Search Results (606)

Industrial equipment fault diagnosis faces dual challenges: significant data distribution discrepancies caused by diverse operating conditions impair generalization capabilities, while underutilized spatio-temporal information from multi-source data hinders feature extraction. To address this, we propose a spatio-temporal collaborative perception-driven feature graph construction and topology mining methodology for variable-condition diagnosis. First, leveraging the operational condition invariance and cross-condition consistency of fault features, we construct fault feature graphs using single-source data and similarity clustering, validating topological similarity and representational consistency under varying conditions. Second, we reveal spatio-temporal correlations within multi-source feature topologies. By embedding multi-source spatio-temporal information into fault feature graphs via spatio-temporal collaborative perception, we establish high-dimensional spatio-temporal feature topology graphs based on spectral similarity, extending generalized feature representations into the spatio-temporal domain. Finally, we develop a graph residual convolutional network to mine topological information from multi-source spatio-temporal features under complex operating conditions. Experiments on variable/multi-condition datasets demonstrate the following: feature graphs seamlessly integrate multi-source information with operational variations; the methodology precisely captures spatio-temporal delays induced by vibrational direction/path discrepancies; and the proposed model maintains both high diagnostic accuracy and strong generalization capacity under complex operating conditions, delivering a highly reliable framework for rotating machinery fault diagnosis. Full article

The stability of modern power systems has become critically dependent on precise inertia estimation of synchronous generators, particularly as renewable energy integration fundamentally transforms grid dynamics. Increasing penetration of converter-interfaced renewable resources reduces system inertia, heightening the grid’s susceptibility to transient disturbances and creating significant technical challenges in maintaining operational reliability. This paper addresses these challenges through a novel Bayesian inference framework that synergistically integrates PMU data with an advanced MCMC sampling technique, specifically employing the Affine-Invariant Ensemble Sampler. The proposed methodology establishes a probabilistic estimation paradigm that systematically combines prior engineering knowledge with real-time measurements, while the Affine-Invariant Ensemble Sampler mechanism overcomes high-dimensional computational barriers through its unique ensemble-based exploration strategy featuring stretch moves and parallel walker coordination. The framework’s ability to provide full posterior distributions of inertia parameters, rather than single-point estimates, helps for stability assessment in renewable-dominated grids. Simulation results on the IEEE 39-bus and 68-bus benchmark systems validate the effectiveness and scalability of the proposed method, with inertia estimation errors consistently maintained below $1\%$ across all generators. Moreover, the parallelized implementation of the algorithm significantly outperforms the conventional M-H method in computational efficiency. Specifically, the proposed approach reduces execution time by approximately $52\%$ in the 39-bus system and by $57\%$ in the 68-bus system, demonstrating its suitability for real-time and large-scale power system applications. Full article

We review a metric theory of gravitation that combines Newtonian gravity with Lorentz invariance. Beginning with a conformastatic metric justified by the Weak Equivalence Principle. We describe, within the Newtonian approximation, the spacetime geometry generated by a static distribution of dust matter. To extend this description to moving sources, we apply a Lorentz transformation to the static metric. This procedure yields, again within the Newtonian approximation, the metric associated with moving bodies. In doing so, we construct a gravitational framework that captures key relativistic features—such as covariance under Lorentz transformations—while remaining rooted in Newtonian dynamics. This approach offers an alternative route to describing weak-field gravitational interactions, without relying directly on Einstein’s field equations. Full article

This paper presents a distributed optimization algorithm for time-varying objective functions utilizing a prescribed-time convergent multi-agent system within undirected communication networks. Departing from conventional time-invariant optimization paradigms with static optimal solutions, our approach specifically addresses the challenge of tracking dynamic optimal trajectories in evolving environments. A novel continuous-time distributed optimization algorithm is developed based on prescribed-time consensus, guaranteeing the consensus attainment among agents within a user-defined timeframe while asymptotically converging to the time-dependent optimal solution. The proposed methodology enables explicit predetermination of convergence duration, representing a significant advancement over existing asymptotic convergence methods. Moreover, two simulation examples on the rendezvous problem and multi-robots control are presented to validate the theoretical results, exhibiting precise time-controlled convergence characteristics and effective tracking performance for time-varying optimization targets. Full article

