Search Results (1,022)

The Galileo High Accuracy Service (HAS) offers an opportunity to enhance the onboard real-time precise orbit determination (POD) and navigation payload design for low-Earth-orbit (LEO) and low-medium-Earth-orbit (MEO) satellites. Spaceopal, in collaboration with the German Aerospace Center, is developing a novel Galileo HAS receiver tailored for real-time POD and LEO/low MEO navigation payloads. This receiver provides autonomous onboard knowledge of the satellite’s orbit with centimeter accuracy in real time, enabling cost-efficient operations, better collision prediction, and accurate payload pointing among other benefits. Additionally, the POD receiver facilitates time synchronization for LEO/low MEO PNT navigation payloads. Full article

Modern UAV swarm operations face strict onboard bandwidth and autonomy constraints, making simultaneous multi-target interception under limited communication a critical unsolved challenge. This paper addresses three-dimensional cooperative interception of maneuvering targets by multiple unmanned aerial vehicles (UAVs) at prescribed line-of-sight (LOS) angles under limited communication resources. In the LOS direction, a fixed-time consensus-based guidance law is designed with remaining flight time as the coordination variable, synchronizing each UAV’s impact time to a freely specified desired value with bounded gains throughout the engagement. Unlike most existing fixed-time cooperative guidance works, the consensus convergence time is rigorously proven to be strictly less than the maximum initial predicted flight time, guaranteeing impact-time agreement is reached before any UAV intercepts the target—a necessary condition for genuine simultaneous salvo attack. A dynamic event-triggered (DET) mechanism is incorporated to reduce inter-UAV communication frequency by adaptively updating the triggering threshold according to consensus state evolution. In the LOS normal directions, a piecewise nonsingular terminal sliding-mode law ensures fixed-time convergence of the LOS angle and its rate to desired values under impact-angle constraints. Fixed-time stability and Zeno-behavior exclusion are rigorously established via Lyapunov analysis. Comparative simulations against existing methods demonstrate clear advantages in impact-time accuracy, guidance smoothness, and communication efficiency. Full article

The integration of artificial intelligence into video-based human behavior analysis enables contactless and continuous monitoring of both motor dynamics and facial reactions. This paper proposes a dual-stream multimodal framework for synchronized modeling of facial expression dynamics and skeletal motion during physical movement from monocular RGB video. The framework consists of two coordinated streams: the motor stream, based on 2D skeletal keypoints, and the facial stream, which extracts features associated with discomfort and affective responses. Person and face detection are performed using YOLO11, while specialized deep learning models handle pose estimation and facial expression recognition. Temporal dependencies and cross-modal relationships are modeled via a bidirectional LSTM, enabling unified temporal modeling of skeletal and facial dynamics. This novel approach allows investigation of how physical movement patterns relate to facial reactions during dynamic activities. By integrating heterogeneous facial and skeletal features in a synchronized temporal model, the framework enables consistent cross-modal analysis of dynamic human behavior. The framework was trained and validated using FER2013 and AffectNet for facial expression recognition, and UI-PRMD and FineRehab for skeletal motion modeling. It achieves 91.2% accuracy in facial expression classification, 94.8% mean Intersection over Union for human detection, and an F1 score of 0.89 for multimodal state assessment. Operating in real-time at 18–28 FPS on standard GPU hardware without requiring wearable sensors, the framework supports applications in behavioral monitoring and safety analysis. Full article

Accurate damage characterization in thermoset Carbon Fiber-Reinforced Polymer (CFRP) composites using Acoustic Emission (AE) requires statistically robust and interpretable models. This study employs multinomial logistic regression with forward selection and Type III analysis to identify the minimal set of AE parameters necessary for classifying damage mechanisms (fiber breaks, delamination, matrix cracks) in quasi-isotropic thermoset CFRP laminates under synchronously recorded load conditions. Starting from 18 conventional time- and frequency-domain descriptors, forward selection yielded seven candidate predictors. However, Type III analysis revealed that only four parameters, Load, Initiation Frequency, Amplitude, and Average Frequency, provide unique, statistically significant contributions (p < 0.05). The remaining predictors became redundant once these four were included. Machine learning and deep learning models trained on this minimal feature set achieved validation accuracies up to 98.7% on external specimens. High-frequency components (>1 MHz), as recorded at the sensor location after propagation and sensor convolution, were associated with fiber break events at elevated loads, while delamination events exhibited higher amplitude and lower-frequency content (<200 kHz) compared to matrix crack events. These observed frequency ranges reflect the combined effects of source mechanisms, guided wave dispersion in the 2.4 mm thick laminate, PWAS sensor response, and HDT-based hit segmentation, and are consistent with established AE damage signatures in literature. The results indicate that this four-parameter set is sufficient to classify the labeled AE waveform classes under monotonic tensile loading of quasi-isotropic [45/90/−45/0]_2s laminates, achieving 98.7% agreement with reference labels assigned via waveform morphology and spectral analysis. The proposed approach reduces computational overhead and enhances interpretability for structural health monitoring applications, pending validation across broader material systems and loading scenarios. A limitation of this study is that reference labels were assigned using waveform morphology and spectral analysis, lacking independent physical validation (e.g., microscopy). Full article

