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    Wyświetlanie 1-4 z 4
    Tytuł:
    Implementation of a Web-based remote control system for qZS DAB application using low-cost ARM platform
    Autorzy:
    Korzeniewski, M.
    Kulikowski, K.
    Zakis, J.
    Jasiński, M.
    Malinowski, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/201276.pdf
    Data publikacji:
    2016
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    ARM processor
    DAB converter controller
    network controller
    web-based user interface
    energy control
    procesor ARM
    kontroler
    konwerter
    kontroler sieciowy
    interfejs użytkownika oparty na sieci Web
    kontrola energii
    DAB
    Opis:
    Continuous development of intelligent network applications drives the demand for deployment-ready hardware and software solutions. Such solutions are highly valued not only by distributed producers of energy but by energy consumers as well. The use of intelligent network applications enables the development and improvement of the quality of services. It also increases self-sufficiency and efficiency. This paper describes an example of such device that allows for the control of a dual active bridge (DAB) converter and enables its remote control in real time over an IP-based network. The details of both hardware and software components of proposed implementation are provided. The DAB converter gives a possibility to control and manage the energy between two DC power systems with very different voltage levels. Not only information, but also the quality of energy, the direction of power flow, and energy storage systems can be easily controlled through an IP-based network and power electronics converters. Information technology, together with intelligent control of power electronics technology, provides a flexible solution, especially for sustainable smart grids.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2016, 64, 4; 887-896
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Execution time prediction model for parallel GPU realizations of discrete transforms computation algorithms
    Autorzy:
    Puchala, Dariusz
    Stokfiszewski, Kamil
    Wieloch, Kamil
    Powiązania:
    https://bibliotekanauki.pl/articles/2173537.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    graphics processing unit
    GPU
    execution time prediction model
    discrete wavelet transform
    DWT
    lattice structure
    convolution-based approach
    orthogonal transform
    orthogonal filter banks
    time effectiveness
    prediction accuracy
    procesor graficzny
    model przewidywania czasu wykonania
    dyskretna transformata falkowa
    struktura sieciowa
    podejście oparte na splotach
    przekształcenia ortogonalne
    ortogonalne banki filtrów
    efektywność czasowa
    dokładność przewidywania
    Opis:
    Parallel realizations of discrete transforms (DTs) computation algorithms (DTCAs) performed on graphics processing units (GPUs) play a significant role in many modern data processing methods utilized in numerous areas of human activity. In this paper the authors propose a novel execution time prediction model, which allows for accurate and rapid estimation of execution times of various kinds of structurally different DTCAs performed on GPUs of distinct architectures, without the necessity of conducting the actual experiments on physical hardware. The model can serve as a guide for the system analyst in making the optimal choice of the GPU hardware solution for a given computational task involving particular DT calculation, or can help in choosing the best appropriate parallel implementation of the selected DT, given the limitations imposed by available hardware. Restricting the model to exhaustively adhere only to the key common features of DTCAs enables the authors to significantly simplify its structure, leading consequently to its design as a hybrid, analytically–simulational method, exploiting jointly the main advantages of both of the mentioned techniques, namely: time-effectiveness and high prediction accuracy, while, at the same time, causing mutual elimination of the major weaknesses of both of the specified approaches within the proposed solution. The model is validated experimentally on two structurally different parallel methods of discrete wavelet transform (DWT) computation, i.e. the direct convolutionbased and lattice structure-based schemes, by comparing its prediction results with the actual measurements taken for 6 different graphics cards, representing a fairly broad spectrum of GPUs compute architectures. Experimental results reveal the overall average execution time and prediction accuracy of the model to be at a level of 97.2%, with global maximum prediction error of 14.5%, recorded throughout all the conducted experiments, maintaining at the same time high average evaluation speed of 3.5 ms for single simulation duration. The results facilitate inferring the model generality and possibility of extrapolation to other DTCAs and different GPU architectures, which along with the proposed model straightforwardness, time-effectiveness and ease of practical application, makes it, in the authors’ opinion, a very interesting alternative to the related existing solutions.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2022, 70, 1; e139393, 1--30
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Execution time prediction model for parallel GPU realizations of discrete transforms computation algorithms
    Autorzy:
    Puchala, Dariusz
    Stokfiszewski, Kamil
    Wieloch, Kamil
    Powiązania:
    https://bibliotekanauki.pl/articles/2173635.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    graphics processing unit
    GPU
    execution time prediction model
    discrete wavelet transform
    DWT
    lattice structure
    convolution-based approach
    orthogonal transform
    orthogonal filter banks
    time effectiveness
    prediction accuracy
    procesor graficzny
    model przewidywania czasu wykonania
    dyskretna transformata falkowa
    struktura sieciowa
    podejście oparte na splotach