Extensive numerical simulations were performed in MATLAB R2020b based on the classical nonlinear Thomson scattering theory and single-electron model, to systematically examine the influence of initial phase in tightly focused linearly polarized laser pulses on the radiation characteristics of multi-energy-level electrons. Through our research, we have found that phase variation from 0 to 2π induces an angular bifurcation of peak radiation intensity, generating polarization-aligned symmetric lobes with azimuthal invariance. Furthermore, the bimodal polar angle decreases with the increase of the initial energy. This phase-controllable bimodal distribution provides a new solution for far-field beam shaping. Significantly, high-harmonic intensity demonstrates π-periodic phase-dependent modulation. Meanwhile, the time-domain pulse width also exhibits 2π-cycle modulation, which is synchronized with the laser electric field period. Notably, electron energy increase enhances laser pulse peak intensity while compressing its duration. The above findings demonstrate that the precise control of the driving laser’s initial phase enables effective manipulation of the radiation’s spatial characteristics. Full article

With the rapid development of digital technologies, data-driven methods have demonstrated commendable performance in the toxic text detection task. However, several challenges remain unresolved, including the inability to fully capture the nuanced semantic information embedded in text languages, the lack of robust mechanisms to handle the inherent uncertainty of text languages, and the utilization of static fusion strategies for multi-view information. To address these issues, this paper proposes a comprehensive and dynamic toxic text detection method. Specifically, we design a multi-view feature augmentation module by combining bidirectional long short-term memory and BERT as a dual-stream framework. This module captures a more holistic representation of semantic information by learning both local and global features of texts. Next, we introduce an entropy-oriented invariant learning module by minimizing the conditional entropy between view-specific representations to align consistent information, thereby enhancing the representation generalization. Meanwhile, we devise a trustworthy text recognition module by defining the Dirichlet function to model uncertainty estimation of text prediction. And then, we perform the evidence-based information fusion strategy to dynamically aggregate decision information between views with the help of the Dirichlet distribution. Through these components, the proposed method aims to overcome the limitations of traditional methods and provide a more accurate and reliable solution for toxic language detection. Finally, extensive experiments on the two real-world datasets show the effectiveness and superiority of the proposed method in comparison with seven methods. Full article

In recent years, domain generalization-based fault diagnosis (DGFD) methods have shown significant potential in rotating machinery fault diagnosis in unseen target domains. However, these methods focus on learning domain-invariant representations via feature distribution adaptation. The generalization of classifiers and the orthogonality between fault-related and domain-related features have not been thoroughly explored, which hinders further improvements in DGFD performance. To address these limitations, an episodic training and feature orthogonality-driven domain generalization (EODG) method is proposed. In this method, episodic training is introduced to jointly improve the generalization capabilities of both the feature extractor and fault classifier, while a novel feature transfer loss is proposed for learning domain-invariant representations. Furthermore, the orthogonality between fault-related and domain-related features is enhanced by minimizing their cosine similarity, thereby improving the generalization capability of the DGFD model. The experimental results validated the effectiveness and superiority of the proposed method on domain generalization-based fault diagnosis tasks. Full article

Poverty is a multifaceted phenomenon impacting millions globally, defined by a deficiency in both material and immaterial resources, which consequently restricts access to satisfactory living conditions. Comprehensive poverty analysis can be accomplished through the application of mathematical and modeling techniques, which are useful in understanding and predicting poverty trends. These models, which often incorporate principles from economics, stochastic processes, and dynamic systems, enable the assessment of the factors influencing poverty and the effectiveness of public policies in alleviating it. This paper introduces a mathematical compartmental model to investigate poverty within a population ($\psi \left(t\right)$), considering the effects of immigration, crime, and incarceration. The model aims to elucidate the interconnections between these factors and their combined impact on poverty levels. We begin the study by ensuring the mathematical validity of the model by demonstrating the uniqueness of a positive solution. Next, it is shown that under specific conditions, the probability of poverty persistence approaches certainty. Conversely, conditions leading to an exponential reduction in poverty are identified. Additionally, the semigroup associated with our model is proven to possess the Feller property, and its distribution has a unique invariant measure. To confirm and validate these theoretical results, interesting numerical simulations are performed. Full article

