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A review of research on absolute angular displacement sensors
Zhang Tianheng, Peng Donglin, Wang Yangyang, Zheng Yong, Duan Zhengnan
Abstract:
In the core components of intelligent control systems, absolute angular displacement sensors play a crucial role. Their measurement accuracy and long-term operational reliability directly determine the performance limits of the entire control system. This article provides a comprehensive review of absolute angular displacement sensors based on different physical principles (inductive, photoelectric, electric field, magnetoelectric). It delves deeply into their core sensing mechanisms and elaborates on the working principles of each type of absolute angular displacement sensor. Sensing electrode shapes (special encoding patterns, rings, sectors, sinusoidal patterns, petal shapes, etc.) are designed to adapt to different sensing mechanisms. Configuration characteristics of sensing units (evolving from grating lines to grating planes and further to arrays) are utilized to enhance sensing accuracy. Sensing media are shifting towards multi-media coupling to improve adaptability in complex environments. Typical structural features of absolute measurement and current research status are included. Their respective advantages are analyzed while also explicitly identifying their inherent limitations. It includes a comparative benchmarking of performance indicators of mainstream products currently on the market and their typical application domains, offering valuable references for engineering selection. This article dissects the absolute time-grating angular displacement sensor. This sensor fuses spatiotemporal modulation technology with diverse sensing mechanisms, achieving high precision and interference resistance through the spatial periodic distribution of sensing units coupled with temporal signal scanning and demodulation Finally, this article outlines future development trends for absolute angular displacement sensors, including improving sensor accuracy and resolution while enhancing robustness and reliability in extreme environments, and advancing multi-sensor information fusion technology to meet the increasing demand for multi-dimensional sensing in complex intelligent control systems.
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Progress and challenge of gallium-based liquid metal flexible sensor in the medical field
Sun Ying, Zhang Jiaqi, Tian Ziye, Liu Weipeng
Abstract:
In the medical field, traditional sensors often face challenges such as discomfort during wear, signal attenuation, and complications arising from invasiveness. Gallium-based liquid metal flexible sensors, with their exceptional advantages including high conductivity, flexibility, and biocompatibility, have shown great potential in medical diagnostics. This paper focuses on four innovative applications and technological advancements of gallium-based liquid metal flexible sensors in medical monitoring: in terms of vital sign sensing, these sensors enable real-time continuous monitoring of dynamic physiological parameters such as pulse, respiration, and surface pressure, providing data support for early disease warning; for motor function sensing, they can accurately detect joint motion trajectories, swallowing coordination, and muscle strength, serving as a basis for rehabilitation assessment in neuromuscular disorders; in multi-parameter surface sensing, the sensors allow non-invasive detection of skin temperature and sweat composition, offering new technical pathways for early identification of health issues such as metabolic disorders; in deep electrophysiological signal sensing, they break through the limitations of traditional methods in collecting weak internal signals such as neural and EEG signals, offering new tools for the diagnosis and treatment of neurological diseases. This paper provides an in-depth analysis of the practical applications of gallium-based liquid metal flexible sensors in early disease warning, postoperative monitoring, rehabilitation management, and personalized medicine. It also summarizes the key challenges in real-world applications, including packaging reliability, multi-physics field signal crosstalk, and weak signal acquisition, and explores corresponding optimization strategies. Finally, the paper discusses the future research prospects of gallium-based liquid metal flexible sensors in the medical field.
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A study of a MEMS gyro based on bidirectional thermal expansion flow
Ma Xuanlin, Piao Linhua, Liu Junyu
Abstract:
A MEMS gyro based on the principle of bi-directional thermal expansion flow is proposed and validated. The gyro generates airflow through the instantaneous temperature change of the heater, which induces a corresponding temperature change in a thermistor. This temperature variation is then converted into an output voltage, enabling accurate detection of angular velocity along the Z-axis. In order to further improve the performance of the gyroscope, reduce the cross-coupling, and optimize the preparation process, structural design optimization is necessary. COMSOL Multiphysics simulation software is used to explore the influence of factors such as the positional distribution of the sensitive components, the placement mode, and the uni-directional and bi-directional thermal expansion flows on the sensitivity. Through systematic simulation analysis, the optimal placement range of sensitive elements is clarified, with the parallel placement determined to be determined to be the most effective configuration. Furthermore, both theoretical and simulation results demonstrate that the bidirectional thermal expansion flow realizes higher output sensitivity and superior cross-coupling suppression compared to its uni-directional counterpart. Finally, based on the above findings, a MEMS gyro is prepared and its performance is experimentally evaluated. The test results show that the gyro is capable of detecting the angular rate in the range of ±600°/s with a sensitivity of 3.04 mV/（°·s-1） and a nonlinearity of 7.09%, under a heater drive signal of 2.5 V, 50% duty cycle, and 10 Hz square wave. The experimental results are consistent with the numerical simulations, confirming that the gyro offers high sensitivity, effective cross-coupling suppression, and a simplified fabrication process. These characteristics make it suitable for applications electronic devices, aerospace and medical instruments.
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Flexible nanowatt microcalorimeter for water droplet evaporation detection
Zeng Peng, Yang Xiaoping, Ji Chenchen, Li Ziheng, Feng Jianguo
Abstract:
Microcalorimeters play a critical role in biomedical and chemical research, such as dynamic monitoring of cellular metabolism, biomolecular interactions, and drug-receptor binding heat analysis, owing to their advantages of minimal sample requirements, rapid detection, and high precision. However, existing high-resolution microcalorimeters predominantly rely on micro-electro-mechanical systems processes, which involve complex photolithography, etching, and vacuum packaging techniques to fabricate microreactors and sensor arrays. These processes result in high costs and prolonged production cycles, limiting their adoption in low-cost, high-throughput scenarios. To address these challenges, this study presents an open-architecture microcalorimeter chip based on flexible printed circuit technology. The chip utilizes a flexible polyimide film substrate, integrates surface-mounted thermistors as temperature sensing units, and combines an open reaction chamber design with differential signal processing circuitry. The device achieves a power resolution of 15.4 nW and a temperature resolution of 48.44 μK under ambient pressure. A thermal reaction monitoring system was developed to perform real-time thermal detection of water droplet evaporation processes. For a 0.4 μL droplet, the measured evaporation enthalpy was 960.9 mJ, deviating by only 1.91% from the theoretical value (979.63 mJ), validating the system′s high reliability and anti-interference capability. The proposed flexible microcalorimeter features simple fabrication, low cost, and scalable production, offering a cost-effective solution for fundamental studies in single-cell metabolic thermodynamics and nanomaterial heat capacity characterization. Furthermore, it demonstrates broad application potential in portable biochemical detection, high-throughput drug screening, and industrial process monitoring, with promising implications for advancing precision medicine, biopharmaceuticals, and green chemical technologies.
