Search Results (201)

Plant surface electrical signals are key representations for non-destructive monitoring of changes in cell membrane potential, enabling real-time reflection of physiological responses and regulatory processes under external stimuli. However, the low-frequency and weak-amplitude characteristics of these signals make them extremely susceptible to interference from multiple complex noise sources, such as environmental, power-line frequency, and inherent instrument noise. Existing denoising methods suffer from issues such as mode mixing and insufficient fidelity, hindering accurate extraction of genuine plant physiological information. This study proposes a novel denoising approach that integrates Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Wavelet Soft Thresholding (WST). By decomposing and filtering noise components with adaptive thresholds based on the SURE criterion, the method achieves multi-scale decomposition and effective suppression of residual noise. Applied to surface electrical signals of maize leaves, the results demonstrated a 48% reduction in permutation entropy (PE) for the entire signal. In the resting potential segment, the root mean square (RMS) decreased by 28.91%, total energy dropped by 9.3%, and waveform stability improved. For the action potential segment, the full width at half maximum (FWHM) increased to 0.747, and although the peak amplitude slightly decreased, the waveform structure remained intact. Signal energy became more concentrated within the 0–2 Hz range, achieving efficient noise suppression and high signal fidelity. This method provides a reliable preprocessing technique for elucidating plant physiological mechanisms based on surface electrical signals and holds significant potential for real-time non-destructive monitoring and early warning systems in smart agriculture. Full article

Microseismic signals induced by mining activities often have low signal-to-noise ratios, and traditional picking methods are easily affected by noise, making accurate identification of P-wave arrivals difficult. To address this problem, this study proposes an adaptive denoising algorithm based on wavelet-threshold-enhanced Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and develops an automatic P-wave arrival picking method incorporating fractal box dimension features, along with a corresponding accuracy evaluation framework. The raw microseismic signals are decomposed using the improved CEEMDAN method, with high-frequency intrinsic mode functions (IMFs) processed by wavelet-threshold denoising and low- and mid-frequency IMFs retained for reconstruction, effectively suppressing background noise and enhancing signal clarity. Fractal box dimension is applied to characterize waveform complexity over short and long-time windows, and by introducing fractal derivatives and short-long window differences, abrupt changes in local-to-global complexity at P-wave arrivals are revealed. Energy mutation features are extracted using the short-term/long-term average (STA/LTA) energy ratio, and noise segments are standardized via Z-score processing. A multi-feature weighted fusion scoring function is constructed to achieve robust identification of P-wave arrivals. Evaluation metrics, including picking error, mean absolute error, and success rate, are used to comprehensively assess the method’s performance in terms of temporal deviation, statistical consistency, and robustness. Case studies using microseismic data from a mining site show that the proposed method can accurately identify P-wave arrivals under different signal-to-noise conditions, with automatic picking results highly consistent with manual labels, mean errors within the sampling interval (2–4 ms), and a picking success rate exceeding 95%. The method provides a reliable tool for seismic source localization and dynamic hazard prediction in mining microseismic monitoring. Full article

Partial discharge (PD) source localization is an essential technology to identify the location of defects in power cables. This paper presents a complete cable PD localization system. To improve localization accuracy and reduce computational cost, the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise—Multiscale Permutation Entropy–Improved Wavelet Threshold (CEEMDAN-MPE-IWT) method is first employed to effectively suppress noise in PD signals. Subsequently, Cross-Correlation (CC) coefficients are calculated between the double-ended signals to eliminate low-quality signals with poor correlation. Furthermore, the retained signals are subjected to time-window cropping to minimize redundant data and enhance computational efficiency. Based on the processed signals, multiple time delay estimates are derived using the Generalized Cross-Correlation (GCC) algorithm, and the K-means clustering algorithm is subsequently applied to determine the final localization result. Finally, a cable PD experimental platform is established to validate the proposed method. Experimental results demonstrate that the proposed approach achieves a relative localization error of less than 3%, indicating high localization accuracy and strong potential for engineering applications. Full article

In recent years, with the development of distributed photovoltaic (PV) systems, their impact on power grids has become increasingly significant. However, the complexity of meteorological variations makes the prediction of distributed PV power challenging and often ineffective. This study proposes a short-term power forecasting method for distributed photovoltaics that can identify seasonal characteristics matching weather types, enabling a deeper analysis of complex meteorological changes. First, historical power data is decomposed seasonally using the adaptive noise complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). Next, each component is reconstructed based on a characteristic similarity approach, and a two-stage feature selection process is applied to identify the most relevant features for reconstruction, addressing the issue of nonlinear variable selection. A CNN-LSTM-KAN model with multi-dimensional spatial representation is then proposed to model different weather types obtained by the K-shape clustering method, enabling the segmentation of weather processes. Finally, the proposed method is applied to a case study of distributed PV users in a certain province for short-term power prediction. The results indicate that, compared to traditional methods, the average RMSE decreases by 8.93%, the average MAE decreases by 4.82%, and the R² increases by 9.17%, demonstrating the effectiveness of the proposed method. Full article

