Search Results (430)

The reliable joining of graphite and Cu holds significant promise for applications in electronic heat dissipation and sliding electrical contacts. However, the substantial differences in their physicochemical properties, poor wettability, and mismatch in coefficients of thermal expansion often result in low joint strength. In this study, a two-step joining strategy combines surface modification with low-temperature sintering, and this is proposed for fabrication of Cu/graphite joints. First, the graphite surface is modified using an AgCuTi active filler alloy under vacuum conditions. Ti preferentially segregates at and reacts with the graphite interface, leading to the formation of an Ag-Cu eutectic modified layer on the graphite surface. Subsequently, low-temperature joining of the modified graphite to a Cu substrate is achieved via a hot-pressing sintering process using a Ag paste. In the sintered joint, the Ag sintered layer forms sound metallurgical bonds with both the Cu substrate and the graphite-modified layer. When the sintering temperature is 250 °C, the joint exhibits a shear strength of 30 MPa, which is significantly higher than that of a directly brazed joint. This strategy effectively reduces thermal residual stress in the joint during cooling and shifts the failure location from the brittle graphite substrate to the ductile Ag sintered layer, thereby substantially enhancing the mechanical performance. Full article

The capability of recovering a portion of braking energy during vehicle deceleration is one of the distinctive advantages of new energy vehicles (EVs) over Conventional Internal Combustion Engine Vehicles (ICEVs). In existing production vehicles, regenerative braking control is commonly implemented using rule-based lookup table methods. Although such approaches are simple, reliable, and easy to implement, they lack the ability to adaptively adjust the braking force allocation according to varying driving conditions, thereby limiting the potential for high efficiency energy recovery. To improve regenerative energy recovery while simultaneously maintaining braking stability, this study introduces an ANFIS-optimized Sliding Mode–Fuzzy Joint Hierarchical Control Strategy (S-FJHCS) for regenerative braking systems. In the upper control layer, an improved tire road friction coefficient estimation algorithm is integrated with a sliding mode controller to ensure consistent slip ratio regulation between the front and rear wheels. In the lower control layer, a fuzzy control algorithm is employed to coordinate the distribution of braking torque between the hydraulic braking system and the hub motors. Furthermore, an Adaptive Neuro-Fuzzy Inference System (ANFIS) is utilized to perform offline optimization of the fuzzy controller, enabling the adaptive adjustment of fuzzy rules and membership functions based on historical operating conditions. Simulation and experimental results demonstrate that the proposed regenerative braking control strategy can improve regenerative energy recovery efficiency by approximately 5–10% compared with a conventional rule based regenerative braking strategy, while maintaining satisfactory braking performance and vehicle stability under various driving conditions. Full article

Text entry remains a bottleneck for productivity-oriented Virtual Reality (VR), especially in scenarios where optical hand tracking is unstable because of self-occlusion, poor lighting, or out-of-view interaction. We present SlideRing, a dual-thumb wearable text-entry method that senses thumb-to-finger micro-gestures with two miniature Inertial Measurement Units (IMUs). SlideRing defines a 30-command interaction space from two hands, three target fingers, and five gesture types, then maps these commands to a full alphabetic keyboard through two complementary strategies: an ergonomic layout optimized for low movement cost and a QWERTY-compatible layout optimized for learnability. To decode subtle inertial signals, we design a dual-stream recognition model with a Statistical Feature Encoder, a Temporal Feature Encoder, and a context-aware gating module for joint finger–action classification. In offline evaluation, the model reaches 96.5% target-finger accuracy and 94.2% action-type accuracy. In a five-day text-entry study, the ergonomic layout improves from 7.43 to 15.75 words per minute (WPM), while the QWERTY-compatible layout improves from 10.55 to 15.25 WPM. The ergonomic layout reduces physical demand, whereas the QWERTY-compatible layout lowers initial mental load. These results suggest that IMU-based thumb-to-finger input has the potential to provide robust, low-visual-demand text entry for constrained VR environments. Full article

