Facial Expression Recognition Methods in the Wild Based on Fusion Feature of Attention Mechanism and LBP.

Facial expression methods play a vital role in human-computer interaction and other fields, but there are factors such as occlusion, illumination, and pose changes in wild facial recognition, as well as category imbalances between different datasets, that result in large variations in recognition rates and low accuracy rates for different categories of facial expression datasets. This study introduces RCL-Net, a method of recognizing wild facial expressions that is based on an attention mechanism and LBP feature fusion. The structure consists of two main branches, namely the ResNet-CBAM residual attention branch and the local binary feature (LBP) extraction branch (RCL-Net).

Optimal Design of Carbon-Based Polymer Nanocomposites Preparation Based on Response Surface Methodology.

Response surface methodology (RSM) and central composite design (CCD) were used to improve the preparation of carbon nanotube and graphene (CNT-GN)-sensing unit composite materials in this study. Four independent variable factors (CNT content, GN content, mixing time, and curing temperature) were controlled at five levels, and 30 samples were generated using the multivariate control analysis technique. On the basis of the experimental design, semi-empirical equations were developed and utilized to predict the sensitivity and compression modulus of the generated samples.

Modeling and Optimization of the Creep Behavior of Multicomponent Copolymer Nanocomposites.

Polymer creep can significantly reduce the safety and dependability of composite applications, restricting their development and use in additional fields. In this study, single-factor and multi-factor analysis techniques were employed to systematically explore the impacts of nickel powder and graphene on the resistive creep of sensing units. The creep model between the rate of resistance changes and the pressure was established, and the material ratio was optimized to obtain a high creep resistance.

Modeling and Optimization of Sensitivity and Creep for Multi-Component Sensing Materials.

Pressure sensors urgently need high-performance sensing materials in order to be developed further. Sensitivity and creep are regarded as two key indices for assessing a sensor's performance. For the design and optimization of sensing materials, an accurate estimation of the impact of several parameters on sensitivity and creep is essential.