Contactless vital signs monitoring is becoming increasingly relevant in scenarios where conventional sensors are impractical or not recommended. In this manuscript, a radar-based contactless system for the joint reconstruction of phonocardiogram (PCG) waveforms and cardiac state segmentation is illustrated. The proposed method exploits a self-attention one-dimensional (1D) U-Net fed by a pre-processed radar-derived input to estimate a PCG-like waveform, its envelope, and the four main cardiac phases: S1, systole, S2, and diastole. The accuracy of our method has been assessed on a public synchronized radar–PCG dataset acquired by means of a 24 GHz Doppler radar and a digital stethoscope. On the test subset, the proposed model achieved a 13.4885 dB reduction in log-spectral distance relative to the radar input signal, indicating a marked improvement in waveform fidelity. Segmentation performance also improved, with Micro-${\mathrm{F}}_1$ increasing from 74.41% to 84.17% and Macro-${\mathrm{F}}_1$ from 68.40% to 80.43% on average. Experimental results demonstrated the viability of real-time low-power embedded hardware deployment for contactless auscultation and continuous cardiac monitoring applications. The findings confirm that respiratory interference and low-amplitude signals complicate S2 detection, especially when exacerbated by subject motion. Full article

This paper investigates the impact of atmospheric conditions on distance determination accuracy in the VDES R-Mode system, based on system development and long-term analytical work conducted within the ORMOBASS project. A dedicated VDES R-Mode transmitter and monitoring station were developed and deployed in Poland, in the Port of Gdynia and at the boatswain’s office in the port of Jastarnia, respectively. Both stations were synchronized in time and frequency using a fiber-optic link and White Rabbit technology, ensuring high-precision and stable operation during long-term measurements. Based on a one-year stationary measurement campaign, a comprehensive dataset combining ranging results and meteorological observations was collected and analyzed. Statistical evaluation demonstrated that atmospheric conditions—particularly rainfall intensity and water vapor density—have a measurable impact on ranging accuracy. These findings motivated the development of a mathematical model describing the relationship between atmospheric conditions and distance measurement errors. The proposed logarithmic regression-based approach was validated using real measurement data; the authors also demonstrated its ability to reduce error variability during changing weather conditions, indicating its potential for future implementation in VDES R-Mode receivers. Full article

Article discusses Digital Twins (DTs) as a solution for artificial intelligence (AI)-based modeling and metrology in Industry 5.0 and Industry 6.0 manufacturing systems. DTs enable the creation of real-time virtual replicas of physical assets, processes, and systems, increasing transparency, prediction, and optimization in manufacturing environments. By integrating AI, machine learning (ML), and advanced sensor data, DT support adaptive, self-learning production models capable of responding to dynamic operating conditions. In metrology, DTs improve measurement accuracy, traceability, and quality assurance by continuously synchronizing data between the physical and virtual domains. This technology improves process simulation, predictive maintenance, and fault detection, reducing downtime and operating costs. Furthermore, DTs facilitate human-centric production by enabling collaborative decision-making between intelligent systems and skilled workers. Their role in sustainable production is significant, supporting energy optimization, waste reduction, and lifecycle performance analysis. In Industry 6.0, DTs go beyond cyber-physical integration to encompass cognitive intelligence, ethical automation, and autonomous optimization. However, challenges remain in data interoperability, cybersecurity, model scalability, and real-time computational performance. DTs represent a revolutionary framework for the development of intelligent, resilient, and precise manufacturing ecosystems in next-generation industrial systems. Full article

To improve the speed regulation performance of permanent magnet synchronous motor (PMSM) drive systems, a composite control strategy consisting of an improved sliding mode controller (ISMC) and an adaptive Luenberger load torque observer (ALLTO) is proposed. The ISMC is constructed based on a novel sliding mode reaching law (NSMRL). The proposed NSMRL overcomes the slow convergence and chattering problems of conventional reaching laws by introducing system state variables and a nonlinear adaptive function, ensuring rapid convergence with reduced chattering. In parallel, the ALLTO is developed to estimate and compensate load disturbances in real time, where its bandwidth is adaptively adjusted according to the speed error to achieve fast response and high estimation accuracy without degrading steady-state performance. Experimental results demonstrate that the proposed control scheme significantly improves the dynamic response and disturbance rejection capability of PMSM drive systems. Full article

