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Advances in measurement uncertainty evaluation: From statistical methods to neural network indirect evaluation
Chen Wenhao, Ding Yinye, Song Rencheng, Zhang Jin, Xia Haojie
Abstract:
As the demand for measurement accuracy intensifies in modern industry and scientific research, the evaluation of measurement uncertainty has become a crucial component in ensuring product quality and optimizing production processes. Traditional methods for assessing measurement uncertainty are widely applied in static and linear systems but have shown limitations when dealing with high-dimensional, nonlinear, or dynamic systems. In recent years, non-statistical methods have effectively complemented uncertainty evaluation for complex systems. In particular, neural network techniques, with their powerful data processing capabilities and nonlinear modeling capabilities, have become essential tools for uncertainty evaluation. This study provides a comprehensive review of statistical methods, including the Guide to the Expression of Uncertainty in Measurement and the Monte Carlo method, as well as non-statistical approaches such as Bayesian inference, grey evaluation, fuzzy evaluation, and maximum entropy evaluation. The fundamental principles, advantages, limitations, and typical application scenarios of each method are systematically analyzed. In addition, the recent trend of integrating these uncertainty evaluation methods with machine learning is discussed. Particular attention is given to emerging neural network-based indirect evaluation methods, including deterministic models, Bayesian neural networks, and ensemble learning frameworks. The modeling and evaluation strategies of these approaches are examined in the context of complex nonlinear systems, highlighting their potential and current limitations. Finally, the applicability of various uncertainty evaluation methods is summarized, and future research directions are outlined. The study suggests that the integration of multiple evaluation paradigms can enhance the modeling capability and reliability of uncertainty estimation in complex measurement systems, reduce dependence on large data samples, and better address the increasingly intricate measurement requirements in modern industry and scientific research.
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Research on dynamic error suppression method of fiber optic gyroscope based on adaptive phase modulation
Huo Zijing, Liu Xin, Xu Jiangyan, Yang Liu, Gao Zhongxing, Zhang Yonggang
Abstract:
To address the error degradation of fiber-optic gyroscopes (FOGs) under high-dynamic conditions, which adversely impacts their performance, this study proposes an adaptive phase-modulation based dynamic error suppression method. Firstly, the principal analysis and system modeling of the FOG closed-loop operating system are conducted to derive the open-loop and closed-loop transfer functions, revealing significant tracking errors for angular acceleration inputs. Secondly, the relationships between the output signal noise, bandwidth of the FOG, and phase modulation depth are analyzed using the random walk coefficient. Subsequently, an adaptive relationship model between phase modulation depth and angular acceleration is established, and a simulation model of the FOG closed-loop operating system is constructed based on this model to validate and analyze the adaptive phase modulation method through simulation. Finally, the adaptive phase modulation method is implemented on a FOG prototype according to the established model and compared with the traditional fixed phase modulation method. Static and dynamic performance tests and analyses are conducted on the FOG prototype under these two different phase modulation methods. The experimental results demonstrate that both the adaptive and traditional phase modulation methods yield a random walk coefficient of 0.006°/h, while the adaptive method reduces the tracking error for angular acceleration inputs by approximately 66.73%. This approach not only effectively enhances the dynamic performance of the FOG while maintaining its static performance but also meets the bandwidth requirements under various dynamic conditions, offering significant theoretical guidance and engineering application value for improving the adaptability of FOG in complex environments.
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Prediction of one-dimensional workbench localization accuracy loss based on dynamic Bayesian network
Li Li, Liu Bingyao, Yang Hongtao, Qin Pengfei, Wang Shen′ao
Abstract:
The worktables of core components in CNC machine may experience wear and failure over extended periods of use, leading to reduced machining accuracy. To accurately predict positioning accuracy loss in workbenches under various factors as a function of usage time, this study proposes a modeling and prediction method for one-dimensional workbench positioning accuracy loss based on dynamic Bayesian network. The effectiveness of the proposed method is validated through comparative analysis of measured error data and predicted data. First, the composition of error sources is determined based on the structural analysis of the one-dimensional workbench. According to the established theoretical model of accuracy loss under complex operating conditions for the one-dimensional workbench, load, speed, temperature, and time are identified as the primary factors influencing the positioning error of the workbench. Second, experimental platform was constructed to measure positioning error data under various influencing factors. The validity of the theoretical model was verified based on cloud map results. Next, we incorporate the time dimension to construct a dynamic Bayesian network prediction model for workbench positioning errors under multi-factor influences. We sequentially determine the basic structure of the dynamic Bayesian network, its network nodes, and the ranges of variable domains. Subsequently, we employ mathematical statistics and the EM algorithm for parameter learning, obtaining the prior probability distribution for root nodes and the conditional probability for non-root nodes. Finally, using forward and backward positioning error as an example, the dynamic Bayesian network clustering inference algorithm was employed to predict workbench positioning error. Simultaneously, the predicted error was compared with the measured error under identical conditions. Results indicate that both the predicted forward and backward positioning error curve and the measured error curve generally increase over time, exhibiting similar trends. The maximum absolute error reached 1.63 μm, while the maximum relative error was 13.471%. This validates the effectiveness of the prediction model.
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Measurement method for volume error of large machine tools based on beam drift compensation and model optimization
Liu Wenzheng, Lan Jinfeng, Tang Chuanzhi, Hu Hongwei, Duan Fajie
Abstract:
Large CNC machine tools are characterized by long guide rail travel and are often subjected to complex environmental disturbances in industrial settings. Existing volumetric error detection methods generally suffer from limited accuracy and low efficiency. To address the above shortcomings, a measurement method for volume error of large machine tools based on beam drift compensation and model optimization is proposed. To mitigate accuracy degradation caused by beam drift in long-distance measurements, a differential compensation method for angular drift is proposed based on the difference in beam polarization states, which is the common transmission of reference light and measurement light and beam splitting detection. At the same time, the effect of the non-parallelism of the two beams on the roll angle measurement was analyzed, and a high-parallelism two-beam generation module was constructed by utilizing the characteristics of retroreflector. On this basis, a five-degree-of-freedom (5-DOF) error online measurement system was developed, which achieved high-precision and high-efficiency acquisition of the geometric error of the machine-sheet axis. In order to further improve the accuracy and applicability of the volume error model of large machine tools, an optimized volume error model suitable for various three-axis machine tools is constructed. Starting from the homogeneous transformation model, the influence mechanism of Abbe error and Bryan error caused by the non-collinearity between the error measurement axis and the motion axis is introduced into the model. Performance tests of the measurement system and compensation experiments for machine tool volume error measurements were carried out in the laboratory and industrial sites respectively. The results show that within the 3 m measurement range, the standard deviation of the angular error of the 5-DOF measurement system is below 0.5″, and the standard deviation of the straightness error is less than 0.6 μm. After error measurement and compensation, the diagonal positioning error of the machine tool is reduced by 51.6%. This method can achieve precise online measurement and active compensation of volume errors of large machine tools, and has good industrial application prospects.
