Search Results (30,375)

Onboard GNSS-based relative baseline determination has emerged as a primary solution for formation-flying satellites dedicated to mapping and remote sensing missions. For ambiguity resolution (AR), the double-difference (DD) method is widely adopted in relative baseline determination. However, this method entails relatively complex satellite pairing, which not only increases computational load and complicates the processing workflow but also imposes higher requirements on onboard embedded computing and storage resources, thereby introducing potential risks to engineering implementation. To address these issues, this paper proposes incremental refinements to the single-difference (SD) model by introducing the combined GPS/BDS uncombined SD method for closely spaced formation satellites. By leveraging the enhanced satellite visibility of the combined GPS/BDS constellation and adopting a purely geometric approach, high-precision real-time relative baseline determination results are achieved. Validation using onboard observation data from the Lutan-1 satellite mission of China demonstrates that centimeter-level relative baseline determination accuracy can be attained. Full article

Digital twin (DT) technology is a key element of Industry 4.0. Despite its rapid development, current research is mainly focused on industrial optimisation and machine-level monitoring. However, its implementation in the educational process lags significantly behind practice. Moreover, existing DT implementations in education often emphasise visualisation or simulation, while neglecting synchronisation and verification of functional equivalence between the physical and virtual systems. This study presents the design, development and experimental verification of a digital twin of a laboratory material handling system. The virtual model created in Tecnomatix Plant Simulation is connected to the physical system controlled by a Siemens PLC SIMATIC S7-1200 and equipped with industrial sensors and an HMI interface. Real-time bidirectional communication is established via the OPC UA protocol using KEPServerEX, ensuring synchronisation between the physical and virtual systems. Experiments confirmed the functional synchronisation of both systems. Additionally, the study presents that DT technology can be adapted for educational purposes and implemented in engineering education. Full article

Accurate vegetable disease detection in complex cultivation environments remains challenging because early lesions are often small, low-contrast, and easily confounded by cluttered backgrounds. To address this issue, we propose VDD-Net, a feature-enhanced detection network based on YOLOv10 for robust vegetable disease detection in protected agriculture. The proposed framework integrates three modules: a receptive field enhancement (RFE) module to improve local perception of small lesions, an adaptive channel fusion (ACF) module to strengthen multi-scale feature aggregation and suppress background interference, and a global context attention (GCA) module to capture long-range dependencies and improve contextual discrimination. Experiments on a custom vegetable disease dataset showed that VDD-Net achieved an mAP@0.5 of 95.2% with only 7.78 M parameters. To further evaluate robustness, zero-shot cross-domain testing was conducted on the PlantDoc dataset, where VDD-Net achieved an mAP@0.5 of 76.5%, outperforming the baseline and showing improved generalization to natural scenes. In addition, after TensorRT optimization and FP16 quantization, the model maintained real-time inference on edge platforms, reaching 89.3 FPS on Jetson AGX Orin and 24.2 FPS on Jetson Nano. These results indicate that VDD-Net provides a practical balance among detection accuracy, cross-domain robustness, and deployment efficiency for intelligent disease monitoring in modern agriculture. Full article

