In the context of carbon neutrality and air pollution prevention, it is of great research significance to achieve high-accuracy prediction of the air quality index. In this paper, Beijing is used as the study area; data from January 2014 to December...
The current serious air pollution problem has become a closely investigated topic in people’s daily lives. If we want to provide a reasonable basis for haze prevention, then the prediction of PM2.5 concentrations becomes a crucial task. However...
Coal is a vital part of China’s energy system, and accurately predicting mine water inflow is crucial for ensuring the safety and efficiency of coal mining. To enhance prediction accuracy, this study introduces a hybrid model—CEEMDAN-OVMD...
A rolling bearing vibration signal fault feature enhancement method based on adaptive complete ensemble empirical mode decomposition with adaptive noise algorithm (CEEMDAN) and maximum correlated kurtosis deconvolution (MCKD) is proposed to address t...
Improving the temperature prediction accuracy for subgrades in seasonally frozen regions will greatly help improve the understanding of subgrades’ thermal states. Due to the nonlinearity and non-stationarity of the temperature time series of su...
In this study, to further improve the prediction accuracy of coal mine gas concentration and thereby preventing gas accidents and improving coal mine safety management, the standard whale optimisation algorithm’s (WOA) susceptibility to falling...
Heating, ventilation, and air-conditioning (HVAC) systems consume approximately 60% of the total energy consumption in public buildings, and an effective way to reduce HVAC energy consumption is to provide accurate load forecasting. This paper propos...
In order to remove noise and preserve the important features of a signal, a hybrid de-noising algorithm based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Permutation Entropy (PE), and Time-Frequency Peak Filtering...
Aiming at the problem that power load data are stochastic and that it is difficult to obtain accurate forecasting results by a single algorithm, in this paper, a combined forecasting method for short-term power load was proposed based on the Complete...
Nitrogen content is one of the essential elements in citrus leaves (CL), and many studies have been conducted to determine the nutrient content in CL using hyperspectral technology. To address the key problem that the conventional spectral data-denoi...
This paper develops a data-driven remaining useful life prediction model for solenoid pumps. The model extracts high-level features using stacked autoencoders from decomposed pressure signals (using complementary ensemble empirical mode decomposition...
The energy demand of the district heating system (DHS) occupies an important part in urban energy consumption, which has a great impact on the energy security and environmental protection of a city. With the gradual improvement of people’s econ...
As rock fractures caused by micro-seismic events has potential safety hazards to underground workers, it is often necessary to accurately locate the micro-seismic source for hidden danger investigation. Micro-seismic data are generated in complex und...
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...
In order to improve the accuracy of the deformation reconstruction method based on the Ko displacement theory, a beam deformation reconstruction method based on CEEMDAN-HT-GMM-KO is proposed in this study. The method uses the CEEMDAN method to decomp...
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...
The concentration series of PM2.5 (particulate matter ≤ 2.5 μm) is nonlinear, nonstationary, and noisy, making it difficult to predict accurately. This paper presents a new PM2.5 concentration prediction method based on a hybrid model of complete ens...
In spring, rivers at middle and high latitudes in the Northern Hemisphere are prone to ice jams, which threaten the safety of hydraulic structures in rivers. Heilongjiang Province is located on the highest latitude in China, starting at 43°26&pri...
Slope entropy (Slopen) has been demonstrated to be an excellent approach to extracting ship-radiated noise signals (S-NSs) features by analyzing the complexity of the signals; however, its recognition ability is limited because it extracts the featur...
To solve the problem of fault signals of wind turbine bearings being weak, not easy to extract, and difficult to identify, this paper proposes a fault diagnosis method for fan bearings based on Complete Ensemble Empirical Mode Decomposition with Adap...
In international trade, it is common practice for multinational companies to use financial market instruments, such as financial derivatives and foreign currency debt, to hedge exchange rate risks. Making accurate predictions and decisions on the dir...
Short-term load forecasting (STLF) is crucial for intelligent energy and power scheduling. The time series of power load exhibits high volatility and complexity in its components (typically seasonality, trend, and residuals), which makes forecasting...
The operational complexities of the elastic tooth drum pepper harvester (ETDPH), characterized by variable drum loads that are challenging to recognize due to varying pepper densities, significantly impact pepper loss rates and mechanical damage. Thi...
Rolling bearings play a crucial role in rotary machinery systems, and their operating state affects the entire mechanical system. In most cases, the fault of a rolling bearing can only be identified when it has developed to a certain degree. At that...