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D motion trajectory measurement method and visualization analysis for double-break contacts of electrical apparatus
Abstract:
Double-break electrical apparatuses are widely used in the fields such as power system, industrial control, and new energy, etc. The motion process and the synchronization of contacts are key factors that affect the electrical life, breaking capability, and operational reliability. Such electrical contacts are prone to positional deviation during motion, making their motion trajectories difficult to measure accurately. To address those issues, A virtual binocular-vision–based approach is proposed to detect the motion of double-break electrical contacts and to enable visual analysis of their motion process. A three-dimensional testing system was developed using a single high-speed camera and two plane mirrors. Markers were placed on each contact to capture motion images during the closing and opening processes. An image processing algorithm was designed to automatically identify and extract the markers. Three-dimensional reconstruction was then used to reconstruct the spatial motion trajectories, generating characteristic curves representing the motion of the dual-contact apparatus. Experimental validation of the test system revealed that the proposed method can achieve a maximum error of 4.28%, with an average error of 1.26%. Testing of the closing and opening processes of a dual-breakpoint high-voltage DC relay revealed that during the closing process, the two contacts experience a certain displacement and time difference along the main motion direction; after opening, the two contacts do not return to their closed positions. Furthermore, displacement deviations between the two contacts are also observed in other directions, indicating dynamic differences and significant asynchrony during the dual-contact motion. Through visual analysis, the internal motion offset state of the double-breakpoint electrical contacts can be clearly presented. This shows that the double-breakpoint detection method based on virtual binocular vision can achieve accurate measurement and visual analysis of the three-dimensional motion trajectory of the two contacts under non-contact conditions.
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Successive variational mode decomposition vital signal frequency detection algorithm for integration of sensing and communication
Pu Qiaolin, Zhang Jielong, Zhou Mu, Tan Mingyi
Abstract:
Integrated sensing and communications (ISAC), as a key enabling technology for 6 G, significantly enhances the application potential of Wi-Fi devices in non-contact vital sign monitoring by deeply integrating communication and sensing capabilities. Accurate monitoring of respiratory and heartbeat rates is crucial for early disease warning and real-time health status monitoring. However, current ISAC-based vital sign detection methods often suffer from suboptimal separation of respiratory and heartbeat signals and limited robustness against interference in complex environments. To address these challenges, a vital sign signal separation and frequency detection algorithm based on successive variational mode decomposition (SVMD) is proposed. Firstly, beamforming feedback information (BFI) is collected via Wi-Fi devices and preprocessed to obtain the beamforming matrix (BFM) signal. Subsequently, the ratio between each pair of elements in the beamforming matrix is calculated, and effective vital sign signals are accurately extracted from complex multipath environments by combining dynamic feature subcarrier screening and multi-stage denoising techniques. Furthermore, SVMD is introduced to leverage its characteristics of sequential extraction and independence from presetting the number of modes K. An adaptive parameter optimization method based on the artificial lemming algorithm (ALA) is designed to determine the key balance parameter in SVMD, enabling high-precision separation of respiratory and heartbeat signals. Finally, respirator and heartbeat rates are estimated using Fast Fourier Transform and peak detection. Experimental results demonstrate that, across various typical application scenarios, including user heterogeneity, deep breathing, post-exercise state, and varying distances, the proposed method effectively mitigates the impact of multipath effects and environmental noise, maintains stable detection performance, and significantly improves the estimation accuracy of respiratory and heartbeat rates compared to existing methods. The proposed algorithm provides a reliable solution for non-contact vital sign detection based on ISAC.
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Design of high-precision asymmetric microgripper based on symmetrical compound half-bridge mechanism
Chen Xiaodong, Tan Huifeng, Wei Yonghe, Tian Fengjie, Chen Xueyan
Abstract:
As the end effector of the micromanipulation system, the microgripper determines the success of the micromanipulation tasks. The parallelogram mechanism is usually used as the final amplification mechanism of the microgripper because of its parallel clamping characteristics. However, the parasitic displacement occurs during the rotation of the parallelogram mechanism. Based on this, this paper proposes a two-stage amplification asymmetric microgripper based on a symmetrical composite half-bridge mechanism driven by a piezoelectric actuator. The piezoelectric actuator is placed inside the mechanism and acts on the input ends of the compound half-bridge mechanism on the left and right sides, thereby driving the parallelogram mechanism to complete the clamping action. During operation, the output force of the left and right composite half-bridge mechanism serves as the input end of the parallelogram mechanism, ensuring equal force application on both sides. Based on the flexible beam theory and coordinate transformation method, the mechanical model of the mechanism is obtained. The performance of the microgripper is obtained by finite element analysis and experimental verification respectively. In terms of parallel clamping characteristics, the rotation angle of the output end of the parallelogram mechanism in the traditional microgripper is 2.1×10-4°, while in the proposed design, it is reduced to 1.15×10-4°. This corresponds to a 45.28% reduction in parasitic displacement along the desired motion direction, significantly improving parallel clamping performance. Regarding displacement amplification, the traditional microgripper has a magnification of 12.6, while the proposed microgripper achieves a magnification of 14.3, representing a 13.5% improvement in the displacement amplification performance of the output end. For the issue of parasitic displacement at the output end, the traditional microgripper exhibits a parasitic displacement of 30.7 nm, whereas the microgripper designed based on the symmetrical compound half-bridge mechanism in this study reduces this to 10.8 nm, corresponding to a 64.8% reduction. In conclusion, compared with the traditional microgripper, the microgripper presented in this paper demonstrates superior overall performance.
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Design and experiment of automatic focusing algorithm for high robustness space camera
Peng Yueyang, Fu Ziyuan, He Yukun, Chen Changzheng, Sha Wei
Abstract:
In order to improve the efficiency and accuracy of on-orbit focusing of earth observation satellites, this paper proposes a method to quantitatively evaluate the defocusing state of the camera based on multiple star images and calculate the optimal detector position. First, the workflow of the auto-focusing algorithm is introduced. The calculation methods of star image preprocessing, image centroid and spatial phase difference (SPD) are described in detail. On the basis of SPD, the point spread function (PSF) is reconstructed and the standard deviation is calculated according to the discrete PSF fitting Gaussian curve. The standard deviation and the detector position are fitted as the focusing curve, and the lowest point of the curve is the optimal detector position calculated by the auto-focusing algorithm. Then, in order to illustrate the effectiveness of the algorithm proposed in this paper, the spatial domain, frequency domain, statistics and feature-based image sharpness evaluation function are briefly introduced as a comparison algorithm. Finally, a test platform is built to test the focusing accuracy and noise robustness of the proposed algorithm, and compared with other clarity evaluation functions. Experimental results indicate that the proposed algorithm achieves with a minimum error of -0.001 3 mm for the optimal detector position calculation, less than 1/6 semi-focus depth, better than the comparison algorithm ; when the image signal-to-noise ratio is not less than 10 dB, the absolute value of the focusing error is less than 0.002 8 mm, and the focusing curve fitting goodness is greater than 0.85 within the range of 1/6 semi-focus depth, and the noise robustness is better than the comparison algorithm. The proposed algorithm acquires multiple star images at different detector positions, quantitatively evaluates the camera′s defocus state through the star images, and the fitted focusing curve can correctly reflect the relationship between the camera′s defocus state and the amount of defocus. The calculation accuracy at the optimal detector position can meet the focusing accuracy requirements of space cameras and has strong noise robustness.
