Search Results (544)

Inertial measurement units (IMUs) in low-cost navigation systems suffer from significant drift and noise errors due to sensor biases, scale factor instability, and nonlinear stochastic noise. This paper proposes a hybrid error compensation approach that combines Fast Orthogonal Search (FOS), a nonlinear system identification technique, with deep Long Short-Term Memory (LSTM) neural networks to improve IMU signal accuracy in GNSS-denied navigation. The FOS algorithm efficiently models deterministic error patterns (such as bias drift and scale factor errors) using a small training dataset, while the LSTM learns the IMU’s complex time-dependent error dynamics from much longer training data. In the proposed method, FOS is first used to predict the output of a high-end IMU based on that of a low-end IMU, and the trained FOS model is then used to extend the training data for an LSTM-based predictor. We demonstrate the efficacy of this FOS–LSTM hybrid on real vehicular IMU data by training with a limited segment of high-precision reference measurements and testing on extended operation periods. The hybrid model achieves high predictive accuracy for predicting the high-end signal based on the low-end signal, with a mean squared error below 0.1% and yields more stable velocity estimates than models using FOS or LSTM alone. Although long-term position drift is not fully eliminated, the proposed method significantly reduces short-term uncertainty in the inertial solution. These results highlight a promising synergy between model-based system identification and data-driven learning for sensor error calibration in navigation systems. Key contributions include FOS-based pseudo-label bootstrapping for data-efficient LSTM training and a navigation-level evaluation illustrating how signal correction impacts dead reckoning drift. Full article

Nonlinear friction in the mechanical transmission system of machine tools induces transient stagnation of the feed axis as its velocity crosses zero, thereby giving rise to contouring errors in multi-axis machining and significantly degrading machining accuracy. To address this issue, a feedforward compensation strategy is proposed based on a simplified static friction model (SSFM) with dual-segment and dual-parameter characteristics. The nonlinear friction is represented by a combination of a linear segment and an exponential segment, while the model incorporates two essential parameters that characterize the maximum friction force and the negative damping effect. Experimental results from two-axis circular trajectory tests show that the proposed SSFM reduces contour errors by approximately 73.4% and 79.2% at 600 mm/min and 2100 mm/min, respectively. To improve compensation under high-speed conditions, an acceleration-dependent dynamic correction is further introduced to establish the SDFM. The results show that the maximum contour error is further reduced to 1.44 μm and 1.49 μm at 3600 mm/min and 5000 mm/min, respectively. Compared with many existing reduced-order or hybrid friction models that rely on more parameters or more complex identification procedures, the proposed method provides a more compact and compensation-oriented modeling strategy for the velocity-reversal region of CNC feed systems. Full article

Position-independent geometric errors crucially impact the accuracy of five-axis machine tools, yet their identification remains challenging due to computational complexities, inadequate measurement pose selection, and disturbances arising from thermal drift and residual uncompensated errors. Existing methods typically rely on linearized kinematic models, heuristic sampling of measurement poses, or computationally expensive global optimization procedures, which collectively limit their effectiveness in industrial environments. This study presents a unified identification framework that overcomes these limitations; it incorporates 3D offset parameters to enhance the decoupling of true geometric errors from non-PIGEs, an observability-driven measurement pose selection strategy to maximize the parameter sensitivity, and an ANN-surrogate model to accelerate high-dimensional global optimization. A genetic algorithm is used to optimize the measurement points based on the observability index of the machine tool. The ANN-surrogate model enhances the identification accuracy of error parameters (11 PIGEs + 3 offsets) through precise kinematic models, global exploration, and final refinement. Experimental validation on a five-axis machine tool demonstrates a volumetric error reduction of 88.615% after compensation, with RMSE decreasing to 0.4337 μm. Sensitivity analysis reveals that PIGEs contribute up to 75.26% of the total inaccuracy, while offset parameters capture 24.74% of the error from thermal and non-PIGE sources. The results confirm the method’s superiority over other techniques in terms of identification accuracy, efficiency, and robustness, providing a practical solution for high-precision applications in the manufacturing industries. Full article

