Abstract:Effective fault diagnosis is a key link in ensuring the safe and stable operation of industrial processes and equipment. Currently, China is undergoing a pivotal transition from an industrial powerhouse to an industrial leader. During this process, smart manufacturing, characterized by digitalization and intelligence, is accelerating, serving as a key support for industrial transformation and upgrading. As intelligent manufacturing advances, industrial processes are becoming increasingly complex, and fault types are becoming more diverse, which imposes higher demands on diagnosis methods. Conventional supervised learning-based fault diagnosis methods depend heavily on substantial labeled samples to train models, focusing on classifying faults that have encountered during training (seen faults). In real scenarios, industrial fault diagnosis often faces challenges such as incomplete fault categories and significant distribution discrepancies. Zero-sample fault diagnosis, which can diagnose unseen fault types without target fault training samples, has emerged as a crucial research direction to address these issues. First, zero-shot scenarios are categorized into traditional and generalized zero-shot settings. After reviewing the development of industrial fault diagnosis techniques, the concepts and connotation of traditional and generalized zero-sample fault diagnosis are combed, and their differences are clarified from conventional methods. Second, the current research on zero-shot fault diagnosis is systematically surveyed from perspectives such as auxiliary knowledge construction, methodological implementation, and application scenarios. Furthermore, key challenges and potential research extensions are discussed for existing approaches. Finally, representative datasets and open-source code are compiled, while future trends and challenges are outlined to provide theoretical references and technical support for zero-sample fault diagnosis.

Abstract:Driven by the dual carbon objective, the offshore wind power has become a key supporting technology for optimizing the energy structure and promoting green and low-carbon transformation. However, offshore wind turbines operate for extended periods in complex marine environments characterized by high salt spray, high humidity, and strong stochastic loads, resulting in highly variable operating conditions and significant operation and maintenance challenges that urgently require advanced fault diagnosis methods. To address the limitations of existing data-driven fault diagnosis models in capturing deep temporal fault features and in effectively integrating fault mechanism knowledge, this article proposes a large artificial intelligence model-based fault diagnosis method for offshore wind turbines. The clustering and a greedy algorithms are first employed to preprocess the turbine data and perform feature selection, thereby identifying key features closely related to fault evolution and reducing data redundancy and noise interference. Subsequently, a large model is adopted as the backbone architecture and integrated with temporal transformation and classification modules to construct the offshore wind turbine fault diagnosis framework. In particular, to enhance the recognition performance and generalization capability of the proposed model, a prior knowledge loss function incorporating physical constraints and class balancing is designed. Finally, the proposed method is evaluated on the EDP public dataset and a dataset collected from offshore wind turbines in Jiangsu Province. Experimental results show that the proposed approach outperforms typical data-driven models, including convolutional neural networks (CNN) and convolutional neural network Transformer hybrid models (CNN-Transformer), in terms of precision, recall, and F1-Score in fault diagnosis, thereby providing reliable technical support for the safe operation of offshore wind turbines.

Abstract:Accurate measurement of blade root loads is of critical engineering value for fatigue life assessment and health management of wind turbine blades. Current direct measurement methods suffer from high hardware cost, insufficient long-term operational reliability and difficulty in large-scale deployment. In contrast, the standard supervisory control and data acquisition (SCADA) system equipped on wind turbines provides a feasible low-cost alternative for indirect blade root load measurement. To address the insufficient measurement accuracy caused by strong coupling and non-stationary characteristics of multi-source SCADA sensing data, this paper proposes a SCADA-data-based virtual sensing method for blade root loads, which employs a symmetry-aware spatiotemporal deformable Transformer (SADeformer) for high-precision load inversion. In the feature extraction stage, the model adopts a collaborative architecture of variable-dimension deformable attention block (V-DAB) and time-dimension deformable attention block (T-DAB) to respectively capture nonlinear inter-variable dependencies and both long short-term temporal correlations, respectively. The model then outputs flapwise and edgewise load sensing results of three blades via a multi-layer perceptron predictor. Moreover, by integrating the prior physical knowledge of wind turbine blade symmetry, a new symmetry loss function is designed to model the spatial consistency of blade flapwise and edgewise loads. Experimental results demonstrate that the proposed method achieves state-of-the-art performance in second-level load data inversion, with a coefficient of determination (R2) of up to 0.952 and a mean absolute percentage error (MAPE) as low as 0.8%, outperforming mainstream time series models in comprehensive performance. The introduction of symmetry physical constraint significantly reduces three-blade load measurement errors, verifying the effectiveness of the proposed method.

Abstract:Wind turbines (WTs) are highly coupled systems, whose safe and efficient operation rely on the supervisory control and data acquisition (SCADA) system. Considering the limitations of sensor placement and practical engineering, some physical quantities cannot be directly acquired during the operation. As a result, the traditional data-driven methods often fail to model the complex operating conditions of WTs effectively due to the lack of relevant physical constraints. To address this issue, this paper proposes a physics-informed graph neural network (PIGNN) method for the WT condition monitoring. First, a graph structure is built by representing the coupling relationships among different WT components, then a graph neural network (GNN) is employed to learn spatiotemporal correlation features of SCADA data and capture the dynamic characteristics of WT. And then a physics-informed neural network (PINN) is embedded into the proposed framework, whose physics-based loss terms can express the physical relationships including power balance, torque balance, voltage balance, temperature constraints and operating boundary constraints. In this way, the physical prior knowledge could be integrated into the data-driven model effectively. Furthermore, an adaptive normalization strategy based on an exponential moving average is developed to address the magnitude inconsistency between the data fitting loss and the physics-based constraint losses. This could balance the magnitude difference of different loss terms and improve the stability of model training. Finally, the experiments are carried out on two real WT cases. Results show that the proposed PIGNN method can balance the data fitting objective and the physics-based constraint objective effectively while providing the high prediction accuracy. It could also improve the performance of anomaly detection and early warning, which verifies the effectiveness of physics-based constraint modeling for WT condition monitoring.