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Effects of gas spectral absorption and heat transfer on endoscopic infrared temperature measurement
Yang Yaoguang, Hu Ruonan, Liu Gongzhi, Feng Lijun, He Bo
Abstract:
The temperature-field measurements obtained by endoscopic infrared thermography for gas-insulated electrical equipment are the combined result of target radiation, background radiation, and gas-selective absorption within the equipment enclosure. Current infrared temperature-calibration methods are mainly developed for atmospheric environments and do not account for the multiple reflections of thermal radiation between cavity surfaces or the energy attenuation caused by short optical paths and high-pressure gas atmospheres. To address this issue, this paper proposes an apparent-temperature compensation method for infrared thermography in closed cavities under specific selectively absorbing gas atmospheres. First, based on a single-path thermal-radiation reflection-transmission model, spectral transmittance calculations and finite-element simulations of gas-state distributions in closed cavities are conducted. The influences of radiation wavelength, gas pressure, gas temperature, and optical path length on thermal-radiation transmittance are analyzed, leading to the establishment of an analytical model for the thermograph′s apparent temperature under composite paths. Subsequently, heating experiments of electrical equipment under air and SF6 atmospheres are carried out. Model parameters for different temperature-field distributions and gas pressures are derived from the measured data, enabling temperature inversion under different gas conditions. The results indicate that the gas transmittance can be approximated by its initial value after equipment inflation even during temperature rise, The gas transmittance between discrete target and background surfaces in the cavity can be treated as a constant, and only the energy attenuation along the optical path before the first radiation reflection needs to be considered. Finally, the mean absolute error of apparent-temperature prediction under different gas atmospheres is less than 1 K. The findings provide a concise computational model for rapidly quantifying the effects of air and SF6 atmospheres on endoscopic infrared temperature measurement in closed cavities.
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Online monitoring method for the accuracy of the rotary table based on its own signals
Xie Jiabo, Zhu Weibin, Huang Yao, Zhu Jin, Ma Li
Abstract:
To address the practical need for maintaining and monitoring the accuracy of rotary tables during long-term operation, this paper proposes an online accuracy monitoring method based on the rotary table′s own signals. Using the positioning accuracy A and repeatability R defined by ISO230-2 as the core monitoring indicators, a mapping model from the circumferential positioning deviation ε(θ) of the rotary table to the monitoring indicators is established. By analyzing the phase relationship of signals output by the dual reading heads and combining it with the Fourier Transform, the circumferential positioning deviation is separated automatically and accuracy indicators are calculated online. On this basis, an online monitoring system based on ZYNQ is designed and implemented, and theoretical analyses were conducted and solutions were proposed for two key issues: signal synchronous acquisition and optimal harmonic order selection. An experimental platform is built, using an autocollimator and a multi-face prism as the reference system. First, the harmonic residuals separated by the online monitoring system are analyzed to determine the current optimal harmonic order. Then, the positioning deviation is separated through Fourier Transform. Experiments show that the positioning deviation obtained by this method is highly consistent with those obtained by the reference device, with a separation accuracy better than 2.10″, validating the accuracy of the positioning deviation separation method. Furthermore, by applying loads to change the accuracy state of the rotary table, when the load gradually increases from no load to 5 kg, the system can effectively monitor the change in positioning accuracy from 289.40″ to 292.70″ and repeatability from 0.23″ to 0.38″, indicating that the self-developed system can monitor accuracy changes at the 0.01″ level. This confirms the feasibility and accuracy of the proposed method for real-time and effective monitoring of accuracy during rotary table operation.
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WiFi CSI-based indoor UAV localization method integrating AGC compensation and multi-scale outlier processing
Jiang Hao, Huang Yi, Chen Jing, Yin Cunyi, Zheng Shaocong
Abstract:
WiFi channel state information (CSI) has emerged as a powerful tool for indoor drone localization, owing to its high temporal resolution and rich environmental features. However, the accuracy of CSI-based systems is significantly compromised by amplitude distortion induced by the receiver′s automatic gain control (AGC) circuit, coupled with multipath effects and dynamic noise interference in complex indoor environments. To address these challenges, this paper proposes a novel CSI-based indoor drone localization method that integrates AGC compensation with multi-scale outlier processing. The automated data collection and annotation system is established using passive CSI sniffing and Aruco visual markers, enabling non-intrusive acquisition of CSI data during the drone′s normal communication. The dynamic AGC compensation algorithm, leveraging real-time hardware gain feedback, is introduced to effectively correct the amplitude distortion and recover the true signal amplitude. Furthermore, the multi-scale outlier processing scheme combining Hampel filtering and density-based spatial clustering of applications with noise (DBSCAN) clustering is employed to respectively identify and filter out isolated pulse noise and dense noise clusters, thereby enhancing the robustness and reliability of signal features in complex settings. The lightweight residual network-one-dimensional convolutional neural network (ResNet-1DCNN) is subsequently constructed to extract deep features from the optimized CSI amplitude sequences for efficient location classification. Comprehensive evaluations demonstrate that the proposed AGC compensation and outlier processing strategies significantly improve CSI signal quality, enabling the model to learn more robust location-specific features. The proposed localization method achieved an overall accuracy of 98% in the test environment, representing a performance improvement of nearly 29% compared to the unoptimized baseline. This work provides a viable and effective solution for high-precision indoor drone localization and confirms its potential for real-time application.
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Research on pantograph-catenary arc detection via multimodal imaging fusion
Cai Zhichao, Wang Jianfen, Cheng Hongbo, Wei Baoquan, Sun Hongyu
Abstract:
Pantograph-catenary arc faults pose a serious threat to the safe and stable operation of high-speed railway and urban rail transit systems. The intense light, high temperature, and electromagnetic interference generated by these arcs accelerate the wear of catenary components, shorten their service life, and may even trigger power supply system failures, leading to serious safety incidents. Traditional visible-light-based pantograph-catenary arc detection methods are susceptible to environmental interferences such as illumination variations, occlusions, and adverse weather conditions, leading to reduced detection accuracy and robustness and thereby limiting their applicability in complex online monitoring scenarios. This paper proposes a multimodal imaging-based arc detection method that integrates visible, infrared, and acoustic signals to enhance performance in complex scenes. Initially, the acoustic signals of arc are collected by a microphone array and transformed into time-frequency matrices. Subsequently, a variational inference-based noise suppression strategy is introduced to attenuate environmental background noise while preserving arc-related acoustic information. Building on this, time-domain beamforming is employed to achieve acoustic source imaging and energy focusing, yielding acoustic intensity maps. The acoustic images are then registered and spatially aligned with visible and thermal imagery to obtain a multimodal representation of arc morphology. The registered images are then fed into a multimodal object detection model to produce arc locations and confidence scores, thereby completing the detection and localization of the arc fault. To evaluate the proposed method, an acoustic propagation model and an experimental platform have been established to analyze the propagation characteristics of arc sources and systematically verify the impact of the noise-suppression strategy on signal-to-noise ratio and imaging performance. The experimental findings demonstrate that, in comparison with single visible-light modality and visible/infrared bimodal schemes, the proposed multimodal imaging fusion method enhances recognition accuracy by 15.9% and 8.1%, respectively, thus providing an effective solution for robust online detection of pantograph-catenary arcs.
