Search Results (385)

Autonomous driving in dense traffic demands policies that ensure safety, accurate path tracking, and ride comfort, yet reinforcement learning (RL) alone suffers from low sample efficiency and weak safety guarantees, while classical artificial potential field (APF) methods lack adaptability to dynamic scenarios. This paper proposes PIPF-TD3, which integrates APF theory with the Twin Delayed Deep Deterministic Policy Gradient (TD3) by embedding composite potential values and Doppler-weighted gradients as physics-informed features into the state vector. A Hybrid A* planner generates a reference path encoded as an attractive field; repulsive fields model nearby obstacles using real-time perception data; and a multi-objective reward function jointly optimizes path tracking, collision avoidance, and ride comfort. Experiments in CARLA 0.9.14 across two scenarios—a highway segment with mixed obstacles and a signalized intersection with conflicting turning movements—show that PIPF-TD3 achieves 100% task completion with zero collisions, whereas TD3 without potential field guidance suffers a 90% collision rate. PIPF-TD3 reduces mean cross-track error to 0.12 m (72.1% reduction over the rule-based FSM baseline), maintains 67.0% larger safety clearance, and yields RMS longitudinal and lateral accelerations of 1.12 and 0.75 m/s², outperforming the FSM by 37.1% and 42.7%. These results confirm that Doppler-weighted physical priors substantially enhance RL-based driving safety and quality in complex traffic conditions. Full article

In the era of urban stock development, the regeneration of historic districts must abandon the misguided approach of large-scale, sweeping transformations and shift toward a micro-regeneration model characterized by small-scale, precise, and incremental interventions. However, as urban renewal enters this stock-based phase, the issues of “physical dissonance” and “cultural discontinuity” in the heritage landscapes of historic districts are becoming increasingly pronounced. To solve this problem, this paper aims to identify the heritage landscape genes of historical districts, explore the characteristics of historical districts, provide operational targets for the micro-renewal of historical districts, guide the implementation of micro-regeneration policies of historical districts, and then improve the quality of historical district heritage landscapes. Taking the Chinese Baroque Block in Harbin as an example, this paper proposes a genetic recognition method for the heritage landscape of historical districts based on the spatial translation of historical information, spatial topology analysis, an improved U-Net deep learning model, and text mining theme analysis. The micro-regeneration path of historical blocks of “gene identification-feature mining-targeted operation-quality improvement” is proposed. The micro-regeneration countermeasures of “gene replacement and texture repair in open space, gene repair and targeted acupuncture in street and alley, gene embedding and catalyst adjustment in courtyard layout, gene recombination and embroidery treatment of architectural style, and retrospective and contextual narrative of intangible genes” are formulated. The heritage landscape gene of historical districts is conducive to the refined control of the characteristics and quality of historical districts and provides new ideas for the micro-regeneration of historical districts in the stock era. Full article

As Global Navigation Satellite Systems (GNSSs) underpin safety-critical infrastructure, their vulnerability to sophisticated spoofing attacks poses severe physical layer security risks. To address the limitations of existing single-antenna defense mechanisms, this paper proposes a rigorous instantaneous spoofing detection framework utilizing a novel “one-primary-two-auxiliary” three-antenna configuration. By embedding the rigid baseline length as a hard geometric constraint into the Integer Least Squares (ILS) model, we derive a specialized constrained LAMBDA algorithm that significantly shrinks the ambiguity search space. A rigorous hypothesis testing mechanism is established based on the Sum of Squared Residuals (SSR), analytically deriving the detection threshold from the central Chi-square distribution and analyzing the sensitivity via the non-central parameter. Through conducting field experiments using commercial receivers and professional GNSS signal simulators, the proposed method was validated using both single-satellite spoofing and full-constellation spoofing scenarios. Results demonstrate that the system achieves a Minimum Detectable Deviation (MDD) of spatial direction as low as $0.33^{\circ }$ and maintains an empirical detection rate of >99% with a negligible false alarm rate. Notably, the method exhibits instantaneous response capabilities, effectively identifying both single-satellite and full-constellation spoofing attacks within a single epoch without requiring prior attitude information or external aiding. Full article

