Search Results (528)

Reliable state estimation in railway environments presents significant challenges due to geometric degeneracy resulting from repetitive structural layouts and point cloud sparsity caused by high-speed motion. Conventional LiDAR-based SLAM systems frequently suffer from longitudinal drift and mapping artifacts when operating in such feature-scarce and dynamically complex scenarios. To address these limitations, this paper proposes SERail-SLAM, a robust semantic-enhanced multi-sensor fusion framework that tightly couples LiDAR odometry, inertial pre-integration, and GNSS constraints. Unlike traditional approaches that rely on rigid voxel grids or binary semantic masking, we introduce a Semantic-Enhanced Adaptive Voxel Map. By leveraging eigen-decomposition of local point distributions, this mapping strategy dynamically preserves fine-grained stable structures while compressing redundant planar surfaces, thereby enhancing spatial descriptiveness. Furthermore, to mitigate the impact of environmental noise and segmentation uncertainty, a confidence-aware filtering mechanism is developed. This method utilizes raw segmentation probabilities to adaptively weight input measurements, effectively distinguishing reliable landmarks from clutter. Finally, a category-weighted joint optimization scheme is implemented, where feature associations are constrained by semantic stability priors, ensuring globally consistent localization. Extensive experiments in real-world railway datasets demonstrate that the proposed system achieves superior accuracy and robustness compared to state-of-the-art geometric and semantic SLAM methods. Full article

To address active power fluctuations in power grids induced by high renewable energy penetration and overcome the limitations of existing probabilistic power flow (PPF) methods that ignore generator frequency regulation constraints, this paper proposes a segmented stochastic power flow modeling method and an efficient analytical framework that incorporates the actions and capacity constraints of regulation units. Firstly, a dual dynamic piecewise linear power injection model is established based on “frequency deviation interval stratification and unit limit-reaching sequence ordering,” clarifying the hierarchical activation sequence of “loads first, followed by conventional units, and finally automatic generation control (AGC) units” along with the coupled adjustment logic upon reaching limits, thereby accurately reflecting the actual frequency regulation process. Subsequently, this model is integrated with the State-Independent Linearized Power Flow (DLPF) model to develop a segmented stochastic power flow framework. For the first time, a deep integration of unscented transformation (UT) and regulation-aware power allocation is achieved, coupled with the Nataf transformation to handle correlations among random variables, forming an analytical framework that balances accuracy and computational efficiency. Case studies on the New England 39-bus system demonstrate that the proposed method yields results highly consistent with those of Monte Carlo simulations while significantly enhancing computational efficiency. The DLPF model is validated to be applicable under scenarios where voltage remains within 0.95–1.05 p.u., and line transmission power does not exceed 85% of rated capacity, exhibiting strong robustness against parameter fluctuations and capacity variations. Furthermore, the method reveals voltage distribution patterns in wind-integrated power systems, providing reliable support for operational risk assessment in grids with high shares of renewable energy. Full article

The Segment Anything Model (SAM), a foundational vision model, struggles with fully automatic segmentation of specific objects. Its “segment everything” mode, reliant on a grid-based prompt strategy, suffers from localization blindness and computational redundancy, leading to poor performance on tasks like Dichotomous Image Segmentation (DIS). To address this, we propose PMG-SAM, a framework that introduces a Pre-Mask Guided paradigm for automatic targeted segmentation. Our method employs a dual-branch encoder to generate a coarse global Pre-Mask, which then acts as a dense internal prompt to guide the segmentation decoder. A key component, our proposed Dense Residual Fusion Module (DRFM), iteratively co-refines multi-scale features to significantly enhance the Pre-Mask’s quality. Extensive experiments on challenging DIS and Camouflaged Object Segmentation (COS) tasks validate our approach. On the DIS-TE2 benchmark, PMG-SAM boosts the maximal F-measure from SAM’s $0.283$ to $0.815$. Notably, our fully automatic model’s performance surpasses even the ground-truth bounding box prompted modes of SAM and SAM2, while using only $22.9$ M trainable parameters ($58.8$% of SAM2-Tiny). PMG-SAM thus presents an efficient and accurate paradigm for resolving the localization bottleneck of large vision models in prompt-free scenarios. Full article