Abstract:To address the problems of insufficient modeling accuracy, weak generalization under multiple operating conditions, and the heavy reliance of traditional deep learning models on empirical parameter tuning in spindle thermal error prediction for computer namerical control (CNC) spiral bevel gear grinding machines, a spindle thermal error modeling method based on whale optimization algorithm (WOA), transfer learning, and a long short-term memory network (LSTM) network is proposed. First, a spindle thermal error experimental platform is established, and temperature data from 10 measuring points together with the axial thermal error of the spindle are synchronously collected under three rotational speeds of 1 000, 1 500, and 2 000 r/min, thereby obtaining complete thermal behavior samples from cold start to thermal equilibrium. Second, K-means clustering combined with grey relational analysis is employed to screen the temperature variables, and T1, T5, and T8 are identified as thermal error-sensitive measuring points, which preserves the main thermal feature information while reducing input redundancy and multicollinearity. Furthermore, WOA is introduced to globally optimize hyperparameters of the LSTM model, including the time step, learning rate, and batch size, and a WOA-LSTM thermal error prediction model is established to improve convergence speed, training stability, and prediction accuracy. On this basis, a model fine-tuning-based transfer learning strategy is constructed. The source-domain pre-training is completed using data under 1 000 and 1 500 r/min conditions, and the model is then transferred to the 2 000 r/min target condition for small-sample fine-tuning, thus enabling cross-condition knowledge reuse and rapid adaptation. Experimental results show that the proposed WOA-LSTM transfer learning model achieves an root mean square error (RMSE) of 1.364×10-3 mm, an mean absolute error (MAE) of 1.361×10-3 mm, and an R-square (R2) of 0.983 9 in spindle thermal error prediction, outperforming the back propagation network (BP), WOA-BP, and conventional LSTM models. The proposed method exhibits good adaptability and cross-condition generalization ability under complex thermal environments, and provides a new and practical approach for thermal error modeling and compensation of machine tools.

Abstract:As a key transmission component in the fields of wind power generation, rail transit, and aerospace, the reliability of permanent magnet synchronous motors (PMSMs) is crucial for the stability of the overall system. In actual operation, the motor is subjected to complex working conditions such as internal armature reaction, high temperatures and alternating loads,which are highly susceptible to irreversible demagnetization of permanent magnets.Therefore, it is significant to realize accurate diagnosis of demagnetization fault. In this article, a fault diagnosis method based on multi-modal data fusion is proposed to solve the coupling representation problem of loss-of-excitation fault in multi-physical signals.Based on the mechanism of electromechanical energy conversion, the relationship between electromagnetic torque harmonic and air gap magnetic field distortion caused by permanent magnet loss of excitation is explained, and the dynamic evolution law of vibration and current signal distortion is revealed. On this basis, a multi-modal feature extraction framework for vibration and current signals is constructed, which achieves in-depth mining and complementary enhancement of fault information by integrating spatial and temporal domain features. To improve the ability of fault identification and localization, a classification mechanism based on fusion features is designed, and the visual expression of diagnosis results was completed. Physical prototypes of motors with normal magnetic field, 10% uniform demagnetization, 20% uniform demagnetization, and local demagnetization are manufactured. Multi-modal data under normal and different demagnetization fault conditions are acquired by an experimental platform for verification. The results show that the diagnostic accuracy of the proposed method can reach 99.3%. Fault information can be collected conveniently and accurately via high-precision sensors without damaging the original structure of the permanent magnet motor. The effectiveness and engineering applicability of multi-modal data fusion for demagnetization fault diagnosis of permanent magnet synchronous motors are verified, showing promising application prospects.

Abstract:To address the prevalent electrical contact failure of high-voltage direct current relays, which is attributed to repeated exposure to high-voltage and large-current impacts in applications such as energy storage and electric vehicles, this article proposes a method for evaluating the electrical contact state of relays based on physically interpretable acoustic features. Traditional monitoring methods relying on the direct current circuit method are difficult to achieve online detection. Meanwhile, existing acoustic and vibration signal monitoring methods yield features with limited interpretability, as they lack grounding in degradation mechanisms. To address this issue, a finite element method (FEM)-boundary element method (BEM) coupled vibro-acoustic simulation model is formulated to elucidate the effects of collision velocity, contact pressure, and contact surface morphology on acoustic signals. Based on this, highly discriminative acoustic signal feature sets strongly related to multiple physical quantities are extracted. Subsequently, dynamic-structural interaction features are established by integrating physical prior knowledge, explicitly representing the implicit coupling relationship as interpretable indicators, and achieving feature enhancement under the constraints of physical laws. Then, a global-local fusion method grounded in statistical correlation is proposed. At the global level, the contribution degree for each of the feature sets is comprehensively quantified based on four dimensions, namely information content, diversity, complementarity, and stability. At the local level, weights are dynamically adjusted to accommodate temporal heterogeneity, effectively capturing the feature differences in the degradation stages. Finally, an ensemble classifier is constructed using random forests, decision trees, and K-nearest neighbors to realize recognition of electrical contact states. The results show that the features proposed in this study exhibit a significantly stronger correlation with contact resistance compared to Mel frequency cepstrum coefficients, and the average accuracy of the proposed method reaches 90.65%. This method enhances the analysis capability of relay degradation process based on acoustic signals, strengthens its adaptability and interpretability of evaluation, and provides effective technical support for relay health management.

Abstract:In response to challenges such as weak early fault characteristic signals in rolling bearings under complex noise environments and the disconnection between condition monitoring and fault diagnosis, this paper proposes an integrated method for condition monitoring and fault diagnosis of rolling bearings based on a hybrid noise model and the maximum likelihood ratio (MLR). First, a statistical model of hybrid noise is constructed, and the expectation-maximization (EM) algorithm is used to estimate parameters and fit the vibration signals collected under healthy conditions, establishing this model as the health baseline; Next, the MLR index is constructed to quantify the probability distribution differences between the monitored signals and the health reference signals. On this basis, the exponential weighted moving average (EWMA) control chart is employed to process the sequence of health monitoring indicators, thereby amplifying the bearing′s degradation trend whilst retaining fault features. Finally, by reusing the band MLR evaluation index, the sub-bands after wavelet packet decomposition are screened to extract the optimal fault-sensitive frequency band. Subsequently, envelope spectrum analysis is performed on the signals of this frequency band to achieve fault diagnosis. Through experiments on two public datasets and comparative analysis with other methods, on the IMS dataset, the MLR index detects early faults 2.048 seconds earlier than the attention Lempel-Ziv complexity method and identifies an outer race fault characteristic frequency of 230 Hz. This result shows only about a 2.1% relative error compared to the theoretical fault characteristic frequency (235 Hz). On the XJTU-SY bearing dataset, the MLR index detects faults 1.28 seconds earlier than the effective weighted sparse kurtosis method and identifies two fault characteristics at 108 Hz and 175 Hz. These correspond to the theoretical outer race fault characteristic frequency (107 Hz) and inner race fault characteristic frequency (172 Hz), with relative errors of 1.4% and 1.7%, respectively. These results verify the timeliness and accuracy of the proposed method for integrated condition monitoring and fault diagnosis of rolling bearings.

