Search Results (1,509)

Manhole shafts in urban drainage systems are prone to accumulating trapped air pockets during intense rainfall, which can lead to sudden bounce of hinged covers and pose significant near-field risks. However, threshold criteria at the prototype scale remain unavailable. To obtain quantitative evidence of cover bounce under full-scale conditions and to clarify the effects of counterweight, dual-shaft coupling, and pressure–displacement phase lag, a series of experiments have been conducted on a prototype platform consisting of two shafts with hinged covers. Tests have been repeated under various counterweight conditions ranging from 0 to 30 kg. Pressure data from multiple transducers and high-speed video recordings have been synchronously acquired, filtered, and temporally aligned. Based on these, the critical overpressure at initial lift-off was identified, and oscillation characteristics and coupling effects have been analyzed. The critical overpressure was found to increase monotonically with added counterweight. When the counterweight was large, the system transitioned into a decaying response, with negligible subsequent bounce. The single-peak “rise–fall” pattern observed in single-shaft conditions no longer appeared when both covers lifted simultaneously. Notably, the critical overpressure did not coincide with the pressure peak, and a significant phase lag was observed between the pressure maximum and the moment of maximum displacement. These findings provide actionable support for the identification, modeling, and rapid mitigation of manhole cover bounce risks in urban drainage systems. Full article

This study investigates the transformation behavior of advanced high-strength dual-phase (DP) steel subjected to thermal cycling, aiming to support improved automotive steel-processing technologies in terms of properties, cost, and speed. The heat treatment applied consisted of 1–7 cycles through the intercritical region at a conventional heating rate. Results were compared with the conventional dual-phase steel treatment currently used in industry, as well as with variants that combine thermal cycling and fast heating, the latter offering potential for carbon-free methods. The goal is to gain a deeper understanding of the transformations that occur in the material and the potential benefits that may result. Characterization was performed using dilatometry, electron microscopy techniques, and Vickers hardness testing. Findings show the initial ferrite–martensite microstructure remained largely unchanged after cycling, though preferential austenite nucleation within ferrite and Mn segregation remained. The resulting microstructure consisted of ferrite, bainite, martensite, and retained austenite. Crystallographic orientation analysis revealed texture memory effects, with preferred orientations persisting after multiple cycles. Grain refinement occurred mainly in transformed zones, while ferrite showed slight growth with more cycles, correlating with a reduced bainite/martensite fraction. Hardness increased significantly after the first cycle but declined with subsequent cycles, reflecting a reduction in bainite/martensite fraction. It is found that when up to two cycles are used, the process can be beneficial for the steel properties; otherwise, other alternatives, such as fast heating, can be applied to optimize production. Full article

End-effectors are becoming increasingly vital in orbital space missions, performing increasingly complex operational tasks. Current on-orbit missions primarily utilize net systems and rigid grippers as manipulators. However, the dynamic analysis of the net system is complicated, and its reliability is insufficient. Moreover, rigid grippers are not impact-resistant, which can lead to the target either rebounding or sustaining damage. This paper designs an adaptive gripper for rapid passive grasping. Adjusting the initial setup of the gripper by altering the cables allows for different degrees of trigger sensitivity to be achieved. The gripper presented in this paper integrates a bistable mechanism with a dual-mode actuation system, achieving performance metrics such as a $16.7\mathrm{ms}$ activation time and a $5.42\mathrm{m}/\mathrm{s}$ capture speed. This combination of rapid passive and controllable active grasping demonstrates a novel and effective solution with significant potential for dynamic on-orbit service and debris removal missions. Full article