Planar linkage mechanisms remain a cornerstone of motion generation and trajectory control, yet the geometric tools that desRcribe their instantaneous behavior, namely centrodes and Bresse’s circles, have been developed almost exclusively for mechanisms with entirely revolute joints, where a sliding pair fundamentally alters the velocity and acceleration fields and disrupts the symmetries on which classical curvature theory relies. This paper presents a comprehensive higher-order kinematic analysis of a planar six-link, single-degree-of-freedom mechanism in which a slider-crank stage and a rocker stage are coupled through a shared prismatic joint that acts simultaneously as output and input. Using vector algebra and a matrix-based loop-closure formulation, the position, velocity, and acceleration analyses are derived in closed form, yielding angular velocity ratios, the instantaneous centers of rotation and acceleration of both coupler links, and their inflection and stationarity circles. The analysis reveals a distinctive geometric constraint on the slider-side coupler’s instantaneous center, a decoupling of the curvature loci of the two couplers, and degenerate configurations, linked to coupler instantaneous-stop and rocker dead-point conditions, that arise at joint-invariant crank angles. Implemented as a computational algorithm and demonstrated on a carton flap-closing mechanism and cross-validated against independent multibody simulation, the framework confirms favorable transmission and dead-point clearance behavior, extending curvature-theory tools to linkages containing sliding pairs. Full article

Conventional masonry infill walls can significantly alter the seismic response of framed buildings and often produce damage patterns incompatible with resilience-based seismic design. Dry-jointed sliding masonry wall systems have therefore emerged as deformation-tolerant alternatives that accommodate drift through controlled interface motion rather than damage accumulation. This study investigates the sequential in-plane (IP) and out-of-plane (OOP) behavior of such systems considering wall thickness, openings, and boundary detailing. Six full-scale specimens were tested, including thick- and thin-wall reference specimens, thick-wall specimens with window openings, and thin-wall specimens with door openings. IP performance was evaluated using global hysteretic and energy-based response parameters, whereas OOP behavior was assessed through load–displacement response, an equivalent acceleration index, and selected image-based displacement fields. The results show that IP drift was mainly accommodated through distributed sliding along horizontal interfaces and local block rotation, without diagonal compression strut formation or brittle cracking, even at drift ratios up to approximately 3.5%. Wall thickness improved IP strength, stiffness, shear resistance, and cumulative energy dissipation, while openings mainly affected deformation compatibility and load-transfer continuity. Under OOP loading, wall thickness and boundary continuity increased stiffness and capacity while enabling resistance mobilization at smaller displacement levels. As inertia-based comparison indicators, boundary-enhanced thick- and thin-wall specimens reached equivalent acceleration capacities of 3.41 g and 1.64 g, respectively. Overall, the system reduced IP damage accumulation, but adequate OOP stability requires appropriate wall thickness, unit geometry, and boundary detailing. Full article

To achieve manipulator trajectory tracking under uncertainties and external disturbances, this study develops a prescribed performance nonsingular sliding mode control strategy. A new sufficient condition for predefined-time stability is established and proved. A predefined-time nonlinear disturbance observer is designed to estimate the lumped disturbance, and a prescribed performance function is introduced to confine the tracking error within predefined bounds. A predefined-time nonsingular sliding mode surface is constructed, while a saturation function and a hyperbolic tangent function are adopted to address singularity and chattering, respectively. Numerical simulations are conducted on a two-degree-of-freedom manipulator subject to 20% parametric uncertainties and time-varying external disturbances. The effectiveness of disturbance compensation is evaluated by comparing the control performance with and without observer compensation, and the proposed method is further compared with fixed-time and finite-time sliding mode controllers. Quantitative results show that, with observer compensation, the integral absolute error (IAE), integral squared error (ISE), and root mean square error (RMSE) are reduced by 71.63%, 12.95%, and 6.60% for Joint 1, and by 79.24%, 35.57%, and 19.55% for Joint 2, respectively. Moreover, compared with the fixed-time method, the proposed controller reduces the IAE by 54.3% for Joint 1 and 63.1% for Joint 2, while the corresponding reductions relative to the finite-time method are 89.0% and 93.3%, respectively. These results verify the effectiveness of the proposed scheme in disturbance rejection and tracking accuracy. Full article