High-speed near-ground flight presents critical challenges for large-inertia UAVs carrying payloads, including complex obstacles and communication-denied environments. Unlike agile small drones, these platforms require both rapid path planning and strict adherence to trajectory tracking constraints for safe obstacle avoidance. This paper proposes a two-stage autonomous navigation framework tailored for large-inertia UAVs. The framework integrates: (1) an enhanced LiDAR model with physical optical noise for improved simulation fidelity; (2) an ESDF + OctoMap dual-map construction method supporting global search and local optimization; and (3) a global BIT* planner combined with a B-spline local optimizer embedding dynamic, smoothness, and tracking accuracy constraints to ensure path feasibility and trackability. Simulation results demonstrate an average planning time of 0.86 ms, outperforming NAVIGATION, Informed RRT*, MPC Planner, and ESDF Optimization by 29.6–52.0%, with a 100% obstacle avoidance success rate and trajectory tracking RMSE of 0.28 m over a 350 m flight distance, along with strong parameter and noise robustness. Actual flight tests on a 9.4 kg quadrotor UAV confirm the algorithm’s effectiveness in map construction, path planning, and obstacle avoidance in environments with 15 obstacles, while maintaining computational overhead suitable for onboard deployment. These results establish the proposed framework as an effective solution for high-speed autonomous navigation of large-inertia UAVs in complex near-ground environments. Full article

To improve unified modeling of steady two-dimensional lid-driven cavity flow across a wide range of Reynolds numbers, this study proposes a Vorticity-Enhanced Physics-Informed Neural Network (VE-PINN). The method augments a standard velocity-pressure PINN with a vorticity-transport residual and uses a logarithmic Reynolds-number embedding, ${\mathit{log}}_{10}Re$, for multi-regime training. Using CFD benchmark data as supervision and evaluation, we conduct systematic ablation studies on network architecture, loss weighting, sampling density, input embedding, and physical constraint over $Re=1000-50000$, together with out-of-range extrapolation tests. The results show that the logarithmic Reynolds-number embedding improves cross-regime training stability and reduces the multi-regime mean relative error, while the vorticity-transport constraint improves the reconstruction of velocity fields and secondary vortical structures with only a modest increase in training cost. Further comparisons based on contour fields, centerline velocity profiles, vortex-core locations, and vorticity intensity indicate that VE-PINN provides improved accuracy, physical consistency, and generalization relative to the baseline PINN in the present benchmark. These findings suggest that, for the steady cavity-flow problem considered here, combining logarithmic parameter embedding with derivative-level physical constraint is a practical and effective strategy for parametric PINN modeling. Full article

Against the global carbon neutrality target, predictive maintenance (PdM) of automotive engines represents a core technical strategy to advance the sustainable development of the automotive industry. Conventional single-modal diagnostic approaches for engine abnormal sound defects suffer from low accuracy and weak anti-interference capability. Existing multi-modal fusion methods fail to deeply mine the physical coupling between cross-modal features and often entail excessive model complexity, hindering deployment on resource-constrained on-board edge devices. To resolve these limitations, this study proposes a Physical Prior-Embedded Cross-Modal Attention (PPE-CMA) mechanism for lightweight multi-modal fusion diagnosis of engine abnormal sound defects. First, wavelet packet decomposition (WPD) and mel-frequency cepstral coefficients (MFCC) are integrated to extract time-frequency features from engine audio signals, while a channel-pruned ResNet18 is employed to extract spatial features from engine thermal imaging and vibration visualization images. Second, the PPE-CMA module is designed to adaptively assign attention weights to audio and image features by exploiting the physical coupling between engine fault acoustic and visual characteristics, enabling efficient cross-modal feature fusion with redundant information suppression. A rigorous theoretical derivation is provided to link cosine similarity with the physical correlation of engine fault acoustic-visual features, justifying the attention weight constraint (β = 1 − α) from the perspective of fault feature physical coupling. Third, an improved lightweight XGBoost classifier is constructed for fault classification, and a hybrid data augmentation strategy customized for engine multi-modal data is proposed to address the small-sample challenge in industrial applications. Ablation experiments on ResNet18 pruning ratios verify the optimal trade-off between diagnostic performance and computational efficiency, while feature distribution analysis validates the authenticity and effectiveness of the hybrid augmentation strategy. Experimental results on a self-constructed multi-modal dataset show that the proposed method achieves 98.7% diagnostic accuracy and a 98.2% F1-score, retaining 96.5% accuracy under 90 dB high-level environmental noise, with an end-to-end inference speed of 0.8 ms per sample (including preprocessing, feature extraction, and classification). Cross-engine and cross-domain validation on a 2.0T diesel engine small-sample dataset and the open-source SEMFault-2024 dataset yield average accuracies of 94.8% and 95.2%, respectively, demonstrating strong generalization. This method effectively enhances the accuracy and robustness of engine abnormal sound defect diagnosis, offering a lightweight technical solution for on-board real-time fault diagnosis and in-plant online quality inspection. By reducing engine fault-induced energy loss and spare parts waste, it further promotes energy conservation and emission reduction in the automotive industry. Quantified experimental data on fuel efficiency improvement and carbon emission reduction are provided to substantiate the ecological benefits of the proposed framework. Full article

