Search Results (15,523)

The force ripple of a permanent magnet synchronous linear motor (PMSLM) caused by multi-source disturbances in practical applications seriously restricts its high-precision motion control performance. The traditional single-mechanism model has difficulty fully characterizing the nonlinear disturbance factors, while the data-driven method has real-time limitations. Therefore, this paper proposes a hybrid modeling framework that integrates the physical mechanism and measured data and realizes the dynamic compensation of the force ripple by constructing a collaborative suppression algorithm. At the mechanistic level, based on electromagnetic field theory and the virtual displacement principle, an analytical model of the core disturbance terms such as the cogging effect and the end effect is established. At the data level, the acceleration sensor is used to collect the dynamic response signal in real time, and the data-driven ripple residual model is constructed by combining frequency domain analysis and parameter fitting. In order to verify the effectiveness of the algorithm, a hardware and software experimental platform including a multi-core processor, high-precision current loop controller, real-time data acquisition module, and motion control unit is built to realize the online calculation and closed-loop injection of the hybrid compensation current. Experiments show that the hybrid framework effectively compensates the unmodeled disturbance through the data model while maintaining the physical interpretability of the mechanistic model, which provides a new idea for motor performance optimization under complex working conditions. Full article

In this paper, a joint Matching Field Processing (MFP) Algorithm based on horizontal uniform circular array (UCA) is proposed for three-dimensional position of underwater wrecked targets. Firstly, a Marine search and rescue position model based on Minimum Variance Distortionless Response (MVDR) and matching field quadratic joint Algorithm was proposed. Secondly, an MVDR beamforming method based on pre-Kalman filtering is designed to refine the real-time DOA estimation of the desired signal and the interference source, and the sound source azimuth is determined for prepositioning. The antenna array weights are dynamically adjusted according to the filtered DOA information. Finally, the Adaptive Matching Field Algorithm (AMFP) used the DOA information to calculate the range and depth of the lost target, and obtained the range and depth estimates. Thus, the 3D position of the lost underwater target is jointly estimated. This method alleviates the angle ambiguity problem and does not require a computationally intensive 2D spectral search. The simulation results show that the proposed method can better realise underwater three-dimensional positioning under certain signal-to-noise ratio conditions. When there is no error in the sensor coordinates, the positioning error is smaller than that of the baseline method as the SNR increases. When the SNR is 0 dB, with the increase in the sensor coordinate error, the target location error increases but is smaller than the error amplitude of the benchmark Algorithm. The experimental results verify the robustness of the proposed framework in the hierarchical ocean environment, which provides a practical basis for the deployment of rapid response underwater positioning systems in maritime search and rescue scenarios. Full article

This paper presents a novel harmonic-selective clocking scheme that facilitates concurrent downconversion of spectrally distant radio frequency (RF) signals using a single low-frequency local oscillator (LO) in an N-path receiver architecture. The proposed scheme selectively generates LO harmonics aligned with multiple RF bands, enabling simultaneous downconversion without modification of the passive mixer topology. The receiver employs a 4-path passive mixer configuration to enhance harmonic selectivity and provide flexible frequency planning.The architecture is implemented on a printed circuit board (PCB) and validated through comprehensive simulation and experimental measurements under continuous wave and modulated signal conditions. Measured results demonstrate a sensitivity of $-55\mathrm{dBm}$ and a conversion gain varying from $-2.5\mathrm{dB}$ to $-9\mathrm{dB}$ depending on the selected harmonic pair. The receiver’s performance is further corroborated by concurrent (dual band) reception of real-world signals, including a GPS signal centered at 1575 MHz and an LTE signal at 1179 MHz, both downconverted using a single 393 MHz LO. Signal fidelity is assessed via Normalized Mean Square Error (NMSE) and Error Vector Magnitude (EVM), confirming the proposed architecture’s effectiveness in maintaining high-quality signal reception under concurrent multiband operation. The results highlight the potential of harmonic-selective clocking to simplify multiband receiver design for wireless communication and global navigation satellite system (GNSS) applications. Full article