With the construction of transportation networks in mountainous areas under the Western Development Strategy, double-row pile foundations on slopes have been widely applied. However, due to the distortion of the soil stress field, their load distribution mechanism under bidirectional loading is extremely complex. To investigate the internal force distribution laws and deformation and failure modes, a systematic study was conducted utilizing theoretical derivation: 60 scale indoor physical model tests, and 3D refined finite element numerical simulations. The results show that the force distribution of double-row piles in slope environments differs significantly: the upper-row piles, affected by active earth pressure and sliding thrust, bear significantly higher load than the lower-row piles; meanwhile, the lower-row piles, constrained by stronger deep soil, can more fully utilize their vertical bearing capacity. Parametric analysis indicates that the terrain slope has a nonlinear amplification effect on the displacement difference at the pile top, with 50° being the critical mutation slope that triggers the failure of connection joints. In addition, the deformation mode of double-row piles undergoes a change when the pile spacing exceeds 5 times the pile diameter. Therefore, in practical engineering design, the traditional concept of symmetrical reinforcement should be abandoned in favor of differentiated bending reinforcement targeting the shallow surface layer of the upper-row piles and the deep inflection point of the lower-row piles. For working conditions with a slope greater than 50°, additional measures such as prestressed anchor cables must be applied to reduce the sliding load. Meanwhile, the row spacing should be strictly controlled within 5 times the pile diameter to fully ensure the diaphragm effect and the overall synergistic stability of the structure. Full article

Multi-sensor fusion of GNSS, IMU, and vision sensors has been extensively applied in urban Internet of Things systems and automated driving to improve positioning accuracy in complex environments. However, conventional FGO algorithms are based on fixed sensor weights, which limit their adaptability to fluctuations in sensor errors caused by environmental changes, thereby compromising positioning performance. To overcome this limitation, a novel multi-sensor adaptive weighted localization algorithm based on joint residuals detection was proposed in this study. The algorithm computes joint residuals by the sliding window accumulation of GNSS, IMU, and vision sensor measurements. By integrating a global weight decay factor into the M-estimation framework, the weights of each sensor were dynamically adjusted, thereby suppressing the effects of outliers on the state estimation. This approach enables high-precision and robust estimation of position, velocity, and attitude. Experimental results demonstrate that, based on validation with the GNSS–Visual–Inertial Navigation System (GVINS) public datasets sports field and complex environments, the proposed method exhibits superior performance in challenging low-altitude economic scenarios such as weak GNSS signals and significant IMU drift—specifically, it improves positioning accuracy by 32.3% and reduces velocity error by 32% compared to traditional FGO algorithms. In scenarios with GNSS signal interference, the system effectively mitigates error accumulation and maintains the stability of position and velocity estimation. The proposed algorithm demonstrates exceptional positioning accuracy and robustness in complex and dynamic environments, making it highly suitable for advanced urban IoT and automated driving applications. Full article

Rotated ship detection in complex synthetic aperture radar (SAR) scenes remains a critical yet challenging task for maritime remote sensing applications. Existing methods are plagued by three core bottlenecks: inconsistent directional responses across multi-scale features, unstable rotation angle regression, and non-uniform supervision quality of positive samples during training, which collectively lead to elevated false alarms, missed detections, and severe localization degradation, especially under high IoU thresholds in complex inshore environments. To address these challenges, we propose CORE-Net, a collaborative optimization framework integrating three dedicated modules in the forward detection stage: a Rotation-Consistent Feature Pyramid (RCFP) to alleviate cross-scale directional mismatch, a Progressive Cascade Rotation Head (PCR Head) to improve progressive angle prediction stability, and an Orientation-Aware Regression Enhancement Unit (OAREU) to strengthen directional geometric representation in regression features, alongside an Uncertainty-Aware Sample Reliability Steering (UARS) module for training-stage optimization to softly downweight the regression contribution of positive samples with high classification confidence but low geometric consistency. Extensive experiments on three public SAR ship detection datasets (RSDD-SAR, SSDD+, and RSAR) demonstrate that the proposed method consistently improves AP_50:95 while maintaining high Recall and Precision, validating that joint optimization of feature representation, rotated regression, and sample reliability is an effective strategy to enhance both the robustness and fine-grained localization capability of rotated ship detection in complex SAR scenes. In addition, large-scene inference experiments on uncropped Sentinel-1 and RSDD-SAR images further demonstrate that CORE-Net can be extended from patch-based evaluation to high-resolution SAR scene interpretation using a sliding-window inference strategy. Full article

