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    Wyświetlanie 1-4 z 4
    Tytuł:
    Assessment of wind energy resources using artificial neural networks – case study at Łódź Hills
    Autorzy:
    Korupczyński, R.
    Trajer, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/199792.pdf
    Data publikacji:
    2019
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    wind speed
    artificial neural network
    wind resources
    measure-correlate-predict
    prędkość wiatru
    sztuczna sieć neuronowa
    zasoby wiatru
    pomiar-korelacja-przewidywanie
    Opis:
    The aim of this paper is to answer the question: Are the Łódź Hills useful for electrical energy production from wind energy or not? Due to access to short-term data related to wind measurements (the period of 2008 and 2009) from a local meteorological station, the measure – correlate – predict approach have been applied. Long-term (1979‒2016) reference data were obtained from ECWMF ERA-40 Reanalysis. Artificial neural networks were used to calculate predicted wind speed. The obtained average wind speed and wind power density was 4.21 ms–1 and 70 Wm–1, respectively, at 10 m above ground level (5.51 ms–1, 170 Wm–1 at 50 m). From the point of view of Polish wind conditions, Łódź Hills may be considered useful for wind power engineering.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2019, 67, 1; 115-124
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Fuzzy interpretation for temporal-difference learning in anomaly detection problems
    Autorzy:
    Sukhanov, A. V.
    Kovalev, S. M.
    Stýskala, V.
    Powiązania:
    https://bibliotekanauki.pl/articles/200233.pdf
    Data publikacji:
    2016
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    anomaly prediction
    Markov reward model
    hybrid fuzzy-stochastic rules
    temporal-difference learning for intrusion detection
    przewidywanie anomalii
    model Markova
    wykrywanie włamań
    hybrydowy algorytm stochastyczny
    Opis:
    Nowadays, information control systems based on databases develop dynamically worldwide. These systems are extensively implemented into dispatching control systems for railways, intrusion detection systems for computer security and other domains centered on big data analysis. Here, one of the main tasks is the detection and prediction of temporal anomalies, which could be a signal leading to significant (and often critical) actionable information. This paper proposes the new anomaly prevent detection technique, which allows for determining the predictive temporal structures. Presented approach is based on a hybridization of stochastic Markov reward model by using fuzzy production rules, which allow to correct Markov information based on expert knowledge about the process dynamics as well as Markov’s intuition about the probable anomaly occurring. The paper provides experiments showing the efficacy of detection and prediction. In addition, the analogy between new framework and temporal-difference learning for sequence anomaly detection is graphically illustrated.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2016, 64, 3; 625-632
    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/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 1-4 z 4

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