The accurate and rapid prediction of hazard gas dispersion fields in urban environments is essential for guiding emergency sensor deployment and enabling real-time risk assessment. However, the computational cost associated with Computational Fluid Dynamics (CFD) simulations hinders their use as real-time forward models, while simplified Gaussian plume models lack the fidelity to resolve building obstruction effects. This study proposes a morphology-guided conditional Generative Adversarial Network (cGAN) framework designed to achieve real-time gas dispersion field modeling in urban environments with complex building configurations. The urban area is discretized into 50 × 50 m grid cells, each characterized by six morphological parameters describing building geometry. K-means clustering categorizes these cells into distinct morphological types. High-fidelity dispersion datasets are then generated for each type using Lattice Boltzmann Method (LBM) simulations. Each sample encodes building geometry, release location, wind speed, and time as multi-channel input images, with the corresponding gas dispersion concentration field is recorded as the output. Two cGAN architectures, Image-to-Image Translation (Pix2Pix) and its high-resolution variant (Pix2PixHD), are employed to learn the mapping from input features to dispersion fields. Model performance is evaluated using four complementary metrics: Fraction within a Factor of Two (FAC2) for prediction accuracy, Normalized Root Mean Square Error (NRMSE) for precision, Fractional Bias (FB) for systematic error, and Structural Similarity Index (SSIM) for spatial pattern fidelity. A case study is conducted across a 1176 km² urban district in China. The results demonstrate that under varying wind speeds (0.5–1.5 m/s) and temporal scales (5–60 s), and across five morphological categories, the Pix2PixHD-based model achieves 92.5% prediction accuracy and reproduces 97.6% of the spatial patterns. The proposed framework accelerates computation by approximately 18,000 times compared to traditional CFD, reducing inference time to under 0.1 s per scenario. This sub-second capability enables real-time concentration field estimation for emergency management, and provides a physically informed, computationally feasible forward model that can potentially support sensor-based gas source localization and detection network planning in complex urban environments. Full article

Accurate crop yield prediction is fundamental to sustainable agricultural management, enabling optimized resource allocation and informed decision-making. However, a critical gap exists in current prediction models: existing approaches overlook the temporal alignment between meteorological conditions and production management phases—defined as the intervals between consecutive agronomic operations (e.g., sowing, fertilization, thinning). This oversight results in suboptimal predictive performance, as conventional whole-season weather aggregation fails to capture phase-sensitive crop–weather interactions. While machine learning (e.g., XGBoost) and deep learning approaches (e.g., CNN, LSTM) have been applied to yield prediction, these models typically treat weather variables as temporally homogeneous inputs, inadequately modeling the correlation between historical yields and phase-specific meteorological patterns. To address this gap, this study proposes CNN-LSTM-AM, an innovative hybrid deep learning model that integrates convolutional neural networks (CNNs), long short-term memory (LSTM), and attention mechanisms (AMs), utilizing weather data explicitly aligned with production management phases as input. The CNN component extracts cross-phase weather patterns, the LSTM captures sequential dependencies across growth stages, and the attention mechanism dynamically weights phase importance based on meteorological conditions. The proposed model is validated using a real-world case study of Bok choy production from an agricultural cooperative in Yunlin County, Taiwan, encompassing 1714 production cycles over eight years (2011–2019). Experimental results demonstrate that CNN-LSTM-AM achieves an RMSE of 1448.24 kg/ha, MAPE of 3.60%, and R² of 0.98, outperforming five baseline models—CNN (RMSE = 2919.18), LSTM (RMSE = 2529.74), CNN-LSTM (RMSE = 1516.44), LSTM-AM (RMSE = 2284.64), and XGBoost (RMSE = 3452.47)—representing a notable reduction in prediction error (58% lower RMSE) compared to XGBoost. Furthermore, prediction accuracy improves progressively as harvest time approaches, and phase-specific weather encoding enhances accuracy by 16.5% compared to whole-season averaging. These findings underscore the critical importance of integrating agronomic domain knowledge into data-driven prediction frameworks. Full article

With the rapid development of Intelligent Transportation Systems (ITSs), traffic prediction, a crucial component of ITSs, has garnered growing scholarly attention. The appli-cation of deep learning into traffic prediction has emerged as a prominent research direction, especially amid the rapid advancement of pretrained large language models (LLMs), which offer substantial benefits in time-series analysis through cross-modal knowledge transfer. In response to this advancement, this study introduces an innovative model for traffic flow prediction, designated as WGLLM. To capture spatiotemporal characteristics inherent in traffic flow data, this model incorporates a sequence embedding layer constructed on the stationary wavelet transform (SWT) and long short-term memory (LSTM), in conjunction with a spatial embedding layer founded on graph convolutional networks (GCNs). Additionally, a fully connected layer is utilized to integrate embeddings into the LLMs for comprehensive global dependency analysis. To verify the effectiveness of the proposed approach, experiments were carried out on two real traffic flow datasets. The experimental results demonstrate that WGLLM achieves superior predictive performance compared to multiple mainstream baseline models, accompanied by a significant enhancement in prediction accuracy. Full article