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 pumps are critical in various industries, and their condition monitoring is essential for reliability. The proposed methodology addresses the limitations of traditional single-sensor fault diagnosis by fusing information from acoustic and vibration signals. CEEMDAN was employed to decompose raw signals into intrinsic mode functions (IMFs), mitigating noise and non-stationary characteristics. Weighted kurtosis was used to select significant IMFs, and a comprehensive set of time, frequency, and time–frequency domain features was extracted. Feature-level fusion integrated these features, and a support vector machine (SVM) classifier, optimized using the crayfish optimization algorithm (COA), identified different health conditions. The methodology was validated on a centrifugal pump with various impeller defects, achieving a classification accuracy of 95.0%. The results demonstrate the efficacy of the proposed approach in accurately diagnosing the state of centrifugal pumps. Full article

Mainlobe interference can severely degrade the performance of distributed phased-array radar systems in the presence of strong jamming or low-reflectivity targets. This paper introduces a signal–data dual-domain cooperative antijamming and localization (SDCAL) framework that integrates adaptive complete ensemble empirical mode decomposition with improved blind source separation and wavelet optimization (CEEMDAN-WOBSS) for signal-level denoising and separation. Following source separation, CFAR-based pulse compression is applied for precise range estimation, and multi-node data fusion is then used to achieve three-dimensional target localization. Under low signal-to-noise ratio (SNR) conditions, the adaptive CEEMDAN–WOBSS approach reconstructs the signal covariance matrix to preserve subspace rank, thereby accelerating convergence of the separation matrix. The subsequent pulse compression and CFAR detection steps provide reliable inter-node distance measurements for accurate fusion. The simulation results demonstrate that, compared to conventional blind-source-separation methods, the proposed framework markedly enhances interference suppression, detection probability, and localization accuracy—validating its effectiveness for robust collaborative sensing in challenging jamming scenarios. Full article

To address the challenge of reduced prediction accuracy caused by capacity regeneration during the use of lithium-ion batteries, this study proposes an RUL (remaining useful life) prediction method based on mode decomposition and an enhanced Informer-LSTM hybrid model. The capacity is selected as the health indicator, and the CEEMDAN (complete ensemble empirical mode decomposition with adaptive noise) algorithm is employed to decompose the capacity sequence into high-frequency and low-frequency components. The high-frequency components are further decomposed and predicted using the Informer model, while the low-frequency components are predicted with an LSTM (long short-term memory) network. Pearson correlation coefficients between each component and the original sequence are calculated to determine fusion weights. The final RUL prediction is obtained through weighted integration of the individual predictions. Experimental validation on publicly available NASA and CALCE (Center for Advanced Life Cycle Engineering) battery datasets demonstrates that the proposed method achieves an average fitting accuracy of approximately 99%, with MAE (mean absolute error) below 0.02. Additionally, both MAPE (mean absolute percentage error) and RMSE (root-mean-square error) remain at low levels, indicating improvements in prediction precision. Full article

To address the challenge of effectively filtering out noise components in GPS coordinate time series, we propose a denoising method based on parameter-optimized variational mode decomposition (VMD). The method combines permutation entropy with mutual information as the fitness function and uses the crayfish (COA) algorithm to adaptively obtain the optimal parameter combination of the number of modal decompositions and quadratic penalty factors for VMD, and then, sample entropy is used to identify effective mode components (IMF), which are reconstructed into denoised signals to achieve effective separation of signal and noise The experiments were conducted using simulated signals and 52 GPS station data from CMONOC to compare and analyze the COA–VMPE–WD method with wavelet denoising (WD), empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) methods. The result shows that the COA–VMPE–WD method can effectively remove noise from GNSS coordinate time series and preserve the original features of the signal, with the most significant effect on the U component. The COA–VMPE–WD method reduced station velocity by an average of 50.00%, 59.09%, 18.18%, and 64.00% compared to the WD, EMD, EEMD, and CEEMDAN methods. The noise reduction effect is higher than the other four methods, providing reliable data for subsequent analysis and processing. Full article