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Research on high-sensitivity fiber-optical Fabry-Perot thermal flow velocity sensing technology based on the vernier effect
Li Taiwen, Liu Zhiyuan, Han Bo, Liao Zuhao, Zhang Junzhe
Abstract:
To address the issues of low sensitivity in high-velocity regions and limited upper measurement range in traditional fiber-optic thermal flow sensors, a high-sensitivity fiber-optic Fabry-Perot thermal flow velocity sensing method based on the Vernier effect and thermosensitive materials is proposed. The fiber-optic sensor employed in this method is fabricated by splicing a single-mode fiber (SMF) with a hollow-core fiber (HCF) filled with thermosensitive material at its end. The sensor achieves the first-stage sensitivity enhancement by utilizing the high temperature sensitivity of the thermosensitive material. Simultaneously, the cascaded Fabry-Perot interferometer (FPI) structure formed between the SMF and the thermosensitive material end-face creates the Vernier effect, which enables the second-stage sensitivity enhancement through the amplification characteristics of the Vernier effect. Through this two-stage enhancement mechanism, the sensor achieves improved flow velocity sensitivity in high-velocity regions and an extended measurement range. The sensing performance of the proposed high-sensitivity fiber-optic Fabry-Perot thermal flow velocity sensing method was theoretically analyzed using PDMS as the thermosensitive material. Concurrently, the sensor fabrication process was investigated, resulting in the successful fabrication of a physical sensor prototype. Experimental analysis was conducted to evaluate key sensing performance metrics, including sensitivity, maximum measurable flow velocity, and repeatability. The experimental results demonstrate that the sensor exhibits a temperature sensitivity of 1.399 nm/℃ and achieves a maximum measurable flow velocity of 25 m/s. Within the range of 17~25 m/s, the sensor′s response curve shows excellent linearity (R2=0.99), with a flow velocity sensitivity of 1.45 nm/(m/s). The repeatability deviation of sensitivity is only 1.24%, indicating excellent consistency. Owing to its high sensitivity, extended velocity measurement range, and compact form factor, this sensing method shows strong potential for industrial applications.
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An integrated insulator voltage-current sensor based on electric field coupling and TMR
Yan Fangfang, Suo Chunguang, Zhang Wenbin, Zhu Junyu
Abstract:
In the application environment of medium-voltage distribution lines and their stable currents, in response to the limitations of traditional voltage and current separate measurement, the internal safety of insulators after the integration of primary and secondary systems, the difficulty in determining the voltage measurement gain, and the anti-interference ability of TMR current measurement. A voltage-current integrated sensor based on electric field coupling and TMR magnetic sensing is proposed for insulators. On the one hand, a simulation-based optimization method is proposed for the structural parameters of the voltage sensing embedded within the insulator. The structural rationality is verified through simulations focusing on electric field distribution and insulation strength. Under the geometric constraints of the insulator model, the optimal structural parameters are obtained to achieve uniform induced electric fields, a well-defined transfer function, and minimized partial discharge after the voltage sensing unit is integrated into the insulator. This ensures the safety of the insulator and improving the voltage measurement accuracy. On the other hand, an open-loop two-stage magnetic ring current sensor based on TMR is proposed, which is placed at the top of the insulator. The dimensional parameters of the magnetic rings are designed through simulation analysis of the magnetic sensitivity characteristics, thereby improving the sensitivity, anti-interference capability, and magnetic field uniformity at the sensing point, enabling accurate measurement ofa wide-range current signal in open-loop conditions. An integrated sensing structure and system are ultimately designed for multiple experiments. Under power frequency conditions, the maximum relative error of the effective voltage measurement from 1 to 14 kV is 1.49%, and 1.41% under 10 kV input a with interference. For current measurements ranging from 1 to 120 A, the error under oscilloscope acquisition for signals above 2 A is within 1.2%, and -1.129% relative error under 20 A input with interference. Experimental results within the test range demonstrate that the proposed voltage-current integrated sensor offers certain anti-interference ability, accuracy, stability, and dynamic range.
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Calibration and error correction method for a line laser train wheel measurement system
Li Xinfei, Yan Ran, Xia Lei, Zhao Qing, Zhang Kaifei
Abstract:
To address the challenges of complex sensor calibration procedures and workpiece placement eccentricity in online 3D geometric parameter measurement of train wheels, this study proposes a high-precision measurement system based on multiple line laser sensors, along with an error correction method. A multi-layer dynamic coordinate transformation model is developed to accurately map sensor data to the wheel′s 3D geometric information without being constrained by placement eccentricity. A stepwise calibration strategy is employed, using cube, cylinder, and profiled rotary calibration blocks to sequentially calibrate the installation pose and position parameters of the sensors. A pose fine-tuning mechanism is further implemented to achieve coplanar calibration of multiple sensors, overcoming coplanarity challenge caused by assembly errors in large-scale scanning systems. To address wheel placement eccentricity, an eccentricity error compensation method based on dynamic polar coordinate correction is introduced. By computing the axis offset in real-time and tracking the workpiece axis trajectory, radial dimension measurement errors are significantly reduced, overcoming the limitations of traditional mechanical centering under special operating conditions. Experimental results show that the system achieves an absolute measurement error is less than ±0.069 mm, a repeatability standard deviation below 0.049 mm, and a fluctuation range of radial dimension errors after correction reduced to within 0.170 mm, meeting industrial-grade precision measurement requirements. In practical engineering applications, the maximum relative error between the system′s measurement results for complex geometric features of a 915KKD-type wheel and reference values obtained by a laser tracker is less than 0.135%, confirming the system′s reliability and engineering applicability. Beyond train wheels, the system can be extended to other rotary workpieces for 3D precision measurements, providing a universal calibration and error correction solution for multi-line-laser-sensor rotational scanning measurement systems.
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Multiphysics-coupled synergistic charging system for lithium portable energy storage under extreme cold conditions
Zhang Xiaocheng, Guo Qiang, Zhao Guangyan, Dai Yunlong, Yang Xinyu
Abstract:
This article addresses lithium-ion battery performance degradation in extreme cold environments, which causes charging inefficiency or failure in portable power banks, by designing a multi-modal cooperative charging system. Low-temperature electrochemical mechanisms and charging behavior are investigated, focusing on coupled electro-thermal relationships. Optimized preheating structures and electro-thermal cooperative dynamic charging strategies are proposed. A polyimide-based flexible heating film integrated with high-thermal-conductivity AlN/graphene significantly improves heat transfer rate and uniformity, rapidly restoring charging capability. To overcome limitations in direct internal temperature measurement, the recursive least squares method with a forgetting factor enables online identification of drifting key parameters (e.g., thermal capacity, resistance). A high-precision, time-varying thermal circuit model is formulated. Combined with an unscented Kalman filter, a dual closed-loop cooperative estimation architecture recursively updates and corrects internal temperature states in real-time. Experimental validation confirms the system achieves a battery internal heating rate of 5℃/min. The thermal model's systematic error stabilizes within 0.2℃. Under various sub-zero conditions (-30℃, -20℃, and -10℃), internal temperature prediction errors remain within ±1℃, with a maximum absolute error of 0.6℃ and maximum root mean square error of 0.4℃. This effectively solves the critical issue of portable power bank charging failure in extreme cold, providing innovative theoretical and engineering foundations for energy assurance systems in such environments.
