Search Results (2,095)

Traditional single-mode Brillouin optical time-domain analysis systems are inherently limited in terms of sensing capacity, susceptibility to bending loss, and spatial resolution. Multi-core fibers present a promising approach to overcoming these limitations. In this study, a seven-core fiber was utilized, with the central core and three asymmetrically positioned off-axis cores selected for sensing. The temperature coefficients of the four selected cores were experimentally calibrated as 1.103, 0.962, 1.277, and 0.937 MHz/°C, respectively. By employing differential pulse techniques within the Brillouin distributed sensing system, temperature-compensated bending measurements were achieved with a spatial resolution of 20 cm. The fiber was wound around cylindrical mandrels with diameters of 7 cm, 10 cm, and 15 cm. Experimental results demonstrate effective decoupling of temperature and bending strain, enabling accurate curvature reconstruction. Error analysis reveals a minimum deviation of 0.04% for smaller diameters and 0.68% for larger diameters. Cross-comparison of measurements conducted at varying temperatures confirms the robustness and effectiveness of the proposed temperature compensation method. Full article

With the continuous advancement of China’s new-type power system construction, wide-range-frequency oscillation accidents in the power grid have become frequent, characterized by multiple modal components and a wide frequency range. Due to the nonlinearity and coupled time-varying characteristic of wide-range-frequency oscillations, it is difficult to accurately identify parameters. Therefore, this paper proposes an improved particle swarm optimization (PSO)–variational mode decomposition (VMD) method for identifying wide-range-frequency oscillations. First, through improved PSO, the number of modal and secondary penalty factors of the VMD are self-optimized. Energy loss is used as the fitness function, and optimum is achieved through dynamic adjustment of the inertial factor of the particle swarm algorithm. Second, the wide-range-frequency oscillation signal undergoes VMD based on the number of obtained modals and the secondary penalty factor. The effective and noisy modal components are separated using the correlation coefficient approach, and signal reconstruction is utilized to reduce noise. Finally, simulation examples were used to verify the feasibility and effectiveness of the method proposed in this paper. Simulation results demonstrate that the proposed method can capture all wide-band-frequency oscillation information of the signal with an identification error below 2%. It provides a theoretical basis and technical support for wide-band-frequency oscillation traceability and mitigation. Full article

This study presents a deep learning–assisted integrated navigation scheme implemented on an autonomous underwater vehicle carrying a Chinese domestically developed strapdown inertial navigation system, designed for operation in surface and littoral environments. The system integrates measurements from SINS, the global positioning system, and a Doppler velocity log, while integrating a Decoder-based covariance estimator into the error state-extended Kalman filter. This hybrid architecture adaptively models time-varying processes and measurement noise from raw sensor inputs, greatly improving robustness for surface navigation in dynamic marine environments. To improve learning efficiency, we design a compact and informative feature representation that can be adapted to navigation error dynamics. The novel structure captures temporal dependencies and the evolution of nonlinear error more effectively than typical sequence models, achieving faster convergence and superior accuracy compared to GRU and Transformer baselines. The experimental results based on real sea trial data show that our method significantly outperforms model-based and learning-based methods in terms of navigation solution accuracy and stability, and the adaptive estimation of noise covariance. Specifically, it achieves the lowest RMSE of 0.0274, reducing errors by 94.6–34.6%, compared to conventional ES-EKF-integrated navigation, Transformer, GRU, and a DCE variant. These findings underscore the practical significance of integrating domain-informed filtering methodologies with deep noise modeling frameworks to achieve robust and accurate AUV surface navigation. Full article

Aiming at the problem of model parameter perturbation in vehicle trajectory tracking control, an improved robust model predictive control (RMPC) method is proposed. Based on the two-degree-of-freedom vehicle model and Serret Frenet error model, a multi-cell hypercube vertex modeling is adopted to map the disturbance range of parameters such as vehicle speed and lateral stiffness to a set of vertices, and dynamic linear combination is achieved through normalized weights. The algorithm design mainly focuses on the dual-layer optimization of the switching mechanism, decomposing the infinite time domain problem into finite time domain optimization and terminal constraints. At the same time, it dynamically updates the vertex parameters to match time-varying uncertainties and then combines Lyapunov theory to design a control invariant set. The results show that in complex road conditions and vehicle state transitions, RMPC can reduce the peak lateral deviation from 1.0 m to 0.2 m, converge the heading deviation to within 2 deg, and significantly reduce the mean and root mean square values of control errors compared to traditional MPC, under the influence of vehicle model parameter perturbations. RMPC has good robustness and real-time performance. Full article