Ice loads continue to pose challenges to the structural safety of offshore wind turbines (OWTs), while the rapid development of offshore wind power in cold regions is enabling the deployment of OWTs in deeper waters. To accurately simulate the dynamic response of an OWT under combined ice–wind loading, this paper proposes a Discrete Element Method–Wind Turbine Integrated Analysis (DEM-WTIA) framework. The framework can synchronously simulate discontinuous ice-crushing processes and aeroelastic–structural dynamic responses through a holistic turbine model that incorporates rotor dynamics and control systems. To address the issue of insufficient prediction accuracy for dynamic responses, we introduced a multivariate time series forecasting method that integrates a secondary decomposition strategy with a hybrid prediction model. First, we developed a parallel signal processing mechanism, termed Adaptive Complete Ensemble Empirical Mode Decomposition with Improved Singular Spectrum Analysis (CEEMDAN-ISSA), which achieves adaptive denoising via permutation entropy-driven dynamic window optimization and multi-feature fusion-based anomaly detection, yielding a noise suppression rate of 76.4%. Furthermore, we propose the F-Transformer prediction model, which incorporates a Frequency-Enhanced Channel Attention Mechanism (FECAM). By integrating the Discrete Cosine Transform (DCT) into the Transformer architecture, the F-Transformer mines hidden features in the frequency domain, capturing potential periodicities in discontinuous data. Experimental results demonstrate that signals processed by ISSA exhibit increased signal-to-noise ratios and enhanced fidelity. The F-Transformer achieves a maximum reduction of 31.86% in mean squared error compared to the standard Transformer and maintains a coefficient of determination (R²) above 0.91 under multi-condition coupled testing. By combining adaptive decomposition and frequency-domain enhancement techniques, this framework provides a precise and highly adaptable ultra-short-term response forecasting tool for the safe operation and maintenance of offshore wind power in cold regions. Full article

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-Transformer—that combines Adaptive Noise Complete Ensemble Empirical Mode Decomposition (CEEMDAN), Optimal Variational Mode Decomposition (OVMD), and the Transformer architecture. This combined model is used to forecast water inflow at the Heidaigou Coal Mine. The experimental results show that the proposed model achieves excellent predictive accuracy, with a Mean Absolute Error (MAE) of 0.507, a Root Mean Square Error (RMSE) of 0.614, a Mean Absolute Percentage Error (MAPE) of 0.010, and a Coefficient of Determination (R²) of 0.948. Compared to the standalone Transformer model, the CEEMDAN-OVMD-Transformer model reduces the MAE by 34.50% and increases the R² by approximately 3.04%, indicating a significant improvement in forecasting accuracy. The findings demonstrate that the CEEMDAN-OVMD-Transformer hybrid model can effectively reduce the complexity of high-frequency components in mine water inflow time series, thereby enhancing the stability and reliability of predictions. This research presents a new and effective approach for mine water inflow forecasting and offers valuable technical guidance for water hazard prevention and control in similar coal mining environments. Full article

There is an obvious correlation between the photovoltaic (PV) output of different physical levels; that is, the overall power change trend of large-scale regional (high-level) stations can provide a reference for the prediction of the output of sub-regional (low-level) stations. The current PV prediction methods have not deeply explored the multi-level PV power generation elements and have not considered the correlation between different levels, resulting in the inability to obtain potential information on PV power generation. Moreover, traditional probabilistic prediction models lack adaptability, which can lead to a decrease in prediction performance under different PV prediction scenarios. Therefore, a probabilistic prediction method for short-term PV power based on multi-level adaptive dynamic integration is proposed in this paper. Firstly, an analysis is conducted on the multi-level PV power stations together with the influence of the trend of high-level PV power generation on the forecast of low-level power generation. Then, the PV data are decomposed into multiple layers using the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and analyzed by combining fuzzy entropy (FE) and mutual information (MI). After that, a new multi-level model prediction method, namely, the improved dual dynamic adaptive stacked generalization (I-Stacking) ensemble learning model, is proposed to construct short-term PV power generation prediction models. Finally, an improved dynamic adaptive kernel density estimation (KDE) method for prediction errors is proposed, which optimizes the performance of the prediction intervals (PIs) through variable bandwidth. Through comparative experiments and analysis using traditional methods, the effectiveness of the proposed method is verified. Full article

