This research addresses the problem of early detection of smoke and open fire on the observed territory by unmanned aerial vehicles. We solve the tasks of improving the quality of incoming video data by removing motion blur and stabilizing the video stream; detecting the horizon line in the frame; and identifying fires using semantic segmentation with Euclidean–Mahalanobis distance and the modified convolutional neural network YOLO. The proposed horizon line detection algorithm allows for cutting off unnecessary information such as cloud-covered areas in the frame by calculating local contrast, which is equivalent to the pixel informativeness indicator of the image. Proposed preprocessing methods give a delay of no more than 0.03 s due to the use of a pipeline method for data processing. Experimental results show that the horizon clipping algorithm improves fire and smoke detection accuracy by approximately 11%. The best results with the neural network were achieved with YOLO 5m, which yielded an F1 score of 76.75% combined with a processing speed of 45 frames per second. The obtained results differ from existing analogs by utilizing a comprehensive approach to early fire detection, which includes image enhancement and alternative real-time video processing methods.

The problem of the unmanned flight vehicle moving along the given route in a perturbed environment comes down to that of pursuing a target which simulates motion of the reference flight vehicle in compliance with its task. A scheme was proposed of a control system selecting the control strategy according to the current states of the flight vehicle and target. Consideration was given to the issues of the lead determination of the point of meeting of the flight vehicle with the target and rearrangement in the transfer functions of the equations of longitudinal and lateral motions of the flight vehicle in a restless tmosphere. The systems of automatic stabilization of the pitch and yaw angles with autopilot are used as the internal control loops. In the experimental part of the paper, the FV motion is modeled under the control constraints.

В работе представлен обзор современных методов обработки данных дистанционного зондирования Земли. Дан анализ работ, посвященных решению задач предварительного анализа снимков, выделения и распознавания целевых объектов для их дальнейшего мониторинга. Сделаны акценты на гибридные методы анализа снимков с применением, в том числе, технологий высокопроизводительной обработки и искусственных нейронных сетей. Показаны особенности, проблемы и тенденции развития технологий обработки больших данных в различных прикладных задачах дистанционного зондирования.

The article is devoted to the analysis of the current state of the supercomputer industry and the prospects for its development. In terms of methodological approach and tools, the work is a continuation of a series of similar analytical reviews by the authors. The main source of information for analysis is the archive of editions (releases) of the world ranking of the five hundred most productive supercomputers in the world. The novelty of this work lies not only in updating the information, taking into account the latest editions of the Top500 list, but also in focusing on the following circumstance: the global supercomputer industry is undergoing a radical restructuring – transition from the “petascale era” to the “exascale era”. Technological trends and features of solutions for the most productive systems in the world in recent years are given. The pace of development of supercomputer technologies, development trends are discussed: hybrid architectures, interconnect technologies and changes in the positions of supercomputer manufacturing companies. Based on the results of the analysis, reliable forecasts were made for the coming years about the general appearance of exascale systems.