Estimating the 3D pose of a human body from monocular images is crucial for computer vision applications, but the technique remains challenging due to depth ambiguity and self-occlusion. Traditional methods often suffer from insufficient prior knowledge and weak constraints, resulting in inaccurate 3D keypoint estimation. In this paper, we propose a method for whole-body 3D pose estimation based on a Transformer architecture, integrating body mass distribution and center of gravity constraints. The method maps the pose to the center of gravity position using the anatomical mass ratio of the human body and computes the segment-level center of gravity using the moment synthesis method. A combined loss function is designed to enforce consistency between the predicted keypoints and the center of gravity position, as well as the invariance of limb length. Extensive experiments on the Human 3.6M WholeBody dataset demonstrate that the proposed method achieves state-of-the-art performance, with a whole-body mean joint position error (MPJPE) of 44.49 mm, which is 60.4% lower than the previous Large Simple Baseline method. Notably, it reduces the body part keypoints’ MPJPE from 112.6 to 40.41, showcasing the enhanced robustness and effectiveness to occluded scenes. This study highlights the effectiveness of integrating physical constraints into deep learning frameworks for accurate 3D pose estimation. Full article

To improve the surface morphology quality of ultra-precision optical components, particularly in the suppression of mid-spatial frequency (MSF) errors, this paper proposes a morphology gradient-aware spatiotemporal coupled smoothing model based on convolutional material removal. By introducing the Laplacian curvature into the surface evolution framework, a curvature-sensitive “peak-priority” mechanism is established to dynamically guide the local dwell time. A nonlinear spatiotemporal coupling equation is constructed, in which the dwell time is adaptively modulated by surface gradient magnitude, local curvature, and periodic fluctuation terms. The material removal process is modeled as the convolution of a spatially invariant removal function with a locally varying dwell time distribution. Moreover, analytical evolution expressions of PV, RMS, and PSD metrics are derived, enabling a quantitative assessment of smoothing performance. Simulation results and experimental validations demonstrate that the proposed model can significantly improve smoothing performance and enhance MSF error suppression. Full article

With the rapid development of the Internet, malicious URL detection has emerged as a critical challenge in the field of cyberspace security. Traditional machine-learning techniques and subsequent deep-learning frameworks have shown limitations in handling the complex malicious URL data generated by contemporary phishing attacks. This paper proposes a novel detection framework, HSSLC-CharGRU (Hybrid Spatial–Sequential Attention Logically constrained neural network CharGRU), which balances high efficiency and accuracy while enhancing the generalization capability of detection frameworks. The core of HSSLC-CharGRU is the Gated Recurrent Unit (Gated Recurrent Unit, GRU), integrated with the HSSA (Hybrid Spatial–Sequential Attention, HSSA) module. The HSSLC-CharGRU framework proposed in this paper integrates symmetry concepts into its design. The HSSA module extracts URL sequence features across scales, reflecting multi-scale invariance. The interaction between the GRU and HSSA modules provides functional complementarity and symmetry, enhancing model robustness. In addition, the LCNN module incorporates logical rules and prior constraints to regulate the pattern-learning process during feature extraction, reducing the model’s sensitivity to noise and anomalous patterns. This enhances the structural symmetry of the feature space. Such logical constraints further improve the model’s generalization capability across diverse data distributions and strengthen its stability in handling complex URL patterns. These symmetries boost the model’s generalization across datasets and its adaptability and robustness in complex URL patterns. In the experimental part, HSSLC-CharGRU shows excellent detection accuracy compared with the current character-level malicious URL detection models. Full article