The synchronous continuum robot (SCR) was developed to emulate biological structures, such as animal tails and elephant trunks, based on continuum robot principles. By synchronizing disk motions, the SCR generates biologically inspired continuous movements while maintaining precise trajectory control. However, its synchronization-based architecture limits adaptability during physical interaction due to rigid trajectory-following characteristics. To address this limitation, this paper proposes a sensorless variable admittance control (VAC)-based compliant motion generation framework for the SCR. A dynamic model-based sensorless disturbance observer is designed to estimate external torques without additional force sensors. To compensate for uncertainties inherent in the cable-driven transmission mechanism, an adaptive term is incorporated into the parameter identification process, improving disturbance estimation accuracy. Based on the estimated external torques, admittance parameters are adaptively modulated according to joint angles, angular velocities, and robot posture, enabling interaction-aware motion speed regulation. Furthermore, the proposed method simultaneously enforces constraints on both joint angles and angular velocities through the adaptive regulation of target positions and velocities, ensuring safe and physically feasible motion. Experimental results under various interaction scenarios demonstrate reliable contact-independent force estimation and effective compliant motion generation. The proposed framework provides an integrated solution for robust force estimation, adaptive compliance control, and simultaneous constraint enforcement in mechanically synchronized continuum robots. Full article

A nonlinear ultrasonic time-domain identification method based on chaos sensitivity was proposed in this study. The Duffing chaotic system was introduced into the weak second harmonic identification to realize early detection and quantitative evaluation of fatigue damage in U71Mn steel. First, to ensure the reliability of nonlinear ultrasonic testing, a probe-pressure monitoring device was designed. Through pressure-stability experiments, 16 N was determined as the optimal pressure, which effectively suppresses contact nonlinearity interference and ensures coupling stability. Subsequently, the Duffing chaos detection system was established. The signal-system frequency-matching problem was resolved through time-scale transformation. Simultaneously, the issue of unknown initial phases was resolved using phase traversal compensation. Based on the chaotic system’s sensitivity to specific frequency signals and immunity to noise, the amplitudes of the fundamental wave and second harmonics in the target signals were quantified to calculate the nonlinear coefficient. Experimental results demonstrate that the proposed method can extract these amplitudes directly in the time domain, thereby effectively overcoming the spectral leakage inherent in traditional frequency-domain methods. The nonlinear coefficient of U71Mn steel exhibits a “double-peak” characteristic as fatigue damage increases. Specifically, the first peak appears at approximately 50% of fatigue life, while the second occurs at approximately 80%. This phenomenon is closely correlated with the distinct stages of internal fatigue crack propagation, reflecting a complex damage-evolution mechanism. This study not only provides a novel method for the precise extraction of weak nonlinear signals but also establishes a critical theoretical and experimental foundation for accurate fatigue life prediction for U71Mn rail steel. Full article

Quasi-resonant (QR) flyback inverters suffer from significant performance degradation under varying thermal conditions. This is because the thermal drift of passive components’ parameters deviates the switching instants from their optimal valley points, leading to increased switching losses and higher grid current distortion. To address this challenge, we propose an online self-tuning control strategy based on a Recurrent Neural Network (RNN) designed for embedded implementation. The RNN model continuously observes a sequence of non-intrusive operational data, including input voltage, input current, and grid current, and directly predicts the optimal time-delay compensation for the valley-switching logic. This end-to-end approach eliminates the need for online parameter identification, complex physical model calculations, or dedicated thermal sensors. The proposed framework was validated through comprehensive MATLAB/Simulink simulations. The results demonstrate that when operating across a wide temperature range (e.g., from 25 °C to 85 °C), the self-tuning control scheme enhances conversion efficiency by over 3.0% and reduces the grid’s current Total Harmonic Distortion (THD) from 5.8% to below 2.0%, thereby significantly improving the inverter’s lifetime performance and reliability. Full article

