Search Results (2,357)

To address natural aggregate scarcity and improve the high-value utilization of Reclaimed Asphalt Pavement (RAP), this study proposes a steel slag–RAP hot recycled asphalt mixture (SSRM) as a sustainable alternative to conventional limestone–RAP mixtures (RM). Unlike previous studies mainly focusing on performance verification, an integrated environmental–economic evaluation framework was developed based on real highway expansion project data under a “cradle-to-gate” boundary and incorporating transportation distance thresholds. SSRM containing 50% RAP and 23% steel slag as coarse aggregate replacement was evaluated through rutting, semi-circular bending (SCB), freeze–thaw splitting (TSR), and skid resistance tests. Compared with RM, SSRM exhibited 14–16% higher dynamic stability and 20–25% higher fracture energy at −10 °C, along with improved moisture stability and skid resistance, mainly attributed to the rough and alkaline characteristics of steel slag enhancing adhesion and aggregate interlocking. Life-cycle assessment (GWP100) and cost analysis indicate that SSRM reduces carbon emissions by 10–11% relative to RM and about 40% compared with conventional virgin mixtures, while initial construction costs decrease by 9–10%. Transportation sensitivity analysis identifies equal-emission and equal-cost thresholds of approximately 590 km and 380 km, respectively. Within typical material supply radii (300–400 km), SSRM demonstrates both environmental and economic advantages, providing a practical framework for low-carbon material selection in highway construction. Full article

Robotic pipe welding represents a key and rapidly evolving technology for the automation of pipe and pipe-joint welding processes with standard, intersecting, and complex geometries. This review analyses 84 studies published over the past three decades, categorising them into four primary research areas: general pipe welding, intersecting pipes, boiler and tube-to-tubesheet welding, and control and modelling. Two separate comparative analyses were conducted: one within intersecting pipe research and another within the control and modelling category. The aggregated findings reveal consistent, complementary patterns: simulation and laboratory experiments clearly dominate validation methods, while industrial-scale evaluations remain scarce. The results further demonstrate that control strategies, sensor integration, and validation levels are strongly interconnected, collectively determining system performance, reliability, and practical applicability. Despite significant progress, challenges remain, including system integration complexity, limited robustness in variable industrial environments, insufficient real-time adaptive control, and inconsistent quantitative performance evaluation. Further research should prioritise the development of digital twins, human–robot collaboration, multi-sensor fusion, reinforcement learning-based adaptive control, and scalable industrial deployment. This review provides an overview of current progress and outlines key directions for developing intelligent and reliable robotic pipe welding systems. Full article

With the increasing penetration of distributed energy resources, distribution networks face elevated risks of power disruptions, which call for rapid and flexible emergency response mechanisms. There are not enough traditional emergency generator vehicles, and they are not highly adaptable when it comes to operations, which makes it hard to meet changing dispatching needs. Electric vehicles (EVs), on the other hand, can be used as distributed emergency resources that can be dispatched through vehicle-to-grid (V2G) interaction. Electric vehicle charging stations (EVCSs), on the other hand, are integrated energy storage units that use existing charging infrastructure to provide on-site grid support. To address this gap, this study proposes a comprehensive V2G-powered pre- and post-disaster coordination framework for enhancing distribution network resilience, with three core novelties: first, a refined individual EV model considering dual power and energy constraints is developed, and the Minkowski summation method is applied to accurately quantify the real-time aggregate regulation potential of EVCSs for the first time; second, a two-stage robust optimization model is formulated for pre-event strategic planning, which jointly optimizes EVCS participant selection and distribution network topology to address photo-voltaic (PV) power generation uncertainties; third, a multi-source collaborative dynamic scheduling model is constructed for post-disaster recovery, which explicitly incorporates the spatiotemporal dynamics of EVs and coordinates EVCSs, gas turbine generators (GTGs) and other resources for the first time. We carried out simulations on a modified IEEE 33-bus system with a 10 h extreme fault scenario. The results show that the proposed strategy raises the average critical load recovery ratio to 97.7% (2% higher than traditional deterministic optimization), lowers the total load shedding power by 0.2 MW and the load reduction cost by 19,797.63 CNY, and gives a net V2G power output of 3.42 MW (86.9% higher than the comparison strategy). The proposed V2G-enabled coordinated pre- and post-disaster fault recovery strategy significantly improves the resilience of distribution networks compared to traditional methods. This makes it easier and faster to recover from extreme disaster scenarios, with the overall load recovery rate reaching 91.8% and the critical load restoration rate staying above 85% throughout the recovery process. Full article