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Non-contact high-precision acquisition of weak current information
Wang Yaoli, Xu Jizheng, Wang Zhibin, Yang Qingdong, Zhang Lei
Abstract:
In the field of industrial monitoring and safety inspection, non-contact high-precision detection of weak current signals is a core technology for ensuring the operational reliability of critical equipment. To address the problems of signal distortion, noise interference at high frequencies, and low detection accuracy in traditional methods, this study proposes a non-contact high-precision acquisition and demodulation method for weak induced currents. By combining down-conversion and digital phase locked detection, the method solves the current monitoring problem ofdevices such as bridge-wire electric detonators, enabling precise measurement of weak currents. The system is optimized through a “down-conversion-filtering digital phase-locking” architecture, achieving high-precision acquisition of weak currents in the range of 5~200 mA. This approach overcomes the bottleneck of electromagnetic interference inherent in traditional contact-based measurements and establishes an integrated system from physical sensing to digital processing. A TMR sensor is used as front-end sensing unit, leveraging its high sensitivity and wide frequency response to address the technical challenges of high-frequency current measurement. In the signal processing stage, multi-band signals are down-converted to 50 kHz using a mixer; low-pass filtering and ADC are then applied to suppress high-frequency noise. At the back end, a digital phase-locked loop based on million-scale multiply-accumulate operations is implemented using an FPGA, which separates target signals from background noise through cross-correlation detection. Experiments results show that in the frequency range of 50 kHz~10 GHz, the system achieves a detection accuracy of ≤±0.65, with real-time performance ensured by FPGA parallel processing. This non-contact detection method, through the synergy of sensor shielding and digital algorithms, provides a low-cost and highly reliable technical solution for the safety monitoring of electric detonators and current measurement of industrial equipment, with broad application prospects.
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Research on the output power prediction method of tidal energy converters based on uncertainty analysis
Xia Hainan, Wang Xiangnan, Guo Yi, Jia Ning, Chen Qiang
Abstract:
The output power of a tidal energy converter is an important indicator for measuring its economic performance. The output power characteristics of the tidal energy converter are crucial for calculating the annual energy production of the tidal energy converter. It is also very important for evaluating the overall economic performance of tidal energy converters. In view of this, this article addresses the scientific problem that the tidal current velocity data obtained during the field testing period of tidal energy converters may not cover the annual variation range of tidal current velocity in the field testing sea area. Uncertainty analysis, theoretical derivation, and model verification methods have been used to analyze the distribution patterns of tidal current velocity data and output power data of the tidal energy converter. The output power prediction method has been studied, a mathematical model for the distribution frequency of current velocity data and electric power data has been established, and the field testing data of the power performance characteristics of tidal energy converters have been applied for verification. The results show that the tidal current velocity data obtained during the field testing do not strictly follow the normal distribution law, and the data with current velocities exceeding 2.0 m/s accounts for about 7.0% of the statistical dataset. However, the output power data of the tested tidal energy converter show an exponential function distribution law, and the fitted curve determination coefficient is 0.99; When the tidal current velocity bin is 2.3 m/s, the difference between the predicted output power by the model and the average power calculated by the bin method reaches its maximum value, which is about 3.5% of the average output power that calculated by the method of bin. The maximum uncertainty of the established two datasets is approximately 2.9% of their output power. The uncertainty propagation coefficient of tidal current velocity generally shows a gradually increasing trend with the increase of tidal current velocity. The research results are expected to provide a reference for the overall economic performance evaluation of tidal energy converters.
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HVDC contactor characteristic parameters measurement based on subband averaging kurtogram and TMSST
Sun Shuguang, Wang Zihang, Wang Jingqin, Cui Yulong, Yan Haolei
Abstract:
Aiming at the demand for non-intrusive measurement of dynamic characteristic parameters in HVDC contactors, as well as the limited adaptability and transferability of short-time analysis methods across different contactor models, a measurement method based on subband averaging kurtogram and time-reassigned multisynchrosqueezing transform (TMSST) is proposed. First, a combination of sliding window framing and dual tree complex wavelet packet transform (DTCWPT) is applied to preprocess the closing acoustic signal of the contactor. Multiple wavelet subbands are obtained and rearranged, and the average kurtosis is calculated to construct the subband averaging kurtogram. The subband with the highest kurtosis is selected as the optimal frequency band for signal reconstruction, enhancing the effective collision-induced impact components. Subsequently, TMSST is employed for time-frequency representation and energy concentration of the collision impact events. The time-frequency coefficients corresponding to the peaks in the time-frequency envelope spectrum are used to characterize the impact features, enabling precise localization of the impact instants of the movable and stationary contact collision and the armature collision, and accurate measurement of the key dynamic characteristic parameters of the contactor, such as closing time and overtravel time. To evaluate the effectiveness of the proposed method, a dynamic characteristic testing system is established, and experimental tests are conducted under various contactor models and sensor layouts. Results show that the proposed method achieves high measurement accuracy in characteristic parameter estimation for various types of HVDC contactors, with both mean absolute error (MAE) and root mean square error (RMSE) maintained within 0.35 ms. Compared with the short time analysis methods, the average MAE and RMSE are reduced by over 39%. In addition, it exhibits low dependence on the sensor layout and strong generalization capability across different contactor types, demonstrating considerable potential for engineering applications.
