Search Results (2,151)

Accurate and reliable positioning is essential for unmanned marine vehicles (UMVs), especially in complex maritime environments. Existing algorithms often underutilize historical information, struggle with nonlinear dynamics, and lack adaptability in the GNSS Measurement Noise Covariance, leading to degraded performance. This study proposes an enhanced Factor Graph Optimization (FGO) method integrated with an adaptive neuro-fuzzy inference system (ANFIS) to overcome these challenges. First, an improved GNSS/Dead Reckoning (DR) factor graph is built using refined error models to enhance baseline accuracy. Second, a marginalization factor is introduced utilizing a sliding window and the Schur complement method to retain informative historical data while reducing computational load, thereby improving stability and field performance. Third, an ANFIS-based adaptive GNSS factor dynamically updates the GNSS Measurement Noise Covariance Matrix (GMNCM) to strengthen robustness under variable maritime conditions. Simulation and field tests demonstrate significant improvements: the proposed method achieves 29.1%, 26.5%, and 9.9% higher accuracy than EKF, UKF, and conventional FGO, respctively. Under GNSS interruptions, EKF and UKF diverge with errors exceeding 500 m, while FGO limits drift to 20 m. The proposed ANFIS–FGO shows the smallest fluctuations and fastest recovery, confirming its strong resilience and practical applicability for UMV navigation. Full article

Wireless Sensor and Actor Networks (WSANs) are critical for industrial automation in the context of Industry 4.0, yet the optimal placement of actors to ensure connectivity and coverage remains an NP-hard problem. This study addresses the Actor Placement Problem (APP) in constrained, two-zone industrial environments. We propose a hybrid system, the PSO-HC-DGA hybrid system, which integrates Particle Swarm Optimization (PSO), Hill Climbing (HC), and the Distributed Genetic Algorithm (DGA). We evaluate four crossover methods (UNDX, SPX, BLX-$\alpha$, and psBLX) combined with two actor replacement methods (RIWM and FC-RDVM) for small-, medium-, and large-scale scenarios. The simulation results demonstrate that psBLX is the most effective of the four crossover methods. In the small-scale scenario, it achieved better load balancing combined with RIWM, while in the medium-scale scenario, psBLX achieved full sensor coverage with RIWM and good load balancing with FC-RDVM. For the large-scale scenario, we compared the performance of the implemented hybrid system with that of a PSO system. The hybrid system showed 100% connectivity and achieved better sensor coverage than the PSO system. The Kruskal–Wallis test confirmed that the performance differences in load balancing were statistically significant. We conclude that the proposed hybrid system using psBLX enables robust and high-performance deployment in two-zone industrial WSANs. Full article

Rapid and uniform seedling establishment is critical for the productivity of direct-seeded rice, particularly in late-season cropping systems where sowing frequently coincides with high-temperature stress. Current seed quality assessment relies predominantly on the Standard Germination Test (SGT); however, this method, conducted under optimal conditions, often fails to predict field performance in thermally stressful environments. To resolve this discrepancy, this study established a High-Temperature Germination (HTG) protocol optimized specifically for late-season rice. Twenty-three diverse rice genotypes—comprising conventional japonica, indica-japonica hybrids, and indica hybrids—were evaluated using SGT and HTG assays at 35 °C, 38 °C, and 41 °C, incorporating a pre-treatment with trichloroisocyanuric acid (TCCA) to standardize initial seed conditions. Validation was conducted through field trials at two distinct locations in Zhejiang, China. The results demonstrated that while SGT indicated high viability (>85%) for most varieties, it exhibited a poor correlation with field emergence (r < 0.31). In contrast, HTG tests conducted at 38 °C and 41 °C showed reliable predictive validity, yielding highly significant correlations with field establishment (r > 0.70, p < 0.001). Significant genotypic variation was observed: hybrid varieties displayed superior thermotolerance and stable germination even at 41 °C, whereas conventional japonica varieties exhibited marked sensitivity to temperatures exceeding 35 °C. These findings highlight the potential of the HTG assay (specifically at 38 °C or 41 °C) as an effective, cost-efficient, and rapid screening tool. By accurately simulating the acute thermal stress of the sowing-to-emergence window, this method facilitates the identification of climate-resilient germplasm and supports reliable stand establishment in direct-seeded rice production. Full article