Abstract:To address the issue in the practical application of the designed intelligent spraying robot for two-component road traffic markings, where the relationship between marking width/thickness and spraying process parameters is unclear, making it difficult to precisely control the quality of the applied marking dimensions. First, by establishing an experimental platform and combining with the mechanistic analysis of the spraying process, this article adopts an experimental design that integrates single-factor and orthogonal experiments to investigate the influence patterns and interactions of various spraying process parameters on marking width and thickness. Then, the least squares method is employed to fit the experimental data, obtaining the optimal fitting forms for the relationship between each spraying process parameter and the marking width/thickness. Subsequently, multivariate nonlinear regression models are established to predict the width and thickness of the two-component road traffic markings, respectively. Furthermore, machine learning algorithms, including the back propagation neural network (BPNN), Gaussian process regression (GPR), and generalized regression neural network (GRNN), are introduced to compare and evaluate the prediction performance. Finally, prototype testing is conducted to validate the accuracy of the predictive models. The experimental results indicate that the coefficients of determination of the selected spraying process parameters on the road traffic marking width and thickness are 99.4% and 99.3%, respectively. The established multivariate nonlinear predictive models effectively overcome the overfitting defects of machine learning models under small-sample engineering conditions, with their predictive fitting degrees (R2) reaching 0.932 3 and 0.978 4, respectively, which meets the construction requirements for two-component road traffic markings. The findings provide a theoretical basis for the intelligent spraying robot to achieve precise control over marking width and thickness by regulating spraying process parameters, offering scientific support for on-site technical decision-making and contributing to the enhancement of the spraying process automation.

Abstract:To meet the requirements for high measurement accuracy, personalized fit, and high wearing comfort in hand biomechanics assessment, this study proposes a high-precision six-channel hand force measurement system. It integrates nine independently adjustable degrees of freedom, including 7 translational and 2 rotational. Through slide rail and rotary hinge mechanisms, it accommodates physiological variations in palm length, palm width, and finger length across subject populations. To alleviate tenderness under high-intensity pressing, the contact interface employs flexible contact units 3D-printed with thermoplastic polyurethane. Finite element simulation using a second-order Mooney-Rivlin hyperelastic model shows 1.422 mm deformation under 100 N axial load, demonstrating excellent cushioning performance. The system integrates six strain-gauge pressure sensors with a 0~200 N range, supporting 100 Hz sampling, and maintains a maximum absolute error of less than 0.5 N within the 0~100 N range. Based on this platform, 53 healthy volunteers and 5 stroke patients are recruited for maximum voluntary contraction （MVC) and 30% MVC constant force holding experiments. This study employs a normalized coupling matrix of relative finger output forces to quantitatively characterize inter-finger enslaving effects, and uses normalized force standard deviation to evaluate steady force control capability. Results show: In healthy individuals, the thumb exhibits highest independence, while the three ulnar fingers demonstrate strong coupling; with aging, the inter-finger coupling matrix becomes denser and the normalized force standard deviation increases; in the stroke group, MVC decreased by 60%~70%, presenting a globalized co-activation pattern, with normalized force standard deviation approximately three times that of the young and middle-aged groups. Results show that the device has significant potential in hand strength monitoring and neuromuscular function evaluation.

Abstract:To address the coupling of residual errors of single-visual geometric localization are coupled with the unknown physical properties of objects in unstructured environments, as well as the lack of low-level dynamic closed-loop compensation in existing visuo-tactile systems leading to uncontrolled contact forces, a visuo-tactile collaborative robotic grasping method based on visual prior guidance and tactile feedback correction is proposed. First, the GraspNet backbone network is replaced by ResUNet, and a center-of-mass constraint mechanism based on the Euclidean distance between the grasp point and the object′s centroid is introduced. This enhances the completeness of point cloud feature extraction, thereby improving the physical stability and accuracy of grasp pose detection. Second, a convolutional neural network (CNN)-Transformer stiffness estimator is trained using a tactile dataset. The online-identified soft, medium, and hard stiffness levels are input into the controller according to the admittance parameter conversion rules mapped by classification probabilities and impedance coefficients. This realizes the dynamic adjustment of grasping contact force, enhances interaction compliance, and reduces the risk of object damage. Finally, based on the deep deterministic policy gradient (DDPG), the rapidly-exploring random tree (RRT) algorithm and hindsight experience replay (HER) mechanism are integrated to construct the RRT-guided hindsight experience replay deep deterministic policy gradient （RHER-DDPG) algorithm, which fuses path guidance and experience reconstruction. By utilizing RRT expert trajectories to narrow the initial action exploration space and combining HER to relabel failed targets as successful samples, the convergence process of the Actor network mapping from high-dimensional states to actions is jointly accelerated, generating efficient grasping strategies. Comparative experiments show that, compared with the original GraspNet, the grasp detection accuracy is improved by 12.8%. Compared with the traditional DDPG algorithm, the model convergence iterations are reduced by approximately 30%. In real-world robotic grasping tests, the success rate reaches 90.3% and the object damage rate is only 7.1%, verifying the effectiveness of the proposed method.

Abstract:With the increasing demand for autonomous operation of ground robots in complex environments, higher requirements are imposed on the accuracy and robustness of simultaneous localization and mapping (SLAM) technology. Aiming at the three core challenges of vertical drift, dynamic interference and cumulative error faced by light detection and ranging (LiDAR) SLAM systems in complex terrain environments including stairs, slopes, dynamic urban scenarios and long corridors, this paper proposes a LiDAR SLAM method for ground robots operating in complex Environments. The proposed method dynamically identifies reliable ground structures through principal normal analysis. It introduces ground constraints in flat regions to suppress vertical drift, and adaptively relaxes constraints in unstructured terrains such as stairs and slopes to mitigate the adverse impact of inappropriate constraints on pose estimation, reducing the vertical component of absolute trajectory error (ATE) by more than 85%. To tackle the interference of dynamic objects, dynamic targets are distinguished based on motion consistency across multi-frame point clouds, and sliding window optimization is employed to improve the efficiency of dynamic point removal. Meanwhile, a LinK3D descriptor based on local geometric structures is introduced to enhance the representation capability of environmental structural information. An adaptive loop closure triggering mechanism integrating fast retrieval of a bag-of-words model and precise Iterative Closest Point registration is adopted to realize efficient and accurate loop closure detection. Experiments conducted on a self-developed ground robot platform demonstrate that the proposed method outperforms mainstream baseline methods in most complex scenarios when loop closure is disabled. After enabling loop closure, its Absolute Trajectory Error is further reduced by approximately 8.7% compared with the version without loop closure. Relative to the best-performing comparative algorithm, the accuracy of the proposed method is improved by about 32.3% with loop closure activated, which fully validates its high precision and strong robustness in real-world complex environments.