Maritime heterogeneous wireless networks are characterized by dynamic topology and significant heterogeneity in bandwidth, latency, and coverage across communication paradigms, rendering traditional terrestrial routing protocols inadequate. To address these challenges, this paper proposes a unified multi-radio access fusion infrastructure featuring a gateway that enables protocol conversion and collaborative resource management across heterogeneous systems. Building upon this infrastructure, we introduce CMPGNN-DQN, an intelligent routing algorithm that integrates Contrastive Message Passing Graph Neural Networks with Deep Reinforcement Learning. Specifically, the algorithm employs k-hop neighbor aggregation to expand the receptive field for routing decisions, and utilizes a dual-view contrastive learning mechanism—encompassing both homogeneous and heterogeneous perspectives—to enhance representation robustness against dynamic topology perturbations. By deeply fusing network topology features with real-time state information, including bandwidth, delay, and queue length, the agent makes hop-by-hop routing decisions via an ε-greedy policy within the DQN framework. Extensive simulations conducted across various scales of dynamic maritime communication scenarios demonstrate that CMPGNN-DQN outperforms state-of-the-art benchmark algorithms, including AODV, DQN, and GCN, across key metrics such as packet delivery ratio, transmission latency, and bandwidth utilization. Quantitatively, compared to the best-performing alternative (MPNN-DQN), our algorithm achieves throughput improvements of 2.06–5.04% under standard traffic loads and 6.6–27.1% under partial link failure conditions, while converging within merely 25 training episodes. Notably, under heavy network loads (40% load rate) or partial link failures, the algorithm maintains stable communication performance, demonstrating strong adaptability to complex dynamic environments. Full article

Robust perception in adverse meteorological conditions remains a critical challenge for autonomous driving systems. Generic object detectors often suffer from feature degradation and domain shifts in foggy environments due to the loss of high-frequency details and contrast attenuation. To address these limitations, we propose the Frequency-Adaptive and Haze-Aware Detection Transformer (FAD-DETR). Building upon the Real-Time Detection Transformer (RT-DETR), an atmospheric scattering suppression module recalibrates the feature distribution in the latent space based on physical scattering priors to mitigate haze interference. Subsequently, an edge-guided enhancement module utilizes Laplacian operators to enhance structural boundaries blurred by fog. Finally, a frequency-adaptive context aggregation module is applied to reduce spectral aliasing during cross-scale feature fusion. Evaluations on the real-world RTTS benchmark demonstrate that FAD-DETR achieves a favorable performance, improving the mAP@50 by 4.1% compared to the RT-DETR-L baseline. Furthermore, zero-shot experiments on the synthetic Foggy Cityscapes dataset indicate the generalization capability of the model without additional fine-tuning. This framework provides a practical solution to visual perception tasks in complex weather. Full article

In real-world applications, the prevalence of heterogeneous graph data has driven the development of heterogeneous graph neural networks (HGNNs) as an effective solution for modeling intricate semantic relationships. A widely adopted strategy involves using meta-paths as high-level structural motifs to direct neighborhood aggregation in HGNNs. Nevertheless, the semantic content inherent in meta-paths themselves is often not fully exploited, even though they are typically employed as guiding signals. This paper introduces a new HGNN architecture that utilizes meta-path sequences, integrating the intrinsic information of meta-paths directly into the semantic fusion mechanism. By representing meta-paths as sequential data—similar to sequences in natural language processing—we are able to capture more detailed semantic patterns through the sequential order of node types in heterogeneous graphs. Using sequence modeling methods, our approach embeds meta-path semantics into the graph neural network, offering not only additional structural insights but also enabling the training of specialized embeddings for node types. We perform extensive experiments, comprising comparative and ablation analyses, on a custom-built dataset and three publicly available medium-scale heterogeneous graph benchmarks. The experimental outcomes validate the efficacy of our method in utilizing sequential characteristics of meta-paths to improve representation learning. Full article