Timely and accurate identification of surface damage in hydraulic structures is essential for maintaining structural integrity and ensuring operational safety. Traditional manual inspections are time-consuming, labor-intensive, and prone to subjectivity, especially for large-scale or inaccessible infrastructure. Leveraging advancements in aerial imaging, unmanned aerial vehicles (UAVs) enable efficient acquisition of high-resolution visual data across expansive hydraulic environments. However, existing deep learning (DL) models often lack architectural adaptations for the visual complexities of UAV imagery, including low-texture contrast, noise interference, and irregular crack patterns. To address these challenges, this study proposes a lightweight, robust, and high-precision segmentation framework, called LFPA-EAM-Fast-SCNN, specifically designed for pixel-level damage detection in UAV-captured images of hydraulic concrete surfaces. The developed DL-based model integrates an enhanced Fast-SCNN backbone for efficient feature extraction, a Lightweight Feature Pyramid Attention (LFPA) module for multi-scale context enhancement, and an Edge Attention Module (EAM) for refined boundary localization. The experimental results on a custom UAV-based dataset show that the proposed damage detection method achieves superior performance, with a precision of 0.949, a recall of 0.892, an F1 score of 0.906, and an IoU of 87.92%, outperforming U-Net, Attention U-Net, SegNet, DeepLab v3+, I-ST-UNet, and SegFormer. Additionally, it reaches a real-time inference speed of 56.31 FPS, significantly surpassing other models. The experimental results demonstrate the proposed framework’s strong generalization capability and robustness under varying noise levels and damage scenarios, underscoring its suitability for scalable, automated surface damage assessment in UAV-based remote sensing of civil infrastructure. Full article

With the increasing deployment of unmanned aerial vehicles (UAVs) in indoor environments, the demand for high-precision six-degrees-of-freedom (6-DoF) localization has grown significantly. Ultra-wideband (UWB) technology has emerged as a key enabler for indoor UAV navigation due to its robustness against multipath effects and high-accuracy ranging capabilities. However, conventional UWB-based systems primarily rely on range measurements, operate at low measurement frequencies, and are incapable of providing attitude information. This paper proposes a tightly coupled error-state extended Kalman filter (TC–ESKF)-based UWB/inertial measurement unit (IMU) fusion framework. To address the challenge of initial state acquisition, a weighted nonlinear least squares (WNLS)-based initialization algorithm is proposed to rapidly estimate the UAV’s initial position and attitude under static conditions. During dynamic navigation, the system integrates time-difference-of-arrival (TDOA) and angle-of-arrival (AOA) measurements obtained from the UWB module to refine the state estimates, thereby enhancing both positioning accuracy and attitude stability. The proposed system is evaluated through simulations and real-world indoor flight experiments. Experimental results show that the proposed algorithm outperforms representative fusion algorithms in 3D positioning and yaw estimation accuracy. Full article

The increasing penetration of renewable energy into power systems has intensified transient power quality (PQ) disturbances, demanding efficient detection and classification methods to enable timely operational decisions. This paper introduces a hybrid framework combining an Improved Adaptive S-Transform (IAST) with a Random Forest (RF) classifier to address these challenges. The IAST employs a globally adaptive Gaussian window as its kernel function, which automatically adjusts window length and spectral resolution based on real-time frequency characteristics, thereby enhancing time–frequency localization accuracy while reducing algorithmic complexity. To optimize computational efficiency, window parameters are determined through an energy concentration maximization criterion, enabling rapid extraction of discriminative features from diverse PQ disturbances (e.g., voltage sags and transient interruptions). These features are then fed into an RF classifier, which simultaneously mitigates model variance and bias, achieving robust classification. Experimental results show that the proposed IAST–RF method achieves a classification accuracy of 99.73%, demonstrating its potential for real-time PQ monitoring in modern grids with high renewable energy penetration. Full article