To address the rigid temporal constraints and high-precision trajectory tracking requirements in modern industrial automation (e.g., high-speed pick-and-place or collaborative assembly), this paper proposes a novel composite control strategy for robotic manipulators that integrates Actor–Critic reinforcement learning with predefined-time sliding mode control (PTC-RLC). Existing predefined-time control (PTC) schemes usually rely on excessively large switching gains when dealing with strong disturbances, which easily triggers severe chattering in the system’s actuators and degrades dynamic performance. To this end, a novel predefined-time sliding surface based on artificial delay feedback is designed, ensuring that the position tracking error can strictly converge within a user-explicitly set time $T_c$ regardless of the system’s initial states, thereby significantly enhancing temporal determinism. Meanwhile, a reinforcement learning agent based on the Actor–Critic architecture is constructed to approximate and dynamically compensate for the system’s lumped unknown dynamics and external disturbances online, minimizing the control law’s reliance on large robust gains. Based on Lyapunov stability theory, the semi-global uniform ultimate boundedness of the closed-loop system is strictly proved. Numerical simulation results demonstrate that under severe operating conditions with parameter mismatches and time-varying disturbances, the proposed control strategy not only achieves high-precision and singularity-free trajectory tracking within the predefined time, but also effectively suppresses high-frequency chattering phenomena compared to the traditional non-singular terminal sliding mode control (NTSMC), outputting a smoother control torque and demonstrating strong potential for practical engineering implementations. Full article

Soft pneumatic robotic joints driven by low-cost on/off solenoid valves are attractive for lightweight and compliant robotic systems, but precise control remains challenging because continuous actuation commands must be realized through discrete valve states subject to minimum pulse-width constraints. This paper presents a model-based constrained equivalent-control PWM (C-EC) framework for a dual-chamber bellows actuator driven by four on/off valves. An ideal duty ratio is derived so that the averaged differential pressure rate matches the desired value required to impose first-order inner-loop error dynamics. To make this law physically implementable, the ideal duty is projected onto the feasible duty set determined by the minimum reliable pulse width of the valves. The resulting duty projection error is explicitly incorporated into a Lyapunov-based analysis, yielding a uniform ultimate boundedness result for the closed-loop system under the proposed implementation and an analytical comparison with conventional discrete sliding-mode control (D-SMC). The valve flow model is parameterized through PWM step-test-based sonic conductance identification. The proposed framework is implemented on a custom 1-DOF rotary joint based on an aluminum-film spiral-duct bellows actuator. Experiments show that C-EC does not uniformly dominate D-SMC over all operating conditions, but it improves $e_{\mathrm{RMS}}$ and $R_{\Delta P}$ in the medium-to-large positive-step regime and in long-hold regulation. In the representative 45°–65°–45° step-hold test, C-EC reduced the RMS tracking error by 39.3% and the differential pressure ripple by 34.5% relative to D-SMC. In the 65° long-hold test, the RMS tracking error and pressure ripple were further reduced by 35.4% and 37.9%, respectively. A loop-period comparison also showed that a 10 ms control period reduced duty projection and pressure ripple relative to 5 ms without degrading tracking accuracy. Full article

This paper addresses the impact of complex mining environments and the nonlinear dynamics of hinged mining trucks on reverse path tracking control for autonomous mining trucks. We propose a neural-network-based sliding mode control (NN-SMC)-based control strategy for reverse motion to improve tracking accuracy and robustness. First, a tractor–trailer dynamic model is built, and the force characteristics at the coupling joint are analyzed to derive the reverse interaction forces, which simplifies trailer modeling and avoids the influence of uncertain tractor parameters. Next, a control scheme matching the simplified model is developed, where an optimized sliding surface is designed and a neural network adaptively tunes control parameters to reduce chattering and improve adaptability to challenging conditions. Finally, hardware-in-the-loop tests validate the simulation results. Both simulation and experiments show that, compared with conventional SMC, the proposed method reduces lateral displacement error by 13.98% and heading error by 18.96%, demonstrating the effectiveness of the control approach. Full article