    przekształcenia ortogonalne
    ortogonalne banki filtrów
    efektywność czasowa
    dokładność przewidywania
    Opis:
    Parallel realizations of discrete transforms (DTs) computation algorithms (DTCAs) performed on graphics processing units (GPUs) play a significant role in many modern data processing methods utilized in numerous areas of human activity. In this paper the authors propose a novel execution time prediction model, which allows for accurate and rapid estimation of execution times of various kinds of structurally different DTCAs performed on GPUs of distinct architectures, without the necessity of conducting the actual experiments on physical hardware. The model can serve as a guide for the system analyst in making the optimal choice of the GPU hardware solution for a given computational task involving particular DT calculation, or can help in choosing the best appropriate parallel implementation of the selected DT, given the limitations imposed by available hardware. Restricting the model to exhaustively adhere only to the key common features of DTCAs enables the authors to significantly simplify its structure, leading consequently to its design as a hybrid, analytically–simulational method, exploiting jointly the main advantages of both of the mentioned techniques, namely: time-effectiveness and high prediction accuracy, while, at the same time, causing mutual elimination of the major weaknesses of both of the specified approaches within the proposed solution. The model is validated experimentally on two structurally different parallel methods of discrete wavelet transform (DWT) computation, i.e. the direct convolutionbased and lattice structure-based schemes, by comparing its prediction results with the actual measurements taken for 6 different graphics cards, representing a fairly broad spectrum of GPUs compute architectures. Experimental results reveal the overall average execution time and prediction accuracy of the model to be at a level of 97.2%, with global maximum prediction error of 14.5%, recorded throughout all the conducted experiments, maintaining at the same time high average evaluation speed of 3.5 ms for single simulation duration. The results facilitate inferring the model generality and possibility of extrapolation to other DTCAs and different GPU architectures, which along with the proposed model straightforwardness, time-effectiveness and ease of practical application, makes it, in the authors’ opinion, a very interesting alternative to the related existing solutions.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2022, 70, 1; art. no. e139393
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Execution time prediction model for parallel GPU realizations of discrete transforms computation algorithms
    Autorzy:
    Puchala, Dariusz
    Stokfiszewski, Kamil
    Wieloch, Kamil
    Powiązania:
    https://bibliotekanauki.pl/articles/2173636.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    graphics processing unit
    GPU
    execution time prediction model
    discrete wavelet transform
    DWT
    lattice structure
    convolution-based approach
    orthogonal transform
    orthogonal filter banks
    time effectiveness
    prediction accuracy
    procesor graficzny
    model przewidywania czasu wykonania
    dyskretna transformata falkowa
    struktura sieciowa
    podejście oparte na splotach
    przekształcenia ortogonalne
    ortogonalne banki filtrów
    efektywność czasowa
    dokładność przewidywania
    Opis:
    Parallel realizations of discrete transforms (DTs) computation algorithms (DTCAs) performed on graphics processing units (GPUs) play a significant role in many modern data processing methods utilized in numerous areas of human activity. In this paper the authors propose a novel execution time prediction model, which allows for accurate and rapid estimation of execution times of various kinds of structurally different DTCAs performed on GPUs of distinct architectures, without the necessity of conducting the actual experiments on physical hardware. The model can serve as a guide for the system analyst in making the optimal choice of the GPU hardware solution for a given computational task involving particular DT calculation, or can help in choosing the best appropriate parallel implementation of the selected DT, given the limitations imposed by available hardware. Restricting the model to exhaustively adhere only to the key common features of DTCAs enables the authors to significantly simplify its structure, leading consequently to its design as a hybrid, analytically–simulational method, exploiting jointly the main advantages of both of the mentioned techniques, namely: time-effectiveness and high prediction accuracy, while, at the same time, causing mutual elimination of the major weaknesses of both of the specified approaches within the proposed solution. The model is validated experimentally on two structurally different parallel methods of discrete wavelet transform (DWT) computation, i.e. the direct convolutionbased and lattice structure-based schemes, by comparing its prediction results with the actual measurements taken for 6 different graphics cards, representing a fairly broad spectrum of GPUs compute architectures. Experimental results reveal the overall average execution time and prediction accuracy of the model to be at a level of 97.2%, with global maximum prediction error of 14.5%, recorded throughout all the conducted experiments, maintaining at the same time high average evaluation speed of 3.5 ms for single simulation duration. The results facilitate inferring the model generality and possibility of extrapolation to other DTCAs and different GPU architectures, which along with the proposed model straightforwardness, time-effectiveness and ease of practical application, makes it, in the authors’ opinion, a very interesting alternative to the related existing solutions.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2022, 70, 1; art. no. e139393
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-4 z 4

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