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    Wyświetlanie 1-7 z 7
    Tytuł:
    Accurate identification on individual similar communication emitters by using HVG-NTE feature
    Autorzy:
    Li, Ke
    Ge, Wei
    Yang, Xiaoya
    Xu, Zhengrong
    Powiązania:
    https://bibliotekanauki.pl/articles/2128146.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    communication emitter
    identification
    feature extraction
    HVG
    NTE
    emiter komunikacji
    identyfikacja
    wyodrębnianie cech
    Opis:
    Individual identification of similar communication emitters in the complex electromagnetic environment has great research value and significance in both military and civilian fields. In this paper, a feature extraction method called HVG-NTE is proposed based on the idea of system nonlinearity. The shape of the degree distribution, based on the extraction of HVG degree distribution, is quantified with NTE to improve the anti-noise performance. Then XGBoost is used to build a classifier for communication emitter identification. Our method achieves better recognition performance than the state-of-the-art technology of the transient signal data set of radio stations with the same plant, batch, and model, and is suitable for a small sample size.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 2; e136741, 1--6
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Accurate identification on individual similar communication emitters by using HVG-NTE feature
    Autorzy:
    Li, Ke
    Ge, Wei
    Yang, Xiaoya
    Xu, Zhengrong
    Powiązania:
    https://bibliotekanauki.pl/articles/2173613.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    communication emitter
    identification
    feature extraction
    HVG
    NTE
    emiter komunikacji
    identyfikacja
    wyodrębnianie cech
    Opis:
    Individual identification of similar communication emitters in the complex electromagnetic environment has great research value and significance in both military and civilian fields. In this paper, a feature extraction method called HVG-NTE is proposed based on the idea of system nonlinearity. The shape of the degree distribution, based on the extraction of HVG degree distribution, is quantified with NTE to improve the anti-noise performance. Then XGBoost is used to build a classifier for communication emitter identification. Our method achieves better recognition performance than the state-of-the-art technology of the transient signal data set of radio stations with the same plant, batch, and model, and is suitable for a small sample size.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 2; art. no. e136741
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Evaluation of the shape and dimensions of cereal seeds and other crops for modeling sowing and seed separation
    Ocena kształtu i wymiarów nasion zbóż i innych roślin uprawnych na potrzeby modelowania procesu wysiewu i separacji nasion
    Autorzy:
    Gierz, Ł.
    Włodarczyk, K.
    Selech, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/335265.pdf
    Data publikacji:
    2017
    Wydawca:
    Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Maszyn Rolniczych
    Tematy:
    grain dimensions
    image binarization
    contour extraction
    photogrammetric measurements
    wymiary ziarna
    binaryzacja obrazu
    wyodrębnianie konturów
    pomiary fotogrametryczne
    Opis:
    The paper presents the results of laboratory tests on the shape and dimensions of cereal seeds and other crop plants necessary for modeling the sowing and separation of these seeds. Individual grains are not usually identical, so their linear dimensions are not uniform in different directions. The ability to determine the shape and dimensions of grains is extremely important from the point of view of basic research as well as in practice as a boundary condition for machine design. During the study a photogrammetric method was used based on the „Gabar” program written at Poznan University of Technology. It allows us to significantly improve and speed up the process of obtaining data with sufficient accuracy and can be applied to the overall assessment of other raw materials such as those from recycling.
    W pracy przedstawiono wyniki badan laboratoryjnych kształtu i wymiarów nasion zbóż i innych roślin uprawnych niezbędnych do modelowania procesu wysiewu i separacji tych nasion. Poszczególne ziarna nie są zazwyczaj identyczne, a więc ich wymiary liniowe nie są jednakowe w różnych kierunkach. Możliwość określenia kształtu i wymiarów ziaren jest niezwykle istotna z punktu widzenia badan podstawowych, jak równie w praktyce jako warunki brzegowe do projektowania maszyn. Podczas badań wykorzystana została metoda fotogrametryczna bazująca na napisanym w Politechnice Poznańskiej programie „Gabar”. Pozwala ona znacznie usprawnić i przyspieszyć proces pozyskiwania danych z wystarczająca dokładnością, jak również może być zastosowana do oceny gabarytowej innych surowców np. recyklingowych.
    Źródło:
    Journal of Research and Applications in Agricultural Engineering; 2017, 62, 2; 37-40
    1642-686X
    2719-423X
    Pojawia się w:
    Journal of Research and Applications in Agricultural Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Fuzzy-Logic Fault Isolation in Large-Scale Systems
    Autorzy:
    Kościelny, J. M.
    Sędziak, D.
    Zakroczymski, K.
