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Wyszukujesz frazę "noise classification" wg kryterium: Temat

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    Wyświetlanie 1-5 z 5
    Tytuł:
    Occupational Exposure to Impulse Noise Associated With Shooting
    Autorzy:
    Lwow, F.
    Jóźków, P.
    Mędraś, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/90390.pdf
    Data publikacji:
    2011
    Wydawca:
    Centralny Instytut Ochrony Pracy
    Tematy:
    weapon
    noise classification
    noise-induced hearing loss
    prevention
    Opis:
    Shooting training is associated with exposure to a considerable amount of unique noise. We wanted to evaluate noise exposure during such training. Our observations especially apply to professional sport shooters, but they are also valid for shooting coaches/instructors. We collected acoustic signals in 10-, 25- and 50-m as well as open-air shooting ranges. The recorded material was analysed with orthogonal, adaptive parameterization by Shur. The mean duration of a single acoustic signal was 250–800 ms with the C-weighted sound peak pressure level of 138.2–165.2 dB. Shooters may be exposed to as many as 600–1350 acoustic impulses during a training unit. The actual load for the hearing organ of a professional shooter or a shooting coach is ~200 000 acoustic stimuli in a year-long training macrocycle. Orthogonal, adaptive parameterization by Shur makes safe scheduling of shooters’ training possible.
    Źródło:
    International Journal of Occupational Safety and Ergonomics; 2011, 17, 1; 69-77
    1080-3548
    Pojawia się w:
    International Journal of Occupational Safety and Ergonomics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Hierarchical Classification of Environmental Noise Sources Considering the Acoustic Signature of Vehicle Pass-Bys
    Autorzy:
    Valero, X.
    Alias, F.
    Powiązania:
    https://bibliotekanauki.pl/articles/176616.pdf
    Data publikacji:
    2012
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    acoustic signature
    environmental noise monitoring
    Gaussian mixture models
    hierarchical classification
    mel-frequency cepstral coefficients (MFCC)
    sound classification
    traffic noise
    vehicle pass-by
    Opis:
    This work is focused on the automatic recognition of environmental noise sources that affect humans’ health and quality of life, namely industrial, aircraft, railway and road traffic. However, the recognition of the latter, which have the largest influence on citizens’ daily lives, is still an open issue. Therefore, although considering all the aforementioned noise sources, this paper especially focuses on improving the recognition of road noise events by taking advantage of the perceived noise differences along the road vehicle pass-by (which may be divided into different phases: approaching, passing and receding). To that effect, a hierarchical classification scheme that considers these phases independently has been implemented. The proposed classification scheme yields an averaged classification accuracy of 92.5%, which is, in absolute terms, 3% higher than the baseline (a traditional flat classification scheme without hierarchical structure). In particular, it outperforms the baseline in the classification of light and heavy vehicles, yielding a classification accuracy 7% and 4% higher, respectively. Finally, listening tests are performed to compare the system performance with human recognition ability. The results reveal that, although an expert human listener can achieve higher recognition accuracy than the proposed system, the latter outperforms the non-trained listener in 10% in average.
    Źródło:
    Archives of Acoustics; 2012, 37, 4; 423-434
    0137-5075
    Pojawia się w:
    Archives of Acoustics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Analysing the impact on underwater noise of changes to the parameters of a ship’s machinery
    Autorzy:
    Buszman, Krystian
    Powiązania:
    https://bibliotekanauki.pl/articles/1573917.pdf
    Data publikacji:
    2020
    Wydawca:
    Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
    Tematy:
    shipping noise
    hydroacoustic passive measurement
    narrowband
    1/3-octave analysis
    diagnosis
    classification
    Opis:
    A ship moving over the surface of water generates disturbances that are perceived as noise, both in the air and under water. Due to its density, water is an excellent medium for transmitting acoustic waves over long distances. This article describes the impact of the settings of a ship’s machinery on the nature of the generated noise. Our analysis includes the frequency characteristics of the noise generated by the moving ship. Data were obtained using an underwater measurement system, and the measured objects were two ships moving on specific trajectories with certain machinery settings. The acquired data were analysed in the frequency domain to explore the possibilities of the acoustic classification of ships and diagnostics of source mechanisms.
    Źródło:
    Polish Maritime Research; 2020, 3; 176-181
    1233-2585
    Pojawia się w:
    Polish Maritime Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Handling class label noise in medical pattern classification systems
    Autorzy:
    Sáez, J. A.
    Krawczyk, B.
    Woźniak, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/333813.pdf
    Data publikacji:
    2015
    Wydawca:
    Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
    Tematy:
    machine learning
    pattern classification
    class noise
    noise filtering
    decision support systems
    uczenie maszynowe
    klasyfikacja wzorców
    filtracja zakłóceń
    filtracja szumów
    systemy wspomagania decyzji
    Opis:
    Pattern classification systems play an important role in medical decision support. They allow to automatize and speed-up the data analysis process, while being able to handle complex and massive amounts of information and discover new knowledge. However, their quality is based on the classification models built, which require a training set. In supervised classification we must supply class labels to each training sample, which is usually done by domain experts or some automatic systems. As both of these approaches cannot be deemed as flawless, there is a chance that the dataset is corrupted by class noise. In such a situation, class labels are wrongly assigned to objects, which may negatively affect the classifier training process and impair the classification performance. In this contribution, we analyze the usefulness of existing tools to deal with class noise, known as noise filtering methods, in the context of medical pattern classification. The experiments carried out on several real-world medical datasets prove the importance of noise filtering as a pre-processing step and its beneficial influence on the obtained classification accuracy.
    Źródło:
    Journal of Medical Informatics & Technologies; 2015, 24; 123-130
    1642-6037
    Pojawia się w:
    Journal of Medical Informatics & Technologies
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Smart Substation Network Fault Classification Based on a Hybrid Optimization Algorithm
    Autorzy:
    Xia, Xin
    Liu, Xiaofeng
    Lou, Jichao
    Powiązania:
    https://bibliotekanauki.pl/articles/227220.pdf
    Data publikacji:
    2019
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    smart substation
    network fault classification
    improved separation interval method (ISIM)
    support vector
    machine (SVM)
    Anti-noise processing (ANP)
    Opis:
    Accurate network fault diagnosis in smart substations is key to strengthening grid security. To solve fault classification problems and enhance classification accuracy, we propose a hybrid optimization algorithm consisting of three parts: anti-noise processing (ANP), an improved separation interval method (ISIM), and a genetic algorithm-particle swarm optimization (GA-PSO) method. ANP cleans out the outliers and noise in the dataset. ISIM uses a support vector machine (SVM) architecture to optimize SVM kernel parameters. Finally, we propose the GA-PSO algorithm, which combines the advantages of both genetic and particle swarm optimization algorithms to optimize the penalty parameter. The experimental results show that our proposed hybrid optimization algorithm enhances the classification accuracy of smart substation network faults and shows stronger performance compared with existing methods.
    Źródło:
    International Journal of Electronics and Telecommunications; 2019, 65, 4; 657-663
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-5 z 5

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