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Error analysis of parametric calibration models for 6-DOF robotic manipulators
Wang Yan, Jiang Wensong, Luo Zai, Yang Li, Jin Xuanjie
Abstract:
The primary sources of multi-parameter identification bias in six degree-of-freedom (DOF) robotic manipulators are the highly coupled model parameters and complex linkage relationships. Such intricate multi-parameter identification chains make it challenging to conduct accurate error assessment, thereby affecting the compensation of the manipulator′s operational accuracy. To address this challenge, a grey-correlation-analysis-based error analysis method for parametric calibration models is proposed, which reveals the error propagation relationships within highly coupled multi-parameter identification chains. First, based on the analysis of error propagation chains for identified parameters, a robotic manipulator calibration error model is established to achieve the quantitative decomposition of Cartesian pose errors in joint space. Second, through the synergistic integration of parameter identification algorithms and error propagation chains, the error values of joint parameter sequences are estimated. To address the strong coupling characteristics of parameter deviations, the grey relational analysis method is introduced. By calculating the correlation coefficients between various parameters, the interrelationships among the characteristic parameters of each joint axis are quantitatively evaluated, thereby determining the priority of error compensation. Experimental results indicate that, compared to translational errors, rotational errors exhibit stronger coupling characteristics during their propagation through the kinematic chain. Through comparative analysis of positioning and orientation errors, it was found that the angular deviations in the first three joints, particularly those along the y-axis direction, contribute most significantly to the end-effector′s overall error. Therefore, these critical parameters should be prioritized in error compensation. Experimental data show that the final positioning error is 3.90 mm, with an orientation error of 0.06°. This study improves the calibration efficiency of robotic arms by decoupling complex parameter linkages, quantifying error contributions, and integrating sensitivity analysis of error parameters to develop an optimized compensation strategy.
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A recognition method of multi-class similar workpieces with weak texture in complex stacking environments
Xie Zhexin, Li Xiaoli, Jiang Jin, Zheng Gaofeng, Zhang Chentao
Abstract:
In the fields of aviation, aerospace, automotive manufacturing, and beyond, industrial robots undertake vital tasks such as automated assembly and meticulous workpiece sorting, relying on precise recognition. Yet, in industrial environments, challenges like intricate backgrounds, workpieces with similar shapes and textures, and stacked arrangements often heighten the vulnerability to incorrect recognition. To surmount these challenges, a binocular structured light camera is first used to acquire of high-fidelity three-dimensional point cloud data. A preprocessing algorithm is then deployed to effectively eliminate background interference and mitigate noise within these complex settings. Subsequently, an innovative point cloud over-segmentation algorithm is proposed, which integrates the moving least squares method to construct local differential geometric constraints. This enables shape-preserving simplification on the raw point cloud, optimizes the supervoxel clustering process, enhances robustness against noise, and effectively mitigates point cloud adhesion. Further, a multi-feature adaptive supervoxel fusion mechanism based on concavity-convexity constraints is designed. This mechanism comprehensively integrates multi-dimensional constraints, including the concavity-convexity relationships between supervoxel clusters and geometric feature similarity, achieving high-precision instance segmentation of multi-class target in complex stacked scenarios. Building upon this foundation, a support vector machine classification architecture driven by "local-global" descriptor sequences is proposed. This architecture constructs a multi-scale cascaded feature description system that jointly characterizes the local geometric details and global morphological features of the targets. This approach effectively solves the misclassification problem caused by stacked targets under small-sample conditions. Finally, an industrial robot platform is devised for algorithm validation. Experimental findings showcase remarkable enhancements in instance segmentation accuracy and classification precision, particularly for similar weak-textured workpieces. The achieved workpiece segmentation accuracy and recognition precision surpass 95%, affirming the effectiveness and robustness of the proposed method.
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Small-sample multi-modal evidence fusion framework for texture classification
Xiong Pengwen, Hu Muye, Huang Yuxuan, Zeng Cheng, Ye Yanhui
Abstract:
Conventional multi-modal fusion approaches assume that quality is uniformly distributed across samples, overlooking dynamic inter-modal reliability. Such static strategies struggle to down-weight low-quality modalities when data heterogeneity is high, rendering the fused representation susceptible to noise, missing modalities, and other degradations, thereby diminishing fusion benefits. Under small-sample conditions, these limitations further erode classifier robustness. To enhance reliability and adaptability in small-sample texture recognition, we propose the small-sample multi modal evidence fusion framework for texture classification (SMEF-TC). Built on subjective logic, SMEF-TC leverages a Dirichlet distribution to jointly model class probabilities and epistemic uncertainty, thereby eliminating extra uncertainty-quantification during inference and the high computational overhead of traditional Bayesian methods. Incorporating modality-specific uncertainties, evidence theory fuses multi-modal information, enabling the model to adaptively recalibrate each modality′s contribution and effectively suppress redundant or noisy cues. By simultaneously accounting for information evidence and predictive uncertainty, SMEF-TC retains high recognition accuracy under conditions of noise, modality absence, and imbalanced quality. Experiments on the public LMT-108 and LMT-184 texture datasets yield accuracies of 96.53% and 94.70%, respectively, confirming that SMEF-TC offers superior precision and robustness for small-sample texture classification compared with existing techniques.
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Research on collision detection and vibration feedback control strategies for cable-driven supernumerary robotic limbs
Qi Fei, Sun Lu, Sun Jie, Ge Yiwei, Liu Xianjun
Abstract:
To address the problems of low safety, poor reliability, and insufficient motion compatibility in human collaboration with supernumerary robotic limbs, this study proposes a collision detection and vibrotactile feedback control strategy to enhance the safety and reliability of human-robot interaction. Firstly, a kinematic model of the cable-driven SRLs is formulated based on the Denavit-Hartenberg method and the constant curvature principle. The workspace is analyzed using the Monte Carlo method. Then, a simplified bounding box model of the SRLs is constructed, and a minimum-distance-based collision detection method is proposed and verified through simulations involving potential collisions between the dual arms of the SRLs. To enable detection and feedback of contact force and position during collisions, a collision detection device based on pressure sensors is designed. The static contact model of the SRLs is studied and calibrated to realize accurate estimation of contact force and location. Finally, a safety control strategy based on vibrotactile feedback is proposed. An experimental prototype platform is developed, and multiple experiments are conducted to validate the proposed kinematic model, collision detection method, and safety control strategy. The results demonstrate that the cable-driven SRLs exhibit good motion performance within the workspace. The proposed collision detection algorithm can detect contact force and collision location within 0.12 seconds, and the vibrotactile glove enables intuitive perception of contact force and safe reactive control. Effective active obstacle avoidance can be achieved within 0.7 seconds, verifying the correctness and effectiveness of the proposed collision detection and vibrotactile feedback control strategy for supernumerary robotic limbs.