Building extraction underpins land-use assessment, urban planning, and disaster mitigation, yet dense urban scenes still cause missed small objects, target adhesion, and ragged contours. We present High-Resolution-Mamba (HR-Mamba), a high-resolution semantic segmentation network that augments a High-Resolution Network (HRNet) parallel backbone with edge-aware and sequence-state modeling. A Canny-enhanced, median-filtered stem stabilizes boundaries under noise; Involution-based residual blocks capture position-specific local geometry; and a Mamba-based State Space Models (Mamba-SSM) global branch captures cross-scale long-range dependencies with linear complexity. Training uses a composite loss of binary cross entropy (BCE), Dice loss, and Boundary loss, with weights selected by joint grid search. We further design a feature-driven adaptive post-processing pipeline that includes geometric feature analysis, multi-strategy simplification, multi-directional regularization, and topological consistency verification to produce regular, smooth, engineering-ready building outlines. On dense urban imagery, HR-Mamba improves F1-score from 80.95% to 83.93%, an absolute increase of 2.98% relative to HRNet. We conclude that HR-Mamba jointly enhances detail fidelity and global consistency and offers a generalizable route for high-resolution building extraction in remote sensing. Full article

Accurate prediction of sensor network data in critical domains such as electric power systems and traffic planning is a core task for ensuring grid stability and enhancing urban operational efficiency. Although deep learning models have achieved significant architectural advancements, their training strategy implicitly assumes that all future events are equally predictable, ignoring that the future evolution of sensor signals intertwines deterministic patterns with stochastic events and that prediction difficulty increases with temporal distance. Forcing a model to fit inherently unpredictable events with a uniform supervision may impair its ability to learn generalizable patterns. To address this, we introduce an Unpredictability Perception loss that dynamically computes a supervision weight. The computation of this weight unifies two assessment dimensions of the intrinsic unpredictability of the forecasting task. The first originates from a posterior analysis of the signal content’s randomness, while the second stems from an a priori consideration of temporal distance. The first dimension, through a complexity-aware weight derived from local spectral entropy, reduces supervision on random segments of the signal. The second dimension, through a temporal decay weight based on exponential decay, lessens supervision for distant future points. Applied to the advanced TimeMixer model, experimental results show that our approach achieves performance improvements across multiple public benchmark datasets. By matching the supervision strength to the intrinsic predictability of the signals, our proposed Unpredictability Perception loss function enhances the forecasting accuracy for sensor network data, providing a more reliable technical foundation for ensuring the stability of critical infrastructures like power grids and optimizing urban traffic systems. Full article

Accurate weed detection in crop fields remains a challenging task due to the diversity of weed species and their visual similarity to crops, especially under natural field conditions where lighting and occlusion vary. Traditional methods typically attempt to directly identify various weed species, which demand large-scale, finely annotated datasets and often suffer from low generalization. To address these challenges, this study proposes a novel dual-model framework that simplifies the task by dividing it into two tractable stages. First, a crop segmentation network is used to identify and remove cabbage (Brassica oleracea L. ssp. pekinensis) regions from field images. Since crop categories are visually consistent and singular, this stage achieves high precision with relatively low complexity. The remaining non-crop areas, which contain only weeds and background, are then subdivided into grid cells. Each cell is classified by a second lightweight classification network as either background, broadleaf weeds, or grass weeds. The classification model achieved F₁ scores of 95.1%, 91.1%, and 92.2% for background, broadleaf weeds, and grass weeds, respectively. This two-stage approach transforms a complex multi-class detection task into simpler, more manageable subtasks, improving detection accuracy while reducing annotation burden and enhancing robustness under the tested field conditions. Full article

Numerical modelling of airflow in underground mines is gaining importance in modern ventilation system design and safety assessment. Computational Fluid Dynamics (CFD) simulations enable detailed analyses of air movement, contaminant dispersion, and heat transfer, yet their reliability depends strongly on the accuracy of the geometric representation of excavations. Raw point cloud data obtained from laser scanning of underground workings are typically irregular, noisy, and contain discontinuities that must be processed before being used for CFD meshing. This study presents a methodology for automatic segmentation and regularization of large-scale point cloud data of underground excavation systems. The proposed approach is based on skeleton extraction and trajectory analysis, which enable the separation of excavation networks into individual tunnel segments and crossings. The workflow includes outlier removal, alpha-shape generation, voxelization, medial-axis skeletonization, and topology-based segmentation using neighbor relationships within the voxel grid. A proximity-based correction step is introduced to handle doubled crossings produced by the skeletonization process. The segmented sections are subsequently regularized through radial analysis and surface reconstruction to produce uniform and watertight models suitable for mesh generation in CFD software (Ansys 2024 R1). The methodology was tested on both synthetic datasets and real-world laser scans acquired in underground mine conditions. The results demonstrate that the proposed segmentation approach effectively isolates single-line drifts and crossings, ensuring continuous and smooth geometry while preserving the overall excavation topology. The developed method provides a robust preprocessing framework that bridges the gap between point cloud acquisition and numerical modelling, enabling automated transformation of raw data into CFD-ready geometric models for ventilation and safety analysis of complex underground excavation systems. Full article