Currently, the PPP-B2b service faces challenges such as long convergence times and re-convergence issues after signal interruptions due to the lack of high-precision atmospheric enhancement. To address this, this study develops a multi-frequency uncombined Precise Point Positioning (PPP) model that accounts for Clock Constant Bias (CCB) based on PPP-B2b products, extracting atmospheric delays from reference stations and performing regional modeling. Considering the spatiotemporal characteristics of the ionosphere, a stochastic model for enhancement information that varies with time and satellite elevation is established. The performance of atmospheric-enhanced PPP-B2b is validated on the user end. Results demonstrate that zenith wet delay (ZWD) and ionospheric modeling generally achieve centimeter-level accuracy. However, during certain periods, ionospheric modeling errors are significant. By adjusting the stochastic model, approximately 98% of modeling errors can be enveloped. With atmospheric constraints, both convergence speed and positioning accuracy of PPP-B2b are significantly improved. Using thresholds of 30 cm horizontally and 40 cm vertically, the convergence times for horizontal and vertical components are approximately (16.7, 21.3) min for single BDS-3 and (3.8, 5.0) min for the dual-system combination, respectively. In contrast, with atmospheric constraints applied, convergence thresholds are met almost at the first epoch. Within one minute, single BDS-3 and the dual-system combination achieve accuracies better than (0.15, 0.3) m and (0.1, 0.2) m horizontally and vertically, respectively. Furthermore, even under high-elevation cutoff conditions, stable and rapid high-precision positioning remains achievable through atmospheric enhancement. Full article

Soil organic carbon (SOC) plays a critical role in the global carbon cycle and serves as a sensitive indicator of climate change impacts, with its dynamics significantly influencing regional ecological security and sustainable development. This study focuses on the Songnen Plain in Northeast China—a key black soil agricultural region increasingly affected by water erosion, primarily through surface runoff and rill formation on gently sloping cultivated land. We aim to investigate the spatiotemporal dynamics of SOC stocks across different cultivated land erosion zones under projected future climate change scenarios. To quantify current and future SOC stocks, we applied a boosted regression tree (BRT) model based on 130 topsoil samples (0–30 cm) and eight environmental variables representing topographic and climatic conditions. The model demonstrated strong predictive performance through 10-fold cross-validation, yielding high R² and Lin’s concordance correlation coefficient (LCCC) values, as well as low mean absolute error (MAE) and root mean square error (RMSE). Key drivers of SOC stock spatial variation were identified as mean annual temperature, elevation, and slope aspect. Using a space-for-time substitution approach, we projected SOC stocks under the SSP245 and SSP585 climate scenarios for the 2050s and 2090s. Results indicate a decline of 177.66 Tg C (SSP245) and 186.44 Tg C (SSP585) by the 2050s relative to 2023 levels. By the 2090s, SOC losses under SSP245 and SSP585 are projected to reach 2.84% and 1.41%, respectively, highlighting divergent carbon dynamics under varying emission pathways. Spatially, SOC stocks were predominantly located in areas of slight (67%) and light (22%) water erosion, underscoring the linkage between erosion intensity and carbon distribution. This study underscores the importance of incorporating both climate and anthropogenic influences in SOC assessments. The resulting high-resolution SOC distribution map provides a scientific basis for targeted ecological restoration, black soil conservation, and sustainable land management in the Songnen Plain, thereby supporting regional climate resilience and China’s “dual carbon” goals. These insights also contribute to global efforts in enhancing soil carbon sequestration and achieving carbon neutrality goals. Full article