Traditional domain adaptation methods often assume balanced data distributions. However, this assumption is frequently violated in real-world industrial scenarios, where normal samples predominate while fault samples are inherently scarce. Under severe class imbalance, conventional decision boundaries tend to shift toward minority fault regions. This shift leads to persistently high misclassification rates for rare fault samples. To overcome this limitation, we propose the Dynamic Maximum Triple-View Classifier Discrepancy (DMTVCD) network, which integrates a Triple-View Classifier (TVC) Architecture and a Primary–Auxiliary Fused Cooperative Loss (PAFL). Specifically, the TVC employs auxiliary binary classifiers to aggregate fine-grained fault sub-classes into a unified “Fault Super-class.” This constructs a robust “normal-fault” binary boundary that effectively counteracts class imbalance. Driven by the PAFL, this boundary acts as a hierarchical geometric constraint to suppress the primary classifier’s tendency to misclassify faults as normal samples, thereby enhancing feature discriminability. Furthermore, a dynamic weighting strategy is introduced to assign large initial weights. This forces the model to bypass simple decision logic dominated by the majority class, ensuring a smooth transition from global exploration to fine-grained alignment. Extensive evaluations on the CWRU and JNU datasets demonstrate that DMTVCD consistently outperforms state-of-the-art approaches under high imbalance ratios (e.g., 20:1). Full article

In electric vehicle (EV) charging systems, DC series arc faults, due to their high concealment and severe hazard, have become one of the important causes of electric vehicle fire accidents. An improved hybrid arc fault model of a charging system was established in Simulink for preliminary study. The results show that the high-frequency noise generated by arc faults affects the output voltage quality of the charger, and this noise is conducted to the battery voltage. Arc faults in a real electric vehicle charging experimental platform were further investigated, where it was found that, during arc fault events, the charging system provides no alarm indication, and the current signals exhibit significant large-amplitude random disturbances and nonlinear fluctuations. Moreover, under normal conditions during vehicle charging startup and the pre-charge stage, the current waveforms also present high-pulse spike characteristics similar to arc faults. Finally, a carefully designed deep neural network-based arc fault detection algorithm, Arc_TCNsformer, is proposed. The current signal samples are directly input into the network model without manual feature selection or extraction, enabling end-to-end fault recognition. By integrating a temporal convolutional network for multi-scale local feature extraction with a sparse Transformer for contextual information aggregation, the proposed method achieves strong robustness under complex charging noise environments. Experimental results demonstrate that the algorithm not only provides high detection accuracy but also maintains reliable real-time performance when deployed on embedded edge computing platforms. Full article

In real-world data, the structural symmetry of time series across temporal scales is often disrupted by the entanglement of trend, seasonal, and high-frequency components. This poses significant challenges to reliable time-series forecasting (TSF) in applications like power dispatch and meteorological analysis. Transformer-based methods typically model mixed signals without explicit inductive bias, while current decomposition-based approaches often rely on linear approximations for trend evolution, leading to unstable extrapolation in long-term forecasting. To overcome these challenges, this paper proposes the Decomposition Patch Time Series Transformer (DecompPatchTST), a hybrid forecasting framework integrating decomposition, trend extrapolation, and patch-based representation. A moving-average operator first decomposes the sequence into trend and residual parts. The trend is extrapolated via a learnable polynomial basis, which adaptively models complex nonlinear trends to ensure smooth long-range dynamics, whereas the residual is divided into temporal patches and modeled by a shared transformer encoder to capture seasonal and high-frequency variations. The final forecast aggregates both components through an additive structure. Experiments on ETT datasets show that DecompPatchTST is more stable with relatively smoother error growth from 96 to 720 forecasting steps. Its practical performance is further demonstrated on real-world Australian electricity data. Full article

