Перегляд за автором "Martovytskyi, V."
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Публікація Approach to building a global mobile agent way based on q-learning(ХНУРЕ, 2020) Martovytskyi, V.; Ivaniuk, O.Today, the problem of navigation of autonomous mobile systems in a space where disturbances are possible is urgent. The task of finding a route for a mobile robot is a complex and non-trivial task. At the moment, there are many algorithms that allow you to solve such problems in accordance with the specified criteria for building a route. Most of these algorithms are modifications of "basic" path planning methods that are optimized for specific conditions. The subject of research in the article is the process of building a global path for a mobile agent. The purpose of the work is to create an algorithm for planning the route of autonomous mobile systems in space using the Q-learning algorithm. The following tasks are solved in the article: development of an approach to training and support of a reinforcement learning algorithm for building a global path of a mobile agent; testing the agent's ability to find a path in environments that are not in the training set. The following methods are used: graph theory, queuing theory, Markov decision-making process theory and mathematical programming methods. The research is based on scientific articles and other materials from foreign conferences and archives in the field of machine learning, deep learning and deep reinforcement learning. The following results were obtained: an approach was formulated to construct the global path of a mobile agent based on the accumulated data in the process of interaction with the external environment. The environment rewards these actions and the agent continues to carry them out. This approach will allow this method to be applied to a wide range of situations and devices. Conclusions: This approach allows accumulating the knowledge of the outside world for further decision-making when planning a route where the robot can acquire the skill of self-learning, studying and training like a human, and finding the path from the initial state to the target state in an unknown environment. In the modern world, the use of robots and autonomous systems is spreading, designed to replace or facilitate human labor, make it safer and speed it up. Adaptive autonomous path finding algorithms are very important in many robotics applications. Thus, navigation tasks with limited information are relevant today, since this is the main task that the agent solves, and one of the tasks that are part of the robot during operation.Публікація Devising an approach to personality identification based on handwritten text using a vision transformer(2025) Shupyliuk, M.; Martovytskyi, V.; Bolohova, N.; Romanenkov, Y.; Osiievskyi, S.; Liashenko, S.; Nesmiian, O.; Nikiforov, I.; Sukhoteplyi, V.; Lapchenkov, Y.The object of this study is the approach to personality identification based on handwritten text using machine learning methods. Increasing the accuracy of personality identification and automating feature extraction could make it possible to perform more accurate analysis of handwritten text. A functional model has been built, and an experimental study of the proposed approach was conducted. The results of the study showed that the proposed approach increased the overall accuracy of identification, compared to other studies, as evidenced by the obtained accuracy values with the lowest indicator of 94.84 % for Friendliness and the highest 99.48 % for Conscientiousness. The accuracy indicator also improved compared to other models, as evidenced by the average accuracy value, which increased from 0.65 to 0.94. Such results were obtained through the use of the "Vision Transformer" method, which makes it possible to remove the need for feature extraction as a separate step, and the scale-invariant feature transformation approach made it possible to extract relevant image patches. An experimental validation was conducted using retrieval and classification approaches, which minimizes the variability of the results. The use of the Big Five model and the CVL dataset improves the accessibility of the study for comparison and reproducibility. In practice, handwriting analysis is widely used in forensics, for personnel selection, as well as in other areas of activity. The results increase the reliability of automated handwriting analysis systems in the area of personality identification, which could help graphologists and handwriting experts in their work both to assess personality traits and to determine whether a certain handwritten text belongs to a specific personПублікація Efficiency of image convolution(2019) Smelyakov, K.; Shupyliuk, M.; Martovytskyi, V.; Tovchyrechko, D.; Ponomarenko, O.The article discusses the main algorithms used to convolve a digital image, experiment is performed on various reduction factors, and discusses the use of convolution algorithms for an image with a large number of fine details, analyzes the effectiveness of the experimental results and selects the most effective convolution algorithms used for images with a large number small parts.Публікація Технологія моніторингу стану функціонування розподілених комп'ютерних систем(ПНТУ, 2022) Martovytskyi, V.; Koltun, Yu.; Holubnychyi, D.; Sukhoteplyi, V.В статті представлена актуальна науково-практична задача покращення показників виявлення аномалій функціонування розподілених комп’ютерних систем в умовах кібернетичних впливів зовнішнього та внутрішнього середовища. Представлена структура та архітектура системи моніторингу з використанням автономних прогрмних агентів. Архітектура системи моніторингу призначена для вирішення завдань збору та зберігання параметрів отриманих від датчиків, що характеризуються не тільки їх цільовими функціями, але і функціонал ьними можливостями. Розроблено технологію процесу функціонування мультиагентної системи моніторингу аномалій при функціонуванні компонентів розподілених комп’ютерних систем, що базуються на методології IDEF1, що деталізують процес моніторингу стану розподілених комп’ютерних систем и дозволяють інтегрувати систему моніторингу з іншими компонентами системи захисту інформації. Представлено результати роботи програмного комплексу системи моніторингу аномалій функціонування розподілених комп’ютерних систем. Ефективність розробленої системи моніторингу підтверджена методом імітаційного моделювання атак на сегмент мережі розподілених комп’ютерних систем. Прототип забезпечує ймовірність прийняття вірного рішення про наявність аномального трафіку склало 91%, помилки першого роду - 5% і помилки другого роду - 4%. Подальші дослідження доцільно спрямувати на вдосконалення та покращення методу оцінки стану обчислювального вузла та стану запущених завдань в комп’ютерній системі.