Abstract:To address the problems of target allocation conflicts, trajectory oscillations in underactuated systems, and degraded control performance under disturbance conditions during the cooperative docking of multiple unmanned surface vessel (USV) clusters, this study proposes a hierarchical, distributed cooperative docking navigation method that integrates potential and differential game theories. First, at the decision-making layer, a distributed optimization framework based on potential games is formulated to resolve conflicts of interest in target allocation among unmanned surface vessels. By designing a global potential function that reflects the total cost of the cluster, self-organization of each unmanned surface vessel is induced, and convergence to a pure-strategy Nash equilibrium is achieved. Second, at the guidance layer, in order to solve the problem of trajectory oscillations caused by the motion coupling characteristics of underactuated systems, a continuously variable weighted guidance strategy with dynamic adjustment based on an S-shaped function is designed, by which a smooth transition from the far-field approach mode to the near-field alignment mode is achieved. Finally, at the control layer, an integral term of the tracking error is introduced into the augmented model, and a nine-dimensional augmented state space is formulated. The docking and collision avoidance requirements are modeled as performance indices and nonlinear potential field constraints. In combination with specific docking requirements, a quadratic cost function is formulated, and the algebraic Riccati equation is solved based on differential game theory and optimal control theory, through which a globally optimal state-feedback control law is obtained. Simulation results verify that the proposed method can successfully accomplish docking tasks in both typical longitudinal and lateral docking scenarios. Disturbances can be effectively suppressed by the integral augmented optimal control structure, the steady-state error can be kept within 0.1 meters, the relative distance among all unmanned surface vessels can be maintained above the safety threshold at all times, the total energy consumption cost can be reduced by approximately 46 percent, and the docking time can be shortened by approximately 20 percent, demonstrating excellent robustness and high efficiency.

Abstract:Measuring the rotation angle of a worm wheel is the core basis for evaluating the dynamic performance of worm gear transmission and for early fault diagnosis, such as machining errors and assembly eccentricity. Currently, existing research mainly focuses on the static measurement of the worm wheel end face. Although high precision can be achieved by optimizing calibration and image processing algorithms, the measurement systems are structurally complex, lack the capability to evaluate transmission stability during operation, and thus have limited engineering applicability. To enhance the flexibility of the measurement system and dynamically evaluate transmission smoothness, this paper proposes a precise rotation angle measurement method considering dynamic tracking of the worm wheel side surface. First, bilateral filtering and CLAHE are applied to the image sequence over a complete rotation cycle of the worm wheel, and the region of interest is determined using the Otsu thresholding method. Second, the Canny operator is introduced to improve the oriented features from accelerated segment test (FAST) and rotated binary robust independent elementary features (BRIEF) (ORB) feature extraction algorithm, so as to obtain a stable feature point set with high response, and a multi-scale pyramidal optical flow method and Kalman filter are integrated to achieve robust tracking of feature points between adjacent frames. Finally, the rotational component is extracted by constructing an inter-frame motion model, thereby completing the calculation of the inter-frame rotation angle and cumulative rotation angle of the worm wheel, and the transmission smoothness is evaluated based on the peak-to-peak value of rotation angle fluctuation. Experimental results show that, under different illumination conditions, rotational speeds, and numbers of teeth, the mean absolute error of the inter-frame rotation angle measured by the proposed method is controlled within 0.003 9°~0.004 7°, the root mean square error is 0.005 3°~0.006 7°, the 95th percentile absolute error is 0.010 7°~0.018 2°, the cumulative rotation angle is highly consistent with the encoder results, and the maximum deviation of rotation angle fluctuation is controlled within 0.024 7°. The proposed method can effectively achieve precise measurement of the worm wheel rotation angle during worm gear transmission without requiring an end-face reference, showing strong resistance to motion interference, good dynamic detection capability, and promising engineering application prospects.

Abstract:In recent years, video-based monitoring has become a well-established non-contact method for measuring river surface velocity and estimating cross-sectional flow. The basic principle involves calculating the pixel displacement velocity of water surface textures or particles, converting it into the actual velocity using the spatial scale corresponding to each pixel, and subsequently using this velocity as the water flow velocity. Accurate acquisition of pixel coordinates within the video frame is an essential process in video-based flow measurement, which directly affects the accuracy of the final velocity calculation. A comparison of several commonly used video calibration methods shows that current video calibration methods commonly suffer from low efficiency, insufficient accuracy, environmental constraints on target placement, and safety risks for personnel. Through theoretical analysis combined with practical application, the author proposes a laser positioning-based video calibration method tailored for scenarios without bank-based references or in complex river environments. This calibration method typically requires less than one hour per site, achieving centimeter-level accuracy. A case study of video calibration at the Xi Xiayuan Hydrological Station confirms that this method achieves an average error of 0.04 m over a hundred-meter distance and an average pixel error of 1.85 pixels, demonstrating a significant reduction in calibration error and improving the accuracy of video-based flow measurements. This paper provides a detailed discussion of this method from the perspectives of required equipment, calculation methodology, applicable environments, and key considerations.

Abstract:To achieve automated, high-precision in-situ diameter measurement of large-scale shaft parts, a robot-based measurement system integrating visual guidance and laser tracking is designed and constructed. The proposed system addresses the challenges of laser point guidance, alignment, and stable measurement under complex operating conditions, including significant initial pose deviations between the robotic arm and the workpiece, as well as highly reflective surfaces. By fusing semantic segmentation with temporal prior information, a robust target measurement region with a safety margin is established. On this basis, an image-based visual servoing (IBVS) pre-alignment strategy is designed to realize closed-loop guidance and real-time adjustment of the laser projection point, ensuring its stably and accurately entry into the target measurement region. Furthermore, a dual-edge joint weighted robust fitting and geometric solution method is employed to obtain the axial direction of the part and the surface normal at the contact point, and the end-effector posture is adjusted through iterative closed-loop control to achieve stable contact and reliable conformance between the roller and the workpiece surface. By utilizing a laser tracker to collect the 3D coordinates of a spherically mounted retroreflector attached to the roller as measurement points, the robotic arm is driven to perform multi-point data sampling within the measurement region. Combined with a cylinder fitting algorithm, this approach enables the automatic selection of measurement regions and diameter estimation across different or identical shaft segments. Experimental results indicate that the minimum absolute deviation between the fitted diameter fitted by the proposed method and the nominal diameter of the part is 0.108 7 mm, with the maximum absolute deviation not exceeding 0.240 0 mm. The standard deviation of the diameter across five repeated measurements remains below 0.180 0 mm, and the results show good consistency with the measured values obtained using a micrometer. The proposed method is suitable for automated, high-precision diameter measurement of large-scale shaft parts in engineering applications, demonstrating good engineering applicability and practical value.

