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Wyszukujesz frazę "Havrysh, B." wg kryterium: Autor

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    Wyświetlanie 1-2 z 2
    Tytuł:
    Design of modern tools for digital output raster scanning
    Autorzy:
    Yushchyk, O.
    Havrysh, B.
    Tymchenko, O.
    Szturo, K.
    Powiązania:
    https://bibliotekanauki.pl/articles/298277.pdf
    Data publikacji:
    2018
    Wydawca:
    Uniwersytet Warmińsko-Mazurski w Olsztynie
    Tematy:
    test-object
    resolution
    output scanning device
    Opis:
    Quality control at all stages of the polygraphic process will allow to establish a feedback between technological processes of printing. The validity of the selected criteria, technological effectiveness and objectivity of the quality assessment methods will allow to organise technically accurate modelling of the processes as well as to promptly interfere with the production process if necessary. The tendency of the development of technologies and control tools as well as computer expansion are the reasons why the objectivity and impartiality of the assessment become the main criteria for the choice of method for quality assessment of the polygrafic product. Methodological purpose of the quality control is to make the polygrafic process technologically driven and stable, and the quality of the received print - more predictable.
    Źródło:
    Technical Sciences / University of Warmia and Mazury in Olsztyn; 2018, 21(2); 157-163
    1505-4675
    2083-4527
    Pojawia się w:
    Technical Sciences / University of Warmia and Mazury in Olsztyn
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Boosting-based model for solving Sm-Co alloy’s maximum energy product prediction task
    Autorzy:
    Trostianchyn, A.M.
    Izonin, I.V.
    Duriagina, Z.A.
    Tkachenko, R.O.
    Kulyk, V.V.
    Havrysh, B.M.
    Powiązania:
    https://bibliotekanauki.pl/articles/24200577.pdf
    Data publikacji:
    2022
    Wydawca:
    Stowarzyszenie Komputerowej Nauki o Materiałach i Inżynierii Powierzchni w Gliwicach
    Tematy:
    Sm-Co alloy
    ensemble learning
    gradient boosting
    prediction accuracy
    Stop Sm-Co
    uczenie zespołowe
    dokładność przewidywania
    Opis:
    Purpose: This paper aims to decide the Sm-Co alloy’s maximum energy product prediction task based on the boosting strategy of the ensemble of machine learning methods. Design/methodology/approach: This paper examines an ensemble-based approach to solving Sm-Co alloy’s maximum energy product prediction task. Because classical machine learning methods sometimes do not supply acceptable precision when solving the regression problem, the authors investigated the boosting ML model, namely Gradient Boosting. Building a boosting model based on several weak submodels, each of which considers the errors of the prior ones, provides substantial growth in the accuracy of the problem-solving. The obtained result is confirmed using an actual data set collected by the authors. Findings: This work demonstrates the high efficiency of applying the ensemble strategy of machine learning to the applied problem of materials science. The experiments determined the highest accuracy of solving the forecast task for the maximum energy product of Sm-Co alloy formed on the boosting model of machine learning in comparison with classical methods of machine learning. Research limitations/implications: The boosting strategy of machine learning, in comparison with single algorithms of machine learning, requires much more computational and time resources to implement the learning process of the model. Practical implications: This work demonstrated the possibility of effectively solving Sm-Co alloy’s maximum energy product prediction task using machine learning. The studied boosting model of machine learning for solving the problem provides high accuracy of prediction, which reveals several advantages of their use in solving issues applied to computational material science. Furthermore, using the Orange modelling environment provides a simple and intuitive interface for using the researched methods. The proposed approach to the forecast significantly reduces the time and resource costs associated with studying expensive rare earth metals (REM)-based ferromagnetic materials. value: The authors have collected and formed a set of data on predicting the maximum energy product of the Sm-Co alloy. We used machine learning tools to solve the task. As a result, the most increased forecasting precision based on the boosting model is demonstrated compared to classical machine learning methods.
    Źródło:
    Archives of Materials Science and Engineering; 2022, 116, 2; 71--80
    1897-2764
    Pojawia się w:
    Archives of Materials Science and Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-2 z 2

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