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Rehabilitation exercise detection method based on 3D human pose estimation
Zhang Kun, Zhang Pengcheng, Chen Xiaohao, Zhang Bin, Hua Liang
Abstract:
In rehabilitation exercise scenarios, motion input is typically in the form of video sequences. However, pseudo-3D solutions based on mainstream 2D human pose estimation methods and depth cameras are incapable of accurately measuring distances between skeletal points within videos, thereby affecting the final assessment performance. To address this issue, this paper proposes a sequence-to-sequence 3D frame-focused pose recognition method tailored for rehabilitation evaluation. The goal is to directly extract more comprehensive and detailed 3D coordinate information from the original noisy 2D scenarios and conduct motion sequence analysis based on this data. The proposed method adopts a four-branch streaming transformer architecture that captures the spatiotemporal interactions across long sequences by independently modeling the temporal and spatial aspects of the raw 2D input. These four branches are integrated through learnable proportional parameters, and an additional module combining a spatial encoder with an enhanced temporal decoder is employed to generate the final output. Our method outperforms state-of-the-art approaches on the Human 3.6M dataset, achieving a mean per-joint position error (MPJPE) of only 14.4 mm, the lowest 3D pose coordinate error reported to date. This demonstrates that the proposed backbone architecture is effective in handling more complex rehabilitation motion video sequence tasks. Moreover, comparative experiments on real-world rehabilitation video sequences further validate the effectiveness of our approach. Based on this advanced human pose estimation method, we have developed a novel multi-dimensional intelligent rehabilitation exercise evaluation and analysis system, capable of estimating motion metrics for 120 joint actions. The system has entered the clinical validation phase and has been tested on over 2 000 patients, achieving an average accuracy of 93.2%.
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Research on intelligent vehicle path planning strategy based on improved JPS
Zhu Zhendong, Yao Qiangqiang, Shi Yiheng, Xie Qilin
Abstract:
Jump point search is a fast graph search algorithm widely used in path planning. However, the complexity of the hopping point search process and the excessive number of node extensions can lead to low search efficiency. To address these limitations, this paper proposes a global path planning strategy based on an improved JPS algorithm. The proposed approach includes a method to reduce redundant node extensions and introduces a safe and smooth path generation strategy. First, a directional priority ranking is introduced, where the search directions are adjusted to prioritize movement toward the goal. Nodes are then expanded sequentially according to this ranking to search for jump points more efficiently. Second, several path optimization strategies are combined, including safe node updating, redundant node elimination and path smoothing to ensure the safety and smoothness of paths. The safe node update strategy reduces the dangerous paths, the redundant node elimination strategy effectively reduces the path length, and the path smoothing strategy improves the smoothness of the paths by three times quasi-uniform B-spline curve processing. Finally, the performance of the improved algorithm is verified through simulation and real scenarios. The simulation results show that the improved JPS algorithm reduces the search time by 19.0% and 99.92% in complex environments compared to the traditional JPS algorithm and A* algorithm, respectively, and the number of expansion nodes of the improved-JPS algorithm is reduced by 56.9% compared to the JPS and 98.9% compared to the A* algorithm. 98.9%. In more complex real-world environments, tests conducted on a ROS-based intelligent vehicle demonstrate search time improvements of approximately 20.5% over A* and 28.0% over JPS. These results confirm that the proposed Improved-JPS algorithm significantly enhances the efficiency and safety of path planning in complex scenarios, validating its effectiveness and superiority.
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Study on defect detection technology for low-magnetization materials based on magnetic eddy current effect
Gao Bo, Yang Lijian, Huang Ping
Abstract:
Magnetic flux leakage detection is one of the most widely used non-destructive testing techniques in online pipeline inspection. The prerequisite for obtaining ideal results in this detection technique is that the excitation system magnetizes the pipeline to a saturated state. This requirement presents significant challenges in the detection of large-wall-thickness pipelines. On the one hand, a large excitation system is needed to achieve sufficient magnetization intensity. On the other hand, the oversized excitation system and the strong magnetic attraction it generates have become the major bottlenecks restricting the practical application of this technique in such scenarios. To address this problem, a defect detection technique based on the magnetic eddy current effect under low magnetization intensity is proposed. This technique focuses on detecting magnetic permeability anomalies at defect locations. The variations in magnetic permeability at defect sites in ferromagnetic materials under low DC magnetic fields are investigated, and the mechanism analysis and the mechanism of inner and outer wall defect detection based on the magnetic eddy current effect is analyzed through finite element simulation. A magnetic eddy current sensor was designed, and comparative experiments on magnetic eddy current and magnetic flux leakage detection of inner and outer wall defects were conducted on 15mm thick steel plates with artificially machined defects of different types and depths (50%, 40%, 30%, 20% and 10% of wall thickness). Experimental results show that when the magnetization current is between 0.7 A and 1.1A, the magnetic eddy current detection technique not only effectively identifies defects of varying depths and types and distinguishes between inner and outer wall defect locations, but also achieves significantly better detection performance than magnetic flux leakage detection under the same conditions. Both simulation and experimental results demonstrate that inner and outer wall defects in ferromagnetic materials can be detected under low magnetization intensity using the magnetic eddy current effect, verifying the feasibility and effectiveness of the proposed detection technique and providing an effective method for the full-wall thickness defect detection in large-wall-thickness pipelines.
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DMIFD: A deep learning-based method for multimodal industrial fault diagnosis
Yin Gang, Zhu Miao, Yan Yuehan, Wang Huaijiang, Jiang Maohua, Liu Qilie
Abstract:
Fault diagnosis based on deep learning is an important research direction in the intelligent management of industrial safety. Failures frequently occur in the actual industry production, leading to reduced production efficiency, and in severe cases, production stoppages or even casualties. Due to the complex and variable production environment, fault features are difficult to extract and recognize, andreal-time monitoring and rapid diagnosis are required at industrial sites. Traditional fault diagnosis methods typically rely on expert experience for feature extraction and pattern recognition, which makes them difficult to adapt to the complex and dynamic industrial environments. To address these issues, a deep learning based multimodal industrial fault diagnosis(DMIFD) method is proposed. Extreme gradient boosting (XGBoost) is employed to select process parameters related to industrial faults, which are then used as the multimodal input data for the model. The deep extreme learning machine (DELM) is used to extract nonlinear and high-dimensional features from the production process parameters to identify equipment in abnormal states. The key parameters of DELM are optimised using the frost and ice optimisation algorithm (FIOA) to achieve optimal model performance. The RIME-DELM module outputs equipment samples in normal states, while the samples of abnormal equipment are further input into a deep belief network (DBN) and a least minimal squares support vector machine (LSSVM) to perform specific fault type classification. The proposed method is applied to the aluminium electrolysis production process to validate its effectiveness. Field conducted in an aluminum electrolysis plant show that the model achieves an abnormal state detection accuracy of 97.96%, an F1-score of 0.975 3, a fault type diagnostic accuracy of 96.75%, and a Macro-F1 score of 0.944 7. Compared with common deep learning models and through ablation experiments, the DMIFD model demonstrates higher diagnostic accuracy and provides more accurate and reliable support for fault diagnosis in practical industrial production.