Piezoelectric synthetic jet actuators typically struggle to generate high-speed jets at low driving frequencies due to the coupling effect between jet frequency and jet intensity. This limitation to some extent restricts their application in flow control within low-speed flow fields. To address this issue, this study presents two methods of signal modulation. The effects of driving signal modulation on dual synthetic jet actuator (DSJA) characteristics were experimentally investigated. A laser displacement meter was used to measure the central point amplitude of the piezoelectric diaphragm, while the velocity at the exit of the DSJAs was measured using a hot-wire anemometer. The effects of signal modulation on the amplitude of the piezoelectric diaphragm, the maximum jet velocity, and the frequency domain characteristics of the dual synthetic jet (DSJ) were thoroughly analyzed. Experimental results demonstrate that driving signal modulation can enhance jet velocity at relatively low driving frequencies. The modulated DSJ exhibits low-frequency characteristics, rendering it suitable for flow control applications that require low-frequency jets. Furthermore, the coupling effect between jet frequency and jet intensity in the piezoelectric DSJA is significantly alleviated. Starting from the vibration displacement of the piezoelectric transducer (PZT), this paper systematically elaborates on the corresponding relationship between PZT displacement and the peak velocity at the jet outlet, and the “low-frequency and high-momentum jet generation method based on signal modulation” proposed herein is expected to break through the momentum–frequency coupling limitation of traditional piezoelectric dual-stenosis jet actuators (DSJAs) and enhance their application potential in low-speed flow control. Full article

This study investigates the communication network (MUAVN) of intelligent reflecting surface (IRS)-assisted high-speed multiple unmanned aerial vehicles, considering that highly dynamic UAVs may incur poor performance due to severe channel fading and rapid channel changes. Our objective is to design an adaptive dual-beam tracking scheme that mitigates beam misalignment, enhances the performance of the worst-case UAV, and sustains reliable communication links in the high-speed MUAVNs (HSMUAVNs). We first exploit an attention-based double-layer long short-term memory network to predict the spatial angle information of each UAV, which yields optimal beam coverage that matches to the UAV’s actual flight trajectory. Then, a worst-case UAV’s received beam components signal-to-interference plus noise ratio (SINR) maximization problem is formulated by jointly optimizing ground base station’s beam components and IRS’s phase shift matrix. To address this challenging problem, we decouple the optimization problem into two subproblems, which are then solved by leveraging semi-definite relaxation, the bisection method, and eigenvalue decomposition techniques. Finally, the adaptive dual beams are generated by linearly weighting the obtained beam components, each of which is well-matched to the corresponding moving UAV. Numerical results reveal that the proposed beam tracking scheme not only enhances the worst-case UAV’s performance but also guarantees a sufficient SINR demanded across the entire HSMUAVN. Full article

The angular momentum flowmeter addresses critical challenges in aviation fuel flow measurement during commercial flight operations. This study designed a visualization platform to observe the dynamic responses of internal components under varying flow conditions. By employing the sliding mesh method coupled with an angular momentum algorithm, it enabled the dynamic rotation simulation of the upstream straight-bladed rotor and provided calculation of the deflection angle in the downstream straight-bladed rotor of an angular momentum flowmeter. Experimental results categorize the flow process into three distinct regimes based on flat and spiral spring response states: pre-spring, single-spring, and dual-spring regimes. Under a flow condition of 0.091 kg/s, the upstream straight-bladed rotor maintained stable rotation at a speed of 1.1 rad/s. At a flow rate of 0.20 kg/s, the flat spring initiated outward expansion, and with further increase in flow rate, the rotational speed of the upstream straight-bladed rotor remained within the range of 25.34–26.21 rad/s. Mathematical analysis demonstrates that the flat spring configuration extends the lower measurement limit and promotes dissipation of the secondary vortex through dominant kinetic energy of the primary vortex during dual-spring operation, thereby improving high-pressure zone stability. This work elucidates the operational mechanism of angular momentum flowmeters and provides a theoretical basis for structural optimization. Full article

Spaceborne Synthetic Aperture Radar (SAR) can be applied for monitoring tropical cyclones (TCs), but co-polarized C-band SAR suffers from signal saturation such that it is improper for high wind-speed conditions. In contrast, cross-polarized SAR data does not suffer from this issue, but the retrieval algorithm needs more deliberation. Previously, many geophysical model functions (GMFs) have been developed using cross-polarized data, which obtain wind speeds using the complex relationships described by radar backscatter, incidence angle, wind direction, and radar look direction. In this regard, the rapid development of artificial intelligence technology has provided versatile machine learning methods for such a nonlinear inversion problem. In this study, we comprehensively compare the wind-speed retrieval performance of several models including Back Propagation Neural Network (BPNN), Support Vector Machine (SVM), Random Forest (RF), and Deep Neural Network (DNN), which were developed based on spatio-temporal matching and correlation analysis of stepped frequency microwave radiometer (SFMR) and dual-polarized Sentinel-1 SAR data after noise removal. A data set with ~2800 samples is generated during TCs for training and validating the inversion model. The generalization ability of different models is tested by the reserved independent data. When using similar parameters with GMFs, RF inversion has the highest accuracy with a Root Mean Square Error (RMSE) of 3.40 m/s and correlation coefficient of 0.94. Furthermore, considering that the sea surface temperature is a crucial factor for generating TCs and influencing ocean backscattering, its effects on the proposed RF model are also explored, the results of which show improved wind-speed retrieval performances. Full article

