Search Results (80)

In-Mold Electronics (IMEs) present a highly promising monolithic integration strategy for manufacturing miniaturized 3D MEMS optical tweezers, offering exceptional environmental adaptability and structural compactness. However, the precision of such optical systems is heavily constrained by the injection molding process. During the molding phase, high-pressure melt scouring and severe thermo-mechanical coupling frequently induce geometric misalignment, manifesting as node displacement, localized warpage, and residual stress accumulation in the embedded circuits. This displacement critically alters the cross-sectional area of conductive traces, leading to resistance fluctuations that can destabilize the driving current. According to American Wire Gauge (AWG) standards, ensuring the geometric fidelity of this sensor-CPU interconnect pathway is fundamental to maintaining signal integrity. To address these manufacturing bottlenecks, this study systematically investigates the process stability of IME circuits Cyclic Olefin Copolymer (COC) is strategically selected as the substrate material over Polycarbonate (PC) and Liquid Silicone Rubber (LSR) due to its ultra-high light transmittance, extremely low water absorption, and superior thermomechanical stability. Based on finite element simulation, a data-driven intelligent optimization framework is developed. Latin Hypercube Sampling (LHS) is first utilized to efficiently sample the multi-dimensional process space, comprising melt temperature, packing pressure, and packing time. To handle the non-stationary nature of process feedback signals, wavelet analysis is introduced to decouple high-frequency noise, extracting Wavelet Energy Entropy (WEE) as a highly robust dynamic metric for process stability. Subsequently, a hybrid NSGA-II-MOPSO multi-objective algorithm is deployed to cooperatively optimize the injection parameters. The simulation-based optimization results demonstrate a substantial enhancement in manufacturing precision. Under the optimal parameter configuration, the average node displacement of the embedded circuits decreases significantly from 0.034 mm to 0.014 mm, achieving a 58.82% reduction. Simultaneously, volumetric shrinkage drops from 5.755% to 4.832% (a 16.04% reduction), while residual stress is maintained well within the structural safety threshold of optical-grade polymers. By clarifying the deformation control mechanism during the manufacturing phase, this study provides a highly reliable, data-driven methodological framework for the precision mass production of micro-nano optical systems. Full article

Indoor companion, rehabilitation, logistics, laboratory transport, and service robot scenarios require mobile platforms that can follow a human operator safely and flexibly under lighting changes, occlusion, texture-poor corridors, and dynamic pedestrian environments. Vision-, LiDAR-, and UWB-based following systems can provide high perception capability, but their deployment cost, environmental dependence, and sensing complexity remain limiting factors for low-perception-dependence applications. This paper presents a passive following system for a Mecanum-wheel mobile platform based on gimbal posture perception and orthogonal odometry fusion. A rope-tensioned two-axis gimbal is mounted above a 300 mm × 300 mm × 150 mm omnidirectional chassis, and a six-axis inertial sensor installed at the top of the gimbal detects pitch and roll changes induced by user traction. A piecewise posture-to-velocity mapping model with a dead zone, saturation, low-pass filtering, and acceleration limiting converts the user’s traction intention into planar velocity commands in the vehicle coordinate frame. To reduce pose errors caused by Mecanum-wheel slip and discontinuous roller-ground contact, two orthogonal passive odometry wheels and inertial attitude estimation are fused to provide planar position feedback for closed-loop following. A prototype was implemented using an Infineon TRAVEO CYT4BB77 controller, TI DRV8701E motor drivers, six-axis IMUs, magnetic encoders, and an embedded display interface. Experiments evaluated attitude estimation accuracy, planar localization accuracy, passive following performance, gyroscope compensation, and open-loop/closed-loop following. The compensated attitude module achieved a static yaw drift of 0.45 deg/h and a dynamic attitude RMSE below 0.56 deg. Orthogonal odometry fusion produced an average positioning error of 3.8 mm over a 3000 mm linear displacement, reducing error by approximately 84.6% compared with pure Mecanum-wheel drive odometry. In a 5000 mm forward traction task, closed-loop following reduced the average distance error from 38.6 mm to 11.5 mm compared with open-loop attitude mapping. The results indicate that the proposed gimbal-orthogonal odometry architecture provides a compact, intuitive, and environment-robust solution for passive following on omnidirectional mobile platforms. Full article

