Advances In Cyber-Physical Systems. – 2023. – Vol. 8, No. 2

Permanent URI for this collectionhttps://ena.lpnu.ua/handle/ntb/61332

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Засновник і видавець Національний університет «Львівська політехніка». Виходить двічі на рік з 2016 року.

Advances in Cyber-Physical Systems / Lviv Polytechnic National University ; editor-in-chief A. Melnyk. – Lviv : Lviv Politechnic Publishing House, 2023. – Volume 8, number 2. – Р. 81–151 : il.

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    An Alternative to Vending Machines
    (Видавництво Львівської політехніки, 2023-02-28) Lozynskyi, Yurii; Yurchak, Iryna; Lviv Polytechnic National University
    In this review article for a smart vending refrigerator, the contours of the future device are thought out and outlined and all its advantages are described. This device will be controlled using Computer Vision and some other features. The main control unit will be Raspberry PI, since it is the best for this device. Also, a web application was developed in which the user registers, and the application itself transmits the user's information through an API that will be developed to communicate with the web server, and the web server will store this information. This article will analyze the systems that have been already on the market and their pros and cons, as well as consider the design, implementation and functionality of a smart vending refrigerator. Also, the paper will consider key requirements for this system, technologies used, and approaches to integration with existing infrastructures. This design plays an important role in providing comfort and productivity in various fields of activity and can be applied in many areas.
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    Cloud Computing With Resource Allocation Based on Ant Colony Optimization
    (Видавництво Львівської політехніки, 2023-02-28) Kniazhyk, Taras; Muliarevych, Oleksandr; Lviv Polytechnic National University
    In this study, we explore the intricacies of cloud computing technologies, with an emphasis on the challenges and concerns pertinent to resource allocation. Three optimization techniques-Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Genetic Algorithm (GA)-have been meticulously analyzed concerning their applications, objectives, and operational methodologies. The study underscores these algorithms' pivotal role in enhancing cloud resource optimization, while also elucidating their respective merits and limitations. As the complexity of cloud computing escalates, devising efficacious strategies for resource management and allocation becomes imperative. Such strategies are paramount in aiding organizations in cost containment and performance amplification. The ensuing comparative analysis has been crafted to offer a holistic insight into the three algorithms, thus empowering cloud providers to judiciously select an optimization technique that aligns with the unique demands and challenges of their cloud computing infrastructure.