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Defects detection of electric fusion welding of polyethylene gas pipeline based on DSG-ResNet34
Ling Xiao, Liu Lu, Sun Baocai, Zhang Zhengtang, Xu Xiaogang
Abstract:
The connection quality of PE gas pipelines can directly affect the normal transmission of medium and low-pressure gas. Structural distortion, cold welding and other defects produced by electrofusion welding will significantly weaken the mechanical properties of the pipeline, which will threaten the stable operation of the gas pipeline network. Therefore, based on the DR Image datasets of PE gas pipelines electrofusion welding defects collected on-site, this paper proposes a defect detection method based on the DSG-ResNet34 model to realize rapid and accurate detection of electrofusion welding defects. This network model consists of three parts: the backbone network CBAM-ResNet34 module, the dynamic sparse gating feature pyramid networks DSG-FPN, and the multi-scale detection head. Firstly, the CBAM-ResNet34 structure of backbone network is used to enhance the network model′s attention to defect features from two dimensions of channel and space. Then, the DSG-FPN, which integrates a dynamic sparse gating module, an Inception module, and sparse connections, dynamically fuses multi-scale defect features-effectively preserving small-target features while suppressing background noise. Finally, the multi-scale detection head converts the enriched features into precise detection outputs. The DSG-ResNet34 model achieves a defect detection accuracy of up to 95.5%, with a P2 layer precision of 82.7% and a minimum recall rate of 85.6% for small targets. The detection speed reaches 68 fps, and the model contains 22.3 million parameters. This model can quickly locate and identify four typical electrofusion welding defects: holes, fused surface inclusion, structural distortion and cold welding. Both its detection performance and speed outperform existing models. This study provides a high-precision solution for intelligent testing of PE pipeline welding quality, which is of great significance for ensuring the safe operation of gas pipeline network.
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Semi-supervised caries segmentation of panoramic X-ray images based on multi-scale convolution and selective kernel dual-attention
Xue Zhonghao, Jiang Jingang, Sun Jianpeng, Pan Jie, Zhang Jiawei
Abstract:
Caries segmentation in panoramic X-ray images is an important prerequisite for early caries detection and subsequent treatment. In order to achieve accurate and automatic segmentation of caries in panoramic X-ray images, a semi-supervised learning framework with multi-scale convolution and selective kernel dual-attention mechanism is proposed. This framework aims to enhance the generalization capability of the model by leveraging a large amount of unlabeled data, while addressing challenges such as blurred lesion boundaries and low contrast in caries-affected regions. The framework adopts a teacher-student dual network architecture. It applies multi-scale convolutional attention to deeply supervise the multilayer decoder in the student network, thereby improving its ability to capture boundary details and distinguish between similar inter-class regions. Meanwhile, a selective kernel attention mechanism is introduced to fuse multi-level predictions from the teacher network, adaptively selecting convolution kernels based on pixel-level uncertainty to generate accurate uncertainty masks that guide the student′s learning process. Experiments conducted on the dataset 1 and dataset 2 demonstrate that, on 265 slices, the dual attention mechanism achieves improvements over the baseline model of 3.91%, 2.14%, and 5.35% in Dice coefficient, precision, and sensitivity, respectively. And on 530 slices, the improvements reach 1.39%, 5.69%, and 12.34%, verifying the method′s stability and adaptability on larger-scale data. Compared with traditional fully supervised models, the proposed method achieves the highest improvements in Dice coefficient, precision, and sensitivity, with increases of 22.27%, 17.64%, and 24.57%, respectively. And compared with recent semi-supervised methods, it achieves improvements of up to 14.54%, 14.81%, and 11.96%, respectively. This study not only significantly enhances caries segmentation performance but also provides an accurate and robust solution for panoramic X-ray images.
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A defect detection method for photovoltaic cells based on MFES-YOLOV8n
Chen Junsheng, Chen Yimeng, Liu Mingjie, Piao Changhao
Abstract:
To address the issues of high missed detection rates for small targets, insufficient robustness against complex background interference, and limited cross-scale defect detection capabilities in existing target detection methods for photovoltaic cell electroluminescence images, a defect detection model based on MFES-YOLOV8n is proposed to enhance detection accuracy and efficiency in industrial scenarios. First, a C2f-ST feature extraction module is embedded into the backbone network, utilizing the window-based self-attention mechanism of Swin Transformer to enhance local-global feature associations for micro-defects, combined with residual connections to preserve shallow-layer detail features. Therefore, the fine-grained feature extraction capabilities are improved. Secondly, an ES-SPPCSPC feature representation module is designed, integrating group convolution with an enhanced SimAM attention mechanism, achieving dynamic suppression of background noise and enhancement of defect-specific features through synergistic optimization of energy-based, channel, and spatial attention. Finally, an MSFF-Neck multi-scale feature fusion module is established, employing scale-sequential feature fusion and triple feature encoding strategies to enable complementary interactions between deep semantic and shallow detail features, mitigating multi-scale feature degradation. Experiments on the PVEL-AD dataset validate the model’s effectiveness. Results show that it achieves an mAP@0.5 of 0.897 with 6.1 M parameters, improving by 3.0% over the baseline YOLOv8n. Through a progressive optimization strategy of “fine-grained feature extraction, cross-scale semantic enhancement, and multi-level feature fusion”, this study overcomes performance bottlenecks in multi-category and cross-scale defect detection of traditional models, providing a high-precision, lightweight, and edge-computing-compatible defect detection solution for industrial scenarios. While maintaining low computational complexity, it meets the demands for real-time performance and reliability in industrial applications, offering technical support for advancing quality control and intelligent maintenance in the photovoltaic industry.
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A multi-domain adversarial transfer method for fault diagnosis of railway bogies
Chu Xiaoyan, Liu Xing, Miao Qiang
Abstract:
The bogie system of a railway train is one of the key subsystems ensuring operational safety. It is significantly affected by factors such as track conditions, time periods, and climate, leading to complex and variable working conditions, while fault-labeled data under specific conditions are scarce. Traditional deep learning-based fault diagnosis methods typically rely on large-scale labeled datasets and often exhibit poor generalization in cross-domain tasks. Existing transfer learning methods for bogie diagnosis generally lack effective alignment of internal feature distributions within the source domain, increasing the risk of negative transfer and limiting the model′s transfer performance. To address these issues, this article proposes an innovative multi-domain adversarial transfer method for fault diagnosis of railway bogies, aiming to align fault features of railway bogies under multiple operating conditions and enhance diagnostic performance across domains. First, pretraining is conducted on a source domain that includes multiple operating conditions. During pretraining, a domain-adversarial fusion method is used to align features across different conditions and to learn generalizable features. Then, the pretrained model is partially frozen and fine-tuned on 20% and 5% of the target domain data, respectively, to adapt to the target task. Testing on the test set achieves average classification recall rates of 98.48% and 93.00%, and average false alarm rates of 0.13% and 0.58%, respectively. Across all evaluation metrics—including recall, false alarm rate, precision, and F1-score—both the average and worst-case values outperform comparison methods. Experimental results show that the proposed method achieves higher diagnostic accuracy for faults in the power transmission chain based solely on easily accessible three phase motor current and rotational speed data. It performs particularly well for confusing fault types and maintains effective diagnostic capabilities even with minimal target domain data by leveraging diverse working condition data from the source domain.
