Informacja

Drogi użytkowniku, aplikacja do prawidłowego działania wymaga obsługi JavaScript. Proszę włącz obsługę JavaScript w Twojej przeglądarce.

English (EN)·Polski (PL)·Yкраїнський (UK)
Przejdź do strony domowej biblioteki Centralna Biblioteka Wojskowa Link otwiera się w nowym oknie Logo biblioteki Prolib Integro - strona głównaCentralna Biblioteka Wojskowa

Wyszukujesz frazę "optymalizacja sieci" wg kryterium: Temat

  Lista filtrów
Wyrównaj
    Wyświetlanie 1-4 z 4
    Tytuł:
    Simultaneous routing and flow rate optimization in energy-aware computer networks
    Autorzy:
    Jaskóła, P.
    Arabas, P.
    Karbowski, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/331329.pdf
    Data publikacji:
    2016
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    MINLP
    MIQP
    network optimization
    green networking
    fairness
    multicriteria
    optymalizacja sieci
    wielokryterialność
    Opis:
    The issue of energy-aware traffic engineering has become prominent in telecommunications industry in the last years. This paper presents a two-criteria network optimization problem, in which routing and bandwidth allocation are determined jointly, so as to minimize the amount of energy consumed by a telecommunication infrastructure and to satisfy given demands represented by a traffic matrix. A scalarization of the criteria is proposed and the choice of model parameters is discussed in detail. The model of power dissipation as a function of carried traffic in a typical software router is introduced. Then the problem is expressed in a form suitable for the mixed integer quadratic programming (MIQP) solver. The paper is concluded with a set of small, illustrative computational examples. Computed solutions are implemented in a testbed to validate the accuracy of energy consumption models and the correctness of the proposed traffic engineering algorithm.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2016, 26, 1; 231-243
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Exact and approximation algorithms for sensor placement against DDoS attacks
    Autorzy:
    Junosza-Szaniawski, Konstanty
    Nogalski, Dariusz
    Rzążewski, Paweł
    Powiązania:
    https://bibliotekanauki.pl/articles/2055151.pdf
    Data publikacji:
    2022
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    DDoS
    sensor placement
    network safety optimization
    umiejscowienie czujnika
    optymalizacja bezpieczeństwa sieci
    Opis:
    In a distributed denial of service (DDoS) attack, the attacker gains control of many network users through a virus. Then the controlled users send many requests to a victim, leading to its resources being depleted. DDoS attacks are hard to defend because of their distributed nature, large scale and various attack techniques. One possible mode of defense is to place sensors in a network that can detect and stop an unwanted request. However, such sensors are expensive, as a result of which there is a natural question as to the minimum number of sensors and their optimal placement required to get the necessary level of safety. Presented below are two mixed integer models for optimal sensor placement against DDoS attacks. Both models lead to a trade-off between the number of deployed sensors and the volume of uncontrolled flow. Since the above placement problems are NP-hard, two efficient heuristics are designed, implemented and compared experimentally with exact mixed integer linear programming solvers.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2022, 32, 1; 35--49
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Learning the naive Bayes classifier with optimization models
    Autorzy:
    Taheri, S.
    Mammadov, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/908351.pdf
    Data publikacji:
    2013
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    Bayesian networks
    naive Bayes classifier
    optimization
    discretization
    sieci bayesowskie
    naiwny klasyfikator Bayesa
    optymalizacja
    dyskretyzacja
    Opis:
    Naive Bayes is among the simplest probabilistic classifiers. It often performs surprisingly well in many real world applications, despite the strong assumption that all features are conditionally independent given the class. In the learning process of this classifier with the known structure, class probabilities and conditional probabilities are calculated using training data, and then values of these probabilities are used to classify new observations. In this paper, we introduce three novel optimization models for the naive Bayes classifier where both class probabilities and conditional probabilities are considered as variables. The values of these variables are found by solving the corresponding optimization problems. Numerical experiments are conducted on several real world binary classification data sets, where continuous features are discretized by applying three different methods. The performances of these models are compared with the naive Bayes classifier, tree augmented naive Bayes, the SVM, C4.5 and the nearest neighbor classifier. The obtained results demonstrate that the proposed models can significantly improve the performance of the naive Bayes classifier, yet at the same time maintain its simple structure.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2013, 23, 4; 787-795
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Optimization of pin fin heat sink by application of CFD simulations and DOE methodology with neural network approximation
    Autorzy:
    Kasza, K.
    Malinowski, Ł.
    Królikowski, I.
    Powiązania:
    https://bibliotekanauki.pl/articles/265135.pdf
    Data publikacji:
    2013
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    wymiana ciepła
    optymalizacja projektu
    radiator
    sieci neuronowe
    modelowanie numeryczne
    polimery
    heat transfer
    design optimization
    heat sink
    neural network approximation
    numerical modeling
    thermally conductive polymer
    Opis:
    A design optimization of a staggered pin fin heat sink made of a thermally conductive polymer is presented. The influence of several design parameters like the pin fin height, the diameter, or the number of pins on thermal efficiency of the natural convection heat sink is studied. A limited number of representative heat sink designs were selected by application of the design of experiments (DOE) methodology and their thermal efficiency was evaluated by application of the antecedently validated and verified numerical model. The obtained results were utilized for the development of a response surface and a typical polynomial model was replaced with a neural network approximation. The particle swarm optimization (PSO) algorithm was applied for the neural network training providing very accurate characterization of the heat sink type under consideration. The quasi-complete search of defined solution domain was then performed and the different heat sink designs were compared by means of thermal performance metrics, i.e., array, space claim and mass based heat transfer coefficients. The computational fluid dynamics (CFD) calculations were repeated for the most effective heat sink designs.
    Źródło:
    International Journal of Applied Mechanics and Engineering; 2013, 18, 2; 365-381
    1734-4492
    2353-9003
    Pojawia się w:
    International Journal of Applied Mechanics and Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-4 z 4

      Ta witryna wykorzystuje pliki cookies do przechowywania informacji na Twoim komputerze. Pliki cookies stosujemy w celu świadczenia usług na najwyższym poziomie, w tym w sposób dostosowany do indywidualnych potrzeb. Korzystanie z witryny bez zmiany ustawień dotyczących cookies oznacza, że będą one zamieszczane w Twoim komputerze. W każdym momencie możesz dokonać zmiany ustawień dotyczących cookies