To better meet the wavefront measurement requirements for large-aperture segmented mirrors after in-orbit deployment, this paper designs a measurement system based on an optical camera and targets. This system utilizes photogrammetry principles to measure target positions, fit the wavefront of the segmented mirror, and form a closed-loop control with the calibration mechanism. Based on the wavefront measurement range and accuracy requirements during the coarse calibration phase of the segmented mirror, the optical system was first designed. The measurement camera features a 16° × 12° rectangular field of view with a 100 mm focal length, achieving near-diffraction-limited imaging quality. The structural fundamental frequency of the measurement camera exceeds 400 Hz. Under a 4 °C temperature rise environment, the surface error of the optical lens remains better than 1/80$\lambda$. Based on error theory, a quantitative analysis of error sources and their impact on target position measurement accuracy was conducted, yielding theoretical measurement errors of ±0.0853 mm in the Z-direction and ±0.1525 mm in the X-direction. Through focal length calibration and imaging tests of the prototype, the measurement camera achieved a modulation transfer function greater than 0.11 with excellent imaging quality. With a focal length of 101.356 mm and a measurement range exceeding ±4 mm, it meets design requirements. Finite element simulation and Monte Carlo methods analyzed wavefront fitting accuracy under different operating conditions, yielding peak-to-valley values of 0.397 mm and root mean square values of 0.073 mm. The wavefront measurement system designed in this paper meets the structural rigidity and temperature adaptability requirements for in-orbit measurement systems. The prototype’s field of view satisfies the wavefront measurement range requirements, the camera’s focal length meets design specifications with good imaging quality, and the wavefront measurement deviation meets the accuracy requirements for the coarse calibration phase. Compared to current wavefront measurement systems, the proposed system significantly expands the measurement range, offering a novel wavefront measurement method for coarse calibration of tiled mirrors. Full article

This paper investigates the degradation of detection performance in FDA–MIMO radar systems caused by signal mismatch under constant-velocity target motion and develops a robust detection strategy to mitigate this effect. Under the effective hypothesis, a stochastic term is introduced into the received radar signal to account for mismatch uncertainty. This term is modeled as a Gaussian random variable whose covariance structure is identical to that of the noise while being scaled by an unknown robustness parameter. Based on the resulting statistical model, three robust detectors are derived using the One-Step Generalized Likelihood Ratio Test (OGLRT), the Two-Step GLRT (TGLRT), and the Gradient test. Simulation results demonstrate that all proposed detectors preserve the Constant False Alarm Rate (CFAR) property under the null hypothesis. Further performance evaluations reveal that, in the absence of signal mismatch, the OGLRT and Gradient detectors provide superior detection performance, whereas under mismatched conditions, all three detectors exhibit improved robustness. These findings provide both theoretical insight and practical guidance for the design and implementation of FDA–MIMO radar systems, contributing to the enhancement and optimization of detection performance in realistic operating environments. Full article

As the primary power transmission conduits, aircraft hydraulic pipelines are critical for actuating flight control surfaces and landing gear systems. Accurate in situ strain evaluation of these pipelines is essential, as installation-induced pre-loads directly compromise fatigue life and sealing performance, threatening overall system reliability. However, such evaluation is frequently hindered by the perspective distortions and limited depth of field inherent in conventional imaging systems. To overcome these metrological limitations, this study presents a novel virtual telecentric camera array system designed for high-precision, non-contact strain measurement. Unlike traditional pinhole models, the proposed system leverages a catadioptric setup with planar mirrors to create a virtual four-eye telecentric array from a single physical lens, ensuring constant magnification within the depth of field. A comprehensive simulation framework was established to rigorously compare the reprojection errors and scale accuracies between telecentric and pinhole projection models, quantitatively demonstrating the superior stability of the telecentric approach. Furthermore, a dedicated calibration strategy for non-overlapping telecentric fields of view was developed and validated. Experimental results from pipeline installation tests indicate a high concordance with strain gauge data, confirming that the proposed telecentric system effectively mitigates parallax errors and provides a robust solution for static and quasi-static micro-scale deformation monitoring in complex assembly environments. Full article

