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    Wyświetlanie 1-3 z 3
    Tytuł:
    Localization in wireless sensor networks: classification and evaluation of techniques
    Autorzy:
    Niewiadomska-Szynkiewicz, E.
    Powiązania:
    https://bibliotekanauki.pl/articles/331429.pdf
    Data publikacji:
    2012
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    sieć sensorowa
    sieć bezprzewodowa
    sieć ad hoc
    system lokalizacji
    symulacja sieci
    wireless sensor network
    ad hoc networks
    localization
    location systems
    network simulation
    Opis:
    Recent advances in technology have enabled the development of low cost, low power and multi functional wireless sensing devices. These devices are networked through setting up a Wireless Sensor Network (WSN). Sensors that form a WSN are expected to be remotely deployed in large numbers and to self-organize to perform distributed sensing and acting tasks. WSNs are growing rapidly in both size and complexity, and it is becoming increasingly difficult to develop and investigate such large and complex systems. In this paper we provide a brief introduction to WSN applications, i.e., properties, limitations and basic issues related to WSN design and development. We focus on an important aspect of the design: accurate localization of devices that form the network. The paper presents an overview of localization strategies and attempts to classify different techniques. A set of properties by which localization systems are evaluated are examined. We then describe a number of existing localization systems, and discuss the results of performance evaluation of some of them through simulation and experiments using a testbed implementation.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2012, 22, 2; 281-297
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Ensemble learning techniques for transmission quality classification in a Pay&Require multi-layer network
    Autorzy:
    Żelasko, Dariusz
    Pławiak, Paweł
    Powiązania:
    https://bibliotekanauki.pl/articles/1838182.pdf
    Data publikacji:
    2021
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    Pay&Require
    ensemble learning
    machine learning
    resource allocation
    QoS
    uczenie zespołowe
    uczenie maszynowe
    alokacja zasobu
    Opis:
    Due to a continuous increase in the use of computer networks, it has become important to ensure the quality of data transmission over the network. The key issue in the quality assurance is the translation of parameters describing transmission quality to a certain rating scale. This article presents a technique that allows assessing transmission quality parameters. Thanks to the application of machine learning, it is easy to translate transmission quality parameters, i.e., delay, bandwidth, packet loss ratio and jitter, into a scale understandable by the end user. In this paper we propose six new ensembles of classifiers. Each classification algorithm is combined with preprocessing, cross-validation and genetic optimization. Most ensembles utilize several classification layers in which popular classifiers are used. For the purpose of the machine learning process, we have created a data set consisting of 100 samples described by four features, and the label which describes quality. Our previous research was conducted with respect to single classifiers. The results obtained now, in comparison with the previous ones, are satisfactory—high classification accuracy is reached, along with 94% sensitivity (overall accuracy) with 6/100 incorrect classifications. The suggested solution appears to be reliable and can be successfully applied in practice.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2021, 31, 1; 135-153
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Data-driven techniques for the fault diagnosis of a wind turbine benchmark
    Autorzy:
    Simani, S.
    Farsoni, S.
    Castaldi, P.
    Powiązania:
    https://bibliotekanauki.pl/articles/330715.pdf
    Data publikacji:
    2018
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    fault diagnosis
    analytical redundancy
    fuzzy system
    neural network
    residual generator
    fault estimation
    wind turbine benchmark
    diagnostyka uszkodzeń
    redundancja analityczna
    system rozmyty
    sieć neuronowa
    estymacja błędu
    turbina wiatrowa
    Opis:
    This paper deals with the fault diagnosis of wind turbines and investigates viable solutions to the problem of earlier fault detection and isolation. The design of the fault indicator, i.e., the fault estimate, involves data-driven approaches, as they can represent effective tools for coping with poor analytical knowledge of the system dynamics, together with noise and disturbances. In particular, the proposed data-driven solutions rely on fuzzy systems and neural networks that are used to describe the strongly nonlinear relationships between measurement and faults. The chosen architectures rely on nonlinear autoregressive models with exogenous input, as they can represent the dynamic evolution of the system along time. The developed fault diagnosis schemes are tested by means of a high-fidelity benchmark model that simulates the normal and the faulty behaviour of a wind turbine. The achieved performances are also compared with those of other model-based strategies from the related literature. Finally, a Monte-Carlo analysis validates the robustness and the reliability of the proposed solutions against typical parameter uncertainties and disturbances.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2018, 28, 2; 247-268
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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