Vibration frequency monitoring is important for IoT-based condition monitoring, where sensing nodes are expected to be compact, low-cost, and easy to deploy. This paper investigates a miniaturized optical tracking sensor for frequency-oriented vibration measurement. The sensor detects surface-relative motion using optical tracking and outputs incremental displacement counts through a digital interface. A prototype system based on an optical tracking sensor and an STM32 microcontroller was developed, and the vibration frequency was extracted from the accumulated count-domain signal using preprocessing and spectral analysis. Experiments were conducted under sinusoidal excitation from 10 Hz to 200 Hz with a sampling frequency of 4 kHz. The results show that the dominant vibration frequency can be identified with mean relative errors below 0.1% within the tested range. Although the time-domain count response becomes less regular at higher frequencies, the frequency component remains distinguishable. With its compact package, low power consumption, non-contact measurement capability, and unit sensor cost below USD 0.1, the proposed sensor shows potential for low-cost distributed vibration monitoring. Full article

Today, distributed optical fiber sensors are widely used in structural health monitoring due to their high sensitivity and long-distance applicability. However, when embedded in pavement structures, distributed optical fiber sensors are always installed in a slotted buried fashion, which not only affects current pavement durability but also reduces pavement construction efficiency. In order to design clear requirements of in situ-embedded distributed optical fiber sensors for pavement construction, this study analyzes the micro-mechanical behavior of optical cables under the ultimate pavement compaction state based on a coupled DEM-FDM approach. According to the study results, it is found that when the pavement subbase was compacted, the maximum contact force of 13.2 mm aggregates in the Z-direction exceeds 150 N, which is the main resistance of the external load during pavement construction. The tight-buffered optical cable without reinforcement element and armored layer cannot withstand the vibration load. The inclusion of GFRP strengthening components and an armored layer decreased maximum stress by 38.2% (X), 30.6% (Y), and 30.9% (Z), as well as displacement by 64.6% (X), 45.5% (Y), and 66.7% (Z). Additionally, the thickness of the outer sheath enhanced the ability to withstand tension but not compression. The increase in the thickness of the armored layer can improve the ability to withstand tension and compression. Full article

Accurate environmental perception is fundamental to safe autonomous driving; however, most existing multimodal systems rely on fixed or heuristic sensor fusion strategies that cannot adapt to scene-dependent variations in sensor reliability. This paper proposes Cross-Modal Adaptive Attention (CMAA), a unified end-to-end Bird’s-Eye-View (BEV) perception framework that dynamically fuses camera, LiDAR, and RADAR information through learnable, context-aware modality gating. Unlike static fusion approaches, CMAA adaptively reweights sensor contributions based on global scene descriptors, enabling the robust integration of semantic, geometric, and motion cues without manual tuning. The proposed architecture jointly performs 3D object detection, multi-object tracking, and motion forecasting within a shared BEV representation, preserving spatial alignment across tasks and supporting efficient real-time deployment. Experiments conducted on the official nuScenes validation split demonstrate that CMAA achieves 0.528 mAP and 0.691 NDS, outperforming fixed-weight fusion baselines while maintaining a compact model size and efficient inference. Additional tracking evaluation using the official nuScenes tracking devkit reports improved tracking performance, while motion forecasting experiments show reduced trajectory displacement errors (minADE and minFDE). Ablation studies further confirm the complementary contributions of adaptive modality gating and bidirectional cross-modal refinement, and a stratified dynamic analysis reveals consistent reductions in velocity estimation error across object classes, motion regimes, and environmental conditions. These results demonstrate that adaptive multimodal fusion improves robustness, motion reasoning, and perception reliability in complex traffic environments while remaining computationally efficient for deployment in safety-critical autonomous driving systems. Full article

In this paper, a low-cost manual micro- and nano-displacement adjustment mechanism is proposed, based on the principle of flexible hinge transmission and micro-displacement scaling. The manual micro- and nano-displacement platform consists of a micrometer input platform, a nano-output platform, a differential head, and a strain displacement sensor. Firstly, a micro-displacement reduction mechanism based on a flexible beam triangular mechanism and a compact asymmetric flexible beam guiding mechanism are proposed, and a theoretical model is established for static mechanical characteristics, such as the displacement reduction multiplier, guiding stiffness, maximum stress, etc., and this is analyzed and verified by finite element simulation. The software and hardware system of the strain displacement sensor is designed and developed, and the calibration experiments of the strain displacement sensor are completed. Finally, the micro-displacement reduction times, resolution, stability, repeat positioning accuracy, load capacity and travel of the manual micro–nano-displacement platform were analyzed and experimented. The results show that when the input range of the micrometer input platform is 0–1 mm, the travel of the nano-output platform is about 0–16 μm; when a differential head with a step resolution of 2 μm is used to input 2 μm micro-displacement, the minimum displacement output of the nano-output platform is about 35.4 nm; the theoretical and simulated values of the reduction multiple of the micro–nano-displacement are 57.29 and 56.69, respectively; the calibration experiment is performed by the self-developed strain sensors, and capacitive displacement sensors measured the reduction multiples of 57.74 and 62.67, respectively, with high consistency; the vibration range of the platform after the displacement adjustment is about ±30 nm, and the load of 0–300 g has less influence on the output characteristics of the platform. Full article