Abstract:To ensure the calibration accuracy of tri-axial vibration sensors, it is necessary for the tri-axial standard vibrator to output low-coupling and low-distortion vibration signals. However, due to the complexity and nonlinearity of the tri-axial standard vibrator structure, the output signals exhibit significant residual coupling and harmonic distortion. To address this issue, this paper proposes a neural network inverse model online iterative decoupling control method to achieve decoupling between axes and improve the accuracy of the output signals. Firstly, taking the leaf-spring-type tri-axial standard vibrator as the research object, the decoupling structure and the coupling characteristics of inter-axis motion were theoretically analyzed, and the orthogonal vibration suppression ratio and harmonic distortion were quantified. Then, the inverse model of the tri-axial standard vibrator system was constructed using the neural network, and the original system was controlled by feedforward series method, which initially improved the inter-axis coupling interference and harmonic distortion. On this basis, to more accurately characterize the dynamic coupling characteristics of the tri-axial standard vibrator, an online iterative learning mechanism was introduced. By dynamically updating the samples, the inverse model was periodically iteratively optimized to gradually improve its fitting accuracy, thereby achieving precise control of the inter-axis coupling and harmonic distortion of the tri-axial standard vibrator. Finally, a vibration acquisition and analysis system was built based on AD7606B, and an experimental test system for the tri-axial standard vibrator was integrated and constructed. The experimental analysis results show that the neural network inverse model online iterative decoupling control method can control the orthogonal vibration suppression ratio of the tri-axial standard vibrator within 2% and the harmonic distortion within 1%, verifying the effectiveness of the decoupling control method and providing a technical reference for high-precision decoupling control of multi-axis vibration test system.

Abstract:High-precision localization is fundamental to autonomous driving. However, the complex underground coal-mine environment severely attenuates global positioning system (GPS) signals, resulting in poor localization accuracy for the underground unmanned transport vehicle. To address this issue, we propose a high-precision localization method for underground unmanned transport vehicles in GPS-denied environments by integrating multiple miniature inertial measurement unit (MIMU) with differential heading and average wheel-speed constraint. By fusing measurements and constraint conditions from multiple low-cost MIMUs, the proposed method effectively enhances vehicle localization accuracy in underground environments. First, a novel distributed multi-MIMU architecture consisting of one body-mounted MIMU and two wheel-mounted MIMUs is proposed, and the system error-state model is formulated based on the simplified PHI-angle error theory and a first-order Gauss-Markov process. Secondly, using measurements from the left and right wheel MIMUs, two measurement error models, namely differential heading and average wheel speed, are formulated. Based on these models and the predicted states, the corresponding observation residuals and observation matrices are further derived. Finally, a centralized error-state Kalman filter framework with the innovation-based adaptive filtering is designed to fuse body and wheel MIMU information, while an abnormal measurement detection mechanism is developed. Together, they realize joint estimation of the vehicle pose and effectively improve localization accuracy. Localization experiments using a four-wheel differential-drive robot in three different scenarios show that, in the GPS-denied underground environment with narrow shafts, the unmanned transport vehicle achieved RMSEs of 0.722 m in position and 0.835° in heading. Compared with using only one MIMU, the positional RMSE improved by 1~2 orders of magnitude, reaching a level comparable to GPS-only positioning. Overall, the proposed method exhibits strong drift suppression and stable localization performance, highlighting the substantial potential of low-cost MIMUs for unmanned transport vehicle localization.

Abstract:The current fluxgate typed current sensors for the high-voltage battery management systems face the several challenges including the complex circuit structures, limited measurement ranges, high power consumption, and difficulty of balancing range expansion with power control. Accordingly, this study proposes a novel full-bridge self-excited fluxgate high-current sensor design based on open-loop principles. By integrating a self-excitation bridge structure based on the Schmitt trigger mechanism, the dead zone limitations of traditional designs are effectively eliminated and the corresponding circuit complexity is reduced. The in-depth analysis of the full-bridge excitation circuit′s operational mechanism establishes the precise mathematical models as function of average excitation current, excitation voltage duty cycle, and measured current. This provides a theoretical foundation for the current detection with the fullbridge excitation circuit, which also identifies the key parameters influencing the sensor′s measurement range and power consumption. Simulation results demonstrate that the designed current sensor achieves wide-range measurement with the excellent stability and linearity while effectively controlling power consumption. The theoretical nonlinearity error remains below 0.06% across all measurement ranges, and the experimental comparison tests confirm the sensor′s capability of measuring currents up to ±1 500 A. The maximum power consumption within the full-scale range was only 5.4 W during the power consumption testing, which provides the synergistic optimization and effectively resolves the trade-off between range expansion and power consumption control of high-current detection scenarios. Compared to the mainstream commercial fluxgate sensors and the traditional excitation schemes, the proposed design demonstrates significant advantages in range, power consumption, and linearity. In conclusion, this research provides an effective solution for the high-current detection in the high-voltage battery management systems, demonstrating the important application value.

Abstract:To address the issues of a wide mainlobe width, insufficient interference suppression capability, and performance degradation in coherent interference environments associated with conventional beamforming algorithms, this paper proposes a null interference suppression technique based on beam sharpening, specifically including the H-LCMV and H-CTMV algorithms. This method divides a uniform linear array into left and right subarrays, computes the null-steering beam weight vectors for each subarray independently, enabling each subarray to form beam outputs with null characteristics. Based on the inherent phase delay between the subarrays, weight factors are constructed, and the outputs of the left and right subarrays are fused through nonlinear hyper-beamforming operations, thereby achieving significant mainlobe sharpening while deepening the null depth in the interference direction. This technique is applicable to both in coherent and coherent interference scenarios. Furthermore, by constructing a correction matrix to replace a single actual interference source with multiple virtual ones of equal intensity, the null is expanded into a notch with a certain width, effectively enhancing the algorithm′s robustness against slight perturbations in the interference direction. Simulation results and experimental data from the South China Sea show that, compared to traditional methods, the proposed algorithm significantly compresses the mainlobe width while achieving a null deepening of over 20 dB in the interference direction, effectively improving target resolution and interference suppression performance. Futhermore, the sharpened algorithm exhibits more precise pointing precision, effectively mitigating beam pointing deviation caused by ocean noise. These results fully validate the effectiveness of the proposed technique for target detection under strong interference and coherent source conditions, providing technical support for improving the detection performance of sonar systems in complex underwater acoustic environments.