Recent years have seen remarkable progress in deep learning on 3D point clouds, with hierarchical architectures becoming standard. Most work has focused on developing increasingly complex operators, such as self-attention, while enhancing the representational capacity of efficient point-wise MLP-based backbones has received less attention. We address this issue by proposing a differentiable module that learns to impose a task-driven canonical structure on local point sets. Our proposed SMA (Sort–Mix–Attend) layer dynamically serializes a neighborhood by generating a geometric basis and using a differentiable sorting mechanism. This enables an efficient MLP-based network to model rich feature interactions, adaptively modulating features prior to the final symmetric aggregation function. We demonstrate that SMA effectively enhances standard backbones for 3D classification and segmentation. Specifically, integrating SMA into PointNeXt-S achieves an Overall Accuracy (OA) of 88.3% on the challenging ScanObjectNN dataset, an improvement of 0.6% over the baseline. Furthermore, it boosts the classic PointNet++ architecture by a significant 5.2% in OA. We also introduce a highly efficient SMA-Tiny variant that achieves 86.0% OA with only 0.3 M parameters, proving the structural superiority, computational cost-effectiveness, and practical significance of our method for real-world 3D perception tasks. Full article

Few-shot semantic segmentation aims to learn accurate pixel-level classification from limited annotated samples, a critical capability for real-world applications where data acquisition is expensive or impractical. However, existing methods often struggle with fine-grained texture details and complex boundaries under data-scarce conditions, particularly when applied to domains with intricate visual patterns (such as batik patterns). To address this few-shot learning challenge, we constructed a few-shot batik pattern dataset and proposed a novel network architecture centered on attention weighting and hierarchical decoding. Our method leverages a pre-trained ResNet101 backbone for transfer learning to establish a strong feature foundation. It incorporates a dual-attention module that combines spatial and channel attention to dynamically highlight semantically rich regions and intricate texture boundaries specific to batik. For multi-scale context aggregation, a lightweight module utilizing parallel dilated convolutions is introduced to efficiently capture features from varying receptive fields. Finally, a hierarchical decoder progressively integrates these enhanced, multi-scale features with high-resolution shallow features to reconstruct precise segmentation maps. Comprehensive evaluations on a dedicated batik dataset show that our model achieves state-of-the-art performance, with a mean Intersection over Union (mIoU) of 79.22% and a pixel accuracy (PA) of 92.47%. It notably improves over the strong DeepLabV3+ baseline by 3.3% in mIoU and 0.95% in PA, demonstrating its effectiveness for the task of batik pattern segmentation under data-scarce conditions. Full article

Zero-trust architecture (ZTA) requires continuous and adaptive identity authentication to maintain security in dynamic environments. However, current federated learning (FL)-based authentication models often struggle to incorporate evolving attack patterns without experiencing catastrophic forgetting. Moreover, non-independent and identically distributed (non-IID) client data and concept drift frequently lead to degraded model robustness and personalization. To address these issues, this paper presents a hybrid learning framework that integrates federated learning with incremental learning (IL) for sustainable authentication. A Dynamic Weighted Federated Aggregation (DWFA) algorithm is developed to mitigate concept drift by adjusting aggregation weights in real time, ensuring that the global model adapts to changing data distributions. This approach enables continuous learning from distributed threat data while maintaining privacy and eliminating the need for historical data retention. Experimental results on real-world traffic datasets indicate that the proposed framework outperforms conventional FL baselines, reducing the overall error rate by approximately 56% and improving the detection rate for novel attack types by over 17.8%. Furthermore, the framework remains stable against performance decay while maintaining efficient communication overhead. This study provides an adaptive, privacy-preserving solution for identity authentication in zero-trust systems. Full article