A vibration energy harvester (VEH) based on the principle of variable magnetic reluctance has been developed to enable wireless and maintenance-free power supply for condition monitoring sensors in vibrating machinery. Conventional battery or wired solutions are often impractical due to limited lifetime and high installation costs, motivating the use of vibration-based energy harvesting. The proposed VEH converts mechanical vibrations into electrical energy through the relative motion of a movable ferromagnetic core within a magnetic circuit. Unlike conventional VEH designs, where the magnet is the moving element, this concept utilizes a movable ferromagnetic core in combination with a stationary pole piece for voltage induction. This configuration enables a compact and easily adjustable proof mass, as neither the coil nor the magnet needs to be moved. The VEH is designed to operate effectively under excitation frequencies between $16 \mathrm{H}\mathrm{z}$ and $50 \mathrm{H}\mathrm{z}$ and acceleration levels from $9.81 {\displaystyle \frac{\mathrm{m}}{{\mathrm{s}}^2}}$ (equivalent to $1 \mathrm{g}$) up to $98.1 {\displaystyle \frac{\mathrm{m}}{{\mathrm{s}}^2}}$ (equivalent to $10 \mathrm{g}$). To ensure a reliable power supply, the VEH must deliver a minimum electrical output of $0.1 \mathrm{m}\mathrm{W}$ at the lowest excitation ($1 \mathrm{g}$) while maintaining structural integrity. Additionally, the maximum permissible displacement amplitude of the movable core is limited to $1.15 \mathrm{m}\mathrm{m}$ to avoid mechanical damage and ensure durability over long-term operation. Coupled magnetic-transient and mechanical finite element method (FEM) simulations were conducted to analyze the system’s dynamic behavior and electrical power output across varying excitation frequencies and accelerations. A laboratory prototype was developed and tested under controlled vibration conditions to validate the simulation results. The experimental measurements confirm that the VEH achieves an electrical output of $0.166 \mathrm{m}\mathrm{W}$ at $9.81 {\displaystyle \frac{\mathrm{m}}{{\mathrm{s}}^2}}$ and $16 \mathrm{H}\mathrm{z}$, while maintaining the maximum allowable displacement amplitude of $1.15 \mathrm{m}\mathrm{m}$, even at $98.1 {\displaystyle \frac{\mathrm{m}}{{\mathrm{s}}^2}}$ ($10 \mathrm{g}$) and $50 \mathrm{H}\mathrm{z}$. The strong agreement between simulation and experimental data demonstrates the reliability of the coupled FEM approach. Overall, the proposed VEH design meets the defined performance targets and provides a robust solution for powering wireless sensor systems under a wide range of vibration conditions. Full article

To address the technical challenge where traditional high-pressure, large-flow emulsion pump stations cannot adapt to the drastic flow rate changes in hydraulic supports due to the fixed displacement of their quantitative pumps—leading to frequent system unloading, severe impacts, and damage—this study proposes an intelligent flow control method based on the digital flow distribution principle for actively perceiving and matching support demands. Building on this method, a compact, electro-hydraulically separated prototype with stepless flow regulation was developed. The system integrates high-speed switching solenoid valves, a piston push rod, a plunger pump, sensors, and a controller. By monitoring piston position in real time, the controller employs an optimized combined regulation strategy that integrates adjustable duty cycles across single, dual, and multiple cycles. This dynamically adjusts the switching timing of the pilot solenoid valve, thereby precisely controlling the closure of the inlet valve. As a result, part of the fluid can return to the suction line during the compression phase, fundamentally achieving accurate and smooth matching between the pump output flow and support demand, while significantly reducing system fluctuations and impacts. This research adopts a combined approach of co-simulation and experimental validation to deeply investigate the dynamic coupling relationship between the piston’s extreme position and delayed valve closure. It further establishes a comprehensive dynamic coupling model covering the response of the pilot valve, actuator motion, and backflow control characteristics. By analyzing key parameters such as reset spring stiffness, piston cylinder diameter, and actuator load, the system reliability is optimized. Evaluation of the backflow strategy and delay phase verifies the effectiveness of the multi-mode composite regulation strategy based on digital displacement pump technology, which extends the effective flow range of the pump to 20–100% of its rated flow. Experimental results show that the system achieves a flow regulation range of 83% under load and 57% without load, with energy efficiency improved by 15–20% due to a significant reduction in overflow losses. Compared with traditional unloading methods, this approach demonstrates markedly higher control precision and stability, with substantial reductions in both flow root mean square error (53.4 L/min vs. 357.2 L/min) and fluctuation amplitude (±3.5 L/min vs. ±12.8 L/min). The system can intelligently respond to support conditions, providing high pressure with small flow during the lowering stage and low pressure with large flow during the lifting stage, effectively achieving on-demand and precise supply of dynamic flow and pressure. The proposed “demand feedforward–flow coordination” control architecture, the innovative electro-hydraulically separated structure, and the multi-cycle optimized regulation strategy collectively provide a practical and feasible solution for upgrading the fluid supply system in fully mechanized mining faces toward fast response, high energy efficiency, and intelligent operation. Full article

