Search Results (50)

Ground Penetrating Radar (GPR) is an important non-destructive sensing technology for subsurface detection. However, strong clutter from surface reflection and heterogeneous media often masks weak target responses in B-scan images. Existing low-rank and sparse decomposition (LRSD) methods, such as Robust Principal Component Analysis (RPCA), can separate clutter and targets but suffer from uniform singular value shrinkage and high computational cost; more critically, they operate only at the original resolution, refining only the spatial sampling grid of the migrated image under the inherent physical resolution limit. In this paper, we propose a Joint Super-Resolution and Weighted Low-Rank Sparse Decomposition (JSR-LRSD) framework that synergistically suppresses clutter and improves the spatial detail and target localization of the migrated image without extending its physical resolution. Unlike cascade approaches, which perform clutter removal first and super-resolution afterward and may amplify residual clutter, our method integrates a differentiable super-resolution network directly into the alternating direction method of multipliers (ADMM) iterations. This allows for the sparse target matrix to be regularized by high-resolution image priors, while the high-resolution estimate adaptively adjusts the weighted nuclear norm via total variation feedback. An adaptive weighted nuclear norm is designed using singular values of the observed data and the total variation feedback from the high-resolution target estimate. A randomized singular value decomposition (SVD) is introduced to accelerate computation. Extensive experiments on simulated and laboratory GPR data demonstrate that the proposed JSR-LRSD achieves a higher signal-to-clutter ratio (SCR) compared to conventional and LRSD-based methods, with clear improvement in spatial detail recovery, while maintaining computational efficiency. Full article

The increasing penetration of inverter-based resources and the growing complexity of modern power systems have made voltage stability assessment a critical aspect of transmission network operation. This paper presents a review of the main methods and indices adopted for voltage stability assessment, with emphasis on their applicability to real-time monitoring and inverter-dominated grids. The review covers both long-term and short-term voltage stability approaches, including L-index methods, Jacobian- and singular-value-based techniques, Lyapunov-exponent-based methods, trajectory-based indices, and recent artificial intelligence applications. For each method, the main advantages, limitations, computational requirements, and suitability for PMU-based monitoring are discussed. The analysis shows that no single indicator can fully describe all voltage instability phenomena. Instead, combining complementary static and dynamic indices provides a more complete basis for wide-area monitoring and real-time operational support in future power systems. Moreover, the main open challenges related to threshold definition, robustness under evolving grid conditions, and the integration of data-driven techniques into operational stability assessment schemes are discussed. Full article

This paper deals with the use of the singular boundary method for analysis of time-harmonic acoustic scattering by penetrable obstacles. There are many formulations of boundary-type meshless methods, distinguished mainly by the manner in which the singularity of the fundamental solution at the source point is treated. The article presents the singular boundary method (SBM), a boundary-only, integration-free collocation technique in which the source points are placed directly on the physical boundary, so that no volume mesh, no auxiliary boundary and no element connectivity are required. The scattered field in the exterior and the transmitted field inside each obstacle are each represented by a single layer of fundamental solutions. The determination of the origin intensity factors (OIFs), which replace the singular self-interaction of the single-layer fundamental solution and of its normal derivative, is the crucial part of the method. A closed-form OIF is employed for the Dirichlet boundary condition and a purely geometric OIF based on the signed boundary curvature is employed for the Neumann boundary condition, so that only two boundary operators are required instead of the four operators used in the direct formulation. The accuracy of the method has been compared with the analytical Mie series and the third-order deltaBEM. Third-order convergence and reasonable accuracy, when compared to the exact solution, are obtained throughout. Full article