Inspection, maintenance, and repair (IMR) operations on underwater infrastructure remain costly and time-intensive because fully teleoperated remote operated vehicle s(ROVs) lack the range and dexterity necessary for precise cooperative underwater manipulation, and the alternative of using professional divers is ruled out due to the risk involved. This work presents and experimentally validates an autonomous, dual-I-AUV (Intervention–Autonomous Underwater Vehicle) system capable of assembling rigid pipeline segments through coordinated actions in a confined underwater workspace. The first I-AUV is a Girona 500 (4-DoF vehicle motion, pitch and roll stable) fitted with multiple payload cameras and a 6-DoF Reach Bravo 7 arm, giving the vehicle 10 total DoF. The second I-AUV is a BlueROV2 Heavy equipped with a Reach Alpha 5 arm, likewise yielding 10 DoF. The workflow comprises (i) detection and grasping of a coupler pipe section, (ii) synchronized teleoperation to an assembly start pose, and (iii) assembly using a kinematic controller that exploits the Girona 500’s full 10 DoF, while the BlueROV2 holds position and orientation to stabilize the workspace. Validation took place in a 12 m × 8 m × 5 m water tank. Results show that the paired I-AUVs can autonomously perform precision pipeline assembly in real water conditions, representing a significant step toward fully automated subsea construction and maintenance. Full article

This study proposes an intelligent patrol planning framework that leverages reinforcement learning, spatiotemporal crime forecasting, and simulated sensor telemetry to optimize autonomous vehicle (AV) navigation in urban environments. Crime incidents from Washington DC (2024–2025) and Seattle (2008–2024) are modeled as a dynamic spatiotemporal graph, capturing the evolving intensity and distribution of criminal activity across neighborhoods and time windows. The agent’s state space incorporates synthetic AV sensor inputs—including fuel level, visual anomaly detection, and threat signals—to reflect real-world operational constraints. We evaluate and compare three learning strategies: Deep Q-Network (DQN), Double Deep Q-Network (DDQN), and Proximal Policy Optimization (PPO). Experimental results show that DDQN outperforms DQN in convergence speed and reward accumulation, while PPO demonstrates greater adaptability in sensor-rich, high-noise conditions. Real-map simulations and hourly risk heatmaps validate the effectiveness of our approach, highlighting its potential to inform scalable, data-driven patrol strategies in next-generation smart cities. Full article

High-strength steel has high strength and low thermal conductivity, and its thin-walled parts are very susceptible to residual stress and deformation caused by cutting heat during the drilling process, which affects the machining accuracy and quality. High-strength steel thin-walled components are widely used in aerospace and other high-end sectors; however, systematic investigations into their temperature fields during drilling remain scarce, particularly regarding the evolution characteristics of the temperature field in thin-wall drilling and the quantitative relationship between drilling parameters and these temperature variations. This paper takes the thin-walled parts of AF1410 high-strength steel as the research object, designs a special fixture, and applies infrared thermography to measure the bottom surface temperature in the thin-walled drilling process in real time; this is carried out in order to study the characteristics of the temperature field during the thin-walled drilling process of high-strength steel, as well as the influence of the drilling dosage on the temperature field of the bottom surface. The experimental findings are as follows: in the process of thin-wall drilling of high-strength steel, the temperature field of the bottom surface of the workpiece shows an obvious temperature gradient distribution; before the formation of the drill cap, the highest temperature of the bottom surface of the workpiece is distributed in the central circular area corresponding to the extrusion of the transverse edge during the drilling process, and the highest temperature of the bottom surface can be approximated as the temperature of the extrusion friction zone between the top edge of the drill and the workpiece when the top edge of the drill bit drills to a position close to the bottom surface of the workpiece and increases with the increase in the drilling speed and the feed volume; during the process of drilling, the highest temperature of the bottom surface of the workpiece is approximated as the temperature of the top edge of the drill bit and the workpiece. The maximum temperature of the bottom surface of the workpiece in the drilling process increases nearly linearly with the drilling of the drill, and the slope of the maximum temperature increases nearly linearly with the increase in the drilling speed and feed, in which the influence of the feed on the slope of the maximum temperature increases is larger than that of the drilling speed. Full article