This article presents the design, fabrication, and experimental validation of a centimeter-scale autonomous robot that achieves bidirectional locomotion and trajectory control through 3D-printed resonators actuated by piezoelectricity and integrated with miniature legs. Building on previous works that employed piezoelectric bimorphs, the proposed system replaces them with custom-designed 3D-printed resonant plates that exploit the excitation of standing waves (SW) to generate motion. Each resonator is equipped with strategically positioned passive legs that convert vibratory energy into effective thrust, enabling both linear and rotational movement. A differential drive configuration, implemented through two independently actuated resonators, allows precise guidance and the execution of complex trajectories. The robot integrates onboard control electronics consisting of a microcontroller and inertial sensors, which enable closed-loop trajectory correction via a PD controller and allow autonomous navigation. The experimental results demonstrate high-precision motion control, achieving linear displacement speeds of 8.87 mm/s and a maximum angular velocity of 37.88°/s, while maintaining low power consumption and a compact form factor. Furthermore, the evaluation using the mean absolute error (MAE) yielded a value of 0.83° in trajectory tracking. This work advances the field of robotics and automatic control at the insect scale by integrating efficient piezoelectric actuation, additive manufacturing, and embedded sensing into a single autonomous platform capable of agile and programmable locomotion. Full article

Robot grinding requires a constant interaction force between the tool and the workpiece, even under inclination changes. This paper proposes a compact single-axis pneumatic constant-force floating compensator (CFFC) to achieve constant force output. The proportional pressure valve and pressure sensor are used to regulate the cylinder’s pressure. Pneumatic components and sensors are integrated into the narrow space between the cylinder and the slide rail. Embedded controller, power, and communication modules are developed and integrated into a control box and interact with the operator by a touch screen. The mathematical models of the compensator are established and the stability and response dynamics are analyzed through transfer functions. A dual-loop force controller based on active disturbance rejection control (ADRC) is designed to address bias load, inclination change, friction, and the sealing cover spring effect. The outer loop is compensated by displacement, tilt, and pressure sensors, and the unmodeled dynamics are estimated by an extended state observer (ESO) and a recursive least square (RLS). Finally, the CFFC is installed on a testing platform to simulate grinding conditions. The experimental results show that even under large floating stroke, inclination changes, and biased load, the CFFC can still quickly and stably output the desired grinding force. Full article

This paper presents a magnetic-flux-based encoder for BLDC drives that maintains high accuracy under rotor eccentricity and dynamic transients. Conventional Hall-sensor-based angle estimators rely on ideal sinusoidal flux assumptions and degrade in the presence of air-gap distortion or misalignment. To overcome these limitations, a nonlinear autoregressive network with exogenous inputs (NARXNet) is proposed as a temporal neural observer that learns the nonlinear, time-dependent mapping between measured flux densities and the true electrical rotor angle. A physics-informed data augmentation framework combines experimentally measured magnetic flux maps with dynamic simulation to generate diverse training scenarios at low and variable speeds. Validation demonstrates mean angular errors below 2°, 95th-percentile errors under 5°, and negligible drift, with enhanced resilience to eccentric displacement and acceleration transients compared to classical methods. The proposed approach provides a compact, data-driven sensing solution for robust, encoderless electric drive control. Full article