    Powiązania:
    https://bibliotekanauki.pl/articles/908284.pdf
    Data publikacji:
    1999
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    logika rozmyta
    wyodrębnianie uszkodzeń
    system na wielką skalę
    diagnostics
    fuzzy logic
    fault isolation
    large-scale systems
    Opis:
    Application of fuzzy logic in fault isolation is proposed. The introduced methods assume the industrial requirements such as integration of different detection algorithms, system complexity, data and knowledge uncertainties. Algorithms of decreasing the calculation expenditures for diagnosing large-scale systems are also introduced. An example of the application is also shown. The proposed technique is a development of the Dynamic State Tables method.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 1999, 9, 3; 637-652
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Rgb-D face recognition using LBP-DCT algorithm
    Autorzy:
    Kumar, Sunil B L
    Kumari, Sharmila M
    Powiązania:
    https://bibliotekanauki.pl/articles/1956066.pdf
    Data publikacji:
    2021
    Wydawca:
    Polskie Towarzystwo Promocji Wiedzy
    Tematy:
    RGB-D
    kinect
    local binary pattern
    pattern recognition
    feature extraction
    histogram
    face recognition
    lokalny wzorzec binarny
    rozpoznawanie wzorców
    wyodrębnianie cech
    rozpoznawanie twarzy
    Opis:
    Face recognition is one of the applications in image processing that recognizes or checks an individual's identity. 2D images are used to identify the face, but the problem is that this kind of image is very sensitive to changes in lighting and various angles of view. The images captured by 3D camera and stereo camera can also be used for recognition, but fairly long processing times is needed. RGB-D images that Kinect produces are used as a new alternative approach to 3D images. Such cameras cost less and can be used in any situation and any environment. This paper shows the face recognition algorithms’ performance using RGB-D images. These algorithms calculate the descriptor which uses RGB and Depth map faces based on local binary pattern. Those images are also tested for the fusion of LBP and DCT methods. The fusion of LBP and DCT approach produces a recognition rate of 97.5% during the experiment
    Źródło:
    Applied Computer Science; 2021, 17, 3; 73-81
    1895-3735
    Pojawia się w:
    Applied Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Speech emotion recognition using wavelet packet reconstruction with attention-based deep recurrent neutral networks
    Autorzy:
    Meng, Hao
    Yan, Tianhao
    Wei, Hongwei
    Ji, Xun
    Powiązania:
    https://bibliotekanauki.pl/articles/2173587.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    speech emotion recognition
    voice activity detection
    wavelet packet reconstruction
    feature extraction
    LSTM networks
    attention mechanism
    rozpoznawanie emocji mowy
    wykrywanie aktywności głosowej
    rekonstrukcja pakietu falkowego
    wyodrębnianie cech
    mechanizm uwagi
    sieć LSTM
    Opis:
    Speech emotion recognition (SER) is a complicated and challenging task in the human-computer interaction because it is difficult to find the best feature set to discriminate the emotional state entirely. We always used the FFT to handle the raw signal in the process of extracting the low-level description features, such as short-time energy, fundamental frequency, formant, MFCC (mel frequency cepstral coefficient) and so on. However, these features are built on the domain of frequency and ignore the information from temporal domain. In this paper, we propose a novel framework that utilizes multi-layers wavelet sequence set from wavelet packet reconstruction (WPR) and conventional feature set to constitute mixed feature set for achieving the emotional recognition with recurrent neural networks (RNN) based on the attention mechanism. In addition, the silent frames have a disadvantageous effect on SER, so we adopt voice activity detection of autocorrelation function to eliminate the emotional irrelevant frames. We show that the application of proposed algorithm significantly outperforms traditional features set in the prediction of spontaneous emotional states on the IEMOCAP corpus and EMODB database respectively, and we achieve better classification for both speaker-independent and speaker-dependent experiment. It is noteworthy that we acquire 62.52% and 77.57% accuracy results with speaker-independent (SI) performance, 66.90% and 82.26% accuracy results with speaker-dependent (SD) experiment in final.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 1; art. no. e136300
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Speech emotion recognition using wavelet packet reconstruction with attention-based deep recurrent neutral networks
    Autorzy:
    Meng, Hao
    Yan, Tianhao
    Wei, Hongwei
    Ji, Xun
    Powiązania:
    https://bibliotekanauki.pl/articles/2090711.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    speech emotion recognition
    voice activity detection
    wavelet packet reconstruction
    feature extraction
    LSTM networks
    attention mechanism
    rozpoznawanie emocji mowy
    wykrywanie aktywności głosowej
    rekonstrukcja pakietu falkowego
    wyodrębnianie cech
    mechanizm uwagi
    sieć LSTM
    Opis:
    Speech emotion recognition (SER) is a complicated and challenging task in the human-computer interaction because it is difficult to find the best feature set to discriminate the emotional state entirely. We always used the FFT to handle the raw signal in the process of extracting the low-level description features, such as short-time energy, fundamental frequency, formant, MFCC (mel frequency cepstral coefficient) and so on. However, these features are built on the domain of frequency and ignore the information from temporal domain. In this paper, we propose a novel framework that utilizes multi-layers wavelet sequence set from wavelet packet reconstruction (WPR) and conventional feature set to constitute mixed feature set for achieving the emotional recognition with recurrent neural networks (RNN) based on the attention mechanism. In addition, the silent frames have a disadvantageous effect on SER, so we adopt voice activity detection of autocorrelation function to eliminate the emotional irrelevant frames. We show that the application of proposed algorithm significantly outperforms traditional features set in the prediction of spontaneous emotional states on the IEMOCAP corpus and EMODB database respectively, and we achieve better classification for both speaker-independent and speaker-dependent experiment. It is noteworthy that we acquire 62.52% and 77.57% accuracy results with speaker-independent (SI) performance, 66.90% and 82.26% accuracy results with speaker-dependent (SD) experiment in final.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 1; e136300, 1--12
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-7 z 7

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