Search Results (68)

Accurately estimating the remaining useful life (RUL) of lithium-ion batteries in energy storage systems is critical for ensuring both the safety and reliability of the power grid. To address the complex nonlinear degradation behavior associated with battery aging, this study proposes a novel RUL prediction framework that integrates ensemble empirical mode decomposition (EEMD) with an ensemble learning algorithm. The approach first applies EEMD to decompose aging data into a residual component and several intrinsic mode functions (IMFs). The residual component is then modeled using a long short-term memory (LSTM) network, while the Kolmogorov–Arnold network (KAN) focuses on learning from the IMF components. These individual predictions are subsequently combined to reconstruct the overall capacity degradation trajectory. Experimental validation on real lithium-ion battery aging datasets demonstrates that the proposed method provides highly accurate RUL predictions, exhibits strong robustness, and effectively captures nonlinear characteristics under varying operating conditions. Specifically, the method achieves R² above 0.96 with absolute RUL errors within 2–3 cycles on NASA datasets, and maintains R² values above 0.91 with errors within 7–15 cycles on CALCE datasets. Furthermore, the optimal KAN hyperparameters for different IMF components are identified, offering valuable insights for multi-scale modeling and future model optimization. Full article

Accurate and efficient short-term load forecasting is crucial for the secure and stable operation and scheduling of power grids. Addressing the inability of traditional Transformer-based prediction models to capture symmetric correlations between different feature sequences and their susceptibility to multi-scale feature influences, this paper proposes a short-term power distribution network load forecasting model based on an enhanced Komodo Mlipir Algorithm (KMA)—Multivariate Variational Mode Decomposition (MVMD)-Crossformer. Initially, the KMA is enhanced with chaotic mapping and temporal variation inertia weighting, which strengthens the symmetric exploration of the solution space. This enhanced KMA is integrated into the parameter optimization of the MVMD algorithm, facilitating the decomposition of distribution network load sequences into multiple Intrinsic Mode Function (IMF) components with symmetric periodic characteristics across different time scales. Subsequently, the Multi-variable Rapid Maximum Information Coefficient (MVRapidMIC) algorithm is employed to extract features with strong symmetric correlations to the load from weather and date characteristics, reducing redundancy while preserving key symmetric associations. Finally, a power distribution network short-term load forecasting model based on the Crossformer is constructed. Through the symmetric Dimension Segmentation (DSW) embedding layer and the Two-Stage Attention (TSA) mechanism layer with bidirectional symmetric correlation capture, the model effectively captures symmetric dependencies between different feature sequences, leading to the final load prediction outcome. Experimental results on the real power distribution network dataset show that: the Root Mean Square Error (RMSE) of the proposed model is as low as 14.7597 MW, the Mean Absolute Error (MAE) is 13.9728 MW, the Mean Absolute Percentage Error (MAPE) reaches 4.89%, and the coefficient of determination (R²) is as high as 0.9942. Full article

Bearings are critical components whose failures in industrial machinery can lead to catastrophic breakdowns and costly downtime; yet, accurate early-stage diagnosis remains challenging due to the non-stationary, nonlinear nature of vibration signals and noise interference. This study proposes a multidataset-integrated bearing fault diagnosis methodology incorporating variational mode decomposition (VMD), refined composite multiscale weighted permutation entropy (RCMWPE) feature extraction, and whale optimization algorithm (WOA)-optimized support vector machine (SVM). Addressing the non-stationary and nonlinear characteristics of bearing vibration signals, raw signals are first decomposed via VMD to effectively separate intrinsic mode functions (IMFs) carrying distinct frequency components. Subsequently, RCMWPE features are extracted from each IMF component to construct high-dimensional feature vectors. To address visualization challenges and mitigate feature redundancy, the t-distributed stochastic neighbor embedding (t-SNE) algorithm is employed for dimensionality reduction. Finally, WOA optimizes critical SVM parameters to establish an efficient fault classification model. The methodology is validated on two public bearing datasets: PRONOSTIA and CWRU. For four-class fault diagnosis on the PRONOSTIA dataset, the model achieves 96.5% accuracy. Extended to ten-class diagnosis on the CWRU dataset, accuracy reaches 99.67%. Experimental results demonstrate that the proposed method exhibits exceptional fault identification capability, robustness, and generalization performance across diverse datasets and complex fault modes. This approach offers an effective technical pathway for early bearing fault warning and maintenance decision making. Full article