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Eddy current testing method for ultra-fine tungsten wires based on energy loss
Abstract:
Addressing the technical challenge of weak signals and low signal-to-noise ratio in eddy current testing of sub-millimeter ultra-fine tungsten wires, which hinders effective micro-defect identification, a novel eddy current testing method based on the energy dissipation principle is proposed. This method innovatively employs energy dissipation as a direct defect characterization parameter, leading to the design of an eddy current detection system centered on micro-power measurement. Numerical simulations based on established impedance and energy dissipation models of the core-type coil reveal that within the 0.1~1.0 mm wire diameter range, the energy dissipation signal decays more gradually with decreasing diameter and remains effective below 0.2 mm, outperforming traditional impedance method. A micro-power measurement system was consequently developed to acquire voltage, current, and phase difference in real time, enabling the calculation of active power to characterize energy dissipation caused by defects. Experimental investigations on diameter fluctuations and crack defects demonstrate that for a 0.05 mm diameter variation, the energy dissipation method yields a signal change of 4.59%, substantially exceeding the 0.11% and 0.21% achieved by traditional impedance and phase detection methods, respectively. For micro-cracks with depths of 0.05, 0.08, and 0.10 mm, the signal variation rates are 0.8%, 0.9%, and 1.1%, respectively, with standard deviations across five repeated measurements all below 0.08 mW, indicating exceptional repeatability, sensitivity, and measurement stability. Furthermore, continuous online testing of 0.40 mm tungsten wires on a production line successfully identified multiple micro-defects with varying locations and magnitudes, confirming the method′s feasibility and effectiveness in real industrial environments. Experimental results show that the proposed energy dissipation method significantly surpasses the traditional impedance approach in sensitivity, stability, and anti-interference capability, offering a new pathway for non-destructive testing of sub-millimeter tungsten wires and other ultra-fine filaments.
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Study on ultrasonic total focus method-intelligent partitioning based on target elements
Shu Yifeng, Long Shengrong, Tan Xiaokang, Li Zhinong
Abstract:
Total Focusing Method (TFM) for ultrasonic imaging, a novel ultrasonic total focus method-intelligent partitioning based on target elements (TFM-IPTE) is proposed. The TFM-IPTE first pre-locates suspected defect areas by analyzing the transmitted and received signals of the main elements. Subsequently, square, horizontal, and vertical division strategies are applied to the full matrix capture (FMC) data, and efficient focused imaging is achieved by combining these divided submatrices with a phase shift algorithm. FMC data were collected through ultrasonic total focusing detection on two carbon fiber composite test blocks containing subsurface layered defects with different depths. Imaging calculations were conducted on the full matrix data and the divided submatrix data respectively, and the imaging effects of the full matrix data were compared with those of the submatrix data obtained by three different division methods. Experimental results show that compared with the traditional total focusing imaging using full matrix data, the TFM-IPTE significantly reduces the computation load and thereby improves imaging efficiency. Among the three division methods, vertical division provides superior performance when detecting shallow, middle, and deep defects. While maintaining imaging resolution and side lobe suppression effect, the relative error of horizontal position prediction for defects are reduced by 7.995 2% and 7.633 4%, 2.603 0% and 2.447 9%, 0.595 2% and 0.496 5% compared with square division and horizontal division respectively. The TFM-IPTE effectively balances imaging efficiency and quality through the data division strategy, providing an efficient solution for automated ultrasonic non-destructive testing (NDT) in fields such as aerospace and nuclear power.
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Motor overshoot prediction and optimization of a two-stage compensation strategy for lower limb exoskeletons
Shi Xin, Tang Jia, Fan Zhirui, Yang Zixiang, Qin Pengjie
Abstract:
Lower limb exoskeleton technology can assist in enhancing human strength and has a wide range of applications. However, exoskeleton motors exhibit dynamic nonlinearity due to electromagnetic inertia and mechanical loads, leading to overshoot that causes mismatches in human-machine collaborative motion responses and increases the risk of biomechanical injury. To address the inertial overshoot issue in exoskeleton motors during the leg-lifting phase of human walking, a two-stage collaborative overshoot prediction and optimization strategy is proposed, integrating inverse system model prediction with forward model optimization. By constructing a reverse system model based on a CNN-LSTM-Attention architecture, the system receives real-time motor target outputs, effectively captures multivariate time-series patterns, and rapidly generates the initial input commands for the exoskeleton motor. A forward optimization model based on a pyramid feature fusion-CNN-bi-LSTM-Transformer (Pyramid-CLT) architecture is constructed. The model employs a gating mechanism together with pyramid-shaped fully connected layers to achieve multi-scale feature integration. The mean squared error between the predicted output and the target output serves as the objective function. To further improve the prediction accuracy, a particle swarm optimization (PSO) algorithm is applied to iteratively refine samples with high mean square error, enabling the generation of precise motor control commands for overshoot compensation. Experimental that the prediction optimization strategy can precisely generate exoskeleton motor input commands based on human movement trajectories, achieving a model correlation coefficient (R2) of 0.985, root mean square error (RMSE) of 0.537, and mean absolute error (MAE) of 0.442; Compared with single-model algorithms and other prediction algorithms, the proposed method achieves real-time dynamic prediction and correction of overshoot, enabling motor output to closely align with human movement trajectories, thereby effectively enhancing human-machine synergy and providing a new method for precise control of lower-limb exoskeletons.
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A colored noise whitening method based on adaptive design of linear phase FIR filter
Abstract:
Gaussian white noise is commonly assumed for data for date in fields such as electrical and electronic engineering. However, optimal systems designed under this assumption experience severe performance degradation in colored noise environments, necessitating effective whitening. Based on the theory of random signal whitening filtering, this paper proposes an adaptive design method for linear-phase finite impulse response (FIR) filters to achieve stable and practical colored noise whitening. First, the mainstream existing whitening filtering methods are briefly summarized and classified into two categories: orthogonal basis-based and transform domain-based approaches. Subsequently, leveraging the covariance matrix whitening principle of orthogonal basis-based methods, it is shown that the key whitening matrix possesses a Toeplitz structure and Hermitian property. Due to the convergence of its elements, it is shown that both the row and column vectors of this matrix possess approximate whitening capability. Furthermore, utilizing the convergence of the approximate whitening vectors, a suitable threshold is set for truncation to automatically determine the filter length. The proposed filter, named the approximate whitening transversal filter (AWTF), is a type of linear-phase FIR filter that can be adaptively designed directly from noise samples. It offers advantages including absolute stability, simple structure, zero phase distortion, and low computational complexity. In addition, the issue of signal detection after whitening filtering is discussed, and a signal detection scheme based on generalized matched filtering is proposed. Simulations are conducted using colored noise generated by autoregressive (AR) models. The results verify the convergence of the approximate whitening vectors and demonstrate that the AWTF can effectively whiten colored noise. When combined with generalized matched filtering, the resulting bit error rate (BER) is essentially consistent with that of traditional optimal methods. Finally, the application of the AWTF in a sky-wave radar system is analyzed. Measured data processing experiments indicate that the radio frequency interference (RFI) conforms to the noise assumption in this paper, and the AWTF can effectively suppress RFI.
