South China University of Technology

About: South China University of Technology is a education organization based out in Guangzhou, China. It is known for research contribution in the topics: Catalysis & Adsorption. The organization has 62343 authors who have published 69468 publications receiving 1251592 citations. The organization is also known as: SCUT & Huánán Lǐgōng Dàxué.

A highly stretchable and sensitive strain sensor based on graphene–elastomer composites with a novel double-interconnected network

Abstract: The construction of a continuous conductive network with a low percolation threshold plays a key role in fabricating a high performance strain sensor. Herein, a highly stretchable and sensitive strain sensor based on binary rubber blend/graphene was fabricated by a simple and effective assembly approach. A novel double-interconnected network composed of compactly continuous graphene conductive networks was designed and constructed using the composites, thereby resulting in an ultralow percolation threshold of 0.3 vol%, approximately 12-fold lower than that of the conventional graphene-based composites with a homogeneously dispersed morphology (4.0 vol%). Near the percolation threshold, the sensors could be stretched in excess of 100% applied strain, and exhibited a high stretchability, sensitivity (gauge factor ∼82.5) and good reproducibility (∼300 cycles) of up to 100% strain under cyclic tensile tests. The proposed strategy provides a novel effective approach for constructing a double-interconnected conductive network using polymer composites, and is very competitive for developing and designing high performance strain sensors.

A Joint Intrinsic-Extrinsic Prior Model for Retinex

Abstract: We propose a joint intrinsic-extrinsic prior model to estimate both illumination and reflectance from an observed image. The 2D image formed from 3D object in the scene is affected by the intrinsic properties (shape and texture) and the extrinsic property (illumination). Based on a novel structure-preserving measure called local variation deviation, a joint intrinsic-extrinsic prior model is proposed for better representation. Better than conventional Retinex models, the proposed model can preserve the structure information by shape prior, estimate the reflectance with fine details by texture prior, and capture the luminous source by illumination prior. Experimental results demonstrate the effectiveness of the proposed method on simulated and real data. Compared with the other Retinex algorithms and state-of-the-art algorithms, the proposed model yields better results on both subjective and objective assessments.

Development and Learning Control of a Human Limb With a Rehabilitation Exoskeleton

Abstract: This paper describes a novel development of a lower limber exoskeleton for physical assistance and rehabilitation. The developed exoskeleton is a motorized leg device having a total of 4 DOF with hip, knee, and ankle actuated in the sagittal plane. The exoskeleton applies forces and learns the impedance parameters of both robot and human. An adaptive control scheme by incorporating learning control approaches into the exoskeleton system is developed to help the leg movement on a desired periodic trajectory and handle periodic uncertainties with known periods. The proposed control approach does not require a muscle model and can be proven to yield asymptotic stability for a nonlinear muscle model and an exoskeleton model in the presence of bounded nonlinear disturbances (e.g., spasticity and fatigue). The performance of the controller is demonstrated through closed-loop experiments on human subjects. The experiments illustrate the ability of the exoskeleton to enable the leg shank to track single and multiple period trajectories with different periods and ranges of motion.