The spatial distribution of soil heavy metals was influenced by both natural and anthropogenic factors, and the multi-scale characteristics of heavy metals played a key role in analyzing their influencing factors. Taking arsenic (As) of an oil refining site in Shandong as an example, the As was firstly decomposed into intrinsic mode functions (IMFs) at different scales and a residual using two-dimensional empirical mode decomposition (EMD). Secondly, the spatial variation scales of As, the IMFs, and the residual were quantified by their semi-variograms, respectively. Finally, local spatial correlation analysis and random forest model were employed to analyze the multi-scale features of As, the IMFs, the residual, and environmental variables. The results indicated that the As was decomposed into IMF1, IMF2, IMF3, and a residual using the two-dimensional EMD method, and the corresponding spatial ranges were 72.60 m, 159.30 m, 448.00 m, and 592.36 m, respectively. IMF3 had the highest percentage of variance with a value of 57.56%, indicating that the spatial variation of As was mainly concentrated on a large scale. There were correlations between As and aspect and land use type. However, after the scale decomposition of two-dimensional EMD, there were significant correlations between oil residue thickness and IMF1, land use type and IMF3, land use type, and aspect and residual, respectively. The IMFs and residual had a significant scale–location dependence on environment variables, and the impact of anthropogenic factors on As was mainly reflected at the small and medium scales, while the influence of natural factors was mainly reflected at the large scale. The developed method can provide a methodological framework for the spatial analysis and pollution control of soil heavy metals. Full article

The agglomeration characteristics of innovation spaces reflect the intrinsic mechanisms of regional resource integration and collaborative innovation. Investigating the contributions of influencing factors to innovation space agglomeration and their spatial differentiation has significant implications for improving urban innovation quality. Taking the Nanjing central urban area as a case study, this research applied gradient boosting regression trees (GBRT) and multiscale geographically weighted regression (MGWR) models to explore the contributions of influencing factors to innovation space agglomeration and its spatial differentiation. Findings demonstrated that (1) Innovation platforms and patents emerged as the most significant driving factors, collectively accounting for 54.8% of the relative contributions; (2) The contributions of influencing factors to innovation space agglomeration exhibited marked nonlinear characteristics, specifically categorized into five distinct patterns: Sustained Growth Pattern, Growth-Stabilization Pattern, Growth-Decline Pattern, Global Stabilization Pattern, and Global Decline Pattern. The inflection thresholds of marginal effects across factors ranged from approximately 12% to 55% (e.g., 40% for metro stations, 13% for integrated commercial hubs); (3) Each influence factor’s contribution mechanism showed pronounced spatial heterogeneity across different regions. Based on these discoveries, governments should optimize innovation resource allocation according to regional characteristics and enhance spatial quality to promote efficient resource integration and transformation. This research provides a novel perspective for understanding innovation space agglomeration mechanisms and offers actionable references for urban policymakers to implement context-specific innovation economic development strategies. Full article

Wind shear at the Hong Kong International Airport (HKIA) poses a significant safety risk due to terrain-induced airflow disruptions near the runways. Accurate assessment is essential for safeguarding aircraft during take-off and landing, as abrupt changes in wind speed or direction can compromise flight stability. This study introduces a hybrid framework for short-term wind shear prediction based on data collected from Doppler LiDAR systems positioned near the central and south runways of the HKIA. These systems provide high-resolution measurements of wind shear magnitude along critical flight paths. To predict wind shear more effectively, the proposed framework integrates a signal processing technique with a deep learning strategy. It begins with optimized variational mode decomposition (OVMD), which decomposes the wind shear time series into intrinsic mode functions (IMFs), each capturing distinct temporal characteristics. These IMFs are then modeled using bidirectional gated recurrent units (BiGRU), with hyperparameters optimized via the Tree-structured Parzen Estimator (TPE). To further enhance prediction accuracy, residual errors are corrected using Extreme Gradient Boosting (XGBoost), which captures discrepancies between the reconstructed signal and actual observations. The resulting OVMD–BiGRU–XGBoost framework exhibits strong predictive performance on testing data, achieving R² values of 0.729 and 0.926, RMSE values of 0.931 and 0.709, and MAE values of 0.624 and 0.521 for the central and south runways, respectively. Compared with GRUs, LSTM, BiLSTM, and ResNet-based baselines, the proposed framework achieves higher accuracy and a more effective representation of multi-scale temporal dynamics. It contributes to improving short-term wind shear prediction and supports operational planning and safety management in airport environments. Full article