In response to the significantly reduced efficiency of magnetic coupling wireless charging for unmanned aerial vehicles (UAVs) caused by their high sensitivity to transmitter and receiver coil alignment, as well as landing point errors, a position identification method based on the detection coil-induced voltage fingerprint and embedded neural network regression is proposed. This enables position alignment through a 2D mechanical structure. Firstly, by means of an S–S compensation topology with a bipolar (BP) symmetrical four-detection-coil array deployed at the transmitter, the system effectively suppresses primary direct coupling, ensuring that the position of the receiver coil predominantly determines the detection signals. Secondly, by establishing a voltage fingerprint database during the offline stage and utilizing a multi-layer perceptron–radial basis function (MLP-RBF) regression model, the system achieves high-precision end-to-end positioning and alignment control during the online stage through induced voltage acquisition and data processing. Finally, experiments demonstrate that the proposed method achieves centimeter-level positioning accuracy, with an average error of approximately 1.2 cm and a maximum error of less than 1.8 cm, presenting excellent deployability and engineering applicability. Full article

Magnetorheological (MR) semi-active suspensions offer clear advantages in improving ride comfort and handling stability, yet their engineering applications are often hindered by strong nonlinear hysteresis of the damper, the randomness of road excitations, and the reliance on manual tuning of controller parameters. To address these issues, this paper proposes an integrated framework of “experimental modeling–semi-active implementation–adaptive control.” First, characteristic tests of the MR damper are conducted, based on which a current-dependent Bouc–Wen forward model is established. Tianji’s Horse Racing Optimization (THRO) is then employed for parameter identification to reproduce the hysteresis behavior accurately. Second, a back propagation (BP) neural network-based inverse current model is developed to achieve rapid mapping from “desired damping force” to “driving current,” enabling semi-active actuation. Furthermore, a radial basis function (RBF) neural network is embedded into the active disturbance rejection control (ADRC) structure to estimate the system Jacobian online and to tune key extended state observer (ESO) gains in real time, forming the proposed RBF-ADRC strategy and thereby enhancing disturbance observation and compensation capability. Simulation results under pulse-road and Class-C random-road excitations show that, compared with the passive suspension, the proposed method reduces the root mean square error values of sprung-mass acceleration, suspension dynamic deflection, and tire dynamic load by 25.14%, 18.71%, and 11.61%, respectively, while also outperforming skyhook control and fixed-gain ADRC. Frequency-domain results further show stronger attenuation in the low-frequency band relevant to body vibration. Under pulse excitation, RBF-ADRC yields smaller peak and trough body accelerations and faster post-impact recovery. Under ±30% sprung-mass variations, it achieves the best worst-case and fluctuation-range robustness among the compared strategies and remains close to offline retuning. These results demonstrate that the proposed method improves both control performance and robustness while reducing the need for repeated manual calibration. Full article

To address the challenge that prolonged interruptions of Global Navigation Satellite System (GNSS) signals—such as those caused by urban obstructions—hinder signal re-locking and thereby reduce the number of available satellites for integrity monitoring algorithms, this study proposes an inertial navigation-assisted GNSS re-locking method based on vehicle motion information constraints. This method leverages vehicle motion constraints to confine the primary direction of Inertial Navigation System (INS) velocity errors to the vehicle’s forward direction. Upon GNSS signal recovery, frequency error compensation is employed to mitigate Doppler errors of the previously obstructed satellites. Simulation results show that this method significantly improves the re-lock capability after a long period of satellite signal interruption, increasing the number of available satellites from 7 to 10 and optimizing the satellite geometry. At a horizontal alarm threshold of 80 m, the availability of the GNSS integrity monitoring algorithm reaches 95.7%, which is 53.7 percentage points higher than the unassisted scheme. Moreover, it can achieve 100% fault detection and identification rate even with a pseudorange deviation of 82 m, significantly improving the performance of the integrity monitoring algorithm. Full article

Electroencephalography (EEG) has attracted growing interest as a biometric modality because it reflects ongoing brain activity and is inherently difficult to counterfeit. At the same time, EEG signals are influenced by internal conditions such as emotions, which may affect identification stability, particularly when recordings are obtained using portable consumer-grade systems. This study examines how emotional states influence EEG-based biometric performance and evaluates deep learning architectures to determine an effective modeling approach for cross-emotion robustness. EEG data were collected from 65 participants using a 14-channel Emotiv EPOC X headset, with 54 subjects retained after self-reported emotional validation. Recordings were acquired under neutral, positive, and negative visual stimuli. To address variability associated with portable acquisition, preprocessing made use of the device’s internal signal quality metrics to select reliable segments, compensate for degraded regions, and reduce noise. Among the evaluated models, a Bidirectional Long Short-Term Memory (BiLSTM) network enhanced with Convolutional Block Attention Module (CBAM) and Multi-Head Self-Attention (MHSA) achieved highest performance in our experiments. The model was trained on neutral-state data and subsequently evaluated under emotional conditions. It reached 95.91% accuracy in the neutral condition and maintained high performance under positive (94.31%) and negative (92.99%) states. Despite a modest decline under negative stimuli, identification performance remained stable. These findings support the feasibility of robust EEG-based biometric authentication using consumer-grade devices in realistic settings. Full article

