Вимірювальна техніка та метрологія. – 2023. – Випуск 84, №3

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Measuring Equipment and Metrology : scientific journal. – Lviv : Lviv Politechnic Publishing House, 2023. – Volume 84, № 3. – 45 р.

Вимірювальна техніка та метрологія

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Author: search.filters.author.Prodan, RomanEnd date: 2023

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    Preventing potential robbery crimes using deep learning algorithm of data processing
    (Видавництво Львівської політехніки, 2023-02-28) Shutka, Denys; Prodan, Roman; Tataryn, Vasyl; Lviv Polytechnic National University
    Recently, deep learning technologies, namely Neural Networks [1], are attracting more and more attention from businesses and the scientific community, as they help optimize processes and find real solutions to problems much more efficiently and economically than many other approaches. In particular, Neural Networks are well suited for situations when you need to detect objects or look for similar patterns in videos and images, making them relevant in the field of information and measurement technologies in mechatronics and robotics. With the increasing number of robbed apartments and houses every year, addressing this issue has become one of the highest priorities in today’s society. By leveraging deep learning techniques, such as Neural Networks, in mechatronics and robotics, innovative solutions can be developed to enhance security systems, enabling more effective detection and prevention of apartment crimes. To evaluate the performance of our trained network, we conducted extensive experiments on a separate test dataset that was distinct from the training data. We meticulously labeled this dataset to obtain accurate ground truth annotations for comparison. By measuring precision scores, we determined the effectiveness of our model in detecting potential crimes. Our experiments yielded an accuracy rate of 97 % in the detection of potential crimes. This achievement demonstrates the capability of YOLO and the effectiveness of our trained network in accurately identifying criminal activities. The high accuracy rate indicates that our system can effectively assist in property protection efforts, providing a valuable tool for security personnel and law enforcement agencies.
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    Measurement and analysis of agricultural field state using cloud-based data processing pipeline Preventing potential robbery crimes using deep learning algorithm of data processing
    (Видавництво Львівської політехніки, 2023-02-28) Prodan, Roman; Shutka, Denys; Tataryn, Vasyl; Lviv Polytechnic National University
    The increasing demand for precision agriculture has prompted the integration of advanced technologies to optimize agricultural practices. This article presents an approach to agricultural field data processing using a cloud-based data pipeline. The system leverages data from various sensors deployed in the fields to collect real-time information on key parameters such as soil moisture, temperature, humidity, etc. The collected data is transmitted to the cloud where it undergoes a series of data processing and analysis stages. The article demonstrates the effectiveness of the cloud-based data pipeline in enhancing agricultural resilience. It facilitates prompt decision-making by farmers and stakeholders based on real-time data analysis. Additionally, the system offers a valuable tool for monitoring and optimizing irrigation strategies, resource allocation, and crop management practices. This research highlights the potential of cloud-based data pipelines in revolutionizing precision agriculture. The ability to measure and analyze agricultural field data accurately and efficiently opens new avenues for sustainable farming practices and mitigating risks related to wildfires and droughts.