In response to the challenges posed by complex and variable operating conditions of rolling bearings and the limited availability of labeled data, both of which hinder the effective extraction of key fault features and reduce diagnostic accuracy, this study introduces a model that combines a spatial attention (SA) mechanism with a multi-scale depthwise separable convolution module. The proposed approach first employs the Gramian angular difference field (GADF) to convert raw signals. This conversion maps the temporal characteristics of the signal into an image format that intrinsically preserves both temporal dynamics and phase relationships. Subsequently, the model architecture incorporates a spatial attention mechanism and a multi-scale depthwise separable convolutional module. Guided by the attention mechanism to concentrate on discriminative feature regions and to suppress noise, the convolutional component efficiently extracts hierarchical features in parallel through the multi-scale receptive fields. Furthermore, the trained model serves as a pre-trained network and is transferred to novel variable-condition environments to enhance diagnostic accuracy in few-shot scenarios. The effectiveness of the proposed model was evaluated using bearing datasets and field-collected industrial data. Experimental results confirm that the proposed model offers outstanding fault recognition performance and generalization capability across diverse working conditions, small-sample scenarios, and real industrial environments. Full article

Quantitative electroencephalography (QEEG) has emerged as a promising tool for detecting Alzheimer’s disease (AD). Among QEEG measures, Multiscale Fuzzy Entropy (MFE) shows great potential in identifying AD-related changes in EEG complexity. However, MFE is intrinsically linked to signal amplitude, which can vary substantially among EEG systems, and this hinders the adoption of this metric for AD detection. To overcome this issue, this study investigates different preprocessing strategies to make the calculation of MFE less dependent on the specific amplitude characteristics of the EEG signals at hand. This contributes to generalizing and making more robust the adoption of MFE for AD detection. To demonstrate the robustness of the proposed preprocessing methods, binary classification tasks with Support Vector Machines (SVMs), Random Forest (RF), and K-Nearest Neighbor (KNN) classifiers are used. Performance metrics, such as classification accuracy and Matthews Correlation Coefficient (MCC), are employed to assess the results. The methodology is validated on two public EEG datasets. Results show that amplitude transformation, particularly normalization, significantly enhances AD detection, achieving mean classification accuracy values exceeding 80% with an uncertainty of 10% across all classifiers. These results highlight the importance of preprocessing in improving the accuracy and the reliability of EEG-based AD diagnostic tools, offering potential advancements in patient management and treatment planning. Full article

This article proposes a multiscale classification framework for detecting voltage disturbances in electrical distribution systems using artificial neural networks (ANNs) combined with the Hilbert–Huang transform (HHT). The framework targets four core power quality (PQ) events defined in the IEEE 1159-2019 standard: normal operation and voltage sag, swell, and interruption. Unlike traditional methods that operate on a fixed disturbance duration, our approach incorporates multiple time scales (0.2 s, 0.4 s, and 0.8 s) to improve detection robustness across varied event lengths, a critical factor in real-world scenarios where disturbance durations are unpredictable. Features are extracted using empirical mode decomposition (EMD) and Hilbert spectral analysis, enabling accurate representation of the signals’ non-stationary and nonlinear characteristics. The ANN is trained using statistical descriptors derived from the first two intrinsic mode functions (IMFs), capturing both amplitude and frequency content. The method was validated in MATLAB on the IEEE 33-bus radial distribution test system using simulated disturbances. The proposed model achieved a classification accuracy of 94.09% and demonstrated consistent performance across all time windows, supporting its suitability for real-time monitoring in smart distribution networks. This study contributes a scalable and adaptable solution for automated PQ event classification under variable conditions. Full article

The coexistence of cloud and snow is very common in remote sensing images. It presents persistent challenges for automated interpretation systems, primarily due to their highly similar visible light spectral characteristic in optical remote sensing images. This intrinsic spectral ambiguity significantly impedes accurate cloud and snow segmentation tasks, particularly in delineating fine boundary features between cloud and snow regions. Much research on cloud and snow segmentation based on deep learning models has been conducted, but there are still deficiencies in the extraction of fine boundaries between cloud and snow regions. In addition, existing segmentation models often misjudge the body of clouds and snow with similar features. This work proposes a Multi-scale Feature Mixed Attention Network (MFMANet). The framework integrates three key components: (1) a Multi-scale Pooling Feature Perception Module to capture multi-level structural features, (2) a Bilateral Feature Mixed Attention Module that enhances boundary detection through spatial-channel attention, and (3) a Multi-scale Feature Convolution Fusion Module to reduce edge blurring. We opted to test the model using a high-resolution cloud and snow dataset based on WorldView2 (CSWV). This dataset contains high-resolution images of cloud and snow, which can meet the training and testing requirements of cloud and snow segmentation tasks. Based on this dataset, we compare MFMANet with other classical deep learning segmentation algorithms. The experimental results show that the MFMANet network has better segmentation accuracy and robustness. Specifically, the average MIoU of the MFMANet network is 89.17%, and the accuracy is about 0.9% higher than CSDNet and about 0.7% higher than UNet. Further verification on the HRC_WHU dataset shows that the MIoU of the proposed model can reach 91.03%, and the performance is also superior to other compared segmentation methods. Full article