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Research on performance evaluation methods of SERF-MEG source localization
Cui Shuhao, Song Xinda, Qi Shengjie, Wang Zheng, Xue Shuncheng
Abstract:
In human magnetoencephalography （MEG） research, the spatial location and orientation information of neural sources inside the human are difficult to obtain directly, making it challenging to intuitively and repeatedly evaluate the performance of spin-exchange relaxation-free (SERF) magnetometer array systems for magnetoencephalography (MEG) imaging. To address this issue, neuronal activity in the human brain can be modeled using equivalent current dipoles (ECDs) characterized by explicit position and orientation information. Based on this premise, we designed a brain-like physical dry phantom supporting multiple orientations with 25 ECDs at different positions, thereby providing controllable and known magnetic source information. Furthermore, a joint optimal orientation estimation method was proposed to simultaneously estimate the single dipole orientation under different signal-to-noise ratios. A potential source space of 3 mm resolution was created within the phantom, and dipole localization experiment was constructed using a 7-channel-SERF magnetometer array. Experimental results showed that, under known-source conditions, the SERF array achieves a mean localization error of 16.86 mm and an average orientation error of 15.35°. These findings indicate that constructing a physical phantom with known magnetic sources provides an effective approach for evaluating the feasibility of multi-channel SERF magnetometer arrays for MEG imaging, and offers a reliable basis for optimizing design under different channel configurations. In addition, the proposed physical phantom exhibits good repeatability and can be employed for routine calibration, performance consistency verification and operational maintenance of SERF-MEG systems. Overall, the physical phantom and orientation estimation method presented in the study provide reference value for performance assessment and engineering implementation of SERF magnetometer array in MEG imaging.
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GPR-initialized self-supervised learning DIC for displacement field measurement of rotating structures
Jin Xu, Zhang Yiming, Li Guang, Xu Zili
Abstract:
Traditional digital image correlation (DIC) methods that rely subset-based correlation calculations are prone to decorrelation and strong parameter sensitivity under rotational motions. To overcome these limitations, this study proposes a Gaussian process regression-guided self-supervised learning DIC method (GPR-SSL-DIC) for accurate rotational displacement field measurement. The method develops a self-supervised learning framework based on the Kolmogorov-Arnold Network (KAN) network, in which a loss function is formulated using the grayscale differences between the reconstructed and reference images together with a displacement-field smoothness constraint, driving adaptive optimization of the displacement field and thereby overcoming the limitations of the conventional subset-matching paradigm in traditional DIC. To improve convergence under large-angle rotations, rotation-invariant SURF feature points are detected in the structural target region, and their displacement information is used to construct sparse observation samples. Furthermore, Gaussian process regression is employed to predict a global displacement field as an initial solution, thereby guiding the network to converge toward the true solution space. Numerical simulations show that the proposed method achieves average end-point errors below 0.001 7 pixels under rigid-body rotation and coupled large-deformation conditions, and below 0.007 4 pixels with sinusoidal displacements are superimposed, corresponding to a 93.5% improvement over traditional DIC. Rotating-blade dispacement experiments further demonstrate that at a 9° inter-frame rotation, the standard deviation of fixed-point distance measurements decreases by 54.3% compared with traditional DIC. At 30°, where traditional DIC fails due to severe decorrelation, the proposed method is still able to obtain the displacement distribution of the blade. These results confirm that the proposed framework is robust under large-angle rotational conditions and offers an effective solution for displacement field measurement in rotating structures.
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Pipe spatial parameter measurement method based on machine vision
Tian Huihui, Su Songbo, He Bobo, Wang Hongxi
Abstract:
The springback during production causes spatial deformation in pipes, and the generated assembly stress increases the probability of pipe failure. Considering the safety of pipes, it is quite important to measure the geometric error between the design and the actual pipe before installation in order to prevent potential hazards caused by excessive assembly stresses. However, it is difficult to measure the spatially complex pipes caused by the installation space. In this paper, a monocular vision-based method is proposed for automatic, highly accurate and efficient measurement of spatially complex pipes. The mono-camera is designed to acquire the pipe 2D images, from which local coordinate systems are established based on the measured pipe parameters. Then importantly, transformation matrixes are established, including the transformation between the pixel coordinate system and the local coordinate system, as well as between adjacent local coordinate systems. Next, spatial parameters of pipes in the global coordinate system are obtained through coordinate unification, and 3D models of spatial pipes are reconstructed. Finally, the measurement system is developed and eight repeated experiments of two different types of pipes are performed to verify the precision of the proposed method. The results show that the repeatability deviations of length, bending angle, rotation angle and node coordinate are 0.305 mm, 0.033°, 0.263°, and 0.325 mm, respectively. Compared with the measurement results of the three-coordinate measuring machines, the deviations of length and angle are within 1 mm and 0.5°, respectively. A detailed analysis and discussion of measurement errors are conducted. This study provides a simple technique for automatic, highly accurate, online measurement of pipes.
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Multi-object tracking for multi-drone systems integrating multi-view projection and spatiotemporal topology
Dang Zhaoyang, Sun Xiaoyong, Guo Runze, Zhou Peida, Sun Bei
Abstract:
To enhance the persistent tracking capability of drones for moving objects and overcome the limitations of single-drone systems, this paper proposes a multi-drone multi-object tracking method that leverages collaborative perception. The approach integrates multi-view projection and the spatiotemporal topology of objects. By utilizing the positional and attitude data of the drones and their onboard photoelectric pods—without relying on image features—rapid projection between views is achieved through a consistency constraint between drone pose and object height. This enables preliminary object association under dynamic, complementary perspectives from multiple drones. Furthermore, bidirectional association matching is performed using the spatiotemporal topological features of objects from different viewpoints. Spatial and temporal cues refine the initial associations, improving cross view object matching accuracy and enhancing tracking robustness in occluded scenarios. Focusing on occlusions during various drone maneuvers such as climbing, descending, circling, and rapid motion, a dedicated multi-drone multi-object tracking dataset (DP-MDMT) incorporating pose data was constructed. Experiments in real-task scenarios show that the proposed method achieves recall, precision, and multi-device association (MDA) score of 60.2%, 85.6%, and 47.1%, respectively, on the DP-MDMT dataset, representing improvements of 6.4%, 13.1%, and 7.4% over the MIA-Net algorithm. The tracking metrics, including the multiple object tracking accuracy (MOTA) and ID F1-score (IDF1) reach 80.1% and 85.1%, respectively, with an average processing efficiency of 29.7 fps, meeting the real-time requirements for multi-drone ground object tracking.
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Measurement and analysis of helicopter rotor blade flapping based on rotating stereo vision
You Tao, Xiong Bangshu, Zhu Jinhao, Ou Qiaofeng
Abstract:
Accurate measurement of helicopter rotor blade flapping, and analysis of its characteristic patterns are essential for evaluating rotor aerodynamic performance and optimizing structural design. To improve flapping visual measurement accuracy and enable full-phase measurement, this study develops a rotating stereo vision measurement system, proposes a corresponding flapping measurement method, and conducts analysis on blade flapping patterns and laws. First, via photoelectric slip rings, the 10-gigabit-level image data transmission from the rotor end to the ground end is realized. Meanwhile, a symmetric camera mounting bracket is designed to ensure the overall dynamic balance between the cameras and the rotor. Additionally, full-phase blade images are acquired on a rotor test rig under different rotational speeds, collective pitches, and cyclic pitches. Second, a small-target detection network is developed to handle complex illumination inference, enabling high-precision localization of the central pixel coordinates of tiny self-luminous marker points and improving the accuracy of their 3D coordinate computation. Third, a hub coordinate system is established for each acquisition phase. Marker 3D coordinates are transformed from camera to hub system to calculate phase-specific flapping, reducing errors from coordinate drift caused by camera-rotor rotation. Finally, third-order polynomial fitting analyzes in-phase flapping spatial patterns, while composite sine fitting analyzes flapping time-domain laws in the rotation cycle, supporting rotor system optimization. Experimental results on the rotor test rig demonstrate that, within a 1.5 m×1.5 m field of view, static and dynamic flapping measurement errors are 0.44 and 0.82 mm, respectively, both flapping patterns and laws models exhibit excellent agreement with experimental data (RMSE<1 mm). These results verify the effectiveness and high-precision characteristics of the proposed measurement system and method, and this system has been applied to the verification of rotor design tests.
