To separate the noise and important signal features of the indoor carbon dioxide (CO2) concentration signal, we proposed a noise cancellation method, based on time-varying, filtering-based empirical mode decomposition (TVF-EMD) with Bayesian optimiza...
In order to separate the sub-signals and extract the feature frequency in the signal accurately, we proposed a parameter-adaptive time-varying filtering empirical mode decomposition (TVF-EMD) feature extraction method based on the improved grasshoppe...
Owing to climate change, industrial pollution, and population gathering, the air quality status in many places in China is not optimal. The continuous deterioration of air-quality conditions has considerably affected the economic development and heal...
Recent empirical studies reveal evidence of the co-existence of heterogeneous data characteristics distinguishable by time scale in the movement crude oil prices. In this paper we propose a new multivariate Empirical Mode Decomposition (EMD)-based mo...
Sufficient cardiac contractility is necessary to ensure the sufficient cardiac output to provide an adequate end-organ perfusion. Inadequate cardiac output and the diminished perfusion of vital organs from depressed myocardium contractility is a hall...
Long-term streamflow forecast is of great significance for water resource application and management. However, accurate monthly streamflow forecasting is challenging due to its non-stationarity and uncertainty. Time series analysis methods have been...
The idea of a bioactive surface coating that enhances bone healing and bone growth is a strong focus of on-going research for bone implant materials. Enamel matrix derivate (EMD) is well documented to support bone regeneration and activates growth of...
Many factors affect short-term electric load, and the superposition of these factors leads to it being non-linear and non-stationary. Separating different load components from the original load series can help to improve the accuracy of prediction, b...
Significant water loss caused by pipeline leaks emphasizes the importance of effective pipeline leak detection and localization techniques to minimize water wastage. All of the state-of-the-art approaches use deep learning (DL) for leak detection and...
Providing accurate load forecasting plays an important role for effective management operations of a power utility. When considering the superiority of support vector regression (SVR) in terms of non-linear optimization, this paper proposes a novel S...
In few-shot image classification (FSIC), the feature extraction module of the traditional convolutional neural networks is often constrained by the local nature of the convolutional kernel. As a result, it becomes challenging to handle global informa...
A method to estimate modal frequency based on empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) is proposed. This method can decrease the difficulties in identifying modal frequency of combine harvesters. First, we u...
The acoustic emission (AE) signal collected by a sensor in the welding process has an overlapping frequency band and weak characteristics under a complex noise background. It is difficult for the wavelet noise reduction method, with single basis func...
The electricity market has experienced an increasing level of deregulation and reform over the years. There is an increasing level of electricity price fluctuation, uncertainty, and risk exposure in the marketplace. Traditional risk measurement model...
Although the development of high-resolution remote sensing satellite technology has made it possible to reconstruct the 3D structure of object-level features using satellite imagery, the results from a single reconstruction are often insufficient to...
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...
This paper presents an EMD (empirical mode decomposition)-based extraction approach to decouple the electric field fringe component impacts on the nonlinearity errors of a micro encoder-like capacitive displacement sensor. A calculated capacitance mo...
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...
The ferromanganese (FeMn) alloy is produced through the smelting-reduction of manganese ores in submerged arc furnaces. This process generates large amounts of furnace dust that is environmentally problematic for storage. Due to its fineness and high...
Earthquakes are sudden and destructive natural events caused by tectonic movements in the Earth’s crust. Although they cannot be predicted with certainty, rapid and reliable detection is essential to reduce loss of life and property. This study...
Real-time and online screening techniques for single load effect signal monitoring are one of the key issues in smart structure monitoring. In this paper, an online signal sifting framework called online recursive empirical mode decomposition (EMD) i...
In response to the challenge of limited model availability for predicting the lifespan of super-high arch dams, a hybrid model named EMD-PSO-GPR (EPR) is proposed in this study. The EPR model leverages Empirical Mode Decomposition (EMD), Gaussian Pro...
Frequency-modulated continuous-wave (FMCW) light detection and ranging (LiDAR) is a high-precision ranging and imaging system that has been widely used in various areas, such as self-driving vehicles and industrial inspection. However, during detecti...
The influence of the control strategy of the power electronic converter obscures the fault characteristics of DC distribution networks. The existence of arc faults over an extended period of time poses a grave threat to the security of power grids an...
Aircraft hydraulic fault diagnosis is an important technique in aircraft systems, as the hydraulic system is one of the key components of an aircraft. In aircraft hydraulic system fault diagnosis, complex environmental noises will lead to inaccurate...
