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    Wyświetlanie 1-3 z 3
    Tytuł:
    Modelling of lake waves to simulate environmental disturbance to a scale ship model
    Autorzy:
    Rak, Andrzej
    Miller, Anna
    Powiązania:
    https://bibliotekanauki.pl/articles/34614624.pdf
    Data publikacji:
    2023
    Wydawca:
    Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
    Tematy:
    wave modeling
    environmental disturbances
    scale ship model
    ship motion simulation
    Opis:
    In the development of ship motion control systems, software simulations or scale model experiments in pools or open water are very often carried out in the verification and testing stages. This paper describes the process of building a software wave simulator based on data gathered on the Silm Lake near Iława, Poland, where scale ship models are used for research and training. The basis of the simulator structure is a set of shaping filters fed with Gaussian white noise. These filters are built in the form of transfer functions generating irregular wave signals for different input wind forces. To enable simulation of a wide range of wind speeds, nonlinear interpolation is used. The lake wave simulation method presented in this paper fills a gap in current research, and enables accurate modelling of characteristic environmental disturbances on a small lake for motion control experiments of scale model ships.
    Źródło:
    Polish Maritime Research; 2023, 3; 12-21
    1233-2585
    Pojawia się w:
    Polish Maritime Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A new method of cardiac sympathetic index estimation using a 1D-convolutional neural network
    Autorzy:
    Kołodziej, Marcin
    Majkowski, Andrzej
    Tarnowski, Paweł
    Rak, Remigiusz Jan
    Rysz, Andrzej
    Powiązania:
    https://bibliotekanauki.pl/articles/2090741.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    epilepsy
    seizure detection
    seizure prediction
    convolutional neural network
    deep learning
    ECG
    HRV
    cardiac sympathetic index
    padaczka
    wykrywanie napadu
    przewidywanie napadu
    splotowa sieć neuronowa
    głęboka nauka
    technika deep learning
    EKG
    wskaźnik współczulny serca
    Opis:
    Epilepsy is a neurological disorder that causes seizures of many different types. The article presents an analysis of heart rate variability (HRV) for epileptic seizure prediction. Considering that HRV is nonstationary, our research focused on the quantitative analysis of a Poincare plot feature, i.e. cardiac sympathetic index (CSI). It is reported that the CSI value increases before the epileptic seizure. An algorithm using a 1D-convolutional neural network (1D-CNN) was proposed for CSI estimation. The usability of this method was checked for 40 epilepsy patients. Our algorithm was compared with the method proposed by Toichi et al. The mean squared error (MSE) for testing data was 0.046 and the mean absolute percentage error (MAPE) amounted to 0.097. The 1D-CNN algorithm was also compared with regression methods. For this purpose, a classical type of neural network (MLP), as well as linear regression and SVM regression, were tested. In the study, typical artifacts occurring in ECG signals before and during an epileptic seizure were simulated. The proposed 1D-CNN algorithm estimates CSI well and is resistant to noise and artifacts in the ECG signal.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 3; e136921, 1--9
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A new method of cardiac sympathetic index estimation using a 1D-convolutional neural network
    Autorzy:
    Kołodziej, Marcin
    Majkowski, Andrzej
    Tarnowski, Paweł
    Rak, Remigiusz Jan
    Rysz, Andrzej
    Powiązania:
    https://bibliotekanauki.pl/articles/2173565.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    epilepsy
    seizure detection
    seizure prediction
    convolutional neural network
    deep learning
    ECG
    HRV
    cardiac sympathetic index
    padaczka
    wykrywanie napadu
    przewidywanie napadu
    splotowa sieć neuronowa
    głęboka nauka
    technika deep learning
    EKG
    wskaźnik współczulny serca
    Opis:
    Epilepsy is a neurological disorder that causes seizures of many different types. The article presents an analysis of heart rate variability (HRV) for epileptic seizure prediction. Considering that HRV is nonstationary, our research focused on the quantitative analysis of a Poincare plot feature, i.e. cardiac sympathetic index (CSI). It is reported that the CSI value increases before the epileptic seizure. An algorithm using a 1D-convolutional neural network (1D-CNN) was proposed for CSI estimation. The usability of this method was checked for 40 epilepsy patients. Our algorithm was compared with the method proposed by Toichi et al. The mean squared error (MSE) for testing data was 0.046 and the mean absolute percentage error (MAPE) amounted to 0.097. The 1D-CNN algorithm was also compared with regression methods. For this purpose, a classical type of neural network (MLP), as well as linear regression and SVM regression, were tested. In the study, typical artifacts occurring in ECG signals before and during an epileptic seizure were simulated. The proposed 1D-CNN algorithm estimates CSI well and is resistant to noise and artifacts in the ECG signal.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 3; art. no. e136921
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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