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Fisheye camera on-orbit calibration method based on starlight vectors
Liao Ping, Sun Peng, Dong Mingli, Zhuang Wei, Yu Kuai
Abstract:
To address the challenges of close-range, ultra-large-scale measurement in on-orbit photogrammetry and variations in the internal parameters of the measurement system, a novel fisheye camera on-orbit calibration method based on starlight vectors is proposed. First, an improved star pattern recognition algorithm is developed based on the state identification method. The algorithm is less sensitive to image distortion and effectively reduces redundancy in fisheye star pattern recognition using a four-star triangle joint decision method, thereby improving recognition speed. Second, to address the loss of recognition accuracy and the limited acquisition of full-field data for calibration caused by variations in the internal parameters of the on-orbit fisheye camera, a self-calibration bundle adjustment method is developed. This method integrates star-pattern recognition with a regional iterative expansion strategy. The algorithm progressively expands the field-of-view region and iteratively optimizes the camera′s internal and external parameters by incorporating recognition results from each region, gradually improving the overall recognition rate until the calibration requirements are satisfied. Finally, experimental validation is carried out using a multi-attitude star pattern dataset constructed from 246 fisheye images of the celestial sphere. Ground-based experiments show that when the recognition threshold is relatively large (0.25°), the improved star pattern recognition algorithm requires only 36.91% of the time consumed by the state identification method, demonstrating a significant improvement in efficiency. Moreover, when the internal parameters of the camera change considerably (principal distance variation of 0.15 mm), the proposed algorithm achieves full-field star recognition, with the recognition rate reaching 98.6%, thereby satisfying the requirement for full-field data in camera calibration. After calibration, the root mean square error of the reprojected star point image coordinates is 1/5 pixels. The experimental results indicate that the proposed algorithm enables high-precision self-calibration of measurement system parameters, providing an effective solution and reference data for addressing the calibration challenges of system parameters in on-orbit visual measurement applications.
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Weak-light binocular fisheye camera calibration cased on global threshold and multi-constraints
Liu Wenhao, Song Tao, Liu Zhaolun, Chan Jia, Li Ning
Abstract:
In order to improve the accuracy of feature point extraction and reduce the error in the reconstructed 3D space in camera calibration, we propose a full-threshold segmentation and multi-constraint binocular fisheye camera calibration algorithm in this paper. To address the problems of large noise in edge regions and insufficient fitting accuracy of feature points in traditional calibration, firstly, based on the unified fisheye imaging model, a Gaussian function is used to iteratively fit the grayscale histogram and a global full-threshold segmentation method is applied to accurately extract circular calibration feature points, thereby achieving high-precision feature identification in distorted images. In addition, multi-constraint optimization function that integrates vertical and collinear constraints is proposed as a supplement to distance and epipolar constraints. It fully exploits 3D information in the inverse-projection stage of spatial point reconstruction, simultaneously considers the overall 3D spatial structure and local geometric relationships of the reconstructed points. In implementation, a circular calibration board combined with a global-threshold-based ellipse boundary detection strategy is adopted, and the multi-constraint objective function is iteratively solved using the Levenberg-Marquardt algorithm, thereby improving the convergence and stability of parameter estimation. Experiments are conducted on a binocular fisheye system, where multiple calibration tests are performed and evaluation metrics including distance error, epipolar error, vertical error, and collinear error were used. The experimental results show that the proposed method attains high accuracy in feature-point detection and parameter optimization: the mean reprojection error (MRE) is reduced by 30.85% compared with traditional methods, the 3D spatial error is reduced by about 45%, and the average error on the test images is below 0.3%, which verifies significant improvements in both accuracy and robustness. This study provides a reliable parameter foundation for high-precision calibration of binocular fisheye vision systems.
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Synergistic decoupling method for scatter and hardening artifacts in CBCT of turbine blades
Xiong Luchen, Gong Xin, Pan Qianghua, Chen Xi, Wu Guanhua
Abstract:
An X-ray polychromatic attenuation correction model that explicitly accounts for scatter is proposed for industrial CT defect inspection of aero-engine turbine blades to address the superimposed scatter and beam hardening in cone-beam CT, which cause reduced image contrast, grayscale distortion, and missed detection of micro-defects. The model is constructed as a cascaded cooperative combination of a scatter term and a hardening attenuation term. In the scatter term, a tilted grating plate is used to scan the turbine blade twice, allowing internal and external scatter fields to be separated. Full-angle scatter distributions are reconstructed using bicubic interpolation and angular spline interpolation to obtain effective projections that approximate scatter-free conditions. In the hardening attenuation term, an exponential hardening curve with projection grayscale as the independent variable is employed, and a weighted beam hardening correction method is developed by deriving a compensation expression based on prior penetration-thickness information and introducing a grayscale trade-off factor. Considering that scatter and hardening are mutually coupled in CT imaging, the results of scatter suppression and beam hardening correction are further unified within a mapping framework between penetration thickness and exposure intensity, yielding a cascaded cooperative correction model that simultaneously suppresses scatter artifacts and cupping artifacts. Experimental results on a 450 kV CBCT system demonstrate that the proposed method increases the signal-to-noise ratio, contrast-to-noise ratio, and average gradient of turbine-blade reconstructions by 42.75%, 75.92%, and 181.25%, respectively, outperforming schemes that apply only scatter correction or only beam hardening correction. For an artificial 0.3 mm film-cooling-hole micro-defect, the depth measurement accuracy reaches 0.28±0.008 mm, and the mean absolute error and mean relative error are reduced by 32.5% and 2.2%, respectively, compared with commercial software, confirming the effectiveness of the method in correcting scatter and beam hardening artifacts in real turbine-blade industrial CT imaging.
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Constant voltage and constant current self-switching output wireless power transmission system based on hybrid topology
Yang Yi, Li Guiyu, Zhang Lu, Li Haixiao, Guo Ke
Abstract:
To address the demand for adaptive switching between constant current output (CCO) and constant voltage output (CVO) during the battery charging process in medium-and low-power wireless charging applications, as well as the power and efficiency degradation caused by magnetic coupling misalignment, this paper proposes a hybrid-topology wireless power transfer (WPT) system with self-switching capability. The system employs a single-switch hybrid compensation topology, enabling automatic switching between CCO and CVO modes according to load variations, without requiring additional control strategies or hardware circuits, thereby simplifying the system architecture. Both the transmitter and receiver adopt grid-type flat spiral (GFSP) coils, which not only achieve natural decoupling of same-side coils but also significantly improve the system’s misalignment tolerance. The proposed topology is validated by MATLAB/Simulink simulations under different load conditions, demonstrating smooth transitions between CCO and CVO modes with efficient and stable output. Furthermore, a 36 V/6 A experimental prototype was developed for verification. Experimental results show that the system can reliably achieves CCO and CVO self-switching under X- or Y-axis offsets of 30 and 60 mm, as well as Z-axis offsets of 10 and 20 mm, while maintaining a peak system efficiency of 90.6% under various operating conditions. These findings confirm the effectiveness of the proposed hybrid topology in improving WPT system output performance, enhancing misalignment tolerance, and simplifying control strategies, offering new design insights and technical support for medium- and low-power wireless charging systems, especially those based on single-switch inverters.
