Адаптивні системи автоматичного управління : міжвідомчий науково-технічний збірник. – 2024. – № 1 (44)

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https://ela.kpi.ua/handle/123456789/66592

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Перегляд Адаптивні системи автоматичного управління : міжвідомчий науково-технічний збірник. – 2024. – № 1 (44) за Автор "Polshakova, O."

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  • Ескіз
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    CNN for solving Computer vision tasks
    (КПІ ім. Ігоря Сікорського, 2024) Kovalchuk, R.; Polshakova, O.
    Аbstract: The paper explores fundamental and current methods addressing computer vision tasks, particularly in classification, segmentation, and image processing implemented in computer vision (CV) systems. It is highlighted that the application of deep learning significantly enhances the speed and accuracy in processing large datasets within CV systems. It is noted that artificial vision systems are proficient in object detection, recognizing serial numbers, and detecting surface defects. Emphasis is placed on the significance of integrating diverse methods for effectively addressing CV tasks. Directions for further research are proposed, such as the application of CV systems in resource-constrained environments and enhancing real-time processing efficiency.
  • Ескіз
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    Development of subject-oriented innovative software products based on artificial intelligence systems
    (КПІ ім. Ігоря Сікорського, 2024) Stenin, A.; Pasko, V.; Lisovychenko, O.; Polshakova, O.; Soldatov, V.
    This article analyzes existing models of the development process of innovative software products for a specific subject area. Taking into account the multialternative nature of solutions at each stage of innovative software products development and compliance with constantly changing requirements, this article proposes an information and logical model of innovative software products development management process based on artificial intelligence systems. The essence of the proposed approach is that the development of an innovative software products is interpreted as an information object that changes in content and structure in the process of its creation. The presence of uncertainty and a large amount of information requires the use of intelligent decision support systems at each stage of ISP development.
  • Ескіз
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    Modern technologies for hiding people's faces using object tracking based on YOLOv5 and deepsort
    (КПІ ім. Ігоря Сікорського, 2024) Shchur, А.; Polshakova, O.
    The object of study is a system for automated blurring of human faces in video. This article provides a detailed overview of modern technologies and principles of tracking objects in video with assigning them unique elements. Since most video editors still leave most of the work to the user, it was decided to optimize this process. The aim of this work is to reduce the time spent on the process of hiding human faces in video files. To achieve this goal, it is proposed to use a modern detector - the YOLO convolutional neural network and the DeepSORT object tracking algorithm, which uses classical approaches to filtering input data and predicting the position of an object in space, as well as a modern neural network capable of distinguishing between people's faces. As a result of this work, among free analogues on the Internet, the acceleration of face blurring was achieved up to 20%, which is a pretty good result.