A novel low-pass filtering self-adaptive (LPFA) denoising method combining complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and a wavelet threshold (WT) strategy is proposed to solve the problem of the aero-engine gas-path...
Financial time series data are characterized by non-linearity, non-stationarity, and stochastic complexity, so predicting such data presents a significant challenge. This paper proposes a novel hybrid model for financial forecasting based on CEEMDAN-...
Grouting power serves as a critical parameter reflecting real-time energy input during grouting operations, and its accurate prediction is essential for intelligent control and engineering safety. Existing prediction methods often struggle to handle...
An inertial guidance system based on a fiber optic gyroscope (FOG) is an effective way to guide long-distance curved pipe jacking. However, environmental disturbances such as vibration, electromagnetism, and temperature will cause the FOG signal to g...
This paper introduces an effective approach for rotatory fault diagnosis, specifically focusing on centrifugal pumps, by combining complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and feature-level integration. Centrifugal...
7 December 2022
Urban rail transit (URT) is a key mode of public transport, which serves for greatest user demand. Short-term passenger flow prediction aims to improve management validity and avoid extravagance of public transport resources. In order to anticipate p...
By relying on the advantages of a uniform site distribution and continuous observation of the Continuously Operating Reference Stations (CORS) system, real-time high-precision Global Navigation Satellite System/Precipitable Water Vapor (GNSS/PWV) dat...
In this article, a method is proposed to effectively extract weak fault features and accurately diagnose faults in ball screws, even in the presence of strong background noise. This method combines singular value decomposition (SVD), complete ensembl...
The analysis of the vibration-transmission path is one of the keys to the vibration control and safety monitoring of a hydropower house, and the vibration source of the hydropower house is complex, making it more difficult to analyze the vibration-tr...
From the viewpoint of BDS bridge displacement monitoring, which is easily affected by background noise and the calculation of a fixed threshold value in the wavelet filtering algorithm, which is often related to the data length. In this paper, a data...
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...
For planetary gear has the characteristics of small volume, light weight and large transmission ratio, it is widely used in high speed and high power mechanical system. Poor working conditions result in frequent failures of planetary gear. A method i...
When utilizing underwater gliders to observe submerged targets, ensuring the quality and reliability of the acquired target characteristic signals is paramount. However, the signal acquisition process is significantly compromised by noise generated f...
Given that the power load data are stochastic and it is difficult to obtain accurate forecasting results by a single algorithm. In this study, a combined forecasting method for short-term power load was proposed based on the Complete Ensemble Empiric...
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)–...
The magnitude of tidal energy depends on changes in ocean water levels, and by accurately predicting water level changes, tidal power plants can be effectively helped to plan and optimize the timing of power generation to maximize energy harvesting e...
Effective diagnosis of vibration fault is of practical significance to ensure the safe and stable operation of power transformers. Aiming at the traditional problems of transformer vibration fault diagnosis, a novel feature extraction method based on...
Short-term load is influenced by multiple external factors and shows strong nonlinearity and volatility, which increases the forecasting difficulty. However, most of existing short-term load forecasting methods rely solely on the original load data o...
Aluminum is globally the most used nonferrous metal. Clarifying the consumption of primary aluminum is vital to economic development and emission reduction. Based on the signal decomposition tool and S-curve model, a new hybrid complete ensemble empi...
It is commonly known that for characteristics, such as long-distance, high-sensitivity, and full-scale monitoring, phase-sensitive optical time-domain reflectometry (Φ-OTDR) has developed rapidly in many fields, especially with the arrival of 5G....
The North China Plain is an important area for agricultural economic development in China. But water shortages, severe groundwater over-exploitation and drought problems make it difficult to exercise the topographic resource advantages of the plain....
Current non-destructive testing (NDT) methods, such as those based on wave velocity measurements, lack the sensitivity necessary to detect early-stage damage in concrete structures. Similarly, common signal processing techniques often assume linearit...
Aiming at improving the prediction accuracy of the gas dissolved in transformer oil which occurs with strong nonlinearity, this paper presents a method named CEEMDAN-PWOA-VMD-BIGRU for gas content prediction. First, Complete Ensemble Empirical Mode D...
In many practical communication environments, the presence of uncertain and hard-to-estimate noise poses significant challenges to cognitive radio spectrum sensing systems, especially when the noise distribution deviates from the Gaussian distributio...
The noise of near-infrared spectra and spectral information redundancy can affect the accuracy of calibration and prediction models in near-infrared analytical technology. To address this problem, the improved Complete Ensemble Empirical Mode Decompo...
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...
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