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Leakage fault location method for branch lines before user meters in low voltage distribution system
Chen Lei, Su Huafeng, Su Sheng, Feng Xiaofei, Li Bin
Abstract:
To address the high concealment of pre-meter leakage locations in low-voltage distribution networks-as well as the limitations of traditional manual inspection methods that rely heavily on maintenance personnel experience and struggle with intermittent leakages-this paper proposes a branch-line leakage localization method targeting users on the customer side of the meter. First, leveraging electrical prior knowledge, a physical model of pre-meter leakage is established, and the underlying mechanism of changes in users′ shortest-path virtual impedance before and after leakage is analyzed. Multivariate linear regression equations are then constructed for users in a distribution area to derive temporal virtual impedance matrices, which are flattened column-wise as model input. A symmetric relative entropy model is proposed to capture both local (adjacent) and global dependencies among users. Its output accuracy is enhanced through a segment aggregation strategy, effectively transforming the leakage localization problem into a time-series anomaly detection task. To improve the model′s sensitivity to subtle feature deviations, a minimax adversarial optimization mechanism is introduced into the reconstruction loss function to amplify differences between normal and leaking users. This is further combined with a collaborative anomaly scoring method based on symmetric relative entropy, enabling robust identification of anomalous users exceeding a predefined threshold. Extensive simulations on the IEEE European low-voltage feeder system under various leakage scenarios are conducted to support hyperparameter tuning and ablation studies. Experimental results demonstrate that the proposed method outperforms existing algorithms in detection accuracy. Moreover, by addressing edge cases-such as outages, no-load users, measurement errors, and electromagnetic interference-the model exhibits strong anti-interference capability. Its effectiveness and generalization ability are further validated through deployment tests on real-world distribution networks.
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Anomaly detection method for motion status of control rod drive mechanism in nuclear power plants by multi-coil joint learning
Lin Weiqing, Miao Xiren, Jiang Hao, Ye Mingxin, Chen Jing
Abstract:
Control rod drive mechanisms (CRDMs), as critical actuators in nuclear power plants, regulate control rod motion by manipulating multiple coil currents through electromagnetic-mechanical coupling. Their operational integrity is vital for ensuring reactor safety. However, existing anomaly detection approaches predominantly model individual coils, neglecting the dynamic interactions among coils and the evolving operational patterns. To address this gap, we propose a multi-coil joint learning (MCJL) model for accurately detecting latent anomalies during control rod operation. In this approach, coil nodes and fully connected edges are defined, and a decay adjacency matrix is introduced to construct a multi-coil interaction graph that captures the dynamic coupling among the lift, moving, and solid coils. A moving graph convolutional network is then employed to efficiently extract local temporal dependencies across coils while jointly reconstructing the current signals of all three coils. The residuals between the reconstructed and actual signals are subsequently calculated, enabling per-cycle anomaly detection using a multi-scale dynamic strategy. Experimental validation using historical data and simulated anomalies from a pressurized water reactor demonstrates that the proposed method effectively captures dynamic inter-coil coupling and temporal patterns, achieving high-precision signal reconstruction. Compared with existing methods, MCJL exhibits superior performance in both reconstruction and anomaly detection. Furthermore, its dynamic thresholding strategy provides flexible decision boundaries and strong fault tolerance.
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Same-scale quantitative assessment method for multiple states of a harmonic reducer based on improved ResNet and MKSVDD
Sun Yulin, Luo Shuang, Kang Shouqiang, Wang Yujing, Liu Liansheng
Abstract:
To address the difficulty in accurately quantifying the fault degree of harmonic reducers and the inability to perform same-scale quantitative analysis for different fault locations, a same-scale quantitative assessment method is proposed for multiple states of harmonic reducer based on improved deep residual network (ResNet) and multi-kernel support vector data description (MKSVDD). First, a new same-scale quantitative assessment framework for multiple states of harmonic reducer is proposed, and continuous wavelet transform is applied to acoustic emission signals sensitive to weak faults to construct a two-dimensional time-frequency image dataset. Then, a convolutionalattention module is used to improve ResNet in order to fully extract the deep features of the two-dimensional time-frequency images. Furthermore, a multi-kernel function is introduced to enhance the support vector data description, and an MKSVDD health state assessment model is constructed based on the deep features of the harmonic reducer in the normal state. Next, the distance between the features of different fault degrees and the center of the hypersphere under the normal condition is calculated to construct the assessment indicators, and the quantitative assessment curve is obtained by fitting these indicators. In addition, based on the structure of the harmonic reducer and the propagation mechanism of acoustic emission signals, a relative distance compensation scheme is proposed to construct the multi-state assessment indicator, thereby achieving quantitative assessment of different health states for harmonic reducer under a unified scale. Through the establishment of a harmonic reducer test bench and multiple comparative experiments on data with unknown fault degrees, the results show that the features extracted by the improved deep residual network are more compact. The proposed method enables same-scale quantitative assessment of different fault locations, with an assessment error not exceeding 3.2%, effectively completing the same-scale quantitative assessment of harmonic reducer in multiple states.
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Fault diagnosis method for harmonic reducers based on federated learning with multi-source imbalanced data
Wang Yujing, Ye Baihong, Kang Shouqiang, Liu Liansheng, Sun Yulin
Abstract:
Aiming at the problems of imbalanced sample sizes across different fault categories in industrial robot harmonic reducers and the limited information obtained from single-source signals, which result in low diagnostic accuracy, a fault diagnosis method for harmonic reducers is proposed based on federated learning under multi-source imbalanced data. This method performs wavelet transform on the multi-source signals of different users to convert one-dimensional signals into two-dimensional images, constructing a time-frequency dataset. An improved data augmentation method is then applied to balance the dataset. The efficient channel attention is introduced, and the output of the residual branches is weighted by learnable weights, which can enhance the model′s adaptability to residual information of different input signals and the ability to extract key features of the data. A modified multimodal variational autoencoder is used to mine the complementary information among multi-source signals for feature fusion, and adopting the focal loss function as the training loss function, the model can pay more attention to category samples with high misclassification frequencies, thus constructing personalized local models for multi-user. The server aggregates the local model parameters at the multi-user and updates the global model, multi-user local island privacy data is protected through federated learning, so as to achieve the fault diagnosis of harmonic reducers under multi-source imbalanced data. The effectiveness of the proposed method is verified by building a signal acquisition experimental platform for harmonic reducers. The proposed method can effectively extract the features from multi-source imbalanced data of harmonic reducers and achieve information fusion, achieving an average fault diagnosis accuracy of 98.8%, outperforming the compared methods.