Vision navigation systems provide significant advantages in agricultural scenarios such as pesticide spraying, weeding, and harvesting by interpreting crop row structures in real-time to establish guidance lines. However, the delay introduced by image processing causes the path and pose information relied upon by the controller to lag behind the actual vehicle state. In this study, a hierarchical delay-compensated cooperative control framework (HDC-CC) was designed to synergize Model Predictive Control (MPC) and Sliding Mode Control (SMC), combining predictive optimization with robust stability enforcement for agricultural navigation. An upper-layer MPC module incorporated a novel delay state observer that compensated for visual latency by forward-predicting vehicle states using a 3-DoF dynamics model, generating optimized front-wheel steering angles under actuator constraints. Concurrently, a lower-layer SMC module ensured dynamic stability by computing additional yaw moments via adaptive sliding surfaces, with torque distribution optimized through quadratic programming. Under varying adhesion conditions tests demonstrated error reductions of 74.72% on high-adhesion road and 56.19% on low-adhesion surfaces. In Gazebo simulations of unstructured farmland environments, the proposed framework achieved an average path tracking error of only 0.091 m. The approach effectively overcame vision-controller mismatches through predictive compensation and hierarchical coordination, providing a robust solution for vision autonomous agricultural machinery navigation in various row-crop operations. Full article

Ensuring the structural integrity of mechanical components operating in fluid environments requires precise and reliable monitoring techniques. This study presents a methodology for reconstructing the full-field deformation of a flexible aluminium plate subjected to unsteady water flow in a water tunnel, using a structural modal reconstruction approach informed by experimental data. The experimental setup involves an aluminium lamina (200 mm × 400 mm × 2.5 mm) mounted in a closed-loop water tunnel and exposed to a controlled flow with velocities up to 0.5 m/s, corresponding to Reynolds numbers on the order of ${10}^4$, inducing transient deformations captured through an image-based optical tracking technique. The core of the methodology lies in reconstructing the complete deformation field of the structure by combining a reduced number of vibration modes derived from the geometry and boundary conditions of the system. The novelty of the present work consists in the integration of the Internal Strain Potential Energy Criterion (ISPEC) for mode selection with a data-driven machine learning framework, enabling real-time identification of active modal contributions from sparse experimental measurements. This approach allows for an accurate estimation of the dynamic response while significantly reducing the required sensor data and computational effort. The experimental validation demonstrates strong agreement between reconstructed and measured deflections, with normalised errors below 15% and correlation coefficients exceeding 0.94, confirming the reliability of the reconstruction. The results confirm that, even under complex, time-varying fluid–structure interactions, it is possible to achieve accurate and robust deformation reconstruction with minimal computational cost. This integrated methodology provides a reliable and efficient basis for structural health monitoring of flexible components in hydraulic and marine environments, bridging the gap between sparse measurement data and full-field dynamic characterisation. Full article

This study systematically examined shock wave velocity induced by millisecond–nanosecond combined-pulse laser (ms–ns CPL) at a fixed ns laser energy density of 6 J/cm², exploring the effects of varying pulse delays of 0 to 3 ms and ms laser energy densities of 226.13 J/cm², 301 J/cm² and 376.89 J/cm². The temporal evolution of shock wave velocity induced by varying laser parameters was predicted by an attention mechanism-based long short-term memory algorithm (Attention-LSTM). The dependence between laser parameters and the evolution of shock wave velocity was captured by the LSTM layer. An attention mechanism was utilized to adaptively increase the weights of important time points during the propagation of the shock wave, thereby improving prediction accuracy. The experimental data corresponding to ms laser energy densities of 226.13 J/cm² and 301 J/cm² were set as the training set. The ms laser energy density of 376.89 J/cm² experimental data was set as test set to evaluate the generalization ability of the model under unknown ms laser energy. The results indicate that when ms laser energy density is 376.8 J/cm², the pulse delay is 2.2 ms. The shock wave velocity induced by the CPL increased by 50.77% compared with that induced by a single ns laser. The proposed Attention-LSTM model effectively predicts the evolutionary characteristics of shock wave velocity. The mean absolute error (MAE), root mean square error (RMSE), mean bias error (MBE) and the correlation coefficient (R²) of the test set are 7.65, 9.01, 1.47 and 0.98, respectively. This study provides a new data-driven approach for predicting the shock wave behavior induced by combined laser parameters and provides valuable guidance for optimizing laser process parameter combinations. Full article