The increasing penetration of renewable generation and flexible loads has made modern power systems operate under highly variable and diverse conditions. For power-system planning studies, static power-system analysis plays an important role in characterizing the security and stability behavior of these operating conditions. In such planning tasks, annual or long-term hourly datasets are often needed to capture temporal variations in renewable generation, load, and power-flow patterns, but performing power-flow-based static analysis for every operating condition can be computationally expensive, especially for targets that require repeated power-flow-based calculations. Therefore, an effective operating-condition reduction framework is needed to select a compact yet representative subset and reconstruct the overall static-analysis profile required for variation trends and distribution analysis. To address this problem, this paper proposes a problem-driven scenario reduction framework based on batch-attention-based self-supervision feature selection (A-SFS) for simplifying large-scale power-flow-based static analysis. Instead of clustering operating conditions only according to their geometric similarity in the original feature space, the proposed framework incorporates the downstream static-analysis target into the reduction process. Target values are first computed for only a small portion of the operating-condition dataset, and A-SFS is then used to learn target-relevant features and their importance weights. Based on the learned weighted feature space, all operating conditions are clustered using weighted K-means++, and the actual operating condition closest to each cluster centroid is selected as the representative scenario. The downstream target evaluation is then performed only on these representative scenarios, and their target values are assigned to the operating conditions within the same clusters to reconstruct the overall target-value profile of the full dataset. The proposed framework is validated on a yearly RTS-GMLC operating-condition dataset using two representative static-analysis targets, namely load margin and the minimum singular value of the power-flow Jacobian, ${\sigma }_{min}$. The results show that the proposed target-aware clustering framework can effectively reconstruct the overall static-analysis profile of the full operating-condition dataset while preserving the relative ranking of different operating conditions. In the best-M comparison, the proposed method achieves MAPEs of 19.56% for load margin and 12.85% for ${\sigma }_{min}$, with corresponding Spearman coefficients of 0.8380 and 0.8755, respectively. Full article

With the increasing penetration of distributed renewable energy sources, such as photovoltaic and wind power, their strong randomness and volatility pose significant challenges to distribution network operation and control. Simultaneously, missing and noisy source-load data in practical distribution network operation further constrain the accuracy of optimization decisions. To address these issues, this paper proposes a data-driven optimization method that integrates low-rank limited-information reconstruction, WGAN-GP-based scenario generation, and source–storage–load coordinated dispatch. Firstly, a low-rank matrix completion model solved by singular value thresholding (SVT) is used to reconstruct incomplete photovoltaic and load profiles. Secondly, a Wasserstein generative adversarial network with gradient penalty (WGAN-GP) is trained on the reconstructed dataset to generate renewable-output scenarios, and five representative scenarios are retained through conditional scenario matching and averaging. Finally, a mixed-integer linear programming (MILP) dispatch model is established by considering energy-storage operating constraints, demand response constraints, and time-of-use electricity prices. The numerical case uses 60 daily profiles with 24 hourly points per day and a 20% random missing-data setting. Case study results show that the proposed reconstruction method reduces the overall RMSE from 177.15 kW to 52.40 kW compared with zero-fill processing. The coordinated dispatch decreases the daily operating cost from 10,060.36 CNY to 9414.67 CNY, corresponding to a 6.42% cost reduction. The limitations of the single-test-day benchmark and simplified active-power dispatch validation are also discussed. Full article

The increasing penetration of renewable energy exposes doubly fed induction generator (DFIG)-based wind power systems to weak-grid conditions, making them susceptible to low-frequency and subsynchronous oscillations. Although grid-forming (GFM) control enhances weak-grid adaptability, the resulting high-order small-signal model complicates stability analysis and controller design. This paper establishes a 15th-order state-space model for a GFM-DFIG system. Eigenvalue analysis is performed to identify the dominant oscillation modes and to reveal their sensitivity to controller parameters and grid strength. To reduce computational burden, a model order reduction method combining singular perturbation theory and participation factor analysis is proposed, yielding an eighth-order model that preserves dominant oscillatory characteristics. An additional damping control strategy is then designed using the reduced model. Simulations validate the reduced model’s accuracy and demonstrate the damping control’s effectiveness in mitigating oscillations. This paper provides an effective framework for stability analysis, reduced-order modeling, and damping control design for GFM-DFIG systems. Full article

