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Wyszukujesz frazę "Nguyen, Thanh-Nghia" wg kryterium: Autor


Wyświetlanie 1-2 z 2
Tytuł:
R peak determination using a WDFR algorithm and Adaptive threshold
Autorzy:
Nguyen, Thanh-Nghia
Nguyen, Thanh-Hai
Ngo, Ba-Viet
Powiązania:
https://bibliotekanauki.pl/articles/38437166.pdf
Data publikacji:
2022
Wydawca:
Polskie Towarzystwo Promocji Wiedzy
Tematy:
ECG signal
wavelet transforms
WDFR algorithm
R peak determination
adaptive threshold
Opis:
The determination of the R peak position in the ECG signal helps physicians not only to know the heart rate per minute, but also to monitor the patient’s health related to heart disease. This paper proposes a system to accurately determine the R peak position in the ECG signal. The system consists of a pre-processing block for filtering out noise using a WDFR algorithm and highlighting the amplitude of the R peak and a threshold value is calculated for determining the R peak. In this research, the MIT-BIH ECG dataset with 48 records are used for evaluation of the system. The results of the SEN, +P, DER and ACC parameters related to the system quality are 99.70%, 99.59%, 0.70% and 99.31%, respectively. The obtained performance of the proposed R peak position determination system is very high and can be applied to determine the R peak of the ECG signal measuring devices in practice.
Źródło:
Applied Computer Science; 2022, 18, 3; 19-30
1895-3735
2353-6977
Pojawia się w:
Applied Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Prediction of Tunnel Cross-Sectional Area After Blastin
Autorzy:
Nguyen, Chi Thanh
Nguyen, Nghia Viet
Powiązania:
https://bibliotekanauki.pl/articles/25212147.pdf
Data publikacji:
2023
Wydawca:
Polskie Towarzystwo Przeróbki Kopalin
Tematy:
ANN
SVR
tunnel
drilling-blasting method
cross-sectional area of tunnel
prediction
tunele
Opis:
In this paper, two methods to predict and calculate the area of the tunnel face after the blasting were used. The first one is an artificial intelligence method using an artificial neural network system (ANN) model, and the second one – the support vector regression (SVR). After building predictive models for the area of the tunnel face after blasting by both methods, on the basis of comparing the results obtained in both methods, the performance of these models was assessed through the root mean square error RMSE and the coefficient of determination R2. RMSE and R2 values of the artificial neural network system (ANN) model were obtained as 0.1473 and 0.903 in training datasets, respectively. These values are 0.1497 and 0.9107 in testing datasets. In the SRV model, RMSE and R2 were equaled to 0.1228 and 0.9331 in training datasets, respectively. These values are 0.1708 and 0.9055, respectively in testing datasets. It can be concluded that artificial intelligence using ANN and SVM models can be used to predict the area of the tunnel face after blasting with high accuracy.
Źródło:
Inżynieria Mineralna; 2023, 2; 39--47
1640-4920
Pojawia się w:
Inżynieria Mineralna
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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