To address the issue that distributed renewable energy grid-connected Virtual Synchronous Generator (VSG) systems are prone to significant power and frequency fluctuations under changing operating conditions, this paper proposes a multi-parameter coordinated control strategy for VSGs based on a fusion framework of fuzzy logic and the Soft Actor–Critic (SAC) algorithm, termed Improved SAC-based Virtual Synchronous Generator control (ISAC-VSG). First, the method uses fuzzy logic to map the frequency deviation and its rate of change into a five-dimensional membership vector, which characterizes the uncertainty and nonlinear features during the transient process, enabling segmented policy optimization for different transient regions. Second, a stage-based guidance mechanism is introduced into the reward function to balance the agent’s exploration and stability, thereby improving the reliability of the policy. Finally, the action space is expanded from inertia–damping to the coordinated regulation of inertia, damping, and active power droop coefficient, achieving multi-parameter dynamic optimization. MATLAB/Simulink R2022b simulation results indicate that, compared with the traditional SAC-VSG and DDPG-VSG method, the proposed strategy can reduce the maximum frequency overshoot by up to 29.6% and shorten the settling time by approximately 15.6% under typical operating conditions such as load step changes and grid phase disturbances. It demonstrates superior frequency oscillation suppression capability and system robustness, verifying the effectiveness and application potential of the proposed method in high-penetration renewable energy power systems. Full article

Decarbonization of transport is a key element of the energy transition and of achieving the Sustainable Development Goals. Integration of renewable energy into transport systems is assessed together with the potential of electric, hybrid, hydrogen, and biofuel-based propulsion to enable low emission mobility. Literature published from 2019 to 2025 is synthesized using structured searches in Scopus, Web of Science, and Elsevier and evidence is integrated through a thematic comparative approach focused on energy efficiency, life cycle greenhouse gas emissions, and technology readiness. Quantitative findings indicate that battery electric vehicles typically require about 18 to 20 kWh per 100 km, compared with about 60 to 70 kWh per 100 km in energy equivalent terms for internal combustion cars. With higher renewable shares in electricity generation, life cycle CO₂ equivalent emissions are reduced by about 60 to 70 percent under average European grid conditions and up to about 80 percent when renewables exceed 50 percent. Energy storage and smart grid management, including vehicle to grid operation, are identified as enabling measures and are associated with peak demand reductions of about 5 to 10 percent. Hydrogen and advanced biofuels remain important for heavy duty, maritime, and aviation segments where full electrification is constrained. Full article

To achieve effective coordination among planning, operation, and service in urban management, and based on the fundamental characteristic of urban spatial development expanding from points to areas, this paper proposes an approach for identifying high-intensity urban development zones based on seed grid growth. First, seed grids are selected using the Getis–Ord $G_i$* of grid floor area ratios as the criterion. Second, drawing on relevant image recognition methods, high-intensity development zones are derived through seed-grid-based zone growth, as well as zone merging and segmentation. Furthermore, the rationality of the geometric morphology and the independence of the spatial relationships of the identified zones are evaluated. Meanwhile, the utilization efficiency of these zones is assessed from the perspectives of population carrying capacity and industrial agglomeration, using data on population, digital brightness of nighttime lights, and points of interest (POI). Finally, the proposed identification and utilization efficiency assessment method is verified through a case study of Shenzhen City. Full article

With the rapid development of the new energy vehicle industry, the quality control of battery pack glue application processes has become a critical factor in ensuring the sealing, insulation, and structural stability of the battery. However, existing detection methods face numerous challenges in complex industrial environments, such as metal reflections, interference from heating film grids, inconsistent orientations of glue strips, and the difficulty of accurately segmenting elongated targets, leading to insufficient precision and robustness in glue dimension measurement and glue break detection. To address these challenges, this paper proposes a battery pack glue application detection method that integrates the YOLOv11 deep learning model with pixel-level geometric analysis. The method first uses YOLOv11 to precisely extract the glue region and identify and block the heating film interference area. Glue strips orientation correction and image normalization are performed through adaptive binarization and Hough transformation. Next, high-precision pixel-level measurement of glue strip width and length is achieved by combining connected component analysis and multi-line statistical strategies. Finally, glue break and wire drawing defects are reliably detected based on image slicing and pixel ratio analysis. Experimental results show that the average measurement errors in glue strip width and length are only 1.5% and 2.3%, respectively, with a 100% accuracy rate in glue break detection, significantly outperforming traditional vision methods and mainstream instance segmentation models. Ablation experiments further validate the effectiveness and synergy of the modules. This study provides a high-precision and robust automated detection solution for glue application processes in complex industrial scenarios, with significant engineering application value. Full article

