Fei Wu

Continuous monitoring of body‐surface vibrations via ambulatory sensing provides critical data for individual healthcare and human health mapping. However, the lack of deep structural integration along the entire skin‐to‐sensor acoustic pathway has rendered wearable mechano‐acoustic sensors incapable of balancing wearability and signal fidelity at the system level. Here, we report a flexible, skin‐interfaced triboelectric patch that addresses impedance mismatch through an internal corrugated sensing structure integrated with a membrane‐based resonant acoustic coupling interface. This architecture substantially augments mechanical‐to‐electrical transduction, encompassing the dominant energy spectra (0–1000 Hz) of physiological motions with a sensitivity of 2062.66 mV·Pa ^{− 1} ·cm ^{− 2} at 168 Hz with a low detection threshold of 45 dB. Then, a bispectrogram classifier based on the Cardiechema database is constructed, tuned, and evaluated via 5 × 3 cross‐validation, achieving a mean accuracy of 96.71% and a macro F1‐score of 96.72%. By synergizing high‐fidelity sensing with machine learning, this robust multimodal framework establishes a general approach for real‐time diagnostics and personalized healthcare, offering profound insights into complex physiological signatures from cardiac pathologies to intricate sleep architectures.

The integrated design of multifunctional acoustic structures provides a cost-effective strategy for low-frequency and broadband noise control, particularly in space-constrained environments. In this Letter, a dual-functional noise control structure based on a hybrid mechanism that combines interference and resonance effects is proposed. The dual functionality is achieved through a symmetric interference–resonance coupling structure, whose identical inlet and outlet ports enable versatile implementation. The structure functions both as an efficient sound absorber that suppresses reflected sound in indoor environments and as a compact silencer that attenuates transmitted sound in duct systems. An adaptive particle swarm optimization algorithm with enhanced sample diversity is employed to optimize the geometric parameters. The optimized structure exhibits an average absorption coefficient exceeding 0.8 within the 700–1700 Hz range and an average transmission loss of 10 dB under grazing incidence. Excellent agreement among theoretical analysis, finite element simulations, and experimental measurements confirms the effectiveness of the proposed design. Owing to the interference–resonance coupling mechanism and the dual functionality, the proposed structure is well suited for broadband noise control at low to mid frequencies.

Achieving stable broadband noise reduction under grazing flow using a structurally simple configuration remains a challenge. To address this issue, a broadband noise control design based on the coupling of interference and resonance mechanisms is proposed. In this study, side branch ducts generate destructive interference, whereas internally extended neck resonators provide resonant damping. The acoustic behavior of the structure was elucidated through analysis of the flow field and phase distribution using the k-ε turbulence model and the linearized Navier–Stokes equations. An analytical model formulated using the transfer matrix method was developed and validated against numerical simulations and experimental measurements conducted in a custom-designed grazing flow duct facility. The results demonstrate that the broadband performance originates from the interaction between interference and resonance effects. Specifically, the side-branch ducts generate stable broadband destructive interference that remains robust under grazing flow, whereas the extended-neck resonators introduce resonant dissipation at discrete frequencies. Notably, at a grazing flow velocity of 60 m/s and a sound pressure level of 140 dB, noise reduction is maintained over the frequency range of 700–2000 Hz. The proposed strategy therefore provides a practical design framework for broadband flow-noise control, with potential for implementation in complex duct systems.

Aiming to enhance the low-frequency noise reduction performance of Helmholtz resonator structures, this study proposes two innovative low-frequency broadband noise suppression configurations based on the negative equivalent parameter theory. These configurations are designed with Helmholtz resonant cavities featuring extended internal necks, which can be arranged in either axial or annular layouts. The four-pole parameter transfer matrix method is employed to calculate the transmission and reflection coefficients for both unit cells and arrays. Subsequently, equivalent parameters are derived through inversion and validated via impedance tube measurements. The results reveal that both configurations exhibit negative equivalent parameters within their respective resonance bands, accompanied by significant noise attenuation. The difference in cavity arrangement leads to variations in the sound pressure phase within the structure, thereby influencing the noise reduction effectiveness. The axial array configuration demonstrates smooth broadband attenuation characteristics due to uniform inter-cavity coupling. In contrast, the circular array exhibits sharp attenuation peaks at multiple resonant frequencies. Moreover, a positive correlation is observed between the bandwidth of negative parameters and the noise suppression performance. This research provides a theoretical foundation and practical design guidelines for developing low-frequency noise control structures based on negative equivalent parameters. The findings have significant potential for widespread application in ventilation systems and industrial noise reduction scenarios.