Statistical process control may lead to false detection results in the presence of measurement error, so it is necessary to deal with the effect of measurement error. The Bayesian exponentially weighted moving average (EWMA) variability control chart, first proposed by Lin et al., is a distribution-free control chart, and it can effectively monitor process variance even if the process skewness varies with time. This paper investigates the influence of measurement error on the Bayesian EWMA variability control chart, and it proposes two designs for the Bayesian EWMA variability control chart in the presence of measurement error. One is to modify the control limits based on the biased error-prone monitoring statistics, called the error-embedded control chart. The other is to design the control limits based on the error-corrected monitoring statistics, called the error-corrected control chart. Simulation results prove that both of the proposed control charts are reliable and have good detection performance in the presence of measurement error. Moreover, the average run lengths of the proposed control charts are exactly the same, indicating that both of them are equivalent control charts. Comparison results show that the existing control chart in Lin et al. is not in-control robust and fails to detect a downward shift in process variance when measurement error is present. Thus, using the error-embedded control chart or the error-corrected control chart to monitor processes with measurement errors is reliable and effective. Moreover, the proposed control charts, where π₁₁ = 1 and π₁₀ = 0, can be applied to monitor processes without measurement errors since their detection performance is equal to that of the existing control chart in Lin et al. Finally, we demonstrate the application of the error-embedded control chart and the error-corrected control chart to analyze the data from the service time system of a bank branch and the data from a semiconductor manufacturing process, showing that the proposed control charts can indeed be applied to data with measurement errors. Full article

In particle image velocimetry (PIV), overexposure is particularly common in regions with high illumination. In particular, strong scattering or background reflection at the liquid–gas interface will make the overexposure phenomenon more obvious, resulting in local pixel saturation, which will significantly reduce the particle image quality, and thus reduce the particle recognition rate and the accuracy of velocity field estimation. This study addresses the overexposure challenges in particle image velocimetry applications, mainly to address the challenge that the velocity field cannot be measured due to the difficulty in effectively detecting particles in the exposed area. In order to address the challenge of overexposure, this paper does not use traditional frame-based high-speed cameras, but instead proposes a particle image velocimetry algorithm based on adaptive integral spike camera data using a neuromorphic vision sensor (NVS). Specifically, by performing target-background segmentation on high-frequency digital spike signals, the method suppresses high illumination background regions and thus effectively mitigates overexposure. Then the spike data are further adaptively integrated based on both regional background illumination characteristics and the spike frequency features of particles with varying velocities, resulting in high signal-to-noise ratio (SNR) reconstructed particle images. Flow field computation is subsequently conducted using the reconstructed particle images, with validation through both simulation and experiment. In simulation, in the overexposed area, the average flow velocity estimation error of frame-based cameras is 8.594 times that of spike-based cameras. In the experiments, the spike camera successfully captured continuous high-density particle trajectories, yielding measurable and continuous velocity fields. Experimental results demonstrate that the proposed particle image velocimetry algorithm based on the adaptive integration of the spike camera effectively addresses overexposure challenges caused by high illumination of the liquid–gas interface in flow field measurements. Full article

Block-based hybrid video coders typically use inter-prediction and bidirectionally coded (B) frames to improve compression efficiency. For this purpose, they employ look-ahead buffers, perform out-of-sequence frame coding, and implement similarity search-based general-purpose algorithms for motion estimation. While effective, these methods increase computational complexity and may not suit delay-sensitive practical applications such as real-time drone video transmission. If future motion can be predicted from external metadata, encoding can be optimized with lower complexity. In this study, a mathematical model for predicting motion vectors in drone video using only flight parameters is proposed. A remote-controlled drone with a fixed downward-facing camera recorded 4K video at 50 fps during autonomous flights over a marked terrain. Four flight parameters were varied independently, altitude, horizontal speed, vertical speed, and rotational rate. OpenCV was used to detect ground markers and compute motion vectors for temporal distances of 5 and 25 frames. Polynomial surface fitting was applied to derive motion models for translational, rotational, and elevational motion, which were later combined. The model was validated using complex motion scenarios (e.g., circular, ramp, helix), yielding worst-case prediction errors of approximately −1 ± 3 and −6 ± 14 pixels at 5 and 25 frames, respectively. The results suggest that flight-aware modeling enables accurate and low-complexity motion vector prediction for drone video coding. Full article

