Search

In this paper, a damage spatial imaging approach based on novel signal extraction is suggested to reconstruct the Lamb wave response signal under strong noise and realize the spatial localization of damage. First, the Variable Mode Decomposition (VMD) parameters are optimized by the improved Grey Wolf optimization method (IGWO) to decompose the response signal. To rebuild the response signal, the correlation coefficient is used to choose the optimal modal component and the residual. To give the best wavelet function and transform level for adaptive denoising of the reconstructed signal without a reference signal, an enhanced Discrete Wavelet Transform (DWT) based on Shannon entropy is proposed. To achieve damage localization imaging, a damage spatial localization model is built utilizing a reconstruction algorithm for probabilistic inspection of damage (RAPID) approach and a convolutional neural network (CNN). The suggested method may successfully increase the signal-to-noise ratio (SNR) of the reconstructed response signal and lower the error of spatial localization under strong noise through experiments. The spatial localization of composite damage using Lamb wave under strong noise is expanded in this paper., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Many challenges exist in the study of using orthotics, exoskeletons or exosuits as tools for rehabilitation and assistance of healthy people in daily activities due to the requirements of portability and safe interaction with the user and the environment. One approach to dealing with these challenges is to design a control system that can be deployed in a portable device to identify the relationships that exist between the gait variables and gait cycle for different locomotion modes. In order to estimate the knee and ankle angles in the sagittal plane for different locomotion modes, a novel multimodal feature-decoupled kinematic estimation system consisting of a multimodal locomotion classifier and an optimal joint angle estimator is proposed in this paper. The multi-source information output from different conventional primary models are fused by assigning the non-fixed weight. To improve the performance of the primary models, a data augmentation module based on the time-frequency domain analysis method is designed. The results show that the inclusion of the data augmentation module and multi-source information fusion modules has improved the classification accuracy to 98.56% and kinematic estimation performance (PCC) to 0.904 (walking), 0.956 (running), 0.899 (stair ascent), 0.851 (stair descent), respectively. The kinematic estimation quality is generally higher for faster speed (running) or proximal joint (knee) compared to other modes and ankle. The limitations and advantages of the proposed approach are discussed. Based on our findings, the multimodal kinematic estimation system has potential in facilitating the deployment for human-in-loop control of lower-limb intelligent assistive devices.

In this study, the chemical graft method was used to connect modified graphene oxide (GO) and Fe 3 O 4 through covalent bonds. To make full use of the tribological properties of graphene, aligned graphene oxide@Fe 3 O 4 /waterborne polyurethane (GO@Fe 3 O 4 /WPU) was prepared in a magnetic field and tribological experiments were carried out on it. The GO@Fe 3 O 4 was characterized by Fourier transform infrared (FTIR) spectroscopy, X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and a transmission electron microscopy (TEM). The characterization results show that Fe 3 O 4 is successfully loaded on the surface of GO and GO@Fe 3 O 4 has better dispersibility in WPU. Among the coatings without alignment inducement of GO@Fe 3 O 4 , 0.5 wt % GO@Fe 3 O 4 /WPU has the lowest friction coefficient and wear rate. In addition, the 0.5 wt % aligned GO@Fe 3 O 4 /WPU composite coating has the lowest friction coefficient and wear rate compared with nonaligned and pure WPU coatings. The excellent tribological properties of the aligned composite coating come from its ability to quickly form a uniform and continuous transfer film on the friction counterpair, which avoids direct friction between the friction counterpair and the coating., Competing Interests: The authors declare no competing financial interest., (© 2021 The Authors. Published by American Chemical Society.)

Stretchability and durability are imperative features for many electronic skins of soft robots, particularly those involving high deformability, cyclic gaits, confined space traverse, rough terrain navigation, and frequent human-robot interaction. This article reports on the design, fabrication, and characterization of highly stretchable and durable interconnections based on conductive knitted fabrics for the skins of soft robots. The core-spun yarn containing an ultrafine metal wire (core diameter: 50 μm) fabricated using a newly developed vortex spinning technology is employed as the conductive trace and is integrated into rib-knitted fabrics together with two types of elastic composite yarns-an elastic filament yarn and an elastic vortex core-spun yarn, respectively. Owing to the structures and properties of the yarns and fabrics, the electrical resistance of the fabrics remains stable at a maximum strain of 425% in unidirectional tensile test and a maximum average membrane strain of 300% in three-dimensional deformation. The fabrics exhibit a fatigue life greater than 1,200,000 loading cycles at 20% tensile strain and 10,000 abrasion cycles. Application of the fabrics is demonstrated by covering an origami paper-fabric composite-based soft extension actuator with the fabric. Performance of the developed conductive knitted fabrics indicates that they have potential to find application in the electronic skins of soft robots.