Sea-surface reflections and wind–wave motion render maritime channels strongly time-varying and statistically non-stationary, while nearshore deployments face sparse infrastructure and co-channel multiuser interference. This study integrates reconfigurable intelligent surfaces (RISs) with rate-splitting multiple access (RSMA) for joint online resource allocation. A physics-inspired time-varying channel model is established by embedding fractional Brownian motion-driven slow statistical drift and reflection-phase perturbations. With imperfect, delayed channel state information (CSI) and discrete RIS phase quantization, a proportional-fairness utility maximization problem is formulated to jointly optimize shore base-station precoding, RIS phase shifts, and RSMA common-rate allocation. To cope with strong non-convexity, high dimensionality, mixed continuous–discrete coupling, and partial observability, a fractal-aware recurrent robust Actor–Critic (FRRAC) algorithm is developed. FRRAC encodes short observation histories using a gated recurrent unit and incorporates a lightweight Hurst-proxy estimator to capture slow channel statistics for robust value evaluation and policy learning. Truncated quantile critics and mixed prioritized–uniform replay further improve value robustness, training stability, and sample efficiency. Simulation results show that FRRAC converges faster and more stably under both conventional and fractal non-stationary channel modeling, and outperforms representative baselines across the objective and multiple statistical metrics, validating its effectiveness for joint resource optimization in maritime RIS–RSMA systems. Full article

This work develops a zero-trust, physics-constrained mathematical framework for detecting stealthy drift perturbations in power system dynamical models. Such perturbations constitute adversarial, statistical deviations that preserve first-order operating trends, making them difficult to identify using classical residual-based estimators or unconstrained data-driven models. We introduce ZETWIN, a spatio-temporal learning architecture formulated as a constrained optimization problem in which the nodal admittance matrix $Y_{bus}$ acts as a graph-structured linear operator embedded directly into the loss functional. This construction enforces Kirchhoff-consistent latent representations and yields a mathematically grounded zero-trust decision rule that flags any trajectory violating physical feasibility, independent of prior attack signatures. The proposed framework is evaluated using a PyPSA-based AC–DC meshed network, demonstrating an AUROC = 0.994, and F1 = 0.969. The formulation highlights how physics-informed constraints, graph operators, and spatio-temporal approximation theory can be combined to construct mathematically interpretable zero-trust detectors for complex dynamical systems. Full article

In structural health monitoring, purely data-driven methods for deformation prediction are often susceptible to time-varying boundary conditions under complex operating scenarios, leading to insufficient physical interpretability and limited generalization across different conditions. To address these challenges, this study proposes a Physics-Informed Dual-branch Long Short-Term Memory framework (PINN-DualSHM). The framework employs dual-branch LSTMs to separately extract temporal features of structural mechanical responses and environmental thermal effects. Dynamic decoupling and fusion of these heterogeneous features are achieved through an adaptive cross-attention mechanism. Furthermore, physical priors, including the thermodynamic superposition principle and structural settlement monotonicity, are embedded into the loss function as regularization terms, complemented by a dual uncertainty quantification system based on heteroscedastic regression and MC Dropout. Experimental results based on long-term measured data from an industrial base project in Shenzhen demonstrate that PINN-DualSHM significantly outperforms baseline models such as LSTM, CNN-LSTM, and GAT-LSTM. Specifically, the Root Mean Square Error (RMSE) is reduced by 65.25%, and the coefficient of determination (R²) reaches 0.925. Physical consistency analysis confirms that the introduction of physical constraints effectively suppresses anomalous predictive fluctuations that violate mechanical laws. Uncertainty decomposition reveals that aleatoric uncertainty is dominant (93.7%), objectively indicating that the current system’s accuracy bottleneck lies in sensor noise rather than model capability. By enhancing prediction accuracy while providing credible quantitative assessments and physical interpretability, the proposed method provides a scientific basis for the operation, maintenance optimization, and upgrading decisions of SHM systems. Full article