This paper proposes a predefined time transient coordinated control strategy based on an adaptive nonlinear extended state observer (ANLESO) to address the adaptability challenges of mode transition control in power-split hybrid electric vehicles (PS-HEVs). Firstly, building upon a conventional dynamic coordinated control framework, the influence of varying acceleration conditions and external disturbances on mode transition performance is analyzed. To enhance disturbance estimation under both positive and negative as well as large and small errors, an ANLESO is developed, which not only improves the speed and accuracy of disturbance observation but also guarantees symmetric convergence performance with respect to estimation errors. Subsequently, a predefined time feedback controller is developed based on the theory of predefined time control. Theoretical stability analysis demonstrates that the convergence time of the system is independent of the initial state and can be guaranteed within a predefined time. Finally, the feasibility and superiority of the proposed control strategy are validated through Hardware-in-the-Loop (HIL) testing and vehicle experimentation. The results show that, compared with PID control based on a linear expansion state observer, the proposed strategy reduces the mode transition time by 45.7% and mitigates drivability shock by 59.2%. Full article

Free chlorine residual is the most widely adopted disinfectant residual in water supply systems. Chlorine is usually applied at treatment works, but it decays as water flows and spends time within the network. Chlorine decay is the result of a bulk and a wall decay component. Bulk decay may be considered invariable through the pipe network (it only depends on water composition) and is often characterized at the entrance to the system through bottle tests, which measure chlorine evolution over time in a laboratory environment to then adjust a model (dependent on one or more coefficients) that represents its behavior. Previous studies have acknowledged that the bulk decay coefficient varies widely and that free chlorine measurements are subject to measurement errors, but they have not quantified the impact of these errors on the bulk decay coefficient. The aim of this paper is to provide a methodology that statistically fits chlorine’s bulk decay coefficient based on bottle test results, with appropriate management of uncertainty effects. The proposal is to use state estimation techniques, which combine free chlorine measurements and system knowledge (in this case, a first-order bulk decay model) to provide the most likely chlorine behavior and its associated uncertainty. This approach goes one step beyond previous studies, which report only a single value of the bulk decay coefficient without accounting for randomness, and thus fail to assess true variability, leading to unrepresentative comparisons. Results for water samples from different sources demonstrate the importance of controlling the fitting process through state estimation to understand and compare the bulk decay coefficient. Full article

This study focuses on the cooperative formation control problem of six-degree-of-freedom (6-DOF) fixed-wing unmanned aerial vehicles (UAVs) under constraints of limited communication resources and strict time requirements. The core innovation of the proposed framework lies in the deep integration of a dynamic self-triggered communication mechanism (DSTCM) with a prescribed-time control strategy. Furthermore, a fuzzy control strategy is designed to effectively suppress system disturbances, enhancing the robustness of the formation. The designed DSTCM not only retains the adaptive triggering threshold characteristic of dynamic event-triggered communication, significantly reducing communication frequency, but also completely eliminates the need for continuous state monitoring required by traditional event-triggered mechanisms. As a result, both communication and onboard computational resources are effectively conserved. In parallel, a novel time-varying unilateral constrained performance function is introduced to construct a prescribed-time controller, which guarantees that the formation tracking error converges to a predefined residual set within a user-specified time. The convergence process is independent of initial conditions and strictly adheres to full-state constraints. A rigorous Lyapunov-based stability analysis demonstrates that all signals in the closed-loop UAV velocity and attitude system are semi-globally uniformly ultimately bounded (SGUUB). Furthermore, the proposed DSTCM ensures the existence of a strictly positive lower bound on the inter-event triggering intervals of the UAVs, thereby avoiding the occurrence of Zeno behavior. Numerical simulation results are provided to verify the effectiveness and superiority of the proposed control scheme. Full article