The research examines regional disparities in the diffusion of e-commerce among enterprises employing at least 10 people in Italy, using an integrated analytical framework that blends econometric modeling, machine learning, and network analysis. Instrumental Variable (IV) panel models overcome endogeneity arising from digital infrastructure, socioeconomic factors, and online business activity, with geographic slope as a suitable instrument for broadband penetration. Machine learning models—regularized regressions, random forests, and boosting—augment causal inference by registering nonlinear effects and sorting variable salience. The results, in all cases, emphasize internet use, household digital connectivity, and the prevalence of remote work as the most important predictors of the diffusion of e-commerce. Cluster analysis identifies regional digital profiles that distinguish northern-central regions from southern-insular regions, characterizing persistently distinct digital divides. The network analysis, in turn, identifies digital inclusion variables—such as internet penetration and ICT infrastructure—that occupy central positions within the entirety of the economic and technological interdependencies’ regime. Innovation and income levels, while practiced, hold peripheral positions, indicating that digital capacity, rather than economic affluence in the singular, drives online business participation. Italy’s case can particularly illustrate this beyond its national borders. Being a high-income economy with significant regional disparities, it reproduces challenges common elsewhere in the world, among which the cases of Spain, Germany, the USA, the Republic of Korea, and Japan come to mind, where regional disparities inhibit inclusive digital development. The Italian case presents, then, a transferable model for the diffusion of digital tools, the reduction in regional disparities, and the encouragement of economic integration. By synthesizing the causal, predictive, and systemic methodologies, the study offers a theoretical and practical response to digital transformation across diverse terrains. Full article

Ballast compaction is a critical factor influencing ballast bed condition and the operational safety of heavy-haul railways. However, existing quantitative evaluation methods often suffer from overly idealized simulation models and limitations in signal processing and assessment frameworks. To address these issues, this study proposes a quantitative analysis approach for ballast compaction by integrating non-uniform medium simulation modeling, wavelet–Singular Value Decomposition (SVD) joint denoising, frequency–wavenumber (F-K) migration imaging, and wavelet packet decomposition (WPD)-based feature extraction. Forward simulations were conducted based on the constructed model, and the proposed methodology was validated using 1.5 GHz (gigahertz, 1 GHz = ${10}^9$ Hz) ground penetrating radar (GPR) data acquired from compaction experiments. The results demonstrate that wavelet–SVD joint denoising effectively suppresses deep coherent noise caused by strong reflections from sleepers, significantly enhancing the identification of deep effective signals and ensuring accurate localization and feature extraction of compaction zones. The Geometric Mean of WPD High/Low-Frequency Energy Ratio (GMHLFER) exhibits a strong positive correlation with the degree of compaction. In simulations, as the proportion of compacted material increased from $9\%$ to $21\%$, the GMHLFER rose from 21.555 to 26.581. In field tests, the value increased from 22.012 to 26.012 as compaction severity progressed from slight to severe, demonstrating stable responses across full-gradient compaction conditions and indicating high robustness and sensitivity. The proposed method provides an effective approach for quantitative characterization of ballast compaction in heavy-haul railways, and offers a promising technical pathway for intelligent inspection and condition assessment of railway ballast beds. Full article

Severe drill string vibrations, particularly stick–slip, significantly compromise drilling efficiency and tool longevity in deep hard formations. Compound percussive drilling (CPD) has emerged as a promising technique to mitigate these vibrations and enhance the rate of penetration (ROP). However, the complex coupling mechanisms between impact loads and bit dynamics remain insufficiently understood. This study aims to elucidate the axial–torsional vibration characteristics of the drill bit and the underlying vibration reduction mechanisms under CPD conditions. A multi-degree-of-freedom (MDOF) dynamic model was first established, integrating both the dynamics of the CPD tool and the regenerative cutting effects inherent in bit–rock interactions. The governing equations were then solved numerically using the fourth-order Runge–Kutta method, followed by a systematic parametric sensitivity analysis to quantify the influence of impact parameters on vibration mitigation. The results show that while CPD induces detrimental axial–torsional vibrations in soft rock formations, it effectively suppresses stick–slip and enhances ROP in hard rock formations. Notably, coupled axial–torsional impact loading exhibits superior vibration suppression capabilities compared to singular axial or torsional impacts. A critical proportional relationship for parameter optimization was identified; specifically, maximizing vibration mitigation requires scaling the axial impact load proportionally with the torsional impact load. For example, when the axial impact load amplitudes are 5 kN and 10 kN, the corresponding optimal torsional impact load amplitudes are approximately 500 N·m and 1000 N·m, respectively. Furthermore, maintaining the impact frequency within the range of 10–30 Hz yields optimal vibration reduction effects. The benefits of CPD become increasingly pronounced with higher rock strength and longer drill strings. These findings confirm the suitability of CPD technology for deep hard rock environments and provide theoretical guidelines for the optimal selection of impact parameters in engineering applications. Full article