Improving absolute accuracy in industrial manipulators remains difficult because rigid-body kinematic calibration cannot fully represent configuration-dependent non-geometric effects. Drawing inspiration from biological brain–body co-adaptation, this study presents an Evolutionary Neuro-Physical Hybrid (Evo-NPH) framework in which rigid geometric parameters and neural compensator weights are treated as a single co-evolving decision vector. In the offline phase, a Dual-Strategy Adaptive Differential Evolution (DS-ADE) optimizer performs global joint identification using complementary exploration–exploitation behaviors and success-history inheritance, analogous to morphology-control co-evolution in biological systems. In the online phase, a Neuro-Physical Hybrid Jacobian (NPHJ) solver augments the analytical Jacobian with gradients from a Graph Kolmogorov–Arnold Network (GKAN), enabling sensorimotor-like real-time compensation on the learned physical manifold. Experiments on an ABB IRB 120 manipulator with 600 configurations (500 training, 100 testing) report a testing distance-residual RMSE of 0.62 mm, STD of 0.59 mm, and MAX of 0.83 mm. Relative to the uncalibrated baseline, RMSE is reduced by 86.75%; compared with the strongest published baseline, RMSE improves by 23.46%. Ablation results show that joint DS-ADE optimization outperforms a sequential pipeline by 32.6%, and the graph-structured KAN outperforms a parameter-matched MLP by 26.2%. Wilcoxon signed-rank tests ($p<0.001$) confirm statistical significance. Full article

Aiming at the challenges in accurately identifying complex pore-space types, significant scale variations, and overlapping log responses in carbonate reservoirs, this study takes the Jurassic Da’anzhai Member in the central Sichuan Basin as the research object. By integrating core observations, cast thin sections, scanning electron microscopy, and well log data, the genetic types and log response characteristics of pore spaces at different scales are systematically analyzed. Building on this, a multivariate distribution identification model for pore-space scales is established based on Bayesian discriminant theory. To enhance the model’s identification accuracy, Z-score normalization is introduced to eliminate dimensional differences. Nonlinear combined features, such as the ratio of the compensated acoustic log (AC) to the gamma ray log (GR) and the logarithmic difference between deep and shallow resistivity logs (RT and RI), are constructed to achieve a multidimensional coupling representation of reservoir physical properties; a class-balancing augmentation method based on Gaussian perturbation is adopted to mitigate decision bias caused by sample imbalance. The results show that the improved Bayesian model achieves F1 scores exceeding 0.80 for large-, small-, and micro-scale pore spaces, with an overall identification accuracy of 84.38%, significantly outperforming the conventional crossplot method’s accuracy of 59.38%. Validation through experiments and well log data demonstrates that the model’s identification results are consistent with core and thin-section observations, indicating that this method can effectively identify large-, small-, and micro-scale pore spaces in strongly heterogeneous carbonate reservoirs. This study provides a valuable approach for reservoir log interpretation and favorable reservoir prediction. Full article