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Online length measurement method for compensation chains via eddy current sensing and peak detection
Xing Qiang, Song Kun, Wang Zebin, Zhang Xiaopin, Wang Zhouyi
Abstract:
Traditional contact-based length measurement methods for elevator compensation chains suffer from inaccuracies caused by slippage during fixed-length production. Furthermore, existing non-contact methods are unsuitable for these chains due to the low flatness, poor reflectivity, and lack of distinctive features on their plastic coating. To solve these problems, this study developed a non-contact online length measurement device for compensation chains based on eddy current sensing and a sliding-window peak detection algorithm. First, the length calculation formula was derived from the chain′s link structure, and its measurement uncertainty was analyzed to confirm the feasibility of the indirect measurement method based on counting links. Second, based on the cross-linked structure of the internal iron rings, a quasi-sinusoidal signal is formed by the variation in distance between the iron rings and the sensor probe as the rings pass an eddy current displacement sensor. The correspondence was established between the number of peaks in this signal and the actual number of chain links. Third, a one-dimensional Kalman filter was used to process the acquired signal. An online peak detection algorithm based on a sliding window was proposed, which accurately identifies the number of peaks in real-time under limited hardware resources. Finally, the measurement device was constructed. It employs a dual-cylinder clamping mechanism and a roller conveyor to reduce the interference of mechanical vibration on the acquired signal, achieving stable length measurement for various types and sizes of compensation chains. Experiments conducted on an actual production line showed that when the chain speed is between 0.5 and 1.0 m/s, the device can measure different types and sizes of chains with a maximum relative error not exceeding 5‰. This result verifies the feasibility of the method and the good stability and accuracy of the device.
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Heterogeneous adaptive ACO: Integration of angle penalty and elite strategy
Zeng Xianyang, Liang Yuansheng, Yu Hao, Liu Chang, Yang Hongli
Abstract:
This paper addresses the limitations of traditional ant colony optimization (ACO) in path planning, such as slow convergence, susceptibility to local optima, and numerous path inflection points, by proposing an improved ACO algorithm. This algorithm integrates a heterogeneous adaptive mechanism, angle penalty, and an elite strategy, and systematically verifies its generalization performance. By constructing a heterogeneous-homogeneous dual-population collaborative architecture, combining two ant populations with different characteristics, the algorithm′s global search capability in various environments is enhanced, effectively avoiding premature convergence. Introducing a direction-aware angle penalty factor avoids unnecessary path inflection points, optimizes path smoothness, and improves the algorithm′s adaptability to complex terrain by adding angle penalties to path planning. The elite-weighted pheromone update strategy allows the influence of excellent solutions to be more fully reflected in the pheromone update process, accelerating the convergence process and improving stability. In the comparative experiment of multi-scale grid maps, the algorithm proposed in this paper showed excellent generalization performance and robustness: in a complex 50×50 environment, compared with the traditional ACO algorithm, the path length was reduced by 14.1% and the inflection point was reduced by 69.4%; compared to existing improved algorithms, the path length was shortened by 8.4%, the inflection point was reduced by 66.6%, and the number of iterations was reduced by 82.6%. The real vehicle experiment of the automated guided vehicle (AGV) further verified the generalization ability of the algorithm in the real scene, the path length was shortened by 11.1%, and the inflection point was reduced by 78.2%. This study innovatively proposed a population heterogeneous adaptive scheduling mechanism, a direction-aware angle penalty strategy, and an elite pheromone weighted update method, which significantly improved the generalization performance of the ACO algorithm and provided reliable technical support for the practical application of mobile robot navigation systems.
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Reconstruction method of non-axisymmetric temperature field based on deflection of light angle analysis of angular momentum conservation
Wu Jun, Luo Hengli, Qin Yang, Deng Xiangji, Cao Gangzheng
Abstract:
The measurement of combustion temperature fields is of great significance for combustion analysis. The background schlieren method, as a non-intrusive optical measurement technology, has the advantages of simple equipment and full-field measurement, and has been widely used in the reconstruction of combustion temperature field in recent years. However, the traditional background schlieren approach relies on the assumption of a parallel paraxial optical path when solving the light-deflection angle, and its accurate analytical solution cannot be established. This leads to large errors in the reconstruction of non-axisymmetric temperature fields and reduces the traceability of the results. Therefore, this paper proposes a non-axisymmetric temperature field reconstruction method based on the conservation principle of angular momentum. The innovation core of this method lies in introducing the law of conservation of angular momentum into the process of solving deflection of light angle, and constructing a more accurate analytical model for light propagation in complex flow fields. Firstly, the background images with and without flames are analyzed using an image cross-correlation algorithm to obtain speckle displacements from multiple viewing perspectives. Based on the principle of conservation of angular momentum, the deflection of light angle is calculated by selecting the offset data on the same height plane, and the normalized refractive index difference distribution is obtained through Radon inverse transformation. Then, according to the Gladstone-Dale relation between refractive index and gas density, combined with the ideal gas state equation, the refractive index distribution is converted into the corresponding temperature field distribution. The experimental results show that, compared with the traditional methods, the precision of the temperature field reconstructed based on the conservation of angular momentum is significantly improved, especially in the peak areas with the largest temperature gradient. The overall reconstruction error is reduced by about 30%, which can realize the reconstruction of non-axisymmetric temperature fields. The proposed method can effectively improve the accuracy of temperature field reconstruction and expand the application range of background schlieren method.
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Design of a crystal oscillation-based capacitive liquid level detection system
Zhang Zhenyang, Jiang Xingyu, Huang He, Li Xiangkun, Tian Liangfei
Abstract:
Liquid-level detection is a critical function in fully automated in vitro diagnostic devices, as its accuracy is directly related to reagent aspiration safety and the reliability of test results. Targeting the requirements for precision and stability in contact-based liquid-level detection, this study designs a crystal-oscillator-based capacitive liquid-level detection system. A crystal oscillator is used to generate a highly stable sinusoidal excitation signal that drives a capacitive voltage divider, When the sensing probe contacts the liquid surface, variations in its parasitic capacitance modulate the amplitude of the excitation signal. By combining synchronous band-selective peak detection with a self-stabilizing pulse conversion mechanism, the system achieves highly sensitive and stable detection of the liquid level. Based on an equivalent capacitance model and transfer function analysis, the key component parameters in the system are optimally configured. Simulink simulations verify that the front-end circuit exhibits a response delay of 150 μs and confirm that the self-stabilizing circuit enters a pulse saturation state when the input voltage variation ΔV > 15 mV or the slew rate > 1 V/ms, thereby ensuring system stability under complex operating conditions. Experimental results show that, due to its higher impedance, deionized water produces a lower output pulse amplitude than physiological saline. When the saline concentration exceeds 0.9%, the pulse amplitude exhibits a nonlinear saturation trend with increasing concentration. Within a liquid volume range of 100~5 000 μL, the system achieves a measurement standard deviation ≤ 50 μm and a coefficient of variation (CV) ≤ 0.75%. Over 10 000 consecutive tests, the deviation between two adjacent measurements remains within ±0.15 mm. The slight linear decrease in liquid level is mainly attributed to liquid evaporation and residual adhesion on the probe wall. By operating the crystal oscillator at its resonant point and jointly optimizing the key parameters, the proposed design achieves medical-device-grade detection accuracy and long-term operational reliability, providing a practical and engineering-ready solution for liquid handling in biochemical analyzers, immunoassay analyzers, urinalysis analyzers, and other in vitro diagnostic instruments.
