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In the absence of vaccines or medicines to stop COVID-19, one of the effective methods to slow the spread of the coronavirus and reduce the overloading of healthcare is to wear a face mask. Nevertheless, to mandate the use of face masks or coverings in public areas, additional human resources are required, which is tedious and attention-intensive. To automate the monitoring process, one of the promising solutions is to leverage existing object detection models to detect the faces with or without masks. As such, security officers do not have to stare at the monitoring devices or crowds, and only have to deal with the alerts triggered by the detection of faces without masks. Existing object detection models usually focus on designing the CNN-based network architectures for extracting discriminative features. However, the size of training datasets of face mask detection is small, while the difference between faces with and without masks is subtle. Therefore, in this article, we propose a face mask detection framework that uses the context attention module to enable the effective attention of the feed-forward convolution neural network by adapting their attention maps’ feature refinement. Moreover, we further propose an anchor-free detector with Triplet-Consistency Representation Learning by integrating the consistency loss and the triplet loss to deal with the small-scale training data and the similarity between masks and occlusions. Extensive experimental results show that our method outperforms the other state-of-the-art methods. The source code is released as a public download to improve public health at https://github.com/wei-1006/MaskFaceDetection .

Energy storage systems are critical elements in electric vehicle (EV) and hybrid electric vehicle (HEV) applications. In these systems, lithium-ion batteries are preferred for their outstanding characteristics and advantages: high power density, long life cycles and high efficiency. For high voltage applications, batteries cells are connected in series, raising the issue of voltage balancing among cells. This paper presents a new structure of active voltage balancing of Li-ion cells associated in series in battery stack. It is based on the use of micro-converters network. The principle of the charge equalization is to derive energy from an overcharged cell and to transfer it to an undercharged cell no matter whether the battery is currently in charge or in use supplying a load. The highlight of this structure is not only its high efficiency and its ease of implement but moreover, its integration capacity in contrast to state of the art passive and active balancing topologies. A monitoring system is required to perform forced balancing. The results from simulations and experimental test bench validate the feasibility and the interest of the proposed balancing circuitry and operating principle.Index Terms -Energy storage systems, lithium-ion, active voltage balancing, and micro-converters network.