The high energy consumption characteristics across all segments of the agricultural supply chain, coupled with rural areas’ excessive reliance on traditional power grids and fossil fuel-based energy supply models, not only result in persistently high energy utilization costs and low efficiency but also inflict ongoing negative environmental impacts. This undermines sustainable development and the achievement of energy security. In response, this paper proposes a multi-timescale robust operation scheme for the coordinated operation of rural integrated energy systems and agricultural supply chains. Its core components are as follows: (1) Establish a collaborative operation framework integrating renewable energy-based rural integrated energy systems with agricultural supply chains; (2) Holistically consider energy consumption characteristics across supply chain segments, leveraging sensor-based environmental parameters for crop yield forecasting and hourly energy consumption assessment. This effectively addresses misalignments between crop growth and energy optimization scheduling, as well as inconsistent energy measurement scales across supply chain segments, thereby advancing agricultural sustainability; (3) Introducing a two-stage robust optimization model to quantify the impact of environmental uncertainty on the collaborative framework and integrated energy system, ensuring optimal operation of supply chain equipment under worst-case conditions; (4) Identifying critical energy consumption nodes in the supply chain through system performance analysis and revealing optimization potential in the collaborative mechanism, enabling flexible load shifting and cross-temporal energy allocation. Simulation results demonstrate that this coordinated operation scheme enables dynamic estimation and optimization of crop growth and energy consumption, reducing system operating costs while enhancing supply chain reliability and renewable energy integration capacity. The two-stage robust optimization mechanism effectively strengthens system robustness and adaptability, mitigates the impact of renewable energy output fluctuations, and achieves spatiotemporal optimization of energy allocation. Full article

Accurate prediction of road traffic crash severity is essential for developing data-driven safety strategies and optimizing resource allocation. This study presents a predictive modeling framework that utilizes Random Forest (RF), Gradient Boosting (GB), and K-Nearest Neighbors (KNN) to estimate segment-level frequencies of fatalities, serious injuries, and slight injuries on Hungarian roadways. The model integrates an extensive array of predictor variables, including roadway geometric design features, traffic volumes, and traffic composition metrics. To address class imbalance, each severity class was modeled using resampled datasets generated via the Synthetic Minority Over-sampling Technique (SMOTE), and model performance was optimized through grid-search cross-validation for hyperparameter optimization. For the prediction of serious- and slight-injury crash counts, the Random Forest (RF) ensemble model demonstrated the most robust performance, consistently attaining test accuracies above 0.91 and coefficient of determination (R²) values exceeding 0.95. In contrast, for fatalities count prediction, the Gradient Boosting (GB) model achieved the highest accuracy (0.95), with an R² value greater than 0.87. Feature importance analysis revealed that heavy vehicle flows consistently dominate crash severity prediction. Horizontal alignment features primarily influenced fatal crashes, while capacity utilization was more relevant for slight and serious injuries, reflecting the roles of geometric design and operational conditions in shaping crash occurrence and severity. The proposed framework demonstrates the effectiveness of machine learning approaches in capturing non-linear relationships within transportation safety data and offers a scalable, interpretable tool to support evidence-based decision-making for targeted safety interventions. Full article

This study proposes a dynamic incentive-based vehicle-to-grid (V2G) strategy grounded in the battery state of health (SOH) to enhance the incentive for electric vehicles (EVs) to participate in grid peak shaving and to mitigate the load fluctuations and grid stability issues caused by large-scale EV grid integration. This strategy constructs a mobility-chain-based charging demand model and establishes a quantitative relationship between the depth of discharge (DOD) and battery lifespan degradation. It incorporates a segmented dynamic incentive mechanism that integrates load fluctuation compensation with SOH degradation compensation. This study employed multi-objective optimization to minimize both grid load fluctuations and user charging costs. Results demonstrate that this strategy effectively achieves optimized regulation of the grid load curve while maximizing economic benefits for EV users. Full article

Indoor landmarks play a crucial role in the process of indoor positioning and route planning for pedestrians or unmanned devices. Indoor structural landmarks, a type of indoor landmarks, can provide rich steering and semantic descriptions for indoor navigation services. However, most traditional indoor landmark extraction methods rely on indoor points of interest and indoor vector map data. These methods face the problem of difficult acquisition of indoor data and overlook the exploration of indoor structural landmarks. Therefore, this paper innovatively proposes a method for extracting indoor structural landmarks based on the commonly available indoor fire protection plan images. First, the HSV model is employed to eliminate noise from the original image, and vector data of indoor components is obtained using the constructed Canny operator. Subsequently, the visibility is calculated based on the grids of indoor space segmentation. Finally, the identification and extraction of indoor structural landmarks are achieved through grid visibility classification, directional clustering analysis, and spatial proximity verification. This approach opens up new ideas for indoor landmark extraction methods. The experimental results show that the method proposed in this paper can effectively extract indoor structural landmarks, the extraction accuracy of indoor structural landmarks reaches over 90%, verifying the feasibility of using indoor fire protection plan data for landmark extraction and expanding the data sources for indoor landmark extraction. Full article