Asymmetry in the supply voltage in three-phase circuits disrupts the flow of currents. This worsens the efficiency of the distribution system and increases the problems in determining the mathematical model of the energy system. Among many power theories, the most accurate is the Currents’ Physical Components (CPC) power theory, which tries to justify the physical essence of each component. Such knowledge can be used to improve efficiency and reduce transmission losses in the power system. This article discusses the method of mathematical decomposition of current components in the case of a three-wire line connecting an asymmetric power source with linear time-invariant (LTI) loads. Special cases where irregularities appear in the results of calculations according to the CPC theory are discussed. The problem of equivalent conductance in the case of a non-zero value of the constant voltage component is discussed. The method of determining symmetrical components for periodic non-sinusoidal waveforms is also discussed. These considerations are supported by numerical examples. Full article

Surface cracks serve as early warning signals for potential geological hazards, and their precise segmentation is crucial for disaster risk assessment. Due to differences in acquisition conditions and the diversity of crack morphology, scale, and surface texture, there is a significant domain shift between different crack datasets, necessitating transfer training. However, in real work areas, the sparse distribution of cracks results in a limited number of samples, and the difficulty of crack annotation makes it highly inefficient to use a high proportion of annotated samples for transfer training to predict the remaining samples. Domain adaptation methods can achieve transfer training without relying on manual annotation, but traditional domain adaptation methods struggle to effectively address the characteristics of cracks. To address this issue, we propose an unsupervised domain adaptation method for crack segmentation. By employing a hierarchical adversarial mechanism and a prediction entropy minimization constraint, we extract domain-invariant features in a multi-scale feature space and sharpen decision boundaries. Additionally, by integrating a Mix-Transformer encoder, a multi-scale dilated attention module, and a mixed convolutional attention decoder, we effectively solve the challenges of cross-domain data distribution differences and complex scene crack segmentation. Experimental results show that UCrack-DA achieves superior performance compared to existing methods on both the Roboflow-Crack and UAV-Crack datasets, with significant improvements in metrics such as mIoU, mPA, and Accuracy. In UAV images captured in field scenarios, the model demonstrates excellent segmentation Accuracy for multi-scale and multi-morphology cracks, validating its practical application value in geological hazard monitoring. Full article

In this paper, I investigate the gluon distributions for the kaon and pion, as well as the improvement of the valence-quark distributions, in the framework of the gauge-invariant nonlocal chiral quark model (NL$\chi$QM), where the momentum dependence is taken into account. I then compute the gluon distributions for the kaon and pion that are dynamically generated from the splitting functions in the Dokshitzer–Gribov–Lipatov–Altarelli–Parisi (DGLAP) QCD evolution. In a comparison with the recent lattice QCD and JAM global analysis results, it is found that the results for the pion gluon distributions at $Q=$ 2 GeV, which is set based on the lattice QCD, have a good agreement with the recent lattice QCD data; this is followed up with the up valence-quark distribution of the pion results at $Q=$ 5.2 GeV in comparison with the reanalysis experimental data. The prediction for the kaon gluon distributions at $Q=2$ GeV is consistent with the recent lattice QCD calculation. Full article

Infrared video satellites have the characteristics of wide-area long-duration surveillance, enabling continuous operation day and night compared to visible light imaging methods. Therefore, they are widely used for continuous monitoring and tracking of important targets. However, energy attenuation caused by long-distance radiation transmission reduces imaging contrast and leads to the loss of edge contours and texture details, posing significant challenges to target tracking algorithm design. This paper proposes an infrared small-target tracking method, the UIMM-Tracker, based on the tracking-by-detection (TbD) paradigm. First, detection uncertainty is measured and injected into the multi-model observation noise, transferring the distribution knowledge of the detection process to the tracking process. Second, a dynamic modulation mechanism is introduced into the Markov transition process of multi-model fusion, enabling the tracking model to autonomously adapt to targets with varying maneuvering states. Additionally, detection uncertainty is incorporated into the data association method, and a distance cost matrix between trajectories and detections is constructed based on scale and energy invariance assumptions, improving tracking accuracy. Finally, the proposed method achieves average performance scores of 68.5%, 45.6%, 56.2%, and 0.41 in IDF1, MOTA, HOTA, and precision metrics, respectively, across 20 challenging sequences, outperforming classical methods and demonstrating its effectiveness. Full article