The existing studies on Small-Scale Embedded Generation (SSEG) have not addressed the net billing framework behavior that applies to different import and export tariff rates. This paper presents the simulation and modeling of a smart net billing electricity meter for SSEG in MATLAB/Simulink R2018b. The model integrates a PV array, MPPT controller, DC-DC boost converter, three-phase voltage source inverter (VSI), LC filter, synchronous generator, and a bidirectional energy meter. A smart billing subsystem was developed to compute real-time energy costs using differential tariff rates consistent with South African utility policies. Simulations were conducted under fixed irradiance, with electrical performance evaluated over a short interval and billing dynamics assessed over an extended period. Results show stable PV generation, proper inverter synchronization with the utility grid, and accurate tracking of imported and exported energy. The system effectively calculates the net bill, demonstrating transparency, automation, and economic accuracy in line with policy-driven net billing frameworks. These outcomes validate the technical feasibility and practical relevance of smart net billing meters in modern grid-connected renewable energy applications. Full article

Tunnel construction requires accurate and timely classification of surrounding rock masses to ensure safety and guide excavation. This research addresses the limitations of conventional methods and unimodal intelligent approaches by proposing a novel hybrid deep model, Random-Mamba, that integrates drilling parameters and digital images for enhanced classification performance. A dataset of 3361 synchronized samples was constructed, containing six drilling parameters, digital face images, and expert-classified rock mass grades. The model employs a dual-branch architecture: a Random Forest processes the drilling parameters, and a MambaVision network extracts visual features, with a multilayer perceptron performing the fusion. The proposed model achieved an overall accuracy of 92.12% and a macro-F1 score of 91.66%, outperforming the most comparable hybrid model by 2.61% in accuracy. It demonstrated particularly high precision in identifying Class III rock with an F_1-score of 93.2%. Ablation and comparative experiments confirmed its superiority over both single-modality models, such as SVM and ResNet, and other hybrid architectures, like Random-Swin. SHAP-based sensitivity analysis further revealed that feed speed was the most influential drilling parameter for classification. The effective fusion of complementary mechanical and visual data provides a robust and practical solution for real-time rock mass assessment in tunneling engineering. Full article

The accurate and efficient acquisition of rock mechanical properties is critical for ensuring the safety and efficiency of underground engineering construction. Traditional laboratory tests are characterized by long cycles, high costs, and an inability to reflect in situ mechanical properties, while existing deep learning models based on while-drilling data suffer from poor noise robustness, insufficient deep feature extraction, and low accuracy in synchronous multi-parameter prediction. To address these limitations, this paper proposes a hybrid deep learning model (CNN-LSTM-MoE) combining a convolutional neural network (CNN), a long short-term memory network (LSTM), and a mixture of experts (MoE) system. The model enables intelligent prediction of elastic modulus, Poisson’s ratio, and yield stress from while-drilling parameters. The proposed model integrates CNN’s local feature extraction capability, LSTM’s temporal dependency modeling capability, and the multi-expert dynamic fusion mechanism of MoE. Furthermore, it incorporates physical constraints from rock fragmentation mechanics and an adaptive multi-objective loss weight optimization strategy to comprehensively enhance the multi-parameter synchronous prediction performance. Experimental results demonstrate that the proposed model achieves coefficients of determination (R²) of 0.8965 for elastic modulus, 0.9193 for Poisson’s ratio, and 0.9813 for yield stress on the laboratory validation dataset, with a mean squared error (mse) of 4.0720. Its prediction performance significantly outperforms benchmark models such as TCN and Transformer time-series architectures. Ablation studies further validate the critical role of the integrated LSTM and MoE modules in improving model accuracy, with the MoE module contributing an average R² improvement of approximately 24%. This study not only provides an effective method for high-precision acquisition of rock mechanical parameters while drilling, but also offers a feasible solution based on numerical simulation for data augmentation to address the common issue of scarce labeled data in deep learning applications within engineering fields. Full article