To address the node voltage fluctuation and over-limit caused by the high penetration of distributed photovoltaic (PV) generation connected to distribution networks, this paper proposes a network partition-based optimal reactive power allocation and sizing method in the active distribution network (ADN). A network index incorporating network partition and critical node identification is introduced to obtain the optimal location for the reactive power compensation. A singular value entropy-based adaptive spectral clustering algorithm is applied to obtain the initial zones and obtain the critical nodes of each zone on the basis of the proposed network indexes. This method avoids the unreasonable scheme and enhances the efficiency and clarity of partitioning. The improved decimal coding method is proposed to improve the efficiency of the proposed method. A case study on the IEEE 33-node distribution system is carried out to verify the feasibility and effectiveness of the proposed method. The results show that compared with the conventional methods, the proposed method can effectively reduce voltage variations and control the voltage within the safe limit. Full article

In the context of the rapid advancement of reconstruction and expansion projects, there has been a significant increase in the demand for the inspection and evaluation of existing asphalt pavements. In order to enhance the efficiency and effectiveness of joint detection using 3D ground-penetrating radar and falling weight deflectometers, this study investigates non-destructive testing methods for existing asphalt pavements based on geostatistical correlation principles. The relationship between crack rate and deflection is analyzed using group average values. The characteristic sections division method based on the crack rate guideline was realized. Research on the prediction method for deflection using Kriging interpolation has been conducted. Research has revealed that there is a positive correlation between the crack rate and the deflection index. The principle of the singularity index can be employed to divide characteristic sections. The falling weight deflectometer is capable of conducting targeted testing in accordance with characteristic sections. Furthermore, the superior performance of Kriging interpolation in predicting deflection compared with linear interpolation has been demonstrated. According to the Kriging interpolation principle, the detection interval of slow lane deflection should not be more than 100 m. Kriging interpolation on one way lane of matrix data has the best effect, and it can predict deflection using a limited amount of slow lane and hard shoulder data. This facilitates analysis of the changing trend of the deflection index in cases where detection conditions are constrained. This method is of great significance for grasping the true performance status of the existing asphalt pavement structure. Full article

The acoustic signals of the drill pipe of a seafloor drill present weak features under noise interference such as marine environmental noise and the mechanical vibration of the seafloor drill. Accurately extracting the features of the weak acoustic signals of a drill pipe under a strong background noise is an effective means of realizing wireless acoustic communication for a seafloor drill. However, the existing short-time Fourier transform and wavelet transform methods have the defects of fixed window length, wavelet basis function, and decomposition layers, which lead to the inability to accurately extract the weak acoustic signal features of a drill pipe. To overcome these challenges, this study investigates the application of S-transform (ST) in the weak acoustic signal feature extraction of a seafloor drill pipe based on its fundamental principles. Firstly, a time-frequency analysis of the drill pipe’s acoustic signal using ST is conducted, which yields the distribution of the signal across the time and frequency axes. Secondly, singular value decomposition (SVD) is applied to mitigate the noise within the time-frequency matrix. Finally, the noise-reduced time-frequency matrix is analyzed to extract the subtle features of the acoustic wave present within the signal. In order to more accurately assess the differences between the different time-frequency analysis methods in the extraction of weak acoustic wave signals, short-time Fourier transform, wavelet transform, and ST are used to extract the weak acoustic wave characteristics of the drill pipe, respectively. The results show that the ST-based method can effectively improve the accuracy of weak acoustic wave signal feature extraction and provide strong support for reliable transmission of cone penetration test data from the seafloor drill. Full article