Abstract:To address the challenge of calibrating the workspace of Delta parallel robots, this study proposes a general solution framework of searching the maximum inscribed working curve based on density field theory. This method constructs a continuous density field through the statistical modeling of spatial point sets, which transforms the complex boundary determination problem into a density gradient extremum detection problem. It effectively avoids the reliance of traditional geometric methods on the explicit modeling of complex surfaces and simultaneously overcomes the inherent accuracy limitations of numerical discretization methods that depend on the sampling density and require the complex parameter tuning. The core innovation lies in the development of a universal solver that does not rely on additional parameters and a dual-path complementary mechanism: On one hand, the analytical formula method based on kinematic equations achieves high-precision generation of the maximum inscribed curve by precisely calculating the proportion of effective points on the circumference, making it suitable for the structural parameter optimization design; On the other hand, the numerical point set method based on kernel density estimation (KDE) converts discrete point clouds into continuous probability density functions, identifies boundaries using extrema and generates engineering-grade curves with built-in safety margins. Experimental results show that the maximum joint angle deviation of the boundary curve obtained by the analytical formula method does not exceed 0.056°, which strictly satisfies the fourth-axis hyperbolic surface constraint and the spherical hinge rotation angle limit, verifying the accuracy of the solution method. The safety margin experiments demonstrate that compared to the boundary curve derived from the analytical formula method, the boundary curve generated by the numerical point set method proactively reserves a safety margin in the radial direction and can serve as a limit boundary of actual motion control. In conclusion, this method combing the theoretical universality with engineering practicality provides a new theoretical tool for the comprehensive study of trajectory planning, motion control and scaling of high-speed parallel robots.

Abstract:To address critical challenges in long-sequence energy consumption prediction for electric vehicles (EVs), including memory decay, high computational complexity of attention mechanisms, and insufficient adaptability to dynamic driving conditions, this paper proposes a memoryaugmented attentionbased prediction model termed deep Q networkadaptive memory augmented gating (DQNAMAG). The model is built upon an adaptive memoryaugmented gating (AMAG) network that incorporates a threelevel collaborative memory architecture consisting of shortterm memory, longterm neural memory, and persistent memory. A surprisedriven decay mechanism is introduced to enhance the modeling of battery degradation and abrupt operating condition variations, enabling effective capture of multiscale temporal dependencies in longhorizon energy consumption sequences. Furthermore, an adaptive Nystrm attention (ANSA) mechanism is developed to perform lowrank approximation of the attention matrix via the Nystrm method with adaptive sampling dimension adjustment. This reduces the computational complexity from O(T2) to O(T·r), significantly improving efficiency and realtime performance in longsequence scenarios. An adaptive multiscale spatiotemporal attention mechanism (AMSTA) mechanism and a hypernetworkbased dynamic forward model are additionally introduced to enhance deep crossmodal fusion between road condition images and battery management system (BMS) timeseries data, strengthening environmental perception capability. Moreover, the AMAG network is embedded into a reinforcement learning framework, where temporal difference learning provides temporalconsistency regularization and enables selfcalibrated parameter optimization. Experimental results based on five years of realvehicle operational data from two vehicle types demonstrate that the proposed model achieves a mean absolute error (MAE) below 02%, a root mean square error (RMSE) below 03%, and Rsquared (R2) above 995% under different stateofhealth (SOH) conditions. The model exhibits superior stability and generalization performance in long-sequence prediction and battery degradation scenarios, significantly outperforming mainstream models such as Transformer, Informer, Mamba, and LSTM.

Abstract:The military portable equipment power sources often employ multiple 18650-type lithium-ion batteries in series-parallel configurations, which possesses the different number of serially or parallel connected cells and heterogeneous battery types across different devices. Thus achieving the accurate identification and adaptive charging of heterogeneous lithium battery packs is significant in the increasingly complex and demanding battlefield environments. The traditional back propagation (BP) neural networks utilized for identifying lithium batteries suffer from the slow convergence, poor identification accuracy and local optima issues. To address these shortcomings, a system identification model based on the mind evolutionary algorithm optimized BP neural network is proposed to tackle the challenges posed by adaptive charging of military portable equipment power sources. This method first takes the widely used 18650-type lithium battery in portable equipment power sources as the research object, tests its charging data under different charging rates, and determines the key features of model construction through in-depth analysis of the lithium battery charging characteristic curve; Additionally in order to construct the system identification model, the mind evolutionary algorithm (MEA) algorithm is used to globally optimize the initial weights and thresholds of the BP neural network with battery type and state of charge (SOC) as output features during the network training period; Finally an experimental prototype based on a four switch Buck-Boost converter is developed, which collaboratively achieves the predictive identification and adaptive charging control of heterogeneous lithium battery packs by combing with the aforementioned adaptive charging strategy. The identification accuracy of the system identification model constructed by the MEA-BP neural network is compared with those of the BP model and the particle swarm optimization (PSO)-BP model. Experiments show that the proposed algorithm improves the identification and prediction accuracy of lithium battery type and SOC with the prediction errors controlled within 1%. It can effectively identify the lithium battery packs with different numbers of cells and SOC states, and achieve the adaptive charging control, demonstrating the superior estimation accuracy and robustness.

Abstract:The microtremor survey method is characterized by the advantage of non-destructiveness to the geographical environment, safety, environmental friendliness and high signal-to-noise ratio, which possesses the broad application prospects in seismology and exploration geophysics. However, the traditional "distributed acquisition-centralized processing" model results in significant delays of shear wave velocity structure imaging, failing to meet the real-time exploration requirements. While the edge computing reduces the latency and network load, it faces challenges such as heterogeneous edge nodes and resource constraints, limiting the instant performance and accuracy of microtremor survey. To address these bottlenecks, this paper proposes a microtremor edge imaging method based on modified genetic optimization algorithm. First, a microtremor edge collaborative imaging system architecture is designed by deploying seismic edge servers to centrally manage sensor nodes, which enables the instant network edge imaging. Additionally in order to minimize the latency and control energy consumption, a multi-node collaborative computing framework is designed by tailoring the heterogeneous node resources and modeling the task allocation challenges. Finally, introduce the modified genetic optimization algorithm(MGOA) integrating crossover, mutation strategies and heuristic rules is introduced to efficiently solve the global optimal task allocation. The EdgeCloudSim simulations demonstrate that compared to traditional genetic algorithms, the proposed method reduces the total instant imaging time by 23.36% while maintaining 100% task coverage, which increases the higher computational efficiency by 29.8% than the CPLEX solver in medium-scale scenarios. The robustness tests show stability scores of 44.12 and robustness scores of 52.12, both surpassing the comparison algorithms. The field tests validate the method′s adaptability across different initial geological models with the low imaging errors and optimized edge device memory utilization at just 53%, making it suitable for the resource-constrained environments. In conclusion, it achieves the coordinated optimization of delay and energy consumption, providing an efficient solution for instant imaging in microtremor survey.

