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Pneumonia is a highly dangerous state that poses serious risks to the health of a patient. In contrast to common pneumonia, lung disease COVID-19 causes a large number of lethal outcomes. The pneumonia of a viral etiology caused by the RNA virus SARS-CoV-2 is visually hardly distinguishable from the bacterial pneumonia or inflammation caused by other viral infections. Now, COVID-19 can be diagnosed using PCR tests or X-rays of the thoracic cage. However, the results of a molecular study take a long time to prepare. In contrast, the radiological images of the thoracic cage can be obtained immediately after the radiological study. Although there exist guiding principles which help radiologists to differentiate COVID-19 from other types of infections, their assessments differ. In addition, doctors who are not radiologists can be assisted in better locating the disease, for instance, by a bounding box. Development of precise computer methods based on artificial intelligence can help medical workers in quickly determining the type of pneumonia and detecting the loci of inflammation. In this study a package of methods is developed to determine the type of pneumonia and detect the ground-glass loci using the appropriate architectures of neural networks, loss functions, augmentations at the training data generation stage, test time augmentation, and computer vision model ensembles. This task is successfully solved in the SIIM-FISABIO-RSNA COVID-19 Detection competition [17] and the proposed algorithm is in the top 10% of the best solutions.

Having actual models for power system components (such as generators and loads or auxiliary equipment) is vital to correctly assess the power system operating state and to establish stability margins. However, power system operators often have limited information about the actual values for power system component parameters. Even when a model is available, its operating parameters and control settings are time-dependent and subject to real-time identification. Ideally, these parameters should be identified from measurement data, such as phasor measurement unit (PMU) signals. However, it is challenging to do this from the ambient measurements in the absence of transient dynamics since the signal-to-noise ratio (SNR) for such signals is not necessarily large. In this paper, we design a Bayesian framework for on-line identification of power system component parameters based on ambient PMU data, which has reliable performance for SNR as low as five and for certain parameters can give good estimations even for unit SNR. We support the framework with a robust and time-efficient numerical method. We illustrate the approach efficiency on a synchronous generator example.