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Research on multi-target trajectory planning of robotic arms for structured light measurement
Li Maoyue, Su Yuanqiang, Yue Caixu, Liu Zelong, Zhang Chenglong
Abstract:
To address the challenge of balancing efficiency and accuracy in measuring complex aero-engine blade surfaces, this paper proposes a robotic-arm trajectory planning method based on an improved multi-objective grey wolf optimization algorithm (ILMOGWO). The kinematic and dynamic models of a robotic arm-structured light hand-eye system are established, where end-effector vibration constraints are transformed into imaging clarity constraints according to structured-light principles. A multi-objective trajectory model minimizing total motion time and maximum vibration velocity is constructed using quintic polynomial interpolation. To enhance optimization performance, ILMOGWO integrates Latin hypercube sampling initialization, a nonlinear convergence factor, adaptive grid-based archive management, and Levy flight perturbation. Simulation and experimental results on a six degrees of freedom (6-DOF) industrial manipulator verify that the proposed method achieves superior Pareto front convergence and effectively suppresses end vibration within 0.115 mm/s. The reconstructed blade point cloud exhibits a maximum deviation of 0.050 9 mm and an average deviation of 0.016 0 mm. The proposed method can significantly improves the imaging quality of structured light and the accuracy of 3D reconstruction while ensuring measurement efficiency, providing a feasible trajectory optimization approach for high-precision measurement of complex surfaces.
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A variable step gain control method with cascaded open-loop and closed-loop for large dynamic interference
Wang Ze, Xin Yedi, Li Yaxing, Xing Jinling, He Fangmin
Abstract:
In the field of wireless communication anti interference, communication receivers often face scenarios with a large dynamic range of interference power and the presence of low-power communication signals. Gain control, by dynamically adjusting the gain of the receiving link amplifier to respond to changes in input signal strength, thereby ensuring the stability and quality of the output signal, is an important means to cope with the instantaneous large dynamic range of interference power. To further improve the convergence speed and stability of gain control methods in large dynamic interference scenarios, this paper constructs a cascaded system model of open-loop gain control and closed-loop variable step size gain control, and proposes a cascaded open-loop and closed-loop variable step size gain control method. An open-loop gain control based on dual-criterion optimization is designed to balance the hard constraint of analog-to-digital converter (ADC) unsaturation and the soft constraint of interference suppression. The relationship between gain compression and noise figure degradation is introduced as a regularization term into the error feedback function, combined with a variable step size mechanism. The feedback iterative equation of closed-loop variable step size gain control is derived, and performance indicators such as the stability and convergence speed of the cascaded system are quantitatively analyzed. Simulation results show that compared with the closed-loop variable step size gain control method and the cascaded open-loop and closed-loop fixed step size control method, the proposed method has a convergence speed improved by 87.5% and 66.6% respectively, lower overshoot, can reduce the noise figure to a lower level, significantly enhance the interference cancellation performance, and effectively alleviate the deterioration of communication bit error rate. Experimental results verify that the proposed method outperforms the closed-loop variable step size method under three typical interferences (pulse, multi-tone, and wideband noise), especially with a significantly reduced bit error rate by 8% to 10% under pulse interference. Low-order modulation has better robustness under strong interference, providde a reference for designing of control methods for wireless communication anti-interference systems.
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Adaptive periodic event-triggered H∞ control of networked PMSM system
Huang Jianglin, Li Shuaihu, Xiao Shenping, Shi Xingyu
Abstract:
To address the problems of limited network resources, load disturbances and transmission delays affecting the control performance of networked permanent magnet synchronous motor (PMSM) systems, an adaptive periodic event-triggered control strategy is proposed. Firstly, based on conventional periodic event-triggered H∞ control, the fixed triggering threshold is replaced with an adaptive threshold that varies with the system state information, and different weighting matrices are used instead of identical ones. In this way, an adaptive periodic event-triggered control (APETC) strategy is developed to further enhance the flexibility and efficiency of the triggering mechanism. Simultaneously, to more accurately characterize the system behavior, a mathematical model of the networked PMSM system considering APETC and transmission delay is established. Second, based on Lyapunov stability theory and the concept of the free weight matrix, deriving the sufficient conditions for the system asymptotic stability and satisfaction of the H∞ performance. Based on these results, a co-design method for the APETC and H∞ controller is developed, which effectively alleviates network congestion and improves the utilization of network resources. Finally, the effectiveness and superiority of the proposed method are demonstrated through comparative experiments, including sudden change operation, loaded operation, and variable-speed operation on a YXSPACE-SP2000 rapid prototype control platform. The experimental results show that, compared with the traditional periodic event-triggered control, the proposed APETC can significantly reduce unnecessary data transmissions while maintaining control performance. Moreover, relative to the PI control, the designed H∞ control exhibits a markedly faster dynamic response and stronger disturbance rejection. These results verify the effectiveness and superiority of the proposed method in improving network resource utilization and enhancing system robustness.
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Terminal centroid calibration method for large component adjustment mechanism
Chu Wenmin, Zhou Kuai, Teng Lichen
Abstract:
The terminal centroid of the large component adjustment mechanism is an important parameter that affects its dynamic model. An inaccurate centroid position can cause the adjustment driving force to deviate from the ideal state, resulting in deformation of the adjustment mechanism and ultimately affecting the alignment accuracy of the assembly features. To meet the requirements for high-precision and compliant positioning of large-scale structures, this paper proposes a centroid position calibration method for the end of the posture adjustment mechanism that accounts for the friction torque of spherical joints. Firstly, a dynamic model of the posture adjustment mechanism is established, and the influence of centroid deviation on the internal force of posture adjustment is analyzed using numerical simulations. Secondly, based on the principle of force and torque transformation between coordinate systems, a centroid calculation model that incorporates the friction torque of ball joints is constructed. Meanwhile, the M-estimate sample consensus (MSAC) algorithm is adopted to optimize centroid parameters, so as to reduce the impact of random sensor errors and motion errors of positioners. Then, the Monte Carlo method is used to study the effects of random errors and centroid calibration strategies on calibration accuracy. The results show that sensor measurement error is the main random factor affecting the accuracy of centroid calibration. Increasing the maximum rotation angle of the terminal and the number of weightings can improve calibration accuracy, and the optimization effect of the MSAC algorithm is significant when the number of weightings is ≥ 6. Finally, centroid calibration of the mechanism terminal and posture adjustment alignment experiments of wing-body structures are conducted in a laboratory environment. The experimental results indicate that the proposed centroid calibration method can significantly improve the calibration accuracy of the terminal centroid. Using the centroid parameters obtained by this method for driving force planning, the average internal posture adjustment force is reduced by 56.0%, and the average coaxiality deviation of the fork ear holes is decreased by 47.6%, verifying that the proposed centroid calibration method can effectively reduce the internal forces of posture adjustment and improve the alignment accuracy of assembly features.
Precision Measurement Technology and Instrument
Information Processing Technology
Visual inspection and Image Measurement
电子测量技术与仪器
Automatic Control 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