Using solid particulates as a heat transfer medium for concentrated solar power (CSP) systems has many advantages, positioning them as a superior option compared with conventional heat transfer media such as steam, oil, air, and molten salt. However, a critical imperative lies in the comprehensive evaluation of the properties of potential solid particulates intended for utilization under such extreme thermal conditions. This paper undertakes an exhaustive examination of both ambient and high-temperature thermophysical properties of four naturally occurring particulate materials, Riyadh white sand, Riyadh red sand, Saudi olivine sand, and US olivine sand, and one well-known engineered particulate material. The parameters under scrutiny encompass loose bulk density, tapped bulk density, real density, sintering temperature, and thermal conductivity. The results reveal that the theoretical density decreases with the increase in temperature. The bulk density of solid particulates depends strongly on the particulate size distribution, as well as on the compaction. The tapped bulk density was found to be larger than the loose density for all particulates, as expected. The sintering test proved that Riyadh white sand is sintered at the highest temperature and pressure, 1300 °C and 50 MPa, respectively. US olivine sand was solidified at 800 °C and melted at higher temperatures. This proves that US olivine sand is not suitable to be used as a thermal energy storage and heat transfer medium in high-temperature particle-based CSP systems. The experimental results of thermal diffusivity/conductivity reveal that, for all particulates, both properties decrease with the increase in temperature, and results up to 475.5 °C are reported. Full article

Work-related musculoskeletal disorders (WMSDs) are a leading concern in industrial ergonomics, often stemming from sustained non-neutral postures and repetitive tasks. This paper presents a vision-based framework for real-time, frame-level ergonomic risk classification using a lightweight Vision Transformer (ViT). The proposed system operates directly on raw RGB images without requiring skeleton reconstruction, joint angle estimation, or image segmentation. A single ViT model simultaneously classifies eight anatomical regions, enabling efficient multi-label posture assessment. Training is supervised using a multimodal dataset acquired from synchronized RGB video and full-body inertial motion capture, with ergonomic risk labels derived from RULA scores computed on joint kinematics. The system is validated on realistic, simulated industrial tasks that include common challenges such as occlusion and posture variability. Experimental results show that the ViT model achieves state-of-the-art performance, with F1-scores exceeding 0.99 and AUC values above 0.996 across all regions. Compared to previous CNN-based system, the proposed model improves classification accuracy and generalizability while reducing complexity and enabling real-time inference on edge devices. These findings demonstrate the model’s potential for unobtrusive, scalable ergonomic risk monitoring in real-world manufacturing environments. Full article

Unmanned platforms such as unmanned aerial vehicles, unmanned ground vehicles, and autonomous underwater vehicles often face challenges of data, device, and model heterogeneity when performing collaborative data processing tasks. Existing research does not simultaneously address issues from these three aspects. To address this issue, this study designs an unmanned platform cluster architecture inspired by the cloud-edge-end model. This architecture integrates federated learning for privacy protection, leverages the advantages of distributed model training, and utilizes edge computing’s near-source data processing capabilities. Additionally, this paper proposes a federated edge intelligence method (DSIA-FEI), which comprises two key components. Based on traditional federated learning, a data sharing mechanism is introduced, in which data is extracted from edge-side platforms and placed into a data sharing platform to form a public dataset. At the beginning of model training, random sampling is conducted from the public dataset and distributed to each unmanned platform, so as to mitigate the impact of data distribution heterogeneity and class imbalance during collaborative data processing in unmanned platforms. Moreover, an intelligent model aggregation strategy based on similarity measurement and loss gradient is developed. This strategy maps heterogeneous model parameters to a unified space via hierarchical parameter alignment, and evaluates the similarity between local and global models of edge devices in real-time, along with the loss gradient, to select the optimal model for global aggregation, reducing the influence of device and model heterogeneity on cooperative learning of unmanned platform swarms. This study carried out extensive validation on multiple datasets, and the experimental results showed that the accuracy of the DSIA-FEI proposed in this paper reaches 0.91, 0.91, 0.88, and 0.87 on the FEMNIST, FEAIR, EuroSAT, and RSSCN7 datasets, respectively, which is more than 10% higher than the baseline method. In addition, the number of communication rounds is reduced by more than 40%, which is better than the existing mainstream methods, and the effectiveness of the proposed method is verified. Full article