As a critical precision transmission element in numerical control (NC) machines, the linear rolling guide (LRG) suffers from surface wear degradation, which significantly impairs machining accuracy and operational reliability. Despite its importance, effective identification methods for LRG degradation remain limited. In this study, a hybrid approach combining multi-scale fuzzy entropy (MFE) with a gray wolf-optimized random forest (GWO-RF) algorithm was proposed to identify the surface wear state of the LRG. Preload degradation and vibration signals were collected at three surface wear stages throughout the LGR’s service life. The vibration signals were decomposed and reconstructed using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), followed by multi-scale fuzzy entropy analysis of the reconstructed signals. After dimensionality reduction via kernel principal component analysis (KPCA), the processed features were fed into the GWO-RF model for classification. Experimental results demonstrated a recognition accuracy of 97.9%. Full article

Accurate carbon price forecasting is essential for market stability, risk management, and policy-making. To address the nonlinear, non-stationary, and multiscale nature of carbon prices, this paper proposes a forecasting framework integrating secondary decomposition, two-stage feature selection, and dynamic ensemble learning. Firstly, the original price series is decomposed into intrinsic mode functions (IMFs), using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). The IMFs are then grouped into low- and high-frequency components based on multiscale entropy (MSE) and K-Means clustering. To further alleviate mode mixing in the high-frequency components, an improved variational mode decomposition (VMD) optimized by particle swarm optimization (PSO) is applied for secondary decomposition. Secondly, a two-stage feature-selection method is employed, in which the partial autocorrelation function (PACF) is used to select relevant lagged features, while the maximal information coefficient (MIC) is applied to identify key variables from both historical and external data. Finally, this paper introduces a dynamic integration module based on sliding windows and sequential least squares programming (SLSQP), which can not only adaptively adjust the weights of four base learners but can also effectively leverage the complementary advantages of each model and track the dynamic trends of carbon prices. The empirical results of the carbon markets in Hubei and Guangdong indicate that the proposed method outperforms the benchmark model in terms of prediction accuracy and robustness, and the method has been tested by Diebold Mariano (DM). The main contributions are the improved feature-extraction process and the innovative use of a sliding window-based SLSQP method for dynamic ensemble weight optimization. Full article

This study suggests a hybrid approach that combines improved feature selection and intelligent diagnosis to increase the operational safety and intelligent diagnosis capabilities of hydropower units. In order to handle the vibration data, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is used initially. A novel comprehensive index is constructed by combining the Pearson correlation coefficient, mutual information (MI), and Kullback–Leibler divergence (KLD) to select intrinsic mode functions (IMFs). Next, feature extraction is performed on the selected IMFs using Refined Composite Multiscale Fluctuation Dispersion Entropy (RCMFDE). Then, time and frequency domain features are screened by calculating dispersion and combined with IMF features to build a hybrid feature vector. The vector is then fed into a CNN-GRU-Attention model for intelligent diagnosis. The improved slime mold algorithm (ISMA) is employed for the first time to optimize the hyperparameters of the CNN-GRU-Attention model. The experimental results show that the classification accuracy reaches 96.79% for raw signals and 93.33% for noisy signals, significantly outperforming traditional methods. This study incorporates entropy-based feature extraction, combines hyperparameter optimization with the classification model, and addresses the limitations of single feature selection methods for non-stationary and nonlinear signals. The proposed approach provides an excellent solution for intelligent optimized diagnosis of hydropower units. Full article

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 linearity and stationarity among the signal data. By analyzing wave attenuation measurements using advanced signal processing techniques, mainly Hilbert–Huang transform (HHT), this work aims to enhance the early detection of damage in concrete. This study presents a novel energy-based technique that integrates complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and Hilbert spectrum analysis (HSA), to accurately capture nonlinear and nonstationary signal behaviors. Ultrasonic non-destructive testing was performed in this study on manufactured concrete specimens subjected to micro-damage characterized by internal microcracks smaller than 0.5 mm, induced through controlled freeze–thaw cycles. The recorded signals were decomposed from the time domain using CEEMDAN into frequency-ordered intrinsic mode functions (IMFs). A multi-criteria selection strategy, including damage index evaluation, was employed to identify the most effective IMFs while distinguishing true damage-induced energy loss from spurious nonlinear artifacts or noise. Localized damage was then analyzed in the frequency domain using HSA, achieving an up to 88% reduction in wave energy via Marginal Hilbert Spectrum analysis, compared to 68% using Fourier-based techniques, demonstrating a 20% improvement in sensitivity. The results indicate that the proposed technique enhances early damage detection through wave attenuation analysis and offers a superior ability to handle nonlinear, nonstationary signals. The Hilbert Spectrum provided a higher time-frequency resolution, enabling clearer identification of damage-related features. These findings highlight the potential of CEEMDAN-HSA as a practical, sensitive tool for early-stage microcrack detection in concrete. Full article