In the face of the COVID-19 pandemic, the World Health Organization (WHO) declared the use of a face mask as a mandatory biosafety measure. This has caused problems in current facial recognition systems, motivating the development of this research. This manuscript describes the development of a system for recognizing people, even when they are using a face mask, from photographs. A classification model based on the MobileNetV2 architecture and the OpenCv’s face detector is used. Thus, using these stages, it can be identified where the face is and it can be determined whether or not it is wearing a face mask. The FaceNet model is used as a feature extractor and a feedforward multilayer perceptron to perform facial recognition. For training the facial recognition models, a set of observations made up of 13,359 images is generated; 52.9% images with a face mask and 47.1% images without a face mask. The experimental results show that there is an accuracy of 99.65% in determining whether a person is wearing a mask or not. An accuracy of 99.52% is achieved in the facial recognition of 10 people with masks, while for facial recognition without masks, an accuracy of 99.96% is obtained.
The COVID-19 pandemic has changed people’s lives and the way in which certain services are provided. Such changes are not uncommon in healthcare services and they will have to adapt to the new situation by increasing the number of services remotely offered. Limited mobility has resulted in interruption of treatments that traditionally have been administered through face-to-face modalities, especially those related to cognitive impairments. In this telerehabilitation approach, both the patient and the specialist physician enter a virtual reality (VR) environment where they can interact in real time through avatars. A spaced retrieval (SR) task is implemented in the system to analyze cognitive performance. An experimental group (n = 20) performed the SR task in telerehabilitation mode, whereas a control group (n = 20) performed the SR task through a traditional face-to-face mode. The obtained results showed that it is possible to carry out cognitive rehabilitation processes through a telerehabilitation modality in conjunction with VR. The cost-effectiveness of the system will also contribute to making healthcare systems more efficient, overcoming both geographical and temporal limitations.
At present, electrical network stability is of the utmost importance because of the increase in electric demand and the integration of distributed generation deriving from renewable energy. In this paper, we proposed a static reactive power compensator model with common direct current voltage sources. Converter parameters were calculated and designed to fulfill specifications. In order to ascertain the device response for different operating modes as reactive power consumer and generator, we developed the model’s power and control circuits in Matlab Simulink. Simulations were performed for different conditions, and as a result, the current and voltage waveforms and the circular power chart were obtained. This paper has theoretically proven it is possible to achieve the consumption or generation of purely active or reactive power by implementing a static reactive power compensator with common DC voltage sources.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.