The transformation of the labor market driven by digital technology has profoundly affected workers’ income. Based on data from the China Family Panel Studies (CFPS) 2014–2022 and the China Labor-force Dynamic Survey (CLDS) 2012–2018, this paper systematically examines the causal effects of standard employment, traditional non-standard employment (labor dispatch), and new non-standard employment (non-contract employment) on income within a unified framework. This study adopts a progressive identification strategy combining the two-way fixed-effects model, individual fixed-effects model, and event study methodology. The findings are as follows: First, new non-standard employment exhibits a significant “income penalty” effect, with its wage level being 14–15% lower than that of standard employment. This effect remains robust after controlling for individual heterogeneity. Second, dynamic analysis shows that transitioning from standard employment to new non-standard employment leads to sustained income loss, with a decline of nearly 10.8% after four years. Third, mechanism testing reveals that workers increase part-time work to compensate for income loss, but job satisfaction significantly declines, leading to a dual dilemma of “exchanging time for income” and “welfare discount.” Fourth, heterogeneity analysis shows that less educated and rural workers suffer greater shocks. The study concludes that new non-standard employment has inherent income suppression characteristics, and its effects are persistent and heterogeneous. It calls for the improvement of a labor rights protection system that adapts to new forms of employment, as well as the implementation of targeted support policies for vulnerable groups, in order to build a more equitable and secure labor market. Full article

Rural revitalization has emerged as a core agenda in the global pursuit of sustainable development, with its success fundamentally hinging on enhancing the resilience of rural households to withstand shocks and restore their livelihoods. In contrast to mainstream research that primarily examines whether Medical Financial Assistance (MFA) reduces medical burden, this paper focuses on MFA as ex-post cash compensation and investigates whether and how it affects the sustainable livelihood recovery of low-income rural households following health shocks, thereby providing empirical evidence for understanding the foundational role of health security in rural revitalization. A quasi-natural experiment is constructed by leveraging the institutional feature that MFA eligibility is activated by exogenous health shocks. Using two-wave balanced panel data (2021–2022) from a nationally designated deep poverty-stricken county in Hebei Province, China, the Propensity Score Matching–Difference-in-Differences (PSM-DID) method and mediation models are employed for causal identification and mechanism testing. The findings indicate that (1) MFA significantly promotes household income recovery. It enables recipient households to recover per capita net income by an average of approximately 13.2% (p < 0.01), demonstrating a protective recovery effect, and simultaneously recovers per capita non-farm labor income by an average of approximately 13.8% (p < 0.05), revealing a developmental recovery effect. The latter is partially mediated by the non-farm labor participation rate (mediation ratio 51.7%, Sobel Z = 2.10). This finding validates the “time release effect,” demonstrating that MFA stimulates endogenous dynamics by restoring health capital and releasing labor previously constrained by family care responsibilities. It thereby extends the application of health capital theory from the individual to the household level. (2) Mechanism analysis shows that the protective recovery effect is fully mediated by the amount of MFA received (mediation ratio 326.7%, Sobel Z = 12.85), providing empirical evidence for precautionary saving theory in the context of targeted social assistance and revealing the potential productive attributes of the social safety net. (3) Heterogeneity analysis reveals clear group targeting and shock thresholds. The protective effect is concentrated among elderly households, while the developmental effect is primarily evident in middle-aged households. Both recovery effects manifest significantly only for households experiencing major disease shocks, confirming the theoretical expectation of “conditional effectiveness,” namely that policy effects are systematically moderated by household life-cycle characteristics and the severity of health shocks. This study demonstrates that MFA serves both as a safety net and an empowerment tool, but its effectiveness is highly contingent upon household characteristics and shock severity. By uncovering the foundational mechanisms through which health security contributes to rural household resilience, this study provides empirical evidence from China for building sustainable poverty prevention systems in the global process of rural revitalization. Full article

This study evaluates the tracking performance of structural damages in disaster environments by combining YOLOv8 detection with the BoT-SORT tracker. Cracks and exposed rebar, characterized by fine and irregular structures, showed high sensitivity to viewpoint changes, with camera motion compensation (CMC) improving $IoU$ by +19.63% and +20.23%. For exposed rebar, the joint use of CMC and re-identification (Re-ID) further increased $IDF1$ by +37.73%, emphasizing the effectiveness of appearance-based matching. In contrast, delamination and concrete debris, with stable morphology and clear boundaries, exhibited limited benefits from CMC, improving $IoU$ by +11.17% and +3.28%. Analysis of $MOTA$, $IDF1$, and $HOTA$ confirms that fine-grained damages require motion- and appearance-based strategies, while stable types maintain high performance through detection consistency. These results highlight the importance of tailored tracking strategies for enhancing disaster-response robots and structural monitoring systems. Full article