Displacement metrology with nanometer-level precision over macroscopic ranges is a key foundation for modern science and engineering. This review provides a comparative overview of Precision Nanometrology, covering measurement ranges from micrometers to meters and accuracies between 0.1 nm and 100 nm. Three main technologies are discussed: the Laser Interferometer (LI), the Grating Interferometer (GI), and the Time Grating Sensor (TGS). The LI is widely regarded as the traceable benchmark for highest resolution; the GI has been developed into a compact and stable solution based on diffraction gratings; and the TGS has emerged as a new approach that converts spatial displacement into the time domain, offering strong resilience to environmental fluctuations. For each technique, the principles, recent progress, and representative systems from the past two decades are reviewed. Particular attention is given to the trade-offs between resolution, robustness, and scalability, which are decisive for practical deployment. The review concludes with a comparative analysis of performance indicators and a perspective on future directions, highlighting hybrid architectures and application-driven requirements in precision manufacturing and advanced instrumentation. Full article

This study investigates the formation mechanism and stress response characteristics of normal faults in coal-bearing strata through large-scale physical simulation experiments. A multi-layer heterogeneous model with a geometric similarity ratio of 1:300 was constructed using similar materials that were tailored to match the mechanical properties of real strata. Real-time monitoring techniques, including fiber Bragg grating strain sensors and a DH3816 static strain system, were employed to record the evolution of deformation, strain, and displacement fields during the fault development. The results show that the normal fault formation process includes five distinct stages: initial compaction, fault initiation, crack propagation, fault slip, and structural stabilization. Quantitatively, the vertical displacement of the hanging wall reached up to 5.6 cm, equivalent to a prototype value of 16.8 m, and peak horizontal stress increments near the fault exceeded 0.07 MPa. The experimental data reveal that stress concentration during the fault slip stage causes severe damage to the upper coal seam roof, with localized vertical stress fluctuations exceeding 35%. Structural planes were found to control crack nucleation and slip paths, conforming to the Mohr–Coulomb shear failure criterion. This research provides new insights into the dynamic coupling of tectonic stress and fault mechanics, offering novel experimental evidence for understanding fault-induced disasters. The findings contribute to the predictive modeling of stress redistribution in fault zones and support safer deep mining practices in structurally complex coalfields, which has potential implications for petroleum geomechanics and energy resource extraction in similar tectonic settings. Full article

The karst terrain of Guangxi, China, characterized by steep slopes and thin residual soils, is highly vulnerable to rainfall-induced shallow landslides. Timely and accurate displacement forecasting is critical for early warning and risk mitigation. However, most existing systems depend on centralized computation, leading to latency and reduced responsiveness. Moreover, conventional forecasting models are often too computationally intensive for edge devices with limited processing resources. To address these constraints, we present EoML-SlideNet, a lightweight forecasting framework designed for resource-limited hardware. It decomposes displacement and triggers into trend and periodic components, then applies the Dual-Band Lasso-Enhanced Latent Variable (DBLE–LV) module to select compact, interpretable features via cross-correlation, LASSO, and VIF screening. A small autoregressive model predicts the trend, while a lightweight neural network captures periodic fluctuations. Their outputs are combined to estimate displacement. All models were evaluated on a single CPU-only workstation to ensure fair comparison. This study introduces floating-point operations (FLOPs), alongside runtime, as practical evaluation metrics for landslide displacement prediction models. A site-specific multi-sensor dataset was developed to monitor rainfall-triggered landslide behavior in the karst terrain of Guangxi. The experimental results show that EoML-SlideNet achieves 2–4 times lower MAE/RMSE than the most accurate deep learning and the lightest baseline models, while offering 3–30 times faster inference. These results demonstrate that low-complexity models can match or surpass the accuracy of deep networks while achieving latency and FLOP levels suitable for edge deployment without dependence on remote servers. Full article

Quasi-zero-stiffness (QZS) structures can provide a near constant force output in a certain range of displacement without force sensors and controllers. Therefore, they can be used in overload protection, vibration isolation, and biomedical application. In this paper, we propose a novel QZS structure based on cosine-curve compliant beams, which have a large QZS stoke and compact layout. The proposed QZS structure is composed of two half-period cosine-curve compliant beams with negative stiffness and two one-period cosine-curve compliant beams with positive stiffness. Then, we conducted the modeling of the force-displacement relationship of the compliant beams and analyzed the influence of the parameters on the mechanical performance. Based on the influence analysis, we propose the optimization processes to achieve QZS and obtain a QZS structure with the required force-displacement behavior. Finally, the mechanical performance of the QZS structure is verified through compression experiments on the prototype. Full article