Understanding and predicting how tourists move through a city is a challenging task, as it involves a complex interplay of spatial, temporal, and social factors. Traditional recommender systems often rely on structured data, trying to capture the nature of the problem. However, recent advances in Large Language Models (LLMs) open new possibilities for reasoning over richer, text-based representations of user context, even without a dedicated pre-training phase. In this study, we investigate the potential of LLMs to interpret and predict tourist movements in a real-world application scenario involving tourist visits to Verona, a municipality in Northern Italy, between 2014 and 2023. We propose an incremental prompt engineering approach that gradually enriches the model input, from spatial features alone to richer behavioral information, including visit histories, time information, and user cluster patterns. The approach is evaluated using six open-source models, enabling us to compare their accuracy and efficiency across various levels of contextual enrichment. The results provide a first insight about the abilities of LLMs to incorporate spatio-temporal contextual factors, thus improving predictions, while maintaining computational efficiency. The analysis of the model-generated explanations completes the picture by adding an interpretability dimension that most existing next-PoI prediction solutions lack. Overall, the study demonstrates the potential of LLMs to integrate multiple contextual dimensions in tourism mobility, highlighting the possibility of a more text-oriented, adaptive, and explainable T-RS. Full article

Flood and flash flood events can generate severe hydraulic actions on civil structures, requiring modeling strategies able to link flow features to structural damage. This paper proposes a two-scale numerical framework based on advanced finite element modeling to assess the vulnerability of structures subjected to inundation and flood-driven impact. At the macroscale, the flood propagation and the interaction with the built environment are simulated through the depth-averaged Shallow Water Equations, adopting a time-explicit interface treatment to capture the evolution of the free surface. The macroscale model provides time-dependent water depth and flow velocity along the external surfaces of the structure, which are then used to derive hydrostatic and hydrodynamic actions, also in comparison with code-based formulations. At the mesoscale, these actions are transferred to a detailed structural model to investigate the nonlinear mechanical response of the building. Structural components are described through a coupled damage–plasticity constitutive law, enabling the prediction of stiffness degradation, cracking-driven damage patterns, and the identification of the most critical structural zones under flood loading. The proposed workflow is finally applied to a real structure located in the municipality of Cosenza (Italy), demonstrating the capability of the approach to combine hydraulic intensity measures with physics-based structural damage assessment, supporting scenario analyses and risk mitigation evaluations. Full article

Automatic Identification System (AIS) data provide valuable insights into ship behavior, supporting maritime safety, situational awareness, and operational efficiency capabilities that are increasingly required for autonomous ship functions and harbor maneuvering assistance. This review synthesizes recent research on AIS-based ship behavior analysis and modeling published between 2022 and 2024 using a structured literature search and screening process informed by PRISMA principles. The review presents a five-stage workflow, spanning data processing, data analysis, knowledge extraction, modeling, and runtime applications with emphasis on how these stages contribute to perception, prediction, and decision support in automated navigation. Four dimensions are considered in data analysis, including statistical analysis, safety indicators, situational awareness, and anomaly detection. The modeling approaches are categorized into classification, regression, and optimization, highlighting current limitations such as data quality, algorithmic transparency, and real-time performance, while also assessing runtime feasibility for onboard or edge deployment. Three runtime application directions are identified: autonomous vessel functions, remote monitoring and control operations, and onboard decision-support tools, with numerous studies focusing on constrained waterways and port-approach scenarios. Future directions suggest integrating multi-source data and advancing machine learning models to improve robustness in complex traffic and harbor environments. By linking theoretical insights with practical onboard needs, this study provides guidance for developing intelligent, adaptive, and safety-enhancing maritime systems. Full article