Eco-driving for vehicle platoons has attracted significant research interest due to its potential to improve traffic efficiency and reduce energy consumption by optimizing vehicle speed trajectories and inter-vehicle spacing. This paper presents a Data-Efficient Iterative Learning-based (DEIL) method for integrated speed planning and control, specifically designed for heavy-duty vehicle platoons. The proposed approach formulates the problem as a finite-horizon constrained optimization, that incorporates both a terminal energy cost and a time-domain-guided terminal safe constraint set. Unlike conventional optimization methods that rely on a one-time pre-planned reference speed, the DEIL leverages historical vehicle states and energy consumption data from previous iterations to approximate the terminal cost and constraint set, enabling closed-loop, data-efficient iterative learning. The approximated terminal energy cost decreases monotonically and converges, facilitating the derivation of an energy-optimal control strategy within a limited number of iterations. Moreover, the method integrates trajectory planning and tracking control without requiring predefined reference profiles. To improve computational efficiency, a dynamic programming (DP) strategy is employed to generate the initial feasible trajectory. Comparative analysis validates the proposed approach, demonstrating its effectiveness in producing energy-optimal trajectories and control strategies for heavy vehicle platooning.

Low-frequency resonant acoustic structures possess similar underlying principles in sound absorption and muffling, but absorption and muffling are applied in different contexts. In this study, we propose an innovative ultra-thin noise reduction structure that integrates sound absorption and muffling functionalities. By modifying the cavity depth distribution and utilizing Helmholtz resonance with an extended inner neck, the design significantly reduces structural thickness while maintaining high noise reduction performance. The sound absorption coefficient and sound transmission loss are calculated using the transfer matrix method. The structure, with a thickness of only 1/73 of the target wavelength, achieves a near-unity sound absorption coefficient under normal incidence. Furthermore, the results demonstrate that when implemented in a ventilation duct, the structure enhances sound transmission loss by 17 dB, leveraging cavity resonance. Under ventilated conditions, low-frequency sound insulation is further improved through Fano resonance, introducing additional insulation peaks. By fine-tuning the parameters of localized units, broadband resonance is achieved, effectively expanding the noise reduction bandwidth. These findings offer a novel approach for compact, high-performance low-frequency noise control, with potential applications in various engineering and industrial environments.

The Helmholtz resonant structure with rectangular extended neck is designed to solve low-frequency broadband sound absorption problem in this work. Theoretical and finite element absorption models are established and be used for low-frequency acoustic design. What makes it interesting is that all parameters of the rectangular extended neck Helmholtz resonator structure can be adjusted to shift the working frequency. Based on the regularity of the structural parameters, four coupling structures with different neck depths, neck opening areas, cavity cross-sectional areas, and cavity depths are designed respectively, each of which exhibited multiple sound absorption coefficient peaks to enhance the low-frequency absorption capacity of the structure. To further analyze the effectiveness of coupling structure, the broadband acoustic absorption mechanism of the coupled structure is analyzed based on particle vibration velocity distribution. It is found that cells with different acoustic impedance contributed differently to the sound absorption, and cells with longer necks provided better noise reduction for low-frequency. The experiment is verified in the impedance tube, result shows that the coupling structure with 9 cells and a cavity depth of only 4 cm achieved an average sound absorption coefficient of above 0.8 at 210–340 Hz, which verified the accuracy of the theoretical model. Overall, the Helmholtz resonant cavity acoustic structure with rectangular extension neck designed in this work has a simple structure with low-frequency broadband acoustic absorption performance. This provides a new approach for designing low-frequency broadband acoustic structure.

In large infrastructure projects, the precision and quality of welding large-scale steel mesh sheets are crucial for the safety of the building structure. Traditional manual welding is inefficient and risky, thus, there is an urgent need to replace manual labor with specialized robots equipped with visual recognition systems. However, identifying features like weld points on diverse steel components remains a bottleneck for the application of robotic welding technology. This paper proposes a framework for simultaneous extraction of multi-type steel mesh features based on measured point clouds to meet the requirements of a visual-guided robotic welding system. Initially, Euclidean clustering method is employed to segment the point cloud and obtain all point cloud clusters that belong to the rebar body. Subsequently, Principal Component Analysis (PCA) and geometric constraints are utilized to merge the point cloud clusters belonging to the same rebar or overlapping rebar. Finally, feature extraction of rebar’s intersection welding points and lap weld seams is performed using methods such as Statistical Outlier Removal (SOR) and Least Squares Cylinder Fitting. Additional ablation experiments were conducted to assess the robustness of the framework. To substantiate the feasibility of the framework, the recommended approach was compared with existing point cloud-based and image-based methods. The results indicate that the framework achieves a balance between efficiency and stable robustness while ensuring recognition accuracy.