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    Wyświetlanie 1-5 z 5
    Tytuł:
    Multicriteria optimization of medical institutions’ schedules on the basis of neuro fuzzy models and evolutionary algorithms
    Autorzy:
    Tkachenko, R.
    Kovalyshyn, O.
    Powiązania:
    https://bibliotekanauki.pl/articles/410857.pdf
    Data publikacji:
    2017
    Wydawca:
    Polska Akademia Nauk. Oddział w Lublinie PAN
    Tematy:
    scheduling of medical institutions
    optimization of schedules
    evolutionary algorithms
    multicriteria assessment
    neuro fuzzy models
    Opis:
    Taking into account the expansion of infrastructure and the growth of hospitals, as well as the increase in the influx of patients, the manual preparation of therapies, in particular, regenerative therapy, becomes ineffective and causes frequent dissatisfaction and complaining of patients. Taking into account the large number of factors forming the schedule, the task of multicriteria optimization is presented in accordance with strict restrictions and immediate wishes of patients. This task can be decomposed into several subtasks that require development of: a reference schedule that would satisfy the strict restrictions imposed by the domain; a method for evaluating the reference schedule and intermediate schedules; the method of optimization of the reference scheduling in order to improve the estimated results. In the course of solving these problems it is necessary: to carry out the construction of relevant criteria for evaluating the quality of the decomposition and turn their qualitative values into quantitative forms; carry out the transition from multi-criteria optimization to one-criterion by minimizing the set of evaluation criteria in the scalar value that can be used in the process of optimization; to avoid local optimum and reach the global optimal solution. The article is devised a method of multicriteria assessment and optimization of medical institutions’ schedules, based on the use of automatic theory to construct the reference scheduling of the functioning of the clinic, the application of methods and means of fuzzy logic and evolutionary algorithms. Using an automated system of construction, multicriteria assessment and optimization of schedules of medical institutions can reduce the amount of manual work, as well as increase the level of satisfaction of patients with the quality of regenerative therapy.
    Źródło:
    ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes; 2017, 6, 3; 53-59
    2084-5715
    Pojawia się w:
    ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A method of making up a clinic schedule with use of a finite-state automaton
    Autorzy:
    Tkachenko, R.
    Kovalyshyn, O.
    Powiązania:
    https://bibliotekanauki.pl/articles/411052.pdf
    Data publikacji:
    2016
    Wydawca:
    Polska Akademia Nauk. Oddział w Lublinie PAN
    Tematy:
    clinic
    schedule
    schedule construction
    abstract automaton
    finite-state automation
    Opis:
    The article investigates the problems of scheduling methods for making up the schedules for medical care institutions, with a view to further optimization the conditions and improvement the patient care. It is noted that a key step in the operation of medical institutions is the scheduling stage process. Depending on the specifics of a particular institution, work plans take different forms, mostly turning into a timetable. It is established that there are a number of scheduling methods, ranging from manual planning and mathematical programming with limitations, ending with the use of artificial intelligence. Most methods consist of two or more stages and initially require the construction of the very schedule that satisfies the strict limitations of the institution functioning. At a later stage the scheduling optimization of one or more criteria is regarded. The types of finite-state automata are analyzed and, with their use, developed a method for constructing the supporting schedules for clinics. An algorithm of finite-state machines for clinic schedule construction is established.
    Źródło:
    ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes; 2016, 5, 3; 131-134
    2084-5715
    Pojawia się w:
    ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Motion control system of autonomous mobile robot
    System sterowania ruchem autonomicznego robota mobilnego
    Autorzy:
    Tsmots, I.
    Vavruk, I.
    Tkachenko, R.
    Powiązania:
    https://bibliotekanauki.pl/articles/407642.pdf
    Data publikacji:
    2014
    Wydawca:
    Politechnika Lubelska. Wydawnictwo Politechniki Lubelskiej
    Tematy:
    robot mobilny
    system sterowania ruchem
    logika rozmyta
    regresja liniowa
    mobile robot
    motion control system
    fuzzy logic
    linear regression
    Opis:
    In this article the motion control system of autonomous mobile robot is described. Four motion modes: motion mode “to the target”, motion mode “obstacles avoidance”, motion mode “along the right wall” and motion mode “along the left wall” are implemented. A method for determining the effective rotation angle of mobile robot which is a linear combination of rotation angles which are obtained in different motion modes and activation coefficients is proposed. Fuzzy-oriented method with high accuracy and performance is used for motion modes implementation and for finding values of activation coefficients.
    W artykule opisano system sterowania ruchem autonomicznego robota mobilnego. Zaimplementowano cztery tryby ruchu: „do celu”, „unikanie przeszkód”, „wzdłuż prawej ściany” oraz „wzdłuż lewej ściany”. Zaproponowano metodę do określania efektywnego kąta obrotu robota, która jest liniową kombinacją kąta obrotu, który jest otrzymywany dla różnych trybów ruchu oraz współczynników aktywacji. Do implementacji trybów pracy oraz znalezienia wartości współczynników aktywacji użyto metodę rozmytą o dużej dokładności i wydajności.
    Źródło:
    Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska; 2014, 4; 89-93
    2083-0157
    2391-6761
    Pojawia się w:
    Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Ensemble-based Method of Fraud Detection at Self-checkouts in Retail
    Autorzy:
    Vitynskyi, P.
    Tkachenko, R.
    Izonin, I.
    Powiązania:
    https://bibliotekanauki.pl/articles/410756.pdf
    Data publikacji:
    2019
    Wydawca:
    Polska Akademia Nauk. Oddział w Lublinie PAN
    Tematy:
    classification
    Ensemble-based method
    Random Forest
    fraud detection
    retail
    Ito decomposition
    imbalanced dataset
    Opis:
    The authors consider the problem of fraud detection at self-checkouts in retail in condition of unbalanced data set. A new ensemble-based method is proposed for its effective solution. The developed method involves two main steps: application of the preprocessing procedures and the Random Forest algorithm. The step-by-step implementation of the preprocessing stage involves the sequential execution of such procedures over the input data: scaling by maximal element in a column with row-wise scaling by Euclidean norm, weighting by correlation and applying polynomial extension. For polynomial extension Ito decomposition of the second degree is used. The simulation of the method was carried out on real data. Evaluating performance was based on the use of cost matrix. The experimental comparison of the effectiveness of the developed ensemble-based method with a number of existing (simples and ensembles) demonstrates the best performance of the developed method. Experimental studies of changing the parameters of the Random Forest both for the basic algorithm and for the developed method demonstrate a significant improvement of the investigated efficiency measures of the latter. It is the result of all steps of the preprocessing stage of the developed method use.
    Źródło:
    ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes; 2019, 8, 2; 3-8
    2084-5715
    Pojawia się w:
    ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes
    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 1-5 z 5

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