The world today is being hit by COVID-19. As opposed to fingerprints and ID cards, facial recognition technology can effectively prevent the spread of viruses in public places because it does not require contact with specific sensors. However, people also need to wear masks when entering public places, and masks will greatly affect the accuracy of facial recognition. Accurately performing facial recognition while people wear masks is a great challenge. In order to solve the problem of low facial recognition accuracy with mask wearers during the COVID-19 epidemic, we propose a masked-face recognition algorithm based on large margin cosine loss (MFCosface). Due to insufficient masked-face data for training, we designed a masked-face image generation algorithm based on the detection of the detection of key facial features. The face is detected and aligned through a multi-task cascaded convolutional network; and then we detect the key features of the face and select the mask template for coverage according to the positional information of the key features. Finally, we generate the corresponding masked-face image. Through analysis of the masked-face images, we found that triplet loss is not applicable to our datasets, because the results of online triplet selection contain fewer mask changes, making it difficult for the model to learn the relationship between mask occlusion and feature mapping. We use a large margin cosine loss as the loss function for training, which can map all the feature samples in a feature space with a smaller intra-class distance and a larger inter-class distance. In order to make the model pay more attention to the area that is not covered by the mask, we designed an Att-inception module that combines the Inception-Resnet module and the convolutional block attention module, which increases the weight of any unoccluded area in the feature map, thereby enlarging the unoccluded area’s contribution to the identification process. Experiments on several masked-face datasets have proved that our algorithm greatly improves the accuracy of masked-face recognition, and can accurately perform facial recognition with masked subjects.

Solar-driven water splitting is an efficient process for converting solar energy into chemical energy. In this process, semiconductor materials are excited by solar energy to generate free electrons to participate in the water-splitting reaction. Among these semiconductor materials, inorganic perovskite oxides have a spatial structure that is easy to control and thereby lead to different energy band structures and photocatalytic properties. More importantly, perovskite oxides can be compounded with other organic/inorganic materials to promote charge separation and improve apparent quantum yield. However, the low solar-to-hydrogen conversion efficiency has not yet reached the requirements of practical applications. In this review, the fundamental principles of solar-driven water splitting based on perovskite materials are introduced according to the most recently published results. In addition, the innovative modification techniques for water splitting based on perovskite oxides have been summarized, focusing on the following methods: element doping, homo/heterojunction formation, Z-scheme, plasmon effect, dye sensitization, carbon enhancement, and surface modifications. Note that the applications in the visible light wavelength range have been described, with emphasis among all these modification materials. Furthermore, the recent water-splitting reaction systems for practical applications are briefly discussed. As a summary, we outline the challenges and potential utilization associated with visible light–driven water splitting based on perovskite oxides for future commercial applications. This review describes various modification methods to improve photochemical performance of perovskite oxides as well as illustrates the potential to employ perovskite oxides as a key material for the practical application of water splitting.

In order to reduce vehicle vibration during driving conditions, a fuzzy sliding mode control strategy (FSMC) for semi-active air suspension based on the magnetorheological (MR) damper is proposed. The MR damper used in the semi-active air suspension system was tested and analyzed. Based on the experimental data, the genetic algorithm was used to identify the parameters of the improved hyperbolic tangent model, which was derived for the MR damper. At the same time, an adaptive neuro fuzzy inference system (ANFIS) was used to build the reverse model of the MR damper. The model of a quarter vehicle semi-active air suspension system equipped with a MR damper was established. Aiming at the uncertainty of the air suspension system, fuzzy control was used to adjust the boundary layer of the sliding mode control, which can effectively suppress the influence of chattering on the control accuracy and ensure system stability. Taking random road excitation and impact road excitation as the input signal, the simulation analysis of passive air suspension, semi-active air suspension based on SMC and FSMC was carried out, respectively. The results show that the semi-active air suspension based on FSMC has better vibration attenuating performance and ride comfort.