Irina I. Shpakovskaya

Every year manufacturers of household appliances improve their devices, trying to make everyday life easier for users. New smart devices have many useful features, but not all users can easily cope with the complexity of the devices. One of the main tasks of household appliance manufacturers is to ensure the convenience of using appliances, taking into account the increasing complexity. Therefore, any manufacturer supplies equipment with a short but useful instruction manual. Practice shows that no printed user manual can compare with a demonstration of the device operation by a professional consultant. Instructions for home appliances using augmented reality technology will allow users to get the necessary detailed information about the device in a short period of time. As part of this work, the task of developing an artificial intelligence-based module is being solved. This module consists of developed classification, matching, and tracking submodules that can provide simple and fast visual instructions to users of household appliances in real time. The identification of household appliances is performed with more than 0.9 accuracy, and the tracking inside an unidentified object using the camera of a mobile device is processed with the success score of about 0.68 and frames per second (FPS) about 7. Mobile applications based on the proposed intelligent modules for Android and iOS were developed.

The paper proposes an algorithm for constructing Lyapunov candidate neural network functions in order to maximize the estimate of the area of attraction of the equilibrium position. To do this, it is necessary that the invariant subset, given by the level set, occupy the largest possible share of the empirical estimate of the attraction region obtained by the simulation. The implementation of this goal is carried out by introducing an additional term in the loss function. The algorithm makes it possible to construct candidate functions for systems with non-linearities of a rather general form. The operation of the algorithm is illustrated by an example.

The article analyzes the data of elderly patients with airway cancer before and after surgery. To reduce the mortality of cancer patients in the postoperative period, it is necessary to identify markers that can predict the deterioration of the patient's condition. The study involved 83 patients, who recorded 216 parameters before and 47 parameters after surgery. The data contain not only general laboratory parameters, but also blood gas parameters, hemodynamic data and autonomic indices, metabolic parameters and cardiorespiratory stress tests at various levels of exercise. Statistical data analysis is carried out using the SPSS program and is based on the following methods: one-sided Kolmogorov-Smirnov test, Mann-Whitney test, chi-square test and the use of logistic regression to build a multivariate model. The investigated variable is the result of the operation, which can take the following values: no complications, complications after surgery, death. The result of this analysis, namely the determination of mortality markers, will determine which of the patients should not undergo surgery, and who need special medical supervision in the postoperative period.