Перегляд за Автор "Siemens, Eduard"

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    Automated workflows for assessing the quality of functioning of scientific and educational institutions
    (National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", 2023) Novogrudska, Rina L.; Siemens, Eduard; Pidnebesnyy, Andrii V.; Shelest, Yegor V.
    Background. The effectiveness of functioning is a particularly important characteristic for organizations involved in the field of science and education, since the future level of state development and the life of its citizens depends on the quality of the services they provide. There are certain generally accepted principles of such organizations evaluation that is set by relevant normative documents. Nowadays important tasks are optimization and automatization of such processes. Objective. The aim of the paper is to propose and develop a workflow model for automating and optimizing the procedure for evaluating scientific and educational institutions. Methods. We use approaches to business process modelling and basic principles of workflow modelling, namely BPMN notation was chosen as the notation to model elements of workflows for assessing the quality of scientific and educational institutions functioning, and Camunda Modeler was used as the development environment. Results. Conceptual model of the work process for assessing the quality of scientific and educational institutions functioning was proposed, which made it possible to generalize and formalize such an assessment process. Developed using BPMN models of workflow were used to test proposed conceptual model and prove its effectiveness for automating the process of assessing the quality of scientific and educational institutions functioning. Conclusions. Mechanism of modelling the process of assessing the quality of scientific and educational institutions functioning proposed in the paper gave basis and means for such process optimization and automatization.
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    Conditionally Infinite Telecommunication Resource for Subscribers
    (Springer, 2021) Globa, Larysa; Skulysh, Mariia; Siemens, Eduard
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    Developing a computer vision re-identification system
    (National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, 2020) Ostapenko, Maksym S.; Shtogrina, Olena S.; Globa, Larysa S.; Astrakhantsev, Andrii A.; Siemens, Eduard
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    Internet of things data transfer method using neural network autoencoder
    (National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", 2023) Siemens, Eduard; Kurdecha, Vasyl V.; Ushakov, Serhii M.
    Background. The number of devices in the Internet of Things is constantly increasing. At the same time, the number of solutions on the market for such technologies is growing. Statistics confirm that these factors lead to an increase in data transfer volumes. This raises the number of resources spent on data transmission. The growing trend in the number of users of the Internet of Things technology leads to the emergence of the problem of a rapid increase in the data transmitted by the network. Objective. The purpose of the paper is to improve the process of data transmission in the Internet of Things by modifying the neural network autoencoder to reduce network resources use. Methods. Analysis of publications dedicated to Internet of things data transmission. Integration of existing data coding solutions based on a neural network autoencoder in the process of transmitting data from the Internet of things. Results. The neural network autoencoder has been improved by using an algorithm that additionally includes an arithmetic encoder and further training a new model on the output of a full-fledged autoencoder. Conclusions. The process of data transmission in the Internet of Things network has been modified by improving the neural network autoencoder by using the training of a smaller neural network on the initial data of the main autoencoder, which has reduced the amount of data transmitted and, accordingly, reduced the use of network resources.
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    Resource Sharing Challenge for Micro Operator Pattern in 5G SDN/NFV Network
    (Edition Hochschule Anhalt, 2020) Skulysh, Mariia; Globa, Larysa; Siemens, Eduard