China is one of the countries severely affected by earthquakes, making precise and timely identification of earthquake precursors essential for reducing casualties and property damage. A novel method is proposed that combines a rock acoustic emission (AE) detection technique with deep learning methods to facilitate real-time monitoring and advance earthquake precursor detection. The AE equipment and seismometers were installed in a granite tunnel 150 m deep in the mountains of eastern Guangdong, China, allowing for the collection of experimental data on the correlation between rock AE and seismic activity. The deep learning model uses features from rock AE time series, including AE events, rate, frequency, and amplitude, as inputs, and estimates the likelihood of seismic events as the output. Precursor features are extracted to create the AE and seismic dataset, and three deep learning models are trained using neural networks, with validation and testing. The results show that after 1000 training cycles, the deep learning model achieves an accuracy of 98.7% on the validation set. On the test set, it reaches a recognition accuracy of 97.6%, with a recall rate of 99.6% and an F₁ score of 0.975. Additionally, it successfully identified the two biggest seismic events during the monitoring period, confirming its effectiveness in practical applications. Compared to traditional analysis methods, the deep learning model can automatically process and analyse recorded massive AE data, enabling real-time monitoring of seismic events and timely earthquake warning in the future. This study serves as a valuable reference for earthquake disaster prevention and intelligent early warning. Full article

Microfluidic platforms offer a powerful approach for ultimately replicating vascularization in vitro, enabling precise microscale control and manipulation of physical parameters. Despite these advances, the real-time ability to monitor and quantify mechanical forces—particularly pressure—within microfluidic environments remains constrained by limitations in cost and compatibility across diverse device architectures. Our work presents an advanced experimental module for quantifying pressure within a vascularizing microfluidic platform. Equipped with an integrated Arduino microcontroller and image monitoring, the system facilitates real-time remote monitoring to access temporal pressure and flow dynamics within the device. This setup provides actionable insights into the hemodynamic parameters driving vascularization in vitro. In-line pressure sensors, interfaced through I2C communication, are employed to precisely record inlet and outlet pressures during critical stages of microvasculature tubulogenesis. Flow measurements are obtained by analyzing changes in reservoir volume over time (dV/dt), correlated with the change in pressure over time (dP/dt). This quantitative assessment of various pressure conditions in a microfluidic platform offers insights into their impact on microvasculature perfusion kinetics. Data acquisition can help inform and finetune functional vessel network formation and potentially enhance the durability, stability, and reproducibility of engineered in vitro platforms for organoid vascularization in regenerative medicine. Full article

Financial market forecasting remains challenging due to complex nonlinear dynamics and regime-dependent behaviors that traditional models struggle to capture effectively. This research introduces the Adaptive Financial Reservoir Network with Hypernetwork Flow (AFRN–HyperFlow) framework, a novel ensemble architecture integrating Echo State Networks, temporal convolutional networks, mixture density networks, adaptive Hypernetworks, and deep state-space models for enhanced financial time-series prediction. Through comprehensive feature engineering incorporating technical indicators, spectral decomposition, reservoir-based representations, and flow dynamics characteristics, the framework achieves superior forecasting performance across diverse market conditions. Experimental validation on 26,817 balanced samples demonstrates exceptional results with an F1-score of 0.8947, representing a 12.3% improvement over State-of-the-Art baseline methods, while maintaining robust performance across asset classes from equities to cryptocurrencies. The adaptive Hypernetwork mechanism enables real-time regime-change detection with 2.3 days average lag and 95% accuracy, while systematic SHAP analysis provides comprehensive interpretability essential for regulatory compliance. Ablation studies reveal Echo State Networks contribute 9.47% performance improvement, validating the architectural design. The AFRN–HyperFlow framework addresses critical limitations in uncertainty quantification, regime adaptability, and interpretability, offering promising directions for next-generation financial forecasting systems incorporating quantum computing and federated learning approaches. Full article