Manual pea shelling is a labor-intensive task facing small-scale farmers in rural areas, requiring substantial physical effort and limiting productivity. This study employed a Design Thinking methodology to co-design an affordable, automatic pea sheller addressing the specific needs of resource-constrained farmers. The methodology comprised five phases: empathizing with farmers through interviews, defining technical specifications from user requirements and benchmarking analysis, ideating preliminary concepts through collaborative brainstorming, prototyping using 3D-printed food-grade materials, and testing with end-users under real operating conditions. The developed sheller features counter-rotating rollers operating at optimized speed with dual compartments for grain and shell separation. Experimental validation demonstrated good extraction efficiency with minimal grain damage, while field testing confirmed substantial time reduction compared to manual shelling and strong user acceptance. The fully 3D-printable design enables affordable, customizable production suitable for small-scale operations, demonstrating how user-centered co-design can create accessible agricultural technology that addresses both technical performance and socioeconomic constraints in rural communities. Full article

This study investigates the aerodynamic effect of Gurney flaps (GFs) of different heights on the performance of a Darrieus vertical axis wind turbine (VAWT). Through numerical simulations, the performance of a baseline airfoil is compared against configurations with GFs of 0.5%c, 1%c, and 1.5%c chord lengths across a range of tip-speed ratios (TSRs). Results identify the 0.5%c GF as the optimal configuration, providing consistent power enhancement across all tested conditions, unlike the taller flaps which showed inconsistent or negative effects. This optimal configuration achieved a peak power coefficient (C_p) of 0.366 at TSR = 2.0, a 3.73% improvement over the baseline, and critically, enhanced the low-speed power by 6.30% at TSR = 0.5, improving the turbine’s self-starting capability. Flow field analysis reveals a dual-benefit mechanism for this superior performance: at low TSRs, the GF delays flow separation during the upwind pass to increase lift, while at higher TSRs, it effectively manages the wake during the downwind pass to reduce drag and mitigate negative torque. The study concludes that the 0.5%c GF strikes an optimal balance between lift augmentation and drag. Full article

The objective of this work was to study the possibility of upgrading the control system of the drum shear mechanism by using neural network PI controllers to improve the efficiency of the sheet-metal cutting process. The developed detailed model of the mechanism, including a dual DC electric drive with three subordinate control loops for the voltage of the thyristor converter, current and speed of the motors, a 6-mass kinematic system with viscoelastic connections as well as a model of the metal cutting process, made it possible to uncover that the interaction of electric drives with the mechanical part leads to significant speed fluctuations during the cutting process, which worsens the quality of the sheet-metal edge. A modified system of current and speed controllers with built-in three-layer fitting neural networks as nonlinear components of proportional-integral channels is proposed. An algorithm for the fast learning of neural controllers using the gradient descent method in each cycle of calculating the controller signal is also proposed. The developed neuro-regulators make it possible to reduce the amplitude of speed fluctuations during the cutting process by four times, ensuring the effective damping of oscillations and reducing the duration of transient processes to 0.1 s. Full article

The rapid growth of smart healthcare improves medical efficiency through electronic data sharing but introduces security risks like privacy leaks and data tampering. However, existing ciphertext-policy attribute-based encryption faces challenges such as single points of failure, weak authentication, and inadequate integrity protection, hindering secure, efficient medical data sharing. Therefore, we propose LDDV, a lightweight decentralized medical data sharing scheme with dual verification. LDDV constructs a lightweight multi-authority collaborative key management architecture based on elliptic curve cryptography, which eliminates the risk of single point of failure and balances reliability and efficiency. Meanwhile, a lightweight dual verification mechanism based on elliptic curve digital signature provides identity authentication and data integrity verification. Security analysis and experimental results show that LDDV achieves 28–42% faster decryption speeds compared to existing schemes and resists specific threats such as chosen plaintext attacks. Full article