Basalt fiber-reinforced concrete is increasingly being used in shotcrete support systems for rock mass excavation engineering due to its superior mechanical properties and durability. Rapid freeze–thaw cycling tests were performed to simulate freeze–thaw conditions in order to meticulously investigate the dynamic and static fracture behaviors of basalt fiber-reinforced concrete in freeze–thaw environments. Then, utilizing a Split Hopkinson Pressure Bar (SHPB) system and rock testing equipment, dynamic and static fracture tests were performed on developed Mode I, mixed-mode I/II, and Mode II platform Brazilian disk specimens. Under freeze–thaw conditions, the dynamic and static fracture propagation velocities of specimens with diverse crack propagation modes were determined. Based on this, LS-DYNA numerical simulations were used to perform inverse evaluations of crack propagation processes in specimens with varied fracture modes and Mode I fracture specimens with variable basalt fiber contents. We were able to calculate the effective stress field distributions during crack propagation with dynamic loading. The data indicate that different fracture modes present significantly distinct crack propagation issues. Pure Mode I fracture specimens exhibit the most straightforward crack propagation, with a maximum effective stress of roughly 25 MPa after crack penetration. With a maximum effective stress of around 31 MPa following crack penetration, the mixed-mode I/II fracture specimens exhibit considerable propagation difficulties. Mode II fracture specimens are the hardest to propagate after crack penetration because of their maximum effective stress of 64 MPa. Additionally, the optimal basalt fiber content was determined to be in the range of 0.35% to 0.45%, at which the concrete exhibited the best fracture toughness and freeze–thaw resistance. Furthermore, the evolution characteristics of the displacement of the crack tip opening under different fracture modes are revealed. A theoretical basis for stability analysis and design of excavation engineering structures under dynamic stress and associated freeze–thaw conditions is provided by the study’s findings. Full article

High-strength concrete (HSC) is vital for large-scale tunnel infrastructure; however, its durability is often compromised by rigorous freeze–thaw cycles in cold-region environments. This study investigates the synergistic effects of incorporating hybrid steel fiber (SF) and polypropylene fiber (PPF) to enhance the frost resistance of HSC. Experimental testing involved 125 freeze–thaw cycles across various fiber dosages and lengths, monitoring mass loss and the relative dynamic modulus of elasticity. Additionally, a concrete damage plasticity (CDP) model was utilized in numerical simulations to analyze thermal stress distribution and damage evolution under coupled freeze–thaw and axial loading. Results indicate that the hybrid fiber integration significantly improved durability, with Group A3 (35 kg/m³ SF and 1.5 kg/m³ of 18 mm PPF) achieving the highest performance. After 125 cycles, Group A3 maintained a relative dynamic modulus of 94.5% and restricted mass loss to 1.42%, a 41% improvement over the fiber-free control. Numerical simulations corroborated these findings, demonstrating that the dual-fiber system preserves load-bearing capacity, limiting compressive strength degradation to just 6.7%. These findings quantitatively validate the synergistic mechanisms of hybrid fibers, providing a robust reference for designing high-durability concrete in cold-climate engineering applications. Full article

The internal thermal insulation structure serves as a vital subsystem within the thermal insulation system of high-temperature devices, playing a crucial role in effectively maintaining a high-temperature environment, reducing energy consumption, and enhancing testing efficiency. However, during the operation of these devices, the internal thermal insulation structure is inevitably subjected to high temperatures. Therefore, it is essential to focus on the heat transfer performance of this structure. Initially, the internal thermal insulation structure is designed, and the relative dimensions and materials of each component are determined. Subsequently, a finite element model of the internal thermal insulation structure is established, and numerical simulations of heat transfer are conducted under the device’s operating conditions to analyze the thermal insulation structure. This analysis is ultimately validated through high-temperature experiments conducted on specimens of the internal thermal insulation structure. The results indicate that the designed internal thermal insulation structure effectively maintains the high-temperature environment within the device and demonstrates excellent thermal insulation performance, with a maximum heat flux of 66.7 W/m² and an outer wall surface temperature of 25.98 °C. This work is significant as it lays the groundwork for the design and construction of such devices. Full article