Abstract:The dual-load inductive power transfer (IPT) system with quadrature double channels (QDC) enables wide-range and high degrees of freedom power delivery for electrical devices. The coupling mutual inductance within the QDC-IPT system can induce circulating currents, which interfere with its normal resonant operation state. For the dual-load QDC-IPT system, a system model considering the direct and cross coupling mutual inductances is established. The transmission path of the circulating current generated by the coupling mutual inductance is revealed, and the coupling circulating current between the channels and its formation mechanism are analyzed. The expressions of the power transfer component and the circulating current component are given, and the effect of the reactive component of the circulating current on the system is clarified. The reflected impedance of the circulating current and the receiving coil circuit at the transmitter coil are derived, revealing that the interactive coupling effect between two channels can be characterized as a series branch of resistance and capacitance. The power flow between the two channels, the system transmission power, and the efficiency are analyzed. The power ratio generated by the input voltage component and the circulating current component is derived, clarifying the roles of the reactive and active components of the circulating current component during the system operation. Furthermore a power interaction mechanism is constructed, in which the reactive power of the circulating current compensates for the reactive power caused by the variable inductance under specific conditions. The expressions of the input voltage amplitude for compensating the channel reactive power through the reactive power of the circulating current are derived at three excitation conditions. The switching criterion for the operation mode based on the quality factor is determined. Finally, a 1 kW system model and a prototype are built. Simulation and experimental results validate the correctness of interaction coupling and power interaction mechanism compensation. At quadrature excitation, the compensation of energy channels is achieved. The power factor of the energy transfer channel increased by 0.11, and the system efficiency was improved to 93%.

Abstract:Permanent magnet synchronous motor (PMSM) servo systems have become a core driving component in high-end equipment and automation due to their high power density, high efficiency, and excellent control performance. As applications such as computer numerical control (CNC) machine tools and industrial robots advance toward higher speeds, precision, and dynamic response, more stringent requirements are imposed on position servo systems in terms of tracking accuracy, response speed, and disturbance rejection capability. The "position + current" single-stage proportional-integral-derivative (PID) position servo control structure is favored for its fast position response; however, its design complexity, combined with disturbances such as parameter uncertainties and unmodeled dynamics, limits its application in high-end servo domains. To address these challenges, this article proposes a single-stage position servo control strategy based on adaptive backstepping. First, an adaptive backstepping design is conducted for the position servo loop to derive a virtual velocity reference. An adaptive observer is then designed to compensate for parameter uncertainties and unmodeled disturbances, and the q-axis current reference for torque control is derived through the virtual velocity closed loop, achieving integrated position-velocity control. A complete position closed-loop model is subsequently constructed to accurately analyze stability. Finally, simulation and experimental comparisons with the PID-based single-stage position control demonstrate that the proposed strategy achieves smaller steady-state and dynamic position errors, as well as stronger robustness against parameter uncertainties and unmodeled disturbances, providing an effective solution for high-performance high-end servo systems.

Abstract:As fuel cell systems evolve towards the higher power outputs, higher requirements are imposed on air supply capacity and compressor driving power. At present, the power of a single-stack fuel cell system has exceeded 350 kW, which further raises the control requirements of air compressors operating under high-power, high-speed, and wide-speed-range conditions. However, the conventional motor control strategies suffer from the reduced speed estimation accuracy, insufficient system robustness, and degraded steady-state performance under the high-speed operation conditions. To address these issues, a motor controller of high-speed, high-power fuel cell air compressors is designed and developed, and an adaptive sliding mode observer with the novel wide-speed-range is proposed. Based on the traditional sliding mode observer structure, a wide-speed-range saturation function with variable boundary layer is introduced. On one hand, its piecewise regulation characteristics enable the smooth switching, effectively suppresses chattering and improving the speed estimation accuracy. On the other hand, its variable boundary layer allows the dynamic adjustment according to motor speed and estimation error, thereby enhancing the steady-state performance under high-speed conditions. As a result, the proposed method effectively improves the speed estimation accuracy under the high-speed operation and enhances the steady-state performance. The controller employs the silicon carbide power modules to construct a three-phase inverter with a maximum output power of 80 kW and a maximum speed of 80 000 r/min, which can meet the air supply requirements of 200~500 kW fuel cell systems. A high-speed air compressor experimental platform is constructed and compared with the traditional sliding mode observer algorithm. The results show that the controller can achieve stable and reliable motor drive, whose speed fluctuation range is significantly reduced under the high-speed operating conditions. The speed fluctuation at 80 000 r/min is decreased from ±150 r/min to ±90 r/min with a reduction rate of approximately 40%, which significantly improves the speed stability and estimation accuracy, thus verifying the effectiveness of the proposed method.

Abstract:To address the issues of signal interference and the difficulty in extracting weak features of assembly defects in the current computer numerical control (CNC) machine tool feed system assembly quality inspection, this paper proposes an online assembly quality detection method based on motor current signature analysis (MCSA). This method leverages the non-contact measurement, rapid response, and convenient data acquisition characteristics of current signals, offering a new approach to tackle the aforementioned inspection challenges. Firstly, from the perspective of mechanical stress, this article systematically analyzes the relationship between the loads on the machine tool feed system and the required motor torque under three motion states: constant speed, acceleration, and deceleration. Subsequently, the machine tool feed system is established under typical assembly defects such as misalignment between the guide rail and the screw, excessive screw preload, and protective cover jamming, to study the linear mapping relationship between the motor current signal and the applied load. This allows for theoretical analysis of how load variations caused by different assembly issues affect the motor current. Finally, a signal acquisition system for the assembly quality inspection of the machine tool feed system was established. Different typical assembly problem conditions of the feed system were simulated, motor current data under different conditions were collected, and correlation verification experiments as well as simulation experiments of common assembly quality problems of the machine tool feed system is conducted. Relevant experimental data indicate that the motor current is positively correlated with the feed system torque; assembly quality problem simulation experiments show that different assembly defects can have specific effects on the motor current signal, causing specific changes in the features corresponding to the motor current signal. By analyzing the abnormal changes in the machine tool motor current during the operation of the feed system, effective detection of the assembly quality of the CNC machine feed system can be realized, laying the foundation for subsequent research on identifying assembly quality issues based on current signal analysis.