Conventional black-box models for electric vehicle (EV) battery State-of-Health (SOH) prediction achieve high accuracy but lack interpretability, limiting their practical deployment in Battery Management Systems (BMSs). To circumvent these limitations, this study proposes a novel Grey-Box Residual-Driven Framework (GBRDF) that synergizes Deep Symbolic Regression (DSR) with a residual-learning BiLSTM network with two contributions: (1) the DSR component derives explicit, interpretable mathematical expressions governing global degradation trajectories based on electrochemical features, and (2) the BiLSTM network models the residual errors to capture high-frequency nonlinearities and complex sequential dependencies not addressed by the symbolic baseline. By fusing the physics-informed transparency of DSR with the data-driven refinement of BiLSTM, the GBRDF significantly enhances forecasting precision. Experimental validation across four independent EV datasets shows that the GBRDF achieves the highest coefficient of determination (R²) of 0.982, and the lowest mean absolute error (MAE) of 0.1398 and root mean square error (RMSE) of 0.3176, significantly outperforming existing methods. Furthermore, the DSR-derived SOH equation shows that battery degradation is primarily driven by high voltage exposure and charging time, with mathematical transformations reflecting how degradation accelerates initially then slows, matching real-world aging patterns where voltage stress dominates over temperature and usage variations. Full article

Industrial predictive maintenance programs often rely on SCADA historian signals characterized by low-frequency sampling and asynchronous reporting intervals. These data constraints, specifically non-uniform scan rates and inter-tag time misalignment, limit the applicability of high-resolution or sensor-intensive prognostic models. This study proposes a lightweight, physics-informed health proxy, the temperature-weighted work (TWW) index, designed to monitor motor stator-winding degradation within these industrial limitations. The TWW index accumulates mechanical work derived from torque and speed measurements, weighted by an adaptive exponential temperature-emphasis function that penalizes operation at elevated temperatures. The formulation is inspired by practical thermal-aging heuristics such as Montsinger’s rule in the qualitative sense that higher temperatures are treated as disproportionately more damaging, but it is not intended as a direct implementation of a fixed absolute-temperature life law. Instead, it is designed as a lightweight adaptive index suitable for online SCADA-based implementation. To address SCADA-specific irregularities, the framework incorporates data synchronization and resampling techniques to align heterogeneous tags, alongside power-thresholding to isolate degradation-relevant load periods. The resulting cumulative index is mapped to a normalized health/RUL proxy using failure-referenced thresholds identified from historical events. Validation using field data from industrial three-phase motors demonstrates that the TWW index provides a monotonic degradation profile that is consistent with documented winding-related failures and proactive removals. Case studies confirm that the model enabled proactive maintenance interventions by signaling the terminal phase of insulation life before catastrophic breakdown, offering a hardware-free and scalable solution for real-time asset management. Full article

Aim: Conventional free-fall kinematic models applied to plyometric push-up assessment treat the upper body as a vertically translating point mass, ignoring the curvilinear trajectory imposed by the ankle pivot and systematically biasing flight-time and height estimates. Methods: A planar rigid-body pendulum pivoting about the ankle axis was formulated via two independent derivation pathways (static moment equilibrium and a gravitational-torque coordinate approach), yielding effective pendulum length L = (M_W/M) × L_OS. Closed-form expressions for flight time, arc displacement, maximum height, and mean mechanical power were derived analytically from energy conservation and compared against free-fall predictions across seven pendulum arm lengths (L_OW = 0.50–2.00 m) and 500 initial hand velocities per length, using adaptive Gauss–Kronrod quadrature (relative tolerance 10⁻¹⁰) with ODE cross-validation (maximum discrepancy < 2.5 × 10⁻⁷ s). Results: Flight time equivalence (t^H = t^G) was formally established. The free-fall model overestimated flight time by up to 18.82% (Δt = 0.096 s; L_OW = 0.50 m, V_H,0 = 2.50 m/s) and maximum height by up to 28.43% (Δh = 0.087 m; L_OW = 0.50 m, t_flight = 0.50 s), with both errors growing nonlinearly with initial velocity. Overestimation in height was proportionally larger at shorter pendulum arm lengths (18.18% at t_flight = 0.30 s for L_OW = 0.50 m vs. 10.91% for L_OW = 1.00 m). Conclusions: The pendulum model provides a physically consistent, analytically tractable framework for geometry-adjusted upper-body power assessment from four field-obtainable anthropometric inputs. These results reflect computational self-consistency; prospective experimental validation against force-plate kinematics is required before applied deployment. Prospective empirical validation against dual force-plate and motion-capture reference data is required to establish the model’s accuracy boundaries under real push-up kinematics. Full article