Accurate fault diagnosis of mining hoisting head sheave systems is critical for ensuring operational safety in harsh underground environments. This study proposes a multi-source fault diagnosis framework that fuses vibration and acoustic information using a Convolutional Neural Network and Random Forest (CNN-RF). To support mechanism understanding and validate the experimental platform, finite element and multi-body dynamics simulations (ANSYS/ADAMS) are employed for physical verification and fault signature analysis, while the CNN-RF model is trained and tested exclusively using experimentally acquired vibration and acoustic data. For feature construction, vibration signals are transformed into time–frequency representations (including STFT, CWT, and generalized S-Transform (GST)), and acoustic signals are characterized using Mel-Frequency Cepstral Coefficients (MFCCs). Experimental results demonstrate that vibration–acoustic fusion improves diagnostic performance compared with single-modality baselines; the best performance is achieved by GST+MFCC with the proposed CNN-RF classifier, reaching an accuracy of 98.96%. Future work will conduct cross-condition validation under varying speeds and loads and investigate missing-modality robustness to further assess generalization and deployment reliability. Full article

In urban environments, autonomous vehicles face critical challenges in localization and perception under extreme lighting conditions, including rapid illumination changes, high contrast, and nighttime low-light scenarios. To address the performance degradation of traditional LiDAR-inertial odometry systems under such conditions, this study proposes a high-precision FAST-LIO2-EC algorithm that fuses event cameras into the FAST-LIO2 framework. Event cameras, with their microsecond temporal resolution and 140 dB dynamic range, provide asynchronous edge information that complements LiDAR point clouds and IMU measurements. We validate the proposed system through real-world road tests conducted on public roads and closed test tracks, covering three typical extreme lighting scenarios: tunnel entrance/exit transitions, high-contrast shadow boundaries, and nighttime sparse-lighting conditions. The experimental platform is equipped with a 32-beam LiDAR, a 6-axis IMU, a DVS event camera, and an RTK-GNSS system for ground truth trajectory acquisition. Real-world results demonstrate that the FAST-LIO2-EC system achieves significant improvements in localization accuracy and robustness. In illumination change scenarios, the Absolute Trajectory Error (ATE) is reduced by 32.5% compared to the baseline FAST-LIO2 system, with zero tracking loss events. The point cloud quality is substantially enhanced, with more uniform distribution and clearer obstacle boundaries. In high-contrast scenarios, both systems maintain comparable performance with ATE below 0.15 m. However, in nighttime scenarios, the fusion system shows moderate improvement (15.3% ATE reduction) but reveals sensitivity to event camera noise, indicating the need for adaptive thresholding strategies. Supplementary simulation experiments validate the system’s robustness under varying speeds and sensor noise levels. This work provides a practical solution for autonomous vehicle deployment in complex urban lighting environments, with a comprehensive analysis of real-world performance boundaries and deployment considerations. Full article

Unmanned Aerial Vehicles (UAVs) have made significant advancements in communication stability and security through techniques such as frequency hopping, signal spreading, and adaptive interference suppression. However, challenges remain in modeling spectrum competition, integrating expert knowledge, and predicting opponent behavior. To address these issues, we propose UAV-FPG (Unmanned Aerial Vehicle–Frequency Point Game), a game-theoretic environment model that simulates the dynamic interaction between interference and anti-interference strategies of opponent and ally UAVs in communication frequency bands. The model incorporates a prior expert knowledge base to optimize frequency selection and employs large language models for episode-level opponent trajectory generation and planning within UAV-FPG, serving as an operationally more challenging simulator adversary for stress-testing anti-jamming policies under our evaluation protocol. Experimental results highlight the effectiveness of integrating the expert knowledge base and the large language model: relative to fixed-path baselines, iterative feedback-conditioned LLM planning tends to generate more adaptive trajectories and achieve higher opponent rewards in UAV-FPG. These findings are confined to the proposed simulation environment and are not intended as general claims about real-world jamming capability or onboard planning performance. UAV-FPG provides a robust platform for advancing anti-jamming strategies and intelligent decision-making in UAV communication systems. Full article