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Denoising method for on-orbit line-of-sight measurement data of remote sensing satellites and its BiLSTM-CNN-based implementation
Gao Yu, Zhang Xu, Li Hong, Zhuang Wei, Zhu Lianqing
Abstract:
To address the challenges posed by complex perturbation environments and noise interference affecting line-of-sight (LOS) micro-angle measurement systems onboard remote sensing satellites during in-orbit operations, this study proposes a denoising method that integrates Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Networks (CNN) to enhance the accuracy and reliability of measurement data. By combining physical modeling of micro-angle measurement with Monte Carlo simulations, the noise distribution and spatiotemporal correlations in the data are systematically analyzed and validated. A high-quality labeled dataset is then constructed from existing in-orbit measurements to ensure robust and generalizable model training. In the proposed BiLSTM-CNN architecture, BiLSTM captures bidirectional temporal dependencies, while CNN extracts local spatial features. A gradient balancing mechanism is incorporated to mitigate issues such as gradient vanishing and overfitting, thereby improving model stability under complex conditions. Experimental comparisons with typical neural network models show that, on the a1-axis, the proposed model reduces MSE, RMSE, and MAE by 7.9%, 4.3%, and 16.4% respectively compared to the best-performing BiLSTM, and on the b1-axis, achieves respective reductions of 4.6%, 2.3%, and 6.4% compared to the best-performing GRU. These results demonstrate the robustness, generalizability, and effective noise suppression capability of the proposed method, offering a practical and promising solution for high-precision satellite attitude measurement.
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Plantar pressure-based identity recognition method combining recurrent plot and LeNet network
Abstract:
Aiming at the variability in the number and positional configuration of sensors in plantar pressure acquisition devices within the field of identity recognition, as well as the issue of increased time cost arising from the conventional reliance on complete segmentation of gait cycle data for extracting plantar pressure features, this article proposes an identity recognition method based on threshold-free recurrence plots of plantar pressure signals and the LeNet neural network. Plantar pressure data are collected during natural, unloaded walking from 28 healthy adult participants without foot or lower limb pathologies. Data acquisition occurs on a standardized concrete surface using a custom-designed plantar pressure measurement system. The raw plantar pressure data are preprocessed using a data reconstruction algorithm to directly convert them into threshold-free recurrence plots. These generated images are then used as input to the LeNet network for feature extraction and identity recognition. Recognition performance across various single-region and multi-region configurations is systematically analyzed and compared. Experimental results show that the optimal configuration—combining the medial heel, lateral heel, second metatarsal head, and hallux regions—achieved superior identity recognition performance with minimal sensor deployment and high accuracy. Specifically, accuracy, precision, recall and F1-score attained 99.25%, 99.22%, 99.39%, and 99.26%, respectively. Recognition performance tends to be influenced by gait phase and plantar pressure magnitude. However, this effect progressively diminishes with increasing number of integrated regions. Furthermore, the proposed method maintains high recognition accuracy without requiring rigorous gait segmentation. It provides new ideas and technical support for the application of identity recognition technology in the field of biometric identification, and has potential application value in public safety and other fields.
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Waveform correlation factor-weighted Lamb wave total focusing imaging for variable-thickness plates
Huang Yin, Wang Qi, Xu Caibin, Deng Mingxi
Abstract:
Variable-thickness plates are widely used in critical fields such as aerospace and nuclear engineering due to their structural advantages, such as lightweight properties, making defect detection essential for ensuring operational safety. However, the geometric non-uniformity of such plates significantly affects the propagation characteristics of Lamb waves, leading to complex wave behavior, intensified dispersion effects, and rendering conventional Lamb wave defect imaging methods designed for uniform-thickness waveguides-ineffective. To address the defect detection challenges in variable-thickness plates, this paper proposes a Lamb wave total focusing imaging method weighted by waveform correlation factors for defect localization and imaging. First, a theoretical model of Lamb wave propagation in variable-thickness plates is established by approximating the thickness-varying waveguide as a series of locally uniform, constant-thickness short waveguides. Then, a propagation path-based virtual backpropagation technique is employed to perform dispersion compensation on defect-scattered wave packets, correcting waveform distortions. On this basis, the waveform correlation coefficients of the dispersion-compensated signals from each channel are calculated and used as weighting factors, which are integrated with the amplitude superposition mechanism of the classical total focusing method to construct a weighted amplitude imaging metric. By introducing the waveform correlation factor, this metric suppresses the contribution of uncorrelated noise to amplitude superposition, thereby improving the signal-to-noise ratio of defect imaging. Numerical simulations on a linearly varying-thickness aluminum alloy plate demonstrate that the proposed method can achieve effective accurate defect localization, with a maximum defect center localization error of less than 4 mm, and the imaging background noise amplitude is significantly lower than that of the conventional total focusing method. Experimental results further verify the effectiveness of the proposed approach. This study provides a valuable reference for defect detection and imaging in variable-thickness plates.
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Research on an ORB-based charging pile localization algorithm integrating color invariant and multi-scale features
Luan Tiantian, Gu Wenlli, Sun Mingxiao, Li Bin, Liu Pengfei
Abstract:
Charging stations serve as essential power infrastructure for unmanned vehicles, where precise localization enables reliable operation. Current positioning algorithms predominantly use template matching and deep learning, yet both face limitations. Template matching performs poorly under perspective changes, while deep learning lacks real-time applicability. To address these issues, this article proposes an improved ORB feature matching algorithm incorporating deblurring and color-invariant processing for scale-invariant charging station localization. The method first applies a multi-scale pyramid and fuzzy layer segmentation for image deblurring. Next, a color invariant model preprocesses template and test images to extract invariant features. A scale space is then constructed for these features, with Fast-Hessian detecting extremum points to obtain scale-invariant keypoints. Feature descriptors are computed using rBRIEF, while Hamming distance and an accelerated RANSAC filter mismatches to derive the inter-image mapping matrix. The charging station′s pose is finally estimated using cooperative target dimensions and PnP. Experimental results show superior deblurring performance over traditional methods. Compared with the conventional ORB, the proposed algorithm resolves feature extraction failures in the same-gray but different-color regions, enhances matching accuracy, ensures scale invariance, and improves feature distribution uniformity, ultimately boosting positioning precision.
传感器技术
电子测量技术与仪器
机器人感知与人工智能
先进感知与损伤评估
Information Processing Technology
About
Organizer：China Association for Science and Technology
Governing Body：China Instrument and Control Society
Chief editorial unitf：Zhang Zhonghua
Address：23rd Floor, Building A, Horizon International Tower,No.6 Zhichun Road, Haidian District,Beijing, China
Zip Code：100088
Phone：010-64004400
Email：cjsi@cis.org.cn
ISSN：11-2179/TH