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    Wyświetlanie 1-4 z 4
    Tytuł:
    Infrasound Signal Classification Based on ICA and SVM
    Autorzy:
    Lu, Quanbo
    Wang, Meng
    Li, Mei
    Powiązania:
    https://bibliotekanauki.pl/articles/31339863.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Czasopisma i Monografie PAN
    Tematy:
    independent component analysis
    fast Fourier transform
    support vector machine
    infrasound signal
    Opis:
    A diagnostic technique based on independent component analysis (ICA), fast Fourier transform (FFT), and support vector machine (SVM) is suggested for effectively extracting signal features in infrasound signal monitoring. Firstly, ICA is proposed to separate the source signals of mixed infrasound sources. Secondly, FFT is used to obtain the feature vectors of infrasound signals. Finally, SVM is used to classify the extracted feature vectors. The approach integrates the advantages of ICA in signal separation and FFT to extract the feature vectors. An experiment is conducted to verify the benefits of the proposed approach. The experiment results demonstrate that the classification accuracy is above 98.52% and the run time is only 2.1 seconds. Therefore, the proposed strategy is beneficial in enhancing geophysical monitoring performance.
    Źródło:
    Archives of Acoustics; 2023, 48, 3; 191-199
    0137-5075
    Pojawia się w:
    Archives of Acoustics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    VMD and CNN-Based Classification Model for Infrasound Signal
    Autorzy:
    Lu, Quanbo
    Li, Mei
    Powiązania:
    https://bibliotekanauki.pl/articles/31339812.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Czasopisma i Monografie PAN
    Tematy:
    infrasound signal
    variational mode decomposition
    convolutional neural network
    Fast Fourier Transform
    Opis:
    Infrasound signal classification is vital in geological hazard monitoring systems. The traditional classification approach extracts the features and classifies the infrasound events. However, due to the manual feature extraction, its classification performance is not satisfactory. To deal with this problem, this paper presents a classification model based on variational mode decomposition (VMD) and convolutional neural network (CNN). Firstly, the infrasound signal is processed by VMD to eliminate the noise. Then fast Fourier transform (FFT) is applied to convert the reconstructed signal into a frequency domain image. Finally, a CNN model is established to automatically extract the features and classify the infrasound signals. The experimental results show that the classification accuracy of the proposed classification model is higher than the other model by nearly 5%. Therefore, the proposed approach has excellent robustness under noisy environments and huge potential in geophysical monitoring.
    Źródło:
    Archives of Acoustics; 2023, 48, 3; 403-412
    0137-5075
    Pojawia się w:
    Archives of Acoustics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Influence of Infrasound on the Alpha Rhythm of EEG Signal
    Autorzy:
    Kasprzak, C.
    Powiązania:
    https://bibliotekanauki.pl/articles/1490447.pdf
    Data publikacji:
    2012-01
    Wydawca:
    Polska Akademia Nauk. Instytut Fizyki PAN
    Tematy:
    87.50.Y-
    Opis:
    The alpha waves were first registered and named by Berger in 1929. They are oscillations in the frequency range 8-12 Hz, originating from the occipital lobe during wakeful relaxation with closed eyes. The alpha blockage is the result of desynchronisation of the bioelectric activity of the brain induced by sensory stimulation. When the subject's eyes are closed, the alpha rhythm is generated. As soon as the eyes are open, alpha disappears. This is called alpha block and may be elicited also by any form of sensory stimulation. This replacement of the alpha rhythm is also called desynchronization because it represents a change of the synchronized activity of neural elements. This state is also called arousal or alerting response. Infrasounds are acoustic waves of frequency below 20 Hz. They are not directly perceived by humans because the natural frequency of vibrations of the part of the basilar membrane distant from the round window is about 20 Hz. The hearing organ, therefore, is well adapted to receive waves with frequency in excess of 20 Hz. The purpose of the experiment was to determine the effects of infrasound waves on variations in the alpha waves. Tests were done on a group of 32 participants. The experiment showed that infrasounds of frequency f = 7 Hz and acoustic pressure level SPL = 120 dB (HP) cause a statistically significant reduction of the alpha rhythm power.
    Źródło:
    Acta Physica Polonica A; 2012, 121, 1A; A-061-A-064
    0587-4246
    1898-794X
    Pojawia się w:
    Acta Physica Polonica A
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    The Influence of Infrasound Noise from Wind Turbines on EEG Signal Patterns in Humans
    Autorzy:
    Kasprzak, C.
    Powiązania:
    https://bibliotekanauki.pl/articles/1197502.pdf
    Data publikacji:
    2014-04
    Wydawca:
    Polska Akademia Nauk. Instytut Fizyki PAN
    Tematy:
    87.50.Y-
    43.28.Dm
    87.19.lr
    Opis:
    The purpose of this paper is to determine the effect of infrasound noise from wind turbines (up to 20 Hz) on the changes in the EEG signal patterns in humans. The experimental study was undertaken to investigate the effect of a 20 minutes long infrasound exposure on humans. The acoustic signal was recorded at a distance of 750 meters from the wind turbine and the frequency components above 20 Hz were then filtered out. Research work undertaken so far to investigate the impacts of wind turbine noise on humans would mostly rely on questionnaire tools and subjective assessment given by respondents. This study focuses on the effects of infrasound noise from wind turbines on variations of EEG signal patterns in an attempt to develop a more objective measure of the infrasound noise impacts. The experimenal study was conducted in a pressure cabin where the EEG procedure was performed. Analysis of the EEG signals reveals the changes between the EEG patterns registered during the three successive stages of the study.The results indicate some changes in EEG signal patterns registered under exposure to wind turbine noise. Moreover, the specific frequency ranges of the EEG signals are found to be altered.
    Źródło:
    Acta Physica Polonica A; 2014, 125, 4A; A-20-A-23
    0587-4246
    1898-794X
    Pojawia się w:
    Acta Physica Polonica A
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-4 z 4

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