The relationships between the pore structure and a single fractal or specific region have been widely reported. However, the intrinsic relationship between multifractal parameters and physical properties have remained uncertain. In this study, micro-computed tomography scanning technology and high-pressure mercury injection technologies were applied to determine the pore structures of tight sandstone at different scales. Subsequently, the multifractal theory was applied to quantitatively evaluate the multiscale pore structure heterogeneity. An evident linear relationship exists between $logX\left(q,\epsilon \right)$ and $\mathit{log}\left(\epsilon \right)$, indicating the pore structure of tight sandstones exhibits significant multifractal characteristics. Multifractal parameters, including $∆\left(\alpha \right), ∆D, D_{min}-D_0,\mathrm{a}\mathrm{n}\mathrm{d}{ D}_0-D_{max}$, can serve as sensitive indicators to assess the multiscale pore structure heterogeneity. In particular, the relative development degree of large-scale pores (>10 μm) can be reflected by $D_{min}-D_{0 }$, which has strong heterogeneity. The heterogeneity of the multiscale structure is closely linked to the mineral components of tight sandstone reservoirs, and the heterogeneity of small-scale pores (1–10 μm) is stronger by clay mineral enrichment. Furthermore, the part of the pore structure corresponding to the combination of pore size range of 10 to 20 μm and throat size range of 20 to 40 μm in a low probability measure area may dominate the permeability of tight sandstone. The findings enhance the understanding of pore structure heterogeneity and broaden the application of multifractal theory. Full article

With the acceleration of urban renewal, directional blasting has become a common method for building demolition. Analyzing the time–frequency characteristics of blast-induced seismic waves allows for the assessment of risks to surrounding structures. However, the signals monitored are frequently tainted with noise, which undermines the precision of time–frequency analysis. To counteract the dangers posed by blast vibrations, effective signal denoising is crucial for accurate evaluation and safety management. To tackle this challenge, a dual denoising model is proposed. This model consists of two stages. Firstly, it applies endpoint processing (EP) to the signal, followed by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to suppress low-frequency clutter. High-frequency noise is then handled by controlling the multi-scale permutation entropy (MPE) of the intrinsic mode functions (IMF) obtained from EP-CEEMDAN. The EP-CEEMDAN-MPE framework achieves the first stage of denoising while mitigating the influence of endpoint effects on the denoising performance. The second stage of denoising involves combining the IMF obtained from EP-CEEMDAN-MPE to generate multiple denoising models. An objective function is established considering both the smoothness of the denoising models and the standard deviation of the error between the denoised signal and the measured signal. The denoising model corresponding to the optimal solution of the objective function is identified as the dual denoising model for blasting seismic wave signals. To validate the denoising effectiveness of the denoising model, simulated blasting vibration signals with a given signal-to-noise ratio (SNR) are constructed. Finally, the model is applied to real engineering blasting seismic wave signals for denoising. The results demonstrate that the model successfully reduces noise interference in the signals, highlighting its practical significance for the prevention and control of blasting seismic wave hazards. Full article

The Loess Plateau region of China has an anomalous climate and frequent geological disasters. Hipparion laterite in seasonally frozen regions exhibits heightened susceptibility to freeze–thaw (F-T) cycling, which induces progressive structural weakening and significantly elevates the risk of slope instability through mechanisms including pore water phase transitions, aggregate disintegration, and shear strength degradation. This study focuses on the slip zone Hipparion laterite from the Nao panliang landslide in Fugu County, Shaanxi Province. We innovatively integrated F-T cycling tests with ring-shear experiments to establish a hydro-thermal–mechanical coupled multi-scale evaluation framework for assessing F-T damage in the slip zone material. The microstructural evolution of soil architecture and pore characteristics was systematically analyzed through scanning electron microscopy (SEM) tests. Quantitative characterization of mechanical degradation mechanisms was achieved using advanced microstructural parameters including orientation frequency, probabilistic entropy, and fractal dimensions, revealing the intrinsic relationship between pore network anisotropy and macroscopic strength deterioration. The experimental results demonstrate that Hipparion laterite specimens undergo progressive deterioration with increasing F-T cycles and initial moisture content, predominantly exhibiting brittle deformation patterns. The soil exhibited substantial strength degradation, with total reduction rates of 51.54% and 43.67% for peak and residual strengths, respectively. The shear stress–displacement curves transitioned from strain-softening to strain-hardening behavior, indicating plastic deformation-dominated shear damage. Moisture content critically regulates pore microstructure evolution, reducing micropore proportion to 23.57–28.62% while promoting transformation to mesopores and macropores. At 24% moisture content, the areal porosity, probabilistic entropy, and fractal dimension increased by 0.2263, 0.0401, and 0.0589, respectively. Temperature-induced pore water phase transitions significantly amplified mechanical strength variability through cyclic damage accumulation. These findings advance the theoretical understanding of Hipparion laterite’s engineering geological behavior while providing critical insights for slope stability assessment and landslide risk mitigation strategies in loess plateau regions. Full article