Maritime dynamic observation missions, such as environmental monitoring, marine ranching inspection, and emergency response, typically require large-scale and high-efficiency operations in complex and variable maritime environments. Unmanned Surface Vehicles (USVs) and Unmanned Aerial Vehicles (UAVs) offer complementary advantages in such missions: USVs provide long endurance and stable platform support, while UAVs enable rapid, high-coverage aerial perception. However, limited UAV battery capacity and dynamic task environments pose significant challenges to autonomous collaborative operations. This study proposes a collaborative operation and energy replenishment strategy for USV–UAV systems in maritime dynamic observation missions. Under a unified framework, task allocation, collaborative path planning, and energy replenishment are jointly optimized, where the USV serves as a mobile replenishment platform to provide energy support for the UAV. The proposed method incorporates dynamic task updates, environmental disturbances, and energy constraints, achieving real-time adaptive collaboration between heterogeneous agents. Validation through both simulations and actual sea trials demonstrates that the proposed strategy significantly outperforms four baseline methods (greedy strategy, static planning, multi-objective genetic algorithm, and reinforcement learning scheduler) across five core metrics: task completion rate (91.74% in simulation/90.85% in sea trials), total energy consumption (1284.66 kJ/1298.42 kJ), mission completion time (40.28 min/41.12 min), average response time (10.21 s/10.35 s), and path redundancy (13.79%/14.03%). Furthermore, ablation experiments verify that the energy replenishment strategy enhances the task completion rate in both simulation and field tests. This method provides a feasible and scalable collaborative solution for autonomous multi-agent systems, offering significant guidance for the practical deployment of future maritime observation and monitoring missions. Full article

CubeSats are increasingly adopted for space missions due to their low cost and short development cycles. However, their attitude determination and control systems (ADCS) often suffer from limited verification environments and constrained hardware configurations. This study addresses the development and verification of a flight-ready ADCS for the INHA RoSAT 3U CubeSat under realistic constraints in hardware, software, and test infrastructure. A model-based design (MBD) approach is adopted to construct an integrated development pipeline covering algorithm design, simulation, automatic C code generation, and integration with flight software (FSW). The generated code is embedded into a closed commercial onboard computer framework while preserving consistency across model-in-the-loop (MIL) and processor-in-the-loop (PIL) verification stages. To compensate for the lack of full hardware-in-the-loop (HIL) facilities, a FlatSat-based Sensor-to-Actuator test strategy is introduced to validate critical hardware–software interfaces including signal polarity, unit consistency, mounting orientation, and data flow using actual flight hardware. Furthermore, a fault-aware hierarchical attitude control scheme is defined in which the controller transitions to an alternative controller upon actuator fault indications. The presented approach demonstrates a practical ADCS development and verification strategy suitable for resource-constrained CubeSat missions, providing guidance for teams facing similar limitations in cost, resources, and test infrastructure. Full article

The rapid expansion of impervious surfaces in urban environments has significantly increased surface runoff and flood risk. Detention basins, implemented as part of Sustainable Urban Drainage Systems (SUDSs), are widely adopted worldwide to control peak discharges and mitigate recurrent flooding. In this study, an explicit flood routing model is applied to simulate the hydraulic behaviour of an urban detention reservoir, offering a computationally efficient alternative to traditional implicit numerical schemes by avoiding iterative solution procedures. In parallel, twenty-eight machine learning (ML) models are evaluated to estimate the percentage reduction in peak discharge required to comply with local regulatory constraints. The proposed framework integrates explicit hydrological routing with data-driven modelling to support decision-making during the design of detention systems. The methodology is applied to an urban catchment in Cartagena, Colombia, comparing an uncontrolled inflow hydrograph (without SUDSs) with an attenuated outflow hydrograph produced by the detention basin. The results demonstrate a substantial reduction in peak discharge and a delay in the time to peak, fully complying with Colombian regulations that require a minimum attenuation of 30%. Among the evaluated ML models, Squared Exponential Gaussian Process Regression achieved the best performance, yielding coefficient of determination (R²) values of 0.999 in both the validation and test sets. The findings confirm the potential of machine learning techniques to quantify peak-flow reduction requirements accurately and to support the planning and design of detention reservoirs in urban environments. The proposed approach constitutes a practical, efficient, and replicable tool for sustainable urban drainage design since the results of this research can be used to design detention pond systems employing ML tools. Full article