By leveraging amplitude differences between reflected and diffracted signals in Ground Penetrating Radar (GPR) data, multiple singular spectrum analysis (MSSA) is considered an attractive approach to separate diffraction, which has identified great potential in their detectability of small-scale geological structures. However, conventional MSSA encounters difficulties in pinpointing the singular value threshold that corresponds to reflection, diffraction, and noise within the singular spectrum, leading to a resolution loss of the extracted diffraction profile. To address this issue, this paper develops a new technique that incorporates multilevel wavelet transform (MWT) and MSSA to separate GPR diffraction. By first implementing the MWT on GPR data decompose, the strategy can obtain various approximate detailed coefficients of multiple transformation levels for the subsequent inverse MWT to construct the corresponding coefficient profile. The issue of coefficient profiles that depict reflections often contains residual diffractions is also addressed by performing multiple singular spectrum SVDs based on the Hankel matrix within the dominant frequency domain. Building upon this, the k-means clustering algorithm is introduced to perform MSSA for classifying singular values into k categories. The diffraction wavefield is rebuilt by combining these outcomes with the coefficient profiles that depict diffractions at various transformation levels. Numerical tests showcase that the biorthogonal wavelet basis function bior4.4 provides remarkably efficient GPR diffraction separation performance, and the number of clusters in the k-means clustering algorithm typically ranges from 9 to 15, accounting for the complexity of the wave components. Compared to plane wave deconstruction (PWD), the proposed MWT-MSSA approach reduces energy loss at the diffraction vertex, decreases residual diffraction energy within the reflection profile, and enhances computational efficiency by approximately 70–80% to facilitate the subsequent subtle imaging. Full article

Cone penetration test often uses the cone factor to calculate the undrained shear strength of silty clay base on the cone tip resistance data, but the accurate determination of the cone factor is challenging due to its wide range of values. This study conducted a laboratory cone penetration test and vane shear test to investigate and calibrate the cone factor for evaluating the undrained shear strength of silty clay at various depths. The cone factor is first identified based on the laboratory cone penetration test and vane shear test, and it is compared with the cone factor generated from the cavity expansion theory. Cone factor calibration has been performed by integrating laboratory model tests with the cavity expansion method, unlike conventional approaches relying on singular methodologies. The proposed cone factor is validated by the in situ undrained shear strength of Shanghai silty clay based on the in situ cone penetration test data. The results indicate that the cone factor varies significantly, ranging from 3 to 27. The cone factor from laboratory results shows good agreement with that based on the cavity expansion method. The calibrated cone factor predicts reasonable undrained shear strength measured from in situ tests. The refining method enables ±30% accuracy in predicting field-measured undrained shear strength values, establishing region-specific guidelines for East China Sea investigations. Full article

The electrical penetration graph (EPG) technique is of great significance in elucidating the mechanisms of virus transmission by piercing-sucking insects and crop resistance to these insects. The traditional method of manually processing EPG signals encounters the drawbacks of inefficiency and subjectivity. This study investigated the data augmentation and automatic identification of various EPG signals, including A, B, C, PD, E1, E2, and G, which correspond to distinct behaviors exhibited by the Asian citrus psyllid. Specifically, a data augmentation method based on an improved deep convolutional generative adversarial network (DCGAN) was proposed to address the challenge of insufficient E1 waveforms. A multi-criteria evaluation framework was constructed, leveraging maximum mean discrepancy (MMD) to evaluate the similarity between the generated and real data, and singular value decomposition (SVD) was incorporated to optimize the training iterations of DCGAN and ensure data diversity. Four models, convolutional neural network (CNN), K-nearest neighbors (KNN), decision tree (DT), and support vector machine (SVM), were established based on DCGAN to classify the EPG waveforms. The results showed that the parameter-optimized DCGAN strategy significantly improved the model accuracies by 5.8%, 6.9%, 7.1%, and 7.9% for DT, SVM, KNN, and CNN, respectively. Notably, DCGAN-CNN effectively addressed the skewed distribution of EPG waveforms, achieving an optimal classification accuracy of 94.13%. The multi-criteria optimized DCGAN-CNN model proposed in this study enables reliable augmentation and precise automatic identification of EPG waveforms, holding significant practical implications for understanding psyllid behavior and controlling citrus huanglongbing. Full article