Accurate reservoir-fluid identification and water saturation prediction are essential for remaining-oil evaluation and water-flooding adjustment in heterogeneous oilfields. However, in the Youshashan Oilfield, southwestern Qaidam Basin, China, thin interbeds, strong reservoir heterogeneity, complex oil–water transitions, and inter-well logging-response differences make conventional single-task interpretation difficult. To address these problems, this study proposes a joint prediction method based on the Youshashan Fluid Prediction Network (YSF-Net) for six-class reservoir-fluid identification and continuous water saturation (S_w) prediction. A total of 200 wells were used and strictly divided by well into 140 training wells, 30 validation wells, and 30 independent test wells to avoid data leakage. Conventional logs were first processed through depth matching, outlier correction, robust standardization, and missing-value masking. Then, sliding-window logging sequences, regional stratigraphic embeddings, and reservoir-prior parameters, including shale volume, porosity, and permeability, were jointly input into the YSF-Net. The model uses a shared feature encoder with classification and regression branches to simultaneously identify oil layers, oil–water layers, water layers, and weakly, moderately, and strongly water-flooded layers, while predicting continuous S_w. A modified Simandoux-based physical consistency constraint was further introduced during training to improve the geological rationality of S_w prediction. Experimental results show that YSF-Net outperforms the CNN, BiLSTM, CNN-BiLSTM, and Transformer. It achieves an Accuracy of 0.926, Macro-F1 of 0.913, Macro-AUC of 0.968, S_w RMSE of 0.061, S_w MAE of 0.047, and S_w R² of 0.947. In direct cross-well testing without fine-tuning, YSF-Net obtains a Cross-well Accuracy of 0.918, Cross-well Macro-F1 of 0.904, and Cross-well S_w RMSE of 0.064. Ablation, transition-boundary, and typical well-interval analyses further demonstrate that regional constraints, reservoir-prior inputs, multi-task learning, and physical consistency improve class-boundary discrimination and S_w prediction reliability. The proposed method provides an accurate, consistent, and practical workflow for intelligent reservoir-fluid interpretation in heterogeneous reservoirs. Full article

Automatic video anomaly detection remains challenging because abnormal events are infrequent, visually heterogeneous, and weakly bounded in time. This study proposes an unsupervised framework trained only with normal video segments. The framework integrates sliding-window segment construction, dual-view perturbation, a two-branch spatio-temporal encoder, exponential moving-average prototype updating, prototype-guided contrastive optimization, and a robust anomaly score composed of prototype deviation, second-order temporal residual, and local-neighborhood sparsity. Experiments were conducted on UCSD Ped2, CUHK Avenue, and ShanghaiTech under the same input size, segment length, optimizer, and threshold protocol. The proposed model achieved AUC values of 97.4%, 91.8%, and 83.7% on the three datasets, respectively, with an average AUC of 91.0% and an average F1 score of 88.1%. Relative to the baseline contrastive model, the average AUC increased by 2.4 percentage points, and the average F1 score increased by 2.8 percentage points. Across three independent runs, the improvement over the contrastive baseline was statistically significant (paired two-sided t-test, p = 0.018). Ablation and sensitivity analyses indicate that the performance gain is mainly attributable to spatio-temporal joint encoding, prototype traction, temporal residual scoring, and local-neighborhood support. These results show that contrastive representation learning, explicit prototype updating, and temporal-aware scoring can jointly produce a stable representation of normal behavior without using abnormal samples during training. Full article

Individual tree crown (ITC) detection from remotely sensed data is fundamental to forest inventory and ecological monitoring, but deep learning approaches remain constrained by limited labelled training data. We systematically evaluate three transformer detectors (the Detection Transformer (DETR), Deformable DETR, and DETR with Improved DeNoising Anchor Boxes (DINO)) paired with two backbones, ImageNet-pretrained ResNet-50 and a Masked Autoencoder (MAE) pretrained on unlabelled Canopy Height Model (CHM) data. These are benchmarked against a classical local maximum and watershed pipeline and Faster R-CNN across four test sets spanning boreal, temperate mixed-wood, and diverse North American forest types at 0.25–1.0 m resolution. Spatially held-out test regions with a one-patch buffer band eliminate sliding-window leakage; headline configurations are reported as mean ± standard deviation across three random seeds. With multi-resolution MAE pretraining, the practical lower bound for non-degenerate single-dataset transformer detection lies between ∼200 and ∼1200 patches. Without MAE pretraining, DETR fails at every dataset size we tested. Multi-dataset joint training reaches $F_1=0.84\pm 0.01$ on the boreal test set and 0.45–0.68 across the temperate-mixed-wood and NEON test sets, while Faster R-CNN narrowly wins on the smallest training pool. Standard DETR with ResNet-50 collapses regardless of the length of training schedule, but the same architecture with an MAE backbone reaches $F_1=0.83\pm 0.01$ at that schedule, showing that DETR’s reported instability is conditional on the combination of backbone initialization and training budget rather than architectural. Resolution and backbone interact: ResNet-50 wins at 0.25 m, and MAE wins at 0.5–1.0 m, consistent with the eight-pixel MAE patch-matching crown scale only at coarser resolutions. Full article