As autonomous driving technology advances, the deployment of autonomous vehicles in urban environments is rapidly increasing. Lens flare—an often overlooked optical artifact in object detection research—can lead to increased false positives or missed detections, particularly in the challenging conditions inherent to autonomous driving. Current mitigation methods are often ill-suited for real-time implementation. This work proposes a solution to alleviate the adverse effects of lens flare by utilizing a lightweight lens flare perception network, eliminating the need for additional hardware or complex image pre-processing. Specifically, we propose a reference-free model utilizing a ResNet18 backbone integrated with a lightweight Multi-Layer Perceptron (MLP) to extract and leverage lens flare information. This model is developed via a teacher–student framework, which was distilled from an end-to-end reference-based model optimized using the Learned Perceptual Image Patch Similarity (LPIPS) metric. Our experiments demonstrate that incorporating lens flare information significantly enhances the performance of the baseline object detection network, outperforming previous mitigation methods by a substantial margin. The proposed method can be seamlessly integrated into existing object detectors and requires only an efficient training process, facilitating its deployment in practical autonomous driving tasks. Full article

This article introduces an intelligent framework using deep learning to recognize and classify different faults through the real-time detection of multiple faults in power distribution systems. A collection of data representing normal operating conditions, alongside various fault scenarios including line-to-ground (LG), line-to-line (LL), double line-to-ground (LLG), and three-phase line (LLL) faults, was created using three phase current signals obtained from the Real-Time Digital Simulator (RTDS) microgrid test system. To properly model the system dynamics, a feature extraction method that integrates phase currents, differential currents, summation currents and magnitude results was developed. The temporal features of the fault signals were identified by using a sliding window approach to fit the data. A one-dimensional convolutional neural network (CNN) was developed to identify different types of faults. This model performed well, obtaining nearly 96.15% accuracy while testing. In order to evaluate the feasibility of the approach, the trained model was loaded on Raspberry Pi 5, NodeMCU, ESP32 and existing sensing devices. The fault classification performed in real-time was time-sensitive. The proposed intelligent framework is applicable to low-scale operation for smart grid fault monitoring and protection and it is an economically viable solution. Full article

Agricultural robots and unmanned farmland management require real-time and precise parsing of crop leaves at the edge to support variable application of pesticides, seedling condition monitoring, and phenotypic analysis. However, the field environment features drastic changes in light, leaf occlusion, and interference from background weeds, which can cause semantic fragmentation and boundary artifacts in lightweight models. This paper presents ES2-LeafSeg, a lightweight framework for leaf semantic segmentation tailored for edge deployment. The method employs EfficientNetV2 as the backbone encoder and introduces the State Space Semantic Enhancement Module (S2FEM) on skip connection features, modeling long-range dependencies and suppressing local texture noise through SSM pooling in row and column directions. Meanwhile, a cross-scale decoder (CSD) and a global context transformation (GCT) are designed to achieve multi-scale semantic fusion and boundary refinement. On the three-class segmentation task of the SoyCotton dataset, ES2-LeafSeg achieved mIoU of 0.817, mDice of 0.869, Fβw of 0.925, and MAE of 0.011, outperforming multiple classic and recent baselines while maintaining 23.67 M parameters and 49.62 FPS. Ablation experiments further verified the complementary contributions of S2FEM and GCT to regional consistency and boundary quality. Full article