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    Tytuł:
    Fatigue Life Predictions of Metal Matrix Composites Using Artificial Neural Networks
    Przewidywania trwałości zmęczeniowej kompozytów metalowych przy użyciu sztucznych sieci neuronowych
    Autorzy:
    Uygur, I.
    Cicek, A.
    Toklu, E.
    Kara, R.
    Saridemir, S.
    Powiązania:
    https://bibliotekanauki.pl/articles/355098.pdf
    Data publikacji:
    2014
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    MMCs
    fatigue life prediction
    artificial neural networks
    przewidywania trwałości zmęczeniowej
    sztuczne sieci neuronowe
    Opis:
    In this study, fatigue life predictions for the various metal matrix composites, R ratios, notch geometries, and different temperatures have been performed by using artificial neural networks (ANN) approach. Input parameters of the model comprise various materials (M), such as particle size and volume fraction of reinforcement, stress concentration factor (Kt), R ratio (R), peak stress (S), temperatures (T), whereas, output of the ANN model consist of number of failure cycles. ANN controller was trained with Levenberg-Marquardt (LM) learning algorithm. The tested actual data and predicted data were simulated by a computer program developed on MATLAB platform. It is shown that the model provides intimate fatigue life estimations compared with actual tested data.
    Zastosowano sztuczne sieci neuronowe (ANN) do przewidywania trwałości zmęczeniowej dla różnych kompozytów metalowych, parametrów R, geometrii karbu, i różnych temperatur. Parametry wejściowe modelu obejmowały: różne materiały (M), o różnym rozmiarze cząstek i objętosci frakcji zbrojącej, współczynnik koncentracji naprężeń (Kt), stosunek parametru R (R), naprężenie szczytowe (S), temperaturę (T), natomiast dane wyjściowe składały się z liczby cykli awarii (SSN). Kontroler ANN był trenowany z użyciem algorytmu uczenia Levenberga-Marquardta (LM). Badane dane rzeczywiste i dane przewidywane symulowane były przez program komputerowy opracowany na platformie MATLAB. Wykazano, że model zapewnia oszacowanie trwałości zmęczeniowej bliską rzeczywistym danym badanym.
    Źródło:
    Archives of Metallurgy and Materials; 2014, 59, 1; 97-103
    1733-3490
    Pojawia się w:
    Archives of Metallurgy and Materials
    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
    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:
    Development of models to study traffic accidents on the final sections of access roads to the cities: a case study of three major Iranian cities
    Autorzy:
    Fallah Tafti, Mehdi
    Roshani, Reza
    Powiązania:
    https://bibliotekanauki.pl/articles/1955155.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    traffic accidents
    city access roads
    entrance roads
    traffic safety
    accident prediction model
    neural networks
    wypadki drogowe
    drogi dojazdowe do miasta
    drogi wjazdowe
    bezpieczeństwo na drodze
    modele przewidywania wypadków
    sieci neuronowe
    Opis:
    The final sections of main access roads to the cities require especial attention as the frequency of accidents in these road sections are considerably higher than other parts of interurban roads. These road sections operate as an interface between the rural roads and urban streets. The previous researches available on this subject are limited and they have also mainly focused on a narrow range of factors contributing to the accidents in these areas. The main contribution of this research is to consider a relatively comprehensive range of potential factors , and to examine their impacts through the development and comparison of both conventional probabilistic models and Artificial Neural Network (ANN) models. For this purpose, information related to the main access roads of three major Iranian cities were collected. This information consisted of accident frequency data together with the field observations of traffic characteristics, roadway conditions and roadside features of these roads. Various ANN and probabilistic models were developed. The frequency of accidents, i.e. fatal, injured, or damaged accidents, was considered as the output of the developed models. The results indicated that a hybrid of ANN models, each comprised of 10 input variables representing traffic, roadway and roadside conditions, outperformed several probabilistic models, i.e. Poisson, Negative binomial, Zero-truncated Poisson, and Zero-truncated Negative Binomial models, also developed under similar conditions in this study. Moreover, effective roadway width, roadway lighting condition, the standard deviation of vehicles speed, percentage of drivers violating the speed limit, average annual daily traffic, percentage of heavy goods vehicles, the density of roadside commercial and industrial land uses, the density of median U-turns, the density of local access roads, and the effective width of the left-side shoulder were identified as the most effective factors contributing to the accidents in these areas. The developed ANN model can be used as a tool to predict accident rates in these road sections, and to estimate a potential reduction in the accident rates, following any improvements in the major factors contributing to the traffic accidents in these areas.
    Źródło:
    Archives of Transport; 2021, 59, 3; 129-148
    0866-9546
    2300-8830
    Pojawia się w:
    Archives of Transport
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-5 z 5

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