Accurate forecasting of mining-induced surface deformation is critical for coal-mine safety assessment and hazard mitigation. InSAR deformation time series are often short, temporally sparse, and strongly nonlinear. These characteristics can make purely data-driven predictors unreliable in small-sample settings. To address this issue, we propose a generation–prediction–interpretation framework that combines generative augmentation with physics-informed forecasting. We first develop a TCN-TimeGAN model to synthesize high-fidelity deformation sequences and expand the training set. Recurrent modules in the generator and discriminator are replaced with causal TCN residual blocks, and a temporal self-attention layer is further stacked on top of the TCN backbone to adaptively reweight informative time steps. We then construct a physics-informed Kolmogorov–Arnold Network, termed PI-KAN. Subsidence-consistency and smoothness priors are embedded in the learning objective to promote physically plausible predictions while retaining spline-based interpretability. Experiments on SBAS-InSAR deformation series from the Guqiao coal mine show that the framework achieves an RMSE of 0.825 mm and an R² of 0.968. It outperforms TGAN-KAN, CNN-BiGRU, and BiGRU under the same evaluation protocol. Visualizations of the learned spline-based edge functions further reveal stronger nonlinear responses for lagged inputs closer to the forecast horizon, providing interpretable evidence of short-term temporal sensitivity under sparse observations. Full article

Hyperspectral image (HSI) classification has achieved substantial progress with deep learning. However, existing methods still underexploit frequency-domain information, particularly the complementary roles of high- and low-frequency components. The recently proposed Kolmogorov-Arnold Network (KAN) shows strong nonlinear feature extraction ability for HSI classification, but its lack of frequency-domain learning and reliance on B-spline activation functions often causes unstable training and convergence issues. To address these limitations, this study introduces a Frequency-aware Filtering Activation-based KAN (FFAKAN) for HSI classification. In this framework, the conventional B-spline activation functions in KAN are replaced with learnable high-pass and low-pass spatial filters, enabling explicit frequency decomposition while preserving spectral sequence modeling capacity. Specifically, the proposed framework includes three modules: spectral-spatial feature embedding (S2FE), adaptive filtering KAN (AFKAN), and sequence feature extraction (SeqFE) modules. First, the S2FE module generates highly discriminative feature representations, providing a strong foundation for subsequent processing. Second, the AFKAN module, serving as the core component, employs learnable cutoff frequencies together with cosine-based smooth transition functions to achieve physically interpretable high-low frequency separation, adaptively capturing fine-grained details and structural characteristics in HSI data. Finally, the SeqFE module leverages multi-layer stacked 3D convolutions to perform deep spectral-spatial correlation modeling, extracting high-level discriminative joint features for the classification task. Experiments on four public HSI datasets demonstrate that FFAKAN consistently outperforms state-of-the-art methods. Overall, the proposed method achieves significant performance gains, with maximum improvements of 6.82%, 1.83%, 4.35%, and 5.93% compared with conventional approaches. Moreover, compared with strong baseline models, FFAKAN further improves the overall accuracy by 1.70%, 0.10%, 0.02%, and 0.37%, respectively. These results clearly demonstrate the effectiveness, robustness, and superior generalization capability of the proposed method across different datasets. This study introduces a new paradigm that incorporates physically interpretable frequency-domain priors, showing strong adaptability and promising potential in complex land-cover scenarios. Full article