Abstract:To address the demand for mouse-equivalent access among people with upper-limb impairments, numerous intelligent interaction frameworks have been proposed. However, existing systems still exhibit significant limitations in terms of perception dimensionality, mapping accuracy, and personalized adaptation. Therefore, starting from the design of the fundamental sensing unit, a novel MXene/MWCNT/MXene sandwich-structured sensitive layer is designed and fabricated using a KOH-ion-induced gelation reaction combined with a layer-by-layer vacuum filtration process. Based on this sandwich-structured sensitive layer, a flexible piezoresistive sensor is assembled by integrating interdigital electrodes and a PDMS encapsulation layer. Benefiting from the robust conductive pathways, the developed sensor exhibits 10.97 kPa-1 sensitivity and 100 ms fast response time, as well as below 5% drift over 800 loading cycles, demonstrating excellent mechanical and electrical stability. On this basis, the proposed sensor is integrated with an inertial measurement unit (IMU) and a rotary potentiometer to construct an intelligent interactive control system that fuses multimodal information, including pressing, rotation, and displacement. The system employs a sliding-window-based multi-scale feature extraction method to construct a fused feature vector that simultaneously captures local dynamic characteristics and global steady-state features. This fused representation is then processed by a graph neural network (GNN), achieving an average recognition accuracy of 97.2% across 12 typical mouse interaction actions. Meanwhile, a user-specific thresholding method that tracks individual behavior vectors boosts overall accuracy by 5-4% while reducing false-trigger rates by 30%. The proposed approach offers a highly robust interaction solution for users with hand-function impairments and lays the groundwork for personalized adaptation in intelligent human-computer interaction systems.

Abstract:Three-dimensional (3D) reconstruction technology is widely applied in fields such as industrial manufacturing and cultural heritage conservation. As industrial measurement accuracy requirements increase, vision only algorithms cannot meet metrology-grade demands, while industrial computed tomography (CT) completely lacks surface color and texture information. To address the balance on geometric accuracy and surface texture in 3D reconstruction, a truncated signed distance function (TSDF) surface texture fusion reconstruction framework guided by high-precision CT surface priors is proposed. First, high-precision global spatial registration of heterogeneous sensors is achieved via Zhang′s calibration method and Lie algebra-based optimization. Subsequently, a bounding volume hierarchy (BVH) accelerated M-ller-Trumbore ray-casting algorithm is employed to calculate pixel-level depths from the high-precision CT mesh, circumventing the uncertainty of visual depth estimation. Finally, a TSDF volumetric field is constructed to fuse multi-view red green blue (RGB) images and depth information, realizing 3D model reconstruction with both precise geometric morphology and authentic surface textures. Experimental results demonstrate that on a texture-rich toy model and a highly reflective metal part datasets, the average distance errors of the reconstruction are 0.035 and 0.024 mm, respectively, representing an approximately 73% reduction compared to mainstream neural radiance field (NeRF) and 3D Gaussian splatting (3DGS) methods. The F1-score exceeds 0.950, while the visual metric structural similarity index measure (SSIM) reaches 0.897, and learned perceptual image patch similarity (LPIPS) is as low as 0.145. The single-view processing time is approximately 0.67 s, and the full-model reconstruction time is shortened to the one-minute level, meeting the real-time requirements of industrial online inspection. This method effectively overcomes the limitations of missing textures in CT reconstruction and insufficient accuracy in optical reconstruction, providing a solution with both metrology-grade geometric accuracy and high-fidelity texture for cultural heritage digitization and precision industrial inspection.

Abstract:Few-shot military target recognition aims to achieve fast and accurate recognition of novel military targets with extremely limited labeled samples, and is of great significance in military remote sensing interpretation, battlefield situation awareness, and decision support. Metric learning-based methods perform recognition by constructing class prototypes and measuring the similarity between query samples and prototypes. Owing to their simple structure, flexible training, and strong transferability, these methods have become a mainstream approach in few-shot learning. However, most existing methods construct class prototypes by mean estimation, which is easily affected by background clutter, imaging noise, and outliers in remote sensing image, leading to prototype deviation. Morever, equal weighting is usually assigned to all dimensions in the feature space, making it difficult to characterize their different contributions to classification. Consequently, when the feature distributions of different classes highly overlap, the discriminative ability of the model is constrained. To address these problems, a spatial metric prototypical network for few-shot military target recognition is proposed. First, a feature extractor is employed to map samples into the embedding space, and feature translation and normalization are introduced to enhance the robustness and stability of feature representations. Subsequently, a prototype enhancement module is designed to adaptively optimize class prototypes within a low-rank subspace. By enhancing the principal discriminative directions and suppressing redundant noise information, the proposed network alleviates the entanglement of low-dimensional discriminative features. Finally, a metric function is constructed by incorporating spatial projection error to achieve fine-grained recognition of query samples. Experimental results on the Ship, MAR20, and NWPU-RESISC45 datasets demonstrate that the proposed method improves the average recognition accuracy by 24.49%, 2.07%, and 4.03% under the 5-way 1-shot setting, and by 26.98%, 8.92%, and 5.43% under the 5-way 5-shot setting, respectively. The results demonstrate the effectiveness and generalization capability of the proposed network in complex remote sensing scenarios.

Abstract:Existing multimodal object detection methods often suffer from limitations in modality selection, spatial modeling, and cross-modal consistency, particularly under challenging conditions such as low illumination, target occlusion, and complex backgrounds. To address these issues, this paper proposes a UAV object detection method based on modality-guided selection and adaptive contrastive learning. First, a modality-guided selection module is designed, which employs global semantic-aware channel attention to dynamically evaluate modal contributions and enable adaptive feature fusion, thereby effectively resolving the modality imbalance issue inherent in conventional fixed-weight fusion strategies. Second, a Modality Enhancement Module is introduced, incorporating locally-globally coordinated relative positional biases and normalized residual connections into a single-branch self-attention structure to enhance spatial perception in complex and occluded scenes. Finally, a detection-aware adaptive crossmodal contrastive learning strategy is proposed, utilizing detection boxes with modality-adaptive temperature scaling to explicitly align multimodal features and improve semantic consistency. Experimental results demonstrate that the proposed method achieves mAP50 scores of 78.6% on the Drone Vehicle dataset and 98.3% on the LLVIP dataset, outperforming existing approaches. Deployment on a real UAV platform achieves 12.61 fps, validating both the accuracy and practical utility of the framework.