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    Wyświetlanie 1-5 z 5
    Tytuł:
    Continuous wavelet and Hilbert-Huang Transforms applied for analysis of active and reactive power consumption
    Autorzy:
    Avdakovic, S
    Bosovic, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/221580.pdf
    Data publikacji:
    2014
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    active power consumption
    reactive power consumption
    continuous wavelet transform (CWT)
    empirical mode decomposition
    Hilbert-Huang transform
    Opis:
    Analysis of power consumption presents a very important issue for power distribution system operators. Some power system processes such as planning, demand forecasting, development, etc.., require a complete understanding of behaviour of power consumption for observed area, which requires appropriate techniques for analysis of available data. In this paper, two different time-frequency techniques are applied for analysis of hourly values of active and reactive power consumption from one real power distribution transformer substation in urban part of Sarajevo city. Using the continuous wavelet transform (CWT) with wavelet power spectrum and global wavelet spectrum some properties of analysed time series are determined. Then, empirical mode decomposition (EMD) and Hilbert-Huang Transform (HHT) are applied for the analyses of the same time series and the results showed that both applied approaches can provide very useful information about the behaviour of power consumption for observed time interval and different period (frequency) bands. Also it can be noticed that the results obtained by global wavelet spectrum and marginal Hilbert spectrum are very similar, thus confirming that both approaches could be used for identification of main properties of active and reactive power consumption time series.
    Źródło:
    Metrology and Measurement Systems; 2014, 21, 3; 413-422
    0860-8229
    Pojawia się w:
    Metrology and Measurement Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Energy of intrinsic mode function for gas-liquid flow pattern identification
    Autorzy:
    Sun, Z.
    Gong, H.
    Powiązania:
    https://bibliotekanauki.pl/articles/221137.pdf
    Data publikacji:
    2012
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    gas-liquid flow pattern
    flow-pattern map
    pressure fluctuation
    bluff body
    signal energy
    intrinsic mode function
    empirical mode decomposition
    Opis:
    Gas-liquid flows abound in a great variety of industrial processes. Correct recognition of the regimes of a gas-liquid flow is one of the most formidable challenges in multiphase flow measurement. Here we put forward a novel approach to the classification of gas-liquid flow patterns. In this method a flow-pattern map is constructed based on the average energy of intrinsic mode function and the volumetric void fraction of gas-liquid mixture. The intrinsic mode function is extracted from the pressure fluctuation across a bluff body using the empirical mode decomposition technique. Experiments adopting air and water as the working fluids are conducted in the bubble, plug, slug, and annular flow patterns at ambient temperature and atmospheric pressure. Verification tests indicate that the identification rate of the flow-pattern map developed exceeds 90%. This approach is appropriate for the gas-liquid flow pattern identification in practical applications.
    Źródło:
    Metrology and Measurement Systems; 2012, 19, 4; 759-766
    0860-8229
    Pojawia się w:
    Metrology and Measurement Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Comparison of multiband filtering, empirical mode decomposition and short-time fourier transform used to extract physiological components from long-term heart rate variability
    Autorzy:
    Adamczyk, Krzysztof
    Polak, Adam G.
    Powiązania:
    https://bibliotekanauki.pl/articles/2052173.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    heart rate variability
    nonstationary signal analysis
    multiband filtering
    empirical mode decomposition
    short-time Fourier transform
    Hilbert transform
    Opis:
    Heart rate is constantly changing under the influence of many control signals, as manifested by heart rate variability (HRV). HRV is a nonstationary, irregularly sampled signal, the spectrum of which reveals distinct bands of high, low, very low and ultra-low frequencies (HF, LF, VLF, ULF). VLF and ULF components are the least understood, and their analysis requires HRV records lasting many hours. Moreover, there are still no well-established methods for the reliable extraction of these components. The aim of this work was to select, implement and compare methods which can solve this problem. The performance of multiband filtering (MBF), empirical mode decomposition and the short-time Fourier transform was tested, using synthetic HRV as the ground truth for methods evaluation as well as real data of three patients selected from 25 polysomnographic records with a clear HF component in their spectrograms. The study provided new insights into the components of long-term HRV, including the character of its amplitude and frequency modulation obtained with the Hilbert transform. In addition, the reliability of the extracted HF, LF, VLF and ULF waveforms was demonstrated, and MBF turned out to be the most accurate method, though the signal is strongly nonstationary. The possibility of isolating such waveforms is of great importance both in physiology and pathophysiology, as well as in the automation of medical diagnostics based on HRV.
    Źródło:
    Metrology and Measurement Systems; 2021, 28, 4; 643-660
    0860-8229
    Pojawia się w:
    Metrology and Measurement Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Effect of Feature Extraction on Automatic Sleep Stage Classification by Artificial Neural Network
    Autorzy:
    Prucnal, M.
    Polak, A. G.
    Powiązania:
    https://bibliotekanauki.pl/articles/220360.pdf
    Data publikacji:
    2017
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    sleep stage classification
    EEG signal
    power spectral density
    discrete wavelet transform
    empirical mode decomposition
    artificial neural network
    Opis:
    EEG signal-based sleep stage classification facilitates an initial diagnosis of sleep disorders. The aim of this study was to compare the efficiency of three methods for feature extraction: power spectral density (PSD), discrete wavelet transform (DWT) and empirical mode decomposition (EMD) in the automatic classification of sleep stages by an artificial neural network (ANN). 13650 30-second EEG epochs from the PhysioNet database, representing five sleep stages (W, N1-N3 and REM), were transformed into feature vectors using the aforementioned methods and principal component analysis (PCA). Three feed-forward ANNs with the same optimal structure (12 input neurons, 23 + 22 neurons in two hidden layers and 5 output neurons) were trained using three sets of features, obtained with one of the compared methods each. Calculating PSD from EEG epochs in frequency sub-bands corresponding to the brain waves (81.1% accuracy for the testing set, comparing with 74.2% for DWT and 57.6% for EMD) appeared to be the most effective feature extraction method in the analysed problem.
    Źródło:
    Metrology and Measurement Systems; 2017, 24, 2; 229-240
    0860-8229
    Pojawia się w:
    Metrology and Measurement Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    New gas-liquid two-phase flow pattern maps based on the energy ratio of pressure fluctuation through a Venturi tube
    Autorzy:
    Sun, Zhiqiang
    Chen, Luyang
    Yao, Fengyan
    Powiązania:
    https://bibliotekanauki.pl/articles/220955.pdf
    Data publikacji:
    2019
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    gas-liquid two-phase flow
    flow pattern map
    Venturi tube
    pressure fluctuation
    energy ratio
    ensemble empirical mode decomposition
    Opis:
    To find effective and practical methods to distinguish gas-liquid two-phase flow patterns, new flow pattern maps are established using the differential pressure through a classical Venturi tube. The differential pressure signal was first decomposed adaptively into a series of intrinsic mode functions (IMFs) by the ensemble empirical mode decomposition. Hilbert marginal spectra of the IMFs showed that the flow patterns are related to the amplitude of the pressure fluctuation. The cross-correlation method was employed to sift the characteristic IMF, and then the energy ratio of the characteristic IMF to the raw signal was proposed to construct flow pattern maps with the volumetric void fraction and with the two-phase Reynolds number, respectively. The identification rates of these two maps are verified to be 91.18% and 92.65%. This approach provides a cost-effective solution to the difficult problem of identifying gas-liquid flow patterns in the industrial field.
    Źródło:
    Metrology and Measurement Systems; 2019, 26, 2; 241-252
    0860-8229
    Pojawia się w:
    Metrology and Measurement Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-5 z 5

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