Maintaining frequency stability in modern multi-area interconnected power systems has become increasingly challenging due to the stochastic nature of wind power and reduced effective system inertia. Under these dynamic conditions, traditional fixed-gain PID controllers frequently fail to provide robust regulation. To address this limitation, this study proposes and evaluates a practical model-free secondary control strategy for multi-area Load Frequency Control (LFC). The proposed hybrid MFAC–PID framework integrates an incremental model-free adaptive control (MFAC) law with a low-gain incremental PID damping term. This combination leverages real-time input–output data to determine primary control actions without relying on an explicit plant model, while the PID component supplies supplementary damping based on recent control errors. Furthermore, the controller utilizes online pseudo-gradient estimation to dynamically adapt to stochastic wind fluctuations and $\pm 5\%$ parametric uncertainty. Simulation results demonstrate that the hybrid design substantially enhances Area Control Error (ACE) regulation. Under wind-disturbed conditions, it reduces the aggregated Integral Absolute Error ($IA{E}_{total}$) from 92.76 to 41.10, representing an improvement of over 50% compared with the fixed-gain PID baseline. Additionally, the controller maintains a low computational overhead of 0.306 milliseconds per control cycle. These findings indicate that the hybrid MFAC–PID structure provides a robust, computationally efficient solution for real-time Automatic Generation Control (AGC) in renewable-integrated multi-area power grids. Full article

Optimizing energy-intensive manufacturing under time-varying electricity tariffs requires scheduling strategies that reduce cost without compromising operational feasibility. This study is grounded in readily available industrial sensing: we exclusively use time-series measurements of aggregated active power and energy at the main distribution board of a quicklime production plant. We propose a tariff-aware load-shifting framework in which a Proximal Policy Optimization (PPO) reinforcement learning agent is trained in a custom Gymnasium environment to apply discrete consumption scaling actions constrained to 80–125% of a baseline profile during the operating shift (08:00–16:00), explicitly accounting for demand-charge exposure in the TOU peak window (13:00–15:00). The reward design combines instantaneous electricity cost with cumulative energy-tracking penalties and terms associated with operational constraints. Multi-day validation over $N=30$ working days shows consistent economic benefits, with a median total cost reduction on the order of 10% (narrow IQR) driven by reduced peak-window energy and demand peaks. However, the script-based binary compliance indicators (viol_energy, viol_prod_min) reveal deviations from the energy-balance criterion and occasional minimum-production shortfalls under the tolerances used, highlighting the cost–production trade-off and the need for stricter constraint handling for industrial deployment. In addition, we benchmark against dynamic programming (DP), an alternative RL policy (DQN), and a greedy heuristic (GREEDY), comparing cost; operational performance; and, when applicable, computational efficiency, which positions PPO as a competitive alternative among the considered methods. Overall, this work demonstrates how learning-based decision making can be coupled with real-world industrial sensing infrastructures, providing a data-driven tariff-aware scheduling layer for industrial energy management under practical constraints. Full article

In smart campus construction, behavior pattern recognition and perturbation analysis serve as the cornerstones for achieving a transition from passive response to dynamic regulation, with intelligent perception and anomaly diagnosis methods based on campus traffic flow underpinning transportation system resilience. Traditional research methods suffer from issues such as privacy risks, coarse modeling, and limitations from single data formats, labeling difficulties, and coverage gaps. This study proposes a refined semantic trajectory construction method that integrates multi-source data (e.g., mobile signaling data, maps and weather conditions), known as the Campus Transportation Semantic Trajectories Space (CTSTSpace) framework. It enables the precise identification of semantic origin–destination points from dynamic personnel trajectories, quantifies service performance through real-time road network mapping, and models multidimensional perturbations, achieving full campus coverage without complex labeling while ensuring robust privacy protection. Under clear weather conditions, the analysis demonstrates accurate recognition of travel behavior patterns (dwelling, aggregation, mobility, and congestion) that synchronize with class schedules, where vehicle speeds drop by over 50% during peak hours. Under rainy weather perturbations, it captured demand shifts (e.g., peak hour offsets of 30–60 min and a 6.8–9.2% reduction in long-distance dining trips) and speed reductions (52.15–73.74%). This approach provides critical insights for resilient smart campus traffic management. Full article