Compared to traditional mechanical zoom lenses, tunable lenses driven by dielectric elastomers provide clear advantages in zoom range, response speed, and lightweight design. However, these lenses generally employ planar dielectroelastomer actuation, resulting in redundant structures. Additionally, the viscoelastic properties of dielectroelastomer materials often make precise control of focal length difficult. To overcome this issue, this paper introduces a compact, spherical dielectroelastomer-driven tunable lens with a radial size of 20 mm and an effective aperture of 8 mm. A dual-sided actuation structure allows for a 55% adjustment in focal length. Using thermodynamic principles and a spring–viscoelastic rheological model, static and dynamic models of the system have been developed. Experimental results demonstrate that the proposed model accurately predicts the lens’s dynamic response, with a root-mean-square error of less than 0.135, thereby providing a reliable theoretical basis for achieving high-precision focal length control. Full article

In advanced semiconductor manufacturing, computational lithography, particularly sub-resolution assist features (SRAFs), is crucial for enhancing the process window. However, conventional SRAF placement methodologies are hampered by a critical trade-off between speed and pattern fidelity, and they largely fail to optimize the complex, curvilinear layouts essential for advanced nodes. This study develops a deep learning framework to replace and drastically accelerate the optical refinement of SRAF shapes. We established a large-scale dataset with coarse, binarized SRAF patterns as inputs. Ground-truth labels were generated via an Level-Set Method (LSM) optimized purely for optical performance. A U-Net convolutional neural network was then trained to learn the mapping from the coarse inputs to the optically optimized outputs. Experimental results demonstrate a dual benefit: the model provides a multi-order-of-magnitude acceleration over traditional CPU-based methods and is significantly faster than modern GPU-accelerated algorithms while achieving a final pattern fidelity highly comparable to the computationally expensive LSM. The U-Net-generated SRAFs exhibit high fidelity to the ground-truth layouts and comparable optical performance. Our findings demonstrate that a data-driven surrogate can serve as an effective alternative to traditional algorithms for SRAF optical refinement. This represents a promising approach to mitigating computational costs in mask synthesis and provides a solid foundation for future integrated optimization solutions. Full article

Surface defects on stay cables are primary contributors to wire corrosion and breakage. Traditional manual inspection methods are inefficient, inaccurate, and pose safety risks. Recently, cable-climbing robots have shown significant potential for surface defect detection, but existing designs are constrained by large size, limited operational speed, and complex installation, restricting their field applicability. This study presents an intelligent robotic system for detecting cable surface defects. The system features a dual-wheel driving mechanism, and a computer vision–based defect recognition framework is proposed. Image preprocessing techniques, including histogram equalization, Gaussian filtering, and Sobel edge detection, are applied. Interfering information, such as sheath edges and rain lines, is removed using the Hough Line Detection Algorithm and template matching. The geometry of identified defects is automatically calculated using connected component analysis and contour extraction. The system’s performance is validated through laboratory and field tests. The results demonstrate easy installation, adaptability to cable diameters from 70 mm to 270 mm and inclination angles from 0° to 90°, and a maximum speed of 26 m/min. The proposed defect recognition framework accurately identifies typical defects and captures their morphological characteristics, achieving an average precision of 92.37%. Full article

This study presents a soft exoskeleton system designed to enhance the safety of electrical maintenance personnel during tower climbing by augmenting the hand grip and providing fall prevention assistance. Inspired by biological principles, a compact, stroke-amplified, and fast-response actuator based on a spring energy storage–release mechanism was developed and evaluated through tensile and speed tests, demonstrating sufficient locking force and a fast response time of 37.5 ms. A dual-sensing module integrating pressure and flexible bending sensors was designed to detect grasping states in real time. System effectiveness was further validated through functional electrical stimulation (FES) and simulated climbing experiments. FES tests confirmed the system’s ability to maintain grasp posture under involuntary hand extension, while climbing experiments verified consistent and reliable transitions between locking and unlocking during movement. Although preliminary, these results suggest that integrating soft exoskeletons with rapid-response actuators offers a promising solution for improving grip stability and operational safety in high-risk vertical environments. Full article