram (ECG) signal de-noising and baseline wander correction method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and wavelet threshold is proposed. Although CEEMDAN is based on empirical mo...
Originally, a rolling bearing, as a key part in rotating machinery, is a cyclic symmetric structure. When a fault occurs, it disrupts the symmetry and influences the normal operation of the rolling bearing. To accurately identify faults of rolling be...
Ship-radiated noise (SN) is one of the most critical signals in the complex marine environment; however, it is inevitably contaminated by the marine environment’s noise as well as noise from other equipment. Thus, the feature extraction and ide...
This paper proposes a model which uses the greedy algorithm to select the optimal intrinsic mode functions (IMFs) of the empirical mode decomposition (EMD), namely the greedy empirical mode decomposition (GEMD) model. The optimal IMFs can more suffic...
Using traditional time-frequency analysis methods, it is possible to delineate the time-frequency structures of ground-penetrating radar (GPR) data. A series of applications based on time-frequency analysis were proposed for the GPR data processing a...
Chatter detection in robotic milling is a difficult issue due to the complex dynamic behavior of robots. In this paper, a novel approach to detecting chatter in the robotic milling process is proposed. The method of improved complete ensemble empiric...
This paper investigates fault detection of a roller bearing system using a wavelet denoising scheme and proper orthogonal value (POV) of an intrinsic mode function (IMF) covariance matrix. The IMF of the bearing vibration signal is obtained through e...
Influenced by the complexity of ocean environmental noise and the time-varying of underwater acoustic channels, feature extraction of underwater acoustic signals has always been a difficult challenge. To solve this dilemma, this paper introduces a hy...
Despite the increased attention that has been given to the unmanned aerial vehicle (UAV)-based magnetic survey systems in the past decade, the processing of UAV magnetic data is still a tough task. In this paper, we propose a novel noise reduction me...
Drought is a stochastic natural feature that arises due to intense and persistent shortage of precipitation. Its impact is mostly manifested as agricultural and hydrological droughts following an initial meteorological phenomenon. Drought prediction...
Noise reduction of underwater acoustic signals is of great significance in the fields of military and ocean exploration. Based on the adaptive decomposition characteristic of uniform phase empirical mode decomposition (UPEMD), a noise reduction metho...
In order to improve wind power prediction accuracy and increase the utilization of wind power, this study proposes a novel complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)–variational modal decomposition (VMD)–...
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...
Marginal spectrum (MS) feature information of humpback whale vocalization (HWV) signals is an interesting and significant research topic. Empirical mode decomposition (EMD) is a powerful time–frequency analysis tool for marine mammal vocalizati...
To eliminate the influence of white noise in partial discharge (PD) detection, we propose a novel method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and approximate entropy (ApEn). By introducing adaptive noi...
In this paper, a novel method is proposed to enhance the accuracy of fault diagnosis for rolling bearings. First, an enhanced complementary empirical mode decomposition with adaptive noise (ECEEMDAN) method is proposed by determining two critical par...
High-G MEMS accelerometers have been widely used in monitoring natural disasters and other fields. In order to improve the performance of High-G MEMS accelerometers, a denoising method based on the combination of empirical mode decomposition (EMD) an...
Real-time ash content control in dense medium coal separation is challenged by time lags between detection and density adjustment, along with nonlinear/noisy signals. This study proposes a hybrid model for clean coal ash content in dense medium separ...
Sea level change is a key indicator of climate change, and the prediction of sea level rise is one of most important scientific issues. In this paper, the gridded sea level anomaly (SLA) data from satellite altimetry are used to analyze the sea level...
Aiming at the problem that fault feature extraction is susceptible to background noises and burrs, we proposed a new feature extraction method based on a new decomposition method and an effective intrinsic mode function (IMF) selection method. Firstl...
Satellite Image Time Series (SITS) have recently been of great interest due to the emerging remote sensing capabilities for Earth observation. Trend and seasonal components are two crucial elements of SITS. In this paper, a novel framework of SITS de...
A common issue in water infrastructure is that it suffers from leakage. The hydroinformatics technique for recognizing the presence of leaks in the pipeline system by means of pressure transient analysis was briefly explored in this study. Various st...
A short-term power load forecasting method is proposed based on an improved Sparrow Search Algorithm (ISSA), Variational Mode Decomposition (VMD), and Bidirectional Long Short Term Memory (BiLSTM) neural network. First, the SSA is optimized by combin...
In the continuous-casting process, mold-level control is one of the most important factors that ensures the quality of high-efficiency continuous casting slabs. In traditional mold-level prediction control, the mold-level prediction accuracy is low,...
In recent years, wearable devices have been popularly applied in the health care field. The electrocardiogram (ECG) is the most used signal. However, the ECG is measured under a body-motion condition, which is easily coupled with some noise, like as...
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