With the increase in photovoltaic installed capacity year by year, accurate photovoltaic power prediction is of great significance for photovoltaic grid-connected operation and scheduling planning. In order to improve the prediction accuracy, this paper proposes a photovoltaic power prediction combination model based on Pearson Correlation Coefficient (PCC), Complete Ensemble Empirical Mode Decomposition (CEEMDAN), K-means clustering, Variational Mode Decomposition (VMD), Convolutional Neural Network (CNN), and Bidirectional Long Short-Term Memory (BiLSTM). By making full use of the symmetric structure of the BiLSTM algorithm, one part is used to process the data sequence in order, and the other part is used to process the data sequence in reverse order. It captures the characteristics of sequence data by simultaneously processing a ‘symmetric’ information. Firstly, the historical photovoltaic data are preprocessed, and the correlation analysis of meteorological factors is carried out by PCC, and the high correlation factors are extracted to obtain the multivariate time series feature matrix of meteorological factors. Then, the historical photovoltaic power data are decomposed into multiple intrinsic modes and a residual component at one time by CEEMDAN. The high-frequency components are clustered by K-means combined with sample entropy, and the high-frequency components are decomposed and refined by VMD to form a multi-scale characteristic mode matrix. Finally, the obtained features are input into the CNN–BiLSTM model for the final photovoltaic power prediction results. After experimental verification, compared with the traditional single-mode decomposition algorithm (such as CEEMDAN–BiLSTM, VMD–BiLSTM), the combined prediction method proposed reduces MAE by more than 0.016 and RMSE by more than 0.017, which shows excellent accuracy and stability. Full article

This study systematically investigates the formation mechanism and development characteristics of the “foamy oil” phenomenon during pressure depletion development of high-viscosity crude oil through a combination of physical experiments and numerical simulations. Using Venezuelan foamy oil as the research subject, an innovative heterogeneous pore-etched glass model was constructed to simulate the pressure depletion process, revealing for the first time that bubble growth predominantly occurs during the migration stage. Experimental results demonstrate that heavy components significantly delay degassing by stabilizing gas–liquid interfaces, while the continuous gas–liquid diffusion effect explains the unique development characteristics of foamy oil—high oil recovery and delayed phase transition—from a microscopic perspective. A multi-scale coupling analysis method was established: molecular-scale simulations were employed to model component diffusion behavior. By improving the traditional Volume of Fluid (VOF) method and introducing diffusion coefficients, a synergistic model integrating a single momentum equation and fluid volume fraction was developed to quantitatively characterize the dynamic evolution of bubbles. Simulation results indicate significant differences in dominant controlling factors: oil phase viscosity has the greatest influence (accounting for ~50%), followed by gas component content (~35%), and interfacial tension the least (~15%). Based on multi-factor coupling analysis, an empirical formula for bubble growth incorporating diffusion coefficients was proposed, elucidating the intrinsic mechanism by which heavy components induce unique development effects through interfacial stabilization, viscous inhibition, and dynamic diffusion. This research breaks through the limitations of traditional production dynamic analysis, establishing a theoretical model for foamy oil development from the perspective of molecular-phase behavior combined with flow characteristics. It not only provides a rational explanation for the “high oil production, low gas production” phenomenon but also offers theoretical support for optimizing extraction processes (e.g., gas component regulation, viscosity control) through quantified parameter weightings. The findings hold significant scientific value for advancing heavy oil recovery theory and guiding efficient foamy oil development. Future work will extend to studying multiphase flow coupling mechanisms in porous media, laying a theoretical foundation for intelligent control technology development. Full article