In order to reduce the noise of a defect electromagnetic signal of the steel cord conveyor belt used in coal mines, a new signal noise reduction method by combined use of the improved threshold wavelet and Empirical Mode Decomposition (EMD) is propos...
Demodulation is one of the most useful techniques for the fault diagnosis of rotating machinery. The commonly used demodulation methods try to select one sensitive sub-band signal that contains the most fault-related components for further analysis....
The complexity of offshore operations demands that offshore platforms withstand the variability and uncertainty of marine environments. Consequently, analyses of platform motion responses must extend beyond single sea state conditions. This study emp...
In this study, we benchmark various machine learning techniques against a synthetic but physically based reference time series (model-simulated (ERA5-Land/FLake) bottom-temperature series) and assess whether decomposition methods (VMD and EMD) improv...
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...
The frequency-domain analysis using the fast Fourier transform (FFT) for diagnosis of eccentricity fault has been widely used in squirrel-cage induction motor (IM). However, with the restriction of sampling frequency and time acquisition, FFT analysi...
In recent years, limited works on EOG (electrooculography)-based biometric authentication systems have been carried out with eye movements or eye blinking activities in the current literature. EOGs have permanent and unique traits that can separate o...
To improve the recognition accuracy of ship-radiated noise, a feature extraction method based on regenerated phase-shifted sinusoid-assisted empirical mode decomposition (RPSEMD), mutual information (MI), and differential symbolic entropy (DSE) is pr...
Air pollution (mainly PM2.5) is one of the main environmental problems about air quality. Air pollution prediction and early warning is a prerequisite for air pollution prevention and control. However, it is not easy to accurately predict the long-te...
Wavelength modulation-tunable diode laser absorption spectroscopy (WM-TDLAS) is a critical tool for gas detection. However, noise in second harmonic signals degrades detection performance. This study presents a hybrid denoising algorithm combining Em...
To accurately forecast the future development trend of vegetation in dry areas, it is crucial to continuously monitor phenology, vegetation health indices, and vegetation drought indices over an extended period. This is because drought caused by high...
To improve the performance of single-channel, multi-fault blind source separation (BSS), a novel method based on regenerated phase-shifted sinusoid-assisted empirical mode decomposition (RPSEMD) is proposed in this paper. The RPSEMD method is used to...
The large-scale integration of coordinated offshore wind and offshore photovoltaic (PV) generation introduces pronounced power fluctuations due to the intrinsic randomness and intermittency of renewable energy sources (RESs). These fluctuations pose...
In gaze-based Human-Robot Interaction (HRI), it is important to determine human visual intention for interacting with robots. One typical HRI interaction scenario is that a human selects an object by gaze and a robotic manipulator will pick up the ob...
Groundwater radon concentrations can reflect the changes of crustal stress and strain. Scholars and scientific institutions have also recorded groundwater radon precursor anomalies before earthquakes. Therefore, groundwater radon monitoring is an eff...
Existing studies have attempted to determine the tool chipping condition using the indirect method of data capture and intelligent analysis techniques considering machine parameters, and tool conditions using signal processing techniques. Due to the...
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 p...
The accurate prediction of significant wave height (SWH) offers major safety improvements for coastal and ocean engineering applications. However, the significant wave height phenomenon is nonlinear and nonstationary, which makes any prediction work...
Heart diseases are among the most common death causes in the population. Particularly, sudden cardiac death (SCD) is the cause of 10% of the deaths around the world. For this reason, it is necessary to develop new methodologies that can predict this...
A photoacoustic (PA) signal of an ideal optical absorbing particle is a single N-shape wave. PA signals are a combination of several individual N-shape waves. However, the N-shape wave basis leads to aliasing between adjacent micro-structures, which...
Measuring displacement response is essential in the field of structural health monitoring and seismic engineering. Numerical integration of the acceleration signal is a common measurement method of displacement data. However, due to the circumstances...
An efficient maintenance is a key consideration in systems of railway transport, especially in high-speed trains, in order to avoid accidents with catastrophic consequences. In this sense, having a method that allows for the early detection of defect...
The prediction of mold level is a basic and key problem of continuous casting production control. Many current techniques fail to predict the mold level because of mold level is non-linear, non-stationary and does not have a normal distribution. A hy...
In order to improve wind power utilization efficiency and reduce wind power prediction errors, a combined prediction model of EMD-KPCA-BilSTM-ATT is proposed, which includes a data processing method combining empirical mode decomposition (EMD) and ke...
Data hiding in videos has been a big concern as their rich redundancy can be used for embedding a lot of secret information. Further, as high efficiency video coding (HEVC) introduces many innovative technologies compared with the previous standard,...
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