Coal-mine-gas-monitoring data exhibits missing phenomena due to the harsh underground operating environment. Accurate imputation of missing values in gas-monitoring sequences serves as a key data foundation for guaranteeing the continuity of gas data, enhancing the reliability of disaster early warning, and improving the accuracy of mine safety situation analysis and judgment. Aiming at the prevalent random and segmented missing issues in coal-mine-gas-monitoring time-series data, and the limitation that existing imputation methods struggle to accurately capture the nonlinear spatiotemporal correlations and long-range temporal dependencies of such data, this study proposes a missing data imputation method for coal mine gas time-series data based on the Spatio-Temporal Graph Attention Network—Echo State Network (ST-GAT-ESN). Firstly, this method extracts temporal features of the gas concentration sequence using a Gated Recurrent Unit (GRU). Subsequently, it models multiple monitoring points as graph nodes through a Graph Attention Network (GAT), constructs an adjacency matrix based on airflow propagation relationships, and adaptively learns the spatial dependency weights between monitoring points to realize the deep fusion of spatiotemporal features. Finally, it designs a dual-channel Echo State Network (ESN), synchronously inputs the spatiotemporal fusion features of the missing regions before and after, efficiently fits the nonlinear evolutionary trend of the data by virtue of the echo state property of the reservoir, and solves the output layer weights through ridge regression to achieve accurate imputation of missing values. Experimental results demonstrate that, compared with the single-ST-GAT-ESN, ESN, and ARIMA models, the proposed method achieves the optimal imputation performance in both random and segmented missing scenarios within the missing rate range of 5–50%. The three evaluation metrics—Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE)—are reduced by 30–80% compared with the benchmark models. Moreover, the imputation curve achieves the best fitting performance with the ground-truth curve at a 50% segmented missing rate. This study confirms that the ST-GAT-ESN model effectively enhances the adaptability and robustness to complex missing patterns via spatiotemporal collaborative modeling and a dual-channel fusion mechanism, providing a high-precision and highly stable technical solution for ensuring the integrity of coal-mine-gas-monitoring data, and also provides theoretical references and engineering insights for the missing-value processing of industrial time-series monitoring data. Full article

Traditional table tennis technique evaluation relies heavily on coaches’ subjective judgment, which limits the objectivity, consistency, and scalability of instructional feedback. To address this problem, this study proposes a multi-dimensional joint quantitative evaluation method for table tennis techniques based on OpenPose and YOLOv8 using consumer-grade high-frame-rate video. A total of 50 participants were recruited and divided into a high-level group and a low-level group. Standardized forehand drive and backhand push tasks were recorded using a synchronized dual-view camera setup. OpenPose was used to extract upper-body keypoint trajectories for kinematic analysis, while YOLOv8 was employed to detect and track the ball, racket, and net for outcome-related feature extraction. Based on these data, seven core indicators covering movement stability, coordination, timing, smoothness, and hitting effectiveness were selected to construct a quantitative scoring model, which was further optimized by ridge regression and validated against expert ratings from three senior athletes/coaches. The results show significant between-group differences in multiple technical dimensions, including impact accuracy, smoothness, trajectory consistency, and limb coordination ($p<0.001$). The model score was strongly correlated with expert ratings ($r=0.882$, $p<0.001$) and demonstrated high reliability ($ICC=0.915$). These findings indicate that the proposed framework can provide a low-cost, non-invasive, and practically effective solution for intelligent table tennis teaching, technical diagnosis, and skill-level evaluation. Full article

Although model predictive control (MPC) has been successfully applied in permanent magnet synchronous motor (PMSM) speed control systems, its performance can degrade under high-dynamic operating conditions and uncertain load disturbances. To address these issues, a continuous-time model predictive control (CTMPC) framework is proposed to improve speed tracking accuracy and robustness. From a symmetry perspective, the proposed method leverages the orthogonal symmetry of Laguerre basis functions and the structural invariance of the continuous-time PMSM speed dynamics, enabling a compact and balanced representation of the control trajectory while preserving prediction accuracy. Specifically, a finite set of orthogonal Laguerre functions, combined with an adaptive smoothing factor and soft constraint mechanism, is employed to reduce computational complexity without compromising performance. In addition, a nonlinear disturbance observer is integrated to achieve real-time estimation and feedforward compensation of load torque variations, thereby enhancing disturbance rejection capability. Comprehensive simulation results demonstrate that the proposed approach significantly improves tracking precision, reduces overshoot, and shortens recovery time following load disturbances compared to conventional MPC methods. Full article

Phase-angle AC control is a low-cost technique for regulating power in resistive loads, but its performance depends on accurate trigger timing. This study quantitatively compares an ESP32-based phase-angle controller implemented in MicroPython and in native C using ESP-IDF. Firing delay was measured over 1000 consecutive cycles at firing angles from 10° to 150° under a 60 Hz supply, and the timing error was converted into equivalent angular deviation. The native C implementation reduced the mean timing error from 218.2–234.7 μs in MicroPython to −10.3–6.1 μs after calibration, corresponding to an average improvement of approximately 225 μs or 4.86° across the tested angles. In the current dataset, the measured standard deviation remained angle-dependent and numerically similar in both environments, ranging from 2.5 to 10.1 μs. Oscilloscope measurements confirmed the expected phase-angle operation and the practical timing displacement between firmware strategies. The results show that the principal advantage of the native implementation is improved absolute synchronization accuracy, whereas the residual short-term jitter remains dominated by the shared detection and triggering chain. Full article