Photonic quantum communication enables secure and high-fidelity information transfer beyond classical limits, with direct relevance to emerging quantum networks operating in free-space environments. While physical-layer models of depolarizing noise, Gamma–Gamma turbulence statistics, entanglement swapping, and decoy-state QKD security bounds are individually well established, prior work typically treats these components in isolation or under fixed network assumptions. In this work, we develop a unified topology-aware analytical framework that simultaneously integrates free-space optical link budgets, turbulence-induced visibility degradation, depolarizing qubit noise, multi-hop entanglement cascade dynamics, teleportation fidelity thresholds, CHSH nonlocality certification, and asymptotic decoy-state secret key rate bounds across star, mesh, and ring graph structures. Rather than introducing new physical channel models, we demonstrate that identical physical links exhibit fundamentally different end-to-end performance once embedded within different network topologies. Mesh architectures minimize visibility cascade through hop-count reduction but incur quadratic hardware scaling. Star topologies minimize link count but concentrate noise and synchronization overhead at the hub. Ring configurations offer linear hardware scaling with multiplicative fidelity degradation. The results establish topology as a first-order design parameter in near-term free-space quantum networks operating without full quantum repeater infrastructures. While motivated by distributed multi-agent architectures, the framework applies broadly to terrestrial, airborne, and satellite-assisted photonic quantum communication systems. Full article

Embedded devices in modern power systems offer increased connectivity and remote reprogrammability/reconfigurability. These features along with interconnections between Information Technology (IT) and Operational Technology (OT) networks enable greater agility, reduced operator workload, and enhanced power system performance and capabilities, as well as expanding the cyber-attack surface. This increased cyber-attack surface, as well as increasingly complex, diverse, and potentially untrustworthy software/hardware supply chains, increases the need for robust real-time monitoring in power systems, and more generally in cyber–physical systems (CPS). We propose a novel framework for real-time monitoring and anomaly detection in CPS, specifically smart grid substations and SCADA systems. The proposed framework enables real-time signal temporal logic condition-based anomaly monitoring by processing raw captured packets from the communication network through a hierarchical semantic extraction and tag processing pipeline into a time series of semantic events and observations, that are then evaluated against expected temporal properties to detect and localize anomalies. We demonstrate the efficacy of our methodology on a hardware in the loop (HITL) testbed under several attack scenarios. The HITL testbed includes multiple physical power system devices (real-time automation controllers and relays) and simulated devices (Phasor Measurement Units—PMUs, relays, Phasor Data Concentrators—PDCs), all interfaced to a dynamic power system simulator. Full article

In the field of digital architectural heritage, the mining of tacit local knowledge embedded in architectural heritage is considered essential for the preservation, inheritance, and application of regional architectural characteristics. Local knowledge can be formally represented through semantic models, by which the automated mining of tacit information can be facilitated. However, due to the incomplete preservation of physical buildings and the fragmented nature of historical records, local knowledge is often represented as semantic fragments. Consequently, existing semantic models are still challenged in terms of knowledge integration and reasoning. In this study, a knowledge graph was developed for representing local knowledge, in which fragmented local semantics were aligned at both the ontological and entity levels. Subsequently, implicit local knowledge mining is achieved through meta-path centrality propagation combined with expert evaluation on a graph visualization platform. The method was applied to eight historical buildings in a case study. The knowledge graph quality assessment results indicate excellent ontology utilization and property utilization. The knowledge mining results demonstrate that graph-based expert evaluation successfully enables knowledge Feature Ranking and knowledge Extinction Warning. Full article

To meet the real-time computational requirements of active suspension control systems, this study shifts from complex microscopic physical equations to a direct nonlinear functional mapping between the relative motion states (displacement and velocity) and the output force of air springs. This approach aims to preserve critical nonlinear hysteresis characteristics while significantly reducing the computational overhead. A progressive modeling strategy is implemented to characterize these complex behaviors. Initially, polynomial fitting is employed to identify key input features; however, its limited capacity to capture intricate nonlinearities necessitates more advanced methods. Subsequently, standard Feedforward Neural Networks (FNNs) are explored for their nonlinear mapping capabilities, yet their inherent “black-box” nature often leads to convergence difficulties and restricted generalization. To address these issues, a Physics-Informed Neural Network (PINN) architecture is introduced, embedding physical governing equations as regularization constraints within the loss function to integrate data-driven flexibility with mathematical rigor. Recognizing that conventional PINNs often encounter convergence challenges due to conflicts between PDE constraints and data-driven loss terms, this research develops a Physics-Embedded Hierarchical Network (PEHN). By deriving specialized PDE constraints tailored to air spring dynamics and designing a hierarchical architecture aligned with these physical requirements, the PEHN effectively balances physical priors with experimental data. Experimental results demonstrate that, compared to the baseline models, the proposed PEHN exhibits stronger stability and superior accuracy in capturing the complex nonlinearities of air spring dynamics. Full article